ai4data/datause-dataset-rechunked huggingface dataset:
The second source of data we draw on is a short survey on COVID-19 vaccination collected inperson as part of the Ethiopia Socioeconomic Survey (ESS 5), a nationally representative household survey that was implemented between April and June 2022 by the Ethiopia Statistical Service with support from the World Bank's LSMS program. This survey contained a similar module as the phone surveys and collected information on the vaccination status of all household members.

The source of administrative data for our study is the Our World in Data (OWID) COVID-19 vaccination dataset (Mathieu et al. 2021) that compiles administrative data on COVID-19 vaccine coverage. Amongst others, the dataset contains information on the number of total doses administered, the share of the country population that has received at least one dose, and the share of the population that is fully vaccinated. [3] It covers the period from December 2020 when the first COVID-19 vaccines achieved approval and is regularly updated as new data becomes available on a per-country basis. The data is compiled from country reports (such as government websites, dashboards, or the social media accounts of national authorities) and in some cases third-party aggregators (where national authorities do not publish data in a machine-readable format) and is regularly audited for inconsistencies and technical errors.

We additionally access a second source of administrative data stemming from the WHO's COVID19 vaccination dashboard (WHO 2020b). The dashboard does not provide longitudinal information for public access but reports the latest available COVID-19 vaccine coverage figures at the time of data access (April 2, 2023, in our case).

Lastly, we use data from the World Bank's Statistical Performance Indicators (SPI) available through the World Bank's Open Data library (World Bank n.d.). The SPI is a composite index between 0 - 100 scoring countries' statistical systems across the five pillars of data use, data services, data products, data sources, and data infrastructure (Dang et al. 2023). To capture the performance of administrative
. The SPI is a composite index between 0 - 100 scoring countries' statistical systems across the five pillars of data use, data services, data products, data sources, and data infrastructure (Dang et al. 2023). To capture the performance of administrative data systems in particular, we also use the SPI's indicator of administrative data capacity (Dimension 4.2) that records the availability of Civil Registration and Vital Statistics (CRVS).
the General Social Survey - showed no trend. This paradox acquired quickly popularity across the

longitudinal data.

of studies, those that rely on one cross-section survey focusing therefore on individuals or

Provided that cross-section and longitudinal data are available, one can of course try to reconcile
Page 9 of 51 Figure 2: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys When these results are analyzed by the type of NSE, we find significant differences between the trends in Part-time and Temporary employment. On the one hand, there is a stable or growing prevalence of part-time employment among the salaried employees, where Uruguay is the only exception, characterized by a decrease in the incidence of this type of employment (Figure 3). In the cases of Peru and Bolivia, we find almost the same prevalence of part-time employment as two decades ago while in the remaining countries (Argentina, Brazil, Chile, Mexico, El Salvador and the Dominican Republic) there is a greater prevalence of part-time employment. Likewise, the prevalence of temporary employment shows a downward trend in most of the analyzed countries in the last 20 years (Figure 4). In fact, three of the five countries in which temporary employment could be identified show a significant drop in the prevalence of this type of employment (Argentina, Brazil, and Chile). El Salvador presents a stable incidence of temporary employment, while Mexico is the only country in our sample for which there is an increase in this type of NSE.
Impacts of Extremism in Refugee ’ s Integration: Evidence from Afghan Refugees in Tajikistan * Laurent Bossavie † Sandra V. Rozo ‡ Mar ´ ıa Jos ´ e Urbina § Keywords: Refugees, Female Education, Extremism JEL Classification: F22, D74, J15, I21, I10 * The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are our own. The team is grateful to the project “ Enhancing Government capacity in hosting temporarily Afghan refugees ”, funded under the Rapid Social Response Program (RSR) multi-donor Trust Fund of the World Bank, for funding data collection and allowing us to use the Afghan refugee survey as one of the multiple sources of data for the paper. The purpose of the project and the data it collected was to identify opportunities for refugee integration in Tajikistan. We acknowledge financial support from the Research Support Budget at the World Bank. We also thank Daniel Garrote for his initial support on instrument design and Merve Demirel for supporting data collection. † World Bank, e-mail: lbossavie @ worldbank. org ‡ World Bank, Dev.
Anderson, J. (2011): “ The Gravity Model, ” Annual Review in Economics, 3 (1), 133 – 160. Angrist and Pischke (2009). Mostly Harmless Econometrics, Princeton University Press. AFAD (Disaster and Emergency Management Presidency of Turkey) (2013). Syrian Refugees in Turkey, 2013: Field Survey Results. Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015a). “ The Impact of Refugee Crisis on Host Labor Markets: The Case of the Syrian Refugee Crisis in Turkey. ” IZA Discussion Paper 8841. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015b). “ The Impact of Refugee Crises on Firm Dynamics and Internal Migration: Evidence from the Syrian Refugee Crisis in Turkey, ” mimeo. Aydemir, Abdurrahman and Murat Kırdar (2013). “ Quasi-Experimental Impact Estimates of Immigrant Labor Supply Shocks: The Role of Treatment and Comparison Group Matching and Relative Skill Composition, ” IZA Discussion Paper 7161. Baez, J. (2011). “ Civil Wars Beyond their Borders: The Human Capital and Health Consequences of Hosting Refugees. ” Journal of Development Economics 96 (2) November: 391 – 408.
**Table 8: Flood Risk Classification of Proposed Subproject Sites**

