Input:
**Annex 6: Procurement Plan — Year 1**

Procurement of goods, works, and non-consulting services will follow the World Bank Procurement Regulations for IPF Borrowers (2016, revised 2020). The Procurement Strategy for Development (PDSD) was agreed during preparation and disclosed on the Bank's website. All contracts above the prior review threshold will be submitted to the Bank's procurement team for no-objection before award. The PIU has been granted access to the STEP system, through which procurement plans and contract awards are recorded and reported. The SIGAF module for procurement will be configured to interface with STEP to avoid duplication of reporting.

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor

A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source


Input:
Data for these measures of German influence are obtained from Eurostat, OECD and UNIDO. Finally, we use data on exports from COMTRADE to construct a measure of Germany's revealed comparative advantage (RCA) relative to Poland and to the world.

Results in this section draw on the Orbis data set, and for this reason refer only to
employment as the outcome variable. Unfortunately, the variable turnover presents
an excessive amount of missing values, which makes it unreliable.

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor

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Input:
**(1)**
**Enrollment at a JFPR**
**school**

Using local-level survey data collected for this purpose, we test whether the evidence supports the standard economic argument that there will be little or no impact on rural roads rehabilitated, given fungibility.

independent administrative data reports that an average of 4.6 kilometers per commune

Roads in Vietnam" (SIRRV) is a panel data set of pre-project baseline and post-project

6 Least cost techniques refer to the minimum-cost engineering solution that ensures a minimum level of
motorized passability.
7 For a more detailed description of the SIRRV see van de Walle (2007).

local-level survey data
independent administrative data

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A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor

A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source


Input: Annual surveys under the project are designed to be nationally representative and use the same sampling frame as the national statistics office's multi-purpose household survey, enabling comparison of project-area trends with national trends. The first annual survey field work was completed in March, covering 4,800 households across the six project provinces. Preliminary review of the annual survey data found data quality to be high, with item non-response rates below 2 percent on all key variables. Cleaned data will be available to the evaluation team by end of April.

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor

A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source


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 “. Usage context: background mention, primary mention, supporting mention.

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

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Input: destination. Building on a growing literature documenting the relationship between subjective welfare and relative income, Fafchamps and Shilpi (2008) show that Nepalese households care about their consumption level relative to that of others in the same location. If this is the case, it is conceivable that migrants choose their destination not so much for the absolute gain in income it may provide but for the gain in relative status that would ensue. For instance, if returns to education and ability are higher in an urban setting, an educated individual may improve his relative position in society by moving from a rural to an urban setting. To investigate this possibility, we estimate equation (4) using the log of relative income (or relative consumption) as dependent variable and construct a predicted relative income measure using the same formula (5). These are shown in the second panel of Table 1. Theories of work migration predict that individuals move to increase their utility or welfare. The 1995 / 96 NLSS asked respondents a number of questions regarding their subjective satisfac- tion level with various dimensions of consumption — namely, food, clothing, housing, health care, and child schooling. They were also asked their subjective satisfaction with their level of total income. Usage context: supporting mention, background mention, primary mention.

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Input: 5. Time Use and Child Labor: The fifth module examined how children spend their leisure time, their involvement in child labor, and interactions with peers. 6. Pro-social Preferences and Migration Outlook: The sixth module concentrated on adolescents ’ pro-social behaviors, such as altruism and trust, and explored their expectations and intentions regarding migration. 7. Socio-emotional and Mental Health: The final module involved the administration of various scales to assess socio-emotional well-being and mental health, includ- ing trauma, behavioral problems, anxiety, and depression. The scales include the Trauma Symptom Checklist for Young Children (TSCYC), Strengths and Difficulties Questionnaire (SDQ), General Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). All these scales and the corresponding outcomes that we evaluated are described in the next subsection. The survey also employed the Peabody vocabulary test to evaluate the cognitive devel- opment of all participating children and adolescents. A summary of the survey modules is depicted in Table A. 1. III. C Sample comparability While Medell ´ ın ranks as the third city with the highest migration in Colombia, it is crucial to recognize the degree to which migrants arriving in the city differ from those migrating to other regions in Colombia. Usage context: primary mention, background mention, supporting mention.

Strengths and Difficulties Questionnaire

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Input: Those who had moved out of the Kagera Region by 2004 experienced consumption growth that was 10 times higher compared to those who remained in their original community. These averages translate into very different poverty dynamics patterns for the physically mobile and immobile. For those who stayed in the community, poverty rates drop by about 4 percentage points over these 13 years. For those who moved elsewhere within the region, poverty rates drop by about 12 percentage points, and for those who moved out of the region, they drop by 23 percentage points. Had we not tracked and interviewed people who moved out of the community – a practice found in many panel surveys – we would have seriously underestimated the extent to which poverty has gone down over the past 13 years in the Kagera Region; we would have reported poverty reduction at about half of its true value. Clemens and Pritchett (2007) raise similar concerns in the context of income growth and international migration. In addition, the data would omit the part of the population with a high information content on pathways out of poverty. Still, these statistics are not evidence that moving out of the community leads to higher income growth. As noted above, we cannot observe the counterfactual: What would income growth have been for migrants had they not migrated? We exploit some unique features of these data to address concerns about unobserved heterogeneity. First, individual fixed effects regressions for movers and stayers produce a difference-in-difference estimation of the impact of physical movement, controlling for any fixed individual factors that affect consumption. Second, we can control for initial household fixed effects in the growth rate of consumption since we observe baseline households in which some individuals migrate

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

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Input: As a result, migration may entail negative eﬀects on the productivity and wages of migrants, often reﬂected in terms of occupa- tional downgrading (Jasso et al. (2000)). For instance, our survey indicates that 72 % of the Venezuelans who migrated to Ecuador report that their skills were used more productively in their jobs back in Venezuela. On the other hand, international migration typically entails moving from low to high productivity countries, which can lead to increases in wages and productivity (Clemens (2011)). Migration driven by natural or man-made disasters, such as the Venezuelan exodus, is much more likely to be of the South-South type and, as a result, this type of productivity gain may be less relevant. An important feature of the EPEC survey is that it contains retrospective information on the last job held by migrants prior to leaving Venezuela. The goal in the remainder of this section is to compare the changes in occupation experienced by Venezuelan migrants 14 Usage context: background mention, primary mention, supporting mention.

