Input:
ratings. Where GDP per capita growth data have been recorded, it appears that FCS countries have stronger growth trends than non-FCS countries and in both groups of countries growth is correlated with stronger outcome ratings.

categorization is derived from the FCS lists for FY06 to FY13. [1] Partial FCS countries are those classified as FCS in at least two years during this period.

7For example that would be detected through the implementation status reports (ISRs).

we rely on the data from Melecky and Podpiera (2013) and the 2003, 2007, and 2012 Bank Regulation

and Supervision Surveys of the World Bank. For banking crisis classification, we rely on Laeven and

Valencia's (2013) database and cross-check our results against the crisis classification by Reinhart and

countries.

8

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_**Regression Model**_

We use the systemic banking crisis database of Laeven and Valencia (2013) to identify banking

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Input:
### 4.2 GLSS Cluster-Level Aggregation

To construct community-level outcome variables from GLSS household data, we aggregate household-level observations to the GLSS enumeration area level using survey-weighted means. We restrict this aggregation to GLSS enumeration areas with at least 10 interviewed households to limit noise from small-cluster estimates. The resulting GLSS cluster-level dataset comprises 378 enumeration areas distributed across all ten regions, with an average of 18 interviewed households per cluster.

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Input: We only found reliable approximations from the International Displacement Monitoring Center (IDMC) between 2003 and 2013. According to IDMC, there were about 12. 5 million internally displaced people in SSA at the end of 2013 (IDMC 2014), more than one third of the total number of IDPs and more than tripling the number of refugees in SSA. Although the number of IDPs in SSA is the highest since 2007, the share of IDPs in SSA has been decreasing from 53 % in 2003. Usage context: background mention, primary mention, supporting mention.

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Input: prior to the pandemic, a period characterized by less distinct time trends in the labor market. Since the focus of the paper is on the impact of COVID on labor market stock and dynamics, we also restrict the sample of interest to include only prime-aged working adults (aged 20 to 59). The dataset contains standard variables expected of a labor force survey, including those denoting employment, unemployment, and inactivity. It also contains information on the intensive margin of the labor supply, including hours worked and full-time and part-time status. Information on employment sector, industry, contract status, health insurance coverage, and mode of work (distinguishing between employees and self-employed, for example) is also available, allowing us to construct indicators of formality and to differentiate different modes of employment. Information on occupation is also available, but only at the level of 2-digit ISCO-08 classification. This information is enough to distinguish between white- and blue-collar occupations but it is not enough to observe additional relevant pandemic-related job characteristics such as the degree of contact with the public. 3. 2 Descriptive statistics Figure (1) tracks the evolution of labor market stocks in the West Bank and Gaza respectively over time. Usage context: primary mention, supporting mention, background mention.

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Input:
Data and Variables Definition The data used to conduct this study are the ARIS-REDS data from the National Council of Applied Economic Research (NCAER). Since 1971, the NCAER has been conducting surveys on a _sample_ of households in 232 villages in the 17 major states of India.

development improves. According to FAO data, in the last five decades per capita milk

development partners) as part of the Living Standard Measurement Study - Integrated Survey  on Agriculture (LSMS-ISA) program [2] . The paper aims to contribute to building an evidence

2 More information on the program is available at www.worldbank.org/lsms-isa.

Living Standard Measurement Study - Integrated Survey  on Agriculture (LSMS-ISA) program

Living Standard Measurement Study - Integrated Survey  on Agriculture (LSMS-ISA)

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Input: Chew et al. (2018) use a baseline convolutional neural network model on a gridded population sampling frame to select a sample of households in Nigeria and Guatemala. The authors found this technique to be on par with human canvassing in terms of accuracy, and to outperform other machine learning models based on crowdsource or remote sensing data. Grais et al (2007) compared an unweighted random point selection methodology to a random walk in their study of vaccination rates in urban Niger. The authors do not find statistically significant differences between the methods, though the sample size was limited and both methods were non-probabilistic. 3. Design and Field Protocols 3. 1. Experiment Design This paper makes use of a dataset from the purposefully designed methodology experiment conducted in one section of the Protection of Civilians site 1 (PoC1, Figure 1), one of the largest IDP camps in Juba, South Sudan. To generate a gold standard as the basis of comparison, a household census was conducted between August and September 2017. During this exercise, 2, 655 households were interviewed using a questionnaire designed to collect demographic information, dwelling characteristics, household consumption, and perception data. At the end of each census interview, households received a unique barcode that could be used to identify them later in the experiment. Usage context: primary mention, supporting mention, background mention.

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Input: To avoid changes in camp composition, immediately following the completion of the census fieldwork, the interviewers returned to the field to implement the experiment. Teams used each of the sample selection methods to identify which households would have been selected had that method been used for a survey. To avoid respondent fatigue, instead of re-asking the questionnaire, the interviewers simply scanned the unique bar code of the selected household. Once scanned, the barcodes created an observation in the method-specific dataset with the information captured in the census. Each sampling technique targeted about 322 interviews so that comparisons could be made between the methods using an identical sample size. There was, however, some non-response for each method if interviewers were not able to contact a household member who could provide access to the barcode, if the barcode had not been retained by the household, or if the barcode was not scanned correctly. Protocols for each individual method are listed below. Usage context: primary mention, supporting mention, background mention.

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Input: Figure A. 2: Injective relations We also isolate many-to-one (bijective) relations. In this case, we have to aggregate the Afro- barometer ethnicities with their unique and more aggregated correspondence in the UNHCR refugee camps data (See Figure A. 3). Figure A. 3: Bijective relations The remaining correspondences are either (i) one-to-many (bijective) but opposite to Figure A. 3 (i. e., many ethnicities from the UNHCR refugee camps data correspond to one ethnicity from the Afrobarometer) or (ii) many-to-many relations. For both cases, we apply a more pragmatic approach: a. In both cases, we disregard ethnicities that do not appear either in the Afrobarometer or in the UNHCR refugee camps data. This means that for the remaining ethnicity that has no counterpart in either the Afrobarometer or the UNHCR refugee camps data, we simply keep the name of the ethnicity as such, i. e., this information is not dropped. b. Then, after ignoring ethnicities that have no occurrence in our datasets, we check whether the one-to-many or the many-to-many relation has not boiled down to a one-to-one resp. many-to- one relation again. If so, we can treat them as above. c. Usage context: background mention, supporting mention, primary mention.

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Input:
At present, the FAO hunger estimates rely on CVs which are not always country specific, do not vary

ranging from 19% when the personal diary is used to 72% when the estimates come from an HCES that

household surveys (HCES-direct method), as opposed to calculating them from a combination of food

balance sheets and household surveys (FBS-CV method) as currently done by the FAO. The FBS-CV

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Input: Compared to the baseline year of 2001, residents were more fearful of crime by 0. 17 sd. These fears are not necessarily unfounded, as we ﬁnd some weak evidence of changes in levels of violence in their parishes. Compared to 2001, a 1-SD increase in refugee presence in 2011 is associated with a 1. 26 percentage point increase in likelihood of a violent event, and in 2016, a 1. 11 percentage point increase. These ﬁndings are in line with our predictions for Uganda. As in Global North contexts, we recognize that out-group members can elicit fears and insecurity. It is unclear from the ACLED dataset, however, whether these violent events involve refugees. It is also possible that ACLED underreports violent incidences that are not reported by the media. Recent scholarship on the relationship between refugees and violent conﬂict ﬁnds that hosting generally has null eﬀects on conﬂict (Zhou and Shaver, 2021); when conﬂicts do occur, refugees tend to be the victims (Savun and Gineste, 2019). Nevertheless, by 2020, like with Afrobarometer respondents reporting feeling more safe, these fears of crime reverse with a negative eﬀect of- 0. 13 sd. Usage context: background mention, supporting mention, primary mention.

