Input:
**6. Conclusions**

This paper has used historical rainfall data to estimate a putative history of payouts on

Indian rainfall insurance policies. We have found that indemnities are concentrated in the

According to Census data (CNBS, 2001), shares of these crops in five of China's provinces

The climate data (monthly temperature and precipitation) were gathered from the National   Meteorological Information Center in China.  The data are based on actual measurements in 753   national meteorological stations that are located throughout China. The temperature and   precipitation data were collected from 1951 to 2001.

Socio-economic data come from China's National Bureau of Statistics (CNBS). The data   were collected by a highly trained, professional enumeration staff in 2001 as part of the annual,   nation-wide Household Income and Expenditure Survey (HIES). The data cover 45,700 farm   households in 4365 villages, 533 counties and 31 provinces.

account for soils, we downloaded a soil map from FAO's website. There are three major soil

types-clay, sand and loam soils. The final set of variables for our analysis was created by

historical rainfall data
Census data
climate data
soil map

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Page 14 of 51 Source: Own calculations based on Household surveys From the education perspective, an improvement in the profile of workers employed as NSE can be observed in the countries analyzed, even though there are some exceptions. In fact, in most of the countries of our sample, the prevalence of workers with secondary and tertiary education increases in detriment of workers with a lower educational level. However, there are some differences by type of non-standard employment. In the case of part-time employment (Figure 10), several countries show an obvious rise in the prevalence of workers with secondary and tertiary education (Argentina, Brazil, Peru, and Mexico). There is a second group of countries that presents a decrease in the prevalence of workers at the secondary level, but, this decrease is more than compensated by the greater incidence of tertiary workers, so that, taken together, workers with secondary or higher education increased their share within part-time employees (Uruguay and Chile). Therefore, we can also conclude from this group that part-time employment presents a better educational profile today than two decades ago. In the Dominican Republic and El Salvador, we find a rise in the prevalence of workers with secondary education but a decrease in the share of workers at the tertiary level. Usage context: background mention, primary mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 3 five methods, it was not possible to consider non-sampling error. This paper goes a step further by using simulations to describe the sampling error and a field experiment in an IDP camp in South Sudan to measure the total survey error of each design compared to a census, allowing for the disaggregation of the total error into sampling and non-sampling components. In addition, we attempt to separate the components of non-sampling error linked to the sample method from those common across all methods, such as interviewers selecting larger households and other issues in properly implementing the household survey protocols. The next section briefly describes each method and highlights the literature as it relates to the relevant selection methods. Section 3 describes the data set and protocols for each method included in the experiment, followed by Section 4, which discusses implementation issues. Section 5 reports the results of the analysis, and section 6 concludes with further discussion of the overall performance and areas for future research. 2. Description of Methods This paper compares five alternatives of second stage selection (satellite mapping, segmentation, grid squares, “ Qibla ” (or “ walk north ”) method, and random walk) to a human canvassing operation.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 14 3. Refugees as Agents of Their Own Destiny 3. 1 The Composition of Africa ’ s Refugee Population and Its Consequences One of the first elements that catch the eye in Figure 6 is the difference in the composition of the refugee population in Africa compared to the rest of the world. The share of children and women among refugees is higher in Africa than elsewhere, in particular East and West Africa stand out here. This is, at least partly, a consequence of Africa ’ s younger, general population, but other forces could be at work as well, e. g. higher mortality of adult males in Africa or adult males staying behind or being separated from the rest of the household. It does mean however that, relative to other areas, more attention should be going to the needs and capacities of women and children in Africa. This means, for example, adaption of and increased supply of schooling and health services. Figure 6. The composition of refugees by age and gender, 2013 Source: Note: UNHCR statistics (UNHCR 2014). Asia excludes Australia, Japan and New Zealand. Americas exclude Canada and the United States. These percentages have been calculated by country when demographic data are available for at least 30 % of the total. Usage context: background mention, supporting mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: to guide and define camp-specific cooking energy options. • In Afar, Gambella, and Melkadida, more than 382,000 refugees and 85,000 hosts have access to alternative cooking fuels and market-based clean electricity from solar-mini grids. • More than 1,739,726 seedlings were planted, 160m3 check dams were built, and 8 km of soil stone band were built to rehabilitate degraded land. Additional GoE GRF Pledges 2023 Climate Action: Protect and restore the environment, manage natural resources, afforestation of degraded lands, and expand renewable energy solutions for the benefit of both refugees and host communities. Recognize vulnerability of women to climate change, and prevent violence against women in all environmental policies and programs, and empower women to have agency and influence in environmental stewardship and adaptation to climate. Human Settlement: Transform selected refugee camps into sustainable urban settlements by enhancing the quality and availability of shelter, infrastructure, and public services, such as roads, electricity, water, sanitation, health, and education by aligning them with adjacent towns’ masterplan, by 2027. Inclusion of refugees into existing national systems: Enhance the capacity of GoE to include 1,000,000 refugees into the national Central Statistics Service (CSS), the national Gender-Based Violence (GBV) prevention and response programs, 814,000 refugees into

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
Since 2006, Rwanda is divided into 30 districts, which are the main service-delivery units. All
household surveys are representative at the district level, but there is no information on districtlevel economic growth. Estimating district-level growth based on nightlights is however
complicated by the low intensity of lights: Four districts did not emit any observable lights at all
during 2000-2011, and ten districts only have nonzero observations towards the end of the 20002011 period. As a result, we can only estimate nighlights-based GDP growth for 16 districts.

