Input:
of data is the National Water Commission; the data consist of daily rainfall measures in

Third, we use the geographic location data of electoral sections obtained from the Department of

elections in 2000 and 2006 by electoral section are public from the Federal Electoral Institute

(IFE) website. In addition to these, we use complementary information on socio-demographic

characteristics of municipalities from the 2000 Population Census and the 2005 Short Census or

2000 Population Census
2005 Short Census

geographic location data of electoral sections

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
To mitigate issues arising from the time interval between the 2007 Population Census and EMDHS 2014, only

EMDHS at regional level. The table shows that measured undernutrition rates in EMDHS and the estimated rates

regression of z-scores is estimated in the EMDHS with addition of the SAE estimates. The regression includes the

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


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.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts All Refugees All Hosts - 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Predicted total expenditure Predicted poverty rate Figure 5.16: Poverty incidence decreases with education of the household head Source: World Bank Staff based on SESRE 2023. Refugees’ Aspirations 52 A larger proportion of refugees work inside the camp across the distribution. Yet, better-off refugees are more likely to work inside the camp. Regarding location, although working outside the camp is shown to have significant wage effects (see Chapter 3), the data show that the poorest in-camp refugees are more likely to work outside the camp than the richest (Figure 5.18d), an effect apparently driven by refugees from South Sudan and Somalia, who are poorer overall. Regression results show that an increase in the share of employed household members is associated with increased household expenditure for in-camp refugees, their hosts, and out-of-camp refugees (Table D.12 in Annex D). The predicted poverty rate decreases with the share of employed household members, indicating that employment is essential to lowering poverty for in- camp refugees (Figure 5.19). 0 10 20 30 40 50 60 70 80 90 100 Poorest

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: of trade with government. Because the dataset used for this study is cross- sectional rather than longitudinal, I am unable to examine whether citizens adjust their beliefs and behavior to relative changes in service delivery. I was only able to test whether there are associations between the absolute service quality across neighborhoods, countries and ethnic groups and deference to the tax department, police and courts. Further, citizens may not be attributing goods and services to the gov- ernment. Rather, citizens may be attributing goods and services, such as roads, electricity grids, sewage systems, health care and education to vari- ous non-state actors including the following: the private sector; NGOs and community-based groups; churches, mosques and other religious institutions; traditional leaders; and, bilateral and multilateral donors. Survey questions on the Afrobarometer only indicate the presence or absence of services and infrastructure, and the quality of these services, but these questions do not probe respondents on who they believe are providing these services. Each of the indicators of perceptions of government performance is sig- niﬁcant at the p < 0. 05 level. Food security is positively associated with a willingness to defer to the tax department. Usage context: background mention, primary mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 14 people may also gain from conflict in terms of income and wealth and this may explain why some people do not move. The defining attributes of the alternative choices are very different from any other model and the task of economics is to understand what these defining attributes should be. In terms of independent variables, “ push ” factors become more important than “ pull ” factors in forced displacement models. The intensity of a conflict may be more important than the income opportunities in potential destination areas. In addition to the classic socioeconomic variables, risk aversion, stress, anxiety, other traits of personality and behavioral factors in general have to be well understood and measured. Hence, one could think of four essential blocks of independent variables including individual or household socioeconomic characteristics, “ push ” factors, “ pull ” factors and behavioral factors. Also, access to and dissemination of information related to the conflict in the place of origin but also in the potential places of destination may be crucial for people to make choices. This is where social psychology, behavioral economics and neuroeconomics may offer insights into such choices. Forced displacement data are also unusual in their form.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 10 measured by the number of days missed during the academic year, can function as an indicator of school attachment or integration for refugee students. To address potential biases from teacher subjectivity, the analysis of score disparities relies on INVALSI test results. The standardized and anonymized nature of these tests helps mitigate subjectivity in assessment. First, the results section presents some summary statistics of the main outcomes across the different categories of students. Second, we use the administrative data to analyze empirically how Ukrainian refugees and newly arrived foreigners compared to other students as regards their education performance. This estimation is based on an OLS model with the following econometric speciϐication: 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 = 𝛽𝛽0 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 + 𝛽𝛽1 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 + 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 + 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 + 𝑓𝑓𝑔𝑔 + 𝑓𝑓𝑠𝑠 + 𝑓𝑓𝑙𝑙 + ϵigs (1) where 𝑌𝑌𝑖𝑖𝑖𝑖𝑖𝑖𝑖𝑖 represents the outcome of interest (such as test scores, absenteeism, or high-track recommendation) for student i in school s, in grade g, and with language l. The variable 𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑢𝑒𝑒𝑖𝑖 is a dummy indicating whether the student is a Ukrainian refugee, and 𝑛𝑛𝑛𝑛𝑛𝑛_𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑖𝑖 indicates if the student is a newly arrived foreigner. 𝐸𝐸𝐸𝐸𝐸𝐸𝑆𝑆𝑖𝑖 represents the Economic, Social, and Cultural Status of the student, and 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑟𝑟𝑖𝑖 indicates the student ’ s gender. Usage context: background mention, primary mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
As has been demonstrated, jobs are not easy to come by for most refugees, and in order to earn
money some choose to invest in small businesses. Some organisations assist refugees in their
livelihoods through allowing them to be able to gain independence and earn their own money. In
2009-2010 CRAT, in conjunction with the US Embassy in Cameroon, began a project entitled
“Improving the Coping Status of Urban Refugees”. This project was initiated after a survey and
research project was done with many of the torture victims that CRAT assists, the results of
which showed a strong link between Post-Traumatic Stress Disorder, anxiety and depression,
with a lack of livelihood support. According to a psychologist at CRAT, when people are unable
to meet their basic needs, it is very difficult to treat their mental health issues.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Significant among these are: (a) the lack of capacity of national statistical agencies in many developing countries to collect robust data on refugees; (b) weak or incomplete monitoring of refugees dispersed within host communities; (c) lack of capacity to maintain up to date information on refugees (reflecting new arrivals, 81 General population registers may also provide opportunities for more elaborate analysis of the integration of refugees in asylum countries, as the data could be linked to other administrative registers, for example on labor and education (UNSD 2014). 82 UNHCR collects, compiles and publishes data on asylum-seekers, refugees and IDPs protected or assisted by UNHCR, including populations in refugee-like or IDP-like situations. 83 Established in 1863, the ICRC ’ s mission is to ensure humanitarian protection and assistance for victims of armed conflict and other situations of violence. Usage context: supporting mention, primary mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: likelihood of working outside the camp, or in monthly earnings. Agriculture is an important source of livelihood for host households, but refugee households have low agricultural holdings, reflecting their inability to own land legally. Refugee households are less than half as likely as hosts to report an agricultural holding with crops (19 percent versus 41 percent of host households, Figure 3.14). Refugee livestock ownership is similarly low (22 percent of households own livestock versus 48 percent for host households) but average livestock ownership is higher for Somali households (41 percent) (Figure 3.15). 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 15-24 25-34 35-44 45-54 55-64 Less than Primary Primary Secondary Figure 3.12: Share employed by age – camp refugees Source: World Bank Staff based on SESRE 2023. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 15-24 25-34 35-44 45-54 55-64 Less than Primary Primary Secondary Figure 3.13: Share employed by age – camp hosts Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 32 When refugees own livestock, the value and flock size of this livestock is low. Somali refugees mostly own sheep, goats, and donkeys, and compared to Somali hosts, they