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Input: We focus on two policy actions: legalization (in the sense of providing legal work and residence permits to all migrants) and measures aimed at improving the quality of employment for highly educated Venezuelan migrants. 6. 1 The Eﬀects of Legalization Based on the EPEC survey, approximately 90 % of Venezuelan migrants in Ecuador lack legal status, in the sense that they are not authorized to work. This has important consequences for Venezuelan workers. As shown in Section 4. 3, lack of legal status is associated with a higher likelihood of informal employment (column 5 in Table 7). Inspired by Clemens et al. (2018), the goal of this section is to use the estimates of these eﬀects to simulate the consequences of providing legal work permits to Venezuelans on the quality of their employment (measured by informality) and, through this channel, on their productivity (measured by wages). The relevant information is collected in Table 10. We classify Venezuelan workers that lack legal status on the basis of their education level (primary, secondary or tertiary) and the quality of their employment (formal or informal). Based on the data in column 3 15 Usage context: primary mention, supporting mention, background mention.

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Input: 11 There may be grounds for skepticism about these estimates for madrassa enrollment. Since the data were collected prior to 2001, geopolitical changes after September 11 could have led to greater madrassa enrollment. In addition, the household-based survey faces the usual problems of accurately estimating a low-probability event — although enrollment is less than 1 percent in these surveys, the sampling error is large (see Bauman, 2001, for a description of similar problems in estimating home-schooling in the United States). Finally, while the census of populations does not face the problem of small samples, it is not that recent (1998) and some may have reservations regarding the quality of government data. 10 The LEAPS census of schooling choice conducted in 2003 provides a rough check on these numbers (see appendix for details). This census was conducted in three districts of Punjab and villages were chosen randomly based on the criterion that each village must have at least one private school. Typically, this means that the villages lies somewhere between fully urban and fully rural populations and are not representative of the districts that they are in. Estimates from the LEAPS census show that as a percentage of enrolled children, the numbers in two of the three districts are slightly higher than those of the population census. Usage context: supporting mention, background mention, primary mention.

household-based survey
census of populations

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Input: 33 In school: an indicator for whether the respondent currently attends regular education (schooling). This does not preclude also being employed. Retired: an indicator for a respondent who declares that they are not engaged in job search because they are retired. Full / part-time employment: an indicator for whether a person works full or part-time for all people in private sector employment (see above definition of employment). Full-time employment is defined as usual working hours of 30 or more hours per week, part-time employment as usual working hours of less than 30 hours per week. We do not use the indicator provided in the LFS data since there seems to be some confusion in which category 30 hours per week falls (with these evenly divided between full and part-time). Education: we classify people into three education categories. Low education is defined as those with no completed formal education. Medium education is defined as those with at least completed primary education but no high school completion. Higher education is defined as people who have at least completed high school. Usage context: supporting mention, background mention, primary mention.

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Input: Figure 3. Wealth Index Distribution (a) Wealth Index- Total 0. 05. 1. 15. 2. 25 k-Density- 5 0 5 10 15 Wealth Index Colombian Venezuelan Venezuelan: Before migrating (b) Adequate Housing Materials (% of Total) 0 1 2 3 4 k-Density 0. 2. 4. 6. 8 1 Average- Dwelling Material Colombian Venezuelan Venezuelan: Before migrating (c) Asset Ownership (# Total) 0. 05. 1. 15 k-Density 0 10 20 30 40 Total Assets Colombian Venezuelan Venezuelan: Before migrating (d) Access to Services (% of Total) 0 1 2 3 k-Density 0. 2. 4. 6. 8 1 Average- Access to Services Colombian Venezuelan Venezuelan: Before migrating Notes: Panel (a) presents the distribution of the wealth index for Colombian and Venezuelan households in our sample in 2022 and pre-migration. Wealth Index is an index measure of the household ’ s cumula- tive living standard constructed following The Demographic and Health Surveys (DHS) methodology. Usage context: primary mention, supporting mention, background mention.

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Input: A number of different road indicators are available and we choose road line type to use in the analysis. Road type is defined by the following: The reference category (0) points out squares with dual lane / divided highways, other primary roads, or road connectors within urban areas (types 1 or 8 in the ESRI dataset). The second category include secondary roads (type 2), and the third combines squares with informal or tertiary roads (tracks, trails or footpaths) or no road registered at all (types 3 and 0, respectively, in the ESRI dataset). Figure 4 overlays the types of roads in the original dataset before our recategorization. The shaded area represents the portion of Africa for which we code Usage context: primary mention, supporting mention, background mention.

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Input:
**Internally Displaced Persons (IDPs)** : Over 652,600 Afghans (approximately 96,000 families) were
newly displaced due to conflict in 2016, adding to a protracted IDP population of over 1 million. Most
IDPs found refuge with host families in neighboring communities, already facing extreme poverty.
Food, adequate shelter, WASH, and health care remain high priority needs, while efforts to raise
awareness of mines and ordnance risks are also ongoing. A majority of IDPs live an insecure existence
in makeshift shelters and informal squatter settlements with irregular access to services, poor

8 8.8% of women aged 20-24 were married or in a union before the age of 15 and 45% before the age of 18 (AfDHS15)

Page **7** of **12**

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Input: In certain contexts, there can be significant overlaps in these two groups; however data systems may be maintained separately for conflict-induced displacement and natural disasters (e. g. in Afghanistan) leading to possible gaps or double counting if these categories are combined. 30 The IOM Displacement Tracking Matrix (DTM) is a system to track and monitor displacement and population mobility. It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route. It has been active in over 40 countries since its inception in 2004. See http: / / www. globaldtm. info /. 31 This is typically defined as nomads not having access to their traditional routes, but routes can vary. 32 IDMC has recently adjusted their methodology to facilitate greater comparability across situations and improvements are reflected in IDMC ’ s end-2015 data. 33 This is not necessarily a problem if the purpose of the registration system is to delineate entitlements to assistance rather than to determine status. Usage context: background mention, supporting mention, primary mention.

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Input: Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer. Usage context: background mention, supporting mention, primary mention.

Linking Ethnic Data from Africa
Afrobarometer
EPR-ER dataset

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Input: discussed earlier, the expenditure of in-camp refugees is almost half that of hosts despite sizeable food aid and significant investments made by the WFP and UNHCR in cash transfers (in selected camps). The significantly lower expenditures (food and non-food) among refugees compared to the host population led to higher poverty rates. The team cross-checked the food aid received by in-camp refugees based on administrative data from the UNHCR and WFP with food consumption data from SESRE. The analysis looks at both separately for information provided by UNHCR and WFP. While WFP is not responsible for distributing non-food items such as mattresses, cooking, feeding utensils, etc., UNHCR provides non-food items; maybe mattresses were distributed in Alemwach since it is a relatively new camp. WFP’s food and cash assistance targets all individuals in refugee households. The information received from UNHCR on food aid provided to refugees in each camp includes quantities per food item per month and cash transfers per person per month for each camp and period. The food items include cereal, wheat, maize, rice, sorghum, CSB/famex (CSB+), pulse, biscuit, date biscuit, dates, oil, vegetable oil, salt, and cash (Table E.4). We have computed the per person per month in-kind aid

administrative data from the UNHCR
food consumption data from SESRE

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Input: The data shows that cohorts that were of school age during the Indonesian occupation achieved higher level of education than older cohorts (i. e. those born in the 1970s compared to those born in the 1960s or before) testifying for an increasing trend as expected. The figure shows that despite the increasing trend a large fraction of individuals have low education levels. Interestingly, all curves start to drop after the 1987 cohort. This decreasing trend is observed among individuals aged 20 or younger in 2007 and provides evidence of a mismatch between the grade attended and the grade that they should have achieved at their age. 4 This is caused by a persistent sluggishness in grade achievement due to the high level of delayed entry to school and high rates of repetition. 5 The impact of the conflict in its different phases and the subsequent reconstruction efforts on schooling levels of children in Timor Leste is therefore unclear. The early years of violence coincided with an education for all policy in which quantity was preferred to quality. In addition, the 1999 violence that followed the withdrawal of Indonesian troops led to the destruction of schools and the removal of children from school. The reconstruction program implemented after 1999 tried to counteract this destruction, and achieved fast progress. However, the education sector was still in very poor shape. In the next section, we investigate in more detail the effects of the conflict on educational outcomes of boys and girls in Timor Leste. 4. Identification strategy and data description 4 Those born in 1992 are 15 in 2007. So they might have at most completed grade 9 and this justifies part of the drop in the curves as the grade completed is right censored. 5 The high levels of school delay are also confirmed by the figures on gross and net enrolment ratios calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007. Usage context: supporting mention, primary mention, background mention.

