ai4data/datause-dataset-rechunked huggingface dataset:
**Appendix G: Comparison of GLSS and DHS Wealth Indices**

The GLSS wealth index and the DHS wealth index are both constructed using principal component analysis but differ in their input variables. The GLSS wealth index draws on 32 asset and housing variables, while the DHS wealth index uses 40 variables including country-specific items added through DHS national adaptation. The correlation between the two indices, estimated on the subsample of GLSS clusters that were also covered by DHS fieldwork within the same 12-month window, is 0.74, suggesting substantial but not complete overlap in what the two measures capture.
6 3 Methodology 3. 1 The A-F method and individual deprivations The Multidimensional Poverty Index (MPI) used in this paper was first presented, with a full methodological discussion, in Admasu et al. (2021). Here we present a general overview of the measure for the individual-level and intrahousehold analyses. The MPI is constructed based on the Alkire-Foster (AF) method of multidimensional poverty measurement (Alkire and Foster 2011). Three key statistics characterize any MPI: incidence or headcount ratio (H), which is the proportion of the population who are multidimensionally poor; intensity (A), which is the average share of weighted indicators in which multidimensionally poor people are deprived; and adjusted headcount ratio (M0 or MPI), which is the product of the incidence and intensity (MPI = H × A). The AF method uses a dual-cutoff counting approach to poverty measurement. Having fixed relative weights across indicators that sum to 100 %, it first identifies who is deprived in each indicator, then sums up the weighted deprivations each person experiences into a deprivation score. A person is identified as poor if their deprivation score meets or exceeds a cross-dimensional poverty cutoff that is greater than 0 and less than or equal to 100 %. It then aggregates this information to compute society-level MPI, incidence, and intensity.
In addition to the publicly available imagery, NOAA has produced, especially for this paper, monthly frequency composites for the 2004 to 2013 period. These satellite-month datasets

from the labor force survey (LFS) ENOE. Specifically, we produce a quarter-year panel of

The national water commission (CONAGUA) provided us with three datasets: (i) Data on historical rainfall at the day-weather station level, this dataset spans the 1920 to 2015 period,

and contains the universe of weather stations. (ii) The weather station-month level triggers for Fonden eligibility. (iii) The mapping between municipalities and representative weather stations.
4 MDL contained 4, 539 datasets at the time of this research (December 2022), some of which were also cross-listed in the UNHCR MDL. The UNHCR MDL contains micro-level datasets that are of concern to UNHCR ’ s mission and mandates. These datasets often – if not always – include a sample of refugees, asylum seekers, IDPs, or stateless people. The datasets come from censuses, registration and administrative exercises, and surveys. This MDL contained 546 datasets at the time of our mapping exercise. One of the key advantages of using these databases is that they offer detailed metadata which help us understand the main characteristics of each dataset. The metadata contains a rich set of attributes pertaining to each micro-level dataset, including the country of data collection, the producer (s) of the dataset, brief description or abstract and thematic scope of a dataset, dates of data collection, unit of analysis (e. g., households, individuals), geographic coverage (e. g., national, regions, camps), data type (e. g., sample survey data, census, administrative), questionnaire modules, as well as sampling strategy. A complete list and explanation of each attribute in the metadata can be found in Annex B: Metadata.
The FAO now uses survey data to calculate the CV and coefficient of
skewness. This revision also took into account the average physical stature of each age-sex group derived from
DHS data. The FAO updated the CV estimate for 37 countries, and, for the other countries (where they did not
obtain HCEs), they used the same CV as in the past.

The FAO method has been widely critiqued by the research community (Svedberg, 1999, de Haen et al.

The FAO calculates the CV of food availability directly for only for a limited number of countries and
Using observations from NASA's OCO-2 platform, we develop the template from the data filtering techniques and econometric analysis employed by Dasgupta, Lall and Wheeler (2022). For a large sample of urban areas, we compare alternative trend estimation models and conclude that the template can be constructed from a simple model that estimates trends directly from OCO-2 data that are prefiltered to isolate local concentration anomalies.

In an additional exercise, we use our regression model results to compute expected emissions from
urban areas. The regression residuals identify the directions and relative magnitudes of departures
from expected values for individual areas. We convert the residuals to their emissions equivalents
using high-resolution gridded information from the EDGAR global database (Crippa et al. 2020). For
1,306 urban areas with populations greater than 500,000, we find a rough balance between cities whose
emissions are higher and lower than their expected values. Converting deviations to percentages of
expected values, we find that percent deviations are typically greater in absolute value for cities with
lower-than-expected emissions.