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 11 survey, we construct the asset index based on the same categories of assets captured in every survey wave, ensuring full comparability over time. Most of these assets are recorded as dichotomous variables, taking 0 / 1 values, while the few categorical variables with multiple categories are manually reorganized along an ordinal scale according to costs. The asset variables are first normalized and then transformed into a single wealth index through principal component analysis, following an approach proposed by Kolenikov and Angeles (2009). Scree plots indicate that the first principal component is highly significant in every wave, while further components carry little information, as desired. The wealth-index is likely to indicate the long-term economic well-being, as many durables captured are typically held by households for many years and are not frequently replaced (Sahn and Stifel 2000). In between the first and second survey waves an administrative reform took place in which the definition of urban areas was revised (Megill 2004), among other things. Some communities previously considered rural were now coded as towns, which, to some extent, explains the sharp increase in the proportion of urban population. Usage context: primary mention, background mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Figure 1: Data and Descriptive Statistics: Clusters, Refugee Camps, and Conflicts Revised refugee diversity indices. We first use Afrobarometer data to construct standard indices of diversity, namely the EF and the EP indices (Bazzi et al., 2019; Esteban and Ray, 1994). The EF index describes the probability that two randomly selected individuals from a given location belong to two different ethnic groups (Alesina et al., 2003, 2016; Gomes, 2020b). The EF index can be defined as EFjt = Njt X e = 1 get (1 − get), (2) where Nj is the number of ethnic groups in cluster j at time t and get is the population share of 14 Usage context: primary mention, supporting mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: the West Bank and Gaza. Out of all the estimated coefficients for each period for both outcomes (job loss and job gain), we only find a negative effect on job gain in 2019Q4, which is very small in magnitude (1 percentage point). Taken altogether, the results presented in this section bolster our confidence that we are correctly identifying the effects of the pandemic shock on labor market outcomes. Figure 12: Placebo effect on labor market flows Notes: The figure shows the output of a placebo test with a set-up analogous to Figures 6 and 9. We perform the same regression as specified in Equation (2). Our sample includes data from 2018Q2 to 2020Q1 and assumes that the pandemic started in 2019Q2. Therefore, the post-pandemic period refers to the quarters between 2019Q2 to 2020Q1. The analysis is restricted to men aged 20-59. 7 Conclusion This paper examines the effect of the pandemic on labor markets in the West Bank and Gaza using quarterly labor market data provided by national labor force surveys. With a focus on men ’ s labor market outcomes, this paper sheds light on how labor markets in the West Bank and Gaza adjusted to the COVID-19 shock examining adjustments at the extensive (employment) and intensive (hours of work) margins. Usage context: primary mention, background mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Having returned (early) to Northern Mali did not always lead to a stable condition either: the transition probabilities from returnee to IDP or to refugee are 1. 1 % and 0. 2 % respectively. As we can see from Figure 1, the majority of the sample is Songhai and Kel Tamasheq (almost everybody identified themselves as Muslim). There are clear differences in migration decisions between ethnic groups. The reaction of most of Arab and Kel Tamasheq origin was to leave the country, while most Songhai people preferred to go south, to Bamako, or, by the time of our survey, had already returned to Northern Mali. In fact, as pointed out in (Etang-Ndip et al., 2015), IDPs and returnees have a similar ethnic composition because 94 % of returnees in our sample were IDPs. Far fewer returnees in the sampled cities of Gao, Tombouctou and Kidal returned from refugee camps in the neighboring countries for the simple reason that most refugees used to live in towns and villages outside the regional capitals of Northern Mali. Displaced Refugee Returnee Total Tamasheq Arab Songhai Peulh Bella Other Analytic weights used Source: LDPS 2014-15 Figure 1: Ethnic composition

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 2017. • 890,825 refugees enrolled in the Level 3 Registration and Biometric Identity Management System (BIMS). Refugee ID cards and proof of registration are issued to 55 and 98 percent of refugees, respectively. Out-of-camp Permit is issued to 48,346 refugees. • One-stop shops have been established in 13 refugee camps (14 are under construction) to provide one-center registration, documentation, and protection services. Annexes 91 • The National Strategy on Violence Against Women and Children (2021 -2026) recognizes refugee women and children. Also, refugees are included in the national Gender Based Violence (GBV). • Digital Request and Compliant System (DRCS) is established and implemented as part of the digitization of refugee protection services. • Refugees are getting mobile courts and free legal aid services. Energy/Environment “Provide market-based sustainable, reliable, affordable, culturally acceptable, environmentally friendly clean/renewable energy solutions for 3 million people. • A National Cooking Fuel Strategy is developed by EEWG to guide and define camp-specific cooking energy options. • In Afar, Gambella, and Melkadida, more than 382,000 refugees and 85,000 hosts have access to alternative cooking fuels and market-based clean electricity from solar-mini grids. • More than 1,739,726 seedlings were planted, 160m3 check dams were built, and 8 km of

Level 3 Registration and Biometric Identity Management System

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: The objective of the survey was to analyze perceptions and priorities with regard to the Peace Accord, to analyze perceptions of security, to determine access to basic infrastructure and school attendance, to understand nutrition levels, and to measure household asset ownership. In addition to this baseline survey, the World Bank was sponsoring a mobile phone survey among IDPs in Bamako, returnees in Gao, Kidal and Timbuktu town and refugees in camps in Niger and Mauritania. This survey interviewed 500 respondents on a monthly basis. In the August 2015 round of this survey, questions about perceptions and priorities with regard to the Peace Accord were included. This paper also makes use of a subset of the responses obtained from that survey, particularly those from refugees in Niger (n = 80) and Mauritania (n = 100) as these sub-populations who live outside Mali ’ s borders are important stakeholders in the peace process whose opinions risk being ignored. 15 To select a household in a village or neighborhood for the baseline survey, random selection was used: the enumerator divided the locality into two parts and selected five 15 For a more elaborate description of this mobile phone survey, see: Etang Ndip, A., J. Hoogeveen and J. Lendorfer (2016). Socioeconomic Impact of the Crisis in Mali on Displaced People. Journal of Refugee Studies. Usage context: primary mention, supporting mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
weather policy, this is deemed a rainfall shock. As the quality of the rainfall data is related to the