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Input: 5.4 Dependency ratio 1.1 1.5 0.5 0.4 0.7 1.3 Female-headed 44% 73% 45% 58% 44% 69% Head’s age 42.3 39.6 42.0 30.9 42.1 37.6 Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 12 2.2 Education Integrating refugee children into educational programs soon after arrival14 avoids the loss of valuable years of education and human capital accumulation that can hinder future prospects. Integrating refugees into national education systems can improve future outcomes for refugee children and their hosts (UNHCR, 2020; Piper et al., 2020; Abu-Ghaida and Silva, 2020; Crawford et al., 2015; Bilgili et al., 2019). Investment in human capital development can enable refugees to contribute to local economies to benefit refugees and hosts alike, and it can contribute to the recovery of countries of origin and the hosting communities. Despite the positive externalities of integrating refugees into public-school systems, a large influx of refugee children can exacerbate existing inefficiencies. Where there are large inflows, the national education system might require additional human and financial resources to integrate newly arrived children. Increasing the supply and improving the quality of schools in affected areas, supported by external assistance and financing, can avoid tension between refugees and host community populations over

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Input:
Lack of livelihood opportunities in areas of displacement combined with wide spread insecurity, lack
of freedom of movement, and limited humanitarian access to AGEs controlled areas is generating an
alarming humanitarian and protection crisis that requires immediate attention and a new strategic
approach by the APC.

## **PATTERNS OF ABUSE**

Analysis of protection risks needs to be improved to better prioritize and mitigate risks in 2017-2018.
The APC will dedicate further time and resources to producing a comprehensive protection risk
analysis. Information available suggest the following patterns of abuse:

1 REACH PIDP study, p. 17

2 IDMC. Afghanistan: New and long-term IDP risk becoming neglected as conflict intensifies.

3 The findings of the Protection community assessment in the East indicate that additional influx of people into communities, that already
have limited service providers’ capacities, overstretches the resources, like water, health and education, leading to the overcrowded
schools, hospitals facing challenges with the number of people and water sources being not sufficient to meet the needs of the local and
displaced population.

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Input: Evidence for Sub-Saharan Africa shows that female-headed households that have previously been highly dependent on male income – notably widow-headed households – are particularly prone to poverty, although other types of female-headed households may not be more likely to be poor than male-headed households (Brown & Van der Walle, 2020). For this reason, while it is important to analyze differences in poverty risk between households with female and male heads, it is also important to understand how different routes to female headship, and the changes in the household composition more broadly, impact poverty risks and expenditure levels. This paper explores level and distributional changes in expenditure among Syrian refugee households in Jordan between 2013 and 2018 using a gender lens. By the end of 2018, UNHCR had registered more than 671, 000 Syrians1 who had fled their homes and settled in Jordan. At the end of the time period covered by this research, UNHCR ’ s 2019 Vulnerability Assessment Framework (VAF) 1 UNHCR ’ s data portal records 671, 551 registered Syrian refugees residing in Jordan on January 13, 2019. See UNHCR (2022b) available at https: / / data. unhcr. org / en / situations / syria / location / 36. Usage context: background mention, supporting mention, primary mention.

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Input: These surveys are part of a long-term data collection effort by the research team (Vinck & Pham 2014) and were collected separately from the focus group discussions. The focus group discussions thus did not directly inﬂuence the design of the surveys, but rather directed the analysis strategy of the surveys that our team has collected at regular intervals in eastern DRC. The survey data are analyzed in two ways. First, 11 surveys collected between 2017 and 2021 8Eastern DRC is a site of ongoing violence, raising a number of ethical, methodological, and practical concerns about collecting data. We discuss the ethical protections we implemented when collecting this survey data in the Appendix, Section B. 9Territoires are sub-provincial administrative units. Additional details on the structure of administrative units are available in the Appendix, Section A. 1. 21 Usage context: supporting mention, primary mention, background mention.

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Input: Do people run away from their home country when a conﬂict breaks out? Does the seriousness of the conﬂict matter in this decision? In which phase of the conﬂict do they leave? When do refugees come back to their home country? In order to answer these questions, we look at the impact of conﬂict incidence on 21We will return to these issues in the policy section. 22According to the UNHCR deﬁnition, this category includes “ individuals recognized under the 1951 Convention relating to the Status of Refugees; its 1967 Protocol; the 1969 OAU Convention Governing the Speciﬁc Aspects of Refugee Problems in Africa; those recognized in accordance with the UNHCR Statute; individuals granted complementary forms of protection; or those enjoying temporary protection; and people in a refugee-like situation ”. 23The UNHCR Global Trends Report 2014 provides evidence that conﬁrms this hypothesis. About 59. 5 million people were forcibly displaced worldwide by the end of year 2014. Among them, 19. 5 million were refugees and 38. 2 million were IDPs. 28 Usage context: supporting mention, primary mention, background mention.

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Input: Deciding on whether to flee or not to flee a conflict (the migration choice) can be an individual or household choice and risk coping strategies may include temporary migration, shuttling between places, migration of only selected members of the households or migration of the whole household. This implies that individuals may stay put throughout the period observed, join or leave the household during the period, or have several episodes of out and immigration. Households may decide to leave and come back several times. In econometric terms, this means that longitudinal data may be left and right censored and have spells within. They are therefore the most complex set of panel data possible and require particular treatment of data and modeling. Survival or duration models can usually accommodate many of these complexities but it is very rare to find similar data sets used in published articles. Collecting such type of data is also not obvious, particularly if conflict is intense and survey areas cannot be reached. This is an issue where empirical economics could provide a real contribution by defining the optimal data format and adapting panel models to this format. Macro models Macroeconomics has attempted to model forced migration using models borrowed from the trade and economic migration literature such as the gravitational model (Echevarria and Gardeazabal, 2016) or used other macro models to test the impact of refugees on trade (White and Tadess, 2010). A more recent body of work is adapting trade models to take into account stochastic shocks in a dynamic framework (Cameron et al., 2007; Artuc et al., 2008). These are rational expectations models that are able to model the unpredictability of shocks, and recent work has tried to adapt these models to the context of violent Usage context: supporting mention, background mention, primary mention.

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Input: Sources of information This work includes a descriptive analysis that allows us to identify how the prevalence of non- standard employment (NSE) has evolved in the last two decades in different regions of the planet, as well as the evolution of the profile of workers in those roles, in terms of their educational level, salary per hour, and type of tasks performed. This analysis was based mainly on periodic surveys of households that included information regarding the employment and educational situation of individuals. Although the denomination of this type of surveys varies from country to country, in all the cases analyzed there is usually a survey of annual or higher frequency that includes information required to identify the labor status of the individuals as well as to analyze the salary profile and education of the employed. However, it should be noted that the identification of the type of work relationship (standard or non- standard) is frequently limited in these data sources. Indeed, it is only possible to identify part-time employment and temporary employment (not in all cases) within the non-standard forms of employment mentioned in the previous section.

