ai4data/datause-dataset-rechunked huggingface dataset:
each country. However, while we are aware of this limitation, the ESS has been extensively

(2012) mentions that these questions in the ESS eliminate ambiguities by referring to _people_   _who come to live in a country_, rather than to _immigrants_ . In countries where citizenship is   based on blood ancestry such as Germany, a translation of immigrants would include people   who were born in the country but are not citizens.

section survey data. The second specification incorporates age, year of birth and survey year   dummy variables as explanatory variables (blue line). According to the model without any   cohort controls, older people are less likely to exhibit positive attitudes toward immigrants than   their younger peers in almost all countries considered.

_Is the period of time long enough to capture life cycle effects?_


It could be argued that the period of time covered by the ESS - from 2002 to 2012 - may not be


long enough to capture life cycle patterns. Preferences may change over a longer period of
There is one limit to our approximation in Equation 4. The ethnic composition of refugees in each year t for a given origin – destination pair of countries obtained from the EPR-ER database is assumed to be homogeneous across camps of the same origin – destination pair of countries for the refugees at year t. This may seem to be a strong assumption; however, the risk of misallocating refugees is reduced as the annual variation in the EPR-ER is generated by just a few dominant groups for a given origin – destination pair and the geographical distribution of refugees by country of origin is highly influenced by the proximity to their countries of origin. 16 As can be seen from panel A of Table B. 2, in refugee-hosting areas, on average, both EF and the EP seem to increase quite significantly when they are revised by incorporating the number of refugees in an 80-km buffer: the mean value of the standard EF index is 25. 58 %, while the mean value of the revised refugee EF index is 37. 90 %. The mean value of the standard EP index is 10. 11 %, while the mean value of the revised refugee EP index is 14. 07 %.
The countries in our sample are Benin, Burkina Faso, Burundi, Cameroon, Gabon, Ghana, Guinea, Ivory Coast, Kenya, Liberia, Malawi, Mali, Mozambique, Namibia, Niger, Nigeria, Senegal, Sierra Leone, Tanzania, Togo, Uganda, Zambia, and Zimbabwe. As described in Table B. 1, we also incorporate information on the quality of our refugee data, which is determined by comparison with official UNHCR bilateral data. Below we describe how these data have been used to define our main variables of interest and present some descriptive statistics in Table B. 2. 11 Conflict. In Equation 1, we first relate variation in ethnic diversity with data on conflict from ACLED (Linke et al., 2010). Two main definitions are used: the incidence of conflict and the intensity of conflict. Incidence is captured by an indicator equal to one if conflict occurred in a particular year within a pre-defined buffer around cluster j. Intensity is measured by summing the number of conflict events occurring in a particular year within the same buffer area. A conflict event is defined as a single altercation wherein force is used by one or more groups for a political end (Linke et al., 2010). We further describe events (non-exclusively) as violent events, non-violent events, violence against civilians, and riots. In our main analysis, we focus on violent conflicts (Section 5. 1) and report results for other outcomes as robustness tests (Section 5. 3). In doing so, we follow a recent and large literature that has combined the ACLED dataset with geographically disaggregated data in Africa (Besley and Reynal-Querol, 2014; Berman and Couttenier, 2015; Michaelopoulos and 11Panel A of Table B. 2 shows descriptive statistics for the data from refugee-hosting areas specifically, whereas panel B of Table B. 2 shows descriptive statistics for our data in all covered areas. 11
for the exam in 2007, we were able to identify 71 percent three years later in ENLACE

we use administrative data from the annual school census ( _Formato_ _911_ ) to estimate the

ENLACE panel has a survival rate that is 6 and 7 percentage points lower vis-a-vis the

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matching. Take-up rate in the ENLACE test is high, 87.3 percent of the population