11The APHRODITE weather data provides information about how many local weatherstations contributed to a
certain rainfall reading. Since some of the rainfall observations are likely to be more accurate than others, I weight
them according to accuracy. If there are no rainfall stations contributing to the APHRODITE data within a .75 _[o]_ x.75 _[o]_

When the regressions are run with the village characteristics from the 2005 Indian census, the coefficients of interest do not change significantly. Also, most village-level characteristics had insignificant coefficients, with the exception that a more literate population

While the BASIX data set does not offer the opportunity to test the direct effects of a cash payment

Robust standard errors in parentheses Obervations weighted by quality of rainfall data

APHRODITE weather data
2005 Indian census
BASIX data set

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 9 Figure 2: The number of FDP micro-level datasets over time Notes: this graph shows a trend in the number of FDP micro-level datasets in the UNHCR / WB MDLs between 1995 and 2022 based on the year of data collection. Note that at the time of this study, many micro-level datasets collected in 2022 were not yet incorporated into the UNHCR / WB MDLs and thus the number for that year should be treated with caution. Project specific datasets Of the 375 FDP datasets, a non-negligible share of them turns out to be project specific, meaning that despite their utility for evaluating and monitoring the impact of a certain project or program, its application for analyzing any broader population of interest is quite limited. We find that 39 percent of the identified datasets sample from a narrow base of beneficiaries from a certain program or project. It is important to draw a representative sample of a broader population of refugees and / or IDPs instead of sampling only from a narrow base. If the sample consists of only beneficiaries from a certain project or program, the generalizability of findings or conclusions drawn from that sample is very much limited and does not extend beyond the small confine of the target population that is relevant only to the project or program itself but not to broader communities of policy makers, development practitioners and scholars alike. Usage context: supporting mention, primary mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
emotional well-being, as well as local integration prospects. In this sense, some Afghan women
have reported a strict control exerted by male family members, which translates into their
confinement in the domestic realm.

##### Protection Risk IV

**Trafficking in persons, forced labor or slavery-like practices.** Although asylum seekers, refugees,
and other forcibly displaced persons have the same labor rights as nationals in Brazil, they
encounter several obstacles to their economic integration. The experience of displacement,
coupled with high levels of unemployment, informal labor, and poverty, heighten the risk of this
population to fall prey of human traffickers. According to official data, between 2021 and 2023,
355 refugees and migrants of all nationalities were rescued from forced labor or slavery-like
practices in Brazil from various economic sectors, primarily in timber trade, cassava cultivation,
clothing manufacturing, road transportation, and tobacco. [33 ]

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Notably, while we cannot rule out that time outside due to employment may play a role (e. g., fresh air may boost one ’ s mood), our time-use data indicates that the average refugee already spends at least three hours outside per day, with no measurable diﬀerence between employed and cash arms. As we are powered to detect changes of at least twenty minutes for each activity, our results suggest that large substitutions away from unsavory activities are unlikely to be driving the improvements in psychosocial well-being, insofar as the respondent recalls. 1718 We also investigate whether those who were more idle prior to being employed beneﬁt more from employment. We ﬁnd no impact along this margin, suggesting that the elimination of boredom per se is not the driving force behind the psychosocial value of employment (Appendix Table A10). 17Most respondents do not track their day by time, making collection of reliable time use data challenging (though recent literature documents the broader unreliability of such data). We piloted a variety of strategies, and settled on asking respondents how much time they spent on a set of activities in the previous day.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: SEIS enumerators were instructed to interview only the household's de facto head, defined as the person with primary decision-making authority over household resources, regardless of gender. In cases where the household head was unavailable, the spouse or a proxy respondent aged 18 or above was interviewed. SEIS interviews were conducted in private to minimize social desirability bias on sensitive questions about protection incidents and the household's experiences of discrimination in access to services.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Looking also at the impact of IDPs in Colombia on wages, Morales (2017) uses a labor force survey, census data and registry data to study short and long-term effects as follows: 𝑆ℎ𝑜𝑟𝑡 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ௧ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ௧ ି ଵ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ௧ ൅ 𝜆 ௠ 𝑋 ௠ ௧ ൅ 𝛾௧ ൅ 𝛿 ௠ ൅ 𝛿 ௠ 𝑇 ൅ 𝜀 ௜ ௠ ௧ 𝐿𝑜𝑛𝑔 െ 𝑟𝑢𝑛: 𝑦 ௜ ௠ ൌ 𝛼 ൅ 𝛽𝑑 ௠ ൅ 𝜆 ௜ 𝑋 ௜ ௠ ൅ 𝜆 ௠ 𝑋 ௠ ൅ 𝛿 ஽ ௠ ൅ 𝜀 ௜ ௠ where y is the log of wages, i, m, and i are individuals, municipalities and time respectively, 𝑋 ௜ ௠ ௧ are individual controls, 𝑋 ௠ ௧ is the log of total population or other municipality controls, 𝛾௧ and 𝛿 ௠ are time and municipality fixed effects, 𝛿 ௠ 𝑇 are municipality time trends, 𝛿 ஽ ௠ are department fixed effects and d is the inflow of IDPs defined as 𝑑 ௠ ௧ ൌ 100 𝑝𝑜𝑝 ௠ ௧ 𝑓 ௠ ௧ where 𝑓 ௠ ௧ is the total number of IDPs arriving in municipality m at time t. The same variable without the t subscript is used for the long-run effects equation. Usage context: primary mention, supporting mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


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: supporting mention, primary mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
Respondents should be asked to estimate the number of purchases made over a fixed reference period, anchoring questions on consumption rather than on acquisitions (as in the 2016 Barbados Survey of Living Conditions and the 2017/18 Jordan Household Income and Expenditure Survey).

**GDP.** We use GDP data from the Word Development Indicators of the World Bank. It is

measured using constant 2010 prices in US dollars. [8] For our analysis, GDP series need to be

filtered in order to extract the business cycle component from the trend. We use three different

8We used the data serie called "NY.GDP.MKTP.KD"
9With this setting, we mostly keep fluctuations that have a frequency between 8 and 32 quarters.
10We keep fluctuations between 32 and 200 quarters, following Comin and Gertler (2006a)
11The concordance table from SITC Rev2 to BEC can be found on the UN Trade Statistics webpage: `[https:](https://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp)`
`[//unstats.un.org/unsd/trade/classifications/correspondence-tables.asp](https://unstats.un.org/unsd/trade/classifications/correspondence-tables.asp)` .

**Trade** **Proximity.** We collect data on bilateral trade flows from the Observatory of Economic Complexity (MIT). This database covers 215 countries over the period 1962-2014.

In this paper, we use the recent GVC indicators from Borin and Mancini (2019). They offer a new toolkit for value-added accounting of trade flows at the aggregate, bilateral, and sectoral levels

2017/18 Jordan Household Income and Expenditure Survey

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
The final steps consist of setting a threshold for when a stream flow is strong enough  to flood the basin and then weighting this with the nightlight data and aggregating up to a  district level.  5  <!-- PAGE 8 --> The earthquake index is constructed from computer generated contour maps by the US  Geological Survey (USGS) of earthquake intensity data, commonly used as potential dam age proxy (De Groeve _et_ _al._, 2008; GeoHazards International and United Nations Centre  for Regional Development, 2001; Federal Emergency Management Agency, 2006).

