Input: UNHCR (2021a) projects that the number of returnees will reach 141, 000 in 2021, up from 41, 000 in 2020. There are no datasets such as the one used in this study to explore the impact of post-2015 returnees on social cohesion and it is not possible to determine the degree to which our findings our applicable to this new context. However, it is possible to explore similarities and differences between the two contexts. Looking at a UNHCR report about the latest wave of returnees, it states that “ almost all returnee households rely on food obtained from their own gardens (93 %) and / or fields- households struggle to get food during the period they do not produce. 81 % of households declared that they are not satisfied with their level of food security because of the low dietary diversity ” (UNHCR 2021a). The report also suggests that “ 88 % of returnee heads of households are subsistence farmers, but most of them declared not having the adequate resources to produce their land. ” This high level of dependence on agriculture and prevalence of food insecurity are similar to the ones in our dataset for returnees and suggests that tensions related to access to agricultural land could also be present for post-2015 returnees. There are also signs of potential differences between current dynamics and the pre-2015 period. Usage context: background mention, primary mention, supporting mention.

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

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Input:
To examine the impact of different electricity price assumptions on hurdle prices, we use data from only the two most comprehensive studies (EC (2008) and IEA (2005)) presented in IEA (2012), which implies that the levelized costs for different technologies are more comparable.

Using aggregate energy consumption data and a nationally representative household survey immediately before the crisis, this paper provides an overview of household energy consumption patterns, highlights Armenia's energy vulnerability, and estimates the direct poverty and distributional impacts of the increase in the cost of imported gas.

Using data from a nationally representative household survey of 2008 and national energy

nationally representative household survey

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Input: Respondents who were untraced were much more likely to be residing outside Kagera (43 %) compared to their counterparts who were re-interviewed (8 %). The consumption data come from an extensive consumption module administered in 1991 and again in 2004. The consumption aggregate includes home produced and purchased food and non-food expenditure. The non-food component includes a range of non-food purchases, as well as utilities, expenditure on clothing / personal items, transfers out and health expenditures. Funeral expenses and health expenses prior to the death of an ill person were excluded. Monetary levels were adjusted to account for spatial and temporal price differences, using price data collected in the Kagera survey in 1991 and 2004, and, for households outside Kagera, data from the National Household Budget Survey. Consumption is expressed in per capita, per annum terms. The poverty line is set at TZS 109, 663, calibrated to yield for our sample of respondents who remained in Kagera the same poverty rate as the 2000 / 1 National Household Budget Survey estimate for Kagera (29 %). 4. Growth, Poverty and Physical Mobility in Kagera In this section, we discuss changes in living standards overall, and the changes for four mutually exclusive groups based on residence in 2004: (i) still residing in the baseline community, (ii) residing in a Usage context: background mention, supporting mention, primary mention.

National Household Budget Survey
Kagera survey

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Input:
<!-- PAGE 2 -->  ## WPS4426 Policy Research Working Paper 4426  # Statistical Analysis of Rainfall Insurance Payouts in Southern India  #### _Xavier Giné_ _Robert Townsend_ _James Vickery_ The World Bank Development Research Group Finance and Private SectorTeam December 2007     <!-- PAGE 3 -->  Policy Research Working Paper 4426  ### **Abstract**   Using 40 years of historical rainfall data, this paper estimates a distribution for payouts on rainfall insurance policies offered to farmers in the State of Andhra Pradesh, India, in 2006.

This paper uses historical rainfall data to estimate the distribution of payouts on a   rainfall index insurance product developed by the general insurer ICICI Lombard and   offered to rural Indian households since 2003.

Correspondingly we can use historical rainfall data to calculate a putative history of   insurance payouts for insurance contracts written against the 2006 monsoon.

Applying the insurance contract terms to historical rainfall data, we calculate the   hypothetical payout on the contract for each station, phase and year.

Dependence on Insurance Payouts**   To calculate the degree of cross-sectional dependence in payouts, we calculate the   standard deviation of phase payouts for each weather station, restricting analysis to the 11   contracts for which we have the most historical rainfall data.

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

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Input: diverse diets and poor food consumption status compared to their hosts. The average household dietary diversity score—the number of food groups consumed out of twelve—is 7.5 for hosts and 6.5 for refugees (Figure 5.10a). Overall, the average dietary diversity score is also lower for in-camp refugees than their hosts. The share of households with acceptable food consumption status—food consumption score of 35 or above—is considerably lower among in-camp refugees (49 percent) than their host (74 percent). The relatively lower dietary diversity could be due to refugees having limited access to diverse food as they depend on aid. Most Addis Ababa refugees and their hosts have an acceptable food consumption status (Figure 5.10b). 5.1.5 Shocks and coping strategies Market-related shocks are common, but refugees are exposed to more diverse shocks than their hosts. While Ethiopian households face a plethora of risks that affect their livelihoods—risks to assets, income, and food supply (Dercon et al 2005; Woldehanna et al 2008)—market shocks related to rising food prices, food shortage, and health shocks appear to be most prevalent (Figure 5.11). High food prices drive the market shocks. Food shortage seems to represent a crucial economic shock among refugees—roughly 31 percent of in-camp refugees are

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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: supporting mention, primary mention, background mention.

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Input: MPI of IDP communities is considerably higher than that of the host communities, so it follows that the censored headcount ratios display a similar gap. The difference by indicator is particularly noticeable in the living standards dimension, where the electricity, cooking fuel, housing, and bank account indicators show over 34 percentage point difference between the censored headcount ratios for the IDP and host communities. Figure 1. Censored headcounts of each indicator in the MPI, by displacement status in Sudan (2018) Usage context: primary mention, background mention, supporting mention.

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Input: 10 Last, specific adjustments are made in the case of Germany and the Republic of Korea. For Germany, bilateral data are available only by nationality. However, these data fail to take adequate account of the large number of ethnic Germans who arrived from other countries between 1944 and 1950 (mainly expellees) and those who arrived after1950 (mainly resettlers). Material from the German 2005 micro-census was therefore used to supplement the data for Germany (see appendix 3). In the case of Korea, data by nationality are readily available for each census round. However, these data fail to account for the large numbers of migrants from the People ‘ s Democratic Republic of Korea living in the Republic of Korea. Since the United Nations Trends in International Migrant Stock details the total migrant stock in the Republic of Korea by the country of birth definition and because citizenship is rarely granted to people from outside, it is simply assumed that the nationality data were comparable to the foreign-born definition. The nationality total was then subtracted from the UN total and the remaining migrants were assigned to the People ‘ s Democratic Republic of Korea. Usage context: primary mention, supporting mention, background mention.

