rafmacalaba/datause-encoder-data huggingface dataset:
developing countries. For example, using the complete Enterprise Survey database of over 70 countries, we find that the average number of firms using trade credit for working capital or investment purposes (the only comparative variable available across countries) is 47% in China, and 51% for the complete sample. Similarly, the average percentage of trade credit used for working capital purposes (averaged across firms that use trade credit) is 48% of total working capital financing in China, versus 37% for the complete sample. These figures provide further support to the generality of our findings, as well as recent institutional changes in China. For example, unique features of the Chinese economy – such as the bias towards state-owned banks and state-owned firms – have been decreasing since 2001 (two years before our survey takes place) and we carefully address related potential biases. Moreover, there are no country-specific regulations on inter-firm financing. Finally, when we replicate our main results using data for Brazil, we find additional support for both the market power and the matching story found in China.<sup>6</sup> The remainder of the paper is organized as follows. Section 2 describes the data. Section 3 develops our main testable hypotheses. Section 4 presents empirical results. Section 5 discusses our results and addresses some related issues. Section 6 concludes.

# **2.  Data and Summary Statistics**

We use firm-level data on about 2,500 Chinese firms, which was collected as part of the _World Bank Enterprise Surveys_ conducted by the World Bank with partners in 76

> 6 Unfortunately data for Brazil does not include as detailed information on supply chain contracts; therefore, we use data for Brazil as only a further robustness check for the evidence found in China.

9
# Appendix E – Descriptive Statistics

Table (10) provides descriptive statistics about the distribution of mahallas in Uzbekistan at the time of the baseline survey in 2018.

**Table 10: Distribution of Mahallas and Population in Rural and Urban Areas of Uzbekistan in 2018 (At Baseline)**

|Province|Total<br>Mahallas|Urban|%|Village/<br>Rural|%|Population|
|---|---|---|---|---|---|---|
|Karakalpakstan|412|163|39.56|249|60.44|1,873,629|
|Andijan|876|383|43.72|493|56.28|3,010,957|
|Bukhara|540|205|37.96|335|62.04|1,874,534|
|Jizzakh|287|138|48.08|149|51.92|1,329,838|
|Kashkadarya|726|329|45.32|397|54.68|3,130,222|
|Navoi|304|136|44.74|168|55.26|989,509|
|Namangan|770|416|54.03|354|45.97|2,651,005|
|Samarkand|1089|405|37.19|684|62.81|3,747,573|
|Syrdarya|222|94|42.34|128|57.66|817,071|
|Surkhandarya|712|238|33.43|474|66.57|2,516,636|
|Tashkent|998|440|44.09|558|55.91|2,881,180|
|Fergana|993|453|45.62|540|54.38|3,522,087|
|<br>Khorezm|499|171|34.27|328|65.73|1,818,182|
|Cityof Tashkent|505|505|100.00|.|0.00|2,476,280|
|National|8933|4076|45.63|4857|54.37|32,638,703|

_Source: Uzbekistan State Statistic Committee and the National Mahalla Committee. Note that the reported figures are based on administrative data, in the absence of a national population census, and use different definitions of rural/urban from the standard previously applied in the Soviet period._ The definition of “rural” and “urban,” as well as the boundaries of Tashkent, have not remained fixed over the relevant period. According to UNFPA, from 2005 to 2009, a list of rural communities with at least 2,000 residents was compiled reclassified as urban settlements. On March 13, 2009, the Cabinet of Ministers adopted Resolution No. 68 “On Additional Measures to Improve the Administrative and Territorial Structure of Communities in the Republic of Uzbekistan,” which granted 965 rural settlements – with a total population of 4.4 million people – the status of urban settlements. Official records indicate that there were 15,364 mahallas in 2009, but that by 2018 these had been consolidated to a total of 8,933. The number of urban mahallas fell slightly from 4268 to 4130, while for rural areas it declined by much more (from 11,096 to 4,803). Tashkent city was one of the few places that added mahallas over this time, going from 474 to 505.

