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. Usage context: primary mention, background mention, supporting mention.

Trends in International Migrant Stock Database

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Input:
The model is estimated using data from the Turkish HBS in 2008 and 2009. The HBS is conducted annually by the Turkish Statistical Institute to collect information on household socioeconomic status, living standards, income, and consumption expenditures.

The survey data are then combined with the actual monthly rate schedule. The Turkish electricity
tariff data have two advantages that are fairly unique to the study. First, in the retail sector,
Turkey has applied a price equalization mechanism to maintain a nationwide uniform tariff until
2011. Under such a system, all residential consumers face a uniform flat-rate price schedule.

ation to fish & fishery products     -    GB 14939Fish Hygienic standard for canned fish CAC 70:1995 NEQ 1994    GB 2715Cereal Hygienic standard for grains - 2005    GB 19303Meat 2003   GB/T 22388Milk 2008   GB/T 23376Tea 2009    Hygienic practice of cooked meat and meatproducts factory    Determination of melamine in raw milk and                                          -                                         dairy products   Determination of pesticides residues in tea                                          -                                         GC/MS method    CAC/RCP13 MOD 1976    Vegetabl  es    GB 2714Hygienic standard for preserved vegetables         -         2003    GB 13104- CAC Sugar Hygienic standard for sugars NEQ 2005 212:1999   _Source:_ Authors‟ calculations based on SAC National Standards Query   20     <!-- PAGE 24 -->  **Figure 2.

To ensure reliability and completeness, the standards have been cross  checked with the "Chinese Bulletin of Standards" (SAC 2011a) and the German-Chinese  Standards Portal (DIN and SAC 2011).

(HS 1992) and tariffs are compiled from the TRAINS data base. Consumption variables are

computed from the Food and Agriculture Organization of the United Nations‟ statistical

database FAOSTAT. [6] We deflated our data US Bureau of Labor Statistic's HS Import Price

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Input: 15 4. 1. 2. Empirical strategy In order to make use of the retrospective information on school attendance provided in the dataset, we have constructed what we have called an ‘ ad hoc panel ’, whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset. In this way, we are able to obtain observations for each individual over three academic years. All key education variables are time- variant, while other individuals and households characteristics are time-invariant. Within these three years, we focus our analysis on individuals that were of primary school age (between 7 and 12 years old) in each year. In practice, we keep all children aged at minimum 7 years old in 1998 and at maximum 12 years old in 2000. As a consequence, our panel data contains children aged 8-11 years in 1999, the year of the violence. 11 Since we are interested in looking at different effects across groups of individuals, we have split the sample between boys and girls and between younger children (aged 8-9 in 1999) and older children (aged 10-11 in 1999). Usage context: primary mention, background mention, supporting mention.

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Input:
The returnee population is relatively more prone to lethal landmine and ERW accidents than the
civilian population living in their community of origin. According to data from DMAC and UNMAS,
travelers including returnees and IDPs account for 30% of all landmine, ERW and Pressure Plate IED
accidents. Returnee populations are particularly vulnerable due to their unfamiliarity with the overall
threats posed by explosive hazards; lack of information about how to identify them and lack of the
potentially life-saving behaviour to adopt in response, in addition to their unfamiliarity with their new
surroundings, including the history of armed clashes and potential for explosive contamination in
areas of settlement.

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Input: (2009) have shown that, in Nepal, subjective welfare is negatively associated with geographical isolation. Census data on total population and population density in each district are used as proxies for urbanization and geographical proximity: the denser the population, the less geographically isolated individuals are likely to be. We also include data on the average elevation in each district. Nepal being a mountainous country, the higher the average elevation of a district, the more costly it is to build roads, raising transport and delivery costs to the district. Ceteris paribus, we expect migrants to seek out districts with a higher population density and a lower elevation. 4 Econometric results 4. 1 Univariate analysis We now investigate the choice of migration destination. We begin with simple univariate analysis. Variables are of the form ∆ h is = xh s − xh i where i is the district of origin of migrant h and s is each of 74 possible districts of destination. We examine the average value of ∆ h is for the destination district and compare it to the value of ∆ h is for alternative destinations. For instance, let xh s be population density in district s. The average value of ∆ h is for the actual destination of the migrant tells us whether the destination district is more densely populated than the district of origin. Usage context: supporting mention, background mention, primary mention.

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

Survey of Income and Living Conditions
Turkish Labor Force Survey

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Input: Figure 19: Trust and Conﬂict in the Cross Section The problem with a lack of trust and strong group identities is that they penetrate and pervert formal institutions. The ethnic politics analyzed by Burgess et al. (2015) is just one example. Shayo and Zussman (2011), for example, use data from Israeli small claims courts to show that Arab and Jewish judges displayed signiﬁcant judicial ingroup bias. Furthermore, this bias is strongly associated with terrorism intensity in the vicinity of the court in the year preceding the ruling. Conﬁdence-building is also a crucial ingredient for the establishment of a fertile investment climate, which in turn is a trigger of economic development post conﬂict. This is the core message of the World Bank Report by Mills and Fan (2006). An important role of increasing trust doubtlessly goes to the media. It has been shown, for example, that hate radio in Rwanda played a critical role in the extent of ethnic violence during the genocide. 59 Other research has shown that media coverage can have strong eﬀects on political preferences more generally. 60 Perhaps the most direct proof of the crucial role played by the media in the post-conﬂict situation comes from DellaVigna et al. (2014). The authors exploit variation in radio reception of na- tionalistic Serbian radio in border regions in Croatia.

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Input: 29 evaluations have been conducted (Renton et al., 2000; Shaw, 2000; Shaw, 2002a; Shaw, 2002b; Paine et al., 2002; White, Greene and Murphy, 2003; Interagency working Group, 2003). For example, the Medical Research Council (from the United Kingdom) conducted an external evaluation of program effectiveness in the Gambia using key informant interviews, focus groups, and a KAP (knowledge, attitudes and practices) survey in four villages, including two program sites and two controls. That study found that the Gambia program improved self-reported attitudes and behaviors related to violence against women. Specifically, the program reduced the social acceptability of wife-beating at the community level and appeared to produce a corresponding drop in that behavior. Qualitative findings from other Stepping Stones sites suggest similar benefits. Program H (Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru) is being carried out by four NGOs. It aims to change gender norms and sexual behaviors in Bolivia, Brazil, Colombia, Jamaica, Mexico and Peru (Barker, 2003; White, Green and Murphy, 2003; Guedes, 2004). Usage context: primary mention, supporting mention, background mention.

