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.

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Input: In 2003, almost 60 % of young girls and 40 % of young boys had no formal schooling (ILO / UNICEF 2005). Data from the Demographic and Health survey shows that more than 40 percent of adult women have no education, compared to fewer than 20 percent of men, while 23 percent of women and 44 percent of men have some secondary schooling (DHS 2007). Happily, access to education is rising rapidly, especially for girls: according to the Liberian labor force survey from 2010, the ratio of girls to boys enrolled in primary school has risen from 72 in 2000 to 90 in 2009. Enrollment levels and sex ratios are lower among older children and youth, as they become increasingly engaged in 1 Both are unweighted averages; Barro-Lee comprises 32 countries with data from 2010; Edstats comprises 43 countries with data from 2007-2011. 2 Defined as without work, available for work, and actively looking for work (LISGIS 2010). 2

Barro-Lee
Demographic and Health survey
Liberian labor force survey
Edstats

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Input: 29 should be entered immediately into the accounts; and (c) Payment must be recorded as soon as they are made. Budget implementation should be reviewed periodically to ensure that programs are implemented effectively and to identify any financial or policy derailment. The review of budget execution should cover financial, physical and other performance indicators. Development budgets are often beset by implementation problems because of insufficient implementation capacities and other factors such as delays in mobilizing external financing, overoptimistic implementation schedules or difficulties in importing supplies. It is thus important to have in place mechanisms for reviewing the most significant or problematic projects. These could consist of a regular monthly or quarterly review of projects within the line ministries and a midyear review involving line ministries and central agencies29. The government has taken steps to improve the tracking of budget expenditure until the intended destination, particularly investments spending, for which a tracking survey was entrusted in 2005 with the Ministry of infrastructures. In addition, the ministries took themselves certain internal initiatives, in particular in health and education sectors, but the action plans of these ministries were not updated as envisaged in 2005, and there are neither reliable benchmark, nor quantitative targets as regards improvement of the arrival of the expenditure at intended destination in these sectors.

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Input: These systems employ a combination of data collection techniques including key respondent interviews, focus group discussions, registration, observations and physical counts, samplings and other statistical methodologies. For examples, UNHCR ’ s population tracking systems identifies and trains local NGOs to monitor key locations such as IDP settlements, bus stations and roads to report on movements. The accuracy of data from movement tracking systems is subject to several caveats. These include: limited access to locations and routes due to insecurity; vast geographical areas to monitor; mixed population flows that include refugees, IDPs, pastoral and seasonal movements and economic migrants; massive population flows that overwhelm monitoring capacity; disinclination of individuals to provide information when there is no assistance being offered; pressures from communities to inflate figures to maximize future assistance; and political pressures to suppress accurate reporting on IDP movements. Additionally, due to the fluid nature of displacement in many contexts and the likelihood of recurring displacements, it is not possible to use movement data to provide estimates of population stocks. Population censuses National population and housing censuses often provide the most comprehensive source of population data and offer the potential for estimating numbers of forcibly displaced people. To estimate the size of displaced populations a census would need to include questions on country (and / or place) or birth, year of (internal) 70 Other data collection methods may be used such as movement tracking systems, registration, big data etc. Usage context: background mention, supporting mention, primary mention.

movement tracking systems
population tracking systems

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Input: 15 causes of death. The Danish Epidemiology Science Centre (1999) found severe malnutrition and high mortality in a survey of 422 refugee children in Guinea ‐ Bissau. They report higher malnutrition and higher mortality for children living in a non ‐ camp setting, compared to children living in a camp. The Goma epidemiology group (1995) found high prevalence of child mortality as well as acute malnutrition among children in refugee camps in Eastern Zaire, especially in female headed households. The magnitude of the difference between ‘ normal ’ mortality in the country under study, in the absence of conflict and the mortality in a refugee camp, depends on several parameters: the health infrastructure in the country as well as in the camp, the food available to camp and non ‐ camp residents, the frequency of visits by nurses or doctors, the intensity of the conflict (e. g. attacks on camps), and so on. Thus, the results are highly dependent on the context. For example, Singh et al (2005) do not find a difference in under 5 mortality among refugee versus non ‐ refugee households in western Uganda and South Sudan, whereas Verwimp and Van Bavel (2005) find higher child mortality and fertility among Rwanda refugees in Congo versus Rwandan women who did not became a refugee.