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Input:
UNHCR
 ProGres
 data
 on
 the
 83,044
 registered
 refugees
 in
 Thailand
 indicates
 an
estimated
 84
 per
 cent
 are
 ethnic
 Karen
 and
 12
 per
 cent
 are
 ethnic
 Karenni.
 The
remaining
 4
 per
 cent
 are
 of
 Burman,
 Shan,
 and
 Mon
 descent,
 and
 other
 groups.
 The
majority
 of
 registered
 refugees
 come
 from
 Kayin
 State
 (65.3
 per
 cent),
 followed
 by
Kayah
 (14.6
 per
 cent),
 Tanintharyi
 (7.3
 per
 cent),
 Bago
 (5.2
 per
 cent)
 and
 Mon
 (5
 per
cent).
 (Annex:
 Myanmar
 Thailand
 Border
 –
 Refugee
 Overview,
 as
 of
 end
 of
 March
 2013)

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Input: source-destination-year level on source-time, destination-time, and source-destination ﬁxed eﬀects in a gravity-like speciﬁcation: Refugeessdt = δst + δdt + δsd + εsdt. We estimate this equation by Poisson Pseudo-Maximum Likelihood (Eaton, Kortum and Sotelo 2012), pooling countries and years (and thus including observations with zero bilateral stocks). We then construct a destination-adjusted refugee stock by subtracting the destination-time eﬀect from the actual stock: AdjustedRefugeessdt = Refugeessdt − δdt. Then, we compute the average distance traveled, share of refugees going to a contiguous country, the Herﬁndahl index of destinations, and share in wealthy OECD countries using this adjusted refugee data set instead of the actual data. Appendix Figure A5 re- ports the results. Netting out destination-time eﬀects prior to carrying out the analysis leaves the main results virtually unchanged. 4 Conclusion Our analysis suggests that the assumption underpinning the debate on responsibility- sharing may need to be partly revisited. Countries neighboring a conﬂict do host a majority of refugees and are hence bearing a disproportionate portion of the respon- sibility for providing asylum to those who are ﬂeeing from violence and oppression. Yet, the share of refugees who move to further-away destinations, including OECD countries, has been growing over time. In other words, responsibilities are increasingly shared across countries. Usage context: primary mention, supporting mention, background mention.

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Input: this way, profiling of IDP situations aims to underpin advocacy, protection and assistance activities as well as support the achievement of durable solutions by informing joint strategies between government, humanitarian and development actors. Profiling provides an overview of displacement-affected populations through the collection and analysis of minimum core data (number of IDPs, disaggregated by location, age and sex) and where possible additional quantitative and qualitative data (causes of displacement, patterns of displacement, protection concerns, humanitarian needs, vulnerabilities, and aspirations and prospects for durable solutions). Profiling may utilize data collection techniques at individual, household and community levels, often combining population estimation methods, a review of secondary data, focus group discussions, household surveys and key informant interviews targeted specifically at forcibly displaced populations (UNSD 2014). 70 Profiling methods focus on displacement situations, rather than only on displaced populations, and therefore includes comparisons to conditions in the host population. IDMC estimates that humanitarian profiling data forms the basis for 18 of their 60 country estimates and around 63 percent of their annual estimates (IDMC 2015), with the largest volume of data on conflict-induced internal displacement provided by OCHA followed by IOM. There are several practical challenges associated with IDP profiling exercises in displacement situations. Insecurity or terrain may impede access to displaced populations in conflict-affected or hard to reach areas. Usage context: background mention, primary mention, supporting mention.

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Input:
The ODHD survey (2011) found that about 7 in 10 gold washers practice
agriculture in parallel, and about 1 in 10 of them practice a trade. The higher estimates of 200,000
to 1 million consider the possibility of artisanal gold mining being practiced as a secondary or
temporary occupation.

On the positive side, mining could create a mini-boom in the local economy-that is, higher
employment and higher wages leading to an increase in local aggregate demand for food crops.
However, due to lack of geocoded agricultural modules in censuses and household budget surveys,

_**Governance**_ **Afrobarometer Question** **Coding**

imagery has inspired many researchers to investigate the
use of earth observation data for monitoring economic
activity around the world. One of the most popular earth
observation data sets is the so-called nighttime lights from
the Defense Meteorological Satellite Program. Researchers have found positive correlations between nighttime
lights and several economic variables.

The study finds that the Defense Meteorological Satellite Program data are quite noisy and therefore the resulting growth elasticities of Defense Meteorological Satellite Program nighttime lights with respect to most of these socioeconomic variables are low, unstable over time, and generate little explanatory power.

Defense Meteorological Satellite Program

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Input: Figure 3 also shows that there is considerable variation in both indexes within our sample when averaging these indexes at the regional level over the period of investigation. 16It is possible that our approximation is noisy and could potentially induce non-random measurement errors. In Section 4. 3, we propose an instrumental variable approach and estimate Shareodet from the EPR-ER data using a gravity model. Our findings concerning the number of ethnic groups across time for a given origin – destination pair are in line with the EPR-ER data. It seems that refugees of a given origin – destination pair mainly belong to two major ethnic groups. This also means that the variation in diversity in refugee-hosting areas is coming from the refugee composition at the camp level. Figure B. 5 shows the movements of refugees from origin to destination countries under scrutiny. Somalia, the Democratic Republic of Congo, Liberia, South Sudan, and Sudan are major source countries for refugees, while Kenya, Tanzania, Uganda, Zambia, and Ghana appear to be countries hosting most refugees. Representing refugees in camps per ethnic group for the top 5 asylum countries over the sample period, Figure B. 9 shows that there is considerable variation in ethnic composition across camps. 17 Usage context: background mention, primary mention, supporting mention.

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Input: Domestic violence experiences and attitudes were measured based on self- report using three questions: (a) In your opinion, is a husband justified in hitting or beating his wife? (b) Did your husband ever hit or beat you? And (c) Are (were) you afraid of your husband: most of the time, sometimes, or never? These questions have been used in the Government of Pakistan Demographic and Health Survey 2017-2018. For the regression analyses, fear of husband was controlled for as it was the only variable that was correlated with all mental health outcomes and child development. Fear of husband was categorized from 1 to 3, with 1 indicating never afraid, 2 indicating sometimes afraid, and 3 indicating afraid most of the time. Community Safety. Perceptions of and experiences of safety in the community (or lack thereof) were determined by exposure to crime (i. e., if anyone has taken or tried taking something from you by using force or threatening to use force in the last 12 months) and perceptions of safety in the community — specifically, feeling safe while home alone at night or while walking through the neighborhood alone after dark. Discrimination. Respondents were asked if, in the past 12 months, they felt discriminated against or harassed on the basis of ethnic origin, immigration or refugee status, sex, sexual orientation, Usage context: supporting mention, background mention, primary mention.