| Site ID | District | Elevation (m) | 50-yr Flood Zone | Design Adjustment |
|---|---|---|---|---|
| S-04 | Rambora North | 312 | Outside | None |
| S-11 | Kalendo East | 287 | Inside | Elevated plinth |
| S-17 | Mwenda Central | 301 | Boundary | Drainage upgrade |
| S-22 | Ngozi West | 318 | Outside | None |

Flood hazard maps used for this classification were prepared by the National Flood Risk Management Authority and validated against historical flood records from the Ministry of Home Affairs' disaster registry.
more recent data. This suggests that ambiguity about index insurance can fall over time, with

loan restructuring after early flooding. The contract would be underwritten against recorded

water levels at a main river gauge station, using this data as a proxy for flood damage. This

10 This form of cat bond trigger is more analogous to a traditional insurance policy with its loss settlement process.
Other triggers are on _modeled losses_ or _industry losses_ . For _modeled losses_, instead of dealing with Proactive's
actual losses, an exposure portfolio is constructed for use with catastrophe modeling software. When there is a
disaster, the event parameters are run against the exposure database in the cat model. If the modeled losses are above
a specified threshold, the bond is triggered. For _industry losses_, the cat bond is triggered when an entire industry loss
from a certain peril for the insurance industry doing business in this country reaches a specified threshold.

information on renewable policy design was obtained mostly from IEA's renewable policy   database, and cross-checked with government websites, legislative texts and other related

