Input: project had to meet a basic minimum literacy level in order to qualify for admission to the program; whereas only half of the 15-29-year-old female respondents to the nationally-representative Core Welfare Indicators Questionnaire (CWIQ) survey report that they can read and write (LISGIS 2007). Fewer than two percent of the girls in the EPAG study responded that they had no education, which is much lower than in the CWIQ survey. Second, the majority of adolescent girls and young women in Liberia reside in rural areas, whereas the survey participants were residing in urban and peri-urban areas, where access to basic social services may be much more improved. Consequently, the results are not representative of adolescent girls and young women in Liberia overall. The results are neither indicative of the average Liberian girl and young woman; nor are they indicative of the average Liberian girl or young woman in the project communities. They are only indicative of the average girl and young woman who are part of the EPAG project. Finally, many of the variables that we examine in this study are measures of self-assessed levels of satisfaction or belief. These are entirely subjective variables, and are subject to significant measurement error. There is considerable evidence that the wording of these questions can affect the answers given, as can the order in which the questions are asked.

Core Welfare Indicators Questionnaire
CWIQ survey

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Input: Table 1 provides descriptive statistics for the years 2011 and 2014. 18 Labor force participation is very low in Turkey, around 54 percent of the working-age population in 2011, though it has been rising. The reason is that female labor force participation is particularly low at about one-third. The majority of employment is private sector, around one-third of the working-age population, compared to 6 percent employed in the public sector. There are a large number of unpaid workers (7 percent) and unemployment is at 5 percent of the working-age population, an unemployment rate of about 10 percent. School attendance has been rising over the period, from 12 to 16 percent of the working-age population, and the fraction retired has been steady at about 5 percent. Correspondingly, educational attainment has been rising though still 13 percent of the working-age population has no formal education, 57 percent at least completed primary education but not high school, and high school completion has risen from 30 to 34 percent. 17 Of those who have an irregular workplace 60 percent are agricultural workers, 14 percent work in construction, 7 percent in transportation and 5 percent in retail and in manufacturing each, and 3 percent as household employees. 18 Note that in 2014 new regulations for the Household LFS were carried out within the framework of European Union criteria. Consequently, statistics are not necessarily entirely comparable across years. Since we do not use aggregate time-series variation for identification this does not affect our empirical strategy, see Section 3. For those interested, the Turkish Statistical Institute provides consistent time-series on their website.

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Input: in Bangladesh). The data set we use does not cover the 5. 1 million Palestinian refugees who are under the man- date of the United Nations Relief and Works Agency for Palestine Refugees in the Near East (UN- RWA). The deﬁnition of Palestinian refugees in international law is distinct from other refugees. Palestinian refugees are people “ whose normal place of residence was Mandatory Palestine be- tween June 1946 and May 1948, who lost both their homes and means of livelihood as a result of the 1948 Arab-Israeli conﬂict ”. Importantly, their patrilineal descendants are also considered refugees regardless of citizenship (UNRWA 2009). For the purpose of the analysis, we do not include Internally Displaced Persons (IDPs), who are deﬁned as “ persons who have been forced or obliged to ﬂee or leave their home or place of habitual residence, in particular as a result of or in order to avoid the effects of armed conﬂict, situations of generalized violence, violations of human rights or natural or human-made disas- ters, and who have not crossed an internationally recognized border ” (United Nations 2004). The categories “ returnees ”, “ Stateless ”, and “ Others of concern ” are also not included.

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Input: 4 household displacement and the destruction of household dwellings during the violent events. We are also able to measure conflict intensity across time – including peaks of violence at various stages of the conflict – at the district level from event data on violence intensity during the conflict in Timor Leste. We focus on primary school effects because only a small percentage of the Timorese population attended secondary school. Our results show mixed evidence for the impact of violent conflict on educational outcomes. Mirroring some of the findings of Bellows and Miguel (2006) and others, we find evidence for a rapid recovery of the education sector in Timor Leste, and of educational outcomes, particularly for girls. However, in line with emerging results in the micro-level literature, we find that the 1999 wave of violence in Timor Leste – as well as peaks of violence in the 1970s and 1980s – resulted in negative effects on primary school attendance and attainment. This effect is particularly strong for boys. We attribute the first result to a process of educational catch-up among girls in Timor Leste that started before the conflict and continued despite the conflict. Usage context: background mention, primary mention, supporting mention.

