Input: These 25 were included in the surveys but have been dropped from the analysis because they were not assigned randomly. 10 The reasons given for not entering the training included: 1) they were back in school, 2) they had moved to a distant location, 3) they were seriously ill, 4) they had found full-time work, 5) they were not interested or able to make such a big time commitment, or 6) they could not be located despite numerous efforts. 11 This includes 1155 of the original 1273 assigned to treatment, plus 36 out of the 39"replacements"- young women from the control group who were offered a chance to be reassigned to round 1. 12 Note that a previously released baseline report for this evaluation was based on 2008 observations. However, after cleaning, 4 were found to be duplicate observations and 15 were not found in the program data and hence were dropped from the midline analysis. This leaves 1989 baseline observations that are included in the midline analysis. 13 These are cases in which the adolescent girl was interviewed but the household head interview was not conducted because the head was not available, could not be found, or declined to take part. 14 This suggests that the loss of the 118 young women who were selected but declined to take part, and the 60 who started but did not complete the program, does not bias the results. 8

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Input: Afrobarometer finds that the vast majority of Malians want their country to remain a single and unified nation and that the attempt by armed groups to create a breakaway state in Mali ’ s northern territories is decisively rejected. See Afrobarometer Policy Paper 10 (Dec 2013). This difference with the Afrobarometer survey can be explained by the fact that the latter survey only focused on Malians inside the country and did not take the views of refugees into account. 5 93 2 6 86 75 20 2 3 94 Independence of the North Autonomy of the North Establish full government control over the North Figure 19: How do you envision the future of Mali? IDPs Refugees Niger Refguees Mauritania Returnees

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Input: Colombian and Venezuelan migrant children and adolescents. VII Discussion In this study, we analyze novel and unique data on forcibly displaced migrants and hosts, focusing on children and adolescents, to highlight the disparities in human development between them. The structure our analysis in two parts. In the first part, we characterize the main trends in the data. We show that forcibly displaced households have a wealth distribution skewed towards lower values relative to Colombian households. This is likely explained by the assets ownership loss that forcibly displaced households expe- rienced after the migration episode. We also identify meaningful lags in human capital accumulation between Colombian and Venezuelan children and adolescents of approxi- mately 1 year. We further note that the Colombian government ’ s supportive policies for Venezuelan forced migrants are evident through high levels of service access and pro- gram participation for migrants. Nevertheless, it remains surprising that participation is not higher, suggesting significant potential for improvement in increasing sisb ´ en and health insurance enrollments. In a second part of our analysis, we document sizeable lags in physical and cognitive de- velopment of Venezuelan children and adolescents, relative to their Colombian counter- parts. However, we were not able to identify any gaps in the socioemotional and mental health between the two groups. Usage context: background mention, primary mention, supporting mention.

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Input: Due to our research design and the complexity of introducing a third grouping who would play outgroup in the behavioral experiments regardless of partner identity, we exclude PRL from the main analyses presented here. 5 Data were also collected six months after the end of the training but this was heavily disrupted due to the outbreak of the COVID-19 pandemic. In Lebanon, this resulted in a change to the method of data collection (from in-person to telephone) and in Jordan, an end to data collection entirely. In Jordan, this had a more pronounced effect on the control group, due to the scheduling of data collection and implementation of restrictions in Jordan. Given these complexities, we do not present results from these analyses. 6 In addition, we attempted to collect information on the extent of social and economic interactions between hosts and refugees. At baseline, almost 95 % of respondents in both the treatment and control group reported such interactions. For this reason, we do not include this information in these analyses. Usage context: background mention, primary mention, supporting 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. Usage context: background mention, primary mention, supporting mention.

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Input: The JLMPS sample is restricted, after matching it to the 2010 school census, to individuals born in Jordan who are aged 25 to 70 in 2010 and who have nonmissing information on age, subdistrict of birth, years of schooling, father ’ s schooling, mother ’ s schooling, and local supply of schools in subdistrict of birth. 9 These exclusions resulted in a sample of 4, 139 males and 4, 131 females, which are referred to as the male and female full samples, respectively. 7. Because of the absence of annual estimates of subdistrict populations, the population used to normalize the supply of schooling at the subdistrict level is the 2004 population of the subdistrict. There are 86 subdistricts in Jordan. If subdistrict populations are growing at different rates, this could introduce some measurement error of the true supply of schooling available to different cohorts. 8. Secondary schools include both general and vocational secondary schools. Public schools include schools under the jurisdiction of: (i) Ministry of Education, (ii) Ministry of Higher Education, (iii) Ministry of Defense, (iv) Ministry of Social Development, (v) Ministry of Religious Endowments (Awqaf), and (vi) UNRWA. 9. The original sample size of all individuals who are aged 25 to 70 years in 2010 and are born in Jordan is 8, 312 observations. The sample restrictions on the missing values result in the exclusion of 34 observations (missing age), 1 observation (missing father ’ s schooling), and 7 observations (missing mother ’ s schooling). Usage context: background mention, primary mention, supporting mention.

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Input:
The FAO estimates of the hunger rate combine aggregate food balance sheets for every country with

representative household consumption expenditure surveys (HCES) available for 129 developing

countries. [1] These HCES are already being used to monitor global poverty trends (Chen and Ravallion

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Input:
Keywords: HIV, AIDS, behavioural surveillance survey, humanitarian emergency,
conflict, post-conflict, refugee, IDP, methodology and quality

Correspondence: Paul B. Spiegel, MD, MPH, UNHCR, Case Postale 2500, Geneva 1211,
Switzerland. Tel: 41 22 739 8289. Fax: 41227397366. E-mail: spiegel@unhcr.org

ISSN 1744-1692 print/ISSN 1744-1706 online # 2006 Taylor & Francis
DOI: 10.1080/17441690600679764

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Input: from UNHCR items valued using SESRE prices Food expenditure (UNHCR in-kind + cash): aggregate food expenditure from UNHCR items valued using SESRE prices plus the cash equivalent transfers Annexes 126 Food aid data received from WFP Food aid information received information from the WFP includes five food items and their quantities distributed to refugees: cereal (mainly wheat but in some camps rice), pulses (mostly yellow split peas), CSB+, vegetable oil, and salt (Table E.5). The per person per month aid (in-kind) are converted into annual values and mapped them to closest food item in SESRE (this was not straightforward as the items are different). We assumed a 50 percent ration until November 2022 (for our Oct/Nov sample) and an 84 percent after Dec 2022 (Dec, Jan, and Feb sample). The WFP data are converted to annual values using household size and prices from SESRE. The data source is the “Revised 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.

Revised cash transfer value from Oct 2022_refugee camps

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Input: 2 Background Information 2. 1 Characteristics of West Bank and Gaza ’ s labor markets The labor markets of the West Bank and Gaza exhibit features typical of the broader Middle East and North Africa (MENA) region, but also have attributes that are highly unique. Additionally, important differences exist between the West Bank and Gaza. This section provides an overview of these characteristics. We use data from the Labor Force Surveys (LFS) of the West Bank and Gaza and we focus on 20-59 years old men. In Section 3. 1, we provide more information about the data sources and sample selection. We divide each labor market into five mutually exclusive and jointly exhaustive states: public sector employment, private formal sector employment, private informal sector employment, unemployment, and out of labor force. 1 We focus our discussion exclusively on men, as women ’ s labor force participation in both the West Bank and Gaza is very low, never reaching values above 25 %. This low participation rate is common in MENA countries and makes the role of the pandemic on women ’ s labor market outcomes relatively less important than other, more relevant structural factors. Usage context: primary mention, background mention, supporting mention.