Notes: Dependent variable is the natural log of GDP in constant LCU. All specifications include country and
year fixed effects. Robust standard errors clustered by countries in brackets. ***: significant at 1%-level; **:
significant at 5%-level; *: significant at 10%-level. Data source: NGDC (2014) and WDI (2014).

In Column (3) we replace lights observed from space by data on electrical power consumption (in kilowatt hours, obtained from the World Development Indicators). As data on electricity consumption are only available for 22 countries in SSA, the number of observations drops from 966 to 425 (not all 22 countries have data for all 21 years). We find a strong and statistically significant association between electricity consumption and GDP in the reduced sample (elasticity of _0.37_ ). The coefficient is however considerably smaller than the one estimated between night lights and GDP ( _0.58_ ). This may potentially be explained by the relatively high use

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
Use of state level data on rental restrictions, together with a nationally representative survey from India, suggests that, contrary to original intentions, rental restrictions negatively affect productivity and equity.

1 Using census figures, Appu (1997) estimates that, to avoid having to give rights to tenants, landlords evicted about 30 mn tenants or about one third of the total agriculturally active population.

The data used in the analysis come from two rounds of NCAER's ARIS/REDS survey conducted in 1982 and 1999, respectively. This survey, the first rounds of which were implemented in 1968-71 to evaluate the impact of an agricultural development program, covers all of India's major states.

of observation 4980 1279 1613 670 1417 7476 1705 2479 1307 1985
_Source:_ Own computation from 1982 and 1999 ARIS/REDS surveys.
All values are in 1982 Rs with 1999 values having been deflated by state level deflators.

state level data on rental restrictions
nationally representative survey
census figures

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Integrating refugees into functioning national education systems can improve future outcomes for refugee children and their hosts (UNHCR, 2020; Piper et al., 2020; Abu-Ghaida and Silva, 2020; Crawford et al., 2015; Bilgili et al., 2019). As Chapter 2 highlighted, more than half of all refugees are children under the age of 15. Although over 70 percent of primary school age children attend primary education, they do not make it past primary education. Integrating refugee children into educational programs soon after their arrival in Ethiopia avoids the loss of valuable years of education and human capital accumulation, hindering prospects. It is critical to address the obstacles that hinder children from transitioning to secondary schooling, such as challenges in accessing school records, language barriers, or distance to schools. Supporting Regional Education Bureaus could increase the accessibility of secondary schools Policy Recommendations 74 Policy Recommendations to camp refugees.51 To improve the educational attainment of refugee children, the focus should be on increasing the number of qualified teachers in primary education, increasing the currently low compensation to incentive teachers with similar qualifications as nationals, improving primary-to- secondary transition rates, and reducing classroom overcrowding. Build an inclusive health system. Good health is an essential requirement

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: In settings with clearly distinguishable individual structures, these methods can also be reasonably accurate for the purposes of rapid estimation of displaced population (Checchi, et al. 2013). 101 The use of unmanned drones is also becoming more popular as the cost of this technology falls. This technique has been used by UNHCR to update its estimates of IDPs in Somalia ’ s Afgooye corridor (IDMC 2015) and by IOM to monitor disaster-induced displacement in Haiti, including the use of Unmanned Aerial Vehicles (UAVs) in collaboration with UNOSAT. (g) Open data initiatives. There are several initiatives to provide free and open data that enable Internet users to independently mine and analyze data and generate customized summaries, charts and visualizations. For example, the Bank has provided free, open access to its development data since the launch of its Open Data Initiative in 2010, however there is little open data on asylum-seekers, refugees and IDPs. JIPS has developed a web-based platform that allows users to explore, analyze and visualize profiling data online, and IDMC has 100 See http: / / www. flowminder. org /. 101 These methods are not effective in settings with connected structures, a complex pattern of roofs or multi-level buildings, as are prevalent in urban areas. Additionally, cloud cover and dense foliage can also obscure structures. Usage context: primary mention, supporting mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 2 1. Introduction The most common sampling approach for cross-sectional household surveys in the developing world is a stratified two-stage design (Grosh and Munoz, 1996). Following stratification based on administrative boundaries, clusters are selected in the first stage with probability proportional to size from a national census-based frame. In the second stage, a canvassing operation is conducted in the selected clusters to compile an updated list from which households are randomly selected. While this methodology is straight forward to implement in the field and reliably produces unbiased estimates, there are several downsides. The first downside is cost. The World Bank ’ s Living Standards Measurement Study team, which provides technical assistance on large-scale household surveys around the world, estimates the field listing operation increases the overall budget for data collection by 25 percent. Due to confidentiality concerns, the data collected during a field listing operation, typically the name of the household head and address or location description of dwellings, does not have any analytical applications beyond as a component of the weight calculations. 2 At a time when typical survey costs are in the USD millions, reducing a significant cost component will increase the financial sustainability of data collection. The second drawback to the traditional design relates to timeliness. Usage context: background mention, primary mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: In order to address these potential sample biases, the CAVR supplemented its documentation with reports produced by Amnesty International and Fokupers (a local NGO). The information contained in these reports was then included into the HRVD database. 9 Note that we do not analyse school completion in 2001 because most children that were of school age in 1999 were still in school in 2001. 10 The questions we used are ― Was [NAME] displaced outside E. Timor in 1999? ‖, and ― Was the [BUILDING] damaged in the violence of 1999? ‖. 14 % of the whole sample surveyed in 2001 report having been displaced, while 26 % report that their house was destroyed. Within our sample of school age children, these figures are 16 % and 25 %, respectively. We have made sure that buildings that are reported to having been destroyed were used for living purposes only.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
The data sources are (1) Romania MoF firm-level data for the period 2011-20, and (2) the World Bank Businesses of the State (BOS) database for Romania, and (3) the taxonomy of sectors developed by Dall'Olio et al.

