Input:
Usual) which would lead to even higher emissions.

We rely on four climate models: CNRM (Gueremy et al. 2005), ECHAM

(Cubasch et al 1997), GFDL (Manabe et al. 1991), and MIROC (Hasumi and Emori

shown in Figure 1. Note that a range of temperature changes are predicted for this

emission scenario.

This study

Using a tropical cyclone generator in each ocean basin, the climate data is used to

project 17,000 tropical cyclone tracks (Emanuel et al. 2008). There are 3,000 tracks in

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Input: 6 3. Data Sources and Methods 3. 1. Administrative data on educational outcomes in Italy Two administrative data sources represent the backbone of this paper. These are the administrative data obtained from the Ministry of Education (MoE) for academic years 2021-22, 2022-23 and 2023- 24, and standardized test score data from the Italian National Institute for the Evaluation of the Educational System (INVALSI) for the 2022-2023 academic year. These datasets offer valuable insights into the educational outcomes of students in Italy, including Ukrainian refugees who entered the Italian school system following the invasion in 2022. Deϐinitions. In both datasets, students are categorized into ϐive demographic groups based on their nationality and timing of entry into the Italian educational system. These groups are Italian students, Ukrainian refugee students, non-refugee Ukrainian students, newly arrived foreign students, and other foreign students. Among Ukrainian students, the distinction between refugees and non- refugees is based on their enrollment date in the Italian education system. Ukrainian refugees are deϐined as Ukrainian students who enrolled in Italian schools after February 2022. In this paper, Ukrainian refugees are labeled “ Ukr post-Feb 2022 “, while non-refugee Ukrainians are labeled “ Ukr pre-Feb 2022 “.

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Input: UNICEF (2017) reports that for boys, child labor, school violence, and the high costs of schooling (for transportation and stationery supplies) are the main barriers to their enrollment. For girls, barriers include the distance to the nearest school, the high cost of transportation, the need to help with household chores, health problems, and families refusing to educate their daughters. According to the JD ‐ HV database, the most common reasons parents gave for their children not attending school were financial constraints (35 percent), lack of capacity in schools (29 percent), or that children were required to work to support their family (14 percent). By 2016, the enrolment rate of Syrian refugee school ‐ age children in Jordan was 83 percent, 54 percent in formal education, and 29 percent in nonformal education (World Bank 2017b). 10 Refugee Employment It has been hard for refugees to find work in Jordan. The slowdown in growth in Jordan pre ‐ dates the arrival of Syrian refugees and the economy has been increasingly unable to absorb new labor market entrants. Between 2010 and 2016, labor force inactivity increased, employment decreased, and unemployment increased in Jordan (Malaeb and Wahba, 2018). Usage context: primary mention, supporting mention, background mention.

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Input: conomic characteristics. The data suggests that both groups were comparable and that those referred by forced migrant organizations were not more vulnerable before migration. Of 15 characteristics analyzed, only the time of settlement in Colombia was statistically different between groups. While this difference is mechanical (because RAMV migrants migrated earlier in general and likely referred other forced migrants who migrated around the same time), it is also small (less than one month). Moreover, in Figure C. 1, we show that date of arrival was uncorrelated with an index constructed with baseline socioeconomic character- istics of migrants in our sample during our period of analysis. We address concerns related to biases introduced by the characteristics of migrants sam- pled through different sources by estimating the local effects for RAMV and non-RAMV migrants who migrated around the RAMV cutoff date. First, we checked the internal valid- ity of this empirical strategy by showing that RAMV and non-RAMV migrants who arrived around the cutoff date were comparable based on a rich set of baseline observables (Table 1). Second, we checked for the comparability of RAMV and non-RAMV referrals from or- ganizations and the comparability of RAMV and non-RAMV referrals from other migrants (Tables B. 2 – B. 3). Usage context: supporting mention, primary mention, background mention.

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

reported consumption data
reported income data

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Input: The 2005 insurance policy's surety bond requirement created delays for three smaller contractors who lacked access to the commercial surety market at competitive rates. To resolve this, the PIU negotiated a framework agreement with two national insurance companies approved by the Insurance Regulatory Authority to provide project performance bonds under the 2005 insurance policy at standardized premium rates. This arrangement reduced average bond issuance time from 22 days to 4 days and has been incorporated into the standard bidding documents for subsequent civil works contracts.

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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].

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Input: Although much more suited to geographically disaggregated analysis than other datasets, this location dataset has some limititations, It does not record changes over time in the center location and extent of conflicts, and it reports the total extent of the conflict zone without distinguishing between areas that saw repeated and extensive fighting and those that only experienced scattered activities or individual events far from the center of the conflict. 3. 2 Disaggregated Dependent Variable: ACLED The ACLED dataset (Raleigh & Hegre, 2005) deals with these problems. The dataset takes the PRIO / Uppsala Armed Conflicts Dataset as its point of departure. The dataset is limited to events within conflicts that fall within the Uppsala conflict definition; conflicts involving two parties, one of which is a government, and fighting resulting in at least 25 battle deaths. 3 ACLED is designed to parse out both the temporal and spatial actions of rebels and governments within civil wars. 3See the PRIO / Uppsala Armed Conflict Data codebook for more information (Strand, Wilhelmsen & Gleditsch, 2004). Usage context: background mention, supporting mention, primary mention.

PRIO / Uppsala Armed Conflicts Dataset
ACLED dataset

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Input: Our analysis exclusively uses data from the 1951-2015 UNHCR Population Statistics Reference database (extracted September 18 2015). Data were provided for 173 countries: 77 percent of these data were based on individual refugee registration, 13 percent on estimates, 5 percent on combined estimation and registration, and 5 percent on other sources. The data are structured as follows: for each situation, the database records annual numbers of persons of concern, which comprise “ Refugees (including refugee-like situations) ”, “ Asylum seekers ”, “ Internally Displaced Persons ”, “ Returnees ”, “ Stateless ” persons, and “ Others of concern ”. A situation is a pair country of origin / country of destination. For example, Somali refugees in Kenya account for one situation, Somali refugees in Ethiopia for another, and South Sudanese refugees in Kenya for yet another. Furthermore, a situation is considered major if it involves more than 25, 000 people. It is referred to as protracted if it is major for at least 5 continuous years. The database, and therefore our analysis, is limited to refugees under UNHCR protection. It does not include asylum seekers, i. e. individuals who have sought international protection under the 1951 Convention but whose claims for refugee status have not yet been determined, and persons in “ refugee-like situations ”, i. e. individuals outside their country or territory of origin who face protection risks similar to those of refugees, but for whom refugee status has, for practical or other reasons, not been ascertained (e. g., undocumented Rohingya originating from Myanmar 4 Usage context: supporting mention, background mention, primary mention.

