rafmacalaba/datause-encoder-data huggingface dataset:
# **I. MEASURING THE BUSINESS ENVIRONMENT**

_What do we understand by business environment?_ The life cycle of a firm resembles that of a person. Firms are born; if they survive birth they grow, or live at a comfortable small size; and eventually they die. This simplistic view of the entrepreneurial process can help to classify the regulations, institutions and policies that shape the business environment of one country or region into those that affect mostly the **entry** , and those that have an impact on the **survival and growth of a firm** .

Of course life is more complicated, and factors that have a direct impact on the dayto-day operations of a firm, like the tax level, can have an impact on the entry decision because expected profits net of taxes might simply not compensate the risks undertaken by an entrepreneur. Other factors affect all stages of the firm cycle, therefore it is difficult to assess whether they belong to one or the other category. One example is corruption and red tape which make it costly and cumbersome to register a business and even more so to run it every day. Lastly, there are factors that do not fit in any of the categories suggested above. One of those factors is the bankruptcy law, which determines the direct cost of exit.

Keeping all those caveats in mind, the classification of institutions into different groups, loosely related to the stages of the entrepreneurial process, helps analyzing systematically the context in which firms are created and operate and its impact upon employment performance.

What follows is a brief discussion of each of the institutions that make the investment climate of a country. Although not exhaustive, the list of institutions provided is the outcome of the theoretical and empirical research carried out in the field of entrepreneurship. For a detailed description of the variables used to measure each of those institutions, please refer to Appendix 2.

# **I.1. What affects the entry decision of entrepreneurs?**

The two main barriers to firm creation are the administrative burdens on firm creation and the access to finance. Those two are the main obstacles identified by Europeans, according to a periodical empirical study on Entrepreneurship carried out by the European Commission.<sup>4</sup>

# **_Administrative burdens on firm creation_**

Administrative burdens on firm creation might have a large impact on the labor market performance of transition economies. Burdens on firm creation have been proven to prevent job creation in the service sector, where firms are smaller and job creation depends heavily on new firm entry. Hence, in a situation of large job destruction in other economic sectors, high administrative costs on firm creation can result in high unemployment. That was the case in Spain and Italy, where many jobs

> 4 Flash Eurobarometer 134 “Entrepreneurship”.  Done by EOS Gallup Europe upon request of the European Commission (DG XXIII), 2000, 2001 and 2002. In general, around 70% of the people surveyed agree that the existing administrative procedures are complex and 76% claim that the main problem is lack of available finance.

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of 15 countries (where data was available) experienced an increase in the Gini coefficient between 2010 and 2012 (Peru, Costa Rica, Mexico and Honduras) while Panama had no change in inequality. The rise of the Gini coefficient in Honduras was particularly high, from 53.4 to 57.4 between 2010 and 2011. Also, the increase in Mexico’s Gini coefficient from 47.8 to 49.4 (between 2010 and 2012) explains a good part of the recent regional slowdown in the decline of income inequality.<sup>15</sup> At the same time, while the gains in inequality reduction continued in 10 countries after 2010, they weakened in Brazil, the most populous country in the region.<sup>16</sup> Figure 6. Annualized changes in Gini coefficient, 2003-10 and 2010-12 Source: Authors’ calculation from SEDLAC (CEDLAS and the World Bank). Note: The figure shows changes in Gini points between 2003-2010 and 2010-2012, or the nearest years in case data for these years are not available (see Table A2). Nicaragua and Guatemala have information available only for the first period.

The significant reduction of inequality experienced between 2003-10 across the countries of Latin America was driven mostly by a solid record of growth of the incomes of the households in the bottom of the income distribution, coupled with moderate growth of the top percentiles (Figure A2 in the annex presents country-specific Growth Incidence Curves, and more detailed analysis of growth of incomes of bottom and top deciles for the 17 Latin American countries is shown in Figure A3).<sup>17</sup> Despite the fact that the country-specific growth incidence curves for the period 2010-12 show downward slopes (that is, income growth is higher for the lower end of the distribution) in countries

> 15 The Gini coefficients in this study are calculated using the SEDLAC database, a regional harmonization effort that generates income aggregates that are comparable across countries and, as a result, often differ from official income aggregates. The trends seen in Mexico’s Gini coefficient are comparable to the trends of the Gini coefficient calculated by INEGI (using “ENIGH tradicional” household survey) which increased from 43.5 to 44.0 between 2010 and 2012. While the Gini coefficient calculated by CONEVAL fell from 50.9 to 49.8 (using “ENIGH MCS” household survey).