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Input: These data are only for three districts in the province of Punjab, but is very recent, was conducted by an independent team of academics, and is a complete census of all households in the selected villages. Consequently, it yields sufficient madrassa enrollment to examine correlations with household attributes in a meaningful manner (this data source provides information on four times as many children as the PIHS). Table A2 in the appendix shows how these different data sources are used in the paper. Each source asks about madrassa enrollment in a slightly different but comparable way. The population census (1998) asks about the field-of-education (“ What is name ’ s field of education? ”) with options that include (for instance) engineering, medicine, or religious education. This question is also asked of all literate adults irrespective of their current enrollment status, allowing for comparisons in the stock of religious education over time. The PIHS rounds ask, “ What type of school is name currently attending? ” with options that include government school, private school, or deeni-madrassa (religious schooling). Finally, the LEAPS census directly asks, “ Is the child enrolled in a madrassa or an Islamic education school? ” Fortunately these different questions all give rise to similar numbers. This is reassuring since it suggests that any one particular result is not driven by the specific question or definition that was used. 7 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 (www. statpak. gov. pk). Usage context: background mention, supporting mention, primary mention.

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Input: 2, about 97 percent of refugee and 39 percent of host households in Addis Ababa live in rented houses. The data show that Addis Ababa refugees pay higher rents (ETB 31,600 per year, per adult equivalent) than hosts (ETB 18,700 per year, per adult equivalent). Moreover, rent expenditures make up 56 percent of refugees’ non-food expenditure. In-camp refugees in Ethiopia are much poorer than their hosts, but because everyone suffers from similarly low expenditures, inequality is also low for refugees. As measured by the Gini 43 For details on the OCP policy, see Box 2.2. 32% 18% 25% 84% 7% 75% In camp Addis Ababa Total Poverty headcount rate (%) Hosts Refugees Figure 5.1: Poverty incidence Source: World Bank Staff based on SESRE 2023. 0 5 10 15 20 25 30 35 40 45 In Camp Addis Ababa Total Hosts Refugees Figure 5.2: Income inequality, Gini index Source: World Bank Staff based on SESRE 2023. Most of the analysis presented in this chapter is based on detailed consumption data from the Socioeconomic Survey of Refugees in Ethiopia (SESRE) conducted between October 2022 and February 2023. All consumption of food and non-food items is included, regardless of whether these items are

Socioeconomic Survey of Refugees in Ethiopia

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A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor

A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source


Input: List of acronyms CAR Central African Republic DHS Demographic and Health Surveys DRC Democratic Republic of Congo DTM Displacement Tracking Matrix FCS Fragile and Conflict-affected Situations GIDD Global Internal Displacement Database GIS Geographic Information Systems IASC Inter-Agency Standing Committee ICRC International Committee of the Red Cross IDMC Internal Displacement Monitoring Centre IDPs Internally Displaced Persons ILO IOM International Labour Organization International Organization for Migration IRRS International Recommendations for Refugee Statistics JIPs Joint IDP Profiling Service LSMS Living Standards Measurement Study MICS Multiple Indicator Cluster Surveys NGOs Non-Governmental Organizations NRC Norwegian Refugee Council OCHA Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat OAU Organization of African Unity ODA Official Development Assistance OECD Organisation for Economic Co-operation and Development SDG Sustainable Development Goal SKOPE Somalia Knowledge for Operations and Political Economy SuTPs Syrians under Temporary Protection UAV Unmanned Aerial Vehicle UNDP United Nations Development Programme UNHCR United Nations High Commissioner for Refugees UNITAR United Nations Institute for Training and Research UNOSAT UNITAR ’ s Operational Satellite Applications Programme UNRWA United Nations Relief and Works Agency for Palestine Refugees in the Near East UNSD United Nations Statistical Commission WFP World Food Programme

International Recommendations for Refugee Statistics

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Input: There is also qualitative evidence that low take-up was at least in part due to some landlords ’ reluctance to make signed legal commitments to refugees for the lease, and with the implementing partner for the necessary construction. This is a setting in which most rental contracts are informal and landlords have ample discretion over their terms and a largely free hand to evict tenants. Despite the appeal of guaranteed rent for a year plus funding for housing upgrades, some landlords preferred not to “ bind ” themselves to the program and the particular recipient refugee household currently residing in their property. 3 One of the study ’ s main empirical ﬁndings is that we detect no signiﬁcant positive impacts of the housing assistance program for refugees along a range of pre-speciﬁed primary outcomes, with the exception of housing expenditures where there is the expected (and somewhat mechanical) drop in spending. Beyond housing expenditures, the other primary pre-speciﬁed outcomes in- 2We do this by surveyed neighbors but excluding landlords, who were often involved in the program. 3The data underlying these ﬁndings is from our implementing partner ’ s Integrated Assessment Shelter Analysis. 3 Usage context: background mention, supporting mention, primary mention.

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A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor

A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source


Input: The SEIS questionnaire includes a dedicated section on labor market status that mirrors the ILO reference week definition used in the national LFS. This alignment allows direct comparison of employment rates between the refugee population captured in SEIS and the national working-age population captured in the LFS, controlling for differences in age structure and educational attainment. Preliminary comparisons show that SEIS-measured employment rates for working-age refugees are 14 percentage points below LFS-measured national rates, net of compositional differences.

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor

A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source


Input: 9 Figure 10. Selected characteristics across Ugandan and refugee households, % Source: RHCS 2018, WB staff calculations. A simple comparison between refugees and Ugandan households demonstrates that refugees lag with regards to selected characteristics found to narrow the poverty gap. For example, refugees are less likely to have access to land than Ugandans. If refugees have access to land, the majority do not own it, but have user rights. The size of land also differs a lot among Ugandan and refugee households. Most Ugandans have at least 0. 05 hectare per capita, while the majority of refugees have less than 0. 05 hectare per capita. Refugee heads of household are also less likely to work and less likely to be literate compared to their Ugandan counterparts. Refugees have higher shares of children and elderly in household size compared to Ugandans. For example, among almost 60 percent of refugee households, more than half of the household members are children and elderly compared to 42 percent of households among Ugandans. Economic inclusion dividend When a development approach to hosting refugees is followed and refugees earn incomes, there are two key beneficiaries. Refugees themselves, who gain dignity, financial autonomy and pathways to self-reliance. Usage context: primary mention, background mention, supporting mention.

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor

A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source


Input: 23 mega-cities, annual growth rates of peripheral population tend to reach around 10-20 percent compared to central business districts. 4. Implications for public policy Hazard management is a task both for the public sector and for private households and firms. For the public sector in cities, this includes ensuring the safety of municipal buildings and public urban infrastructure, encouraging and supporting private sector hazard risk reduction, and developing first response capacity. A considerable share of hazard risk stems from relatively small but frequent events which cause localized damage and few injuries or deaths (Bull-Kamanga et al. 2003). For instance, an analysis of detailed records of 126 thousand hazard events in Latin America showed that more than 99 percent of reported events caused less than 50 deaths or 500 destroyed houses (ISDR 2009). In aggregate, these accounted for 16. 3 percent of total hazard related mortality and 51. 3 percent of housing damage. The probability of larger events may or may not be predictable. For instance, a city may be in an earthquake risk zone, but the location specific ground shaking probabilities are not known. Individual dwelling unit level mitigation is therefore necessary everywhere in the general area of high earthquake probability. For other hazard types like landslides and floods, potential risk areas can be more easily delineated. Usage context: primary mention, supporting mention, background mention.

detailed records of 126 thousand hazard events in Latin America

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor

A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source


Input: Figure A4: Participation certiﬁcate to boost ‘ resume ’ CERTIFICATE THIS ACKNOWLEDGES THAT I engaged with Pulse Bangladesh to do data collection Notes: The wording of the certiﬁcate was made such that it could be applied to both arms; cash-only arms participated in weekly surveys along with all other experiment participants, so technically also engaged in data collection for our project. 60 Usage context: background mention, supporting mention, primary mention.

