Input: 5.1.3 Multidimensional poverty ............................................................................................................................................................................................................. 46 5.1.4 Food security........................................................................................................................................................................................................................................... 47 5.1.5 Shocks and coping strategies....................................................................................................................................................................................................... 48 5.2 Determinants of welfare......................................................................................................................................................................................................................................... 49 5.3 Cost of basic needs for refugees ..................................................................................................................................................................................................................... 53 6. Markets and Opportunities ....................................................................................................................................................................................................................................... 56 6.1 Spatial disparities in refugees labor market access and outcomes...................................................................................................................................... 57 6.2 Effects of local factors on refugees’ labor market outcomes...................................................................................................................................................... 61 7. Social Cohesion...................................................................................................................................................................................................................................................................... 63 7.1 Attitudes between refugees and hosts...................................................................................................................................................................................................... 64 7.2 Social interactions ...................................................................................................................................................................................................................................................... 68 8. Policy Recommendations ........................................................................................................................................................................................................................................... 72 References ............................................................................................................................................................................................................................. 77 Annexes ................................................................................................................................................................................................................................. 82 Annex A: Description of Refugees by Country of Origin.............................................................................................................................................. 83 Annex B: Refugee Policies in Ethiopia.............................................................................................................................................................................. 85 Annex C: Survey Design and Methodology.................................................................................................................................................................... 92 Annex D: Descriptive Statistics and Regression Results............................................................................................................................................. 97 Annex E: Robustness Checks of Refugees’ Consumption.......................................................................................................................................... 124 Annex F: Comparison of Results from Skills Profile Survey and SESRE................................................................................................................. 128 TABLE OF CONTENTS Figure ES.1: Desired location in three years ����������������������������������������������������� iv Figure ES.2: Expected location in three years ��������������������������������������������������� iv Figure ES.3: Poverty incidence........................................................................... v Figure ES.4: Food insecurity scale...................................................................... v Figure ES.5: Refugee employment and proximity to resource hubs .................. vi Figure ES.6: Host response to “Refugees are good people” ............................ vii Figure ES.7: Host response to “Would you feel comfortable having

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Input: informal sector in surrounding communities or the camps., this may include selling aid rations on the local market, informal trade, and economic exchange, or working for local NGOs and UN agencies (ReDSS, 2018). Extensive research has shown that not being able to enter local labor markets legally is detrimental to refugees’ ability to earn for their families, and for them to find an occupational match that maximizes the benefits they contribute to Ethiopia (World Bank, 2023). Because of conflict, the SESRE data collection could not include refugees in the Tigray region in Ethiopia, where most Eritrean refugees were hosted before the conflict. Since outbreak of the conflict in November 2020, many Eritrean refugees moved to Addis Ababa, and many fled from the Mai Ani and Adi Harush refugee camps in Tigray to the newly established refugee hosting site of Alemwach, Dabat in the Amhara region. Between February and July 2022, over 15,000 refugees relocated from the Tigray camps to Alemwach, and an additional 7,000 refugees were resettled in November 2022 following the cessation of hostilities (UNHCR, 2022). Before the conflict, 64 percent of all Eritrean refugees were hosted in camps in Tigray and 36 percent in camps in Afar (UNHCR,

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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: primary mention, supporting mention, background mention.

UNHCR statistical population online dataset

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Input: Combining almost 50, 000 responses to 11 cross-sectional surveys between 2017 and 2021, displacement is negatively associated with perceptions of social cohesion in aggregate. But at the individual level, those who report hosting displaced populations in their communities often have higher perceptions of social cohesion. These results are strongest among respondents who self-report hosting IDPs as opposed to refugees, but important heterogeneity across indicators, local context, and gender should guide policy meant to promote social cohesion in forced displacement. This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at ppham @ hsph. harvard. edu. Usage context: primary mention, supporting mention, background mention.

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

international bilateral migrant stock data

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Input: 15 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). As the Sudanese sample surveys IDPs living in the Abu Shouk and El Salam camps, we must understand these deprivations with the background that these settlements were created as emergency and crisis responses rather than durable, long-term solutions (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019). Although Sudan does have a national electric grid that supplies electricity to the urban and peri-urban areas of the nearby city El Fasher, IDP communities living in the camps report limited connection to the city ’ s electricity supply, reflected in the high deprivations in the electricity and cooking fuel indicators. The ad-hoc construction of dwellings in the two camps explains why 71 % of the IDP households in Abu Shouk and 65 % in El Salam live in tukuls or other permanent mud or wood structures (Sudanese Government ’ s Joint Mechanism for Durable Solutions 2019: p. 50), both of which register as unimproved housing types. Usage context: primary mention, background mention, supporting mention.

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Input:
data source listed most prominently in the announcement was the Emergency Food Security  and Nutrition Assessment (EFSNA), which was conducted between November and December

the IPC classification. For example, the EFSNA avoided the two most conflict-affected gover norates, was potentially plagued by a host of logistical difficulties given the security situation

by the WFP used in this analysis and the Gallup World Poll both find that the size of the

displaced population is up to three times as large as is being identified by the Task Force for  Population Movement (TFPM), which is the official source used in the IPC 2017 classification

occur. This article utilizes the mobile Vulnerability and Assessment Mapping Survey (mVAM)

Emergency Food Security  and Nutrition Assessment (EFSNA)

Emergency Food Security  and Nutrition Assessment

Task Force for  Population Movement (TFPM)

mobile Vulnerability and Assessment Mapping Survey (mVAM)

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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).

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Input:
Our main specification for the poverty line estimations defines an expenditure threshold of R$500 to
classify lumpy (all above) and not lumpy (all below) items. Items that are above the threshold are thus not
included in the consumption aggregate as it is assumed that they are infrequently purchased, and in many
cases, an accurate incorporation into the poverty line calculation would be by estimating a value of the
flow of services they provide to the owner. The data required to run those estimations is not available in
POF 2017/18.