United Nations Trends in International Migrant Stock

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Input: displaced children and adolescents residing in host communities, rather than in refugee camps. Particularly, VenRePs-Kids collects data on 2, 556 households including 1, 338 Colombian and 918 Venezuelan households, respectively. The study collects rich and comprehensive data on children ’ s and adolescent ’ s development including anthropometric measures, vo- cabulary ability tests, and socio-emotional and mental health assessments. It also collects data on risk behaviors, time use, social integration measures, prosocial behaviors, and parents and caregivers sociodemographics, among other dimensions. 2 The study includes Colombian children and adolescents as the comparison group to high- light the developmental differences of Venezuelan forcibly displaced children. This choice stems from the fact that comparing Venezuelan children in Colombia with their counter- parts remaining in Venezuela is impractical due to the latter ’ s exposure to a severe eco- nomic and humanitarian crisis, marked by limited access to services and food. This envi- ronment severely hampers their potential for normal human development. Additionally, many Venezuelan children and adolescents have spent more of their lives in Colombia than in Venezuela. Therefore, Colombian children and adolescents serve as the most ap- propriate benchmark for assessing the developmental gaps of their Venezuelan peers.

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Input: This equation expresses the model in the case of a dichotomous measure of conflict. Here, it gives the odds ratio of IPV if the district experienced any conflict compared to the odds of IPV if the district had experienced no conflict. X_ij contains individual, household and partner characteristics summarized in Table 1. As noted before, the independent variables are added to the model in blocks to examine for possible confounding or effect modification with the main association. For all analyses, significance was assessed using an alpha of 0. 05. For each country, a multilevel model assessed the association between IPV and forced displacement. Stepwise model fitting was undertaken to assess how the main association was affected by sequential blocks of variables. The final model results for each country are given in the tables below. Sample Weights In order to account for the complex survey design of the DHS, the survey weights for the DV module were included in all analyses, using the probability weight or pweight option within the gllamm survey command. The probability weight is defined as the inverse probability of the respondent ’ s being included in the sample. The pweight command assumes weights are specified at least two levels in the data. Since the data were not weighted at the district level, the level-1 weight within Stata was specified as 1. The level-2 weights were calculated using the Usage context: background mention, supporting mention, primary mention.

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Input:
These climate scenarios are then used to predict how tropical cyclones may change into the future using a newly developed tropical cyclone simulator (Emanuel et al. 2008).

The historic relationship between aggregate damages and the magnitude of each storm is estimated using data from storms that have hit the United States since 1960.

The damages are matched with characteristics of the storm including minimum barometric pressure, maximum wind speed, and location at landfall (NOAA 2009).

speed, and location at landfall (NOAA 2009). Estimates of county income and

population density are inferred from Census data for the five counties closest to the point

of contact. A regression estimates the relationship between storm intensity and damages.

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

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Input: refugees can find opportunities to earn money or work—assuming that assistance is the gap between the consumption of refugees to the poverty line— reduces the cost by 44 percent to an annual US$221 per person. Further, the cost of basic needs could decrease to an estimated US$78 per year if the country adopts a “full inclusion” scenario 32% 18% 25% 84% 7% 75% In camp Addis Ababa Total Poverty headcount rate (%) Hosts Refugees Figure ES.3: Poverty incidence Source: World Bank staff based on SESRE 2023. 4.0 2.1 2.9 8.1 3.4 7.0 In Camp Addis Ababa Total Hosts Refugees Figure ES.4: Food insecurity scale Source: World Bank Staff based on SESRE 2023. Executive Summary vi where in-camp refugees have equal opportunities as hosts. The results show that refugee integration has considerable potential to save money, creating an “economic-inclusion dividend” that could be allocated to other interventions. Markets and Opportunities Ethiopia’s 24 refugee camps2 are spatially dispersed, and location matters significantly in terms of refugee’s ability to work. About 88 percent of refugees in Ethiopia remain in camps (based on SESRE data). The different camp areas have different geographic, social, and economic contexts, and are in different ecological zones, with different

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Input: 1% 1% 2% Difficulty with self-care 1% 1% 1% 1% 1% 2% Communicating 0% 1% 1% 1% 1% 1% Any disability 5% 8% 3% 4% 6% 5% Source: World Bank Staff based on SESRE 2023. Table D.4: Living conditions by survey domains Eritrean Somali South Sudanese Hosts Refugees Hosts Refugees Hosts Refugees Dwelling type Owned 57% 0% 77% 21% 72% 11% Rented 36% 2% 13% 0% 22% 2% UN/NGO temporary 0% 61% 0% 28% 1% 16% UN/NGO permanent 0% 36% 0% 50% 1% 71% Other 7% 0% 10% 1% 4% 0% Housing quality Overcrowded 22% 66% 44% 53% 42% 56% Improved wall 16% 7% 16% 5% 4% 0% Improved roof 72% 64% 75% 81% 58% 8% WASH Improved source of drinking water 80% 99% 97% 99% 64% 79% Improved bathing facilities 30% 28% 5% 9% 25% 14% Improved toilet facility 38% 39% 60% 59% 18% 34% Improved waste disposal method 24% 64% 30% 37% 10% 8% Source of lighting Electricity (meter) 74% 13% 25% 5% 29% 1% Electricity (meter, generator, solar) 89% 91% 38% 17% 34% 4% Source: World Bank Staff based on SESRE 2023. Annexes 100 0 10 20 30 40 50 60 70 80 90 100 < 15

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Input:
Furthermore, these actors and concerned government counterparts
provided their inputs and contributed to the development of a
durable solutions strategy for Sudanese IDPs and refugees and
supported the works of the HLP and DSWGs.

##### 4.1- Funding data

The total funding requirement for **HRP 2021** of Sudan was **USD**

**1.94bn,** out of which **USD 149,928,919 was planned for protection**

**activities.** However **, only 20%** (USD 30,559,006 **) was provided,** and
USD 116,369,913 remained unmet. **The same amount of USD**

**1.94bn is the funding requirement for HRP 2022, with USD**

**161,917,433 for protection activities that incudes General**

**Protection, Child Protection and GBV. However, as of 05 April 2022,**

**only 1,705,350 in funding has been received** **.**

If the funding situation continues as such, and the government does
not take concrete steps towards the implementation of the NPPOC,
protection agencies and actors will not be able to provide effective
assistance, including under:

**a) protection** : conduct protection assessments, protection
monitoring by presence and remotely, referrals to specialised
services, provide legal assistance, psycho-social support, emergency
cash assistance, awareness-raising, implement community support
projects, support community-based protection networks, capacity
building of service providers, conduct advocacy, protective
accompaniment under.