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

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A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source


Input: Surveys of employers consistently find that more than hard, technical skills, employers value these harder-to-quantify skills of honesty and integrity, problem-solving ability, work ethic, communication skills, the ability to work productively with others, responsibility and dependability (Blom and Hobbs 2007). The AGI program has focused on the development and measurement of these softer attributes that matter for employment as well as those that matter more to the individual, such as self-confidence and empowerment. Despite the challenges of measuring such subjective outcomes, the survey instruments included panels of questions designed to elucidate a nuanced picture of the personality and psychosocial characteristics that are most relevant for labor market success. Table 6A presents results on empowerment and decision-making. The first series of questions have to do with control over resources, spending decisions and earnings. Respondents were asked how much control they had over how to spend their own earnings; also, whether they had money of their own for basic uses that they alone could decide how to use, without having to ask for permission. The EPAG baseline survey found that respondents reported a high degree of control over resources even before the program started. Usage context: primary mention, background mention, supporting mention.

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

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A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source


Input:
Martin et al. (2018) propose a measure of business relationships' stickiness based on the duration of firm-to-firm trade and use firm level data in France to measure a "stickiness" index for more than 4,000 HS6 products.

We use data consolidated banking statistics from the Bank for International Settlement to construct an index of financial proximity. [12] We use the total bilateral cross-border claims (including bank and non-bank sectors for all maturities)

**Foreign** **Direct** **Investments.** Data from the UNCTAD's Bilateral FDI Statistics provides up-to-date and systematic FDI data for 206 economies around the world, covering inflows, outflows, inward stock and outward stock by region and economy.

an index of " _proximity_ _in_ _sectoral_ _composition_ " based on the World Development Indicators. We use the share in value added of main sectors: service and agricultural sectors and we decompose manufacturing sectors into 7 main sub-sectors.

UNCTAD's Bilateral FDI Statistics
World Development Indicators

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Input: In the third (Rahim Yar Khan) there is a large difference, with the census reporting that 1 percent of all school-going children attended madrassas, and the LEAPS showing that the fraction is closer to 3. 7 percent (Table II). There are three potential explanations for this difference. First, the LEAPS data is not representative of the district and could be off the mark for districts with wide variation in madrassa enrollment across rural and urban samples. Second, the experience of the last five years could have varied dramatically across districts — in some, the enrollment fractions did not change and in others it increased substantially. Third, the data could point to systematic problems with the census estimates from certain districts, or the statistical problems that arise when we try to estimate low-probability events. 3. 3 Explaining the Differences A number of reasons could account for differences between the estimates presented here and those in the popular press. 1. Differences in the sampling unit. Our estimates are all based on household surveys — an interviewer goes to a household and asks about the enrollment status of every child. Some census estimates of home rather than religious schooling in the United States — the former ranges from 1 to 2 percent (Bauman 2001) while the latter is closer to 8 percent (National Center for Education Statistics, 2001). 10 In our own analysis, we find the quality of the data generated by the Federal Bureau of Statistics in Pakistan to be consistently high. We have used the FBS Census of Private Schools (PEIP, 2000) to guide our fieldwork and feasibility study for LEAPS and found it tallied with the situation on the ground quite well, even in remote villages. Usage context: primary mention, supporting mention, background mention.

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Input: 19 As property prices in the worst affected areas reduced the most, low income households responded by moving into low-rent housing being offered in these locations. On the other hand, middle income households moved away to avoid risk, and the wealthy, for whom insurance and self-protection was the most affordable, did not change where they lived. Poor people “ sort ” into low rent locations – which are often at higher risk to natural hazards. The problem is particularly acute in developing countries where there is a divide between the formal and informal markets for land. While formal developments may respect land use regulations, informal settlements are often located in hazard prone locations, such as on hill slopes, close to river banks, or near open drains and sewers. In Dhaka for example, informal settlements are developing across the metropolitan area, with many residents lacking basic public services and in locations at risk from flooding. In fact, most informal settlements do not have access to a public toilet within 100 meters, and 7, 600 households in 44 slums live within 50m of the river (World Bank 2005, Dhaka Urban Poverty Assessment). For the city of Bogotá, we use the same database discussed earlier to examine if poor people are at greater risk from natural hazards – particularly earthquakes.

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Input: The wealth index used in this analysis is computed from GLSS household asset data following the principal component analysis method developed by Filmer and Pritchett (2001). The GLSS wealth index is based on ownership of durable assets (television, refrigerator, mobile phone, bicycle, motor vehicle), housing characteristics (wall and roof materials, number of rooms per capita), and access to utilities (electricity, type of toilet, drinking water source). This index has been validated against consumption-based welfare measures in prior applications of GLSS data to poverty analysis.

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Input: of being available on a yearly basis independently of the quality of local statistical oﬃces and data gathering. While it comes with its own problems it can shed light on local economic activity where gathering of statistical data is incomplete. 7 This makes it a great ﬁt for measuring growth in a context of civil conﬂict. Conﬂict incidence is measured through the number of battle-related deaths from UCDP / PRIO dataset. We run the following regression for country i at time t: git = β × incidenceit + µi + ηt + ϵit (1) where git is economic performance per capita growth of country i in year t, incidenceit is conﬂict incidence, µi and ηt are respectively country and year ﬁxed eﬀects. A cross-country analysis as in equation (1) bears considerable potential for both reverse causality and omitted variable bias. Thus, a priori, a convincing causal link is hard to establish. However, here we expect the resulting bias to be small for two rea- sons. Usage context: background mention, primary mention, supporting mention.

