rafmacalaba/data-use-mentions-extended huggingface dataset:
# **3.1. Flooding hazard data**

Floods can be caused by a variety of factors. The most common types are considered in this analysis:

**Fluvial flooding** occurs when intense precipitation or snow melt causes rivers to overflow. **Pluvial flooding** occurs when rainwater builds up beyond the absorptive capacity of soil. Country-level pluvial and fluvial flood maps, developed by Fathom Ltd, are used in this analysis (Smith et al. 2015). The data sets provide information on flood extents and flood depth at an approximate spatial resolution of 90 meters and are available for all countries. The maps are based on a global hydrological and topographic model. The model simulates flood events with return periods of 5, 20, 50, 100, 250, and 500 years. This study considers flooding with a 100-year return period. A 1-in-100-year event, for example, is expected to occur once every 100 years on average (i.e. it has a probability of 1% of occurring in any given year). As with all global flood maps, the effects of artificial flood protection structures like dikes are not incorporated.

Two editions of the Fathom global flood maps are available; 2016 and 2019. Wherever available, the newer version has been used. The 2019 version differs from the 2016 version in the resolution of the digital elevation model that has been used to simulate event scenarios. The 2019 edition uses the newest DEM MERIT elevation model that corrects for multiple errors, including tree and building height adjustments.

**Coastal flooding** is caused by storm surges and high tides in coastal areas. A commonly used global coastal flood risk map is used in this study: the Global Tide and Surge Reanalysis (GTSR) data set by Muis et al. 2016. As it uses a digital terrain model that does not account for common errors such as stripe noise and tree heights (unlike for instance DTM MERIT), this map is expected to provide a lower bound or conservative
to be the subject of a full listing operation, which could then serve as a sampling frame.<sup>6</sup> Finally, in all three countries, as in other developing countries, forcibly displaced populations are excluded from the available national census and sampling frames. Hence, we used UNHCR data on registered Syrian refugees, which had to be combined with host population estimates, typically at the sub-district level. For more details on the sampling strategy, please refer to (Aguilera, et al., 2019).

The sample was stratified into groups of interest, depending on the country context. In Lebanon, the survey is representative of the host community and the Syrian refugee population. In Kurdistan, the scope of the survey was expanded to include internally displaced persons (IDPs), so that the survey is representative of the host community, of Syrian refugees in camps and outside camps, and of IDPs in camps and outside camps. In Jordan, the original design could not be implemented because we could not access the new sample frame based on the 2015 Jordanian census. The design was then amended to include a representative sample of the Za’atari and Azraq camps (which account for the vast majority of the Syrian refugee camp population in Jordan). It was complemented by purposive samples of the surrounding governorates, Mafraq and Zarqa (where the sample included areas physically proximate to the camp, and other areas of high Syrian refugee prevalence). In the Amman governorate, a purposive sample was drawn, combining a geographically distributed sample with a sample of high prevalence Syrian refugee areas as indicated by the Jordanian Department of Statistics. In what follows, _Jordan sample_ refers to the households surveyed in Za’atari and Azraq camps, areas surrounding the camps in Mafraq and Zarqaa governorates, and Amman governorate, except in regression analysis, where we only use the Amman sample.

The survey instrument was administered across Lebanon, Jordan, and KRI, with slight modifications depending on the structure of refugee living conditions.
non-farm agricultural household enterprises a large share is in urban areas and two main sectors are trading and manufacturing. The latter was analyzed using RPED data, while the former is least understood. It is clear that in trading there is low capital involved and very seldom hired labor utilized, making it a substantially household enterprise activity, as opposed to most manufacturing which compared to trading has more capital and more hired labor. Other activities such as transport, construction and services also employ a large share of non household members, while transport by far has the highest capital component of any informal sector activity.

Informal sector activities play an important role in the lives of poor people. It can be easily seen from the following table that income derived from informal sector 28
# _Privacy_

Privacy will be a key consideration in any implementation. As demonstrated in this paper, the scoring model can be estimated with anonymous data, by anonymizing the identifier that links phone and lending data. However, to generate a prediction for a lending decision, the model must be run on that potential borrower’s data. An implementation can be designed to mitigate privacy risks. It can be opt-in, so that only consumers who consent are scored with the system.<sup>20</sup> It can reveal to lenders only a single number summarizing default risk, rather than the underlying features describing behavior. Additionally, it can be restricted to use features that are less sensitive, such as top up behavior rather than the network structure of an individual’s contacts.