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

Displaced People Survey
Afrobarometer perception survey

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Input:
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cities in the sample, providing a consistent mid-day activity benchmark for comparing CO2
concentration anomalies. [3] OCO-2 has an observation repeat time of 16 days. We have downloaded
georeferenced measures of XCO2 (the column-averaged dry air mole fraction of CO2).

We compute the daily median XCO2 for each 10-degree latitude band and linearly interpolate the result to each OCO-2 observation with 1-degree resolution.

Mistry (2019) has provided global estimates of monthly heating and cooling degree days at 25 km
resolution. [4] We compute population at 25 km resolution by aggregating data from CIESIN (2021) at
5 km resolution. Monthly estimates are interpolated from data provided for 2010, 2015, and 2020. We
use two sources to construct our georeferenced measure of income per capita. From the G-Econ
database (Nordhaus et al.

We merge the results with annual UN estimates of GDP per capita in constant $US 2015 (UN 2021), and use the cell ratios to estimate annual GDP per capita for each cell.

data from CIESIN
UN estimates of GDP per capita

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Input: MIS data extracted at 30 September showed that cumulative cash transfer disbursements had reached 78 percent of the annual target, with 14,822 households having received at least one payment. The MIS disbursement module flagged 312 records with payment anomalies, of which 287 were resolved through validation against the beneficiary registration database and 25 were referred to the compliance officer for further investigation. The MIS was updated to add a duplicate detection algorithm that screens new registrations against existing records before activation.

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Input: 23 mega-cities, annual growth rates of peripheral population tend to reach around 10-20 percent compared to central business districts. 4. Implications for public policy Hazard management is a task both for the public sector and for private households and firms. For the public sector in cities, this includes ensuring the safety of municipal buildings and public urban infrastructure, encouraging and supporting private sector hazard risk reduction, and developing first response capacity. A considerable share of hazard risk stems from relatively small but frequent events which cause localized damage and few injuries or deaths (Bull-Kamanga et al. 2003). For instance, an analysis of detailed records of 126 thousand hazard events in Latin America showed that more than 99 percent of reported events caused less than 50 deaths or 500 destroyed houses (ISDR 2009). In aggregate, these accounted for 16. 3 percent of total hazard related mortality and 51. 3 percent of housing damage. The probability of larger events may or may not be predictable. For instance, a city may be in an earthquake risk zone, but the location specific ground shaking probabilities are not known. Individual dwelling unit level mitigation is therefore necessary everywhere in the general area of high earthquake probability. For other hazard types like landslides and floods, potential risk areas can be more easily delineated.

detailed records of 126 thousand hazard events in Latin America

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Input: count of existing contributions and the diﬀering capacities and resources among States. ” It formally “ intends to provide a basis for predictable and equitable burden- and responsibility-sharing among all United Nations Member States, together with other rel- evant stakeholders as appropriate. ” Underpinning the global debate on responsibility- sharing is the assumption that “ the grant of asylum may place unduly heavy burdens on certain countries ” (UN General Assembly 2018), typically countries neighboring a conﬂict area. In this perspective, the number of refugees a country is to host is simply a function of its geography. This paper examines empirically the proposition that the hosting of refugees falls disproportionately on neighboring countries, which in most cases are in the developing world. To do so, we use data on worldwide bilateral refugee stocks compiled by UNHCR to examine the spatial distribution of refugees and its evolution over time. Our period of analysis is 1987-2017. Our main ﬁndings can be summarized as follows. While refugees still remain over- whelmingly in a country neighboring their country of origin, the past decades have seen a trend towards greater geographic diﬀusion. We begin by showing that the global pop- ulation of refugees has been increasingly dispersed across host countries, as captured by a falling Herﬁndahl index of host-country shares. Usage context: primary mention, background mention, supporting mention.

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Input: Historical trends and patterns of forced displacement: Insights from available global data This section provides an overview of the available global data on conflict-induced forced displacement, drawing largely on UNHCR ’ s published data on asylum-seekers, refugees and IDPs. 41 Data are presented visually in a series of figures to highlight the scope and character of the current global forced displacement crisis and identify historical trends and anomalies. These data largely focus on the scale and trends in conflict-induced displacement (i. e. the numbers of forced displaced) with some coverage of other elements such as demographics, location and accommodation. Globally, there has been an unprecedented increase in the numbers of displaced people over the last decade, largely explained by the expansion in the number of reported IDPs. Historical data show a substantial increase in the numbers of forced displaced (see Figure 3), however the expanding geographical scope and quality of displacement monitoring systems are likely to account for much of the increase in forced displacement figures. The numbers of refugees under UNHCR ’ s mandate have recorded a number of variations over time, peaking in the early 1990s (at a level 10 percent over 2015 numbers) with the conflict and displacement associated with the end of the Cold War. The number of Palestinian refugees steadily has increased steadily over time, largely as a result of natural growth. IDP numbers (for which the underlying data are the least robust) have recorded the largest progression as a consequence of: (a) the expanded scope of monitoring efforts (IDPs were not counted before 1989 and methodologies were 41 UNHCR ’ s data only include IDPs protected or assisted by the agency. Usage context: supporting mention, primary mention, background mention.

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Input: Similarly, regional representation of the sample was found to be in line with actual regional distribution of the population in the north. 16 The code of the day is the sum of the two figures of the date, i. e. if it ’ s the 25th of August the code of the day is 2 + 5 = 7. The enumerater will chose house number 7 as a starting point. Arab 3 % Tamashek 32 % Songhai 45 % Peulh / Foulbe 7 % Other ethnicities 13 % Figure 2: Ethnic composition of the North, 2009 Census Arab 5 % Tamashek 36 % Songhai 49 % Peulh / Foul be 7 % Other ethnicities 3 % Figure 3: Ethnic composition of sample

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Input:
When the stable lights data set is examined, most countries display positive growth in _AoL_ and negative
growth in _R_ . There is a weak negative relationship in levels (Figure 7) and a somewhat stronger negative
relationship in growth rates (Figure 8). This outcome is not surprising. For all but a handful of rapidly
growing countries, the periphery of an expanding _AoL_ is not as bright as the center. Thus, _R_ must fall
because _R_ = _SoL_ ÷ _AoL_ .

We use four independent variables. Data for GDP, non-agricultural GDP, and manufacturing value-added
are all measured in constant 2005 US dollars. These data come from the World Development Indicators
supplied by the World Bank. Data for electricity consumption in kilowatt-hours come from the
International Energy Agency via the World Development Indicators. Data for population come from the
UN Population Division, again via the World Development Indicators. Data for the value of the stock of
physical capital, denominated in constant 2005 US dollars, come from the Penn World Tables Version 8.0.

