rafmacalaba/data-use-mentions-extended huggingface dataset:
for KUR but had been rejected, others had applied for KUR but had withdrawn their application before receiving KUR, and others did not apply for KUR. These interviews covered the following topics: perception and information received about KUR, KUR loan application process, reasons and notification of KUR loan rejection or withdrawal, alternative financing sources available, adjustments to the business plan or scale after rejection or withdrawal, the impact of COVID-19 on the business, and suggestions for improving the KUR program. As with the qualitative data collection of KUR borrowers, interviews were conducted over the phone, and interviewers had the flexibility for which topics to explore in more depth with each respondent.

# **3. Results**

3.1 KUR’s subsidized credit reaches many first-time borrowers, helping them enter the formal financial sector and invest in their firms KUR primarily targets borrowers who are excluded from the formal financial system: most KUR borrowers (61%) had never received any type of loan, including loans from friends or family, prior to accessing KUR. These borrowers typically relied on personal savings and reinvested profits to meet both their investment and working capital needs before accessing KUR (Figure 4). In fact, only a small percentage of borrowers relied on formal financing arrangements, such as commercial banks, microfinance institutions, and input suppliers. KUR also finances business plans that may not be implemented at the same scale otherwise. Qualitative interviews with rejected KUR applicants reveal that less than half were able to find alternative financing for their projects, and those who did typically reduced the scope of their investment.

Figure 4: Most KUR borrowers relied on savings to finance business investments and working capital prior to receiving KUR

**Main source of capital prior to receiving KUR**

<!-- Start of picture text --> 38%<br>Personal savings 35%<br>21%<br>Savings from business/reinvested profits 37%<br>Friends or family other than spouse (loans or gifts) 6
); i = 1, 2, 3 ........ 2202 households included in the survey; and Xij tj, and a;j are as defined in equation 2.

The application of equation 2 to household income as recorded in the 1992 survey yielded potential revenue amounting to Lev 4.1 million with an average income tax payment of Lev 1850 per capita, or a potential average income tax rate of 8.3 percent. This amount was substantially higher than the actual 1992 income tax payment of Lev 1.5 million, giving an
//doi.org/10.17863/CAM.52477.</u>

- Aksoy, C.G., B. Eichengreen, O. Saka. “The political scar of epidemics.” NBER Working Paper No. 27401.

- Alfano, V., S. Ercolano (2020). “The efficacy of lockdown against COVID-19: A cross-country panel analysis.” _Applied Health Economics and Health Policy_ 18, 509–517.

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- Barrios, J.M., Y. Hochberg. 2020. “Risk perception through the lens of politics in the time of the COVID19 Pandemic.” NBER Working Paper No. 27088.

- Bartik, A.W., M. Bertrand, Z.B. Cullen, E.L. Glaeser, M. Luca, C.T. Stanton. 2020a. “How are small businesses adjusting to COVID-19? Early evidence from a survey,” NBER Working Paper No. 26989.

21
Policy Research Working Paper 8869

# **Abstract**

The goals of ending extreme poverty by 2030 and working toward a more equal distribution of income are prominent in international development and agreed upon in the United Nations’ Sustainable Development Goals 1 and 10. Using data from 164 countries comprising 97 percent of the world’s population, this paper simulates a set of scenarios for global poverty from 2018 to 2030 under different assumptions about growth and inequality. This allows for quantifying the interdependence of the poverty and inequality goals. The paper uses different assumptions about growth incidence curves to model changes in inequality and relies on the Model-based Recursive Partitioning machine-learning algorithm to model how growth in GDP is passed through to growth as observed in household surveys.