secondary in 2013. The difference in graduation or survival rates between the ENLACE
restrictions on their employment. Legal restrictions (i.e., not having work permits) and location often prevent refugees from working, which limits their ability to generate income and improve their economic situation. As a result, many refugees rely on food aid and have limited access to necessities such as housing and electricity. Multidimensional poverty tends to be high among refugees. Low living standards and low education primarily drive multidimensional poverty. Standard of living indicators, low-quality cooking fuel, inadequate housing and low asset ownership, contribute half to non-monetary poverty. Moreover, deprivation in education and child malnutrition also contribute most to multidimensional poverty among refugees. Refugee households tend to have worse food security than hosts. In-camp refugees have less diverse diets, suffer food insecurity, and have low consumption status compared to hosts (Figure ES.4). Broadly, there is a need to enhance the economic self-sufficiency and food security of in-camp refugees and host communities by improving their livelihood opportunities. For in-camp refugees, consumption (expenditures) tends to increase with certain characteristics. These include higher education, access to mobile phones, owing a non-farm business, possessing a bank account, and being closer to a market town or and Woreda capitals. Education (of the household head) and employment strongly
to inform decisions such as the size of schools to ensure progress toward inclusive systems that support refugees and their hosts can be made. Strengthening the use of statistics includes facilitating access to data and disseminating results. The SESRE is an excellent start to this initiative. Yet, the need to systematically integrate refugees in every round of the national household surveys and other data collection activities is key to allowing for evidence-based policy making. 77 Abdelhady D., and Al Ariss, A. (2023). How Capital Shapes Refugees’ Access to the Labour Market: The Case of Syrians in Sweden. The International Journal of Human Resource Management, 34(16), 3144-3168, DOI: 10.1080/09585192.2022.2110845 Abu-Ghaida, D., and Silva, K. (2020). Forced Displacement and Educational Outcomes: Evidence, Innovations, and Policy Indications. Second issue. Quarterly Digest on Forced Displacement. Washington, D.C.: World Bank Group, UNHCR and JDC. Adda, J., Dustmann, C., and Görlach, J. S. (2022). The Dynamics of Return Migration, Human Capital Accumulation, and Wage Assimilation. The Review of Economic Studies, 89(6), 2841-2871. Alix-Garcia, J., Walker, S., Bartlett, A., Onder, H., and Sanghi, A. (2018). Do Refugee Camps Help or Hurt Hosts? The Case of Kakuma, Kenya. Journal of Development Economics, 130, 66-83. Alkire, S., Kanagaratman, U.,
and their hosts by: (i) understanding different dimensions of welfare, such as monetary poverty, inequality, multidimensional poverty, food security, and shocks; and (ii) understanding determinants of welfare and estimating the cost to meet basic needs through a combination of assistance and some economic inclusion of refugees into national systems. Chapter 6 aims to understand how the location of camps determines labor market outcomes, highlighting the importance of refugees’ location as part of the development strategy for refugees in Ethiopia. Chapter 7 looks at social cohesion by showcasing attitudes between refugees and hosts and the level of social integration of refugees. Chapter 8 highlights policy directions based on the conclusions of the report to maximize the benefits of hosting refugees while minimizing the costs. (vi) Differences in poverty estimation: The poverty measurement methodology is distinct for each survey with SESRE applying the same methodology as HoWStat. Despite the difference in methodology used in the Skills Profile Survey (SPS) and SESRE, we find similar patterns in some of the indicators common in both surveys among in camp refugees such as demographic composition, primary and secondary net enrollments, housing condition, access to basic infrastructures, employment and attitude of hosts toward refugees. Moreover, both
effects is Calahorrano (2011). Using panel data for Germany between 1999 and 2008, she finds   that immigration concerns decrease over the life-cycle.

similar to Calahorrano (2011). However, given the lack of comparable panel data surveys for a   large group of countries, we use pooled cross-sections from the European Social Survey (ESS)
Because the DHS cluster displacement algorithm can misassign households at cluster boundaries to the wrong treatment group, we conduct a robustness check in which we exclude all DHS clusters located within 10 km of the treatment threshold. This boundary exclusion sample represents 74 percent of the original sample. Point estimates in this restricted sample are almost identical to those in the full sample, and the exclusion of boundary clusters reduces rather than inflates the estimated treatment effect, allaying concerns about bias from spatial misassignment.
2 Data from the EM-DAT database, created and maintained by the Center for Research on the Epidemiology of Disasters (CRED) at the Catholic University of Louvain. 3 There are multiple formal definitions.

For a disaster to be listed in the EM-DAT database, at least one of the following criteria should be met: (i) 10 or more people are reported killed; (ii) 100 people are reported affected; (iii) a state of emergency is declared; (iv) a call for international assistance is issued.

