ai4data/datause-dataset-rechunked huggingface dataset:
The magnitudes are similar as we saw with the overall conﬂict index. A one standard deviation decline in quarterly casualties, which is about 90, is associated with a 2 % higher return rate. The use of chemical weapons and incidents of skirmishes and ﬁghting all have a signiﬁcant negative eﬀect on returns to Syria. In results not shown, we ﬁnd less robust relationships between the number of air strikes and incidents of artillery and carpet bombing and refugees ’ decision to return. 4. 1. 1 Vignette Analysis We complement the study of returns that have already happened with data from hy- pothetical vignettes. There are advantages and disadvantages of both of these data sources. The returns observed to date are still a very small percentage of the overall refugee population, and therefore the factors aﬀecting their return decision may not be representative of the larger Syrian refugee population. The hypothetical vignettes, while clearly weaker in that they represent hypothetical scenarios, seek to provide in- sights into the external validity of the earlier results.
We exploit the LFS, which does not collect consumption data and has a different design from the HEIS, to fill the missing poverty data problem in Jordan for the years the HEIS is absent. We validate our imputation-based estimates of poverty against those obtained from the actual consumption data (or design-based estimates) for the two years 2008 and 2010 when consumption data are available, before imputing estimates for other years when consumption 4 See, for example, King et al. (2001) and Honaker and King (2010) for examples of adaptation of multiple imputation methods in the field of political science. 6
and hourly wages. For this objective, we merge upper secondary ENLACE test scores

limitations, large-scale standardized tests like ENLACE capture skills that are important

una comparación entre ENLACE, Excale y Pisa," 2014. Nexos.

_Promise_, Washington, DC: World Bank, 2018.


28



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#### **Tables**

Table 1: ENLACE Panel       - Means and Standard Deviations
-0.0232 -0.0248 (0.0246) (0.0258) (0.0269) HH access electricity -0.0637 -0.0571 -0.1051+ (0.0489) (0.0499) (0.0559) Proximity and market access Level two -0.3000*** (0.0713) Level three -0.3988*** (0.0821) Level four -0.4862*** (0.0832) Distance to Zone city (Km) -0.002* (0.0008) Market accessibility indicator 0.0897** (0.0307) Observations 787 782 787 Chi-square test 0.0000 0.0000 0.0000 R2 0.2871 0.2738 0.2538 Source: World Bank Staff based on SESRE 2023. Standard errors in parentheses. All estimates are controlled for regions. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001 Annexes 121 Results on Social Cohesion Table D.18: Regression analysis of host and refugee attitudes (1) (2) (3) Hosts: Attitudes Index Hosts: Trusts Refugees Refugees: Trusts Hosts Male -0.021 -0.025 -0.029 (0.060) (0.036) (0.039) Age 0.004 0.003 -0.003 (0.009) (0.005) (0.006) Age Sq. -0.000 -0.000 0.000 (0.000) (0.000) (0.000) Educ: Primary incomplete - - - Educ: Completed primary -0.094 -0.040 -0.009 (0.066) (0.050) (0.052) Educ: Completed secondary -0.003 0.033 -0.083 (0
- - Educ: Completed primary -0.094 -0.040 -0.009 (0.066) (0.050) (0.052) Educ: Completed secondary -0.003 0.033 -0.083 (0.098) (0.055) (0.066) Educ: Completed post-sec. -0.074 -0.073 -0.055 (0.103) (0.050) (0.106) Agrees on improved local services 0.408*** 0.116** (0.103) (0.055) Years in Ethiopia 0.002 (0.005) Agrees hosts culturally similar 0.215*** (0.055) Region Fixed Effects Yes Yes Yes N 1724 1666 1613 Source: World Bank Staff based on SESRE 2023.
**Figure** **2.** Source: WDR 2020 team, based on World Bank's World Development Indicators (database).
Note: The blue line represents the average of all pair-wise GDP correlation taken over all country-pairs
that include China. The orange line represents the average taken over all countries that include the
United States. The grey line represents country-pairs that contain neither the United States nor China.
The date corresponds to the midpoint of a 10-year rolling window.

Change in Production Connectivity


**Figure 4.** WDR 2020 team, based on World Bank's World Development Indicators (database) and World
Integrated Trade Solution (database). Note: Each dot represents a pair of regions-for example, East
Asia and Pacific and Sub-Saharan Africa, and Latin America and Caribbean and South Asia are two
different observations). The horizontal axis measures the change over time in production connectivity
defined as the total trade in intermediates as a share of GDP of both regions. The vertical axis measures
the proportional change in GDP correlation over time.