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

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Input:
Production of soybeans QSB 41,000 metric tonnes Faostat [a] Imports of soybeans MSB 27 metric tonnes Faostat [b] Export of soybeans XSB 14,445 metric tonnes Faostat [b] Diesel consumption D 596.84 mil. liters Energy Sector Report [c]

Diesel price PD 0.92 $/liter Energy Sector Report [c]

Report of the Status of
Diesel tax t 0.30 $/liter
Petroleum Industry [d]

MPG biodiesel/MPG diesel λ 0.91 de Gorter et al. (2011)
Local price of soybeans at Beira linked to Argentian price PSB 370.24 $/metric tonne HighQuest Partners (2011)

Biodiesel price in Germany PB 1.64 $/liter UFOP [e]

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Input: Geospatial data from the national land registry, including parcel boundary polygons, ownership classification codes, and historical transaction records, were reviewed during project preparation to assess land tenure risks in the areas earmarked for infrastructure works. The geospatial data revealed that 23 percent of subproject sites intersected with unresolved customary land claims, triggering the preparation of individual resettlement plans for the affected sites. Geospatial data will be updated following the completion of land demarcation activities planned under Component 1.

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Input:
**Component 3: Institutional Strengthening and Capacity Building (US$2.1 million)**

This component will finance: (i) a functional review of the national disaster risk management agency's M&E systems, including an assessment of the SIGAF modules currently in use for project accounting; (ii) training of 120 national and subnational staff in financial management, procurement, and environmental and social risk management; and (iii) development of a communications strategy to disseminate project results to beneficiaries and the public. Capacity building activities will be designed around the competency gaps identified through a training needs assessment completed during project preparation.

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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, background mention, supporting 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).

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Input:
**Procurement Performance**

As of the midterm review, all prior review contracts have been awarded and counter-signed, and 83 percent of goods and non-consulting services contracts have been awarded against the Year 2 procurement plan. Delays in two civil works contracts are attributed to a late ESMF clearance for one subproject and to difficulties in obtaining land access certification for a second. Both contracts are now unblocked, and the STEP system has been updated to reflect the revised award dates. No misprocurement has been identified during Bank reviews of contract documentation.

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Input:
Our nightlights data set is composed of 22 satellite-year composites for the period 2000 2013. Each composite covers Central America and contains information on 604,473 one kilometer square grid-cells.

To measure the distribution of surface winds from hurricanes in Central America during our sample period, we use the wind field model developed by Pita et al. (2015). This model uses an asymmetric Holland equation that has been specifically calibrated for Central America.

Data on the distribution of mangroves come from two sources. The first is a collection of harmonized maps, 1960 to 1996, that was assembled for the Mangrove World Atlas (Spalding et al., 1997). We use this map to identify areas that have historically supported mangrove habitats.

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Input: standard ﬁxed or random eﬀect approach is not suﬃcient to ensure correct inference; clustering standard errors by individual is necessary. This is what we do. Having described how the dependent variable is constructed, we turn to regressors. We begin by describing how we construct an estimate of g E [yhs | zh], the level of income (or consumption) yh s that a migrant with characteristics zh can expect to earn in district s. To construct such estimate, we use the 1995 / 96 NLSS data. The reason for using the 1995 / 96 data instead of the 2002 / 3 NLSS survey is to avoid reverse causation, i. e., migration causing a change in income patterns. Migrants are unlikely to be able to accurately predict the evolution of incomes in each district over time. Income and consumption levels observable before migration are thus a reasonable starting point.

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Input: 24 According to Table 12, all countries but Sudan show a significant relationship for school- age children ’ s experience of intrahousehold inequality and their displacement status, although sample sizes mean that only Nigeria and Somalia ’ s results are robust. In Nigeria, most children experiencing intrahousehold inequality reside in non-displaced households – although it is crucial to note that these children constitute most of the school-age children (71. 1 %), and the levels of intrahousehold inequality are nonetheless far higher than anticipated if displacement status had no effect. In Somalia, displaced children are significantly more likely to experience intrahousehold inequality in school attendance, as they constitute 63. 5 % of school-age children experiencing intrahousehold inequality even though they only make up 35. 3 % of the school-age children population in the sample. For the years of schooling indicator, the overall lack of intrahousehold inequality among the MPI poor in years of schooling obscures meaningful or robust differences by displacement status. Gender and displacement status appear to jointly have significant impacts in school attendance in Ethiopia, Somalia, and South Sudan. In Northeast Nigeria, it appears that displacement status has larger effects than gender. In Somalia, forcibly displaced school children experience intrahousehold inequality more often than non-displaced children, to the disadvantage of girls. Usage context: supporting mention, background mention, primary mention.

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Input: 2017 SESRE 2023 (In camp) Hosts Refugees Hosts Refugees Country of origin South Sudanese 23% 53% Somali 24% 30% Eritrean 25% 5% Sudan 28% Demographics Female headed 35% 66% 44% 73% Children (0 to 14) 55% 61% 47% 53% Youth (15 to 24) 13% 17% 19% 21% Adults (25 to 64) 29% 22% 31% 24% Elderly (>=65) 2% 1% 3% 2% Education Net primary enrollment 74% 79% 75% 69% Net secondary enrollment 35% 13% 39% 22% Living conditions Own a house 72% 5% 69% 14% Overcrowding 32% 59% 36% 56% Improved sources of water 96% 98% 91% 100% Access to electricity (grid) 46% 8% 41% 3% Improved toilet facility (shared/not shared) 51% 69% 38% 43% Employment Employed 61% 22% 48% 25% Unemployed 2% 6% 9% 19% Inactive, not in school 23% 44% 20% 23% Inactive, in school 14% 27% 24% 33% Poverty incidence US$1.9 per capita per day 27% 65% 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:
Landscan Population Data. Oak Ridge National
Laboratory.
Hallegatte, S., Bangalore, M., Bonzanigo, L., Fay, M., Kane, T., Narloch, U., Rozenberg, J., Treguer, D., Vogt-
Schilb, A., 2016.

