rafmacalaba/data-use-mentions-extended huggingface dataset:
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Given the program was oversubscribed, half of the applicants were randomly assigned to the entrepreneurship track (“treatment group”) and the other half were assigned to continue with the standard curriculum (“control group”). Randomized assignment was conducted at the project level, stratified by gender and by the subject students were reading for (divided into 14 groups<sup>6</sup> ). The study assigned 757 projects to the treatment group (658 individual projects, and 99 projects in pairs), and 742 projects to the comparison group (652 individual projects; 97 projects in pairs). Students participated in the entrepreneurship track between February and June 2010, and graduation took place in June 2010. Two-thirds of the beneficiaries of the entrepreneurship track who graduated are women.

A first round of follow-up data was collected through face-to-face interviews between April and June 2011, approximately nine to twelve months after the end of the academic year. Despite the high mobility of the population of graduates, thorough tracking procedures led to low nonresponse rates at follow-up: 92.8 percent of the 1,702 applicants were tracked. Attrition was balanced and uncorrelated with treatment status.

A second round of follow-up data was collected through face-to-face interviews between March and June 2014, approximately four years after graduation. The instrument included similar questions as the first follow-up survey. Additional modules were introduced, including on employment history since graduation, information on the process taken by individuals to set up projects, and on constraints faced by women.

The population of university graduates is highly mobile, and the endline survey took place three years after the midline survey, and nearly four years after students’ graduation. Therefore, extensive efforts were undertaken to track individuals. The non-response rate at endline survey was 14.75 percent: 1,451 of the 1,702 applicants were tracked and interviewed. Attrition was balanced and uncorrelated with treatment status.<sup>7</sup> The short-term study documented good balance between the full treatment and control group
Policy Research Working Paper 10308

# **Abstract**

This paper studies the effects of differential exposure to COVID-19 on educational outcomes in Guatemala. The government adopted a warning index (ranging from 0 to 10) to classify municipalities by infection rates in 2020, which was then used by the Ministry of Education in 2021 to establish a “stoplight” system for in-person instruction. Using administrative panel data for all students in Guatemala, the study employs a difference-in-differences strategy that leverages municipal differences over time in the warning index to estimate the effects of the pandemic on dropout, promotion, and school switching. The results show that municipalities with a higher warning index had significantly larger dropout, lower promotion rates, and a greater share of students switching from private to public schools. These effects were more pronounced during the first year of the pandemic. The findings show differential effects by the level of instruction, with greater losses for younger children in initial and primary education. The results are robust to specification choice, multiple hypothesis adjustments, and placebo experiments, suggesting that the pandemic has had heterogeneous consequences.

This paper is a product of the Education 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 a.ham@uniandes.edu.co, evazquez@cedlas.org, and myanezpagans@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views
structure of these surveys, which includes half of the 9,400 households interviewed in at least two of the three rounds, we assess how environmental risks are related to consumption differences between households and consumption changes over time.

A number of recently‐developed data sets are used to assess 8 environmental risks: (i) outdoor air pollution as a proxy for health risks through respiratory diseases (from Brauer et al., 2015); (ii) tree cover loss as a proxy for the loss of forest resources and ecosystem function (from Hansen et al., 2013); (iii) land degradation as a proxy for productivity decline and agricultural production risks (from Vu et al., 2014b); (iv) slope as a proxy for fragile environments vulnerable to erosion and landslides (from World Soil Database); (v‐vi) long‐term rainfall and temperature variability as a proxy for the variation of weather conditions (from the Climate Research Unit (CRU)); (vii) flood hazards as a proxy for the risks of being flooded (from Bangalore et al., 2016); and (viii) drought hazards as a proxy risks to become affected by drought events (from Winsemius et al., 2015).

Based on these data, the paper addresses three questions: (1) Are there spatial hotspots of high environmental risks and high poverty incidence? (2) Do poorer households live in communes with higher environmental risks? (3) Do environmental risks relate to consumption differences between households and consumption changes over time? These analyses are complementary to other recent work for Vietnam analyzing the income effects of current weather shocks (Narloch, 2016) and the exposure of poor people to future flood risks under different climate change scenarios (Bangalore et al., 2016).