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Input: Newly Arrived Immigrants into the Labour Market. Urban Studies, 50(6). https://doi.org/10.1177/0042098012461674 References 79 Holzaepfel, E. A. (2015). Evaluating the Effectiveness of Livelihoods Programs for Refugees in Ethiopia. Washington, D.C: United States Department of State. Hvidtfeldt, C., Schultz-Nielsen, M. L., Tekin, E., and Fosgerau, M. (2018). An Estimate of the Effect of Waiting Time in the Danish Asylum System on Post-Resettlement Employment among Refugees: Separating the Pure Delay Effect from the Effects of the Conditions under Which Refugees Are Waiting. Edited by Bernardo, L. Q. PLOS ONE 13 (11), e0206737. https:// doi.org/10.1371/journal.pone.0206737. Ibáñez, A.M., Moya, A., Adelaida Ortega, M., Rozo, S.V, and Urbina, M.J. (2022). Life Out of the Shadows: Impacts of Amnesties in the Lives of Migrants. Working Paper 15049. IZA Discussion Papers. https://www.econstor.eu/handle/10419/250710. Immergluck, D. (1998). Job Proximity and the Urban Employment Problem: Do Suitable Nearby Jobs Improve Neighborhood Employment Rates? A Reply. Urban Studies, 35(12), 2359-2368. https://doi.org/10.1080/0042098983926 IOM. (2023). A Region on the Move 2022: East and Horn of Africa. IOM, Nairobi. IPCC. (2019). Special Report on the Ocean and Cryosphere in a Changing Climate. Retrieved from https://www.ipcc.ch/srocc/ Kalter, F., and Kogan, I. (2014). Migrant Network and Labor Market Integration of Immigrants from the Former Soviet Union in

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Input: 20 capacity to offer these services. Many may be at risk of violence or trafficking. These are very important aspects from the perspective of welfare economists interested in measuring well ‐ being but these measurements are complex and not usually included in multidimensional indicators of deprivation or poverty. The dimensions of deprivations to consider are more numerous and more complex to measure. Again, there is very little research in welfare economics dedicated to the special needs of these populations. Risks and vulnerabilities. The analysis of risk and vulnerability is also much more complex in the context of the forcibly displaced. Welfare economics has only approached these topics recently, in the past decade or so. Essentially, the idea is to measure the risk of being poor or falling poor in the future using cross ‐ section or panel data studying spells of poverty over time. This is work that requires accurate and complex data sets that would be rarely available in a refugee or IDP context. More importantly, the nature of the problem changes. Refugees and IDPs are by definition more at risk and more vulnerable than regular populations and these vulnerabilities are not only linked to skills and efforts but to legal status, discrimination, limited mobility and other factors that are unique or much more acute with refugees and IDPs.

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Input: It has been previously used in Pakistan and found to be reliable (Afridi et al., 2021). The Cronbach ’ s alpha of the scale was. 91, indicating high internal consistency reliability. Asset Index. An asset index was calculated by a principal components analysis of a series of questions related to household materials, access to water, and home ownership, among other resource-related factors. For each household, based on respondent report, an asset quintile was created to categorize by quintile from lowest asset index values (quintile 1) to highest asset index values (quintile 5). Flooding Impact. Respondents were asked a series of questions about the impact of flooding on their day-to-day life in both 2022 and 2023. Households were categorized as being “ impacted by flooding ” if they answered “ yes ” to at least one question about whether flooding impacted their day-to-day life in 2022 or 2023. Domestic Violence. Usage context: background mention, primary mention, supporting mention.

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

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Input: Geographic Information Systems (GIS) and geospatial analysis can be used to map, monitor and analyze data on forced displacement. Triangulation of this information with socio-economic and other indicators can provide a rich source of data and enable insights into underlying patterns and trends over time. (e) Use of big data (mobile phone data, news scraping, social media). IDMC is pursuing big data approaches to capturing displacement data in real time in order to report on displacement situations as they are happening and to provide updates on how they are evolving (IDMC 2015). These data are not necessarily representative but can be used in conjunction with other methods to triangulate trends. For example, the Swedish NGO, Flowminder, has pioneered the use of de-identified data from mobile operators to track population displacement caused by natural disasters such as earthquakes in Haiti in 2010 and Nepal in 2015, and these techniques may also have applications in conflict-induced displacement crises. 100 (f) High-resolution satellite imagery and unmanned drones. High resolutions satellite imagery can be used to map physical structures in refugee and IDP camps including changes to the number and type of these over time, support the remote detection of displaced populations in hard to reach or insecure settings; and conduct rapid assessments during or immediately after a mass displacement (Harvard Humanitarian Initiative 2014). Usage context: primary mention, supporting mention, background mention.

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Input: (micro-characteristics of refugees, conﬂict dynamics, and the conditions in the countries of asylum and origin) has made the analysis of return decisions possible. The next section will discuss how we leverage the diﬀerent dimensions of this data set for our purposes. 3. 2 Empirical Strategy The analysis proceeds in two parts: we ﬁrst analyze a panel data set, constructed at the sub-district level within Syria, 12 to understand the relationship between returns and se- curity and access to utilities in Syria. Formally, we estimate the following speciﬁcation: ln (returnssmt) = α + β1 ∆ CEIdmt + β2 ∆ luminositydmt + β3AoCdmt (1) + δd + τt + εdmt, where returnssmt is the number of refugees originally from sub-district s in Syria who returned to Syria in month m of year t. Also included are district ﬁxed eﬀects δd and year ﬁxed eﬀects, τt. 13 Since refugees will make the decision to return home based on past conﬂict events and recent changes in standards of living like electricity reliability, we look at a lag of both the conﬂict events index (CEI) and luminosity. Usage context: supporting mention, background mention, primary mention.

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Input: This distinction is also applied to foreign students, with those entering the system after February 2022 referred to as “ Migrant post-Feb 2022 ” and others referred as “ Migrant pre-Feb 2022 ”. 7 Administrative data from MoE. The Ministry data includes information for all students who enrolled at any point during the academic year. 8 This information covers academic year, grade, gender, birth date, birthplace, citizenship. They also include school-speciϐic information such as the name and identifying code of the institution where the student is enrolled. Furthermore, the dataset includes a variety of school outcome variables, including grades in English, Italian, Mathematics, overall GPA calculated as the average across all subjects, behavior scores from grade 9 to grade 12, guidance council evaluations from lower secondary school, records of absences, late entries, and early exits. Given the timing of this study, data for academic year 2022-23 are the most complete. For academic year 2022-2023, school enrollment data at the provincial level was provided for 4, 269, 348 enrolled students across the 8 years of Italian lower and upper secondary school, encompassing both public and private institutions. The dataset includes nearly all students in the country irrespective of their citizenship. 9 Table 1 shows the distribution of the different groups of students by grade. In the 7 For ease of reference, we refer to non-Italian and non-Ukrainian students as migrants. However, we acknowledge that some of these students may be refugees or displaced students. 8 At the time of writing this paper, both MoE and INVALSI data were not fully available and, as such, only the information on enrollment was used for the academic year 2023-24. 9These numbers do not include students enrolled in Provincial centers for adult education (CPIA). See footnote 5.