The  data are then combined with the nightlight data and aggregated up to district level.  Finally, the 2004 Christmas tsunami has been modeled following a method where Heger  (2016) uses inundation maps to construct a district level damage index assuming uniform  6  <!-- PAGE 9 --> damage across all flooded areas.

aggregation of the disaster indices in the main paper. To weight the indices, nightlight data  are used as a proxy for economic activity.  **A.1** **Flood** **Index**  The flood index is made from stream flow modeled in GeoSFM, a software that uses remotely  sensed data as inputs, which are weather and soil and terrain based.

the contour maps as a base for damage infliction, we combine them with the nightlight and  building type data from the USGS building inventory for earthquake assessment to create  fragility curves by building type; see Jaiswal & Wald (2008) and GeoHazards International  and United Nations Centre for Regional Development (2001).

nightlight data
earthquake intensity data

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: The long-term consequences of these trends are signiϐicant, as diminished educational outcomes and social isolation can hinder successful integration into host communities. Conversely, sustained social and educational integration efforts are vital for positive outcomes. For instance, studies indicate that long-term integration can be hampered by social, economic, and institutional barriers (Chiswick and Miller, 2014), while interventions focused on language support and community engagement can lower these barriers (Ozden and Wagner, 2020). Further, speciϐic interventions aimed at removing obstacles to education for children on the move can contribute signiϐicantly to better integration outcomes (Schuettler and Caron, 2020). This paper beneϐits from key information coming from administrative data on educational records of Ukrainian refugees in Italy for the academic years 2021-2022 to 2023-2024 for grades 6 to 13. This provides a unique opportunity to examine enrollment, attendance, test performance, and other indicators of integration into the Italian educational system. Supplemented by survey data collected in 2023-2024, this study offers an overview of the challenges and opportunities faced by Ukrainian students in secondary schools and highlights areas for potential policy development. This study advances the literature by adding empirical evidence on the short- to medium-term educational impacts of displacement on young refugees within a European host country, offering insights into the role of education policy in mitigating human capital losses. It also contributes to discussions on human development by identifying factors that support or hinder integration, highlighting pathways for improving educational and social outcomes for refugee students. Results highlight that despite gradual improvements, enrollment rates remain signiϐicantly lower among refugees compared to native and other foreign students. Ukrainian refugees also demonstrate higher absenteeism and lower academic performance, particularly in subjects requiring language proϐiciency such as Italian and English. However, good performance in mathematics suggests potential strengths linked to their prior educational backgrounds. Despite these challenges, teachers seem to be more inclined to recommend Ukrainian refugees for high-track education compared to other newly arrived foreigners, indicating potential optimism about their academic capabilities. The Usage context: supporting mention, primary mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: intention to migrate abroad (1) (2) Camp-Based Refugees OCP Refugees Male 0.022 -0.001 (0.019) (0.008) Age Under 30 - - Age 30-44 -0.000 -0.007 (0.020) (0.008) Age 45-64 -0.082*** -0.088** (0.027) (0.036) Education: Primary incomplete - - Education: Completed primary -0.008 0.004 (0.036) (0.009) Education: Completed secondary 0.016 0.006 (0.064) (0.009) Education: Completed post-secondary -0.016 -0.010 (0.067) (0.034) Years in Ethiopia -0.000 -0.000 (0.004) (0.001) Region Fixed Effects Yes No N 3,069 830 Source: World Bank Staff based on SESRE 2023. Note: The outcome positively responds to the question, ‘Do you intend to migrate abroad?’. The sample includes all refugees aged 15 or over who were born abroad. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 112 Results on Welfare and Equity Table D.11: Poverty headcount rate by subgroups Characteristics Subgroups In-camp Addis Ababa Hosts Refugees Hosts Refugees Location or domain Eritrean 16% 73% Somali 38% 76% South Sudanese 36% 89% Addis Ababa 18% 7% Sex of head Female 34% 87% 16% 10% Male 30% 74% 19% 3% Head education No education 38% 87% 35% 16% Primary incomplete 30% 81% 26% 13% Primary complete 29% 79% 14% 6%

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
**Annex 3: Environmental and Social Risk Assessment**