The remainder of this paper is structured as follows. Section 2 describes the data used in the analyses. Section 3 shows the district‐level overlay of environmental risks and poverty in 2010. Section 4
represents a challenge, as it is difficult to link each economic unit with the level of wind by which it has been hit.<sup>7</sup> In this study we consider three approaches based on 3 different indexes, as outlined in the recent literature. The first one is simply using the maximum sustained wind speed (MSWS) as generated by the Pita et al. model, representing a fully exogenous measure of hurricane intensity. This variable being fully exogenous, comprises potentially high levels of measurement error.

The second approach, proposed by Hsiang and Jina (2014), is the Wind Exposure Index (WEI) describing a spatially‐weighted index by area exposed to hurricane wind speed within a sub‐national unit (see Equation 2). This index aims to recover the average effect of hurricane exposure on an average grid. As Hsiang and Jina (2014) suggest, intuitively the WEI index represents the exposure all units of land would have if the measure of a storm is “spread out” evenly across all locations in a country. If exposed area is correlated with socioeconomic damages, but the overall intensity of a storm is not correlated with populated or economically active regions or countries, measurement error is reduced.

> 7 Another aspect that needs to be addressed in the characterization of hurricanes is the temporal and spatial mismatch between socioeconomic data and wind speed data.  Socioeconomic data are often collected, calculated or released at an aggregate level by country or region on a yearly basis. Meanwhile, wind speed data are available at a finer resolution (1km<sup>2</sup> ) for each point in time. In the case of the GDP analysis (section 3), indexes are aggregated at the country level, while in the case of the household analysis (section 4), indexes are aggregated at the sub‐national level (regions).

5
In 2019, 64 countries participated in TIMSS, including Saudi Arabia. In addition to providing countries with robust data on mathematics and science achievement, TIMSS collects a wealth of contextual data from students, parents, teachers, and school principals. In Saudi Arabia, 5,453 grade 4 students (mean age 9.9 years; 49.6 percent male) across 220 public and private schools and 5,680 grade 8 students (mean age 13.9 years; 49.2 percent male) across 209 schools took part in TIMSS 2019. The TIMSS 2019 data were collected using a stratified two-stage cluster sample design, with a sample of schools selected randomly at the first stage and one or more classes of students selected per each of the sampled schools at the second stage (LaRoche, Joncas, and Foy 2020). The Saudi sample of schools was drawn systematically in order for the sampled schools to represent the populations of grade 4 and grade 8 students nationally, with representation from 13 regions and a balance between male and female schools. Implicit stratification methods were used also to ensure representation of the various school types (such as public versus private schools). The IEA requires high participation rates and adherence to standardized administration procedures for participating countries to be included in the international results. The IEA calculates and provides sampling weights (to ensure that the final sample of participating students can be generalized to the national populations of grade 4 and grade 8 students) and plausible values<sup>6</sup> for mathematics and science scores (to ensure accurate population-level estimates of achievement) in order to facilitate appropriate analyses, taking the complex nature of the data into account.

> 5 Representative regional-level data from NALO could be exploited to examine the varying availability of resources and variability in practices across the different regions of Saudi Arabia, and how region-level differences are related to differences in achievement and the gender gap.

> 6 Plausible values are generated by imputing a set of values (five values in the case of TIMSS)
Rutherford and Tarr, 2009); Tanzania (Jensen, Rutherford and Tarr, 2010), Kazakhstan (Jensen and Tarr, 2006) and Armenia (Jensen and Tarr, 2012).