<!-- PAGE 8 -->
the period 2011 to 2020. It is based on financial statements and contains balance sheet information such as
firm tax identification number, year of incorporation, operating revenue, average number of employees,
number of employees at the end of the year, labor cost, fixed assets, total assets, amount of subsidies
received from the government, [6] 4-digit NACE industry code, and the county location of the firm.

The Romania MoF firm-level data did not include a variable that identifies the ownership status of the firm (whether the firm is an SOE), which is the key explanatory variable of interest.

<!-- PAGE 14 -->
2014 428,618 1,031 0.24 86.8 13.2 25.2 71.2
2015 440,445 1,092 0.25 87.5 12.5 23.7 72.7
2016 457,273 1,136 0.25 88.1 11.9 22.9 73.7
2017 475,757 1,209 0.25 88.9 11.1 21.4 75.4
2018 491,289 1,277 0.26 89.4 10.6 20.5 76.4
2019 511,863 1,299 0.25 89.5 10.5 20.1 76.9
Total 4,027,783 0.24 87.6 12.4 23.7 72.8
Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. For purposes of providing the
summary statistics, the sample is restricted to firms with revenue and employment data. SOEs are defined as firms
where national or subnational governments have ownership stake of

World Bank Businesses of the State (BOS) database

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
.8
Source: World Bank staff analysis using Romania MoF firm-level data, 2011-2019. For purposes of providing the
summary statistics, the sample is restricted to firms with revenue and employment data. SOEs are defined as firms
where national or subnational governments have ownership stake of at least 10%, while POEs are firms where national
or subnational governments own 0-9.9%. The percentages for centrally owned or locally owned may not add up to
100 because there are "other" SOEs that are neither owned by the central or local government.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 15 causes of death. The Danish Epidemiology Science Centre (1999) found severe malnutrition and high mortality in a survey of 422 refugee children in Guinea ‐ Bissau. They report higher malnutrition and higher mortality for children living in a non ‐ camp setting, compared to children living in a camp. The Goma epidemiology group (1995) found high prevalence of child mortality as well as acute malnutrition among children in refugee camps in Eastern Zaire, especially in female headed households. The magnitude of the difference between ‘ normal ’ mortality in the country under study, in the absence of conflict and the mortality in a refugee camp, depends on several parameters: the health infrastructure in the country as well as in the camp, the food available to camp and non ‐ camp residents, the frequency of visits by nurses or doctors, the intensity of the conflict (e. g. attacks on camps), and so on. Thus, the results are highly dependent on the context. For example, Singh et al (2005) do not find a difference in under 5 mortality among refugee versus non ‐ refugee households in western Uganda and South Sudan, whereas Verwimp and Van Bavel (2005) find higher child mortality and fertility among Rwanda refugees in Congo versus Rwandan women who did not became a refugee. Usage context: supporting mention, background mention, primary mention.