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Input: This database lists seven categories: refugees, asylum-seekers, returned refugees, internally displaced persons (IDPs), returned IDPs, stateless persons and others of concern. For each group the database provides yearly information about their composition by loca- tion of residence and origin. We exploit only the data on “ refugees ”. 22 In particular, we are interested in the annual stock of refugees for each country of residence, i. e. how many people with refugees status have left their home country each year. We focus on these numbers as they appear to be the most comparable across time and countries. However, this is likely to capture only the tip of the iceberg in some cases. The number of IDPs is extremely high in some instances but cannot be captured with the same level of conﬁdence as refugees generally. 23 Cross-country data about conﬂict is provided by the UCDP / PRIO. As for the index of country-level economic activity, we use again information provided by the Penn World Table and World Bank databases. As mentioned above, our aim is to explore the dynamics of refugees during conﬂicts. In other words, we attempt to answer several questions. Usage context: primary mention, supporting mention, background mention.

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Input: 10 15 20 25 30 35 40 45 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Refugees Hosts In camp Addis Ababa Total Sanitation problem Long waiting time Shortage of health professionals Too expensive Shortage/unavailability of medicines Unavailability of laboratory Shortage of medical equipment Lack of cooperativeness of the staﬀ Percent Percent Figure D.8: Problems faced in health institutions Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Government/public Private Mission/Religious/NGO Other Percent Figure D.7: Type of health institutions Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese In camp Addis Ababa Total Female Male Percent Figure D.9: Stunting by gender of children Source: World Bank Staff based on SESRE 2023. a. Faced any problem b. Types of problems faced Annexes 103 - 500 1,000 1,500 2,000 2,500 3,000 3,500 Hosts Refugees Hosts Refugees

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Input: include the rice price — which appears with the wrong sign but is only marginally signiﬁcant — and elevation and population density — which are no longer signiﬁcant. Comparing Tables 7 and 5, we ﬁnd that in the smaller NLSS 2002 / 3 dataset none of the anticipated consumption variables is statistically signiﬁcant. Other results are as before. 4. 4 Magnitude To assess the relative magnitude of our results, we multiply coeﬃcients estimated in Tables 4 and 5 by the standard deviation of their respective regressors. We then average over the various regressions reported in Tables 4 and 5. Calculations are summarized in Table 8. The larger the value, the more inﬂuence the regressor has on the choice of a destination district. We see that the most important regressors in terms of magnitude are travel time to the near- est road, elevation, language similarity, and the price of rice. Consumption variables have an eﬀect on migration destination that is smaller in magnitude: a one standard deviation increase in anticipated relative consumption, for instance, has an eﬀect on destination that corresponds to a third of the eﬀect of a one standard deviation in elevation — and one-sixth of a one stan- dard deviation in distance from the nearest road. Income variables have a negligible eﬀect on migration decisions.

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Input: No data on religiosity was collected as part of the census and a more recent and detailed household survey that includes information on time-use elicits little variation — everyone reports high mosque attendance and regular prayers. An alternative, suggested by David Evans at Harvard University, which we pursue here, is to use recent developments in the use of “ names. ” Research by Fryer and Leavitt (2004) demonstrates the increasing use of names to define race identity in the United States. We postulate that households who named (at least) one child “ Osama ” (also spelt Usamah, Usamma or Usama) are more likely to favor a radical brand of Islam. The use of the name Osama was minimal until 1998, and then peaks in 1998 and 2001, following disruptive events. Of course, the naming of the child may reflect name recognition rather Usage context: supporting mention, background mention, primary mention.

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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, supporting mention, background mention.

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Input: Page 9 of 51 Figure 2: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys When these results are analyzed by the type of NSE, we find significant differences between the trends in Part-time and Temporary employment. On the one hand, there is a stable or growing prevalence of part-time employment among the salaried employees, where Uruguay is the only exception, characterized by a decrease in the incidence of this type of employment (Figure 3). In the cases of Peru and Bolivia, we find almost the same prevalence of part-time employment as two decades ago while in the remaining countries (Argentina, Brazil, Chile, Mexico, El Salvador and the Dominican Republic) there is a greater prevalence of part-time employment. Likewise, the prevalence of temporary employment shows a downward trend in most of the analyzed countries in the last 20 years (Figure 4). In fact, three of the five countries in which temporary employment could be identified show a significant drop in the prevalence of this type of employment (Argentina, Brazil, and Chile). El Salvador presents a stable incidence of temporary employment, while Mexico is the only country in our sample for which there is an increase in this type of NSE. Usage context: primary mention, supporting mention, background mention.

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Input: On average, the tax-to-GDP ratio in Sub-Saharan Africa is around 21 percent, compared with the OECD average of about 32 percent. In Tanzania and Uganda, the total tax share drops to about 10 percent. Historical data suggests that the tax share of many European countries did not reach 15 percent of GDP until World War II when incomes were substantially higher than they are in many African countries (Fjeldstad and Rakner, 2003, 3). The types and amount of taxes citizens pay varies both within and be- tween countries. We do know there are taxes on agricultural crops, but the rates and processes of collection vary within countries (Kasara, 2007). User fees from electricity, water, sanitation, and other services comprise the major- ity of local revenue in South Africa (Hoﬀman, 2007). In Tanzania, Fjeldstad and Semboja (2001) count ten major categories of taxes, eighteen major categories of licenses, forty groups of charges and fees, and seventeen items listed as other revenue sources. In some countries including Kenya, Malawi, 11 Usage context: primary mention, supporting mention, background mention.

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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: supporting mention, background mention, primary mention.

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Input: Wages: the earnings measure we use is the response to the question “ how much did you earn from your main job activity during the last month? ” In the LFS 2011 there is further information on how much of that income was irregular, for example a bonus payment, but the LFS 2014 no longer provides that breakdown. There is also a measure of the “ number of hours per week worked in the main job ” (both usual and total hours), which can be used to construct hourly wages. Since the hours worked measure does not correspond exactly to the earnings measure and introduces additional measurement error, our preferred wage measure is the monthly wage. We exclude wage observations were respondents report having usual working hours of less than 14 or more than 84 hours per week.

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Input:
**Annex 10: Social Assessment Summary**

Beneficiary assessments, stakeholder consultations, and a desk review of secondary sources were conducted between September and November of the preparation year to inform the social assessment. Key findings from the beneficiary assessments include: (i) 61 percent of households in the target area report income below the national poverty line; (ii) land tenure insecurity affects approximately 35 percent of households, particularly those without formal documentation; and (iii) access to financial services is limited to approximately 18 percent of households, with mobile money adoption exceeding bank account ownership.

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Input: This study aims to assess the consequences of forced migration on ethnic diversity and conflict in Sub-Saharan Africa. We combine a unique dataset on refugee camps with individual data from the Afrobarometer Surveys across 23 African countries for the 2005 — 2016 period. We construct two standard measures of ethnic diversity: indices of ethnic fractionalization (EF) and ethnic polarization (EP). Ethnic fractionalization measures the probability that two individuals drawn from the society at random will belong to two different ethnic groups and thus increases with the number of ethnic groups present. Ethnic polarization captures antagonism between individuals and is maximized when the society is divided into two equally sized and distant ethnic groups. Although these indices have been widely used, little variation over time has been found, making causal inference difficult. The innovative aspect of our analysis is that we use data on the precise locations of refugee camps, their yearly size, and — most importantly — their annual composition in terms of countries of origin. Combined with the Ethnic Power Relations- Ethnicity of Refugees 2019 dataset, we are able to predict changes in ethnic diversity induced by refugee inflows. We then assess the relationship between refugee diversity and the likelihood of conflict. In an additional analysis, we also assess how refugee-induced changes in diversity affect the incidence of theft and violence, participation in protests, and perceptions of ethnic attachment, inter-personal trust, and institutional trust. Other studies have investigated the links between displacement and social conflict or social co- 3 Usage context: background mention, supporting mention, primary mention.