Below we list the items that were excluded in our main specification by POF module.

by floods, and which strategies they use to cope and adapt.
These insights are based on firm survey data collected in

2018 using a tailored questionnaire, covering a sample of

more than 800 firms. To assess the impact of disasters on
businesses, the study considers direct damages and indirect
effects through infrastructure systems, supply chains, and
workers.

This study presents evidence from a dedicated Tanzanian firm survey conducted with 837 businesses in Dar es Salaam and the provinces of Tanga and Dodoma.

The survey's target population was all 58,959 firms in Dar es Salaam, Tanga, and Dodoma that were registered with the National Bureau of Statistics' (NBS) in 2015.

firm survey data
dedicated Tanzanian firm survey

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Input: using a regression analysis to compare the earnings of refugees and hosts and how this wage gap changes after adjusting for the effects of demographic and job characteristics. Column 1, Annex D, Table D.6 shows that, after controlling for the domain, monthly earnings is 70 percent lower for refugees than for hosts. After controlling for age, gender, and education in Column 2, this earnings gap remains at 64 percent. This means 0 20 40 60 80 100 Camp Hosts Male Camp Refugees Male Camp Hosts Female Camp Refugees Female Employed Unemployed, searching Percent Unemployed, not searching Inactive not in school Inactive in school Figure 3.5: Work status by gender Source: World Bank Staff based on SESRE 2023. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 Female Male Figure 3.6: In-camp refugee share employed by age Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 30 that, after adjusting for any differences explained by age, gender, and education, refugees still earn significantly less than hosts. Column 3 indicates that refugees face this same earnings gap even after adjusting for differences explained by occupation and industry. Only after restricting the

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Input: First, there is reason to believe that better service delivery may aﬀect citizens ’ willingness to defer to the tax department only through its eﬀect on improved outcomes that matter for citizens ’ livelihoods. Unless improved services and infrastructure have a positive impact on citizens ’ welfare, indi- viduals are unlikely to credit the government for these outputs (Sacks and Levi, 2010). The Afrobarometer ’ s objective measures of service delivery only denote the presence or absence of infrastructure and services. The data do not indicate the condition of the services and infrastructure. Citizens may perceive and reward relative improvements or sanction de- teriorations in services, rather than the absolute level of service quality they receive. If services deteriorate or improve, taxpayers may alter their beliefs about governments ’ performance and should attempt to adjust their terms 10I also tested whether there is a relationship between the presence of a concrete road, health clinic, post oﬃce and electricity grid in the enumeration areas and respondents ’ willingness to pay taxes. None of these objective indicators except for the presence of an electricity grid were signiﬁcant at the p < 0. 05 level. The presence of an electricity grid is negatively associated with the willingness to defer to the tax department. 17

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Input: 3 restrictions on job search, but they do not benefit from the same humanitarian assistance refugees in camps benefit from (Betts et al., 2017b). We investigate the determinants of the vulnerability of Syrian refugees to the COVID-19 shock in Jordan. In particular, we examine whether Syrian disadvantage during the shock is completely explained by institutional barriers reflected in precarious jobs and residency in camps, or if they experience added vulnerability by virtue of their refugee status. We use retrospective data on the job histories of young Jordanian and Syrian men between the ages of 16 and 30 years from the Survey of Young People in Jordan (SYPJ) (Assaad et al., 2021a; OAMDI, 2022) to construct a synthetic semi-annual panel dataset that tracks the job finding and separation experiences of respondents. We compare the trends of job finding and separation across the two populations before and after the onset of the COVID-19 pandemic. We find that Syrians have generally experienced lower job finding rates and higher job separation rates compared to Jordanians, although the differences were generally not statistically significant prior to the pandemic. With the onset of the pandemic, Syrians became significantly disadvantaged on both measures. Controlling for the type of employment showed that workers whose last job was informal were disadvantaged on both measures compared to formal workers regardless of nationality. Usage context: supporting mention, primary mention, background mention.

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Input:
Score Grade 6 512.2 527.3 550.0 527.8 515.6 (100.8) (98.96) (95.74) (105.8) (100.7) Enlace taker Grade 9 0.712 1 1 0.769 0.764 (0.453) (0) (0) (0.421) (0.425) Enlace taker Grade 12 0.323 0.453 1 0.370 0.379 (0.468) (0.498) (0) (0.483) (0.485) Girl 0.469 0.498 0.524 0.473 0.535 (0.499) (0.500) (0.499) (0.499) (0.499) Private School Grade 6 0.0822 0.0970 0.119 0.0578 0.0954 (0.275) (0.296) (0.323) (0.233) (0.294) Mother has lower secondary 0.539 (0.499) Father has lower secondary 0.586 (0.493) Mother is white collar 0.114 (0.318) Father is white collar 0.207 (0.405)

Notes: The table shows the mean and standard deviations of all students matched in the ENLACE panel in 2007
(column 1), 2010 (column 2) and 2013 (column 3). Column 4 displays additional variables from student and parents
surveys that were applied to a sample of ENLACE takers. Column 5 reports statistics for the sample of identified
twins in grade 6. Data: ENLACE panel.

Enlace Score 0.213 0.375 -0.0630 0.114 (0.854) (0.856) (0.778) (0.808) College Student 0.630 1 0 0.437 (0.483) (0) (0) (0.496) Employed 0.379 0.263 0.577 1 (0.485) (0.440) (0.494) (0) Upper Secondary GPA -0.0219 0.135 -0.289 -0.0835 (1.007) (0.995) (0.970) (0.987) Girl 0.564 0.546 0.596 0.502 (0.496) (0.498) (0.491) (0.500) Private Upper Secondary 0.175 0.203 0.129 0.134 (0.380) (0.402) (0.335) (0.340) Urban resident 0.848 0.912 0.740 0.814 (0.359) (0.283) (0.439) (0.389) Age 19.18 19.16 19.21 19.23 (

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Input:
However, specifying a control variable for which national accounts or survey data is needed (such as the share of agriculture in GDP or the share of the population engaged in agriculture) would run counter to the purpose of this study, which is to see whether proxies such as nightlights can be used to estimate growth in areas (sub-national or supra-national) for which there are no national accounts or survey data. One option is to use rainfall or vegetation data, which share many of the desirable features of nightlights data, to proxy the performance of the agricultural sector.

