rafmacalaba/datause-encoder-data huggingface dataset:
the bias of the coe¢ cient on tari¤s and lead to higher elasticity estimates.

In addition, as a robustness check, we split the HS 6-digit products into homogeneous versus di¤erentiated products according to Rauch’s classi...cation. As expected, the homogeneous products are signi...cantly more elastic than the di¤erentiated products. At the sample mean, the trade elasticity for the homogeneous products is -6.2, while the trade elasticity for the di¤erentiated products is -4.2, and the di¤erence is statistically signi...cant even after we controlled for the importer-exporter pair ...xed e¤ects.<sup>24</sup> Finally, instead of using the market shares of importing and exporting countries to measure market powers in Equation 16, we use the logs of the GDPs of the importing and exporting countries as a robustness check. This is because larger countries may have more market power to in‡uence world prices according to the terms of trade theory. One problem with using the log of GDP is that it does not vary by products. In other words, we are assuming that larger countries have more market power for all products, which may not be the case empirically and may introduce bias in the estimations. This is why we prefer our original market share variables which measure the market power of each country for each product. Nevertheless, results based on interacting the log of GDP of the importing and exporting countries are quite consistent with our original results. The correlation between the AVE estimated using market shares and the AVE estimated using GDPs is 0.36, while the correlation between the import demand elasticity estimated using market shares and the import demand elasticity estimated using GDPs is 0.58. Regressing the AVE estimated using GDPs on the AVE estimated using market shares results in a R-square of 0.74. So both sets of estimates are positively correlated. Overall we are con...dent that the results based on our original market share estimations are robust and consistent.

> 24Regression results are available upon request.

26
_de jure_ failure is more relevant in a systemic financial crisis. On the other hand, investigating which distressed intermediaries were closed and which were kept open allows us to gain insights on the political economy of dealing with financiat intermediary distress. This also provides some insights on the causes of the East Asian financial crisis as well as on the appropriateness of policy responses during the evolution of the crisis. As an additional insight, the performance of early warning systems based on financial ratios in developing countries can be compared with that for developed countries. It is often argued that traditional bank financial ratios and market-based indicators used in industrial countries are not effective in developing countries as these countries' accounting and reporting practices are often less reliable than those in developed countries. At the same time, the degree of risk-taking in emerging markets is often higher than in developed countries, which may also mean that risk can more easily detected using financial ratios, Since we use for our analysis the same data items as typically used in a CAMEL rating system-the most often used early warning system employed in developed countries to identify financial institutions in troubleirectly insight on this claim is obtained.

# 3. Data Sources

We investigate the distress and closure decisions for 186 banks and 97 nonbank financial institutions-which include finance companies, investment banks, merchant banks and specialized banks-from five crisis-affected East Asian countries: Indonesia, Korea, Malaysia, the Philippines and Thailand. The breakdown of the data by country is as follows: (i) 78 commercial banks and 9 nonbank financial institutions in Indonesia; (ii) 28 commercial banks and 30 nonbank financial institutions in Korea; (iii) 36 commercial banks and 28 nonbank financial institutions in Malaysia; (iv) 31 commercial banks and 5 nonbank financial institutions in the Philippines; and (iv) 13 commercial banks and 25 nonbank financial institutions in Thailand (see Table I).

We gathered financial statements for these 283 intermediaries from BANKSTAT, a comprehensive database of balance sheet and income statement data for individual financial institutions across the world. BANKSTAT collects annual reports and financial statements from individual financial institutions, which are prepared according to the various national accounting standards. BANKSTAT makes some adjustments to the reported data to make them comparable across countries and conform as much as possible to international accounting standards. Coverage of the national financial sector in terms of total assets is high for all five countries and substantial in terms of number of institutions for Korea, Malaysia and Thailand. In terms of number of local commercial banks and of both local and foreign commercial banks, the coverage of our sample is

> 6 For instance, Rojas-Suarez (1998) argues that this is the case particularly in Latin America, and develops an alternative set of indicators of bank problems which takes into account the specific characteristics of these countries' banking systems.

6
developed by the World Bank Group, varies between 0 and 1. It is constructed by averaging 9 dummy variables that have a value of 1 if there is gender equality in a specific area and a 0 if there is not; lower values indicate therefore more pronounced gender discrimination. The dimensions include equality in Law regarding ownership rights, inheritance, capacity before the law, rights of married men compared to married women, as well as a set of work related issues such as tax liability, industry or work hours discrimination, and within these issues, discrimination towards women who are pregnant or nursing. Figure 1 shows that the African countries have a _Women, Business and the Law index_ substantially lower than that of the rest of the world, with the average index value for African countries being 0.5, while for the rest of the world it is 0.85.

