Input: 4 The only publicly available sources of data to document patterns of enrollment and available educational options for Pakistani families are household-based surveys. These are the official 1998 Census of Population (Government of Pakistan) 3, the 1991, 1998, and 2001 rounds of the Pakistan Integrated Household Survey4, and a 2003 census of schooling choice conducted by our research team. The fact that three sources use different definitions of madrassa enrollment, and were collected at different times by individuals with very different institutional affiliations provides independent verification of enrollment estimates and allows us to determine the sensitivity of our results. The household data tell us whether a child is enrolled full-time in a madrassa, but not whether a child goes for an hour on any given day to study the Quran. Therefore this data does not confound full-time with part-time attendees — a child who attends a public school during the day and a madrassa in the evening is recorded as enrolled in a public school. This is an important distinction since parents might use a modicum of madrassa or mosque based education to teach their children about religion. Consequently, if we contrast these household-based numbers with numbers from establishment-based reports, discrepancies can arise. From virtually any policy perspective, including evening quran classes in enrollment figures seems misguided.

Pakistan Integrated Household Survey
1998 Census of Population

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Input: V. B Impacts of exposure to Taliban territorial control on educational attainment Table 4 reports estimates for the effects of refugees ’ exposure to Taliban, contested terri- tories, and U. S. allies on refugees ’ educational attainment. First, this reveals that the im- pact of Taliban exposure is negative across four variables we examined: literacy, formal schooling attendance, primary education completion, and secondary education comple- tion. However, it is worth noting that the estimated effects do not reach statistical signif- icance for the completion of primary education. Nevertheless, when we combine these four variables into an education index (computed as their average), we observe enhanced precision, allowing us to document the significant negative effects of Taliban exposure on the education index. These effects are both substantial and meaningful. For instance, the coefficients in column (3) and Panel A indicate that a one-unit increase in the aver- age share of exposure to Taliban presence (representing 100 % presence in the province of birth during the five years between 2017 and 2021) is associated with an 18. 5 percentage point decrease in the share of individuals with some level of education. The estimates further demonstrate strong and adverse effects of refugee exposure to con- tested territories on educational attainment. Usage context: primary mention, supporting mention, background mention.

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Input: New Research. World Bank. (2023b). Welfare in Forcibly Displaced Populations: From Measuring Outcomes to Building Capabilities. Leveraging Harmonized Data to Improve Welfare among Forcibly Displaced Populations and their Hosts: A Technical Brief Series. World Bank. (2023c). Do Legal Restrictions Affect Refugees’ Labor Market and Education Outcomes? Evidence from Harmonized Data World Bank. (2023d). Ethiopia Economic Opportunities Program: Aide Memoire. World Bank Group. 2021. “IDA19 Mid-Term Refugee Policy Review.” Washington, D.C.: World Bank Group. Zanfrini L., and Giuliani, C. (2023). Look at Me, but Better: The Experience of Young NEET Migrant Women between Vulnerability and Stifled Ambitions. Social Sciences 12, 110. https://doi.org/10.3390/socsci12020110. Zetter R., and Ruaudel, H. (2016). Refugees’ Right to Work and Access to Labor Markets – An Assessment, Part 1. World Bank Global Program on Forced Displacement (GPFD) and the Global Knowledge Partnership on Migration and Development (KNOMAD) Thematic Working Group on Forced Migration. http://bit.ly/KNOMAD-Zetter-Ruaudel-2016-1 Zhou, Y. Y., Grossman, G., and Ge, S. (2022). Inclusive Refugee Hosting in Uganda Improves Local Development and Prevents Public Backlash. References ANNEXES Annexes 83 E thiopia hosts refugees from some 24 countries. By far the largest groups are refugees from South Sudan, Somalia, and Eritrea. Each of these groups is described below, including

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

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

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

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

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

annual average particulate matter concentrations from the RIVM

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Input: The study: (i) compares the socio- economic profile of refugees with that of the Syrian population before the crisis and with the hosting populations of Jordan and Lebanon; (ii) provides a welfare and vulnerability assessment of Syrian refugees including a poverty profile, the socio-economic characteristics of higher poverty and where pockets of deep poverty are located; (iii) analyzes key drivers of welfare and poverty; and (iv) models monetary and non- monetary vulnerability.  In Lebanon, Jordan and Iraq, the Bank is leading an initiative to evaluate the socio-economic impact of the regional crises on the welfare of Syrian refugees and host communities in neighboring countries [ongoing]. Data on living conditions, access to services and economic opportunities, coping strategies and economic status are to be collected via a specialized household survey and a sub-component of the survey will be carried out on a semi-annual basis to continue to monitor and adapt support as needed.  A recent Bank paper, “ Turkey ’ s Response to the Syrian Refugee Crisis and the Road Ahead ” [completed in 2015] assessed the impact of Syrian refugees on host areas in various sectors. It found that the presence of Syrian refugees is placing a strain on municipal services, housing rental markets, social relations, and education services for Turkish households. Usage context: supporting mention, background mention, primary mention.

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Input:
In the absence of objective measures of these factors at the commune level, we leverage the Afrobarometer [15] surveys to investigate at the individual level the effect of the proximity to gold-mining activities on perceptions of governance and quality of public services.

