ai4data/datause-dataset-rechunked huggingface dataset:
interactions between hosts and refugees may help improve hosts’ attitudes (Betts et al., 2023). In some contexts, refugee inflows have been found to harden in-group identification and increase support for ideological extremes. This was the case with refugee inflows in Denmark, for instance, but only in rural areas (Dustmann et al., 2019). On the other hand, refugees hosted in Austrian municipalities for extended periods, as opposed to those who passed through, were found to reduce support for anti-immigrant parties, pointing to the benefits of refugee-host interactions (Steinmayr, 2021). All-in-all, there is little evidence that refugee hosting tends to worsen attitudes toward refugees in the Global South (World Bank, 2023b). 7.1 Attitudes between refugees and hosts SESRE data show that, while some hosts have negative attitudes towards refugees, most attitudes are generally positive. Sixty-five percent of hosts agree that refugees are friendly and good people, and only 20 percent are uncomfortable with having a refugee neighbor. This is an important finding, highlighting the potential for integration policies. Host attitudes are generally most favorable in the Somali region and most negative around South Sudanese camps; the share not comfortable with having a refugee neighbor increases to 37 percent in the South Sudanese
6 However, this may reflect differences in rainfall or farming intensity.
7 We use two poverty maps based on the General Population and Housing Censuses of 1998 and 2009. The poverty
map for 1998 was developed by GREAT (Applied and Theoretical Economics Research Group) and combines the
1998 census and the household survey ELIM (Integrated Light Household Survey) of 2006.
violent conﬂict. Besley and Mueller (2015a) argue that foreign investors seem to know that growth volatility changes with strong executive constraints and therefore react signiﬁcantly to their adoption. In summary, the literature suggests that a lack of constraints on executive power at the country level could play a key role in building inequalities across regions and ethnic groups. In the absence of strong executive constraint, we expect regions populated by ethnic groups that have access to executive power to perform better relative to others due to ethnic favoritism. Conversely, excluded ethnic groups should experience relatively worse economic performance compared to other groups in the absence of such constraints. 46 To test these hypothesis we use data on ethnic groups ’ access to executive power and night light intensity from the GROWup Research Front-End (RFE Release 2. 0) dataset and executive constraint data from the Polity IV dataset. We use night light intensity as a proxy for economic activity at the ethnic group level. 47 Night light data has the beneﬁt of being available on a yearly basis and of being measured at the local level where there is poor availability of statistical data.
These data are only for three districts in the province of Punjab, but is very recent, was conducted by an independent team of academics, and is a complete census of all households in the selected villages. Consequently, it yields sufficient madrassa enrollment to examine correlations with household attributes in a meaningful manner (this data source provides information on four times as many children as the PIHS). Table A2 in the appendix shows how these different data sources are used in the paper. Each source asks about madrassa enrollment in a slightly different but comparable way. The population census (1998) asks about the field-of-education (“ What is name ’ s field of education? ”) with options that include (for instance) engineering, medicine, or religious education. This question is also asked of all literate adults irrespective of their current enrollment status, allowing for comparisons in the stock of religious education over time. The PIHS rounds ask, “ What type of school is name currently attending? ” with options that include government school, private school, or deeni-madrassa (religious schooling). Finally, the LEAPS census directly asks, “ Is the child enrolled in a madrassa or an Islamic education school? ” Fortunately these different questions all give rise to similar numbers. This is reassuring since it suggests that any one particular result is not driven by the specific question or definition that was used. 7 This is complemented with the census of private schools carried out by the Federal Bureau of Statistics in 2000 (www. statpak. gov. pk).
animal-drawn carts. Across all domains, refugees have a lower value of productive assets such as farming tools and construction equipment. They are also less likely to own commercial cars, motorcycles, or Bajaj, a cause of a significant portion of the gap in total value of assets between refugees and hosts. With low employment rates, earnings, and value of household income-generating activities, in-camp refugee households rely heavily on aid. Expanding refugee access to agricultural land, livestock, and legal work outside of camps are vital for refugees to maintain their livelihoods without depending on donations. On average, 78 percent of in-camp refugee households report that NGOs or government donations are their primary source of income (Figure 3.20), increasing to 88 percent for South Sudanese households. On the other hand, their host counterparts rely most on employment, and 32 percent rely on agricultural income compared to only 3 percent of refugee households. This contrasts with refugees’ previous livelihoods in their country of birth, where they relied on traditional income sources, especially agriculture and remittances, along with a smaller amount of aid. 0 0.1 0.2 0.3 0.4 0.5 0.6 Camp Hosts Camp Refugees Figure 3.14: Household owns crops Source: World Bank Staff based on
The activity monitoring dashboard provides a consolidated real-time view of implementation progress across the three project components. It is updated daily by the MIS system's automated batch processes and accessed by national and regional coordinators through a password-protected web portal. Dashboard visualizations include cumulative beneficiary counts by gender and age group, disbursement rates by component, and a map of active subproject sites. The dashboard does not constitute an official reporting instrument; official results are reported through the structured Interim Financial Reports and annual progress reports.
The COVID-19 Panel Phone Survey of Households sample is nationally representative, representative of Bamako, and representative of both urban and rural areas.