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Input: Figure 17: Peace and Foreign Inﬂow Across Cut-oﬀs and Data Sources The basic results are in Table 8 which shows results for equation (9) using the threshold of 0. 008 battle-related deaths per 1000 population. According to this the investment from OECD countries was almost 70 percent larger in peacetime than during conﬂict. The ﬂows from other data sources shows an increase of between 35 and 50 percent. The consistency of this result across very diﬀerent datasets is striking. Note also that the average change in inﬂows implied by these rates is very large. In 2012, average inﬂows in the World Bank dataset were over 9. 5 billion USD and over 3 billion USD in the OECD data. Our estimates therefore imply a gain of between 2 billion and 4 billion USD in yearly inﬂows for countries which emerge from conﬂict. In order to understand the dynamics of recovery it is useful to understand the dynamics of this change around the end of conﬂict. For this purpose we add a set of dummies to the equation above. We construct a dummy that indicates the start of recovery and add three forward and lag dummies to trace average investment around this date. As before we always lag the explanatory variables by one year. Results for the OECD data are shown in Figure 18.

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Input:
The coefficient for storm intensity was estimated using aggregate damages per storm and storm characteristics at landfall from US storms since 1960 (NOAA 2009).

and income at each impacted coastal area. These data were inferred for each year from

decennial Census data by county (US Census of Population 1960, 1970, 1980, 1990,

A separate damage analysis was then conducted of tropical cyclones around the globe (EMDAT 2009). The international data set was used to estimate the coefficients for vulnerability (income and population density).

Note: There were 111 observations in the damage regression and 40 observations in the fatality regression. The t statistics are in parenthesis. The functional form of the regression is log log. Source: NOAA(2009).

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Input:
# **Population** **Data Analysis**

### **Regional Bureau** **for Southern Africa**

**September 2022**

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Input:
This paper combines district-level government spending data from Indonesia and natural disaster damage indices to analyze the extent to which districts are forced to reallocate their expenditures across categories after the incidence of floods, earthquakes, and volcanic eruptions.

local economic activity. The natural disaster damage indices in Skoufias _et_ _al._ (2017) are  constructed by modeling the local strength of each disaster using its physical characteristics  and taking account of local exposure to these aspects using nightlight intensity derived from  satellite imagery.

<!-- PAGE 7 --> ##### **2 Natural Disaster Damage Indices** The methodology and data sources used to make damage indices for natural disasters are  extensively covered in Skoufias _et_ _al._ (2017), and there are also additional details in Ap pendix A.

Based on Remotely Sensed Data: An Application to Indonesia. _Policy_ _Research_ _Working_

_Paper_, **8188** . https://openknowledge.worldbank.org/handle/10986/28365.

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Input: 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Addis Ababa All camp Total Total consumption Pre-assistance income Percent Figure 5.21: Poverty incidence at consumption and pre-assistance consumption levels Source: World Bank Staff based on SESRE 2023. Note: Poverty rates are calculated based on $2.15 in the 2017 PPP line using total consumption and pre-assistance income Scenario 1: no economic opportunities Scenario 2: current scenario Scenario 3: full inclusion USD per capita per year, Dec 2022 prices Figure 5.22: Costs of basic needs per refugee per year under different scenarios Source: World Bank Staff based on SESRE 2023. Note: The costs are in December 2022 prices. Refugees’ Aspirations 55 In addition to their own resources, refugees rely on humanitarian aid to cover their expenditures on food, sanitation, hygienic products, and essential non-food items. “Successful” integration and economic inclusion—that is, earning sufficient income to be no longer poor and to consume more than the (international) poverty line—of refugees bring higher self-reliance and less reliance on humanitarian assistance. This opens two tracks for investigation: (i) First, how much aid would be needed if the policy objective were to bring refugee consumption up to the poverty line. The answer to this

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Input: As summarized by Robinson (2017), “ fortunately, Afrobarometer respondents comprise stratified random samples at all levels, making population estimates based on them unbiased: thus, the major concern with using Afrobarometer sample data to construct demographic measures is unbiased measurement error. ” Based on a comparison of census-based and survey-based diversity indexes across five African countries, Robinson (2017, 224) found that a “ sample-based measure tends to underestimate the overall degree of diversity compared to census data ”. In theory, this should make it more difficult to observe the true relationship between ethnic diversity and some outcomes at the local level. Diversity indices are more likely to be measured with noise in highly diverse communities at the local level. We nonetheless argue that such a concern should not be overestimated, for three reasons. First, such noise cannot easily explain the contrast between the coefficients corresponding to the pre-revised and revised indices and the opposite results found for the revised refugee fractionalization and the revised polarization. This set of results can be explained by the fact that our identification comes from the annual changes in refugees flows. Second, the IV approach is likely to deal with the measurement errors if they are correlated with our main variables of interest. Our IV estimates therefore capture a local average treatment effect coming from the plausibly exogenous increase in annual refugee flows of particular ethnic groups. The similarity of the IV results to the OLS results supports this interpretation. Third, at the cost of introducing attenuation bias30, we also aggregate the number of conflict events at the regional level. Lines B and C of Table 7 confirm the negative and positive effects found for the revised fractionalization and polarization indexes, respectively, whether or not 30Another risk highlighted by Robinson (2017) is the fact that ethnic diversity may also capture different theoretical mechanisms at aggregated levels. 34 Usage context: primary mention, background mention, supporting mention.

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Input:
Source: Electricity Demand Forecast by Planning Power of National Transmission and
Dispatch Company (NTDC) (2014).

15

<!-- PAGE 18 -->
**Figure 3 System Average Interruption Frequency Index**

30 Manufacture of other transport equipment 89
31 Manufacture of furniture 39
32 Other manufacturing 480
Source: Authors' calculations using 2010-11 Census of Manufacturing Industries.

Force Defense Meteorological Satellite Program-Operational Linescan System (DMSP

The source for hurricane tracks is the HURDAT Best Track Data, which provides

end quarterly series of national GDP is available for the Dominican Republic.  We thus

Census of Manufacturing Industries
HURDAT Best Track Data

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Input: These are the women living in the DHS clusters affected by wartime conflict in Mali and make up the treatment group in our study (see figure 2). Figure 2: Wartime conflict occurring within 10km of communities surveyed by the Mali Demographic and Health Survey 2 We check that the results are robustness to changes in the definition of the distance away from conflict events by exploiting other distance definitions- 20km to 50km. This is especially important since the GPS coordinates for randomly selected respondents in the DHS are displaced to protect the confidentiality of respondents. Usage context: background mention, supporting mention, primary mention.

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Input: 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 data enables better planning (and decisions). Integrating refugees as part of the national household survey system could provide high-quality data on a regular basis. This would include the ability to disaggregate data to a subset of the population surveyed and compare refugees with other population groups. This requires strengthening data collection and dissemination mechanisms at all levels. The GoE pledged to include refugee data in national statistics. This would ensure that systems are systematically built to serve all people in a particular “place” regardless of status. This includes the need for a full population count (including refugees) across Ethiopia’s territory 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

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Input: In all these cases violence probably did not aﬀect the entire economy notably. In what follows we focus on positive net ﬂows, i. e. we subtract outﬂows from inﬂows and code negative numbers as 0s. Our results are robust to using gross inﬂows but as these are not provided by all sources. 40See Frome (1983) for a discussion of using the Poisson model to study rates. For a general discus- sion of count data models, see Cameron and Trivedi (2013). Our results are also robust to using year ﬁxed eﬀects instead of exposure. 41The reason is that the OECD data, the Dutch Central Bank data and the UN data allows us to distinguish between net ﬂows and gross ﬂows. 42We also distinguish two diﬀerent ways of calculating the cut-oﬀof intensity using contemporaneous and average population in a country. In total we therefore have 14 diﬀerent estimates per cut-oﬀ. 43Each coeﬃcient is also estimated quite precisely at this cut-oﬀ. 50 Usage context: background mention, supporting mention, primary mention.

