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.

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Input: 2 1. INTRODUCTION Refugees pose a massive moral, political and economic challenge for potential host countries. 1 The scale of the challenge is larger than ever, with 60 million people forcibly displaced by conflicts across the world (UNHCR, 2014). War in Syria has produced more refugees than any other conflict of the past two decades: around 4. 6 million have fled the country, with an additional 7. 6 million internally displaced. 2 About 2. 5 million Syrians have found refuge in Turkey, making it the largest refugee-hosting country worldwide. This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turkish Labour Force Survey to assess the impact on Turkish employment and wages. The large majority (85 percent) of Syrians have left the refugee camps and entered the Turkish labor market. 3 They are overwhelmingly employed informally, since they were not issued work permits. This makes their arrival a well-defined supply shock to informal labor, and a particularly good context in which to test the predictions of basic economic theory. We instrument for refugee flows using travel distance between 13 origin governorates in Syria and 26 Turkish subregions (338 origin-destination pairs). This allows us to also control for distance from the Syrian border, and thus any confounding factors that are correlated with proximity to Syria. Usage context: background mention, supporting mention, primary mention.

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Input:
Page | 7

Priority 4 of the CBP and
Outreach strategy aims to
contribute to prevention of
violence through Community
safety action plans to reduce
violence in the community
community makes contributions to pay the salary of the Imam and may pool together funds to provide
support to persons living in poverty, vulnerable persons (i.e. widows, orphans, PWSN), and persons
seeking financial assistance for medical treatment or to pursue educational opportunities.
Positive Coping Mechanisms [Questions about how people cope with stress and what activities in the community help persons
overcome traumatic events].
For stress relief, males referenced different coping mechanisms such as: discussing their issues with
elders and/or religious scholars, engaging in prayer to seek help and guidance from their faith, and
gathering with their friends or relatives to discuss issues (peer counseling). Those who can afford it,
engage in sports, go to recreational spaces or restaurants with
friends, or gather in common places (Hujras) to play cards or
other games. Negative coping mechanisms reported by males
include domestic violence against women and children, or
“unusual religious activities” such as going beyond the required
number of prayers and excessive citation of the Qur’an.
Due to movement restrictions, females do not have the same
availability of stress relief opportunities outside the home. As a
result, females reported that to cope with stress they may isolate themselves in the home and cry, seek
medication, and talk to relatives to find support and solutions (peer counseling). Females also mentioned
that due to stress, a negative coping mechanism is emotional and physical abuse of children. The findings
reinforce previous PA findings and lend strong support for Community safety plans as a means to
empower the community to address the negative coping mechanisms.
Voluntary Repatriation [Questions about the decision making process of return to Afghanistan, how persons receive information on
return to Afghanistan and how return to Afghanistan has affected community dynamics].
The majority of community members report that they have relatives, friends or know of community
members that have returned to Afghanistan, for the following reasons3:

Police harassment in Pakistan

Uncertainty of POR card extension in Pakistan

Poverty and lack of employment opportunities in Pakistan

Strict border crossing control between Pakistan and Afghanistan

Desire to join relatives and/or communities back in Afghanistan
Community members report they receive information on repatriation from4:

UNHCR/partner helplines

Print and electronic media

Contact with friends and relatives back in Afghanistan via phone and internet

Information from persons that have recently traveled to Pakistan from Afghanistan
As a result of return, the community reports a decrease in livelihoods5 opportunities and increased
exposure to security issues as community numbers reduce and government authorities actively support
returns. Furthermore, loneliness and sadness due to friends and family departing Pakistan is reported as a
negative impact as well as decreased educational opportunities for children due to teachers returning.

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Input: According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006. 1 A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large. 2 Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010). 3 In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese 1 On the quality of public services indicator, Lebanon ’ s score declined from 2. 5 in 2006 to 0. 8 in 2012 on a 4-point scale. On the territorial coverage indicator, its score went down from 2. 7 in 2006 to 1. 5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men.

Middle East and North Africa Survey Project

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Input: and purchase goods at better prices. In Ethiopia, refugees’ locations differ vastly in terms of proximity to resource and economic hubs. About 37 percent of refugees live within 10 kilometers of the nearest Woreda city and another 43 percent live within 10 to 20 kilometers. Zone capital cities are farther away, but almost half (45 percent) of in-camp refugees live within 20 kilometers of the nearest Zone capital city (Figure 6.1a). Borders seem farther, with 18 percent of refugees living within 30 46 Labor Force and Migration Survey 2021. Markets and Opportunities 58 kilometers of the nearest border. When defining mutually exclusive location categories to measure proximity to resource hubs, we see that 44 percent of refugees live closest to the nearest Zone capital city. Another 28 percent live closest to a Woreda City, which is not a Zone capital city. About 11 percent live close to a border but not the Zone capital or Woreda city, and 17 percent of in-camp refugees live in remote areas far from a Zone capital city, Woreda city, or a border. When looking at accessibility, as defined by a market accessibility index, more than one-third of the refugees are located in areas with

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Input:
Specifically, the monthly mVAM began in August 2015 and has been conducted in nearly every month since. [7] The 10-minute survey is conducted by mobile phone and reaches approxi mately 2400 households every month through random digit dialing. The survey is stratified by

Every month, the survey collects information necessary to construct the Food Consumption Score (FCS) [10], information on food coping strategies necessary to construct the Reduced Coping Strategy Index (rCSI) [11], information on whether the household received food assistance in

publicly available. However, the WFP publicly shares each month the governorate-level averages and confidence intervals of all key variables collected [13], and these monthly governorate-level  estimates are used in the empirical analysis to analyze changes in food assistance and food access following the 2017 IPC announcement. [14] We further merge the mVAM data with data

Food Consumption Score (FCS)
Reduced Coping Strategy Index (rCSI)

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Input: Disaggregated data on IDPs who are not protected or assisted by UNHCR are collected by other agencies, including IOM, but data are not comprehensive and therefore not published in IDMC ’ s global reports. 93 The difficulties of collecting disaggregated data on locations of IDPs are compounded by the fluidity of IDP movements — IDPs might suffer multiple displacements or they might resort to changing locations as a coping strategy (e. g. moving between their homes and place of displacement or testing different locations before deciding where to stay) (Brookings 2011). In recent years, efforts have been made to improve data collection for IDPs living outside of camps by employing a range of techniques including: (a) profiling; (b) household surveys; (c) collecting information on IDPs who come to camps to visit family members or collect relief items; and (d) community outreach programs (Brookings 2013). Usage context: supporting mention, background mention, primary mention.

