ai4data/datause-dataset-rechunked huggingface dataset:
HIV behavioural surveillance surveys 149

Table I. Internationally accepted key BSS indicators.

Knowledge International standard

Prevention Percentage of young women and men
aged 15 �24 years who, in response to
prompted questions, say that:
1) people can protect themselves from
contracting HIV by having sex with only one
faithful, uninfected partner. [a]

UNGASS, MDG, PEPFAR

2) people can protect themselves from UNGASS, MDG, PEPFAR
contracting HIV by using condoms. [a]

Misconceptions Percentage of young women and men
aged 15 �24 years who, in response to
prompted questions, correctly reject that:
1) A person can get HIV from mosquito UNGASS, MDG, PEPFAR
bites. [a]

2) A person can get HIV from sharing a UNGASS, MDG, PEPFAR
meal with someone who is infected. [a]

General Percentage of young women and men
aged 15 �24 who, in response to
prompted questions, know that:
1) A healthy-looking person can have UNGASS, MDG, PEPFAR
HIV. [ab]

Attitudes

Care and support The number of respondents who report
an accepting or supportive attitude of:
1) Would be willing to care for a family
member who became sick with the AIDS
virus.

PEPFAR

2) Would buy fresh vegetables from a vendor PEPFAR
whom they knew was HIV�/.
twins) variation on 6th grade ENLACE, identifying, therefore, the relationship between

as captured by ENLACE, on future outcomes and the (within-family) individual-level

characteristics. In the ENLACE panel, this last specification uses the sample of students

who answered the ENLACE context questionnaire to control for differences in household

ENILEMS-ENLACE panel regressions, _Xi_ _[′]_ [includes] [upper] [secondary] [school] [grade] [point]
<!-- PAGE 3 -->
**Estimating a Poverty Line for Brazil Based on the 2017/18 Household Budget Survey** **[+]**

**Keywords:** poverty lines; food poverty line; basic needs; household budget survey; Brazil
FAO (2012) being the latest. More recently, the FAO indicator is used to track progress toward the first

country. [2] Combining this with population data allows the FAO to estimate the total kilo calories available

number of HCES. [3] For most countries the CV was kept constant across years and only the mean was

revised. [4] Finally, the FAO estimates the required energy of a population by determining age-sex specific
**3.3** **Open Street Map (OSM) Road Network Data Set**

In addition to the five public transportation networks, we also added the drive as well as the pedestrian network leveraging the Open Street Map data set to account for commuters who walk or drive to work.

**3.4** **Fathom Flood Maps**

<!-- PAGE 10 --> Pluvial and fluvial flood maps from the Fathom global flood model were first clipped to the bounding box [9] of the Kinshasa city and then mosaiced together using the maximum operator so that the maximum flood depth from both fluvial and pluvial flood estimates were preserved.