# **Literature Review: The Impact of Services Availability and Services Liberalization on Productivity**<sup>**6**</sup>

**Studies Based on Firm Level Data.** First, several studies using firm level data have established a link between increased access to services and increases in productivity.  Using panel level data and controlling for FDI endogeneity, Arnold, Javorcik and Mattoo (2011) establish a positive link between total factor productivity (TFP) of manufacturing firms and liberalization of the services sector. They analyze the impact of liberalization of services on the performance of approximately ten thousand manufacturing firms in the Czech Republic in 19982003. The link is stronger for the firms that use services inputs more intensively.  Employing a similar methodology, Fernandes and Paunov (2012) find that forward linkages from foreign direct investment in services to downstream manufacturing industries account for almost 5 percent of the observed increase in the Chilean manufacturing productivity growth.  Employing a panel data set for India of about 4,000 firms, Arnold, Javorcik, Lipscomb and Mattoo (2012) find that services reforms in the telecommunications, insurance and transport sectors significantly increased productivity of manufacturing firms. Both Indian firms and foreign firms operating in India benefited from services reforms, but the foreign firms reaped the larger productivity increases.  Arnold, Mattoo and Narciso (2008) find a statistically significant positive relationship between productivity in a sample of over 1000 firms in ten Sub-Saharan countries and the performance of the three service industries for which they collected data. Several papers have

> 6 See Francois and Hoekman (2010) for a more complete review of the literature on the impact of services availability and services liberalization on productivity and growth.

5
past year|82|LB, WVS|
||Share of population that says racist behavior is very or quite frequent in their<br>neighborhood|74|WVS|
||Share of population that voted in the last national elections|106|LB, ASB, WVS|
||Share of population that has ever attended a demonstration or protest march|102|AF, AB, ASB,<br>WVS|
||Share of population that agrees they are free to express what they think|42|AF, AB, ASB|
||Share of population that agrees they are free to join any organization they like<br>without fear|42|AF, ASB, AB|
||Share of population that got together with others to try to resolve local<br>problems|14|ASB|
||Share of population that are active members of organizations|78|ASB, WVS|
||Share of population that participates in voluntary associations, organizations<br>or community groups|43|AF, AB|
||Fatalities due to violence index (0-1, low to high)|205|ACLED|
||Number of violent events in 2020|205|ACLED|
||Homicide index (0-1, low to high)|90|WDI|
|**Process Legitimacy**||||
||Rule of Law<br>[The extent to which agents have confidence in and abide by the rules of<br>society, including the quality of contract enforcement and property rights, the<br>police, and the courts, as well as the likelihood of crime and violence]|209|WGI|
||Government effectiveness<br>[The quality of public services, the capacity of the civil service and its<br>independence from political pressures, and the quality of policy formulation]|209|WGI|
||Control of corruption<br>[The extent to which public power is
first wave of the pulse survey differed across countries–collection time averaged around 4 weeks per country and spanned from mid-April through the end of August. The survey captures businesses at different stages of the COVID-19 shock in each country (see Figure A1) and to correct for these differences in the timing of the survey, we also include dummies for the number of weeks before or after the peak of the COVID-19 shock, which we proxy using Google mobility data around transit stations (Google, 2020). For each country, we identify the date when mobility reached the trough and then count the number of weeks between the date of the interview and the trough. For countries where Google mobility data are not available, we predict mobility using the stringency of the lockdown restrictions provided in Hale et al. (2020). We describe this exercise and detail on the harmonization and trimming of variables in the appendix. Finally, to control for differences in the number of observations in each sample, we weight our results using the inverse of the number of observations in each country, that is, each country has the same weight in our summary statistics.<sup>9</sup>

# **3. Impact of the crisis**

The COVID-19 outbreak led many firms to close or reduce operations, with large drop in sales, and significant adjustments in employment, but with large heterogeneity in effects across firms. This section assesses the impact of the outbreak across these three dimensions: operation status, sales, and employment. While operation status (e.g. open or temporary closed) refers to the status of the business at the moment of the interview, the results on sales and employment refer to changes in these variables 30 days before the interview compared to the same period of 2019. Because the survey was implemented in different countries at different time, and those countries were facing diverse stages of the COVID-19 outbreak, the data provide a comprehensive picture of the way businesses have been performing and adjusting under the current
their private counterparts, consistent with previous evidence. The authors show that financial regulatory changes introduced in 2018, which tightened credit conditions, further increased credit misallocation in favor of government-run firms because of their government guarantees.