Ethnic Power Relations- Ethnicity of Refugees 2019

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Input: 20 capacity to offer these services. Many may be at risk of violence or trafficking. These are very important aspects from the perspective of welfare economists interested in measuring well ‐ being but these measurements are complex and not usually included in multidimensional indicators of deprivation or poverty. The dimensions of deprivations to consider are more numerous and more complex to measure. Again, there is very little research in welfare economics dedicated to the special needs of these populations. Risks and vulnerabilities. The analysis of risk and vulnerability is also much more complex in the context of the forcibly displaced. Welfare economics has only approached these topics recently, in the past decade or so. Essentially, the idea is to measure the risk of being poor or falling poor in the future using cross ‐ section or panel data studying spells of poverty over time. This is work that requires accurate and complex data sets that would be rarely available in a refugee or IDP context. More importantly, the nature of the problem changes. Refugees and IDPs are by definition more at risk and more vulnerable than regular populations and these vulnerabilities are not only linked to skills and efforts but to legal status, discrimination, limited mobility and other factors that are unique or much more acute with refugees and IDPs. Usage context: primary mention, supporting mention, background mention.

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Input:
most recent year in our CRU dataset). We begin by computing average annual rainfall

and temperature for the nine datasets (CRU; 8 GCMs). Then we use a least-squares fit

generates nine benchmark annual datasets - CRU and eight GCMs - for each of the 372

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Input:
**Annex 11: GIS-Supported Targeting Methodology**

Beneficiary targeting in rural subcomponents relied on GIS to overlay poverty incidence maps, displacement flow data, and existing service facility locations. Spatial clustering algorithms were applied to identify communities with the highest unmet need scores within each target district. The GIS analysis was conducted by the Ministry of Planning's geospatial unit using ArcGIS Pro and the outputs validated against field-verification data collected by community mobilizers. GIS-derived targeting lists are subject to a 10% random audit by the Bank's task team during each implementation support mission.

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Input: A UNESCO report written during the height of Liberia ’ s second civil war reported that Liberia had one of the lowest gender parity scores globally, and girls were disproportionately represented in the number of out-of-school children in Liberia. This disruption in education presaged lifelong consequences for this cohort of women (Kirk, 2003). In 2007, the literacy rate for adult men was 55 % and women was 41 %-- the number lowering to 26 % for rural women (OECD et al., 2009). In 2001 an education law was enacted making primary education free and compulsory, however, as of 2006, primary school enrollment in urban areas was 63. 7 % for girls and 33. 1 % for girls in urban areas (Woodon, 2012). Liberia also scores in the bottom eight countries that reported women having fewer than half of the years of education as men (Klugman, 2010). Five years after the war ended, women ’ s labor force participant was high, with women accounting for 54 % of the labor force. However, women were disproportionally represented in the informal sector (CWIQ, 2007). According to data from the 2017 Women, Peace, and Security Index, Liberia still has a number of gaps to fill particularly related to education, financial inclusion, legal discrimination and intimate partner violence (WPS, 2017). Usage context: supporting mention, background mention, primary mention.

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Input: of Stunting among under-five Children in Ethiopia: A Multilevel Mixed-effects Analysis of 2016 Ethiopian Demographic and Health Survey Data. In BMC Pediatrics 19 (1). BioMed Central Ltd. .org/10.1186/s12887-019-1545-0 Ginn, T., Resstack, R., Dempster, H., Arnold-Femandez, E., Miller, S., Guerrero, M., and Kanyamanza, B. (2022). 2022 Global Refugee Work Rights Report. Center for Global Development, Refugees International, and Asylum Access. Ginn, T. (2023). Labor Market Access and Outcomes for Refugees. World Bank – UNHCR Joint Data Center. Quarterly Digest on Forced Displacement, Seventh Issue. Washington, D.C.: World Bank Group. Giordano, N., Ercolano, F., and Makhoul, M. (2021). Skills and Labour Market Transitions for Refugees and Host Communities: Case Studies and Country Practices on Including Refugees in Technical and Vocational Education and Training (TVET) and Employment. UNHCR Gobillon, L. and Selod, H. (2014). Spatial Mismatch, Poverty, and Vulnerable Populations. In: Fischer, M.M., Nijkamp, P. (eds) Handbook of Regional Science. Springer, pp.93-107. https://doi.org/10.1007/978-3-642-23430-9 Hagos, S. Z. (2021). Refugees and Local Power Dynamics: The Case of The Gambella Region of Ethiopia. Hahn, E., Richter, D., Schupp, J., and Back, M. D. (2019). Predictors of Refugee Adjustment: The Importance of Cognitive Skills and Personality. Collabra: Psychology, 5(1), 23. Hainmueller, J., and Hopkins, D. J. (2014).

2016 Ethiopian Demographic and Health Survey Data

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Input: Wages: the earnings measure we use is the response to the question “ how much did you earn from your main job activity during the last month? ” In the LFS 2011 there is further information on how much of that income was irregular, for example a bonus payment, but the LFS 2014 no longer provides that breakdown. There is also a measure of the “ number of hours per week worked in the main job ” (both usual and total hours), which can be used to construct hourly wages. Since the hours worked measure does not correspond exactly to the earnings measure and introduces additional measurement error, our preferred wage measure is the monthly wage. We exclude wage observations were respondents report having usual working hours of less than 14 or more than 84 hours per week. Usage context: supporting mention, primary mention, background mention.

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Input:
The UNCRPD defines a person with disability “ _as those who have long-term physical, psychosocial, intellectual or sensory_
_impairments which, in interaction with various barriers, may hinder their full and effective participation in society on an equal_
_basis with others_ ”. However, obtaining accurate data on persons with disabilities in South Sudan remains a challenge. The
latest reliable data originates from the 2008 census, reflecting a disability prevalence of 5.1%. In the absence of current data,
the World Health Organization (WHO) approximates that 16% of any population has a disability. Nonetheless, collaborative
insights from various humanitarian and development organisations suggest this estimate might even be higher, given the
influence of additional factors such as poverty, conflict, climate shocks, and inadequate healthcare facilities. An
anthropological study conducted in Pibor in 2022 by Humanity and Inclusion (HI) underscores the interplay of poverty,
prolonged conflict, and societal barriers leading to exclusion and marginalization. [vi]

**BARRIERS TO AN EFFECTIVE SYSTEM TO ENSURE THE RIGHTS OF PERSONS WITH DISABILITY AND OLDER PERSONS**

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Input: GIS layers depicting soil type, slope gradient, and land cover were used by the project's environmental specialist to conduct a preliminary erosion risk assessment for all proposed earthworks sites. Sites classified as having a high erosion risk under the GIS analysis were required to submit an erosion and sediment control plan developed by a qualified engineer before earth-moving works could begin. The GIS-based erosion risk layer was created by combining existing national datasets using weighted overlay analysis in QGIS.

