Input: years) ......... 103 Figure D.12: Average annual per capita health expenditure ....................... 103 Figure D.13: Types of disability..................................................................... 104 Figure D.14: Rent expenditure (Refugees and hosts in Addis Ababa) ........ 104 Figure D.15: Hand washing facility................................................................. 104 Figure D.16: Top 3 difficulties with being a refugee by survey domains ..... 108 Figure D.17: Work status by survey domains ������������������������������������������������ 108 Figure D.18: Type of work by survey domains ���������������������������������������������� 108 Figure D.19: Occupation by survey domains ������������������������������������������������� 108 Figure D.20: Work location by survey domains ��������������������������������������������� 108 Figure D.21: Hours per week by survey domains ����������������������������������������� 108 Figure D.22: Hourly earnings by survey domains ����������������������������������������� 109 Figure D.23: Household owns crops ............................................................. 109 Figure D.24: Household owns livestock......................................................... 109 Figure D.25: Total value of livestock ............................................................ 109 Figure D.26: Value per tropical livestock unit ������������������������������������������������ 109 Figure D.27: Household has non-farm business ����������������������������������������� 109 Figure D.28: Value of productive assets in households with business ........ 110 Figure D.29: Primary source of income pre-post migration by survey domains......................................................................... 110 Figure D.30: Youth work status by survey domains �������������������������������������� 110 Figure D.31: In-camp refugee locations by ecological Zone ........................ 115 Figure D.32: Refugee’s labor market performance ��������������������������������������� 115 Figure D.33: Economic sector........................................................................

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Input: Therefore, the final sample size consisted of 759 youth, of which 473 treatment and 286 comparison. Detailed baseline data were collected through face-to-face interviews from July to September 2015 prior to implementation. The actual implementation varied between projects and ranged between the second half of August and end of December 2015. Sampled youth from both selected and non- selected NGOs were invited to fill out a questionnaire with detailed information on volunteers ’ socio-economic backgrounds, education levels, interests and attitudes towards volunteering, employment, soft skills, as well as social cohesion values. Follow-up data were collected between November 2016 and March 2017, approximately one year following the start of implementation, through phone and face-to-face interviews. The questionnaire contained the same modules asked and collected at baseline. Despite the high 8 Proposals were ranked based on four main selection criteria: institutional appraisal (25 points), technical appraisal (40 points), project impact (25 points), and financial appraisal (10 points). There was also a fifth criterion related to sustainability of volunteering activities, which was assigned a bonus score (5 points). Usage context: background mention, primary mention, supporting mention.

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Input:
The only question included in the WVS related to attitudes towards immigrants - and available   for a sufficient number of survey years - is: _On this list are various groups of people.

Using household surveys for 24 countries between 2002 and 2012, we find that in most

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Deaton, Angus. The analysis of household surveys: a microeconometric approach to

Source: Attitudes toward migrants were estimated from ESS (see data section for more details). The gap reflect the difference between the positive attitudes of

group as the majority (ESS). _Proimmigdiff_ is the average pop. share (%) who would like many or some immigrants of a _different_

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Input: 10 Finally, we divide the communities based on land scarcity before the 1993 conflict in order to explore the possible role of posterior rules regarding land provision to returnees. Respondents in communities that had more and less pre-war land available have broadly similar attitudes towards return. 4. Research design 4. 1 The survey We collected the data for this project during January to March 2015 as part of a nationwide survey on issues related to migration for the Labour Market Impacts of Forced Migration (LAMFOR) project. The survey had two components. First, a household survey in which 15 households were interviewed in 100 communities (i. e. sous-collines) across the 17 provinces of the country. Second, a community survey in which a local leader was interviewed in each of the 100 communities. The number of communities selected in each province was based on information from the 2008 Census. Figure 4 indicates the location of the communities surveyed. Figure 4 – Location of communities surveyed in Burundi Note: Geolocation of the 100 communities (i. e. sous-collines) sampled in the survey. Each community corresponds to a dot. Fifteen households and a local leader were interviewed in each community. The number of communities selected in each province was based on information from the 2008 Census. In the analysis below we focus on rural areas. Usage context: supporting mention, background mention, primary mention.

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Input: throughout the day prior to the survey day. We develop our own questions around self- worth rather than employing the more standard Rosenberg Self-Esteem Scale, which we found inappropriate given the Rohingya ’ s recent experiences. Speciﬁcally, we construct an index of self-worth from two questions designed to elicit respondents ’ beliefs about how they contribute to their family and community. Finally, we adapt the Cantril Self-Anchoring Striving Scale (Cantril, 1965) to measure how stable respondents feel in their present lives and in the future. We additionally examine the impacts of each treatment on physical health, cognitive function, economic decision making, time-use, and consumption. We capture respondents ’ sense of physical health by asking how many days they have fallen sick in the past thirty days and cognitive function by employing a digit-span memory test and a series of basic arithmetic problems. We explore economic decision making along two dimensions: incentivized time preferences (Andreoni and Sprenger, 2012; Gin ´ e et al., 2018) and incentivized risk preferences (Holt and Laury, 2002).

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Input: NGOs. Initiatives such as JIPS, a collaborative project of UN and NGO actors, have also been established to support governments and operational organizations to design and implement data collection processes. There are a variety of data sources for generating statistics on forced displacement, each of which has strengths and weaknesses. Despite the significant challenges, large amounts of data are collected and disseminated every year. The main data sources and methods for the generation of statistics on forcibly displaced populations include: (a) registration of refugees and asylum-seekers; (b) registration of IDPs; (c) profiling of IDPs; (d) population movement tracking systems; (e) national population censuses; (f) sample surveys; (g) border crossings; (h) administrative records and registers; (i) general population registers; and (j) a variety of estimation methods for producing statistics when adequate and reliable data on individuals are unavailable (UNSD 2014). Several of these data sources might be used together to triangulate estimates of stocks and flows for a particular displacement situation. Usage context: primary mention, background mention, supporting mention.

sample surveys
general population registers

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Input:
**SOUTH SUDAN** | October 2023

#### **CONTEXT**

**YOUNG POPULATION AND PREVALENCE OF DISABILITIES**

South Sudan is a young nation with an average age of 18.8 years. In the absence of an updated census, it is estimated that 1.2
million people, or 16% of the population have disabilities. The most commonly identified disabilities include visual
impairments, hearing impairments, and physical disabilities. [iv] Additionally, the proportion of older people in South Sudan is on
the rise, accounting for 5.1% of the population in 2016, an increase from 3.9% in 2008, and an indication that the number is
growing each year. [v] Notably, most older people in South Sudan reside in impoverished rural areas, where they play a crucial
role as caregivers for children whose parents have often perished in the conflict.

