rafmacalaba/data-use-mentions-extended huggingface dataset:
for women to spending time with men with whom they are sexually active, and this is more so for younger girls in villages struck by high levels of disruption. This has implications related to fertility and risky behaviors, that we come to below after examining the impacts of ELA on the social ties of young girls and women.

# **5.4.2 Social Networks**

As Figure 8 and Table 8 show, the reallocation of leisure time caused by ELA is not just from time spent with men, but also from time spent with friends, alone or engaged in volunteering/church activities: this is the case for both age cohorts and in low and high disruption villages.

The reduction in time spent with friends can have one of two interpretations. Girls might be substituting this with time spent at ELA clubs, or there might be an overall weakening of social ties between girls that persists post-crisis. Given the different implications of each, we probe further by exploiting a network survey module fielded at baseline and endline. This asked about the number (and identity) of ties to other girls in the village along four dimensions: friendship, for business (income generation), who intimate topics are discussed with, and for credit/finance.

Given network ties are censored at zero, we estimate (1) using a Tobit model. The results in Table 9 reveal the following. The estimates show that for older girls, high Ebola-related disruption weaken networks along two dimensions: business and credit ties. Such weakening of social network structures due to the aggregate shock has both micro and macro implications in the long run [Fogli and Veldkamp 2016].<sup>35</sup> However, ELA clubs curb the loss in social ties for both younger and older cohorts along multiple dimensions in high disruption villages. For younger girls, these offsetting impacts are _._ 337 friendship ties (16% of the baseline mean), _._ 459 for business ties (63% of the baseline
changes in household level food insecurity in Vietnam, and Lentz et al. (2019) predict food insecurity for village clusters in Malawi. Martini et al. (2021); Foini et al. (2022) develop high-frequency now-casting capabilities at a global scale focused on the food consumption score and Balashankar et al. (2021) extract leading signals from news streams. Andr ́ee et al. (2020) predict the outbreaks of new food crises up to a full year ahead at the administrative level in 21 highrisk countries using the price data from Andr ́ee (2021a,b) combined with conflict data and remote sensing data. Using the data set from Andr ́ee et al. (2020), Wang et al. (2020, 2022) also provide a stochastic simulation model that projects

> 3Beyond immediate impacts, food crises also have long-lasting development consequences for countries, including inter-generational health and education effects (Galler and Barrett, 2001; Veenendaal et al., 2013; Galler and Rabinowitz, 2014; Asfaw, 2016; Li et al., 2017), and so they may rapidly alter the long-term development prospects of a country.

> 4This interplay between economic outcomes, food insecurity outcomes, and policy decisions was particularly highlighted by the events that unfolded during and after the Arab Spring in 2011. Food insecurity following the 2008 and 2011 global food price spikes exacerbated existing frustrations and disrupted the social and political order of several countries, culminating in the Arab Spring in 2011 (Barrett, 2014). This event had long-lasting regional impacts which triggered financial stress dynamics from which some countries arguably never recovered (Elsayed and Yarovaya, 2019). The food security policies that governments adopted during the Arab Spring played an important role in shaping the nature and severity of subsequent protest (Soffiantini, 2020). More recently, rising food prices have put several countries in major financial distress and at risk of default as governments first sought to maintain price controls on staple foods, and then faced the consequences once prices
- SO2 Sulfur dioxide (SO2) is a colorless gas with a sharp odor Health: Hospital admissions and emergency that is readily soluble in water. It is predominantly room visits linked to asthma and other derived from the combustion of fossil fuels such as coal. respiratory conditions. <u>Climate: Acid rain</u>

- BC Black carbon (BC) is a major component of PM2.5 and Health: Short- and long-term exposure to BC is it is sometimes referred to as soot. Its main sources are associated with cardiovascular health effects from incomplete combustion of fossil fuels, biofuels, and premature mortality. <u>and biomass.</u>

- CO2 Carbon dioxide (CO2) is the primary source of manClimate: Global warming made GHG emitted mainly from the combustion of <u>fossil fuels.</u>

Source: (WHO, 2021) The compilation of normalized average emission factors for brick kilns in South Asia and Nepal is summarized in Tables A2.7 – A2.9.