1 Recent research has used Google Traffic to infer vehicular emissions from high-resolution traffic congestion data for
some cities (Heger et al. 2018; Dasgupta, Lall, and Wheeler 2021). However, no currently available technology enables
direct estimation of global vehicular emissions at 25 km resolution.
2 Household air conditioning is powered by fossil-fired home generators in many hot low-income areas where utility-scale
power is either nonexistent or unreliable. For a detailed assessment, see Lam et al. (2019).

The design of OCO-2 supports comparative exercises like our analysis. It follows a sun-synchronous
near-polar orbit, crossing the equator in ascending mode around 1330 hours local time. This means
that the OCO-2 observations for our study are collected between 1200 and 1500 local time for all
displacees in their communities, the survey asked an additional question in which respondents re- ported whether their communities hosted IDPs (displaced persons from within DRC) or refugees (displaced persons from Burundi, Rwanda, Uganda, South Sudan, or other countries). These re- sponses are used to create a categorical variable that measures whether respondents report hosting IDPs, hosting refugees, or not hosting displaced persons in their communities. Table 3 summarizes the measurement strategies for hosting status. It is possible that respondents may misreport whether they are displaced or whether displaced people are present for a number of reasons. First, they might not know that displaced persons are present, a risk that is especially acute for IDPs. Because the paper is primarily interested in how knowledge of hosting impacts perceptions of social cohesion, this measurement challenge is not as acute a problem as it may at ﬁrst seem. Respondents must know that IDPs or refugees are present in their community for hosting to impact their perceptions of social cohesion. If they are not aware of the presence of IDPs or refugees, their presence is unlikely to systematically impact their perceptions. Second, there might be incentives to either hide or, alternatively, to over-claim the presence of IDPs or refugees.
The study mainly relies on household data from the VHLSS 2010, 2012, and 2014. These surveys are conducted by the General Statistics Office (GSO) with technical support from the World Bank in Vietnam. They are nationally and regionally representative and contain detailed information on individuals, households and communes. In total 9,400 households nationwide are included in each round. Half of these households were also interviewed in the previous round so that the data set includes a short-term panel.

**1)** **Air pollution** is measured by the area-weighted mean of concentration (measured as micrograms per cubic meter) of particulate matter with a diameter of 2.5 micrometers or less (PM2.5) taking the 10 years- average value for 2000-2010. The data is based on satellite imaginary using the total column aerosol optical depth from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer satellite instruments, which is combined with chemical transport model simulations, and ground measurements from 79 countries to produce a global spatial data set with 0.1° × 0.1° resolution (Brauer et al., 2015). PM2.5 includes dust, dirt, soot, smoke, and liquid droplets, which can lodge deeply into the lungs due to their small size. PM2.5 air pollution has been identified as a leading risk factor for global diseases (Forouzanfar et al., 2015).

**2)** **Tree cover loss** is calculated as the share of the area under tree cover in 2000 that suffered from a tree cover loss between 2000 and 2010. Tree cover is defined as canopy closure for all vegetation taller than 5m in height and is calculated from imagery from the Landsat 4, 5, 7, and 8 satellite data used to produce a global

**3)** **Land degradation** is measured by the share of land area that experienced a significant biomass decline. This loss is calculated based on the inter-annual mean
, 5, 7, and 8 satellite data used to produce a global

