rafmacalaba/data-use-mentions-extended huggingface dataset:
# **Appendices**

Table A1: <u>Clustering — Impacts of hosting refugees on household welfare and poverty</u>

|||All househol<br>|ds<br>||
|---|---|---|---|---|
||Consumption|Consumption poverty|Wealth|Wealth poverty|
||(1)|(2)|(3)|(4)|
|Refugee Intensity|-0.189**|0.179***|0.047|-0.007|
||(0.076)|(0.059)|(0.049)|(0.044)|
|Constant|9.725***|-0.896**|0.455|0.651**|
||(0.446)|(0.375)|(0.339)|(0.324)|
|Round FEs|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|
|R-squared|0.068|0.043|0.188|0.115|
|# of Woredas|317|317|317|317|
|# of EAs|433|433|433|433|
|N|12937|12937|13522|13522|

_Notes:_ This table provides estimates of the impacts of logarithm of refugee intensity on logarithm of total household consumption expenditure per capita (column (1)), a binary measure of household consumption poverty status (column (2)), “started log” of household wealth score (column (3)), and a binary measure of household wealth poverty status (column (4)). Household sample weights from ESS2 applied. All columns control for household (HH
students provided personal and family information on registration forms. For instance, we gathered information on enrolled children’s age, gender, household composition, mother’s education level, and adult supervision exposure, among other characteristics. We also collected school records of math, reading, and science grades; behavior reports;<sup>17</sup> and absenteeism data from both enrolled and nonenrolled children. Appendix 1

> 17In El Salvador, teachers complete behavior reports each quarter, which are presented in the following discrete scale: Excellent (E), Very Good (MB), Good (B), and Regular (R). It can be translated in a continuous scale that is comparable to course grades. In this paper, we use a reversed continuous scale to facilitate the interpretation and comparability to self-reported measures of violence and crime.

12
financial markets, through which people can also access financial instruments such as life, health and auto insurance, for example.

# **4. Methodology**

In order to assess the correlation between insurance penetration and our measures of institutional quality and financial market development taking into account GDP per capita, we estimate the following regression using a panel of 180 countries from year 1996 to 2016: where _i_ indexes the country, _r_ indexes the country region and _t_ indexes the year. As our dependent variable, we use insurance penetration as a percentage of GDP. In the main analysis, we only report the results using life and non-life insurance penetration, but we also report the results using total (life and non-life) insurance penetration in the Online Appendix.

Since we want to estimate partial correlations controlling for GDP per capita, we always include GDP per capita on the right-hand side of our equation. Inspired by the model developed by Greenwood and Jovanovic (1990) and previous work by Enz (2000), among others, we include both a linear (GDPPC) and a quadratic term (GDPPC<sup>2</sup> ) to partially capture the non-linear correlation between insurance penetration and GDP per capita. All variables on the right-hand side are lagged by one year to mitigate concerns of endogeneity. We also include region fixed effects (γr) and year fixed effects (λt) in all specifications and country-specific time trends (tθi) in some specifications.

GDP per capita, institutional quality and financial market development are arguably strongly correlated, so we take a parsimonious approach and run separate regressions for each one of our measures of interest apart from GDP per capita. These variables are included in vector Xit�1. The coefficient of interest is β3 and should be interpreted as the partial correlation between variable X and insurance penetration once GDP per capita is taken into account.

When analyzing institutional quality, not only do we estimate Equation 1 using the index that summarizes the quality of the institutions of each country, but we
status and a well-functioning data ecosystem. Of the 19 economies in the MENA region, 15 report quarterly data on GDP. Some economies lack information for the year 2020 entirely. Economies in conflict such as Libya (2014) and Yemen (2017) have outdated data. Only 10 of the 19 MENA economies have monthly or quarterly information on industrial production—for the remaining nine economies, information is not readily available. Only eight economies report quarterly unemployment data, while none have monthly data. The benchmark country, Mexico, reports unemployment data monthly. A caveat is that the table does not consider quality. For example, definitions of unemployment may be inconsistent with international standards (Arezki et al., 2020).