Source: WDR 2020 team, based on World Bank's World Development Indicators (database) and a classification of sticky vs. non sticky trade from Martin et al. (2018).
increase in earnings. Most importantly, the relationship between completing secondary and post-secondary schooling and wage earnings becomes significant only when the refugee sample is restricted to workers outside the camps (see Annex D, Table D.7). This indicates that refugees only benefit from education and are incentivized to invest in education if they can access work outside the camps. This highlights the importance of refugees’ access to the labor market without restrictions, particularly outside the camps, as a critical component to achieving positive long- term effects. Refugee outcomes in the labor market do not improve over time in Ethiopia. Annex D, Table D.7 also shows that, after adjusting for domain and demographic characteristics, there is a slight increase in the probability of working for each year that a refugee is in Ethiopia, by around one percentage point per year, but no change in the likelihood of being in a high-skill occupation, 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
Global Public Health, June 2006; 1(2): 147�156

# HIV behavioural surveillance surveys in conflict and post-conflict situations: A call for improvement

P. B. SPIEGEL & P. V. LE

United Nations High Commissioner for Refugees
The third graph represents the kernel density of our productivity loss measure for observations with positive loss, which represents around 11 percent of country-year observations in our dataset. It gives an idea of the distribution of this variable across country-years. The distribution shows a long and thin upper tail driven by countries with repetitive and highly intense conﬂict history like Afghanistan or the Lebanon. The average productivity loss is 15 percent in this sample and one fourth of all country-year observations are associated with losses of more than 20 percent of productivity. Even if these estimates were drastically overestimated they indicate that the long run impact of mass violence through this channel could be substantial. 5. 2 Macro Evidence In this subsection, we investigate the correlation between the aggregate loss measure and output. For this purpose, we use the height loss measure from the previous sub- section to estimate the marginal eﬀect of an extra cm loss on log GDP. This serves two objectives. First, we explore whether the micro evidence can be used as a conduit for understanding the long-term damage to output from conﬂict. Second, we check whether the aggregate loss in output that we get is consistent with the micro estimates of marginal economic return to health.
HIV behavioural surveillance surveys 151