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Input: As explained in section 4, the HRVD dataset contains data on the number of human rights violations occurred since the start of the conflict in 1975 until its end in 1999 for each district. The types of violations recorded are killings, deaths due to deprivation and disappearances. We use only the number of killings to identify years and districts affected by the conflict. We exclude the deaths due to deprivation because the districts in which this occurred may very likely not be those where the conflict was most intense, but were simply places were the victims were hiding as a consequence of escaping from the troops, and died for starvation. In addition, since killings are less likely to affect entire families than deaths due to deprivation, there is a lower underreporting bias attached to the former measure relative to the latter one (Silva and Ball 2006). We also exclude disappearances as, according to HRVD data, they do not show enough time and geographical variation in order to identify individuals more or less exposed to the conflict. We believe that the number of killings proxies quite well the intensity of the conflict across time and space as their occurrence largely tracked the movements of the Indonesian military operations. The other two types of violations do not seem to show the same pattern (Silva and Ball 2006). For the same reason, we believe that it proxies quite well the destruction of houses and infrastructure and the displacement of people given the way in which the last wave of violence occurred (i. e. the scorch-earth technique employed by Indonesian troops as they moved towards West Timor). Usage context: background mention, supporting mention, primary mention.

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Input: We complete this data with exact geographic location data from MineAtlas (2013), where satellite imagery shows the actual mine boundaries, which allows us to identify and update the center point of each mine. The production data and ownership information are double-checked against the companies ’ annual reports. For Ghana, this exercise results in 17 industrial mines tracked over time. We have annual production levels from 1990 until 2012. As mentioned, Table 1 shows the mining companies active in Ghana during recent decades, with opening and closing years (although some were closed in between, and are not presented in the table). Figure 2 shows the geographic distribution of these mines. Figure 2 Gold mines and DHS clusters in Ghana Panel A Gold mines and 20 km buffer zones Panel B Gold mines, DHS clusters, and 100 km buffer zones 4 The distances are radii from mine center point, and form concentric circles around the mine. 5 The DHS and the GLSS data are representative at the regional level, and not at the district level. Since the regional level is too aggregated, we do the analysis at the district level, but note that the sample may not be representative. Usage context: primary mention, background mention, supporting mention.

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Input: 5. Time Use and Child Labor: The fifth module examined how children spend their leisure time, their involvement in child labor, and interactions with peers. 6. Pro-social Preferences and Migration Outlook: The sixth module concentrated on adolescents ’ pro-social behaviors, such as altruism and trust, and explored their expectations and intentions regarding migration. 7. Socio-emotional and Mental Health: The final module involved the administration of various scales to assess socio-emotional well-being and mental health, includ- ing trauma, behavioral problems, anxiety, and depression. The scales include the Trauma Symptom Checklist for Young Children (TSCYC), Strengths and Difficulties Questionnaire (SDQ), General Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). All these scales and the corresponding outcomes that we evaluated are described in the next subsection. The survey also employed the Peabody vocabulary test to evaluate the cognitive devel- opment of all participating children and adolescents. A summary of the survey modules is depicted in Table A. 1. III. C Sample comparability While Medell ´ ın ranks as the third city with the highest migration in Colombia, it is crucial to recognize the degree to which migrants arriving in the city differ from those migrating to other regions in Colombia.

Trauma Symptom Checklist for Young Children

Strengths and Difficulties Questionnaire

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Input: (see Annex E for detailed information). Food aid information received from WFP includes five food items and their quantities distributed to refugees: cereal (mainly wheat but in some camps rice), pulses (mostly yellow split peas), CSB+, vegetable oil, and salt. We have computed the per person, per month in-kind aid quantities into annual values using the same prices as other food items based on SESRE data, mapping them to the closest food item in SESRE (this was not straightforward as the items are different). We further considered the changes in quantities of food rations that took place across survey months due to funding shortages, which can significantly affect the overall wellbeing of refugees in Ethiopia. Based on this information, we compare items refugees should have received with what refugees reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food aid admin data for every item except biscuits. Refugee households still report lower quantities, even when valuing the food ration quantities indicated as sold in markets. Possible explanations for lower food quantities are that food rations are only received once a month, which may not coincide with the interview date. Moreover, SESRE asks what food people

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Input: (0.0814) Distance to Zone city (Km) -0.0030* (0.0013) Market accessibility indicator 0.4056** (0.1381) Observations 2024 2024 2024 2024 2205 2024 Chi-square test 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 R2 0.1373 0.1373 0.1373 0.1373 0.1172 0.1373 Source: World Bank Staff based on SESRE 2023. Note: Average marginal effects are estimated. a refers whether a household ran out of food in the last 12 months. b refers if most of the land is covered in the community by agriculture activities i.e., less built-up and shops. c refers recent (5 years) internal migrants from rural to urban centers. Distance to the nearest bank variable is excluded from proximity model II, as it is captured by effect of distance to the nearest zone city. The left side for years in exile, Head education level, HH size, most land cover, and proximity level is less than 15 years, Head with no education, non-working age members, most land cover by built-up and shops, and nearest to Zone capital city, respectively. All estimates are controlled for refugee camps. Standard errors in parentheses. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001 Variables Basic Model Local Market Proximity Market access Model I Model II Model I Model II Annexes 119