We use the 2005, 2012, and 2014 rounds of the nationally representative Afrobarometer survey in
Mali. We focus on variables of political responsiveness, corruption, and quality of public services
that are similar across the three rounds. Geocoded enumeration areas are combined with location
of industrial mine sites to construct our treatment variable, which is the cumulative gold production
within 20 kilometers of the mine at the time of the survey. This assumes that both distance to mines
and intensity of production matter for the effect. We then estimate the following logistic regression
of the form:

<!-- PAGE 24 -->
**Figure 17 Locations of Afrobarometer enumeration areas**

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Input:
The raw data has been sourced from Visible Infrared Imaging Radiometer Suite (VIIRS) sensors mounted on US satellites, and has been cleaned to remove irregularities, such as lighting used for the year-round growing of dragon fruit.

In order to analyze the exposure of industrial activity to natural hazards, a dataset of 372 industrial zones in Vietnam was obtained (World Bank, 2020).

This analysis circumvents the lack of available data sources by using data from the OpenStreetMap (OSM)
project. OSM is an open and free initiative that aims to create and continuously update a map of the world
that is created entirely from user-contributed data. Next to detailed street networks, the locations of
various points of interests are published by community volunteers. From this source, a dataset of 5,658
hotels, hostels, guest houses, and motels was downloaded in early 2019 (OpenStreetMap, 2019). Each
datapoint includes an exact point location. Of the hotels, 3,309 are located in coastal provinces and 1,531
are located within 5 kilometers of the coastline.

Insights into the exposure of health care facilities are based on a dataset provided by a collaboration between the Government of Vietnam, the World Bank, and the World Health Organization. The dataset contains 1,583 geocoded public hospitals and district health centers.

As no official datasets of school locations could be obtained for this analysis, just as with hotels, volunteer-contributed geolocations of schools were sourced from the OSM project (OpenStreetMap, 2019).

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

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Input:
We develop and use a model to estimate expected asset losses and expected welfare losses, and quantify socioeconomic resilience in 117 countries. The model builds on Hallegatte et al. (2016b), which only considered river floods and 90 countries. Here we add new countries thanks to new socioeconomic data, and we introduce additional hazards - coastal floods and storm surge, windstorm, earthquakes, and tsunamis - using the risk assessment provided in the Global Assessment Report (UN-ISDR 2015). We also simplify the model - focusing on the factors that were found to have most influence on our previous assessment and disregarding other factors - and improve our modeling of insurance and social protection.

In our national indicator, we include the role of early warning system using data reported in the context of the Hyogo Framework for Action [7] (UN-ISDR, 2015a). The priority for action #2 ("Identify, assess and monitor disaster risks and enhance early warning") includes an indicator (P2-C3) related to "Early warning

<!-- PAGE 16 --> overlays flood maps from the GLOFRIS global model and poverty maps from the World Bank. For countries where this study does not provide data, we use an older, similar study by (Winsemius et al. 2015). This other World Bank study overlays the same GLOFRIS flood maps as above with geo-localized household surveys (using the Demographic and Health Surveys [8] ) to assess the exposure of poor people to river floods relative to the exposure of non-poor people.

We calculate the average capital productivity as output-side GDP divided by total reproducible capital within a country, both variables from Penn World Tables.

GLOFRIS
Demographic and Health Surveys
Penn World Tables

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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: Figure 22: Numbers of Refugees in Ongoing Refugee Situations end-2014 Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes Palestinian refugees under UNRWA ’ s mandate. D. Data on asylum-seekers, refugees and IDPs: sources, applications and credibility In this section, a distinction is made between: (a) the collection of source data; and (b) the compilation of data across sources (within a country or across countries). In general, there is a delineation of roles between data collectors and data compilers, however there are organizations, such as UNHCR, IOM and the Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat (OCHA), 59 that are involved in both data collection and compilation activities. Data collection: Sources for refugees, asylum-seekers and IDPs60 Collection of primary data on forcibly displaced persons is generally undertaken by national governments through their national statistical offices, line ministries or immigration agencies. However, where countries lack the capacity to undertake this work, they may rely on international organizations as well as international and local NGOs to collect data or undertake estimates. 61 In general, governments tend to collect data on refugees in developed countries, while UNHCR and NGOs tend to collect data on refugees in developing countries. Usage context: primary mention, supporting mention, background mention.

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Input: Restrictive norms perpetuated by men continue to severely restrict women ’ s access to the formal labor market. Fortunately, the Jordanian government has worked in recent years to address this problem. For example, in the amendment made to the Jordanian labor law in 2019, the provision of childcare services was made mandatory in every business that has 15 or more employees, male or female, with children under 5 (World Bank, 2019). However, implementation is not consistent and low-income households report less access to childcare services (Weldali, 2022). Home-based businesses can also provide important access to economic opportunities for women, albeit within a limited number of sectors and the Government of Jordan ’ s 2017 amendments of the regulations governing the licensing of home-based businesses is particularly important for female entrepreneurs in both host and refugee communities (Slimane et al, 2020; Turner, 2019). 3. Data and descriptive statistics We use two rounds of household-level data collected by UNHCR in 2013-14 and 2017-18. 6 These data come from two sources, namely, the Profile Global Registration System (ProGres) and Jordan Home 6 For simplicity, from here on we refer to the first wave as the 2013 wave, and to the second wave as the 2018 wave. Usage context: primary mention, background mention, supporting mention.

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Input:
Recall that, by definition, attritors are not enrolled in a JFPR school. Arguably, the probability of

6 Smith and Welch parametrize this as the enrollment ratio between attritors and non-attritors. We report this ratio
in columns 2 and 5 of Table 3.

