ai4data/datause-dataset-rechunked huggingface dataset:
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Priority 4 of the CBP and 
Outreach strategy aims to 
contribute to prevention of 
violence through Community 
safety action plans to reduce 
violence in the community 
community makes contributions to pay the salary of the Imam and may pool together funds to provide 
support to persons living in poverty, vulnerable persons (i.e. widows, orphans, PWSN), and persons 
seeking financial assistance for medical treatment or to pursue educational opportunities.  
Positive Coping Mechanisms [Questions about how people cope with stress and what activities in the community help persons 
overcome traumatic events].  
For stress relief, males referenced different coping mechanisms such as: discussing their issues with 
elders and/or religious scholars, engaging in prayer to seek help and guidance from their faith, and 
gathering with their friends or relatives to discuss issues (peer counseling). Those who can afford it, 
engage in sports, go to recreational spaces or restaurants with 
friends, or gather in common places (Hujras) to play cards or 
other games. Negative coping mechanisms reported by males 
include domestic violence against women and children, or 
“unusual religious activities” such as going beyond the required 
number of prayers and excessive citation of the Qur’an.  
Due to movement restrictions, females do not have the same 
availability of stress relief opportunities outside the home. As a 
result, females reported that to cope with stress they may isolate themselves in the home and cry, seek 
medication, and talk to relatives to find support and solutions (peer counseling). Females also mentioned 
that due to stress, a negative coping mechanism is emotional and physical abuse of children. The findings 
reinforce previous PA findings and lend strong support for Community safety plans as a means to 
empower the community to address the negative coping mechanisms.  
Voluntary Repatriation [Questions about the decision making process of return to Afghanistan, how persons receive information on 
return to Afghanistan and how return to Afghanistan has affected community dynamics].  
The majority of community members report that they have relatives, friends or know of community 
members that
Repatriation [Questions about the decision making process of return to Afghanistan, how persons receive information on 
return to Afghanistan and how return to Afghanistan has affected community dynamics].  
The majority of community members report that they have relatives, friends or know of community 
members that have returned to Afghanistan, for the following reasons3: 
 
Police harassment in Pakistan 
 
Uncertainty of POR card extension in Pakistan 
 
Poverty and lack of employment opportunities in Pakistan 
 
Strict border crossing control between Pakistan and Afghanistan 
 
Desire to join relatives and/or communities back in Afghanistan  
Community members report they receive information on repatriation from4: 
 
UNHCR/partner helplines 
 
Print and electronic media 
 
Contact with friends and relatives back in Afghanistan via phone and internet 
 
Information from persons that have recently traveled to Pakistan from Afghanistan 
As a result of return, the community reports a decrease in livelihoods5 opportunities and increased 
exposure to security issues as community numbers reduce and government authorities actively support 
returns. Furthermore, loneliness and sadness due to friends and family departing Pakistan is reported as a 
negative impact as well as decreased educational opportunities for children due to teachers returning.
According to the 2012 Institutional Profiles report, the quality of public services and its territorial coverage, which was weak to being with, have significantly deteriorated since 2006. 1 A combination of rising poverty, rising insecurity, and deteriorating public services have further strained inter-communal relations and contributed to deteriorations in social cohesion. Many Lebanese youth do not trust their state and become disillusioned as they are not able to affect their own life or contribute productively to society at large. 2 Political and civic engagement is reported to be low (Status of Women in the Middle East and North Africa Survey Project, 2010). 3 In an already fragile context with a highly complex political, religious and social landscape consisting of 18 religious sects, numerous political parties, and large numbers of refugees, many Lebanese 1 On the quality of public services indicator, Lebanon ’ s score declined from 2. 5 in 2006 to 0. 8 in 2012 on a 4-point scale. On the territorial coverage indicator, its score went down from 2. 7 in 2006 to 1. 5 in 2012. 2In a Gallup World Poll, Lebanese reported low confidence in (a) their national government (37 percent) and the judiciary, (b) the honesty of elections (15 percent), and (c) the honesty of government (4 percent) (World Bank, 2016). 3 According to the SWMENA survey, only 18 percent of Lebanese women are members of an organization, compared to 34 percent of men. Men are more likely to be members of a political organization than women (21 percent of men vs. 7 percent of women), whereas women are more likely to be active in religious groups and charity organizations than men.
and purchase goods at better prices. In Ethiopia, refugees’ locations differ vastly in terms of proximity to resource and economic hubs. About 37 percent of refugees live within 10 kilometers of the nearest Woreda city and another 43 percent live within 10 to 20 kilometers. Zone capital cities are farther away, but almost half (45 percent) of in-camp refugees live within 20 kilometers of the nearest Zone capital city (Figure 6.1a). Borders seem farther, with 18 percent of refugees living within 30 46 Labor Force and Migration Survey 2021. Markets and Opportunities 58 kilometers of the nearest border. When defining mutually exclusive location categories to measure proximity to resource hubs, we see that 44 percent of refugees live closest to the nearest Zone capital city. Another 28 percent live closest to a Woreda City, which is not a Zone capital city. About 11 percent live close to a border but not the Zone capital or Woreda city, and 17 percent of in-camp refugees live in remote areas far from a Zone capital city, Woreda city, or a border. When looking at accessibility, as defined by a market accessibility index, more than one-third of the refugees are located in areas with
Specifically, the monthly mVAM began in August 2015 and has been conducted in nearly every month since. [7] The 10-minute survey is conducted by mobile phone and reaches approxi mately 2400 households every month through random digit dialing. The survey is stratified by