In the first step, we estimate national GDP based on specification (1). The estimated GDP is divided into two parts based on the share of agriculture in the economy (based on actual national accounts data): Agricultural GDP and industrial and services GDP. In the second step, we distribute industrial and services GDP among the subnational units in proportion to their share of national nightlights.

6 This is in contrast to Ghosh and others (2010) who use the Landscan data to distribute agricultural GDP among
sub-units.

16

<!-- PAGE 19 -->
##### **5.1 Kenya**

survey data
rainfall or vegetation data
nightlights data
national accounts data

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Input: In 2003, almost 60 % of young girls and 40 % of young boys had no formal schooling (ILO / UNICEF 2005). Data from the Demographic and Health survey shows that more than 40 percent of adult women have no education, compared to fewer than 20 percent of men, while 23 percent of women and 44 percent of men have some secondary schooling (DHS 2007). Happily, access to education is rising rapidly, especially for girls: according to the Liberian labor force survey from 2010, the ratio of girls to boys enrolled in primary school has risen from 72 in 2000 to 90 in 2009. Enrollment levels and sex ratios are lower among older children and youth, as they become increasingly engaged in 1 Both are unweighted averages; Barro-Lee comprises 32 countries with data from 2010; Edstats comprises 43 countries with data from 2007-2011. 2 Defined as without work, available for work, and actively looking for work (LISGIS 2010). 2 Usage context: supporting mention, primary mention, background mention.

Liberian labor force survey
Demographic and Health survey
Barro-Lee
Edstats

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Input:
For this reason, the World Bank's Development Economics Vice Presidency (DEC) has established an open web facility, the XCO2 database, [19] that pre-filters the OCO-2 data and publishes monthly mean concentration anomalies for all terrestrial cells of a 25 km global grid (G25). The website will also publish annual change estimates for urban areas by statistical significance class and provide information that links G25 grid cell IDs to IDs for urban areas and national administrative units (levels 0, 1 and 2). We believe that this web facility will contribute to the global effort to reduce CO2 emissions as rapidly as possible.

Byers, L., J. Friedrich et al. 2021. A Global Database of Power Plants. World Resources
Institute.

JPL/NASA. 2021. OCO-2 Data Set. Jet Propulsion Laboratory, California Institute of
Technology. https://co2.jpl.nasa.gov/?mission=oco-2

XCO2 database
Global Database of Power Plants

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Input:
survey, which did not include a benchmark measure as did the Dantlait survey. Figure 3

Dantlait survey results can be extrapolated to a sample of households in other parts of Niger,

It is important to note, however, that the Dantlait survey was limited to cattle. Small

precision of these estimates, and the Dantlait survey only collected data on cattle. Throughout

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Input:
using spatial information. Geographical coordinates of enumeration areas in GLSS are from

Ghana Statistical Services (GSS). [2] Point coordinates (global positioning system [GPS]) for the

The Raw Materials Data are from InterraRMG (2013). The data set contains information on

past or current industrial mines. All mines have information on annual production volumes,

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Input: nizations such as UNHCR, and national and international non-governmental organizations. Data is compiled from a number of sources, including but not restricted to individual registration of refugees and asylum seekers (information typically includes name, gender, date of birth, country of origin, marital status, and place of displacement), tracking of population movement in situa- tions where the movement is ﬂuid or continuous, standardized surveys such as Living Standards Measurement Study (LSMS) surveys, Labor Force Surveys (LFS), Demographic and Health Sur- veys (DHS), and Multiple Indicator Cluster Surveys (MICS), administrative records and registries. Yet, data collection is a difﬁcult exercise, due to both methodological issues (UNHCR 2014) and practical challenges, especially in situations of heightened insecurity or mass refugee situations. To date, UNHCR maintains the most comprehensive statistical database under a uniform methodology. UNHCR publishes annual data on refugee ﬂows and stocks by countries of resi- dence and origin dating back to 1951, shortly after the Ofﬁce was established. UNHCR publishes annual statistical reports ranging from “ Global Trends ”, “ Mid-year trends ”, “ Asylum trends ”, to a “ Statistical Yearbook ”. There is a consensus that these data provide the most reliable source of information (Sarzin 2016).

Labor Force Surveys
Multiple Indicator Cluster Surveys
Living Standards Measurement Study

individual registration of refugees and asylum seekers

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Input: 23 and females. 10 The results are shown in table S. 1 in the supplemental appendix. The baseline effects of basic schools on educational attainment and their effects on mobility are stronger and often more significant than those estimated in table 4 for both males and females. A second concern is that Jordan received a large influx of Palestinian refugees in the aftermath of the 1967 Arab-Israeli War. While refugees benefited from the UNRWA basic schools, their educational attainment and intergenerational educational mobility were perhaps subject to a different set of constraints than those facing other Jordanians. Thus, as a robustness check, individuals who are likely to be Palestinian refugees were excluded from the sample. Since the JLMPS 2010 does not allow directly identifying Palestinian refugees who are now mostly Jordanian citizens, two indirect methods were employed to identify individuals who are likely to be Palestinian refugees. Method 1 excludes individuals born in subdistricts where the percentage of individuals who were ever enrolled (or are currently enrolled) in an UNRWA school exceeds ten percent out of all individuals below 36 years of age in the sample. Method 2 excludes individuals born in subdistricts where the percentage of UNRWA schools exceeds ten percent of the total number of schools. The results for the restricted sample according to both methods are shown in tables S. 2 and S. 3 respectively.

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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: background mention, supporting mention, primary mention.

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Input: Figure A2: Refugees a burden for SSA? Panel A: Not weighted by economic capacity Usage context: background mention, supporting mention, primary mention.