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Input: (a) High frequency sample surveys using smartphone and cellular technologies. For example, a high-frequency survey initiative in Somalia employs a dynamic questionnaire loaded onto smartphones, which enables data to be collected from household interviews in 60 minutes. This approach was developed to overcome the challenges of insecurity, limited data gathering capacity and budgetary constraints. (b) The use of mobile phones to conduct surveys or follow up interviews following face-to-face household surveys. For example, a Bank paper on the impact of the 2012 crisis in Mali on IDPs, refugees and returnees used information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. This combination provided a mechanism to monitor the impact of conflict on hard-to-reach populations who at times live in areas inaccessible to enumerators. And in Sierra Leone and Liberia, the Bank supported the use of mobile phones to collect key socio-economic data on the effects of the Ebola virus. (c) Crowdsourcing data on displacement. Platforms such as the Kenyan Ushahidi has crowd- sourced data on displacement in Kenya and eastern DRC by encouraging IDPs and host communities to report incidents using their mobile phones or the internet, including information about living conditions. The platform references these reports geo-spatially. (d) Geo-mapping of data on displaced populations and affected host communities. Usage context: supporting mention, background mention, primary mention.

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Input: Second, the Turkish LFS asks respondents whether they had previously lived in a different province (one of Turkey ’ s 81 NUTS 3 regions), and if so in what year they moved to their current province. We estimate the impact of refugees on the probability a native moved to a subregion in the past year. Table 9 reports OLS and IV estimates of the impact of refugee on net population growth in subregion (Columns 1 and 2) and gross population inflows (Columns 3 and 4). Net population growth is estimated at the level of NUTS 2 subregions. Population inflows to a subregion are estimated at the individual level (and standard errors clustered by subregion- year). All regressions include subregion and year fixed effects and a year-specific control for log distance from the Syrian border. The first column presents the estimates for the whole sample, subsequent columns for different sub-samples by gender, age and education. For the full sample the net population growth in a subregion is positively correlated with refugee flows, while the IV point estimate is negative (though neither estimate is statistically significant). The probability of a Turkish person migrating to a subregion is negatively correlated with refugee flows (the OLS estimate is highly statistically significant). The IV estimate is of a similar magnitude, but no longer statistically significant. This same pattern broadly holds for both women and men. The only other statistically significant IV estimates are a decrease in the population aged 15 – 24, an age group that is likely more mobile, and of those with medium educational attainment. There is also a decrease in the inflow of low Usage context: supporting mention, background mention, primary mention.

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Input: that are at a maximum distance of 80 km from the 7, 547 clusters. 12 Figure 1 shows the locations of these refugee camps and clusters. Clusters are represented in green, while clusters in the vicinity of a refugee camp are represented in red. Refugee camps are designated with a red + sign. There are some important limitations associated with this data. First, the data only provides information on refugees residing in camps monitored by the UNHCR. In Figure B. 7, we combine the UNHCR refugee camp data on the annual number of refugees and the UNHCR official statistics on refugees (which includes people in refugee-like situations) at the country level. 13 Although the overall trends match, our constructed dataset clearly underestimates the true refugee population in Africa, which is not surprising since our camp-specific data does not contain dispersed refugees or refugees living outside of camps. While our data seem to represent quite fairly the number of refugees in camps, there is significant heterogeneity across countries. Based on the visual inspection of Figure B. 8, the quality of the refugee data appears to be less reliable for the following countries in our sample: Gabon, Mali, Senegal, and Togo. Usage context: primary mention, supporting mention, background mention.

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Input: Road data from the provincial transport authority's annual condition census were used to validate the project's prioritization model. The road data include pavement condition index scores, GPS coordinates of assessed segments, and traffic count estimates for 312 road links in the target area. Cross-checking the road data against community consultations confirmed that communities with the lowest road condition scores also reported the highest frequency of missed health facility appointments due to inaccessibility during the rainy season.

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Input:
40% of the respondents reported that their community **38%**
or living area is not currently experiencing elevated
tensions and frequent clashes. This perception is
consistent with the available surveys and assessments
run by the NWS PC, on the escalation of hostilities
started in October 2023 and still ongoing.
 The survey also explored the **presence of Unexploded Ordnance (UXO)** in the living areas of youth in
northwest Syria, along with their experiences of receiving support for clearance and awareness, 13%
reporting presence in their area. Among those who reported the presence of UXO, 25% received help or
support for clearance, and 79% of those who received support reported that it included an awareness
session about explosive ordnance exchange.

**Is your community experiencing elevated**

**tensions and frequent clashes?**

**38%**

**7**

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Input: 6 3. The Data The Kagera Health and Development Survey (KHDS) was originally conducted by the World Bank and Muhimbili University College of Health Sciences (MUCHS), and consisted of about 915 households interviewed up to four times from fall 1991 to January 1994 (at 6-7 month intervals) (see World Bank, 2004, and http: / / www. worldbank. org / lsms /). The KHDS 1991-1994 serves as the baseline data for this paper. Initially designed to assess the impact of the health crisis linked to the HIV-AIDS epidemic in the area, it used a stratified design to ensure relative appropriate sampling families with adult mortality. Comparisons with the 1991 HBS suggest that in terms of basic welfare and other indicators, it can be used as a representative sample for this period for Kagera (results not shown but available upon request). The objective of the KHDS 2004 survey was to re-interview all individuals who were household members in any round of the KHDS 1991-1994 and who were alive at the last interview (Beegle, De Weerdt and Dercon, 2006). This effectively meant turning the original household survey into an individual longitudinal survey. Each household in which any of the panel individuals live would be administered the full household questionnaire. Usage context: background mention, supporting mention, primary mention.

KHDS 1991-1994
KHDS 2004 survey
Kagera Health and Development Survey

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Input:
We develop and use a model to estimate expected asset losses and expected welfare losses, and quantify socioeconomic resilience in 117 countries. The model builds on Hallegatte et al. (2016b), which only considered river floods and 90 countries. Here we add new countries thanks to new socioeconomic data, and we introduce additional hazards - coastal floods and storm surge, windstorm, earthquakes, and tsunamis - using the risk assessment provided in the Global Assessment Report (UN-ISDR 2015). We also simplify the model - focusing on the factors that were found to have most influence on our previous assessment and disregarding other factors - and improve our modeling of insurance and social protection.

In our national indicator, we include the role of early warning system using data reported in the context of the Hyogo Framework for Action [7] (UN-ISDR, 2015a). The priority for action #2 ("Identify, assess and monitor disaster risks and enhance early warning") includes an indicator (P2-C3) related to "Early warning

<!-- PAGE 16 --> overlays flood maps from the GLOFRIS global model and poverty maps from the World Bank. For countries where this study does not provide data, we use an older, similar study by (Winsemius et al. 2015). This other World Bank study overlays the same GLOFRIS flood maps as above with geo-localized household surveys (using the Demographic and Health Surveys [8] ) to assess the exposure of poor people to river floods relative to the exposure of non-poor people.

We calculate the average capital productivity as output-side GDP divided by total reproducible capital within a country, both variables from Penn World Tables.