# _Manipulation_

Some indicators are ‘gameable’ in the sense that a subscriber may be able to manipulate their score if they knew the algorithm. The feasibility of manipulation depends on the complexity of the final model and the susceptibility of individual indicators to manipulation. Both dimensions of the model can be tailored to reduce the probability of manipulation. For example, it is preferable to use indicators that are less susceptible (e.g., manipulating spending or travel can be costly).

# _Heterogeneity in performance by subgroup_

An extension to this paper evaluates performance by different subgroups (Björkegren & Grissen, 2018), replicated in Figure S3. The model is trained on all individuals except the omitted fold, and performance is reported for the given subgroup (women, men, and residents in or outside the capital) within the omitted fold. Although our error bars can be wide, performance is not widely heterogeneous across groups, suggesting the method may be able to score different types of individuals.

> cannot be used to score our population of interest, and our method scores this population at a higher level of performance within this range.

> 20 Potential borrowers who opt in may be differentially selected from the broader population, in which case a
**Table 1b: Sample Composition (Kitchens): Airmetrics MiniVol Air Sampler**

|**Fuel Type**||**Kitchen Type**||**Construction Ma**|**terial**|
|---|---|---|---|---|---|
|Gas, Electricity,<br>Kerosene|35|Single room dwelling,<br>no separate kitchen|36|Wall: Thatch|100|
|Firewood, Twigs,<br>Leaves|89|Kitchen with a<br>partition (4 walls and<br>a roof)|37|Wall: Tin|37|
|Cow Dung|42|Separate, attached<br>kitchen (4 walls and a<br>roof)|43|Wall: Mud|39|
|Rice Husks, Straw|16|<br>Separate, detached<br>kitchen (4 walls and a<br>roof)|89|Wall: Brick/ Mud,<br>Roof: Other than<br>Concrete|9|
|Jute Sticks,<br>Bagasse, Sawdust|54|Outside/ open kitchen<br>(0 walls, no roof)|31|Wall: Brick/ Mud,<br>Roof: Concrete|20|
|Total number of<br>households|236|Total number of<br>households|236|Total number of<br>households|205|

air at 5 liters/minute through PM10 and PM2.5 particle size separators (impactors) and then through filters. The particles were caught on the filters, and the filters were weighed preand post exposure with a microbalance. We operated the air samplers at 236 locations.

# **3.  Sample Evidence: PM10 vs. PM2.5**

We focus on PM10 in this paper because our monitoring sample is much larger.

However
international migration over 1960–2000. Section V discusses some implications of the study.

# _I. PRELIMINARIES_

Migration data are complex. They almost always come from destination countries, because it is difficult for origin countries to collect demographic data on people who are not living in the country. Unlike trade and financial statistics, which are recorded by both transacting parties, the quality of migration statistics depends almost entirely on the rigor with which destination countries survey the migrants within their borders. In addition, destination countries‘ recording and dissemination methods can differ greatly. Understanding the analysis in this article requires an understanding of the subtle differences in various sources and definitions, together with an understanding of the inherent inconsistencies between them.<sup>3</sup>

# _Who Are Classified As Migrants?_

The United Nations (1998, p. [6]) defines a migrant as ―any person that changes his or her country of usual residence.‖ This broad definition implies a movement from one location to another, the most relevant concept for economic analysis. However, official records apply many different definitions of what constitutes an international migrant. Most common criteria are based on country of birth, country of citizenship, purpose of visit or visa type, place of last permanent residence, and duration of stay.

The two main definitions of migration—being born in or being a citizen of a foreign country—are used most consistently over time and across countries. Citizenship is important for determining an individual‘s legal rights for employment, voting, and access to public services. The place of birth definition is superior for determining physical movement. Destination countries typically publish migration statistics by either category, mainly according to national migration and citizenship laws. Historically, countries in the Americas and Oceania favor the country of birth definition whereas countries in Asia, Africa, and Europe traditionally adopt a mix of the two definitions.

Individuals may be classified as migrants or nonmigrants depending on the definition. Many destination countries grant citizenship to foreign-born people who are
& Furey, 2016) The importance of access to water supply cannot be overstated. Apart from meeting the human necessity of water, it has immediate health impacts (Hyland and Russ, 2019) which in turn affects other developmental outcomes including education attainment and even long run poverty outcomes (Zhang and Xu, 2016; Mangyo, 2008; Alderman et al., 2001). Evidence indicates that lack of functioning infrastructure will undermine economic growth (Agenor, 2010; Barbier, 2004). Moreover, evidence also indicates that infrastructure—including water and sanitation—is likely to offset moderate macroeconomic shortcomings during the initial stages of economic development (Moller and Wacker, 2017; Gibson and Rioja, 2017; Amann et al., 2016).

