metadata: authors: 0: author: Amin,Mohammad
1: author: Islam,Asif Mohammed
count: World
docna: 0: docna: Exports and Women Workers in Formal Firms
docty: Policy Research Working Paper
subtopic: Labor Markets,Gender and Development,International Trade and Trade Rules,Legal Reform,Legislation,Judicial System Reform,Labor Law,Legal Products,Social Policy,Regulatory Regimes,Labor & Employment Law
teratopic: Social Development,Law and Development,Governance,Social Protections and Labor,Public Sector Development,International Economics and Trade,Gender
abstracts: cdata!: Theory suggests several ways in which
            exporting may benefit women’s employment. However, the
            empirical evidence is mixed and limited, especially for
            developing countries. This paper uses firm-level survey data
            for 91 developing countries to estimate the relationship
            between exporting and the share of women workers at the
            firm.  The analysis pays close attention to endogeneity
            concerns. First, it proxies a given firms’ exports by the
            average exports of all other firms in the same
            country-year-industry cell. Second, it exploits the repeated
            cross-section nature of the data and analyzes how changes
            over time in exporting activity are associated with changes
            in the share of women workers. The strategy is more immune
            to endogeneity problems than pure cross-section regressions.
            Third, it tests several mechanism or mediating factors as
            predicted by the theory through which exporting impacts
            women’s employment prospects. The predictions are confirmed
            in the data, an unlikely scenario if exports were a mere
            proxy for other correlated drivers of women’s employment.
            The results show a large, positive impact of higher exports
            on the share of women workers. A conservative estimate is
            that for each percentage point increase in the ratio of
            exports to total sales, the share of women workers increases
            by 0.16 percentage point. Consistent with the theoretical
            predictions, this positive relationship is much larger (more
            positive) in industries that rely more on women workers, in
            country-industry pairs where competitive pressure is largely
            from international markets in comparison to less competitive
            domestic markets, when social attitudes and labor laws are
            more favorable toward women’s work, and when the law and
            order situation is more business friendly.
repnme: repnme: Exports and Women Workers in Formal Firms
display_title: Exports and Women Workers in Formal Firms
listing_relative_url: /research/2021/01/32782228/exports-women-workers-formal-firms
topicv3: Trade m1326924 698,Labor and Employment Laws and Regulations
            m1327247 848,Gender m1327621 615,Jobs m1503931 2670,Female Workers m1504031 2728
publishtoextweb_dt: 2021-01-27T00:00:00Z
historic_topic: Social Development,Law and Development,Governance,Social Protections and Labor,Public Sector Development,International Economics and Trade,Gender
origu: Global Indicators Group - WB (DECIG)
no_of_pages: 91
author_keys: 0: author_key: 717499
1: author_key: 950662
model_extractions: Policy Research Working Paper 9527 Exports and Women Workers in Formal Firms _Mohammad Amin Asif M. Islam_ Development Economics Global Indicators Group January 2021
Policy Research Working Paper 9527

# **Abstract**

Theory suggests several ways in which exporting may benefit women’s employment. However, the empirical evidence is mixed and limited, especially for developing countries. This paper uses firm-level survey data for 91 developing countries to estimate the relationship between exporting and the share of women workers at the firm. The analysis pays close attention to endogeneity concerns. First, it proxies a given firms’ exports by the average exports of all other firms in the same country-year-industry cell. Second, it exploits the repeated cross-section nature of the data and analyzes how changes over time in exporting activity are associated with changes in the share of women workers. The strategy is more immune to endogeneity problems than pure cross-section regressions. Third, it tests several mechanism or mediating factors as predicted by the theory through which exporting impacts women’s employment prospects. The predictions are confirmed in the data, an unlikely scenario if exports were a mere proxy for other correlated drivers of women’s employment. The results show a large, positive impact of higher exports on the share of women workers. A conservative estimate is that for each percentage point increase in the ratio of exports to total sales, the share of women workers increases by 0.16 percentage point. Consistent with the theoretical predictions, this positive relationship is much larger (more positive) in industries that rely more on women workers, in country-industry pairs where competitive pressure is largely from international markets in comparison to less competitive domestic markets, when social attitudes and labor laws are more favorable toward women’s work, and when the law and order situation is more business friendly.

This paper is a product of the Global Indicators Group, Development Economics. 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 mamin@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
# **Exports and Women Workers in Formal Firms**

**By: Mohammad Amin* and Asif M. Islam****

Keywords: Trade, Exports, Gender, Women workers, Globalization JEL: F15, F16, J21, F66 We would like to thank Norman Loayza, Jorge Luis Rodriguez Meza and participants at a seminar organized by Enterprise Surveys, World Bank for providing helpful comments. All remaining errors are our own.

* Corresponding author. Enterprise Analysis Unit, World Bank Country Office in Kuala Lumpur, Malaysia and Washington DC, USA. Email: mamin@worldbank.org. Phone: +1 202-412-5723. ** Office of the Chief Economist, Middle East and North Africa Region, World Bank, Washington DC. Email: aislam@worldbank.org. Phone: 202-458- 5246.
# **1. Introduction**

Trade in goods increased from about 43 percent of GDP in 1995 to almost 60 percent in 2017 (World Bank 2020). Increasing globalization has sparked interest along several dimensions. One such dimension, the focus of the present study, is the impact of exporting activity on job market prospects of women relative to men. Theory suggests several reasons for such a relationship. For instance, greater competition associated with international trade raises employers’ cost of discriminating against female workers and thereby increases female employment. Reallocation of resources from non-exporting firms to exporting firms can affect female employment depending on how intensively females are employed by the two types of firms. Access to foreign technology and greater mechanization linked to trading activity shifts job requirements from less “brawn” to more “brains”, favoring females more than males.

The empirical validity of the relationship between exporting activity and female employment remains to be properly established, especially for the case of developing countries. The present paper attempts to fill this gap in the literature. We do so by using firm-level survey data for several developing countries to estimate the relationship between exporting activity and female (relative to male) employment. We provide several endogeneity checks. We also pay close attention to the mechanisms at play and the mediating factors that may enhance or mitigate the impact of exporting on female employment. These checks not only help to uncover the relationship between exporting activity and female employment, but also help raise our confidence against possible endogeneity concerns (discussed below).

There is a growing literature on the impact of trade on female employment. However, most of the studies in the area are restricted to specific countries, and the cross-country studies that do exist tend to rely on macro-level data. Available empirical evidence is mixed. Some of the studies 2
that find greater trade openness to be associated with higher female employment include, for example, Ozler (2000), Wood (1991), Cagatay and Ozler (1995), Cagatay and Berik (1990), Kasnakoglu and Dikbayir (1997), Chen et al. (2013), Juhn et al. (2014), Ederington et al. (2010), Aguayo-Tellez et al. (2010), Spieldoch (2004), Gaddis and Pieters (2012), Pradhan (2005), Bussman (2009), Rocha and Winkler (2019), World Bank (2020) and United Nations (2009). In contrast, studies showing no effect or a negative effect or contradictory effects of trade liberalization on female employment in developing countries include, for example, Meyer (2006), Wamboye and Seguino (2014), Joekes (1995), Gray et al. (2006) and Cooray et al. (2012).

Our study provides several contributions to the literature. First, we use firm-level survey data for 44,539 manufacturing firms across 91 (mostly) developing economies covering multiple waves of surveys between 2006 and 2017. Second, regressing female employment in a firm on exporting activity of the same firm is fraught with endogeneity problems. Thus, we follow the broader literature and proxy firms’ exporting activity by the average exporting activity of all other firms in the same country-year-industry cell (“cell average”). As discussed below, use of cell averages has been made in the literature and it helps to considerably reduce the possible spurious correlation problem. We go beyond and exploit the repeated cross-section nature of the data. That is, the identification of our main results comes from changes in exporting activity of firms within a country-industry pair over time and the associated change in female employment. This helps limit several avenues of omitted variable bias that plague pure cross-sectional regressions.

Third, we test for several predictions or implications of the theoretical models in the related literature. These tests not only serve to provide a better understanding of the relationship, but also raise our confidence against endogeneity concerns. For instance, it is argued that greater competition that exporters face relative to non-exporters raises the cost of discriminating against 3
female workers to the employer. The higher cost results in lower equilibrium discrimination and therefore higher female employment. This effect is likely to be more pronounced in countries and industries where domestic competition is low to begin with. Thus, we predict that the relationship between exporting and female employment is larger (more positive) when domestic competition is low. The prediction is unlikely to hold if exporting activity is a mere proxy for other correlated variables such as the age of the firm, industry to which the firm belongs, firm-size, etc. Our empirical results confirm this prediction. Other predictions that we test for include how the strength of the relationship between exporting and female employment depends on the dependency of sectors on female relative to male workers, social attitudes towards women’s work, gender disparity in laws in the area of employment, and the law and order situation proxied by the quality of the functioning of courts.

Fourth, the firm-level survey data we use provide information on several firm characteristics and the quality of the business environment. This allows us to control for several firm and industry characteristics that may spuriously affect our main results.

Our results show a positive, statistically significant and quantitatively large relationship between the export orientation of the firms and female employment. A conservative baseline estimate indicates that the share of female workers in a firm is higher by about 16 percentage points for firms that export all their output compared to firms that do not export at all. This is a large difference given that the mean share of female workers is about 29 percent. Furthermore, the positive relationship between female employment and exporting activity is much stronger (more positive) in countries with higher competitive pressure in international markets relative to domestic markets, in industries that rely more on female workers, in countries where social attitudes towards women’s work are more favorable, in countries where laws discriminate less against women’s 4
work relative to men’s work, and when the law and order situation is more business friendly. These results are consistent with the theoretical predictions mentioned above and discussed in detail in the next section.

# **2. Conceptual framework**

There are several mechanisms identified in the literature that could lead exporting activity to affect female employment. First, studies point towards a positive association between trade openness and higher levels of income and growth (Frankel and Romer 1999, Irwin and Tervio 2002, Noguer and Siscart 2005, Dollar and Kraay 2003). Economic development and growth can translate to more jobs, and especially so for women (World Bank 2011). Note that at least some of the proposed benefits here of trade openness apply to exporting as well as non-exporting firms. Thus, this channel has limited relevance for us as our empirical strategy is based on the difference in female employment between less vs. more exporting firms within a country.

Second, gender discrimination is a preference with a significant efficiency cost. Firms may discriminate against female workers for pure “taste” reasons or because females are less mobile than males due to a lack of opportunities or cultural and legal restrictions (Barth and Dale-Olsen, 2009; Boal and Ransom, 1997; Webber, 2016). The idea that greater market competition can reduce discriminatory behavior was first suggested by Becker (1957). In a competitive setting, non-discriminating firms will be able to produce at a lower cost by hiring females. Thus, in the long run, discriminating firms will sustain huge efficiency losses that will drive them out of the market (see for example, Weichselbaumer and Winter-Ebmer, 2007; Elson, 1999; Heyman and Vlachos, 2013; Hellerstein et al.., 2002). If we assume that exporting firms selling in international markets face greater competition than firms selling domestically, higher exporting activity is likely 5
to result in greater pressure to cut costs, leading to less discrimination against females and therefore greater female employment (see for example, Bhagwati 2004, Chen et al. 2013, Ederington et al. 2010, Juhn et al. 2014, and World Bank 2011).

Third, recent studies have examined the comparative advantage of men vs. women within industries and across occupations. One argument here is that females enjoy a comparative advantage in cognitive vs. physical skills (Galor and Weil 1996, Juhn et al. 2014, Do et al. 2011). One possibility suggested here by Juhn et al. (2014) is that by lowering the cost of entering foreign markets, trade liberalization causes some firms to start exporting and adopt modern technologies. The use of modern technology, such as use of computers, reduces the need for routine physical tasks, improving females’ labor market outcomes in the blue-collar tasks, while leaving them unchanged in the white-collar jobs. However, the issue is far from settled. Depending on the type of technology adopted, greater export orientation can lead to worsening of female employment. T echnological upgrading and improvements on product quality following trade liberalization may lead to higher capital and skill intensity of production processes. This is likely to adversely affect female employment (see for example, Berik 2000, Joekes 1995, Pearson 1995).

Fourth, according to the Hecksher-Ohlin theory, a country specializes and exports the product that uses the relatively more abundant factor of production in the country more intensively. Since, developing countries are abundant in semi and unskilled labor relative to skilled labor, demand for semi and unskilled labor should increase with exporting activity. In as much as women gravitate toward low-skilled jobs and men cluster toward high-skilled jobs, trade expands job opportunities for females, in both absolute and relative (to males) terms (see for example, Wood 1991, Cagatay and Ozler 1995, Joekes 1995, Cagatay and Berik 1990, Ozler 2000, United Nations 2011).

6
Last, females may constitute a cheaper source of labor considering not just the wage rate but also manual dexterity, conscientious application to monotonous production processes, employers’ contribution to social wage and working conditions. As mentioned above, trade openness makes such cheap labor more attractive, increasing job opportunities for females (Ozler 2000, Black and Brainerd 2004, Pearson 1998, Fussell 2000, Standing 1989, Elson 1996, Seguino 1997, 2000). One caveat here is that exporting firms may demand more flexible workers. The burden of care as well as the other forms of discrimination may reduce the flexibility of female workers, and thus women may be less likely to be favorably affected by trade (Bøler et al. 2018).

We test for several predictions that follow from the theoretical models above. As argued above, higher exporting activity causes firms to reduce discrimination leading to more female workers hired. The larger the gap in the level of competition faced by exporters vs. non-exporters, the bigger the pro-competitive impact of exporting on female employment. Assuming that all exporting firms in an industry face similar a level of competition in the international market, the pro-competitive effect of exporting on female employment will be higher the lower the level of competition in the domestic markets. Thus, the first prediction we test is that the positive relationship between exporting and female employment is greater (more positive) the lower the level of competition domestically. We test this prediction using alternative measures of the level of domestic competition. The test serves the additional purpose of raising our confidence against endogeneity concerns with our main result. That is, while it is possible that exports may proxy for other correlated effects on female employment, there is no reason to expect such spurious correlation to vary systematically with the level of competition in the domestic markets. The logic extends to other predictions that we test for (discussed below).

7
Next, we argue that while exporting may increase the demand for female workers, the increase in equilibrium employment will be restricted if the supply of female workers is not forthcoming. We focus on three supply side bottlenecks: unfavorable social attitudes towards women’s employment, labor laws that are more restrictive for women than men, and poor law and order situation as women may be more affected than men by high crime and lawlessness. Thus, our null hypothesis is that the positive relationship between exporting and female employment is much stronger (more positive) when social attitudes towards women’s work are more favorable, labor laws are less discriminatory against women workers, and the law and order situation is more business friendly.

Further, as argued above, exporting increases women’s employment more when relative to non-exporters, exporters use female workers more intensively than male workers. To test for this prediction, we follow the related literature and classify industries by their dependence on female workers. The estimates for the dependence on female workers are taken from Do et al. (2011) and based on UNIDO data. The testable hypothesis is that the positive relationship between exporting and female workers is stronger (more positive) in sectors that rely more on female workers.

# **3. Data and main variables**

The main data source we use is firm-level surveys for 91 (mostly developing) countries. These surveys were conducted by the World Bank’s Enterprise Surveys (ES) between 2006 and 2017. The ES are nationally representative surveys of the non-agricultural private sector of the economies. A common sampling methodology, stratified random sampling, is followed in all the surveys together with a common questionnaire. For each country, the sample is stratified by industry, firm-size, and location within the country. Weights are provided in the survey and used 8
throughout the analysis to correct for oversampling and ensure that the sample is representative of the non-agricultural private sector of the economy.

We focus on the sample of manufacturing firms and all countries for which data are available.<sup>1</sup> The two most recent rounds of the ES in the country are used in the regressions. Note that since the ES do not track firms over time, the sample used is a repeated cross-section rather than firm-panel. Our baseline sample consists of 44,539 manufacturing firms. We complement the ES with other data sources such as World Development Indicators (WDI), World Bank, Doing Business (World Bank), World Value Surveys, and so on. In the online appendix, Table A1 provides the list of countries in our baseline sample; Table A2 provides the summary statistics of all the variables used in the regressions; and Table A3 contains the correlations between our main explanatory variable, exports (defined below), and other variables used as controls.