survey of 422 refugee children in Guinea ‐ Bissau

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Input:
**SIGAF Reconciliation Status**

As of 31 December, cumulative project expenditures recorded in SIGAF amount to USD 14.7 million across all disbursement categories. This figure reconciles to within USD 12,000 of the Bank's Client Connection disbursement records, a variance attributable to timing differences in exchange rate application. The SIGAF balance will be reconciled in the first week of January following the receipt of the final bank statement for the Designated Account. No unexplained variances were identified in the year-end SIGAF reconciliation.

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Input: Not all cross-sectional studies have multiple rounds of comparable data, covering the period before and after the crisis. When comparing impacts between locations within a country, cross-sectional data also usually does not allow to capture impacts on those who moved out and to differentiate impacts between those who were already there before the shock and those who moved in afterwards. Some of the models based on administrative areas qualify as spatial econometrics models in that they use estimation methods that derive from this literature and are published in spatial econometrics journals. Studies that compare different areas within a country are not only confronted with the potential endogeneity of the size and skill composition of the inflow and the choice of destination, but also with the endogenous reactions of the host community. Local workers might respond to the labor supply shock by dropping out of the labor force, investing in education, occupational upgrading or moving to other areas and diffusing the impact of the inflow. Even if local workers do not respond to wage variations, capital flows may equalize capital / labor ratios within the country, labor-intensive industries might move towards the regions with a high refugee or IDP influx or firms might use more labor-intensive production technologies. The reactions of the host country workers, investors and firms are medium-to long-term in nature and will play less of a role in the short-term if there are large, sudden and geographically concentrated inflows. Some of the papers explicitly analyze these potential channels, notably migration of local workers, and, to a lesser extent, occupational upgrading. Outmigration of hosts is a critical complement to the labor market analysis and excluding this outcome can lead to an underestimation of the impacts of forced displacement on the labor market outcomes of natives. The papers we reviewed that looked at tasks complexities and the question of substitution vs complementarities between refugees and natives found occupational upgrading among

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

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Input: There is one limit to our approximation in Equation 4. The ethnic composition of refugees in each year t for a given origin – destination pair of countries obtained from the EPR-ER database is assumed to be homogeneous across camps of the same origin – destination pair of countries for the refugees at year t. This may seem to be a strong assumption; however, the risk of misallocating refugees is reduced as the annual variation in the EPR-ER is generated by just a few dominant groups for a given origin – destination pair and the geographical distribution of refugees by country of origin is highly influenced by the proximity to their countries of origin. 16 As can be seen from panel A of Table B. 2, in refugee-hosting areas, on average, both EF and the EP seem to increase quite significantly when they are revised by incorporating the number of refugees in an 80-km buffer: the mean value of the standard EF index is 25. 58 %, while the mean value of the revised refugee EF index is 37. 90 %. The mean value of the standard EP index is 10. 11 %, while the mean value of the revised refugee EP index is 14. 07 %. Usage context: primary mention, supporting mention, background mention.

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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: The model includes ϐixed effects for grade, school, and language spoken. 14 The results are presented in Table 3. We then narrow our focus to foreign students who joined Italian schools after February 2022, speciϐically comparing Ukrainian refugees to other newly arrived foreign students. This approach allows us to examine how Ukrainian refugees compare to other foreign students who entered the education system around the same time. By restricting the sample to these two categories of students, we estimate the following regression: 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 + 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 + 𝑓𝑓𝑔𝑔 + 𝑓𝑓𝑠𝑠 + 𝑓𝑓𝑙𝑙 + 𝜖𝜖𝑖𝑖𝑖𝑖𝑖𝑖 (2), with variables as deϐined in (1), and results presented in Table 4. Our analysis also aims to explore potential mechanisms that could explain results derived from equations (1) and (2). Using the administrative data, we investigate whether being placed in a smaller class inϐluences school achievement in the sample of Ukrainian refugees. The results are presented in Table 5. We then draw on ϐindings from the survey data to unpack and analyze how Ukrainian refugees feel in Italy, the challenges they face, and their aspirations. 4. Results 4. 1. Integration challenges faced by Ukrainian refugees in Italy Low enrollment and substantial dropout rates At the end of the 2021-2022 school year, the enrollment rate of Ukrainian refugee children in Italian schools was low. In the months following Russia ’ s full-scale invasion of Ukraine in 2022, 3, 320 Ukrainian refugees were enrolled into Italian secondary schools. This ϐigure constitutes 24 % of the 14, 106 Ukrainian refugees aged between 11 and 18 years who sought temporary protection as of 14 This variable is included to account for the potentially greater ease of learning experienced by students who speak languages that are considered closer to Italian. Usage context: primary mention, supporting mention, background mention.