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Input: The Refugees and Host Communities Household Survey expanded the national Household Consumption and Informal Sector Survey to include a representative sample of refugees and host communities, including Sudanese and host communities located in the east of the country. The remainder of this note is organized as follows. Section 2 presents a short discussion of the literature on the economic participation of refugees. Section 3 compares the characteristics of newly arrived refugees from Sudan with previous arrivals for whom survey data is available, to find that both groups are highly comparable. Section 4 uses the existing data to explore how the basic needs refugees are covered from own-income. Sections 5 and 6 dig deeper by exploring econometrically the correlates of higher incomes of refugees. A discussion of the results and their policy implications follows in section 7, after which section 8 concludes. 2. Benefits of economic participation of refugees Whether or not the arrival of Sudanese refugees in Chad contributes to economic growth is of limited immediate relevance as concerns about the safety of fellow humans drive the response. Nor does any decision maker suggest that hosting refugees is a development strategy Chad should pursue. Usage context: primary mention, supporting mention, background mention.

Refugees and Host Communities Household Survey

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Input: Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10044 Most refugee hosting communities are characterized by high levels of poverty with precarious livelihood conditions, low access to public services, and underdeveloped infrastructure. While the unexpected inflow of refugees might bring both constraints and opportunities for improving and maintaining local livelihoods in these communities, the understanding of these effects remains limited. Using a household level micro data set from a 2018 baseline survey of the Ethiopia Development Response to Displacement Impacts Project, this paper assesses the impact of refugee inflow on the livelihood strategies of host communities with respect to diversification and agricultural commercialization. Usage context: primary mention, supporting mention, background mention.

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Input: “relaxed” definition of labor force participation includes anyone who is available to work. The “strict” definition of labor force participation includes only those who are available to work and also actively searching for work. Employment-to- population ratio is the share of working-age people who are employed. 41 In Ethiopia, in-camp refugees can work as incentive workers, with standardized pay scales according to their skills, in different organizations, including RRS, a government entity. Thus in-camp refugees who indicated that they work in the public sector were assumed to be incentive workers under RRS. 0 10 20 30 40 50 60 70 80 90 100 Camp Hosts Camp Refugees Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure 3.3: Work type Source: World Bank Staff based on SESRE 2023. Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals 0 10 20 30 40 50 60 70 80 90 100 Camp Hosts Camp Refugees Percent Figure 3.4: Occupation Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 29 Female refugees have high employment rates relative to male refugees of all ages and make a critical contribution to refugee household incomes. Figure 3.5

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Input: Papaioannou, 2016; Berman et al., 2017; Harari and Ferrara, 2018; Eberle et al., 2020; McGuirk and Burke, 2020b). As a further robustness check, we also use data on conflict incidence and intensity from the UCDP, which uses a more conservative definition of conflict. The UCDP dataset is manually curated and compiled with automated computer assistance (Sundberg and Melander, 2013). The UCDP defines an armed conflict event as “ an incident where armed force was used by an organized actor against another organized actor, or against civilians, resulting in at least one direct death at a specific location and a specific date ” (Pettersson et al., 2020). We extract daily event observations from the UCDP dataset if the location of the actual event is exactly known, the event location is within a radius of less than 25 km around a known point, or at least the administrative district where the event happened is known. As pointed out by Eberle et al. (2020), the UCDP events are more likely to capture violence between large-scale and more structured groups. Table B. 2 shows that on average, conflict events seem to occur more in refugee-hosting areas. This is of course not a causal interpretation but a simple correlation. Usage context: supporting mention, primary mention, background mention.

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Input: 14 people may also gain from conflict in terms of income and wealth and this may explain why some people do not move. The defining attributes of the alternative choices are very different from any other model and the task of economics is to understand what these defining attributes should be. In terms of independent variables, “ push ” factors become more important than “ pull ” factors in forced displacement models. The intensity of a conflict may be more important than the income opportunities in potential destination areas. In addition to the classic socioeconomic variables, risk aversion, stress, anxiety, other traits of personality and behavioral factors in general have to be well understood and measured. Hence, one could think of four essential blocks of independent variables including individual or household socioeconomic characteristics, “ push ” factors, “ pull ” factors and behavioral factors. Also, access to and dissemination of information related to the conflict in the place of origin but also in the potential places of destination may be crucial for people to make choices. This is where social psychology, behavioral economics and neuroeconomics may offer insights into such choices. Forced displacement data are also unusual in their form. Usage context: background mention, primary mention, supporting mention.

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Input: Table 2: Ethnic composition of IDPs, refugees, returnees in the North Ethnicity IDPs in Bamako (%) Refugees Niger (%) Refugees Mauritania (%) Returnees (%) Total I + R + R (%) Ethnic composition of the North (%) Songhai 75 21- 71 43 45 Kel Tamasheq 12 56 69 12 38 32 Arab 3- 28 4 11 3 Peulh 4 21- 6 4 7 Other 6 11 3 7 4 12 Total (%) 100 100 100 100 100 100 Total (n) 100 81 100 220 501 1, 268, 009 Source: Listening to Displaced People Survey, 2014 and 2009 Population and Housing Census. The ethnic composition of IDPs and returnees is almost identical. This is a reflection of the fact that 94 % of returnees were displaced within Mali. Only 6 % returned from outside the country. The reason why few returned refugees are in the returnee sub-sample is explained by their place of residence prior to the crisis: only 5 % of the refugees in Mauritania and Niger lived in Timbuktu town before their displacement; 2 % lived in Gao town and 1 % in Kidal town. The remaining 92 % lived in 27 different towns and villages in northern Mali, locations not covered by the survey.

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Input: The data on total gross proﬁts and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key diﬀerence from the Chamber of Commerce data is that the sales and proﬁts data include all businesses including self- proprietorships. 9 Data were provided for the years between 2010 and 2014 and are re- ported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by deﬁnition and they are likely to be less accurate and complete for smaller ﬁrms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and proﬁt ﬁgures. 10 We scale the variables according to province size by dividing sales and proﬁts by the pop- ulation of the provinces. If we use sales and proﬁts in absolute terms, we get qualitatively similar results. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12 Usage context: supporting mention, primary mention, background mention.

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Input: of the groups and the distances between them. For instance, (Bazzi et al., 2019) shows that polariza- tion increases ethnic attachment. Others have highlighted the reduction in trust, either interpersonal trust or institutional trust Alesina and Ferrara (2002); Beugelsdijk and Klasing (2016). To assess the importance of alternative explanations, we first replicate our analysis using individual data on violence. In addition to participation in protests, we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault. We then assess the relationship between the revised refugee diversity indices and alternative individual outcomes such as ethnic vs. national identity, generalized trust, trust in neighbors, and institutional trust (trust in government). The questions from the Afrobarometer mentioned below are used as a proxy for these outcomes: 32 1 Attack: Over the past year, how often (if ever) have you or anyone in your family: Been physically attacked? 2 Crime: Over the past year, how often (if ever) have you or anyone in your family: Feared crime in your own home? 3 National identity: Let us suppose that you had to choose between being a [Ghanaian / Kenyan / etc.] and being a [respondent ’ s identity group]. Usage context: background mention, primary mention, supporting mention.

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Input: 60 80 100 Camp Hosts Female Camp Refugees Female Elementary occupations Machine operators/assemblers Craf/related trade workers Skilled agricultural workers Service/sales workers Clerical support workers Tech/associate professionals Managers/professionals Percent Figure 3.8: Female occupations Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 31 0 10 20 30 40 50 60 Camp Hosts Male Camp Refugees Male Camp Hosts Female Camp Refugees Female Figure 3.11: Hourly earnings 0 5 10 15 20 25 30 35 40 45 Camp Hosts Male Camp Hosts Female Camp Refugees Male Camp Refugees Female Figure 3.10: Hours per week Source: World Bank Staff based on SESRE 2023. Note: Hourly earnings are past-month earnings in the main occupation in Birr divided by the typical hours worked in a month over the past year. Only refugees working outside of camps start to see earnings improve with schooling. Working outside the camp is associated with a 42 percent increase in earnings. Most importantly, the relationship between completing secondary and post-secondary schooling and wage earnings becomes significant only when the refugee sample is restricted to workers outside the camps (see Annex D, Table D.7). This indicates that refugees only benefit from education and are incentivized to invest in education

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Input:
The 355 municipalities are grouped into 25 GGD areas, each covering a population of approximately 600,000 inhabitants. The GGD borders are visible in figure 5 which visualizes the hospital admissions. The data is combined with demographic statistics (2019) obtained from the Dutch Central Bureau of Statistics.