When holding within-country inequality unchanged and letting GDP per capita grow according to International Monetary Fund forecasts, the simulations suggest that the number of extreme poor (living below $1.90/day) will remain above 550 million in 2030, resulting in a global extreme poverty rate of 6.5 percent. If the Gini index in each country decreases by 1 percent per year, the global poverty rate could reduce to around 5.4 percent in 2030, equivalent to 100 million fewer people living in extreme poverty. Reducing each country’s Gini index by 1 percent per year has a larger impact on global poverty than increasing each country’s annual growth 1 percentage point above the forecasts, suggesting an important role for inequality on the path to eliminating extreme poverty.

This paper is a product of the Development Data Group, the Development Research Group, and the Poverty and Equity Global Practice. 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 clakner@worldbank.org, dmahler@worldbank.org, mnegre@worldbank.org
Guebas et al., 2005; Sandilyan and Kathiresan, 2015). In terms of width, mangrove belts are still found in the region despite significant deforestation (Spalding et al., 2010). Wide mangrove belts are likely to be an important feature of protection against storm surge as argued by Zhang et al. (2012).

To estimate whether mangroves can reduce hurricane damage, we divide Central America into one km<sup>2</sup> grid cells and construct a cell-year panel for the 2000 to 2013 period. The panel combines measures of economic activity, potential hurricane damage, and mangrove protection. Specifically, we use remote sensing data on nightlights to measure local economic activity. Nightlights have been shown to be a good proxy of economic activity (see Donaldson and Storeygard (2016) for a review), and their high spatial resolution is ideal because the economic impact of tropical cyclones has been shown to be highly localized (e.g., Bertinelli and Strobl, 2013; Elliott et al., 2015). We measure potential hurricane damages using predicted wind speed from the wind field model of Pita et al. (2015). The model is calibrated for Central America and has been validated with historical data. To translate wind speed into potential destruction, we use the damage function proposed by Emanuel (2011). To measure the degree of mangrove protection, we follow Das and Vincent (2009) and calculate the width of mangrove, as measured in 2000, along the closest path to the coast.

We estimate the causal impact of hurricanes on our proxy of economic activity by regressing nightlights on our damage index. The identifying assumption is that hurricane strikes are exogenous conditional on cell and year fixed effects. We then explore the heterogeneity in the impact of hurricanes by interacting our damage index with the predetermined width of mangrove on the path to the coast.

Our results fall into two categories. The first set of results show that, consistent with previous literature, hurricanes have negative short-run
products machine operators|815|4.0|0.7|1.0|2.3|29|
|31|<br>Vehicle, window, laundry and other hand cleaning workers|912|4.0|0.7|1.0|2.3|29|
|32|<br>Domestic, hotel and office cleaners and helpers|911|3.6|0.1|1.4|2.2|23|
|33|<br>Food processing and related trades workers|751|3.6|0.6|1.1|1.9|33|
|34|<br>Other personal services workers|516|3.2|0.4|0.6|2.2|35|
|35|<br>Secondary education teachers|233|2.9|0.1|0.5|2.4|45|
|36|<br>Nursing and midwifery professionals|222|2.5|0.6|0.2|1.7|41|
|37|Primary school and early childhood teachers|234|2.1|0.3|0.2|1.6|41|
|38|Cooks|512|1.0|0.7|0.0|0.3|33|

_Source_ : Authors’ calculations based on World Bank-Burning Glass Malaysia online job postings data (2016-18). _Notes_ : Darker red colors show higher magnitudes within the “share of digital skills” or “O*Net digital score” column while darker blue colors show lower magnitudes within each set of columns. O*NET digital score, used as a benchmark here, is based on occupations’ three digitalization aspects in measured by O*NET for occupations in the U.S. —computer & electronics, programming, and interacting with computer— following the methodology by Muro
br>0.2 0.5 5<br>200<br>0.0 0.0<br>0 0<br>Internet users (per 100 people)<br>Internet users (per 100 people)<br>International Internet bandwidth (bits per second per person)<br>International Internet bandwidth (bits per second per person)<br>Internet users<br>Internet users<br>International internet bandwidth International internet bandwidth<br>2000 2001 2002 2003 2004 2005 2006 2007 2008<br><!-- End of picture text --> _Source:_ World Bank, including AICD analysis.