World Development Indicators
Penn World Tables Version 8.0

International Energy Agency via the World Development Indicators

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Input: Further, as Jordan and the international community develop new approaches that respond holistically to the specific needs of refugee and host communities, more evidence is needed about how gender ‐ based constraints and vulnerability affect refugee women ’ s ability to take up economic opportunities and to access the services and resources they need to enable their families to move out of poverty. Our analysis applies a gender lens to a rich set of microdata on Syrian refugees in Jordan collected by UNHCR between 2011 and 2014. As these data do not capture how the changes in policies affect refugees and the constant evolution of their situation since 2014, the analysis is not intended to directly inform current policy choices and decisions. Instead, our aim is to devise an approach that can provide greater insights into gender ‐ specific barriers, based on the premise that the experiences and potential vulnerabilities of women, men, and children are significantly different in refugee settings. We use household ‐ level data to examine the relationship between poverty and gender for Syrian refugees. Our approach is informed by a body of work in the academic literature that has used household survey data to examine the relationship between the gender of the household head and household 2 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 3 http: / / www. unhcr. org / globaltrends2017 /, accessed August 2, 2018. 4 https: / / data2. unhcr. org / en / documents / download / 64568, accessed August 2, 2018. 5 https: / / reliefweb. int / sites / reliefweb. int / files / resources / 64114. pdf, accessed August 2, 2018.

household survey data
household ‐ level data

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Input:
1 We use the generic term HCES to refer to a range of household survey efforts to capture total household
consumption expenditures. This can include surveys described as household budget surveys, living standards
surveys, or others.

We explore a unique survey experiment which randomly assigned seven different HCES methods to

of Sub-Saharan environments where, according to the FAO, the proportion of hungry people is highest

survey experiment, described below, ensure that any differences in derived hunger numbers are solely

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Input: described above, HSP households received not only full rental subsidies for 9 to 18 months but also physical upgrades to the home environment. These proximate outcome measures can be seen as capturing successful program implementation. To measure physical improvements to the shelter, we constructed an index of self-reported ﬂoor, roof, and wall quality; electricity and water access; and crowding. Housing expen- ditures were measured in two ways: First, in the midline phone survey, respondents re- ported their total monthly housing expenditures, including the payments provided by the implementing organization. Second, in the endline in-person survey, once assistance had ended, participants reported their monthly out-of-pocket housing expenditures (excluding the amount paid by the implementer). The latter is our preferred measure of the direct effect of the program on housing expenditures, because it allows us to test whether treated house- holds experienced rental savings. Because the endline measure was collected after assistance ended, any observed expenditure reductions would most likely be an underestimate of the beneﬁts experienced during the program. Recall that HSP also required the landlord to not raise rent for an additional year after the assistance ended, which could help account for any persistent effects on housing expenditures in the treatment group. 2. Usage context: primary mention, supporting mention, background mention.

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Input: 40 Sub ‐ Saharan Africa 8604000 5895000 7055000 5406100 5068000 N. A. MENA 6230000 8000000 6675000 8592900 10892000 N. A. Asia and Pacific 4325000 2405000 3392000 2128800 5490000 N. A. (excl. Australia, Japan, New Zealand) Americas 1126000 1280000 2176000 2900000 3661000 N. A. (excl. North America) Note: Refugee data are from the UNHCR statistical population online dataset, accessed in September 2014. Refugee data from 2007 to 2013 include people in refugee ‐ like situations (see note, Figure 1). IDP data from 1990 to 2008 are aggregated based on Center for Systematic Peace (http: / / www. systemicpeace. org /). As indicated in Figure A1, these data are much lower compared to those provided from 2003 by IDMC but provide a longer time series. UNHCR assisted ‐ IDP are compiled based on several UNHCR (1995, 1996, 2007, 2008, 2009, 2010, 2011, 2012, 2013, and 2014) reports. UNHCR ‐ assisted IDPs are only IDPs who are protected / assisted by UNHCR. These are also not necessarily representative of the entire IDP population in a given country. Many of the world's IDP situations are not covered by UNHCR and are thus not reflected. Usage context: supporting mention, primary mention, background mention.

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Input: 10 scored on a Likert scale running from 0 (significantly worse) – 10 (significantly better). The survey questions on optimism were collected at outreach, baseline and endline. Employment status: Due to slight differences in access to labor markets for refugees in Jordan and Lebanon and differences in how we were able to ask about employment status, we tabulate employment status as whether or not an individual is employed. Participants were asked at outreach about their employment status, and, in subsequent rounds, whether or not this had changed. This variable is coded 0 for not currently employed and 1 for employed. Economic scarcity: We collect survey questions on individual perceptions on: ability to meet current needs; ability to meet future needs; expectation that access to jobs is fair; expectation that salaries are fair; and belief that unfair access to labor markets fuels tensions. Ability to meet current and future needs are coded on a Likert scale running from 1 (completely unable) to 5 (fully able). The “ fairness ” indicators are coded: 0 (unfair) or 1 (fair). Whether or not competition around employment contributes to tensions is captured on a 1 (not at all) to 5 (absolutely) Likert scale.

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Input:
In order to take account of the building types in Indonesia we use  information from the USGS building inventory for earthquake assessment, which provides  estimates of the fractions of building types observed by country; see Jaiswal & Wald (2008).  The data provides the share of 99 different building types within a country separately for  urban and rural areas, where - due to lack of other information - a homogenous distribution  of buildings is assumed.  Then fragility curves by building type are derived from the curves constructed by the  Global Earthquake Safety Initiative project; see GeoHazards International and United Na tions Centre for Regional Development (2001).

In order to use these vulnerability curves for Indonesia we first allocated each of the 99  building types given in the USGS building inventory to one of the 9 more aggregate cate gories of the GESI building classification.

of ongoing eruption as a threshold of when to include an eruption in the data set or not.  Second, images containing sulphur dioxide data from the OMI/AURA satellite are used to  model the intensity of the eruptions.

To construct an inundation map of the affected areas, a  map based on MODIS satellite pictures from Anderson _et_ _al._ (2004) is used with spatial al gorithms to detect the difference in color between inundated and non-inundated areas.

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Input:
ASPIRE does not cover all countries in the world. To expand the set of countries in this study, we proceed as follows. First, we estimate an econometric model that explains the share of income from transfers, for poor and nonpoor people in each country by the share of GDP spent on social protection at the national level (from the International Labour Organization), an indicator of government effectiveness (from the Worldwide Governance Indicators), and whether the country used to be in the Soviet Union. (Other explanatory variables we tested include World Bank income category of the country, region, and GINI coefficient, but they turn out to have no explanatory power.) This provides an estimate of income from transfers in 26 additional countries (from 98 using only ASPIRE data).