**Table A2.7** : Normalized average production-based emission factors for brick kilns (g/kg of fired brick)

|||Particu|late Matt|er||Flue gas|pollutant|||
|---|---|---|---|---|---|---|---|---|---|
|Kiln type|Data source|SPM|PM10|PM2.5|BC|SO2|NOx|CO|CO2|
|CK|2|1.60|1.60|0.97|0.290|3.60|0.07|-|-|
||4|-|-|0.64|0.001|1.05|-|6.50|-|
|DDK|1|1.56|-|0.97|0.
Overall, we find that FDI does not stimulate growth in economies with excessive business and labor regulations, after controlling for some other relevant determinants of observed changes in GDP growth rates. However, this outcome is restricted to the top 20 or 30 percent most regulated countries, indicating that there is a threshold level for which our results do hold up. This outcome has some powerful policy implications, as governments have first to improve the regulatory quality in their countries to be able to benefit from increased openness to foreign capital in the form of direct investment.

The paper is structured as follows: The next section describes the regulation data used, whereas Section 3 presents the specification of the econometric model. We use standard OLS regression analysis and, since a number of the independent variables are likely to be endogenous, an instrumental variable approach. The results will be presented in Section 4 and finally, Section 5 summarizes the main results and concludes with some policy implications.

# **2. Measuring Regulations**

For a long time, researchers who examined the impact of institutions and regulations across countries had to rely on a few sources, such as the indicators on political risk provided by the PRS Group (2005) in their International Country Risk Guide or the government effectiveness indicators from the annual Global Competitiveness Report supplied by the World Economic Forum (2005). These organizations publish a large variety of relevant indicators, for instance, on the level of corruption or the quality of the bureaucracy, which are clearly relevant for the effects of institutions and government regulations. These indicators, however, are derived from the results of executive opinion surveys and thus presents the perceived level of corruption or the rule of law. Predominantly, they do not use factual information to measure differences in institutions and regulations across countries.

Recently, the World Bank started to publish results from their Doing Business project (World Bank 2004). The Doing Business database provides objective measures of business regulations and their enforcement. The Doing Business indicators are comparable across economies and indicate
During the first month of each legislative session, committees are formed with one senator appointed as chairman. For each borrower and quarter, we define the dummy variable _chairman_ that equals one if the borrower is headquartered in a state with at least one senator serving as the chairman in that quarter and zero otherwise.

# **_III_ .B. Empirical Strategy**

We now discuss the methodology used to analyze whether banks offered better loans to firms in a state with a relevant chairman, and whether these loans are more likely to eventually default in the future. We focus on four credit margins: loan size (in logs), price of the loan (interest rate spread), original maturity (in months) and a dummy on the existence of collateral.<sup>24</sup> We also examine the loan's ex-post performance by creating an indicator variable of whether loans were more than 90 days in arrears. Appendix A presents the detailed definitions of each variable.

> 20 To ensure the homogeneity of the data, we exclude sole proprietorships from the sample because consumption credit can be confused for commercial credit.

> 21 Many loan-level credit registry data sets are truncated from below. For example, the threshold is 1.5 million euro in Germany and 41,300 euros in Italy (Sapienza, 2004), which suggests that small and medium firms may be excluded in those credit registries.

> 22 Therefore, we are able to calculate the recovery rate of defaulting loans.

> 23 Legislative session LIX started in September of 2003, while LX and LXI started in September of 2006 and September of 2009 respectively.

> 24 We calculate the interest rate spread as the difference between the loan interest rate charged and the annual cost of funds reported monthly by commercial banks to the Bank of Mexico.

10
addition to tangible innovation inputs‐ such as technology, equipment, and physical production facilities, innovation requires intangible assets such as human capital, scientific and creative capital and, more importantly, managerial and organizational capital. These inputs require in turn specific innovation activities depending on the degree of novelty and technology sophistication of the innovations introduced. Firms invest in training in order to increase their human capital available. In addition, firms can also invest in R&D, software and digitalization or copyrights, patents and licenses, and equipment in order to increase their scientific or innovative capital. Finally, innovation also requires organizational capital through adoption of new business models, design and prototyping or corporate alliances and networks. There is, however, uncertainty about the outcomes that can be achieved with the combination of inputs as innovation outcomes depend on each firm’s ability, the sector and country context, and the enabling environment and policy framework.

When measuring innovation, the emphasis can be on measuring inputs and innovation activities and/or innovation outcomes. The earlier literature on innovation measurement focused on a specific set of innovation inputs that were more easily quantifiable, such as R&D or the technological intensity used. These early efforts were subsequently complemented with the framework provided by the Oslo manual, currently the main reference for innovation surveys, which mainly focuses on measuring innovation outcomes such as new products and/or processes, product and process improvements or patents at the firm level (OECD/EUROSTAT, 2015). A third generation of synthetic innovation indicators, such as the OECD STI scoreboard, was developed later on. These indicators combine innovation inputs and outputs in order to facilitate cross‐country benchmarking and comparisons (OECD, 2015). However, to date, the Oslo manual framework, implemented originally through the Community Innovation Surveys (CIS) in the EU, has dominated innovation measurement in most countries, to the extent that even countries that do not conduct CIS have adopted the same questions at least in relation to the measurement of innovation outcomes
(WUAs). To date, these institutions have been quite effective at improving access to water and strengthening communitylevel governance. Supportive national regulatory environment will be instrumental to allow for further development of these organizations.