Second, we evaluate to what extent females are likely to establish a formal business. For this purpose, Table 6 shows the share of female and male entrepreneurs across different legal ownership types. The share of female-owned firms is low in the case of sole proprietorships compared with other types of firms. Only 23 percent firms with female ownership participation are sole proprietorships compared to 34 percent of firms with male ownership participation. This seems to indicate that in our sample women are less likely to be entrepreneurs and more likely to be one of several owners of an enterprise.

Third, we test whether certain enterprise characteristics differ by gender ownership participation. While we cannot test for gender differences in inherent characteristics, we can test for observable differences, such as size or the tendency to innovate. We therefore estimate the following regression where the dependent variables are indicators of size, product innovation and process innovation.

14
|No-till practices through<br>a new assessment model<br>applied to the global<br>area under cereal crops<br>(Powlson, et al. 2014)|Abundant evidence of<br>benefits on increasing<br>soil quality in many, yet<br>not all, situations|Beneficial for climate<br>adaptation|Equivocal evidence on<br>carbon sequestration|
|---|---|---|---|
|No-till practices from a<br>global meta-analysis<br>using 5,463 paired yield<br>observations from 610<br>studies across 48 crops<br>and 63 countries in Asia,<br>Africa, North and South<br>America, Europe, and<br>Australia (Pittelkow, et<br>al. 2015)|No-till reduced yield by<br>5.7% overall, although<br>the impact could be<br>positive under certain<br>local conditions; but no-<br>till in combination with<br>the residue retention and<br>crop rotation<br>significantly increased<br>rainfed crop productivity<br>in dry climates by 7.3%||An increase in SOC<br>stocks by 20% and an<br>increase in nitrogen by<br>24% when<br>regenerative practices<br>were deployed|
|Legume-based cover<br>crops, no-till, and<br>grazing the crop field<br>with livestock on 76<br>cornfields in the US<br>(LaCanne and Lundgren<br>2018)|Regenerative corn fields<br>produced 29% less corn<br>grain but 78% higher<br>profits than conventional<br>operations, driven by<br>lower fertilizer costs and<br>higher revenue from<br>meat production.|||
|Conservation tillage,<br>cover crops, and biochar<br>applications from a<br>meta-analysis of 3,049<br>paired measurements<br>from 417 global peer-<br>reviewed articles|||Biochar applications<br>represented the most<br>effective approach for<br>increasing SOC<br>content (39%),<br>followed by cover<br>crops (6%) and|

10
1985 in 2007 as compared to 2003. The estimated coefficient on the interaction between the "war cohort" dummy and the survey dummy is positive and significant (0.016, significant at 1% level). The estimated coefficient on this term in the regressions with a dependent variable "completed secondary school of more" is also positive but not statistically significant. The estimated coefficient on the interaction between the war-cohort and living in the more war-affected area is negative and statistically significant in the regressions for men. The last result indicates that men who were of school age during the conflict and lived in the more affected areas, were about seven percentage points less likely to complete secondary school as compared to men of the same age who lived in the lesser affected regions (Table 2.1, Col. 4 and 5).

The results from the regressions for women on the pooled sample (Table 2.2, Col. 1 and 2) indicate that women from the “war-cohort” in the more affected regions were on average 2 percentage points (significant at 1% level) less likely to complete basic schooling as compared to similar women from the lesser affected regions. The estimated coefficient increases to 2.8 percentage points when I add to the regression terms interacted with the survey dummy (Col. 3). The stand-alone war-cohort term is not significant at a level higher than 10% in any of the regressions that use "completion of basic education" as a dependent variable. The estimated coefficients on the “war-cohort” dummy term are negative and statistically significant in the regressions with the dependent variable "Completed secondary schooling or more", indicating that women who were of school age during the war were about 4.2 percentage points less likely to complete this level of education than women who were age 19-26 when the conflict started (born in 1966-1973) (Table 2.2, col. 4 and 5). However, the estimated coefficient on the interaction between the war-cohort dummy and the survey year is positive and statistically significant suggesting that on overall between 2003 and 2007, across Tajikistan, women from the war-affected cohort were able to catch-up to older women in the completion of the secondary level of education. However, the estimated coefficient on the triple interaction between the “war cohort”, "survey 2007" and “residence in the conflict affected area” dummies is positive but not statistically significant. This result suggests that in the more conflict-affected areas women were not able to catch up on the lost years of schooling between 2003 20
# **1 Introduction**

As Herrendorf et al. (2014), among many others, argue, the process of structural transformation, that is, the reallocation of inputs to more productive activities, is recognized as an important feature of successful economic development. Focusing particularly on the three largest sectors of the economy, we can further characterize this process as the reallocation of productive resources from agriculture to manufacturing and services, and later from agriculture and manufacturing to services.<sup>1</sup> Access to public infrastructure services and, particularly, to modern electricity services is another important process that has recently gained a prominent place in the development economics literature. Electricity is as an important public good, and consequently improving electricity access in developing countries is a significant goal of the international community. The Sustainable Development Goals and the United Nations initiative Sustainable Energy for All (SE4All), for example, call for providing electricity to the 1.1 billion hitherto nonelectrified people worldwide by 2030.