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

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Input: 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: 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: primary mention, supporting mention, background mention.

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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: Income and consumption index: includes the logarithm of monthly household income and consumption. Because these variables are continuous the index is constructed following the methodology outlined in Kling, Liebman and Katz (2007). Labor market index: includes a dichotomous variable for employment, and the logarithm of monthly wages and weekly hours worked. Descriptive statistics for each of the outcomes and their components variables are de- scribed in Table 1. III. B Data for the Taliban ’ s Geographical Presence To obtain information on the Taliban ’ s geographical presence, we scraped online data from the Foundation for Defense of Democracies ’ Long War Journal on the districts con- trolled by Taliban, by U. S. Allies, and contested territories between the two groups. 9 The data includes a yearly evolution of the territorial control in Afghanistan. The classification is based on open-source information from NATO ’ s data in Afghanistan, press reports, in- formation provided by government agencies, and the Taliban. To categorize the districts, they identify who provides government services, ensures security within the district, ad- ministers the district openly, and oversees local courts (see Appendix B. Usage context: primary mention, background mention, supporting mention.

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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: While most existing theories of madrassa enrollment are based on household attributes (for instance, a preference for religious schooling or the household ’ s access to other schooling options) the data show that among households with at least one child enrolled in a madrassa, 75 percent send their second (and / or third) child to a public or private school or both. Widely promoted theories simply do not explain this substantial variation within households. 1 Corresponding Author: Tahir Andrabi (tandrabi @ pomona. edu). This study would not have been possible without the enthusiasm and continuous support we received from Tara Vishwanath. Charles Griffin first encouraged us to look at the data. We thank Veena Das, Shehla Andrabi, Sehr Jalal, Ritva Reinikka and Carolina Sánchez for their encouragement and to Hedy Sladovich for her excellent editorial suggestions. The paper has also benefited from comments by Ismail Radwan, Naveeda Khan, Shahzad Sharjeel and Shanta Devarajan. The research department of the World Bank provided funding for this study through the Knowledge for Change trust fund. The findings, interpretations and conclusions expressed in this paper are those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the governments they represent. Working papers describe research in progress by the authors and are published to elicit comments and to further debate. Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized Public Disclosure Authorized WPS3521

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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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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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Input: thirty years of autocratic rule that followed the Congolese independence from Belgium colonial rule paved the way to a violent transition, culminating in two internationalized wars from 1996 to 1997, in the aftermath of the Rwandan genocide, and from 1998 to 2003. Since the end of the Second Congo War in 2003, eastern Congo has remained unstable and violent, with many domestic and foreign-backed armed groups – including elements of the state military, FARDC – using violence against civilians and each other (Autesserre 2010). The violence and instability have resulted in poor living conditions and regular forced displacement for Congolese civilians. This project focuses on three provinces of eastern DRC that are especially impacted by forced displacement and political violence: North Kivu, South Kivu, and Ituri. 4 These three provinces account for 4. 5 million out of an estimated 5. 268 million total (85 %) IDPs in DRC 2020 (UNHCR Operational Data Portal: Democratic Republic of Congo 2021). Other provinces not included in this study but hosting IDPs include southern and central provinces such as Kasai, Kasai-Central, Kasai-Oriental, Lomani, Sankuru, and Tanganyika. The analysis in this paper focuses exclusively on dynamics in eastern Congo where the authors collected data.

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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.