event data on violence intensity during the conflict

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Input: Figure 19: Trust and Conﬂict in the Cross Section The problem with a lack of trust and strong group identities is that they penetrate and pervert formal institutions. The ethnic politics analyzed by Burgess et al. (2015) is just one example. Shayo and Zussman (2011), for example, use data from Israeli small claims courts to show that Arab and Jewish judges displayed signiﬁcant judicial ingroup bias. Furthermore, this bias is strongly associated with terrorism intensity in the vicinity of the court in the year preceding the ruling. Conﬁdence-building is also a crucial ingredient for the establishment of a fertile investment climate, which in turn is a trigger of economic development post conﬂict. This is the core message of the World Bank Report by Mills and Fan (2006). An important role of increasing trust doubtlessly goes to the media. It has been shown, for example, that hate radio in Rwanda played a critical role in the extent of ethnic violence during the genocide. 59 Other research has shown that media coverage can have strong eﬀects on political preferences more generally. 60 Perhaps the most direct proof of the crucial role played by the media in the post-conﬂict situation comes from DellaVigna et al. (2014). The authors exploit variation in radio reception of na- tionalistic Serbian radio in border regions in Croatia. Usage context: supporting mention, primary mention, background mention.

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Input: Columns 2, 3, and 4 show that age, school attendance, and employment status at baseline are all correlated with survey attrition. To further investigate whether these characteristics lead to differential attrition between the treatment and control groups, we interact treatment with particular characteristics at baseline. Columns 5 and 6 show that, conditional on being in the control group, the most likely predictor of attrition is having a child. By itself, being a mother does not predict attrition, but when interacted with treatment, we see that control group mothers are more likely to have dropped out of the panel than their treated counterparts. Employment at baseline, while positively correlated with attrition, does not differentially affect treated and untreated individuals. Because the differential attrition between treatment and control groups may bias our results, we use Inverse Probability Weighting (IPW) as outlined in Wooldridge (2002) to adjust the estimates of our key outcomes, using the inverse probability of inclusion in the panel as a probability weight. As a first step, we use the probit model in Column (6) of Table 10 to regress the likelihood of being observed twice on baseline individual characteristics, including those likely to affect attrition, such as employment and parental status. In the second step, we use the inverse of the predicted values from that probit model as probability weights to redo the difference-in-difference regressions for our key outcomes of interest. This method gives more weight to the individuals with the highest chance of attrition, giving them more influence on the estimate of the impact than those with a low probability of attrition. The results are reported in Table 11. The results show a high degree of similarity between the original (unadjusted) and the adjusted estimates. Across all outcomes, the point estimates and standard errors vary only slightly. 27 Of these 305 cases, nine are dropped from the attrition analysis because the household head was not interviewed at baseline, hence the household level control variables are not available. 23

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Input:
7 Gibson and Kim (2012) use a HCES with direct measures of consumption from food stocks and find an error of up to 300 KCal per person per day from ignoring destocking of one major calorie-source (rice) that is subject to bulk buying and storage.

these reports when made by people who never transact in metric units. A typical HCES consumption

Once the food reported in an HCES is converted into calories, the household's calorie intake is compared

While the potential sources of mismeasurement in HCES are numerous, this study systematically

using a survey experiment conducted in Tanzania. There were a total of eight alternate designs, which

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Input: 9 this oversubscribed list. Selection into the training group was based on a “ vulnerability score ” that gave priority to younger, female and unemployed individuals. Despite this approach, intake was “ fuzzy ”- participants were ordered by their vulnerability score, with the most vulnerable entering up until capacity. In some intakes, individuals with comparatively high scores were not taken into the program. In others, individuals with comparatively low scores were included. We construct our treatment and control groups from these intake decisions. Data were collected from members of the host and refugee communities in each country. 4 In both Jordan and Lebanon, the intervention was implemented on a rolling basis. As soon as one training cycle was completed, another would begin. Data were collected in three waves during each training cycle. First, during an “ outreach ” phase, where data were collected in order to assign treatment status. Second, at “ baseline ”, which occurred before the training had begun but after treatment assignment was known. Third, data were collected at “ endline ”, immediately following the end of the training. Data collection for those assigned to the treatment and control followed the same pattern. 5 Outreach and baseline data collection took place less than a week apart and were collected between July 2018 and September 2019.