collect generator-level wind installation data from a global wind farm dataset [20] to match
The initiative includes four components, namely: a) training professionals to work with young men in the area of health and gender-equity using a set of manuals and videos; b) social marketing of condoms; c) promoting health services; and d) evaluating changes in gender norms. In 2002, PROMUNDO and Horizons began a 2-year evaluation to measure the effectiveness of two different approaches, compared to a control site. Researchers have developed a"Gender-Equitable Men"(Leichert) scale with 24 items for measuring attitudes. Methods include pre and post-tests as well as a six-month follow-up community-based survey. In addition, they are gathering qualitative information among men and their female partners. Preliminary results suggest that the program has been successful at increasing gender equitable norms and reducing behavior that puts men at increased risk of HIV / AIDS. ReproSalud (Peru): Manuela Ramos launched ReproSalud in 1995 as a USAID-funded rural reproductive health program. ReproSalud used participatory rural appraisal (PLA) to help women's groups identify women's reproductive health needs and to organize community meetings to design strategies to address those needs. Domestic violence and forced sex within marriage emerged as important problems in those communities. In response, ReproSalud organized workshops for women and men on gender issues, carried out community awareness campaigns and established a microcredit program for women.
3 quest for imaginative and convincing instruments for migration (see the review of the migration and poverty literature by McKenzie and Sasin, 2007, and the references therein). An additional hurdle is the need for panel data to study migration and economic mobility. The costs and difficulties in re-surveying migrants mean that attrition may be relatively high for this group and may also result in the loss of some of the most relevant households to study this process (Beegle, 2000; Rosenzweig, 2003). This paper uses unique data from a region in Tanzania to address this key question: What is the impact of physical movement out of the original community on poverty and wealth? Although we do not have experimental data, the nature of our data allows us to limit the potential sources of unobserved heterogeneity considerably. Building on a detailed panel survey conducted in the early 1990s, we re- interviewed individuals in 2004, making a notable effort to track individuals who had moved. The tracking of individuals to new locations proves crucially important for assessing welfare changes among the baseline sample. The average consumption change of individuals who migrated was more than four times higher than that of individuals who did not moved.
Expected location in three years ��������������������������������������������������� iv Figure ES.3: Poverty incidence........................................................................... v Figure ES.4: Food insecurity scale...................................................................... v Figure ES.5: Refugee employment and proximity to resource hubs .................. vi Figure ES.6: Host response to “Refugees are good people” ............................ vii Figure ES.7: Host response to “Would you feel comfortable having a refugee as a neighbor?” ��������������������������������������������������������������� vii Figure 1.1: Refugees
������������������������������������������� vii Figure 1.1: Refugees and asylum seekers in Ethiopia by country of origin, 1984-2023.................................................................................... 1 Figure 2.1: Refugees by survey domain ���������������������������������������������������������� 10 Figure 2.2: Country of birth............................................................................. 10 Figure 2.3: Refugees arrival in Ethiopia (15 years and above) ..................... 10 Figure 2.4: Age structure........................................
.. 10 Figure 2.4: Age structure................................................................................. 11 Figure 2.5: Gender composition....................................................................... 11 Figure 2.6: Marital status (18 years and above) ��������������������������������������������� 11 Figure 2.7: Education level (18 years and above) ������������������������������������������ 13 Figure 2.8: Youth (15 to 24) education level �������������������������������������������������� 13 Figure 2.9: Refugees’ education outside of Ethiopia (18 years and above) .... 15 Figure 2.10: Children currently attending school ���
��������������� 13 Figure 2.9: Refugees’ education outside of Ethiopia (18 years and above) .... 15 Figure 2.10: Children currently attending school ����������������������������������������������� 15 Figure 2.11: Gross Enrollment Rate (GER) �������������������������������������������������������� 16 Figure 2.12: Net Enrollment Rate (NER) ������������������������������������������������������������ 16 Figure 2.13: Share of children and youth above school age in education ....... 16 Figure 2.14: Reasons for not currently attending school ���������������������������������� 17 Figure 2.15:
Figure 7.17: Share or refugees involved in a community representative body 70 Figure 7.18: Share or refugees engaged in a community representative body by demographic group ���������������������������������������������������� 70 Figure 7.19: Discrimination and harassment ������������������������������������������������ 71 Figure 7.20: Discrimination and harassment by demographic group ....... 71 Figure D.1: Age group by gender................................................................ 100 Figure D.2: Refugees’ education document ����������������������������������������������� 100 Figure D.3: Share of school-age children in education per household ..... 100 Figure D.4: School-age children currently attending school by gender .... 101 Figure D.5: Reasons for not currently attending school by gender ........... 101 Figure D.6: Average annual household education expenditure (in ETB) ... 101 Figure D.7: Type of health institutions....
for not currently attending school by gender ........... 101 Figure D.6: Average annual household education expenditure (in ETB) ... 101 Figure D.7: Type of health institutions......................................................... 102 Figure D.8: Problems faced in health institutions ��������������������������������������� 102 Figure D.9: Stunting by gender of children ������������������������������������������������� 102 Figure D.10: Childbirth in health institutions (children under five years) 103 Figure D.11: No birth evidence available (children under five years) ......... 103 Figure D.12: Average annual per capita health expenditure ....................... 103 Figure D.13: Types of disability..................................................................... 104 Figure D.14: Rent expenditure (Refugees and hosts in Addis Ababa) .
................................. 104 Figure D.14: Rent expenditure (Refugees and hosts in Addis Ababa) ........ 104 Figure D.15: Hand washing facility................................................................. 104 Figure D.16: Top 3 difficulties with being a refugee by survey domains .....
Beneficiary assessments were conducted in all 24 target communities between February and May using a structured questionnaire administered by trained community development officers. The assessments captured household composition, livelihood strategies, access to basic services, and self-reported protection concerns. Results from the beneficiary assessments informed the design of component-level activities and the weighting of eligibility criteria used in the targeting formula applied during beneficiary registration.
Several of the refugees interviewed for this research indicated that no one wanted to give them
work because of their status as a refugee in Cameroon. When discussing the treatment they
received from the host population and the ability to find a job, all refugees interviewed explained
that it was difficult for them “because I am a stranger”, “from the outside” or because “I am
different”.

Although there are many similarities between the refugee population and the host community,
there is also a clear distinction that exists; all refugee interviewed indicated that they experienced
some sort of harassment and/or discrimination because they were refugees, and most connected
this directly to their ability to find wage-earning work in Yaoundé. However, several refugees
stated that the discrimination they face is not experienced with all Cameroonians, in some cases
they indicated that the locals supported them and treated them well; simply put by one refugee
from CAR, “some Cameroonians are nice, some are not.”