The country has a troubled history marked by decades of armed conflict, leading to displacement, economic instability, and
increased climate hazards such as flooding and droughts. These factors have further weakened an already fragile state and
limited governance capacity, disproportionately impacting older individuals and those with disabilities.

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Input: In all speciﬁcations, conﬂict incidence correlates nega- tively with country-level economic performance. The estimated coeﬃcients of conﬂict incidence are statistically signiﬁcant and negative. We also ﬁnd that the coeﬃcients 7For a discussion see Henderson et al. (2012). In order to calculate light per capita we use popu- lation data that is provided by a World Bank dataset. 8The standard reference here is Miguel et al. (2004). Ciccone (2011) shows that high rainfall levels three years earlier seem to be best predictors of conﬂict in the reduced form. Miguel and Satyanath (2011) argue that lagged negative growth shocks are a predictor of conﬂict onset. In any case, there is no evidence from this literature that contemporaneous growth declines cause conﬂict. Bazzi and Blattman (2014) corroborate the view that the relationship between income shocks and conﬂict is not straightforward. They do not ﬁnd evidence of an eﬀect of price shocks on conﬂict onset and only weak evidence on incidence. 9Results from this are presented in the Appendix. 10We take the threshold from Mueller (2016) who shows that a threshold like this leads to a similar number of coded civil wars as the threshold of 1000 battle-related deaths often used in the conﬂict literature. In the context here, this is a conservative approach as it is not the threshold which yields the biggest diﬀerence between conﬂict and non-conﬂict countries. 10 Usage context: primary mention, supporting mention, background mention.

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Input: Despite notable disparities in the characteristics of our sample compared to the VenRePS and RAMV samples, understanding the interaction of these attributes within a frame- work of self-selection among migrants into Medellin is challenging. Moreover, it is cru- cial to note two primary distinctions between our survey and the VenRePS and RAMV surveys. Firstly, the inherent differences in the sampling frames of each survey stem from their distinct measurement objectives. Second, both surveys were conducted at different times compared to our survey. The RAMV survey was undertaken in 2018 in response 10See Ib ´ a ˜ nez et al. (2022) for specific survey and sampling details. 11Refer to Ib ´ a ˜ nez et al. (2022) for further details. 12Since the VenRePS and RAMV surveys lack information regarding children and adolescents within households, our analysis concentrates only on the household and household head characteristics that are available in all three surveys. 20

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Input: 14 3. Refugees as Agents of Their Own Destiny 3. 1 The Composition of Africa ’ s Refugee Population and Its Consequences One of the first elements that catch the eye in Figure 6 is the difference in the composition of the refugee population in Africa compared to the rest of the world. The share of children and women among refugees is higher in Africa than elsewhere, in particular East and West Africa stand out here. This is, at least partly, a consequence of Africa ’ s younger, general population, but other forces could be at work as well, e. g. higher mortality of adult males in Africa or adult males staying behind or being separated from the rest of the household. It does mean however that, relative to other areas, more attention should be going to the needs and capacities of women and children in Africa. This means, for example, adaption of and increased supply of schooling and health services. Figure 6. The composition of refugees by age and gender, 2013 Source: Note: UNHCR statistics (UNHCR 2014). Asia excludes Australia, Japan and New Zealand. Americas exclude Canada and the United States. These percentages have been calculated by country when demographic data are available for at least 30 % of the total. Usage context: background mention, primary mention, supporting mention.

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Input: (Dercon et al 2005; Woldehanna et al 2008)—market shocks related to rising food prices, food shortage, and health shocks appear to be most prevalent (Figure 5.11). High food prices drive the market shocks. Food shortage seems to represent a crucial economic shock among refugees—roughly 31 percent of in-camp refugees are affected by food shortage—but not for their host communities. 4.0 2.1 2.9 8.1 3.4 7.0 In Camp Addis Ababa Total Hosts Refugees Figure 5.9: Food insecurity scale for refugees and hosts Source: World Bank Staff based on SESRE 2023. 44 Food insecurity experience is measured based on a scale that ranges between 0 and 10 and calculated by adding household’s experience related to the following events in the past year: (i) worried about having enough food, (ii) unable to eat healthy/nutrition food, (iii) only ate a few kinds of food, (iv) had to skip a meal, (v) adults ate less, (vi) ran out of food, (vii) adults were hungry but did not eat, (viii) went without eating for a whole day, (ix) restricted consumption so kids could eat, and (x) borrowed food or relied on friend/relative for help. 7.0 8.4 7.8 6.0 8.2 6.5 In Camp Addis Ababa Total Hosts

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Input: 4 single caregivers, are an extremely vulnerable group and especially so if principal applicant is a woman or girl. Moreover, poverty gaps between male and female principal applicant ’ s for these households remain after humanitarian assistance is received. To understand how gender differentiates the poverty experienced by the Syrian refugees, we use two UNHCR datasets: The Profile Global Registration System (ProGres) and Jordan Home Visits round 3 (JD ‐ HV). The ProGres database for Jordan includes information on refugees ’ registration since 1935. The registration process assigns refugees a unique registration number that serves as a reference for recording data at the initial registration and in all subsequent activities, including decisions on refugee status and right of return or resettlement in a third country, as applicable. UNHCR issues refugees residing in camps a ‘ proof of registration ’ document, which they hold while they remain there. For those who live outside the camp, UNHCR provides an asylum seeker certificate stating that those on the certificate are persons of concern. The asylum seeker certificate allows Syrians to access United Nations (UN) services and assistance provided outside the camps, such as monthly cash support, nonfood goods, and healthcare (NRC and IHRC 2016). Usage context: background mention, supporting mention, primary mention.