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Input: sectoral distribution of employment, the poorest refugees—in- and out-of- camp—tend to be employed in the service sector. While employment in the industry sector is low for refugees, the poorest are less likely to be employed in the industry sector than the richest. The poorest hosts of in-camp refugees are more likely to be employed in agriculture, and the richest appear to be employed in the industry or service sectors (Figure 5.18b). Not surprisingly, the poorest hosts of in-camp refugees and the poorest refugees work in low (or medium)-skilled occupations, while the richest are employed in high-skill occupations (Figure 5.18c). - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 No education Primary incomplete Primary complete Secondary incomplete Secondary complete Post- secondary No education Primary incomplete Primary complete Secondary incomplete Secondary complete Post- secondary In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts All Refugees All Hosts In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts All Refugees All Hosts - 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Predicted total expenditure Predicted poverty rate Figure 5.16: Poverty incidence decreases with education of the household head Source: World Bank Staff based on SESRE

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Input: This paper combines newly available data on the distribution of Syrian refugees across Turkey and the Turkish Labour Force Survey to assess their labor market impact. Syrian refugees are overwhelmingly employed informally, since they were not issued work permits, making their arrival a well-defined supply shock to informal labor. Consistent with economic theory our instrumental variable estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupational upgrading, there are increases in formal employment for the Turkish- though only for men without completed high school education. Women and the high-skilled are not in a good position to take advantage of lower cost informal labor. The low educated and women experience net displacement from the labor market and, together with those in the informal sector, declining earning opportunities.

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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:
### INTRODUCTION

Within UNHCR public health covers various areas including primary health
care, nutrition and food security, reproductive health and HIV, mental
health and integrated refugee Health Information Systems (iRHIS). Sectoral
programming in a comprehensive response context means applying a wholeof-government (i.e. relevant national and local authorities for health and
nutrition response) multi-stakeholder approach and planning with relevant
partners. The overall responsibility of coordinating the health sector
response in refugee-only situations will be with the Ministry of Health, with
support of UNHCR and relevant partners. A wide range of partners play
a role in planning and delivering public health interventions in different
areas and at different stages of the refugee response. For an effective and
comprehensive response it is therefore essential to know how and when to
engage these various partners. Though the establishment of refugee-specific
services may be needed in the early phases of a refugee situation, longer
term solutions are required to ensure that refugees have access to services
through the national health system. Host countries may require assistance
from other partners, including international organizations but also local
partners, to make the necessary adjustments to comprehensively include
refugee health needs into national development and local health plans,
to strengthen/reinforce national and local resilience of national and local
health systems to meet the health needs of refugees and host communities.

integrated refugee Health Information Systems

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Input:
The rapid humanitarian assessments of newly conflict-induced displaced populations often detect
children amongst those injured by the armed clashes. Aside from material hardships, the psychological
impact of the conflict and subsequent flight is deemed to be severe. Recruitment and use of children
by armed forces and armed groups remains a significant risk in light of the fragmentation of NSAG and
varying degrees of interest in compliance with IHL. Active conflict led to 11,418 civilian casualties [5 ] in
2016 - approximately 11% were women and 31% were children. In 2016, the Country Taskforce on
Monitoring and Reporting (CTFMR) verified 57 incidents of recruitment and use of children in the
conflict (89 boys) who were recruited and mainly used for planting IEDs, transporting explosives,
carrying out suicide attacks and spying. Forced recruitment is primary reason given by Afghan asylum
seekers in Sweden and Norway. Poverty, coercion and lack of livelihood opportunities, including
during the more prolonged phases of displacement, is also a factor that contributes to the recruitment
of children, particularly adolescents. Access to education in displacement is generally hindered by
several factors: Poverty and destitution, with a loss of assets and means of livelihood, often forces
displaced families to engage children in support of family resilience and interim livelihood strategies.
Lack of civil documentation, cultural and social norms, threats and intimidation, social status and
poverty are significant obstacles. In 2016, 423 schools were intermittently closed due to conflict and

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Input: only 12 percent relied on crops or livestock. This contrasts with the Eritreans in camps, who previously relied primarily on agricultural and labor income, and demonstrates the large differences between Eritrean households that could and could not acquire OCP status. This, coupled with the finding that OCP refugees have much higher levels of education compared to in- camp Eritrean refugees (as highlighted in Chapter 2), again indicates that OCP refugees were relatively well-off before displacement, and they still have family members or other support systems. Refugees across all regions of Ethiopia have benefited from livelihood training interventions provided by RRS, domestic and international NGOs, and humanitarian organizations. For example, the Ikea Foundation, through UNHCR, invested around US$100 million in the Dollo Ado camps in Somalia between 2012 and 2019. Much of this funding supported economic development and livelihood opportunities for refugees and the host community, including creating livelihood cooperatives in agriculture, livestock value chain, energy, firewood, and microfinance (Betts et al., 2020). In the north of the country, UNHCR worked with various partners to provide Eritrean refugees with vocational skills training, tools, and start-up capital for crafts, such as leather products, weaving, and tailoring. UNHCR records indicate that more than

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Input: related to education, gender, and age. The results in Figure 3.26 show that refugees are under-represented in high-skill occupations and services and sales relative to what we would expect based on their age, gender, and education, and substantially over-represented in crafts and related trades. Among employee workers, OCP refugees also earn less than hosts, though the wage gap is smaller than for in-camp refugees. Annex D, Table D.9 shows that refugees in Addis earn 25 percent less than their hosts. Even after adjusting for demographic characteristics, occupation, and sector of work, this wage gap remains at around 19 percent. 0 20 40 60 80 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female - Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure 3.22: Work status by gender Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure 3.23: Work type by gender Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Addis Hosts Male

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Input: area probability sample. Specifically, we first stratify the sample according to the main sub-national unit of government (state, province, region, etc.) and by urban or rural location. Area stratifi- cation reduces the likelihood that distinctive ethnic or language groups are left out of the sample. Afrobarometer occasionally purposely oversamples certain populations that are politically significant within a country to ensure that the size of the sub-sample is large enough to be analyzed. ” Afrobarometer provides geocoded data for 6 rounds, which correspond to the 1991 – 2016 period, with the information on an individual ’ s ethnicity available from round 3 (corresponding to 2005 – 2006). We therefore restrict our analysis to the 2005 – 2016 period. The selection of countries is driven by data availability. Among the 33 countries with available Afrobarometer data, we exclude Botswana, Cape Verde, Lesotho, Madagascar, Mauritius, Sao Tome and Principe, South Africa, and Swaziland, for which no data is available on refugee camps or from the EPR-ER. We also exclude Sudan since the question on individual ethnicity is not asked in this country ’ s survey.