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Input: The employment arm also signiﬁcantly improves cognitive function as measured through an index of memory and basic arithmetic tests, a ﬁnding consistent with a large psychol- ogy literature documenting the relationship between cognitive processes and depression (Semkovska et al., 2019). As with physical health, improvements to cognitive function are unlikely to be a direct product of the employment task itself, which was speciﬁcally de- signed to require no literacy or mathematical skill. Rather, these results are suggestive of a downstream impact to reducing depression through the experience of employment. Finally, we ﬁnd no change in time preferences: treated individuals are no more or less likely to discount the future relative to control counterparts, although results may have diﬀered had we engaged participants in an eﬀort or consumption-based time preference game rather than a ﬁnancial one. However, we ﬁnd a substantial increase in risk tolerance among the employed. A greater preference for risk-taking may be indicative of employment serving as a form of psychological ‘ insurance ’ that allows participants the mental bandwidth to exercise greater risk. This is consistent with the positive impacts of employment on stability as well as with a key motive underlying universal basic income (UBI) in the developing world (Banerjee, Niehaus, and Suri, 2019). Interestingly, however, we document no parallel increase in risk tolerance in the cash transfer arm. Our result on risk preference also echoes a potential consequence of depression and anxiety described in Ridley et al. (2020), although empirical 15 Usage context: supporting mention, primary mention, background mention.

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Input: The third graph represents the kernel density of our productivity loss measure for observations with positive loss, which represents around 11 percent of country-year observations in our dataset. It gives an idea of the distribution of this variable across country-years. The distribution shows a long and thin upper tail driven by countries with repetitive and highly intense conﬂict history like Afghanistan or the Lebanon. The average productivity loss is 15 percent in this sample and one fourth of all country-year observations are associated with losses of more than 20 percent of productivity. Even if these estimates were drastically overestimated they indicate that the long run impact of mass violence through this channel could be substantial. 5. 2 Macro Evidence In this subsection, we investigate the correlation between the aggregate loss measure and output. For this purpose, we use the height loss measure from the previous sub- section to estimate the marginal eﬀect of an extra cm loss on log GDP. This serves two objectives. First, we explore whether the micro evidence can be used as a conduit for understanding the long-term damage to output from conﬂict. Second, we check whether the aggregate loss in output that we get is consistent with the micro estimates of marginal economic return to health.

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Input:
The majority of surveys reported the same or similarly worded internationallyaccepted HIV indicators for prevention and misconception (except for the
misconception question on the possibility of getting HIV from sharing a meal with
someone who is infected with HIV); however, few reported disaggregated
indicators by age and gender (Table III). The majority of surveys did not report
the same or similarly worded internationally-accepted HIV indicators for practice
and attitudes; few reported disaggregated indicators by age or gender (Table III).
Twenty-six (83.9%) of the surveys asked an HIV practice question with 14
(45.2%) asking the question that was the same as or similar to the internationallyaccepted practice indicator that we chose (Table I); five (35.7%) disaggregated
the indicator by gender and age (Table III). Twenty-one (67.7%) of the surveys
asked HIV attitude questions with less than the majority asking at least one
question that was the same as or similar to the internationally-accepted attitude
indicators (Table I); few disaggregated by gender and age (Table III).

internationallyaccepted HIV indicators for prevention and misconception

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Input: destination. Building on a growing literature documenting the relationship between subjective welfare and relative income, Fafchamps and Shilpi (2008) show that Nepalese households care about their consumption level relative to that of others in the same location. If this is the case, it is conceivable that migrants choose their destination not so much for the absolute gain in income it may provide but for the gain in relative status that would ensue. For instance, if returns to education and ability are higher in an urban setting, an educated individual may improve his relative position in society by moving from a rural to an urban setting. To investigate this possibility, we estimate equation (4) using the log of relative income (or relative consumption) as dependent variable and construct a predicted relative income measure using the same formula (5). These are shown in the second panel of Table 1. Theories of work migration predict that individuals move to increase their utility or welfare. The 1995 / 96 NLSS asked respondents a number of questions regarding their subjective satisfac- tion level with various dimensions of consumption — namely, food, clothing, housing, health care, and child schooling. They were also asked their subjective satisfaction with their level of total income. Usage context: background mention, primary mention, supporting mention.

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Input: violent conﬂict. Besley and Mueller (2015a) argue that foreign investors seem to know that growth volatility changes with strong executive constraints and therefore react signiﬁcantly to their adoption. In summary, the literature suggests that a lack of constraints on executive power at the country level could play a key role in building inequalities across regions and ethnic groups. In the absence of strong executive constraint, we expect regions populated by ethnic groups that have access to executive power to perform better relative to others due to ethnic favoritism. Conversely, excluded ethnic groups should experience relatively worse economic performance compared to other groups in the absence of such constraints. 46 To test these hypothesis we use data on ethnic groups ’ access to executive power and night light intensity from the GROWup Research Front-End (RFE Release 2. 0) dataset and executive constraint data from the Polity IV dataset. We use night light intensity as a proxy for economic activity at the ethnic group level. 47 Night light data has the beneﬁt of being available on a yearly basis and of being measured at the local level where there is poor availability of statistical data. Usage context: primary mention, supporting mention, background mention.

GROWup Research Front-End
Polity IV dataset

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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: background mention, supporting mention, primary mention.

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Input: Language barriers, mental health challenges, and uncertain futures are identified as major obstacles to integration. The study highlights the importance of tailored interventions, such as psychological support and more dedicated teaching time, to foster refugee students ’ academic and social inclusion. This paper is a product of the Development Data Group, Development Economics and the Social Protection and Labor Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at michela_carlana @ hks. harvard. edu; pcastaing @ worldbank. org; mtestaverde @ worldbank. org; and mtiberti @ worldbank. org. Usage context: supporting mention, background mention, primary mention.

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

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Input:
HIV behavioural surveillance surveys 153

Table III. HIV/AIDS knowledge, practices and attitudes questions and indicators (N �/31).

Number* Percent

Knowledge
Did the study include prevention questions, even if 26 83.9%
they did not include the standard questions below?