Profile Global Registration System
Jordan Home Visits round 3

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Input: 5.1.4 Food security Refugees and hosts perceive that household living standards have deteriorated over time. To capture subjective well-being, the survey asks if the living standard of the household or their community has improved or worsened in the past five years (Figure 5.8a) and in the past 1 year (Figure 5.8b). Overall, most households feel that their living standards have deteriorated. While in-camp refugees are more pessimistic about the changes in their households’ living standards, there is no significant difference in perceptions among OCP refugees in Addis Ababa and their hosts. The considerably high negative perception about changes in household living standards indicates that well-being has been worsening for everyone over the past few years, but even more so for refugees in camps. Refugees, on average, have poor food and nutrition security outcomes compared to their hosts. The extent of food insecurity measured by the food insecurity scale is significantly higher for refugees than hosts, both for in-camp and for out-of-camp refugees (Figure 5.9). While the food insecurity Refugee and host communities could differ in multiple dimensions over and above consumption. The Multidimensional Poverty Index (MPI) explores this multiple deprivation, capturing differences across three dimensions of well-being: health, education, and

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

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Input: environment. Any issues discovered during these monitoring visits were brought to the attention of service providers and resolved swiftly in conjunction with the project coordination team at MoGD. Eligibility: The EPAG program was targeted to young women who: i) were age 16 to 27, ii) possessed basic literacy and numeracy skills, iii) were not enrolled in school within several months prior of the program initiation, and iv) resided in one of nine target communities in and around Monrovia. 6 These eligibility criteria stemmed from the project's objectives to reach young women at an early enough age to significantly improve the trajectory of their working years, to focus on girls who already had the basic literacy and numeracy skills needed to succeed in the labor market, and to avoid incentivizing applicants to drop out of school.

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

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Input: Journal on Education in Emergencies, Inter-agency Network for Education in References 80 References Emergencies, 5(2). https://doi.org/10.33682/f1wr-yk6y. Ravallion, M. (1998). Poverty Lines in Theory and Practice: Living Standards Measurement Study. LSMS Working Paper, Issue 133. https://documents1.worldbank.org/curated/en/916871468766156239/pdf ReDSS. (2018). Local Integration Focus: Refugees in Ethiopia - Gaps and Opportunities for Refugees Who have Lived in Ethiopia for 20 Years or More. ReDSS. Rotter, J. B. (1966). Generalized expectancies for internal versus external control of reinforcement. Psychological monographs: General and applied, 80(1), 1. RRS. (2017). Road Map for Implementing the Federal Democratic Republic of Ethiopia Government Pledges and the Practical Application of the CRRF in Ethiopia. RRS RRS. (2019). Directive to Determine Conditions for Movement and Residence of Refugees Outside Camps, Directive No. 01/2019. RRS RRS. (2019a). Directive to Determine the Procedure for Refugees Right to Work. Directive No. 02/2019. RRS. (2019b). Refugees and Returnees Grievances and Appeals Handling Directive. Directive No. 03/2019. RRS, and UNHCR. (2021). Ethiopia GRF Pledge Progress Report. RRS and UNHCR Shamsuddin, M., Acosta, P.A., Schwengber, R.D., Fix, J., and Pirani, N. (2021). Integration of Venezuelan Refugees and Migrants in Brazil. Policy Research Working Papers 9605. http://hdl.handle.net/10986/35358. Schuettler, K., and Caron, L. (2020). Jobs Interventions for Refugees and Internally

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

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

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Input: A growing number of low and middle-income countries have tried to improve the law enforcement response to gender-based violence by training professionals, reorganizing police and courts, and trying to provide a more comprehensive response to survivors. Evidence of effectiveness is relatively limited; most well-evaluated initiatives come from high-income countries, and the lessons learned may not be applicable to developing countries. Evaluations of law enforcement reforms in low and middle income countries have typically been limited to case study approaches drawing from police records (notorious for under-reporting), qualitative perspectives from key informant interviews, intermediate outcomes such as changes in attitudes and knowledge among police and judges, and interviews with small numbers of women who have sought legal redress. Population-based data collection, control groups, or follow-up among more than a handful of survivors are rare. Nonetheless, the following initiatives illustrate the types of efforts that have produced important lessons learned. Training personnel in the police and judiciary and other parts of the justice system Throughout the world, organizations have launched efforts to improve the knowledge, attitudes, and practices of justice sector personnel regarding gender-based violence. Some law enforcement institutions organize training internally, as did South Africa following passage of the 1998 Domestic Violence Act (Usdin et al., 2000). Usage context: background mention, primary mention, supporting mention.

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Input: As expected the positive correlation between refugee flows and 2011 school attendance rates is significant at the one percent significance level (controlling for the gender, age and education composition of a subregion the point estimate is- 0. 17). Even once we instrument for refugee flows the correlation remains significant at the one percent significance level (a point estimate of- 0. 30). However, once we control for the log distance for the Syria border there is no longer a statistically significant relationship, in either the OLS or IV, between school attendance rates in 2011 and subsequent refugee flows. This suggests that, on account of the inclusion of our distance from the border control, we can rule out the 2012 education reform confounding our estimates. 5. 3 Robustness to Varying Sample of Turkish NUTS 2 Subregions Throughout this paper we use all 26 NUTS 2 subregions of Turkey for identification. However, the results are robust to varying the particular sample of subregions. We report results for two alternative samples. First, we drop the Gaziantep subregion from the estimation. Gaziantep has the highest refugee to population ratio among all regions and reportedly towns with a refugee share of over 30 percent. The inclusion of Gaziantep may skew results if there are any non-linearities in the impact of refugees. Second, we follow Ceritoglu et al. (2015) in only considering nine subregions of Turkey. These are the five Syrian border regions of southeastern Anatolia (Hatay, Gaziantep, Sanliurfa, Mardin, and

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Input: also face high turnover of teachers as they take on better-paid jobs (UNHCR, 2017). Secondary education, on the other hand, is provided for refugees in camp-based refugee schools and in government administered public schools, with support from UNHCR’s NGO partner (Development and Inter-Church Aid Commission, DICAC). This relies on qualified national teachers exclusively. UNHCR and partners are working towards progressive transfer of secondary school administration from DICAC to Regional Education Bureaus. The MoE is responsible for managing refugee and national education for higher education. However, enrollment of refugees in higher institutions is low due to low absorption capacity of higher education institutions (UNHCR, 2020a). At the same time, the Ministry of Skills and Labor (MoLS) is responsible for managing Technical and Vocational Education and Training (TVET). OCP refugees receive education at the same levels as nationals through the national education system. Refugee education data is integrated into the MoE’s Education Management Information System (EMIS) (UNHCR, 2020), and a separate chapter on refugee education is included in the annual education statistics report of the MoE. The GoE included expanding primary and secondary education for refugees in the national five-year Education Sector Development Programme VI (ESDP), covering 2020 to 2025, but little