At the national-level analysis, we overlay the flood hazard maps developed for this study with spatial socioeconomic data. For Vietnam, the World Bank has produced estimates of the number of people within each district who live below the poverty line: this "poverty map" is displayed in Map 4a, and the full methodology can be found in (Lanjouw, Marra, and Nguyen 2013). In addition, we use gridded population density data with a 1km resolution from Landscan (Geographic Information Science and Technology 2015). This "population map" is displayed in Map 4b.

The spatial socioeconomic data set used for Ho Chi Minh City is a data set of potential slum areas and of urban expansion from 2000 to 2010, from the Platform for Urban Management and Analysis (PUMA), a city-level data set developed by the World Bank (World Bank 2015).This data was collected via satellite in the year 2012, through a combination of visual interpretation of various sources and vintages of imagery.

The inundation maps were used in an earlier flood risk study of HCMC (Lasage et al. 2014), and were
composed with the MIKE 11 hydraulic modeling software (DHI 2003).

Platform for Urban Management and Analysis (PUMA)

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Input: However, we can- not exclude the possibility that refugees would sort non-randomly into areas with particular ethnic characteristics. 19 In order to address this potential endogeneity, we implement an instrumental variable (IV) ap- proach. We are particularly concerned about certain ethnic groups from certain countries of origin moving to destination countries with similar ethnic characteristics. Such endogenous selection would be reflected in the EPR-ER data. To deal with the plausibly endogenous nature of the resulting refugee EF and EP indices, we implement a gravity model to predict the number of refugees of a certain ethnic group e moving from country o to d at time t, based on EPR-ER data. The predicted (and plausibly exogenous) number of refugees by ethnic group e is then used to create other (plausibly exogenous) diversity indices to be used as instrumental variables. More specifically, we estimate the following gravity model: 17We also use this method to link data from EPR-ER on the ethnicities of refugees with data from the Murdock Atlas on their historical homeland (Section 4. 3). 18As a robustness check (Section 5. 3), we use an alternative linkage based on the relations between sets of language nodes associated with two groups. 19Another source of selection may come from the fact ethnic groups are more likely to be displaced when they share territory with regime supporters in their countries of origin (Lacina et al., 2017). Since similar ethnic groups are likely to share common borders (Michaelopoulos and Papaioannou, 2016), it is not impossible to think conflict might spill over through this channel. 19 Usage context: primary mention, background mention, supporting mention.

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Input: The instrument becomes even weaker if we use refugee to population ratios rather than the number of refugees, therefore we use the absolute number of refugees throughout. 4Including 2014 data in the DD estimations generally results in more statistically signiﬁcant and larger coeﬃcients but does not change the direction of the results. 5The weights are calculated using the Stata package synth provided by the authors. We use the option nested to calculate the weights through the nested optimization procedure described in Abadie et al. (2011). 6Tunceli ’ s 2011 export and import values are missing for 2011, therefore only 2009 and 2010 values could be used in calculating Tunceli ’ s average. 7The Chamber of Commerce also provides information on the number of ﬁrms that shut down. However, reporting exits is not mandatory and the indicator is therefore less reliable. We found no signiﬁcant eﬀects in both the IV and DD estimates on the number of ﬁrms that shut down. 8Since the number of new foreign ﬁrms is 0 in several observations, we add 1 to the value. As an alternative, we used the hyperbolic inverse sine transformation which does not have the same problem with 0s as log transformation and found similar results (Burbidge et al., 1988). 9Publicly available data from the Ministry of Science, Industry and Technology indicate that less than 10 % of total revenue is from micro-establishments. Most of the net sales and gross proﬁts reported stem from larger ﬁrms that should be included in the Chamber of Commerce data. 10All ﬁrms exceeding 200, 000 Turkish Liras (ca. $ 85, 000) in sales are obligated to report detailed balance sheets. Smaller ﬁrms may still report their balance sheets but would be doing so on a voluntary 25 Usage context: supporting mention, background mention, primary mention.

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Input:
Oseni and Pollitt (2013) use cross-sectional data on electricity outages collected by the 2007 World Bank Enterprise Survey of 6,854 firms in 12 African countries.

** **Data** We used the social accounting matrix (SAM) of year 2007.

We use a choice experiment study to fill this knowledge gap and identify preferences for

goods, when market data are not available for assessing these valuations. A choice experiment

survey presents the respondent with choice scenarios where each choice scenario has

The enumerators spoke local languages and were trained in conducting choice   experiment surveys (and most had previous experience with collecting choice experiment data

social accounting matrix (SAM) of year 2007

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Input: After a short respite in November, Israel imposed a third lockdown from December to February the next year. Gradual easing continued throughout March. During this lockdown, vaccination was rolled out, including to commuters with valid permits. Given this context, we expect the main shock to occur in 2020Q2 in the West Bank, with negative effects of smaller magnitudes to persist over the next quarters. In Gaza, we expect another major shock in 2020Q3 that carries over to Q4. We expect the commuters ’ outcomes to instead track events in Israel and occupied territories more closely, with a major shock in 2020Q2, recovery in Q3, and another dip in Q4. 3 Data 3. 1 Data sources and definitions We employ data from the Labor Force Surveys (LFS) of the West Bank and Gaza, collected by the Palestinian Central Bureau of Statistics and prepared by the Economic Research Forum. The surveys are conducted on a quarterly basis, covering periods from 2000 onwards. The LFS is meant to represent all households whose ordinary residence is in the West Bank and Gaza, though their place of work need not be. The LFS is representative at the region level (respectively of the West Bank and Gaza), as well as at the level of locality types (urban, rural, and refugee camps) (Palestine- Labor Force Survey, LFS, 2021). Importantly for the purpose of our analysis, the data have a panel dimension, en- abling the study of labor market transitions. The sample rotation scheme is described 9 Usage context: primary mention, background mention, supporting mention.

