Input:
REGIONAL BUREAU OF SOUTHERN AFRICA

PROTECTION MONITORING DASHBOARD ON INCIDENTS IN DRC AND MOZAMBIQUE

As of 30 September 2022

MAP SHOWING THE NUMBER OF REPORTED PROTECTION INCIDENTS PER

Province in Mozambique

(MOZ) covered by
protection monitoring

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Input:
- Significant number of new IDPs (141,775 as of June 2017) due to the recurrent military clashes,
decrease of 25% compared to 2016;

- Increase number of refugee returnees as of June 2017 compared to the previous year due to
shrinking protection space in Pakistan and Iran;

- Increased trends in short term displacement due to the lack of humanitarian access, with
people often returning to their place of origin after the engagement has finished (ex. Situation
in Kunduz in 2016: 118,166 people were displaced from Kunduz in September-October, and
69,916 Kunduz IDPs that were displaced within the Northern and North-Eastern provinces
returned to their places of origin within several weeks – a month period);

- Pattern of secondary and multiple displacement in rural and urban centers (according to the
REACH study on prolonged displacement, some 23% IDPs were displaced twice or more [1] );

- Pattern of return in unsafe areas due to limited livelihood opportunities in urban centers and
the need to tend to crops [2] ;

- Pattern of local integration in urban center of protracted IDPs due to lack of security in area
of origin;

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Input: In a 2004 note to its Executive Com- mittee, UNHCR established the average at 17 years at the end of 2003 (Executive Committee of the High Commissioner ’ s Programme 2004). This number has been widely quoted by media, ac- tivists, humanitarian agencies, and development institutions (Milner 2014; United Nations 2016; UNHCR 2015). The rest of the paper is organized as follows. Section 1 gives some deﬁnitions and background information on the refugee population. In section 2, we provide some summary statistics from our main source of data, the UNHCR Population Statistics Database. Section 3 describes the method followed to construct duration statistics and presents a few stylized facts. The results of our anal- ysis are presented in section 4. Section 5 concludes. 1 Background: Deﬁnitions and Data Under the terms of the 1951 Convention Relating to the Status of Refugees – henceforth the Convention – later amended by the 1967 Protocol, a refugee is a person, who “ owing to a well-founded fear of being persecuted for reasons of race, religion, nationality, membership of a particular social group or political opinion, is outside the country of his nationality, and is unable to, or owing to such fear, is unwilling to avail himself of the protection of that country. ” Data on refugees and asylum seekers are collected by individual countries, international orga- 3 Usage context: supporting mention, background mention, primary mention.

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Input: In this sense, it is necessary to take into consideration that all indicators of prevalence of NSE and its profile will be limited to a subset of this kind of workers. 4. 1 Latin America and the Caribbean This section focuses on the Latin America and the Caribbean region, where a set of 9 countries, that we consider representing the different realities of the region in an exhaustive way, was analyzed. Specifically, the analysis was conducted for Argentina, Brazil, Bolivia, Chile, El Salvador, Mexico, Peru, Dominican Republic and Uruguay. 5O * NET is the successor of DOT (Dictionary of Occupational Titles) which is no longer updated. O * NET was launched in 1998 on the basis of the BLS Occupational Employment Statistics codes. In 2003, it was changed to SOC which implies that the consistent measures of task content are calculated from 2003. Usage context: background mention, primary mention, supporting mention.

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Input: Border crossings The registration of people crossing internationals borders is conducted in many countries, and in some cases these data are used to estimate migration flows. Identifying refugees among people crossing borders is a significant challenge, particularly if individuals decide not to apply for asylum or refugee status (UNSD 2014). Additional problems associated with the collection of data on border crossings include: (a) difficulties distinguishing migrants from other people crossing a border, such as tourists, commuters, traders and truck drivers; (b) lack of capacity of many border posts and officials to handle large migration flows; (c) less scrutiny and diligence of emigration flows compared with immigration flow; and (d) lack of tight controls at most borders and the high incidence of undocumented or irregular crossings (UNSD 2014). Administrative records and registers Many countries have administrative records or registers of immigrants that could generate statistics on asylum-seekers and refugees. In particular, data on residence permits issued to refugees or asylum- seekers could be used to generate statistics on both flows and stocks of refugees. 79 For example, Eurostat collects and disseminates data on residence permits granted to those with refugee status and subsidiary protection (UNSD 2014). Usage context: background mention, supporting mention, primary mention.

data on residence permits granted to those with refugee status and subsidiary protection

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Input: It is diﬃcult to draw causal inference from observational data. This study is no exception. The results presented here are nevertheless suﬃciently suggestive to cast doubt on the theory that the choice of migration destination is driven primarily by income diﬀerentials. Other factors seem to play a strong — and probably more important — role. References 1. Adams, Richard, Remittances, Investment, and Rural Asset Accumulation in Pakistan, 30 Usage context: supporting mention, background mention, primary mention.

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Input: 9 Bank Account No member has a bank or mobile money account. 1 / 12 Many of the indicators align with goals identified in the 2030 Agenda for Sustainable Development, such as no hunger, good health, access to quality education, clean water and sanitation, and decent work, as well as indicators that are especially relevant for displaced people, such as possession of legal identification, physical safety, and food security. The focus on gendered dynamics justifies health indicators related to pregnancy care, combining information on prenatal care, assisted delivery, and early marriage. A full discussion of the MPI ’ s indicator selection can be found in Admasu et al. (2021). We focus on six of these 15 indicators that use individual-level data – viz years of schooling, school attendance, pregnancy care, early marriage, legal identification, and unemployment. Our intrahousehold analysis drops the two health indicators due to data limitations; the question about age at marriage was only asked to the household head in Ethiopia, Nigeria, and South Sudan, whereas in Somalia and Sudan, it was applied to more members than the head.

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Input: The LFS employment module follows ILO definitions: a person is classified as employed if they performed at least one hour of paid or unpaid work during the reference week, or if they were temporarily absent from their regular job. The LFS therefore captures both formal wage employment and informal self-employment in subsistence agriculture, a distinction that is important for understanding the full scope of labor market adjustment to the economic shocks we study. The LFS does not collect wage information for self-employed individuals, which limits our ability to estimate wage effects for that subgroup.