56
the move is unrelated to other determinants of primary completion, I also estimate these effects within households, using plausibly exogenous migration outflows, and instrumenting destinations with past migration patterns. All these exercises produce results that are qualitatively similar. Moreover, I explore potential heterogeneity by estimating childhood exposure effects by sub-populations and I explore an alternative outcome that computes upward mobility with secondary education as the level of interest. I do not find much evidence of heterogeneity and the results of the estimates using secondary education show similar patterns to the baseline exercise but with larger convergence rates.

This paper is related to three strands of the economic literature. First, it is related to the recent literature that estimates intergenerational mobility (IGM) in education within countries and documents important variation across places in developing countries (see for example, Alesina, Hohmann, Michalopoulos, & Papaioannou, 2020; Alesina et al., 2021; Asher, Novosad, & Rafkin, 2021; Munoz, 2021a, 2021b; Van der Weide, Ferreira de Souza, & Barbosa, 2020). This is an extension of the literature on intergenerational income mobility within countries initiated by Chetty, Hendren, Kline, and Saez (2014) that is mostly focused on high-income economies, and the intergenerational mobility in education at the countrylevel (see Torche, 2019, for a survey focused on developing countries).<sup>2</sup> Second, it adds to the literature that studies how the place in which children grow up matters for medium or long-term socioeconomic outcomes (see Chyn & Katz, 2021, for a recent survey about place effects). This includes studies that use random or quasi-random variation to identify the effects (see for example Chetty, Hendren, & Katz, 2016; Chyn, 2018; Damm & Dustmann, 2014) and those using observational data exploiting samples of movers (Alesina et al., 2020, 2021; Chetty & Hendren, 2018a; Deutscher, 2020; Lalibert ́e, 2021; Ward, 2020). Third, this paper relates to the set of papers studying the different drivers of intergenerational mobility with the help of data at the sub-national level and the application of quasi-experimental methods (see for example, Card, Domnisoru, & Taylor, 2022; Derenoncourt, 2021; Sharkey

> 2Important recent contributions are Narayan et al. (2018); Van der Weide, Lakner, Gerszon Mahler, Narayan, and Ramasubbaiah (2021) documenting IGM in education at the country-level for 153 economies.

3
Table 10: Estimated Export Supply and Import Demand Functi;ns

|Dependent Variable|Real<br>Non-Fuel<br>Exports|Real<br>Non-Fuel<br>Imports|
|---|---|---|
|Sample|1967 to 1988|1966 to 1988|
|Constant|9.107|0.7z4|
|Export Price/GDP Deflator (t-l)|<br>0.135*<br>(1.675)||
|Import Price/GDP Deflator||-1.135--<br>(3.56)|
|Actual Trend/Real Output (t-l)|<br>-0.349"'<br>(3.020)||
|Real Exchange Rate|-0.279<br>(3.726)|-0.468<br>(2.15)|
|Real Output||1.067"'<br>(5.52)|
|Flow of Forex||0.587<br>(5.52)|
|Dummy for 1986|0.156**<br>(3.073)||
|Adjusted R<sup>2</sup><br>Durbin Watson|0.82<br>1.81|0.80<br>1.45|

Note: All variables except dummies are in logs.

A one-shot raJl depreciation of 15% lowers private investment sharply but returns to the no-depreciation baseline level in two years. A similar pattern is obtained for the gradual real depreciation. However, the gradual 35
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school along with full enrollment histories in preschool and primary school. We also collected information on parenting practices, the quality of preschools in the village, and other child and family background characteristics.

We find substantial gender gaps in both cognitive and non-cognitive skills in early primary grades, with substantial female advantage in language and mathematics assessments as well as in emotional maturity and social competence. We also find that the rate and duration of enrollment in preschools for girls is higher than for boys in the prime preschool-going ages of 3 to 5. In addition, gender gaps are observed in parenting practices for children age 7 to 9, suggesting that there are some gender differences in parent-child interactions in rural Indonesia. In our preferred decomposition analyses, we find that these observed gender gaps in cognitive and non-cognitive skills are in part explained by one’s early schooling environment.

The rest of the paper is organized as follows. In the next section, we discuss relevant literature and in section 3, we describe the country context. The data and key variables are described in section 4. In section 5, we describe the gender differences in cognitive and noncognitive skills in the early years of primary school and explore whether there are differences in early schooling and family environment between girls and boys. In section 6, we use decomposition analyses to investigate the extent to which these school and family factors contribute to the gender gaps we observe in the first few years of primary school. The paper concludes by discussing the results in relation to prior studies on the topic of gender gaps in early childhood developmental outcomes.