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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

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Input: 8 neighboring community, (iii) residing elsewhere in the Kagera Region and (iv) residing outside the Kagera Region. Table 3 shows that the basic needs poverty rate declined 8 percentage points in the full sample. This figure masks significant differences in changes between subgroups based on migration. For those found residing in the baseline community, poverty rates dropped by 4 percentage points, but rates dropped by 11, 13 and 23 percentage points for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. A similar pattern is found for consumption per capita. While consumption per capita grew by $ 65 overall, it grew by only $ 30 for those found in the same community and by $ 65, $ 100 and $ 287 for those who moved to neighboring communities, elsewhere in Kagera Region and outside the Kagera Region respectively. Dividing consumption into food and non-food components gives the same result. The most basic assessment of welfare changes would have been wrong if we had focused only on individuals still residing in the community, a practice found in many panel data surveys. We would have underestimated the growth in consumption by half of its true increase. The differences in consumption changes of groups in Table 3 are statistically significant, as shown in Table 4. Usage context: supporting mention, primary mention, background mention.

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Input: The SIGAF system's fixed assets module was used to register all project-procured equipment at point of delivery. Each item received a unique SIGAF asset code printed on a tamper-proof label, and asset records were updated to reflect depreciation annually using the straight-line method over the estimated useful life. The SIGAF fixed assets register is reconciled against the physical inventory count conducted by the Ministry's internal auditors at the end of each fiscal year.

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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:
Data sources: UNHCR PRIMES, UNHCR Resettlement Statistics Report.   For more information or to contribute, please contact UNHCR RBSA DIMA (rsarbdima@unhc

**1,729**

**154**

**57**

**53**

**34**

**13**

**12**

**4**

**3**

**2**

**COD**

**BDI**

**SOM**

**RWA**

**TUR**

**ZAM**

**ETH**

**UGA**

**ANG**

**AFG**

**COD**

**BDI**

**SOM**

**RWA**

**ETH**

**PAK**

**TUR**

**CAR**

**BOT**

**ERT**

**3,619**

**263**

**235**

**220**

**40**

**36**

**19**

**14**

**8**

**8**

UNHCR PRIMES
UNHCR Resettlement Statistics Report

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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: 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: Refugees Hosts Refugees Eritrean Somali South Sudanese In Camp Addis Ababa Total Average household expenditure on education (children in school) Average household expenditure on education per child (school age (4 to 18 years)) - 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18.000 Figure D.6: Average annual household education expenditure (in ETB) Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Boys Girls Boys Girls 8 to 14 years 15 to 18 years Need to work Unable to cover education expenses (fee and materials) School too far Too young Marriage or pregnancy Family not willing Sickness/injury or natural or human calamites Negative perception towards the benefit of education Other Percent Figure D.5: Reasons for not currently attending school by gender Source: World Bank Staff based on SESRE 2023. Annexes 102 0 5 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

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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: 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

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Input: This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turkish Labour Force Survey to assess their labor market impact. Syrian refugees are overwhelmingly employed informally, since they were not issued work permits, making their arrival a well-defined supply shock to informal labor. Consistent with economic theory our instrumental variable estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupational upgrading, there are increases in formal employment for the Turkish- though only for men without completed high school education. Women and the high-skilled are not in a good position to take advantage of lower cost informal labor. The low educated and women experience net displacement from the labor market and, together with those in the informal sector, declining earning opportunities. Usage context: background mention, primary mention, supporting 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:
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:
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: the power calculations performed to determine the sample size. 3. 3 Randomized Assignment and Job Applications Each week we randomly selected 300 job ads from the sample of ads meeting our inclusion criteria. Among these 300 ads, each job ad was randomly assigned to a single applicant profile. Job ads are stratified to ensure that our treatment and comparison units are balanced on key variables. We use two variables for our strata: company size and company location. Company size is a dummy variable that takes the value of 1 if the company has up to 50 employees, and takes the value of 0 if companies have 51 or more employees. Company location is a dummy variable that takes the value of 1 if the company is located in greater Kuala Lumpur, the capital and largest metropolitan area in Malaysia, and 0 otherwise. 6 Our stratified randomization procedure guarantees balance in the assignment of job profiles to specific characteristics of companies. The application process was carried out manually from May 17 to July 28, 2023. At the beginning of each week, a research assistant was given a list of randomly assigned jobs for each applicant profile. Applications were completed on Mondays, Wednesdays, and Fridays of every week (with day of the week randomly assigned).