All our regressions use Huber-White robust standard errors clustered at the country-ES time-industry level, where “ES time” is the final or initial ES (survey round) in the country; industry grouping is at the 2-digit ISIC Rev. 3.1 level (31 industries in the baseline sample).

# 3.1 _Estimation method_

The baseline regression exercise involves estimating the following equation:

> 1 Enterprise Surveys do not cover the primary sector, public sector, mining, and services sectors such as education and health.

9
where subscript _i_ denotes the firm, _j_ the industry (2-digit ISIC rev. 3.1) to which the firm belongs, _t_ denotes ES survey round (latest vs. first round), and _k_ denotes the country where the firm is located. The dependent variable, _Y_ , is the percentage of female workers at the firm; _Exports_ , our main explanatory variable, is a measure of exporting activity of the firm (defined in detail below); CIFE is a set of dummy variables indicating the country-industry group to which the firm belongs (Country-Industry fixed effects); YFE is year fixed effects where year is the calendar year covered by the ES; Firm Controls and Country Controls include the various controls used and discussed in detail below; u is the usual error term.

As discussed above, we go beyond and explore how the relationship between female workers and exports varies depending on several country and industry characteristics. These heterogeneities are estimated using the following equation: Equation (2) differs from equation (1) in two ways. First, it includes the interaction term between our exports variable and country and/or industry characteristics captured by Z. These interaction terms estimate how the relationship between female workers and exports varies with factors such as the dependence of sectors on female workers, level of competition in the domestic markets, social attitudes towards women’s right to scarce jobs and other rights, law & order situation, and gender disparity in labor laws. The interaction terms are included one by one (separate regressions) 10
and all simultaneously. The second change in equation (2) from equation (1) is that it includes additional controls for interaction terms such as between exports and GDP per capita, etc.

# 3.2 _Dependent variable_

Our main dependent variable is the percentage of all permanent full-time workers employed at the firm at the end of the last fiscal year (from the date of the ES) that are females ( _Female Workers_ ). In our baseline sample, the mean value of the variable is 29.2 percent and the standard deviation equals 28.

We would like to note that since our dependent variable is female employment _relative_ to total employment, factors such as overall economic development, job availability and labor market conditions that may affect the employment of males and females equally do not pose any omitted variable bias problem.

# 3.3 _Main explanatory variable_

Our main explanatory variable is a measure of export orientation of the firms. Information is available in the ES on the percentage of firms’ sales made abroad (direct exports). However, this variable cannot be used directly in the regressions as it is likely to be endogenous to (share of) female workers. That is, reverse causality from the female workers to exports cannot be ruled out as cheap labor provided by females can help firms in the international markets. Similarly, firm characteristics such as firm-size, industry to which it belongs, age of the firm, foreign ownership, availability of skilled workers, etc., that are likely to impact exports may also affect female employment – omitted variable bias problem.

11
One solution suggested in the broader literature is to proxy a given firms’ exports by the average level of exports of all other firms in the same location-industry cell (henceforth, cell average). Note that the cell average excludes the firm in question. Thus, reverse causality from the share of female workers at the firm to exporting activity of other firms in the cell is highly unlikely. Similarly, firm characteristics that may impact a given firms’ share of female workers are less likely to be correlated with other firms’ exporting activity than with the exporting activity of the own firm. Using the cell average also helps to control for potential measurement error if some firms choose not to respond or misreport the regulatory burden (Pounov 2016). The use of cell averages to mitigate endogeneity concerns has been made in the literature. See for example, Amin and Soh (2020), Aterido et al. (2011) and Fisman and Svensson (2007).

Thus, we define our main explanatory variable, _Exports_ , as the average of the percentage of firms’ sales made abroad (direct exports) where the average is taken over all firms in the country-ES time-industry cell and excluding the firm in question. Industry grouping here is at the 2-digit ISIC Revision 3.1 level. In our baseline sample, there are 31 industries. To ensure adequate thickness within the cells, all cells with fewer than 5 firms are excluded from the sample. Thus, we are left with 1,659 cells in our baseline sample. In this sample, the mean value of _Exports_ equals 9.45 percent and the standard deviation equals 13.96 percent. For later reference, the term “CI avg.” denotes the cell average at the country-ES time-industry level in the sense defined in this paragraph.

12
# 3.4 _Baseline controls_

To further raise our confidence against omitted variable bias problem, we control for several potential drivers of female employment. The controls are motivated by the existing literature on the drivers of female employment and discussed in detail below.

We take advantage of the fact that multiple rounds of survey data are available for the same country-industry pair over time. Thus, in all our regressions, we control for dummy variables indicting the country-industry pair to which a firm belongs (Country-Industry fixed effects). Note that the “country” here is the geographical region and independent of ES round. Use of countryindustry fixed effects as controls implies that our regressions control for all time invariant countryindustry specific characteristics. It also implies that all time invariant country characteristics such as culture, social attitudes towards women’s work, legal origin, etc., that may impact female employment are accounted for in the regressions. Similarly, all time invariant characteristics common to firms globally within an industry (that is, industry fixed effects) are captured by the country-industry fixed effects. For instance, studies report a heavier concentration of females relative to males in some industries than in others (Juhn et al. 2014, Amin and Islam 2014, Rendall 2010, Do et al. 2011). Some of the reasons suggested for this in the literature include, for example, the brawn vs. brains content of jobs, the ease with which work can be combined with family responsibility, and culture. Our results are unaffected by such industry-wide features.

Thus, identification of the relationship between female workers and exports comes from differences over time and within a country-industry pair rather than across countries and industries. In this sense, our results are less susceptible to the omitted variable bias problem than is typically the case with pure cross-country regressions.

13
The ES rounds were conducted in different years across countries. It is possible that global shocks to female employment could bias our estimation results. Thus, all our regressions control for dummy variables for the year the ES was conducted (Year fixed effects).

The remaining controls can be divided into macro and micro level controls. First, consider the controls at the micro or firm-level. Firm-size and age are known to be highly positively correlated with exporting activity. Firm size and age are also important proxy measures for various firm characteristics, potentially correlated with several firm attributes such as the tendency to innovate, firm-efficiency and growth (Acs and Audretsch 1988, Pagano and Schivardi 2003, Cohen and Klepper 1996, Soderbom and Teal 2004, Diaz-Mayans and Sanchez 2008). The relationship between women’s employment and the age and size of the firm is not obvious but it cannot be ruled out. For example, being more visible to the public, large firms may discriminate less against women workers than the small firms. On the other hand, small firms may be more flexible, offering a better work-family life balance that is important for women’s participation in the labor market. Younger firms may be less tied to traditional notions of gender roles and therefore more open to hiring women workers. Thus, we control for firm-size proxied by the (log of) number of permanent full-time employees at the firm at the end of the last fiscal year ( _No. of Workers_ ). For age, we use the (log of) age of the firm at the time the ES was conducted in the country.

Women workers are also more likely in firms that have women owners or women top managers as women owners and managers are less likely to discriminate against women workers than men owners and managers. The empirical evidence on this issue, however, is somewhat mixed (Nelson and Bridges 1999, Penner and Toro-Tulla 2010). Apart from exports, another aspect of globalization is the presence of foreign firms and foreign ownership among domestic firms. There is little research on how foreign ownership affects women’s employment. One possibility is that 14
foreign ownership may come with foreign values and culture that tend to be more favorable for women’s employment. Women may be particularly sensitive to the law and order situation and the prevalence of crime as criminals tend to target women more than men (Glaeser and Sacerdote 1999; Islam 2013).

Based on the discussion in the previous paragraph, we control for the following variables: a dummy variable equal to 1 if the firm has one or more female owners and 0 otherwise; proportion of firms’ ownership that is with foreign individuals or companies ( _Foreign Ownership_ ); a dummy variable equal to 1 if the firm experienced losses due to crime, theft and disorder and 0 otherwise ( _Crime Losses_ ); and a measure of the quality of courts based on a question in the ES on how severe is the (lack of proper) functioning of courts as an obstacle for firms’ current operations (on 0-4 scale). Like for exports, we use cell averages at the country-ES round-industry level of the responses to the question on courts’ functioning ( _Courts Obstacle_ ).

The remaining firm-level controls are intended to capture recent investment by the firm in physical capital and various aspects of the business environment as experienced by the firm. The assumption here is that recent investment and business environment are likely to impact firm efficiency and therefore its ability to compete in international markets. If the share of female workers also happens to vary systematically with the quality of the business environment, our main results for the relationship between exports and female workers could suffer from omitted variable bias problem. For recent investment, we use as a control a dummy variable equal to 1 if the firm purchased fixed assets during the last year and 0 otherwise. For the business environment we control for hours of power outages experienced by the firm in a typical month in the last year ( _Power Outages_ ); a dummy variable equal to 1 if the firm was inspected by tax officials during the last year and 0 otherwise ( _Firm Inspected_ ); and severity level (on 0-4 scale) of the following 15
variables as obstacles for firms’ current operations: high tax rates, labor laws, inadequately educated workers ( _Skills Obstacle_ ), and lack of proper transport facilities.

We now consider macro-level controls. Unless stated otherwise, we use 1 year lagged (from the year the ES was conducted) values of all the macro-level variables.

We argued above that one of the ways in which trade affects women’s employment is by raising incomes and overall economic development. However, differences in the level of development across countries cannot be solely attributed to trade. This implies that at least part of the observed relationship between exports and women’s employment may be spuriously driven by aspects of overall economic development unrelated to exporting activity. To guard against this problem, we control for the (log of) GDP per capita, PPP adjusted and at constant 2011 international dollars. The data source for the variable is WDI, World Bank.

As discussed above, greater competition in the domestic markets makes discrimination against female workers more costly to the employer. Thus, a positive relationship between greater competition domestically and female workers is expected. This could bias our main results if exporting activity happens to vary systematically with the level of competition in the domestic markets. Thus, we control for two proxy measures of the competitive pressure faced by the firm in the domestic markets. The first measure is based on entry regulations as measured by the World Bank’s Doing Business sub-indicator Starting a Business. We use the “distance to frontier” summary measure for the Starting a Business indicator. Higher values of the variable imply fewer entry restrictions and therefore greater competition in the domestic markets. The second measure of competition that we use is the traditional Herfindahl-Hirschman index for the annual sales of the firms in the ES. The index is defined at the country-ES time-industry level. Industry grouping is at the same level as used for computing cell averages for our exports variable. Note that higher 16
values of the Herfindahl-Hirschman index imply greater concentration of sales/output among firms in the industry and therefore lower competition.

Next, we control for supply-side factors. These include the proportion of women in total population and the fertility rate. The data source for these variables is WDI, World Bank. Fertility rate represents the number of children that would be born to a woman if she were to live to the end of her childbearing years and bear children in accordance with current age-specific fertility rates. High fertility rates have a direct effect on women’s involvement in the labor market via less time available for working (see for example, Bloom et al. 2009; Morrison et al. 2007; World Bank 2011). Regarding the proportion of women in total population, a higher proportion implies a larger supply of women workers and therefore proportionately more women in the workforce.

Labor laws that discriminate against women vs. men can have significant impact on women’s participation (relative to men’s) in the formal labor market. For instance, using firmlevel survey data for a cross-section of countries, Amin and Islam (2016) find that the share of women in total workers among registered private firms is significantly higher in countries that have implemented laws that prohibits discrimination against women in hiring practices. Islam et al. (2019) reach a similar result for an overall measure of gender-based disparity in the laws. Following this body of work, we control for an overall measure of disparity in the laws as they apply to women vs. men in the area of starting a job. We control two additional variables for disparity in the laws that we believe may alter the strength of the relationship between exports and female workers. These variables are a dummy variable equal to 1 if women can work in the same industries as men and 0 otherwise, and a dummy variable equal to 1 if women can work the same night hours as men and 0 otherwise. Data for all the controls for legal gender disparity are from Women, Business and Law (WBL), World Bank.

17
There is some indication in the literature that urbanization alters women’s position inside and outside the household. More job opportunities, better access to education, lower fertility rates, and more favorable attitudes towards women’s paid work are some of the channels through which urbanization may positively impact women’s employment. Thus, we control for the percentage of the country’s population living in urban areas ( _Urbanization_ ). The data source for the variable is WDI, World Bank.

Differences in fixed costs associated with different technologies, production methods, degree of mechanization and automation imply that market size may play important role in the choice of production technology and therefore the demand for female relative to male workers. Thus, we control for market size proxied by the (log of) total population in the country taken from WDI, World Bank. Last, we control for macroeconomic stability using the rate of inflation based on the consumer price index as a proxy. The data source for the variable is WDI, World Bank.

# 3.5 _Robustness controls_

In the robustness section, we show that our main result of a positive and significant relationship between exports and the share of female workers continues to hold with additional controls. Some of these controls are not included in the baseline controls because they involve a noticeable decline in sample size (missing data). Hence, we use them for robustness purposes only.

We argued above that women owners and top managers are less likely to discriminate against women workers than men owners and top managers. Information on the gender of the top manager is available in the ES but missing for about 15 percent of our baseline sample. Thus, our first robustness control is a dummy variable equal to 1 if the top manager of the firm is a female and 0 otherwise.

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Next, we control for possible self-selection of female workers into less productive and less dynamic firms. To control for firm productivity, we use labor productivity of the firm equal to (log of) total sales of the firm (deflated to 2009 USD) in the last fiscal year divided by the total number of workers employed at the end of last fiscal year ( _Labor Productivity_ ). We complement this with a dummy variable equal to 1 if the firm has an internationally recognized quality certificate and 0 otherwise ( _Quality Certificate_ ). For less vs. more dynamic firms, we use as control the annual growth rate of total employment at the firm over the last 3 fiscal years ( _Employment Growth_ ).

Next, we include controls for digitization and ICT use, provision of training by the firm, and expenses on security incurred by the firm. Greater digitization, use of computers and ICT is considered to improve women’s chances to employment. Thus, we control for a dummy variable equal to 1 if the firm has own website and 0 otherwise. In addition, since women tend to lag behind men in education and technical skills in many parts of the developing world, provision of training to employees by firms may be particularly attractive to women especially when informal networks in the firm are male-dominated (see for example, Rowley 2013, Ragins and Sundstrom 1989). Thus, we control for a dummy variable equal to 1 if the firm provides training to its workers and 0 otherwise. We argued above that more than men, women workers are concerned about the law & order situation, crime and security. We complement the controls in the baseline for crime and law order with a dummy variable equal to 1 if the firm spent on security (personnel, equipment, etc.) during the last fiscal year and 0 otherwise.

While differences in GDP per capita across countries reflect long-term and structural forces, the growth rate of GDP per capita in the recent past is a useful proxy measure of the current state of demand in the labor market (see for example, Wamboye and Seguino 2014). Further, compulsions of democracy may lead to force government to implement policies for better access 19
to jobs for women. Hence, we control for the annual real growth rate of real GDP per capita. The data source for the variable is World Development Indicators, World Bank; and for the quality of democracy proxied by the “Polity” index taken from the Polity IV database.

There are several ways in which more education for all (men and women) and especially for women may improve women’s labor market outcomes. For example, improved access to education for women is likely to add to their skills and also create awareness among them about available opportunities in the labor markets; it raises the cost to women of staying home; more educated women in society helps to change social attitudes towards women’s work, making women’s labor market activity socially more acceptable; (World Bank 2011, Chioda et al. 2011, Contrareras and Plaza 2010, Morrison et al. 2007). More education overall (among men and women) can also result in a more favorable attitude towards women’s work and their rights. Thus, we control for two measures related to access to education: gross enrollment rate in primary education in the country, and the gender parity index for gross enrollment rate in primary education. The data source for the variable is WDI, World Bank.

# **4. Estimation results**

4.1 Base regression results Our baseline regression results are provided in Table 1. For all the specifications considered, the relationship between female workers and exports is large, positive and statistically significant at the 1 percent level. The estimated coefficient value of exports ranges between 0.160 and 0.191. Thus, for each percentage point increase in exports, the associated increase in female workers equals 0.160 to 0.191 percentage points.