# **2. Literature review**

Early achievement gaps can lead to large educational disparities in later life (Currie and Thomas 1999, Paxson and Schady 2005). As a result, there is considerable interest in understanding when these gaps in child development appear. Recent studies from developed countries have examined the emergence of gender gaps in cognitive and non-cognitive outcomes in the first year of schooling (Anderson 2008, Cornwell et al. 2013, DiPrete and Jennings 2012, Fryer and Levitt 2010, Janus and Duku 2007). Evidence from nationally representative data of kindergarten students in the United States shows that while there are no mean differences in math scores between boys and girls in kindergarten, girls lose ground to boys by more than 0.2 standard deviation (SD) by fifth grade (Fryer and Levitt 2010). Other studies using the same data set also 3
**Table 13: Only firms with calendar year reporting—By model design size—Fixed effects—regression on firm profitability as dependent variable**

||No Dummies|Year Dum.|+Reg. Dum.|Full Model|
|---|---|---|---|---|
|**Treatment (ITT)**|0.000970|0.00174|0.00171|0.00181|
||(0.07)|(0.12)|(0.12)|(0.13)|
|Reform Period|-0.0106*|-0.0448***|-0.0450***|-0.0450***|
||(-1.65)|(-3.01)|(-3.03)|(-3.02)|
|Constant|0.0474***|0.0569***|0.0998|-0.000932|
||(14.36)|(11.91)|(0.93)|(-0.01)|
|Year Dum.|No|Yes|Yes|Yes|
|Region Dum.|No|No|Yes|Yes|
|Sector Dum.|No|No|No|Yes|
|Observations|840 610|840 610|840 610|840 610|

**t-statistics shown in parentheses, treatment variable bolded**

***p<0.1          **p<0.05          ***p<0.01**

**Table 14: Short reform period (2007-2010)—By model design size—Random effects— regression on firm profitability as dependent variable**

||All firms|Small|Medium|Large|
|---|---|---|---|---|
|**Treatment (ITT)**|0.0115|0.0155|-0.551|-0.0673|
||(0.83)|(1.26)|(-0.83)|(-0.62)|
|Pre-reform<br>youth share|0.0562***|0.0640***|0.568|-0.0426|
||(5.46)|(7.45)|(1.03)|(-0.60)|
|Reform Period|-0.0472***|-0.0475***|0.0619|-0.0257|
||(-3.17)|(-3.07)|(0.91)|(-1.03)|
|Pre-reform<br>Education level|-0.0858***|-0.0801***|-0.801*|0.0407|
||(-6.39)|(-6.19)|(-1.76)|(1.13)|
|Pre-reform firm<br>size|-0.0000176**|-0.00500***|0.000475|0.00000152|
||(-1.98)|(-9.76)|(1.18)|(0.49)|
|Constant|0.275***|0.284***|1.246|-0.0850|
||(9.70)|(10.28)|(1.62)|(-0.57)|
|Year Dum.|Yes|Yes|Yes|Yes|
|Region Dum.|Yes|Yes|Yes|Yes|
|Sector Dum.|Yes|Yes|Yes|Yes|
|Observations|1 099 578|1 066 831|27 665|5 643|

**t-statistics shown in parentheses, treatment variable bolded**

- ***p<0.1          **p<0.05          ***p<0.01**

25
randomized at the individual parent level. Cell phone numbers and names of parents in the 26 schools were collected during a meeting at the beginning of the school year. A random sample of 20 parents in each school were selected; a target of half of them were randomly selected to receive a series of SMSs with the remainder not receiving SMSs.<sup>12</sup> All 20 parents were interviewed in the endline survey. Last, in Sumbawa, where 41 primary schools were available for the pilot, a facilitated meeting to explain the content of a poster was carried out in 21 randomly selected schools and 20 formed the control group. Twelve parents in each of these schools were randomly selected for the endline survey.