Results

A total of 40 BSS or KAP surveys were identified, of which 31 (77.5%) were
eligible according to the inclusion criteria. The 31 eligible surveys were undertaken between 1998 and 2005 in 14 countries. There were 12 countries (25
surveys) in Africa: Angola (1), Eritrea (1), Ethiopia (2), Kenya (2), Rwanda (2),
Sierra Leone (3), Somalia (1), South Africa (1), Sudan (2), Tanzania (3), Uganda
(5) and Zambia (2); and 2 countries (six surveys) in Asia: Nepal (2) and Thailand
(4). Eight (25.8%) surveys were undertaken in conflict settings, nine (29.0%) in
post-conflict settings and 14 (45.2%) in relatively stable countries hosting
refugees. Among some of the eligible surveys, more than one affected population
was studied; refugees were included in 28 (90.3%) surveys, IDPs in six (19.4%)
surveys, returnees in three (9.7%) surveys and surrounding host populations
in six (19.4%) surveys. The primary organizations responsible for the surveys
were NGOs, (23 surveys, 74.2%), CDC four surveys (12.9%), a United Nations
agency three surveys (9.7%) and one government survey (3.2%). The sample
sizes of the surveys ranged from 148 to 7,484, with a mean of 1,261 and a median
of 549 persons. Fourteen (45.2%) of the 31 surveys were classified as
reproducible (see Table II). Surveys undertaken by NGOs were significantly
less reproducible than those undertaken by non-NGOs (chi-square test,
p �/0.05).
5.1.3 Multidimensional poverty ............................................................................................................................................................................................................. 46 5.1.4 Food security....................................................................................................................................................................
......................................................................................................................... 47 5.1.5 Shocks and coping strategies....................................................................................................................................................................................................... 48 5.2 Determinants of welfare...............................................
Determinants of welfare......................................................................................................................................................................................................................................... 49 5.3 Cost of basic needs for refugees ..........................................................................................................................................
............................................................................................................................. 53 6. Markets and Opportunities ....................................................................................................................................................................................................................................... 56 6.1 Spatial disparities in refugees labor market access and outcomes.........
............................ 56 6.1 Spatial disparities in refugees labor market access and outcomes...................................................................................................................................... 57 6.2 Effects of local factors on refugees’ labor market outcomes...................................................................................................................................................... 61 7. Social Cohesion........................................
..... 61 7. Social Cohesion...................................................................................................................................................................................................................................................................... 63 7.1 Attitudes between refugees and hosts.......................................................................................................
................................................................................................................................................. 64 7.2 Social interactions .........................................................................................................................................................................................................................................
............................................................... 68 8. Policy Recommendations ........................................................................................................................................................................................................................................... 72 References ...............................................................................
................................................................................................................................................................................................ 77 Annexes ..............................................................................................................................................................................................
..................................................................................... 82 Annex A: Description of Refugees by Country of Origin.............................................................................................................................................. 83 Annex B: Refugee Policies in Ethiopia..........................................................................................................................................
...................................................................................... 85 Annex C: Survey Design and Methodology.................................................................................................................................................................... 92 Annex D: Descriptive Statistics and Regression Results.....................................................................................................................
.......................................................................... 97 Annex E: Robustness Checks of Refugees’ Consumption.......................................................................................................................................... 124 Annex F: Comparison of Results from Skills Profile Survey and SESRE................................................................................................................. 128 TABLE OF CONTENTS Figure ES.1: Desired location in three years ����������������������
.............. 128 TABLE OF CONTENTS Figure ES.1: Desired location in three years ����������������������������������������������������� iv Figure ES.2: 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
.. vi Figure ES.6: Host response to “Refugees are good people” ............................ vii Figure ES.7: Host response to “Would you feel comfortable having
informal sector in surrounding communities or the camps., this may include selling aid rations on the local market, informal trade, and economic exchange, or working for local NGOs and UN agencies (ReDSS, 2018). Extensive research has shown that not being able to enter local labor markets legally is detrimental to refugees’ ability to earn for their families, and for them to find an occupational match that maximizes the benefits they contribute to Ethiopia (World Bank, 2023). Because of conflict, the SESRE data collection could not include refugees in the Tigray region in Ethiopia, where most Eritrean refugees were hosted before the conflict. Since outbreak of the conflict in November 2020, many Eritrean refugees moved to Addis Ababa, and many fled from the Mai Ani and Adi Harush refugee camps in Tigray to the newly established refugee hosting site of Alemwach, Dabat in the Amhara region. Between February and July 2022, over 15,000 refugees relocated from the Tigray camps to Alemwach, and an additional 7,000 refugees were resettled in November 2022 following the cessation of hostilities (UNHCR, 2022). Before the conflict, 64 percent of all Eritrean refugees were hosted in camps in Tigray and 36 percent in camps in Afar (UNHCR,
data source listed most prominently in the announcement was the Emergency Food Security  and Nutrition Assessment (EFSNA), which was conducted between November and December

the IPC classification. For example, the EFSNA avoided the two most conflict-affected gover norates, was potentially plagued by a host of logistical difficulties given the security situation

by the WFP used in this analysis and the Gallup World Poll both find that the size of the

displaced population is up to three times as large as is being identified by the Task Force for  Population Movement (TFPM), which is the official source used in the IPC 2017 classification

occur. This article utilizes the mobile Vulnerability and Assessment Mapping Survey (mVAM)
Our main specification for the poverty line estimations defines an expenditure threshold of R$500 to
classify lumpy (all above) and not lumpy (all below) items. Items that are above the threshold are thus not
included in the consumption aggregate as it is assumed that they are infrequently purchased, and in many
cases, an accurate incorporation into the poverty line calculation would be by estimating a value of the
flow of services they provide to the owner. The data required to run those estimations is not available in
POF 2017/18.

Below we list the items that were excluded in our main specification by POF module.

by floods, and which strategies they use to cope and adapt.
These insights are based on firm survey data collected in

2018 using a tailored questionnaire, covering a sample of

more than 800 firms. To assess the impact of disasters on
businesses, the study considers direct damages and indirect
effects through infrastructure systems, supply chains, and
workers.

This study presents evidence from a dedicated Tanzanian firm survey conducted with 837 businesses in Dar es Salaam and the provinces of Tanga and Dodoma.