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Input: 2 International migration — the movement of people across national borders — has important economic, social, and political implications. Despite the recent emergence of a dynamic literature, empirical analysis of migration flows and their impact lags behind the policy debate and the theoretical literature. The main reason is the absence of comprehensive and reliable data on international migration patterns and migrant characteristics at either the aggregate or the household level. The objective of this article is to use data from more than one thousand national censuses and population registers to estimate a complete global origin – destination migration matrix for each decade over 1960 – 2000. These 226 * 226 matrices, comprising every country, major territory, and dependency around the world, are divided into periods corresponding to the last five completed census rounds. The gender dimension of international migration over this period is also presented. The primary source of the raw data is the United Nations Population Division ‘ s Global Migration Database, created through the collaboration of the United Nations Population Division, the United Nations Statistics Division, the World Bank, and the University of Sussex (United Nations [2008]). This unique data repository comprises 3, 500 individual census and population register records1 for more than 230 destination countries and territories over the last five decades. Usage context: background mention, supporting mention, primary mention.

national censuses and population registers

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Input: The report focuses on intent-to-treat (ITT) estimates, measuring the impact of offering volunteering opportunities and soft skills training independently of actual take-up. 9 We estimate the following individual-level intent-to-treat regression: 𝑌 ௜ ௧ ൌ 𝛼 + 𝜇 ௡ ൅ 𝛽𝑇௧ + 𝛾𝐷 ௜ + 𝛿ሺ𝑇 ∗ 𝐷ሻ ௜ ௧ + 𝜀 ௜ ௧ (1) where 𝑌 ௜ ௧ is the outcome of interest for respondent i in period t, 𝑇௧ is a post-treatment year binary variable, 𝐷 ௜ is a binary variable for being assigned to the treatment, and 𝜇 ௡ is a fixed effect for NGOs. 𝛼 represents the baseline average for the outcome of interest for non-selected youth. 𝛽 is the difference in after-and- before intervention in outcomes for non-selected youth. 𝛽 ൅ 𝛿 is the difference in after-and- before intervention in outcomes for selected youth. 𝛾 is the difference in 9 Due to some procurement delays that caused a big time-lag between baseline data collection and actual NGO project implementation, many of the volunteers who belonged to selected NGOs and who were randomly selected to participate in the impact evaluation study dropped out after their baseline data were collected and were replaced by other volunteers. Project monitoring data reveal that 23 percent of volunteers assigned to treatment did not actually end up participating in the NVSP. Given the relatively high number of non-compliance, we are unable to perform Local Average Treatment Effects (LATE) analyses to understand the impact of participating in NVSP on outcomes of interest.

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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.

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Input:
www.gasflaretracker.ng](http://www.gasflaretracker.ng/) has the geographic coordinates of each flaring point in Nigeria as well as monthly estimates of the flare volume from each location.

satellite-detected estimates with the official estimates reported by the Department of Petroleum

Resources [DPR] (2018). The satellite-detected estimates are broken down by location, while the

The paper uses the Romanian firm-level data from the Ministry of Finance covering enterprises of all sizes from 2011 to 2020, combined with the new World Bank Businesses of the State dataset, which tracks ownership of state business entities with at least 10 percent stake in Romania.

World Bank Businesses of the State dataset

Romanian firm-level data from the Ministry of Finance

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Input: The household and family dynamics in Liberia, as in other African settings, can be complex, with families often sending their children to live with relatives who are more able to provide for their schooling and basic needs. Young parents, in particular, often leave home to migrate for work, leaving their children back home with relatives until they are able to establish themselves and send for their children. If the economic success of the EPAG participant allowed her to bring non-resident family members into her household (including but not limited to her own children), then overall household size may have been expected to increase as a result of the program. This does not appear to have happened, at least in the short term. The results in Table 8 show that overall household size was not affected by the program. It is possible that the increase in earnings due to EPAG was too small, or too short-lived, to have induced the kinds of migrations described above. Other measures of household well-being, including food security and asset ownership, reflect shorter- term investments that might be influenced by the economic success of EPAG participants. Panel B of Table 8 shows the impact of EPAG on a broad range of food security measures.

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Input: We use two main data sources for our estimation of a poverty line for Brazil: the 2017/18 Household Budget Survey (Pesquisa de Orçamentos Familares; POF) and the Brazilian Table of Food Composition (Tabela Brasileira de Composição de Alimentos; TBCA). POF is a nationally representative semiregular survey on income and expenditures in Brazil, conducted every six to nine years.

Tabela Brasileira de Composição de Alimentos

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Input: 23 86 % of the IDPs, 91 % of the refugees and 88 % of the returnees are confident or fully confident that a coalition like this would be capable of providing security. Source: Listening to Displaced People Survey, 2014. In an open-ended question on who can be trusted most when it comes to ensuring security in the North, survey results suggest that the majority of refugees in Mauritania (86 %) trust the armed rebel groups as opposed to the army or police. This does not hold for refugees in Niger of whom 75 % trust the army and police. Similar results hold for IDPs and returnees, who put much more confidence in state authorities when it comes to securing the North: most trust is placed in the army and police (72 % of the IDPs and 66 % of the returnees) while little to no trust is placed in armed rebel groups (3 % of IDPs, 1 % of returnees). Usage context: supporting mention, primary mention, background mention.

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Input: Having experienced the work task and therefore able to realistically value the work, we oﬀer individuals in the employment arm an additional [surprise] week of work at a series of wages following the incentivized Becker-DeGroot-Marschak (BDM) method. We inform participants that we have a limited amount of funds remaining and are therefore unable to pay everyone their previous wage. This strategy realistically motivates the reservation wage elicitation exercise and makes clear that there will be no further opportunities for work. We piloted this exercise extensively. To maximize comprehension, we employ a multiple price list strategy, embed repeated conﬁrmations, and conduct a trial run of the exercise for each respondent before the real exercise; this mimics the procedure employed in Burchardi et al. (2021) for which participants in another low-income country ﬁeld context exhibited high comprehension. For those individuals who express willingness to work at a wage of zero, we oﬀer an alternative option of answering a brief survey at the end of the week for a small, randomized fee; we then use the fraction of respondents who are willing to forego this paid option and instead work for free as an estimate of the proportion of respondents who have a negative reservation wage of at least the foregone magnitude. 16 Usage context: primary mention, background mention, supporting mention.