**Figure 1: Evaluation design**

<!-- Start of picture text --> Printed materials SMS School Meeting<br>(Tulungagung) (Malang) (Sumbawa)<br>81 schools 520 parents in 41 schools<br>26 schools<br>Randomly selected Randomly selected Randomly selected<br>21<br>27 27 schools<br>27 schools schools 20 schools<br>schools Pocket<br>Control School Control<br>Letter Book<br>Meeting<br>264<br>256<br>270 270 270 parents 252 240<br>parents<br>Parents Parents Parents Control Parents Parents<br>sent SMS<br>Group<br><!-- End of picture text -->

# **_2.4 Data_**

> 12 While carried out centrally, the randomization didn’t always result in equal numbers of parents selected for treatment and control in each school (22 or the 26 schools have between 8 and 12 parents in the treatment group).

10
defined as declining average rates as the stage of processing increases. A "mixed" rating, "3" is given to countries where average tariffs are higher for final goods - but there is no significant difference between raw materials and intermediates, or, as is sometimes the case, the average for intermediates is _higher than_ for raw materials. A rating of zero is given when escalation is low or not significant.

The Table shows that while progress has been made in reducing tariff escalation in some country schedules in the post UR period (e.g. Korea, Mexico, South Africa, Thailand), the tariff schedules of 38 per cent of the countries on which information is available showed substantial escalation. In some cases, (e.g. Pakistan), countries have noted that the escalation in the tariff schedules has been designed explicitly to promote industrialization. Whatever the justification and merits of the policy, the facts are that tariff escalation in a large number of developing countries is quite extensive and involves higher degree of effective protection than escalation in developed countries (see below).

Unfortunately, while effective rates of protection were calculated in some TPRs , not enough information was available on a systematic basis to present in a tabular form. But it is clear that countries whose tariff schedules contain substantial escalation as shown in Table 7 as well as high average applied rates, i.e 30 per cent or more as shown in Tables 4 and 5, and high dispersion (e.g. Egypt, Kenya, Pakistan, Philippines, Tunisia), are likely to have effective rates of tariff protection of several hundred per cent in a number of products/sectors.

The other issue that the TPR bring out quite clearly, is the large differences between the applied MFN rates and the rates actually paid by importers. The differences are due to two factors: first, there is a growing number of preferential trade arrangements (PTA), usually of a regional nature, resulting in an increasing proportion of imports coming in at lower preferential rates; second, most developing countries provide tariff exemptions on a large number of products for a variety of purposes. Practically all countries exempt from duties and other taxes goods imported by international and charitable organizations, 17
18

**Figure 2**

# **Unemployment rate and turnover indicators from 1998 to 2007**

<!-- Start of picture text --> 14<br>12<br>10<br>8<br>6<br>4<br>2<br>0<br>1998 1999 2000 2001 2002 2003 2004 2005 2006 2007<br>Unemployment rate Turnover<br>Involuntary separations Employee separations<br>Percent<br><!-- End of picture text -->
economic growth can increase the opportunities for small and medium firms to expand relative to large firms that may have reached their optimal size. Such an expansion may allow small and medium firms to protect themselves better, thereby reducing the degree of crime they face.

Given the wealth of theoretical mechanisms available in the literature, the objective of this study is to provide empirical verification of the effect of per capita economic growth on crime experienced by firms. Depending on which mechanisms dominate, we may have a positive or negative association between crime and economic growth.

We structure the rest of the paper as follows. Section 2 describes the data, section 3 provides the estimation and results, and sections 4, 5, and 6 provide instrumental variable estimations, robustness checks, and conclusions, respectively.

# **2 Data and Main Variables**

The data for firm level variables are collected by the World Bank’s Enterprise Surveys (ES). The Enterprise Surveys use standard survey instruments to collect firm-level data on a country's business environment from business owners and top managers. The surveys cover a broad range of topics including access to finance, corruption, infrastructure, crime, competition, labor, obstacles to growth and performance measures. The survey is designed to be representative of a country’s private non-agricultural economy and only registered firms with at least five employees are included in the sample. The data consist of a random sample of 12,000 firms across 27 developing countries in different regions stratified by firm size, location, and sector using a consistent methodology. The survey year ranges between 2007 and 2009. Police data are 7
The upper panel of Table 1 includes summary statistics for the outcome variables split up by municipalities that had at least one Azteca branch in the fourth quarter of 2002 and by municipalities that didn’t have any Azteca branch at that time, but that had at least one branch from a different bank. The data in Table 1 are for the pre-Banco Azteca period, including only observations between 2000-II and 2002-III. The averages in Columns 1 and 2 of Table 1 show that, in municipalities with Azteca, 8.2 percent of people in the sample owned an informal business in the pre-Azteca period. This number was higher for municipalities without Azteca, 13.8 percent. There is a somewhat smaller percentage of formal business owners, 7.9 in municipalities with Azteca and 6.6 in other municipalities. Another 50 (44) percent of our sample worked as wage earners in municipalities with (without) Azteca. The remaining 34 (36) percent of the sample were not employed.