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Without any other controls (except for year and country-industry fixed effects), the estimated coefficient value of exports equals 0.169 (column 1). The coefficient value increases to 0.178 when we control for GDP per capita (not shown); it remains roughly unchanged equaling 0.180 when we also control for firm-size (column 2). Adding the remaining firm-level controls to the previous specification causes the coefficient value to increase further to 0.191 (column 5). It declines to 0.160 when we add all the remaining macro-level controls to the previous specification (column 5).

Several controls show a significant relationship with female workers and in the expected direction. Unless stated otherwise, all the relationships discussed in this paragraph are significant at the 5 percent level or less. Greater competition as captured by lower values of the HerfindahlHirschman index and higher values of the Starting a Business (DTF) measure is associated with a significantly higher share of female workers. This is consistent with the general understanding in the literature that greater competition forces managers to higher more females than they would do otherwise. Greater gender parity in the laws as reflected in higher values of the WBL index is associated with a significant increase in female workers, confirming some of the earlier findings in the literature (discussed above). For the remaining macro-level controls, the share of female workers is significantly higher in countries that are smaller (population wise) and in countries with greater urbanization, larger share of females in total population, and lower fertility rates. The result for country size should be treated with due caution as it disappears when we include additional country-level controls (section 4.2). GDP per capita is also significantly positively correlated with the share of female workers, but this relationship becomes insignificant when we control for some of the other macro-level variables (column 5). Regarding firm-level controls, the share of female workers is significantly higher in firms with a female owner vs. all male owners, in relatively large 21
firms, and for firms that report skills shortages to be a more severe obstacle; the share is significantly lower for firms that are relatively old, experience more power outages, and for firms that purchased fixed assets during the last year (significant at the 10 percent level).

# 4.2 _Robustness for other controls_

Starting with the final specification in Table 1, we now add sequentially more controls for robustness purposes. Note that as a result, the sample size declines substantially (missing data). The robustness results are provided in Table 2. Column 1 includes the controls for firms’ labor productivity, sales growth rate, and having quality certificate. Controls for gender of the top manager of the firm, legal form of the firm, and whether the firm spent on security in the last year are introduced in column 2. Controls for having own website and provision of training to workers are added in column 3. The remaining macro-level controls for the polity index, growth rate of GDP per capita, gross enrollment rate in primary education, and the gender parity index in primary education are added in column 4.

Our main result survives the robustness check. That is, the estimated coefficient value of exports remains large, positive and significant (at the 5 percent level or less) when we include the additional controls in the specification. It declines from 0.160 in the final baseline specification to 0.149 (column 4, Table 2) with all the additional controls included in the regression. However, this decline seems to be due to the decline in sample size because of missing data on the additional controls.

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# **5. Interaction term results**

In this section, we discuss the results for the interaction terms between exports and several other variables including the dependence of sectors on female workers, level of competition in domestic markets, social attitudes towards women’s work and rights, gender-based disparity in labor laws, and law and order situation at the sub-national level.

A natural concern with the interaction term results is that the interaction terms could be picking up the differential effect of exports on female employment in rich vs. poor countries. For instance, social attitudes towards women’s work are likely to be correlated with income level (GDP per capita). So, the interaction term between exports and social attitudes could simply be a proxy for the interaction term between exports and overall economic development or GDP per capita. Hence, we guard against this problem by controlling for the additional interaction term as controls. These controls are the interaction terms between exports and GDP per capita, and between firm-size and the variable that is interacted with exports (listed in the previous paragraph). For brevity, results are shown only for the final baseline specification with and without controlling for the interaction term controls. Regression results for the remaining baseline specifications are provided in the online appendix (stated below).

# 5.1 _Competition_

Regression results for the interaction term between exports and the Herfindahl-Hirschman index for the final baseline specification are provided in columns 1 and 2 of Table 3. Results for the remaining baseline specifications are provided in Table A4 in the online appendix. To reiterate, our expectation is that the positive impact of higher exports on the share of female workers due to greater competition in international vs. domestic markets is larger when competition in the 23
domestic markets is low. Thus, a positive interaction term between exports and the HerfindahlHirschman index is predicted.

The results in columns 1 and 2 of Table 3 and in Table A4 confirm the prediction. That is, the interaction term between exports and the Herfindahl-Hirschman index is positive, large and significant at the 5 percent level. Based on the results in column 1 in Table 3, each percentage point increase in exports is associated with an increase of 0.099 percentage points in female workers (significant at the 10 percent level) at the lowest value of the Herfindahl-Hirschman index (highest domestic competition). The corresponding increase at the highest value of the HerfindahlHirschman index (lowest domestic competition) is much larger at 0.872 percentage points (significant at the 1 percent level).

Regression results for the interaction term between exports and Starting a Business are provided in columns 3 and 4 of Table 3 and more detailed results are provided in Table A5 in the online appendix. The prediction here is that the interaction term is negative reflecting the fact that higher exports lead to a much larger increase in the share of female workers when domestic competition is low (low values of Starting a Business). Regression results in Table 3 and Table A5 confirm this prediction. That is, the interaction term between exports and Starting a Business is negative, large and significant at the 1 percent level. For instance, results for the final baseline specification (column 3, Table 3) indicate that a one percentage point increase in exports is associated with an increase of 0.398 percentage points (significant at the 1 percent level) in female workers at the lowest value of Starting a Business (lowest domestic competition). The corresponding increase at the highest value of Starting a Business (highest domestic competition) equals a mere 0.066 percentage points, insignificant at the 10 percent level.

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# 5.2 _Dependence of sectors on female workers_

Results for the interaction term between exports and the dependence of sectors on female workers for the final baseline specification are provided in columns 5 and 6 of Table 3. More detailed results are provided in Table A6 in the online appendix. As discussed above, our prediction is that the sectors more dependent on female workers are more likely to benefit from higher exports in terms of female employment.

Regression results in Table 3 and in Table A6 confirm the prediction. That is, the interaction term between exports and the index for the dependence of sectors on female workers is positive, large and statistically significant at the 10 percent level or less.<sup>2</sup> For the results based on the final baseline specification (column 5, Table 3), a one percentage point increase in exports is associated with an increase in the share of female workers by 0.043 percentage points (insignificant at the 10 percent level) in sectors least dependent on female workers. The corresponding increase in sectors most dependent on female workers is much larger equaling 0.306 percentage points (significant at the 1 percent level).

# 5.3 _Social attitudes towards women’s work and their rights_

As discussed above, increase in demand for female workers (due to higher exports) is likely to result in a larger increase in female workers when supply of female workers is forthcoming. This is more likely to occur when social attitudes towards women’s work and their rights are more favorable to women. Thus, we predict that the interaction term between exports and our proxy measures of the social attitudes is positive.

> 2 The interaction term is significant at the 5 percent level in some specifications and at the 10 percent level in other specifications including the final baseline specification (see Table A6 in the online appendix).

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Regression results for the interaction term between exports and social attitudes towards the rights of women vs. men to scarce jobs are provided in columns 1 and 2 of Table 4 (for the final baseline specification) and in Table A7 in the online appendix (all baseline specifications). Likewise, results for the interaction term between exports and the rights of women vs. men in general are provided in columns 3-4 in Table 4 and in Table A8 in the online appendix. Note that all these results are based on a much smaller sample size due to missing data on social attitudes. Thus, the results should be treated with due caution.

The results confirm our prediction above. That is, the interaction term between exports and more favorable attitudes towards women’s work and women’s rights is positive, large and significant at the 1 percent level. To provide an example, consider the results based on the final baseline specification in column 1 of Table 4. A one percentage point increase in exports is associated with a _decrease_ in female workers by 0.472 percentage points (significant at the 5 percent level) when social attitudes towards women’s right to scarce jobs are least favorable for women. The corresponding change in female workers when the attitude is most favorable for women is an _increase_ of 0.638 percentage points (significant at the 1 percent level).

# 5.4 _Courts’ quality_

Interaction term results between exports and the quality of courts functioning as perceived by the firms and defined at the country-industry level (for each ES round separately) are provided in columns 5 and 6 of Table 4 (final baseline specification) and the more detailed results are provided in Table A9 in the online appendix. The prediction is that worse functioning courts, reflecting worse law and order and less security for women, dampens the positive impact of exports on female employment.

26
Regression results confirm the prediction. That is, the interaction term between exports and courts obstacle is large, negative and significant at the 5 percent level in most specifications and at the 10 percent level in one specification. Based on the results for the final baseline specification (column 5, Table 4), a one percentage point increase in exports is associated with an increase in female workers by 0.254 percentage points (significant at the 1 percent level) when courts functioning is at its best. In contrast, when the functioning of courts is at its worst, the corresponding change in female workers is a _decline_ of 0.137 percentage points, insignificant at the 10 percent level.

# 5.5 _Legal gender disparity_

Next, we consider the role of gender-based labor laws in enhancing or mitigating the positive impact of exports on female employment. Results for the interaction term between exports and a dummy variable equal to 1 if women can work night shifts like men and 0 otherwise are provided in columns 1 and 2 of Table 5 (final baseline specification) and in Table A10 in online appendix (all baseline specifications). Similarly, results for the interaction term between exports and a dummy variable equal to 1 if women can work in the same industries as men and 0 otherwise are provided in columns 3 and 4 of Table 5 (final baseline specification) and in Table A11 in online appendix (all baseline specifications). We make one change in the specification from above. That is, we now include as controls the interaction term between the WBL index and exports and between the WBL index and firm-size. These interaction terms ensure that our results for the gender-based labor laws are not spuriously picking up the effect of the broader gender-based disparity in the laws related to the employment of women.

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As discussed above, the prediction is that the interaction term between exports and the two gender-based labor laws is positive, implying that exports increase female employment more when labor laws do not hinder female relative to male employment. The regression results confirm this prediction. That is, the interaction term between exports and the dummy for night work for women is positive, large and significant at the 1 percent level in all the specifications considered. For instance, for the final baseline specification, a one percentage point increase in exports is associated with an increase in the share of female workers by 0.301 percentage points (significant at the 1 percent level) when women can work in the same industries as men. The corresponding increase in female workers when the women cannot work in the same industries as men is much lower equaling 0.030 percentage points (insignificant at the 10 percent level).

The interaction term between exports and the dummy for women allowed to work in the same industries as men is also large, positive and significant at the 1 percent level in the final specifications (column 3 and 4, Table 5), significant at the 5 percent and 10 level in some of the other specifications (see Table A11 in the online appendix). However, as can be seen from column1 of Table A11, the interaction term is insignificant at the 10 percent level in the specification with no other controls (except for year and country-industry fixed effects).

We also experimented by controlling for the interaction terms between exports and both the gender specific labor laws simultaneously. However, this did not change the qualitative nature of the results discussed above (see Table A12 in the online appendix).

# 5.6 _All interaction terms simultaneously_

Last, we include all the interaction terms discussed above simultaneously except that only one of the competition variables (Herfindahl-Hirschman index and Starting a Business) is included at a 28
time. This is because the two competition variables are close substitutes and intended to capture the same phenomenon. Table A13 and Table A14 in the online appendix provide the regression results. The tables show that the qualitative nature of the results for the various interaction terms discussed above remain unchanged.

# **6. Conclusion**

Rapid globalization over the last few decades has brought numerous economic benefits. However, it has also raised concerns about how vulnerable sections such as female workers are impacted. Theory suggests that several avenues through trade may impact female workers, but these come with important caveats. Empirical evidence on the issue is mixed. It is also limited, especially for the case of developing countries. The present paper attempts to fill this gap in the literature. It does so by using firm-level survey data for 91 (mostly) developing countries to estimate the relationship between exporting activity and the share of females in total workers in firms. We pay close attention to endogeneity concerns. We also test for several predictions of the theoretical models, which helps raise our confidence against endogeneity concerns.

The results show that there is a large positive impact of higher exports (to sales ratio) on the proportion of female workers. Further, consistent with theoretical predictions, this positive relationship is much larger (more positive) in industries that rely more on female workers, in country-industry pairs where domestic competition is low, in countries where social attitudes and labor laws are more favorable towards women’s work, and when the law and order situation is better.

Several questions remain to be answered. We highlight a few of them to motivate future research in the area. First, our results show that exporting activity benefits females in terms of jobs, 29
but the impact on wages is not analyzed due to data limitations. The concern with female employment is not only about the number of jobs but also the quality of jobs. Closing the gender disparity gap requires not only more jobs for females but also better paying jobs. It will be interesting to see how exporting activity impacts the wages of female relative to male workers. Second, our analysis focused on the formal private sector. However, available evidence indicates that more people work in the informal sector than the formal sector, especially females. Do the interlinkages between the formal and informal sectors imply that exporting activity in the formal sector affects job prospects of females in the informal sector? This is an important question that remains to be answered. We hope that the present paper motivates future work in this area.

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38
||**Tabl**|**e 1: Base reg**|**ression resu**|**lts**|||
|---|---|---|---|---|---|---|
|Dependent variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Exports (%, cell average)|0.169***|0.180***|0.166***|0.171***|0.191***|0.160***|
||(0.058)|(0.057)|(0.060)|(0.061)|(0.064)|(0.054)|
|No. of Workers (logs)||0.547**|0.714***|0.753***|0.916***|0.926***|
|||(0.244)|(0.258)|(0.280)|(0.293)|(0.294)|
|GDP per capita (logs)||14.162***|14.390***|16.082***|15.807***|5.808|
|||(5.166)|(5.369)|(5.393)|(5.801)|(5.811)|
|Herfindahl-Hirschman Index|||-6.588*|-5.237|-9.126**|-6.158*|
||||(3.459)|(3.504)|(4.085)|(3.700)|
|Age of Firm (logs)|||-0.524|-0.913***|-0.689*|-0.843**|
||||(0.359)|(0.341)|(0.364)|(0.360)|
|Foreign Ownership|||-0.940|-0.181|0.006|0.085|
|(proportion)|||||||
||||(0.845)|(0.879)|(0.969)|(0.961)|
|Female Ownership Y:1 N:0||||6.169***|5.972***|5.939***|
|||||(0.615)|(0.650)|(0.651)|
|Skills Obstacle (0-4)||||0.625***|0.699***|0.656**|
|||||(0.228)|(0.270)|(0.270)|
|How Much Of An Obstacle:||||-0.031|0.022|-0.007|
|Transport?|||||||
|||||(0.193)|(0.209)|(0.209)|
|Firm Purchased Fixed Assets||||-0.955*|-1.096*|-1.061*|
|Y:1 N:0|||||||
|||||(0.526)|(0.560)|(0.557)|
|Power Outages (days)|||||-0.176**|-0.155**|
||||||(0.070)|(0.068)|
|Courts Obstacle (cell|||||0.100|-0.486|
|average)|||||||
||||||(0.777)|(0.743)|
|Crime Losses Y:1 N:0|||||-0.603|-0.657|
||||||(0.667)|(0.669)|
|Firm Inspected Y:1 N:0|||||-0.824|-0.879|
||||||(0.606)|(0.606)|
|How Much Of An Obstacle:|||||-0.209|-0.251|
|Tax Rates|||||||
||||||(0.231)|(0.229)|
|How Much Of An Obstacle:|||||<br>0.012|<br>0.031|
|Labor Regulations?|||||||
||||||(0.301)|(0.301)|
|Population (logs)||||||-38.758***|
|||||||(10.891)|

39
|Urbanization (%)<br>WBL Index||||||1.682***<br>(0.422)<br>17.365**<br>(8.736)|
|---|---|---|---|---|---|---|
|Women can work the same<br>night hours as men Y:1 N:0||||||2.816|
|||||||(2.830)|
|Women can work in the same<br>industries as men Y:1 N:0||||||-1.293|
|||||||(2.066)|
|Ratio of Female to Male||||||2.502***|
|Population|||||||
|||||||(0.567)|
|Fertility Rate||||||-16.125***<br>(3.677)|
|Starting a Business (DTF)||||||0.079**<br>(0.039)|
|Inflation (%)||||||0.057<br>(0.065)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry fixed|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>effects|||||||
|Constant|28.879***|-93.348**|-94.276**|-113.860**|-110.443**|417.745**|
||(1.998)|(44.156)|(46.013)|(46.173)|(49.689)|(172.440)|
|Number of observations|<br>44,539|<br>44,539|<br>43,616|<br>41,444|<br>36,944|<br>36,944|
|R-squared|0.476|0.477|0.483|0.501|0.510|0.513|
|Standard errors in brackets. All<br>Sample size varies due to missi|standard error<br>ng data.|s are Huber-|White robust|and clustered o|n country-tim|es-industry.|