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Input: to documentation: Enhance digital infrastructure in refugee hosting areas to facilitate refugee inclusion to the digital economy including digitally enabled livelihood opportunities and financial inclusion as well as to foster their access to socio- economic e-services, including standardized travel documents. Source: Ethiopia GRF Pledge Progress Report (RRS & UNHCR, 2021 and 2023) Pledges Progress as of 2023 Annexes 92 S ESRE is a separate but integrated survey alongside the Ethiopian Household Welfare Statistics Survey (HoWStat),54 the national household survey to measure poverty and other socio-economic outcomes. Like most national poverty surveys, HoWStat excludes displaced populations—Internally Displaced People (IDPs) or refugees—including in Ethiopia. To have up-to-date information on the socio-economic outcomes and poverty levels of refugees and to allow comparison to Ethiopian host communities, the SESRE applied the same questionnaire and data collection methods as the HoWStat, with some modifications. Training of the enumerator team and implementation arrangements of the survey followed the same standards and procedures as the HoWStat. SESRE data was not collected alongside HoWStat due to security concerns at the time of data collection for HoWStat, especially in the refugee areas. The SESRE aimed to solve two problems: (i) gaps in data on the socioeconomic dimensions of refugees,

Ethiopian Household Welfare Statistics Survey

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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: Aldo Morri for his excellent editorial support. Finally, we extend our deepest gratitude to the survey respondents for their willingness to share their experiences which has been instrumental in deepening our understanding of the challenges and needs faced by both refugees and host households. ACKNOWLEDGEMENTS ii Introduction E thiopia, with its long history of hosting refugees, is grappling with the complex challenges of accommodating close to 1 million refugees and asylum seekers. These come primarily from neighboring countries like South Sudan, Somalia, Eritrea, and Sudan housed in camps in mostly rural areas spread around the country near border areas. While Ethiopia has adopted progressive refugee policies, including the Comprehensive Refugee Response Framework (CRRF), challenges persist in translating these policies into tangible socioeconomic outcomes for refugees. Despite Ethiopia’s efforts to shift from a camp-based approach to a more inclusive model promoting self-reliance and integration, refugees live largely in camps, are reliant on humanitarian aid, and face barriers to accessing employment and education. The country’s new Refugee Proclamation grants refugees the right to basic services, work, and freedom of movement, but implementation delays hinder their realization. To address these challenges and achieve better development outcomes for both refugees and host communities, a

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Input: The LFS's rotating panel design introduces attrition that is potentially non-random: households that relocate between LFS interview waves are excluded from panel analyses, and mobile households may differ systematically from stayers in their labor market outcomes. We test for selective attrition by comparing baseline characteristics of households observed in all four LFS waves with those lost to follow-up due to residential relocation. Attritors are somewhat younger and have lower educational attainment on average, but observable baseline differences are small in magnitude.

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Input: Furthermore, in some contexts, registration as an IDP can expire after a prescribed timeframe without regard to whether the person achieved a durable solution (e. g. after five years in Russia). The accuracy of IDP registers is greatly impacted by political considerations, particularly a government ’ s willingness to acknowledge internal displacement and to enable the humanitarian community to respond. Some countries may be reluctant to acknowledge the presence of IDPs, or may be inclined to understate numbers to demonstrate progress in military operations or limit assistance provided to IDPs. For example, in Kenya, registration of individuals displaced by the 2007 and 2008 post-election violence excluded ‘ integrated ’ IDPs, i. e. those who had sought refuge with host communities or rented accommodation in urban areas, as assistance was limited to registered IDPs. Alternatively, aggregate numbers of IDPs in particular countries may be inflated to suggest a deterioration of the situation or to maximize humanitarian assistance. Therefore, access to IDP areas and the willingness of IDPs to be counted may be largely dependent on government policies. These political considerations can lead to disagreements on the data, undermine cooperation and in some cases even lead to reduced humanitarian funding. Profiling of IDP situations Profiling of IDP situations is a collaborative process aimed at generating reliable data that can be broadly agreed upon. As a collaborative process, it can be a crucial tool for generating agreement on persistent questions such as who is recognized as internally displaced within a given context, what are the most prevalent vulnerabilities caused by displacement, and how do IDPs fare compared to host populations. In sought refuge with host communities or rented accommodation in urban areas, as assistance was limited to registered IDPs. 68 However, in many conflict-affected countries, governments lack the basic capacity to maintain Civil Registration and Vital Statistics (CRVS) systems including the registration of births and deaths in non-displacement situations, let alone the registration of IDPs displaced due to natural disasters or conflict. 69 IOM has introduced biometric registration systems in South Sudan, Sudan, DRC and Nigeria to circumvent these problems.

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Input: While there is a large literature on the role of immigrants on the native-born population in terms of labor market competition, there is a limited amount of studies that examine the effect from displaced populations. Many conclusions from the traditional literature on the study of immigrants ’ impact on natives cannot be applied to the case of Syrians in Turkey. There are many differences between the inflow of Syrians and other flows of extended family and economic immigrants. First, the sheer volume of Syrian refugees and the short time- frame in which they entered Turkey is unprecedented. For the case of Syrians in Turkey, or displaced populations in general, large movements of refugees are not restricted due to humanitarian reasons. Second, formal immigration processes are controlled, limited, and regulated by destination countries. Therefore, results from literature on “ immigrants ” are very different than a focus on displaced or refugee populations. Recent literature on the labor market effects of SUTPs estimates negative impacts on host community employment rates. The negative displacement results are largest for the young, women, informal workers, 2 United Nations High Commissioner for Refugees (UNHCR) – Syrian Regional Refugee Response, Inter-agency Information Sharing Portal 3 (UNHCR) – Syrian Regional Refugee Response, Inter-agency Information Sharing Portal Usage context: primary mention, supporting mention, background mention.