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Input: challenge in some cases, notably among South Sudanese, refugees generally believe they are culturally similar to their hosts. This rate averages 78 percent for all refugees. It is lowest in South Sudanese camps at 68 percent and highest in Somali camps at 87 percent. Many of these refugees who say they are culturally similar to their hosts respond that they have no Ethiopian friends and that social interactions with Ethiopians are complex. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Refugees Figure 7.16: Share of refugees who agree they are “culturally similar to hosts” Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 Age Under 30 Age 30-44 Age 45-64 Age Over 64 Female Male Figure 7.18: Share or refugees engaged in a community representative body by demographic group Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 Eritrean Somali South Sudanese Addis Ababa All Refugees Figure 7.17: Share or refugees involved in a community representative body Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 71 Low refugee social integration is not

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Input: Africa. The Afro Barometer Working Papers No. 200. Woldehanna, T., Hoddinott, J., and Dercon, S. (2008). Poverty and Inequality in Ethiopia:1995/96 – 2004/05. May. World Bank (2017). Forcibly Displaced: Toward a Development Approach Supporting Refugees, the Internally Displaced, and Their Hosts. Washington, D.C: World Bank. https://doi.org/10.1596/978-1-4648-0938-5. World Bank. (2019). Informing the Refugee Policy Response in Uganda. Results from the Uganda Refugee and Host Communities 2018 Household Survey. World Bank. (2019b). Better Opportunities for All: Vietnam Poverty and Shared Prosperity Update Report. Washington DC: World Bank Group. World Bank (2020). Ethiopia Regional Poverty Report: Promoting Equitable Growth for All Regions. Washington, D.C.: World Bank Group. World Bank (2020b). Ethiopia Poverty Assessment: Harnessing Continued Growth for Accelerated Poverty Reduction. Washington, D.C.: World Bank Group. World Bank (2023). World Development Report 2023: Migrants, Refugees, and Societies. Washington, D.C: World Bank Group. https://doi:10.1596/978-1-4648-1941-4 World Bank. (2023a). Social Cohesion and Forced Displacement: A Synthesis of 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

Uganda Refugee and Host Communities 2018 Household Survey

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Input: 0.10, ** p < 0.05, *** p < 0.01 Annexes 114 The regression specification used is: where is the vector of control variables that include demographic characteristics (sex of household head, age of household head, family size, years of schooling completed by the head), assets and wealth (mobile phone ownership, livestock ownership in tropical livestock units, land ownership, bank account, non-farm business ownership, electricity access), employment (share of employed members), resource and market access (market accessibility and proximity to resource hubs), and shocks (health, market, employment, drought, political). The regression also controls for survey domain and survey time (month) fixed effects to account for the effects of location and time on welfare. Table D.13: Determinants of welfare for in-camp refugees (1) In-camp refugees Head years since refugee status (from 2022) 0.00 (0.00) Head wants to go back to own/parents 0.02 (0.03) Ration change -0.43*** (0.10) Head: has any relative in own/parents’ COB 0.05 (0.04) HH received humanitarian food aid in past 12 months -0.00 (0.04) Distance to woreda capital (log) -0.10*** (0.01) Distance to border (log) -0.03 (0.02) Medium market accessibility -0.02 (0.06) High market accessibility 0.29*** (0.05) Constant 11.10*** (0.34) Survey domain Yes Survey time Yes Observations 1,266 Source:

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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: primary mention, supporting mention, background mention.

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Input: Overall, 27 % of the transitions that occurred had an imputed month. Two things to keep in mind when considering this table. This does not show all the respondents-time periods in our sample (8, 240 observations). Since imputation will only occur when there is a transition in or out of a job, we are only including in the table those transitions and not all the periods where the individual is staying in a certain job. The total number of transitions is 726 which is less than the total number of individuals in the sample. That is because for some individuals all transitions occur prior to 2016 so they are not captured in our synthetic panel. Table 2 Transitions in and out of jobs by half, nationality, camp residency and imputation status of the month where the transition occurred Half Jordanian Syrian camp = 0 camp = 1 Usage context: primary mention, supporting mention, background mention.

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

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

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Input: Consequently, they may fear detection by authorities when asked to respond to surveys or participate in public initiatives. 1 Furthermore, collective repre- sentative and longitudinal data on forcibly displaced migrants, a population with high mobility rates, is difficult and costly (Ib ´ a ˜ nez et al. 2024). This complexity is compounded when focusing on children and adolescents, given the need for enumerators to receive specific training to interact with such a vulnerable demographic and for migrant parents to authorize their children ’ s involvement despite prevailing distrust issues. To address this knowledge gap, we launched the Venezuelan Refugee Panel Study for Kids (VenRePs-Kids) in Medell ´ ın, Colombia. VenRePs-Kids is a longitudinal study repre- sentative of forcibly displaced Venezuelan and Colombian children and adolescents aged 5 to 17. To our knowledge, it is the first study to gather panel data specifically on forcibly 1This concern is also prevalent among undocumented migrants in the United States, as highlighted by Amuedo-Dorantes and Lopez (2015). 2 Usage context: supporting mention, primary mention, background mention.

VenRePs-Kids
Venezuelan Refugee Panel Study for Kids

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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: 26 value of consumption flow of durable goods. 22 While monetary poverty can measure temporal resource holdings, multidimensional poverty, as a more comprehensive measure, includes chronic and exacerbating sources of poverty. This difference explains the existence of mismatches between individuals identified as monetary versus MPI poor, which are often more prominent in poorer countries (Evans et al 2020). This section examines these differences in the contexts of displacement. Table 9. Percentage of the sample in each poverty category: Rows sum to 100 % Non-poor by both measures Only Monetary Poor Only Multidimensional Poor Monetary and multidimensional poor Ethiopia 38 % 23 % 12 % 27 % N. E Nigeria 13 % 69 % 4 % 15 % Somalia 20 % 32 % 14 % 34 % Sudan 33 % 47 % 4 % 17 % Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). This table presents the distribution of households in each of the categories in the columns. Thus, each row adds up to 100 %. South Sudan is excluded from this analysis as monetary data is not available for the country.

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Input: Staff based on SESRE 2023. Most of the analysis presented in this chapter is based on detailed consumption data from the Socioeconomic Survey of Refugees in Ethiopia (SESRE) conducted between October 2022 and February 2023. All consumption of food and non-food items is included, regardless of whether these items are purchased on the market, come from own production, or received as gifts. For own-consumption and gifts, the quantities consumed are valued at prevailing prices in the enumeration area. Although consumption is expressed annually, the reference period used during data collection varies based on the nature of the items. For example, questions related to information on food and food-related items was asked by visiting households twice a week using the “last three days” and “last four days” as reference periods. For house rent, durable goods, clothing, health and education expenditures, and some other categories, the survey questions used the “last three months” and “last 12 months” as references. Imputed rent for owner-occupied houses is calculated by the Ethiopian Statistical Service (ESS) team and is included in the consumption expenditure data shared with the Bank team. Spatial and temporal price deflators adjust for price variations across time and space. First, nominal consumption

Socioeconomic Survey of Refugees in Ethiopia

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Input: 12 employment status and education level also significant at 10 %. Marital status and education are important predictors of attrition. As we might expect, these imbalances suggest some threats that, if left uncorrected, could undermine the parallel trends assumption of difference-in-difference estimators. That said, we see no sign of differences between treatment and control, or attritors and non-attritors, over the key GRIT personality features. This suggests that members of the treatment group are not, for example, more motivated to succeed than members of the control group. To account for these biases, we generate a series of inverse probability weights to balance the data. These weights define the probability of an individual with particular characteristics (e. g. host or refugee status) being in each of the treatment and control groups at baseline and endline and are used to rebalance the data in order to closer support the parallel trends assumption. Results are shown in Column 3 of Table 4. Following weighting, data balances on all key factors, including nationality. This suggests that the parallel trends assumption is more reasonable under the weighted dataset than in the raw treatment / control data. 11 Based on these analyses, we conclude that it is safe to use weighted OLS-based approaches.