survey of 422 refugee children in Guinea ‐ Bissau

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 6. Robustness Checks 6. 1. Sample attrition Many challenges were encountered during survey implementation, due to the context – the densely populated and impoverished communities in and around Monrovia are difficult settings in which to find and track respondents — as well as the transience of a young study population. Despite vigorous efforts to track and interview each individual in the sample, a certain amount of survey attrition was expected. As the survey response rates in Table 1 show, 1622 (or 80 %) of the individuals in the study sample were successfully interviewed in both the baseline and midline surveys. Another 305 respondents were interviewed at baseline but not at midline and hence are not in the panel used for the analysis in this paper. 27 This survey attrition, while not much higher than other program evaluations in Africa, may cause concern that the results of this evaluation are biased, especially if the loss to follow up is correlated with individual characteristics that might affect the outcomes. To address this concern, Table 10 presents regressions on the likelihood of panel inclusion, that is, the likelihood of being interviewed at both baseline and midline. The first column indicates that treated individuals are significantly more likely than control to have been interviewed twice. This result persists even after controlling for individual characteristics and community dummies.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Some data challenges common to refugees and IDPs Irrespective of the specific questions related to refugee and IDP data, there are also general questions that refer to the forcibly displaced in general and that are distinct from data collection of regular populations or even migrant populations. We explore here selected issues including sampling, unit of analysis, welfare measurement, multidimensional aspects, and the measurement of risks and vulnerabilities. Sampling. As mentioned, the UNHCR is really the only statistical agency for refugees and the UNHCR registry the only population census. As for any other populations, sampling requires the preparation of a master sample that derives from the population census. With various degrees of knowledge and accuracy, this is also what happens with refugees. However, the master sample is more difficult to construct than for regular populations because refugees live in camps and outside camps and are diluted in a host population with different types of arrangements. Some households rent, others stay at relatives ’ places, other live in makeshift shacks and others stay in camps. The information available in the UNHCR registry (the census) can also be quite inaccurate, as already discussed, and the degree of accuracy changes for different groups of refugees. Stratification by urban and rural areas, a typical approach in sampling, may mean little for a population that is mostly in urban areas whether in camps or outside camps. Refugees and IDPs are also mobile and more difficult to track over time than other populations. Several statistical institutes worldwide have developed methodologies to track and measure mobile populations such as herders, nomads or homeless people. However, tracking refugees from other countries has been in the Usage context: primary mention, background mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: We developed pseudo-EAs from the list by location (sub-city and Woreda); some EAs covered more than one Woreda, and multiple EAs were in a single Woreda. We selected a sample of EAs and households from each EAs in collaboration with UNHCR. Finding refugees in Addis Ababa was challenging, as they change their location frequently. To minimize the burden of searching for selected refugees, representatives of the selected households were contacted before the survey to ask them to come to a UNHCR center to collect preliminary information, including their current residential address. Since many Eritrean refugees in Addis Ababa had fled from the conflict in Tigray, out- of-camp refugees in Addis Ababa were stratified into two domains: refugees who arrived before the start of the conflict in November 2020, and those who arrived after November 2020. (c) Host Communities Populations around the refugee camps under each domain were meticulously identified in consultation with UNHCR and RRS. We used the ESS EA maps to assess the settlement of communities around camps, ensuring a precise fit to the definition of a “host community”. The assessment highlighted that using the list of EAs obtained from the new cartographic frame57 meets the definition of host