40
|**Tabl**|**e 2: Robustness f**|**or other controls**|||
|---|---|---|---|---|
|Dependent variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|
|Exports (%, cell average)|0.189***|0.154**|0.138**|0.149**|
||(0.059)|(0.063)|(0.066)|(0.068)|
|No. of Workers (logs)|0.959***|1.182***|0.812**|0.917**|
||(0.344)|(0.392)|(0.411)|(0.415)|
|Herfindahl-Hirschman Index|-1.344|-12.534**|-11.431**|-7.554|
||(3.792)|(5.142)|(5.708)|(5.838)|
|Age of Firm (logs)|-1.518***|-1.367***|-1.421***|-1.513***|
||(0.477)|(0.507)|(0.515)|(0.519)|
|Foreign Ownership (proportion)|-0.458|0.720|0.750|0.731|
||(1.163)|(1.443)|(1.475)|(1.484)|
|Female Ownership Y:1 N:0|6.504***|3.947***|3.876***|4.020***|
||(0.740)|(0.766)|(0.790)|(0.799)|
|Skills Obstacle (0-4)|0.791***|0.832***|0.827**|0.795**|
||(0.301)|(0.311)|(0.322)|(0.332)|
|How Much Of An Obstacle:|-0.028|0.005|0.009|0.030|
|Transport?|||||
||(0.232)|(0.268)|(0.272)|(0.278)|
|Firm Purchased Fixed Assets Y:1|-0.580|-0.745|-0.718|-0.928|
|N:0|||||
||(0.600)|(0.620)|(0.635)|(0.637)|
|Power Outages (days)|-0.129*|-0.066|-0.065|-0.049|
||(0.074)|(0.085)|(0.086)|(0.087)|
|Courts Obstacle (cell average)|-0.299|-0.395|-0.339|-0.314|
||(0.900)|(0.950)|(0.974)|(1.066)|
|Crime Losses Y:1 N:0|-1.179|-2.207**|-2.301**|-1.995**|
||(0.813)|(0.925)|(0.931)|(0.955)|
|Firm Inspected Y:1 N:0|-0.814|-0.859|-0.734|-0.552|
||(0.659)|(0.693)|(0.703)|(0.711)|
|How Much Of An Obstacle: Tax|-0.279|-0.305|-0.297|-0.314|
|Rates|||||
||(0.257)|(0.263)|(0.271)|(0.280)|
|How Much Of An Obstacle: Labor|0.080|0.061|0.009|0.089|
|Regulations?|||||
||(0.333)|(0.358)|(0.366)|(0.371)|
|GDP per capita (logs)|1.201|3.400|5.696|7.525|
||(6.818)|(8.539)|(8.715)|(10.362)|
|Population (logs)|-42.358***|3.824|5.107|3.793|
||(12.527)|(23.050)|(23.512)|(30.625)|

41
|Urbanization (%)|2.141***|1.306*|1.400*|1.189|
|---|---|---|---|---|
||(0.492)|(0.755)|(0.762)|(0.808)|
|WBL Index|12.840|5.369|6.806|9.124|
||(8.897)|(11.969)|(11.956)|(12.614)|
|Women can work the same night<br>|3.770|-3.051|-2.936|-3.790|
|hours as men Y:1 N:0|||||
||(3.457)|(4.147)|(4.105)|(4.175)|
|Women can work in the same|-0.319|-4.090|-4.243|-3.966|
|industries as men Y:1 N:0|||||
||(2.323)|(2.741)|(2.785)|(3.024)|
|Ratio of Female to Male Population|2.739***|9.206|10.477|9.140|
||(0.712)|(6.852)|(7.043)|(7.425)|
|Fertility Rate|-16.438***|2.041|3.243|4.626|
||(4.520)|(4.315)|(4.408)|(4.740)|
|Starting a Business (DTF)|0.108**|0.056|0.071|0.024|
||(0.046)|(0.063)|(0.064)|(0.079)|
|Inflation (%)|-0.012|-0.084|-0.084|-0.090|
||(0.073)|(0.071)|(0.072)|(0.079)|
|Labor Productivity (in 2009 USD,|-0.316|-0.522*|-0.598**|-0.585*|
|logs)|||||
||(0.256)|(0.292)|(0.299)|(0.303)|
|Quality Certificate Y:1 N:0|0.857|0.768|0.269|-0.042|
||(0.880)|(0.890)|(0.911)|(0.936)|
|Employment Growth (%)|-0.052***|-0.050***|-0.051***|-0.050***|
||(0.016)|(0.018)|(0.017)|(0.018)|
|Legal form of the firm fixed effects||Yes|Yes|Yes|
|||(2.513)|(2.570)|(2.629)|
|Firms' Top Manger is Female Y:1||8.659***|8.694***|8.703***|
|N:0|||||
|||(1.029)|(1.026)|(1.055)|
|Firm Spent on Security Y:1 N:0||0.012|0.011|0.012|
|||(0.007)|(0.008)|(0.008)|
|Firm Has Own Website Y:1 N:0|||1.601*|1.279|
||||(0.854)|(0.857)|
|Firm Provides Training to Workers|||1.266*|1.506*|
|Y:1 N:0|||||
||||(0.751)|(0.771)|
|Polity index||||1.461|

42
|Real growth rate of GDP per capita||||(1.189)<br>0.197|
|---|---|---|---|---|
|||||(0.160)|
|Primary Enrollment (Gross, %)||||-0.011|
|||||(0.177)|
|Primary Enrollment (Gross, %):<br>Gender Parity Index||||34.610<br>(44.324)|
|Year fixed effects|Yes|Yes|Yes|Yes|
|Country-Industry fixed effects|Yes|Yes|Yes|Yes|
|Constant|481.765**|-603.977|-716.956|-676.810|
||(201.234)|(651.132)|(668.326)|(786.331)|
|Number of observations|29,279|24,966|23,890|23,635|
|R-squared|0.536|0.551|0.552|0.551|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-timesindustry. Sample size varies due to missing data.

43
|**Table 3: Interaction ter**|**m results for C**|**ompetition an**|**d Dependenc**|**e of Sectors o**|**n Female Wor**|**kers**|
|---|---|---|---|---|---|---|
|Dependent variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Exports (%, cell<br>average)*Herfindahl-Hirschman|0.772**<br>(0.390)|0.951**<br>(0.380)|||||
|Index|||||||
|Exports (%, cell|||-0.003**|-0.005***|||
|average)*Starting a Business|||(0.002)|(0.002)|||
|<br>(DTF)|||||||
|Exports (%, cell|||||0.418*|0.403*|
|average)*Female Dependence<br>of Sectors|||||(0.219)|(0.215)|
|Exports (%, cell average)|0.099*|-1.000**|0.398***|-0.573|0.009|-0.718|
||(0.053)|(0.504)|(0.126)|(0.509)|(0.092)|(0.449)|
|Herfindahl-Hirschman Index|-10.670***|1.930|-5.828|-5.922|-0.835|-1.032|
||(3.836)|(7.299)|(3.648)|(3.654)|(4.327)|(4.324)|
|Starting a Business (DTF)|0.076*|0.075*|0.120***|0.147**|0.070*|0.074*|
||(0.039)|(0.039)|(0.045)|(0.061)|(0.040)|(0.039)|
|Female Dependence of Sectors|||||48.382***|41.236***|
||||||(6.366)|(9.237)|
|Exports (%, cell average)*GDP||0.120**||0.116**||0.081|
|per capita (logs)|||||||
|||(0.055)||(0.055)||(0.053)|
|No. of workers||-4.336**|||||
|(logs)*Herfindahl-Hirschman|||||||
|<br>Index|||||||
|||(1.726)|||||
|No. of Workers (logs)*Starting<br>a Business (DTF)||||-0.004|||
|||||(0.014)|||
|No. of Workers (logs)*Female||||||2.199|
|Dependence of Sectors|||||||
|||||||(1.897)|
|No. of Workers (logs)|0.917***|1.318***|0.898***|1.166|0.977***|0.242|
||(0.291)|(0.332)|(0.292)|(1.038)|(0.308)|(0.638)|
|GDP per capita (logs)|<br>8.307|<br>5.893|<br>5.817|<br>3.360|<br>1.297|<br>-0.696|
||(5.356)|(5.480)|(5.669)|(5.718)|(6.179)|(6.428)|
|Age of Firm (logs)|-0.806**|-0.800**|-0.832**|-0.846**|-0.736*|-0.705*|
||(0.355)|(0.357)|(0.359)|(0.362)|(0.383)|(0.383)|

44
|Foreign Ownership (proportion)|0.163|0.218|0.049|-0.004|-0.569|-0.623|
|---|---|---|---|---|---|---|
||(0.962)|(0.960)|(0.963)|(0.964)|(1.021)|(1.017)|
|Female Ownership Y:1 N:0|5.939***|5.949***|5.975***|5.978***|5.944***|5.982***|
||(0.652)|(0.650)|(0.652)|(0.652)|(0.710)|(0.710)|
|Skills Obstacle (0-4)|0.646**|0.659**|0.651**|0.649**|0.740**|0.745**|
||(0.269)|(0.267)|(0.269)|(0.268)|(0.293)|(0.292)|
|How Much Of An Obstacle:|-0.006|-0.004|-0.006|0.000|-0.036|-0.042|
|Transport?|||||||
||(0.209)|(0.209)|(0.209)|(0.209)|(0.223)|(0.223)|
|Firm Purchased Fixed Assets|-1.041*|-0.987*|-1.063*|-1.029*|-0.606|-0.587|
|Y:1 N:0|||||||
||(0.556)|(0.557)|(0.557)|(0.557)|(0.588)|(0.588)|
|Power Outages (days)|-0.145**|-0.146**|-0.152**|-0.156**|-0.084|-0.087|
||(0.068)|(0.067)|(0.068)|(0.068)|(0.075)|(0.074)|
|Courts Obstacle (cell average)|-0.536|-0.404|-0.562|-0.438|-1.390*|-1.336|
||(0.738)|(0.734)|(0.742)|(0.739)|(0.811)|(0.813)|
|Crime Losses Y:1 N:0|-0.664|-0.671|-0.626|-0.650|-0.809|-0.831|
||(0.670)|(0.670)|(0.668)|(0.668)|(0.701)|(0.704)|
|Firm Inspected Y:1 N:0|-0.869|-0.899|-0.880|-0.885|-1.325**|-1.327**|
||(0.606)|(0.604)|(0.604)|(0.604)|(0.658)|(0.659)|
|How Much Of An Obstacle:|-0.243|-0.231|-0.258|-0.258|-0.351|-0.345|
|Tax Rates|||||||
||(0.229)|(0.228)|(0.229)|(0.228)|(0.255)|(0.255)|
|How Much Of An Obstacle:|0.032|0.034|0.024|0.041|0.075|0.080|
|Labor Regulations?|||||||
||(0.301)|(0.300)|(0.301)|(0.301)|(0.329)|(0.329)|
|Population (logs)|-38.368***|-37.069***|-42.373***|-43.149***|-46.382***|-46.717***|
||(10.833)|(10.729)|(10.841)|(10.831)|(11.098)|(11.036)|
|Urbanization (%)|1.630***|1.633***|1.611***|1.616***|1.378***|1.418***|
||(0.406)|(0.411)|(0.407)|(0.409)|(0.456)|(0.457)|
|WBL Index|15.209*|14.790*|15.746*|15.280*|28.689***|28.240***|
||(8.451)|(8.393)|(8.524)|(8.382)|(8.905)|(8.822)|
|Women can work the same|2.882|2.999|2.756|2.816|1.375|1.508|
|night hours as men Y:1 N:0|||||||
||(2.879)|(2.861)|(2.818)|(2.785)|(3.206)|(3.190)|
|Women can work in the same|-1.485|-0.858|-1.410|-0.815|-2.446|-1.892|
|industries as men Y:1 N:0|||||||
||(2.114)|(2.094)|(2.087)|(2.066)|(2.184)|(2.189)|
|Ratio of Female to Male|2.474***|2.720***|2.562***|2.852***|2.956***|3.113***|
|Population|||||||
||(0.556)|(0.566)|(0.562)|(0.561)|(0.715)|(0.706)|

45
|Fertility Rate|-14.636***|-13.769***|-15.971***|-15.471***|-15.366***|-14.999***|
|---|---|---|---|---|---|---|
||(3.089)|(2.959)|(3.466)|(3.372)|(4.758)|(4.767)|
|Inflation (%)|0.041|0.030|0.053|0.046|0.054|0.051|
||(0.063)|(0.062)|(0.063)|(0.063)|(0.065)|(0.065)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|391.879**|374.651**|474.786***|489.949***|546.231***|559.370***|
||(170.766)|(168.749)|(172.119)|(172.173)|(174.131)|(173.806)|
|Number of observations|36,944|36,944|36,944|36,944|27,366|27,366|
|R-squared|0.514|0.514|0.513|0.514|0.523|0.524|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

46
|**Table 4: Inter**|**action term re**|**sults for social a**|**ttitudes toward**|**s women and la**|**w & order situ**|**ation**|
|---|---|---|---|---|---|---|
|Dependent variable:<br>_Female Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Exports (%, cell<br>average)*Men have<br>more right to scarce<br>job (WVS)|0.624***<br>(0.215)|0.610***<br>(0.209)|||||
|Exports (%, cell|||0.345***|0.278***|||
|<br>average)*Women &<br>men have same rights<br>(WVS, 20 yr.<br>average)|||(0.099)|(0.099)|||
|Exports (%, cell|||||-0.098**|-0.087*|
|average)*Courts<br>Obstacle (cell<br>average)|||||(0.046)|(0.046)|
|Exports (%, cell|-1.177***|-1.238*|-2.822***|-3.212***|0.253***|-0.239|
|<br>average)|(0.445)|(0.739)|(0.826)|(0.939)|(0.068)|(0.528)|
|Courts Obstacle (cell|-1.080|-1.081|-0.599|-0.494|0.353|0.501|
|<br>average)|(1.471)|(1.466)|(1.034)|(1.040)|(0.833)|(1.413)|
|Exports (% cell||0.009||0.102||0.053|
|,<br>average)*GDP per<br>capita (logs)||(0.071)||(0.076)||(0.056)|
|No. of Workers||||0.175|||
|(logs)*Women & men||||(0.459)|||
|<br>have same rights<br>(WVS, 20 yr.|||||||
|average)|||||||
|No. of Workers||-1.949**|||||
|(logs)*Men have||(0.858)|||||
|<br>more right to scarce<br>jobs (WVS)|||||||
|No. of Workers||||||-0.056|
|(logs)*Courts<br>Obstacle (cell<br>average)||||||(0.359)|
|No. of Workers (logs)|1.151***|5.168***|0.671**|-0.781|0.926***|0.992**|
||(0.398)|(1.826)|(0.329)|(3.744)|(0.294)|(0.475)|