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Input: The GIS-based beneficiary targeting map was reviewed during the midterm assessment to verify that the communities selected for Component 2 infrastructure investments remained those with the highest unmet need. Updated displacement figures from UNHCR and poverty scores from the Ministry of Social Welfare were overlaid in the GIS to check whether the original targeting ranks had shifted. The GIS reanalysis confirmed that 41 of the original 47 target communities remained in the highest-need tertile; the six communities that fell below the threshold were replaced through a targeted consultation process.

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Input: However, an endline survey was conducted after the second round of the EPAG program as per the timeline depicted in Figure 1. Examination of this endline survey data will permit a descriptive analysis of the outcomes of the first group of trainees 12 months after they completed the EPAG program, as well as examination of the outcomes of the second batch of trainees. The second round included not only the control group from this impact evaluation but also newly recruited participants who were offered brief basic literacy and numeracy training program prior to program entry. Work is already underway to design and implement the third round of EPAG, with a substantial redesign of the Job Skills track, an emphasis on reaching younger girls with lower literacy, and expansion to communities outside of Monrovia. If the high success rates found in this study are replicated for these future cohorts, the EPAG program should serve as a model for policy makers in Africa and the world seeking to improve lives and livelihoods of all youth, male and female. 25 Usage context: primary mention, background mention, supporting mention.

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Input: the standard ethnic diversity indices to include the annual variation in refugee ethnicities. 8 We then construct a measure of proximity between the clusters in the host country and refugees in surrounding camps by defining an 80-km buffer around each cluster. 9 To control for unobserved heterogeneity and changes within a given cluster, we introduce cluster and year fixed effects, αj and δt. To minimize the risk of confounding the refugee-induced changes in diversity with the annual changes in refugee numbers, we also control for the presence of refugees based on the same buffer as the one used to construct the refugee-induced change in diversity. More specifically, the variable Refugeesjt − 1 counts the number of refugees present in cluster j at year t − 1 within the predefined buffer. The variable is also transformed into an inverse hyperbolic sine to ease interpretation. Finally, Qjt controls for yearly shocks at the cluster level, such as weather shocks. In particular, we control for rain and temperature anomalies. Standard errors are clustered at the Afrobarometer cluster level. 4. 2 Data and descriptive statistics Our analysis combines various sources of data: Afrobarometer, UNHCR refugee camp data, Armed Conflict Location and Event Data (ACLED), Uppsala Conflict Data (UCDP), and the Ethnic Power Relations- Ethnicity of Refugees (EPR-ER) 2019 dataset. Usage context: supporting mention, primary mention, background mention.

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Input: Production data for the agricultural cooperative subcomponent were obtained from the Ministry of Agriculture's crop monitoring unit, which collects yield estimates from extension officers at the district level following each harvest season. These production data cover 14 crop types and are disaggregated by farm size category and irrigation status. Baseline production data will be compared against endline estimates to assess whether project-supported cooperatives achieved productivity gains consistent with the expected rate of return in the economic analysis.

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Input:
A joint study conducted by HI and IOM in Bentiu in 2023 highlights the limited capacity of the South Sudanese authorities to
address the needs and rights of persons with disabilities, compounding the challenges. The gaps in disability disaggregated
data also hinder effective assistance, although at times there may be valid reasons for not reporting on specific vulnerabilities
when providing mass assistance like awareness raising or information sharing activities. The reported numbers of persons with
disabilities benefiting from individualized assistance services through the 5W system are very low. Together with the reliance
on the global estimate as the basis for planning, this indicates a need for improved data collection, analysis, and utilization to
better support persons with disabilities.

Data from the Protection Cluster's Protection Monitoring System (PMS) collected between October 2022 and March 2023
further emphasizes the severe impact on persons with disabilities. Approximately 76% of key informants consistently report
violations of persons with disabilities' ability to access humanitarian aid, especially in crucial areas such as food, shelter, and
health services. Moreover, according to the PMS data, they face disproportionate challenges in accessing justice and
addressing Housing, Land, and Property issues.

Protection Cluster's Protection Monitoring System

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Input: food item per month and cash transfers per person per month for each camp and period. The food items include cereal, wheat, maize, rice, sorghum, CSB/famex (CSB+), pulse, biscuit, date biscuit, dates, oil, vegetable oil, salt, and cash (Table E.4). We have computed the per person per month in-kind aid quantities into annual values using the household size and prices from SESRE and mapped them to the closest food item in SESRE (this was not straightforward as the items are different) considering food ration change periods. The food ration scaling factor is 50 percent vs. 84 percent. Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food aid admin data for every item except Biscuits. Refugee households still report lower quantities, even correcting for shares indicated as sold. Annexes 125 Table E.2: Food aid and consumption comparisons Items Quantity (per capita/year) Expenditure (per capita/year) SESRE UNHCR SESRE UNHCR Nonzero All Net of sold ration* Cereals/other cereals 23.2 186.1 175.5 493 12404 Wheat 60.5 133.2 116.6 1427 4710 Maize 39.6 125.5 118.3 312 4267 Rice 21.3 48.5

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Input: The UNHCR data identify principal applicants for each household and our analysis examines differences in household poverty between households with a female rather than male principal applicant. 6 The principal applicant is the person who receives assistance from UNHCR for the family and is self ‐ selected or selected by the family. This definition of female headship has advantages over the way that household headship is commonly identified in household surveys. An often ‐ noted drawback of the headship variable is that female headship may reflect the enumerators ’ perception about who should be considered a family head rather than who has the most responsibility for the family ’ s welfare in practice. 7 Social norms can also affect whether female respondents self ‐ identify as household heads. For example, some Eritrean returnees who would in other cultural settings be regarded as de jure female headed (single mothers, widows, divorcees, separated women) reported being male ‐ headed. Other Eritrean female returnees who would be considered de facto heads reported headship by absent husbands or male relatives (Kibreab, 2003). Our approach is therefore to distinguish between different types of female and male principal applicant households, using a typology that reflects some of the indicators of vulnerability used by UNHCR. We find that distinguishing between different types of female principal applicant households is important in the setting of Syrian refugees in Jordan. Usage context: primary mention, supporting mention, background mention.