**Table 3 MENA Macro Data Availability (as of January 2022)**

|**Economy**|**GDP Data**|**GDP Data**<br>**latest**<br>**(calendar**<br>**year)**|**Industrial**<br>**Production**<br>**Index Data**|**Industrial**<br>**Production**<br>**Index latest**<br>**(calendar year)**|**Unemployment**<br>**Data**|**Unemployment**<br>**Data latest**<br>**(calendar year)**|
|---|---|---|---|---|---|---|
|Algeria|Quarterly|Q2 2021|Quarterly|Q3 2021|Semi-Annually|May 2019|
|Bahrain|Quarterly|Q3 2021|n/a|n/a|Only Census|2020|
|Djibouti|Annually|2018|n/a|n/a|n/a|n
**Figure A1. Share of electricity in the total consumption of goods and services by various sectors in 2012 (%)**

<!-- Start of picture text --> Households 1%<br>Service 3%<br>Transport 0%<br>Trade 8%<br>Mining 21%<br>Gas 0%<br>Electricity 10%<br>Construction 1%<br>Other manufacturing 19%<br>Machinary & Equipment 11%<br>Metals & products 28%<br>Non Metalic Minerals 18%<br>Petroleum 0%<br>Fertilizers & Chemicals 21%<br>Jute, textile, Leather 0%<br>Food & Beverage 1%<br>Forest & products 9%<br>Agriculture 1%<br><!-- End of picture text --> Source: Social accounting matrix used for this study 34
obstacles (e.g. rough terrain) or other barriers (e.g. border delays), and relative performance measures (e.g. processing time at ports or when crossing a border).

As with any modeling exercise, it should be noted that our network is a simplified version of reality and results are affected by the quality of inputs. For example, we do not include some elements that could affect routing, such as rail gauge, freight or passenger lines, service frequency, etc. An additional limitation that needs to be acknowledged comes from the fact that the Belt and Road Initiative includes some efforts that are not easily introduced to the network: improving efficiency of customs, special economic zones, reducing border delays or an improved management of economic corridors. As an extension of our main database, in Section 5 we present two scenarios where those elements are explicitly taken into account. Finally, our results are driven by a number of assumptions regarding the speed along different path as well as the choice of transportation mode, the processing time when reaching a port and the time it takes to cross a border. We describe those assumptions in detail below.

# **b. Transport network and BRI‐related transport infrastructure projects**

We start with an initial network of links connecting all cities considered in our analysis, and we then add additional links and improved speed corresponding to transport infrastructure projects that are linked to the Belt and Road Initiative. Our initial network comes from Delorme Atlas of the Earth (DAE), 2015 release. For our analysis, maritime features were generated separately for the Pacific, Atlantic and Indian Oceans, and for the Caribbean Basin and Seas of Indonesia. To simulate plausible trans‐oceanic shipping routes, great‐circle arcs<sup>7</sup> were generated from port locations along the boundary of Exclusive Economic Zones. Within the Caribbean Basin, around Indonesia, and near land, connectivity to major ports was manually digitized using a map of Global Shipping
# IPF Results: Road Transport

As with water catchment projects, transport projects are ranked by SEI and FEI scores separately. The calculations for SEI and FEI scores are detailed in Annexes 6, 7, and 8, while the graphical results by each index are summarized in Figures 10, 11 and 12. Figures 10 and 11 show the SEI ranking based on the simple poverty indicator and the multi-criteria poverty index, respectively.