Every month, the survey collects information necessary to construct the Food Consumption Score (FCS) [10], information on food coping strategies necessary to construct the Reduced Coping Strategy Index (rCSI) [11], information on whether the household received food assistance in

publicly available. However, the WFP publicly shares each month the governorate-level averages and confidence intervals of all key variables collected [13], and these monthly governorate-level  estimates are used in the empirical analysis to analyze changes in food assistance and food access following the 2017 IPC announcement. [14] We further merge the mVAM data with data
Disaggregated data on IDPs who are not protected or assisted by UNHCR are collected by other agencies, including IOM, but data are not comprehensive and therefore not published in IDMC ’ s global reports. 93 The difficulties of collecting disaggregated data on locations of IDPs are compounded by the fluidity of IDP movements — IDPs might suffer multiple displacements or they might resort to changing locations as a coping strategy (e. g. moving between their homes and place of displacement or testing different locations before deciding where to stay) (Brookings 2011). In recent years, efforts have been made to improve data collection for IDPs living outside of camps by employing a range of techniques including: (a) profiling; (b) household surveys; (c) collecting information on IDPs who come to camps to visit family members or collect relief items; and (d) community outreach programs (Brookings 2013).
We complete this data with exact geographic location data from MineAtlas (2013), where satellite imagery shows the actual mine boundaries, which allows us to identify and update the center point of each mine. The production data and ownership information are double-checked against the companies ’ annual reports. For Ghana, this exercise results in 17 industrial mines tracked over time. We have annual production levels from 1990 until 2012. As mentioned, Table 1 shows the mining companies active in Ghana during recent decades, with opening and closing years (although some were closed in between, and are not presented in the table). Figure 2 shows the geographic distribution of these mines. Figure 2 Gold mines and DHS clusters in Ghana Panel A Gold mines and 20 km buffer zones Panel B Gold mines, DHS clusters, and 100 km buffer zones 4 The distances are radii from mine center point, and form concentric circles around the mine. 5 The DHS and the GLSS data are representative at the regional level, and not at the district level. Since the regional level is too aggregated, we do the analysis at the district level, but note that the sample may not be representative.
(see Annex E for detailed information). Food aid information received from WFP includes five food items and their quantities distributed to refugees: cereal (mainly wheat but in some camps rice), pulses (mostly yellow split peas), CSB+, vegetable oil, and salt. We have computed the per person, per month in-kind aid quantities into annual values using the same prices as other food items based on SESRE data, mapping them to the closest food item in SESRE (this was not straightforward as the items are different). We further considered the changes in quantities of food rations that took place across survey months due to funding shortages, which can significantly affect the overall wellbeing of refugees in Ethiopia. Based on this information, we compare items refugees should have received with what refugees reported regarding food consumption. The results show that refugees reported quantities lower than UNHCR food aid admin data for every item except biscuits. Refugee households still report lower quantities, even when valuing the food ration quantities indicated as sold in markets. Possible explanations for lower food quantities are that food rations are only received once a month, which may not coincide with the interview date. Moreover, SESRE asks what food people
(0.0814) Distance to Zone city (Km) -0.0030* (0.0013) Market accessibility indicator 0.4056** (0.1381) Observations 2024 2024 2024 2024 2205 2024 Chi-square test 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 R2 0.1373 0.1373 0.1373 0.1373 0.1172 0.1373 Source: World Bank Staff based on SESRE 2023. Note: Average marginal effects are estimated. a refers whether a household ran out of food in the last 12 months. b refers if most of the land is covered in the community by agriculture activities i.e., less built-up and shops. c refers recent (5 years) internal migrants from rural to urban centers. Distance to the nearest bank variable is excluded from proximity model II, as it is captured by effect of distance to the nearest zone city. The left side for years in exile, Head education level, HH size, most land cover, and proximity level is less than 15 years, Head with no education, non-working age members, most land cover by built-up and shops, and nearest to Zone capital city, respectively. All estimates are controlled for refugee camps. Standard errors in parentheses. +p<0.10, * p<0.05, ** p<0.01, *** p<0.001 Variables Basic Model Local Market Proximity Market access Model I Model II Model I Model II Annexes 119
sectoral distribution of employment, the poorest refugees—in- and out-of- camp—tend to be employed in the service sector. While employment in the industry sector is low for refugees, the poorest are less likely to be employed in the industry sector than the richest. The poorest hosts of in-camp refugees are more likely to be employed in agriculture, and the richest appear to be employed in the industry or service sectors (Figure 5.18b). Not surprisingly, the poorest hosts of in-camp refugees and the poorest refugees work in low (or medium)-skilled occupations, while the richest are employed in high-skill occupations (Figure 5.18c). - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 No education Primary incomplete Primary complete Secondary incomplete Secondary complete Post- secondary No education Primary incomplete Primary complete Secondary incomplete Secondary complete Post- secondary In Camp Refugees In Camp Hosts Addis Ababa Refugees Addis Ababa Hosts All Refugees All Hosts In 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
### INTRODUCTION