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
**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.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: For example, IDPs who subsequently cross international borders may be counted as both IDPs and refugees (e. g. in the case of the Syrian displacement crisis). Table 4: Stocks and Flows Stock Increases Decreases Asylum- seekers  New applications for asylum, separately identifying individuals who were previously IDPs  Positive decisions (convention status, complementary protection status)  Rejected  Otherwise closed Refugees  Spontaneous arrivals (group recognition, temporary protection, individual recognition), separately identifying individuals who were previously IDPs  Resettlement arrivals  Births  Administrative corrections  Repatriation  Resettlement  Cessation  Naturalization  Deaths  Administrative corrections IDPs  New internal displacement  Births  Administrative corrections  Cross border flight, becoming an asylum-seeker or refugee  Return  Settlement elsewhere in the country  Local integration  Administrative corrections Source: UNHCR Global Trends, IDMC Forced Displacement Data Model Usage context: supporting mention, background mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 8 as self-confidence, responsibility, and respect. Additionally, the curriculum includes workplace readiness skills, such as interviewing and time management. SECTION 2: STUDY TIMELINE & DATA A quasi experimental impact evaluation design was embedded into the NVSP. As mentioned before, the NVSP received 38 applications from eligible NGOs. Per well-developed selection criteria, 8 the highest 22 ranked proposals were selected to receive funding. Each of the 38 proposals included a list of 50 youth (the minimum number of youth set by the NVSP) who would benefit from the project if selected for funding. However, as mentioned before, the 22 selected projects benefited a total of 1, 296 youth, exceeding the set target of 1, 100 volunteers. Of the 50 volunteers included in each of the 38 proposals, 22 youth per proposal were randomly selected to participate in the impact evaluation study. Therefore, the initial sample size of the study comprised a total of 825 youth: 473 youth who served as the treatment group (representing the 22 selected NGOs that received NVSP funding) and 352 youth who served as the comparison group (representing the 16 non-selected NGOs). However, two NGOs refused to participate in the study once informed that their proposals had not been selected for funding. Usage context: supporting mention, background mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: We find no evidence that Pashtun households are more likely to send their children to Madrassas compared to the rest of the sample, suggesting that geopolitical factors and geographical proximity to Afghanistan matter more than cultural preferences. 16 Similarly there is no evidence for religiosity or household preference-based models of madrassa enrollment. The radical religiosity argument suggests that children are more likely to be sent to madrassas when the family favors a radical brand of Islam. If true, what are we to make of the fact that more than 75 percent of all households with a child in a madrassa also send a child to a public or private school? In a multivariate context we checked whether households identified as “ radically Islamic ” were more likely to send their child to a madrassa. 17 Again, we found no 16 The data from the LEAPS census asked about ethnic and caste identity, and households that classified themselves as “ Pathan ” or “ Afghani ” were used to represent Pashtun households. In line with the usual residential patterns of individuals with Pashtun backgrounds, most of these households are in district Attock in the North of Punjab. 17 In a largely Islamic country it is difficult to find good measures of religiosity. Usage context: supporting mention, background mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
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.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 5 implemented in six areas: Bamako, the regional capitals of Gao, Timbuktu, and Kidal as well as one refugee camp in Mauritania and one in Niger. Bamako was selected because it is home to a large number of IDPs. The refugee camps were selected to obtain a sample of refugees. Returnees were identified in the regional capitals of Timbuktu, Gao and Kidal where the phone network was (still) functional. The approach to selecting respondents differed by location and depended on the availability of pre-existing population information.  Bamako: Listing information of all households with IDPs was obtained from the International Organization for Migration (IOM). Based on this data 10 districts were selected and in each district 10 households were randomly identified.  Gao, Timbuktu and Kidal: No listing data was available and the cities were divided into different sectors. The enumerator was assigned a starting point in a sector, a direction (North, South, East, West) and based on the code of the day 9 the enumerator selected the first household. If the code of the day was 4, the enumerator would choose the 5th house to conduct the first interview. No more than 6 houses were to be interviewed from one starting point. Usage context: background mention, supporting mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Religious School Enrollment in Pakistan A Look at the Data Tahir Andrabi1 Pomona College Jishnu Das The World Bank Asim Ijaz Khwaja Harvard University Tristan Zajonc Harvard University Abstract Bold assertions have been made in policy reports and popular articles on the high and increasing enrollment in Pakistani religious schools, commonly known as madrassas. Given the importance placed on the subject by policy makers in Pakistan and those internationally, it is troubling that none of the reports and articles reviewed based their analysis on publicly available data or established statistical methodologies. This paper uses published data sources and a census of schooling choice to show that existing estimates are inflated by an order of magnitude. Madrassas account for less than 1 percent of all enrollment in the country and there is no evidence of a dramatic increase in recent years. The educational landscape in Pakistan has changed substantially in the last decade, but this is due to an explosion of private schools, an important fact that has been left out of the debate on Pakistani education. Moreover, when we look at school choice, we find that no one explanation fits the data. Usage context: supporting mention, background mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Border crossings The registration of people crossing internationals borders is conducted in many countries, and in some cases these data are used to estimate migration flows. Identifying refugees among people crossing borders is a significant challenge, particularly if individuals decide not to apply for asylum or refugee status (UNSD 2014). Additional problems associated with the collection of data on border crossings include: (a) difficulties distinguishing migrants from other people crossing a border, such as tourists, commuters, traders and truck drivers; (b) lack of capacity of many border posts and officials to handle large migration flows; (c) less scrutiny and diligence of emigration flows compared with immigration flow; and (d) lack of tight controls at most borders and the high incidence of undocumented or irregular crossings (UNSD 2014). Administrative records and registers Many countries have administrative records or registers of immigrants that could generate statistics on asylum-seekers and refugees. In particular, data on residence permits issued to refugees or asylum- seekers could be used to generate statistics on both flows and stocks of refugees. 79 For example, Eurostat collects and disseminates data on residence permits granted to those with refugee status and subsidiary protection (UNSD 2014). Usage context: primary mention, supporting mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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. Usage context: primary mention, supporting mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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. Usage context: background mention, supporting mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 11 There may be grounds for skepticism about these estimates for madrassa enrollment. Since the data were collected prior to 2001, geopolitical changes after September 11 could have led to greater madrassa enrollment. In addition, the household-based survey faces the usual problems of accurately estimating a low-probability event — although enrollment is less than 1 percent in these surveys, the sampling error is large (see Bauman, 2001, for a description of similar problems in estimating home-schooling in the United States). Finally, while the census of populations does not face the problem of small samples, it is not that recent (1998) and some may have reservations regarding the quality of government data. 10 The LEAPS census of schooling choice conducted in 2003 provides a rough check on these numbers (see appendix for details). This census was conducted in three districts of Punjab and villages were chosen randomly based on the criterion that each village must have at least one private school. Typically, this means that the villages lies somewhere between fully urban and fully rural populations and are not representative of the districts that they are in. Estimates from the LEAPS census show that as a percentage of enrolled children, the numbers in two of the three districts are slightly higher than those of the population census. Usage context: primary mention, supporting mention, background mention.

census of populations
household-based survey

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
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

Moderate Resolution Imaging Spectroradiometer (MODIS)

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
, 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).

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: of the groups and the distances between them. For instance, (Bazzi et al., 2019) shows that polariza- tion increases ethnic attachment. Others have highlighted the reduction in trust, either interpersonal trust or institutional trust Alesina and Ferrara (2002); Beugelsdijk and Klasing (2016). To assess the importance of alternative explanations, we first replicate our analysis using individual data on violence. In addition to participation in protests, we follow McGuirk and Burke (2020b) in using the Afrobarometer survey data on interpersonal crime and physical assault. We then assess the relationship between the revised refugee diversity indices and alternative individual outcomes such as ethnic vs. national identity, generalized trust, trust in neighbors, and institutional trust (trust in government). The questions from the Afrobarometer mentioned below are used as a proxy for these outcomes: 32 1 Attack: Over the past year, how often (if ever) have you or anyone in your family: Been physically attacked? 2 Crime: Over the past year, how often (if ever) have you or anyone in your family: Feared crime in your own home? 3 National identity: Let us suppose that you had to choose between being a [Ghanaian / Kenyan / etc.] and being a [respondent ’ s identity group]. Usage context: primary mention, background mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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 Usage context: supporting mention, primary mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Afrobarometer finds that the vast majority of Malians want their country to remain a single and unified nation and that the attempt by armed groups to create a breakaway state in Mali ’ s northern territories is decisively rejected. See Afrobarometer Policy Paper 10 (Dec 2013). This difference with the Afrobarometer survey can be explained by the fact that the latter survey only focused on Malians inside the country and did not take the views of refugees into account. 5 93 2 6 86 75 20 2 3 94 Independence of the North Autonomy of the North Establish full government control over the North Figure 19: How do you envision the future of Mali? IDPs Refugees Niger Refguees Mauritania Returnees Usage context: background mention, supporting mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
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.