**3)** **Land degradation** is measured by the share of land area that experienced a significant biomass decline. This loss is calculated based on the inter-annual mean trend of Normalized Difference Vegetation Index based on data from Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration (NOAA) satellite between 1982 and 2006, which is corrected for climate effects to only measure human-induced degradation (Vu et al., 2014b). Soil fertility, agricultural productivity and ultimately food security of smallholder farmers can be largely compromised on degraded lands (Von Braun et al., 2013).
In particular we construct ∆ CEIdmt as the change in the conﬂict events index for district d between the quarter immediately prior to month m in year t from the previous quarter. 14 The second term on the right hand side ∆ luminositysmt is analogously constructed using the same lagged time periods. The third term AoCsmt is a series of Area of Control dummy variables, which capture who is in control of sub-district s in month m in year 12Syria is a unitary state, but for administrative purposes it is divided into 14 governorates, which are further divided into 65 districts and 281 sub-districts. 13As a robustness check, we also include month of the year ﬁxed eﬀects to pick up seasonal changes in migration patterns. 14Note that the conﬂict data is only available at the district level. 18
A panel database tracking outcomes for a cohort of 2,400 households across six districts was established during the baseline survey phase. The panel database will be updated at 18-month intervals through follow-up surveys using the same household roster, allowing the evaluation team to track changes in income, consumption, and access to services at the individual household level. Attrition in the panel database will be documented and analyzed to assess whether survey non-response is systematic and whether inverse probability weighting adjustments are required.
The activity monitoring dashboard was identified as a critical tool for maintaining oversight of the project's rapid cash transfer component, where disbursement targets are time-sensitive and beneficiary eligibility windows are short. The dashboard interface was customized to display daily transfer volumes disaggregated by district, gender of primary recipient, and payment modality (mobile money vs. agent banking). When daily transfer volumes fall below 80% of the planned schedule, an automated notification is sent to the relevant district coordinator and the PIU operations manager.
3 UNHCR Pakistan Monthly Protection Trends Report, May-Aug 2017, cites similar reasons for return to Afghanistan including the 
following pull factors: reunion with family in Afghanistan, employment / livelihoods, no longer fear of persecution, UNHCR 
assistance, happy to return and the following push factors: strict border entry requirements, uncertainty of POR card extension, no 
overall protection value of POR cards, overall deterioration of security situation in Pakistan, arrest and detentions, denial of access 
to services.  
4 UNHCR Pakistan Monthly Protection Trends Report, July 2017, reflects similar means for persons to receive information on 
repatriation, collected during VolRep Exit Interviews and Encatchment Center reports with Afghan returnees, stating they receive 
information on Afghanistan from the Afghan community in Pakistan, UNHCR, and visits to Afghanistan.  
5 Example provided of carpet weaving businesses returning to Afghanistan and thus resulting in a number of carpet weavers out of 
work in Khyber Pakhtunkhwa.
14 The fundamental unit of observation in ACLED is the event. Figure 2 illustrates the ACLED data for Central Africa for the 1980s and the 1990s. Each location of a conflict event is represented by a symbol. In several of these locations, multiple events occured over the periods. Events always involve two actors – a rebel group and a government – and are coded to occur at a specific point location and on a specific day. Most of the events are battles, but the dataset also records other activities. The dataset includes information on and distinguishes between six types of events: battles resulting in no change of territory, battles resulting in a transfer of territory to the rebel actor, battles
Language barriers, mental health challenges, and uncertain futures are identi­fied as major obstacles to integration. The study highlights the importance of tailored interventions, such as psycho­logical support and more dedicated teaching time, to foster refugee students ’ academic and social inclusion. This paper is a product of the Development Data Group, Development Economics and the Social Protection and Labor Global Department. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http: / / www. worldbank. org / prwp. The authors may be contacted at michela_carlana @ hks. harvard. edu; pcastaing @ worldbank. org; mtestaverde @ worldbank. org; and mtiberti @ worldbank. org.
hotspot areas and regularly issues protection of civilians’ advocacy
briefs. The Sector also disseminates monthly updates and daily and
weekly updates in times of crisis.

Funding situation permitted, the UN protection agencies, Protection
Cluster, its AoRs, and their partners implemented activities
mentioned under funding data.

While most of these activities were conducted in response to several
emergencies in Darfur, South, West Kordofan and Blue Nile states,
some activities were also implemented in the areas of protracted
displacement.