Within UNHCR public health covers various areas including primary health
care, nutrition and food security, reproductive health and HIV, mental
health and integrated refugee Health Information Systems (iRHIS). Sectoral
programming in a comprehensive response context means applying a wholeof-government (i.e. relevant national and local authorities for health and
nutrition response) multi-stakeholder approach and planning with relevant
partners. The overall responsibility of coordinating the health sector
response in refugee-only situations will be with the Ministry of Health, with
support of UNHCR and relevant partners. A wide range of partners play
a role in planning and delivering public health interventions in different
areas and at different stages of the refugee response. For an effective and
comprehensive response it is therefore essential to know how and when to
engage these various partners. Though the establishment of refugee-specific
services may be needed in the early phases of a refugee situation, longer
term solutions are required to ensure that refugees have access to services
through the national health system. Host countries may require assistance
from other partners, including international organizations but also local
partners, to make the necessary adjustments to comprehensively include
refugee health needs into national development and local health plans,
to strengthen/reinforce national and local resilience of national and local
health systems to meet the health needs of refugees and host communities.
The rapid humanitarian assessments of newly conflict-induced displaced populations often detect
children amongst those injured by the armed clashes. Aside from material hardships, the psychological
impact of the conflict and subsequent flight is deemed to be severe. Recruitment and use of children
by armed forces and armed groups remains a significant risk in light of the fragmentation of NSAG and
varying degrees of interest in compliance with IHL. Active conflict led to 11,418 civilian casualties [5 ] in
2016 - approximately 11% were women and 31% were children. In 2016, the Country Taskforce on
Monitoring and Reporting (CTFMR) verified 57 incidents of recruitment and use of children in the
conflict (89 boys) who were recruited and mainly used for planting IEDs, transporting explosives,
carrying out suicide attacks and spying. Forced recruitment is primary reason given by Afghan asylum
seekers in Sweden and Norway. Poverty, coercion and lack of livelihood opportunities, including
during the more prolonged phases of displacement, is also a factor that contributes to the recruitment
of children, particularly adolescents. Access to education in displacement is generally hindered by
several factors: Poverty and destitution, with a loss of assets and means of livelihood, often forces
displaced families to engage children in support of family resilience and interim livelihood strategies.
Lack of civil documentation, cultural and social norms, threats and intimidation, social status and
poverty are significant obstacles. In 2016, 423 schools were intermittently closed due to conflict and
only 12 percent relied on crops or livestock. This contrasts with the Eritreans in camps, who previously relied primarily on agricultural and labor income, and demonstrates the large differences between Eritrean households that could and could not acquire OCP status. This, coupled with the finding that OCP refugees have much higher levels of education compared to in- camp Eritrean refugees (as highlighted in Chapter 2), again indicates that OCP refugees were relatively well-off before displacement, and they still have family members or other support systems. Refugees across all regions of Ethiopia have benefited from livelihood training interventions provided by RRS, domestic and international NGOs, and humanitarian organizations. For example, the Ikea Foundation, through UNHCR, invested around US$100 million in the Dollo Ado camps in Somalia between 2012 and 2019. Much of this funding supported economic development and livelihood opportunities for refugees and the host community, including creating livelihood cooperatives in agriculture, livestock value chain, energy, firewood, and microfinance (Betts et al., 2020). In the north of the country, UNHCR worked with various partners to provide Eritrean refugees with vocational skills training, tools, and start-up capital for crafts, such as leather products, weaving, and tailoring. UNHCR records indicate that more than