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Input: Ghana is experiencing its third gold rush, and this paper sheds light on the socioeconomic impacts of this rapid expansion in industrial production. Using a rich dataset consisting of geocoded household data combined with detailed information on gold mining activities, the authors conduct two types of difference-in-differences estimations that provide complementary evidence. The first is a local-level analysis that identifies an economic footprint area very close to a mine, and the second is a district-level analysis that captures the fiscal channel. The results indicate that men are more likely to benefit from direct employment as miners compared to men further away, and that women in mining communities may more likely gain from indirect employment opportunities and earn cash for work. Authors also find that infant mortality rates decrease significantly in mining communities, compared to the evolution in communities further away. Usage context: supporting mention, background mention, primary mention.

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Input: As explained in section 4, the HRVD dataset contains data on the number of human rights violations occurred since the start of the conflict in 1975 until its end in 1999 for each district. The types of violations recorded are killings, deaths due to deprivation and disappearances. We use only the number of killings to identify years and districts affected by the conflict. We exclude the deaths due to deprivation because the districts in which this occurred may very likely not be those where the conflict was most intense, but were simply places were the victims were hiding as a consequence of escaping from the troops, and died for starvation. In addition, since killings are less likely to affect entire families than deaths due to deprivation, there is a lower underreporting bias attached to the former measure relative to the latter one (Silva and Ball 2006). We also exclude disappearances as, according to HRVD data, they do not show enough time and geographical variation in order to identify individuals more or less exposed to the conflict. We believe that the number of killings proxies quite well the intensity of the conflict across time and space as their occurrence largely tracked the movements of the Indonesian military operations. The other two types of violations do not seem to show the same pattern (Silva and Ball 2006). For the same reason, we believe that it proxies quite well the destruction of houses and infrastructure and the displacement of people given the way in which the last wave of violence occurred (i. e. the scorch-earth technique employed by Indonesian troops as they moved towards West Timor). Usage context: primary mention, background mention, supporting 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: Refugee Policies on Self-Reliance and Resilience. Journal of Refugee Studies, 33(1), 22-41. Kvittingen A., Valenta M., Tabbara H., Baslan D., and Berg B. (2018). The Conditions and Migratory Aspirations of Syrian and Iraqi Refugees in Jordan. Journal of Refugee Studies 32(1), 106-124. https://doi.org/10.1093/jrs/fey015. Lebow, J. (2023). Immigration and Occupational Downgrading in Colombia. Journal of Development Economics, Forthcoming Levenson, H. (1981). Differentiating among Internality, Powerful Others, and Chance. Research with the Locus of Control Construct, 1, 15-63. Liu, D. and Kwan, M. (2020). Measuring Spatial Mismatch and Job Access Inequity Based on Transit-Based Job Accessibility for Poor Job Seekers. Travel Behaviour and Society, 19, 184-193. https://doi.org/10.1016/j.tbs.2020.01.005 ESS and World Bank. (2023). Ethiopia Socioeconomic Panel Survey Report – Wave 2, 2021/22. Marbach, M., Hainmueller, J., Hangartner, D. (2018). The Long-Term Impact of Employment Bans on the Economic Integration of Refugees. Science Advances, 4. https://doi.org/10.1126/sciadv.aap9519. Martin, S. F. (2010). The Causes and Consequences of Forced Migration. Paper presented at the Annual Meeting of the Population Association of America, Dallas, TX. Maystadt, J. F., and Verwimp, P. (2014). Winners and Losers among a Refugee-Hosting Population. Economic Development and Cultural Change, 62(4), 769-809. MoE. (2022). Education Statistics Annual Abstract (ESAA). MoE MoLSA. (2019). Revised Directives

Ethiopia Socioeconomic Panel Survey Report

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: 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.