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

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Input: 10 Figure 3: Level of education age 25 +; compared with regional average (%) (*) INSTAT refers to census 2009 Source: Listening to Displaced People Survey, 2014. With regards to ownership of consumer durables, IDPs, refugees and returnees were better endowed than the average citizen of the North (see Figure 4). As was the case for education, they are more comparable to the average citizen in Bamako than to the average citizen in the regions of Gao, Timbuktu and Kidal. Figure 4: Asset ownership compared with regional average Source: Listening to Displaced People Survey, 2014 and EMOP 2011 (INSTAT). The main occupation of IDPs, refugees and returnees before the crisis was commerce (Table 5). This held for over half of the IDPs, 37 % of refugees and 34 % of returnees. 18 % of the refugees 51 85 60 47 85 89 87 15 6 18 29 11 8 8 34 9 22 25 5 3 5 IDPs Refugees Returnees Bamako (INSTAT) Gao (INSTAT) Timbuktu (INSTAT) Kidal (INSTAT) Secondary or Higher Primary None 0 100 200 300 400 500 600 IDPs Refugees Returnees Bamako (Instat) Gao (Instat) Timbuktu (Instat) Kidal (Instat) Percentage Mobile Phone Car / Motorized Vehicle Motorbike / scooter Bicycle Refridgerator TV CD

Displaced People Survey
Listening to Displaced People Survey

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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

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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: 10 as the Demographic and Health Surveys (DHS), though researchers have reduced under-reporting by providing special training to interviewers, placing greater emphasis on respondents ’ privacy and safety, and allowing women multiple opportunities to disclose their experiences (Ellsberg et al., 2001a; Garcia Moreno et al., 2003; Ellsberg et al., forthcoming). Estimates of the magnitude of the problem Population-based surveys have found that between 10-70 % of women report being physically assaulted by an intimate male partner at some point in their lives (Heise, Ellsberg and Gottemoeller, 1999). See Annex A for estimates from many recent population based studies (Ellsberg et al., forthcoming). Findings from a multi-country study on domestic violence and women ’ s health carried out by the World Health Organization in fifteen sites and ten countries found that between 13-62 % of women had experienced physical violence by a partner over the course of their lifetime, and between 3-29 % of women reported violence within the past year (Figure 1. 1). Figure 1. 1. Usage context: primary mention, background mention, supporting mention.

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Input: Poverty remains widespread and social indicators are well below the average for Sub-Saharan Africa. Chad is ranked 173 among the 177 countries surveyed in the 2006 UNDP Human Development Report. The incidence of poverty (defined as the proportion of households with annual spending below what is necessary to meet minimal needs) is estimated at 55 percent according to a 2003 household survey; an estimated four-fifths of the population of about 8. 8 million is living on less than a dollar a day3. Of the population over 15 years old, more than 73 percent (and 76 percent of women) are illiterate. Access to potable water has improved over past years, but is still limited to one out of three people in 2005. Less than two percent of the population has access to electricity and only 1021 kilometers of roads has been paved on a surface area of over 1. 2 million square kilometers. As already mentioned, Chad has recently become oil producing country; however, the economy remains largely agricultural and pastoral. About 80 percent of the country ’ s population lives in rural areas and continue to make their living4 from agriculture and livestock. Cotton is the principal cash crop, employing about 300, 000 families. Usage context: background mention, primary mention, supporting mention.

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Input: 34 Any statistics on the imputed welfare will based on the set of imputed welfares for each household. The estimator takes the form, with R denotes the number of simulation: ܪ ෡ ൌ 1 ܴ ෍ ݄ ሺݕ ෤ ௥ ሻ ோ ௥ ୀ ଵ where ݄ ሺݕሻ is a function that converts the vector y with (log) incomes for all households into a poverty measure (such as the head-count rate or bottom 40 %), and where ݕ ෤ ௥ denotes the r-th simulated imputed welfare. Figure 6. Survey-to-Survey Imputation Methodology, an illustration For the case of Turkey, we use the Survey on Income and Living Conditions survey to impute to the Labor Force Survey. Income is used instead of consumption for this paper ’ s analysis. The model included variables related to: household demographics (age, gender, age composition, etc.), household characteristics (education, labor activity, etc.), household head ’ s characteristics (age, gender, labor, education, marital status, etc.) and household assets holding (both livestock and durables). Based on that model the simulated values of consumption (at household level) were imputed for the households in the corruption survey.

Survey on Income and Living Conditions survey

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Input: displaced due to armed conflict, situations of generalized violence and violations of human rights. 28 Data on IDPs monitored by IDMC are disaggregated and currently published separately for conflict-induced displacement and disaster-induced displacement. 29 At the country level the IOM ’ s Displacement Tracking Matrix (DTM) 30 provides data on IDPs in both conflict and natural disaster settings (activated in all major natural disaster contexts in recent years). Global data on conflict-induced internal displacement reflect variations in how IDPs are defined across situations. There is no consensus on how far a person must flee in order to be considered internally displaced. The definition of internal displacement for nomadic populations, which account for a significant share of IDPs in the Horn of Africa and increasingly in the Sahel, is open to controversy. 31 Moreover, while some countries register IDP children born in displacement (e. g. Azerbaijan, Cyprus and Georgia), other countries do not (IDMC 2015). The crafting of a definition for IDPs and its application in a particular context may be heavily influenced by local and national politics in conflict and post-conflict countries, as well as the direct link between estimates of displaced populations and humanitarian assistance, which can lead to both over- and under-reporting. Usage context: primary mention, supporting mention, background mention.