Diversification also comes from financial inclusion. We use the fraction of the population with savings at a financial institution, from the Global Financial Inclusion Database (FINDEX, 2015). The Findex database provides these values for the bottom 40% and for the top 60%, which we use for poor and nonpoor people respectively. We assume that the fraction of income that is diversified increases by 10% for people who have bank accounts.

Rather, we proxy the ability of a country to borrow with sovereign credit rating, using the average (long-term) rating of from three agencies: S&P, Moody's and Fitch.

Our study takes advantage of newly available panel data from the Indian Human Development Survey (IHDS), which includes a question asking households to estimate the daily average duration of outages.

Worldwide Governance Indicators
Global Financial Inclusion Database
Indian Human Development Survey (IHDS)
ASPIRE data
Findex database

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Input: The univariate analysis showed that migrants on average move to destinations where they are on average less likely to ﬁnd people like them. The results presented in Table 4 present a diﬀerent picture. Conditional on the other regressors, the ethnicity and language proximity indices are signiﬁcant with the anticipated sign: social proximity between the migrant and the population of the destination district is higher than in alternative destinations. The religion proximity index is not signiﬁcant. Taken together, these results suggest that, conditional on material beneﬁts from migration, migrants prefer to move to a destination where they integrate more easily — and possibly enjoy network beneﬁts in terms of access to jobs and housing (Munshi 2003, Beaman 2006). 24

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Input:
8 Flood return periods of 5, 10, 20, 50, 75, 100, 200, 250, 500 and 1000 are included in the Fathom data product.

We leverage a unique large-scale experiment on consumption measurement in Iraq designed for the Iraq Household and Socio-Economic Survey (IHSES) in 2012.

[1] In recall interviews, households are typically asked to report on food _consumption_ during the specified period and 1The World Bank's Living Standards Measurement Study (LSMS) survey finder includes a list of household consumption and expenditure surveys: of almost 90 surveys from more than 25 countries, more than 75 use recall.

(2017) compare recall questions on food spending from the Canadian Food Expenditure Survey to data from expenditure diaries.

The availability of repeated diary-recall measurements of food spending or acquisition is not unique to our setting, the Consumer Expenditure Surveys in the United States and the Canadian Food Expenditure Survey being notable examples.

Iraq Household and Socio-Economic Survey

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Input: We offer less restrictive assumptions and formal tests for these assumptions where data are available, provide more insights into the selection of control variables for model building, and offer simpler variance formulae. Our framework can be generally applied to imputation either from one survey 27 We also experimented with imputation from the HEIS into the DHS. However, one major issue is the latter survey ’ s most recent two rounds are in 2009 and 2012, which do not overlap with the HEIS, thus making it difficult to benchmark the DHS. We tried benchmarking both rounds of the DHS using the HEIS in 2010, and found a qualitatively similar decreasing trend in poverty across these two survey rounds. 28 It is also more demanding to make the distributions of the explanatory variables comparable for smaller population groups (e. g., as disaggregated by regional characteristics or other distributional characteristics such as quintiles) in surveys of different designs. We leave this extension for further research. 33 Usage context: primary mention, supporting mention, background mention.

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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, background mention, supporting mention.

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Input: 3 The Government of Chad subscribes to this logic as evidenced by the application decree of Chad ’ s Asylum Law signed in 2023, 1 and the National Response Plan to the Impact of the Sudanese Crisis which is under preparation. The Law and the Plan promote the local integration of refugees, aim to avoid settling refugees in permanent camps and promote self-sufficiency. They offer refugees the right to own land, to engage in formal employment and commercial activities, to move freely, and to access to banking services. While participation is the stated policy objective, the reality is that previous arrivals are almost exclusively living in camps, and that the new arrivals live in “ organized sites ” (as humanitarians now call them), presumably to cope with massive arrivals but with little concrete evidence for the onward movement of refugees. This note explores the size of this four-way benefit (for refugees, hosts, the Chadian state and the international community) by estimating how much could be saved on aid for basic needs consumption by enabling refugees from Sudan to realize their economic potential. For its empirical work the note draws primary on data from the ECOSIT4 survey. 2 In 2018 – 19, Chad became one of the first countries in Africa to capture refugees and host communities in its national household survey. Usage context: supporting mention, primary mention, background mention.

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Input: Ethiopia has experienced steady economic growth for much of the last two decades, but even before the country’s concurrent crises, economic growth did not transform the labor market structure. Between 2004 and 2020, Ethiopia’s GDP annual growth averaged 10 percent, helping to reduce the poverty by about ten percentage points. But during this period, the distribution of workers across sectors and geographies shifted very little. According to newly released 2021 Labor Force Survey (LFS) data, about 80 percent of Ethiopians live in rural areas, where roughly 75 percent work in agriculture. In urban areas, approximately 70 percent of people work in services. Nationally, self-employment accounts for about half of jobs, with unpaid family work second most common and wage work a distant third. Again, the urban context is different: wage employment is prevalent but often poorly paid. Over the last decade, negative repercussions from the concurrent overlapping crises have threatened this marginal progress. In 2020, the COVID pandemic closed markets, albeit relatively briefly, and this immediately decreased job opportunities. While most reentered the labor market, many changed their work situation, and some permanently exited the workforce or reduced working hours. At the same time, more and more people— rural women

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Input:
9 Beans, for instance, which are one of the most important food purchases and items in Brazil, have a raw calorie intake of 306 kcal per 100 g in TBCA (Feijão, carioca), while in the prepared form, TBCA assigns 71 kcal per 100 g, assuming a composition of 50 percent beans and 50 percent water to the meal.

Table 2 shows the top 10 food groups in POF in terms of average calorie intake per 100g. It shows that
'Meat' is the most expensive food group, followed by 'Breads, Cakes and Pies'. On the other hand, 'Kitchen
Oil' is displayed as the most caloric food group, as expected. It is followed by 'Flour Derivatives' (which
includes pasta). The cheapest food group is 'Beans and Legumes', while the least caloric is 'Fruits'.
Therefore, the data - including our calorie mapping - show some expected patterns of caloric and price
distribution across food groups.

_Source:_ Own calculations using POF 2017/18 data and TBCA, based on food items categorizations by IBGE.

For this, we construct a consumption aggregate using the data from POF. The consumption aggregate is based on household expenditures on goods and services.