The Protection Sector and its AoRs also actively supported the
development of the One UN Protection of Civilians strategy and
support plan for the National Plan for the Protection of Civilians
(NPPOC). Before the military coup of 25 October 2021, the Sector was
preparing to organise a joint national and several workshops at the
state level to develop further and prioritise proposed activities. The
Protection Sector and AoRs also actively advocated for establishing
the state-level protection of civilians’ committees and working
relationships with the National Mechanism on the Protection of
Civilians.
In a mortality study in Iraq, Galway et al. (2012) used GIS and Google earth imagery for household sampling. The method used gridded population data for selection of clusters. The first cluster sampling stage of their study used the ‘ Create Spatially Balanced Points ’ (CSBP) function in the ArcGIS (v10) software. Boo et al. (2020) introduces a sampling design based on gridded population estimates as their sampling frame to implement a PPS design and derive sample size estimates for the number of grid cells. Assuming the grid square method is applied to the area itself rather than a selected PSU, the weights for the grid method are similar to those for segmentation, where the cells are the PSUs, but without the additional step of selecting segments. The weights can therefore be represented as 𝑤𝑤𝑖𝑖 ′ = (𝑁𝑁𝑘𝑘) ൫𝑁𝑁𝑘𝑘𝑘𝑘൯൫𝑁𝑁𝑘𝑘𝑘𝑘𝑘𝑘൯ 𝑘𝑘𝑘𝑘𝑘𝑘. 2. 4. North Method The “ Qibla method ” described in Himelein et al. (2017), or what is called in this paper the “ North method ” method, is an attempt to assign probability weights to random point selection methods. Several random point selection methods can be found in the literature, particularly in relation to epidemiological studies.
on behalf of refugee beneficiaries. III DATA III. A Data for Afghan Refugees in Tajikistan The primary dataset is a detailed census of Afghan refugees in Tajikistan carried out by the World Bank at the end of 2022, after the Taliban regained complete control of Afghanistan in 2021. The questionnaire administered to refugee families was compre- hensive; it collected detailed information on the socio-economic background, migration history, labor market outcomes and current socio-economic situation of Afghan refugee household and their members, and some measures of socio-economic integration. The approximate duration of the interviews was about two hours. The sampling frame for the census is the administrative database of Afghan refugees registered with the United Nations Refugee Agency (UNHCR) in Tajikistan. All households recorded in the registry were interviewed, leading to a total of 1, 958 Afghan refugee households and 9, 763 indi- viduals. Figure 2 displays the dates of arrival of the refugees in the census. As shown in the figure, the vast majority of migrants arrived to Tajikistan in 2021 when the Taliban gained complete control of Afghanistan. We first use these data to describe the main socioeconomic characteristics of the popula- tion of Afghan refugees in Tajikistan and illustrate general patterns, as described in Ap- pendix A.
refugees, it is higher compared to their hosts. Moreover, refugees have higher access to improved waste disposal methods38 than hosts. Both refugee and host households have low access to electricity, except for hosts of Eritrean refugees. Hosts around Eritrean refugees have better access to electricity (meter private or shared) for lighting (74 percent). The use of solar energy is common among Eritrean refugees, with 78 percent of Eritrean refugee households get lighting from solar energy. Almost all South Sudanese refugee households have no access to electricity either from meter or solar sources. All refugees in Addis Ababa use electricity for lighting, similar to their hosts. 0 20 40 60 80 100 120 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Improved toilet facility Improved waste disposal method Percent Figure 2.23: Access to toilet facility and waste disposal Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis Ababa Total Electricity (meter) Electricity (meter, generator, solar) Percent Figure 2.24: Source of lighting Source: World Bank Staff based on SESRE 2023. 0 20 40 60 80 100 Hosts Refugees Hosts Refugees Hosts Refugees In camp Addis
8 areas are listed so that a given number of households can be randomly chosen from the list to be interviewed. The advantage of this approach is that it only requires knowledge of any FDP residing in an enumeration area, so that this person can be included in the sampling frame. The more accurate the number of FDPs in an enumeration area before listing households, the more efficient is the resulting sample. This approach requires appropriate enumeration area maps that can be easily defined through satellite images for camps, but might not be available across the country for FDPs living in host communities. Implementation can be expensive especially with limited knowledge about the number of FDPs in enumeration areas. However, area-based sampling has the big advantage of not requiring FDPs to register. In contrast, list-based sampling uses an existing list of all FDPs and randomly chooses a sample among them. If additional characteristics are available, like location or country of origin, the sample can be stratified. However, the sample will only be representative of registered FDPs. Hence, it is rarely used in the context of IDPs unless a proper registration system is in place.
based on SESRE 2023. 0 10 20 30 40 50 60 70 80 Eritrean Somali South Sudanese Addis Ababa All Refugees Figure 7.17: Share or refugees involved in a community representative body Source: World Bank Staff based on SESRE 2023. Markets and Opportunities 71 Low refugee social integration is not because they have a low willingness to engage with their communities. Refugees have an extremely high rate of involvement in refugee community representative bodies. This includes participation in refugee central committees, refugee outreach volunteers, refugee community leaders, and leaders in women and youth associations. Sixty percent of refugees participate in an organization like this, the lowest in Eritrean camps at 32 percent. On average, these rates are similar across age and gender groups. Main immediate refugee integration challenges are: (i) to expand involvement outside of refugee communities, and (ii) to better understand the social integration barriers refugees in Ethiopia face. In principle, the positive attitudes among many hosts towards refugees, the high degree of cultural similarity between groups, and the willingness of refugees to be engaged in their community are all promising signs for social integration. Yet, even in the Somali domain, where cultural similarity and host attitudes are greatest,
higher for refugees than hosts, both for in-camp and for out-of-camp refugees (Figure 5.9). While the food insecurity Refugee and host communities could differ in multiple dimensions over and above consumption. The Multidimensional Poverty Index (MPI) explores this multiple deprivation, capturing differences across three dimensions of well-being: health, education, and living standards (Alkire et al., 2021). MPI provides a general picture of the extent of deprivation. In this context, deprivation in education is assessed using school attendance for school-age children and years of schooling among adults. Health is proxied by the presence in the household of a stunted child or death of a child in the last 12 months before the survey. Living standards are assessed by access to electricity, improved water, sanitation, cooking fuel source, housing, and economic assets. The MPI ranges from 0 to 1, with 1 representing a high level of deprivation. It is the product of two partial indices: the headcount ratio (H) and the intensity of poverty (A) i.e. (MPI = H*A). The headcount ratio is the share of poor people in the population, while the intensity shows how much deprivation poor people experience on average. A cut-off point of 0.33 is used for the