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Input: Ethiopia. Box 1.1: Comparison of SPS 2017 and SESRE 2023 Sociodemographic Profile 9 2. Sociodemographic Profile T his chapter presents results on sociodemographic outcomes of refugees and hosts. It provides the context for refugees and their hosts, covering demographic characteristics, human capital, living conditions, and displacement experience, which are crucial to understand refugees’ context in Ethiopia. These results are presented across the eight domains: Eritrean, Somali, South Sudanese, and refugees in Addis Ababa and their hosts, as well as broad categories between in-camp and out-of-camp refugees and hosts. 2.1 Demographic characteristics In the SESRE sample, most refugees are from South Sudan, accounting for 53 percent of all refugees.11 South Sudanese refugees reside in camps in Gambella and Benishangul-Gumuz regions. Somali refugees living in camps in the Somali region constitute 30 percent of the refugee sample. Eritrean refugees who reside in camps in the Amhara and Afar regions and Addis Ababa under the Out-of-Camp Policy (OCP) constitute 5 and 12 percent of the sample, respectively (Figure 2.1). More than 30 percent of refugees in camps are born in Ethiopia (Figure 2.2). Somali refugees have a higher share of refugees born in Ethiopia (38 percent). According to UNHCR estimates (2023)12, around 1.9

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Input: In 2008, trained enumerators conducted face-to-face interviews in local languages with 26, 513 respondents across 19 countries. 5 The sample is designed as a representative cross-section of all citizens of voting age in a given country. The dataset used for this paper has a multilevel structure; individuals are nested within primary sampling units (PSUs), which are nested within countries. The PSUs are the smallest, well-deﬁned geographic units for which reliable population data are available and they tend to be socially homoge- nous, thereby producing highly clustered data. In most countries, these will be Census Enumeration Areas (Afrobarometer, 2005, 37-38). Although re- spondents were not sampled based on their ethnic aﬃliation, there is likely to be a high level of clustering in the dataset around ethnicity. In other work, I discuss the advantages of multilevel modeling (Levi and Sacks, 2009). Treating the dependent variable as a binary outcome and taking into account the multilevel nature of our data, I estimate random intercepts for 5I excluded Zimbabwe from the analysis because of missing data on key variables. 9 Usage context: primary mention, supporting mention, background mention.

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Input: The data on total gross proﬁts and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key diﬀerence from the Chamber of Commerce data is that the sales and proﬁts data include all businesses including self- proprietorships. 9 Data were provided for the years between 2010 and 2014 and are re- ported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by deﬁnition and they are likely to be less accurate and complete for smaller ﬁrms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and proﬁt ﬁgures. 10 We scale the variables according to province size by dividing sales and proﬁts by the pop- ulation of the provinces. If we use sales and proﬁts in absolute terms, we get qualitatively similar results. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12 Usage context: primary mention, supporting mention, background 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: 4 Internal Conﬂicts and Refugees A particularly serious aspect of internal conﬂicts is the human suﬀering they generate. This is not only those who are killed or injured in conﬂict but the large number of people who are forced to leave their homes. The issue of refugees has received particular attention in Western media in recent years as refugee ﬂows from Northern Africa, the Middle East and Afghanistan are increasingly reaching Europe. These refugee streams are linked to a severe humanitarian crisis with considerable funding needs for international donors and heavy strains on host countries. 21 The current refugee crisis, however, is in no way unique. Civil war has always been closely linked to humanitarian crisis and refugee streams are one way to capture this. In this section we provide a cross-country analysis aimed at investigating how the stock of refugees evolves when a civil conﬂict hits a country. In the analysis we will focus entirely on showing changes in the stock of refugees across time to illustrate the dimensions involved. We will base our later analysis on these population movements. We exploit country-level data gathered from several sources. Data about refugees is provided by the UNHCR Population Statistics Database. The database provides in- formation about UNHCR ’ s populations of concern from the year 1951 up to 2014. Usage context: supporting mention, background mention, primary mention.

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Input: arise. The field visits helped to understand the camp administrative structure and environment of the teams facilitating the camp and to test the accessibility of sampled refugee households inside the camp, in Addis Ababa, and the identification of the host. ESS provided detailed feedback on the fieldwork procedures and adjustments made before the fieldwork began. For instance, in Afar (Asayita), the visit helped to identify challenges in tracing sampled refugee households and to take the necessary corrective measures. Likewise, in Addis Ababa, the visit assisted in designing an appropriate strategy to select host communities. The survey created a good opportunity for a collaborative effort between different government institutions and development partners. This collaboration allowed the sharing of experiences across institutions and knowledge for ESS to implement such unique surveys in the future. The survey process, from preparation to implementation, focused on ensuring data quality for refugee data collection. During preparation, ESS translated the survey instrument into different main languages and undertook an in- depth training of supervisors and enumerators for enumerators to understand better the concepts of the questions related to the refugee context. Moreover, a close follow-up and coordination in the field helped to get better quality data and

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Input: 14 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 5. 2 Composition of poverty Unpacking the headline numbers further, important patterns emerge about the composition of multidimensional poverty among forcibly displaced and host communities in these countries. Overall, the censored headcount ratios (proportion of people who are poor and deprived in a given indicator) are lower among non-displaced communities than among refugees and IDPs, but there are large differences in which indicators are the most salient in different countries. The indicators with the largest difference between the two populations are bank account and cooking fuel in Ethiopia, years of schooling in Somalia, electricity in Sudan, drinking water in South Sudan, and legal identification in Nigeria. These findings reinforce the need for policies and programming that take into account the measured experiences of IDPs and refugees. In this way, the MPI can function both as tool to monitor, track, and bear witness to the lived experiences of forcibly displaced communities, as well as advise on evidence-based interventions that address the needs of the local population. Figure 1 shows the censored headcounts of each indicator in Sudan ’ s MPI, with large differences appearing by displacement. Usage context: background mention, primary mention, supporting mention.

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Input: Ethiopia–Household Welfare Statistics Survey (HoWStat)—excludes the majority of displaced populations (Internally Displaced People [IDPs] or refugees) from its sample of households. Thus, we have limited in-depth information on the socio-economic outcomes—including on poverty— for refugees across all camps in Ethiopia to compare with Ethiopian hosts. SESRE collected data from November 2022 to January 2023, from a nationally representative sample of 3,452 refugee households and their hosts. The SESRE covers all currently operating refugee camps of major refugee groups: Eritreans, South Sudanese, and Somalis, as well as the out-of- camp refugees of Addis Ababa and their respective host communities. The survey was aligned with the HoWStat methodology, allowing comparability between refugees and their host communities. The World Bank, Ethiopia’s RRS, Ethiopia’s Statistical Service, and UNHCR collaborated to implement SESRE10 and was the first of its kind, building on the “Skills Profile Survey 2017, A Refugee and Host Community Survey” conducted in 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

Ethiopia–Household Welfare Statistics Survey

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Input: The paper extends the GTAP 8 database by separating Lebanon, Jordan, Syria, Iraq, and West Bank and Gaza from the rest of the Western Asia aggregate and Algeria and Libya from the rest of North Africa. Kuwait, Qatar, Bahrain, Saudi Arabia, UAE, and Oman are aggregated into a GCC composite group. In addition, the 57 sectors in the GTAP 8 database are aggregated into 22 sectors based on their importance for the countries in the MENA region (Table 1). The resulting MENA-specific database contains 26 countries, among which are the six Levant economies of interest in this paper (Turkey, Lebanon, Syria, Iraq, Jordan, and Egypt) and the rest of the developing MENA countries (Table 1). The procedure used to construct the individual country information employs data from several sources. The UN Statistics Division data for 2007 is the source for the six components of GDP – agriculture, hunting, forestry, and fishing (ISIC A-B); mining, manufacturing, and utilities (ISIC C-E); construction (ISIC-F); transport, storage, and communication (ISIC I); wholesale, retail trade, restaurants and hotels (ISIC G-H); and other activities (ISIC J-P).