The ESMF (Environmental and Social Management Framework) outlines the screening criteria applied to all civil works under Components 1 and 2. Site-specific Environmental and Social Management Plans (ESMPs) will be prepared for each subproject prior to commencement of works. The ESMF also includes a Labor Management Procedures (LMP) section detailing requirements for contractor compliance with national labor law, prohibition of child and forced labor, and worker grievance mechanisms. The LMP will be incorporated by reference into all civil works contracts.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 16 Source: Authors ’ calculations based on data from the High Frequency Surveys of Ethiopia (2017), Nigeria (2018), Somalia (2017), South Sudan (2017) and Sudan (2017). 6. Gender differences in multidimensional poverty Next we examine differences in multidimensional poverty outcomes by the gender of the household head. Existing literature points out the limitation of household level MPI analysis in masking the intrahousehold distribution of deprivations, and thus being less sensitive to gender based differences in individual outcomes within the family unit, which might lead to underestimation of inequality and gender gaps (Espinoza-Delgado and Klasen 2018; Franco 2017; Klasen and Lahoti 2020, Rodriguez, 2016). However, as the MPI identifies poverty at the household level, our initial analysis focuses on disaggregated results by the gender of the household head. 19 We acknowledge that this approach has several limitations since most women reside in male-headed households, and the composition of households can change after displacement due to separation of family members, and widowhood. Regardless, the analysis at the household level remains relevant given the high prevalence of female-headed households that emerge after displacement, with the analysis showing large differences across countries between households based on the gender of the head. Usage context: primary mention, background mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: In other contexts, deregistration signifies not the achievement of a durable solution but rather the end of state or international support (IDMC 2015). 33 The absence of a clear and operational approach to defining the ‘ end ’ of internal displacement may be one of the factors behind the continued overall increase in the global numbers of IDPs. Lack of clarity around when displacement ends also leaves room for political manipulation. Governments may find it politically expedient to artificially prolong IDP status by deterring returns or local integration, for example in Azerbaijan and Georgia to promote claims over territory (Beau 2003). In other contexts, national 28 UNHCR ’ s IDP data focus only on internally displaced populations to which it extends protection or assistance. IDMC coverage of IDP data is more expansive and in 2015 included additional data on: (a) 26 countries accounting for 4. 5 million IDPs including some significant IDP hosting countries (Turkey, India, Ethiopia, Bangladesh and Kenya); and (b) IDPs in countries where UNHCR is active who are not protected or assisted by the agency. In 2015, IDMC ’ s aggregate figure for conflict-induced internal displacement was 3. 3 million higher than UNHCR ’ s aggregate figure for IDPs protected or assisted by the agency. 29 IDMC ’ s 2016 report presents both data sets alongside each other. Usage context: supporting mention, background mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: as sold. Annexes 125 Table E.2: Food aid and consumption comparisons Items Quantity (per capita/year) Expenditure (per capita/year) SESRE UNHCR SESRE UNHCR Nonzero All Net of sold ration* Cereals/other cereals 23.2 186.1 175.5 493 12404 Wheat 60.5 133.2 116.6 1427 4710 Maize 39.6 125.5 118.3 312 4267 Rice 21.3 48.5 39.8 560 3392 Sorghum 12.7 132.8 125.7 4 5652 Pulses 7.1 18.9 17.5 248 1514 Vegetable oil/oil 3.7 9.7 8.9 627 1774 Salt 1.6 7.9 7.7 57 232 Biscuits 5.4 4.5 4.4 16 112 Dates 0.0 4.2 3.7 0 . CSB+ 19.3 15.0 14.0 36 532 Other food 565.9 - - 2558 Peas 5.7 - - 137 All cereals 78.2 - - 2994 All pulses 8.2 - - 385 Aggregate ration/month 46.7 - - Source: UNHCR and World Bank Staff based on SESRE 2023. Valuing food aid quantities with prices from SESRE suggests that if UNHCR food aid quantities were received/ reported by refugees, refugees’ food expenditures would be much more comparable to those of hosts (Based on this information, we compare how the distribution list shared what refugees should have received to what they reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Introducing the UCDP Georeferenced Event Dataset. Journal of Peace Research. 1989-2019. 50 (4). Taylor, J., M. Filipski, and M. Alloush (2016). Economic impact of refugees. Proceedings of the National Academy of Sciences 113 (27), 7449 – 53. United Nations High Commissioner for Refugees (2020). Global Trends: Forced Displacement in 2019. Geneva. Verme, P. and K. Schuettler (2021). The impact of forced displacement on host communities a review of the empirical literature in economics. Journal of Development Economics 102606. Verwimp, P. and J. Maystadt (2015, December). Forced Displacement and Refugees in Sub-Saharan Africa. An Economic Inquiry. World Bank Policy Research Working paper 7517. Vogt, Manuel, N.- C. B. S. R. L.- E. C. P. H. and L. Girardin (2015). Integrating Data on Ethnicity, Geography, and Conflict: The Ethnic Power Relations Data Set Family. 51

Ethnic Power Relations Data Set Family
UCDP Georeferenced Event Dataset

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Applications were submitted during the same time span each of these days (8am-12pm CT). 7 The order of applications (grouped by profile) each day was also randomized. The application process is straightforward, it consists of submitting the application and completing an optional pitch. However, some jobs have a mandatory pre-scan questionnaire. For these type of ads we standardized answers and recorded the job ads that implemented these questionnaires. 3. 4 Monitoring Job Applications Job applications were monitored using a web scraping algorithm. For each job application, the following data was scraped from the website every Tuesday, Thursday, and Saturday (between 8am- 12pm CT): 1. Number of times the profile was viewed by the employer 6The locations we classify as Greater Kuala Lumpur are: Kuala Lumpur, Putrajaya, Petaling Jaya, Klang / Port Klang, Kajang / Bangi / Serdang, Subang Jaya, Ampang, Cyberjaya, Seremban, Selangor, Selangor- Others, Selayang, Semenyih, Shah Alam / Subang, and Central. 7If the website is under maintenance, which is common, applications will be delayed until the website is available. 10 Usage context: supporting mention, primary mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: A growing number of low and middle-income countries have tried to improve the law enforcement response to gender-based violence by training professionals, reorganizing police and courts, and trying to provide a more comprehensive response to survivors. Evidence of effectiveness is relatively limited; most well-evaluated initiatives come from high-income countries, and the lessons learned may not be applicable to developing countries. Evaluations of law enforcement reforms in low and middle income countries have typically been limited to case study approaches drawing from police records (notorious for under-reporting), qualitative perspectives from key informant interviews, intermediate outcomes such as changes in attitudes and knowledge among police and judges, and interviews with small numbers of women who have sought legal redress. Population-based data collection, control groups, or follow-up among more than a handful of survivors are rare. Nonetheless, the following initiatives illustrate the types of efforts that have produced important lessons learned. Training personnel in the police and judiciary and other parts of the justice system Throughout the world, organizations have launched efforts to improve the knowledge, attitudes, and practices of justice sector personnel regarding gender-based violence. Some law enforcement institutions organize training internally, as did South Africa following passage of the 1998 Domestic Violence Act (Usdin et al., 2000). Usage context: primary mention, supporting mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Source: UNHCR Statistical Online Population Database Note: Only includes refugee situations greater than 25, 000 people. Excludes high-income (OECD and non-OECD) countries. Excludes Palestinian refugees under UNRWA ’ s mandate. Usage context: primary mention, background mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: First, I include a measure of whether citizens believe that a large portion of tax administrators is corrupt. Second, I in- clude a variable indicating whether citizens approve of how well their local government is handling the collection of license fees on bicycles, carts and barrows. 8 Third, both the size of a country and the size of the government may aﬀect a government ’ s ability to detect and punish evaders. I include the 7I also include a country-level indicator of government performance, the World Bank Governance indicator of government eﬀectiveness, in the model. This indicator measures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implemen- tation, and the credibility of the government ’ s commitment to such policies (Kaufmann, Kraay and Mastruzzi, 2006, 4). This variable is not signiﬁcant at the p <. 05 level. 8I included two additional measures in the model neither of which were signiﬁcant at the p < 0. 05 level. One is a measure of citizens ’ approval of how well their local government council provides citizens with the information about the councils budget (i. e. revenues and expenditures). The other, the World Bank governance indicator, control of corruption, measures the extent to which public power is exercised for private gain, as well as capture of the state by elites and private interest (Kaufmann, Kraay and Mastruzzi, 2006, 4). 13 Usage context: background mention, supporting mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: When national or international actors provide assistance, there may be an incentive for people to register in camps even if they are staying elsewhere, or to register in multiple locations (Brookings 2011). 69 Furthermore, registration data provide only a snapshot of the stock of IDPs at a particular point in time and may be out of date if registers are not maintained regularly. Registration methodologies can vary across displacement situations. For example, families may be registered rather than individuals and the population estimated based on an assumption of average family size, which can differ among organizations (UNSD 2014). IDPs may be required to present documentation, meet specific criteria or re-register periodically to maintain their benefits, which affects aggregate numbers (IDMC 2015). For example, in Ukraine, pre-requisites for IDP registration (including valid documentation, arrival from a recognized conflict zone and permanent residence registration in recognized conflict zone) means that people displaced within a non-government controlled area, people displaced from a non- recognized conflict zone in a government controlled area, unaccompanied children or people without current / valid identification are not counted as IDPs (IDMC 2015). Usage context: primary mention, supporting mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
Survey data, for example from large household survey programs such as the Demographic and Health Surveys (DHS), Multiple Indicators Cluster Survey (MICS), or Living Standards Measurement Study (LSMS) is independent of public record keeping and can also capture vaccination obtained through private or non-governmental providers

The data that support the findings of this study are available from the from the Central Statistical Office of Poland and the Orbis database of Bureau Van Dijk.