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Input: A number of different road indicators are available and we choose road line type to use in the analysis. Road type is defined by the following: The reference category (0) points out squares with dual lane / divided highways, other primary roads, or road connectors within urban areas (types 1 or 8 in the ESRI dataset). The second category include secondary roads (type 2), and the third combines squares with informal or tertiary roads (tracks, trails or footpaths) or no road registered at all (types 3 and 0, respectively, in the ESRI dataset). Figure 4 overlays the types of roads in the original dataset before our recategorization. The shaded area represents the portion of Africa for which we code

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Input: III THE VENREPS-KIDS STUDY In this section, we offer a detailed overview of the VenRePs-kids Study, covering its de- sign, implementation, the questionnaire utilized, and the primary outcomes that will be employed to evaluate the human development disparities between forcibly displaced children and adolescents and their peers in host communities. III. A Design Location. Our study is conducted in Medell ´ ın, Colombia ’ s second-largest city, following Bogot ´ a. Medell ´ ın was chosen for this study because it hosts the third-largest Venezue- lan migrant population in the country, trailing only Bogot ´ a and C ´ ucuta, as indicated by the 2018 population census data. Additionally, previous research has demonstrated that survey response rates among migrants in Medell ´ ın are notably high. For instance, a na- tionally representative survey of Venezuelan migrants conducted in 2018 — which was representative across Colombia — revealed that Medell ´ ın had the highest response rates among migrants, whereas Bogot ´ a recorded the lowest (Ib ´ a ˜ nez et al. 2022). This finding supports the decision to focus our study exclusively on Medell ´ ın, also considering the challenges and high costs associated with tracking a highly mobile population longitu- dinally in previous research efforts. Usage context: background mention, supporting mention, primary mention.

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Input: The data on total gross proﬁts and net sales acquired from the Turkish Ministry of Science, Industry and Technology are compiled from administrative taxation data and was provided upon request by the ministry. The key diﬀerence from the Chamber of Commerce data is that the sales and proﬁts data include all businesses including self- proprietorships. 9 Data were provided for the years between 2010 and 2014 and are re- ported in nominal Turkish Liras (TL). It is worth noting that the administrative data will not include any informal activities by deﬁnition and they are likely to be less accurate and complete for smaller ﬁrms. Firms whose sales do not exceed an annually determined limit do not have to report their balance sheets which includes sales and proﬁt ﬁgures. 10 We scale the variables according to province size by dividing sales and proﬁts by the pop- ulation of the provinces. If we use sales and proﬁts in absolute terms, we get qualitatively similar results. The IV estimations use data from the years 2011 and 2014. Since the number of refugees was still relatively small in 2011 and really started picking up only in 2012, we 12 Usage context: background mention, supporting mention, primary mention.

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Input:
Data Sources: proGres v4 (PRIMES) hosts the data of refugees and asylum-seekers in 11 countries. In South Africa, the data are managed by the
government. In Angola, DRC, Zambia and Zimbabwe, some portions of the data are external. For IDP data, the source of DRC’s IDP figure is the OCHA;
the sources in Mozambique and Zimbabwe are the Displacement Tracking Matrix (DTM) of the IOM; and the source in the Republic of Congo is the
government, the Ministry of Social Affairs and Humanitarian Action (MASAH).

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Input: followed the same standards and procedures as the HoWStat. SESRE data was not collected alongside HoWStat due to security concerns at the time of data collection for HoWStat, especially in the refugee areas. The SESRE aimed to solve two problems: (i) gaps in data on the socioeconomic dimensions of refugees, and (ii) gaps in analytical studies presenting the socioeconomic outcomes of refugees and hosts. Lack of up-to-date evidence is a significant obstacle to designing effective policies and support for refugees and host communities. To this end, the availability of the data helps to analyze refugee hosting areas’ social dynamics and longer-term socioeconomic viability by focusing on the: (i) social impact of refugees on host communities, (ii) socioeconomic interaction, (iii) social inclusion, and (iv) social relations among refugees and between refugees and host communities. The data provides valuable information to development partners and governments to inform policies to facilitate refugees’ integration and improve their lives, along with refugee hosting communities. The SESRE covers all current major refugee camps: Eritreans, South Sudanese, and Somalis, as well as the out-of-camp refugees in Addis Ababa. In addition, the survey covers the respective host communities around the camps, including the host communities of Addis Ababa.

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Input:
We test the two parts of the redistribution hypothesis using the most recent data from Luxembourg Income Study for 20 OECD countries covering the period 1967-2005 (total number of country/years is 110).

_Economy_ (Milanovic, 2000) and which for the first time used household level data derived from household income surveys to test the hypothesis, and in the process

individual national identification", which he argues, based on World Values Survey data, to be stronger among the poor voters, and which reduces their propensity to vote for

seemingly depending on author's preferences, availability of the data (OECD provides the 90-10, 90-50 and 50-10 gross wage ratios), or perhaps contingent on the formulation

is why, in their analysis of eight advanced economies, they use scores of "intended generosity" (developed by Scruggs, 2004) of the pension system, unemployment benefits, child benefits and social assistance.

Luxembourg Income Study
World Values Survey

household income surveys
gross wage ratios

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Input: the intensity of conflicts, and iv) population density in refugee host countries by region. Appendix B presents the list of variables along with description and summary statistics. 5. Empirical strategy To estimate the impact of refugee inflow19 on host community ’ s livelihood strategy choice, we use the following basic econometric model: 𝑌𝑌𝑖𝑖 = 𝛼𝛼 + 𝛽𝛽𝑅𝑅𝑅𝑅𝑖𝑖 + 𝛾𝛾𝑋𝑋𝑖𝑖 + 𝜈𝜈 + 𝜀𝜀𝑖𝑖 (1) Where, 𝑖𝑖 indexes a household, 𝑌𝑌𝑖𝑖 is an outcome variable of interest (livelihood diversification or commercialization of agriculture), 𝑅𝑅𝑅𝑅𝑖𝑖𝑖𝑖 is the measure of refugee inflow, i. e., the refugee population (average of 2017 and 2018) in the nearest refugee camp weighted by the inverse of distance of the household to the refugee camps, 𝑋𝑋𝑖𝑖 is a set of household controls, 𝜈𝜈 is kebele fixed effects, and 𝜀𝜀𝑖𝑖 is the error term. Several variables, from the DRDIP data set, were used as controls in our model. Usage context: primary mention, supporting mention, background mention.