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Input: 12 crop products (e. g., wheat, potatoes) and the value of five livestock products (i. e., milk, egg, butter, hides, and honey) sold in the market. 14 Several other data sources were utilized. First, the Ethiopian refugee camps location data set from the Humanitarian Data Exchange (HDX) 15 and the total number of refugees by camps from the United Nations High Commissioner for Refugees (UNHCR), Addis Ababa office. We use data from 26 official UNCHR refugee camps in Ethiopia that were operational in 2018 (see Figure 1; 3). Second, we use administrative data sets for Ethiopia and refugee source countries from the database of Global Administrative Areas (GADM). 16 We also use the conflict data set from the Armed Conflict Location and Event Data Project (ACLED) 17 and the population data from the Gridded Population of the World (GPW) data set. 18 On the basis of these data sets and the location of sample households from Ethiopia DRDIP data set, we generated the following variables: i) distance of sample households to the nearest refugee camp, the nearest region (administration level 1 in GADM) to the refugee camps, ii) distance of the refugee camps to the nearest border of the refugee source country, iii) Usage context: primary mention, supporting mention, background mention.

Gridded Population of the World (GPW) data set

Ethiopian refugee camps location data set

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Input: Since the set of household members at baseline have subsequently moved, and usually not as a unit, the 2004 round results in more than 2, 700 household interviews (from the baseline sample of 912 households). Although the KHDS is a panel of respondents and the concept of a ‘ household ’ after 10-13 years is a vague notion, it is common in panel surveys to consider re-contact rates in terms of households. Excluding households in which all previous members are deceased (17 households with 27 people), the field team managed to re-contact 93 % of the baseline households. This is an excellent rate of recontact compared to panel surveys in low-income countries and high-income countries. The KHDS panel has an attrition rate that is much lower than that of other well-known panel survey summarized in Alderman et al. (2001) in which the rates ranged from 17. 5 % attrition per year to the lowest rate of 1. 5 % per year. Most of these surveys in Alderman et al. (2001) covered considerably shorter time periods (two to five years). Figure 1 charts the evolution of households from baseline to 2004. One-half of all households interviewed were tracking cases, meaning they did not reside in the baseline communities. Of those households tracked, only 38 % were located nearby the baseline community. Overall, 32 % of all households were not located in or relatively nearby the baseline communities. While tracking is costly, it is an important exercise because migration and dissolution of households are often hypothesized to be important Usage context: background mention, primary mention, supporting mention.

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Input: Significant among these are: (a) the lack of capacity of national statistical agencies in many developing countries to collect robust data on refugees; (b) weak or incomplete monitoring of refugees dispersed within host communities; (c) lack of capacity to maintain up to date information on refugees (reflecting new arrivals, 81 General population registers may also provide opportunities for more elaborate analysis of the integration of refugees in asylum countries, as the data could be linked to other administrative registers, for example on labor and education (UNSD 2014). 82 UNHCR collects, compiles and publishes data on asylum-seekers, refugees and IDPs protected or assisted by UNHCR, including populations in refugee-like or IDP-like situations. 83 Established in 1863, the ICRC ’ s mission is to ensure humanitarian protection and assistance for victims of armed conflict and other situations of violence. Usage context: primary mention, background mention, supporting 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

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Input: Treatment Randomization: The housing subsidy program was randomized geographically at the community level. 8 In the ﬁrst step, 158 communities in Irbid and Mafraq were randomized into treatment or control for HSP assistance, stratifying on governorate and district population quartile. One third of communities were randomly assigned to treatment, while the remaining two thirds were randomly assigned to control. All eligible applicants living in the treatment communities were assigned to treatment. In all analysis below, error terms are clustered at the community level. There were several reasons for the cluster randomized design, including the ability to streamline implementation (and thus lower program costs), to reduce conﬂict among refugee households as- signed to treatment versus control, and to improve the analysis of treatment spillovers onto host community members by boosting the local saturation of program assistance, thereby making it more salient to neighbors. Refugee Sample: The survey sample consists of all refugees assigned to treatment and an 7See AER RCT Registration Number AEARCTR-0006141. 8Communities correspond to the Jordanian government ’ s administrative unit of “ localities ”. Communities in Mafraq have 3, 866 people on average, while those in Irbid are somewhat larger at 14, 626 individuals on average. 10 Usage context: supporting mention, primary mention, background mention.

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Input:
The paper assembles a new, regionwide panel data set that measures local economic activity using nightlights, potential hurricane damages using a detailed hurricane windstorm model, and mangrove protection by mapping the width of mangrove forests on the path to the coast.

Specifically, we use remote sensing data on nightlights to measure local eco nomic activity. Nightlights have been shown to be a good proxy of economic activity (see Donaldson and Storeygard (2016) for a review), and their high spatial resolution is ideal because the economic impact of tropical cyclones has been shown to be highly localized

We measure potential hurricane damages using predicted wind speed from the wind field model of Pita et al. (2015). The model is calibrated for Central America and has been validated with historical data.

We measure local economic activity using imagery from four weather satellites that are part of the United States Air Force Defense Meteorological Satellite Program. These satellites record daily cloud formation by measuring the amount of moonlight reflected by clouds at night.

Specifically, we use the annual composites produced by the National Oceanic and Atmospheric Administration (NOAA). These composites predominantly measure man-made lights because they only use information from cloudfree days and because NOAA's methodology filters transient sources of light.

composites produced by the National Oceanic and Atmospheric Administration (NOAA)

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Input: Bank branches in El Fasher have limited capital for small businesses as their headquarters in Khartoum regard the area as too great a risk and IDPs themselves as riskier investments than their host community peers (UN- HABITAT 2009: p. 8). Exposure to unclean cooking fuels and inadequate housing can lead to poor health outcomes, while lack of access to electricity and a bank account further excludes individuals from labor market integration and livelihood opportunities that would empower forcibly displaced persons to overcome their multiple, overlapping deprivations. Clearly, displacement status puts individuals at a greater risk of poverty than their host community neighbors, and we can unpack those risks in greater detail using the MPI. Results can also be broken down to show the percentage contribution of each indicator to multidimensional poverty (see Figure 2). Among refugees in Ethiopia, lack of a bank account is the largest contributor to poverty, while among host communities, the largest contributor is years of schooling. Usage context: supporting mention, background mention, primary mention.