GLOFRIS
Demographic and Health Surveys
Penn World Tables

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Input: According to Council on Foreign Relations, the estimated number of people killed since December 2013 is over 50, 000, and over 1. 6 million people are internally displaced66. Economic Opportunity South Sudan has abundant natural resources. Before its oil production fell sharply, the government relies on oil for its revenue. It also has very fertile soils and abundant water supplies. South Sudan has struggled with economic development since its independence and its economic conditions have deteriorated since January 2012 when the government decided to 61 IMF, Six Things to Know about Somalia ’ s Economy, April 11, 2017, http: / / www. imf. org / en / News / Articles / 2017 / 04 / 11 / NA041117 ‐ Six ‐ Things ‐ to ‐ Know ‐ About ‐ Somalia ‐ Economy 62 UNHCR, Somalia, http: / / reporting. unhcr. org / node / 2550? y = 2016 # year 63 Human Rights Watch, World Report, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / south ‐ sudan 64 Human Rights Watch, World Report, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / south ‐ sudan 65 UNHCR, South Sudan http: / / reporting. unhcr. org / node / 2553 66 Council on Foreign Relations (CFR) https: / / www. cfr. org / global / global ‐ conflict ‐ tracker / p32137 #! / conflict / civil ‐ war ‐ in ‐ south ‐ sudan

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Input: We have constructed two different variables that try to account for the degree of severity of the conflict. 10 The first variable identifies individuals belonging to households that were displaced due to the 1999 wave of violence (all members displaced). The second variable identifies individuals in households that report having their house completely destroyed by the violent attacks in 1999. The TLSS 2001 contains also useful retrospective information on school attendance and grade attained across three different academic years: 1998 / 99, 1999 / 00 and 2000 / 01. We are 7 Commission for Reception, Truth and Reconciliation & Benetech Human Rights Data Analysis Group. ― Human Rights Violations Database. ‖ 9 February, 2006. Website: http: / / www. hrdag. org / resources / timor-leste_data. shtml. 8 There may be potential sample biases in the statement taking procedure given the voluntary nature of the process. It is possible that those living in more remote or mountainous areas, those living far away from the areas where the statements were taken, the sick, old and disabled and those with no access to the media or means of mass communication have a lower probability of being part of the sample. By contrast, those more active in local communities are more likely to have provided a testimony.

Human Rights Violations Database
TLSS 2001

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Input:
### Financial Covenants

The Loan Agreement includes the following financial covenants: (i) the borrower will maintain the SIGAF accounting module for the project active and updated within five business days of any transaction; (ii) annual project financial statements will be prepared in accordance with International Public Sector Accounting Standards (IPSAS) on a cash basis; (iii) external auditors will be appointed within four months of project effectiveness; and (iv) the external audit report, together with the management letter, will be submitted to the Bank within six months of each fiscal year-end.

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Input: All civil works contracts under the project include as a condition precedent to payment the submission of a workers' accommodation standard certification and a signed code of conduct from contractor supervisory staff, consistent with the LMP. The PIU's E&S officer has developed a compliance checklist aligned with the LMP requirements, which is used during quarterly site inspections. Instances of non-compliance identified during inspections are recorded in a corrective action register and followed up within 30 days.

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Input: V. B Impacts of exposure to Taliban territorial control on educational attainment Table 4 reports estimates for the effects of refugees ’ exposure to Taliban, contested terri- tories, and U. S. allies on refugees ’ educational attainment. First, this reveals that the im- pact of Taliban exposure is negative across four variables we examined: literacy, formal schooling attendance, primary education completion, and secondary education comple- tion. However, it is worth noting that the estimated effects do not reach statistical signif- icance for the completion of primary education. Nevertheless, when we combine these four variables into an education index (computed as their average), we observe enhanced precision, allowing us to document the significant negative effects of Taliban exposure on the education index. These effects are both substantial and meaningful. For instance, the coefficients in column (3) and Panel A indicate that a one-unit increase in the aver- age share of exposure to Taliban presence (representing 100 % presence in the province of birth during the five years between 2017 and 2021) is associated with an 18. 5 percentage point decrease in the share of individuals with some level of education. The estimates further demonstrate strong and adverse effects of refugee exposure to con- tested territories on educational attainment. Usage context: primary mention, supporting mention, background mention.

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Input:
widespread among Shona households, and access to external support was minimal. Although employment levels

began to recover a year later, they remained below pre-pandemic levels, underscoring the prolonged adverse

impact of the pandemic on this already vulnerable community. [[9]]

_Figure 1: Labor force status after the COVID-19 outbreak (18-64 years)_

_Source: Kenya COVID-19 Rapid Response Phone Surveys (RRPS)_ _[[9]]_

**Two rounds of household surveys taking place over five years before and after the transition of the Shona in**

**Kenya from statelessness to citizenship help us to understand this community – and present one of the first**

**socioeconomic pictures of the impact of citizenship on the welfare of stateless persons.** Between 2019 and 2024,

the median age of Shona household members remained unchanged at 18 years, underscoring a predominantly

youth demographic profile. Gender distribution is even, showing only a marginal change, with the female population

increasing slightly from 49 percent to 50 percent. Average household size has risen modestly from 4.9 to 5.4

members, which may reflect greater household consolidation or socio-economic stability following their

naturalization. The most notable shift is the significant reduction in the proportion of female-headed households,

Kenya COVID-19 Rapid Response Phone Surveys

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Input:
which collected heretofore unavailable expenditure data on each type of subsidized food

4. New 1997 Household Budget Survey

Egyptian food subsidy system.' [8] This study used expenditure data from the 1990/91

Household and Income Expenditure Survey (HIES), which was a large, nationally

and Statistics (CAPMAS).1 [9] One of the main problems with this 1990/91 HIES survey

Household and Income Expenditure Survey (HIES)

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Input: 14 4. 1 Variation across Districts Pakistan is divided administratively into four provinces with 102 districts — Punjab, Balochistan, North-West Frontier Provinces (NWFP), and Sindh — plus the federal capital Islamabad, the Federally Administered Tribal Areas (FATA), the federally administered Northern Areas and Azad Jammu and Kashmir (AJK). The four provinces or Punjab, Balochistan, Sindh and NWFP, together with Islamabad, account for more than 97 percent of the population. Geographically, parts of Balochistan, the NWFP and FATA border Afghanistan. Sindh and Balochistan are sparsely populated provinces, with the exception of Karachi in Sindh, which is the single biggest metropolis in the country with a population approaching 10 million. We use data from the population census, 1998, as well as the census of private schooling, 2000, to provide estimates of madrassa, private, and government school enrollment in each district except for those in the province of FATA. The geographical dispersion of madrassa enrollment depends on how we define madrassa prevalence. There are three alternatives. We could present a geographical breakdown of the total number of children enrolled in madrassas. This number is related to the total population of the district, and may thus reflect only the size of the district relative to others. Usage context: supporting mention, primary mention, background mention.

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Input: Surveys, 76 Demographic and Health Surveys (DHS), 77 and Multiple Indicator Cluster Surveys (MICS). 78 The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner (UNSD 2014). There are only a few cases where modules or questions on forced displacement have been integrated into survey instruments (most notably in Somalia, Uganda, West Bank and Gaza, Azerbaijan, Bosnia and Herzegovina, Serbia, Ghana, as well as health surveys in Albania, Ukraine and Moldova). There are several challenges associated with ‘ mainstreaming ’ forced displacement into household surveys: (a) there is huge demand for adding sector-specific or thematic modules to international surveys; (b) it is relatively difficult to convince national statistical agencies to modify their county-specific surveys; (c) disaggregating survey results by specific vulnerable groups (e. g. refugees, IDPs, migrant populations) requires these distinctions to be integrated into the sampling frame and sometimes there is insufficient information to do this or a lack of resources to expand the sample size; (d) lack of access to displacement- affected areas; and (e) difficulties associated with integrating an inherently political topic into less controversial surveys. Usage context: background mention, primary mention, supporting mention.