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Input: 11 there is another data source that provides independent information on conflict events. The Uppsala Conflict Data Program, Conflict Encyclopedia Database (UCDP) provides similar information that spans the time period of interest. In contrast, UCDP data is not available for Liberia- so each country draws on similar but distinct data set to incorporate independent information about conflict intensity. The study data set contains one observation for every woman in the DHS individual recode, i. e., the woman level data and variables showing the annual number of ACLED or UCDP events and fatalities beginning in the year the woman was interviewed up to 10 years preceding the survey. Providing conflict for the 10 years preceding the DHS survey is key to the methodology and helps establish temporality in how conflict may affect IPV outcomes. A 10-year period was chosen because this captured hostilities from both Liberian civil conflicts and allowed the data set to reflect the long-standing impact of the conflict in each country. Each conflict data set used is described below. Uppsala Conflict Data Program (UCDP) Data- Colombia The Uppsala Conflict Data Program (UCDP) Georeferenced Event Dataset is an event data set that disaggregates three types of organized violence: state-based conflict, non-state conflict, and one-sided violence. Usage context: supporting mention, primary mention, background mention.

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Input: under the Out-of-Camp Policy (OCP) constitute 5 and 12 percent of the sample, respectively (Figure 2.1). More than 30 percent of refugees in camps are born in Ethiopia (Figure 2.2). Somali refugees have a higher share of refugees born in Ethiopia (38 percent). According to UNHCR estimates (2023)12, around 1.9 million children were born as refugees between 2018 and 2022 globally. Overall, Ethiopia’s refugee situation is protracted; refugees have been in Ethiopia for an average of about 14 years. Refugees differ by country of origin. For example, Eritrean refugees have been in Ethiopia for average of slightly more than 16 years, Somalis for just under 16 years, and South Sudanese for roughly 15 years. On the other hand, refugees in Addis Ababa arrived nine years ago, on average (Figure 2.3a). Globally, the number of refugees in protracted situations—at least 25,000 refugees from the same country who lived in exile for more than five consecutive years—increased over time, accounting for 40 percent of all refugees in 2021 (World Bank, 2023). In Ethiopia, 95 percent of all refugees live in a protracted situation, according to SESRE data. 11 According to UNHCR mid-2022 statistics, South Sundanese, Somali and Eritrean refugees constitute 46, 29

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Input: recently introduced the first version of their Global Internal Displacement Database (GIDD) that allows users to explore, filter and sort IDMC ’ s data to produce graphs and tables, and export underlying data. 102 Such platforms need to incorporate safeguards to protect the privacy and confidentiality of individuals ’ data. UNHCR and Statistics Norway are currently leading an initiative to improve forced displacement statistics with the participation of national statistical agencies. This process began with the presentation of the “ Report on Statistics on Refugees and IDPs ” at the 46th session of the UN Statistical Commission in March 2015, 103 followed by an international conference in Turkey in October 2015. 104 The conference set in motion a process for national statistical agencies to collaborate to develop a set of recommendations that both countries and international organizations can use to improve data collection, reporting, data disaggregation, and overall quality, including the preparation of International Recommendations for Refugee Statistics (IRRS). Progress on this agenda was discussed at the 47th session of UNSD held in New York in March 2016, where it was recommended that the expert group should also include IDPs in its scope of work (UNSD 2016). 105 The current initiative is focused on refugees, asylum-seekers and IDPs but would ideally be extended to host communities and returnees. Usage context: background mention, supporting mention, primary mention.

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

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Input: months” and “last 12 months” as references. Imputed rent for owner-occupied houses is calculated by the Ethiopian Statistical Service (ESS) team and is included in the consumption expenditure data shared with the Bank team. Spatial and temporal price deflators adjust for price variations across time and space. First, nominal consumption is adjusted for price differences across survey domains using spatial deflators calculated using the Household Welfare Statistics (HoWStat 2021) survey data. Second, spatially-deflated consumption levels are expressed in December 2022 prices using the food and non-food Consumer Price Indexes produced and provided by the ESS. Finally, to adjust for variations in household size and composition, the spatially and temporally adjusted consumption expenditure is divided by household size. This is because the poverty rates presented in this chapter are calculated using the international poverty line of USD 2.15 per capita in 2017 PPP. The US$2.15 poverty line was converted to local currency in 2017 using the PPP conversion factor, and then the value was inflated to December 2022 prices using the national CPI. Given that international poverty estimates reported at the global level are based on consumption aggregates not spatially deflated, the poverty reports presented in this report are not strictly

Household Welfare Statistics (HoWStat 2021) survey

food and non-food Consumer Price Indexes

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Input:
**Annex 12: CAT DDO Trigger Conditions and Drawdown Procedure**

The CAT DDO facility may be drawn down upon declaration of a state of natural disaster by the Council of Ministers, provided that the macroeconomic trigger conditions specified in the Loan Agreement are satisfied. The declaration must be accompanied by a damage and needs assessment prepared by the national disaster management agency and reviewed by a Bank-approved assessment team. Drawdown requests under the CAT DDO are processed within ten business days of receipt of a complete and satisfactory trigger documentation package.

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Input:
Additionally, local and
upstream shares of forest cover are measured using satellite data from the European Space Agency.

###### 3. Empirical Strategy

Our econometric specification uses a panel fixed-effects model to link data on droughts to data on
economic growth at the level of 0.5-degree grid cells (approximately 56 kilometers x 56 kilometers at the
equator) between 1991 and 2014, the period for which economic data is available at a granular scale.

In robustness checks we also make use of the Standardized Precipitation Evapotranspiration Index (SPEI) (Vicente-Serrano, et al., 2010) that integrates evapotranspiration into the standard precipitation index.

We use data from the ESA CCI project to determine the share of cropland within each cell at the beginning of the period (ESA starts in 1992) and split the sample based on different shares of cropland ranging from less than 20 percent to more than 75 percent.

2 UNCCD Report "Drought in Numbers 2022."
3 Defined here as shocks that are at least 2 standard deviations (SDs) below the long-term mean. Note that a 2 SD dry
shock is a very rare event and includes the driest 2.5 years in a century.

The magnitudes of the effects are also consistent with the recent 2022 Global Assessment Report on Disaster Risk Reduction which looks at all types of disasters from rapid onset events like typhoons, floods, earthquakes to other events like droughts, saltwater intrusion, air pollution.