47
|Herfindahl-Hirschman<br>Index (HHI)|-11.004|-11.442|-4.669|-5.691|-6.664*|-6.700*|
|---|---|---|---|---|---|---|
||(6.920)|(7.321)|(4.247)|(4.356)|(3.627)|(3.655)|
|Age of Firm (logs)|-0.874**|-0.842*|-0.164|-0.151|-0.864**|-0.870**|
||(0.437)|(0.435)|(0.436)|(0.433)|(0.361)|(0.362)|
|Foreign Ownership|2.175*|2.309*|1.698|1.707|0.071|0.055|
|(proportion)|||||||
||(1.204)|(1.193)|(1.118)|(1.117)|(0.959)|(0.960)|
|Female Ownership<br>Y:1 N:0|5.267***|5.206***|5.457***|5.443***|5.945***|5.937***|
||(0.771)|(0.776)|(0.720)|(0.717)|(0.650)|(0.652)|
|Skills Obstacle (0-4)|0.398|0.405|0.502|0.498|0.658**|0.657**|
||(0.387)|(0.386)|(0.349)|(0.348)|(0.269)|(0.269)|
|How Much Of An<br>Obstacle: Transport?|-0.254|-0.251|-0.051|-0.040|-0.013|-0.009|
||(0.300)|(0.299)|(0.274)|(0.276)|(0.209)|(0.209)|
|Firm Purchased Fixed<br>Assets Y:1 N:0|-0.520|-0.482|-0.650|-0.619|-1.046*|-1.033*|
||(0.819)|(0.818)|(0.671)|(0.673)|(0.557)|(0.558)|
|Power Outages (days)|-0.205*|-0.192*|-0.074|-0.072|-0.153**|-0.156**|
||(0.105)|(0.106)|(0.082)|(0.083)|(0.068)|(0.068)|
|Crime Losses Y:1 N:0|-1.432|-1.378|-1.285*|-1.299*|-0.670|-0.679|
||(0.910)|(0.908)|(0.733)|(0.734)|(0.668)|(0.668)|
|Firm Inspected Y:1|-0.564|-0.552|-0.190|-0.188|-0.875|-0.879|
|N:0|||||||
||(0.849)|(0.850)|(0.754)|(0.752)|(0.605)|(0.605)|
|How Much Of An<br>Obstacle: Tax Rates|-0.415|-0.426|-0.448|-0.438|-0.254|-0.252|
||(0.321)|(0.322)|(0.276)|(0.275)|(0.229)|(0.229)|
|How Much Of An<br>Obstacle: Labor|-0.193|-0.173|-0.082|-0.076|0.041|0.051|
|Regulations?|||||||
||(0.401)|(0.399)|(0.360)|(0.359)|(0.300)|(0.300)|
|GDP per capita (logs)|-38.168***|-42.889***|-20.342**|-21.450***|6.526|5.296|
||(9.297)|(10.036)|(8.271)|(8.223)|(5.760)|(5.807)|
|Population (logs)|-46.729|-57.415|-100.948***|-94.482***|-37.758***|-37.641***|
||(40.569)|(40.654)|(36.755)|(36.360)|(10.909)|(10.849)|
|Urbanization (%)|-1.699|-1.623|-1.582**|-1.486**|1.709***|1.723***|
||(1.360)|(1.355)|(0.745)|(0.736)|(0.423)|(0.427)|
|WBL Index|-5.633|-0.854|17.887|17.764|16.988*|17.157**|
||(12.997)|(13.437)|(11.050)|(10.931)|(8.755)|(8.677)|
|Women can work the<br>same night hours as<br>men|-26.481***|-24.642***|1.877|1.469|2.962|2.967|

48
||(6.178)|(6.282)|(3.140)|(3.172)|(2.814)|(2.807)|
|---|---|---|---|---|---|---|
|Women can work in|4.173|3.185|-27.171***|-25.654***|-0.860|-0.608|
|the same industries as|||||||
|men|||||||
||(6.741)|(6.843)|(3.744)|(3.879)|(2.009)|(2.053)|
|Ratio of Female to|-1.399|-0.822|-11.870**|-11.328*|2.352***|2.486***|
|Male Population|||||||
||(1.338)|(1.382)|(5.789)|(5.785)|(0.572)|(0.566)|
|Fertility Rate|-25.246***|-28.560***|-46.903***|-46.026***|-15.380***|-15.262***|
||(8.963)|(9.345)|(7.940)|(7.955)|(3.649)|(3.646)|
|Starting a Business|0.025|0.038|0.035|0.035|0.081**|0.081**|
|(DTF)|(0.088)|(0.089)|(0.081)|(0.081)|(0.039)|(0.039)|
|Inflation (%)|1.153**|1.158**|0.640**|0.677**|0.059|0.056|
||(0.525)|(0.531)|(0.284)|(0.282)|(0.065)|(0.065)|
|year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry<br>fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|1,418.287**|1,612.929**|2,736.879***|2,601.910***|397.642**|398.045**|
||(640.445)|(648.725)|(832.182)|(824.868)|(171.873)|(171.380)|
|Number of|27,517|27,517|25,587|25,587|36,944|36,944|
|observations|||||||
|R-squared|0.503|0.504|0.515|0.516|0.514|0.514|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

49
|**Table 5: Interaction te**<br>Dependent variable:_Female Workers_|**rm results for**<br>**(1)**|**gender-based**<br>**(2)**|**laws**<br>**(3)**|**(4)**|
|---|---|---|---|---|
|Exports (%, cell average)*Women can work<br>the same night hours as men Y:1 N:0|0.318***<br>(0.091)|0.305***<br>(0.095)|||
|Exports (%, cell average)*Women can work<br>in the same industries as men Y:1 N:0|||0.272***<br>(0.100)|0.280***<br>(0.098)|
|Exports (%, cell average)|-0.128|-0.626|0.03|-0.723|
||(0.083)|(0.535)|(0.055)|(0.507)|
|Women can work the same night hours as|0.503|-0.971|||
|<br>men Y:1 N:0|(2.945)|(3.954)|||
|Women can work in the same industries as|||-4.652*|-3.746|
|men Y:1 N:0|||(2.459)|(3.127)|
|Exports (%, cell average)*GDP per capita||0.065||0.096|
|(logs)||(0.078)||(0.072)|
|Exports (%, cell average)*WBL Index||-0.112||-0.165|
|||(0.392)||(0.370)|
|No. of Workers (logs)*Women can work the<br>same night hours as men Y:1 N:0||0.445<br>(0.726)|||
|No. of Workers (logs)*Women can work in<br>the same industries as men Y:1 N:0||||-0.202<br>(0.529)|
|No. of Workers (logs)*WBL Index||-3.368*<br>(1.732)||-2.942*<br>(1.712)|
|No. of Workers (logs)|0.902***<br>(0.291)|2.887**<br>(1.271)|0.916***<br>(0.290)|3.066**<br>(1.225)|
|GDP per capita (logs)|5.876|4.791|7.974|6.291|
||(5.710)|(5.74)|(5.494)|(5.524)|
|Herfindahl-Hirschman Index|-5.197|-5.022|-7.021**|-6.775*|
||(3.662)|(3.709)|(3.446)|(3.482)|
|Age of Firm (logs)|-0.833**|-0.831**|-0.834**|-0.834**|
||(0.358)|(0.359)|(0.355)|(0.358)|
|Foreign Ownership (proportion)|0.103<br>(0.961)|0.130<br>(0.962)|0.183<br>(0.961)|0.218<br>(0.961)|
|Female Ownership Y:1 N:0|5.975***|5.987***|5.905***|5.890***|
||(0.652)|(0.654)|(0.652)|(0.653)|
|Skills Obstacle (0-4)|0.636**|0.636**|0.625**|0.622**|
||(0.269)|(0.269)|(0.270)|(0.269)|
|How Much Of An Obstacle: Transport?|<br>-0011|<br>-0016|<br>-001|<br>-0012|
||.<br>(0.209)|.<br>(0.209)|.<br>(0.209)|.<br>(0.209)|
|Firm Purchased Fixed Assets Y:1 N:0|-1.031*|-0.994*|-1.042*|-0.999*|
||(0.556)|(0.558)|(0.557)|(0.558)|

50
|Power Outages (days)|-0.154**|-0.155**|-0.151**|-0.152**|
|---|---|---|---|---|
||(0.068)|(0.068)|(0.067)|(0.068)|
|Courts Obstacle (cell average)|-0.454|-0.422|-0.644|-0.558|
||(0.739)|(0.737)|(0.740)|(0.739)|
|Crime Losses Y:1 N:0|-0.633|-0.672|-0.688|-0.703|
||(0.669)|(0.674)|(0.671)|(0.672)|
|Firm Inspected Y:1 N:0|-0.847|-0.822|-0.811|-0.78|
||(0.603)|(0.604)|(0.603)|(0.604)|
|How Much Of An Obstacle: Tax Rates|-0.253|-0.264|-0.257|-0.269|
||(0.229)|(0.23)|(0.229)|(0.228)|
|How Much Of An Obstacle: Labor|0.036|0.051|0.034|0.057|
|Regulations?|(0.301)|(0.300)|(0.301)|(0.300)|
|Population (logs)|-35.364***|-35.099***|-35.761***|-35.328***|
||(10.96)|(11.115)|(10.819)|(10.887)|
|Urbanization (%)|1.673***<br>(0.415)|1.642***<br>(0.405)|1.857***<br>(0.399)|1.840***<br>(0.391)|
|WBL Index|17.580**|29.339**|17.226**|28.064**|
||(8.651)|(11.496)|(8.478)|(11.187)|
|Women can work in the same industries as<br>men Y:1 N:0|-2.120<br>(2.049)|-1.808<br>(2.127)|||
|Women can work the same night hours as|||3.258|3.246|
|men Y:1 N:0|||(2.973)|(2.949)|
|Ratio of Female to Male Population|3.454***<br>(0.693)|3.508***<br>(0.708)|2.614***<br>(0.587)|2.680***<br>(0.619)|
|Fertility Rate|-15.658***|-15.345***|-15.721***|-15.204***|
||(3.56)|(3.521)|(3.33)|(3.275)|
|Starting a Business (DTF)|0.066*|0.064*|0.062|0.061|
||(0.039)|(0.039)|(0.039)|(0.039)|
|Inflation (%)|0.068|0.071|0.059|0.059|
||(0.064)|(0.065)|(0.062)|(0.064)|
|Year fixed effects|Yes|Yes|Yes|Yes|
|Country-Industry fixed effects|Yes|Yes|Yes|Yes|
|Constant|315.970*|312.122*|336.227*|331.879*|
||(174.209)|(179.319)|(171.634)|(174.104)|
|Number of observations|36,944|36,944|36,944|36,944|
|R-squared|0.514|0.514|0.514|0.515|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-timesindustry. Sample size varies due to missing data.

51
# **Online Appendix**

|**Table A1: Coun**<br>|**tries included in the ba**<br>|**seline sample**<br>|
|---|---|---|
|Afghanistan|Gambia, The|Nigeria|
|Albania|Georgia|Pakistan|
|Angola|Ghana|Panama|
|Argentina|Guatemala|Paraguay|
|Armenia|Guinea|Peru|
|Azerbaijan|Honduras|Philippines|
|Bangladesh|Hungary|Poland|
|Belarus|Indonesia|Romania|
|Benin|Kazakhstan|Russian Federation|
|Bhutan|Kenya|Rwanda|
|Bolivia|Kosovo|Senegal|
|Bosnia and Herzegovina|Kyrgyz Republic|Serbia|
|Botswana|Lao PDR|Sierra Leone|
|Bulgaria|Latvia|Slovak Republic|
|Burundi|Lesotho|Slovenia|
|Cambodia|Liberia|Tajikistan|
|Cameroon|Lithuania|Tanzania|
|Chad|North Macedonia|Timor-Leste|
|Chile|Madagascar|Togo|
|Colombia|Malawi|Turkey|
|Congo, Dem. Rep.|Mali|Uganda|
|Côte d'Ivoire|Mauritania|Ukraine|
|Croatia|Mexico|Uruguay|
|Czech Republic|Moldova|Uzbekistan|
|Dominican Republic|Mongolia|Venezuela, RB|
|Ecuador|Montenegro|Vietnam|
|Egypt, Arab Rep.|Myanmar|Yemen, Rep.|
|El Salvador|Namibia|Zambia|
|Estonia|Nepal|Zimbabwe|
|Eswatini|Nicaragua||
|Ethiopia|Niger||

52
|**Table A2:**|**Summary**|**Statistics**||||
|---|---|---|---|---|---|
|Variable|Mean|Std.<br>deviation|Min.|Max.|No. of<br>Observations|
|Female Workers (% of total workers)|29.217|28.009|0|100|44539|
|Exports (%, cell average)|9.452|13.96|0|100|44539|
|No. of Workers (logs)|3.061|1.231|0|10.06|44539|
|Herfindahl-Hirschman Index (HHI)|0.102|0.147|0|1|44539|
|Age of Firm (logs)|2.53|0.833|0|5.737|43911|
|Foreign Ownership (proportion)|0.089|0.266|0|1|44217|
|Female Ownership Y:1 N:0|0.34|0.474|0|1|43506|
|Skills Obstacle (0-4)|1.35|1.301|0|4|43892|
|How Much Of An Obstacle: Transport?|1.259|1.282|0|4|44074|
|<br>Firm Purchased Fixed Assets Y:1 N:0|0.481|0.5|0|1|44182|
|Power Outages (days)|1.316|3.541|0|30|42105|
|Courts Obstacle (cell average)|1.016|0.715|0|3.955|43198|
|Crime Losses Y:1 N:0|0.178|0.383|0|1|43173|
|Firm Inspected Y:1 N:0|0.598|0.49|0|1|44070|
|How Much Of An Obstacle: Tax Rates|1.768|1.322|0|4|43885|
|How Much Of An Obstacle: Labor Regulations?|0.963|1.132|0|4|44003|
|GDP per capita (logs)|8.598|1.005|6.462|10.264|44539|
|Population (logs)|16.266|1.35|13.329|19.345|44539|
|Urbanization (%)|48.113|19.99|0|95.045|44539|
|<br>WBL Index|0.691|0.164|0.176|1|44539|
|Women can work the same night hours as men|0.825|0.38|0|1|44539|
|Women can work in the same industries as men|0.463|0.499|0|1|44539|
|Ratio of Female to Male Population|50.251|5.534|0|56.84|44539|
|Fertility Rate|3.296|1.693|1.23|7.57|44539|
|<br>Starting a Business (DTF)|67.956|20.73|0|95.81|44539|
|Inflation (%)|7.028|7.885|-8.975|81.138|44539|
|Labor Productivity (2009 USD, logs)|9.738|2.145|-5.089|22.663|38742|
|Quality Certificate Y:1 N:0|0.185|0.389|0|1|43171|
|<br>Employment Growth (%)|4.874|17.859|-97.044|100|40435|
|Firms' Top Manger is Female Y:1 N:0|0.166|0.372|0|1|37489|
|<br>Firm Spent on Security Y:1 N:0|54.704|49.779|0|100|44409|
|Firm Has Own Website Y:1 N:0|0.394|0.489|0|1|44381|
|Firm Provides Training to Workers Y:1 N:0|0.32|0.467|0|1|42208|
|Polity index|4.028|5.596|-9|10|44539|
|Real growth rate of GDP per capita|3.7|5.457|-22.331|38.69|44539|
|Primary Enrollment (Gross, %)|102.905|14.373|47.618|144.338|<br>42363|

53
|Primary Enrollment (Gross, %): Gender Parity|0.95|0.086|0.406|1.072|42363|
|---|---|---|---|---|---|
|Index||||||
|Men have more right to scarce job (Agree-|2.054|0.349|1.13|2.796|32683|
|Disagree)||||||
|Women & men have same rights (Disagree-|8.236|0.632|6.913|9.316|30328|
|Agree)||||||
|Female Dependence of Sectors|0.314|0.165|0.08|0.71|33328|

Sample size varies due to missing data.