Multiple Indicator Cluster Surveys
Demographic and Health Surveys

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Input: 18 household socioeconomic information. In some cases, socioeconomic information is collected at the individual or household level but more often information is collected at the community level. It is extremely rare to have unit record data sets on IDPs, which explains why we found only 18 studies in the Econpaper repository as already documented. Recognizing the problem of scarcity and availability of information on IDPs, international organizations have set up in several countries coordination mechanisms to count IDPs usually coordinated by IOM, UN ‐ OCHA or the UNHCR. There are also global efforts to centralize this information on the part of organizations such as the UNHCR, UN ‐ OCHA, the international Displacement Monitoring Centre (iDMC) or the Joint IDP Profiling Services (JIPS). These efforts are making good progress on harmonizing counts of IDPs but remain short of establishing proper data collection systems that could deliver in the years to come unit data of quality for research. Hence, research on IDPs remains constrained by lack of data, lack of a blueprint on how to collect data and lack of an organization dedicated to IDP data collection. Usage context: background mention, supporting mention, primary mention.

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Input: Page 7 of 51 In this sense, it is necessary to draw attention to the fact that the non-standard employment statistics presented below are not homogeneous among countries. In countries where both forms of non- standard employment were identified, we define non-standard employment as those occupations that satisfy at least one of the conditions, that is, either corresponds to a part-time occupation or a temporary job. In countries where temporary employment was not identified in the data, our non- standard employment category will coincide with part-time employment. Note that in either case, as other non-standard employment modalities are not identified, the indicators presented in this paper indicate a lower level with respect to the true dimension of the phenomenon. The only aspect addressed that required the use of additional information was the analysis linked to the profile of tasks that are developed in the framework of non-standard jobs. To carry out this analysis, the information available in the O * NET (Occupational Information Network) database was used in conjunction with the Household surveys. This database provides information referring to the content of tasks of the occupations. Usage context: primary mention, supporting mention, background mention.

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Input: In addition to these categories, the 2018 home visit survey reports expenditure data on water, transportation, infant needs, essential household items, hygiene items, debt payment, and telecommunications. This difference in expenditure categories could inflate reported expenditure in the second wave. This is because survey respondents tend to reveal more accurate information when asked item by item, than if asked for an expenditure or income aggregate. In part, this may be due to an intention to withhold information to obtain the monetary assistance, and in part, it has to do with the recall error that this type of questionnaire elicits. To aggregate expenditure, we restrict the number of categories to those in both waves which represent a sizeable portion of a household expenditure; that is, rent, utilities, food, water, and transportation. These five categories average 97. 5 percent and 77 percent of household expenditure in the first and the second waves, respectively. While spending on healthcare, education, and household items were also recorded in both waves, the amounts were not large and were excluded because health care and education expenditures may reflect a vulnerability (for example, caused by illness or disability in the case of health care), or a luxury for those who can access the service, and 8 The monthly per capita cash assistance Syrian refugees receive is roughly 60 %- 65 % of the Survival Minimum Expenditure Basket (SMEB) and is intended to cover rent, water and sanitation costs. Assistance levels are mainly determined by family size. Since 2014, PAs often make the withdraws or their allocated cash assistance using iris scan enabled ATM machines (UNHCR, 2018b). Usage context: primary mention, background mention, supporting mention.

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Input: 11 however, since they cannot distinguish between a country where the population is concentrated in one cluster covering 10 % of the territory and one where the population is concentrated in two clusters of 5 % each, but with a considerable geographical distance between them. Population and conflict geography in the Democratic Republic of Congo (DRC) corresponds to these arguments regarding population concentration and dispersion. Concentrations of language-based minorities are evident throughout eastern DRC. Due to the limited access of the government, the close proximity to international borders, and the dense population concentrations, these concentrated minorities have a higher potential of conflict than other, more accessible, sparsely populated areas of DRC. Figure 1 shows the population concentrations in 1990 (CIESIN data) for Central Africa. Heavily populated areas are shaded in deeper tones of red / grey. Civil conflict in DRC has overwhelmingly occurred in the eastern portion of the state, which is the most densely populated area and also geographically peripheral to the capital, Kinshasa. Of the eleven Congolese rebel groups accounted for in the dataset used in this paper, all have operated either exclusively or partially in the eastern and southern areas of DRC. Usage context: supporting mention, background mention, primary mention.

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Input: MPI of IDP communities is considerably higher than that of the host communities, so it follows that the censored headcount ratios display a similar gap. The difference by indicator is particularly noticeable in the living standards dimension, where the electricity, cooking fuel, housing, and bank account indicators show over 34 percentage point difference between the censored headcount ratios for the IDP and host communities. Figure 1. Censored headcounts of each indicator in the MPI, by displacement status in Sudan (2018)

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Input: 20 between 1948 and 1965, with increases in the percentage of adults with religious education in the cohorts born after this date. There is also wide geographical dispersion in the prevalence of madrassa education in Pakistan. Although all districts report that less than 2. 5 percent of children in the relevant age group (children between the ages of 5 and 19) are going to madrassas, the Pashto speaking belt that borders Afghanistan stands out in terms of the popularity of madrassas as an educational choice. The notion that the madrassa movement coincided with resistance to the Soviet invasion of Afghanistan is supported by the 1998 data from the population census. The increase in the stock of religiously educated individuals starts with the cohort that came of age in 1979 (the year of the Soviet invasion of Afghanistan) and the largest increase is for the cohort co-terminus with the rise of the Taliban. Combined with the fact that the largest enrollment percentage in Pakistan is in the Pashtun belt bordering Afghanistan, this suggests events in neighboring Afghanistan influence madrassa enrollment. Is there something intrinsic about Pashtun sensibility or tribal culture that leads to higher madrassa enrollment? The differentiation of the Pashtun and non-Pashtun districts does not extend to Pashtun and non-Pashtun households in the LEAPS data. Usage context: primary mention, supporting mention, background mention.

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Input: Impacts on the labor market are unclear; initial research suggests that there has been a supply shock to informal labor markets. This has had a large-scale impact on the employment of natives in the informal sector. At the same time, research suggests there has been a boost to formal employment for the Turks, but this has been uneven: the low educated and women experience net displacement from the labor market and, together with those in the informal sector, declining earning opportunities.  The “ Socio-economic Assessment of the Impact of Syrians under Temporary Protection (SuTPs) on Turkish Hosting Communities ” [ongoing], to be undertaken in partnership with the Government of Turkey, will include a nationally representative household survey with SuTP and local Turkish households including camp and non-camp environments. The questionnaire will cover welfare (assets, income, expenditure), municipal services, labor and employment, education, social networks and quality of life. F. Options to improve forced displacement statistics Significant efforts are needed to enhance the reliability, comparability, quality and scope of the global data on forced displacement. In particular, more robust estimates are needed of the scale (stocks, flows and locations) and typology (demographics, location and accommodation) of forced displacement crises. This requires substantial improvements in the rigor of data collection and compilation methodologies including: (a) Harmonization of definitions and methodologies used in the collection and analysis of statistical data on forced displacement — covering stocks and flows of refugees, asylum- seekers and IDPs — to ensure comparability across regions and countries; Usage context: background mention, supporting mention, primary mention.