Inter-agency Information Sharing Portal 3

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Input:
**Annex 2**

**FGDs Questionnaire**

FGDs Questions :

1. Torture, Arbitrary detention, and Forced Disappearance, are some of the protection risks faced by youth
in NWS, from your point of view What solutions or actions would help mitigate/overcome these
protection risks?
2. The survey results conducted earlier in NWS indicated that 66% of youth are facing discrimination, if you
agree with that statement, can you please clarify and elaborate the factors contributing to
discrimination, exclusion, and stigmatization among youth in the region?
3. What challenges do young people encounter when trying to acquire official documents?
4. Which are the top 5 risks you believe young males face? and which are the tops 5 protection risks young
females face?
5. How can organizations engage young people in NWS in the rebuilding of their communities?
6. 54% of the surveyed youth in NWS who themselves or their family have properties and 83% of them
have property documents, could you explain more by whom it is occupied?
7. What are the main protection risks related to your/your family's properties?

Demographics:

Female Participants: Total of 50 across all FGDs, 25 in Aleppo/ A'zaz and 25 in Idleb/Idleb.

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Input: tegrated in Tajik labor markets relative to their previous situation in Afghanistan. This is a typical characteristic of forcibly displaced populations as it naturally take time for them to recover their livelihood. Moreover, females have lower labor market participa- tion rates, hours worked, and wages relative to men. We also see that student enrollment for individuals of schooling age is dramatically lower in Tajikistan relative to Afghanistan. Finally, not surprisingly, household size for refugees is smaller in Tajikistan, which likely originates from family separation due to forced displacement from Afghanistan. Outcome variables. Using the census data, we construct five index variables for each of the outcomes of interest: refugee integration, educational attainment, mental health, income and consumption, and labor market outcomes. Indexes for dichotomous vari- ables were constructed as the average of all outcomes. Indexes that include continuous variables were constructed standardizing each variable, averaging all variables, and stan- dardizing the average once again. The methodology to construct indexes was adopted from Kling, Liebman and Katz (2007). Appendix B. A describes in detail all the variables included in each of the indexes. In sum, each of the indexes comprises the following: Integration index: Includes four variables that measure the feelings of belonging of refugees in Tajikistan. These are measured using a Likert scale. Usage context: supporting mention, background mention, primary mention.

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

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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: groupement level data cannot measure the character of the displacement in any meaningful way, but analysis at the individual level can. 5. 2 Individual Level Relationships Two survey waves posed additional questions on local displacement dynamics. While these ad- ditional questions restrict comparison with other survey waves, they provide the opportunity to unpack the mixed results found in the aggregate analysis. Poll 14 is a special survey that only sam- ples cities (Ville de Goma, Ville de Beni, Ville de Butembo, Ville de Bukavu, Ville d ’ Uvira, Ville de Bunia and Irumu in particular) while Poll 15 is a representative sample of all territoires in the three provinces. 11 These survey waves are labeled as “ Cities ” and “ General ” samples in the indi- vidual analysis. Analyzing these two surveys together enables the comparison of relationships by the local context, which may distort the impact that hosting has on perceptions of social cohesion. Figure 5 plots the coefﬁcients from series of logistic regressions to account for the binary na- ture of the dependent variables. The regressions include Province ﬁxed effects and groupement clustered standard errors to account for unmeasured context-speciﬁc dynamics. Responses are weighted by the inverse proportion of selection at the territoire in each regression.

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Input: The resulting migration matrices should be viewed as work in progress, but they are an important step in an ongoing global effort to improve migration data. The matrices can be readily updated as additional or superior information surfaces, and they can easily be extended to include future census rounds. Bilateral datasets of international migration are rare. Attempts to create them have focused almost exclusively on industrialized countries as destinations because these countries have more accurate and more frequently produced data. Harrison and others (2003) calculate bilateral remittances for the countries of the Organisation for Economic Co-operation and Development (OECD) together with the 27 largest nonmembers. These estimates are based on international bilateral migrant stock data that the authors also provide, although many of the data are derived from the Trends in International Migration (OECD 2002). This report, published annually since 1973, was arguably the most comprehensive guide to international migration for many years and has been the basis for many studies (see, for example, Mayda 2007). More recently, the OECD has developed a database that provides a comprehensive overview of migration to OECD countries in 2000 (OECD 2008). These data are disaggregated by a number of covariates including age, gender, educational attainment, and place of birth. Usage context: primary mention, supporting mention, background mention.

international bilateral migrant stock data

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Input: We, therefore, first examine whether or not there is structure, both, to selection into the treatment group and attrition, which could undermine our econometric approach, where we rely on difference-in-difference estimators. Imbalance between treatment and control groups could undermine the key assumption of parallel trends. For example, given that men and women face different barriers in the labor market, we should not expect employment to evolve in the same way for men and women after the treatment. We would expect to observe a difference-in-differences for a treatment group where women are more common than in the reference group, even without the program. To test for imbalances, we run a simple regression of treatment and attrition indicators at baseline on the socio-economic and demographic controls, GRIT indicators, self-reported optimism, employment status and risk. Table 4 (Column 1 for the treatment analysis, Column 2 for the attrition analysis) shows some signs of structure. In particular, host status and risk preferences are significantly different between treatment and control, with 9 For example, “ hummus ” is used to refer to chickpeas in general but can also be used for the dish involving mashed chickpeas, tahini, lemon and garlic in Lebanon. In other dialects, some qualifiers are required to specify this dish (e. g. hummus ne ’ em, or smooth hummus). This is akin to identifying a British or American individual using similar variations in foodstuffs such as courgette / zucchini; coriander / cilantro; etc. 10 Specifically, we regress variables with missing observations on the list of all variables with a complete record. We then use the predicted values from this regression to populate the missing variables. Where appropriate, predicted values are rounded to the nearest integer and within answer codes of that variable. In a second round, this process is repeated on the full set of actual and predicted values from the first stage.