> 14 Very Small Aperture Terminal.

> 15 International bandwidth was estimated at between 14 to 16 megabits per second (Mbps) in 2007 (World Bank 2009e).

> 16 Digital subscriber line.

32
<mark>Fehr, Ernst and Simon Gächter (2000), Fairness and Retaliation: The Economics of Reciprocity,</mark> _<mark>Journal of Economic Perspectives</mark>_ <mark>, 14(3), 159-181.</mark> <mark>Ferrando, Mery, Alina Machado, Ivone Perazzo, and Adriana Vernengo. 2011. “Una Primera Evaluación de los Efectos del Plan CEIBAL en Base a Datos de Panel.” Montevideo, Uruguay: Instituto de Economía de la FCEydeA.</mark> Filmer, Deon, Amer Hasan, and Lant Pritchett. 2006. “A Millennium Learning Goal: Measuring Real Progress in Education.” Working Paper 97. Washington, DC: Center for Global Development.

Fredriksen, Birger, Sukhdeep Brar, and Michael Trucano. 2015. _Getting Textbooks to Every Child in Sub-Saharan Africa: Strategies for Addressing the High Cost and Low Availability Problem. Directions in Development--Human Development._ Washington, DC: World Bank. <u>https://openknowledge.worldbank.org/handle/10986/21876</u> <mark>Fredriksen, Birger and Sukhdeep Brar. 2015.</mark> _<mark>Getting textbooks to every child in Sub-Saharan Africa: strategies for addressing the high cost and low availability problem</mark>_ <mark>. Washington, DC: World Bank.</mark> <u><mark>https://doi.org/10.1596/978-1-4648-0540-0</mark></u> Fuchs, Thomas and Ludger Woessmann. 2004. “Computers and Student Learning: Bivariate and Multivariate Evidence on the Availability and Use of Computers at Home and at School.” CESifo Working Paper Series No. 1321. CESifo Group Munich.

<mark>Glewwe, Paul W., Eric A. Hanushek, Sarah D. Humpage, and Renato Ravina. 2011. “School Resources and Educational Outcomes in Developing Countries: A Review
2008 (figure 22). This is largely attributable to significant reduction in network losses, which were as high as 35 percent in 2003 but were reduced to 22 percent by 2008. While tariffs have risen slightly, they have not kept pace with the rising costs of production Nevertheless, a 60 percent expansion of power production all sold at a loss, has inflated absolute hidden costs from $306 million in 2003 to $484 million in 2008 Expressed in terms of the size of the economy, these hidden costs shaved off 2.2 percent of GDP in 2003, falling to 1.5 percent of GDP in 2008.

**Figure 22. Large hidden costs at the NEC due to underpricing and unaccounted losses**

<!-- Start of picture text --> 200<br>180<br>160<br>140<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>2003 2004 2006 2007 2008<br>Unaccounted Losses UnderPricing Collection Inefficiencies<br>(%)<br>Hidden costs as a share of revenue<br><!-- End of picture text --> _Source:_ Derived from Briceño-Garmendia, Smits, and Foster (2009) using data from World Bank (2007) and Sudan Central Bureau of Statistics handbooks.

The NEC’s hidden costs look high when compared to that of other African utilities (figure 23). They represent about 100 percent of the NEC’s revenues and are double that of the top-performing countries such as Senegal, Côte d’Ivoire, and Benin. Nevertheless, they are far from being the worst in Africa.

> <sup>32</sup>
spread thinly across Bangladesh. BMET limited the number of Phase 1 winners in each village to at most one applicant. Unions, which typically have about 6,000 households from a few villages, had an average of two Phase 1 (group T1) winners. To decrease the geographic scope of data collection efforts, we limited the survey to Dhaka (Mymensingh) and Chittagong Divisions.<sup>8</sup> We then randomly selected 49 of 223 upazilas from these divisions.<sup>9</sup> The survey was conducted in all 522 unions within the selected 49 upazilas.