In this paper, we utilize information from the Tanzania Water Point Mapping (WPM) compiled in November 2016 to identify the reasons for the failure of water points in the country. Using a variety of statistical methods, we seek to understand the impact of the following factors on water points failure: (i) the age, (ii) technology type; (iii) geographic patterns; (iv) hydrological characteristics; (v) type of promoter; and (vi) day to day management type of these points. The results indicate that water points 2
are calculated automatically by the insurer based on measured rainfall, without households needing to file a claim or provide proof of loss. This significantly reduces administrative expenses.

In Andhra Pradesh, insurance is sold to households by BASIX, a microfinance institution with an extensive rural network of local agents known as Livelihood Services Agents (LSAs). These LSAs have close, enduring relationships with rural villages and sell a range of financial services including microfinance loans and other types of insurance. In our Gujarat study areas, rainfall insurance is marketed by SEWA, a large NGO that serves women.

_Actuarial values, observed payouts and pricing. –_ For four policies in Table 1, we are able to calculate a measure of expected payouts using historical rainfall data. In each case, we

> 4 As a point of reference, the average daily wage for agricultural laborers in our survey areas at the time of the study is around Rs. 50, although incomes for landed farmers or more skilled workers are significantly higher.

6
The two countries that are found to impact our results most are Vietnam, whose omission from the sample would raise the estimate to 47.9 million people, and Pakistan, whose exclusion would lower the final count to 38.2 million people.

As a second robustness check, we calculate global poverty implications of higher food prices without using country-specific information on domestic price changes for selected commodities. Redoing our calculations using only the global price changes scaled by the domestic import shares, we arrive at a significantly lower estimate of poverty change of 30.9 million people, which suggests that our assumption for pass-through parameters may be conservative, and that domestic food price changes in developing countries may, in fact, be more responsive to global price changes than assumed.

As an additional check on our results, we calculate their responsiveness of our estimates to poverty lines higher or lower than the internationally standard $1.25 per day measure for extreme poverty. We repeat our calculations for four additional poverty lines in the range of the standard definition of extreme poverty. Our results (Table 6) show that while the estimates of global poverty vary depending on the poverty line chosen—the poverty estimates decline as the poverty line rises—the results are not vastly different within the range from $1 to 2 that we consider.

The observed negative relationship between the poverty impacts and the poverty line appears to arise primarily from the declining share of expenditure on food as incomes rise. While the shares of net food sales rises with income, this appears to be a less important determinant of the overall resul than the decline in the share of food in total consumption. These findings are reflected in the last two columns of Table 6 which show little relationship between gross reductions in poverty and poverty line, and a much stronger negative relationship between the poverty line and gross increases in poverty.

# **Comparison with the impacts of the 2008 food price crisis**

The recent surge in food prices, which has raised the World Bank food price index
# **Candle in the Wind? Insights from COVID-19 Emergency Cash Transfers to Informal Sector Workers in**

# **Sierra Leone**<sup>**1**</sup>

# **Samik Adhikari**

# **Suneha Seetahul**

**_Keywords_** _: Unconditional Cash Transfer, Covid-19, Sierra Leone_

# **_JEL codes_** _: I38_

> 1 The authors would like to thank the National Commission for Social Action (NaCSA), Statistics Sierra Leone (Stats SL), and other stakeholders involved in implementing the ECT Program. We would also like to thank Elizabeth Mary Foster for timely collaboration on collecting data for ECT Program Beneficiaries through the CIMS high-frequency phone surveys and Wendy Cunningham and Yuko Okamura for their valuable comments. We are indebted to Shreya Chatterjee for her role in conducting the preparatory data analysis for the design of the ECT and in preliminary data analysis for this paper. Finally, we would like to thank the World Bank Sierra Leone Social Protection and Jobs team: Junko Onishi, Abu Kargbo, Steisianasari Milieva, Judith Sanford, Sumati Rajput, Thomas Vaughn Bowen, Mpumelelo Nxumalo and Hannah Buya Kamara for their input and feedback on various drafts of this paper. Data files available upon request.
The combined electoral and criminal effects of forced displacement exacerbated already negative impacts on development and fueled the autocratic regime. Amid less pressure for reforms and more reliance on illicit activities and coercive enforcement, the incumbent government had few incentives to improve conditions for private and human capital investment. We leverage a mediation analysis to show that these two channels explain approximately 40 percent of the effect of forced displacement on development.