The EHCVM sample itself covered 8,390 households across Mali and is nationally representative, representative of Bamako, and representative of both urban and rural areas. The survey relies on a multi-module instrument covering topics including a household's socio-economic characteristics, time use, production activities, and welfare indicators such as consumption expenditure and food security.

use sampling weights derived from the 2018 EHCVM sampling frame and adjusted for response rates


in the COVID-19 Panel Phone Survey of Households. These sampling weights are applied both in our

In this study, we use the FAO's Food Insecurity Experience Scale (FIES) as primary outcome of interest. The FIES aims to measure food insecurity based on the direct experiences of people relating to food security (Ballard _et_ _al._, 2013; Smith _et_ _al._, 2017).
We also simulate the livestock effects across three AOGCM climate scenarios. The AOGCM scenarios predict a 2-3% increase in the probability of owning livestock by 2020, a 4-7% increase by 2060, and a 0-13% increase by 2100.

Washington W et al., 2000. Parallel Climate Model (PCM): Control and transient scenarios.
_Climate Dynamics_ 16: 755-774.

<!-- PAGE 28 -->
**Table 7: AOGCM climate scenarios**

The empirical analysis is based on a household survey conducted of 11 countries across Africa: Burkina Faso, Cameroon, Egypt, Ethiopia, Kenya, Ghana, Niger, Senegal, South Africa, Zambia and Zimbabwe (for more information about the entire study, see Dinar et al. 2006).

In this study, we relied on monthly temperature data collected from US Department of Defense satellites (Basist et al. 2001). This set of polar orbiting satellites obtain measurements at a given location on earth at 6am and 6pm every day. The satellites are equipped with sensors that measure surface temperature by detecting microwaves that pass through clouds (Weng & Grody 1998).
The GLSS follows a multi-stage stratified cluster design with 580 enumeration areas selected in proportion to population size. Within each GLSS cluster, 20 households are systematically selected using a random start. The GLSS sample is designed to be representative at the regional level and for urban/rural disaggregation within each region. Analytical weights supplied with the GLSS public-use file must be applied to produce nationally representative estimates; unweighted analyses will produce biased results due to differential sampling rates across strata.
This is exactly what we will do in the present paper. Using data from 333 municipalities
( _comuna_ s) in Chile, we will estimate short-run relationships between climate variables


1
For example, an extensive drought in Central America might damage its coffee-production and the reduced
supply of coffee would increase the world price of coffee, which could benefit coffee-producers in other parts
of the world.

Two different types of climate change will be assessed. First, the documented recent
climate change in each of the 333 municipalities, as estimated from average monthly
temperature series from 1948 to 2008 for all the Chilean meteorological stations that have
contributed systematically to the Monthly Climatic Data for the World (MCDW)
publication of the US National Climatic Data Center. Second, we will use the predictions
of the Fourth Assessment Report of the Intergovernmental Panel on Climate Change
(IPCC4) climate models to simulate the likely effects of projected future climate change in
Chile.

The municipal level cross-section data-base, which is used to estimate the relationship between climate and development in Chile, is constructed using data from different sources. Table 1 lists the variables, their definitions, and the sources of the information.
The model is estimated using data from the Turkish HBS in 2008 and 2009. The HBS is conducted annually by the Turkish Statistical Institute to collect information on household socioeconomic status, living standards, income, and consumption expenditures.

The survey data are then combined with the actual monthly rate schedule. The Turkish electricity
tariff data have two advantages that are fairly unique to the study. First, in the retail sector,
Turkey has applied a price equalization mechanism to maintain a nationwide uniform tariff until
2011. Under such a system, all residential consumers face a uniform flat-rate price schedule.

ation to fish & fishery products     -    GB 14939Fish Hygienic standard for canned fish CAC 70:1995 NEQ 1994    GB 2715Cereal Hygienic standard for grains - 2005    GB 19303Meat 2003   GB/T 22388Milk 2008   GB/T 23376Tea 2009    Hygienic practice of cooked meat and meatproducts factory    Determination of melamine in raw milk and                                          -                                         dairy products   Determination of pesticides residues in tea                                          -                                         GC/MS method    CAC/RCP13 MOD 1976    Vegetabl  es    GB 2714Hygienic standard for preserved vegetables         -         2003    GB 13104- CAC Sugar Hygienic standard for sugars NEQ 2005 212:1999   _Source:_ Authors‟ calculations based on SAC National Standards Query   20     <!-- PAGE 24 -->  **Figure 2.