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Input: Surveys, 76 Demographic and Health Surveys (DHS), 77 and Multiple Indicator Cluster Surveys (MICS). 78 The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner (UNSD 2014). There are only a few cases where modules or questions on forced displacement have been integrated into survey instruments (most notably in Somalia, Uganda, West Bank and Gaza, Azerbaijan, Bosnia and Herzegovina, Serbia, Ghana, as well as health surveys in Albania, Ukraine and Moldova). There are several challenges associated with ‘ mainstreaming ’ forced displacement into household surveys: (a) there is huge demand for adding sector-specific or thematic modules to international surveys; (b) it is relatively difficult to convince national statistical agencies to modify their county-specific surveys; (c) disaggregating survey results by specific vulnerable groups (e. g. refugees, IDPs, migrant populations) requires these distinctions to be integrated into the sampling frame and sometimes there is insufficient information to do this or a lack of resources to expand the sample size; (d) lack of access to displacement- affected areas; and (e) difficulties associated with integrating an inherently political topic into less controversial surveys.

Multiple Indicator Cluster Surveys
Demographic and Health Surveys

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Input: Refugees In camp Addis Ababa Total Attending school Primary school Secondary school 0 10 20 30 40 50 60 70 80 90 100 Primary school (7 to 14 years) Secondary school (15 to 18 years) Percent Percent Figure 2.10: Children currently attending school Source: World Bank Staff based on SESRE 2023. a. Attended education b. Education level a. All children b. School-age children 20 The Primary Gross Enrollment Rate (GER) is the ratio of the number of children enrolled in primary school irrespective of age to the number of children of primary school age (age 7 to 14). 21 The Primary Net Enrollment Rate (NER) is the ratio of the number of children of primary school age enrolled in primary school to the number of children of primary school age (age 7 to 14). 22 The secondary Gross Enrollment Rate (GER) is the ratio of the number of children enrolled in secondary school irrespective of age to the number of children of secondary school age (age 15 to 18). 23 Secondary Net Enrollment Rate (NER) is the ratio of the number of children of secondary school age enrolled in secondary school to the number of children of secondary school age

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Input: The UNHCR supported in engaging refugee communities and leaders. The RRS facilitated access to all camps for the survey teams; this is the first time that the RRS facilitated access to all camps for such an extensive survey. Notably, the UNHCR and RRS facilitated the collaboration of the field workers with refugee leaders in each camp and Addis Ababa to support the teams in identifying sampled households and maintaining the safety of the field workers, and the sampled households during the entire survey period. The World Bank team led the collaboration between ESS, UNHCR, and RRS and provided technical support to the ESS since the project’s inception. The SESRE used a logistics plan similar to HoWStat. Six ESS branches were responsible for administering the survey: the Asayita, Gondar, Jigjiga, Negele, Gambella, Assosa, and Addis Ababa branches. Twenty-four field teams carried out the fieldwork, each consisting of one statistician, one team supervisor, and four enumerators. All field staff involved in the SERSE participated in the HoWStat survey. Enumerators were knowledgeable about local cultures and languages and could detect inconsistencies and misunderstandings during interviews to ensure high-quality data. Supervisors were additionally trained on how to troubleshoot standard technical issues with tablets. The

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Input: Another ex- planation of these results could be the vulnerability of the Colombian population who in many cases has also a long history of internal forced displacement and violence. V. D Social Cohesion We also delve into the differences in secondary outcomes among Colombian and Venezue- lan adolescents concerning social cohesion. We focus on assessing altruism, trust, iden- tity towards specific domains, networks, and experiences of discrimination. To measure altruism and trust, we employ the questions from the Global Preference Survey, a tool developed by Falk et al. (2022) to elicit risk, time, and social preferences. Specifically, to measure altruism we ask the adolescents how much of a fictional endowment would they be willing to donate to a good cause. To measure trust, we include the 7-itme ques- 38

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Input:
This assessment is made possible through the use of state of the art transportation modeling techniques and by combining three data sets: 1) An innovative General Transit Feed Specification (GTFS) data set collected for the purpose of this analysis under normal (dry) and flooded (wet) conditions, 2) a travel survey data set with travelers' socioeconomic attributes and Origin-Destination (OD) information, as well as 3) a set of high-resolution global flood maps that capture both the extent and depth of pluvial and fluvial floods.

**3.2** **General Transit Feed Specification (GTFS) Public Transit Feed**

Wong, J. C. (2013). _Use of the general transit feed specification (GTFS) in transit performance measurement_ [Thesis,
Georgia Institute of Technology]. https://smartech.gatech.edu/handle/1853/50341

To understand the impact of flooding on public transit, we compared the GTFS transit feed mapped under dry and wet conditions and evaluated changes in headways, blockages of roads and rerouting, and travel speeds.

General Transit Feed Specification (GTFS)

General Transit Feed Specification (GTFS) Public Transit Feed

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Input: Sudan domain (.48 standard deviations below the mean). It is higher for female than male hosts in both domains, by .14 standard deviations from average in the Somali domain and .19 standard deviations in the South Sudan domain. The difference in attitudes between male and female hosts is not statistically significant, and inter-group attitudes vary little by age or education. We can study the characteristics associated with attitudes by putting this index into a regression 0 10 20 30 40 50 60 70 80 90 100 Hosts Refugees Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Addis Ababa All Hosts All Refugees Neither are trustworthy Only my group Only the other group Both are trustworthy Percent Figure 7.9: Are most Ethiopians/refugees in Ethiopia trustworthy? Source: World Bank Staff based on SESRE 2023. Note: Combines two questions regarding trust in Ethiopians and refugees, with identical wording to both Ethiopians and refugees. 2.3 2.4 2.5 2.6 2.7 2.8 2.9 3 3.1 Eritrean Somali South Sudanese Addis Ababa All Hosts Figure 7.10: Host attitudes Index Source: World Bank Staff based on SESRE 2023. Note: This index is an average of ten questions regarding beliefs about refugees’ character, the rights they should

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Input: their earnings on household expenses than men. Most of these studies have focused on adult women, specifically married women with children. It is not known whether the same holds true for young women, who may have other spending priorities, have less experience in managing households, and have younger children. Given the large increases in employment and earnings documented above, the EPAG program serves as a good setting to examine these types of spillovers. The evaluation included detailed interviews with the heads of the household in which EPAG participants were residing. The purpose of the household questionnaires was precisely to examine the hypothesis that investing in young girls would benefit her household. A secondary hypothesis was that EPAG participation may change gender-related attitudes in the participants ’ households. Household data was collected for 1601 out of the 1622 individuals who were interviewed at both baseline and midline; this same sample of 1601 individuals serves as the basis for both the individual and household level analysis in this paper. The estimated impact of the program on a broad range of household outcomes is summarized in Tables 8 and 9. Panel A of Table 8 examines the household size. Usage context: primary mention, background mention, supporting mention.