54
# **Table A3: Correlation between Exports and other variables used** **<u>in the regressions as controls</u>**

|Variable|Exports (%, cell<br>average)|
|---|---|
|Exports (%, cell average)|1|
|No. of Workers (logs)|0.169|
|Herfindahl-Hirschman Index (HHI)|-0.105|
|Age of Firm (logs)|0.015|
|Foreign Ownership (proportion)|0.091|
|Female Ownership Y:1 N:0|0.067|
|Skills Obstacle (0-4)|0.029|
|How Much Of An Obstacle: Transport?|-0.040|
|Firm Purchased Fixed Assets Y:1 N:0|0.055|
|Power Outages (days)|-0.075|
|Courts Obstacle (cell average)|0.010|
|Crime Losses Y:1 N:0|-0.008|
|Firm Inspected Y:1 N:0|-0.038|
|How Much Of An Obstacle: Tax Rates|0.051|
|How Much Of An Obstacle: Labor|0.060|
|Regulations?||
|GDP per capita (logs)|0.176|
|Population (logs)|-0.184|
|Urbanization (%)|0.041|
|WBL Index|0.181|
|Women can work the same night hours as|0.067|
|men||
|Women can work in the same industries as|-0.016|
|men||
|Ratio of Female to Male Population|0.072|
|Fertility Rate|-0.217|
|Starting a Business (DTF)|0.058|
|Inflation(%)|-0.104|

55
||**Table A4: In**|**teraction term**|**results for th**|**e Herfindahl-**|**Hirschman Ind**|**ex**||
|---|---|---|---|---|---|---|---|
|Dependent variable:<br>_Female Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Exports (%, cell<br>average)*Herfindahl<br>-Hirschman Index|0.748|0.889*|0.972*|1.071**|1.153**|0.772**|0.951**|
||(0.508)|(0.505)|(0.552)|(0.519)|(0.545)|(0.390)|(0.380)|
|Exports (%, cell|0.106**|0.107**|0.088*|0.084*|0.098*|0.099*|-1.000**|
|average)||||||||
||(0.052)|(0.051)|(0.051)|(0.050)|(0.054)|(0.053)|(0.504)|
|Herfindahl-|<br>-10.006***|<br>-10.794***|<br>-11.705***|<br>-11.128***|<br>-15.403***|<br>-10.67***|<br>1.930|
|Hirschman Index|(3.823)|(3.533)|(3.618)|(3.607)|(4.214)|(3.836)|(7.299)|
|Exports (%, cell<br>average)*GDP per<br>capita (logs)|||||||0.120**|
||||||||(0.055)|
|No. of workers|||||||-4.336**|
|(logs)*Herfindahl-<br>Hirschman Index||||||||
||||||||(1.726)|
|No. of Workers||0.544**|0.714***|0.749***|0.906***|0.917***|<br>1.318***|
|(logs)||||||||
|||(0.244)|(0.257)|(0.279)|(0.291)|(0.291)|(0.332)|
|GDP per capita||16.228***|16.436***|18.519***|18.865***|8.307|5.893|
|(logs)||||||||
|||(4.853)|(4.915)|(4.867)|(5.196)|(5.356)|(5.480)|
|Age of Firm (logs)|||<br>-0.494|<br>-0.886***|<br>-0.656*|<br>-0.806**|<br>-0.800**|
||||(0.358)|(0.337)|(0.359)|(0.355)|(0.357)|
|Foreign Ownership|||<br>-0.881|<br>-0.103|<br>0.125|<br>0.163|<br>0.218|
|(proportion)||||||||
||||(0.846)|(0.883)|(0.970)|(0.962)|(0.960)|
|Female Ownership||||<br>6.182***|<br>5.978***|<br>5.939***|<br>5.949***|
|Y:1 N:0||||||||
|||||(0.615)|(0.652)|(0.652)|(0.650)|
|Skills Obstacle (0-4)||||<br>0.609***|<br>0.676**|<br>0.646**|<br>0.659**|
|||||(0.226)|(0.267)|(0.269)|(0.267)|
|How Much Of An||||<br>-0.036|<br>0.017|<br>-0.006|<br>-0.004|
|Obstacle: Transport?||||||||
|||||(0.193)|(0.209)|(0.209)|(0.209)|
|Firm Purchased||||<br>-0.927*|<br>-1.066*|<br>-1.041*|<br>-0.987*|
|Fixed Assets Y:1||||||||
|N:0||||||||
|||||(0.522)|(0.557)|(0.556)|(0.557)|

56
|Power Outages<br>|||||-0.157**|-0.145**|-0.146**|
|---|---|---|---|---|---|---|---|
|(days)||||||||
||||||(0.068)|(0.068)|(0.067)|
|Courts Obstacle (cell<br>|||||-0.098|-0.536|-0.404|
|average)||||||||
||||||(0.735)|(0.738)|(0.734)|
|Crime Losses Y:1<br>|||||-0.625|-0.664|-0.671|
|N:0||||||||
||||||(0.670)|(0.670)|(0.670)|
|Firm Inspected Y:1<br>|||||-0.813|-0.869|-0.899|
|N:0||||||||
||||||(0.607)|(0.606)|(0.604)|
|How Much Of An<br>|||||-0.199|-0.243|-0.231|
|Obstacle: Tax Rates||||||||
||||||(0.231)|(0.229)|(0.228)|
|How Much Of An<br>|||||0.012|0.032|0.034|
|Obstacle: Labor||||||||
|Regulations?||||||||
||||||(0.301)|(0.301)|(0.300)|
|Population (logs)||||||-38.37***|-37.069***|
|||||||(10.833)|(10.729)|
|Urbanization (%)||||||1.630***|1.633***|
|||||||(0.406)|(0.411)|
|WBL Index||||||15.209*|14.790*|
|||||||(8.451)|(8.393)|
|Women can work the||||||2.882|2.999|
|same night hours as<br>||||||||
|men Y:1 N:0||||||||
|||||||(2.879)|(2.861)|
|Women can work in||||||-1.485|-0.858|
|the same industries<br>||||||||
|as men Y:1 N:0||||||||
|||||||(2.114)|(2.094)|
|Ratio of Female to||||||2.474***|2.720***|
|Male Population||||||||
|||||||(0.556)|(0.566)|
|Fertility Rate||||||-14.64***|-13.769***|
|||||||(3.089)|(2.959)|
|Starting a Business||||||0.076*|0.075*|
|(DTF)||||||||
|||||||(0.039)|(0.039)|
|Inflation (%)||||||0.041|0.030|
|||||||(0.063)|(0.062)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry<br>fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

57
|Constant|28.48***|-111.39***|-111.93***|-134.88***|-136.74***|391.88**|374.65**|
|---|---|---|---|---|---|---|---|
||(2.168)|(41.395)|(41.960)|(41.599)|(44.528)|(170.766)|(168.749)|
|Number of<br>observations|44,539|44,539|43,616|41,444|36,944|36,944|36,944|
|R-squared|0.476|0.477|0.483|0.502|0.511|0.514|0.514|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

58
||**Table A5: I**|**nteraction ter**|**m results for**|**Starting a Bus**|**iness (DTF)**|||
|---|---|---|---|---|---|---|---|
|Dependent variable:<br>_Female Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Exports (%, cell<br>average)*Starting a|-0.004***<br>(0.002)|-0.004**<br>(0.002)|-0.004**<br>(0.002)|-0.005**<br>(0.002)|-0.004**<br>(0.002)|-0.003**<br>(0.002)|-0.005***<br>(0.002)|
|<br>Business (DTF)||||||||
|Exports (%, cell average)|0.458***|0.444***|0.426***|0.488***|0.457***|0.398***|-0.573|
||(0.131)|(0.132)|(0.146)|(0.152)|(0.162)|(0.126)|(0.509)|
|Starting a Business (DTF)|0.092**|0.089**|0.104***|0.086**|0.064|0.120***|0.147**|
||(0.038)|(0.036)|(0.037)|(0.036)|(0.043)|(0.045)|(0.061)|
|Exports (%, cell|||||||0.116**|
|average)*GDP per capita<br>(logs)|||||||(0.055)|
|No. of Workers|||||||-0.004|
|(logs)*Starting a Business<br>(DTF)|||||||(0.014)|
|No. of Workers (logs)||0.504**|0.668***|0.712**|0.886***|0.898***|1.166|
|||(0.243)|(0.256)|(0.279)|(0.292)|(0.292)|(1.038)|
|GDP per capita (logs)||13.923***|14.248***|15.284***|14.997**|5.817|3.360|
|||(5.053)|(5.257)|(5.321)|(5.827)|(5.669)|(5.718)|
|Herfindahl-Hirschman|||-6.833**|-5.371|-8.963**|-5.828|-5.922|
|Index|||(3.410)|(3.421)|(4.054)|(3.648)|(3.654)|
|Age of Firm (logs)|||<br>-0.526|<br>-0.897***|<br>-0.681*|<br>-0.832**|<br>-0.846**|
||||(0.354)|(0.339)|(0.362)|(0.359)|(0.362)|
|Foreign Ownership|||-0.957|-0.221|-0.038|0.049|-0.004|
|(proportion)||||||||
||||(0.846)|(0.881)|(0.972)|(0.963)|(0.964)|
|Female Ownership Y:1||||6.229***|6.017***|5.975***|5.978***|
|N:0||||||||
|||||(0.614)|(0.651)|(0.652)|(0.652)|
|Skills Obstacle (0-4)||||0.610***|0.692**|0.651**|0.649**|
|||||(0.227)|(0.269)|(0.269)|(0.268)|
|How Much Of An||||-0.042|0.020|-0.006|0.000|
|Obstacle: Transport?||||||||
|||||(0.193)|(0.209)|(0.209)|(0.209)|
|Firm Purchased Fixed||||-0.992*|-1.110**|-1.063*|-1.029*|
|<br>Assets Y:1 N:0||||||||
|||||(0.527)|(0.560)|(0.557)|(0.557)|
|Power Outages (days)|||||<br>-0.172**|<br>-0.152**|<br>-0.156**|
||||||(0.070)|(0.068)|(0.068)|
|Courts Obstacle (cell|||||-0.030|-0.562|-0.438|
|average)||||||||
||||||(0.759)|(0.742)|(0.739)|

59
|Crime Losses Y:1 N:0|||||-0.572|-0.626|-0.650|
|---|---|---|---|---|---|---|---|
||||||(0.666)|(0.668)|(0.668)|
|Firm Inspected Y:1 N:0|||||-0.833|-0.880|-0.885|
||||||(0.606)|(0.604)|(0.604)|
|How Much Of An|||||-0.214|-0.258|-0.258|
|Obstacle: Tax Rates||||||||
||||||(0.230)|(0.229)|(0.228)|
|How Much Of An|||||-0.004|0.024|0.041|
|Obstacle: Labor|||||(0.301)|(0.301)|(0.301)|
|Regulations?||||||||
|Population (logs)||||||-42.373***|-43.149***|
|||||||(10.841)|(10.831)|
|Urbanization (%)||||||1.611***|1.616***|
|||||||(0.407)|(0.409)|
|WBL Index||||||15.746*|15.280*|
|||||||(8.524)|(8.382)|
|Women can work the||||||2.756|2.816|
|same night hours as men<br>Y:1 N:0||||||||
|||||||(2.818)|(2.785)|
|Women can work in the||||||-1.410|-0.815|
|same industries as men||||||||
|Y:1 N:0||||||||
|||||||(2.087)|(2.066)|
|Ratio of Female to Male||||||2.562***|2.852***|
|Population||||||||
|||||||(0.562)|(0.561)|
|Fertility Rate||||||-15.971***|-15.471***|
|||||||(3.466)|(3.372)|
|Inflation (%)||||||0.053|0.046|
|||||||(0.063)|(0.063)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|19.617***|-100.241**|-103.572**|-116.419***|-110.468**|474.786***|489.949***|
||(4.674)|(42.257)|(44.161)|(44.513)|(48.331)|(172.119)|(172.173)|
|Number of observations|44,539|44,539|43,616|41,444|36,944|36,944|36,944|
|R-squared|0.476|0.477|0.483|0.501|0.510|0.513|0.514|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

60
||**Ta**|**ble A6: Depen**|**dence of secto**|**rs on female**|**workers**|||
|---|---|---|---|---|---|---|---|
|Dependent variable:<br>_Female Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Exports (%, cell<br>average)*Female|0.586***<br>(0.220)|0.535**<br>(0.216)|0.471**<br>(0.214)|0.526**<br>(0.207)|0.418*<br>(0.223)|0.418*<br>(0.219)|0.403*<br>(0.215)|
|Dependence of<br>Sectors||||||||
|Exports (%, cell<br>averae)|-0.055|-0.025|-0.006|-0.024|0.039|0.009|-0.718|
|g<br>Female Dependence<br>of Sectors|(0.101)<br>43.106***|(0.100)<br>43.900***|(0.101)<br>45.375***|(0.103)<br>45.061***|(0.109)<br>48.872***|(0.092)<br>48.382***|(0.449)<br>41.236***|
||(5.772)|(5.753)|(5.709)|(5.882)|(6.343)|(6.366)|(9.237)|
|Exports (%, cell<br>average)*GDP per<br>capita (logs)|||||||0.081|
||||||||(0.053)|
|No. of Workers<br>(logs)*Female<br>Dependence of|||||||2.199|
|Sectors|||||||(1.897)|
|No. of Workers (logs)||0.605**|0.800***|0.827***|0.971***|0.977***|0.242|
|||(0.258)|(0.271)|(0.297)|(0.310)|(0.308)|(0.638)|
|GDP per capita (logs)||15.497***|14.906**|13.987**|10.334|1.297|-0.696|
|||(5.678)|(5.965)|(6.141)|(6.548)|(6.179)|(6.428)|
|Herfindahl-Hirschman|||-1.272|0.690|-1.336|-0.835|-1.032|
|Index||||||||
||||(3.955)|(4.029)|(4.759)|(4.327)|(4.324)|
|Age of Firm (logs)|||-0.434|-0.873**|-0.586|-0.736*|-0.705*|
||||(0.381)|(0.362)|(0.386)|(0.383)|(0.383)|
|Foreign Ownership<br>(proportion)|||-1.549*|-0.670|-0.671|-0.569|-0.623|
||||(0.881)|(0.934)|(1.028)|(1.021)|(1.017)|
|Female Ownership<br>Y:1 N:0||||6.154***|5.981***|5.944***|5.982***|
|||||(0.669)|(0.713)|(0.710)|(0.710)|
|Skills Obstacle (0-4)||||0.664***|0.769***|0.740**|0.745**|
|||||(0.250)|(0.293)|(0.293)|(0.292)|
|How Much Of An||||<br>-0.046|<br>-0.011|<br>-0.036|<br>-0.042|
|Obstacle: Transport?||||||||
|||||(0.205)|(0.223)|(0.223)|(0.223)|
|Firm Purchased Fixed<br>Assets Y:1 N:0||||-0.770|-0.658|-0.606|-0.587|

61
|||||(0.552)|(0.590)|(0.588)|(0.588)|
|---|---|---|---|---|---|---|---|
|Power Outages (days)|||||-0.108|-0.084|-0.087|
||||||(0.078)|(0.075)|(0.074)|
|Courts Obstacle (cell<br>|||||-0.707|-1.390*|-1.336|
|average)||||||||
||||||(0.850)|(0.811)|(0.813)|
|Crime Losses Y:1 N:0|||||-0.737|-0.809|-0.831|
||||||(0.699)|(0.701)|(0.704)|
|Firm Inspected Y:1|||||-1.273*|-1.325**|-1.327**|
|N:0||||||||
||||||(0.656)|(0.658)|(0.659)|
|How Much Of An|||||-0.290|-0.351|-0.345|
|Obstacle: Tax Rates||||||||
||||||(0.258)|(0.255)|(0.255)|
|How Much Of An|||||0.034|0.075|0.080|
|Obstacle: Labor||||||||
|Regulations?|||||(0.330)|(0.329)|(0.329)|
|Population (logs)||||||-46.382***|-46.717***|
|||||||(11.098)|(11.036)|
|Urbanization (%)||||||1.378***|1.418***|
|||||||(0.456)|(0.457)|
|WBL Index||||||28.689***|28.240***|
|||||||(8.905)|(8.822)|
|Women can work the||||||1.375|1.508|
|same night hours as||||||||
|men Y:1 N:0||||||||
|||||||(3.206)|(3.190)|
|Women can work in<br>||||||-2.446|-1.892|
|the same industries as<br>men Y:1 N:0||||||(2.184)|(2.189)|
|Ratio of Female to||||||2.956***|3.113***|
|Male Population||||||||
|||||||(0.715)|(0.706)|
|Fertility Rate||||||-15.366***|-14.999***|
|||||||(4.758)|(4.767)|
|Starting a Business||||||0.070*|0.074*|
|(DTF)||||||||
|||||||(0.040)|(0.039)|
|Inflation (%)||||||0.054|0.051|
|||||||(0.065)|(0.065)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|<br>fixed effects||||||||

62
|Constant|13.193***|-119.999**|-114.549**|-111.287**|-79.525|546.231***|559.370***|
|---|---|---|---|---|---|---|---|
||(2.858)|(48.274)|(50.775)|(52.185)|(55.616)|(174.131)|(173.806)|
|Number of|33,328|33,328|32,576|31,001|27,366|27,366|27,366|
|observations||||||||
|R-squared|0.489|0.490|0.496|0.512|0.519|0.523|0.524|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