Standardized Precipitation Evapotranspiration Index (SPEI)

satellite data from the European Space Agency

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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

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Input: 26 Buller et al. (2016) conducted a mixed methods study in Ecuador, combining secondary analysis from a field experiment on the impact of a transfer program on IPV with in-depth interviews and focus group discussions with male and female beneficiaries. The qualitative component aimed to better understand the mechanisms underlying the quantitative results, which showed substantial reductions in physical and sexual violence among beneficiaries of the cash and in-kind food transfer program. These qualitative interviews revealed a similar finding to Arugay et al. (forthcoming). Specifically, a core feature of women ’ s reported joint decision making was asking their spouse or partner for input into a decision, so that they would not be blamed if something went wrong. Similarly, an analysis of qualitative data from eight projects in Africa and Asia focused on understanding women ’ s empowerment finds that joint decision making can be empowering for women. In particular, in focus groups in Ghana, women stressed the importance of family harmony and in the individual interviews, “ women indicated that they want more input on decisions, but do not want full responsibility for decisions in case they go wrong (Meinzen-Dick et al, 2019, p. 20). The qualitative evidence provides one interpretation of our empirical results. Couples who agree that they jointly make decisions may also be sharing responsibility for these decisions. Usage context: primary mention, supporting mention, background 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, background mention, supporting mention.

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Input: refugee situation and to devise policy directions. The SESRE covers three types of groups: (i) refugees in camps; (ii) refugees out-of-camps in Addis Ababa; and (iii) host communities; all of which require a distinct sampling procedure. The sampling frame for refugee camps is based on UNHCR’s proGRES database. SESRE is a representative survey of the refugee population 1 Formerly the Household Consumption and Expenditure Survey and Welfare Monitoring Survey. EXECUTIVE SUMMARY iii of Eritrean, South Sudanese, and Somali origin living in camps in Ethiopia, refugees living in Addis Ababa, and their respective host communities. Host communities are defined as Ethiopian non-displaced households living enumeration areas adjacent to the refugee camps. SESRE data was collected from November 2022 to January 2023, from a nationally representative sample of 3,452. The following represents a summary of findings stemming from the SESRE data and associated statistical regression work using this data. Sociodemographic Ethiopia is a second home for close to one million refugees who predominantly originate from South Sudan, Somalia, and Eritrea. Around 88 percent of refugees live in camps, and the rest reside in urban areas under the Out-of-Camp Policy (OCP) regime. Refugees fled from their country mainly due to conflict and violence.

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Input: Appendix A Data Appendix A. 1 Linking Ethnic Data from Africa (LEDA) LEDA offers an interface — a language tree — to flexibly link ethnic groups from different databases to each other and calculate the linguistic distances between them. LEDA is currently structured around lists of ethnic groups from 12 original datasets, which are the following:  Afrobarometer Surveys  All Minorities at Risk (AMAR)  Census data from IPUMS  Ethnic Power Relations (EPR) dataset  Ethnologue languages  Political Relevant Ethnic Groups from Posner (2004)  Ethnic groups in Francois, Trebbi & Rainer (2015)  Ethnic groups from Fearon (2003)  GREG Data (based on the Russian Atlas Miradova)  Demographic and Health Surveys  Murdock Atlas  Spatially Interpolated Data on Ethnicity (SIDE) These lists are structured in LEDA ’ s interface by data source, country, year, or in the case of survey data, survey rounds. In our analysis, we use Afrobarometer, EPR, and Murdock Atlas data; therefore, we can use LEDA functions to link the different ethnic groups to each other. LEDA consists of three main linkage types: binary linking based on the relations of sets of language nodes associated with two groups; binary linking based on linguistic distances; and a full computation of dyadic linguistic distances.

Spatially Interpolated Data on Ethnicity

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Input: Due to the variations in weight and height ratios among children and adolescents according to gender and age, the benchmarks for determining nutritional status are specifically tailored to these factors. We adhere to WHO guidelines to calculate the standardized BMI values for minors. A BMI exceeding one standard deviation (SD) from the mean suggests overweight, while a BMI less than minus one SD indicates underweight. 16 Health status is assessed through a binary variable, assigned a value of one if the caregiver has reported any health issues such as disease or chronic pain, accidents, dental pain, surgical interventions, or preg- 14For the Colombian households the wealth index is measured with contemporaneous data. 15This procedure restricts the sample to the common support of the propensity score for being a forced migrant and weights observations for Colombian kids by a non-parametric function of the propensity score. This procedure has been shown to increase the estimate ’ s efficiency. 16Furthermore, a BMI greater than 2SD is indicative of obesity risk, and less than- 2SD signals a risk of severe thinness. 33 Usage context: primary mention, background mention, supporting mention.

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Input: which increases signiﬁcantly under both treatment arms. In other words, the psychosocial value to employment appears to be driven largely by the non-pecuniary dimensions of the employment experience. 5. 4 Impacts of employment on reported physical health, cognitive function, and economic decision-making The positive eﬀects of employment extend to other measures beyond psychosocial health. Table 3 presents results on reported physical health, cognitive function, and incentivized measures of risk and time preference. We observe a signiﬁcant increase in the days reported healthy. This eﬀect may be due to ‘ real ’ health improvements from increased exercise (which has also been documented to translate to improved mental health (Herbert et al. (2020))) from the employment task or ‘ perceived ’ health improvements in which improved psychoso- cial well-being translates into feeling less physically ill. Should the channel be exercise, we may expect health improvements to grow over time. Our weekly data on days healthy sug- gests this is not the case: we observe the treatment eﬀect on health from the ﬁrst week of working, and the gap remains steady throughout the following two months (Appendix Figure A3). Usage context: primary mention, background mention, supporting mention.