Multiple Indicators Cluster Survey (MICS)

Living Standards Measurement Study (LSMS)

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
Poor households in Bangladesh depend heavily on wood, dung and other biomass fuels
for cooking. This paper provides a detailed analysis of the implications for indoor air
pollution, drawing on new monitoring data for respirable airborne particulates (PM10) in a
large number of Bangladeshi households. Concentrations of 300 ug/m [3] or greater are
common in our sample, implying widespread exposure to a serious health hazard. For
comparison, Galassi, Ostro, et al. (2000) find substantial health benefits for PM10
reduction in eight Italian cities whose annual concentrations are far lower: 45-55 ug/m [3] .

households, using new air monitoring data from Bangladesh. Recent technical advances

In this paper we have investigated the determinants of indoor air pollution in

Bangladesh, using monitoring data for a stratified sample of 236 households in the region

of Dhaka. Extrapolating from our results, we have estimated indoor air pollution levels

for a random sample of 600 rural, peri-urban and urban households in six regions:

3 The 24-hour cycle of ambient PM10 concentrations is very similar to the pattern of average hourly
residuals from a panel regression that controls for differences in average PM10 concentrations for
households that use biomass fuels.
4
For comparison, we cite PM10 concentration measured by the Bangladesh Air Quality Management
Program monitor situation at the Parliament building in Dhaka. From March, 2002 to February, 2003, the
mean daily concentration was 137 ug/m [3] . Our thanks to our colleague Paul Martin for this contribution.

new monitoring data for respirable airborne particulates (PM10)

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
The Normalized Deviation Vegetation Index (NDVI) is used to determine the exposure of households to
the drought. The NDVI is derived from satellite images measuring the health of vegetation.

In controlling for potential confounding factors, we rely on geo-coded conflict fatality data provided by the Armed Conflict Location Event Dataset (ACLED) and on data on the percentage of target beneficiaries reached with aid by pre-war region coming from the Food Security Cluster Somalia (Table A.1).

Armed Conflict Location Event Dataset (ACLED)

geo-coded conflict fatality data provided by the Armed Conflict Location Event Dataset

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: the degree to which the global response should include a development element. We ﬁnd that the average stood at around 10. 3 years at the end of 2015, with a median duration of 4 years, and signiﬁcant sensitivity to a few situations. Such numbers re-emphasize the importance of effective humanitarian interventions on the right scale. They suggest that development actors have a role to play but that they need to focus their interventions on a set of discrete protracted situations. To produce these numbers, we rely on the Population Statistics Database compiled and main- tained by UNHCR. The database records the number of “ persons of interest ” to UNHCR in each year since 1951 and for each situation, where a situation consists of a pair host-origin countries. The calculation of duration of exile is obtained under a no-turnover assumption, whereby a de- crease in the number of refugees for any given situation is fully attributed to exits from refugee status, while increases are assumed to be fully accounted for by new cases. Although such ap- proach tends to over-estimate the true duration of exile, the lack of individual-level data on regis- tration precludes reﬁning the estimate further. Attempts to estimate similar statistics have been limited. Usage context: background mention, supporting mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 77% Proximity to resource hubs Nearest to zone 34% 93% Nearest to woreda 24% 72% Nearest to border 37% 81% Remote 33% 78% Source: World Bank Staff based on SESRE 2023. Annexes 113 Table D.12: Determinants of welfare (total expenditure per capita) (1) (2) (3) (4) In-camp refugees In-camp hosts OCP refugees OCP hosts Female headed 0.00 0.03 -0.01 0.02 (0.03) (0.03) (0.04) (0.05) Age of head (year) 0.00** 0.00 -0.00 -0.00 (0.00) (0.00) (0.00) (0.00) Household size -0.12*** -0.09*** -0.20*** -0.16*** (0.01) (0.01) (0.01) (0.02) Head years of schooling 0.02*** 0.01*** 0.03*** 0.03*** (0.00) (0.00) (0.01) (0.00) Mobile phone 0.10*** 0.19*** 0.39*** 0.17 (0.03) (0.03) (0.11) (0.15) Household has electricity 0.15*** 0.22*** 0.14 0.19 (0.04) (0.03) (0.27) (0.13) HH owns any livestock 0.04 0.03 -0.42* (0.03) (0.04) (0.25) HH member has bank account 0.13*** 0.13***

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: .13) HH owns any livestock 0.04 0.03 -0.42* (0.03) (0.04) (0.25) HH member has bank account 0.13*** 0.13*** 0.07 0.16 (0.03) (0.03) (0.10) (0.26) HH has agricultural holding -0.02 -0.08* -0.47* (0.03) (0.04) (0.24) HH operates a nonfarm enterprise 0.07** -0.00 -0.05 0.26*** (0.03) (0.03) (0.10) (0.07) Share of employed members 0.35*** 0.57*** 0.14** 0.06 (0.08) (0.06) (0.06) (0.08) Health shock 0.07 -0.02 -0.04 -0.20 (0.06) (0.06) (0.10) (0.18) Market shock -0.01 -0.09*** -0.04 -0.19*** (0.02) (0.03) (0.04) (0.05) Employment shock 0.01

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
**KEY HIGHLIGHTS** **[1]**

 Most of the youth in NWS **face protection risks,** with 53% reporting homelessness and 55% experiencing
exploitation, while 44% face gender-based violence.

 75% of youth **lack access to protection services**, highlighting a significant gap in availability and
accessibility.
 77% of respondents indicate **no organizations offer tailored activities or services for youth** in NWS,

**Specific youth groups**, such as those with disabilities 70%, adolescent females 40%, IDPs 48%, and
homelessness 36%, are identified as experiencing significant protection risks.
 82% of **youth believe newly graduated youth have fewer working opportunities**, 66% of Youth actively
participate in community-based structures, with 74% volunteering and 53% involved in outreach teams,
promoting skill development, relationship building, and community contribution.

 76% of youth in northwest Syria **perceive unequal opportunities for social participation,** with
discrimination (85%), exclusion (34%), and stigmatization (15%) as key contributing factors.

 Only 12% of **youth have access to capacity-building or empowerment activities**,

 Expanded initiatives for **youth empowerment, leadership, and conflict resolution** are needed, with a
focus on essential skills development and resilience building.