A number of surveyed health
statistics (2016) have been obtained as well from the RIVM (maps can be viewed in the
source link). [11] . The data is based on a survey of 457,000 people and includes the share of
population in each district with a documented long-term illness (illnesses over 6 months), the
prevalence of overweight and obesity, alcohol abuse, smoking and noise due to traffic.

For the main analysis, annual average particulate matter concentrations from the RIVM are used to capture long-term exposure (2017, published September 2019).

he temporal lag in the pollution data also ensures that there is no endogeneity due to feedback between case incidence and changes in pollution levels that follow lock-down policies. To test whether the main findings of the analysis generalize to other pollution data sets, a second analysis presented in the appendix uses the coarser grids from the global PM2 _._ 5 data set of van Donkelaar et al. (2016).

Cases are reported to the RIVM by the Municipal Health Service (GGD).The GGD is organized as collaboration between municipalities to provide base level public health service

annual average particulate matter concentrations from the RIVM

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Input:
In doing so, this paper also relates to recent research using firm-level data to document effects of foreign acquisitions on firm performance, including Arnold and Javorcik (2009), Bloom et al.

In order to estimate equations (1), (2) and (3), we use data from the Orbis database
of Bureau Van Dijk. The Orbis dataset provides retrospective information on
company ownership and reports the country of origin of each foreign shareholder,
although the share value is often unknown.

The data covers about 58% of Polish firms as reported by Eurostat and coverage varies significantly across regions (Farole et al., 2017). [5] Figure A.4 shows the evolution of the percentage of companies with at least one foreign shareholder from either Germany, French, the United States or the United Kingdom.

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Input: 5 interviewed (i. e., on the premises when the enumerators began their interviews) were included in this survey. Enumerators selected households within the catchment area randomly. Among households with an age-eligible child present (i. e., 0-35 months or 36-72 months), enumerators explained the purpose of the study and invited the target child ’ s primary caregiver to be interviewed. Respondents were supported to withdraw their consent anytime during or after the interview and such cases were dropped from analyses. For children 0-35 months of age, assuming a minimum power level of. 8 and an alpha of. 05, the study design predetermined that the minimum desired detectable difference in average ECD Z- score measured by the Caregiver Reported Early Development Instrument (CREDI) was a 0. 2 standard deviation (SD) difference, which has been shown to be the average difference between urban and rural Pakistani children assessed using the CREDI (Hentschel et al., 2024). For children between 36 and 72 months of age, assuming a minimum power level of. 8 and an alpha of. 05, the study design predetermined that the minimum detectable difference in average ECD score (measured by the AIM-ECD score) is a 0. 23-point difference (the difference between males and females in Hentschel et al., 2024). Usage context: supporting mention, primary mention, background mention.

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Input: 8 households in each. To select a starting point the enumerator used a code of the day16 and chose every second house in rural areas and every fifth house in urban areas. The selection of individuals within the household to answer the questionnaire was conducted as follows: the head of household (male or female) was selected to answer the first part of the questionnaire dealing with general questions about the households. Using the roster of household members which was compiled during the first part of the interview, another member of the household aged 18 or above was selected randomly to answer the second part of the questionnaire in which perception questions were asked. Alternation between male and female was ensured. The survey thus generated data that are reflective of the opinions of those aged 18 and above in northern Mali. To assess the representativeness of the data, which were collected under rather challenging circumstances, the ethnic composition of the sample was compared with the ethnic composition in the North as reported by the 2009 Census. Usage context: background mention, primary mention, supporting mention.

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Input: In contrast, the self-assessed entrepreneurial score is based on questions asking how well the respondent believes she could perform a series of six tasks related to starting or running a business, and can be considered a task-oriented measure of self-efficacy. The aggregate measure of entrepreneurial ability increased by roughly nine percentage points among EPAG beneficiaries relative to those in the control group, equivalent to a quarter of a standard deviation. Enhancing participants ’ self-confidence to perform these tasks was one of the main immediate objectives of the BDS training program. Table 6B summarizes the results on a series of questions on attitudes and self-confidence that were added during the midline survey only (hence no panel analysis is possible). EPAG graduates report a more positive attitude: they feel more in control and more comfortable, and they have greater confidence in their own business abilities as well as in their personal and social lives. They are also more confident than the control group in their personal relationships with spouses and partners, consistent with the findings in Table 6A. Usage context: background mention, supporting mention, primary mention.

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Input:
Nevertheless, we believe that this analysis provides suggestive   13As mentioned earlier, this is per capita income for the three months prior to the ENIGH survey.

CADENA now offers weather index insurance   for a variety of perils (e.g., drought, flood, hail), as well as area-based yield index insurance, which   2Subsidies from the federal government depend on the marginality index of the insured municipality, as computed by CONAPO.

Note. The data used to created this table comes from the 2010 census. % Illiterate is defined as the number of
illiterate individuals over the age of 15 divide by the total population over 15.

**Karlan,** **Dean,** **Robert** **Osei,** **Isaac** **Osei-Akoto,** **and** **Christopher** **Udry**, "Agricultural

Decisions after Relaxing Credit and Risk Constraints," _Quarterly_ _Journal_ _of_ _Economics_, 2014,

In addition, this study computes environmental risks at the commune level [2] to relate it to household information based on the Vietnam Household Living Standard Surveys (VHLSS) for 2010, 2012, and 2014. Benefiting from the panel

Vietnam Household Living Standard Surveys (VHLSS)

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Input: For a detailed definition see section 4 Household size Number of people included in the case records of each PA in Individual ProGress dataset Wage Income 1 if the household receives income from employment and / or daily or irregular work Income from remittances 1 if the household receives income from remittances Income per capita Raw sum of household income from all sources; work, pension, assets in Syria transfers, donations, other organizations'humanitarian aid, and other divided by household size Male Adults Number of males above 18 (inclusive) in the household Marital Status Categorical variable. The classification includes married PAs with spouse in the household, married PAs without spouse in the household, widowed, single or engaged, and divorced or separated. Proportion of female Number of female divided by the household size Location Categorical variable for 11 Governorates / cities. Ajloun City, Aqaba, Balqa, Irbid Jerash, Karak, Maan, Madaba, Mafraq, Tafilah, Zarqa. In Camp 1 if the household is located in a refugee camp Poverty before UNHCR and WFP assistance 1 if household expenditure before UNHCR plus WFP assistance is below the poverty line (JD50) Poverty before UNHCR assistance 1 if household expenditure after WFP assistance but before UNHCR assistanc is below the poverty line (JD50) Source: Authors ’ elaboration.

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Input: obtained from the multilevel logistic model. Thus, I am conﬁdent that I did not lose substantial statistical information by dichotomizing this variable. 6. 2 Independent Variables 6. 2. 1 Socio-Demographic Variables I control for standard socio-demographic variables that can aﬀect citizens ’ acceptance of government ’ s right to make people pay taxes. A question prob- ing respondents on their household income was not included in the fourth round of Afrobarometer surveys. Asking respondents to quantify their in- come can be problematic in the context of developing economies, where in- dividuals are often embedded in barter or commodity exchange, rather than, market economies. There are, however, reasonably good proxies including whether respondents own a television, radio, car, and mobile phone, and use the internet. Age, education, employment, and urban or rural residence are demographic factors that also aﬀect household resources. 6. 2. 2 Experience with Paying Taxes or Fees It is diﬃcult to assess just how ubiquitous taxes are in ordinary Africans ’ lives. There has not been any systematic eﬀort to take stock of the types and amount of taxes citizens pay across Africa. Similar to pre-modern European states, African states ’ revenue raising capacity is generally low.