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Input: percentage points less overall and 34 and 18 percentage points less in Eritrean and Somali camps, respectively. There is little benefit to working outside the camp to reduce aid reliance in South Sudanese households. On the other hand, years spent in Ethiopia are only associated with a gradual decrease in dependence on donations, with only a 1 percentage point reduction for every year spent in the country. The lack of employment outcomes contrasts with other refugee-hosting countries in East Africa, highlighting the lack of labor market access for Ethiopian refugees. Evidence suggests that refugees 0 5,000 10,000 15,000 20,000 25,000 Camp Hosts Camp Refugees Figure 3.19: Value of productive assets among households with non-farm business 0 0.05 0.1 0.15 0.2 0.25 Camp Hosts Camp Refugees Figure 3.18: Household has non-farm business Source: World Bank Staff based on SESRE 2023. Productive assets include the subset of assets with production value, such as farm tools and water pumps, sewing and building equipment, and commercial cars. Outliers are treated, and values are adjusted for inflation. 0 20 40 60 80 100 Camp Hosts Camp Refugees Current Camp Refugees COB Other (rental income, PSNP, pension) Remittances (local/international) Donations (NGO/gov) Crops/livestock Salary (employment/casual labor) 0

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Input: 16 { Tables 4 and 5 here} Among the first six categories that are based on raw census data, three categories (raw scaled, R & R not scaled, and R & R scaled) are constructed through the summation of bilateral raw numbers and disaggregations of some aggregate categories in the original censuses. Since these categories together constitute around 45 percent of migrants in each census round, the original bilateral portion of each cell was compared with the final number assigned to them after the various calculations as a check on accuracy. For each decade, therefore, the overall percentage contribution of the raw bilateral data to the total is calculated (table 6). 23 In each census round, at least 92 percent of all those categories are derived from the raw data. { Table 6 here} Simulating Missing Data Finally, to examine the reliability of the estimated missing census data and test the methodologies, several scenarios are assumed. All bilateral observations for a single year for four countries (Australia, United States, Switzerland, and Chile) in different parts of the world are deleted and the missing cells are filled using one of five methods. 24 The first simulation assumes that all bilateral data for 2000 are missing but that the total number of migrants is available. Usage context: supporting mention, background mention, primary mention.

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Input: to severe based on the score. Moreover, depression is screened with the Patient Health Questionnaire, a 9-item tool administered directly to adolescents. These instruments collectively gauge a broad spectrum of psychological and emotional states, including post-traumatic stress, emotional disturbances, behavioral issues, inat- tention, peer relationships, prosocial behavior, anxiety, and depression, offering a com- prehensive view of the mental health and socioemotional development of the minors in our study. Figures B. 3 and B. 4 depict the distribution of the raw scores for Venezuelan and Colom- bian minors for each of the four scales. Surprinsingly, we do not observe any stinking differences on the distribution of any of these scores across groups. We are also not able to distinguish statistical differences between Colombian and Venezuelan children in any of the scales, when we estimate the specification highlighted in equation (1) as illustrated in Table 7. This is an unexpected result considering that typically, forcibly displaced pop- ulations have a high prevalence of socioemotional and mental health issues, but might be related to the fact that Venezuelan migrants have not faced war (as many forced migrants have in other contexts) directly and as such, these issues are less prevalent.

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Input: sexual encounter. Finally, about 10 % of study respondents have experienced a forced sexual encounter in their lifetimes, which is consistent with the national figure of 13 % for this age group (DHS 2007). 17 Table 2B presents baseline balance tests for the household characteristics of respondents in the study sample. More than 40 % of households are headed by females, and the average household has slightly fewer than five members. The mothers of EPAG respondents tended to have very low education levels (almost 60 % had never been to formal school), while the fathers had more variance in their education (about a quarter had no schooling, but over 60 % had at least some secondary education). In both treatment and control households, a high proportion of school-aged children are in fact enrolled in school. Less than half of young people aged 13-30 in study households have any employment. Housing conditions are also similar across experimental groups. Tables 2A and 2B include a representative, but not exhaustive, list of indicators that were tested for balance by the authors. We conclude that the presence of very few significant differences between the individual or household characteristics between the two experimental groups indicates a high degree of internal validity for the study. 3. 5. Usage context: primary mention, supporting mention, background mention.

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Input: The MHPSS program evaluation conducted at 18 months assessed changes in functional impairment, social participation, and perceived support among beneficiaries who received structured psychosocial support compared with a waitlist control group. Facilitators documented session attendance and content coverage using a standardized session log. The evaluation found meaningful reductions in psychological distress scores among participants, though the absence of clinical diagnostic instruments limited the ability to measure changes in diagnosable mental health conditions.

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Input: A significant increase in non-participation among men can be clearly seen in the West Bank in 2020Q2, mostly at the expense of the private infor- mal sector. The labor market then quickly bounces back. By 2020Q3, labor market stocks in the West Bank appear indistinguishable from pre-pandemic periods. Gaza, on the other hand, experienced three consecutive quarters of depressed employment from 2020Q2 to 2020Q4. Non-participation spiked twice, first in 2020Q2 and then in 2020Q4, corresponding respectively to the initial lockdown orders and the subsequent outbreak in Gaza. Recovery also appears to be slow and uneven. Figure (3) shows the labor market flows. We exploit the specific panel structure of the LFS dataset, described in Section 3. 1 by focusing on one cohort of the same respon- dents who were surveyed in 2019Q1, 2019Q2, 2020Q1, 2020Q2, and finally 2020Q4. This cohort of individuals allows us to observe labor market transitions into the pan- demic; to compare with a period over the same quarters in 2019; and, finally, to observe their recovery outcomes in 2020Q4. Overall, Figure (3) shows two labor markets with high levels of churning. On average, 29 % of individuals in the sample would change their labor market states after just one quarter. These churns are especially prominent between informal employment and unemployment, and in Gaza between unemployment and non-participation. The figure also illustrates the significant differences between the West Bank and Gaza in labor market dynamics, differences already observed in the labor market stocks presented in Figure (1). In addition to the significant flows between unemployment 11 Usage context: primary mention, background mention, supporting mention.

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Input: 1 Introduction Company owners and managers make two decisions with important implications in the labor market: what skills are demanded and who to hire. On the demanded skills, a driver of trends in employment is the changing demand for soft skills (Heckman and Kautz (2012), Weidmann and Deming (2021)). However, we know relatively little about what kinds of soft skills employers value in modern entry- level jobs (Heller and Kessler, 2022). On the decision of who to hire, it is in the best interest of companies to hire based on workers productivity. However, several studies have documented the existence of labor market discrimination in a wide range of contexts (Bertrand and Duflo (2017), Neumark (2018)) and it remains unclear how discrimination operates throughout the hiring process and how the existent empirical evidence on discrimination is linked to economic theory (Bertrand and Duflo, 2017). We conducted a correspondence study in 2023 using a large online job platform to assess demand for soft skills in the context of hiring discrimination in Malaysia. Malaysia is a particularly interesting setting because it is an upper-middle-income economy, home to multiple ethnicities representing large shares of the population, and previously documented gender gaps in labor force participation and wages.

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Input: by the migration episode. These include the minors age and sex, the parent ’ s and grand- parent ’ s education pre-migration, and a wealth index constructed with retrospective in- formation on the household conditions pre-migration. 14 Grandparent ’ s education is used a proxy for living standards that is unaffected by the migration episode from Venezuela to Colombia and the Venezuelan crisis, which intensified in 2016. ϵij depict the standard errors clustered at the household level to correct for intra-household correlation. For robustness, we will present the estimates of equation 1 with and without controls. As further robustness, we use propensity-score weights (Hirano and Imbens 2001, Hirano, Imbens and Ridder 2003). 15 V. A Physical development: Body Mass Index and health status In our study, we examine disparities in nutritional and health status among Colombian and Venezuelan children aged 5 to 10 years, focusing on standardized body mass index (BMI), instances of overweight and underweight, and overall health status. The BMI serves as an indicator of nutritional status for both adults and children, calculated as an individual ’ s weight in kilograms divided by their height in meters squared, according to World Health Organization (WHO) guidelines. Usage context: background mention, supporting mention, primary mention.