> 8 The Mymensingh Division was formed in 2015, after the G2G lottery program, by combining the northern districts of the Dhaka Division. The survey was conducted in current Dhaka, Mymensingh, and Chittagong divisions.

> 9 The data extract we got from BMET had data from applicants in 223 of 258 upazilas. The discrepancy could be a result of the lottery not collecting data from upazilas with very high urban penetration.

10
To apply our occupation-level measures to other countries, we aggregate our SOC8-digit measures to the SOC2-digit level using U.S. employment weights from the Current Population Survey (CPS).

The second step is to assess the actual availability of internet services by occupation and country. For this step, we combine information on the share of internet users by country and income level from the Gallup World Poll 2019 with data on average wages by occupation (at the two-digit disaggregation) from ILOSTAT. Gallup survey data provide the share of internet access at home among the top 60 percent and the bottom 40 percent of the income distribution in each country. This distinction enables us to account for the fact that internet use is positively correlated with income levels. The ILOSTAT data allow us to rank occupations by their average wages and assign them to either the top 60 percent or the bottom 40 percent of the income distribution in their corresponding country, which in turn allows us to concord them with the Gallup data to construct country-specific measures of internet penetration by occupation.

Once we have both the share of DN2020 jobs that require internet and the internet penetration across occupations, we calculate the share of jobs that can be done from home by summing the share of jobs that can be done from home and do not require internet with the share of jobs that can be done at home and need internet multiplied by the relevant internet access rates at home from Gallup.

Most of our global analysis relies on the ILOSTAT database (See ILO 2020 for details) that provides information on wages and employment numbers per occupation for over 180 countries. We restrict the country coverage to 107 countries for which 2-digit occupations are available. We also use individual data from the most recent (2018) European Labor Force Survey (EU LFS) as well as the labor force surveys from several large countries – Brazil (2017), India (2012), Mexico (2018), and Turkey (2018). These data sets are the most recent surveys and include education level, formality
) in shallow aquifers (see Ahmed et al. 2006, Johnston et al. 2014, and Pfaff et al. 2017). The problem, due to the widespread presence in the country of geological conditions conducive to accumulation of arsenic in groundwater, is compounded by millions of households in rural areas relying on water from privately owned, unregulated shallow tube wells for drinking and cooking. Using nationwide data from 2009, Flanagan et al. (2012) estimated that, in a country of more than 150 million people, about 20 million were likely exposed to arsenic levels above the official Bangladesh standard of 50 ppb, while almost one third of the population was likely exposed to levels above the significantly lower WHO guideline of 10 ppb.

The most visible health consequences of chronic exposure to arsenic from drinking tube well water in South Asia, such as cancerous skin lesions and loss of limb, were recognized in the state of West Bengal, India in the mid-1980s (Smith et al. 2000). It has since then been shown on the basis of longterm studies in neighboring Bangladesh that arsenic exposure increases mortality due to cardiovascular disease, and may inhibit intellectual development in children and be detrimental for mental health (Wasserman et al. 2007, Argos et al. 2010, Rahman et al. 2010, Chen et al. 2011, Chowdhury et al. 2016). These health effects are accompanied by significant economic impacts: exposure to arsenic has been estimated to reduce household labor supply by 8% (Carson et al. 2011) and household income by 9% per every earner exposed (Pitt et al. 2015), while Flanagan et al. (2012) calculated that a predicted arsenic-related mortality rate of 1 in every 18 adult deaths represents an additional economic burden of USD13 billion in lost productivity alone over the next 20 years.

Piped water from regulated and monitored supplies would likely be the most effective policy answer, but such a solution would require immense investments in infrastructure that may not be sustainable 3
of the time follows a similar trend as the actual EFNX, though it is usually ‡atter.