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Input: We have constructed two different variables that try to account for the degree of severity of the conflict. 10 The first variable identifies individuals belonging to households that were displaced due to the 1999 wave of violence (all members displaced). The second variable identifies individuals in households that report having their house completely destroyed by the violent attacks in 1999. The TLSS 2001 contains also useful retrospective information on school attendance and grade attained across three different academic years: 1998 / 99, 1999 / 00 and 2000 / 01. We are 7 Commission for Reception, Truth and Reconciliation & Benetech Human Rights Data Analysis Group. ― Human Rights Violations Database. ‖ 9 February, 2006. Website: http: / / www. hrdag. org / resources / timor-leste_data. shtml. 8 There may be potential sample biases in the statement taking procedure given the voluntary nature of the process. It is possible that those living in more remote or mountainous areas, those living far away from the areas where the statements were taken, the sick, old and disabled and those with no access to the media or means of mass communication have a lower probability of being part of the sample. By contrast, those more active in local communities are more likely to have provided a testimony. Usage context: primary mention, supporting mention, background mention.

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Input: Bank branches in El Fasher have limited capital for small businesses as their headquarters in Khartoum regard the area as too great a risk and IDPs themselves as riskier investments than their host community peers (UN- HABITAT 2009: p. 8). Exposure to unclean cooking fuels and inadequate housing can lead to poor health outcomes, while lack of access to electricity and a bank account further excludes individuals from labor market integration and livelihood opportunities that would empower forcibly displaced persons to overcome their multiple, overlapping deprivations. Clearly, displacement status puts individuals at a greater risk of poverty than their host community neighbors, and we can unpack those risks in greater detail using the MPI. Results can also be broken down to show the percentage contribution of each indicator to multidimensional poverty (see Figure 2). Among refugees in Ethiopia, lack of a bank account is the largest contributor to poverty, while among host communities, the largest contributor is years of schooling.

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Input: We sourced bilateral trade value data from WITS and bilateral tariff data from a medley of sources, presented in Appendix Table A1. As part of this procedure, all entries in the two composite regions (rest of Western Asia and rest of Northern Africa) were split and assigned the split values to the newly created economies, while all entries for the two composite regions from the GTAP database were removed from the database. Each entry was split using the most thematically relevant external source. Sectoral GDP shares were used to split consumption and production values, trade data were used to split export and import values, and tariff information was used to assign tariff values. Export shares were used to split further production and consumption information into the final set of industries presented in Table 1. For internal consistency purposes, the required accounting relationships were imposed on the split database 8

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Input: Staff based on SESRE 2023. Note: Columns 3-4 are restricted to workers, and Columns 5-6 are restricted to workers in camps. High-skill occupations include managers, professionals, and associate professionals (around 7% of refugees). Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 124 Table E.1: Food aid data/information received from UNHCR Item Remark Assumptions Cereal Not clear Other cereals Wheat Matched Maize Matched Rice Matched Sorghum Matched CSB/famex (CSB+) Not in SESRE Average of other cereals/pulses Pulse Not clear Peas Biscuit Matched Date biscuit Not in SESRE Merged with biscuits Dates Matched Oil Merged with edible oil Vegetable oil Merged with edible oil Salt Matched Cash - - Source: UNHCR Annex E: Robustness Checks of Refugees’ Consumption This Annex discusses assessing the disparity between refugee ration aid and reported consumption quantities. This is reported as a robustness check. As discussed earlier, the expenditure of in-camp refugees is almost half that of hosts despite sizeable food aid and significant investments made by the WFP and UNHCR in cash transfers (in selected camps). The significantly lower expenditures (food and non-food) among refugees compared to the host population led to higher poverty

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Input:
In the recent past, the number of persons affected by drought has been comparable to
that of victims of hurricanes and floods (146 million, on average, between 2000 and
2005 according to the EM-DAT). The latest report of the IPCC predicts increased
water shortages in Africa (74 to 250 million people affected in 2020) and Asia:
"Freshwater availability in Central, South, East and Southeast Asia particularly in
large river basins is projected to decrease due to climate change which, along with
population growth and increasing demand arising from higher standards of living,
could adversely affect more than a billion people by the 2050s." (Intergovernmental
Panel on Climate Change 2007a, 10). Case studies, however, paint a contrasting
picture. The effect of a lack of drinking and irrigation water on migration is actually
less sudden than that of the meteorological events mentioned in the previous chapter,
and only generates progressive departures.

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Input: the main government department responsible for refugee affairs, housed within the former National Intelligence and Security Services (NISS). The former ARRA was elevated to an agency level in accordance with Proclamation No. 1097/2018, which defines the Powers and Duties of the Executive Organs of the Government and established the Agency for Refugees and Returnees Affairs (ARRA) under the Ministry of Peace in 2018. In 2021, a new government announced through the Definition of Powers and Duties of the Executive Organs Proclamation No. 1263/2021, during which it reestablished ARRA as Refugees and Returnees Service (RRS). The RRS became one of the executive organs accountable to the NISS which is accountable to the Prime Minister’s Office and oversees the Immigration and Citizenship Service other than RRS. Introduction 4 Refugees in Ethiopia have been severely affected by ongoing conflict and unrest across Ethiopia. The country has dealt with multiple crises, including rampant inflation, a devastating war, and frequent droughts and floods. SESRE data collection was carried out between November 2022 and January 2023, marked by drought, inflation, and insecurity, posing significant threats to the livelihoods of refugees and host communities in an already fragile economy. The conflict in Northern Ethiopia continued until 2022

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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

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Input: : 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: National Poverty line 32% 84% Food security High food insecurity 26% 67% Food insecurity scale 4.0 8.1 Social cohesion Economic competition 33% 49% Increased insecurity 37% 39% Source: Pape et al. (2018) and World Bank Staff based on SESRE 2023.

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Input: 21 Results suggest that 66 % of the returnees trust the Malian police and army most when it comes to providing security in the North. Almost half believe that the Malian army is brave and well trained. The vast majority of returnees believe that the government ’ s policies regarding reconciliation, security and social cohesion are good or very good. They also support the government ’ s approach towards decentralization and providing infrastructure such as access to potable water and electricity. As the next section will illustrate this differs strongly with the opinions of refugees. 6. Prospects for Peace IDPs, refugees and returnees have comparable opinions with regard to the requirements for peace: (i) addressing the ongoing crisis, (ii) improving security and (iii) reconciliation. Although there is agreement on what needs to be done, there is little consensus on what happened during the crisis, who the culprits are and who the main victims. Figure 15: What is the most important problem the Government needs to resolve today? (%) Source: Listening to Displaced People Survey, 2014. Usage context: supporting mention, background mention, primary mention.