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Input:
different social norms and customs, as well as ethnic prejudice. Card et al (2012) use the ESS to

countries. In order to identify age patterns using repeated cross-section data, Deaton and

degree of pro-immigration attitudes, we estimate the models using stacked micro-data from   the cross-section surveys instead of averaging over time-invariant characteristics.

the cross-section surveys instead of averaging over time-invariant characteristics. As a

To identify the effect of age on attitudes toward migration, we append household surveys from

repeated cross-section data
stacked micro-data
cross-section surveys
household surveys

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Input: Page 8 of 51 In the 9 cases mentioned, it was possible to identify part-time workers, while temporary workers were only identified in the cases of Argentina, Brazil, Chile, Mexico and El Salvador. 4. 1. 1 Variation of the NSE as a percentage of total employment The prevalence of NSE in the total employment has not shown very significant variations in the countries considered in the last two decades (Figure 1). Indeed, most of the countries analyzed show non-standard employment registers similar to those observed in the mid-1990s. The exceptions where the variation is a little more relevant are Brazil and Uruguay, where there are contractions in the incidence of the NSE of the order of 10 and 5 percentage points respectively and Mexico, where there is an increase of 5 percentage points. Figure 1: Prevalence of NSE among salaried employees. (Mid-90s / Mid-2010s) Source: Own calculations based on Household surveys Analyzing the prevalence of NSE by types of occupations, considering the ISCO classification at one digit, we find a quite similar pattern across countries. Indeed, in most of the considered countries, the “ Elementary Occupations ” are the category where the prevalence of NSE is higher. Usage context: background mention, primary 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. Usage context: background mention, primary mention, supporting mention.

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Input: Medium ‐ intensity, on the other hand refers to “ regular armed clashes between governments, government forces and insurgents. ” (IISS 2017) Within the first category, according to the Armed Conflict Survey in 2016 are Afghanistan, Syria, Somalia, South Sudan, and Iraq, while Myanmar, Central African Republic, Democratic Republic of the Congo, and Sudan are in medium ‐ intensity conflict. (Eritrea is not included in the Armed Conflict Survey in 2017, discussed in Annex). Minorities in Albania, Kosovo and Serbia are often the objects of discrimination but countries are not in conflict. A capsule summary of the security and social situation in each of the other countries included in this survey can be found in Annex 2. The survey illustrates how impractical return is today in the countries in conflict, such as Syria, Afghanistan, and Iraq, and voluntary return in large 36 Best practice and new methods in return policy, July 2017, http: / / www. bamf. de / SharedDocs / Meldungen / EN / 2017 / EMN / 20170504 ‐ emnjahrestagung ‐ rueckkehr. html Usage context: primary mention, background mention, supporting mention.

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Input: IDMC reports that of the 52 countries it monitored in 2015, it was only able to obtain data on new displacements in 20 countries, 92 on returns in 20 countries, on integration in one country, on resettlement in two countries, on children born in displacement in two countries and on deaths in one country; no data was obtained for any county on cross-border flight in 2015 (IDMC 2016). Moreover, existing systems for collecting data on refugees and asylum-seekers make it difficult to know how many were formerly IDPs, and it is possible for some people to be simultaneously counted in both categories, e. g. in the case of the Syrian displacement crisis (IDMC 2016). If no data on returns are available, IDMC risks overstating the number of IDPs (IDMC 2015).

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Input: among refugees and their best-preferred pulse. CSB is a corn soya blend with added essential micronutrients and vitamins called super cereal. Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than WFP food aid admin data for every item except for CSB+ and salt (Table E.6). Even when correcting for shares indicated as sold, refugee households still reported lower quantities. Using the WFP food aid information, we found a picture similar to UNHCR’s. Annexes 127 Table E.5: Food quantity and expenditure comparisons Items Quantity (per capita/year) Expenditure (per capita/year) SESRE WFP SESRE WFP Nonzero All Net of sold ration* Cereals 82.0 77.9 2,179 Wheat 60.5 1,427 Pulse 17.1 15.6 1,366 Peas 5.7 137 Vegetable oil 3.7 5.4 5.0 627 967 CSB+ 19.3 11.1 10.1 36 394 Salt 1.6 1.8 1.7 57 63 All cereals 76.9 - - 2,994 All pulses 8.3 - - 385 Aggregate ration/month 46.7 - Source: WFP and World Bank Staff based on SESRE 2023. Note: *Net of sold ration = quantity*share of ration sold (we asked the share of ration sold in SESRE) Table

WFP food aid information
WFP food aid admin data

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Input: 34 Any statistics on the imputed welfare will based on the set of imputed welfares for each household. The estimator takes the form, with R denotes the number of simulation: ܪ ෡ ൌ 1 ܴ ෍ ݄ ሺݕ ෤ ௥ ሻ ோ ௥ ୀ ଵ where ݄ ሺݕሻ is a function that converts the vector y with (log) incomes for all households into a poverty measure (such as the head-count rate or bottom 40 %), and where ݕ ෤ ௥ denotes the r-th simulated imputed welfare. Figure 6. Survey-to-Survey Imputation Methodology, an illustration For the case of Turkey, we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey. Income is used instead of consumption for this paper ’ s analysis. The model included variables related to: household demographics (age, gender, age composition, etc.), household characteristics (education, labor activity, etc.), household head ’ s characteristics (age, gender, labor, education, marital status, etc.) and household assets holding (both livestock and durables). Based on that model the simulated values of consumption (at household level) were imputed for the households in the corruption survey. Usage context: primary mention, background mention, supporting mention.

Survey on Income and Living Conditions survey

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Input: Hosting new neighbors: Perspectives of host communities on social cohesion in eastern DRC * Phuong Pham, † Thomas O ’ Mealia, ‡ Carol Wei, § Kennedy Kihangi Bindu, ¶ Anupah Makoond, | | & Patrick Vinck * * * Pham and O ’ Mealia are co-ﬁrst authors. Acquisition of the data used in this manuscript was supported by the United Nations Development Programme (UNDP). The funder played no role in the analysis, inter- pretation or writing of the results and decision to submit the manuscript. This paper was commissioned by the World Bank Social Sustainability and Inclusion Global Practice as part of the activity “ Preventing Social Conﬂict and Promoting Social Cohesion in Forced Displacement Contexts. ” The activity is task managed by Audrey Sacks and Susan Wong with assistance from Stephen Winkler. This work is part of the program “ Building the Evidence on Protracted Forced Displacement: A Multi-Stakeholder Partnership ”. The program is funded by UK aid from the United Kingdom ’ s Foreign, Commonwealth and Development Ofﬁce (FCDO), it is managed by the World Bank Group (WBG) and was established in partnership with the United Nations High Commissioner for Refugees (UNHCR). The scope of the program is to expand the global knowledge on forced displacement by funding quality research and disseminating results for the use of practitioners and policy makers. Usage context: background mention, primary mention, supporting mention.