To ensure reliability and completeness, the standards have been cross  checked with the "Chinese Bulletin of Standards" (SAC 2011a) and the German-Chinese  Standards Portal (DIN and SAC 2011).

(HS 1992) and tariffs are compiled from the TRAINS data base. Consumption variables are

computed from the Food and Agriculture Organization of the United Nations‟ statistical

database FAOSTAT. [6] We deflated our data US Bureau of Labor Statistic's HS Import Price
15 4. 1. 2. Empirical strategy In order to make use of the retrospective information on school attendance provided in the dataset, we have constructed what we have called an ‘ ad hoc panel ’, whereby we exploit the time-variation of the variables of interests (age, attendance status and grade attained by the respondents) by reshaping the cross-sectional structure of the TLSS 2001 dataset. In this way, we are able to obtain observations for each individual over three academic years. All key education variables are time- variant, while other individuals and households characteristics are time-invariant. Within these three years, we focus our analysis on individuals that were of primary school age (between 7 and 12 years old) in each year. In practice, we keep all children aged at minimum 7 years old in 1998 and at maximum 12 years old in 2000. As a consequence, our panel data contains children aged 8-11 years in 1999, the year of the violence. 11 Since we are interested in looking at different effects across groups of individuals, we have split the sample between boys and girls and between younger children (aged 8-9 in 1999) and older children (aged 10-11 in 1999).
The returnee population is relatively more prone to lethal landmine and ERW accidents than the
civilian population living in their community of origin. According to data from DMAC and UNMAS,
travelers including returnees and IDPs account for 30% of all landmine, ERW and Pressure Plate IED
accidents. Returnee populations are particularly vulnerable due to their unfamiliarity with the overall
threats posed by explosive hazards; lack of information about how to identify them and lack of the
potentially life-saving behaviour to adopt in response, in addition to their unfamiliarity with their new
surroundings, including the history of armed clashes and potential for explosive contamination in
areas of settlement.
(2009) have shown that, in Nepal, subjective welfare is negatively associated with geographical isolation. Census data on total population and population density in each district are used as proxies for urbanization and geographical proximity: the denser the population, the less geographically isolated individuals are likely to be. We also include data on the average elevation in each district. Nepal being a mountainous country, the higher the average elevation of a district, the more costly it is to build roads, raising transport and delivery costs to the district. Ceteris paribus, we expect migrants to seek out districts with a higher population density and a lower elevation. 4 Econometric results 4. 1 Univariate analysis We now investigate the choice of migration destination. We begin with simple univariate analysis. Variables are of the form ∆ h is = xh s − xh i where i is the district of origin of migrant h and s is each of 74 possible districts of destination. We examine the average value of ∆ h is for the destination district and compare it to the value of ∆ h is for alternative destinations. For instance, let xh s be population density in district s. The average value of ∆ h is for the actual destination of the migrant tells us whether the destination district is more densely populated than the district of origin.
<!-- PAGE 7 -->
cities in the sample, providing a consistent mid-day activity benchmark for comparing CO2
concentration anomalies. [3] OCO-2 has an observation repeat time of 16 days. We have downloaded
georeferenced measures of XCO2 (the column-averaged dry air mole fraction of CO2).

We compute the daily median XCO2 for each 10-degree latitude band and linearly interpolate the result to each OCO-2 observation with 1-degree resolution.

Mistry (2019) has provided global estimates of monthly heating and cooling degree days at 25 km
resolution. [4] We compute population at 25 km resolution by aggregating data from CIESIN (2021) at
5 km resolution. Monthly estimates are interpolated from data provided for 2010, 2015, and 2020. We
use two sources to construct our georeferenced measure of income per capita. From the G-Econ
database (Nordhaus et al.