UNHCR Pakistan Monthly Protection Trends Report

Encatchment Center reports with Afghan returnees

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: this way, profiling of IDP situations aims to underpin advocacy, protection and assistance activities as well as support the achievement of durable solutions by informing joint strategies between government, humanitarian and development actors. Profiling provides an overview of displacement-affected populations through the collection and analysis of minimum core data (number of IDPs, disaggregated by location, age and sex) and where possible additional quantitative and qualitative data (causes of displacement, patterns of displacement, protection concerns, humanitarian needs, vulnerabilities, and aspirations and prospects for durable solutions). Profiling may utilize data collection techniques at individual, household and community levels, often combining population estimation methods, a review of secondary data, focus group discussions, household surveys and key informant interviews targeted specifically at forcibly displaced populations (UNSD 2014). 70 Profiling methods focus on displacement situations, rather than only on displaced populations, and therefore includes comparisons to conditions in the host population. IDMC estimates that humanitarian profiling data forms the basis for 18 of their 60 country estimates and around 63 percent of their annual estimates (IDMC 2015), with the largest volume of data on conflict-induced internal displacement provided by OCHA followed by IOM. There are several practical challenges associated with IDP profiling exercises in displacement situations. Insecurity or terrain may impede access to displaced populations in conflict-affected or hard to reach areas. Usage context: primary mention, supporting mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
**pandemic, regional drought, inflation crisis and continuing global economic turbulence.** According to data from

the Kenya COVID-19 Rapid Response Phone Survey (RRPS), the Shona community experienced significant job

losses during the pandemic, particularly between July and September 2020, when unemployment increased

dramatically. [[9]] This period of economic disruption caused by the pandemic led to fluctuating employment rates, as

seen in the figure below.

_Figure 3: Labor force participation as a percentage of working age population, between 2019 and 2024_

_Source: 2019 Shona socioeconomic survey,_ [[8]] _Kenya COVID-19 Rapid Response Phone Surveys (RRPS),_ _[[9]]_ _and the authors’ calculation_

_of 2024 survey data._

**While employment has started to improve, it has not yet returned to pre-pandemic levels, especially for women.**

The employment rate for women decreased from 72 percent in 2019 to 61 percent in 2024, which can largely be

attributed to the job losses incurred during the pandemic and slower recovery in female-dominated sectors that

were further setback by the food price crisis and global economic headwinds. Meanwhile, male employment

increased slightly from 74 percent to 76 percent during the same period, reflecting a quicker rebound in male
dominated occupations.

Kenya COVID-19 Rapid Response Phone Survey

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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 Usage context: supporting mention, background mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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. Usage context: background mention, supporting mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
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.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 26 Source: Listening to Displaced People Survey, 2014. 95 % of the refugees in Mauritania envision an independent or autonomous North and 26 % of the refugees in Mauritania even state the independence of Azawad (= the north) as a main condition for returning home. Stark differences can also be observed with regard to the discussion around a possible federalist solution for the North that was ongoing when the monthly phone interviews were conducted in October. As illustrated in the Figure 20 below, 80 % of the refugees in Mauritania support a federalist solution, while the majority of IDPs, returnees and refugees in Niger are not in favor. Of those who do not support a federalist solution (96 % of the IDPs, 88 % of the refugees in Niger and 95 % of the returnees), the majority of IDPs (61 %) and returnees (70 %) as well as 38 % of the refugees in Niger suggest decentralization as a possible solution to resolve the conflict. 13 % of the refugees in Niger also mention war and 27 % the integration of the North. Nonetheless, 49 % of IDPs, 86 % of returnees and 89 % of refugees believe a stable and sustainable peace accord can be achieved. by 4 % of the population living Timbuktu, 2 % in Gao and nobody in Kidal. Usage context: supporting mention, background mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Section 6 presents the differing views of IDPs, refugees and returnees on what happened during the crisis and prospects for peace. Section 7 concludes the paper. 2. The Listening to Displaced People Survey The Listening to Displaced People Survey (LDPS) combines a baseline face-to-face survey with mobile phone follow-up interviews. During the baseline survey respondents were identified and information on household and respondent characteristics was collected. Once the baseline interview was completed, respondents were given a mobile phone and started to receive, at monthly intervals, phone interviews from a call center in Bamako. During these phone interviews structured questions were asked about welfare of the household. Phone interviews are standard practice in developed countries and they are increasingly being used in less developed countries, as the coverage of cell phone networks expands. Not only do these kinds of surveys allow for low cost, high frequency representative data collection (Hoogeveen et