> 16 Brazil is home to 37 percent of the total population living in the 17 countries under analysis.

> 17 As recent literature based on tax unit records has documented, household surveys fail to measure top incomes accurately. As a result, household surveys may underestimate inequality and fail to reveal changes in inequality given that they do not capture the growth rates of those at the very top of the income distribution.

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2000 was 0.41 in the U.S. and 0.55 in Mexico, while in the same year the income share of the highest 10 percent was 43 percent in Mexico, compared to 30 percent in the U.S. (World Bank, 2004). All else equal, one would therefore predict negative self-selection among Mexican emigrants.

This prediction assumes that all migration costs are proportional to wages at home and therefore do not determine self-selection patterns. However, in practice international migration is costly, involving upfront monetary costs, search and information costs, and psychological costs. Such costs are unlikely to be constant across education levels, but instead be decreasing in skills (Chiquiar and Hanson, 2005; Cuecuecha, 2005a). For example, fixed costs of migration represent fewer hours of work and can be met with no or lower borrowing costs by more educated individuals, and education can help in seeking information. As Chiswick (1999, p. 182) puts it, the more able are also more efficient in migration. If migration costs are large enough, and credit constraints sufficiently binding, one should therefore expect to see positive selection in terms of education as individuals with low education find it too costly to move.

Given this, the pattern of self-selection should depend on how costly migration is from a given community. Migration networks act to lower the costs of migrating in a number of ways: they provide information on job opportunities and labor market conditions at destination, information on border crossing, including on ways to find and deal with smugglers, assistance in job search and housing, and help relaxing credit constraints (Massey, 1988; Orrenius, 1999, Orrenious and Zavodny, 2005, Dolfin and Genicot, 2006). These effects are likely to benefit low-skill migrants the most. In part this is due to them being more likely to be credit-constrained, but may be due also to the fact that ethnic enclaves provide services mainly to migrants with low skills in general and low levels of host-language fluency in particular (Borjas, 1999, Chiswick and Miller, 2005, Bauer et al., 2005).<sup>1</sup> As a result we should expect to see a greater degree of negative self-selection in communities with larger migration networks.

> 1 Indeed, Bauer et al. (2005) show that enclaves selectively attract people with limited language skills while all else equal migrants with better host-country language proficiency choose destinations with smaller home-country networks.

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in precision from doing so. All in all, then, there is no signi fi cant productivity difference between sharecropped and owner-cultivated plots.

How informative is our failure to reject the null hypothesis of equal productivity? Andrews’ (1989) inverse power (IP) function allows us to quantify the set of alternatives against which our test has power. Based on yield speci fi cation (3) in Table 2, we would be equally likely as not to reject the null if the true yield differential were 6.5%; this fi gure demarcates the region of low power. On the other hand, if the true yield differential were 13%, we would be 95% certain of rejecting the null. Thus, our test has high power against yield differentials exceeding 13%.

Although 13% is a respectable number, we may do better with the more restrictive village fi xed effects speci fi cation. This estimator, the results of which are reported in column (5) of Table 2, is not robust to the selection problem outlined above, but may be more efficient than household fi xed effects, in part because it exploits a much larger 2,807 plot sample (see Table 1). In addition to village fi xed effects, we allow for household random effects to deal with the correlation across plots within multi-plot households. As it happens, the village fi xed effects estimates of γ are less negative than their household fi xed effects counterparts, which would be contrary to expectations except for the fact that this difference is not remotely signi fi cant; the two estimates are within a standard error of each other. Selection bias, therefore, does not appear to be a serious problem.<sup>11</sup> With an almost 40% lower standard error, however, the village fi xed effects estimator of γ is much more precise than the ones based on household fi xed effects. As a consequence, we can now be 95% certain that the true yield differential is no greater than 8%.

# 2.6 Labor results

A more direct test for moral hazard is to compare the cultivator’s family labor input on sharecropped versus owned plots. IFPRI-93 (but not PRHS-01) provides information on plot-level labor inputs. These data are collected for each type of worker (adult male and female, and male and female child), but since the vast majority of farm labor is supplied by adult men in this sample, we sum the days of all worker types. Farm labor inputs are also disaggregated by task (plowing/irrigation, sowing, weeding, harvesting, and threshing), which we initially combine. On average, families supply a total of 16 days annually for each acre cultivated.

> 11 Again using the IP function, we can be 95% certain that the household fi xed effect estimate of the yield differential is within 11 percentage points of the village fi xed effect (household random effect) estimate. In other words, we would be very likely to detect moderate selection bias if it existed.