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Input: The MPI can be decomposed by any groups for which the data are representative and broken down by indicator to show the composition of multidimensional poverty, adding to the policy relevance of the analysis. To tackle individual-level and intrahousehold analyses, we build on the work of Alkire, Ul Haq and Alim (2019). The focus is on individual deprivations, and we call the persons with individual-level data in each indicator the eligible household members. For example, children aged 6-16 years might be eligible for deprivations in terms of school attendance, but not those older or younger. For individual-level indicators, we identify who and how many household members are deprived: their gender and their age, and what proportion of eligible household members are deprived. This is a powerful and potentially informative steppingstone for analysis. Consider two households, each of which has five eligible members with data on nutrition. The aggregation rule in this example is that if any household member is undernourished then the household is undernourished. So, both households are deprived in terms of Usage context: supporting mention, background mention, primary mention.

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Input: The descriptive characteristics of foreign-born and host community households are still comparable, and therefore allow for comparisons between 2013 and 2014. It is possible to make some inferences on welfare changes by looking at changes in host community employment rates and labor market characteristics. Overall, this paper finds no negative effects on host community welfare from an increasing population of SUTPs. As other authors have stated, the influx of SUTPs has had both positive and negative impacts. It seems on average, the host community has been strong and adaptive, and not negatively impacted. This is not to disregard that real strains do exist in some regions where the SUTP population is very large. Nor do these results undermine findings of displacement effects in the labor market that certain types of workers are experiencing. However, on average nationally, we do not see a systematic decline in the welfare of the host community between 2011 and 2013. The remainder of the paper is organized as follows. Section 2 outlines the data availability and technical issues. Section 3 discusses descriptive statistics of the foreign-born and host community. Section 4 explores the impact of the foreign-born population on host community welfare. 1. DATA AND TECHNICAL ISSUES Data Sets Data availability limits which data set can be used to identify the foreign-born population and geographic location while measuring poverty. The Turkish Statistical Institute (TUIK) has been conducting three nationally representative surveys annually since 2005; the Household Income and Consumption Expenditure Survey (HICES), the Survey on Income and Living Conditions (SILC) and the Labor Force Survey (LFS). However the HICES, which is the national survey that is used to measure official poverty, 5 UNCHR, 2013pg 13 6 UNHCR (22 November 2013), UNHCR (15 September 2014) 7 UNHCR (22 March 2013), Erdogan (2014), pg 14 8 This technical issue will be discussed further later in the paper.

Household Income and Consumption Expenditure Survey

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Input: Ethiopia in 2017. The SPS 2017 was conducted in refugee camps and host communities in four regions in Ethiopia. The survey was used to draw a profile for skills and potential opportunities for refugees and host 9 See Annex C for detailed information on survey design and methodology. 10 Financial support was provided by the World Bank and UNHCR Joint Data Center on Forced Displacement. Introduction 7 communities to design a better mix of approaches that could help the government in designing livelihood opportunities for these communities. Due to differences in scope, sampling design, and methodology, results based on the SPS cannot be directly compared with those of SESRE. Box 1.1 summarizes the similarities and differences between SPS 2017 and SESRE 2023. The Skills Profile Survey (SPS), conducted in 2017, is a household survey focused on collecting data on refugees from South Sudan, Somali, Eritrea, and Sudan living in camps in Ethiopia, as well as from host communities. The sample frame for the survey was derived from the list of all refugee camps, sites, and locations provided by UNHCR-Ethiopia as of January 2017, covering the four main regions that host refugees: Tigray, Afar, Gambella, Benishangul-Gumuz, and Somali. The SPS specifically

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

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Input: Disaggregated data on IDPs who are not protected or assisted by UNHCR are collected by other agencies, including IOM, but data are not comprehensive and therefore not published in IDMC ’ s global reports. 93 The difficulties of collecting disaggregated data on locations of IDPs are compounded by the fluidity of IDP movements — IDPs might suffer multiple displacements or they might resort to changing locations as a coping strategy (e. g. moving between their homes and place of displacement or testing different locations before deciding where to stay) (Brookings 2011). In recent years, efforts have been made to improve data collection for IDPs living outside of camps by employing a range of techniques including: (a) profiling; (b) household surveys; (c) collecting information on IDPs who come to camps to visit family members or collect relief items; and (d) community outreach programs (Brookings 2013).

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Input: impression of the state ’ s role in development and give credit to the state for helping to leverage external resources. Citizens are also likely to give the state credit where mechanisms to voice complaints about non-state actors exist and where bureaucrats are able to eﬀectively respond to complaints. Under these conditions, non-state service provision is likely to strengthen the ﬁscal contract. 5 Data and Methods I explore the relationship between external service provision and deference to government using Afrobarometer survey data from 19 Sub-Saharan African countries (see Table 1). Africa is an especially good place to examine these issues because of the large amount of variation both within and across African countries in the extent to which non-state actors, donors and other states are active in service provision and the extent to which governments are relatively eﬀective and fair. Government responsiveness, corruption and reliance on non-public resources vary considerably among localities with consequences for citizen understanding of and relationship to government (Gibson and Hoﬀman, 2005). This project relies on the fourth round of Afrobarometer data that surveys Africans ’ views towards democracy, economics, and civil society with random, stratiﬁed, nationally representative samples.

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Input: I expect citizens who perceive tax collection to be the responsibility of non-state actors to be less likely to be willing to pay taxes to the state ’ s tax department than citizens who perceive tax collection to be the responsibility of the state. Perceptions of the eﬀectiveness of donor and non-state actor provision of services are assessed using the following items. Respondents were probed on how much they believe the following non-state actors and donors do to help their country: the United Nations; international donors and NGOs; international businesses and investors; China; and the United States. 9 I also include Freedom House ’ s political liberties and civil rights ratings for the 19 countries in the sample. These two variables should capture the relative equality of inﬂuence in making policy. They indicate whether citizens are able to express their voice without fear of repression and whether elections are free and fair. Neither of these variables are signiﬁcant at the p < 0. 05 level. 14

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Input: Outcome Variable Collection Periods Basline Midline Weekly Endline Psychological Well-being PHQ9 X X Life Satisfaction Index X X Stress Index X X X Sociability (Total) X X X Sociability (Positive) X X X Self-Worth Index X X Locus of Control X X Allocation Decision Game X X Stability Index X X Physiological Well-being Index X X Gender Dynamics Gender Perceptions- Work X X Gender Perceptions- Violence (IPV) X X Financial Well-being Savings X X ∗ X Borrowing X X Economic Decision Making Risk Preference X X Time Preference X X Other Outcomes Cognitive Ability X X ∗ X Physical Health X X ∗ X Notes: The “ Baseline ” survey was conducted with respondents before treatment assignment was revealed. The “ Midline ” survey were questions asked immediately after treatment assignments were disclosed after the baseline survey, but before the work task had begun. “ Weekly ” surveys were conducted after each week of work (if any).