Hazard maps for countries can provide this information<br>(with uncertainty) based on historical data; hazard maps<br>can be adjusted based on climate models to investigate<br>future conditions (even larger uncertainty)

nd evacuation<br>schemes.<br>Information and education<br>campaigns on risk maps|
|Vulnerability|The vulnerability of the<br>exposed capital𝑉 (or<br>equivalently, total asset<br>losses )|Fraction of the population covered by an early warning<br>system and with ability to prepare and evacuate|Fraction of the population covered by an early warning<br>system and with ability to prepare and evacuate|
|Macro-economic<br>resilience ( )|The interest rate and<br>marginal<br>capital<br>productivity ( );|Macroeconomic data provide this information|Policies<br>to<br>improve<br>the<br>macroeconomic context|

_Figure B1: Roofer wages in an area where losses have been significant after the 2004 hurricane season in_
_Florida. Data from the Bureau of Labor Statistics, Occupational Employment Surveys in May 03, Nov 03,_
_May 04, Nov 04, May 05, May 06, May 07._

Figure B2 is a classical quantity-price plot, showing the long-term demand and supply curves for a goods
or service aggregated at the macroeconomic level. The green line is the demand curve: it shows how the
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.
33 Ratha, D., and W. Shaw. 2007. ― South-South Migration and Remittances. ‖ World Bank Working Paper 102, World Bank, Washington, DC. United Nations Statistics Division. 1998. Recommendations on Statistics of International Migration Revision 1. New York: United Nations. United Nations, Department of Economic and Social Affairs, Population Division. [2008]. United Nations Global Migration Database. New York: United Nations. http: / / esa. un. org / unmigration — — —. 2006. Trends in Total Migrant Stock 1960 – 2000, 2005 Revision. Database. POP / DB / MIG / Rev. 2005 / Doc. New York: United Nations. — — —. 2009. Trends in International Migrant Stock: The 2008 Revision. Database. POP / DB / MIG / Stock / Rev. 2008. New York: United Nations. http: / / www. un. org / esa / population /. — — —. 2010. ― World Population Prospects: The 2009 Revision, Highlights ‖, Working Paper No.
The lack of certainty means children are trying to prepare for further studies (e. g., in universities) in two separate systems with varying requirements. The survey results indicate that students enrolled in both systems spend as much time in Italian schools as those attending only Italian schools, averaging 31 hours per week. However, students participating in both systems spend an additional 8 hours per week on online Ukrainian classes. This puts an extra burden on these children. Connectedness to Italy is correlated with demographic characteristics and social environment of refugee children. Additionally, Table 6 shows that making new friends in the country of destination and speaking Italian are strongly associated with higher connection to Italy. The mental distress resulting from displacement is a key barrier to educational integration for many Ukrainian refugees in Italy. The link between poor mental health and low school attendance and performance is widely acknowledged in the literature (see Fiining et al., 2019 for a systematic review). In the World Bank survey data, children and caregivers reported signs of mental distress, with 16 % of children and 24 % of refugee caregivers reported experiencing psychological distress 61 % 35 % 31 % 68 % 50 % 36 % 23 % 68 % 59 % 26 % 26 % 59 % Would like to continue living in Italy Would like to move back to Ukraine Feel strongly connected to Italy Feel strongly connected to Ukraine Caregivers (N = 283) Children between 9 and 14 years old (N = 141) Children between 15 and 20 years old (N = 96)
The study is based on two-period panel data collected by the IHDS, which was jointly carried out by researchers from the University of Maryland and the National Council of Applied Economic Research (NCAER) in New Delhi. This nationally representative survey covers a wide-ranging set of topics, including energy use, income, expenditure, education, health, and employment. The survey covers all of India's key states and union territories except Andaman and Nicobar Islands and Lakshadweep. The first round of the survey was carried out in 2004-05 (mostly in 2005) and collected information on 41,554 households in 33 states and union territories, 383 districts, 1,503 villages, and 971 urban blocks. The second one, conducted in 2011-12 (mostly in 2012), re-interviewed 83 percent of the original households and split households (if located within the same village or town), and interviewed 2,134 new households, for a total of 42,152 households.

According to IHDS 2012 data, the average duration of power outages of households who have less than 24 hours of power supply is about 12.5 hours a day. Increasing the supply of electricity to 24 hours a day would lead to an estimated income gain for the rural population of US$6.5 billion annually.