1) Sex with only 1 partner
Same or similar wording 21 67.7%
Disaggregated by gender and age 6 (N�/20) [�] 30.0%

2) Using condoms
Same or similar wording 26 83.9%
Disaggregated by gender and age 6 (N�/25) [�] 24.0%
Did study include misconception questions, even if 20 64.5%
they did not include standard questions below?

1) Mosquito bites
Same or similar wording 16 51.6%
Disaggregated by gender and age 3 (N�/15) [�] 20.0%

2) Sharing a meal
Same or similar wording 14 45.2%
Disaggregated by gender and age 5 (N�/13) [�] 38.4%

3) Healthy-looking person can have/transmit HIV
Same or similar wording 18 58.1%
Disaggregated by gender and age 4 (N�/17) [�] 23.5%

Practices
Did the study include practice questions, even if they 26 83.9%
did not include standard questions below?

1) Condom at last sex with a high risk partner
Same or similar wording 14 45.2%
Disaggregated by gender and age 5 (N�/14) 35.7%

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Input: 7 other disciplines it is relatively rare. A review of published public health literature by Chen et al. (2018) found most surveys use probabilistic designs in the first stage, but random walk or similar methods in the second stage. Lupu and Michelitch (2018) suggest that the combination of random walk and quota sampling is the common approach for political science-themed surveys conducted in the developing world, with 77 percent of respondents to their expert survey using a variation on this design. Diaz de Rada and Martínez (2014) compare a combination of random walk and quota sampling (based on age and gender) to probability designs and find a more accurate estimation of age and educational attainment in the combined method than in the probability methods, but that the probability methods perform better for measuring unemployment. The authors cite the replacement protocols for the probability methods as a reason for the bias and attribute the use of quota sampling for the success in estimating age and education, compared to the gold standard of a high-quality probability sample design. There are also a limited number of papers which directly compare two or three of the methods, but none that consider this wide range of alternatives. Usage context: primary mention, supporting mention, background mention.

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Input: However, another regressor indicates that security may still be pivotal: those who owned a weapon were up to 30 percentage points more likely to plan to go back. In addition to this, it is quite surprising that, if the respondent thought that the Northern Mali crisis was improving, he or she was less likely to plan a return to that area. From a technical point of view, we should point out that we have used an LPM even if the dependent variable was a binary outcome. This choice has been made since in this linear model it is straightforward to add fixed-effects. Furthermore, the coefficients can be interpreted as average partial effects. A simple logit or probit model would not have allowed the inclusion of individual fixed-effects because of the incidental parameter problem. An alternative approach would have been to estimate a conditional logit model. However, since the distribution of the fixed effects is unknown, it would not have been possible to estimate the average partial effects in this model, but only the effect of the regressors on the log-odds ratio. 13 We conclude by stressing that the monthly phone interviews were relatively short, so we did not have a rich panel data set. This may have led to omitted variable biases. Indeed, there may still be time varying factors which could have affected both the probability of being employed and the respondents ’ intentions to go back. Nevertheless, we believe that our model managed to control for 13 See (Wooldridge, 2010) page 639. Conclusions from the conditional logit model are qualitatively similar.

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Input: Since forced migrants are a hard- to-reach population, we constructed the sampling frame for the survey using the RAMV census, referrals from other forced migrants, and databases of local migrant organizations. 2 The survey data enabled us to examine the impact of PEP on three groups of outcomes: socioeconomic and health well-being, access to rights and services, and labor market con- sequences. This paper concentrates on the ﬁrst dimension — socioeconomic and health well- being — while the latter two allow us to discern possible mechanisms. Each dimension in- cludes a series of individual outcomes and a summary index. The survey took place between October 2020 and February 2021. This analysis thus provides a picture of PEP ’ s short-term effects two years after its enactment. Despite the advantages for causal identiﬁcation produced by the circumstances of PEP ’ s rollout, registration in RAMV and PEP was voluntary, so self-selection could potentially confound the identiﬁcation of effects. For this reason, our empirical analysis follows a fuzzy regression discontinuity methodology that compares forced migrants who arrived before 2Importantly, as shown in the analysis, migrants in these three data sources were otherwise similar across socioeconomic characteristics in Venezuela and in Colombia before the program was launched. 4 Usage context: supporting mention, background mention, primary mention.

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

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Input: 27 Source: Listening to Displaced People Survey, 2014. 7. Conclusion The 2012 crisis in northern Mali led to widespread displacement. The welfare of the displaced – and those who returned – is monitored by combining a baseline survey with structured monthly follow-up interviews carried out by mobile phone. This innovative approach allows tracking changes in welfare with high frequency – even for those who returned to areas that are insecure and inaccessible to enumerators. After 6 rounds of follow-up interviews attrition rates are very low (more than 99 % response rate), demonstrating that it is possible to collect robust and representative data from hard-to-reach, conflict-affected populations. The results show that those who fled were better educated, better off and less affected by violence than the average population in the North. Those who fled lost significant amounts of durable goods (20-60 %) and livestock (50-90 %); many of their children ended up being taken out of school and their welfare (measured subjectively and by the number of meals consumed) declined considerably. Over time, the impact of the crisis on welfare has lessened and by February 2015 the majority of eligible children of the displaced were going to school and levels of employment and number of meals consumed were at pre-crisis levels. Usage context: background mention, primary mention, supporting mention.

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Input:
20 For more details on the 1997 IFPRI Egypt Integrated Household Survey, see Guarav Datt, Dean Joliffe

***Total food subsidies in real terms calculated by deflating nominal costs by CAPMAS Consumer Price
Index for urban areas (1 986-87 = 100).
n.a. = Not available.
Sources: Food subsidy data:
1970-71 to 1980-81 from Harold Alderman, Joachim von Braun and Sakr A. Sakr, Egypt's Food Subsidy and
Rationing System: A Description, Research Report 34 (Washington, DC: International Food Policy Research
Institute, 1982), Table 2.
1981-82 to 1996-97 from unpublished data, Ministry of Planning, and Ministry of Trade and Supply.