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Input: These include topics like labor, finance (e. g., credit, debt, banking), agriculture / livestock / fishery, and education whereas FDP datasets are relatively rich in health, food insecurity, and water and sanitation in addition to coping strategies and protection. Furthermore, a large majority of questionnaires for these existing micro-level datasets do not include a core set of questions needed for proper identification of displacement status. This calls for further efforts 2 See https: / / www. unhcr. org / news / stories / unhcr-s-grandi-110-million-displaced-indictment-our-world. 3 The UNHCR MDL is available at https: / / microdata. unhcr. org / index. php / about. 4 The WB MDL is available at https: / / microdata. worldbank. org / index. php / about. 5 We focus on low-income and middle-income countries because they account for roughly 95 percent of FDP (refugees + IDPs) in the world based on data from UNHCR Refugee Finder and IDMC. 6 A representative sample means that when analyzed, the observed characteristics of the sample reflect the true characteristics in the target population that is being researched (Baal and Ronkainen, 2017). Usage context: primary mention, supporting mention, background mention.

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Input: The weight given to each province in constructing the synthetic control is based on pre-treatment outcomes. We use the pre-treatment average of the outcome dimension, Y, the unemployment rate, employment rate and the import and export per capita of the province to determine the degree of similarity between control group provinces and the treated provinces, which in turn determines the weight assigned to control provinces. The unemployment and employment rates are included to control for the general economic performance while trade values are added to control for the degree of ’ openness ’ of the province. 6 The treated unit i = 1 is constructed by taking the mean of the outcome variables in the provinces hosting refugees in 2012 or 2013. 5 Data We use several data sources for the analysis. The IV estimations use data from years 2011 and 2014 while the DD estimations use data from 2009 to 2014. The numbers of refugees up to 2012 are treated as 0. The refugee data for 2012 and 2013 are obtained from UNHCR ’ s oﬃcial weekly statements in December. Data on the number of refugees in 2014 is from Erdo ˘ gan (2014), who uses statements released by the Ministry of the Interior to compile his data. All refugee data we use in the analysis is provided at the level of 81 provinces.

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Input:
_replications. Contemporaneous cross-unit correlations are generated by computing the cross-correlations of residuals from_ _AR(I) regression of the first difference of Gross State Product for 20 U.S. states from 1963 to 1986 and of Gross Domestic_ _Product for 24 O.E. C.D. economies from 1960 to 1991._

areas, of which 72 percent depends on agriculture (Census of India, 2011). Agriculture accounts for 17 percent of GDP and employs 51 percent of the total labor force (World Bank, 2010). [2]

_Source_ : Author's analysis based on the 1982 and 2006 rounds of the Additional Rural Incomes Survey and Rural Economic & Demographic Survey (ARIS-REDS)   from the National Council of Applied Economic   Research.

Additional Rural Incomes Survey and Rural Economic & Demographic Survey

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Input:
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ILRI, and the World Bank (as part of the LDIA and LSMS-ISA projects [2] ) to start the survey

validation work that is documented in this paper.

2.2 _Milk production recall methods_

Using data from two microenterprise surveys in Sri Lanka, De Mel et al. (2009) find that

the same survey experiment in Tanzania to obtain evidence on the nature of measurement

Scott and Amenuvegbe (1990) conduct an experimental study on 135 households in Ghana.

Dantlait survey. The fieldwork was managed by two experienced enumerators, and a

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Input:
market outcomes (Kotsadam and Tolonen 2016). Mining is also associated with more economic

activity measured by nightlights (Benshaul-Tolonen, 2019; Mamo et al, 2019).

Kotsadam and Tolonen (2016) use DHS data from Africa, and find that mine openings cause

and GLSS with production data for 17 large-scale gold mines in Ghana. We find that a new

large-scale gold mine changes economic outcomes, such as access to employment and cash

increase in total production that rose from 541,147 oz in 1990 to 3,119,823 oz in 2009 according

official Ghana statistics (Bloch and Owusu, 2012). This production increase led to an increased

South Africa
Teberebie 1990 2005 Anglogold Ashanti South Africa
Wassa 1999 active Golden Star Resources USA
_Source:_ InterraRMG 2013.
_Note:_ Active is production status as of December 2012, the last available data point.

production data
official Ghana statistics

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Input: displaced due to armed conflict, situations of generalized violence and violations of human rights. 28 Data on IDPs monitored by IDMC are disaggregated and currently published separately for conflict-induced displacement and disaster-induced displacement. 29 At the country level the IOM ’ s Displacement Tracking Matrix (DTM) 30 provides data on IDPs in both conflict and natural disaster settings (activated in all major natural disaster contexts in recent years). Global data on conflict-induced internal displacement reflect variations in how IDPs are defined across situations. There is no consensus on how far a person must flee in order to be considered internally displaced. The definition of internal displacement for nomadic populations, which account for a significant share of IDPs in the Horn of Africa and increasingly in the Sahel, is open to controversy. 31 Moreover, while some countries register IDP children born in displacement (e. g. Azerbaijan, Cyprus and Georgia), other countries do not (IDMC 2015). The crafting of a definition for IDPs and its application in a particular context may be heavily influenced by local and national politics in conflict and post-conflict countries, as well as the direct link between estimates of displaced populations and humanitarian assistance, which can lead to both over- and under-reporting.

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Input: 12 crop products (e. g., wheat, potatoes) and the value of five livestock products (i. e., milk, egg, butter, hides, and honey) sold in the market. 14 Several other data sources were utilized. First, the Ethiopian refugee camps location data set from the Humanitarian Data Exchange (HDX) 15 and the total number of refugees by camps from the United Nations High Commissioner for Refugees (UNHCR), Addis Ababa office. We use data from 26 official UNCHR refugee camps in Ethiopia that were operational in 2018 (see Figure 1; 3). Second, we use administrative data sets for Ethiopia and refugee source countries from the database of Global Administrative Areas (GADM). 16 We also use the conflict data set from the Armed Conflict Location and Event Data Project (ACLED) 17 and the population data from the Gridded Population of the World (GPW) data set. 18 On the basis of these data sets and the location of sample households from Ethiopia DRDIP data set, we generated the following variables: i) distance of sample households to the nearest refugee camp, the nearest region (administration level 1 in GADM) to the refugee camps, ii) distance of the refugee camps to the nearest border of the refugee source country, iii)

Gridded Population of the World (GPW) data set

Ethiopian refugee camps location data set

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Input: However, should ASM respond to large-scale activities, either by increasing or decreasing activity in the close geographic area, we will end up estimating the impact of these sectors jointly. In a later stage, should the opportunity arise, we encourage researchers to try to disentangle the effects of small-scale and large-scale mining. 3 Data To conduct this analysis, we combine different data sources using spatial analysis. The main mining data is a dataset from InterraRMG covering all large-scale mines in Ghana, explained in more detail in section 3. 1. This dataset is linked to survey data from the DHS and GLSS, using spatial information. Geographical coordinates of enumeration areas in GLSS are from Ghana Statistical Services (GSS). 2 Point coordinates (global positioning system [GPS]) for the surveyed DHS clusters3 allow us to match all individuals to one or several mineral mines. We do this in two ways. First, we calculate distance spans from an exact mine location given by its GPS coordinates, and match surveyed individuals to mines. These are concentric circles with radiuses of 10, 20, and 30 kilometers (km), and so on, up to 100 km and beyond. In the baseline analysis where 2 The data was shared by Aragón and Rud (2013) 3 Both the DHS and GLSS enumeration area coordinates have a 1-5 km offset. The DHS clusters have up to 10km displacement in 1 % of the cases. Usage context: background mention, supporting mention, primary mention.