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Input:
This rate decreases as we move away from mining areas (figure 12a and 12b). Not only are the rates higher in the first two types of communes mentioned, but the rates in those communes also increased faster from 1998 to 2009, the two years being the years of the last two General Population and Housing Censuses in Mali.

|Figure 12a Net primary enrollment (%)|Figure 12b Net primary enrollment (%) mining
communes|
|---|---|
|
|
|
|_Source:_RGPH (General Population and Housing Census) 1998 and 2009.|_Source:_RGPH (General Population and Housing Census) 1998 and 2009.|

5 We could not use the 1987 census data because communes were not created at that time.

RGPH (General Population and Housing Census)

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Input:
Population Data Analysis – September 2022

Overview

As of the end of September 2022, Southern Africa hosts around **8.6 million persons of concern (PoCs) to UNHCR** .
This includes 1.1 million refugees and asylum-seekers and 6.9 million internally displaced persons (IDPs), as well as
others of concern, refugee returnees and IDP returnees. **The Democratic Republic of Congo (DRC) represents 77**

**per cent of the regional data.**

Refugees, Asylum-Seekers and Others of concern

The region hosts **785,000 refugees, 278,000**

**asylum-seekers** **and** **36,000** **others** **of**

**concern** . Among those 1.1 million PoCs, 74 per
cent of them are from the countries outside of
the Southern Africa region. [1] The top five
countries of origin are Central African Republic
(243,000), Rwanda (242,000), DRC (228,000),
Burundi (84,000) and Ethiopia (61,000).

Internally Displaced Persons
(IDPs)

In Southern Africa, there are **6.9 million**

**internally displaced persons (IDPs)** . Most of
them are conflict-induced, 6.4 million, but there
are also natural disaster-induced IDPs, 0.5
million. The data on IDPs are reported in DRC,
Congo, Mozambique and Zimbabwe (see
Figure 1).

Durable Solutions

Figure 1. Number of IDPs in RBSA by Cause as of 30 September 2022

---
[1] The Southern Africa region refers to the 16 countries covered by the Regional Bureau for Southern Africa of UNHCR including Angola,

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Input:
**6.2 Baseline and Endline Survey Design**

The 2024 survey data constitute the project's baseline. They were collected using computer-assisted personal interviewing (CAPI) on Android tablets, with GPS coordinates recorded for each interview to enable geographic analysis. The 2024 survey data cover consumption expenditure, agricultural production, access to markets, and child schooling outcomes, structured to mirror the indicators in the results framework. A pre-analysis plan was registered with the AEA RCT Registry prior to data collection to protect against specification searching in the impact evaluation.

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Input: We use the sum of conflict events occurring in the historic homeland of ethnic group e in the previous year t − 1, denoted as Conflictet − 1, and we use the mean distance between the historic homeland of ethnic group e and the border of country d to predict the number of refugees of a certain ethnic group e moving from country o to d at time t. 22 In order to be consistent with EPR-ER data construction, we restrict our analysis to all origin – destination country pairs that are at a maximum distance ≤ 950 km from each other. Predicted numbers of refugees are then transformed into predicted shares for the three largest groups to follow the logic used by the EPR-ER dataset. We then plug in these predicted shares in the following way: X \PredictedRefcet = Refocdt ∗ \Shareodet. (6) The predicted shares of refugees per camp c are then used to compute (as documented above) refugee diversity indices to be used as instrumental variables. The first-stage equations corresponding to the 2SLS-equivalent of Equation 1 can be expressed as 20We conduct a robustness check on Equation 5, replacing the dyadic origin – destination fixed effects with separate origin and destination fixed effects (Section 5. 4). 21More information on LEDA can be found in Appendix A. 1. 22The construction of the IV follows a long tradition in using the gravity model to predict bilateral migration flows (Ravenstein, 1985, 1989; Crozet, 2004; Mayda, 2010; Garcia et al., 2015; Beine et al., 2016). In our analysis, a major difference is that we have an additional dimension: the ethnic group e. 20