37. The majority of survey respondents have identified electricity as the single most important priority for government spending on climate change adaptation. Indeed, stable power supply prevents deforestation and is essential for development of non-farm economic activities. Similarly, investments in roads and bridges will be necessary to connect remote rural communities to local and regional markets, facilitate trade and enable farmers to access distant pastures thus preventing overgrazing.

# **Disaster Management**

38. Climate change is expected to increase the frequency and severity of natural disasters, particularly mudflows, floods and droughts, posing additional risks to livelihoods, and infrastructure. However, vulnerability to natural hazards depends not only on the severity of weather events but also on availability of protective infrastructure and institutional capacity to prevent and respond to disasters. Disaster prevention rather than a mere response to their occurrence should become a priority for government and donor support and funding. A number of donor-supported projects have been very successful in preventing disasters and minimizing the impact of their occurrence by working with communities to identify local disaster risks, supporting small scale infrastructure improvements (e.g. construction of flood protection walls), developing evacuation maps, constructing shelters, and training of volunteers for the search and rescue brigades. While significant investments are necessary to scale these initiatives to all vulnerable areas, the international experience shows that disaster risk

> 18 TLSS (Tajikistan Living Standards Survey). 2007. The World Bank, UNICEF and Tajikistan State Statistics Committee. Dushanbe

18
Coal Industry," p. 13.

> **13** _Survey of Current Business,_ May 1996, Table 1.14, p. IO.

> 14 By way of contrast, Schaffer cites "costs of social provision" in a 1988 survey of West European industry varying from 1.4% to 12.0% of total labor costs in different countries, but still concludes that "employees in Western firms receive social benefits from their employers which are about as costly in relative terms" as those in transition countries (Schaffer, _Economics of Transition Round Table,_ p.248).
on the other performance variables are close to zero and not significant, exception made for energy efficiency that is positive, as in the case of Mexico. We return below to the possible interpretation of the more muted effect of electricity prices on performance among Indonesian plants.

A comparison with the OLS counterparts of these estimates (see Table A2 in the appendix) reveals two interesting patterns. First, the endogeneity appears to exert a downward bias to the estimated fuel coefficient, which tends to be positive but smaller than the IV coefficient for Indonesia and close to zero for Mexico. This could be the case for instance if an unobserved positive productivity shock to the plant (e.g. the hiring of a good manager) improves performance and reduces the fuel price paid by the company (e.g. the manager is able to source fuel more cheaply). Second, for Indonesia, the endogeneity bias goes in the opposite direction. This would be consistent for example with a positive shock (e.g. an increase in demand) leading to an increase in the plant’s installed electricity capacity, which moves the plant to a higher electricity tariff. On the other hand OLS estimates are very similar to the 2SLS coefficients for Mexico. That is expected, because as discusses in Section 4.1 energy prices are computed on the basis of institutional factors and so less subject to plant-specific sources of endogeneity.

One important difference between the two samples is the longer time period covered in the Indonesia dataset (1998-2015) as opposed to Mexico (2009-2015). Table A3 of the appendix shows that similar results hold when we restrict the Indonesia data on the period 2009-2015.<sup>36</sup> .

To the best of our knowledge, such an asymmetry in the impact of prices across different sources of energy has not been documented before. In particular, the result that an increase in fuel prices can boost performance is remarkably similar across the two countries in spite of the differences in the data, the instrumental
adult children may have returned to live locally in the village in order to provide instrumental care as well as financial or in-kind support (e.g., Giles and Mu, 2007).

Apart from the dependent variable, net transfer per capita, and pre-transfer income per capita, the linear portion of the model (3) includes controls for gender, age, years of schooling, and a dummy indicating presence of a spouse for the oldest household resident. At the household level, controls are included for whether there are any household members who are currently enrolled in school, and numbers of household residents in different demographic categories. To control for the size and _quality_ (or earnings ability) of a potential transfer network, the regressors include numbers of non-resident children and their spouses in different demographic categories, and shares of these children and spouses who have completed middle and high school, respectively. Finally, the model estimated in (3) includes village dummy variables to control for time-invariant features of the local economic environment, and province-year dummies to control for macroeconomic shocks.