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Input:
#### 4. RESPONSE

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

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

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Input: Excluding respondents who have relocated would omit those with the higher rates of income growth and poverty reduction. Table 5 reports confidence intervals for the incremental samples (which are not mutually exclusive); it gives a more detailed picture of how inference on consumption growth and poverty reduction would have changed if we had not tracked movers. It is apparent that inference from a ‘ simple ’ panel survey of respondents continuing to reside within the original communities would have produced underestimates of actual consumption growth and poverty reduction in this population. These conclusions are robust across the distribution of consumption, as well as at the mean and poverty line. Panel A in Figure 2 depicts the cumulative density function for consumption per capita for those people who remained living in the same community. Panels B, C and D make the same graph for respondents found residing in neighboring communities, elswehere in Kagera Region and outside Kagera Region. As respondents were located further from their location in 1991, so the difference between the 1991 and 2004 graphs becomes more pronounced. Note how, for people who remained in the baseline community, the 1991 and 2004 distributions lie close to each other under the poverty line and diverge above it, while for other mobility categories there is more divergence at the bottom of the graph. Usage context: primary mention, supporting mention, background mention.

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

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

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Input:
Chinese Exports in Agriculture and Food Products**   Value of Exports in Billion USD        _Source_ : COMTRADE Database, Authors calculation    19     <!-- PAGE 23 -->  **Table 1.

6 [http://faostat.fao.org/](http://faostat.fao.org/)   6     <!-- PAGE 10 -->  standards and heavy regulation (Chen and Findlay 2008).Chinese exports in agricultural and  food products are increasing.

Although there is no data on the implementation of  voluntary standards, the China Statistical Yearbook of Certification and Accreditation (cited in  Jin et al.

China Statistical Yearbook of Certification and Accreditation

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Input: access for refugees by easing restrictions and providing work permits. ◆ Integrate refugee children into national education system to improve their long-term prospects. ◆ Strengthen inclusive healthcare systems to address the health needs of refugees. Focus on place-based interventions: ◆ Invest in refugee hosting areas to benefit both refugees and host communities. ◆ Direct additional educational resources to districts hosting refugees to support integration. ◆ Expand access to social safety nets for vulnerable refugees and hosts. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Comfortable Neutral Not comfortable Percent Figure ES.7: Host response to “Would you feel comfortable having a refugee as a neighbor?” Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Eritrean Somali South Sudanese Addis Ababa All Hosts Strongly agree Agree Disagree Strongly disagree Percent Figure ES.6: Host response to “Refugees are good people” Source: World Bank Staff based on SESRE 2023. Executive Summary viii Continue implementation of progressive policies: ◆ Implement concrete actions to fulfill Government pledges and proclamations to move away from encampment toward mobility based on economic opportunities. ◆ Harmonize national and sub-national

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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: supporting mention, primary mention, background mention.

Gridded Population of the World (GPW) data set

Ethiopian refugee camps location data set

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Input: By highlighting socioeconomic interactions and outcomes, SESRE aims to guide development interventions and facilitate refugee integration. The survey covers various aspects, including demographic profiles, livelihoods, welfare patterns, and social cohesion, offering valuable insights for policymakers and humanitarian actors. SESRE is a separate but integrated survey alongside the Ethiopian Household Welfare Statistics Survey (HoWStat),1 the national household survey to measure poverty and other socio-economic outcomes. Like most national poverty surveys, HoWStat excludes displaced populations—Internally Displaced People (IDPs) or refugees—including in Ethiopia. To have up-to-date information on the socio-economic outcomes and poverty levels of refugees and to allow comparison to Ethiopian host communities, the SESRE applied the same questionnaire and data collection methods as the HoWStat, with some modifications. The World Bank, Ethiopia’s RRS, Ethiopia’s Statistical Service, and UNHCR collaborated to implement SESRE and was the first of its kind. This report uses data from the SESRE extensively to analyze the Ethiopian refugee situation and to devise policy directions. The SESRE covers three types of groups: (i) refugees in camps; (ii) refugees out-of-camps in Addis Ababa; and (iii) host communities; all of which require a distinct sampling procedure. The sampling frame for refugee camps is based on UNHCR’s proGRES database. SESRE is

Ethiopian Household Welfare Statistics Survey

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Input: ICRC ’ s work is based on the Geneva Conventions of 1949, their Additional Protocols, its Statutes — and those of the International Red Cross and Red Crescent Movement — and the resolutions of the International Conferences of the Red Cross and Red Crescent. 84 WFP is the food assistance branch of the United Nations and the world's largest humanitarian organization addressing hunger and promoting food security. 85 See: popstats. unhcr. org. 86 IDP data are only included from 1998 onwards. 87 See: http: / / data. unhcr. org. Currently the Burundi situation, Yemen (regional refugee and migrant response plan), DRC regional refugee response, Mediterranean (refugees / migrants emergency response), CAR, Côte d ’ Ivoire, Syria Emergency, Sahel Emergency, South Sudan Situation, Horn of Africa Emergency, and the Liberia Portal. 88 IOM ’ s new Global Migration Data Analysis Centre provides limited data on global migration trends such as data on asylum application in Europe and selected countries (including demographics, country of origin, and country of asylum).

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Input: 8 permits and subsequently for the right to work. In practice, this is a long and cumbersome process and by late 2015 at most several thousand had been issued. 14 The economic impact of Syrian refugees in Turkey extends beyond changes in the potential labor supply of informal workers in important ways. There has been extensive humanitarian aid provided to the refugees, overwhelmingly by the Turkish government. Reportedly, by early 2015 the Turkish state had spent $ 6 billion (with total outside contributions $ 300 million). 15 Much of these funds have been spent on food, various services, non-food items such as medicines, clothing, shelter, and housing-related goods. In particular, there are 20 accommodation centers (camps) in 10 cities in Turkey. 2. 2 Data Sources We use the Turkish Household Labor Force Survey (LFS) micro-level data sets compiled and published by the Turkish Statistical Institute. The data contains a rich set of labor market variables along with individual-level characteristics and the region of residence. We primarily rely on two years of LFS data: 2011 (just before the arrival of the refugees) and 2014 (the last year available). 16 By design the LFS does not contain any information on Syrian refugees.