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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. Usage context: background mention, supporting mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: In certain contexts, there can be significant overlaps in these two groups; however data systems may be maintained separately for conflict-induced displacement and natural disasters (e. g. in Afghanistan) leading to possible gaps or double counting if these categories are combined. 30 The IOM Displacement Tracking Matrix (DTM) is a system to track and monitor displacement and population mobility. It is designed to regularly and systematically capture, process and disseminate information to provide a better understanding of the movements and evolving needs of displaced populations, whether on site or en route. It has been active in over 40 countries since its inception in 2004. See http: / / www. globaldtm. info /. 31 This is typically defined as nomads not having access to their traditional routes, but routes can vary. 32 IDMC has recently adjusted their methodology to facilitate greater comparability across situations and improvements are reflected in IDMC ’ s end-2015 data. 33 This is not necessarily a problem if the purpose of the registration system is to delineate entitlements to assistance rather than to determine status. Usage context: background mention, primary mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: In this sense, it is necessary to take into consideration that all indicators of prevalence of NSE and its profile will be limited to a subset of this kind of workers. 4. 1 Latin America and the Caribbean This section focuses on the Latin America and the Caribbean region, where a set of 9 countries, that we consider representing the different realities of the region in an exhaustive way, was analyzed. Specifically, the analysis was conducted for Argentina, Brazil, Bolivia, Chile, El Salvador, Mexico, Peru, Dominican Republic and Uruguay. 5O * NET is the successor of DOT (Dictionary of Occupational Titles) which is no longer updated. O * NET was launched in 1998 on the basis of the BLS Occupational Employment Statistics codes. In 2003, it was changed to SOC which implies that the consistent measures of task content are calculated from 2003. Usage context: supporting mention, background mention, primary mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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. Usage context: primary mention, supporting mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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,

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 21 Results suggest that 66 % of the returnees trust the Malian police and army most when it comes to providing security in the North. Almost half believe that the Malian army is brave and well trained. The vast majority of returnees believe that the government ’ s policies regarding reconciliation, security and social cohesion are good or very good. They also support the government ’ s approach towards decentralization and providing infrastructure such as access to potable water and electricity. As the next section will illustrate this differs strongly with the opinions of refugees. 6. Prospects for Peace IDPs, refugees and returnees have comparable opinions with regard to the requirements for peace: (i) addressing the ongoing crisis, (ii) improving security and (iii) reconciliation. Although there is agreement on what needs to be done, there is little consensus on what happened during the crisis, who the culprits are and who the main victims. Figure 15: What is the most important problem the Government needs to resolve today? (%) Source: Listening to Displaced People Survey, 2014. Usage context: primary mention, supporting mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


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

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

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

Lastly, we use data from the World Bank's Statistical Performance Indicators (SPI) available through the World Bank's Open Data library (World Bank n.d.). The SPI is a composite index between 0 - 100 scoring countries' statistical systems across the five pillars of data use, data services, data products, data sources, and data infrastructure (Dang et al. 2023). To capture the performance of administrative

Our World in Data (OWID) COVID-19 vaccination dataset

Statistical Performance Indicators (SPI)

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: . The SPI is a composite index between 0 - 100 scoring countries' statistical systems across the five pillars of data use, data services, data products, data sources, and data infrastructure (Dang et al. 2023). To capture the performance of administrative data systems in particular, we also use the SPI's indicator of administrative data capacity (Dimension 4.2) that records the availability of Civil Registration and Vital Statistics (CRVS).

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 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. Usage context: primary mention, background mention, supporting mention.

Middle East and North Africa Survey Project

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Page 16 of 51 Figure 11: Education profile of Temporary employees. (Mid-1990s / Mid-2010s) Source: Own calculations based on Household surveys However, it should be noted that the improvement of the educational profile of workers is a generalized trend in the countries considered and cannot be considered a specific characteristic of non-standard employment since it is also observed in standard wage employment and self-employed workers. Statistics regarding the educational profile of standard employees are included in Annex II of the paper. Figure 12 presents the Kernel distribution of the labor income per working hour by country and type of employment. When we analyze what has happened at the salary level and distribution in the period under analysis, two important conclusions emerge. On the one hand, a shift to the right of the wage distribution is observed in all the countries analyzed, indicating an increase in their average. This growth in wages is simply a consequence of the economic growth experienced by these economies. Note that this average wage increase is also observed in the counterpart of standard employment in all cases.. 6 The second trend identified as generalized in the countries of study is the increase in the variance of the wage distribution. In fact, in most of the countries considered, non-standard employment wages currently show a significantly greater dispersion than that registered a decade ago. Usage context: background mention, primary mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Women experience particularly pronounced displacement in the informal sector and no formal job gains. In contrast, for men displacement in the informal sector is fully offset by employment growth in formal sector, with no net job losses. Clearly, our main findings do not depend on the particular sample of subregions we analyze, and importantly are robust to restricting the analysis to a more homogenous group of regions. 6. CONCLUSIONS This paper combines newly available data on the 2014 distribution of 1. 6 million Syrian refugees across subregions of Turkey and the Turkish LFS, to assess the impact on Turkish labor market conditions. The Syrian refugees in Turkey are overwhelmingly employed informally, since they were not issued work permits, and so their arrival was a well-defined supply shock to informal labor. Consistent with economic theory our IV estimates, which also control for distance from the Turkish-Syrian border, suggest large-scale displacement of natives in the informal sector. At the same time, consistent with occupational upgrading, there are increases in formal employment for the Turkish. This increase though only occurs among men without completed high school education. The employment patterns of women and the high-skilled mean they are not in a good position to take advantage of lower cost 36 Results are also robust to dropping all subregions with close to no refugees. Usage context: background mention, primary mention, supporting mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
the General Social Survey - showed no trend. This paradox acquired quickly popularity across the

longitudinal data.