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Input:
**Component 4: Project Management, Monitoring, and Evaluation (US$3.8 million)**

This component will finance the operating costs of the Coordination Unit and support the rollout of the Management Information System (MIS) across all twelve participating district offices. The MIS will track beneficiary registration, service delivery milestones, and grievance redress outcomes, providing real-time dashboards for national coordinators. Quarterly progress reports generated from the MIS will form the basis of implementation support missions. The component also covers the cost of the endline evaluation, contracted to an independent firm, and capacity-building workshops for district-level M&E officers.

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Input: The Women ’ s Empowerment in Agriculture Index (WEAI) (Alkire et al. 2012) uses individual-level data, and the linked Gender Parity Index reflects inequalities across women and men ’ s deprivation scores within the same household. Alkire, Apablaza and Jung (2014) design and implement an exploratory individual-level MPI for 31 European countries over six waves of data using EU-SILC data sets, finding no cases in which are women significantly less poor than men, and in many cases, they are significantly poorer. Espinoza-Delgado and Klasen (2018) create an individual-level MPI to understand differences in poverty between women and men in Nicaragua, finding similar overall incidence, but much higher intensity of poverty among women. Bessell (2015) and Pogge and Wisor (2016) explore deeply contextual gendered poverty measures and elucidate the ways that participatory consultations can inform the design and uses of gendered measures. Rogan (2016) uses the global MPI to analyze the gender poverty gap in South Africa. Alkire, Ul Haq, and Alim (2019) use individual-level data alongside MPI data to expose gendered and intrahousehold differences among MPI poor and non-poor children. Usage context: primary mention, background mention, supporting mention.

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

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Input: 25 less cohesion. The increase in solidarity is less apparent among refugees where the majority (53 %) stated that the crisis had no effect on solidarity. Figure 18: Levels of trust, by group (June) (%) Source: Listening to Displaced People Survey, 2014. Perceptions that different groups have of others are important elements of peace. When asking for the degree to which neighbors, other villagers and people from other ethnic groups can be trusted the survey finds positive outcomes. Although all groups trust people from other ethnic groups slightly less, the general level of trust is high and it remains stable over time. Finally, consider how IDPs, refugees and returnees envision the future of Mali. The majority of refugees in Mauritania vie for an independent or autonomous North, while the majority of IDPs, returnees and refugees in Niger wish to see full government control over the North. 20 20This contradicts, in part, findings of an Afrobarometer perception survey on causes and consequences of the conflict in Mali conducted in December 2013. Usage context: supporting mention, background mention, primary mention.

Displaced People Survey
Afrobarometer perception survey

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Input: the Refugee Proclamation of 2019, while encampment undermines achieving the goals set out in the Proclamation. The GoE pledged to transform camps into settlements and facilitate mobility for refugees to take advantage of opportunities in the labor market and increase work authorization to allow for the formalization of working conditions. Humanitarian and development partners should strongly support this pledge by swiftly investing in and accelerating inclusive approaches through the engagement of line ministries. Yet, large gaps in financing remain to fill the needs of refugees and host communities. Better coordination and engaging line ministries can achieve better outcomes for refugees and their hosts. Implementing an overarching coordination mechanism across line ministries to track investments and progress on refugee inclusion could leverage the existing humanitarian resources to deliver the first mile investment into inclusive development approaches, led by development actors. Improved communication, collaboration, and connections between RRS and line ministries could support initiatives seeking to mainstream refugees into existing governance structures. Encouraging these collaborative efforts of departments and agencies of the GoE can achieve a successful implementation of development solutions. Efforts to improve the coverage, accuracy, reliability, quality, and comparability of data can provide the analytical underpinning for policy decisions. Better

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Input: 5 implemented in six areas: Bamako, the regional capitals of Gao, Timbuktu, and Kidal as well as one refugee camp in Mauritania and one in Niger. Bamako was selected because it is home to a large number of IDPs. The refugee camps were selected to obtain a sample of refugees. Returnees were identified in the regional capitals of Timbuktu, Gao and Kidal where the phone network was (still) functional. The approach to selecting respondents differed by location and depended on the availability of pre-existing population information.  Bamako: Listing information of all households with IDPs was obtained from the International Organization for Migration (IOM). Based on this data 10 districts were selected and in each district 10 households were randomly identified.  Gao, Timbuktu and Kidal: No listing data was available and the cities were divided into different sectors. The enumerator was assigned a starting point in a sector, a direction (North, South, East, West) and based on the code of the day 9 the enumerator selected the first household. If the code of the day was 4, the enumerator would choose the 5th house to conduct the first interview. No more than 6 houses were to be interviewed from one starting point.

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Input: Protection monitoring interviews conducted in the three northern border districts revealed persistent gaps in access to civil documentation, with 42 percent of respondents reporting that at least one household member lacked a valid national identity document. Barriers cited most frequently included costs of administrative fees, distance to documentation offices, and limited awareness of entitlements. Protection monitoring interviews were conducted with 1,840 households across 18 communities between January and April, with samples drawn using systematic random sampling from updated household registration lists.