Total government expenditure data:
1970-71 to 1979 from Alderman, von Braun and Sakr, op. cit.
1980-81 to 1991-92 from unpublished data, Central Agency for Public Mobilization and Statistics.
1992-93 to 1996-97 from unpublished data, Ministry of Trade and Supply.

Source: IFPRI Egypt Integrated Household Survey, 1997.

applying it to data from a household survey in northern Mali. In this application, we find

1997 IFPRI Egypt Integrated Household Survey

Total government expenditure data
household survey in northern Mali

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Input: 18 corridor, so only aggregate numbers can be compared. For this comparison, mid-year estimates of the world migrant stock for 1990 – 2000 are taken from the 2008 edition and estimates for the earlier censuses, 1960 – 1980, are taken from the 2005 edition (table 8). The analysis subtracts the estimated number of refugees from the total mid-year estimates of the world migrant stock from the Trends in International Migrant Stock database to yield the net number of migrants in each decade. These numbers are then compared with the decadal estimates generated through this project, both the total and the net, after subtracting estimates of migrants within the Soviet Union for 1960 – 1980 (data for 1990 and 2000 should be directly comparable) and the number of ethnic German migrants added to the German censuses. { Table 8 here} The aggregate estimates are remarkably close (the two net totals), differing at most by around 1 million migrants, except in 1990. There are several possible explanations for these differences. First, the census totals from the current work may not match because censuses do not always make allowances for temporary workers. For example, Singapore ‘ s official 2000 census records 563, 430 foreign-born migrants. The United Nations, however, reports 1, 351, 806 foreign-born migrants for 2000. Usage context: supporting mention, primary mention, background mention.

Trends in International Migrant Stock database

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Input: 5 if they are built to code. 2 These spillovers or externalities are absent in more sparsely populated rural areas where damages to smaller sized and dispersed dwellings will cause less or no collateral damage. Exposure The main reason why urban risk is large and increasing is the rise in exposure. Although urbanization statistics suffer from a lack of standard definitions of what should be considered ‘ urban ’, the assumption of half the world ’ s population living in cities seems realistic. Urban populations are growing in practically all developing countries. About 40-60 percent of this growth can be attributed to natural growth, i. e., fertility of urban dwellers (Montgomery 2009). The remaining growth is due to urban expansion and migration, reducing the share of rural residents except where rural fertility is vastly larger. The latest UN urban population estimates suggest that, globally, urban population exceeded rural population for the first time in 2008 (UN 2008). In less developed regions, this threshold is expected to be reached by 2019. This continuing urbanization process will lead to an increase of exposure of people and economic activity in hazard prone urban areas. Although we can only speculate about the global distribution of disaster damage in cities today and in the future, newly available geographically referenced data yield some estimates of urban exposure to natural hazards.

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Input: In addition, we observe several characteristics of the firm and job that are not typically observable to researchers, which we will use to conduct a rich heterogeneity analysis. Most importantly, we observe the number of applicants applying to a specific job, and the number of similar jobs posted on the platform. This unique data facilitates our third contribution, in which we analyze the role of labor market competition in hiring discrimination, an area which to date has not been widely explored. de Haan et al. (2017) show that discrimination against disadvantaged groups is more likely in the presence of competition of workers from a non-discriminated group than in a non-competitive scenario. Along these lines, we hypothesize that firms will discriminate less often when there is a low supply of applicants. Unlike de Haan et al. (2017), we uniquely observe quality indicators of the applicant pool, which we use to test our hypothesis that discrimination decreases when the relative quality of applicants in the pool is low. Furthermore, we exploit our unique data to test whether firms discriminate less often when there is high demand for specific job positions. We know of no studies that have previously considered competition on the demand side. The rest of the paper is organized as follows. Section 2 provides background information of Malaysia. Section 3 details the experimental design, and section 4 provides summary statistics of the data. Section 5 explores if there is discrimination in the Malaysian labor market, section 6 studies 4 Usage context: primary mention, supporting mention, background mention.

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

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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: primary mention, supporting mention, background mention.

UNHCR Population Statistics Reference database

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Input: Using Afrobarometer ’ s geocoded surveys, we focus on clusters as our unit of observation. 10 Our sample consists of 7, 547 such locations and 76, 518 individuals in 23 countries in Sub-Saharan Africa. “ The sampling universe normally includes all citizens age 18 and older. As a standard practice, they [we] exclude people living in institutionalized settings, such as students in dormitories, patients in hos- pitals, and persons in prisons or nursing homes. ” (Afrobarometer, https: / / afrobarometer. org / surveys − and − methods / sampling − principles) Since the sampling frame is based on recent censuses, with the aim of representing all citizens of voting age in a given country, the Afrobarometer samples are unlikely to include refugees. Note also that “ the sample design is a clustered, stratified, multi-stage, 8We explain the construction of theses indices in Section 4. 2. 9We test the robustness of our results with a smaller (40 km) and a larger (120 km) radius in Section 5. 3. This choice of buffer size assures us that between 75 percent and virtually all refugee camps fall within a cluster buffer. Other studies relying on Afrobarometer data construct buffers ranging from 25 km (e. g., Michaelopoulos and Papaioannou (2011), investigating ethnic-specific pre-colonial institutional structures) to 100 km (e. g., McGuirk and Burke (2020a), analyzing the impact of food-price shocks on conflict). 10Afrobarometer is a pan-African research network conducting public attitude surveys on democracy, governance, the economy, and society in African countries that are repeated on a regular basis (Afrobarometer, 2020). 10 Usage context: background mention, supporting mention, primary mention.

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Input: tionnaire from Falk et al. (2022), along with a question on how much do the subjects in our sample trust the Colombian government. Additionally, we inquired about the ado- lescents ’ social networks by asking about their number of friends of each nationality and whether they have felt discriminated in school. While these outcomes were not the pri- mary focus of the VenReps Kids survey, they collectively provide a general assessment of social cohesion, a crucial aspect in understanding the overall well-being of children and adolescents. We measure the average differences in altruism between Venezuelan and Colombian ado- lescents following the estimation of equation 1. Table 8 presents the results for altruism, trust, discrimination and social ties in panels A, B and C respectively. As in the previous tables, the first column for each outcome reports the results of the estimates of equation 1 without controls, and the second and third columns report the results of the estimation with all controls and the propensity score matching respectively. As for altruism, we see that Venezuelan adolescents are on average willing to donate 17 % more of their imagi- nary money endowment to a good cause relative to Colombian adolescents. As for trust, the results are mixed when analyzing the trust items separately. Usage context: primary mention, background mention, supporting mention.