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Input: 5 (UNHCR, 2014), developing countries hosted 10. 1 million refugees or 86 percent of the world ’ s refugees. Contrary to what has been sometimes claimed in popular media, refugees are not “ invading ” the higher ‐ income countries. Actually, SSA has been hosting more refugees than sending them since 1990. The divergence of trends occurring in 2005 is certainly related to large inflows of refugees from North Africa and the Middle East. The second peak in 2011 corresponds to the uprisings that spread across several Arab countries (Egypt, Libya, Syria, Tunisia and Yemen), and the recent one in 2013 to the large outflows of refugees from Iraq, Syria and Yemen. Figure 2. Refugees and Internally Displaced People in SSA, 1990 ‐ 2013 Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). Due to changes in classification and estimation methodology in a number of countries, 2007 figures are not fully comparable with pre ‐ 2007 figures (see also footnote 1). Gathering data on internally displaced people is much more challenging since most existing data on IDPs are incomplete or unreliable. Usage context: background mention, supporting mention, primary mention.

UNHCR statistical population online dataset

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Input: refugees’ social integration (ReDSS, 2018). Box 7.1: Socio-political tensions in the Gambella Region Figure 7.3: Host response to “Refugees are good people” by gender Source: World Bank Staff based on SESRE 2023. Figure 7.4: Host response to “Would you feel comfortable having a refugee as a neighbor?” by gender Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Male Female Male Female Male Female Male Female Eritrean Somali South Sudanese Addis Ababa Strongly agree Agree Disagree Strongly disagree Percent Markets and Opportunities 66 Most hosts do not think they have experienced adverse effects from refugees. However, a sizeable minority are concerned about the effects on employment, inflation, security, and deforestation in their communities, with significant differences across domains. The most consistent perceived effects are economic competition and price increases. Across domains, 29 to 39 percent of hosts think they have experienced either wage or employment competition due to refugees.50 Beliefs that refugees have increased prices are especially prevalent in the Eritrea and Addis Ababa domains (70 and 81 percent, respectively), possibly reflecting concerns over housing costs in Addis Ababa. Other studies have shed light on this phenomenon in more detail.

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Input: 5 narrative around the regressions and aims to explain why not more people migrate when benefits of doing so are so high. 2. The Setting: Tanzania and Kagera, 1994-2004 In the last decade, Tanzania has experienced a period of relatively rapid growth, attributed to liberalization, a renewed trade orientation, a stable political context, and a relatively positive business climate to boost economic performance. Real GDP growth was of the order of 4. 2 % per year between 1994 and 2004, while annual population growth was around 3. 2 % in the same period (URT, 2004). There is also evidence that growth had accelerated in the last few years compared to the 1990s. However, this growth has not been sufficiently broad-based to result in rapid poverty reduction. On the basis of the available evidence, poverty rates have declined only slightly and most of the poverty reduction progress has been made in urban areas. According to the Household Budget Survey (HBS), between 1991 and 2000 / 01, poverty declined from 39 percent to 36 percent in mainland Tanzania. The decline in poverty was steep in Dar es Salaam (from 28 % to 18 %) but minimal in rural Tanzania (from 41 % to 39 %). Usage context: primary mention, supporting mention, background mention.

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

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Input: 13 Proposition 7 State-Level Effects of Population Size: The risk of civil war events at a location varies with the size of the population of the country to which the location belongs, controlling for the local effects. 3 Research Design 3. 1 Unit of Analysis To distinguish between the different theoretical statements regarding how population sizes, population concentrations and locations relate to risk of conflict, we need to investigate exactly where conflicts occur. We have created a dataset using a Geographic Information Systems (GIS) program which converted large territories into smaller portions of 8. 6 km x 8. 6 km, totaling 74 square kilometers. Each of these grid squares are our units of observation (we will refer to them as squares). This approach is similar to that of Buhaug & Rød (2006), with two important differences. First, their squares are much larger (100x100km). Second, they code the dependent variable considerably more crudely than is done in the ACLED dataset described below. Buhaug & Rød (2006) use the `scope'and `location'variables in the Uppsala / PRIO dataset. Usage context: background mention, primary mention, supporting mention.