## Road Transport SEI and FEI Rankings

Figure 10. Road Transport SEI Ranking Results from Input and Weighting with Pov_c <!-- Start of picture text --> 100<br>90 Social and Environmental Indicator (SEI) - Pov_c<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Figure 11. Road Transport SEI Ranking Results from Input and Weighting with Pov_multi<br>100<br>90 Social and Environmental Indicator (SEI) - Pov_multi<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Figure 12. Road Transport Project FEI Ranking Results<br>100 Financial and Economical Indicator (FEI)<br>90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>P31 P12 P10 P26 P34 P4 P46 P19 P23 P13 P39 P28 P14 P17 P44 P5 P21 P25 P18 P29 P8 P16 P30 P6 P9 P43 P22 P20 P35 P49 P11 P32 P42 P48 P3 P33 P2 P41 P47 P1 P50 P15 P38 P36 P27 P37 P40 P45 P7 P24<br>P31 P19 P12 P10 P34 P26 P5 P18 P14 P46 P28 P39 P23 P44 P17 P4 P25 P21 P1 P13 P20 P11 P35 P6 P30 P22 P9 P48 P29 P49 P50 P3
<mark>more migrant workers than households in the rural lowlands, and correspondingly receive higher amounts of remittances.</mark> <mark>The results suggest that vulnerability to climate change and variability varies substantially across regions and agro-ecological zones in ways that are not a priori obvious. The vulnerability index varies according to socio-economic and institutional development while exposure and elevation exert smaller influences: geography is not destiny. Like all indeces, these results are of course a function of assumptions made when constructing the index but they are far from trivial, as geographic and meteorological data carry considerable weight in the index formula. The results indicate that urban areas are by far the least vulnerable while the eastern Region of Republican Subordination (RRS) mountain zone is the most vulnerable. The results also suggest that Tajikistan’s most remote and sparsely populated high-altitude mountains have medium vulnerability while prime agricultural valleys are among the more vulnerable areas. This implies that relatively vulnerable geographic areas can overlap centers of population and economic activity. Adaptation planners therefore do not necessarily face a trade-off between defending the most vulnerable areas and defending the economically most important areas from the impacts of climate change.</mark> <mark>After this introduction, the paper is organized as follows. Section 2 discusses vulnerability and adaptive capacity. Section 3 introduces the data and methodology used for constructing the index. Section 4 presents the results and Section 5 offers concluding remarks.</mark>

# **<mark>2. Vulnerability and adaptive capacity</mark>**

<mark>We define vulnerability as the risk of experiencing poverty or some other deprivation during some time interval, consistent with the social constructivist framework for understanding vulnerability (Füssel and Klein, 2006) and the Social Risk Management framework (Holzmann and Jorgensen, 2000; Heltberg, Siegel, and Jorgensen, 2009). Estimates of vulnerability to poverty normally focus on the risk of the household falling below the poverty line as a result of changes in income resulting from risky events. An individual or
# **References**

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- Aker, Jenny C, Rachid Boumnijel, Amanda McClelland, and Niall Tierney. 2016. “Payment Mechanisms and Antipoverty Programs: Evidence from a Mobile Money Cash Transfer Experiment in Niger.” _Economic Development and Cultural Change_ 65 (1): 1–37.

Ambler, Kate, Alan de Brauw, and Susan Godlonton. 2020. “Cash Transfers and Management Advice for Agriculture: Evidence from Senegal.” _World Bank Economic Review_ 34(3): 597– 617. doi: 10.1093/wber/lhz005

- Anderson, Jock R., and Gershon Feder. 2007. “Agricultural Extension.” In _Handbook of Agricultural Economics_ , edited by Robert E. Evenson and Prabhu Pingali, 3:2343–78. Amsterdam and New York: North Holland. https://doi.org/10.1016/S1574-0072(06)03044-1.

- Barrett, Christopher B., and Leah E. M. Bevis. 2015. “The Self-Reinforcing Feedback between Low Soil Fertility and Chronic Poverty.” _Nature Geoscience_ 8 (12): 907–12. https://doi.org/10.1038/ngeo2591.

- Beaman, Lori, Ariel BenYishay, Jeremy Magruder, and Ahmed Mushfiq Mobarak. 2018. “Can Network Theory-Based Targeting Increase Technology Adoption?” NBER Working Paper 24912. Cambridge, MA: National Bureau of Economic Research.

