rafmacalaba/data-use-mentions-extended huggingface dataset:
that legal equality in the areas covered by the WBL index is only one of the many factors that may shape a female entrepreneur’s decision to begin operations informally.

For firms in Sub-Saharan Africa that are entirely owned by men, an increase in legal equality is associated with a marginally higher probability that the firm would begin operations informally. This coefficient may suggest that, in this region, there is some degree of crowding out that happens when more female-owned firms are in a position to begin operating as formal enterprises. To try to understand this unexpected relationship, we disaggregate the WBL index into two broad areas of the law. As noted in section 2.1, there are 35 data points underlying the WBL index; 18 of these are collected through the administration of a survey on family law, 14 are based on labor laws and the remaining three relate to laws on violence against women. As laws on violence only comprise three of the 35 data points, we allocate two of these data points (those that relate to sexual harassment in the workplace) into the broader area of labor law, and one (specifically, the data point assessing domestic violence laws) to the area of family. Column (1) of 27
firms.<sup>9</sup> Bernard et al. (2007) indeed show that U.S. exporters are significantly larger than nonexporters, by approximately 97 percent for employment and 108 percent for shipments. Verhoogen (2008) reports that on average Mexican exporters are twice as large as their non-exporting counterparts in terms of employment, revenues, and domestic sales. Bustos (2011) documents that on average in Argentina continuing exporters are substantially larger than nonexporters, by approximately 150 percent for employment and 177 percent for domestic sales. Data from the Enterprise Surveys of the World Bank Group further confirm this observation especially for middle- and low-income countries: for example, in 2013 in Bulgaria export represented 4.5% of sales for small (5-19 employees) and medium-sized (20-99 employees) firms and 32% for large firms ( _>_ 100 employees); and in Peru it accounted for 0.7% of sales for small firms, 4.1% for medium-sized firms, and 16.1% for large firms.

Prior literature documents that net entry rates into export mask a substantial turnover of exporters, and especially so in middle- and low-income countries, the bulk of our sample. Eaton et al. (2008) find that, in a typical year, about half of all Colombian exporters were not exporters in the previous year. Volpe Martincus and Carballo (2009) confirm similar large exit, entry and turnover rates of exporters for Peru. In line with this finding of the literature, in our sample the mean entry rate into export roughly equals 58% while the mean exit rate from export equals 57%. There is significant variation in the entry and exit rates of exporters. As shown in Figure 1, Panel C, countries with very similar net entry rates into export exhibit very different export sector dynamism, as captured by the degree of turnover of exporters. And Supplementary Table S1 reveals that the interquartile range for the share of exports of the 5% largest exporters exceeds
# _Individual-level skills and industrial emissions_

To test the hypothesis that skills of employees in an industry are associated with lower emissions per unit of output of the industry, the first data set was created by merging the OECD’s Programme for International Assessment of Adult Competencies (PIAAC) data sets with industry-level data from the EU’s air emissions accounts (EUROSTAT 2021a) and national accounts aggregates and employment by industry (EUROSTAT 2021b, 2021c). PIAAC is a nationally representative household survey of individuals aged 15 to 64 which collected data on individual literacy, numeracy and problem solving skills based on a standardized test as well as background data on employment including earnings, education and on other demographics. Included in the employment data is the industry employment for those employed using the ISIC Rev 4 coding. PIAAC data for the EU countries used in this study were conducted in 2012. The EU emissions accounts data set provides data on various types of emissions by industry based on the NACE rev 2 coding system. There were values for approximately 60 industries per country, depending on the country. For this study, carbon emissions were used. The EU’s national account aggregates and employment data sets provide data on output in terms of value-added and employment numbers for each industry also coded using the NACE rev 2 system. Both the level for 2012, to match the PIAAC year was used as well as the annualized growth from 2010 to 2019.

Merging the EUROSTAT data to the OECD PIAAC data by industry was conducted by aggregating the EUROSTAT data to ISIC Rev 4 coding using a NACE rev 2 and ISIC Rev 4 mapping data set provided by European Commission (2021a). This was used to map values for the variables of interest derived from the EUROSTAT datasets for the four types of ISIC Rev 4 coding, the 4-, 3-, 2- and 1- digit levels. In cases where NACE Rev 2 value mapped to more than one ISIC code, particularly at the 4- and 3- digit levels, the same value was
16, its client base grew from 1 to 11 million people making Equity Bank the largest bank in Africa in terms of customers. Allen et al. (2021) use the case of Equity Bank to explore the relationship between bank branch expansion, financial inclusion, and bank profitability. They find that the presence of Equity Bank branches significantly increased the likelihood of having a bank account or a loan, especially for Kenyans who were less educated, did not own their own homes, and lived in rural and arid areas.<sup>13</sup> Branch-level data on the profitability, credit quality, and financial structure of Equity Bank branches revealed that it was able to achieve profitability, even in remote areas, through aggressive pricing (including on loan interest rates), reducing its delinquency rates, and securing stable, inexpensive funding from retail deposits in those areas.