2 In the following, we use the terms "access" and "grid-connection" interchangeably.
3 World Bank Enterprise Surveys (http://www.enterprisesurveys.org).
Beneficiary assessments completed during the project preparation phase established that the primary constraint to economic inclusion among the target population was lack of legal work authorization, cited by 74 percent of respondents, followed by limited language proficiency in the dominant national language (58 percent) and absence of recognized educational credentials (51 percent). These beneficiary assessments were conducted through a combination of individual household interviews and focus group discussions with community representatives, using a standardized protocol developed with the Ministry of Labor.
Night-time lights data are a beneficial by-product of a meteorological satellite program. The data are
collected by the United States Air Force Defense Meteorological Satellite Program (DMSP). DMSP
satellites have been circling the earth since the 1970s in a polar orbit that allows observations of every

1. Number of illuminated pixels with _DN_ >=6 within the borders of a country.
2. Average _DN_ within all illuminated pixels.
_Sources:_ NGDC v4, World Development Indicators for land area, and author's calculations.

Long-run patterns in _AoL_ and _R_ are consistent with some country circumstances. For example, as shown
in Table 1, a country with a rapidly expanding _AoL_ is more likely to be a country with a high urban
population growth rate. China, Indonesia, Malaysia, Vietnam, and Yemen are examples. A country with
shrinking _AoL_ could be in the early, painful stages of transition from a planned economy to a market
economy. Azerbaijan, Tajikistan and Ukraine are examples. Countries with growing average radiance, _R_,

Souknilanh et al (2015) find their night-light based _GDP_ estimates are improved when supplemented by ground cover data from a second satellite (MODIS).

16 Normally there is a quasi-fixed ratio of intermediates to gross output which slowly falls as productivity improves. Countries that import most of their intermediates will be subject to external shocks (trading partner demand, terms of trade) that disrupt this relationship. 17 From a sample of 166 countries in 2010, from the World Development Indicators.

[26] The result is the v.4 DMSP stable lights data set with between 20 and 100 observations per year per pixel depending upon circumstances (Baugh et al.
than 500, 000 refugees, together accounting for half of all refugees and people in refugee-like situations (excluding Palestine refugees). Major refugee hosting countries are typically the neighbors of countries of origin. For example, Syria ’ s neighbors (Turkey, Lebanon, and Jordan) together accounted for 27 percent of total refugee numbers; Afghanistan ’ s neighbors (Pakistan and the Islamic Republic of Iran) together accounted for 16 percent; and Somalia ’ s and South Sudan ’ s neighbors (Ethiopia, Kenya and Uganda) together accounted for 11 percent. Some countries (Lebanon, Jordan and Turkey) are hosting a particularly large share of refugees relative to their population (see Figure 10). 45 However, in all other countries, the number of refugees as a percentage of the population is 3 percent or lower, and most often below 1 percent. Figure 7: Top 15 Host Countries as a Share of Total Refugees and Asylum-Seekers 1991 – 2015 Source: UNHCR Statistical Online Population Database Note: Includes refugees, people in refugee-like situations and asylum-seekers. Excludes Palestinian refugees under UNRWA ’ s mandate. 45 Nauru is a special case since the Australian government funds the offshore processing center where refugees and asylum-seekers intercepted at sea are detained pending determination of their status.
The firm-level financial data for 1997 are primarily from the Worldscope database. The World

in Australia and Canada, respectively. Using industry data from Mexico, Blomstrom and Persson

Commission on an annual basis. We use [group-affiliation data from the 1994-1997 lists of business]

by the level of growth of a sector or a country. Indeed, using firm level data, Haddad and Harrison
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.
In the alternative specification, an additional step has been taken to assign calories to food items registered in the POF household expenditure questionnaire on frequent purchases (POF 3). The cost per calorie in this alternative scenario is minimally higher, leading to a food poverty line of R$259 in 2018 urban Southeast prices.

_Source:_ Own calculations using POF 2017/18.
_Note:_ Main specification estimates the cost per calorie based on assigning food items in POF to the TBCA and imputation for food
items at the 5-digit level (i.e., steps 1 and 2 described above). The alternative specification extends the imputation to larger food
groups based on categorization by IBGE (2020).

**Lower** **Upper**
**Total poverty line** R$455 R$1,061
𝒔 [𝑭𝑭] 0.235 0.243
_Source:_ Own calculations using POF 2017/18 data.
_Note:_ These total poverty lines are based on the food poverty line of R$258, coming from our main specification, which is based
on the minimum calorie requirement of 2,100 calories per person per day and average cost per calorie of the bottom 40 percent
of the welfare distribution, with calories having been assigned in a two-step procedure and deflated by household-specific foodprice Paasche indices, as described earlier in this section.