BenYishay, Ariel, and A. Mushfiq Mobarak. 2019. “Social Learning and Incentives for Experimentation and Communication.” _Review of Economic Studies_ 86: 976–1009. https://doi.
7 Our exit survey of the 1,113 health care students and 1,029 medical professionals<sup>4</sup> who participated in our experiments further confirm the salience of these difficulties in treating poor patients in Burkina Faso. We asked the participants:

- “Reflecting on why it is more difficult for health clinics to treat poor patients than non-poor patients, how important it is that the poor can have more complex health problems than nonpoor patients?”

- “Reflecting on why it is more difficult for health clinics to treat poor patients than non-poor patients, how important it is that the poor patients are more difficult to understand?”

- “Reflecting on why it is more difficult for health clinics to treat poor patients than non-poor patients, how important it is that it takes longer for health clinics to treat poor patients than nonpoor patients?”

Figures 3a, 3b, and 3c display the answers to each of these questions. An overwhelming majority of both students and professionals agree that there are three disincentives involved in treating poor patients: complexity, communication and time. Policies meant to increase equity in public service delivery do not, however, regularly distinguish between the differences in difficulty of delivery to the poor and non-poor. The model in the next section predicts that failure to recognize effort differences in the structure of pecuniary incentives can have a substantial negative effect on service delivery to the “difficult-to-serve”.

> 4 The results reported in this paper are for the subset of these subjects who participated in the four treatments that focus on the effect of quantity-based bonuses on the allocation of effort by health workers.  We also conducted treatments focusing on the impact of quality-based bonuses, not reported here.  Experiments with student samples focused on the impact of medical treatment-based profits on accuracy and on disentangling the effects of task and mission motivation. These results are in the process of being reported in separate articles (see Banuri et al. 2018; and Banuri, Keefer, and de Walque, 2018 as examples).
However, when excluding Nigeria these numbers become 1.42 and 1.46 (instead of 1.46 and 1.76), which makes the point about the lack of international reserves even stronger.

# _What is the impact of ODA debt forgiveness?_

This section assesses the impact of ODA debt relief on the recipient countries using the import bill capacity indicator introduced above. We do this in two steps, first using averages and second on a marginal basis.

_Have countries receiving more ODA debt relief, on average, been able to import more?'_<sup>_4_</sup> Over the past decade African countries, as a whole and on average, have been able to finance an increasing import bill, as defined above, in part because of an increasing trend in grants. In fact, import capacity and pure grants to the sample countries almost doubled between 1984-88 and 1989-93 (measured in current U.S. dollars). Still, pure grants were insufficient to finance the transfer of resources from abroad, and the gap was covered by new lending from both multilateral and bilateral sources (Chart 4).'5 Indeed, during 198493 the share of financial needs import capacity covered by pure grants for all the sample

> example, there is an improvement in the balance sheet of lenders (typically export promotion agencies) in donor countries.

> **_14_** The results reported have refer only to 31 countries. The smaller sample resulted from excluding Nigeria, which in preliminary estimates and analysis appears as an outlier. This pattem repeated systematically throughout the analysis. Nigeria's large oil reserves and its strong export performance may be causing such divergence. Several examples of this are provided in footnotes. IS For all data sources see appendix 3 below.

10
(quantity, labor, capital, energy, material inputs and import intensity) are expressed as natural logarithms while the export and R & D indicator are discrete variables. There are 544 export entrants where an export entrant is as defined in subsection 4.2. The data set contains information on the number of units sold as well as total sales value of the product for each product of the firm. This gives us unit values of the product. There are data on about 150 _,_ 000 product year observations. Among the firms, 1 _,_ 658 are single-product firms throughout the period while the remaining firms sell two or more products at least atsome point in the period. Products are assigned a product code based on the product classification developed by CMIE Prowess. Each product code is matched to a five-digit industry.

I follow Eslava et al. (2004), Eslava et al. (2010), Ornaghi (2008) and construct firm specific price deflators as Tornqvist indices of the weighted average growth of prices of all products produced by the firm (see appendix A.3). I report the summary statistics for price deflators constructed from the firm data as well as industry deflators from the price series data from the Government of India Statistics Division in the last two rows of Table 3.

> 19I exclude minor industries such as printing, coke and petroleum products etc., where the number of firm-year observations is too few to allow for estimation of the production function parameters.