11

<!-- PAGE 13 -->
probability of other girls who were turned down for scholarships and did not enroll in a JFPR school, but

scholarships and did not enroll in a JFPR school, but whose enrollment status could be established; this

enrollment of attrited recipient and non-recipient girls, the estimated JFPR program effect is 0.191. Note,

girls who received JFPR scholarships and other girls-they correspond to the raw difference in

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

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Input: Combining almost 50, 000 responses to 11 cross-sectional surveys between 2017 and 2021, displacement is negatively associated with perceptions of social cohesion in aggregate. But at the individual level, those who report hosting displaced populations in their communities often have higher perceptions of social cohesion. These results are strongest among respondents who self-report hosting IDPs as opposed to refugees, but important heterogeneity across indicators, local context, and gender should guide policy meant to promote social cohesion in forced displacement. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at ppham @ hsph. harvard. edu. Usage context: primary mention, background mention, supporting mention.

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Input: In comparison, 39 percent of hosts and 55 percent of refugees trust both groups. Once again, hosts’ trust towards refugees is highest in the Somali domain (67 percent) and lowest in the South Sudanese domain (29 percent). On the other hand, refugee trust towards hosts is lowest in the Eritrean camps (38 percent). Results of combining the survey answers into an index echo our prior findings; attitudes toward refugees are better in Somali areas, worse in South Sudan areas, and slightly better among women. The above questions on attitudes and trust towards refugees, the rights refugees should have, and the effects refugees have had on Ethiopia can be combined into an index. To construct the index, we average the response to the ten questions examined above, rescaled to range from 1-4. The index is highest in the Somali domain (.46 standard deviations above the mean) and lowest in the South Sudan domain (.48 standard deviations below the mean). It is higher for female than male hosts in both domains, by .14 standard deviations from average in the Somali domain and .19 standard deviations in the South Sudan domain. The difference in attitudes between male and female hosts is not statistically

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Input:
<!-- PAGE 16 -->
**Appendix 1: Comparing applicants with full application forms to those with only partially**
**completed forms**

**All applicants** **Full application form**
**Outcomes**
Enrolled in JFPR school 0.81 0.82
Attending on the day of school visit 0.74 0.75
Enrolled in any school 0.87 0.87

Das, Jishnu, Quy-Toan Do, and Berk Özler. 2005. "Reassessing Conditional Cash Transfer Programs."
_World Bank Research Observer_ 20(1): 57-80.

Deolalikar, Anil. 1993. "Gender Differences in the Returns to Schooling and School Enrollment Rates in
Indonesia." _Journal of Human Resources_ . 28(4): 899-932.

"Educational Attainment and Enrollment Profiles: A Resource Book based on an Analysis of Demographic and Health Survey Data." Development Research Group.

<!-- PAGE 19 -->
**Figure 1: Attendance and enrollment status by decile, JFPR application data**

JFPR applicants: Enrollment at a JFPR school, by JFPR applicants: Attendance on day of school visit, scholarship status and decile by scholarship status and decile

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Input:
154 P. B. Spiegel & P. V. Le

Discussion

Overall, the majority of surveys that met the inclusion criteria were of insufficient
methodological rigor to be reproducible. Other important methodological issues,
such as households or persons who were absent or refused to participate, as well
as whether replacement of such households or persons occurred, were not stated
in most of the reports. Sample sizes had a wide range and some may have been of
insufficient size to be precise enough to interpret results or make meaningful
comparisons with future surveys. The majority of questionnaires were not field
tested or back-translated nor were qualitative methods used to complement the
quantitative methodology used in many of these surveys. Only a minority of
surveys obtained informed consent from participants. Few provided definitions
of essential terms, such as high risk sex or non-regular partners. Although most
of the surveys included training of surveyors, few reported gender balance of
interviewers. All of the above methodological flaws inject sufficient biases into
these surveys to make the most of them unacceptable.
The majority of the written reports lacked sufficient detail and structure to be
analysed and interpreted by the reader in a meaningful way. Most reports
provided objectives for the survey as well as conclusions and recommendations.
However, few stated limitations and biases and only half appended the
questionnaire to the report. Important methodological details were missing in
many surveys, including information on how the sample size was chosen, the
specifics of the sampling methodology and whether replacement was used. Most
of the analyses were descriptive in nature with few reports using comparative
statistics. The majority of reports used some variation of the internationallyaccepted HIV indicators for knowledge (e.g. prevention and misconceptions) but
only a minority used some variation of these indicators for practice and attitudes.
The disaggregation by age or gender varied considerably among reports which led
to difficulties in comparing results; furthermore, few studies reported indicators
according to both gender and age.
There are limitations to this article. Despite an attempt to search as widely as
possible in the published and grey literature, as well as to contact organizations
known to undertake BSSs in conflict and post conflict settings, some surveys will
have been missed. In addition, those included are not just BSSs but reproductive
health KAP surveys with an HIV component. The latter may not contain as much
detail on HIV as BSSs, however, this would neither affect the basic methodological weaknesses nor the absence of key internationally-accepted standardized
HIV indicators. Some of the BSSs examined in this report were conducted before
2002 when the internationally accepted UNGASS indicators were developed; this
together with the changing of indicators over time makes it difficult to interpret
the usage of internationally-accepted indicators. Misclassification may have
occurred because results were based on findings written in the reports reviewed.
For those reports that omitted key methodological issues or results, the data were
recorded in a negative fashion (e.g. if type of sampling was not mentioned, the
survey was recorded as not employing random sampling).

internationally accepted UNGASS indicators

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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: Protection monitoring data from the last four cycles indicate that freedom of movement restrictions remain the most frequently reported protection incident, accounting for 34 percent of cases. The Protection Monitoring Tool was updated in cycle 5 to include an additional question distinguishing between formal government-imposed restrictions and informal social barriers to movement, following a recommendation from the protection analysis technical advisor. This change improved the actionability of protection monitoring findings for both advocacy and programmatic response teams.