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Input: Gender Perceptions- Violence (IPV) The standardized total score of ﬁve questions regarding norms for inti- mate partner violence (IPV) from the Demographic and Health Survey (DHS) (The important decisions in the family should be made only by the men of the family. How often would you agree? The wife has the right to express her opinion even when she disagrees with what her husband is saying. How often would you agree? A wife should tolerate being beaten by her husband in order to keep the family together. How often would you agree? A husband has the right to beat his wife. How often would you agree? It is more important to send a son to school than it is to send a daughter. How often would you agree?). Financial Well-being Savings Response to the question “ How much money do you currently have in savings? ” During the collection surveys (midlines) this question instead asked “ How much money did you save in the past week? ” Borrowing Total amount of money the household has borrowed. Economic Decision Making Risk Preference Measured using incentivized responses to the multiple price list deci- sions adapted from Holt-Laury and Sprenger (2002). Usage context: primary mention, background mention, supporting mention.

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Input: The study will conduct an ex-ante micro- simulation using pre-crisis household data (IHSES 2012) and macroeconomic projections for 2014 to gauge the distributional impact of the crises across groups (e. g. individuals and / or households, sectors, IDPs and host communities) and space (e. g. urban / rural, governorates).  The Economic and Social Impact Assessment for Kurdistan Region of Iraq [completed in 2015] provides an analysis of the impact of displaced people on access to and quality of service delivery across several sectors.  The Lebanon Economic and Social Impact Assessment of the Syria Conflict [completed in 2013] provides an analysis of the impact of displaced people on access to and quality of service delivery across several sectors.  The Bank and UNHCR undertook a welfare assessment of Syrian refugees living in Jordan and Lebanon [completed in 2016] focusing on welfare, poverty and vulnerability.

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Input:
Our results suggest that any comparative assessment of hunger prevalence using HCES should clearly

from HCES, such as poverty counts and inequality measures (Beegle et al. 2012). The reason is that the

expected when measuring hunger directly from HCES and how some of these errors likely differ by

hunger numbers across the various arms of the survey experiment and verifies whether the magnitude

Agriculture Organization (FAO) of the United Nations in a series of reports tracking world hunger, with

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Input:
Table 2 presents hunger estimates derived from HCES alone. The calorie measure displays a great

One of the arguments for favoring the HCES-direct method over the FBS-CV method is that it allows for

patterns of hunger are sensitive to the type of HCES that is used. For example, for each standard

our experiment to calculate a CV of calorie availability, following FAO (1996, Appendix 3). [11] Specifically,

we collapse the data to 10 deciles of daily per capita kilocalories available and then calculate the CV

FAO motivates these manipulations, which serve to lower the CV, by the desire to purge the CV of

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Input: 3. Secondary outcomes among refugees: These include a broader set of variables that were grouped into 14 broad families in the PAP including: (1) dwelling characteristics and house- hold structure, (2) consumption and expenditure, (3) ﬁnancial participation, (4) earnings, labor, and occupational choice, (5) migration, (6) physical, mental health, and sleep, (7) marriage and fertility, (8) child outcomes, (9) social capital, (10) political attitudes, (11) time use, (12) education and cognition, (13) behavioral games and preferences, and ﬁnally, (14) speciﬁc COVID-19 related outcomes. The three primary outcomes noted above are a subset of these measures. Certain outcomes were collected in each of the three survey rounds, for instance, the food security measures (the number of meals eaten and the frequency of go- ing to bed hungry). Respondents also reported information on child school attendance, and completion of learning activities when schools were closed due to the COVID-19 pandemic. Usage context: supporting mention, background mention, primary mention.

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Input:
We use the FAO Food Composition Tables for the Near East to convert daily food quantities into kilocalories; we then divide by the effective household size to get per capita daily caloric intake. [6]

The amount of assistance delivered during conflict is increas ing to support the growing share of the extreme poor living in these settings. However, little is known about how well assistance is targeted, which can have implications for conflict itself. Using a novel data source in Yemen that tracks food assistance, which accounts for the largest share of assistance in the country, the authors find that the share of households receiving food assistance significantly increased following the U.N. announcement of a food emergency and that the increases were larger in regions identified by the U.N. as being closer to famine. Furthermore, the increases in

This article investigates the change in humanitarian assistance following a particularly in teresting event- the announcement of a food emergency in Yemen in 2017. The United Na tions announced the 2017 Integrated Food Security and Phase Classification (IPC) in March

utilizes a novel mobile phone survey conducted by the World Food Programme (WFP) that pro vides high-frequency data on food assistance and food access that are regionally disaggregated.

_∗_ The authors thank the World Food Programme for sharing the data from the November 2017 Yemen mVAM survey and commend the team on the impressive data they have collected over the course of the conflict in Yemen. We thank the editor and two anonymous reviewers for excellent comments and suggestions.

FAO Food Composition Tables for the Near East

Integrated Food Security and Phase Classification (IPC)

novel data source in Yemen that tracks food assistance

mobile phone survey conducted by the World Food Programme

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Input: Notably, while we cannot rule out that time outside due to employment may play a role (e. g., fresh air may boost one ’ s mood), our time-use data indicates that the average refugee already spends at least three hours outside per day, with no measurable diﬀerence between employed and cash arms. As we are powered to detect changes of at least twenty minutes for each activity, our results suggest that large substitutions away from unsavory activities are unlikely to be driving the improvements in psychosocial well-being, insofar as the respondent recalls. 1718 We also investigate whether those who were more idle prior to being employed beneﬁt more from employment. We ﬁnd no impact along this margin, suggesting that the elimination of boredom per se is not the driving force behind the psychosocial value of employment (Appendix Table A10). 17Most respondents do not track their day by time, making collection of reliable time use data challenging (though recent literature documents the broader unreliability of such data). We piloted a variety of strategies, and settled on asking respondents how much time they spent on a set of activities in the previous day. Usage context: background mention, supporting mention, primary mention.

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Input: To this eﬀect, we replace yh i with yh i / yi in equation (1) and proceed as outlined above. If migration decisions are based on relative rather than absolute income, then the coeﬃcients of eδs − eδi and (eηs − eηi) zh should be positive and signiﬁcant only when they are computed using yh i / yi. In addition to relative and absolute income diﬀerences, the analysis also examines the re- spective roles of various location characteristics such as housing and food prices, availability of public services, and density of human settlement. 8An alternative strategy for the estimation of pre-migration income distribution in cross-section data is sug- gested by Bayer, Khan and Timmins (2008). 11 Usage context: primary mention, supporting mention, background 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: primary mention, background mention, supporting mention.

Armed Conflict Location and Events Dataset

conflict event data
geographically disaggregated data

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