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Input: migration, and reason for movement including asylum / refugee protection (or conflict-induced internal migration) (UNHCR 2016), or a specific question to identify IDPs or refugees. However, not all censuses cover refugees and asylum-seekers (if foreigners are considered outside the scope of the census or because they are considered a special category), 71 nor is it common practice for national censuses to include questions related to forced displacement. 72 Nevertheless, there are several examples of national censuses that have included relevant questions on forced displacement. 73, 74 In the case of protracted internal displacement situations, IDPs are likely to be included in national censuses; however, census instruments may be subject to manipulation for political purposes. There are several drawbacks of population censuses including their cost, the significant training required for enumerators to ensure consistent answers to questions on forced displacement, impediments to field operations and data processing (such as weather conditions and technical problems), the relative infrequency with which they are carried out, and the long processing time before data and statistics become available, which have consequences for the timeliness of data. Moreover, often censuses are not conducted in contested territory or conflict zones where many displaced persons reside, and this limits the completeness of the data. Usage context: background mention, primary mention, supporting mention.

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

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Input: Another ex- planation of these results could be the vulnerability of the Colombian population who in many cases has also a long history of internal forced displacement and violence. V. D Social Cohesion We also delve into the differences in secondary outcomes among Colombian and Venezue- lan adolescents concerning social cohesion. We focus on assessing altruism, trust, iden- tity towards specific domains, networks, and experiences of discrimination. To measure altruism and trust, we employ the questions from the Global Preference Survey, a tool developed by Falk et al. (2022) to elicit risk, time, and social preferences. Specifically, to measure altruism we ask the adolescents how much of a fictional endowment would they be willing to donate to a good cause. To measure trust, we include the 7-itme ques- 38 Usage context: supporting mention, background mention, primary mention.

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Input: Women experience particularly pronounced displacement in the informal sector and no formal job gains. In contrast, for men displacement in the informal sector is fully offset by employment growth in formal sector, with no net job losses. Clearly, our main findings do not depend on the particular sample of subregions we analyze, and importantly are robust to restricting the analysis to a more homogenous group of regions. 6. CONCLUSIONS This paper combines newly available data on the 2014 distribution of 1. 6 million Syrian refugees across subregions of Turkey and the Turkish LFS, to assess the impact on Turkish labor market conditions. The Syrian refugees in Turkey are overwhelmingly employed informally, since they were not issued work permits, and so their arrival was a well-defined supply shock to informal labor. Consistent with economic theory our IV estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupational upgrading, there are increases in formal employment for the Turkish. This increase though only occurs among men without completed high school education. The employment patterns of women and the high-skilled mean they are not in a good position to take advantage of lower cost 36 Results are also robust to dropping all subregions with close to no refugees.

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

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Input: The database provides information on international bilateral migrant stocks (by citizenship2 or place of birth), sex, and age. There is considerable variation, however, in how destination countries collect, record, and disseminate immigration data. Meaningful comparison of destination country records over time is thus often confounded. In constructing global bilateral migration matrices, several challenges arise. First, destination countries typically classify migrants in different ways — by place of birth, citizenship, duration of stay, or type of visa. Using different criteria for a global dataset generates discrepancies in the data. Second, many geopolitical changes occurred between 1960 and 2000, with many international borders redrawn as new countries emerged and others disappeared. In addition to creating millions of migrants overnight — as when the Soviet Union collapsed — these events complicate the tracking of migrants over time. Third, even when national censuses of destination countries include data on international migrant stocks, the data are presented along aggregate geographic categories rather than by country of origin. Data therefore need to be disaggregated to the country level. Finally, the greatest hurdle is dealing with omitted or missing census data. Very few destination countries — especially developing countries — have conducted rigorous censuses or population registers during every census round over the second half of the twentieth century. Wars, civil strife, lack of funding, and political intransigence are but a few reasons why records may be discontinuous. 1 Of the 3, 500 sources detailed in the overarching UN Global Migration Database, 1, 107 were suitable for analysis, once repeated censuses had been removed or combined. Global Migration Database should not be confused with the Trends in International Migrant Stock Database, which lists aggregate migrant stocks for each destination country in the world at five year intervals (United Nations 2006) 2 The article treats the concepts of nationality and citizenship as analogous and uses the terms interchangeably.

Trends in International Migrant Stock Database

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

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Input:
**Box 3: The Role of GIS in Project Site Selection**

During project preparation, GIS was used to overlay three spatial datasets: (i) displacement hotspot maps produced by UNHCR's Field Information and Coordination Support section; (ii) a poverty concentration index derived from the national household budget survey; and (iii) road network accessibility scores from the Ministry of Transport's infrastructure database. The GIS analysis identified 47 communities meeting the dual threshold of high displacement concentration and limited infrastructure access. These communities form the initial targeting list for Component 1 activities.

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Input:
–– Ensuring that a **situation analysis and mapping of relevant actors**
(Government, UN agencies, NGOs, multilaterals and donors) in the health
sector is done in collaboration with the line ministry to inform the design of a
response in each area of public health and nutrition and for every stage of the
response.

–– **Facilitating data driven responses** Facilitate and support the collection,
compilation, analysis, interpretation and dissemination of health program
data. Support inclusion of refugees in national data systems and tools
including disaggregation of data by nationality to the extent possible.

–– **Refugee participation and consultation** : wherever possible, continue to
develop and support consultative processes that enable refugees and host
community members to assist in designing appropriate, accessible and
inclusive responses.

–– **Providing technical expertise & support** : UNHCR will seek to provide
or facilitate technical and general support to partners on program
implementation and support for inclusion of refugees in national data
systems..