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Input: seekers. 81 There are several challenges associated with using central population registers to estimate refugee and asylum-seeker populations, including: consistency of the definition of refugees with the definition in the 1951 Convention and 1967 Protocol; difficulty and cost of establishing and maintaining a population register (UNSD 2014); and confidentiality safeguards. Compilation of statistics on forcibly displaced populations Several international organizations are involved in the compilation, analysis and dissemination of statistics on forced displacement including UNHCR, 82 Eurostat, IDMC, OCHA, International Committee of the Red Cross (ICRC), 83 WFP84 and IOM. Each of these actors has their own thematic focus and specific objectives, and applies their own methodologies. Asylum-seekers and refugees UNHCR is the principal organization responsible for the compilation, analysis and dissemination of data on asylum-seekers and refugees. UNHCR maintains a publicly available statistical online database85 with data for the period 1951-2014 on refugees (including people in refugee-like situations), asylum-seekers (pending cases), returned refugees, IDPs protected or assisted by UNHCR, returned IDPs previously protected or assisted by UNHCR, stateless persons and others of concern to UNHCR, disaggregated by country of origin and asylum. 86 Data are also provided on demographics, location, asylum-seekers (refugee status determination and monthly data) and resettlement. Usage context: background mention, supporting mention, primary mention.

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Input: education. In addition, information on their registration status is recorded, including refugee status, arrival and, where applicable, return date, and sub-district-level location information for last residence in Syria and current residence in the country of asylum. It also identiﬁes kinship of individuals within each “ case"(e. g., familial relationships of everyone within a case to the principal applicant, ranging from members of the nuclear family to extended family, such as in-laws and aunts). Following the initial registration, entries are updated in subsequent contacts. Up- date frequencies vary from one operation to another, with at least 5 percent of all observations being updated in a given month. 8 Therefore, although information on single-shot events like arrival and return dates is ﬁxed, other information like occupa- tion, education and marital status may change over time. In the case of education and marital status, these changes are largely driven by the aging of the refugee. However, the occupation variable is more problematic, since it could refer to current employ- ment or past employment (including in Syria) depending on when it was last updated. Therefore, while we are able to use all of the demographic and registration information of the ProGres database, we exclude the occupation variable from the main analysis. Usage context: primary mention, background mention, supporting mention.

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Input: 1 POST-CONFLICT TRANSITIONS WORKING PAPER NO. 16 Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis * Håvard Hegre Centre for the Study of Civil War, PRIO (CSCW) Clionadh Raleigh CSCW, PRIO & University of Colorado at Boulder Abstract Why do larger countries have more armed conflict? This paper surveys three sets of hypotheses forwarded in the conflict literature regarding the relationship between the size and location of population groups: Hypotheses based on pure population mass, on distances, on population concentrations, and some residual state-level characteristics. The hypotheses are tested on a new dataset – ACLED (Armed Conflict Location and Events Dataset) – which disaggregates internal conflicts into individual events. The analysis covers 14 countries in Central Africa. The conflict event data are juxtaposed with geographically disaggregated data on populations, distance to capitals, borders, and road networks. The paper develops a statistical method to analyze this type of data. The analysis confirms several of the hypotheses. World Bank Policy Research Working Paper 4243, June 2007 The Post-Conflict Transitions Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about post-conflict development (more information about the Post- Conflict Transitions Project can be found at http: / / econ. worldbank. org / programs / conflict). Usage context: background mention, primary mention, supporting mention.

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

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Input:
3 The exact list of socioeconomic variables in the DHS is not identical to that on the JFPR application form. We
derive the index of socioeconomic status in the DHS from variables describing: the ownership of a bicycle, cart,
boat, motorbike, car, truck, radio, television; the conditions of the dwelling such as hard roofing and finished
flooring; the availability of electric lighting; the main source of drinking water; the type of toilet facilities; and the
main type of cooking fuel used.

beneficiaries was done by the Local Management Committee (LMC) of a JFPR _secondary_ school. In

The JFPR scholarship program established a cut-off in the maximum number of scholarships

Finally, the paper presents evidence of heterogeneity in the JFPR program effects. For this

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Input:
14 % of respondents think that a **go and see visit** prior to the return would be useful. From
those who responded positively, 63 % would like to send community leaders for the go and
see visit, while remaining 37 % male family members.

9 % were aware about some kind of information campaign on the return. It is to be noted
that at the time of the interviewed conducted, the information campaign on return has not
yet been officially launched.

In 64 % of interviewed families, the response on **who decides on the return** was political
administration **.** This percentage is relatively high compared to other return intention
surveys conducted by protection cluster. In 23 % it is the community elders who reportedly
make the decision on return, followed by family members in 13 % of cases (which is usually
the highest category reported). Only 49 % of respondents felt that they **participate in the**