 20% of **youth have been threatened or felt afraid**, while 24% sometimes experience such feelings,
impacting their well-being.

---
[1] The Protection Monitoring and Analysis working Group (PMA WG) of the Northwest Syria Protection Cluster, with support from the

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Hosts Refugees Eritrean Somali South Sudanese Figure D.24: Household owns livestock Source: World Bank Staff based on SESRE 2023. 0 0.1 0.2 0.3 0.4 0.5 0.6 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.23: Household owns crops Source: World Bank Staff based on SESRE 2023. 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.25: Total value of livestock Source: World Bank Staff based on SESRE 2023. 0 5,000 10,000 15,000 20,000 25,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.26: Value per tropical livestock unit Source: World Bank Staff based on SESRE 2023. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.27: Household has non-farm business Source: World Bank Staff based on SESRE 2023. Annexes 110 0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 Hosts Refugees Hosts Refugees Hosts Refugees Eritrean Somali South Sudanese Figure D.28: Value of productive assets in households with business Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 Percent 70 80 90 100 Hosts Refugees Hosts Refugees

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: As in previous sections we follow Henderson et al. (2012) who argue that the relationship between GDP and night light at the country level can be expressed fairly well in a constant elasticity model in which an increase of night light by 1 percent implies an increase of GDP of about 0. 25 percent. Hodler and Raschky (2014) also look at the relationship between log nighttime light intensity and log GDP at the regional level using the panel data of regional GDP per capita assembled by Gennaioli et al. (2013) 48 and they conﬁrm that the relationship is linear and also ﬁnd an elasticity of around 0. 3. Access to political power is ranked on a scale from 1 to 7 in the GROWup dataset. Ethnic groups are"powerful"(monopoly of power or dominant group in power), have access to central power through a formal system of power sharing (as"Senior"or"Ju- nior"partner) or are “ excluded ” from power (self excluded, powerless or discriminated). Strong executive constraint is measured as a dummy indicating whether or not we have executive parity or subordination of the executive at the country level, a value 7 for “ xconst ” variable in Polity IV dataset. Usage context: background mention, primary mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Considering that these statistical standards and recommendations were established in recent years, it may not be surprising that the overall level of compliance with EGRISS recommendations is still evolving. Figure 6: Prevalence of Identifying Questions in FDP Datasets A) Refugee identification questions B) IDP identification questions Notes: Panel A shows the percent of FDP datasets containing each of the core identification questions recommended by EGRISS for refugees (in all publicly available datasets designed to sample from refugees that are not project specific and collected after 2010) whereas Panel B shows IDP equivalent of that. For refugees, we do not make a distinction between the question on the country of citizenship and the acquisition of citizenship because many datasets we study often collapse these questions into one by simply asking respondents to indicate their nationality (or nationalities). 5. Conclusion With an ever-growing number of people forced to flee their homes due to conflict, violence, fear of persecution, and human rights violations, data on FDP has become ever more important. A solid evidence base is crucial to inform effective policy responses to address their challenges and to establish such a base, 35 More than 95 percent of the FDP datasets asked questions on either the country of birth or citizenship. 36 While the country of birth does not change, nationality or citizenship can. And it is the country of citizenship or acquisition of citizenship that determines whether refugees and refugee related persons should still be regarded as refugees by the national authorities even though their country of birth may be different from that of their citizenship or nationality and how citizenship interplays with refugee status varies by country (EGRISS 2019, p. 28). Usage context: background mention, supporting mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
_returns to education are heterogeneous across countries. The second set of Mincerian returns are those estimated by Montenegro and Patrinos (2014)._
_Source: The data has been collected from PWT 9.0 (Feenstra, Inklaar and Timmer 2015)._

natural capital: (i) it represents only the contribution of the physical capital stock from PWT 9.0 in the conventional

<!-- PAGE 12 -->
decomposition, and (ii) it shows the contribution of the physical capital stock (from PWT 9.0) and natural capital (Lange

et al. 2018) in the natural resource decomposition. The average annual rate of growth in output per worker for the

respectively- are then computed using the estimated relative income shares (which vary across groups) and the PWT   9.0 labor share (which varies across countries and over time).

Since  is calculated from the share of labor force in PWT 9.0 and 𝛼�1 �𝛾� is proxied by the ratio of natural resource

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
Polity data - political regime or patterns of authority. Countries with larger positive (negative) polity values have a more democratic (autocratic) system.

This paper describes an approach to forecasting future climate at the local level using historical weather station and satellite data and future projections of climate data from global climate models (GCMs) that is easily understandable by policymakers and planners.

policymakers. It draws on historical climate data from weather stations and satellites;

We begin with monthly temperature and rainfall data for the period 1961-2000

historical climate data from weather stations and satellites

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Proclamation of Ethiopia provided a long list of areas52 exclusively reserved for Ethiopian nationals, including small businesses that would have been of interest to refugees (FDRE, 2020). Hence, it allows many business activities to go beyond the reach of refugees. A draft MoU signed between RRS and MoLS/MoTRI/ MoR proposed exceptional treatment of refugees engaged in business due to their vulnerability. According to the MoU, refugees can own a business in agriculture, manufacturing, services, small and medium enterprises, handicrafts, and trade sectors through establishing private limited companies or cooperative societies. Hence, refugees can obtain a business license as a private business or association by providing proof of refugee ID, support letter from RRS on the type of business, source of capital, utilization of profits, qualification certification (depending on the sector), and tax identification number. A business license renewal requires a valid refugee ID, tax clearance, and audit report for a limited liability company. 52 Restaurants, tearooms, coffee shops, bars, nightclubs, catering services, producing bakery products and pastries, barbershop and beauty salon services, smithery, tailoring, sawmilling, timber manufacturing, brick and block manufacturing, quarrying, laundry services, translation secretarial services, security services, brokerage services, and attorney and legal consultancy. Annexes 87 Following the