The survey's target population was all 58,959 firms in Dar es Salaam, Tanga, and Dodoma that were registered with the National Bureau of Statistics' (NBS) in 2015.
using a regression analysis to compare the earnings of refugees and hosts and how this wage gap changes after adjusting for the effects of demographic and job characteristics. Column 1, Annex D, Table D.6 shows that, after controlling for the domain, monthly earnings is 70 percent lower for refugees than for hosts. After controlling for age, gender, and education in Column 2, this earnings gap remains at 64 percent. This means 0 20 40 60 80 100 Camp Hosts Male Camp Refugees Male Camp Hosts Female Camp Refugees Female Employed Unemployed, searching Percent Unemployed, not searching Inactive not in school Inactive in school Figure 3.5: Work status by gender Source: World Bank Staff based on SESRE 2023. 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 Female Male Figure 3.6: In-camp refugee share employed by age Source: World Bank Staff based on SESRE 2023. Jobs and Livelihoods 30 that, after adjusting for any differences explained by age, gender, and education, refugees still earn significantly less than hosts. Column 3 indicates that refugees face this same earnings gap even after adjusting for differences explained by occupation and industry. Only after restricting the
3 restrictions on job search, but they do not benefit from the same humanitarian assistance refugees in camps benefit from (Betts et al., 2017b). We investigate the determinants of the vulnerability of Syrian refugees to the COVID-19 shock in Jordan. In particular, we examine whether Syrian disadvantage during the shock is completely explained by institutional barriers reflected in precarious jobs and residency in camps, or if they experience added vulnerability by virtue of their refugee status. We use retrospective data on the job histories of young Jordanian and Syrian men between the ages of 16 and 30 years from the Survey of Young People in Jordan (SYPJ) (Assaad et al., 2021a; OAMDI, 2022) to construct a synthetic semi-annual panel dataset that tracks the job finding and separation experiences of respondents. We compare the trends of job finding and separation across the two populations before and after the onset of the COVID-19 pandemic. We find that Syrians have generally experienced lower job finding rates and higher job separation rates compared to Jordanians, although the differences were generally not statistically significant prior to the pandemic. With the onset of the pandemic, Syrians became significantly disadvantaged on both measures. Controlling for the type of employment showed that workers whose last job was informal were disadvantaged on both measures compared to formal workers regardless of nationality.
Score Grade 6 512.2 527.3 550.0 527.8 515.6 (100.8) (98.96) (95.74) (105.8) (100.7) Enlace taker Grade 9 0.712 1 1 0.769 0.764 (0.453) (0) (0) (0.421) (0.425) Enlace taker Grade 12 0.323 0.453 1 0.370 0.379 (0.468) (0.498) (0) (0.483) (0.485) Girl 0.469 0.498 0.524 0.473 0.535 (0.499) (0.500) (0.499) (0.499) (0.499) Private School Grade 6 0.0822 0.0970 0.119 0.0578 0.0954 (0.275) (0.296) (0.323) (0.233) (0.294) Mother has lower secondary 0.539 (0.499) Father has lower secondary 0.586 (0.493) Mother is white collar 0.114 (0.318) Father is white collar 0.207 (0.405)

Notes: The table shows the mean and standard deviations of all students matched in the ENLACE panel in 2007
(column 1), 2010 (column 2) and 2013 (column 3). Column 4 displays additional variables from student and parents
surveys that were applied to a sample of ENLACE takers. Column 5 reports statistics for the sample of identified
twins in grade 6. Data: ENLACE panel.

Enlace Score 0.213 0.375 -0.0630 0.114 (0.854) (0.856) (0.778) (0.808) College Student 0.630 1 0 0.437 (0.483) (0) (0)
.375 -0.0630 0.114 (0.854) (0.856) (0.778) (0.808) College Student 0.630 1 0 0.437 (0.483) (0) (0) (0.496) Employed 0.379 0.263 0.577 1 (0.485) (0.440) (0.494) (0) Upper Secondary GPA -0.0219 0.135 -0.289 -0.0835 (1.007) (0.995) (0.970) (0.987) Girl 0.564 0.546 0.596 0.502 (0.496) (0.498) (0.491) (0.500) Private Upper Secondary 0.175 0.203 0.129 0.134 (0.380) (0.402) (0.335) (0.340) Urban resident 0.848 0.912 0.740 0.814 (0.359) (0.283) (0.439) (0.389) Age 19.18 19.16 19.21 19.23 (
However, specifying a control variable for which national accounts or survey data is needed (such as the share of agriculture in GDP or the share of the population engaged in agriculture) would run counter to the purpose of this study, which is to see whether proxies such as nightlights can be used to estimate growth in areas (sub-national or supra-national) for which there are no national accounts or survey data. One option is to use rainfall or vegetation data, which share many of the desirable features of nightlights data, to proxy the performance of the agricultural sector.

In the first step, we estimate national GDP based on specification (1). The estimated GDP is divided into two parts based on the share of agriculture in the economy (based on actual national accounts data): Agricultural GDP and industrial and services GDP. In the second step, we distribute industrial and services GDP among the subnational units in proportion to their share of national nightlights.

6 This is in contrast to Ghosh and others (2010) who use the Landscan data to distribute agricultural GDP among
sub-units.


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##### **5.1 Kenya**