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Input:
**Total Population**

**11,735 Individuals**

**(6,548 Cases)**

**Vulnerabilities***

Unaccompanied or

separated child

Woman at risk

Single parent

Child at Risk

Disability

Chronic Illness

**136**

_*One individual may have multiple specific needs_

**2,288**

2020 2021 2022 2023 2024

_*Pre-registration data refers to a headcount upon arrival or at disembarkation sites. Some individuals departed prior to registration with UNHCR._

UNHCR / 1 November 2024 3

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Input:
cereal production (in kilograms) per residential [household member reported by the] household head in the immediate post harvest [period as the measure of agricultural] production.

index. [8] Here, we construct a food variety score (FVS) to combine the diversity of a

person's diet into a single index (Hatloy, _et al.,_ 1998). The FVS is based on the number

of different food items eaten over a registration period. We evaluate two versions: 1) a

associated with food shortages into a numerical index. Our third alternative indicator is

an index of these 'coping strategies'. We asked the most knowledgeable woman within

1996 and the World Bank's forthcoming World Development Report on Poverty 2000/1, the demand for such comprehensive measures is more urgent than ever.

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Input:
each country. However, while we are aware of this limitation, the ESS has been extensively

(2012) mentions that these questions in the ESS eliminate ambiguities by referring to _people_   _who come to live in a country_, rather than to _immigrants_ . In countries where citizenship is   based on blood ancestry such as Germany, a translation of immigrants would include people   who were born in the country but are not citizens.

section survey data. The second specification incorporates age, year of birth and survey year   dummy variables as explanatory variables (blue line). According to the model without any   cohort controls, older people are less likely to exhibit positive attitudes toward immigrants than   their younger peers in almost all countries considered.

_Is the period of time long enough to capture life cycle effects?_

It could be argued that the period of time covered by the ESS - from 2002 to 2012 - may not be

long enough to capture life cycle patterns. Preferences may change over a longer period of

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

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Input: The countries in our sample are Benin, Burkina Faso, Burundi, Cameroon, Gabon, Ghana, Guinea, Ivory Coast, Kenya, Liberia, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, Tanzania, Togo, Uganda, Zambia, and Zimbabwe. As described in Table B. 1, we also incorporate information on the quality of our refugee data, which is determined by comparison with official UNHCR bilateral data. Below we describe how these data have been used to define our main variables of interest and present some descriptive statistics in Table B. 2. 11 Conflict. In Equation 1, we first relate variation in ethnic diversity with data on conflict from ACLED (Linke et al., 2010). Two main definitions are used: the incidence of conflict and the intensity of conflict. Incidence is captured by an indicator equal to one if conflict occurred in a particular year within a pre-defined buffer around cluster j. Intensity is measured by summing the number of conflict events occurring in a particular year within the same buffer area. A conflict event is defined as a single altercation wherein force is used by one or more groups for a political end (Linke et al., 2010). We further describe events (non-exclusively) as violent events, non-violent events, violence against civilians, and riots. In our main analysis, we focus on violent conflicts (Section 5. 1) and report results for other outcomes as robustness tests (Section 5. 3). In doing so, we follow a recent and large literature that has combined the ACLED dataset with geographically disaggregated data in Africa (Besley and Reynal-Querol, 2014; Berman and Couttenier, 2015; Michaelopoulos and 11Panel A of Table B. 2 shows descriptive statistics for the data from refugee-hosting areas specifically, whereas panel B of Table B. 2 shows descriptive statistics for our data in all covered areas. 11 Usage context: primary mention, supporting mention, background mention.

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Input:
for the exam in 2007, we were able to identify 71 percent three years later in ENLACE

we use administrative data from the annual school census ( _Formato_ _911_ ) to estimate the

ENLACE panel has a survival rate that is 6 and 7 percentage points lower vis-a-vis the

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matching. Take-up rate in the ENLACE test is high, 87.3 percent of the population

secondary in 2013. The difference in graduation or survival rates between the ENLACE

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Input: restrictions on their employment. Legal restrictions (i.e., not having work permits) and location often prevent refugees from working, which limits their ability to generate income and improve their economic situation. As a result, many refugees rely on food aid and have limited access to necessities such as housing and electricity. Multidimensional poverty tends to be high among refugees. Low living standards and low education primarily drive multidimensional poverty. Standard of living indicators, low-quality cooking fuel, inadequate housing and low asset ownership, contribute half to non-monetary poverty. Moreover, deprivation in education and child malnutrition also contribute most to multidimensional poverty among refugees. Refugee households tend to have worse food security than hosts. In-camp refugees have less diverse diets, suffer food insecurity, and have low consumption status compared to hosts (Figure ES.4). Broadly, there is a need to enhance the economic self-sufficiency and food security of in-camp refugees and host communities by improving their livelihood opportunities. For in-camp refugees, consumption (expenditures) tends to increase with certain characteristics. These include higher education, access to mobile phones, owing a non-farm business, possessing a bank account, and being closer to a market town or and Woreda capitals. Education (of the household head) and employment strongly

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Input: to inform decisions such as the size of schools to ensure progress toward inclusive systems that support refugees and their hosts can be made. Strengthening the use of statistics includes facilitating access to data and disseminating results. The SESRE is an excellent start to this initiative. Yet, the need to systematically integrate refugees in every round of the national household surveys and other data collection activities is key to allowing for evidence-based policy making. 77 Abdelhady D., and Al Ariss, A. (2023). How Capital Shapes Refugees’ Access to the Labour Market: The Case of Syrians in Sweden. The International Journal of Human Resource Management, 34(16), 3144-3168, DOI: 10.1080/09585192.2022.2110845 Abu-Ghaida, D., and Silva, K. (2020). Forced Displacement and Educational Outcomes: Evidence, Innovations, and Policy Indications. Second issue. Quarterly Digest on Forced Displacement. Washington, D.C.: World Bank Group, UNHCR and JDC. Adda, J., Dustmann, C., and Görlach, J. S. (2022). The Dynamics of Return Migration, Human Capital Accumulation, and Wage Assimilation. The Review of Economic Studies, 89(6), 2841-2871. Alix-Garcia, J., Walker, S., Bartlett, A., Onder, H., and Sanghi, A. (2018). Do Refugee Camps Help or Hurt Hosts? The Case of Kakuma, Kenya. Journal of Development Economics, 130, 66-83. Alkire, S., Kanagaratman, U.,

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