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Input:
**Persons of concern in Southern Africa, Data as of 30 September 2022**

Notes: *'Other' in the location refers to any known location other than camp or settlement sites, covering both urban and rural areas; **self-settled refers to the individuals without available information such as their names and locations, and their locations are categorised to be 'unknown'; those by location in Congo, Democratic Republic of
the Congo and Zimbabwe could be different from the numbers operation report due to inconsistency in proGres v4.

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Input: Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer. Usage context: supporting mention, primary mention, background mention.

Linking Ethnic Data from Africa
EPR-ER dataset
Afrobarometer

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Input: Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10099 Situations of forced displacement create unique challenges for social cohesion because of the major disruption of social dynamics among both displaced persons and host communities. This paper uses a sequential mixed method approach to analyze the relationship between hosting displaced persons and perceptions of social cohesion in eastern Democratic Republic of Congo. First, participatory research methods in focus groups empowered participants to pro-duce a locally driven definition of social cohesion. The results from these exercises inform the quantitative assessment by dictating measurement strategies when analyzing original surveys. Usage context: background mention, primary mention, supporting mention.

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Input: The third proxy for economic activity is given by per capita growth of night light, computed using satellite data from the National Oceanic and Atmospheric Administration (NOAA). 6 Night light data has the beneﬁt 6Satellite data is available for a shorter time period, 1992-2013. 9 Usage context: primary mention, background mention, supporting mention.

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Input: 9 Figure 10. Selected characteristics across Ugandan and refugee households, % Source: RHCS 2018, WB staff calculations. A simple comparison between refugees and Ugandan households demonstrates that refugees lag with regards to selected characteristics found to narrow the poverty gap. For example, refugees are less likely to have access to land than Ugandans. If refugees have access to land, the majority do not own it, but have user rights. The size of land also differs a lot among Ugandan and refugee households. Most Ugandans have at least 0. 05 hectare per capita, while the majority of refugees have less than 0. 05 hectare per capita. Refugee heads of household are also less likely to work and less likely to be literate compared to their Ugandan counterparts. Refugees have higher shares of children and elderly in household size compared to Ugandans. For example, among almost 60 percent of refugee households, more than half of the household members are children and elderly compared to 42 percent of households among Ugandans. Economic inclusion dividend When a development approach to hosting refugees is followed and refugees earn incomes, there are two key beneficiaries. Refugees themselves, who gain dignity, financial autonomy and pathways to self-reliance.

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

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Input: 13 Commission for Reception, Truth and Reconciliation (CAVR). 7 This information has been collected from deponents to the Commission ‘ s statement-taking process. 8 We make use of data on the number of killings that occurred during the war in order to derive patterns and variation of violence in Timor Leste over time and across space. We use this data to identify districts and years that experienced high and low violence-intensity, both at the start of the occupation and following the withdrawal of Indonesian troops in 1999. This allows us to estimate both the impact of the first years of the conflict and the impact of the last wave of violence in 1999. 4. 1. Identification strategy: The impact of violence on school attendance in 2001 We first investigate the short-term impact of the 1999 violence. The empirical questions being addressed are: (i) whether the violence in 1999 imperiled school attendance9 and school grade deficit, and (ii) whether different channels of exposure to conflict – displacement and house destruction – affected boys and girls and different age groups differently. 4. 1. 1. Primary school attendance and grade deficit rates in 2001 We make use of information in TLSS 2001 collected at the individual and household levels on displacement and house destruction to identify conflict-affected individuals.

data on the number of killings that occurred during the war

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Input:
Malaita is one of largest of the Solomon Islands, with an estimated population of 170,883 (according to
2012/13 HIES). The three primary roads of Malaita, namely the North Road (112.2 km), South Road (75.6
km) and East Road (41.7 km), together constitute nearly 60% of the road network on the island and carry
the majority of vehicular traffic (Figure 1). These roads connect 19 of the 33 wards and provide access to
70% of the population.

It combines nationally representative pre- pandemic household survey data with follow-up phone survey data from Mali and exploits sub- national variation in the intensity of pandemic-related disruptions between urban and rural areas.

We combine nationally representative data collected between October 2018 and July 2019 with follow-up phone survey data collected between May and June 2020.

nationally representative data collected between October 2018 and July 2019

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Input:
prediction model between the z-score and observable household and location characteristics in EMDHS. This

related to the sampling design of EMDHS, as it is designed with few EAs within each region and relatively large

areas, the sample from EMDHS 2014 is used.

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

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Input:
from the Ethiopia Demographic and Health Surveys. International Journal of African Development 1.
8. Christiaensen L, Alderman H (2004) Child malnutrition in Ethiopia: Can maternal knowledge augment the role of

for height). The z-scores were calculated using EMDHS and the 2006 WHO growth standards [24].