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

Provided that cross-section and longitudinal data are available, one can of course try to reconcile

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Page 9 of 51 Figure 2: Prevalence of NSE by categories of occupations ISCO. (Mid-2010s) Source: Own calculations based on Household surveys When these results are analyzed by the type of NSE, we find significant differences between the trends in Part-time and Temporary employment. On the one hand, there is a stable or growing prevalence of part-time employment among the salaried employees, where Uruguay is the only exception, characterized by a decrease in the incidence of this type of employment (Figure 3). In the cases of Peru and Bolivia, we find almost the same prevalence of part-time employment as two decades ago while in the remaining countries (Argentina, Brazil, Chile, Mexico, El Salvador and the Dominican Republic) there is a greater prevalence of part-time employment. Likewise, the prevalence of temporary employment shows a downward trend in most of the analyzed countries in the last 20 years (Figure 4). In fact, three of the five countries in which temporary employment could be identified show a significant drop in the prevalence of this type of employment (Argentina, Brazil, and Chile). El Salvador presents a stable incidence of temporary employment, while Mexico is the only country in our sample for which there is an increase in this type of NSE.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Impacts of Extremism in Refugee ’ s Integration: Evidence from Afghan Refugees in Tajikistan * Laurent Bossavie † Sandra V. Rozo ‡ Mar ´ ıa Jos ´ e Urbina § Keywords: Refugees, Female Education, Extremism JEL Classification: F22, D74, J15, I21, I10 * The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. All errors are our own. The team is grateful to the project “ Enhancing Government capacity in hosting temporarily Afghan refugees ”, funded under the Rapid Social Response Program (RSR) multi-donor Trust Fund of the World Bank, for funding data collection and allowing us to use the Afghan refugee survey as one of the multiple sources of data for the paper. The purpose of the project and the data it collected was to identify opportunities for refugee integration in Tajikistan. We acknowledge financial support from the Research Support Budget at the World Bank. We also thank Daniel Garrote for his initial support on instrument design and Merve Demirel for supporting data collection. † World Bank, e-mail: lbossavie @ worldbank. org ‡ World Bank, Dev. Usage context: supporting mention, primary mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: Anderson, J. (2011): “ The Gravity Model, ” Annual Review in Economics, 3 (1), 133 – 160. Angrist and Pischke (2009). Mostly Harmless Econometrics, Princeton University Press. AFAD (Disaster and Emergency Management Presidency of Turkey) (2013). Syrian Refugees in Turkey, 2013: Field Survey Results. Republic of Turkey Prime Ministry Disaster and Emergency Management Presidency. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015a). “ The Impact of Refugee Crisis on Host Labor Markets: The Case of the Syrian Refugee Crisis in Turkey. ” IZA Discussion Paper 8841. Akgündüz, Y. E., M. van der Berg, and W. Hassink (2015b). “ The Impact of Refugee Crises on Firm Dynamics and Internal Migration: Evidence from the Syrian Refugee Crisis in Turkey, ” mimeo. Aydemir, Abdurrahman and Murat Kırdar (2013). “ Quasi-Experimental Impact Estimates of Immigrant Labor Supply Shocks: The Role of Treatment and Comparison Group Matching and Relative Skill Composition, ” IZA Discussion Paper 7161. Baez, J. (2011). “ Civil Wars Beyond their Borders: The Human Capital and Health Consequences of Hosting Refugees. ” Journal of Development Economics 96 (2) November: 391 – 408. Usage context: supporting mention, primary mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input:
**Table 8: Flood Risk Classification of Proposed Subproject Sites**

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

Flood hazard maps used for this classification were prepared by the National Flood Risk Management Authority and validated against historical flood records from the Ministry of Home Affairs' disaster registry.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.


Input: 11 however, since they cannot distinguish between a country where the population is concentrated in one cluster covering 10 % of the territory and one where the population is concentrated in two clusters of 5 % each, but with a considerable geographical distance between them. Population and conflict geography in the Democratic Republic of Congo (DRC) corresponds to these arguments regarding population concentration and dispersion. Concentrations of language-based minorities are evident throughout eastern DRC. Due to the limited access of the government, the close proximity to international borders, and the dense population concentrations, these concentrated minorities have a higher potential of conflict than other, more accessible, sparsely populated areas of DRC. Figure 1 shows the population concentrations in 1990 (CIESIN data) for Central Africa. Heavily populated areas are shaded in deeper tones of red / grey. Civil conflict in DRC has overwhelmingly occurred in the eastern portion of the state, which is the most densely populated area and also geographically peripheral to the capital, Kinshasa. Of the eleven Congolese rebel groups accounted for in the dataset used in this paper, all have operated either exclusively or partially in the eastern and southern areas of DRC. Usage context: primary mention, supporting mention, background mention.

A data mention with a proper name: dataset, survey, database, index, census, registry, or information system.

A data mention described by its characteristics or producer rather than a proper name.

A data mention that only generally references data, information, or statistics.