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Input: The authors argue that Croats listened to Serbian radio for its consumption value but reacted negatively to national- istic messages intended for Serbian ears. Election results and street surveys are used to elicit preference for extremist nationalist parties among Croats who are able to listen to Serbian radio and those that do not. The authors ﬁnd that 3 to 4 percent of those 59See Yanagizawa-Drott (2014). 60See, for example, Enikolopov et al. (2011) and DellaVigna and Kaplan (2007) who ﬁnd large eﬀects on voting shares. 70

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Input: 12 employment status and education level also significant at 10 %. Marital status and education are important predictors of attrition. As we might expect, these imbalances suggest some threats that, if left uncorrected, could undermine the parallel trends assumption of difference-in-difference estimators. That said, we see no sign of differences between treatment and control, or attritors and non-attritors, over the key GRIT personality features. This suggests that members of the treatment group are not, for example, more motivated to succeed than members of the control group. To account for these biases, we generate a series of inverse probability weights to balance the data. These weights define the probability of an individual with particular characteristics (e. g. host or refugee status) being in each of the treatment and control groups at baseline and endline and are used to rebalance the data in order to closer support the parallel trends assumption. Results are shown in Column 3 of Table 4. Following weighting, data balances on all key factors, including nationality. This suggests that the parallel trends assumption is more reasonable under the weighted dataset than in the raw treatment / control data. 11 Based on these analyses, we conclude that it is safe to use weighted OLS-based approaches. Usage context: background mention, supporting mention, primary mention.

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Input: not better positioned to work in the formal private or public sector, their participation in trade and service relates to economic 47 This analysis uses the relaxed definition to measure the current employment status of the host community and refugees. 48 As the previous chapter highlighted, most of refugees engaged in agriculture activity are livestock holders (see Chapter 3). 44 37 42 55 LFPR Nearest to Zone Nearest to Woreda but not Zone Nearest to border but not Zone & Woreda Remote 40 40 49 LFPR High accessibility Medium accessibility Low accessibility Figure 6.2: Labor force participation rate by proximity to resource hub, market accessibility Source: World Bank Staff based on SESRE 2023. Note: High, medium, and low accessibility refers to the level of market access, with >0, [-0.5, 0], and <-0.5 standard deviations from the average, respectively. 60 40 56 44 68 32 46 54 Employment rate Unemployment rate Nearest to Zone Nearest to Woreda but not Zone Nearest to border but not Zone & Woreda Remote 64 36 60 40 50 50 Employment rate Unemployment rate High accessibility Medium accessibility Low accessibility Figure 6.3: Refugees’ labor market outcomes Source: World Bank Staff based on SESRE 2023. Note: High,

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Input: Since the poverty headcount only reflects the proportion of poor people, it is usually complemented by a measure of the depth of deprivation, the poverty gap, which estimates the average gap between poor individuals and the poverty line. All poverty measures constructed in such a way are based on an underlying welfare metric. In most cases, the metric is defined at the household level and needs to be transformed into an individual measure. The transformation either divides household welfare by household size, providing a per capita measure or, in a more sophisticated way, takes into account differences in household composition (usually age and gender), leading to a per-adult equivalent measure. To measure household welfare, different metrics have been proposed and can generally be classified either into monetary or non-monetary metrics. Monetary metrics equate levels of well-being to a monetary indicator of utility (Samuelson, 1974), usually income, consumption or expenditure. Non-monetary metrics define normatively dimensions of deprivations, e. g., access to education and clean water, and aggregate them either by using dimension-specific thresholds or an aggregated threshold. The most commonly used approach in this class is the Multidimensional Poverty Index by UNDP and Oxford University (MPI; OPHI, 2018). While it is not uncommon to find experts with a strong preference for one measure over the other, both classes of poverty measures are in most cases complementing each other. While the monetary metric has the advantage of its theory-grounded definition without normative choices, it is complex to measure Usage context: supporting mention, background mention, primary mention.

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Input: Addis Ababa Eritrean Somali South Sudanese Addis Ababa Eritrean Somali South Sudanese Addis Ababa Has hand washing place/item Water available Detergent available Hosts Refugees Percent Figure D.15: Hand washing facility Source: World Bank Staff based on SESRE 2023. Annexes 105 Table D.5: Labor force statistics by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Participation (strict) 57% 38% 44% 35% 55% 28% Employment (strict) 89% 72% 94% 77% 96% 82% Unemployment (strict) 11% 28% 6% 23% 4% 18% Participation (relaxed) 62% 60% 51% 49% 57% 38% Employment (relaxed) 81% 45% 81% 55% 91% 60% Unemployment (relaxed) 19% 55% 19% 45% 9% 40% Source: World Bank Staff based on SESRE 2023. Table D.6: Determinants of refugee-host earnings gap (1) (2) (3) (4) Earnings Earnings Earnings Earnings Refugee (% difference from hosts) -69.8*** -63.7*** -62.1*** -40.7*** (0.077) (0.123) (0.122) (0.126) Control: Region Yes Yes Yes Yes Control: Demographics Yes Yes Yes Control: Occupation/Sector Yes Yes Sample: Working outside camp Yes Sample Size 743 742 742 572 Source: World Bank Staff based on SESRE 2023. Note: Monthly earnings are collected for employees only (including work for government, NGOs, and private households). Log earnings are winsorized at the 1st and

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Input: Each respondent was presented with three vignettes, where key aspects of the sce- narios were randomly varied across respondents. These three vignettes were designed to probe the impact of diﬀerent pull and push factors on the refugees ’ return deci- sion, allowing us to go beyond the data limitations of the above analysis. That is, the vignettes not only explore the impact of security on return decisions, but also of employment prospects in both the country of asylum and Syria, the status of property in the home community, and the availability of ﬁnancial assistance. In particular, the ﬁrst vignette probes three questions: ﬁrst, whether the ability to 11According to this classiﬁcation, Jebel Saman, Deir-ez-Zor, Homs, Al Ma ’ ra, and Duma are high- conﬂict districts. 16 Usage context: supporting mention, primary mention, background mention.