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Input: 17 significant for both selected and non-selected youth (see the 𝛽 ൅ 𝛿 estimate and the 𝛽 estimate for columns 2 & 3). Lack of results also holds for specification 3, where imputing missing data with upper and lower bound estimates to account for the potential bias introduced by attrition did not alter the lack of impact of the program, as well as for specification 4 (see the 𝛿 estimate). Figures 1, 2, and 3 plot the distribution of soft skills scores at baseline for both selected and non- selected youth. The figures indicate that scores across the three skills are concentrated towards the end of the scale, suggesting that soft skills training offered by NVSP might have been ineffective or too basic for this pool of volunteers. 19 Indeed, as table 1 shows, a high percentage of selected youth (71 percent) and non-selected youth (63 percent) had taken previous training in soft skills prior to NVSP. Results from a process evaluation conducted separately support this explanation. The majority of NVSP volunteers in focus group discussions and interviews mentioned that they would have welcomed more advanced trainings on soft skills, as well as on technical topics and job-relevant skills that can support their employability. 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

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: NGOs. Initiatives such as JIPS, a collaborative project of UN and NGO actors, have also been established to support governments and operational organizations to design and implement data collection processes. There are a variety of data sources for generating statistics on forced displacement, each of which has strengths and weaknesses. Despite the significant challenges, large amounts of data are collected and disseminated every year. The main data sources and methods for the generation of statistics on forcibly displaced populations include: (a) registration of refugees and asylum-seekers; (b) registration of IDPs; (c) profiling of IDPs; (d) population movement tracking systems; (e) national population censuses; (f) sample surveys; (g) border crossings; (h) administrative records and registers; (i) general population registers; and (j) a variety of estimation methods for producing statistics when adequate and reliable data on individuals are unavailable (UNSD 2014). Several of these data sources might be used together to triangulate estimates of stocks and flows for a particular displacement situation. Usage context: primary mention, supporting mention, background mention.

general population registers
sample surveys

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

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

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


Input: Figure 2. Location of Households in the VenReP-Kids Sample Notes: The figure depicts the exact geographic location of all the households in the VenRePs-Kids study. Green and blue dots depict the location of Colombian and Venezuelan households, respectively. The map in the upper right corner illustrates the location of Medell ´ ın (blue pin) with the department of Antioquia (highlighted in red). survey focuses on strata 1 through 4, intentionally omitting strata 5 and 6 to avoid bias toward higher-income groups which are less likely to include migrants in need of sup- port. 14

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: Operators/Assemblers Craf/Related Trade Workers Skilled Agricultural Workers Service/Sales Workers Clerical Support Workers Tech/Associate Professionals Managers/Professionals Percent Figure D.19: Occupation by survey domains Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Inside the camp Outside the camp Percent Figure D.20: Work location by survey domains Source: World Bank Staff based on SESRE 2023. 0 5 10 15 20 25 30 35 40 45 50 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.21: Hours per week by survey domains Source: World Bank Staff based on SESRE 2023. Annexes 109 0 10 20 30 40 50 60 70 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.22: Hourly earnings by survey domains Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.24: Household owns livestock Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.23: Household owns crops Source: World Bank Staff based on SESRE 2023. 0

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:
UNHS. Data were collected over a 12-month period. The sample used in this paper is

characteristics also differ between the two groups. Table 1 summarizes relevant socio
economic and child anthropometric variables by ownership and per capita consumption

8

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expenditure terciles for the 2005/06 UNHS and the 2009/10 UNPS. [3] Descriptive statistics on

DHS. (2011). Uganda Demographic and Health Surveys, 2011. Data accessed via & Hautvast, J. (1989)
Increased Risk of Vitamin B-12 and Iron Deficiency in Infants on Macrobiotic Diets. _The_
_American Journal of Clinical Nutrition_, 50, 818-824.

PAGE 31 --> Kabubo-Mariara, J., Ndenge, G. & Mwabu, D. (2009). Determinants of Children's Nutritional Status in Kenya: Evidence from Demographic and Health Surveys. _Journal of African_ _Economies_, 18(3), 363-387.

Uganda Demographic and Health Surveys
Demographic and Health Surveys

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: 13 degree of group bias. Thus, should the program reduce bias, 𝜓𝜓7 < 0. 𝑋𝑋is the same 𝑛𝑛 × 𝑘𝑘 matrix of control variables. 𝜓𝜓 is a 𝑘𝑘 × 1 vector of regression coefficients; and 𝜔𝜔 is the idiosyncratic error. We employ two small deviations from these approaches to produce the full set of results. First of all, as we do not have two sets of control variables from outreach to baseline, we run a fixed effects analysis to understand the impact of assignment to treatment status on life and economic optimism. Second, due to a data collection error in the field, indicators of economic scarcity were not collected from all of the control group at baseline. Instead, we seek to approximate the effect of treatment on these indicators by triangulating comparisons in two dimensions. First, we test whether or not these indicators improved for the treatment group from baseline to endline. Second, we test whether or not there are differences between the treatment and control groups at endline. This stops short of causality but still reveals interesting information about the dynamics at play. We produce five outputs for each analysis, with the exception of the economic scarcity indicators. First, we use uncontrolled OLS. Second, we introduce control variables. Third, we remove the controls but add inverse probability weights.

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: Cross-sectoral programming designed to meet families ’ holistic needs would also increase the odds of maternal and child well-being, including increasing provision of cash transfers through BISP, expanding access to ECE programs, ensuring food availability and affordability for mothers, and implementing emergency-response programs for continuity of basic services in the context of floods and other disruptive disasters to protect vulnerable mothers and young children. Both direct interventions that provide mental health services and indirect interventions that reduce exposure to stressors within communities and households are approaches to alleviate mental health concerns among mothers of young children in the FCV setting of the current study. In addition to individual-level impacts, societal and economic impacts result from reduced maternal depression, anxiety and stress. For example, costing estimates from Pakistan suggest that there is a $ 16. 6 billion cost per cohort of unaddressed maternal mental health concerns (Bauer et al., 2024). Limitations The data collection coincided with a politically sensitive time in Khyber Pakhtunkhwa, inclusive of security concerns for enumerators, ongoing election campaigns, and occasional disruptions to data collection, which could have impacted the accuracy and consistency of data collected. Further, repatriation efforts being implemented during the data collection period led researchers to avoid self-report determination of refugee (documented and undocumented) status and rely on enumerator report using observable indicators as outlined above. This may have therefore impacted the accuracy of the data on identifying refugee households. There are also several measurement constraints to be considered. The tools selected to measure maternal mental health may not cover all relevant dimensions of symptoms and experiences specific to the region of Pakistan studied, even though tools were specifically selected based on previous adaptation, validation and use in the province. Usage context: primary mention, supporting 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:
There are different ways to arrive at a value for housing services consumed by nonrenters, which have different advantages and disadvantages (see Deaton and Zaidi, 2002). In POF, households that own their dwelling (even if still paying the mortgage) or that

_Temporal and spatial price adjustment._ To make the consumption aggregate and average costs per calorie
comparable across households, we adjust expenditure values temporally and spatially. POF was collected
throughout a full year and its reference periods go back up to 12 months. Figure 2 shows the temporal
variation of prices for different Brazilian states over the reference period of the survey. On average, prices
increased by 26.8 percent over the reference period - a monthly average of 1 percent. IBGE (2020)
provides temporal deflators in the publicly available data, which we use to deflate prices and the
consumption aggregate to express all monetary values in January 2018 values.