Flood models are an integral tool for understanding and managing flood risks on transportation networks. In
the past decades, increased computing power and precision of remote sensing data sets have led to the
development of multiple global flood models (Bernhofen et al., 2018; Wood et al., 2011). Among them, the
lastest flood map products from Fathom (A. Smith et al., 2015) are selected to provide flood depth and extent
estimates in Kinshasa for the following reasons: 1) the map products from Fathom have relatively high
spatial resolution (90 meters) which is sufficient for this study given the geographic extent of the Kinshasa
city; 2) flood extent and depth estimates for both pluvial and fluvial floods under 10 return periods [8] are
included in this data product; 3) it uses 2D hydrologic flood model which is more advanced in mapping flood
plain and modeling dynamic water flows.
In practice, most of those approved for the OCP have family and friends in Ethiopia who support them with remittances—fewer than 1,500 OCP work permits were issued by 2022. The permit allows refugees to freely move and establish residence in all areas of the country except restricted areas. OCP refugees are systematically different from in-camp refugees, as seen in their livelihood strategies. Therefore, the remainder of this chapter divides the analysis between in-camp refugees and OCP refugees. 3.1 Labor market outcomes of in-camp refugees and their hosts In-camp refugees have high inactivity rates (not working or unemployed) and low labor force participation. Table 3.1 shows that only 31 percent of all in-camp refugees aged 15-64 “participated” in the workforce (in the week before the survey), meaning they were employed or available to work and actively searching (strict unemployment). This compares to 52 percent for hosts. This figure increases to 43 percent for refugees and 57 percent for hosts if you include all available to work regardless of whether they are searching (relaxed unemployment). The remaining 57 percent of refugees are inactive, and just over half are currently in school, leaving 23 percent of refugees neither working nor studying, compared to
camp refugee households with improved bathing facilities36 is low, especially among Somali refugees and their hosts. In addition, few refugee and host households have a place or item designated for hand washing in their dwellings. Availability of water or detergent for hand washing is low, especially among refugees (Annex D, Figure D.15). Regarding rented homes, OCP refugee households live in houses with better bathing facilities (82 percent) than hosts (66 percent). Also, around 80 percent of refugee and host households in Addis Ababa have a place for hand washing, and more than half of refugees and hosts have water or soap. 32 Overcrowding occurs when if more than three people live per room (UN-Habitat). 33 Improved wall is made of stone & cement, blocks-plastered with cement or bricks. 34 Improved roof is made of corrugated iron sheet or concrete/cement. 35 Improved sources of drinking water are piped, bottled, sachet, or tanker water. 36 Improved bathing refers private or shared bathtub, shower, separate room for bathing. 0 20 40 60 80 100 120 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Overcrowded Improved wall Improved roof Percent Figure 2.21: Housing quality Source: World Bank Staff based on SESRE
16 5. 3 Desire to return (Y / N) Keeping our attention on refugees and IDPs, we wanted to deepen our understanding about their migration plans. In particular, we would like to discover which characteristics are associated with the desire to return to Northern Mali. To this end, we estimated a probit model using the same regressors as in the previous sections. The dependent variable was set equal to one when the respondent was considering the possibility to eventually go back to the North, zero otherwise. The estimated marginal effects have been reported in Table 3 for all respondents (Column 1-2), as well as for only the household heads or their spouses (Column 3-4). The strongest predictor of a planned future return was refugee status: individuals living abroad in refugee camps were up to 25 percentage points more willing to go back than IDPs. Joining this result with those on unemployment presented in the descriptive statistics, we may wonder whether this desire to go back home may have resulted from a more general malaise experienced by these respondents forced to migrate and halted in a limbo not fully integrated with the local community and labor market.
Produced by the Research Support Team Abstract The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development / World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Policy Research Working Paper 10631 This paper aims to understand the existing gaps in micro- level data on forcibly displaced people — refugees and internally displaced persons. The paper undertakes a com­prehensive review of all existing micro-level data sets in the United Nations High Commissioner for Refugees Micro­data Library and the World Bank Microdata Library. It first identifies a corpus of micro-level data sets that are designed to have a representative sample of refugees and / or internally displaced persons and assesses gaps in geographical and the­matic coverage. The paper then evaluates whether the data sets contain a core set of questions that are essential for the proper identification of refugees and internally displaced persons.