As discussed previously, identity documentation is a big problem for refugees living in
Cameroon, as many authorities and institutions do not recognise UNHCR identity cards and
3 The main contributions of this article lie in identifying and overcoming these challenges in order to construct a consistent and complete set of origin – destination matrices of international migrant stocks for 1960 – 2000, disaggregated by gender. The starting point is a master set of 226 origin or destination countries and regions. Despite border changes, all migrants are assigned to this master set so that migrations can be meaningfully tracked over time. These assignments, especially in cases where only aggregate data are available, are made using several alternative propensity measures based either on a destination country ‘ s propensity to accept international migrants or on an origin country ‘ s propensity to send migrants abroad. Cases of omitted data occur when destination countries do not collect or publicly disseminate the information on migrants. When data from census rounds are missing altogether, the approach taken depends on the extent of the omission (see appendices 3 and 4). When sufficient data are available for other decades, interpolation is used. When not enough data are available, propensity measures are used to generate bilateral data. When a gender breakdown is missing, gender splits are calculated based on supplementary statistics or other data in the matrices (see appendix 5).
The novelty is that a new, more spatially and temporally detailed data set for nightlights, the Visible Infrared Imaging Radiometer Suite (VIIRS) product from the National Oceanic and Atmospheric Administration (NOAA), following the work of Elvidge et al. (2017), is employed. In addition, settlement layers have been used to remove nightlight cells with zero population to reduce the impact that "empty" cells could have on the analysis.

This paper utilizes a newer nightlight data set, the VIIRS Day/Night Band (DNB) provided by
The Earth Observations Group (EOG) at NOAA/NCEI. The data are produced following the
methodology of Elvidge, et al.

The VIIRS data have a number of advantages over the widely used, but now discontinued,
Defense Meteorological Satellite Program (DMSP). Firstly, they have a higher resolution,
450m by 450m compared to 1km by 1km for the DMSP.

The WorldPop data sets have been used to identify settlement areas in Myanmar and Vietnam
(WorldPop 2013) (Worldpop 2016). These data sets have a spatial grid cell resolution of
approximately 100 meters at the equator and estimate the number of persons per square.

\n<!-- PAGE 13 -->\nFor Indonesia, the Philippines and Thailand high resolution settlement layers from Facebook Connectivity Lab and Center for International Earth Science Information Network - CIESIN - Columbia University was used.
First, most of the studies only consider a subset of occupation or livelihood activities (mainly employee-based) and do not provide a full picture of the livelihood impacts of refugee presence on host communities. We consider an exhaustive set of livelihood activities in which households (individuals) in the impacted communities may engage. 7 Second, prior studies tend to focus on the different livelihood activities separately (i. e., whether the individual adult members or the household engage in each of the livelihood activities). 8 Therefore, they are unable to infer whether households are diversifying or specializing their livelihoods or are engaging more on the commercialization of activities. 9 The current paper goes beyond the allocation of labor to individual (specific) 7 As the data we used does not have a good welfare indicator (e. g., income, consumption, and assets), we could not explore the welfare impact of refugee inflow. 8 We examined households ’ engagement in individual livelihood activities as a mechanism for household livelihood strategies. 9 Generally, households tend to diversify their livelihood when facing negative shocks (e. g., conflicts, droughts) to minimize risk (Ellis 2000a, b). In the case of refugee inflow, households may either diversify or specialize as refugee inflow could be both a negative shock (through increase competition for resources, services, and employment) and a positive shock (through creating opportunities, such as high demand agricultural products, provision of cheap labor).
cope with the emergencies that arise all too often. 20 To facilitate successful coping and provide a safe place to save, the EPAG program assisted each participant to set up a savings account at a local bank if she did not already have one. Table 5 presents midline survey results that indicate that the treatment group were nearly 50 percentage points more likely to have savings than the control group, and were saving on average LD 2500 (nearly US $ 35) more than the control group. EPAG graduates were also twice as likely as the control group to have outstanding loans (six percent v. three percent), and have loans from formal lenders (five percent v. two percent), 21 although the overall rate of obtaining credit remains extremely low. 4. 3. Empowerment The Adolescent Girls ’ Initiative is based on the hypothesis that livelihood and life skills training for young women will improve their lives in more than just narrowly-defined economic dimensions. In addition, evidence is increasing that these soft skills are also essential for success in employment (Borghans et al. 2008).
2 Over the past three or four decades, the Arab world has experienced a massive expansion in educational attainment. According to the Barro and Lee educational attainment dataset, seven out of the top 20 countries in terms of increase in number of years of schooling from 1980 to 2010 were Arab countries (Barro and Lee 2013). 1 Jordan, the subject of this paper, had the seventh highest increase in educational attainment in the world, with an increase of about five years in the average years of schooling over the period. This increase is widely believed to be attributable to a massive public investment in the supply of schooling in the postindependence period in the context of a state-led development model, which virtually guaranteed employment in the public sector for graduates (Assaad 2014; Saleh 2016). The rapid increase in educational attainment has continued unabated despite the fall in returns to education that accompanied the demise in the state-led model and its employment guarantee schemes (Pritchett 2001). A slew of recent literature on the drivers of the Arab Spring protests, some of which occurred in Jordan, has identified the low economic returns to this massive increase in education as the single most important cause of the uprisings (Goldstone 2011; Campante and Chor 2012a, 2012b, 2014; Sanborn and Thyne 2014).