**decision making process** on the return.

**Despite the fact that IDPs wish to return to their area of origin, 26 % indicated that not**

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Input: analysis of social integration outcomes (1) (2) (3) (4) (5) Has in Ethiopia: Easy to do: Family Friend Market Interactions Social Interactions Sharing Resources Male 0.007 0.067** 0.033 0.010 -0.076** (0.017) (0.033) (0.027) (0.036) (0.035) Age Under 30 - - - - - Age 30-44 -0.030 0.033 0.007 0.004 -0.048* (0.021) (0.025) (0.025) (0.029) (0.028) Age 45-64 -0.041 0.024 0.002 0.011 0.012 (0.025) (0.038) (0.038) (0.050) (0.041) Age Over 64 -0.009 -0.062 0.100** 0.041 0.046 (0.046) (0.058) (0.043) (0.089) (0.073) Educ: Primary incomplete - - - - - Educ: Completed primary -0.004 0.121*** 0.008 0.028 0.063** (0.027) (0.033) (0.035) (0.041) (0.032) Educ: Completed secondary -0.026 0.218*** -0.020 0.061 0.060 (0.028) (0.054) (0.051) (0.060) (0.057) Educ: Completed post-sec. 0.003 0.238* 0.100** -0.020 0.238 (0.045) (0.131) (0.040) (0.095) (0.155) Years in Ethiopia 0.003** 0.006* 0.008*** -0.001 0.006 (0.002) (0.004) (0.003) (0.004) (0.004) Agrees hosts culturally similar 0.021 0.047 0.020 -0.010 -0.030 (0.016) (0.039) (0.037) (0.051) (0.036) Region Fixed Effects Yes Yes Yes Yes Yes N 1667 1667 1667 1667 1667 Source: World Bank Staff based on SESRE 2023. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 123 Table D.20: Regression

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Input: 21 Lebanon. It was the obvious step forward in overcoming these problems and the answer to the increasing demand for evidence around the Syrian crisis. The study benefitted from a unique and unprecedented set of data. The UNHCR registry data in Jordan and Lebanon were among the better quality registry data available worldwide and the UNHCR also conducted home visits in Jordan that, at the time of the study, covered over a third of all refugees. There were also sample surveys in both Jordan and Lebanon that were small in size but representative of the population present in the registry. The home visits and the surveys included questions on income and expenditure that could be used for the welfare assessment. Using these data, the study addressed ten questions defined as follows: 1) Who are the refugees?; 2) How different are refugees from “ regular ” populations?; 3) How poor are refugees?; 4) What are the main predictors of refugees ’ welfare and poverty?; 5) How vulnerable are refugees from a monetary and non ‐ monetary perspective?; 6) Do poverty and vulnerability statuses overlap?; 7) How effective are refugee assistance programs?; 8) What is the potential for alternative policies?; 9) How does welfare compare across countries and data sets?; 10) How transferable are

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Input: Figure 1: Data and Descriptive Statistics: Clusters, Refugee Camps, and Conflicts Revised refugee diversity indices. We first use Afrobarometer data to construct standard indices of diversity, namely the EF and the EP indices (Bazzi et al., 2019; Esteban and Ray, 1994). The EF index describes the probability that two randomly selected individuals from a given location belong to two different ethnic groups (Alesina et al., 2003, 2016; Gomes, 2020b). The EF index can be defined as EFjt = Njt X e = 1 get (1 − get), (2) where Nj is the number of ethnic groups in cluster j at time t and get is the population share of 14

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Input: Survey instruments enable detailed questions to be asked about the characteristics and situations of households, and if they identify displaced populations based on self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status. Additionally, more innovative tools and technologies for data collection, analysis and compilation should be explored and leveraged. For example, new methodologies (such as high resolution satellite imagery and unmanned drones) may expand the coverage of data collection efforts in insecure or inaccessible areas. Additionally, new techniques could be explored to improve the collection of robust data on flows of refugees and IDPs. Organizations such as the World Bank, UNHCR, IOM and IDMC are already exploring and in some cases are beginning to use more innovative data collection tools. These techniques include: 99 Several standardized international sample surveys have been designed for special purposes including Living Standards Measurement Studies, Labor Force Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys. The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner

Labor Force Surveys
Multiple Indicator Cluster Surveys
Demographic and Health Surveys
Living Standards Measurement Studies

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Input: Beneficiary assessments confirmed that the most significant barrier to cooperative membership for women was the requirement to present land title documentation as proof of agricultural practice. Since women in the project area rarely hold formal land title independently of their husbands, this requirement effectively excluded female farmers from cooperative membership. The project's operational manual was revised to accept a sworn declaration of land use from the village council as an alternative to formal land title for female applicants.

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Input: the nearest international border but not within a radius of 20km from the Zone capital city and 10km from Woreda city. We classify level four as “remote”; that is, not located 30km from the nearest international border, not within a radius of 20km from the Zone capital city, and not 10km from Woreda city. The market access indicator in Ethiopia is measured at the Woreda level. Accessibility for a Woreda is estimated as the sum of the travel time of the weighted population to the destination Woredas. With Woreda- to-Woreda origin-destination matrices, we calculate market accessibility by the following equation (Donaldson and Hornbeck, 2016; World Bank, 2019b): where is market access at Woreda “o”, is the trade cost between two Woredas “o” and “d”, is the population of Woreda “d”, and is the trade elasticity. Trade costs between two Woredas, is defined by =exp ( ) with = 0.02 and the optimal travel time between Woredas using the transport network of 2020. The trade elasticity, has a value of 8.28 (Eaton and Kortum, 2002). Box 6.1: Measurement of proximity and market access index in Ethiopia 37 43 19 To Woreda City 1-10km 10-20km >20km 1-20km 20-100km >100km 1-30km 30-50km >50km

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Input: 2 1. Introduction The most common sampling approach for cross-sectional household surveys in the developing world is a stratified two-stage design (Grosh and Munoz, 1996). Following stratification based on administrative boundaries, clusters are selected in the first stage with probability proportional to size from a national census-based frame. In the second stage, a canvassing operation is conducted in the selected clusters to compile an updated list from which households are randomly selected. While this methodology is straight forward to implement in the field and reliably produces unbiased estimates, there are several downsides. The first downside is cost. The World Bank ’ s Living Standards Measurement Study team, which provides technical assistance on large-scale household surveys around the world, estimates the field listing operation increases the overall budget for data collection by 25 percent. Due to confidentiality concerns, the data collected during a field listing operation, typically the name of the household head and address or location description of dwellings, does not have any analytical applications beyond as a component of the weight calculations. 2 At a time when typical survey costs are in the USD millions, reducing a significant cost component will increase the financial sustainability of data collection. The second drawback to the traditional design relates to timeliness. Usage context: primary mention, background mention, supporting mention.