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Input: 33 In school: an indicator for whether the respondent currently attends regular education (schooling). This does not preclude also being employed. Retired: an indicator for a respondent who declares that they are not engaged in job search because they are retired. Full / part-time employment: an indicator for whether a person works full or part-time for all people in private sector employment (see above definition of employment). Full-time employment is defined as usual working hours of 30 or more hours per week, part-time employment as usual working hours of less than 30 hours per week. We do not use the indicator provided in the LFS data since there seems to be some confusion in which category 30 hours per week falls (with these evenly divided between full and part-time). Education: we classify people into three education categories. Low education is defined as those with no completed formal education. Medium education is defined as those with at least completed primary education but no high school completion. Higher education is defined as people who have at least completed high school.

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Input:
**6. Conclusions**

This paper has used historical rainfall data to estimate a putative history of payouts on

Indian rainfall insurance policies. We have found that indemnities are concentrated in the

According to Census data (CNBS, 2001), shares of these crops in five of China's provinces

The climate data (monthly temperature and precipitation) were gathered from the National   Meteorological Information Center in China.  The data are based on actual measurements in 753   national meteorological stations that are located throughout China. The temperature and   precipitation data were collected from 1951 to 2001.

Socio-economic data come from China's National Bureau of Statistics (CNBS). The data   were collected by a highly trained, professional enumeration staff in 2001 as part of the annual,   nation-wide Household Income and Expenditure Survey (HIES). The data cover 45,700 farm   households in 4365 villages, 533 counties and 31 provinces.

account for soils, we downloaded a soil map from FAO's website. There are three major soil

types-clay, sand and loam soils. The final set of variables for our analysis was created by

historical rainfall data
Census data
climate data
soil map

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Input: Page 14 of 51 Source: Own calculations based on Household surveys From the education perspective, an improvement in the profile of workers employed as NSE can be observed in the countries analyzed, even though there are some exceptions. In fact, in most of the countries of our sample, the prevalence of workers with secondary and tertiary education increases in detriment of workers with a lower educational level. However, there are some differences by type of non-standard employment. In the case of part-time employment (Figure 10), several countries show an obvious rise in the prevalence of workers with secondary and tertiary education (Argentina, Brazil, Peru, and Mexico). There is a second group of countries that presents a decrease in the prevalence of workers at the secondary level, but, this decrease is more than compensated by the greater incidence of tertiary workers, so that, taken together, workers with secondary or higher education increased their share within part-time employees (Uruguay and Chile). Therefore, we can also conclude from this group that part-time employment presents a better educational profile today than two decades ago. In the Dominican Republic and El Salvador, we find a rise in the prevalence of workers with secondary education but a decrease in the share of workers at the tertiary level. Usage context: background mention, primary mention, supporting mention.

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Input: 3 five methods, it was not possible to consider non-sampling error. This paper goes a step further by using simulations to describe the sampling error and a field experiment in an IDP camp in South Sudan to measure the total survey error of each design compared to a census, allowing for the disaggregation of the total error into sampling and non-sampling components. In addition, we attempt to separate the components of non-sampling error linked to the sample method from those common across all methods, such as interviewers selecting larger households and other issues in properly implementing the household survey protocols. The next section briefly describes each method and highlights the literature as it relates to the relevant selection methods. Section 3 describes the data set and protocols for each method included in the experiment, followed by Section 4, which discusses implementation issues. Section 5 reports the results of the analysis, and section 6 concludes with further discussion of the overall performance and areas for future research. 2. Description of Methods This paper compares five alternatives of second stage selection (satellite mapping, segmentation, grid squares, “ Qibla ” (or “ walk north ”) method, and random walk) to a human canvassing operation.

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Input: 14 3. Refugees as Agents of Their Own Destiny 3. 1 The Composition of Africa ’ s Refugee Population and Its Consequences One of the first elements that catch the eye in Figure 6 is the difference in the composition of the refugee population in Africa compared to the rest of the world. The share of children and women among refugees is higher in Africa than elsewhere, in particular East and West Africa stand out here. This is, at least partly, a consequence of Africa ’ s younger, general population, but other forces could be at work as well, e. g. higher mortality of adult males in Africa or adult males staying behind or being separated from the rest of the household. It does mean however that, relative to other areas, more attention should be going to the needs and capacities of women and children in Africa. This means, for example, adaption of and increased supply of schooling and health services. Figure 6. The composition of refugees by age and gender, 2013 Source: Note: UNHCR statistics (UNHCR 2014). Asia excludes Australia, Japan and New Zealand. Americas exclude Canada and the United States. These percentages have been calculated by country when demographic data are available for at least 30 % of the total. Usage context: background mention, supporting mention, primary mention.

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Input: to guide and define camp-specific cooking energy options. • In Afar, Gambella, and Melkadida, more than 382,000 refugees and 85,000 hosts have access to alternative cooking fuels and market-based clean electricity from solar-mini grids. • More than 1,739,726 seedlings were planted, 160m3 check dams were built, and 8 km of soil stone band were built to rehabilitate degraded land. Additional GoE GRF Pledges 2023 Climate Action: Protect and restore the environment, manage natural resources, afforestation of degraded lands, and expand renewable energy solutions for the benefit of both refugees and host communities. Recognize vulnerability of women to climate change, and prevent violence against women in all environmental policies and programs, and empower women to have agency and influence in environmental stewardship and adaptation to climate. Human Settlement: Transform selected refugee camps into sustainable urban settlements by enhancing the quality and availability of shelter, infrastructure, and public services, such as roads, electricity, water, sanitation, health, and education by aligning them with adjacent towns’ masterplan, by 2027. Inclusion of refugees into existing national systems: Enhance the capacity of GoE to include 1,000,000 refugees into the national Central Statistics Service (CSS), the national Gender-Based Violence (GBV) prevention and response programs, 814,000 refugees into

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Input:
Since 2006, Rwanda is divided into 30 districts, which are the main service-delivery units. All
household surveys are representative at the district level, but there is no information on districtlevel economic growth. Estimating district-level growth based on nightlights is however
complicated by the low intensity of lights: Four districts did not emit any observable lights at all
during 2000-2011, and ten districts only have nonzero observations towards the end of the 20002011 period. As a result, we can only estimate nighlights-based GDP growth for 16 districts.

Notes: Dependent variable is the natural log of GDP in constant LCU. All specifications include country and
year fixed effects. Robust standard errors clustered by countries in brackets. ***: significant at 1%-level; **:
significant at 5%-level; *: significant at 10%-level. Data source: NGDC (2014) and WDI (2014).

In Column (3) we replace lights observed from space by data on electrical power consumption (in kilowatt hours, obtained from the World Development Indicators). As data on electricity consumption are only available for 22 countries in SSA, the number of observations drops from 966 to 425 (not all 22 countries have data for all 21 years). We find a strong and statistically significant association between electricity consumption and GDP in the reduced sample (elasticity of _0.37_ ). The coefficient is however considerably smaller than the one estimated between night lights and GDP ( _0.58_ ). This may potentially be explained by the relatively high use

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Input:
Use of state level data on rental restrictions, together with a nationally representative survey from India, suggests that, contrary to original intentions, rental restrictions negatively affect productivity and equity.