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Input: 5. 5 Ethnic diversity at different levels Despite the use of sampling weights in the construction of the diversity indices, we have no guarantee that our diversity indices are representative at the local level. Although similar ethnic diversity indices have been used at the local level (Nunn and Wantchekon, 2011; Rohner et al., 2013; Robinson, 2017; Desmet et al., 2020; Gomes, 2020b, a; Hodler et al., 2020), we cannot exclude the possibility that a lack of representativeness at the local level introduces some noise into our estimates. Ideally, we would have liked to construct our local diversity indices based on census data. However, such data are not available on an annual basis and only a minority of African countries include ethnicity questions on their censuses (Robinson, 2017). Robinson (2017) highlights other benefits but also warns against the risk of using non-random samples or of the size of samples introducing significant errors. Usage context: background mention, primary mention, supporting mention.

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Input: attempt at distinguishing the currently available methods in statistics and economics as well as incorporating the advances from the former into the latter. This is consistent with similar ongoing efforts in other disciplines that build on the multiple imputation method in statistics to better address their own disciplinary needs. 4 Empirically, we illustrate our method with an application to Jordan, a particularly interesting case for analysis. Not much is known about poverty trends since Jordan ’ s Department of Statistics (DOS) last conducted its Household Expenditure and Income Survey (HEIS) in 2010. In the meantime, this country ’ s economy has experienced several major events such as the introduction of new poverty-reduction policies by the government (e. g., in accordance with its recent Poverty Reduction Strategy), economic reforms (e. g., reducing its petroleum subsidies and implementing a targeted cash transfer), and shocks due to higher energy prices. Socio-political change and unrest in neighboring Syria and Egypt also add further uncertainty to the economy. Given this fast evolving context, policy makers are keenly interested in tracking poverty trends on a more frequent and timely basis. In contrast with the HEIS survey which was last conducted in 2010, DOS administers the Employment-Unemployment Survey, a labor force survey (LFS) with wide geographical coverage, on a quarterly basis. Usage context: background mention, supporting mention, primary mention.

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Input: 13 Second, each individual in the JLMPS restricted sample is matched to the 2010 Jordanian school census. The matching process determines for each individual the number of sex- appropriate public basic and secondary schools per 1, 000 individuals available in the individual ’ s subdistrict of birth when the individual was of age to accede to this educational level (six years of age for the basic level and 15 years of age for the secondary level). 7 A school is considered sex-appropriate for a female if it is a girls ’ or a mixed school and for a male if it is a boys ’ or mixed school. The empirical analysis is also performed by entering boys ’, girls ’, and mixed schools separately. 8 Measuring the local supply of public schools at the subdistrict of birth of the individual (i. e., the child) mitigates potential endogeneity originating from parents who had a higher taste for schooling moving to subdistricts where public schooling was more abundant when their child was of school age, although it is not possible to rule out that parents might have moved across subdistricts prior to the birth of their child. Usage context: background mention, supporting mention, primary mention.

2010 Jordanian school census
JLMPS restricted sample

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

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Input: Children and adolescents ’ long-term exposure to households with a more equalized division of domestic labor, as a result of violent or political conflict, warrants further investigation. 2. 3 Country contexts The countries with subnational regions covered in this study, using data from 2017 or 2018, are Ethiopia, Nigeria, Somalia, South Sudan, and Sudan. All are located in Sub-Saharan Africa, have undergone or are currently involved in armed conflict, and are affected by environmental issues such as drought, famine or flooding. Despite some commonalities, each faces a unique set of social, political and economic challenges, which cannot be accurately covered in this study. However, to contextualize the findings, a brief introduction of the country context is presented alongside the poverty estimates by the $ 1. 90 / day measure and the global Multidimensional Poverty Index (MPI). 3 3 An international measure of acute multidimensional poverty, aligined with the 2030 Agenda, that captures deprivations in health, education, and living standards for more than 100 countries (Alkire and Jahan 2018; Alkire, Kanagaratnam and Suppa 2020).

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Input:  In Somalia, the Mogadishu Household Survey covered both residential areas and IDP camps, collecting data on expenditures, demographics and living conditions [survey completed; analysis forthcoming]. As part of the Somalia Knowledge for Operations and Political Economy (SKOPE) initiative, the Puntland Household Survey will also cover both residential and IDP populations [ongoing].  An IDP study in South Sudan [ongoing] aims to assess the economic needs of IDPs and host communities in urban areas, covering livelihoods, water and sanitation, infrastructure as well as intentions and conditions to return. Basic information about education, employment and general health variables will also be collected.  The Iraq Crisis Response Study [ongoing] will assess the impact of the Islamic State and oil price-related crises on IDPs and households left behind in IS controlled areas.

Iraq Crisis Response Study
Mogadishu Household Survey
Puntland Household Survey

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Input: Most male principal applicants are one of a married couple with children whereas most female principal applicants are single care ‐ givers, single persons or living in non ‐ traditional family groups. While on average female principal applicant households are no more likely to be poor than male principal applicant ones, poverty rates for some types of households are higher when these households have a female principal applicant. Households that have formed because of the unpredictable dynamics of forced displacement, such as sibling households, unaccompanied children, and 6 Identification of the head of the case (as family groupings are referred to in the UNHCR ProGres database) is determined by who best represents the family for case management purposes. It is not assumed that the household will be best represented by a man; a woman or even a child can be a head of a case, depending on standard operating procedures. 7 Even when traditional household survey data are gathered at the individual level, the information is often collected from a single respondent. The respondent is usually the self ‐ identified ‘ most knowledgeable ’ household member, which overwhelmingly corresponds to the ‘ head ’ of the household. In the case of a household survey that solicits information on ‘ headship ’, this information is gathered often through the question: “ Who is the head of this household? ” Usage context: background mention, supporting mention, primary mention.

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Input: isted, including cellphone credit, supermarket vouchers, and electronic transfers. Appendix D discusses VenRePS and the data collection procedures in more detail. III. C Outcomes The analysis of PEP ’ s impact focuses on three groups of outcomes: the socioeconomic well- being of forced migrants (herein “ migrants ”), their access to rights and services, and their labor market outcomes. The ﬁrst dimension, well-being, is the focus of this article, while the latter two delve into potential mechanisms. Each dimension includes the individual outcomes described below and an index estimated following Kling et al. (2007) to summarize each dimension. Speciﬁcally, the three dimensions of outcomes are: (i) socioeconomic well-being encom- passes consumption, income, and employment; (ii) access to rights and services captures effective access to PEP ’ s direct beneﬁts and services that are not available to migrants with- out it; 10 and (iii) labor market outcomes include holding a formal job, hours worked, reser- vation wage, job satisfaction (measured as the inverse of the desire to ﬁnd a different job), and self-employment. We deﬁned these outcomes and dimensions of interest following a preanalysis plan reg- istered before data collection (see Ib ´ a ˜ nez et al. 2020). Usage context: background mention, primary mention, supporting mention.