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Input: capital, and concentration in low wage jobs. Furthermore, most of these jobs are in the service sector which potentially could have freed up time, especially for Peruvian women to be more engaged in the labor market, as well as lowered the costs for these types of goods and services. Venezuelans also might have expanded opportunities by increasing the demand for certain goods and services. One widespread claim mentioned in some media reports is that Venezuelan migration led to an increase in crime (Freier et al., 2021). We test whether this claim is supported by the data in Table 6, where we use administrative information on the number of non-violent and violent crimes reported in each municipality, the personal security index from Gallup, and reports on whether crime is perceived as a major problem in ENAHO. The structure of this table is the same as the previous with Panel C our preferred speciﬁcation. Consistent with the idea that Venezuelan inﬂow lead to labor market conditions improving, we observe that locations that received a larger number of immigrants have lower number of reported non-violent crimes (columns 2). This eﬀect is large with a double of Venezuelans in a province leading to a 42 % decline in reported non-violent crimes. Usage context: supporting mention, background mention, primary mention.

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Input:
#### 4. RESPONSE

The Protection Sector in Sudan comprises General Protection, Child
Protection, GBV and Mine action AoRs. There is also active Housing
Land and Property and Durable Solutions Working groups that work
closely with Protection Cluster. These structures meet every month
at the national and state level (five Darfur, South Kordofan and Blue
Nile). The Protection Sector developed its strategy and work plan.
The Sector developed the Protection of Civilians’ Incidents Tracking
tool in consultation with other agencies. It also publishes maps of

health care providers and called on Sudanese authorities to enforce
international”, Report of the Secretary-General, 2 March 2022

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Input: The imputation of income poverty is done using the Turkish Labor Force Survey (LFS), and with information and modeling parameters determined from the Survey of Income and Living Conditions (SILC). More details and validation of this methodology is discussed throughout this paper. While explicit identification of Syrians in available surveys is not feasible, there is evidence of an increase in the amount of foreign-born individuals that is being captured in the LFS. The arrival year of foreign-born migrants is available in the data which allows for identification of “ Settled Migrants ” and “ Recent Migrants ”. The latter is used as a proxy for Syrian refugees for the purposes of this paper. National official surveys that are conducted under-report the refugee population. Yet, since about 10 percent of Syrian refugees are in camps and the remaining are residing throughout the country, it is not surprising that they are accessible to interviews by the LFS. Despite data limitations, there are strong and significant trends in the poverty rates for the recent foreign- born, especially for those near the Syrian border. In 2013, recent migrants near the Syrian border were the poorest group4 in Turkey. While this statistic in itself is not initially surprising, fluctuating welfare trends of recent migrants over time is noteworthy.

Survey of Income and Living Conditions
Turkish Labor Force Survey

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

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Input: The comparison between ∆ h is for actual and hypothetical destinations tells us whether the actual district of destination is more densely populated than alternative destinations. Results are presented in Table 3 for all variables used in the analysis. We begin with district log income eδs. We have two estimates of eδs, one obtained using reported income data, and the other based on reported consumption data. Given that most respondents to the NLSS survey are self-employed, measurement error is typically larger for income than for consumption. We see that our estimates of log income and consumption eδs are on average 20 % and 8 % higher in 20 Usage context: supporting mention, primary mention, background mention.

reported consumption data
reported income data

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Input: The cooperative medical scheme's benefit package was reviewed during project preparation to assess its adequacy for the health care needs of the target population. The review found that the scheme covered primary care visits and generic medicines at contracted facilities but excluded dental care, mental health outpatient services, and optical care. The project will support a targeted benefit expansion for enrolled refugees and host community members in the lowest two wealth quintiles, covering MHPSS outpatient consultations at no additional premium cost.

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Input: E Nigeria Somalia Sudan Host community Refugees Host community IDP Host community IDP Host community IDP No earners 40 % 51 % 62 % 50 % 32 % 64 % 7 % 45 % Remittance recipients only 7 % 8 % 76 % 69 % 16 % 37 %-- Female single earner 12 % 52 % 23 % 37 % 35 % 70 % 24 % 55 % Male single earner 8 % 23 % 4 % 14 % 24 % 50 % 8 % 39 % Majority female earners 3 % 57 % 9 % 28 % 9 % 28 % 15 % 45 % Equal contribution 10 % 24 % 17 % 16 % 23 % 63 % 8 % 41 % Majority male earners 5 % 16 % 15 % 19 % 20 % 52 % 9 % 39 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). Overall, the results show that besides gender, displacement status and the number of household contributors plays a key role in the identification and level of poverty. In comparison with female-headed non-displaced households, more female-headed displaced households are classified as multidimensionally poor.

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Input:
We also use a project level database constructed from provincial and central

However, the household survey was designed in view of combining it with the nationally representative 1998 Vietnam Living Standards Survey (VLSS) to predict baseline consumption expenditures for SIRRV households (van de Walle, 2006).

We then compare this to the independent administrative data on the aggregate allocation.