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: 21 Lebanon. It was the obvious step forward in overcoming these problems and the answer to the increasing demand for evidence around the Syrian crisis. The study benefitted from a unique and unprecedented set of data. The UNHCR registry data in Jordan and Lebanon were among the better quality registry data available worldwide and the UNHCR also conducted home visits in Jordan that, at the time of the study, covered over a third of all refugees. There were also sample surveys in both Jordan and Lebanon that were small in size but representative of the population present in the registry. The home visits and the surveys included questions on income and expenditure that could be used for the welfare assessment. Using these data, the study addressed ten questions defined as follows: 1) Who are the refugees?; 2) How different are refugees from “ regular ” populations?; 3) How poor are refugees?; 4) What are the main predictors of refugees ’ welfare and poverty?; 5) How vulnerable are refugees from a monetary and non ‐ monetary perspective?; 6) Do poverty and vulnerability statuses overlap?; 7) How effective are refugee assistance programs?; 8) What is the potential for alternative policies?; 9) How does welfare compare across countries and data sets?; 10) How transferable are 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: Consequently, they may fear detection by authorities when asked to respond to surveys or participate in public initiatives. 1 Furthermore, collective repre- sentative and longitudinal data on forcibly displaced migrants, a population with high mobility rates, is difficult and costly (Ib ´ a ˜ nez et al. 2024). This complexity is compounded when focusing on children and adolescents, given the need for enumerators to receive specific training to interact with such a vulnerable demographic and for migrant parents to authorize their children ’ s involvement despite prevailing distrust issues. To address this knowledge gap, we launched the Venezuelan Refugee Panel Study for Kids (VenRePs-Kids) in Medell ´ ın, Colombia. VenRePs-Kids is a longitudinal study repre- sentative of forcibly displaced Venezuelan and Colombian children and adolescents aged 5 to 17. To our knowledge, it is the first study to gather panel data specifically on forcibly 1This concern is also prevalent among undocumented migrants in the United States, as highlighted by Amuedo-Dorantes and Lopez (2015). 2

VenRePs-Kids
Venezuelan Refugee Panel Study for Kids

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: These tariff rate modifications are essential for this analysis as suggested by the substantial differences between the tariff rates available in the GTAP 8 database, especially those implied for Jordan, Iraq, Lebanon, and Syria (Figure 1), and the updated tariff rates, presented by country, product, and source in Appendix Tables B1-B6. Since the GTAP tariffs attributed to Jordan, Iraq, Lebanon, and Syria are composite rates, they do not correspond to the actual trade profile of these countries. Therefore, the new tariff rates differ from the GTAP ones both because of differences in the tariff lines and trade composition. By contrast, the tariff information on Egypt and Turkey in the GTAP 8 database represents relatively accurately existing preferences (Figure 1). 3. Simulation design The pre-war efforts for deeper trade integration in the Levant are reflected in the pre-simulation analysis. Starting from the newly constructed database, the pre-simulation analysis implements the deep trade initiatives discussed by the Levant countries prior to the onset of the Syrian war in 2011. The context for these reforms and the shocks associated with each of these reforms are presented in section 3. 1.

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Input:
rents to GDP from WDI), we can implicitly compute 𝛾.

Source:_
_Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._

Figure 2 plots the sources of growth for the Sub-Saharan Africa region as well as the different groups according

Source: Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015)._

Frankel, J. A., & Romer, D. H. (1999). "Does trade cause growth?" American Economic Review 89(3): 379-399.

reported in the Penn World Tables 9.0 (Feenstra, Inklaar and Timmer 2015). [15]

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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: supporting mention, primary mention, background mention.

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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:
_Source: UNHCR Internal movement of refugees in Sudan as of 16 July 2023_

_4 See Sudan Protection Brief, June 2023 and UNHCR Sudan- Overview of refugees and asylum seekers distribution and_

_movement in Sudan Dashboard as of 16 July 2023_

UNHCR 4

UNHCR Internal movement of refugees in Sudan

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: Between outreach and baseline, one full survey round was collected due to potential survey fatigue and on the understanding that nothing of importance would likely change in such a short period. Basic demographic information, such as age, gender, marital status and employment status were collected at outreach. At baseline, additional indicators were collected, relating to the behavior, attitudes, opinions and personalities of the participants. The only exception to this is data on optimism, which were collected at both outreach and endline. This allowed us to test whether or not the intake decision had effects, even before the training began. Endline data were collected between July 2018 and November 2019 and repeated the combined outreach and baseline surveys and experiments. Variables: We collected a range of survey and experimental indicators in order to assess our key research questions and associated hypotheses: 6 Economic and life optimism: We collected two survey questions about optimism at outreach, baseline and endline. These questions ask individuals to rank their expectation that their life and economic situation will be better in one year than it is now. Answers are 4 In addition, data were collected from Palestinian Refugees in Lebanon (PRL). Usage context: background mention, primary mention, supporting mention.