# 5.2 Financial Development Results

## 5.2.1 Cross-sectional Speci...cations

We start with the cross-sectional OLS regression. We estimate equation (12) using the averages of the left-hand side and all of the controls for the entire time period, 1970-99.<sup>11</sup> The results are presented in Table 2, with White robust standard errors in parentheses. Column 1 reports the bivariate relationship between ...nancial development and simple trade openness. While trade openness is signi...cant at 10% level, the relationship is not close, with the R-squared of 0.05. When instead we use EFNXc, as is done in Column 2, the R-squared is 0.28, and the variable of interest is signi...cant at the 1% level, with a t-statistic of 4.1. Column 3 includes both the trade openness and the external ...nance need of exports. The coe¢ cient on EFNX is virtually unchanged. Columns 4 and 5 attempt to control for other determinants of ...nancial development. We ...rst include the legal origin dummies from La Porta et al. (1998), and then per capita income. The latter is meant to capture a country’s overall level of development. While in both of these speci...cations the coe¢ cient on EFNXc is about one third smaller, it nonetheless remains signi...cant at the 1% level. Finally,

> 11 Note that since we have an unbalanced panel, our procedure results in averaging over di¤erent numbers of years for di¤erent countries.

14
using a uniform test, EGRA. Two advantages of EGRA data are that (1) EGRAs are included in the World Bank Harmonized Learning Outcomes database and (2) they are often used to assess the impact of interventions and policies. In Appendix Figure B7, we compare variation within a country to variation across countries as a benchmark of whether variation within a country is large. We find that for a sample of 39 countries for which we have EGRA HLO scores, the average cross-country standard deviation is 53, whereas the within-country standard deviation is often higher than 53, with a density skewed to the right tail. This finding indicates that within-country variation is often quite large, casting doubt on the assumption that a sample would necessarily represents the nation. As a specific example, control-group status-quo gains in India in our sample of studies range from 0.5 _σ_ to 0.76 _σ_ , implying high variance from study to study.

In summary, we find that the assumptions for using control-group learning trajectories as our measure of the typical status-quo learning in a country are unlikely to be robust. We instead rely on national-level learning trajectories, which are easily interpretable and can be converted to a global metric. An additional advantage of using national-level learning trajectories is a practical one: greater data availability. Whereas control-group information is often missing from published papers, national learning trajectories can be calculated using HLO scores, which are available for 164 countries.

28
Two weeks after the registration process and before program implementation, enumerators went to schools to collect baseline information on social-emotional skills, fluid intelligence, and emotion regulation. To reduce the risk of fatigue among students, we collected these data over two days. On the first day, we asked students to complete the tasks used to collect data on social-emotional skills on a tablet. On the following day, the students completed the tasks used to collect fluid-intelligence and emotion-regulation data. In each school, we organized several classrooms in which students were scheduled to arrive in groups at specific times. Once the groups arrived, we gave the instructions to them and each student a tablet. Enumerators followed with students who had gave up questions.

Before the intervention commenced, we were given access to students’ administrative records, which included behavior at school and academic performance (specifically, math grades). Teachers submitted this information as printed reports for their students enrolled in the school. We collected, digitized, and cleaned all these paper reports for the students registered in the program and enrolled in the participating schools in El Salvador, Guatemala, and Honduras. One field coordinator per country was responsible for checking that the digitized data were consistent. Because of ethical protocol, we were unable to keep the records of students who did not consent to participate in the study.

We collected data on the characteristics of the participant schools from the 2018 Educational Censuses for El Salvador and Guatemala and from a school-level survey conducted by the authors for Honduras. We were able to obtain information on school location, enrollment, equipment available, and infrastructure, among other aspects. Last, we collected data on homicide rates at the municipality level from each country’s national police, which we use as a stratification variable.

**Short-term follow-up data collection.** As presented in the project timeline in Figure 3, the implementation of the ASP and intervention curricula was completed by the end of September 2019. We started short-term follow-up data collection in October 2019, while the