Our findings are robust to additional validity tests. The estimates are robust to alternative cluster specifications for standard errors, to approximating forced displacement using inverse distance to Colombia’s border crossing points, and to alternative difference-in-difference estimators (Abadie, 2005; Sant’Anna and Zhao, 2020). Another potential concern is the possible manipulation of electoral data. To address this, we conduct data checks for abnormal patterns and find no evidence of manipulation before 2018, in line with reports from the humanitarian and international organizations that validated those elections.<sup>6</sup> However, we do find evidence of manipulation (or the effects of a political boycott by the opposition) in the 2018 presidential election. For this reason, we include opposition-collected data for the 2024 presidential election, which indicate that Maduro lost by a landslide. Additionally, we analyze mayoral elections across 335 municipalities, comprising five election years and 1,675 observations. The scale of these elections makes systematic manipulation unlikely. The estimates remain consistent and support the validity of our findings.

We contribute most directly to recent research on the impacts of cross-border migration on migrant-origin socioeconomic conditions (Yang 2008; Dinkelman and Mariotti 2016; Theoharides 2020; Dinkelman et al. 2024; Godlonton and Theoharides 2025). Closest to our work, Khanna et al. (2022) and Testa (2021) focus on the effects of emigration on economic growth at origin locations. Our paper complements their work by examining the effects of forced mi-

> and corruption. The name derives from the sun insignias ( _soles_ ) worn by Venezuelan generals and the term is commonly used
done so as to ensure a minimum of 10 EAs per combination of urban‐rural and state distinction, according to the following rule: where n� is the sample size in stratum _h, n_ is the total sample size, _H_ is the total number of strata, N� is the total population of stratum _h_ , _N_ is the total overall population, and S� is the standard deviation in stratum _h._ The results from the sample size calculations are shown in Appendix B, Table 3. The chosen sample allocation provides estimates that are representative at the national, urban/rural, and state level. Sampling weights were calculated on the basis of the 5<sup>th</sup> Sudan Population and Housing Census from 2008 (Appendix B). In cases where fewer than 12 households were interviewed in an EA, the sampling weights were adjusted at the EA level to reflect this.

Data collection was intended to be implemented in two phases, by randomly splitting each stratum into two equal‐ sized parts, where each phase of data collection would cover half of the sample. The advantage of a two‐phased approach was early availability of representative data after half of the survey was implemented. The two‐phased approach reduces the risk that an eruption of violence during field work invalidates the representativeness of the survey. However, such an approach is not guaranteed to maintain representativeness if some areas remain inaccessible throughout the entirety of fieldwork. It also comes at the cost of optimizing the organization of fieldwork by reducing the enumerators’ ability to sweep over their intended area.

# Survey Implementation

The survey was implemented using tablets as survey devices. The data collection system consisted of Samsung Galaxy Tablet computers equipped with SIM cards, mobile data plans, microSD cards (16 GB capacity), and external battery packs.<sup>13</sup> Teams of four enumerators and one supervisor were provided with a mobile generator using fuel to ensure that tablets could be charged overnight. Computer Assisted Personal Interviewing (
# **4. Empirical Strategy**

To further unravel the factors affecting mobile internet adoption, following the approach of Forenbacher et al. (2019) and Hasbi and Dubus (2020), this analysis tries to estimate the probability of using mobile internet through the following model: where _Mobile Interneti_ is a binary variable that equals 1 if individual _i_ uses mobile internet, and 0 if not. Given that the dependent variable is a binary variable, a logit model is adopted for the estimation to predict the probability of an individual adopting mobile internet. By logarithmically transforming the outcome variable, it allows the examination of a nonlinear association in a linear way. The coefficient βk measures, ceteris paribus, the effect of a one-unit change in xik on the dependent variable.

Following the conceptual framework, which categorizes factors affecting the digital divide into three groups, variables in the model include: (1) infrastructure factors (3G network coverage and household access to electricity); (2) socioeconomic factors such as gender, income level, education level, and rural/urban location; and (3) social network effects (table 1). Those factors also reflect what the descriptive statistics show to be the main obstacles to owning an internet-enabled phone or using internet, as outlined in the data section above.

Infrastructure factors include whether an individual’s location is covered by a 3G mobile network signal, and whether the individual’s household has access to electricity. Whether an address is covered by a 3G mobile signal is identified by mapping the household survey data (which feature GPS location data at the individual level) with the global mobile network coverage data from the Collins Bartholomew data set.<sup>12</sup> Socioeconomic factors include gender, age, education level, income level, marital status, and urban/rural location. Demographic information at the individual level is taken directly from the household survey. Lastly, the number of close friends using an online social network like Facebook or Twitter is used as