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Input: ministries to achieve better outcomes for refugees and their hosts. ◆ Improve the coverage, accuracy, reliability, quality, and comparability of data to provide the analytical underpinning for policy decisions. Executive Summary 1 C onflict, political unrest, environmental disruption, and economic instability has forcibly displaced millions of people globally (Ferris, 2010; Black, 2001). Over the last decade, the number of forcibly displaced persons has continuously increased. In mid-2023, there were 36.4 million refugees worldwide (UNHCR, 2023d). As development reduces global poverty, extreme poverty is increasingly concentrated among vulnerable groups; refugees are among these vulnerable groups (World Bank, 2017). Therefore, the plight of the forcibly displaced poses significant challenges to broad development efforts to eradicate extreme poverty and achieve the Sustainable Development Goals (SDGs). Ethiopia has a long history of hosting refugees and has one of the largest refugee populations in Africa. The refugee situation in Ethiopia is characterized by both complex humanitarian emergencies and protracted refugee status. Forced displacement is a pressing issue in the country, a result of conflict, drought, flood, economic instability, and political instability in neighboring countries (Martin, 2010; UNHCR, 2020d; IPCC, 2019). As of year-end 2023, more than 922,000 refugees and asylum seekers 1. Introduction 0 200,000

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Input: Maternal depression was measured by the Self-Reporting Questionnaire (SRQ-20), which has previously been used in Pakistan and has been found to be valid and reliable for screening for depressive disorders in rural Pakistan (Husain et al., 2006). The SRQ-20 contains 20 yes / no questions, with higher scores indicating higher levels of depression, and was found to have a high internal consistency reliability, with a Cronbach ’ s alpha of. 89 in this sample. Anxiety. Maternal anxiety was assessed using the General Anxiety Disorder 7-item questionnaire (GAD-7). This 7-item questionnaire has been found to be valid in the Gilgit province of Pakistan (Ahmad et al., 2017) and has subsequently been used in other assessments of anxiety in Pakistan (Yasmin et al., 2021). The GAD-7 was found to have a high internal consistency reliability, with a Cronbach ’ s alpha of. 80 in this sample. Parenting Stress. Parenting stress was measured using an adapted version of the Parental Stress Scale (PSS), which has previously been validated in Urdu in Punjab (Bilal et al., 2021). The adapted version used in this survey was designed by Ugarte et al., 2024 for Rohingya refugees in Bangladesh. Usage context: supporting mention, primary mention, background mention.

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Input: -0.000* -0.001*** -0.001*** -0.000 0.000 -0.000 0.001 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) Educ: < Primary - - - - - - - - Educ: Primary -0.028 0.058*** 0.127 -0.042* 0.077** -0.104 -0.172 0.121 (0.020) (0.021) (0.107) (0.022) (0.032) (0.069) (0.122) (0.361) Educ: Secondary 0.115*** 0.506*** 0.700*** -0.045 0.460*** -0.200** 0.098 0.849*** (0.031) (0.046) (0.100) (0.045) (0.107) (0.080) (0.212) (0.311) Educ: Post-sec 0.231*** 0.716*** 0.812*** 0.004 0.493*** -0.073 0.222 0.528** (0.024) (0.037) (0.078) (0.117) (0.121) (0.149) (0.367) (0.232) Years in Ethiopia 0.009*** 0.001 0.001 0.003 0.006 (0.003) (0.002) (0.004) (0.012) (0.015) Work outside camp 0.352*** (0.128) Region fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Restrict to workers outside camp Yes N 3321 1626 494 3,069 830 975 238 73 Source: World Bank Staff based on SESRE 2023. Note: Columns 2 and 5-6 are restricted to working respondents. Columns 3 and 7-8 are restricted to employees. High-skill occupations include managers, professionals, and associate professionals. Ln Earnings is the log of monthly earnings winsorized at the 1st and 99th percentiles. All other models are linear probability models. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes

