ai4data/datause-dataset-rechunked huggingface dataset:
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.
We also use a project level database constructed from provincial and central

However, the household survey was designed in view of combining it with the nationally representative 1998 Vietnam Living Standards Survey (VLSS) to predict baseline consumption expenditures for SIRRV households (van de Walle, 2006).

We then compare this to the independent administrative data on the aggregate allocation.
_Source: UNHCR Internal movement of refugees in Sudan as of 16 July 2023_

_4 See Sudan Protection Brief, June 2023 and UNHCR Sudan- Overview of refugees and asylum seekers distribution and_

_movement in Sudan Dashboard as of 16 July 2023_

UNHCR 4
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
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
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.
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.
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
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.
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.
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.
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.
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
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.
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
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.
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
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
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
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.
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.
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.
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.