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Input: the original variables used to predict fragility as controls without changing results. It is only in the post-war period that exclusion and refugees become a factor that inﬂuences foreign investment ﬂows. Finally, the results we ﬁnd are robust across all datasets of foreign investment we use. These results make it at least plausible that political exclusion and refugees matter because they predict a relapse to more intense violence. As ﬁnal piece of evidence for this idea we use data on political short and mid-term credit risk from the Belgian insurer Delcredere Ducroire (ONDD). We collected data on political risk evaluations from ONDD who, according to their annual report, insured transactions worth about 7 billion EUR in 2011. The variable we use measures the risk of a credit default for rea- sons beyond the control of the debtor, i. e. due to political or ﬁnancial macroeconomic events. We choose this variable because it provides the most consistent time-series in the ONDD data. ONDD measures both short- and mid-term risk on a scale from 1 (low risk) to 7 (high risk). Table 12, columns (1) and (4) show that risk ratings are decreasing in peacetime. Note that, as before, we control for country ﬁxed eﬀects which implies that we look at changes within country. Usage context: primary mention, supporting mention, background mention.

data on political short and mid-term credit risk

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Input: The lack of certainty means children are trying to prepare for further studies (e. g., in universities) in two separate systems with varying requirements. The survey results indicate that students enrolled in both systems spend as much time in Italian schools as those attending only Italian schools, averaging 31 hours per week. However, students participating in both systems spend an additional 8 hours per week on online Ukrainian classes. This puts an extra burden on these children. Connectedness to Italy is correlated with demographic characteristics and social environment of refugee children. Additionally, Table 6 shows that making new friends in the country of destination and speaking Italian are strongly associated with higher connection to Italy. The mental distress resulting from displacement is a key barrier to educational integration for many Ukrainian refugees in Italy. The link between poor mental health and low school attendance and performance is widely acknowledged in the literature (see Fiining et al., 2019 for a systematic review). In the World Bank survey data, children and caregivers reported signs of mental distress, with 16 % of children and 24 % of refugee caregivers reported experiencing psychological distress 61 % 35 % 31 % 68 % 50 % 36 % 23 % 68 % 59 % 26 % 26 % 59 % Would like to continue living in Italy Would like to move back to Ukraine Feel strongly connected to Italy Feel strongly connected to Ukraine Caregivers (N = 283) Children between 9 and 14 years old (N = 141) Children between 15 and 20 years old (N = 96)

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Input: Figure 1 ‐ Number of Forcibly Displaced Persons (1951 ‐ 2015) Source: Constructed from UNHCR population data (http: / / popstats. unhcr. org / en / time_series). Note: 2015 data are mid ‐ year and lower than end of year data. 5 http: / / www. unhcr. org / en ‐ us / figures ‐ at ‐ a ‐ glance. html. 0 10000000 20000000 30000000 40000000 50000000 60000000 70000000 1951 1953 1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 Usage context: supporting mention, primary mention, background mention.

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Input: Similarly, regional representation of the sample was found to be in line with actual regional distribution of the population in the north. 16 The code of the day is the sum of the two figures of the date, i. e. if it ’ s the 25th of August the code of the day is 2 + 5 = 7. The enumerater will chose house number 7 as a starting point. Arab 3 % Tamashek 32 % Songhai 45 % Peulh / Foulbe 7 % Other ethnicities 13 % Figure 2: Ethnic composition of the North, 2009 Census Arab 5 % Tamashek 36 % Songhai 49 % Peulh / Foul be 7 % Other ethnicities 3 % Figure 3: Ethnic composition of sample Usage context: background mention, supporting mention, primary mention.

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Input: But, given the presence of thousands of refugees in the country, the constrained fiscal space the authorities face, and the likelihood that international assistance for refugees will taper in the future, an imminent policy question is how to ensure that refugees can be hosted in a sustainable manner, without becoming a fiscal burden in the future. The fear that refugees are (or might become) a fiscal burden is driven by a broadly held perspective about forcibly displaced persons in general, and refugees in particular, namely that they are humanitarian subjects, vulnerable and worthy of public assistance (Betts and Collier 2017). This perspective is not universal, however. The economic contributions of refugees have been extolled for years, from posters 1 https: / / www. ecoi. net / en / file / local / 2091861 / 645b938a4. pdf. 2 Enquête sur la Consommation des ménages et le Secteur Informel au Tchad. The survey was carried out jointly with the National Statistics Office (Institut national de la statistique, des études économiques et démographiques, INSEED) and the UNHCR in Chad.

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Input: poverty and well-being. With increasing income, more affluent households are more likely to spend a greater share of their budget on high-value food items such as animal- origin diets, processed food, and food away from home, as well as on non-food items. Except for Addis Ababa, food consumption patterns, as indicated by expenditure shares, differ by food groups (Figure 5.6). Overall, refugees expenditures are higher on cereals and less on animal-origin food items associated with the types of food aid provided. This could be because refugees receive assistance for cereals/grains, not animal-origin food items. Food away from home is lower for refugees than hosts, except in Addis Ababa. - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 Hosts Refugees Hosts Refugees Hosts Refugees In Camp Addis Ababa Total Food Non-food non-durables Durables Rent Figure 5.3: Expenditure components (in birr) Source: World Bank staff based on SESRE 2023. Note: The expenditures are in December 2022 values. 61% 58% 59% 68% 56% 65% In Camp Addis Ababa Total Hosts Refugees Figure 5.4: Shares of food expenditure Refugees’ Aspirations 45 Expenditures for in-camp refugees is almost half that of hosts, despite the sizeable food aid and cash transfers (in selected camps) the WFP and

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Input: Beneficiary assessments revealed significant differences in protection concerns between newly arrived and long-staying refugees: new arrivals cited documentation and freedom of movement as primary concerns, while long-stayers reported livelihood insecurity and access to education as more pressing. These findings from the beneficiary assessments were used to differentiate the intensity and content of case management services offered to households by time-since-arrival cohort. Cohort-specific program designs are reflected in the operational manual's guidance for intake officers.

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Input: Some data challenges common to refugees and IDPs Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities. Sampling. As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay at relatives ’ places, other live in makeshift shacks and others stay in camps. The information available in the UNHCR registry (the census) can also be quite inaccurate, as already discussed, and the degree of accuracy changes for different groups of refugees. Stratification by urban and rural areas, a typical approach in sampling, may mean little for a population that is mostly in urban areas whether in camps or outside camps. Refugees and IDPs are also mobile and more difficult to track over time than other populations. Several statistical institutes worldwide have developed methodologies to track and measure mobile populations such as herders, nomads or homeless people. However, tracking refugees from other countries has been in the

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Input:
Table 2 presents summary statistics for the ENILEMS-ENLACE panel dataset. Columns

12 ENLACE scores than individuals out of college (by around 0.38 SD), but they are also

We are interested in the predictive power of ENLACE test scores over future schooling

individual's ENLACE test score in Grade 6 as a predictor of future education outcomes or

We use the ENLACE panel to study the relationship between Grade 6 test scores

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Input:
Several studies have examined the benefits of rural electrification in India. For example, van de Walle et al. (2015) estimate the long-run effects of electrification on household consumption in rural India based on data for 1981-98; Burlig and Preonas (2016) investigate the effect of India's national rural electrification program on labor force participation, living standards, and other village-wide outcomes using census data for 2001 and 2011; and Khandker et al. (2014) estimate the benefits of electrification projects in rural India based on cross-sectional data for 2005.

