ai4data/datause-dataset-rechunked huggingface dataset:
Figure A. 2: Injective relations We also isolate many-to-one (bijective) relations. In this case, we have to aggregate the Afro- barometer ethnicities with their unique and more aggregated correspondence in the UNHCR refugee camps data (See Figure A. 3). Figure A. 3: Bijective relations The remaining correspondences are either (i) one-to-many (bijective) but opposite to Figure A. 3 (i. e., many ethnicities from the UNHCR refugee camps data correspond to one ethnicity from the Afrobarometer) or (ii) many-to-many relations. For both cases, we apply a more pragmatic approach: a. In both cases, we disregard ethnicities that do not appear either in the Afrobarometer or in the UNHCR refugee camps data. This means that for the remaining ethnicity that has no counterpart in either the Afrobarometer or the UNHCR refugee camps data, we simply keep the name of the ethnicity as such, i. e., this information is not dropped. b. Then, after ignoring ethnicities that have no occurrence in our datasets, we check whether the one-to-many or the many-to-many relation has not boiled down to a one-to-one resp. many-to- one relation again. If so, we can treat them as above. c.
At present, the FAO hunger estimates rely on CVs which are not always country specific, do not vary

ranging from 19% when the personal diary is used to 72% when the estimates come from an HCES that

household surveys (HCES-direct method), as opposed to calculating them from a combination of food

balance sheets and household surveys (FBS-CV method) as currently done by the FAO. The FBS-CV
Compared to the baseline year of 2001, residents were more fearful of crime by 0. 17 sd. These fears are not necessarily unfounded, as we ﬁnd some weak evidence of changes in levels of violence in their parishes. Compared to 2001, a 1-SD increase in refugee presence in 2011 is associated with a 1. 26 percentage point increase in likelihood of a violent event, and in 2016, a 1. 11 percentage point increase. These ﬁndings are in line with our predictions for Uganda. As in Global North contexts, we recognize that out-group members can elicit fears and insecurity. It is unclear from the ACLED dataset, however, whether these violent events involve refugees. It is also possible that ACLED underreports violent incidences that are not reported by the media. Recent scholarship on the relationship between refugees and violent conﬂict ﬁnds that hosting generally has null eﬀects on conﬂict (Zhou and Shaver, 2021); when conﬂicts do occur, refugees tend to be the victims (Savun and Gineste, 2019). Nevertheless, by 2020, like with Afrobarometer respondents reporting feeling more safe, these fears of crime reverse with a negative eﬀect of- 0. 13 sd.
We focus on two policy actions: legalization (in the sense of providing legal work and residence permits to all migrants) and measures aimed at improving the quality of employment for highly educated Venezuelan migrants. 6. 1 The Eﬀects of Legalization Based on the EPEC survey, approximately 90 % of Venezuelan migrants in Ecuador lack legal status, in the sense that they are not authorized to work. This has important consequences for Venezuelan workers. As shown in Section 4. 3, lack of legal status is associated with a higher likelihood of informal employment (column 5 in Table 7). Inspired by Clemens et al. (2018), the goal of this section is to use the estimates of these eﬀects to simulate the consequences of providing legal work permits to Venezuelans on the quality of their employment (measured by informality) and, through this channel, on their productivity (measured by wages). The relevant information is collected in Table 10. We classify Venezuelan workers that lack legal status on the basis of their education level (primary, secondary or tertiary) and the quality of their employment (formal or informal). Based on the data in column 3 15