The third column of Table 1 reports the differences in pre-reform averages for municipalities with and without Azteca and their statistical significance. Individuals who live in municipalities with Azteca are more likely to be employed, more likely to work as wage earners, and more likely to own a registered business. However, they are less likely to own an informal business. Moreover, individuals in municipalities with Azteca have a higher average monthly income than individuals in municipalities without Azteca.

The fact that pre-Azteca outcomes were different across our group of treatment and control municipalities does not invalidate the identification strategy. As discussed in Section II above, the identification strategy assumes that changes in outcome variables (not levels) are the same in municipalities with and without Azteca. The lower panel of Table 1 compares pre-period changes in outcome variables across Azteca and non-Azteca municipalities. There was no statistically significant difference in average changes across these municipalities for the informal and formal business owner dummies. The fraction of wage earners, however, decreased in Azteca municipalities in the pre-Azteca period, while it increased in non-Azteca municipalities. This is also reflected in the fact that the fraction of people who are not employed increased in Azteca municipalities while it decreased in non-Azteca municipalities. These differences in changes are statistically significant and would bias our results against finding a positive effect on the fraction of wage earners. Similarly, the fraction of people who earn above minimum wage and also log income increased more in non-Azteca municipalities than in Azteca municipalities, which could also bias our results against finding a positive 11
The first credit constraints instrument captures the fact that the intensity of deleveraging varied significantly across banks, even those within the same country. Firms that were surrounded by branches of banks that had to boost their Tier 1 capital ratio more during the crisis, found it more difficult to access bank credit; therefore, we expect a positive relationship between the increase in local banks’ average Tier 1 capital ratio and the likelihood that nearby firms were credit constrained. To create the instrument , we combine the information on the geographic coordinates of both firms and the bank branches that surround them, and measures the change in average Tier 1 capital ration over the period ΔTTC PP1 2007 (just before the global financial crisis) to 2014 (after both the global financial crisis and the ΔTTC PP1 subsequent Eurozone crisis) for all banks in a firm’s vicinity, where the latter is defined as a circle with a 15km radius.

The second credit constraints instrument is motivated by credit constraints being determined by local credit market conditions. This instrument is an average credit constraints indicator of firms within a 15km radius, excluding those from a firm’s own sector .<sup>6</sup> This is similar to the “leave-one-out” strategy pursued in “jack-knife” approaches (Angrist et al, 1999), and is calculated as follows:<sup>7</sup> CC(CC) 1 C C C C C = ∑ C C C C , (3) iissii jj∈[ss(jj)≠ss(ii)&dd(ii,jj)≤15kkmm] jj where CCPPC is the distance between firms CCPPC 1OO NN and . CCPPC CCPPC CC The green management instrument is motivated by green management being determined by the local CC(CC, jj) CC jj The green management instrument is motivated by green management being determined by the local diffusion of management practices. It is unlikely, though, that firms learn from every other firm in their vicinity; they are more likely to learn from larger firms only. We divide the firms in deciles based on the number of permanent, full-time employees and then calculate average green management z-score of firms within a 15km radius in deciles above the firm’s own decile. This implies that firms in the top decile in such a radius do not have any firms in the vicinity based on which we can calculate such average green management z-score; in such cases, we set this average to 0 and define an indicator variable equal to 1. The green management instrument is thus calculated as follows:

> 6 Sectors used can be found in Table A.3 in the Appendix.

> 7 Similar approaches have been used in a number of other studies, including Fisman and Svensson (2007), Aterido et al (2011), and Commander and Svejnar (2011).

19