In all, Equity Bank’s experience in Kenya and those of a handful of banks in other developing countries suggest that it might be possible to generate sustainable profits with a business model focused on the provision of financial services to population segments that are typically underserved by commercial banks.<sup>14</sup> At the same time, the number of studies is small (and most are not from African countries) making it hard to identify the factors that lead to successful adaptation of those strategies.

A government-supported microcredit expansion program can also help provide not only access to basic financial services to previously unbanked population segments, but also provide an entry

> 13 In their regression models, the presence of Equity Bank in a new district increased the probability of having a bank account by five percentage points. The share of Kenyan adults who had a bank account was 14% in 2006 and 23% in 2009.

> 14 For similar studies, see Bruhn and Love (2014) on the extension of Banco Azteca in Mexico, Burgess and Pande (2005) on an exogenous change in branching restrictions in India to identify the effects of increased branch
0.02, whereas the changes in producer surplus and SNAP consumer surplus are around 0.1 for each marginal dollar of SNAP.

Third, our baseline calculations assume that non-SNAP consumers face the same change in prices as SNAP consumers for simplicity. However, as shown in Section 4, price responses are stronger in stores located in regions with higher SNAP participation rates.<sup>54</sup> The exact incidence calculations would vary across different regions. For non-SNAP consumers shopping in stores with a smaller proportion of SNAP consumers, we would expect them to face a smaller decrease in consumer surplus and vice versa. Furthermore, SNAP consumers and non-SNAP consumers also buy different goods within the same store. Using income-group specific weights for products based on the Nielsen Consumer Panel to construct alternative store-level price indices, we find that price responses are smaller for high-income groups, but the difference in price response between the top and bottom income quartile is only about 0.01, which does not affect incidence calculations substantially.

Fourth, if we account for the distortionary cost of taxation as in Hendren (2020) by using efficient welfare weights, surplus to SNAP consumers could be weighted 1.5-2x more than surplus to non-SNAP consumers who are richer. Likewise, we may assume producer surplus is redistributed to shareholders of retailers who are richer than SNAP consumers. Implementing these weights would raise the economic efficiency of SNAP-benefit changes. On the other hand, the relative burden for non-SNAP consumers may particularly fall on those consumers whose incomes are just high enough to make them ineligible for SNAP, although the absolute burden for any single non-SNAP consumer is found to be small.

# **5.5 Multi-Product Pricing**

To better understand the price and quantity movements seen in Table 6, we extend pass-through formulas to a multi-product setting. In the case of perfect competition, we show in Appendix Section K that we can rewrite the equilibrium condition with demand _Q_<sup>_D_</sup> _j_<sup>(</sup>
magnitude.

In sum these results suggest that our data’s aggregate deviations from SICONFI totals are partially correlated with municipalities’ characteristics, although only weakly. Researchers using our data may want to control for such characteristics depending on the research question at hand.

# **4 Applications**

## **4.1 Local firm contracting in public procurement**

Using our newly created dataset on local public procurement in Brazil, we investigate how much geographical variation exists in the location of government suppliers. According to García-Santana and Santamaría (2023), government purchases are highly locally concentrated worldwide. This phenomenon may occur due to supply factors such as regional economic specialization and lack of a diverse local production sector, or frictions such as transportation costs, geography or information asymmetries. It may also be explained by demand factors such as buy-local policies. These policies are ubiquitous across countries, but there is limited evidence regarding whether governments unofficially favor local suppliers even if a formal 11
available at the district level. This district level indicator may reflect urban-rural disparities in a district, rather than individual disparities. We therefore constructed an indicator based on educational attainment disparities from the Census, for males aged 20-30. The measure encompasses not only differences in access to education for a comparable age group, but also is indicative of income inequality as earnings are highly correlated with education. We find that in rural areas, higher inequality in this measure is associated with higher conflict, a result we also find when applying the provincial dummies.

# **_Hypothesis: Unemployment and current lack of economic opportunity is associated_**

**_with increased conflict._** Localities with higher unemployment shares (based on the PODES) were associated with higher conflict.  This finding is not surprising, as especially among young men it can be a factor underlying conflict situations (Barron and Madden 2003).