They exclude other categories of displaced people who were not forced to move because of conflict or violence, such as economic migrants and victims of natural or environmental disasters. 4 Of course, many people cannot be simply categorized in these groups and this makes statistics on FDPs gross estimates, but the growth and relevance of these numbers are undisputed. The growth in the number of FDPs poses a challenge to the measurement of global and national poverty. Those who are forcibly displaced and in need of international protection tend to be persons who have lost their assets, financial resources, and social networks. They are typically very poor with no obvious path out of poverty. For refugees, their number vanishes from poverty statistics of their own country because they are no longer counted in the place of origin. Both IDPs and refugees are also not properly accounted for in the country in which they reside. Their numbers – even though high in absolute terms – are often low relative to the non-displaced population (with some exceptions like Lebanon and South Sudan). Hence, they do not explicitly show up in official statistics. Even if – as in some but not all countries – their locations are appropriately included in the sampling frame, they are unlikely to be sampled due to their small proportion relative to the population and high clustering in specific locations. 3 https: / / www. unhcr. org / refugee-statistics / 4 Note that the IASC definition of IDPs explicitly includes those fleeing from disasters.
milk and milk products, oils and fats, sugar/honey, and others. Food consumption status is determined based on food consumption score. a. Dietary diversity score (out of 12 groups) b. Food consumption status Refugees’ Aspirations 49 Moreover, insecurity and displacement-related shocks are common for Eritrean refugees, with 14 percent having experienced a recent displacement event. This result is driven by refugees in the Alemwach refugee hosting site, all of whom moved to the refugee site within a few months before the survey as a result in the conflict in Tigray, and would have reported a recent displacement event. Both refugees and host communities use “consumption-smoothing” to cope with the various shocks they face. Households utilize a mix of coping strategies to mitigate harm to their welfare that shocks cause. “Consumption smoothing”, among the major risk coping strategies, mainly involves relying less on preferred food and more on less expensive food (diet changes) and reducing the number of meals eaten daily (negative food intake). Borrowing food or cash from friends and relatives and purchasing food on credit second represent the second and third most common coping strategies. Refugees in camps and in Addis Ababa are more likely to rely on these coping
ESS translated the survey instrument into different main languages and undertook an in- depth training of supervisors and enumerators for enumerators to understand better the concepts of the questions related to the refugee context. Moreover, a close follow-up and coordination in the field helped to get better quality data and provided timely responses to challenges faced during data collection. During the survey implementation period, the main challenge was tracking sampled refugee households in all refugee domains. One of the Eritrean camps, Alemwach Camp, was newly established at time of data collection. Tracing the originally sampled and backup households initially took a lot of work. The issue of missing households in Asayita camp was severe during the second data collection phase. Moreover, some camps were very large; for example, there were more than 100,000 refugees in one camp, creating challenges for field workers in tracing the sampled households. Refugees in Addis Ababa live in rented houses; the team faced challenges in tracing some refugees due to changes in their residential locations. Challenges related to identifying the eligible sample households were also observed due to outdated names of the household heads in UNHCR lists, and UNHCR’s registration of names which is not
Road data quality in the southern provinces is lower than in other regions due to the absence of electronic traffic count equipment on rural roads and the reliance on visual condition assessments conducted by untrained district public works staff. The project will support a road condition data quality improvement program under Component 3 that trains district engineers in standardized pavement condition index methodology and equips them with ruggedized tablets for electronic data entry. Improved road data quality is expected to reduce the cost of prioritization errors in future maintenance planning.
The SEIS was fielded over a 14-week period by a nationally certified survey firm using CAPI on Android tablets. Interviewers received five days of training covering the survey instrument, tablet operation, and protocols for interviewing households with protection concerns. The SEIS questionnaire was translated into four languages in addition to the national language to accommodate the linguistic diversity of the target population. Response rates exceeded 94 percent in all settlement types, and back-checks were completed for 12 percent of interviews, revealing no systematic interviewer effects.
come ratios relative to extractives,<br>2008-13|
|---|---|
||Kayes<br>Sikasso<br>Other<br>regions<br>Total<br>Agriculture<br>6%<br>37%<br>60%<br>40%<br>**Extractives**<br>**100%**<br>**100%**<br>**100%**<br>**100%**<br>Industry<br>111%<br>50%<br>110%<br>85%<br>Construction<br>14%<br>75%<br>413%<br>214%<br>Services (tradable)<br>129%<br>68%<br>107%<br>84%<br>Services (non tradable)<br>76%<br>73%<br>183%<br>126%|
|_Source:_ CPS/SME (Mining and Energy Sector Planning and<br>Statistics Unit), 2013.|_Source:_ EPAM (Permanent Household Survey), 2010.|
Ideally, we would conduct a comparative analysis of the characteristics of migrants residing in Medellin versus those in other parts of the coun- try to discern the extent of these differences. However, the lack of comprehensive data regarding the living conditions of this population makes such analysis unfeasible. To explore how this population compares with other migrant groups in the country, we turn to the only two available data sources on migrants. First, we use the Venezuelan Refugees Panel Survey (VenRePS), conducted by Ib ´ a ˜ nez et al. (2022), which captures a 19
composite measure for human capital. We extract real GDP and human capital data from Penn World