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

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Input: 17 Figure 10: Children aged 7-12 attending school (%) Source: Listening to Displaced People Survey, 2014 and 2015. 5. Challenges Faced by Returnees The results suggest that returnees were less affected by the crisis than IDPs and refugees. This is reflected in data on asset and livestock ownership, but also in the information pertaining to exposure to violence. Returnees reported fewer victims; fewer returnees reported to have lost income as a consequence of the crisis; more of their children were able to continue schooling; and relative to IDPs and refugees, fewer perceived being poorer in June 2014 than before the crisis. Returnees are also the group that feels most secure, that has high levels of trust in the Malian army and police and that has a positive attitude towards most government policies. 88 88 97 79 76 78 99 88 92 55 74 72 91 98 92 96 100 85 90 91 96 90 86 87 87 95 89 76 75 90 94 92 97 86 73 98 Bamako Gao Timbuktu Kidal Niger Mauritania IDPs Returnees Refugees 14-Aug 14-Oct 14-Nov 14-Dec 15-Jan 15-Feb Usage context: primary mention, background mention, supporting mention.

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Input: of the groups and the distances between them. For instance, (Bazzi et al., 2019) shows that polariza- tion increases ethnic attachment. Others have highlighted the reduction in trust, either interpersonal trust or institutional trust Alesina and Ferrara (2002); Beugelsdijk and Klasing (2016). To assess the importance of alternative explanations, we first replicate our analysis using individual data on violence. In addition to participation in protests, we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault. We then assess the relationship between the revised refugee diversity indices and alternative individual outcomes such as ethnic vs. national identity, generalized trust, trust in neighbors, and institutional trust (trust in government). The questions from the Afrobarometer mentioned below are used as a proxy for these outcomes: 32 1 Attack: Over the past year, how often (if ever) have you or anyone in your family: Been physically attacked? 2 Crime: Over the past year, how often (if ever) have you or anyone in your family: Feared crime in your own home? 3 National identity: Let us suppose that you had to choose between being a [Ghanaian / Kenyan / etc.] and being a [respondent ’ s identity group]. Usage context: primary mention, supporting mention, background mention.

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Input: 21 Distance from Rebel Group Headquarters We coded the location of the headquarters of the rebel groups participating in the conflicts under study, and calculated the distance from each square to the most proximate rebel group headquarters (we do not know a priori which rebel group or government that will act in a particular square). As the `distance from capital'variable, it was coded as the distance in terms of squares and log-transformed. Border Square We coded squares as border squares if a national border runs through it. Such squares belong to more than one country and are not straightforward to code. We coded national- level information for border squares according to the following rule: A border square was considered to belong to the country that was most frequent among the eight neighboring squares. In tie cases, we assigned nationality randomly between the tied countries. Interaction country-square population This variable was created to test the population settlement pattern hypothesis. It is an interaction between population count at a location (square) as a portion of the country's total population. Road type Road type is a variable by ESRI that is available in the Digitial Chart of the World Data. It is a high resolution dataset at 1: 1, 000, 000 scale and consists of arcs which indicate road mass. Usage context: primary mention, background mention, supporting mention.

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Input: ethnic group e at time t. It can also be expressed as one minus the Herfindahl index (Alesina et al., 2016). The EP index gives more weight to intergroup differences at the expense of within group homo- geneity. It can be defined as (Esteban and Ray, 1994, 1999; Montalvo and Reynal-Querol, 2005) 14 EPjt = Nrt X e = 1 (g2 et) (1 − get). (3) We compute this index for each cluster at the time of each Afrobarometer survey to assess how refugee-induced changes in diversity differ from standard indices of diversity. In order to construct the revised refugee diversity indices according to ethnicity e, we first combine information about the country of origin of refugees hosted in refugee camps c in year t with the data from the EPR-ER 2019 dataset. The EPR-ER records the ethnic composition of refugee stocks originating from neighboring countries and countries in proximity to each other (maximal distance between country borders ≤ 950 km) with at least 2, 000 refugees and provides the ethnic composition of refugees (Vogt and Girardin, 2015). More specifically, the EPR-ER dataset gives us the share of refugees from ethnic group e moving from country o to country d at year t. The EPR-ER data gives us the three main ethnic groups. Usage context: background mention, primary mention, supporting mention.

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Input: of Forced Migration. Paper presented at the Annual Meeting of the Population Association of America, Dallas, TX. Maystadt, J. F., and Verwimp, P. (2014). Winners and Losers among a Refugee-Hosting Population. Economic Development and Cultural Change, 62(4), 769-809. MoE. (2022). Education Statistics Annual Abstract (ESAA). MoE MoLSA. (2019). Revised Directives on the Issuance of Work Permits for Expatriates in Ethiopia. MoTRI, MoLS , MoR and RRS. (2023). Memorandum of Understanding (MoU) to allow recognized asylum seekers and refugees to engaged in formal business. MoLS and RRS. (2023). MoU on technical and vocational training, work permit, job creation and livelihood improvement, and related issues for refugees. MoLS and RRS. (2023). Technical and vocational training for refugees, job creation and livelihood improvement, work permit and related issues service delivery manual (Standard Operating Procedure). Muna, S. (2019). In Pursuit of Self-reliance: Perspectives of Refugees in Jordan. Archnet-IJAR, 13(3), 612-626. https://doi. org/10.1108/ARCH-04-2019-0085 Nguyen, N. T. V., Savadogo, A., and Tanaka, T. (2021). Refugees in Chad: The Road Forward. The World Bank. Norman, T., Borjesson, M., and Anderstig, C. (2017). Labor Market Accessibility and Unemployment. Journal of Transport Economics and Policy. Pape, U. J., Petrini, B., and Iqbal, S. A. (2018). Informing Durable Solutions by

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Input:
**Focus on key protection risks**, **responsive and rehabilitation actions** : Mainly related to Protection of
civilian in high combat intensity areas as well as hard-to-reach populations in AGEs controlled areas
should access allowed, new displacement due to the conflict, and equity of access to services by
vulnerable groups.

**Severity of needs and response capacity** : Consideration will be given to the number of affected
populations, especially children, in the contested, IEA and government controlled areas. Presence of
operational partners and response capacity will be also taken into consideration. As of 1 March 2017
the access snapshot indicates approximately 54% of the territory is controlled or contested by NSAG.
As of January 2017, the 3W Map shows the humanitarian community to be present as follows (if BPHS
staff are included then the number of staff will increase in all areas): Areas under control of
government (30,349 staff), contested areas (13, 279 staff), NSAG control (428 staff).