The economics literature has demonstrated that improved access to electricity positively affects employment (Dinkelman, 2011), industrial output (Rud, 2012), human development and housing values (Lipscomb et al., 2013, Lewis and Severnini, 2017), household income (Chakravorty et al., 2014), industrial and agricultural productivity (Fisher-Vanden et al., 2015, Allcott et al., 2016, Assunção et al., 2018), infant health (Lewis, 2018), and firms’ entry and exit (Kassem, 2018).

However, our understanding of how improved electricity access interacts with the structural transformation process and affects national income levels is limited. To fill the gap, this paper assesses the significance of electricity access, and the different constraints that surround it, in the allocation of resources among sectors and in the formation and growth of

> 1We adopt the definition of these three sectors based on Herrendorf et al.’s (2014) survey of the literature on structural transformation. In particular, the agriculture sector includes cultivation and breeding of animals, plants, and fungi. The services sector refers to the tertiary sector: financial, business, distribution and personnel services. Finally, the manufacturing sector captures all production activities that fall outside agriculture and services.

2
19 control panel.<sup>14</sup> We aggregated the number of cases at the quarterly and municipal levels. Then, we used population size at the municipal level to estimate municipal COVID-19 incidence rates and included this rate as a control variable. Second, to account for trade across the border as a potential confounder, we also controlled for per capita exports at the municipal level using data from INEGI (Instituto Nacional de Estad ́ıstica y Geograf ́ıa).

## **3.2 A measure of exposure to US unemployment**

Given that we did not know the labor market status of Mexican migrants by municipality of birth and US state of residence, we created a Bartik-style measure of exposure of each municipality _m_ to US state _s_ unemployment of Hispanics of Mexican origin: where _Mms_ corresponds to the share of migrants from municipality _m_ in each state _s_ (fixed over time), and _Ust_ indicates the state-level unemployment rate at time _t_ . To estimate _Ust_ , we use the unemployment of Hispanics of Mexican origin living in the United States.

# **4 Empirical Strategy**

We estimate the following equation: where _Rmt_ is the log of remittances received by municipality _m_ in semester _t_ , and _C_ -19 is a dummy that takes the value of one for every period during 2020. _Distm_ is a measure of distance from municipality _m_ to the nearest border crossing, and _Ecomt_ is our measure of local labor demand shocks in municipality _m_ during our period of analysis, which controls for the potential increase in remittances in municipalities hit hardest by the pandemic (altruism mechanism) that might also be closer to the northern border.

Additionally, _λt_ and _δm_ are time and municipality fixed effects that control for shocks common to all municipalities and time-invariant characteristics of the municipalities, respectively.<sup>15</sup> All models also include municipality trends _γmt_ . This is important given the increase in formal remittances, which is likely

> 14See https://www.coronavirus.gob.mx/datos/.

> 15In this model, remittances do not depend on distance and economic shocks before COVID-19, as these effects are absorbed by

8
Figure 4. Macroeconomic developments during government debt accumulation episodes

# A. Government debt

<!-- Start of picture text --> Percent of GDP<br>60 With crisis **<br>Without crisis<br>45<br>30<br>15<br>0<br>t+2 t+8<br>C. Investment and consumption<br>Index, t = 100 With crisis<br>180 *** Without crisis<br>160<br>*<br>140<br>120 **<br>100<br>t+2 t+8 t+2 t+8<br>Investment Consumption<br><!-- End of picture text --> <!-- Start of picture text --> B. Output and per capita output<br><!-- End of picture text --> <!-- Start of picture text --> Index, t = 100 With crisis<br>160 Without crisis<br>***<br>140<br>***<br>120<br>100<br>t+2 t+8 t+2 t+8<br>Output Output per capita<br>D. International reserves and external debt<br>Percent of GDP With crisis Percent of GDP<br>16 60<br>Without crisis<br>12 *<br>40<br>8<br>20<br>4<br>0 0<br>t+2 t+8 t+2 t+8 t+2 t+8<br>International Short-term External debt<br>reserves external debt (RHS)<br><!-- End of picture text --> Source: International Monetary Fund, World Bank.

Note: Figures show the median evolution of macroeconomic variables in debt accumulation episodes with data available for at least 8 years from the beginning of the episode. Year “t” refers to the beginning of rapid government debt accumulation episodes. All variables are scaled to 100 at t=0. Episodes associated with crises are those experience financial crises (i.e., banking, currency, and debt crises, as in Laeven and Valencia 2018) during or within two years after the end of episodes. *, **, and *** denote that medians between episodes associated with crises and those with no crises are statistically different at 10 percent, 5 percent, and 1 percent levels, respectively, based on Wilcoxon rank-sum tests.