1998 Vietnam Living Standards Survey
VLSS

project level database
independent administrative data

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Input: 40 Sub ‐ Saharan Africa 8604000 5895000 7055000 5406100 5068000 N. A. MENA 6230000 8000000 6675000 8592900 10892000 N. A. Asia and Pacific 4325000 2405000 3392000 2128800 5490000 N. A. (excl. Australia, Japan, New Zealand) Americas 1126000 1280000 2176000 2900000 3661000 N. A. (excl. North America) 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). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). As indicated in Figure A1, these data are much lower compared to those provided from 2003 by IDMC but provide a longer time series. UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Usage context: supporting mention, primary mention, background 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: 14 3. ANALYSIS Has the increase in Syrian refugees impacted the welfare and socioeconomic conditions of the host community? Summary statistics in the previous section showed clear trends of increasing poverty among recent migrants throughout the country, both near the Syrian border and across rest of the country. While the poverty rates of recent migrant households spiked in 2013, poverty of host community households maintained a relatively constant level in the whole country. From these trends, it appears that there at least has not been an increasing trend in poverty among the host community over the latest years. The empirical model is shown in Equation 1. Regressions are estimated at the NUTS2-year level and using data from only the years 2011 and 2013. The dependent variable of interest is the host community poverty rate by region and year, where the poverty rate is based on spatially deflated imputed household income. Unlike the computation of the poverty rates, “ recent migrant ” information is not used for the analysis. Only the host community poverty rates are calculated using the LFS and the number of Syrians are taken from government sources.

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

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Input:
**Statistical Note: Clustering in DHS-Based Analyses**

In all regression tables, _t_-statistics are computed using standard errors clustered at the DHS cluster level. This clustering is essential because: (a) the DHS sampling design introduces within-cluster homogeneity that violates the assumption of independent errors; and (b) our treatment variable — mine proximity — is constant within a DHS cluster, so ignoring within-cluster correlation would produce artificially small standard errors. The effective sample size after clustering is substantially smaller than the nominal number of DHS interviews.

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

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Input: described above, HSP households received not only full rental subsidies for 9 to 18 months but also physical upgrades to the home environment. These proximate outcome measures can be seen as capturing successful program implementation. To measure physical improvements to the shelter, we constructed an index of self-reported ﬂoor, roof, and wall quality; electricity and water access; and crowding. Housing expen- ditures were measured in two ways: First, in the midline phone survey, respondents re- ported their total monthly housing expenditures, including the payments provided by the implementing organization. Second, in the endline in-person survey, once assistance had ended, participants reported their monthly out-of-pocket housing expenditures (excluding the amount paid by the implementer). The latter is our preferred measure of the direct effect of the program on housing expenditures, because it allows us to test whether treated house- holds experienced rental savings. Because the endline measure was collected after assistance ended, any observed expenditure reductions would most likely be an underestimate of the beneﬁts experienced during the program. Recall that HSP also required the landlord to not raise rent for an additional year after the assistance ended, which could help account for any persistent effects on housing expenditures in the treatment group. 2. Usage context: primary mention, supporting 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: to the Venezuelan migratory crisis, whereas the VenRepPS survey was conducted during the pandemic in 2020. These temporal inconsistencies result in varying sample composi- tions across surveys, diverging from the landscape we observe in 2022. Notably, forced migrants in the VenReps Kids survey migrated during the crisis but have since remained in the country for several years, potentially leading to disparities in household integration outcomes. IV GENERAL DESCRIPTIVE STATISTICS IV. A Key characteristics of adults Table 2 provides descriptive statistics for the adults in our study, encompassing the pri- mary caregiver, mother and father (if residing with the child), and the individual finan- cially responsible for the child (should they be different from the aforementioned per- sons). Typically, the roles of primary caregiver and financial provider are fulfilled by either the mother or the father. The table is organized into three panels for clarity: Panel A details key individual characteristics, Panel B outlines adults ’ access to services, and Panel C focuses on labor market characteristics. Within the table, columns (1) and (2) present average values for adults from Colombia and Venezuela, respectively, while the final column displays the results of mean difference tests between these two groups, with standard errors noted in brackets. Usage context: background mention, primary mention, supporting mention.

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Input: In aggregate, we expect to observe a negative relationship between the percentage of the population that reports displacement, but at the individual level we expect that experience with hosting may, in certain circumstances, be positively associated with perceptions of social cohesion, regarding perceptions of relationships and solidarity. 4 Displacement and Social Cohesion: Survey Evidence To empirically evaluate the relationships between hosting displaced populations and social cohe- sion, this paper analyzes a series of surveys of civilian adults conducted in eastern DRC. 8 Each survey uses a multi-stage cluster sampling strategy capturing all territoires9 in North Kivu, South Kivu and Ituri provinces. The ﬁnal sampling units are randomly selected adults above the age of 18 to avoid bias toward men and / or heads of households. Multiple attempts are made over the course of one day to contact selected respondents and if necessary, appointments are made for in- terview. Surveys are enumerated by Congolese college students or professionals and interviews are conducted by members of the same gender and ethnicity as respondents to minimize enumerator- induced response bias. Further methodological details have been published (Vinck, Pham, Bindu, Bedford & Nilles 2019) elsewhere and additional details and sample size calculation are detailed in Appendix.