# **4. Results**

## **Do Private Transfers Respond to Low Income of Elderly Households?**

In Figure 4 below, we show the estimated transfer response, _f_ ( _I i_ ) , calculated by implementing (3) and (4), for different levels of pre-transfer income,<sup>_I_</sup> _i_<sup>. It is likely that transfer responsiveness</sup> from migrants may change as time away from home villages increases.<sup>14</sup> We examine changes in transfer responsiveness over time by splitting the sample into an early period (1995 to 1998) and

> 14The World Bank Poverty Assessment (Chaudhury and Datt, 2009) emphasized showed that 65 percent of rural migrants had lived in the current city where they were working for more than three years, and this duration was significantly longer than reported in a similar survey conducted in 2001.

19
# Training Microentrepreneurs over Zoom: Experimental evidence from Mexico<sup>*</sup>

Elwyn Davies, _World Bank_ Peter Deffebach, _Boston University_ Leonardo Iacovone, _World Bank_ David McKenzie, _Development Research Group, World Bank_ _JEL Classification:_ O12, O17, L26, J24 _Keywords:_ Business training; Microenterprises; Digital Delivery

> *We thank Nayelli Hernández Crespo, Giovanna Hernandez Constantino and the staff of CREA for their collaboration on this project. Innovations for Poverty Action Mexico led the fieldwork on this project, ably managed by Cosma Gabaglio, Alina Bitran and Julia Tobias. This study is registered in the AEA RCT Registry and the unique identifying number is: AEARCTR-0008318. Human Subjects Approval was received from the Innovations for Poverty Action IRB (Protocol 15375). Computational reproducibility verified by DIME Analytics https://reproducibility.worldbank.org/index.php/catalog/60. We gratefully acknowledge funding from the InfoDev/E4D trust fund and the World Bank’s Research Support Budget.
given country. Among other things, the reports generally include a table that reports on the country’s financial structure broken down into institutional categories such as banks or pension funds. The aggregation level of institutional categories varies across reports. There is no standardized categorization of institutions; while one report may have “banks” as one institutional category, another report may have “private banks” and “state-owned banks” as institutional categories instead, which combined would be equivalent to the category “banks” in the former report. The table typically provides the following information for each institutional category: number of institutions, assets in (mostly) local currency units, assets as a percentage of total financial sector assets and assets as percentage of GDP. Note that not all reports report data in all four categories and while reports generally include a couple of years of historic data they may record data in one category for one year but not the next and often data just for one or two years are reported.<sup>5</sup> Using this financial structure information, we build a database from all financial structure information reported in table form in FSAP reports from the beginning of the program until mid 2009.

For some countries, more than one FSAP report is available. Unfortunately, the reporting structure is almost never the same as in the previous report(s) for the same country and crosschecks of the data revealed that the reported information is not even necessarily consistent across reports for the same country. We therefore assume that the most recent report contains the most accurate information and only keep observations from the most recent report available. Our final database consists of an unbalanced panel for 89 countries over the years 1995-2008. We convert any variables in local currency units into 2000 constant US dollars using exchange rates from the IMF’s International Financial Statistics.

While we have data available for a broader array of institutions, we focus on three types. First, low-end financial institutions which include credit unions, building societies, community banks,
The school fixed-effects model relies on the variation in subject-matter knowledge between teachers across different classrooms. And as endogenous matching within schools seems unlikely to be happening at scale, the statistically significant results we find in the results section (4) indicate that teachers with more subject-matter knowledge are indeed better teachers. While this finding is important in its own right, it does not imply that teachers with more subject-matter knowledge are better _because_ they have more knowledge. It could be, for example, that teachers who score higher on subject-matter tests tend to be more motivated, and that it is the additional motivation that makes them perform better. For the purpose of policymaking it is important to establish the causal effect of additional subject-matter knowledge, by isolating the subject-matter knowledge effect from other factors such as motivation and other (correlated) skills.<sup>13</sup> In an attempt to do this, we estimate a **student-teacher fixed-effects model** . The idea of the model is to only rely on variation in test scores for a single student-teacher pair, across three different subjects: math, science and Indonesian language. If a teacher’s subject-matter knowledge were truly important we would observe that students tend to do better in one subject (say math) than in another subject (say Indonesian language) if his or her teacher is better in math than languages. The idea of using variation across different subjects in an attempt to isolate the causal effects of teachers’ subject-matter knowledge on student learning is not new. Metzler and Woessman (2012) already used the idea on Peruvian data and find that a 1.0 standard deviation increase in the subject-matter knowledge of teachers leads to a 0.10 standard deviation increase in student learning. Metzler and Woessman (2012) however estimate their model on a cross section of data only.

# **3.2 Measurement error in test scores: From true achievement to observed test scores**

The _x_<sup>_∗_</sup> ’s and
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