11 There may be grounds for skepticism about these estimates for madrassa enrollment. Since the data were collected prior to 2001, geopolitical changes after September 11 could have led to greater madrassa enrollment. In addition, the household-based survey faces the usual problems of accurately estimating a low-probability event — although enrollment is less than 1 percent in these surveys, the sampling error is large (see Bauman, 2001, for a description of similar problems in estimating home-schooling in the United States). Finally, while the census of populations does not face the problem of small samples, it is not that recent (1998) and some may have reservations regarding the quality of government data. 10 The LEAPS census of schooling choice conducted in 2003 provides a rough check on these numbers (see appendix for details). This census was conducted in three districts of Punjab and villages were chosen randomly based on the criterion that each village must have at least one private school. Typically, this means that the villages lies somewhere between fully urban and fully rural populations and are not representative of the districts that they are in. Estimates from the LEAPS census show that as a percentage of enrolled children, the numbers in two of the three districts are slightly higher than those of the population census.
33 In school: an indicator for whether the respondent currently attends regular education (schooling). This does not preclude also being employed. Retired: an indicator for a respondent who declares that they are not engaged in job search because they are retired. Full / part-time employment: an indicator for whether a person works full or part-time for all people in private sector employment (see above definition of employment). Full-time employment is defined as usual working hours of 30 or more hours per week, part-time employment as usual working hours of less than 30 hours per week. We do not use the indicator provided in the LFS data since there seems to be some confusion in which category 30 hours per week falls (with these evenly divided between full and part-time). Education: we classify people into three education categories. Low education is defined as those with no completed formal education. Medium education is defined as those with at least completed primary education but no high school completion. Higher education is defined as people who have at least completed high school.
Figure 3. Wealth Index Distribution (a) Wealth Index- Total 0. 05. 1. 15. 2. 25 k-Density- 5 0 5 10 15 Wealth Index Colombian Venezuelan Venezuelan: Before migrating (b) Adequate Housing Materials (% of Total) 0 1 2 3 4 k-Density 0. 2. 4. 6. 8 1 Average- Dwelling Material Colombian Venezuelan Venezuelan: Before migrating (c) Asset Ownership (# Total) 0. 05. 1. 15 k-Density 0 10 20 30 40 Total Assets Colombian Venezuelan Venezuelan: Before migrating (d) Access to Services (% of Total) 0 1 2 3 k-Density 0. 2. 4. 6. 8 1 Average- Access to Services Colombian Venezuelan Venezuelan: Before migrating Notes: Panel (a) presents the distribution of the wealth index for Colombian and Venezuelan households in our sample in 2022 and pre-migration. Wealth Index is an index measure of the household ’ s cumula- tive living standard constructed following The Demographic and Health Surveys (DHS) methodology.
**Internally Displaced Persons (IDPs)** : Over 652,600 Afghans (approximately 96,000 families) were
newly displaced due to conflict in 2016, adding to a protracted IDP population of over 1 million. Most
IDPs found refuge with host families in neighboring communities, already facing extreme poverty.
Food, adequate shelter, WASH, and health care remain high priority needs, while efforts to raise
awareness of mines and ordnance risks are also ongoing. A majority of IDPs live an insecure existence
in makeshift shelters and informal squatter settlements with irregular access to services, poor