related to education, gender, and age. The results in Figure 3.26 show that refugees are under-represented in high-skill occupations and services and sales relative to what we would expect based on their age, gender, and education, and substantially over-represented in crafts and related trades. Among employee workers, OCP refugees also earn less than hosts, though the wage gap is smaller than for in-camp refugees. Annex D, Table D.9 shows that refugees in Addis earn 25 percent less than their hosts. Even after adjusting for demographic characteristics, occupation, and sector of work, this wage gap remains at around 19 percent. 0 20 40 60 80 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female - Employed Unemployed, searching Unemployed, not searching Inactive not in school Inactive in school Percent Figure 3.22: Work status by gender Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Addis Hosts Male Addis Refugees Male Addis Hosts Female Addis Refugees Female Self-employed Employee Public NGO/RRS Private household Unpaid Percent Figure 3.23: Work type by gender Source: World Bank Staff based on SESRE 2023. 0 10 20 30 40 50 60 70 80 90 100 Addis Hosts Male
to documentation: Enhance digital infrastructure in refugee hosting areas to facilitate refugee inclusion to the digital economy including digitally enabled livelihood opportunities and financial inclusion as well as to foster their access to socio- economic e-services, including standardized travel documents. Source: Ethiopia GRF Pledge Progress Report (RRS & UNHCR, 2021 and 2023) Pledges Progress as of 2023 Annexes 92 S ESRE is a separate but integrated survey alongside the Ethiopian Household Welfare Statistics Survey (HoWStat),54 the national household survey to measure poverty and other socio-economic outcomes. Like most national poverty surveys, HoWStat excludes displaced populations—Internally Displaced People (IDPs) or refugees—including in Ethiopia. To have up-to-date information on the socio-economic outcomes and poverty levels of refugees and to allow comparison to Ethiopian host communities, the SESRE applied the same questionnaire and data collection methods as the HoWStat, with some modifications. Training of the enumerator team and implementation arrangements of the survey followed the same standards and procedures as the HoWStat. SESRE data was not collected alongside HoWStat due to security concerns at the time of data collection for HoWStat, especially in the refugee areas. The SESRE aimed to solve two problems: (i) gaps in data on the socioeconomic dimensions of refugees,
Aldo Morri for his excellent editorial support. Finally, we extend our deepest gratitude to the survey respondents for their willingness to share their experiences which has been instrumental in deepening our understanding of the challenges and needs faced by both refugees and host households. ACKNOWLEDGEMENTS ii Introduction E thiopia, with its long history of hosting refugees, is grappling with the complex challenges of accommodating close to 1 million refugees and asylum seekers. These come primarily from neighboring countries like South Sudan, Somalia, Eritrea, and Sudan housed in camps in mostly rural areas spread around the country near border areas. While Ethiopia has adopted progressive refugee policies, including the Comprehensive Refugee Response Framework (CRRF), challenges persist in translating these policies into tangible socioeconomic outcomes for refugees. Despite Ethiopia’s efforts to shift from a camp-based approach to a more inclusive model promoting self-reliance and integration, refugees live largely in camps, are reliant on humanitarian aid, and face barriers to accessing employment and education. The country’s new Refugee Proclamation grants refugees the right to basic services, work, and freedom of movement, but implementation delays hinder their realization. To address these challenges and achieve better development outcomes for both refugees and host communities, a
The LFS's rotating panel design introduces attrition that is potentially non-random: households that relocate between LFS interview waves are excluded from panel analyses, and mobile households may differ systematically from stayers in their labor market outcomes. We test for selective attrition by comparing baseline characteristics of households observed in all four LFS waves with those lost to follow-up due to residential relocation. Attritors are somewhat younger and have lower educational attainment on average, but observable baseline differences are small in magnitude.