1 Using census figures, Appu (1997) estimates that, to avoid having to give rights to tenants, landlords evicted about 30 mn tenants or about one third of the total agriculturally active population.

The data used in the analysis come from two rounds of NCAER's ARIS/REDS survey conducted in 1982 and 1999, respectively. This survey, the first rounds of which were implemented in 1968-71 to evaluate the impact of an agricultural development program, covers all of India's major states.

of observation 4980 1279 1613 670 1417 7476 1705 2479 1307 1985
_Source:_ Own computation from 1982 and 1999 ARIS/REDS surveys.
All values are in 1982 Rs with 1999 values having been deflated by state level deflators.

state level data on rental restrictions
nationally representative survey
census figures

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Input: Integrating refugees into functioning national education systems can improve future outcomes for refugee children and their hosts (UNHCR, 2020; Piper et al., 2020; Abu-Ghaida and Silva, 2020; Crawford et al., 2015; Bilgili et al., 2019). As Chapter 2 highlighted, more than half of all refugees are children under the age of 15. Although over 70 percent of primary school age children attend primary education, they do not make it past primary education. Integrating refugee children into educational programs soon after their arrival in Ethiopia avoids the loss of valuable years of education and human capital accumulation, hindering prospects. It is critical to address the obstacles that hinder children from transitioning to secondary schooling, such as challenges in accessing school records, language barriers, or distance to schools. Supporting Regional Education Bureaus could increase the accessibility of secondary schools Policy Recommendations 74 Policy Recommendations to camp refugees.51 To improve the educational attainment of refugee children, the focus should be on increasing the number of qualified teachers in primary education, increasing the currently low compensation to incentive teachers with similar qualifications as nationals, improving primary-to- secondary transition rates, and reducing classroom overcrowding. Build an inclusive health system. Good health is an essential requirement

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Input: In settings with clearly distinguishable individual structures, these methods can also be reasonably accurate for the purposes of rapid estimation of displaced population (Checchi, et al. 2013). 101 The use of unmanned drones is also becoming more popular as the cost of this technology falls. This technique has been used by UNHCR to update its estimates of IDPs in Somalia ’ s Afgooye corridor (IDMC 2015) and by IOM to monitor disaster-induced displacement in Haiti, including the use of Unmanned Aerial Vehicles (UAVs) in collaboration with UNOSAT. (g) Open data initiatives. There are several initiatives to provide free and open data that enable Internet users to independently mine and analyze data and generate customized summaries, charts and visualizations. For example, the Bank has provided free, open access to its development data since the launch of its Open Data Initiative in 2010, however there is little open data on asylum-seekers, refugees and IDPs. JIPS has developed a web-based platform that allows users to explore, analyze and visualize profiling data online, and IDMC has 100 See http: / / www. flowminder. org /. 101 These methods are not effective in settings with connected structures, a complex pattern of roofs or multi-level buildings, as are prevalent in urban areas. Additionally, cloud cover and dense foliage can also obscure structures. Usage context: primary mention, supporting mention, background mention.

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

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Input: In order to address these potential sample biases, the CAVR supplemented its documentation with reports produced by Amnesty International and Fokupers (a local NGO). The information contained in these reports was then included into the HRVD database. 9 Note that we do not analyse school completion in 2001 because most children that were of school age in 1999 were still in school in 2001. 10 The questions we used are ― Was [NAME] displaced outside E. Timor in 1999? ‖, and ― Was the [BUILDING] damaged in the violence of 1999? ‖. 14 % of the whole sample surveyed in 2001 report having been displaced, while 26 % report that their house was destroyed. Within our sample of school age children, these figures are 16 % and 25 %, respectively. We have made sure that buildings that are reported to having been destroyed were used for living purposes only.

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Input:
The data sources are (1) Romania MoF firm-level data for the period 2011-20, and (2) the World Bank Businesses of the State (BOS) database for Romania, and (3) the taxonomy of sectors developed by Dall'Olio et al.

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the period 2011 to 2020. It is based on financial statements and contains balance sheet information such as
firm tax identification number, year of incorporation, operating revenue, average number of employees,
number of employees at the end of the year, labor cost, fixed assets, total assets, amount of subsidies
received from the government, [6] 4-digit NACE industry code, and the county location of the firm.

The Romania MoF firm-level data did not include a variable that identifies the ownership status of the firm (whether the firm is an SOE), which is the key explanatory variable of interest.

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2014 428,618 1,031 0.24 86.8 13.2 25.2 71.2
2015 440,445 1,092 0.25 87.5 12.5 23.7 72.7
2016 457,273 1,136 0.25 88.1 11.9 22.9 73.7
2017 475,757 1,209 0.25 88.9 11.1 21.4 75.4
2018 491,289 1,277 0.26 89.4 10.6 20.5 76.4
2019 511,863 1,299 0.25 89.5 10.5 20.1 76.9
Total 4,027,783 0.24 87.6 12.4 23.7 72.8
Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. For purposes of providing the
summary statistics, the sample is restricted to firms with revenue and employment data. SOEs are defined as firms
where national or subnational governments have ownership stake of at least 10%, while POEs are firms where national
or subnational governments own 0-9.9%. The percentages for centrally owned or locally owned may not add up to
100 because there are "other" SOEs that are neither owned by the central or local government.

World Bank Businesses of the State (BOS) database

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Input: 15 causes of death. The Danish Epidemiology Science Centre (1999) found severe malnutrition and high mortality in a survey of 422 refugee children in Guinea ‐ Bissau. They report higher malnutrition and higher mortality for children living in a non ‐ camp setting, compared to children living in a camp. The Goma epidemiology group (1995) found high prevalence of child mortality as well as acute malnutrition among children in refugee camps in Eastern Zaire, especially in female headed households. The magnitude of the difference between ‘ normal ’ mortality in the country under study, in the absence of conflict and the mortality in a refugee camp, depends on several parameters: the health infrastructure in the country as well as in the camp, the food available to camp and non ‐ camp residents, the frequency of visits by nurses or doctors, the intensity of the conflict (e. g. attacks on camps), and so on. Thus, the results are highly dependent on the context. For example, Singh et al (2005) do not find a difference in under 5 mortality among refugee versus non ‐ refugee households in western Uganda and South Sudan, whereas Verwimp and Van Bavel (2005) find higher child mortality and fertility among Rwanda refugees in Congo versus Rwandan women who did not became a refugee. Usage context: supporting mention, background mention, primary mention.