Table 10 (Feenstra et al., 2015), using the "rgdpna" series, which measures real GDP in constant 2017

schooling and returns to education (Inklaar and Timmer, 2013). We extract the population from World

Development Indicators (WDI).

#### **3.3 Sample Construction**

The baseline populations served by the East Road (within the buffer zones) were established by utilizing
WorldPop open source data. To estimate the number of people affected by rain event, however, a series
of models were run to simulate the locus of impacts for each rain event (3-, 10-, and 30-year events) and
their subsequent effects on access. Repeated events might affect the same people, and so, the cumulative
impacts over the lifetime of the road can be thought of as 'person-disruptions' - i.e., the sum of individual
disruptions. If, for example, an individual living on the East Road was cut off from access to hospitals three
times over the thirty-year period, this experience would account for three person-disruptions.

Using Global Positioning System (GPS) tracking records for the Malaita East Road from Atori to Dala, 5,856
road segments with gradient details were delineated and mapped using geographic information system
(GIS) mapping software. These segments were mapped for the current road and also used to model surface
conditions defined by the proposed upgrading projects.

Cost data for similar road projects also informed assumptions about per-unit (km) repair costs
for each type of road surface subject to various damage levels.
a relatively higher percentage of married individuals. In Addis Ababa, 60 percent of refugees are unmarried. 16.2 15.7 14.9 8.8 14.1 Eritrean Somali South Sudanese Addis refugees All 0 20 40 Percent 60 80 100 Eritrean Somali South Sudanese Addis refugees Conflict and violence Natural/man-made disaster Personal threat and political persecution Social and economic reasons Hope to go to a Western country Other reasons Figure 2.3: Refugees arrival in Ethiopia (15 years and above) Source: World Bank Staff based on SESRE 2023. a. Years since arrival b. Reasons for leaving the country of birth 13 For details on OCP refugees, please see Box 2.2. Sociodemographic Profile 11 Refugees have larger households, younger heads, and a higher proportion of female heads than hosts. Household size is higher among in-camp refugees compared to hosts. Across in-camp refugees, South Sudanese refugees have the highest number of household members, averaging roughly seven members per household. The dependency ratio— the ratio of dependents of those under age 15 and above age 64—to working members in a household, is also higher for in-camp refugees relative to hosts and highest among South Sudanese refugees. OCP refugees have the smallest average household size and the lowest dependency ratio.
The ESMF prepared for this project builds on environmental screening templates developed under the predecessor operation and incorporates updated guidelines on asbestos management and e-waste disposal issued by the Ministry of Environment in 2023. Subproject screening will be conducted using the ESMF's standardized checklist, and outputs will be retained in the project's environmental and social documentation register. Where screening identifies potential impacts on natural habitats or physical cultural resources, a qualified environmental specialist will prepare an ESMP prior to works commencement.
Kreibaum (2016) corroborate the ﬁnding that host communities near refugee settlements in Uganda have relatively higher consumption levels using data on three south-western districts, though the eﬀect is small in magnitude. d ’ Errico et al. (2021) ﬁnd that the presence of refugees has only modest eﬀects on local households ’ consumption levels. The authors attribute this ﬁnding not to an increase in demand for local produce, but to greater participation by host households in paid employment in aid agencies, and to the resulting increase in wage incomes. However, the eﬀects they observe are small, and concentrated in areas very near refugee settlements. 11 Most relevant to our study, Kreibaum (2016) reports that access to private primary schools (but not public schools or health services) has increased at a greater rate as a function of refugee presence. They measure refugee presence at the district level (refugee share of the district popu- lation) and focus on the south-western districts between 2002 and 2010. Thus, by including all of Uganda at the parish-year level as well as the post-2014 inﬂux, we extend their analysis. In sum, our study is the ﬁrst to measure the eﬀects of refugee presence on service delivery outcomes in Ugandan host communities. 10See Gianvenuti, Jalal and Kirule (2020) for more details. 11Note that both Zhu et al. (2016) and d ’ Errico et al. (2021) use original cross-sectional surveys. Without pre-treatment data, it is harder for them to make causal claims. 12
Although 86 % of the refugee sample was surveyed at least once, there was some differential attrition between the treatment and control refugee sam- ples, and therefore we estimate bounded effects following Lee (2009) (and the text highlights the results for which both estimated bounds take the same sign unless otherwise noted). These data are analyzed largely following the econometric models and primary outcomes speciﬁed in an AEA pre-registration (AEARCT # 0006141) and associated pre-analysis plan, while making note of ad- ditional and exploratory results. Another core contribution of this study is to examine the host community reaction to refugee assistance, utilizing a detailed survey of the attitudes and experiences collected among a repre- sentative sample of the Jordanian neighbors (N = 2, 146) of both treatment and control households. To our knowledge, this is among the ﬁrst studies to experimentally examine how humanitarian assistance to refugees affects the views of the local communities who do not directly beneﬁt. Speciﬁcally, we examine whether refugee-targeted transfers — in this case via rental payments 1Statistic calculated directly from data on the universe of Syrian refugees in Jordan registered with UNHCR. 2
14 do selected volunteers increase their teamwork / leadership and their communication skills? Do they increase their self-esteem / self-satisfaction? iii) As a result of their assignment to NVSP volunteering experience, are selected youth more likely to find a job than non-selected ones? (a) IMPACTS ON SOCIAL COHESION VALUES The two main indicators that measure improvements in social cohesion values are tolerance and a sense of belonging to the Lebanese community. Measuring social cohesion values in large-scale surveys is challenging. We are unable to use extensive measures, but rely instead on brief measures adapted from Harb (2010). The tolerance measure relies on a series of 12 questions, each of which is ranked on a four-point scale, which makes the total possible score range between 12 and 48 points. The sense of belonging to the Lebanese community measure consists of 18 questions, each of which is ranked on a seven-point scale, which makes the total possible score range between 18 and 126. Thus, higher scale values indicate higher tolerance values and a stronger sense of belonging to the Lebanese community. Both values are internally standardized so that they have a mean of 0 and a standard deviation (S. D.) of 1 in the comparison group at baseline.
14 Figure 3 Sample Distribution for Refugee Children Compared to Global Distribution (n = 92) Figure 4 Sample Distribution for Children Living in Rural Areas Compared to Global Distribution (n = 902) AIM-ECD direct assessment scores were created by calculating the percent correct out of the 77 direct assessment tasks and questions asked. There were four early literacy tasks (listening comprehension, letter identification, initial sound discrimination and name writing), six numeracy