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per year attached to the compensation scheme which is widely used by the Chinese government-that is, minimum income support'<sup>8</sup> t of approximately 250 Yuan plus 50 Yuan for medical expenses per month per worker, and considering to illustrate an average unemployment spell of one year, the program would cost 0.96% of 1996 GDP for 43% of SOE industrial labor transfer (about 22.49 million workers'<sup>9</sup> ). This one short cost is close to the benefits of reform that would accrue year after year. It thus seems as a very good deal from a pure financial/fiscal perspective, both on cash flow (small loss in year 1, pure gains after), and from a stock perspective (net present value gains in the order of 10 percent of GDP).

But how does it compare to the likely losses born by labor? The 1997 figures that are available to us show that private sector wage income is 9092 Yuan per worker, versus 9128 Yuan per worker in state sector. Since these are about equal, the main loss experienced by workers is the drop in their income during their search for a new job, which is large given the ongoing levels of compensation if job search lasts long. The minimum support for example is about a third of average wages only. In addition, while housing is almost always provided for SOE employees, it is far from clear that laid-off workers would continue to be able to use the same facilities, is The national "minimum living income" was set at 164 Yuan in 1996, 155 Yuan in 1995, and 136 Yuan in 1994 and 114 Yuan in 1993. However, layoff allowances are set in different regions. For example, allowance in Beijing is 260 yuan every month, relatively high, the National average standard is 200 Yuan in 1993. A national survey in 1998 shows that 93.2% of the Xiagang workers have received the monthly allowance, ranging from 140 yuan to 380 yuan in different cities.

'9 The department of Labor predicted that form 1995 to 2000, the number of laid off worker will reach 18 million.

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sity of Venezuelans in the corresponding population, and adopt a difference-in-difference estimation strategy that compares the evolution of our labor market indicators in regions that received immigrant inflows to regions that did not around the time of arrival of Venezuelan migrants. Our analysis of the migrants’ location choices also makes use of data on their point of entry into the country and measures of aggregate income at the regional level.

Our paper contributes to the recent literature on the effects of forced migration on the host labor markets, which has focused on the exodus of Syrians primarily to Turkey (e.g. Ceritoglu et al. (2017), Tumen (2016), DelCarpio and Wagner (2015) and Loayza et al. (2018)) and Venezuelans to Colombia (Caruso et al. (2019) and Penaloza-Pacheco (2019)), which we review in detail below. Our contribution is twofold. First, we provide the first analysis of the labor-market effects of Venezuelan migration to Ecuador, one of the main receivers of Venezuelans over the last few years. Secondly, we make use of new data based on cell phone contracts and usage that allow us to measure with a high degree of accuracy the density of Venezuelan migrants across Ecuador’s geography, and merge these data with the household labor force survey and administrative records on migrants’ points of entry.<sup>3</sup> The main findings of the study are the following. First, based on our data, 470,095 Venezuelans were living in Ecuador in the first quarter of 2019, which amounts to almost 3% of Ecuador’s population. We also observe that four _cantons_ (out of a total of 221) host 52% of Venezuelan migrants.<sup>4</sup> Secondly, Venezuelan migrants are highly geographically mobile, mainly choosing to locate in higher income regions, with the point of entry into Ecuador having very little explanatory power. Specifically, we estimate that _cantons_ with 10% higher income receive 5% more Venezuelan migrants than otherwise similar regions.

Our difference-in-difference estimates do not uncover any effects on the workforce as a whole for the regions with the largest inflows of Venezuelans relative to their population, even when restricting to female workers. However, our estimates clearly indicate a reduction in the quality of employment and earnings among young, low-educated Ecuadoran workers. Compared to similar workers in regions with a small inflow of Venezuelans (relative to population), young, low-educated Ecuadoran workers in high-migration regions have experienced a 6 percentage-point drop in the rate of _adequate_ employment,

> 3Beine et al. (2019) have also used cell phone records in the context of Syrian refugees in Turkey, but their analysis is limited to refugees’ location choices and they do not analyze labor market outcomes.

> 4The cantons containing the cities of Quito and Guayaquil host over 85,000 Venezuelan migrants each.

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response program. This curriculum was adapted by the NGO to the Central American context from the program developed by the Luster Learning Institute after the NGO conducted a pilot program among 200 Salvadoran students in 2017.<sup>27</sup> The program included directed meditation to reduce stress and anxiety and to control automatic responses. It also used thought techniques to help participants develop self-awareness, mental concentration, and inner calm. Through a series of activities, _Mindful_ provided students with tools to manage their stress more effectively and to regulate their emotions.