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Input: Most male principal applicants are one of a married couple with children whereas most female principal applicants are single care ‐ givers, single persons or living in non ‐ traditional family groups. While on average female principal applicant households are no more likely to be poor than male principal applicant ones, poverty rates for some types of households are higher when these households have a female principal applicant. Households that have formed because of the unpredictable dynamics of forced displacement, such as sibling households, unaccompanied children, and 6 Identification of the head of the case (as family groupings are referred to in the UNHCR ProGres database) is determined by who best represents the family for case management purposes. It is not assumed that the household will be best represented by a man; a woman or even a child can be a head of a case, depending on standard operating procedures. 7 Even when traditional household survey data are gathered at the individual level, the information is often collected from a single respondent. The respondent is usually the self ‐ identified ‘ most knowledgeable ’ household member, which overwhelmingly corresponds to the ‘ head ’ of the household. In the case of a household survey that solicits information on ‘ headship ’, this information is gathered often through the question: “ Who is the head of this household? ”

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

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Input: 7 Figure 4 ‐ Research on Migration, Refugees and IDPs (% of total hits) Source: Authors ’ estimations based on Econpapers, SSRN and Google Scholars searches. This phenomenon can be explained by essentially two factors. The first relates to the humanitarian ‐ development nexus. For the longest time, refugees and IDPs remained the quasi monopoly of humanitarian organizations whose mandate is essentially the humanitarian protection of refugees and IDPs. These organizations are not typically staffed by economists and analysists but by field workers and lawyers. There was, therefore, little demand for hard economics on forced displacement for a very long time. This is changing as development organizations typically staffed by economists have started to work on forced displacement situations. The second factor relates to lack of good data. As we will see in the data section, data collection of mobile populations is complex and the main organizations in charge of data collection of refugee and IDPs data are humanitarian organizations that do not necessarily have the complex skills required for issues like sampling, questionnaire design and data analysis and have a duty to protect data by mandate. This, in turn, has resulted in very few micro data that would be both of good quality and accessible to researchers. Usage context: background mention, primary mention, supporting mention.

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

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Input: One interesting pattern is that We ﬁnd that more educated Venezuelans are more likely to report being discriminated against. A potential explanation for this, consistent with the previous results, is that higher skilled Venezuelans are disappointed with their situation in Peru, especially when they settle in areas with strong labor markets, and this lack of opportunity is either caused by or perceived as discrimination (Guerrero-Ble et al., 2020). In the second part of our analysis, we examine the impact of immigration in terms of changes in the number of Venezuelans as a share of the local population in a province on a wide-variety of outcomes. We rely on administrative data to measure the number of Venezuelans newly registered in each district in Peru on a monthly basis between January 2015 and December 2020. We aggregate this information at the province level, which roughly corresponds to a labor market. Having a time-varying measure of the presence of Venezuelans in each of the 198 provinces allows us to use repeated cross-sectional data on outcomes for Peruvians and control for location and time ﬁxed eﬀects, as well as, location-speciﬁc time-trends. Hence, we identify the impact of the presence of Venezuelans by examining how outcomes for Peruvians change when more Venezuelans arrive in a province, conditional on the trend in that outcome. However, it is possible that local shocks impact both the destination choice of Venezuelans and outcomes for Peruvians, hence we also use an instrumental variable strategy where we in- strument for the number of Venezuelans in a location with the presence of Venezuelans in that 3 Usage context: background mention, supporting mention, primary mention.

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Input: 15 3. Methodology and Data 3. 1. Data The UNHS survey is used by the GoU to monitor the well-being of the population. The Uganda Bureau of Statistics (UBOS) has conducted the survey every three to four years since the 1990s, providing a series of comparable data stretching more than 20 years. The last UNHS survey was conducted between July 2016 and June 2017, collecting data from around 15, 000 households in all 112 existing districts of Uganda at the time. The UNHS 2016 / 17 is representative at the national, rural-urban, regional and sub-regional level, and gathers information on the socio-economic characteristics of the household (including income and consumption) and its members (including educational attainment, health status and use of services, etc.). Thus, it is possible to allocate all fiscal interventions for each household in the survey, as explained in detail in the next sections. 3. 2. Summary of CEQ Methodology14 The CEQ Assessment takes specific fiscal policy elements, programs, expenditures, or revenue collections and allocates them to individuals and households appearing in the UNHS 2016 / 17. Usage context: supporting mention, primary mention, background mention.

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Input: These findings were also echoed in the context of other developing countries such as India (Allard et al., 2022) and Zimbabwe (Mabugu, Maisonnave, Henseler, Chitiga-Mabugu, & Makochekanwa, 2023). In the MENA economies ’ context, the evidence is considerably sparcer. Early work on the impact of the COVID-19 pandemic on labor markets in the MENA region re- lied on high-frequency phone surveys and highlight important job losses among wage workers and an uneven impact across industries (Krafft, Assaad, & Marouani, 2021, 2022). Providing evidence from Labor Force Surveys in the Islamic Republic of Iran, Dang and Salehi-Isfahani (2023) find that the pandemic exacerbated the pre-existing low participation of females in the labor force. Wahby and Assaad (2023), on the other hand, focus on the impact of the pandemic on Syrian refugees in Jordan and find a divergence in job finding and separation rates of Syrian refugees relative to their Jorda- nian hosts after the onset of the pandemic. Focusing on cross-border commuters in the West Bank and Gaza, Adnan and Etkes (2022) find that undocumented commuters benefited relative to their documented peers after the pandemic, as Israeli policies inadvertently created incentives for employers to favor the former. This sharply con- trasts the results by Borjas and Cassidy (2020) on the impact of the pandemic on immigrants in the United States. The rest of this paper is organized as follows. Section 2 provides background information on labor markets in the West Bank and Gaza, as well as background in- formation on the COVID-19 pandemic and government responses. Section 3 describes the data. Section 4 discusses our methodology. Section 5 presents the main regression results and investigates heterogeneous effects. Section 6 provides robustness checks. Finally, we provide concluding remarks in Section 7. 4

Labor Force Surveys
high-frequency phone surveys

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Input: If that person is a 10, where would you put yourself? ” Locus of Control The standardized total score from responses to four locus of control questions. “ In the last 7 days, how many days did you feel that to a great extent your life is controlled by accidental / chance happenings... ” Allocation Decision Game Indicator (yes / no) for response to an oﬀer to participate an allocation committee to decide how money is spent. Participants are oﬀered the opportunity to make a resource allocation decision for their community or have another individual (an NGO worker, an “ expert ”, or another refugee) make the decision. Stability Index The standardized total score from responses to two stability questions using a Cantril ladder. “ How secure [do you feel / think you will feel] [at present / ﬁve years from now] ” Physiological Index An inverse-covariance weighted average of PHQ, Stress, Life Satisfac- tion, Sociability (Total), Self-Worth, Locus of Control, and Stability indices. Gender Dynamics Gender Perceptions- Work The standardized total score of two questions regarding women ’ s work, “ How often would you agree that women should be allowed to work for a living [inside / outside] the block? ” 64 Usage context: background mention, primary mention, supporting mention.