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Input: used in the Skills Profile Survey (SPS) and SESRE, we find similar patterns in some of the indicators common in both surveys among in camp refugees such as demographic composition, primary and secondary net enrollments, housing condition, access to basic infrastructures, employment and attitude of hosts toward refugees. Moreover, both surveys find that in camp refugees in Ethiopia are much poorer than host community households and poverty rates are heterogenous across refugee groups: South Sudanese refugees have the highest incidence of poverty, while Eritrean refugees have the lowest poverty incidence amongst the refugees. However, different poverty estimation methodologies and different poverty lines are used. Likewise, both surveys indicate that refugees are more food insecure than the host community. Annex F, Table F.1 shows a summary of these findings. Source: Pape, U. J., Petrini, B., and Iqbal, S. A. (2018). Informing Durable Solutions by Micro-Data: A Skills Survey for Refugees in Ethiopia. Box 1.1: Comparison of SPS 2017 and SESRE 2023 Sociodemographic Profile 9 2. Sociodemographic Profile T his chapter presents results on sociodemographic outcomes of refugees and hosts. It provides the context for refugees and their hosts, covering demographic characteristics, human capital, living conditions, and displacement experience, which are crucial

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Input: 8 permits and subsequently for the right to work. In practice, this is a long and cumbersome process and by late 2015 at most several thousand had been issued. 14 The economic impact of Syrian refugees in Turkey extends beyond changes in the potential labor supply of informal workers in important ways. There has been extensive humanitarian aid provided to the refugees, overwhelmingly by the Turkish government. Reportedly, by early 2015 the Turkish state had spent $ 6 billion (with total outside contributions $ 300 million). 15 Much of these funds have been spent on food, various services, non-food items such as medicines, clothing, shelter, and housing-related goods. In particular, there are 20 accommodation centers (camps) in 10 cities in Turkey. 2. 2 Data Sources We use the Turkish Household Labor Force Survey (LFS) micro-level data sets compiled and published by the Turkish Statistical Institute. The data contains a rich set of labor market variables along with individual-level characteristics and the region of residence. We primarily rely on two years of LFS data: 2011 (just before the arrival of the refugees) and 2014 (the last year available). 16 By design the LFS does not contain any information on Syrian refugees. Usage context: background mention, supporting mention, primary mention.

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Input:
### Financial Management Risk and Mitigation

The overall FM risk is rated **Substantial** due to the multiplicity of implementing agencies and the limited capacity of district-level accountants. Key mitigation measures include: (i) appointment of a dedicated FM specialist at the central PIU; (ii) mandatory use of the SIGAF government accounting system for all transactions above USD 5,000; (iii) quarterly internal audits by the Ministry's internal audit department; and (iv) annual external audits by a firm acceptable to the Bank, with reports submitted within six months of fiscal year-end.

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Input: The upcoming section provides an overview of the situation facing Venezuelan migrants in Colombia, setting the stage for understanding the context within which our study is situated. Section three offers a comprehensive description of the VenRePS-Kids study, covering aspects such as the sampling frame, the instrument used for data collection, the representativeness of the study, its implementation process, and an overview of descriptive statistics. Section four delves into the human development disparities observed among forcibly displaced chil- dren and adolescents, providing detailed insights into the nature of these gaps. In section 9

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Input: This assistance is crucial and has been reported to support a meal a day, a better roof, and dignity for Syrians who have fled to Jordan. 8 The JD ‐ HV database has detailed information on expenditure, sources of income, and indicators of household ‐ level welfare, for example, as reflected by recourse to coping mechanisms, standard of accommodation, or access to water, sanitation, and hygiene (WASH). JD ‐ HV data collected between October 2013 and December 2014 were first analyzed in Verme et al. (2016) who produced welfare aggregates and poverty measures to help target benefits and assistance to those most in need. Verme et al. (2016) draw attention to the precarious circumstances of Syrian refugees in Jordan and Lebanon. Around 55 percent of refugees in Jordan are vulnerable to monetary poverty and more than half are vulnerable to food shocks. Family size increases the probability of being poor, with the poverty rate almost doubling if the size of the family goes from one to two members and increasing by 17 percent when the number of children increases from one to two.

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Input: An alternative version of the database that has been mapped to the United Nations (2006, 2009) Trends in International Migrant Stock database is available from the authors. These data are standardized over time in terms of the years to which they refer. { Table 3 here} Calculating Missing Gender Splits Although common in the underlying data, bilateral migration data disaggregated by gender are sparser than aggregate migrant totals (see table 1). An important contribution of the current work is in estimating the gender breakdown of all migrants in destination countries in the global migration matrices. Similar to the allocation from aggregated categories in the Global Migration Database to specific origins in the master list, two measures are used for calculating gender splits; they are described in appendix 5. Combining Migrant Definitions Only a single definition of a migrant (foreign born or foreign citizen) can be applied to each destination country in the final matrices. Switching definitions over time 17 The subregions used for the disaggregations are the 21 UN regions (see http: / / unstats. un. org / unsd / methods / m49 / m49regin. htm, with the countries of Oceania aggregated into a single subregion. They do not match the large World Bank regions used in the analysis in section IV. 18 While this propensity measure is clearly inappropriate, less than 1 percent of all migrants and observations are assigned on this basis. This method is included so that every migrant in the underlying data is accounted for. Usage context: background mention, primary mention, supporting mention.

Trends in International Migrant Stock
Global Migration Database

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Input: III THE VENREPS-KIDS STUDY In this section, we offer a detailed overview of the VenRePs-kids Study, covering its de- sign, implementation, the questionnaire utilized, and the primary outcomes that will be employed to evaluate the human development disparities between forcibly displaced children and adolescents and their peers in host communities. III. A Design Location. Our study is conducted in Medell ´ ın, Colombia ’ s second-largest city, following Bogot ´ a. Medell ´ ın was chosen for this study because it hosts the third-largest Venezue- lan migrant population in the country, trailing only Bogot ´ a and C ´ ucuta, as indicated by the 2018 population census data. Additionally, previous research has demonstrated that survey response rates among migrants in Medell ´ ın are notably high. For instance, a na- tionally representative survey of Venezuelan migrants conducted in 2018 — which was representative across Colombia — revealed that Medell ´ ın had the highest response rates among migrants, whereas Bogot ´ a recorded the lowest (Ib ´ a ˜ nez et al. 2022). This finding supports the decision to focus our study exclusively on Medell ´ ın, also considering the challenges and high costs associated with tracking a highly mobile population longitu- dinally in previous research efforts. Usage context: supporting mention, primary mention, background mention.

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Input: Anderson, J. (2011): “ The Gravity Model, ” Annual Review in Economics, 3 (1), 133 – 160. Angrist and Pischke (2009). Mostly Harmless Econometrics, Princeton University Press. AFAD (Disaster and Emergency Management Presidency of Turkey) (2013). Syrian Refugees in Turkey, 2013: Field Survey Results. Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015a). “ The Impact of Refugee Crisis on Host Labor Markets: The Case of the Syrian Refugee Crisis in Turkey. ” IZA Discussion Paper 8841. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015b). “ The Impact of Refugee Crises on Firm Dynamics and Internal Migration: Evidence from the Syrian Refugee Crisis in Turkey, ” mimeo. Aydemir, Abdurrahman and Murat Kırdar (2013). “ Quasi-Experimental Impact Estimates of Immigrant Labor Supply Shocks: The Role of Treatment and Comparison Group Matching and Relative Skill Composition, ” IZA Discussion Paper 7161. Baez, J. (2011). “ Civil Wars Beyond their Borders: The Human Capital and Health Consequences of Hosting Refugees. ” Journal of Development Economics 96 (2) November: 391 – 408.

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Input: Formula (5) can be decomposed into two parts: eδs, which measures the average income level in district s, and eηszh ≡ eβs (Eh s − Es) + eχs (Hh s − Hs) which captures individual-speciﬁc variation in income. Migration models predict that, other things being equal, the choice of migration destination should depend on g E [yhs | zh]. This means that if we regress the choice of destination separately on eδs and eηszh, they should have the same coeﬃcient. The same methodology is used to construct other variables that may aﬀect the choice of 9The literature has often emphasized that migrations often serve an important role in household formation. For migrants, the prospect of forming a large, successful household is likely to be one of the purposes of migration. 10The 1995 / 96 NLSS survey adopted the following sampling strategy. Within each district a small number of wards were selected at random. Within each ward, 12 randomly selected households were interviewed. Because the wards diﬀer widely in terms of population, applying sampling weights is essential in order to obtain consistent estimates of δs. 16 Usage context: supporting mention, primary mention, background mention.

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