For example, using night lights data, Min and Gaba (2016) show that many villages in India that were officially classified as electrified under the RGGVY program remained in the dark for years after the completion of electrification projects.

This paper estimates the welfare impact of rural electrifi cation in India using nationally representative household panel survey data for 2005 and 2012.

nationally representative household panel survey data

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Input: labor markets shape the employment trajectories of refugees. Restrictions on land access for refugees restrict their access to rural labor markets, primarily shaped by agricultural activities. Livelihood activities in cities or work similar to that found in urban areas are most promising for refugees to utilize their labor and skills. Yet, many refugee camps are in more agrarian locations, and local labor market characteristics and connectivity drive refugees’ labor market outcomes (Hedberg and Tammaru, 2013; Kalter and Kogan, 2014; Kogan and Kalter, 2020; Schuettler and Caron, 2020; Dorian and Burmann, 2023). The GoE vision to create sustainable livelihood opportunities and build refugees’ self-reliance and resilience has yet to be fully implemented; roughly 88 percent of refugees in Ethiopia remain in camps based on SESRE data. Globally, approximately one- quarter of all refugees live in camps, a proportion that varies widely by country income status. Roughly half of refugees hosted in low-income countries live in camps (UNHCR, 2022b), but this share is much higher in Ethiopia (88 percent). Long-term encampment policies leave refugees isolated with limited or no economic rights, a situation that wastes their human capital and capacity for work (World Bank, 2017; Ibáñez et al., 2022). Although it may

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Input: cash transfer value from Oct 2022_refugee camps” file received from the WFP document that helps to get information regarding the changes in cereal cash equivalent – data on cereal cash equivalent for cash camps which is used to calculate cereals provided in those camps and cash transfer value per year. Based on this information, we compare how the list shared with us on what refugees should have received to what they reported regarding food consumption. Table E.4: Food aid data/information received from WFP Item Remark Assumptions Cereal Not clear Mapped to wheat* Pulse Not clear Mapped to peas* Vegetable oil Mapped to edible oil CSB/famex (CSB+) Not in SESRE Average of other cereals/pulses Salt Matched Cash - - Source: UNHCR Note: Rice was distributed for some months in Afar and Somali Dollo area camps, though wheat remained the main cereal distributed. YSP (Yellow Split Pea) was the main pulse distributed among refugees and their best-preferred pulse. CSB is a corn soya blend with added essential micronutrients and vitamins called super cereal. Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor

A general reference to data, information, or statistics that contains a data noun but lacks enough specificity to identify the exact source


Input: 9 where a government might be a real or perceived contributor to displacement or is harboring an overt or covert policy to reduce the number of refugees in the country. With limited results in including FDPs in national sampling frames, alternative approaches remain necessary. The UNHCR, the World Bank and numerous scholars and practitioners worldwide are experimenting with satellite images and phone surveys to try detecting refugee and IDP populations that may escape the UNHCR and national registers with some initial encouraging results. In Lebanon, Jordan and the Kurdistan region of Iraq, Aguilera et al. (2020) designed sampling strategies for Syrian refugees with known ex-ante selection probabilities. They used a variety of data sources, including data collected by humanitarian agencies, and also employed geospatial segmenting to create enumeration areas where they did not exist. Systematic field experiments are also underway to test different sampling approaches for IDPs living in camps. For example, Himelein, Pape and Wild (forthcoming) compare the performance of five alternative sampling approaches (satellite mapping, segmentation, grid squares, “ Qibla method, ” and random walk). Different indicators are assessed including household size, consumption, poverty and ownership of assets. Usage context: background mention, supporting mention, primary mention.

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A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor

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Input:
**5.2 Geospatial Data Integration in M&E**

Geospatial data from the national statistics office's 2020 census cartographic database have been integrated into the MIS to enable automatic generation of geographic coverage reports. When a community is registered in the MIS, its geographic code is linked to the corresponding census enumeration area boundary in the geospatial data layer, allowing the M&E team to produce maps of beneficiary coverage against the total population of each target district without additional data processing.

A dataset, survey, database, index, census, registry, or information system with a specific proper name that can be cited, queried, or downloaded as a data source

A data source described by its characteristics or producer rather than a proper name, containing a data noun and at least one identifying descriptor

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Input: Figure 17: Peace and Foreign Inﬂow Across Cut-oﬀs and Data Sources The basic results are in Table 8 which shows results for equation (9) using the threshold of 0. 008 battle-related deaths per 1000 population. According to this the investment from OECD countries was almost 70 percent larger in peacetime than during conﬂict. The ﬂows from other data sources shows an increase of between 35 and 50 percent. The consistency of this result across very diﬀerent datasets is striking. Note also that the average change in inﬂows implied by these rates is very large. In 2012, average inﬂows in the World Bank dataset were over 9. 5 billion USD and over 3 billion USD in the OECD data. Our estimates therefore imply a gain of between 2 billion and 4 billion USD in yearly inﬂows for countries which emerge from conﬂict. In order to understand the dynamics of recovery it is useful to understand the dynamics of this change around the end of conﬂict. For this purpose we add a set of dummies to the equation above. We construct a dummy that indicates the start of recovery and add three forward and lag dummies to trace average investment around this date. As before we always lag the explanatory variables by one year. Results for the OECD data are shown in Figure 18. Usage context: primary mention, supporting mention, background mention.

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Input: Institutional arrangements would need to delineate data collector and data compiler roles, as well as reflect the following principles to ensure sustainability: (a) Pursue additional activities within the overall framework of existing initiatives to ensure coherence with the activities of other actors. (b) Primary responsibility for data collection rests with national statistical agencies (with adequate arrangements in the case of IDPs to mitigate political risks). (c) Definitions and methodologies should be harmonized across countries, through a process managed under the auspices of the UN Statistical Commission. (d) Agencies such as UNHCR and IDMC can play a leading role in ensuring quality, providing technical assistance as may be needed, and aggregating data for global analyses. 102 See http: / / www. internal-displacement. org / database. 103 See http: / / unstats. un. org / unsd / statcom / doc15 / 2015-9-RefugeeStats-E. pdf. 104 See conference documentation at http: / / www. efta. int / seminars / refugee. 105 See http: / / unstats. un. org / unsd / statcom / 47th-session / documents / 2016-14-Refugee-statistics-E. pdf. Usage context: primary mention, supporting mention, background mention.