The _Mindful_ program included 16 breathing, stretching, relaxation, and focusing activities, which were implemented throughout all of the sessions. During each session, the volunteers explained each technique for about three minutes and then demonstrated and led the students in the practices throughout the session. These activities were supplemented with the _Clubs_ activities. For example, in the I am Calm activity, students used and consciousness such as and sun and breathing techniques deep breathing butterfly breathing to try to calm themselves. During the Sun Breathing activity, participants were invited to sit comfortably in their chairs with their feet on the floor. Then they were asked to breathe for 10 seconds while being conscious of their breathing. They were instructed to inhale through their noses, stretch their arms above their heads, and then lower their arms slowly after 5 seconds. They repeated this exercise 10 times, after which they inhaled and exhaled for 20 to 30 seconds, opened their eyes, and discussed how they felt.<sup>28</sup>

# **2.2 Conceptual Framework**

Our analysis is organized around the conceptual framework we present in Figure 1, which was preregistered at the AEA RCT Registry. This framework approaches the incipient problem of high incidence of school-based violent behaviors in low- and middle-income countries and how an ASP can help to address it. We expect that the SEL component can affect at least three primary outcomes. By learning new skills and techniques from the

> 27For more details on the original program, visit www.calmclassroom.com.

> 28This activity is presented in Figure A3 in the Appendix. A manual covering the complete intervention, including support materials along with the activity schedule for every academic year, is available in Spanish here.

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candidates compete for limited access to higher education. The alternative argument is that vocational schooling’s overall objective may restrict access to higher education because:

- (1) They offer a more restrictive curriculum with less emphasis on advanced core subjects, such as math and science, and result in a lower level of achievement in standardized test for selection into higher education;

- (2) Achievement at vocational schools may be negatively affected due to an environment in which there are fewer highly motivated peers;

- (3) The perception that vocational schools are less prestigious dampens expectations and aspirations;

- (4) Less classroom time is allocated to core cognitive skills; and

- (5) Generally, less experienced and effective teachers are employed at vocational schools.

Overall, the argument is that vocational schools are not designed or intended to prepare students for college. Therefore, they actively reproduce inequities, particularly as they generally attract students from lower socioeconomic strata.

However, there are also strong counter-arguments in favor of vocational education. In particular, some studies find that the benefit-cost ratio of vocational schooling is higher than for general education; and that it provides a safety net for members of disadvantaged groups by reducing their risk of failing to achieve employability.

Neuman and Ziderman (1989) examine the labor market outcomes associated with vocational school education in Israel, comparing these with outcomes associated with academic schools. Using data from the 1983 population census, the study shows vocational schooling (which accounts for half of the secondary school enrollment in Israel) to be more cost-effective than general school education. Specifically, they find that students who complete vocational school and work in occupations related to the course of study pursued at school earn up to 10 percent annually more than their counterparts who attended general education.

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implementation was not correlated with pre-existing trends in cognitive achievement.<sup>12</sup> Table 4 reports results of regressions of the form: where _i_ indexes individual children, _d_ districts, and _t_ age cohorts. Standard errors are clustered by district. Note that this data reflects a single cross section, so differences in birth cohorts are identified by comparing children of different ages, controlling for an age fixed effect. The IHDS tested children at four years of age (8 to 11), but our paper studies only TSC implementation in 2001, 2002, and 2003. Therefore, for robustness we use three different samples, matching birth cohorts to TSC years in three different ways:

- _8-10 sample_ : children aged 8 in 2005 are assigned to 2003 TSC data; 9 to 2002; 10 to 2001.

- _9-11 sample_ : children aged 9 in 2005 are assigned to 2003 TSC data; 10 to 2002; 11 to 2001.

- _8-11 sample_ : children aged 8 in 2005 are assigned to 2004 TSC data (which is not otherwise used in our analysis); 9 to 2003; 10 to 2002; 11 to 2001.

Additionally, we replicate results with a simple indicator that in a particular district or year, the TSC had “started,” by which we mean any latrines are reported built.

As table 4 shows, in no specification is the within-district trend in TSC implementation associated with the within-district cohort-to-cohort trend in test scores, in this sample of children who are too old to have been exposed to the TSC during early-life development. Indeed, only 2 of 12 point estimates are positive, and in only 4 of 12 cases does the _t_ -statistic

> 12Although we are assuming that any effect of the TSC on cognitive scores would reflect early-life exposure (we explore this timing of the effect in detail in section 7) if we are incorrect, then any effect of later childhood exposure to the TSC would make these estimates more _positive_ , biasing against our purpose in this section, which is to demonstrate a lack of association.

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