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Input: Figure 18: Significant Refugee Returns by Country of Origin 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Countries selected based on their cumulative returns of refugees over the period 1991-2015. Return does not necessarily lead to the full reintegration of a person into their home country or area of origin. In the absence of global data on the success of reintegration following return, data on returns appear to be taken as indication of sustainable return. In reality, many returnees face impediments to reintegration and continue to have specific economic and social vulnerabilities linked to their displacement. They may not be able to reclaim land, access sufficient financial resources (e. g. accumulated during their displacement) or reestablish social networks in areas of origin, which are critical factors for successful reintegration (World Bank 2015). Sustainable refugee return is therefore not a one-off event but a process that provides returnees with adequate safety, housing, livelihoods and services that address their specific vulnerabilities and reduce the likelihood of secondary displacement (World Bank 2015). Figure 19: Voluntary Returns of Refugees 1991 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015 Note: Includes returns of refugees and people in refugee- like situations protected or assisted by UNHCR. Usage context: supporting mention, primary mention, background 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:
3) Female teacher who is HIV�/ but not sick
should be allowed to continue teaching in
school.

PEPFAR

4) Would not want to keep the HIV�/ status PEPFAR
of a family member a secret.

Practices

Condom use 1) Percent of men and women (aged 15 �24)
who used a condom at last sex with a
non-marital, non-cohabiting partner, of
those who have had sex with a non-marital,
non-cohabiting partner in the last 12
months. [bc]

UNGASS, MDG, PEPFAR

a prior to the UNGASS indicators in 2002, the UNAIDS stated indicators did not specify youth
(15 �24 years).
b the MDG indicator replaces ‘have’ with ‘transmit’.
c the MDG indicators do not specify ‘non-marital, non-cohabiting’ but add ‘high risk’. PEFPAR
uses 15 �49 years.

with the surrounding host population response and a sub-regional approach
undertaken in order to take into account the displacement cycle (UNHCR 2005).
Timely and accurate data are needed to provide targeted and effective
interventions in conflict and post-conflict settings. Unfortunately, due to unstable

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: 12 though somewhat lower than that of the Turkish at 30- 50 percent. Child labor is also quite prevalent, though there have been extensive efforts made to ensure that refugee children attend school. 19 Publicly available information on refugees comes from an AFAD survey of 2, 700 households in June and July 2013. Figure 2, using data from AFAD (2013), provides an overview of the Syrian governorates from which the refugees to Turkey originated. The refugees primarily come from northwest Syria. The largest source regions are Aleppo (36 percent), Idleb (21 percent) al-Raqqah (11 percent), Lattakia (9 percent), and Hamah (8 percent). Consistent with travel distance being a good predictor of refugee flows to Turkey, 80 percent of respondents report that they chose to flee to Turkey, instead of another country, due to the ease of transportation. The refugees in Turkey, unlike the later 2015 refugee flows to Western Europe, are nearly 50 percent female. Slightly over 50 percent are minors (under the age of 18). These facts reflect that to large extent Syrian families fled to Turkey together.

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Input: When national or international actors provide assistance, there may be an incentive for people to register in camps even if they are staying elsewhere, or to register in multiple locations (Brookings 2011). 69 Furthermore, registration data provide only a snapshot of the stock of IDPs at a particular point in time and may be out of date if registers are not maintained regularly. Registration methodologies can vary across displacement situations. For example, families may be registered rather than individuals and the population estimated based on an assumption of average family size, which can differ among organizations (UNSD 2014). IDPs may be required to present documentation, meet specific criteria or re-register periodically to maintain their benefits, which affects aggregate numbers (IDMC 2015). For example, in Ukraine, pre-requisites for IDP registration (including valid documentation, arrival from a recognized conflict zone and permanent residence registration in recognized conflict zone) means that people displaced within a non-government controlled area, people displaced from a non- recognized conflict zone in a government controlled area, unaccompanied children or people without current / valid identification are not counted as IDPs (IDMC 2015). 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

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