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Input: 3 trust in the government and its institutions and perspectives on conflict resolution. By analyzing the impact of the crisis on welfare, the consequences of returning home versus remaining in displacement and by comparing immediate with longer term impacts, this paper contributes to the literature on refugee, IDP and returnee populations. The paper combines data from a face-to-face baseline survey with information collected via mobile phone interviews from respondents identified during the baseline. This innovative approach to data collection makes it possible to collect welfare data with high frequency (monthly) – important in a volatile crisis situation – and allows measuring changes over time. It also permits following displaced and refugee households once they return, even if they return to areas that are inaccessible to enumerators. The remainder of this paper is organized as follows. Section 2 provides a brief overview of the methodology, the sample and sample selection. Section 3 discusses the characteristics of the displaced and returnees, looking specifically at ethnic composition, place of origin, household size, education, asset ownership and employment status. Section 4 considers how the crisis affected food consumption, employment, assets and school attendance. Section 5 is devoted to the specificities of returnees who turn out to be, on aggregate, less affected by the crisis and better off than IDPs or refugees.

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Input: Expenditure). In the calculation, values for the measuring poverty rate (P) and design factor (deft) 1.5, the expected Relative Standard Error (RSE) of 4.63%, and finally, an adjusted Response Rate of 99% at a 95% Confidence level used to represent the expected precision is acceptable at the domain level. To select a representative sample from this population, first, the initial sample size was determined by using the following scientific formula: where the deft is the design factor defined as the ratio between the square root of standard error using the given sample design and the standard error resulting from a simple random sample used. Based on the above scenario, total sample size =3,456 Households, and EAs = 288 . An equal allocation method was used to ensure that the survey precision was comparable across domains, where 36 EAs were selected from each domain. Based on a fixed sample take of 12 households per cluster, Equal Allocation formula Where: = total number of sample households and = Number of sample households allocated to stratum Table C.1: The distribution of sampled and surveyed households by domains EA HH Sampled Covered Sampled Covered Eritrean refugee domain 36 36 432 432 Somalian refugee domain

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Input:
15 We followed Anginer, Demirgüç-Kunt, and Zhu's (2013) methodology and computed the index by aggregating the answers to 14 selected questions on supervisory powers that were collected in the 2003, 2007 and 2011 surveys conducted by Barth, Caprio, and Levine (2008). 16 The methodology for assessing central banks' FSRs was introduced by Čihák (2006) in the first worldwide survey of FSRs. 17 Alternatively, one could use as the control the availability or even better the actual implementation of macroprudential tools prior to the 2008 crisis.

It derives from the database developed by Melecky and   Podpiera (2013) and the Bank Regulation and Supervision Surveys carried out by the World Bank in

empirical link between a simple publication of FSRs and financial stability. The data on FSR publication

are available for only 78 countries, covering 22 of the 25 crisis countries. The data on the quality of FSRs

database developed by Melecky and   Podpiera

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Input: Sarvimaki (2011) uses the elements of the government ’ s placement policy as instruments (i. e. the proportion of a municipality ’ s population speaking Swedish and the hectares of potential agricultural land). Other authors focus instead on the counterfactual group testing alternative designs of the control group, sometimes including placebo groups and other times recurring to matching methods. The choice of matching methods varies from ordinary methods such as nearest neighbor to more recent advances such as Synthetic Control Methods (Abadie and Gardeazabal, 2003). The inclusion of fixed effects is common to almost all papers although the choice of fixed effects can be very different, as described above. Only one paper uses Fixed Effects (FE) and Random Effects (RE) formal models in conjunction and tests for differences (Esen and Binatli 2017). Cross-section econometrics is, by far, the method of choice even if time is included into the equations but we also found three papers employing time-series models (Carrington and de Lima 1996, Makela 2017, Fakih and Ibrahim 2015). Only few papers are able to exploit panel data (Foged and Peri 2015, Depetris-Chauvin and Santos 2017) and several of them use the same data set (Maystadt and Duranton 2018, Maystadt and Verwimp 2014; Ruiz and Vargas-Silva 2015, 2016, 2017). Usage context: primary mention, background mention, supporting mention.

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Input: 21 Results suggest that 66 % of the returnees trust the Malian police and army most when it comes to providing security in the North. Almost half believe that the Malian army is brave and well trained. The vast majority of returnees believe that the government ’ s policies regarding reconciliation, security and social cohesion are good or very good. They also support the government ’ s approach towards decentralization and providing infrastructure such as access to potable water and electricity. As the next section will illustrate this differs strongly with the opinions of refugees. 6. Prospects for Peace IDPs, refugees and returnees have comparable opinions with regard to the requirements for peace: (i) addressing the ongoing crisis, (ii) improving security and (iii) reconciliation. Although there is agreement on what needs to be done, there is little consensus on what happened during the crisis, who the culprits are and who the main victims. Figure 15: What is the most important problem the Government needs to resolve today? (%) Source: Listening to Displaced People Survey, 2014.

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Input:
**Annex 9: Implementation Support Plan**

World Bank supervision missions will be conducted biannually and will focus on: (i) review of implementation progress against the results framework; (ii) fiduciary compliance, including review of IFRs and audit reports; (iii) environmental and social risk management, including field visits to at least two active subproject sites; and (iv) assessment of data quality for MIS-generated indicators. Between missions, task team members will maintain monthly contact with PIU coordinators via video conference and review outputs from the activity monitoring dashboard.

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Input: Figure A2: Refugees a burden for SSA? Panel A: Not weighted by economic capacity Usage context: primary mention, background mention, supporting mention.

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Input: hinders future prospects. Globally in 2019, almost half of all refugee children were out of school (UNHCR, 2020d). Of those attending school, most do not make it past basic education; gross enrolment in primary education stood at 77 percent in 2019. Yet, the contrast between primary and secondary school enrolment remains stark, and only 31 percent of refugee children were enrolled in secondary school, much below the global average of secondary school enrolment. A recent study of refugee children in Kakuma refugee camp in Kenya, for example, found that literacy and learning outcomes for refugee children were significantly lower than in immediate host community or the rest of Kenya (Piper et al., 2020). This report shows that education outcomes for children in Ethiopia are low across all population groups and ages but particularly for refugee children. COVID-19 exacerbated this situation for many refugee children (Wieser, 2020). 1.2 How does Socio-Economic Survey of Refugees in Ethiopia (SESRE) contribute to the debate on policies? The Socio-Economic Survey of Refugees in Ethiopia (SESRE) is a representative survey of the refugee population in Ethiopia and their host communities, the first of its kind.9 Ethiopia made significant progress over the past few years in articulating

Socio-Economic Survey of Refugees in Ethiopia

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Input: One of the main contributions of this paper is the use of panel data which allows us to examine the effect of the pandemic on labor market transitions — job loss and job gain rates — in addition to the effect on labor market stocks. Studying both stocks and flows provides a comprehensive framework to analyze the impact of the pandemic on labor markets and allows for a better understanding of the underlying mechanisms 30 Usage context: primary mention, supporting mention, background mention.

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