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Input: Medium ‐ intensity, on the other hand refers to “ regular armed clashes between governments, government forces and insurgents. ” (IISS 2017) Within the first category, according to the Armed Conflict Survey in 2016 are Afghanistan, Syria, Somalia, South Sudan, and Iraq, while Myanmar, Central African Republic, Democratic Republic of the Congo, and Sudan are in medium ‐ intensity conflict. (Eritrea is not included in the Armed Conflict Survey in 2017, discussed in Annex). Minorities in Albania, Kosovo and Serbia are often the objects of discrimination but countries are not in conflict. A capsule summary of the security and social situation in each of the other countries included in this survey can be found in Annex 2. The survey illustrates how impractical return is today in the countries in conflict, such as Syria, Afghanistan, and Iraq, and voluntary return in large 36 Best practice and new methods in return policy, July 2017, http: / / www. bamf. de / SharedDocs / Meldungen / EN / 2017 / EMN / 20170504 ‐ emnjahrestagung ‐ rueckkehr. html Usage context: background mention, primary mention, supporting mention.

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Input: According to Council on Foreign Relations, the estimated number of people killed since December 2013 is over 50, 000, and over 1. 6 million people are internally displaced66. Economic Opportunity South Sudan has abundant natural resources. Before its oil production fell sharply, the government relies on oil for its revenue. It also has very fertile soils and abundant water supplies. South Sudan has struggled with economic development since its independence and its economic conditions have deteriorated since January 2012 when the government decided to 61 IMF, Six Things to Know about Somalia ’ s Economy, April 11, 2017, http: / / www. imf. org / en / News / Articles / 2017 / 04 / 11 / NA041117 ‐ Six ‐ Things ‐ to ‐ Know ‐ About ‐ Somalia ‐ Economy 62 UNHCR, Somalia, http: / / reporting. unhcr. org / node / 2550? y = 2016 # year 63 Human Rights Watch, World Report, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / south ‐ sudan 64 Human Rights Watch, World Report, https: / / www. hrw. org / world ‐ report / 2017 / country ‐ chapters / south ‐ sudan 65 UNHCR, South Sudan http: / / reporting. unhcr. org / node / 2553 66 Council on Foreign Relations (CFR) https: / / www. cfr. org / global / global ‐ conflict ‐ tracker / p32137 #! / conflict / civil ‐ war ‐ in ‐ south ‐ sudan Usage context: primary mention, background mention, supporting mention.

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Input: 2 regards to who is poor, how poor they are, and the composition of their poverty. Based on the Alkire-Foster (AF) method, the index provides a summary measure of poverty for the population that can be disaggregated by displacement status and gender of the household head to analyze the variation in deprivations. The MPI can be further broken down by indicator to show the proportion of the population who are poor and deprived in each area. These features of the MPI can inform better policy responses, with interventions and programs targeting the most deprived communities and indicators with the highest headcount ratios. The paper proceeds as follows. Section 2 of the paper reviews some of the existing literature to provide the background and motivation for the analysis, including a summary of the different country contexts covered by the data analyzed in this paper. Section 3 outlines the Alkire- Foster method and the selected dimensions and indicators used to construct the MPI, followed by Section 4, which introduces the data. Section 5 presents the findings, first for results at the national level and then results disaggregated by displacement status. Section 6 analyzes differences in multidimensional poverty by gender of the household head to improve understanding of the gendered aspects of multidimensional poverty in these contexts. Usage context: primary mention, supporting mention, background mention.

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Input:
Findings from the 2014 Agricultural Census suggest there are over 317,000 family farms [4] of 1.48 hectares on average (Statistical Committee of the Republic of Armenia, 2016), which produce approximately 95% of the country's gross agricultural output (Statistical Committee of the Republic of Armenia, 2021).

4 This estimate of over 317,000 farms includes those farms that had their own land (including leased land) at the time of the 2014 Agricultural Census, as well as farms that leased land but did not have their own land.

Statistical Committee of the Republic of Armenia. (2016). Main Findings of Agricultural Census 2014 of the
Republic of Armenia. [http://armstat.am/en/?nid=82&id=1860](http://armstat.am/en/?nid=82&id=1860)

<!-- PAGE 8 --> of enumeration areas was based on the Population Census 2011, and a fresh household listing was conducted in each of the selected EAs to attain a current household sampling frame from which to randomly select 12 households in each EA.

Todorov, A., & Kirchner, C. (2000). Bias in proxies' reports of disability: data from the National Health
Interview Survey on disability. _American Journal of Public Health_, 90(8), 1248.

UNECE. (2005). Inventory of Land Administration Systems in Europe and North America. Produced and
published by HM Land Registry. London, United Kingdom on behalf of the UNECE Working Party
on Land Administration.

2014 Agricultural Census
Population Census 2011

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Input: In a second paper evaluating the same UCT program, the authors find significant impacts (7. 8 percentage points) on enrollment of children 12 and older but no impacts on younger children. The finding that cash transfers had no impact on the enrollment of children under 12 could be related to the fact that primary (but not secondary) education is free in Kenya. The literature on the effectiveness of assisting return migrants to Afghanistan includes an evaluation of a shelter assistance program implemented by UNHCR. UNHCR provided post-return shelter assistance to Afghans between 2009 and 2011. Loschmann, Parsons, and Siegel (2005) use a multidimensional poverty index comprising four dimensions: economic welfare, health, education, and basic services. To address selection bias, they use a propensity score matching approach. They estimate that the shelter assistance 2 The cash transfer programs studied were implemented in Honduras, Indonesia, Morocco, Mexico (two programs), Nicaragua, and the Philippines. Usage context: background mention, primary mention, supporting mention.

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

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Input:
Additionally, insights were drawn from the Kenya COVID-19 Rapid Response Phone Surveys (RRPS), [[9]] conducted

between 2020 and 2022, which included the Shona as a distinct stratum. The RRPS monitored the impact of the

pandemic on vulnerable groups, capturing critical data on employment disruptions, income losses, and food

insecurity during the crisis.

**The Shona community in Kenya has faced significant socioeconomic challenges, exacerbated by their stateless**

**status and the impacts of the COVID-19 pandemic.** Before acquiring citizenship, Shona households faced a 24

percent higher likelihood of living in poverty compared to the urban Kenyan population and were more likely to

reside in larger, overcrowded households. Although primary school enrollment rates were similar to those of their

Kenyan counterparts, many Shona children struggled to transition to secondary education. Limited access to formal

employment, driven by a lack of legal documentation, has forced many Shona individuals into self-employment or

informal work. Even with high employment rates within the community, this does not translate into reduced

poverty, as most of the employment is low-income and lacks stability. [[8] ]

**[www.unhcr.org](http://www.unhcr.org/)** 3

Kenya COVID-19 Rapid Response Phone Surveys

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