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Input: Following the approach used for the Global Costing of Refugee Inclusion, successful inclusion is defined as earning sufficient income to be no longer poor and to consume more than the (international) poverty line. This definition opens two tracks for investigation: first, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this question is found by identifying the poverty gap for refugees. This opens the second track which explores the factors that determine, or at least that are associated with, the size of the poverty gap. The note is organized as follows. The next section discusses data and presents some key descriptive statistics on refugees and host communities in Uganda. This is followed by a methodological section discussing how own income and aid are complements and how an analysis of poverty gaps informs about the need for assistance. This is followed by two analytical sections. The first identifies refugee poverty gaps, and assistance needs for refugees with distinct characteristics. The following section estimates how much has been saved by including refugees in the economy and explores how more could be saved. Conclusions follow. 2 The poverty numbers in World Bank (2019) are based on the official poverty line adopted in Uganda in 1997. There was a need to update this line as it was too old and producing a very low poverty rate. For example, using this line produced a national poverty rate of about 21 percent in 2019 / 20 compared to more the than 40 percent international poverty rate using the USD 2. 15 2017 PPP daily poverty line. In order to address this criticism, the poverty line was revised by the Uganda Bureau of Statistics in 2021, but it is not available for the 2018 Refugee and Host Communities Household Survey used in this note. Instead, we are using the international poverty line throughout. Usage context: primary mention, supporting mention, background mention.

2018 Refugee and Host Communities Household Survey

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Input: The comparison between ∆ h is for actual and hypothetical destinations tells us whether the actual district of destination is more densely populated than alternative destinations. Results are presented in Table 3 for all variables used in the analysis. We begin with district log income eδs. We have two estimates of eδs, one obtained using reported income data, and the other based on reported consumption data. Given that most respondents to the NLSS survey are self-employed, measurement error is typically larger for income than for consumption. We see that our estimates of log income and consumption eδs are on average 20 % and 8 % higher in 20 Usage context: primary mention, background mention, supporting mention.

reported consumption data
reported income data

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Input: Addis Ababa All Hosts Increased economic opportunity Increased insecurity Are taking our land Figure 7.6: Host beliefs about refugee impact in Ethiopia Source: World Bank Staff based on SESRE 2023. 50 The numbers are combined here, but mainly measures employment competition since very few are concerned about wage competition. 0 5 10 15 20 25 30 35 40 Eritrean Somali South Sudanese Addis Ababa All Hosts Improved Infrastructure Improved Services Figure 7.8: Positive experience due to refugees Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 67 Overall, hosts and refugees show similar trust rates in each other; still, refugees are generally more trusting. Questions about the trustworthiness of hosts and refugees reveal that most people either trust both (hosts and refugees) or neither group. Only 17 percent of hosts trust other Ethiopians but not refugees, and only 10 percent of refugees trust other refugees but not Ethiopians. In comparison, 39 percent of hosts and 55 percent of refugees trust both groups. Once again, hosts’ trust towards refugees is highest in the Somali domain (67 percent) and lowest in the South Sudanese domain (29 percent). On the other hand, refugee trust towards hosts is lowest in the Eritrean

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Input: For the remaining one-to-many relations, we keep these ethnicities in the Afrobarometer as such and consider them as a single ethnic group. Some manual treatment can even further improve 4

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

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Input: 13 genocide in 1994, with estimates that approximately 300, 000 to 400, 000 women suffered rape; Somalia in the early 1990s; the conflict in Kashmir; the 15-year-long civil war in Peru; and the recent civil war in Sudan (See, for example, McGinn 2000; El Jack 2003; Human Rights Watch 1995, 1996; McGinn 2000; Swiss and Giller 1993). A global review of 50 countries found significant increases in gender-based violence following major wars (World Bank 2011). Estimates of sexual and gender-based violence can suffer in both wartime and peacetime from serious underreporting (i. e., because people are unwilling or afraid to report gender-based violence, especially when the perpetrator is a family member) or overreporting, when incidence statistics are inflated because reporting improves with time (Nordås and Cohen 2011). This situation also occurs in peacetime, making it very difficult to accurately assess the increases in sexual and gender-based violence that are associated with conflict. Recent evidence highlights variations in the prevalence of sexual and gender-based violence in war situations and relates this variation to combatant norms and group cohesion. This evidence shows that Bosnia and Rwanda are anomalous cases of wartime rape being used as a war weapon for ethnic cleansing. Usage context: primary mention, supporting mention, background mention.

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Input: The cross-dimensional poverty cut-off is defined as k = 50 %, with those deprived in half or more of the weighted indicators identified as multidimensionally poor. 4. Data Data on forcibly displaced populations are scarce, with many household surveys excluding refugees and IDPs from the sample framework. To ensure that MPI results are representative of these communities and that they can be disaggregated for comparative analysis, an initial review of possible data sets was conducted. Feasibility was determined based on the availability of sufficient sample sizes for forcibly displaced persons for quantitative analyses, as well as inclusion of many of the indicators (on health, education, living standards, etc.) 14 A household is deprived if the respondent reports feeling moderately or very unsafe when alone at home, walking alone after dark, or walking around during the day. In Sudan, the indicator on the ‘ feeling safe from crime and violence when at home ’ was not available, and the indicator only considers answers to the questions on safety when walking alone. 15 Unprotected dug well, unprotected spring, carts with tank, tanker-truck, surface water, or other are considered as unsafe water sources according to international guidelines. See https: / / washdata. org / monitoring / drinking-water. 16 Pit latrine without slab, bucket, hanging toilet, and no facility (open defecation) are considered as unimproved sanitation facilities according to international guidelines. See https: / / washdata. org / monitoring / sanitation. 17 According to the ILO definition, those who did not participate in employment in the last four weeks (and have no work to return to) are actively looking for work and are available to start, or those currently waiting to start work are classed as unemployed. See https: / / www. ilo. org / ilostat-files / Documents / description_UR_EN. pdf.

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