63
|**Table A**|**7: Interaction**|**term results**|**for social atti**|**tudes toward**|**s women's rig**|**ht to scarcejob**|**s**|
|---|---|---|---|---|---|---|---|
|Dependent<br>variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Exports (%, cell<br>average)*Men have<br>more right to scarce<br>job (WVS)|0.605***<br>(0.211)|0.617***<br>(0.207)|0.658***<br>(0.213)|0.644***<br>(0.213)|0.671***<br>(0.214)|0.624***<br>(0.215)|0.610***<br>(0.209)|
|Exports (%, cell<br>average)|-1.178***<br>(0.429)|-1.190***<br>(0.419)|-1.272***<br>(0.432)|-1.247***<br>(0.433)|-1.283***<br>(0.438)|-1.177***<br>(0.445)|-1.238*<br>(0.739)|
|Exports (%, cell<br>average)*GDP per<br>capita (logs)|||||||0.009<br>(0.071)|
|No. of Workers|||||||-1.949**|
|(logs)*Men have<br>more right to scarce<br>jobs (WVS)|||||||(0.858)|
|No. of Workers<br>l||1.072***|1.117***|1.213***|1.159***|1.151***|5.168***|
|(ogs)||(0.327)|(0.350)|(0.386)|(0.398)|(0.398)|(1.826)|
|GDP per capita<br>||<br>-12.017|<br>-11.785|<br>-13.799*|<br>-21.442***|<br>-38.168***|<br>-42.889***|
|(logs)||(8.120)|(8.180)|(7.865)|(7.883)|(9.297)|(10.036)|
|Herfindahl-<br>|||-11.842**|-10.150*|-9.926|-11.004|-11.442|
|Hirschman Index||||||||
||||(5.534)|(5.409)|(6.465)|(6.920)|(7.321)|
|Age of Firm (logs)|||<br>-0.519|<br>-1.007**|<br>-0.904**|<br>-0.874**|<br>-0.842*|
||||(0.421)|(0.417)|(0.435)|(0.437)|(0.435)|
|Foreign Ownership<br>(proportion)|||<br>0.351|<br>1.922*|<br>2.104*|<br>2.175*|<br>2.309*|
||||(1.098)|(1.124)|(1.204)|(1.204)|(1.193)|
|Female Ownership<br>Y:1 N:0||||<br>5.461***|<br>5.263***|<br>5.267***|<br>5.206***|
|||||(0.730)|(0.773)|(0.771)|(0.776)|
|Skills Obstacle (0-||||0.069|0.380|0.398|0.405|
|4)||||||||
|||||(0.316)|(0.387)|(0.387)|(0.386)|
|How Much Of An<br>Obstacle:<br>Trnrt?||||<br>-0.432|<br>-0.258|<br>-0.254|<br>-0.251|
|aspo||||(0.266)|(0.298)|(0.300)|(0.299)|
|Firm Purchased||||<br>-0.899|<br>-0.562|<br>-0.520|<br>-0.482|
|Fixed Assets Y:1||||||||
|N:0||||||||
|||||(0.758)|(0.815)|(0.819)|(0.818)|

64
|Power Outages|-0.187*|-0.205*|-0.192*|
|---|---|---|---|
|(days)||||
||(0.102)|(0.105)|(0.106)|
|Courts Obstacle<br>|-1.432|-1.080|-1.081|
|(cell average)||||
||(1.322)|(1.471)|(1.466)|
|Crime Losses Y:1<br>|-1.510*|-1.432|-1.378|
|N:0||||
||(0.907)|(0.910)|(0.908)|
|Firm Inspected Y:1|-0.590|-0.564|-0.552|
|N:0||||
||(0.846)|(0.849)|(0.850)|
|How Much Of An|-0.400|-0.415|-0.426|
|Obstacle: Tax||||
|Rates||||
||(0.321)|(0.321)|(0.322)|
|How Much Of An|-0.185|-0.193|-0.173|
|Obstacle: Labor||||
|Regulations?||||
||(0.401)|(0.401)|(0.399)|
|Population (logs)||-46.729|-57.415|
|||(40.569)|(40.654)|
|Urbanization (%)||-1.699|-1.623|
|||(1.360)|(1.355)|
|WBL Index||-5.633|-0.854|
|||(12.997)|(13.437)|
|Women can work||-26.481***|-24.642***|
|the same night<br>||||
|hours as men Y:1||||
|N:0||||
|||(6.178)|(6.282)|
|Women can work||4.173|3.185|
|in the same||||
|industries as men||||
|Y:1 N:0||||
|||(6.741)|(6.843)|
|Ratio of Female to||-1.399|-0.822|
|Male Population||||
|||(1.338)|(1.382)|
|Fertility Rate||-25.246***|-28.560***|
|||(8.963)|(9.345)|
|Starting a Business||0.025|0.038|
|(DTF)||||
|||(0.088)|(0.089)|
|Inflation (%)||<br>1.153**|<br>1.158**|
|||(0.525)|(0.531)|

65
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|---|---|---|---|---|---|---|---|
|Country-Industry|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|fixed effects||||||||
|Constant|41.516***|148.750**|147.390**|162.212**|233.291***|1,418.287**|1,612.929**|
||(1.940)|(74.266)|(75.054)|(72.244)|(72.807)|(640.445)|(648.725)|
|Number of|32,683|32,683|31,932|30,553|27,517|27,517|27,517|
|observations||||||||
|R-squared|0.464|0.465|0.476|0.494|0.501|0.503|0.504|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

66
|**Ta**|**ble A8: Inter**|**action term r**|**esults for soc**|**ial attitudes**|**towards wom**|**en's rights**||
|---|---|---|---|---|---|---|---|
|Dependent variable:<br>_Female Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Exports (%, cell<br>average)*Women &<br>men have same<br>rights (WVS, 20 yr.<br>average)|0.273***<br>(0.093)|0.276***<br>(0.095)|0.289***<br>(0.096)|0.333***<br>(0.100)|0.410***<br>(0.105)|0.345***<br>(0.099)|0.278***<br>(0.099)|
|Exports (%, cell<br>average)|-2.226***<br>(0.766)|-2.247***<br>(0.782)|-2.363***<br>(0.785)|-2.710***<br>(0.818)|-3.339***<br>(0.865)|-2.822***<br>(0.826)|-3.212***<br>(0.939)|
|Exports (%, cell|||||||0.102|
|average)*GDP per<br>capita (logs)|||||||(0.076)|
|No. of Workers|||||||0.175|
|(logs)*Women &<br>men have same<br>rights (WVS, 20 yr.<br>|||||||(0.459)|
|average)<br>No. of Workers<br>(los)||0.613**|0.551*|0.710**|0.675**|0.671**|-0.781|
|g||(0.275)|(0.297)|(0.321)|(0.334)|(0.329)|(3.744)|
|GDP per capita||-0.141|1.758|-0.369|-1.444|-20.342**|-21.450***|
|(logs)||||||||
|||(5.887)|(6.072)|(5.904)|(6.587)|(8.271)|(8.223)|
|Herfindahl-<br>Hirschman Index|||-5.382|-4.185|-7.547|-4.669|-5.691|
||||(4.415)|(4.309)|(4.688)|(4.247)|(4.356)|
|Age of Firm (logs)|||0.321|-0.252|-0.072|-0.164|-0.151|
||||(0.414)|(0.420)|(0.441)|(0.436)|(0.433)|
|Foreign Ownership<br>(proportion)|||-0.016|1.153|1.685|1.698|1.707|
||||(1.127)|(1.141)|(1.151)|(1.118)|(1.117)|
|Female Ownership<br>||||5.506***|5.295***|5.457***|5.443***|
|Y:1 N:0||||||||
|||||(0.703)|(0.728)|(0.720)|(0.717)|
|Skills Obstacle (0-4)||||0.543*|0.704**|0.502|0.498|
|||||(0.311)|(0.349)|(0.349)|(0.348)|
|How Much Of An<br>Obstacle: Transport?||||<br>-0.270|<br>-0.075|<br>-0.051|<br>-0.040|
|||||(0.237)|(0.270)|(0.274)|(0.276)|
|Firm Purchased||||-1.102*|-0.804|-0.650|-0.619|
|Fixed Assets Y:1<br>N:0||||||||

67
|(0.628)|(0.672)|(0.671)|(0.673)|
|---|---|---|---|
|Power Outages|-0.056|-0.074|-0.072|
|(days)||||
||(0.080)|(0.082)|(0.083)|
|Courts Obstacle|-0.454|-0.599|-0.494|
|(cell average)||||
||(0.991)|(1.034)|(1.040)|
|Crime Losses Y:1|-1.341*|-1.285*|-1.299*|
|N:0||||
||(0.741)|(0.733)|(0.734)|
|Firm Inspected Y:1|-0.170|-0.190|-0.188|
|N:0||||
||(0.746)|(0.754)|(0.752)|
|How Much Of An<br>|-0.418|-0.448|-0.438|
|Obstacle: Tax Rates||||
||(0.276)|(0.276)|(0.275)|
|How Much Of An|-0.140|-0.082|-0.076|
|Obstacle: Labor||||
|Regulations?||||
||(0.362)|(0.360)|(0.359)|
|Population (logs)||-100.948***|-94.482***|
|||(36.755)|(36.360)|
|Urbanization (%)||-1.582**|-1.486**|
|||(0.745)|(0.736)|
|WBL Index||17.887|17.764|
|||(11.050)|(10.931)|
|Women can work||1.877|1.469|
|the same night hours||||
|as men Y:1 N:0||||
|||(3.140)|(3.172)|
|Women can work in||-27.171***|-25.654***|
|the same industries||||
|as men Y:1 N:0||||
|||(3.744)|(3.879)|
|Ratio of Female to||-11.870**|-11.328*|
|Male Population||||
|||(5.789)|(5.785)|
|Fertility Rate||-46.903***|-46.026***|
|||(7.940)|(7.955)|
|Starting a Business||0.035|0.035|
|(DTF)||||
|||(0.081)|(0.081)|
|Inflation (%)||0.640**|0.677**|
|||(0.284)|(0.282)|
|Year fixed effects<br>Yes<br>Yes<br>Yes<br>Yes|Yes|Yes|Yes|

68
|Country-Industry|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|---|---|---|---|---|---|---|---|
|fixed effects||||||||
|Constant|35.350***|35.262|16.331|31.827|42.448|2,738.231***|2,601.910***|
||(1.972)|(53.742)|(55.741)|(54.459)|(60.774)|(832.266)|(824.868)|
|Number of|30,328|30,328|29,626|28,299|25,587|25,587|25,587|
|observations||||||||
|R-squared|0.480|0.480|0.484|0.502|0.508|0.515|0.516|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

69
||**Ta**|**ble A9: Intera**|**ction term res**|**ults for Courts**|**Obstacle**|||
|---|---|---|---|---|---|---|---|
|Dependent<br>variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|Exports (%, cell<br>average)*Courts|-0.108**<br>(0.046)|-0.114**<br>(0.045)|-0.103**<br>(0.045)|-0.097**<br>(0.043)|-0.123***<br>(0.045)|-0.098**<br>(0.046)|-0.087*<br>(0.046)|
|<br>Obstacle (cell<br>average)||||||||
|Exports (%, cell<br>average)|0.280***<br>(0.075)|0.298***<br>(0.075)|0.277***<br>(0.078)|0.273***<br>(0.077)|0.306***<br>(0.078)|0.253***<br>(0.068)|-0.239<br>(0.528)|
|Courts Obstacle|0.515|0.646|0.525|0.972|1.103|0.353|0.501|
|(cell average)|(0.965)|(0.943)|(0.896)|(0.838)|(0.874)|(0.833)|(1.413)|
|Exports (%, cell<br>average)*GDP per<br>capita (logs)|||||||0.053<br>(0.056)|
|No. of Workers<br>(logs)*Courts<br>Obstacle (cell<br>average)|||||||-0.056<br>(0.359)|
|No. of Workers||0.570**|0.721***|0.768***|0.914***|0.926***|0.992**|
|(los)||||||||
|g||(0.248)|(0.263)|(0.285)|(0.293)|(0.294)|(0.475)|
|GDP per capita<br>(los)||<br>19.410***|<br>20.073***|<br>21.258***|<br>16.495***|<br>6.526|<br>5.296|
|g||(5.241)|(5.537)|(5.513)|(5.654)|(5.760)|(5.807)|
|Herfindahl-<br>Hih Id|||-6.963*|-6.519*|-9.685**|-6.664*|-6.700*|
|rscman nex|||(3.728)|(3.649)|(3.973)|(3.627)|(3.655)|
|Age of Firm (logs)|||<br>-0.472|<br>-0.919***|<br>-0.720**|<br>-0.864**|<br>-0.870**|
||||(0.371)|(0.349)|(0.365)|(0.361)|(0.362)|
|Foreign Ownership<br>(proportion)|||<br>-0.840|<br>-0.038|<br>0.005|<br>0.071|<br>0.055|
||||(0.866)|(0.900)|(0.966)|(0.959)|(0.960)|
|Female Ownership<br>Y:1 N:0||||<br>6.148***|<br>5.979***|<br>5.945***|<br>5.937***|
|||||(0.621)|(0.650)|(0.650)|(0.652)|
|Skills Obstacle (0-||||<br>0.638***|<br>0.695**|<br>0.658**|<br>0.657**|
|<br>4)||||||||
|||||(0.233)|(0.270)|(0.269)|(0.269)|
|How Much Of An||||-0.001|0.015|-0.013|-0.009|
|Obstacle:<br>Transport?||||||||

70
||(0.196)|(0.208)|(0.209)|(0.209)|
|---|---|---|---|---|
|Firm Purchased|-1.046**|-1.073*|-1.046*|-1.033*|
|Fixed Assets Y:1|||||
|N:0|||||
||(0.527)|(0.560)|(0.557)|(0.558)|
|Power Outages<br>||-0.173**|-0.153**|-0.156**|
|(days)|||||
|||(0.070)|(0.068)|(0.068)|
|o.iv_h30_ci||(dropped)|(dropped)|(dropped)|
|Crime Losses Y:1||-0.623|-0.670|-0.679|
|N:0|||||
|||(0.666)|(0.668)|(0.668)|
|Firm Inspected Y:1||-0.813|-0.875|-0.879|
|N:0|||||
|||(0.605)|(0.605)|(0.605)|
|How Much Of An||-0.216|-0.254|-0.252|
|Obstacle: Tax Rates|||||
|||(0.230)|(0.229)|(0.229)|
|How Much Of An||0.027|0.041|0.051|
|Obstacle: Labor||(0.300)|(0.300)|(0.300)|
|Regulations?|||||
|Population (logs)|||-37.758***|-37.641***|
||||(10.909)|(10.849)|
|Urbanization (%)|||1.709***|1.723***|
||||(0.423)|(0.427)|
|WBL Index|||16.988*|17.157**|
||||(8.755)|(8.677)|
|Women can work|||2.962|2.967|
|the same night|||(2.814)|(2.807)|
|hours as men Y:1|||||
|N:0|||||
|Women can work|||-0.860|-0.608|
|in the same|||(2.009)|(2.053)|
|industries as men|||||
|Y:1 N:0|||||
|Ratio of Female to|||2.352***|2.486***|
|Male Population|||(0.572)|(0.566)|
|Fertility Rate|||-15.380***|-15.262***|
||||(3.649)|(3.646)|
|Starting a Business|||0.081**|0.081**|
|<br>(DTF)|||||

71
|Inflation (%)||||||(0.039)<br>0.059<br>(0.065)|(0.039)<br>0.056<br>(0.065)|
|---|---|---|---|---|---|---|---|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|fixed effects||||||||
|Constant|27.536***|-139.744***|-144.453***|-159.812***|-117.836**|397.642**|398.045**|
||(1.995)|(44.704)|(47.329)|(47.118)|(48.419)|(171.873)|(171.380)|
|Number of|43,198|43,198|42,299|40,237|36,944|36,944|36,944|
|observations||||||||
|R-squared|0.479|0.481|0.486|0.504|0.510|0.514|0.514|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