13 There are several differences between our consumption aggregate and other studies using POF data. In one of the
most recent ones (Oliveira et al. 2016), the authors aim to measure and analyze welfare, poverty (using the
administrative "poverty'' line equivalent to half a minimum wage), inequality, and vulnerability.

Midwest-Rural 0.997 0.671 0.933
_Source:_ Own calculations using POF 2017/18 data.
_Note:_ Prices in the urban Southeast are the reference prices. While we use household-specific price indices in our estimations,
for ease of presentation we calculated regional averages of household-specific indices and divided them by the
Southeast-Urban average.

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:
Notes: The table displays the mean and standard deviations of several characteristics of all students
matched in the ENILEMS-ENLACE (column 1), students that reported to be in college (column 2),
out of college (column 3) or employed (column 4). Data: ENILEMS-ENLACE panel.

30

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Table 3: OLS - ENLACE test scores and secondary school outcomes: simple correlations

Notes: (1) The table displays the estimation of the effect of early test scores on grade 9 and 12 test scores and enrollment by subject and
grade. (2) Specifications for each subject and grade include as independent variables grade 6 test scores of the same subject. (3) Sample:
Twins (students in the same school in grade 6, with identical last names and birth date. (4) All specifications include twins fixed effects. (5)
Robust standard errors are reported in parentheses. *** p _<_ 0.01, ** p _<_ 0.05,  - p _<_ 0.1.

<!-- PAGE 46 -->
Table A.4: OLS       - ENLACE mathematics and Spanish test scores and post-secondary school outcomes

Enlace Score 0.102*** -0.00750 0.0681** -0.000444 (0.0154) (0.0297) (0.0304) (0.0298) Upper Secondary GPA 0.0688*** 0.0412* -0.00579 0.00507 (0.0146) (0.0233) (0.0268) (0.0254) Girl -0.0500* -0.237*** -0.0321 0.00994 (0.0263) (0.0442) (0.0501) (0.0478) Private Upper Secondary 0.113*** -0.0962 0.0606 -0.0842 (0.0320) (0.0649) (0.0736) (0.0653) Urban resident 0.269*** 0.0373 0.0944* 0.137** (0.0375) (0.0472) (0.0535) (0.0566)

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: The age categories are 15 – 19, 20 – 24, 25 – 29, 30 – 34, 35 – 39, 40 – 44, 45 – 49, 50 – 54, 55 – 59, and 60 – 64 years. There are 183 groups since we exclude groups containing less than 40 observations. 24 An additional advantage of the IV approach is that it helps deal with measurement problems. Despite the improved measures of refugee numbers in Turkey by subregion starting in 2014, there is likely considerable measurement error, resulting in attenuation bias in the OLS estimates. For the IV estimates to be consistent, it is only necessary that- conditional on the fixed effects and control variables- the flows of Syrian refugees are uncorrelated with the instrument. 25 Using data from AFAD (2013) we can also weight the aggregate refugee numbers using the Syrian source governorates of refugees in 2012-13 (see Figure 2). Results are qualitatively robust to this alternative instrument and first-stage F-statistics about the same. We prefer the use of the pre-war distribution of population in Syria, Usage context: primary mention, supporting 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: 6. Robustness Checks 6. 1. Sample attrition Many challenges were encountered during survey implementation, due to the context – the densely populated and impoverished communities in and around Monrovia are difficult settings in which to find and track respondents — as well as the transience of a young study population. Despite vigorous efforts to track and interview each individual in the sample, a certain amount of survey attrition was expected. As the survey response rates in Table 1 show, 1622 (or 80 %) of the individuals in the study sample were successfully interviewed in both the baseline and midline surveys. Another 305 respondents were interviewed at baseline but not at midline and hence are not in the panel used for the analysis in this paper. 27 This survey attrition, while not much higher than other program evaluations in Africa, may cause concern that the results of this evaluation are biased, especially if the loss to follow up is correlated with individual characteristics that might affect the outcomes. To address this concern, Table 10 presents regressions on the likelihood of panel inclusion, that is, the likelihood of being interviewed at both baseline and midline. The first column indicates that treated individuals are significantly more likely than control to have been interviewed twice. This result persists even after controlling for individual characteristics and community dummies. 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

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:
Firstly, the Hydrosheds variant of SRTM is used, both at 3 and 30 arc second resolutions. A number of additional corrections are applied to the terrain data including a systematic vegetation correction procedure in vegetated areas and an urban correction procedure in urbanized areas. A detailed description of the modeling framework is provided by (Sampson et al. 2015).

For this study, we developed flood hazard maps representing riverine, flash-flood and coastal flood risk for Vietnam. These flood hazard maps estimate the inundation depth at a grid cell level of 3 arc-seconds, (~ 90m) and provide coastal surge hazard layers, along with pluvial and fluvial layers. The maps provide information on the extent and depth of flood hazard for a specific location. For the coastal component, we explicitly model four return periods - 25, 50, 100, and 200 year events, under current and future climate conditions.

Data on natural capital is sourced from a World Bank dataset on national wealth. The dataset disaggregates the three components of national wealth-produced capital, intangible capital and natural capital-and it decomposes the natural wealth stock into renewable and nonrenewable resources.

The data on income inequality is sourced from the World Bank's "All-the-Ginis Database,"
which was last updated in 2014. [8] The database collects Gini indexes from multiple sources
into long time series. The data has been standardized for this analysis via the so-called
"choice-by-precedence approach," which reflects each dataset's reliability, degree of
variable standardization, and consistency of geographical coverage. GDP and population
figures have been collected from the United Nation's UNCTAD-STAT database. Table 2
presents the descriptive statistics for per capita GDP and the Gini index by country
income group. The Gini index peaks among the upper-middle-income group, falls among
the lower-middle-income and low-income groups and is lowest among the high-income
group. These data are consistent with the relationship between inequality and GDP
described by Kuznets (1955).

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