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

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Input: 18 Figure 11: Timing of Return (%) Source: Listening to Displaced People Survey, 2014. 94 % of the returnees were displaced inside Mali and 6 % outside the country. 67 % went to Bamako, 11 % in Koulikoro, 9 % to Ségou, 5 % to Mopti and 3 % went elsewhere (Kidal, Gao and Sikasso). The majority returned between June and October 2013 a period that followed the signing of a peace deal between the interim government and rebel factions to allow presidential elections to be held in July (first round) and August (second round) 2013. In October security in the North worsened again and ever since the number of people returning has been very limited. The main challenges reported by returnees in June 2014 were (i) poverty and food insecurity; (ii) lack of infrastructure (including lack of safe drinking water) and (iii) unemployment. 11 % of the returnees stated not to be facing any challenges (Figure 12). Usage context: background mention, supporting mention, primary mention.

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Input: After a short respite in November, Israel imposed a third lockdown from December to February the next year. Gradual easing continued throughout March. During this lockdown, vaccination was rolled out, including to commuters with valid permits. Given this context, we expect the main shock to occur in 2020Q2 in the West Bank, with negative effects of smaller magnitudes to persist over the next quarters. In Gaza, we expect another major shock in 2020Q3 that carries over to Q4. We expect the commuters ’ outcomes to instead track events in Israel and occupied territories more closely, with a major shock in 2020Q2, recovery in Q3, and another dip in Q4. 3 Data 3. 1 Data sources and definitions We employ data from the Labor Force Surveys (LFS) of the West Bank and Gaza, collected by the Palestinian Central Bureau of Statistics and prepared by the Economic Research Forum. The surveys are conducted on a quarterly basis, covering periods from 2000 onwards. The LFS is meant to represent all households whose ordinary residence is in the West Bank and Gaza, though their place of work need not be. The LFS is representative at the region level (respectively of the West Bank and Gaza), as well as at the level of locality types (urban, rural, and refugee camps) (Palestine- Labor Force Survey, LFS, 2021). Importantly for the purpose of our analysis, the data have a panel dimension, en- abling the study of labor market transitions. The sample rotation scheme is described 9

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Input: 4 The article employs a unique data source from Jordan, the 2010 Jordan Labor Market Panel Survey (JLMPS 2010), which includes information on parents ’ schooling for every adult in the sample, along with the 2010 School Census produced by the Jordanian Ministry of Education (Hashemite Kingdom of Jordan, 2010). The school census provides the subdistrict, type, and date of establishment of every school in Jordan, allowing us to measure the local supply of each type of schools in each subdistrict in every year (under the presumption that there were no significant school closures or changes in type over time, which is likely the case). The exposure of an individual in the JLMPS 2010 sample to the supply of public schooling is then determined by the number of sex-appropriate basic (or secondary) public schools (per 1, 000 individuals) that were available to them in their subdistrict of birth at the time they were of age to enroll in that school level (six years of age for basic and 15 years for secondary). The richness of the data set makes it the first in the Middle East to allow such a study.

2010 School Census
2010 Jordan Labor Market Panel Survey

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Input:
**7.1 Summary of Key M&E Instruments**

| Instrument | Frequency | Managed By | Output |
|---|---|---|---|
| MIS routine data | Continuous | PIU M&E | Monthly dashboard |
| Annual surveys | Yearly | External firm | PDO indicator updates |
| Protection monitoring | Quarterly | UNHCR / NRC | Protection situation reports |
| GRM reports | Monthly | PIU coordinator | Grievance resolution log |
| ESMF field audits | Semi-annual | PIU E&S officer | Compliance report |

All monitoring instruments feed into the annual progress report submitted to the Association by April 30 of each year.

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

Turkish Labor Force Survey
Survey of Income and Living Conditions

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Input: 7 Database. Where both censuses and population registers are available, censuses receive priority. Censuses, generally conducted decennially, are retrospective tools for surveying an entire population (or in some cases, a representative sample) at a single point in time. In addition to their universal coverage, their greatest strength is the inclusion of questions on place of birth and nationality. Censuses also typically aim to enumerate the resident population, whether documented or undocumented (Bilsborrow and others 1997). So although some migrants have a strong incentive to provide false information to enumerators, many undocumented migrants will be captured in these matrices. 7 The size and scope of the census questionnaires vary enormously, both over time and in different destination countries. And there is potential variation in the quality of censuses both across countries and over time. Richer countries have many resources at their disposal to design questionnaires, train interviewers, employ statisticians, and disseminate results. Researchers have little choice but to accept the data at face value. However, where the underlying census is clearly substandard (when there are errors that are obviously not coding errors or not easily corrected), these data are omitted from the analysis. Popular in many parts of Europe, population registers are continuous reporting systems providing up-to-date demographic and socioeconomic information for everyone surveyed.

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Input:
- **Physical Constraints:** Poor infrastructure, damaged roads, and impassable routes during the rainy season make it difficult
for humanitarian vehicles to access remote areas. Shortages of clean water, fuel, and electricity also affect operations.

- **Lack of Awareness on Inclusion:** Specific needs of diverse at-risk groups, such as people with disabilities and older
individuals, are often considered under one group of ‘the vulnerable’ without an analysis of underlying causal factors.
The limited awareness on inclusion and the specific risks is likely to translate to insufficient support for disability, gender
and age inclusion in humanitarian settings and requires enhanced efforts to include disability inclusion as part of the
existing coordination structures.

**CRITICAL GAPS IN FUNDING AND POPULATION REACHED**

- Limited resources due to conflicts and instability.

- Coordination structures are still in need of awareness in relation to the needs and barriers of people with disabilities and
older persons.

- Accessibility and inclusion are seen as an add on or specific requirements which are not included in budgeting from the
on-set due to perceived high costs of technical support for disability and age inclusion.

- Data gaps that hinder advocacy with donors and humanitarian actors.

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Input: Figure 2. Sample and Distribution of Venezuelans in Colombia Notes: The left-hand panel of the ﬁgure illustrates in shades the number of Venezuelans registered in the RAMV census; the red circles depict the surveys carried out per municipality. The right-hand panel illustrates the number of Venezuelans per municipality reported in the 2018 Colombian census, a proxy of the overall distribution of migrants in the country. The correlation between the sample and the 2018 Colombian census registry is 0. 93. 39 Usage context: supporting mention, primary mention, background mention.