8 8.8% of women aged 20-24 were married or in a union before the age of 15 and 45% before the age of 18 (AfDHS15)

Page **7** of **12**
In certain contexts, there can be significant overlaps in these two groups; however data systems may be maintained separately for conflict-induced displacement and natural disasters (e. g. in Afghanistan) leading to possible gaps or double counting if these categories are combined. 30 The IOM Displacement Tracking Matrix (DTM) is a system to track and monitor displacement and population mobility. It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route. It has been active in over 40 countries since its inception in 2004. See http: / / www. globaldtm. info /. 31 This is typically defined as nomads not having access to their traditional routes, but routes can vary. 32 IDMC has recently adjusted their methodology to facilitate greater comparability across situations and improvements are reflected in IDMC ’ s end-2015 data. 33 This is not necessarily a problem if the purpose of the registration system is to delineate entitlements to assistance rather than to determine status.
Figure 3: Ethnic Fractionalization and Ethnic Polarization Ethnicity. A major task for the construction of our dataset is the combining of data on ethnicity from various sources. Indeed, linking ethnic groups is challenging as ethnic identities are socially constructed and there are different definitions, categorizations, and even conceptual approaches when it comes to identifying ethnicities in various databases or scientific disciplines. This makes the task of treating, combining, and analyzing ethnicities extremely daunting as it requires substantial back- ground knowledge on hundreds of ethnicities and a manual treatment would inevitably lead to incon- sistencies, errors of manipulation, and / or subjective choices. Fortunately, we can rely on the Linking Ethnic Data from Africa (LEDA) open-source software package constructed by M ¨ uller-Crepon et al. (2020), which contains a full pipeline to link ethnic datasets from Africa in a consistent and replicable way. We obtain ethnicities of refugees from the EPR-ER dataset, while the ethnicities of individuals in the hosting areas stem from the Afrobarometer.
discussed earlier, the expenditure of in-camp refugees is almost half that of hosts despite sizeable food aid and significant investments made by the WFP and UNHCR in cash transfers (in selected camps). The significantly lower expenditures (food and non-food) among refugees compared to the host population led to higher poverty rates. The team cross-checked the food aid received by in-camp refugees based on administrative data from the UNHCR and WFP with food consumption data from SESRE. The analysis looks at both separately for information provided by UNHCR and WFP. While WFP is not responsible for distributing non-food items such as mattresses, cooking, feeding utensils, etc., UNHCR provides non-food items; maybe mattresses were distributed in Alemwach since it is a relatively new camp. WFP’s food and cash assistance targets all individuals in refugee households. The information received from UNHCR on food aid provided to refugees in each camp includes quantities per food item per month and cash transfers per person per month for each camp and period. The food items include cereal, wheat, maize, rice, sorghum, CSB/famex (CSB+), pulse, biscuit, date biscuit, dates, oil, vegetable oil, salt, and cash (Table E.4). We have computed the per person per month in-kind aid
The data shows that cohorts that were of school age during the Indonesian occupation achieved higher level of education than older cohorts (i. e. those born in the 1970s compared to those born in the 1960s or before) testifying for an increasing trend as expected. The figure shows that despite the increasing trend a large fraction of individuals have low education levels. Interestingly, all curves start to drop after the 1987 cohort. This decreasing trend is observed among individuals aged 20 or younger in 2007 and provides evidence of a mismatch between the grade attended and the grade that they should have achieved at their age. 4 This is caused by a persistent sluggishness in grade achievement due to the high level of delayed entry to school and high rates of repetition. 5 The impact of the conflict in its different phases and the subsequent reconstruction efforts on schooling levels of children in Timor Leste is therefore unclear. The early years of violence coincided with an education for all policy in which quantity was preferred to quality. In addition, the 1999 violence that followed the withdrawal of Indonesian troops led to the destruction of schools and the removal of children from school. The reconstruction program implemented after 1999 tried to counteract this destruction, and achieved fast progress. However, the education sector was still in very poor shape. In the next section, we investigate in more detail the effects of the conflict on educational outcomes of boys and girls in Timor Leste. 4. Identification strategy and data description 4 Those born in 1992 are 15 in 2007. So they might have at most completed grade 9 and this justifies part of the drop in the curves as the grade completed is right censored. 5 The high levels of school delay are also confirmed by the figures on gross and net enrolment ratios calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007.
calculated using the TLSS 2001 and 2007: primary gross enrolment ratio was 105 percent in 2001 and 128 percent in 2007, while net enrolment ratios were 74 and 94 percent, respectively, in 2001 and 2007.
5.4 Dependency ratio 1.1 1.5 0.5 0.4 0.7 1.3 Female-headed 44% 73% 45% 58% 44% 69% Head’s age 42.3 39.6 42.0 30.9 42.1 37.6 Source: World Bank Staff based on SESRE 2023. Sociodemographic Profile 12 2.2 Education Integrating refugee children into educational programs soon after arrival14 avoids the loss of valuable years of education and human capital accumulation that can hinder future prospects. Integrating refugees into national education systems can improve future outcomes for refugee children and their hosts (UNHCR, 2020; Piper et al., 2020; Abu-Ghaida and Silva, 2020; Crawford et al., 2015; Bilgili et al., 2019). Investment in human capital development can enable refugees to contribute to local economies to benefit refugees and hosts alike, and it can contribute to the recovery of countries of origin and the hosting communities. Despite the positive externalities of integrating refugees into public-school systems, a large influx of refugee children can exacerbate existing inefficiencies. Where there are large inflows, the national education system might require additional human and financial resources to integrate newly arrived children. Increasing the supply and improving the quality of schools in affected areas, supported by external assistance and financing, can avoid tension between refugees and host community populations over
Lack of livelihood opportunities in areas of displacement combined with wide spread insecurity, lack
of freedom of movement, and limited humanitarian access to AGEs controlled areas is generating an
alarming humanitarian and protection crisis that requires immediate attention and a new strategic
approach by the APC.

## **PATTERNS OF ABUSE**

Analysis of protection risks needs to be improved to better prioritize and mitigate risks in 2017-2018.
The APC will dedicate further time and resources to producing a comprehensive protection risk
analysis. Information available suggest the following patterns of abuse:

1 REACH PIDP study, p. 17

2 IDMC. Afghanistan: New and long-term IDP risk becoming neglected as conflict intensifies.

3 The findings of the Protection community assessment in the East indicate that additional influx of people into communities, that already
have limited service providers’ capacities, overstretches the resources, like water, health and education, leading to the overcrowded
schools, hospitals facing challenges with the number of people and water sources being not sufficient to meet the needs of the local and
displaced population.
Evidence for Sub-Saharan Africa shows that female-headed households that have previously been highly dependent on male income – notably widow-headed households – are particularly prone to poverty, although other types of female-headed households may not be more likely to be poor than male-headed households (Brown & Van der Walle, 2020). For this reason, while it is important to analyze differences in poverty risk between households with female and male heads, it is also important to understand how different routes to female headship, and the changes in the household composition more broadly, impact poverty risks and expenditure levels. This paper explores level and distributional changes in expenditure among Syrian refugee households in Jordan between 2013 and 2018 using a gender lens. By the end of 2018, UNHCR had registered more than 671, 000 Syrians1 who had fled their homes and settled in Jordan. At the end of the time period covered by this research, UNHCR ’ s 2019 Vulnerability Assessment Framework (VAF) 1 UNHCR ’ s data portal records 671, 551 registered Syrian refugees residing in Jordan on January 13, 2019. See UNHCR (2022b) available at https: / / data. unhcr. org / en / situations / syria / location / 36.
These surveys are part of a long-term data collection effort by the research team (Vinck & Pham 2014) and were collected separately from the focus group discussions. The focus group discussions thus did not directly inﬂuence the design of the surveys, but rather directed the analysis strategy of the surveys that our team has collected at regular intervals in eastern DRC. The survey data are analyzed in two ways. First, 11 surveys collected between 2017 and 2021 8Eastern DRC is a site of ongoing violence, raising a number of ethical, methodological, and practical concerns about collecting data. We discuss the ethical protections we implemented when collecting this survey data in the Appendix, Section B. 9Territoires are sub-provincial administrative units. Additional details on the structure of administrative units are available in the Appendix, Section A. 1. 21
Do people run away from their home country when a conﬂict breaks out? Does the seriousness of the conﬂict matter in this decision? In which phase of the conﬂict do they leave? When do refugees come back to their home country? In order to answer these questions, we look at the impact of conﬂict incidence on 21We will return to these issues in the policy section. 22According to the UNHCR deﬁnition, this category includes “ individuals recognized under the 1951 Convention relating to the Status of Refugees; its 1967 Protocol; the 1969 OAU Convention Governing the Speciﬁc Aspects of Refugee Problems in Africa; those recognized in accordance with the UNHCR Statute; individuals granted complementary forms of protection; or those enjoying temporary protection; and people in a refugee-like situation ”. 23The UNHCR Global Trends Report 2014 provides evidence that conﬁrms this hypothesis. About 59. 5 million people were forcibly displaced worldwide by the end of year 2014. Among them, 19. 5 million were refugees and 38. 2 million were IDPs. 28
Sources of information This work includes a descriptive analysis that allows us to identify how the prevalence of non- standard employment (NSE) has evolved in the last two decades in different regions of the planet, as well as the evolution of the profile of workers in those roles, in terms of their educational level, salary per hour, and type of tasks performed. This analysis was based mainly on periodic surveys of households that included information regarding the employment and educational situation of individuals. Although the denomination of this type of surveys varies from country to country, in all the cases analyzed there is usually a survey of annual or higher frequency that includes information required to identify the labor status of the individuals as well as to analyze the salary profile and education of the employed. However, it should be noted that the identification of the type of work relationship (standard or non- standard) is frequently limited in these data sources. Indeed, it is only possible to identify part-time employment and temporary employment (not in all cases) within the non-standard forms of employment mentioned in the previous section.
(a) High frequency sample surveys using smartphone and cellular technologies. For example, a high-frequency survey initiative in Somalia employs a dynamic questionnaire loaded onto smartphones, which enables data to be collected from household interviews in 60 minutes. This approach was developed to overcome the challenges of insecurity, limited data gathering capacity and budgetary constraints. (b) The use of mobile phones to conduct surveys or follow up interviews following face-to-face household surveys. For example, a Bank paper on the impact of the 2012 crisis in Mali on IDPs, refugees and returnees used information from a face-to-face household survey as well as follow-up interviews with its respondents via mobile phones. This combination provided a mechanism to monitor the impact of conflict on hard-to-reach populations who at times live in areas inaccessible to enumerators. And in Sierra Leone and Liberia, the Bank supported the use of mobile phones to collect key socio-economic data on the effects of the Ebola virus. (c) Crowdsourcing data on displacement. Platforms such as the Kenyan Ushahidi has crowd- sourced data on displacement in Kenya and eastern DRC by encouraging IDPs and host communities to report incidents using their mobile phones or the internet, including information about living conditions. The platform references these reports geo-spatially. (d) Geo-mapping of data on displaced populations and affected host communities.
Second, the Turkish LFS asks respondents whether they had previously lived in a different province (one of Turkey ’ s 81 NUTS 3 regions), and if so in what year they moved to their current province. We estimate the impact of refugees on the probability a native moved to a subregion in the past year. Table 9 reports OLS and IV estimates of the impact of refugee on net population growth in subregion (Columns 1 and 2) and gross population inflows (Columns 3 and 4). Net population growth is estimated at the level of NUTS 2 subregions. Population inflows to a subregion are estimated at the individual level (and standard errors clustered by subregion- year). All regressions include subregion and year fixed effects and a year-specific control for log distance from the Syrian border. The first column presents the estimates for the whole sample, subsequent columns for different sub-samples by gender, age and education. For the full sample the net population growth in a subregion is positively correlated with refugee flows, while the IV point estimate is negative (though neither estimate is statistically significant). The probability of a Turkish person migrating to a subregion is negatively correlated with refugee flows (the OLS estimate is highly statistically significant). The IV estimate is of a similar magnitude, but no longer statistically significant. This same pattern broadly holds for both women and men. The only other statistically significant IV estimates are a decrease in the population aged 15 – 24, an age group that is likely more mobile, and of those with medium educational attainment. There is also a decrease in the inflow of low