In the second strand of research, early work by Buera et al. (2011) explores the role of financial distortions in generating capital and talent misallocation, showing that distortions account for a significant part of the cross-country differences in real outcomes such as output, output per worker, and total factor productivity (TFP). Similarly, Midrigan and Xu (2014) exploit plant-level data for the Republic of Korea to evaluate the role of financial frictions in determining TFP. The parametrization of the developed model shows that financial frictions reduce aggregate real outcomes primarily through two different channels, inefficiently low levels of firm entry and technology adoption. Gopinath et al. (2017) also confirm the role of financial frictions in determining productivity losses from capital misallocation. Using data from manufacturing firms in Spain, the paper shows that the decline in real interest rates, often attributed to the euro convergence process, led to important real losses, as capital inflows were allocated to unproductive firms. More recently, Whited and Zhao (2021) extend the seminal framework of Hsieh and Klenow (2009) to analyze the effect of finance misallocation on the real economy.<sup>5</sup> The paper also quantifies the reallocation gains for China relative to the U.S., showing that state-owned firms stand to gain less in percentage terms than non–state-owned firms.

Our paper differs from both strands of research in several regards. First, we unpack the black box of financial distortions and identify one, state ownership, which generates non-trivial real losses by distorting financial markets. Second, we advance the literature and estimate the effect of such a distortion on the cost of production. Third, we use a mirror approach–finance instead of factor markets–to deal with mea5The paper shows that financial distortions reduce aggregate output through two different
publicly available.

In the fifth segment, the capital charge equation of Basel II (2006) is used to compute bank and asset class specific risk weights which incorporate an adjustment term for maturity mismatches based on the average residual maturity of a bank’s asset class. Additionally, we include a penalty function to individualize asset performance correlations for each bank and asset class, based on the relative concentration of a bank’s lending within that asset class. For this, we use the share of the ten largest borrowers in the asset class or the Herfindahl-Hirschman (HH) index as a measure of concentration.<sup>6</sup> We further condition the maturity transformation adjustment term on the average maturity of a bank’s liabilities.

In the fifth segment we also compute a number of outcome indicators of interest. These include the difference between Loan Loss Reserves (LLR) and Expected Losses (EL) as a share of regulatory capital, which is an indicator of the scale of potential underprovisioning, along with the number of underprovisioned banks in the banking system and the absolute amount of missing provisions in the system. Further, we construct the effective capital buffer as the ratio of the regulatory capital adjusted for the difference between profits, loan loss reserves and expected losses to the risk weighted assets that account for the aggregate macroeconomic and bank specific economic risks that we consider.

# **3. Construction of Macroeconomic Scenarios**

When constructing a macroeconomic scenario, it is important that a point of reference is established that corresponds to the long-run equilibrium or steady-state of the economic

> 6One can also use the regional and/or sectoral concentration of bank lending in that asset class as an alternative measure.

12
with stricter social and cultural norms, women-managed firms face higher credit constraints on the intensive margin compared to other firms. Cultural barriers, such as explicit discrimination in allocation of credit or implicit bias that limits access to information and network opportunities, or requires additional guarantors (Brock and De Haas, 2023), may explain our results.

_Third_ , there is suggestive evidence of distortion in allocation of capital away from women-managed firms, particularly in countries with more restrictive gender social and cultural norms. For instance, using the average revenue product of capital as an indicator of capital misallocation, female-managed firms have a 15% higher average return than male-managed firms. Finally, we formally illustrate that female-led firms could benefit from accessing higher levels of credit.

The paper beyond this point is organized as follows. Section 2 summarizes the data description and Section 3 presents the estimation strategy and the base results, followed by additional robustness checks. Section 4 concludes with some policy implications.

# **2. Data and Measurement**

This section outlines the data sources used in our research and presents some of the descriptive stylized facts from this data.

## **2.1 Firm-level data**

Our main source of data is the World Bank Enterprise Surveys (WBES) which are nationally representative surveys of formal firms with 5 plus employees.<sup>5</sup> Although the WBES covers both manufacturing and services, our sample is limited to the manufacturing sector only because capital is not reported for the latter. Beyond surveying a wide range of countries, the survey instrument used in WBES includes several questions on access to finance for firms. These questions can help in understanding the sources of finance; the reasons for not applying for formal finance; rejection of credit application, if they do apply; collateral requirements; and the loan amount received, conditional on successful application.

To measure differences by gender at the firm level, most studies use the gender of the firm owner, sometimes even restricting to sole proprietorship firms (e.