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

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Input: 8 agreements that did not fully satisfied either party (Ndayirukiye and Takeuchi 2014). This tension related to land, and who has a claim to the land, can lead to social tensions in communities with higher levels of return. Figure 3 – Refugees in Tanzania in 2005 by province of origin in Burundi Note: The number in brackets is the number of refugees in Tanzania in 2005 which was originally from the given province in Burundi. This information comes from (UNHCR 2021b). The number in parenthesis is that number as a percentage share of the population of the provinces as estimated in 1990 Burundi Census. An important question for our hypotheses is the degree to which there is evidence of migration-related societal divisions in the country. There is no direct quantitative evidence on identity issues (i. e. returnees versus stayees), but we have data on attitudes towards emigration, remittances and return that can provide insights on these identities and even be a proxy for migration-related identity in some cases. Overall, attitudes towards emigration and return are mixed and show that there is scope for the existence of migration-related divisions. In Table 1 we report the share of respondents who agreed with different statements regarding emigration, remittances and return. Usage context: supporting mention, primary mention, background mention.

data on attitudes towards emigration, remittances and return

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Input: The SEIS sampling frame was validated against UNHCR's registration database prior to fieldwork. SEIS clusters that could not be matched to valid registration records — typically due to secondary displacement between registration and survey fieldwork — were replaced using a random draw from the list of eligible replacement clusters. The SEIS documentation records 31 cluster replacements, representing 5.3 percent of the original SEIS sample, and replacement clusters were confirmed to be statistically comparable to the dropped clusters on observed baseline characteristics.

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Input:
Over 11,000 indigenous Venezuelans currently residing in Brazil have been identified by UNHCR
and partners. Compared to the overall Venezuelan population, they face compounded challenges
accessing basic rights and services, including higher rates of food insecurity (58% vs 52%), health
care needs (75% vs 59%) and out of school children (21% vs 15%). [3] Language barriers and limited
formal education of adults (indigenous refugees are 5 times more likely to have no formal
education when compared to the general Venezuelan population in Brazil), significantly affect their
prospects for successful integration. [4]

### Protection brief in graphics

Population category Population category

Breakdown by nationality

_2 World Bank and UNHCR (2021), Integration of Venezuelan Refugees and Migrants in Brazil,_

_[https://documents1.worldbank.org/curated/en/498351617118028819/pdf/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf;](https://documents1.worldbank.org/curated/en/498351617118028819/pdf/Integration-of-Venezuelan-Refugees-and-Migrants-in-Brazil.pdf)_
_ACNUR & Ministerio do Trabalho e Emprego (2024), Informe sobre o mercado de trabalho formal para Haitianos no Brasil,_
_[https://www.acnur.org/br/sites/br/files/2024-11/informe-mercado-trabalho-formal-haitianos-brasil-jun-2024.pdf](https://www.acnur.org/br/sites/br/files/2024-11/informe-mercado-trabalho-formal-haitianos-brasil-jun-2024.pdf)_
_ACNUR & Ministerio do Trabalho e Emprego (2024), Informe sobre o mercado de trabalho formal para pessoas refugiadas afegãs no Brasil_
https://www.acnur.org/br/sites/br/files/2024-11/informe-mercado-trabalho-formal-pessoas-afegas-no-brasil-junho-2024.pdf

---
[3] R4V (2023), Refugee and Migrant Needs Analysis, https://rmrp.r4v.info/rmna2023/](https://rmrp.r4v.info/rmna2023/) p. 85

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Input: Income and consumption index: includes the logarithm of monthly household income and consumption. Because these variables are continuous the index is constructed following the methodology outlined in Kling, Liebman and Katz (2007). Labor market index: includes a dichotomous variable for employment, and the logarithm of monthly wages and weekly hours worked. Descriptive statistics for each of the outcomes and their components variables are de- scribed in Table 1. III. B Data for the Taliban ’ s Geographical Presence To obtain information on the Taliban ’ s geographical presence, we scraped online data from the Foundation for Defense of Democracies ’ Long War Journal on the districts con- trolled by Taliban, by U. S. Allies, and contested territories between the two groups. 9 The data includes a yearly evolution of the territorial control in Afghanistan. The classification is based on open-source information from NATO ’ s data in Afghanistan, press reports, in- formation provided by government agencies, and the Taliban. To categorize the districts, they identify who provides government services, ensures security within the district, ad- ministers the district openly, and oversees local courts (see Appendix B. Usage context: primary mention, background mention, supporting mention.

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Input: 1 Introduction Company owners and managers make two decisions with important implications in the labor market: what skills are demanded and who to hire. On the demanded skills, a driver of trends in employment is the changing demand for soft skills (Heckman and Kautz (2012), Weidmann and Deming (2021)). However, we know relatively little about what kinds of soft skills employers value in modern entry- level jobs (Heller and Kessler, 2022). On the decision of who to hire, it is in the best interest of companies to hire based on workers productivity. However, several studies have documented the existence of labor market discrimination in a wide range of contexts (Bertrand and Duflo (2017), Neumark (2018)) and it remains unclear how discrimination operates throughout the hiring process and how the existent empirical evidence on discrimination is linked to economic theory (Bertrand and Duflo, 2017). We conducted a correspondence study in 2023 using a large online job platform to assess demand for soft skills in the context of hiring discrimination in Malaysia. Malaysia is a particularly interesting setting because it is an upper-middle-income economy, home to multiple ethnicities representing large shares of the population, and previously documented gender gaps in labor force participation and wages. Usage context: supporting mention, background mention, primary mention.

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Input:
To estimate the fraction of income of the poor and non-poor that comes from social protection and transfers, we calibrate �� using the "average per capita transfer from all social protection and labor (for beneficiaries)" and the coverage in each quintile from the ASPIRE database [10] :

<!-- PAGE 18 --> that quintile. Poor and non-poor correspond to the bottom 20% and the top 80% respectively. ASPIRE at present does not cover developed countries, so we use data from the US Consumer Expenditure Survey (CES, 2015), Canadian National Household Survey (CNHS, 2015), Australian Household Wealth and Wealth Distribution Survey (AWWDS, 2015), and European Union Survey of Income and Living Conditions (EU- SILC, 2015).

Australian Household Wealth and Wealth Distribution Survey

European Union Survey of Income and Living Conditions

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