17
and are difficult to be captured in the available survey and satellite data.

Although the initial sample was 240 households per experiment arm, we had some attrition at different stages. In the weekly visit group, 20 households dropped out of which 9 were replaced with a final sample of 229 households. In the recall group, 7 households dropped out leaving a final sample of 233 households. In all subsequent analysis, households that were not interviewed at the endline are excluded, as well as households in the weekly visit arm that dropped out before week 16 (even if they re-appeared at endline).

**Table 1** summarizes household and plot characteristics across the weekly visit and recall arms, drawing on the baseline survey. For most of the traits presented in this table, households are well balanced across survey arms, but there are some exceptions. In the weekly visit group, the average household consists of 5.8 members, of whom 1.6 are children younger than 10 years old, and cultivates 2.6 plots. The average plot is located within 54 minutes’ walk from the households’ residence. Of all individuals aged 10 and older, 55 percent worked on one or more household plots between the start of land preparations and the baseline survey. However, households in the recall group are significantly smaller, have fewer cultivated plots and are further away from their plots, compared to households in the weekly visit group. Because of these baseline differences, our empirical analysis controls for household size, number of plots, and the average distance between the plots and the residence at baseline. Cropping patterns are very similar across arms, with maize being the most important crop in both arms, followed by yam and groundnuts.

8
98|155|
|||_False Positive_(α=.40)|_True Positive (1-β=.66)_||
||Total|144|149|293|

Source: Authors’ computation.

Using the reverse engineering method, hence, yields promising results, yet the methodology needs to be improved, which stresses the need to collect high frequency data on displacement and socio-economic outcomes at the disaggregated level. Including other NTL related regressors did not improve the performance of the model, neither did the inclusion of additional controls on economic activities (average settlement income and share of employment in services, in 2020).<sup>11</sup> To date, alternative statistical methodologies do not yield better predictive power. For example, machine learning algorithm such as the least absolute shrinkage and selection operator (Lasso) or the random forest model do not outperform the current analysis, due to the low sample size (294 settlements). This paper works as a proof-of-concept, and calls for an expansion of the analysis to the whole Afghanistan, as a means to increase both statistical power and representativity.

# 6. Conclusion

This work investigated whether geospatial data could be used to proxy for displacement flows and settlement expansion, using NTL data. By focusing on Badghis province it provided a relevant proof of concept for a province that was particularly affected by recent climatic shocks, including the 2018 drought. We find that large displacement movements can indeed be observed by NTL satellite imagery. Settlements classified as high net inflow of displaced persons over the 2018-2020 timespan (high inflow receivers) have larger NTL growth than others. Results remain robust when looking at the net inflow of persons as a share of the existing population. Accounting for population in 2017 as a proxy for initial levels of urbanization does not change our findings. We can use these insights to predict which settlements are experiencing a large inflow of displaced people, with a true positive rate of 66 percent.

This paper provides a first basis for better understanding the
995|2004-2012|
|Uruguay|_Ury_|429,685|61,383|2006-2012|
|Total Obs.||8,488,348|||

_Notes_ : Information is based on SEDLAC (World Bank and CEDLAS). Only individuals in the labor force are counted as observations.

try.<sup>4</sup> A summary of the countries along with the years used in this study are shown in Table 1.

All observations in this study represent an individual who is in the labor force, _i.e._ either employed or looking for employment. If an individual is not in the labor force, then we cannot make an inference about their welfare as that may be their preferred state. If a worker is in the labor force, but unemployed, then we can infer that a job would positively influence their welfare. Therefore, we keep unemployed individuals in the sample when we are looking at job quality. Brazil dominates the sample with just under 35% of the observations, whereas Nicaragua represents less than 0.25% of the sample.

The summary statistics are shown in Table 2. Unless otherwise noted, the vari-

> 4As discussed later, only years that reported at least 5 dimensions of job quality were used in this study. This resulted in 9.88% of the observations being trimmed from the sample, equal to 34 country-years and 930,591 observations.

8