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Input: 10 inflow). We then use the composite variable as an instrument to identify the causal impact of refugee presence on livelihood diversification and commercialization in host communities. Figure 2: Household livelihood strategy framework under refugee inflow Source: Adapted from Nielsen et al. (2013) and Walelign and Jiao (2017) 4. Data sources The major data source of the current study is the World Bank ’ s Development Response to Displacement Impacts Project (DRDIP) 12 baseline survey from Ethiopia. The Ethiopia DRDIP survey was administered between September 2017 and August 2018. The survey covers 113 Kebeles (wards) in 16 Woredas (districts) from the top five refugee-hosting regions in Ethiopia. The selection of the sample households follows stratified random sampling with proportion to size (the number of households) using Woredas as a geographic stratum. The sample originally comprised a total of 3, 390 households, who were selected using systematic random sampling within each Woreda. We used data from 3, 375 households, as 15 of them were excluded due to missing location information (GPS). Usage context: primary mention, background mention, supporting mention.

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Input:
As with the earlier two matching estimations, we matched the children within each DHS year.

This is around 7, 6, and 5 percentage points for the 2007, 2011, and 2014 DHS, respectively.

"The Effect of Water and Sanitation on Child Health: Evidence from the Demographic and Health Surveys 1986-2007.

16 According to the Joint Monitoring Programme (JMP), an improved sanitation facility is defined as "one that hygienically separates human excreta from human contact.

" The JMP considers the following categories as improved sanitation: flush to piped sewer system, flush to septic tank, flush to pit (latrine), flush to unknown place, ventilated improved pit latrine, pit latrine with slab, and composting toilet.

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Input: Most papers with few exceptions use standard OLS estimators or some of its variants (Table 2). Two papers use general equilibrium models (Bodvarsson, Van den Berg, and Lewer 2008; Hercowitz and Yashiv 2002) and two papers simply compare means between treated and non-treated groups resulting in simple difference estimations (Card, 1990 and Alix-Garcia and Bartlett, 2015). [Table 2] The unit of observation varies depending on the data at hand. Most studies rely on household survey data where individuals or households are the unit of observations and most studies include some regional dimension (more frequently administrative areas). Where longitudinal or panel data are available time is also included. Other choices for unit of observations include skills or education level, various types of population groups (based on gender, age etc.), and, in a few cases, economic sectors, industry or labor market segments. The use of fixed effects varies. Some papers use the full set of parameters depicting units of observation (for example, household, region and time fixed effects in equations where the unit of observation is constructed using household, region and time). Other papers use subsets of these parameters whereas other papers introduce variables that are not used to identify the unit of observation.

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Input: Programs designed to enhance employment among young people can focus on the supply side of the labor market, including skills training programs for both wage employment and entrepreneurship training; or programs to augment demand, such as wage subsidies, public works, and community service programs; or programs to help the labor market clear, such as job search assistance and placement services. In addition, it may be that the constraints facing young people are not in the labor market itself, but in other markets, such as for credit. The vast majority of jobs programs have focused on the supply side: training programs make up about 79 percent of over 600 cases included in the World Bank ’ s Youth Employment Inventory database. 3 Although rigorous and general evidence of success is limited, it seems that successful skills training programs share a few key features: they are responsive to local market conditions, they provide more than just technical skills in a specific area (including, for example, “ life skills ”), and they include ancillary services that alleviate other constraints preventing successful labor market integration (e. g., access to credit). Among the most celebrated are the Jovenes programs that provide demand-driven technical training, plus social skills that help in the labor market, plus internships. Usage context: background mention, primary mention, supporting mention.

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Input: Another way to view the connection between low return on education and youth frustrations is that the rapid intergenerational mobility in education has failed to yield similar mobility in either income or social status. This delinking between educational and occupational mobility has been documented for Egypt by Binzel and Carvalho (2013). While there is no similar work on Jordan, this article contributes to this agenda by documenting the first step in this process, which is the link between public investment in schooling and the educational mobility across generations. 1. This is based on version 2. 0 of the Barro-Lee dataset for educational attainment among the total population 15 and older.

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Input: We have constructed two different variables that try to account for the degree of severity of the conflict. 10 The first variable identifies individuals belonging to households that were displaced due to the 1999 wave of violence (all members displaced). The second variable identifies individuals in households that report having their house completely destroyed by the violent attacks in 1999. The TLSS 2001 contains also useful retrospective information on school attendance and grade attained across three different academic years: 1998 / 99, 1999 / 00 and 2000 / 01. We are 7 Commission for Reception, Truth and Reconciliation & Benetech Human Rights Data Analysis Group. ― Human Rights Violations Database. ‖ 9 February, 2006. Website: http: / / www. hrdag. org / resources / timor-leste_data. shtml. 8 There may be potential sample biases in the statement taking procedure given the voluntary nature of the process. It is possible that those living in more remote or mountainous areas, those living far away from the areas where the statements were taken, the sick, old and disabled and those with no access to the media or means of mass communication have a lower probability of being part of the sample. By contrast, those more active in local communities are more likely to have provided a testimony. Usage context: supporting mention, background mention, primary mention.

Human Rights Violations Database
TLSS 2001

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Input:
Usual) which would lead to even higher emissions.

We rely on four climate models: CNRM (Gueremy et al. 2005), ECHAM

(Cubasch et al 1997), GFDL (Manabe et al. 1991), and MIROC (Hasumi and Emori

shown in Figure 1. Note that a range of temperature changes are predicted for this

emission scenario.

This study

Using a tropical cyclone generator in each ocean basin, the climate data is used to

project 17,000 tropical cyclone tracks (Emanuel et al. 2008). There are 3,000 tracks in

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Input: 6 3. Data Sources and Methods 3. 1. Administrative data on educational outcomes in Italy Two administrative data sources represent the backbone of this paper. These are the administrative data obtained from the Ministry of Education (MoE) for academic years 2021-22, 2022-23 and 2023- 24, and standardized test score data from the Italian National Institute for the Evaluation of the Educational System (INVALSI) for the 2022-2023 academic year. These datasets offer valuable insights into the educational outcomes of students in Italy, including Ukrainian refugees who entered the Italian school system following the invasion in 2022. Deϐinitions. In both datasets, students are categorized into ϐive demographic groups based on their nationality and timing of entry into the Italian educational system. These groups are Italian students, Ukrainian refugee students, non-refugee Ukrainian students, newly arrived foreign students, and other foreign students. Among Ukrainian students, the distinction between refugees and non- refugees is based on their enrollment date in the Italian education system. Ukrainian refugees are deϐined as Ukrainian students who enrolled in Italian schools after February 2022. In this paper, Ukrainian refugees are labeled “ Ukr post-Feb 2022 “, while non-refugee Ukrainians are labeled “ Ukr pre-Feb 2022 “.

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Input: UNICEF (2017) reports that for boys, child labor, school violence, and the high costs of schooling (for transportation and stationery supplies) are the main barriers to their enrollment. For girls, barriers include the distance to the nearest school, the high cost of transportation, the need to help with household chores, health problems, and families refusing to educate their daughters. According to the JD ‐ HV database, the most common reasons parents gave for their children not attending school were financial constraints (35 percent), lack of capacity in schools (29 percent), or that children were required to work to support their family (14 percent). By 2016, the enrolment rate of Syrian refugee school ‐ age children in Jordan was 83 percent, 54 percent in formal education, and 29 percent in nonformal education (World Bank 2017b). 10 Refugee Employment It has been hard for refugees to find work in Jordan. The slowdown in growth in Jordan pre ‐ dates the arrival of Syrian refugees and the economy has been increasingly unable to absorb new labor market entrants. Between 2010 and 2016, labor force inactivity increased, employment decreased, and unemployment increased in Jordan (Malaeb and Wahba, 2018). Usage context: primary mention, supporting mention, background mention.

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