A. Government debt in percent of GDP two and eight years after the beginning of the government debt accumulation episode (t).

B.C. Based on real growth rates for output, output per capita, investment and consumption.

21
secondary level are actually enrolled at the lower secondary level in Indonesia in 2015. While this is the majority of the age cohort, the relationships between aspirations, expectations, and outcomes could be different if out-of-school adolescents were still in school and considered, especially since they likely have either lower aspirations or face greater constraints to continuing their education.

# **5. Results**

## **5.1. What do students aspire to and expect when imagining their future education?**

## _High aspirations, lower expectations_

Indonesian adolescent students in our sample dream of completing high educational levels. When asked what level of education they would like to complete if they had no constraints and could study as long as they liked, three-quarters of students aspired for postsecondary education, dominantly university education (Figure 1).<sup>5</sup> This proportion is similar to the average for a handful of low- and middle-income countries for which we have similar data: the 74 percent aspiring for university in Indonesia are similar to Vietnam in which 75 percent of 12-year-olds —including those who are not students anymore— respond to a similar question that they aspire to complete university, higher than Ethiopia (69 percent) and India (68 percent) but lower than Peru (79 percent) (Favara et al. 2018).<sup>6</sup> Among students in Indonesia, almost half aspire for a PhD —the highest level representing eight years of postsecondary education and more than twenty years of education in total— that is three times more than any other level, be it postsecondary magister or technical and vocational high school.<sup>7</sup>

> 5 Only 3 percent of the students wish for a vocational postsecondary education.

> 6 Data on aspirations for these countries are from the Young Lives Surveys, which measure educational aspirations with a similar survey question in: “Imagine you had no constraints and could study for as long as you liked, or go back to school if you have already left. What level of formal education would you like to complete?” (Favara 2017, Ross 2019).

> 7 The proportion of students aspiring to a doctorate may seem high, considering that many students of this age might not actually know that the doctorate level exists nor what it entails. Given that students were presented this question on a paper they were filling themselves with an ascending list of educational levels, they might have checked the highest one, possibly with limited knowledge of it. In this case, this could be interpreted as a strong signal that students see value in education and want the most of it if they had no constraints.

11
# **3. The Relationship between Labor Market Regulations and Outcomes**

Employment protection legislation is intended to protect workers from unfair treatment by employers, to counter imperfections in financial markets that limit workers’ ability to insure themselves against job loss and to encourage workers’ commitment to firms and firms’ investment in human capital. At the same time, however, employment protection legislation that is too strict can hinder the mobility in the labor market and the allocation of labor to the most productive jobs, reducing productivity and growth (Scarpetta, 2014).

Several studies suggest that excessively strict employment protection legislation reduces workers’ exit rates from employment and unemployment. Such employment protection legislation is found to reduce employment, with the most adverse impact being on youth, marginal workers and unskilled workers (Heckman and Pagés, 2004). High levels of protection for permanent employees can also contribute to greater inequality in the labor market, exacerbating the differences between workers with stable, long-term employment and workers in insecure, temporary jobs with no easy access to permanent employment.

During recent years, in response to the 2008–09 crisis, several EU and OECD countries have undertaken substantial reforms in labor market legislation with the aim of containing the short-term impact of the crisis while at the same time tackling more structural concerns (OECD, 2013). These reforms included employment protection legislation measures aimed at decreasing the discrepancy in the degree of protection between permanent and temporary employees to reduce labor market segmentation. Between 2008 and 2013 there was a visible reduction in the level of protection for permanent employees—as measured by the OECD employment protection legislation index—in several countries, including Greece, Italy, Portugal, Spain and the United Kingdom.<sup>11</sup> In Sweden, by contrast, the level of employment protection as measured by the OECD index remained constant, as it did in such countries as Austria, Belgium, Finland and Luxembourg (European Commission, 2013).

The analysis that follows focuses on the links between labor regulations and several labor market outcomes in Sweden: the unemployment rate and annual hours worked per worker; temporary and part-time employment (voluntary and involuntary); and entrepreneurship and self-employment.

# **3.1. Labor cost and unemployment**

Economic theory does not provide clear-cut guidance on the net effect of employment protection laws on unemployment because such laws both decrease layoffs, which _reduces_ unemployment, and decrease the demand for new hires because of the lower number of separations and higher cost of

> 11 There was also a visible reduction in the level of protection for permanent employees as measured by the OECD employment protection legislation index in Estonia, Greece, Hungary, Italy, the Slovak Republic, Slovenia and Spain.

22