survey of 422 refugee children in Guinea ‐ Bissau

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Input: 6. Robustness Checks 6. 1. Sample attrition Many challenges were encountered during survey implementation, due to the context – the densely populated and impoverished communities in and around Monrovia are difficult settings in which to find and track respondents — as well as the transience of a young study population. Despite vigorous efforts to track and interview each individual in the sample, a certain amount of survey attrition was expected. As the survey response rates in Table 1 show, 1622 (or 80 %) of the individuals in the study sample were successfully interviewed in both the baseline and midline surveys. Another 305 respondents were interviewed at baseline but not at midline and hence are not in the panel used for the analysis in this paper. 27 This survey attrition, while not much higher than other program evaluations in Africa, may cause concern that the results of this evaluation are biased, especially if the loss to follow up is correlated with individual characteristics that might affect the outcomes. To address this concern, Table 10 presents regressions on the likelihood of panel inclusion, that is, the likelihood of being interviewed at both baseline and midline. The first column indicates that treated individuals are significantly more likely than control to have been interviewed twice. This result persists even after controlling for individual characteristics and community dummies.

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Input: Some data challenges common to refugees and IDPs Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities. Sampling. As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay at relatives ’ places, other live in makeshift shacks and others stay in camps. The information available in the UNHCR registry (the census) can also be quite inaccurate, as already discussed, and the degree of accuracy changes for different groups of refugees. Stratification by urban and rural areas, a typical approach in sampling, may mean little for a population that is mostly in urban areas whether in camps or outside camps. Refugees and IDPs are also mobile and more difficult to track over time than other populations. Several statistical institutes worldwide have developed methodologies to track and measure mobile populations such as herders, nomads or homeless people. However, tracking refugees from other countries has been in the Usage context: primary mention, background mention, supporting mention.

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Input: We developed pseudo-EAs from the list by location (sub-city and Woreda); some EAs covered more than one Woreda, and multiple EAs were in a single Woreda. We selected a sample of EAs and households from each EAs in collaboration with UNHCR. Finding refugees in Addis Ababa was challenging, as they change their location frequently. To minimize the burden of searching for selected refugees, representatives of the selected households were contacted before the survey to ask them to come to a UNHCR center to collect preliminary information, including their current residential address. Since many Eritrean refugees in Addis Ababa had fled from the conflict in Tigray, out- of-camp refugees in Addis Ababa were stratified into two domains: refugees who arrived before the start of the conflict in November 2020, and those who arrived after November 2020. (c) Host Communities Populations around the refugee camps under each domain were meticulously identified in consultation with UNHCR and RRS. We used the ESS EA maps to assess the settlement of communities around camps, ensuring a precise fit to the definition of a “host community”. The assessment highlighted that using the list of EAs obtained from the new cartographic frame57 meets the definition of host

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Input:
**Appendix G: Comparison of GLSS and DHS Wealth Indices**

The GLSS wealth index and the DHS wealth index are both constructed using principal component analysis but differ in their input variables. The GLSS wealth index draws on 32 asset and housing variables, while the DHS wealth index uses 40 variables including country-specific items added through DHS national adaptation. The correlation between the two indices, estimated on the subsample of GLSS clusters that were also covered by DHS fieldwork within the same 12-month window, is 0.74, suggesting substantial but not complete overlap in what the two measures capture.

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Input: For example, IDPs who subsequently cross international borders may be counted as both IDPs and refugees (e. g. in the case of the Syrian displacement crisis). Table 4: Stocks and Flows Stock Increases Decreases Asylum- seekers  New applications for asylum, separately identifying individuals who were previously IDPs  Positive decisions (convention status, complementary protection status)  Rejected  Otherwise closed Refugees  Spontaneous arrivals (group recognition, temporary protection, individual recognition), separately identifying individuals who were previously IDPs  Resettlement arrivals  Births  Administrative corrections  Repatriation  Resettlement  Cessation  Naturalization  Deaths  Administrative corrections IDPs  New internal displacement  Births  Administrative corrections  Cross border flight, becoming an asylum-seeker or refugee  Return  Settlement elsewhere in the country  Local integration  Administrative corrections Source: UNHCR Global Trends, IDMC Forced Displacement Data Model Usage context: supporting mention, background mention, primary mention.

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Input: 8 as self-confidence, responsibility, and respect. Additionally, the curriculum includes workplace readiness skills, such as interviewing and time management. SECTION 2: STUDY TIMELINE & DATA A quasi experimental impact evaluation design was embedded into the NVSP. As mentioned before, the NVSP received 38 applications from eligible NGOs. Per well-developed selection criteria, 8 the highest 22 ranked proposals were selected to receive funding. Each of the 38 proposals included a list of 50 youth (the minimum number of youth set by the NVSP) who would benefit from the project if selected for funding. However, as mentioned before, the 22 selected projects benefited a total of 1, 296 youth, exceeding the set target of 1, 100 volunteers. Of the 50 volunteers included in each of the 38 proposals, 22 youth per proposal were randomly selected to participate in the impact evaluation study. Therefore, the initial sample size of the study comprised a total of 825 youth: 473 youth who served as the treatment group (representing the 22 selected NGOs that received NVSP funding) and 352 youth who served as the comparison group (representing the 16 non-selected NGOs). However, two NGOs refused to participate in the study once informed that their proposals had not been selected for funding. Usage context: supporting mention, background mention, primary mention.

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Input: 6 3 Methodology 3. 1 The A-F method and individual deprivations The Multidimensional Poverty Index (MPI) used in this paper was first presented, with a full methodological discussion, in Admasu et al. (2021). Here we present a general overview of the measure for the individual-level and intrahousehold analyses. The MPI is constructed based on the Alkire-Foster (AF) method of multidimensional poverty measurement (Alkire and Foster 2011). Three key statistics characterize any MPI: incidence or headcount ratio (H), which is the proportion of the population who are multidimensionally poor; intensity (A), which is the average share of weighted indicators in which multidimensionally poor people are deprived; and adjusted headcount ratio (M0 or MPI), which is the product of the incidence and intensity (MPI = H × A). The AF method uses a dual-cutoff counting approach to poverty measurement. Having fixed relative weights across indicators that sum to 100 %, it first identifies who is deprived in each indicator, then sums up the weighted deprivations each person experiences into a deprivation score. A person is identified as poor if their deprivation score meets or exceeds a cross-dimensional poverty cutoff that is greater than 0 and less than or equal to 100 %. It then aggregates this information to compute society-level MPI, incidence, and intensity.

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