To obtain estimates of undernutrition rates at the woreda level, EMDHS is combined, through SAE, with the

The EMDHS and a 10 percent sample of the 2007 census are available on request from the Central Statistical

Ethiopia Demographic and Health Surveys
EMDHS

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Input: Figure 4: Refugees and Asylum-Seekers by Migratory Path 1951 – 2015 Source: UNHCR Statistical Online Population Database, UNHCR Global Trends 2015

UNHCR Statistical Online Population Database

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Input: For refugees, the situation is more varied, with most staying close to their country of origin while a smaller minority fled to countries further away. UNHCR (2020) estimates that three-quarters of all refugees were hosted by neighboring countries. To reflect the increase in forced displacement over the last decade and enable sustainable and long-term solutions to refugee situations, the UN Statistical Commission approved a new indicator, SDG Indicator 10. 7. 4, in early 2020 to measure and track the “ proportion of population who are refugees, by country of origin ” (UNHCR 2020). While the specific challenges for displaced communities depend on the country or host community context, often, in new locations, key challenges confronting IDPs and refugees include food insecurity, lack of livelihood opportunities, and tensions and competition over resources with host communities. The multiplicity of deprivations faced by displaced

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Input: It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / econ. worldbank. org. The author may be contacted at asacks @ worldbank. org. and managing these resources. The author assesses these competing hypotheses using multi-level analyses of Afrobarometer survey data. The sample, drawn from a continuum of developing societies in Africa, allows for analysis of associations between donor and non-state actor service provision and the sense of obligation to comply with the tax authorities, the police and courts. The findings yield support for the hypothesis that the provision of services by donors and non-state actors is strengthening, rather than undermining, the relationship between citizens and the state. Usage context: primary mention, supporting mention, background mention.

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Input: 5 (UNHCR, 2014), developing countries hosted 10. 1 million refugees or 86 percent of the world ’ s refugees. Contrary to what has been sometimes claimed in popular media, refugees are not “ invading ” the higher ‐ income countries. Actually, SSA has been hosting more refugees than sending them since 1990. The divergence of trends occurring in 2005 is certainly related to large inflows of refugees from North Africa and the Middle East. The second peak in 2011 corresponds to the uprisings that spread across several Arab countries (Egypt, Libya, Syria, Tunisia and Yemen), and the recent one in 2013 to the large outflows of refugees from Iraq, Syria and Yemen. Figure 2. Refugees and Internally Displaced People in SSA, 1990 ‐ 2013 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). Due to changes in classification and estimation methodology in a number of countries, 2007 figures are not fully comparable with pre ‐ 2007 figures (see also footnote 1). Gathering data on internally displaced people is much more challenging since most existing data on IDPs are incomplete or unreliable.

UNHCR statistical population online dataset

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

Kagera survey
National Household Budget Survey

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Input: For refugees, the situation is more varied, with most staying close to their country of origin while a smaller minority fled to countries further away. UNHCR (2020) estimates that three-quarters of all refugees were hosted by neighboring countries. To reflect the increase in forced displacement over the last decade and enable sustainable and long-term solutions to refugee situations, the UN Statistical Commission approved a new indicator, SDG Indicator 10. 7. 4, in early 2020 to measure and track the “ proportion of population who are refugees, by country of origin ” (UNHCR 2020). While the specific challenges for displaced communities depend on the country or host community context, often, in new locations, key challenges confronting IDPs and refugees include food insecurity, lack of livelihood opportunities, and tensions and competition over resources with host communities. The multiplicity of deprivations faced by displaced Usage context: background mention, primary mention, supporting mention.

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Input: 5 (UNHCR, 2014), developing countries hosted 10. 1 million refugees or 86 percent of the world ’ s refugees. Contrary to what has been sometimes claimed in popular media, refugees are not “ invading ” the higher ‐ income countries. Actually, SSA has been hosting more refugees than sending them since 1990. The divergence of trends occurring in 2005 is certainly related to large inflows of refugees from North Africa and the Middle East. The second peak in 2011 corresponds to the uprisings that spread across several Arab countries (Egypt, Libya, Syria, Tunisia and Yemen), and the recent one in 2013 to the large outflows of refugees from Iraq, Syria and Yemen. Figure 2. Refugees and Internally Displaced People in SSA, 1990 ‐ 2013 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). Due to changes in classification and estimation methodology in a number of countries, 2007 figures are not fully comparable with pre ‐ 2007 figures (see also footnote 1). Gathering data on internally displaced people is much more challenging since most existing data on IDPs are incomplete or unreliable. Usage context: background mention, primary mention, supporting mention.

UNHCR statistical population online dataset

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Input: Figure 22: Numbers of Refugees in Ongoing Refugee Situations end-2014 Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes Palestinian refugees under UNRWA ’ s mandate. D. Data on asylum-seekers, refugees and IDPs: sources, applications and credibility In this section, a distinction is made between: (a) the collection of source data; and (b) the compilation of data across sources (within a country or across countries). In general, there is a delineation of roles between data collectors and data compilers, however there are organizations, such as UNHCR, IOM and the Office for the Coordination of Humanitarian Affairs of the United Nations Secretariat (OCHA), 59 that are involved in both data collection and compilation activities. Data collection: Sources for refugees, asylum-seekers and IDPs60 Collection of primary data on forcibly displaced persons is generally undertaken by national governments through their national statistical offices, line ministries or immigration agencies. However, where countries lack the capacity to undertake this work, they may rely on international organizations as well as international and local NGOs to collect data or undertake estimates. 61 In general, governments tend to collect data on refugees in developed countries, while UNHCR and NGOs tend to collect data on refugees in developing countries.

UNHCR Statistical Online Population Database

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Input: Survey instruments enable detailed questions to be asked about the characteristics and situations of households, and if they identify displaced populations based on self-reported migration history (including patterns and causes) they can enable the disaggregation of detailed data by displacement status. Additionally, more innovative tools and technologies for data collection, analysis and compilation should be explored and leveraged. For example, new methodologies (such as high resolution satellite imagery and unmanned drones) may expand the coverage of data collection efforts in insecure or inaccessible areas. Additionally, new techniques could be explored to improve the collection of robust data on flows of refugees and IDPs. Organizations such as the World Bank, UNHCR, IOM and IDMC are already exploring and in some cases are beginning to use more innovative data collection tools. These techniques include: 99 Several standardized international sample surveys have been designed for special purposes including Living Standards Measurement Studies, Labor Force Surveys, Demographic and Health Surveys, and Multiple Indicator Cluster Surveys. The advantage of these surveys is that they cover a wide range of countries and are conducted in a regular or systematic manner Usage context: background mention, supporting mention, primary mention.

Living Standards Measurement Studies
Labor Force Surveys
Demographic and Health Surveys
Multiple Indicator Cluster Surveys

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