Logit coefficient variables are difficult to interpret directly. Unlike standard OLS estimates, the marginal impact of a variable cannot be directly read off the coefficient, but depends on the relative value of all other values (Long and Freese 2003). Unemployment was reported as 4 percent in the average locality. Although this measure certainly masks a high degree of underemployment, it serves as a general measure of economic conditions or shocks in a village. The estimate of the marginal effect indicates that a 10 percent point increase in unemployment is associated with a 0.6 percent point (or 10 percent based on an average probably of 6-7 percent) increase in the likelihood of conflict.

# **_Hypothesis:  Shocks increase the likelihood of conflict_** _._ A recent natural disaster in a

locality was positively associated with conflict across the range of specifications. A natural disaster in the community increases the likelihood of conflict by 1.7 percent point (evaluated for all other variables at their mean in equation 1). The probability of conflict in rural areas 24
# **I. Introduction**

A large and growing strand of the recent trade literature - both theoretical and empirical - focuses on firms and how they export. Since the seminal work of Eaton and Kortum (2002) and Melitz (2003), a number of papers have developed models to understand the micro foundations of export growth.<sup>5</sup> On the empirical front, issues related to exporter behavior and dynamics in general have been heavily studied.<sup>6</sup> These studies have in common the use of micro datasets on export transactions within firms from a specific country or in a region. While the conclusions from many of the studies should in principle be comparable across countries, to date there has not been a comprehensive effort to produce analytical work covering an extensive set of countries, particularly developing countries. It would be of interest to see if for example, the empirical findings identified in this growing literature generalize across countries in different regions and at different levels of development, and whether cross-country evidence could provide new insights about how firms export and expand.

To fill this gap, we gather exporter-level customs information from 38 developing and 7 developed countries around the world to build the _“Exporter Dynamics Database”_ (henceforth referred to as the Database), containing comparable measures of exporter, product and market dynamics and which is available to researchers and policy-makers worldwide. The Database contains measures at different levels of disaggregation: a) country-year, b) country-year-product (HS 2-digit, HS 4-digit, or HS 6-digit) and c) country-year-destination mostly for the period between 2003 and 2010 (with longer time series for some countries). The Database covers

> 5 See Bernard, Eaton, Jensen, and Kortum (2003), Bernard, Redding, and Schott (2007), Das, Robert, and Tybout (2007), Chaney (2008), Arkolakis (2010), Bernard, Redding, and Schott (2011), Eaton, Kortum and Sotelo (
Policy Research Working Paper 10454

# **Abstract**

There is limited evidence on the role of participating in international trade in the diffusion of technologies. This paper analyzes the impact of exporting on firms’ adoption of more sophisticated technologies, using a novel dataset, the Firm-level Adoption of Technology survey, which includes more than 1,500 firms in Brazil. The survey provides detailed information on the use of more than 300 technologies, combined with data from Brazil’s census of formal workers and export data from the Ministry of Trade. To address critical endogeneity concerns, the analysis applies difference-in-differences with multiple periods to examine the effects of entering export markets on technology adoption. The findings show that exporting has a positive effect on firms’ likelihood of adopting advanced technologies in business functions related to business administration, production planning, supply chain management, and quality control, which are important for managing tasks associated with export activities.

This paper is a product of the Finance, Competitiveness and Innovation Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at xcirera@worldbank.org or asmartins@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
(COICOP).<sup>25</sup> Moreover, these data contain rich information at the individual level including age, gender, years of education, income, marital status, and relation with the head of family.<sup>26</sup> The Iranian data are ideal for studying the effects of family income shock on children for two reasons. First, I can link children to their parents and observe their family characteristics (such as family income, parents’ education, family size, and family expenditure). Second, the HIES contain children’s years of schooling, enrollment at different levels of education, and family education spending. Information on the education spending includes payments for books, tuition, private tutoring, and donation to the school for different levels of education (pre-primary, primary, secondary, post-secondary non-tertiary, tertiary, and education not definable by level).

I restrict my main sample to the households with children aged 6-24 because children start school at age 6 and most individuals complete their education by age 24 in Iran.<sup>27</sup> Children aged six who were born at the start of the academic year (September 23th) or later are excluded because they are not eligible to enroll in school. I choose my sample period to include all observations from years 1995 to 2013 (1374 to 1392 in Persian Calendar), 12 years before and 7 years after the implementation of the sanctions. I exclude the years 2014 and 2015 when Iran and P5+1 were negotiating over the nuclear program, and people would expect the sanctions to be terminated. I re-conduct the analysis including 2014 and 2015 as a robustness check in section 6. I consider only those children who live with their parents as the data do not report parental characteristics for those who do not live with their parents. 87% of individuals age 18-24 live with their parents.<sup>28</sup> The sanctions could have had an impact on the composition of households
data over the raw COMTRADE data is that the applied revisions reconcile the discrepancies found between the exporter-reported and importer-reported trade volumes and adjust for data reliability. The data also include information about whether missing values are the result of no trade (“true zeros”) and unreported positive flows. The empirical work that follows employs the first of two possible adjustment for the zero/missing data. The CEPII data also include Gross Domestic Product (GDP), distance (measured as the population-weighted