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: In contrast, districts of origin are distributed widely across the country. This reﬂects the fact that much work migration is from remote rural areas to towns and cities. The main characteristics of work migrants are reported in Table 1, together with those of non- migrant adult males. We see that work migrants are on average younger and better educated. The census contains detailed information about ethnicity, language, and religion. In the Nepal census, the term ‘ ethnicity ’ is used to capture a hodgepodge of caste and tribal distinctions. The census distinguishes up to 103 ethnic categories. Most of these categories only account for a tiny proportion of the total population. In terms of the total adult population, the most common ethnic categories are Chhetri, Brahmin, and Newar who, together, account for 35 % of 13 Usage context: primary mention, background mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Colombian and Venezuelan migrant children and adolescents. VII Discussion In this study, we analyze novel and unique data on forcibly displaced migrants and hosts, focusing on children and adolescents, to highlight the disparities in human development between them. The structure our analysis in two parts. In the first part, we characterize the main trends in the data. We show that forcibly displaced households have a wealth distribution skewed towards lower values relative to Colombian households. This is likely explained by the assets ownership loss that forcibly displaced households expe- rienced after the migration episode. We also identify meaningful lags in human capital accumulation between Colombian and Venezuelan children and adolescents of approxi- mately 1 year. We further note that the Colombian government ’ s supportive policies for Venezuelan forced migrants are evident through high levels of service access and pro- gram participation for migrants. Nevertheless, it remains surprising that participation is not higher, suggesting significant potential for improvement in increasing sisb ´ en and health insurance enrollments. In a second part of our analysis, we document sizeable lags in physical and cognitive de- velopment of Venezuelan children and adolescents, relative to their Colombian counter- parts. However, we were not able to identify any gaps in the socioemotional and mental health between the two groups. Usage context: supporting mention, background mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Challenges around skill mismatch exacerbate exclusion, as many young Lebanese lack the skills and competencies demanded by private sector employers, particularly ‘ soft skills ’. To address some of these challenges, the Lebanese government (GOL) identified volunteerism as a mechanism to enable diverse youth to work together for improved community assets and service delivery as well as increased employability. In September 2012, the GOL issued a Decree (Number 8924 / 2012) that created a new extra curriculum program that requires secondary school students to complete 60 hours of civil work. In addition, the Ministry of Social Affairs (MOSA), through its Volunteering Department, launched annual action plans for the implementation of youth volunteer summer camps across Lebanon. 4 Father ’ s education and residence (region and location of school) are the two largest contributors to inequality of opportunity in students ’ math test scores, accounting for 44 and 23 percent of total inequality, respectively (World Bank, 2016). 5 According to the 2013 Gallup Poll, 90 percent of respondents in Lebanon agreed with the statement that knowing people in high positions is critical to getting a job. Usage context: background mention, supporting mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: (b) Disaggregation and geo-mapping of data by location (current location and location of habitual residence; urban, peri-urban or rural location), accommodation (organized camp versus non- camp), and demographics (age and sex); (c) Expanded coverage of data collection exercises to include all areas of affected countries (security permitting); (d) Improved coverage and detailed data on displaced populations living outside of organized camps; (e) Improved coverage of ‘ flows ’, i. e. new displacement, durable solutions (returns, integration, resettlement), births, deaths, and in the case of IDPs, the numbers that flee across international borders becoming refugees; (f) Systematic data collection beginning from the earliest moment following displacement, following up as populations disperse, and continuing until sustainable / durable solutions have been achieved; and (g) Better aggregation, analysis and presentation of forced displacement data currently compiled separately by UNHCR, IOM, IDMC and UNRWA. Additional efforts are required to address the gaps in the data required for development policy and planning. These data are critical for informing the design of development policies and assistance programs. Usage context: primary mention, background mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
The government of Chad continues to ensure the security of refugees and asylum-seekers through the
Humanitarian Workers and Refugee Detachment (DPHR). Generally, refugees are not more exposed to
[existing violence and crime. According to protection data collected through Project 21](https://response.reliefweb.int/west-and-central-africa/protection/projet-21) (‘regional protection
monitoring’) approximately 15 per cent of refugees and asylum-seekers claim to have been victims of
physical assault, but it is noteworthy that these cases occurred in the country of origin at the time of fleeing.

While rape and child marriage remain prohibited by Law, other forms of gender-based violence (GBV)
are insufficiently covered by existing Laws and policies to protect the Chadian and refugee populations.
The national policy to respond to GBV, in effect since 2011, applies in refugee camps and refugee hosting
villages but faces challenges due to limited resources (human and logistic) to fully implement this policy. The
referral mechanism for refugee victims for medical, legal and psychosocial support remains in place but its
effectiveness depends on the human and financial resources available in the relevant hosting areas.

protection data collected through Project 21

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: A map highlighting Medell ´ ın ’ s geographic position and the locations of the households interviewed for this study is provided in Figure 2, offering visual context to our research setting. Representativeness and stratification. VenRepS-Kids is designed to be representative of two groups of youth. The first group consists of Colombian children and adolescents, aged 5 to 17, born to Colombian parents. The second group encompasses Venezuelan migrant children and adolescents of the same age range, born to Venezuelan parents, who mi- grated to Colombia between 2016 and 2020. The sample was further stratified by gender and socioeconomic levels, using Colombia ’ s neighborhood income-based classification system that ranges from 1 to 6, where six indicates the wealthiest neighborhoods. Our 13 Usage context: supporting mention, primary mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: High-skill occupations include managers, professionals, and associate professionals. Ln Earnings is the log of monthly earnings winsorized at the 1st and 99th percentiles. All other models are linear probability models. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Annexes 107 Table D.8: Refugee Household Reliance on NGOs/Donations (1) (2) (3) (4) All Eritrea Somali South Sudan Years in Ethiopia -0.007** -0.017** -0.011** -0.001 (0.003) (0.006) (0.005) (0.005) Member works outside the camp -0.080** -0.335*** -0.180*** -0.001 (0.035) (0.099) (0.051) (0.041) Region Fixed Effects Yes No No No Demographic Controls Yes Yes Yes Yes N 1252 423 412 417 Source: World Bank Staff based on SESRE 2023. Note: Each column is a linear probability model where the outcome is a binary indicator of whether the household relies primarily on donations for income. Demographic controls include the household size and the share within each age and education group. Standard errors clustered at the EA level. * p < 0.10, ** p < 0.05, *** p < 0.01 Table D.9: Determinants of refugee-host earnings gap (1) (2) (3) Earnings Earnings Earnings Refugee (% difference from hosts) -24.9** -22.4** -18.5* (0.137) (0.115) (0.114)

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: The MIS was designed to serve as the single source of truth for beneficiary-level data across all three components. It captures individual registration records, service delivery events, transfer payments, and complaint submissions. Each implementing partner has read access to data from their own operation area, while the central PIU has system-wide read and write access. The MIS generates automated exception reports flagging duplicate registrations, inconsistent household composition records, and transfers issued outside the scheduled payment windows.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 10 as the Demographic and Health Surveys (DHS), though researchers have reduced under-reporting by providing special training to interviewers, placing greater emphasis on respondents ’ privacy and safety, and allowing women multiple opportunities to disclose their experiences (Ellsberg et al., 2001a; Garcia Moreno et al., 2003; Ellsberg et al., forthcoming). Estimates of the magnitude of the problem Population-based surveys have found that between 10-70 % of women report being physically assaulted by an intimate male partner at some point in their lives (Heise, Ellsberg and Gottemoeller, 1999). See Annex A for estimates from many recent population based studies (Ellsberg et al., forthcoming). Findings from a multi-country study on domestic violence and women ’ s health carried out by the World Health Organization in fifteen sites and ten countries found that between 13-62 % of women had experienced physical violence by a partner over the course of their lifetime, and between 3-29 % of women reported violence within the past year (Figure 1. 1). Figure 1. 1.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.