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Input: global-trends-report-2021.html. UNHCR. (2022c). Ethiopia Country Refugee Response Plan (ECRRP) Jan Dec 2022. https://data.unhcr.org/en/documents/ details/94099 UNHCR. (2022d). Serdo Refugee Camp Profile April 2022. https://data.unhcr.org/fr/documents/details/92436 UNHCR. (2022e). “Global Report 2021.” http://reporting.unhcr.org/globalreport2021/pdf. 81 UNHCR. (2023). Ethiopia Global Refugee Forum Pledge Progress Report. UNHCR. (2023a). Addis Ababa Quarterly Urban Factsheet, April 2023. UNHCR. (2023b). UNHCR Resettlement Handbook. UNHCR. (2023c). Culture, Context and Mental Health and Psychological Well-being of Refugees and Internally Displaced Persons from South Sudan. UNHCR. (2023d). Mid-year Trends. Geneva. https://www.unhcr.org/mid-year-trends-report-2023 UNHCR. (2023e). Ethiopia Country Refugee Response Plan 2023. https://reporting.unhcr.org/ethiopia-country-refugee- response-plan-summary UNHCR. (2024). Global Refugee Forum 2023 Pledges. https://globalcompactrefugees.org/pledges-contributions UNHCR. (2024a). Press Release (Ethiopia Launches Inclusive ID System for Refugees, Boosts Access to National Services). https://www.unhcr.org/africa/news/press-releases/ethiopia-launches-inclusive-id-system-refugees-boosts-access- national-services UNHCR, and World Bank Group. (2020). Understanding the Socioeconomic Conditions of Refugees in Kenya Volume A: Kalobeyei Settlement. Results from the 2018 Kalobeyei Socioeconomic Survey. Washington, D.C. UNICEF. (2018). Situation and Access to Services of Persons with Disabilities in Addis Ababa. Briefing Note. UNICEF. (2019). Birth Registration for Every Child by 2030: Are WE on Track? New York. UNICEF. (2021). Education in South Sudan. Briefing Note. Vemuru, V., Sarkar, A., and Woodhouse, A.F. (2020). Impact of Refugees on Hosting Communities in Ethiopia: A Social Analysis. World

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:
To further control for geological and agro-ecological characteristics at the community level, elevation figures were included, taken from the GLOBE 1 kilometer elevation database (GLOBE Task Team et al. 1999). Elevation differences were also incorporated as a measure of terrain roughness. These were computed using the SRTM3 data (Jarvis et al. 2006) by subtracting the minimum elevation in a 1 kilometer grid from the maximum elevation in the 1 kilometer grid. Finally, urban/rural and regional indicator variables help capture unobserved (time invariant) location specific characteristics related to the urban and rural livelihood systems as well as those related to regional variation in the agro-ecological, economic and political environment. Year dummies were included to control for the survey year.

Given the focus here on riverine and coastal floods and to better pinpoint the locations that actually experienced riverine or coastal floods, the DFO flood maps were overlaid with the location of the rivers taken from the Hydroshed project (Lehner et al., 2008). Only the major rivers were mapped as these will have sufficiently large catchment areas exposing the downstream areas they traverse to (non-localized) flooding. Riverine and coastal flood bands were then derived by considering DFO flooded areas within 2, 5 and 8 meters elevation difference from the closest point on the major river or the coast using a Digital Elevation Model from GLOBE (GloBe Task team, 1999).

To reflect access to public services, indicator variables are included that take the value of one if clean water [32], sanitary latrines, [33] or electricity are present in the community, and zero otherwise. To proxy a household's integration in the overall economy, the community data were further augmented with the distance of the centroid of the community (in meters) to the nearest part of the nearest primary or secondary road, as designated in the VMAP0 dataset (NIMA 1997). These measures were computed that using Arc View 3.2. In addition, the estimated travel time to the nearest town or city of at least 25,000 people, the nearest city of at least 100,000 people, and the nearest city of at least 500,000 people were included. The travel times were computed in Arc View from a friction grid that assigned differing travel times to various classes of roads, urban areas, water bodies, off-road, and crossing international boundaries. [34]

The expenditure data are expressed in January 2002 prices using the CPI provided by the Government Statistical Office. They are also corrected each year for regional and rural/urban differences across the 8 regions.

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Input: 9 Visits (JD-HV). ProGres records demographic information of each individual refugee and their household at the time of registration with UNHCR including the relationship of each household member with the principal applicant. In this paper, we refer to the self-identified principal applicant as the household head – the term used in the poverty research literature. Self-identification is commonly used as a means of identifying the household head in household surveys of income, consumption and expenditure surveys which are used to estimate poverty rates (see Hanmer et al, (2020)). Home visits began at the same time as the UNHCR cash assistance program was launched. The program aimed to assist vulnerable Syrian households to meet their basic needs. During the home visits, UNHCR determined whether a household met the criteria to qualify for assistance, considering economic poverty and the presence of members in the household requiring protection. Since 2013, these visits have been made to every registered Syrian refugee household in Jordan outside the camps (UNHCR, 2014). At registration, each household is assigned a unique registration number that serves to cross- reference the initial registration data with Home Visits data. Home Visits data are collected for about one-third of the registered households. Usage context: supporting mention, background mention, primary mention.