We merge the results with annual UN estimates of GDP per capita in constant $US 2015 (UN 2021), and use the cell ratios to estimate annual GDP per capita for each cell.
MIS data extracted at 30 September showed that cumulative cash transfer disbursements had reached 78 percent of the annual target, with 14,822 households having received at least one payment. The MIS disbursement module flagged 312 records with payment anomalies, of which 287 were resolved through validation against the beneficiary registration database and 25 were referred to the compliance officer for further investigation. The MIS was updated to add a duplicate detection algorithm that screens new registrations against existing records before activation.
23 mega-cities, annual growth rates of peripheral population tend to reach around 10-20 percent compared to central business districts. 4. Implications for public policy Hazard management is a task both for the public sector and for private households and firms. For the public sector in cities, this includes ensuring the safety of municipal buildings and public urban infrastructure, encouraging and supporting private sector hazard risk reduction, and developing first response capacity. A considerable share of hazard risk stems from relatively small but frequent events which cause localized damage and few injuries or deaths (Bull-Kamanga et al. 2003). For instance, an analysis of detailed records of 126 thousand hazard events in Latin America showed that more than 99 percent of reported events caused less than 50 deaths or 500 destroyed houses (ISDR 2009). In aggregate, these accounted for 16. 3 percent of total hazard related mortality and 51. 3 percent of housing damage. The probability of larger events may or may not be predictable. For instance, a city may be in an earthquake risk zone, but the location specific ground shaking probabilities are not known. Individual dwelling unit level mitigation is therefore necessary everywhere in the general area of high earthquake probability. For other hazard types like landslides and floods, potential risk areas can be more easily delineated.
count of existing contributions and the diﬀering capacities and resources among States. ” It formally “ intends to provide a basis for predictable and equitable burden- and responsibility-sharing among all United Nations Member States, together with other rel- evant stakeholders as appropriate. ” Underpinning the global debate on responsibility- sharing is the assumption that “ the grant of asylum may place unduly heavy burdens on certain countries ” (UN General Assembly 2018), typically countries neighboring a conﬂict area. In this perspective, the number of refugees a country is to host is simply a function of its geography. This paper examines empirically the proposition that the hosting of refugees falls disproportionately on neighboring countries, which in most cases are in the developing world. To do so, we use data on worldwide bilateral refugee stocks compiled by UNHCR to examine the spatial distribution of refugees and its evolution over time. Our period of analysis is 1987-2017. Our main ﬁndings can be summarized as follows. While refugees still remain over- whelmingly in a country neighboring their country of origin, the past decades have seen a trend towards greater geographic diﬀusion. We begin by showing that the global pop- ulation of refugees has been increasingly dispersed across host countries, as captured by a falling Herﬁndahl index of host-country shares.
Historical trends and patterns of forced displacement: Insights from available global data This section provides an overview of the available global data on conflict-induced forced displacement, drawing largely on UNHCR ’ s published data on asylum-seekers, refugees and IDPs. 41 Data are presented visually in a series of figures to highlight the scope and character of the current global forced displacement crisis and identify historical trends and anomalies. These data largely focus on the scale and trends in conflict-induced displacement (i. e. the numbers of forced displaced) with some coverage of other elements such as demographics, location and accommodation. Globally, there has been an unprecedented increase in the numbers of displaced people over the last decade, largely explained by the expansion in the number of reported IDPs. Historical data show a substantial increase in the numbers of forced displaced (see Figure 3), however the expanding geographical scope and quality of displacement monitoring systems are likely to account for much of the increase in forced displacement figures. The numbers of refugees under UNHCR ’ s mandate have recorded a number of variations over time, peaking in the early 1990s (at a level 10 percent over 2015 numbers) with the conflict and displacement associated with the end of the Cold War. The number of Palestinian refugees steadily has increased steadily over time, largely as a result of natural growth. IDP numbers (for which the underlying data are the least robust) have recorded the largest progression as a consequence of: (a) the expanded scope of monitoring efforts (IDPs were not counted before 1989 and methodologies were 41 UNHCR ’ s data only include IDPs protected or assisted by the agency.
When the stable lights data set is examined, most countries display positive growth in _AoL_ and negative
growth in _R_ . There is a weak negative relationship in levels (Figure 7) and a somewhat stronger negative
relationship in growth rates (Figure 8). This outcome is not surprising. For all but a handful of rapidly
growing countries, the periphery of an expanding _AoL_ is not as bright as the center. Thus, _R_ must fall
because _R_ = _SoL_ ÷ _AoL_ .

We use four independent variables. Data for GDP, non-agricultural GDP, and manufacturing value-added
are all measured in constant 2005 US dollars. These data come from the World Development Indicators
supplied by the World Bank. Data for electricity consumption in kilowatt-hours come from the
International Energy Agency via the World Development Indicators. Data for population come from the
UN Population Division, again via the World Development Indicators. Data for the value of the stock of
physical capital, denominated in constant 2005 US dollars, come from the Penn World Tables Version 8.0.
1 We use the generic term HCES to refer to a range of household survey efforts to capture total household
consumption expenditures. This can include surveys described as household budget surveys, living standards
surveys, or others.

We explore a unique survey experiment which randomly assigned seven different HCES methods to

of Sub-Saharan environments where, according to the FAO, the proportion of hungry people is highest

survey experiment, described below, ensure that any differences in derived hunger numbers are solely