> 6 Data are available at http://www.cepii.fr/cepii/en/bdd_modele/bdd.asp.

pg. 8
opportunities rather than available offers for formal/informal job opportunities), we correct for this possible selection bias using a standard Heckman correction.<sup>10</sup>

# _<u>Estimating the Productivity Functions</u>_

The distributions _Gj (x|y)_ , with _j_ ∈ {Formal; Informal}; are assumed to be drawn from a logistic distribution with a mean equal to _y + kj_ , where _kj_ is a form of unobserved individual-specific heterogeneity that can influence the mean of the _x_ distribution. It is assumed that _kj_ is a Bernoullidistributed random variable. Recovering the productivity distributions requires a four step procedure:

- a. Estimate the part of observed wages, _wj j_ ∈ {Formal; Informal}, that is not due to the worker’s type. This is: _wj - yhat_ . To estimate _yhat_ we use the coefficients _Υ_ of the determinants used in the recovery of _y_ from the self-employed.

- b. Recover the part of this residual that is explainable by unobserved characteristics, and use this to characterize the distribution of the heterogeneity component _kj_ .

- c. Recover the value of the draw that is added to the mean _yhat + kj_ ; and

- d. Calculate the distribution of these draws.

The result of these calculations is a distribution (centered around _y_ or _y +kj_ ) of means for the draws from the _x_ distribution. Subtracting this mean from the observed wage gives an estimator of the value of the draw from the _x_ distribution.

The remaining parameters to calibrate the model need to be estimated, recovered from the data, or withdrawn from the labor law. We estimate directly from the MLFS the shares of population in each employment state (unemployment, formal employment, informal employment and self-

> 9 Our Mincerian estimations control for strata, region, gender, age, age squared, level of education and sector of employment (agriculture/primary, manufacturing/secondary, services/tertiary).

> 10 Estimates are available upon
market during the period covered by the EU-LFS. The analysis in this section is based on the ME APC decompositions.

<u>Figure 6 shows the decomposition of the age effect by gender, educational level, region, and type</u> of contract. The age effect is increasing and similar for all subsamples. Panel a of figure 6 reveals a steeper age profile of tenure for male workers than for female workers. These results are consistent with other studies that report shorter tenure among female workers (Burgess and Rees <u>1998; Hollister and Smith 2014). The gender differences in tenure among older age groups may</u> also be attributed to the different official retirement ages for male and female workers (Turner and <u>Morgavi 2020).</u> 13
through a high-voltage line. A greater share of losses therefore occurs over the distribution network, maintained at a lower-voltage (at 11kV), compared to transmission lines (for example, at 142kV).

To calculate transmission losses, a data set from Rajasthan’s state transmission company containing the total energy generated and total transmission losses for each day between May 2016 to March 2018 is used. Following McRae and Wolak (2019), this information is used to first fit a quadratic relationship between input energy and energy losses, as follows: T u 0 1 2 )2 dd dd where, is the energy inserted into the transmission network and ii = ββ + ββ QQii + ββ (QQii + εεT uii are technical losses at time . We differentiate the above equation to calculate marginal transmission loss, dd T u 1 2 ∗ ii ii QQ TT TT ∑ , where ∑ is the electricity dispatched by all generators at 15-minute intervals ( ). ii = ββ + 2 ββ nn ii nn ii Data on generator-wise energy dispatched at 15-minute time intervals ( ) comes from Rajasthan’s ii=1 RR,ii ii=1 RR,ii QQ QQ TT SLDC. Although this high-frequency data set captures the impact of peak load on technical losses, the ii RR,ii QQ data set suffers from three limitations, for which we make certain assumptions and adjustments. First, the data is available only for the calendar year 2018. We use this data under the assumption that the distribution of marginal transmission losses at 15-minute intervals in financial year 2016-17 is not different from calendar year 2018. Second, the SLDC data set provides electricity dispatch information for the entire state of Rajasthan. The state of Rajasthan is serviced by three utility companies, only one of which is JVVNL. The latter serves approximately 40 percent<sup>23</sup> of the total state-level demand. We rescale the state-wide dispatch figures by 40% to estimate JVVNL’s share of dispatch at 15-minute intervals. Finally, this data set contains