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Input: Religious School Enrollment in Pakistan A Look at the Data Tahir Andrabi1 Pomona College Jishnu Das The World Bank Asim Ijaz Khwaja Harvard University Tristan Zajonc Harvard University Abstract Bold assertions have been made in policy reports and popular articles on the high and increasing enrollment in Pakistani religious schools, commonly known as madrassas. Given the importance placed on the subject by policy makers in Pakistan and those internationally, it is troubling that none of the reports and articles reviewed based their analysis on publicly available data or established statistical methodologies. This paper uses published data sources and a census of schooling choice to show that existing estimates are inflated by an order of magnitude. Madrassas account for less than 1 percent of all enrollment in the country and there is no evidence of a dramatic increase in recent years. The educational landscape in Pakistan has changed substantially in the last decade, but this is due to an explosion of private schools, an important fact that has been left out of the debate on Pakistani education. Moreover, when we look at school choice, we find that no one explanation fits the data. Usage context: supporting mention, primary mention, background mention.

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Input:
**Electricity** **Capital** **GDP** **Consumption** **Stock** **Population** Median 3.84 3.65 3.45 1.44 Average 3.48 4.10 3.88 1.43 Standard Deviation 4.37 6.87 2.77 0.97 Skewness -1.58 1.56 0.81 0.11 Kurtosis 9.83 20.48 1.10 -0.33 _Sources: World Development Indicators, Penn World Tables Version 8, and author's calculations._

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#### **VII. Hypothesis Testing**

We explore two basic questions. First, could night-time lights data serve as a good proxy for any of the
economic variables considered in this paper?

A new satellite is generating superior night-time lights data. The data are generated by the Suomi National Polar-orbiting Partnership (SNPP) satellite series operated by the NASA - NOAA Joint Polar Satellite System that was launched in late 2011.

observed in cross-section surveys - even when controlling for other observable characteristics -   cannot directly be interpreted as reflecting a change in attitudes over the life-cycle.

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Input: Given that we control for the effects of conflict in addition to the usual socio-economic determinants of fertility and given that conflict may also affect these other variables, the estimated coefficient of the conflict variable can be interpreted as the ‘ pure conflict ’ effect. This makes our calculations conservative estimates of the total effects of conflict on fertility. For example, conflict is likely to reduce the educational attainment of children exposed to violence during their school-age (Akresh and de Walque 2008) and of girls in particular (Shemyakina 2006), hence inter alia raising their fertility. In both (1a) and (1b), measures of each woman ’ s socio-economic characteristics include three age categories (young: 15-24 years, middle: 25-34 years, and old: 35-49 years), the number of sons and daughters born before the time span of interest, her education (no education, some primary education, and some secondary or higher education), a dummy variable indicating whether she is currently in a union, 9 a dummy variable indicating whether she has had more than one union, a continuous household wealth index (see Section 4. 1), a dummy variable indicating whether she has always lived in the same community, a dummy variable indicating whether the current location is urban, mortality rates of children under five years at the district 9 This variable is not included in the estimation of equation (1b), given that widowhood is strongly correlated with being in union. Usage context: background mention, primary mention, supporting mention.

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Input:
school report cards and a related online platform. Still, few schools viewed ENLACE

By design, ENLACE had a national mean score of 500 and a standard deviation of 100

and excellent. ENLACE's methodology followed item response theory (IRT), allowing

In 2008, SEP decided to use ENLACE scores to measure teacher performance in

subject to yearly evaluations. In 2008, ENLACE scores among their students were given

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

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

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

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

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

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

countries.

8

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_**Regression Model**_

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

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Input:
### 4.2 GLSS Cluster-Level Aggregation

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

household-based survey
census of populations

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

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

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

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

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

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

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

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

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

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

Linking Ethnic Data from Africa
Afrobarometer
EPR-ER dataset

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

administrative data from the UNHCR
food consumption data from SESRE

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

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

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

## **PATTERNS OF ABUSE**

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

1 REACH PIDP study, p. 17

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

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

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

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

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

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

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

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

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

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

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

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