72
||**Table A**|**10: Interactio**|**n term results**|**forgender sp**|**ecific labor law**|**s**|||
|---|---|---|---|---|---|---|---|---|
|Dependent variable:_Female Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Exports (%, cell average)*Women<br>can work the same night hours as<br>men Y:1 N:0|0.247***<br>(0.087)|0.250***<br>(0.087)|0.231***<br>(0.089)|0.265***<br>(0.097)|0.276***<br>(0.098)|0.318***<br>(0.091)|0.301***<br>(0.097)|0.305***<br>(0.095)|
|Exports (%, cell average)|-0.056|-0.047|-0.044|-0.066|-0.054|-0.128|-0.618|-0.626|
||(0.081)|(0.081)|(0.080)|(0.088)|(0.089)|(0.083)|(0.514)|(0.535)|
|Women can work the same night|-9.626***|-9.150***|-8.745***|-6.629**|-6.797**|0.503|0.482|-0.971|
|<br>hours as men Y:1 N:0|(2.700)|(2.767)|(2.760)|(2.872)|(2.889)|(2.945)|(3.945)|(3.954)|
|Exports (%, cell average)*GDP per|||||||0.056|0.065|
|<br>capita (logs)|||||||(0.059)|(0.078)|
|Exports (%, cell average)*WBL<br>Index||||||||-0.112<br>(0.392)|
|No. of Workers (logs)*Women can|||||||0.055|0.445|
|<br>work the same night hours as men|||||||(0.715)|(0.726)|
|<br>Y:1 N:0|||||||||
|No. of Workers (logs)*WBL Index||||||||-3.368*<br>(1.732)|
|No. of Workers (logs)||0.533**|0.699***|0.743***|0.903***|0.902***|0.867|2.887**|
|||(0.243)|(0.257)|(0.280)|(0.291)|(0.291)|(0.655)|(1.271)|
|GDP per capita (logs)||13.906***|14.101***|15.962***|15.592***|5.876|4.688|4.791|
|||(5.151)|(5.346)|(5.348)|(5.748)|(5.710)|(5.768)|(5.740)|
|Herfindahl-Hirschman Index|||<br>-6.321*|<br>-4.724|<br>-8.467**|<br>-5.197|<br>-5.364|<br>-5.022|
||||(3.499)|(3.528)|(4.117)|(3.662)|(3.714)|(3.709)|
|Age of Firm (logs)|||<br>-0.522|<br>-0.914***|<br>-0.689*|<br>-0.833**|<br>-0.842**|<br>-0.831**|
||||(0.357)|(0.339)|(0.362)|(0.358)|(0.359)|(0.359)|
|Foreign Ownership (proportion)|||<br>-0.904|<br>-0.156|<br>0.030|<br>0.103|<br>0.081|<br>0.130|
||||(0.844)|(0.879)|(0.970)|(0.961)|(0.962)|(0.962)|
|Female Ownership Y:1 N:0||||6.203***|6.013***|5.975***|5.968***|5.987***|
|||||(0.615)|(0.651)|(0.652)|(0.653)|(0.654)|

73
|Skills Obstacle (0-4)|0.597***|0.666**|0.636**|0.638**|0.636**|
|---|---|---|---|---|---|
||(0.228)|(0.269)|(0.269)|(0.269)|(0.269)|
|How Much Of An Obstacle:|-0.035|0.017|-0.011|-0.008|-0.016|
|Transport?||||||
||(0.193)|(0.209)|(0.209)|(0.209)|(0.209)|
|Firm Purchased Fixed Assets Y:1|-0.920*|-1.058*|-1.031*|-1.018*|-0.994*|
|N:0||||||
||(0.524)|(0.558)|(0.556)|(0.557)|(0.558)|
|Power Outages (days)||-0.171**|-0.154**|-0.156**|-0.155**|
|||(0.070)|(0.068)|(0.068)|(0.068)|
|Courts Obstacle (cell average)||-0.005|-0.454|-0.383|-0.422|
|||(0.776)|(0.739)|(0.734)|(0.737)|
|Crime Losses Y:1 N:0||-0.571|-0.633|-0.651|-0.672|
|||(0.667)|(0.669)|(0.672)|(0.674)|
|Firm Inspected Y:1 N:0||-0.814|-0.847|-0.854|-0.822|
|||(0.604)|(0.603)|(0.604)|(0.604)|
|How Much Of An Obstacle: Tax||-0.224|-0.253|-0.250|-0.264|
|Rates||||||
|||(0.230)|(0.229)|(0.230)|(0.230)|
|How Much Of An Obstacle: Labor||0.029|0.036|0.046|0.051|
|Regulations?||||||
|||(0.301)|(0.301)|(0.301)|(0.300)|
|Population (logs)|||-35.364***|-35.332***|-35.099***|
||||(10.960)|(10.933)|(11.115)|
|Urbanization (%)|||1.673***|1.692***|1.642***|
||||(0.415)|(0.419)|(0.405)|
|WBL Index|||17.580**|17.641**|29.339**|
||||(8.651)|(8.608)|(11.496)|
|Women can work in the same|||-2.120|-1.756|-1.808|
|industries as men Y:1 N:0||||||

74
|||||||(2.049)|(2.108)|(2.127)|
|---|---|---|---|---|---|---|---|---|
|Ratio of Female to Male Population||||||3.454***|3.536***|3.508***|
|||||||(0.693)|(0.687)|(0.708)|
|Fertility Rate||||||-15.658***|-15.462***|-15.345***|
|||||||(3.560)|(3.549)|(3.521)|
|Starting a Business (DTF)||||||0.066*|0.067*|0.064*|
|||||||(0.039)|(0.039)|(0.039)|
|Inflation (%)||||||0.068|0.065|0.071|
|||||||(0.064)|(0.064)|(0.065)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|36.458***|-83.892*|-84.872*|-107.330**|-102.805**|315.970*|319.540*|312.122*|
||(2.850)|(44.257)|(46.085)|(46.025)|(49.565)|(174.209)|(175.277)|(179.319)|
|Number of observations|44,539|44,539|43,616|41,444|36,944|36,944|36,944|36,944|
|R-squared|0.476|0.477|0.483|0.501|0.510|0.514|0.514|0.514|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

75
||**Table**|**A11: Interacti**|**on term result**|**s forgender sp**|**ecific labor law**|**s**|||
|---|---|---|---|---|---|---|---|---|
|Dependent variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Exports (%, cell average)*Women<br>can work in the same industries as<br>men Y:1 N:0|0.166<br>(0.102)|0.198**<br>(0.100)|0.195*<br>(0.103)|0.206**<br>(0.102)|0.254**<br>(0.107)|0.272***<br>(0.100)|0.273***<br>(0.101)|0.280***<br>(0.098)|
|Exports (%, cell average)|0.091*|0.089*|0.076|0.074|0.072|0.030|-0.698|-0.723|
||(0.055)|(0.052)|(0.053)|(0.054)|(0.057)|(0.055)|(0.487)|(0.507)|
|Women can work in the same|-5.258**|-4.799**|-4.890**|-3.761*|-4.087*|-4.652*|-3.052|-3.746|
|industries as men Y:1 N:0|(2.278)|(2.213)|(2.220)|(2.151)|(2.405)|(2.459)|(3.170)|(3.127)|
|Exports (%, cell average)*GDP|||||||0.080|0.096|
|per capita (logs)|||||||(0.054)|(0.072)|
|Exports (%, cell average)*WBL||||||||-0.165|
|Index||||||||(0.370)|
|No. of Workers (logs)*Women|||||||-0.394|-0.202|
|can work in the same industries as<br>men Y:1 N:0|||||||(0.539)|(0.529)|
|No. of Workers (logs)*WBL||||||||-2.942*|
|<br>Index||||||||(1.712)|
|No. of Workers (logs)||0.543**|0.707***|0.745***|0.910***|0.916***|1.114***|3.066**|
|||(0.242)|(0.255)|(0.278)|(0.289)|(0.290)|(0.360)|(1.225)|
|GDP per capita (logs)||15.421***|15.569***|17.722***|18.160***|7.974|6.209|6.291|
|||(4.869)|(5.019)|(5.067)|(5.441)|(5.494)|(5.559)|(5.524)|
|Herfindahl-Hirschman Index|||-7.033**|-5.831*|-9.966***|-7.021**|-7.080**|-6.775*|
||||(3.314)|(3.329)|(3.855)|(3.446)|(3.491)|(3.482)|
|Age of Firm (logs)|||-0.525|-0.912***|-0.688*|-0.834**|-0.846**|-0.834**|
||||(0.356)|(0.336)|(0.358)|(0.355)|(0.356)|(0.358)|
|Foreign Ownership (proportion)|||-0.854|-0.085|0.117|0.183|0.169|0.218|
||||(0.842)|(0.878)|(0.968)|(0.961)|(0.960)|(0.961)|

76
|Female Ownership Y:1 N:0|6.148***|5.943***|5.905***|5.880***|5.890***|
|---|---|---|---|---|---|
||(0.616)|(0.652)|(0.652)|(0.653)|(0.653)|
|Skills Obstacle (0-4)|0.600***|0.662**|0.625**|0.620**|0.622**|
||(0.227)|(0.269)|(0.270)|(0.269)|(0.269)|
|How Much Of An Obstacle:|-0.037|0.020|-0.010|-0.004|-0.012|
|Transport?||||||
||(0.193)|(0.209)|(0.209)|(0.209)|(0.209)|
|Firm Purchased Fixed Assets Y:1|-0.940*|-1.076*|-1.042*|-1.017*|-0.999*|
|N:0||||||
||(0.525)|(0.558)|(0.557)|(0.558)|(0.558)|
|Power Outages (days)||-0.168**|-0.151**|-0.154**|-0.152**|
|||(0.069)|(0.067)|(0.067)|(0.068)|
|Courts Obstacle (cell average)||-0.075|-0.644|-0.531|-0.558|
|||(0.765)|(0.740)|(0.737)|(0.739)|
|Crime Losses Y:1 N:0||-0.633|-0.688|-0.693|-0.703|
|||(0.670)|(0.671)|(0.671)|(0.672)|
|Firm Inspected Y:1 N:0||-0.759|-0.811|-0.809|-0.780|
|||(0.602)|(0.603)|(0.604)|(0.604)|
|How Much Of An Obstacle: Tax||-0.216|-0.257|-0.253|-0.269|
|Rates||||||
|||(0.230)|(0.229)|(0.228)|(0.228)|
|How Much Of An Obstacle: Labor||0.018|0.034|0.053|0.057|
|Regulations?||||||
|||(0.301)|(0.301)|(0.300)|(0.300)|
|Population (logs)|||-35.761***|-35.262***|-35.328***|
||||(10.819)|(10.743)|(10.887)|
|Urbanization (%)|||1.857***|1.882***|1.840***|
||||(0.399)|(0.401)|(0.391)|
|WBL Index|||17.226**|17.122**|28.064**|
||||(8.478)|(8.398)|(11.187)|

77
|Women can work the same night||||||3.258|3.327|3.246|
|---|---|---|---|---|---|---|---|---|
|hours as men Y:1 N:0|||||||||
|||||||(2.973)|(2.950)|(2.949)|
|Ratio of Female to Male||||||2.614***|2.781***|2.680***|
|Population|||||||||
|||||||(0.587)|(0.592)|(0.619)|
|Fertility Rate||||||-15.721***|-15.311***|-15.204***|
|||||||(3.330)|(3.311)|(3.275)|
|Starting a Business (DTF)||||||0.062|0.063|0.061|
|||||||(0.039)|(0.039)|(0.039)|
|Inflation (%)||||||0.059|0.056|0.059|
|||||||(0.062)|(0.062)|(0.064)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|30.577***|-102.522**|-102.738**|-126.646***|-129.034***|336.227*|331.347*|331.879*|
||(2.066)|(41.677)|(43.065)|(43.395)|(46.667)|(171.634)|(170.693)|(174.104)|
|Number of observations|44,539|44,539|43,616|41,444|36,944|36,944|36,944|36,944|
|R-squared|0.476|0.477|0.483|0.501|0.510|0.514|0.514|0.515|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

78
||**Table A**|**12: Interactio**|**n term results**|**forgender spe**|**cific labor law**|**s**|||
|---|---|---|---|---|---|---|---|---|
|Dependent variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(9)**|
|Exports (%, cell average)*Women<br>can work the same night hours as<br>men Y:1 N:0|0.196**<br>(0.088)|0.190**<br>(0.085)|0.169**<br>(0.085)|0.204**<br>(0.092)|0.200**<br>(0.091)|0.235***<br>(0.088)|0.212**<br>(0.093)|0.218**<br>(0.092)|
|Exports (%, cell average)*Women|0.127|0.158|0.159|0.161|0.209*|0.222**|0.227**|0.234**|
|<br>can work in the same industries as<br>men Y:1 N:0|(0.104)|(0.102)|(0.104)|(0.104)|(0.109)|(0.102)|(0.102)|(0.099)|
|Exports (%, cell average)|-0.069|-0.066|-0.062|-0.087|-0.084|-0.159*|-0.726|-0.754|
||(0.080)|(0.078)|(0.078)|(0.084)|(0.083)|(0.081)|(0.487)|(0.506)|
|Women can work the same night|<br>-8.333***|<br>-7.915***|<br>-7.468***|<br>-5.639**|<br>-5.771**|<br>1.469|<br>0.831|<br>-0.390|
|<br>hours as men Y:1 N:0|(2.594)|(2.633)|(2.627)|(2.768)|(2.794)|(3.045)|(3.976)|(4.011)|
|Women can work in the same|-3.485|-3.124|-3.290|-2.507|-2.610|-4.647*|-2.901|-3.433|
|industries as men Y:1 N:0|(2.240)|(2.156)|(2.158)|(2.105)|(2.379)|(2.417)|(3.128)|(3.094)|
|Exports (%, cell average)*GDP per<br>capita (logs)|||||||0.065<br>(0.056)|0.083<br>(0.072)|
|Exports (%, cell average)*WBL<br>Index||||||||-0.206<br>(0.367)|
|No. of Workers (logs)*WBL Index||||||||-3.269*<br>(1.737)|
|No. of Workers (logs)*Women can|||||||0.256|0.577|
|<br>work the same night hours as men<br>Y:1 N:0|||||||(0.709)|(0.720)|
|No. of Workers (logs)*Women can|||||||-0.464|-0.326|
|work in the same industries as men|||||||(0.534)|(0.524)|

No. of Workers (logs)*Women can work in the same industries as men Y:1 N:0 79
|No. of Workers (logs)|0.534**|0.697***|0.740***|0.901***|0.900***|0.922|2.862**|
|---|---|---|---|---|---|---|---|
||(0.242)|(0.254)|(0.278)|(0.289)|(0.289)|(0.663)|(1.267)|
|GDP per capita (logs)|15.040***|15.198***|17.308***|17.645***|7.628|6.274|6.446|
||(4.897)|(5.046)|(5.080)|(5.455)|(5.472)|(5.550)|(5.507)|
|Herfindahl-Hirschman Index||-6.789**|-5.322|-9.395**|-6.153*|-6.288*|-5.955*|
|||(3.348)|(3.357)|(3.891)|(3.459)|(3.528)|(3.516)|
|Age of Firm (logs)||-0.521|-0.912***|-0.684*|-0.828**|-0.842**|-0.832**|
|||(0.355)|(0.336)|(0.357)|(0.354)|(0.355)|(0.356)|
|Foreign Ownership (proportion)||-0.842|-0.089|0.114|0.178|0.162|0.206|
|||(0.842)|(0.878)|(0.968)|(0.961)|(0.960)|(0.960)|
|Female Ownership Y:1 N:0|||6.177***|5.978***|5.938***|5.919***|5.940***|
||||(0.617)|(0.653)|(0.653)|(0.654)|(0.655)|
|Skills Obstacle (0-4)|||0.581**|0.642**|0.616**|0.611**|0.612**|
||||(0.227)|(0.269)|(0.270)|(0.269)|(0.269)|
|How Much Of An Obstacle:|||-0.038|0.017|-0.013|-0.007|-0.016|
|Transport?|||(0.193)|(0.209)|(0.209)|(0.209)|(0.209)|
|Firm Purchased Fixed Assets Y:1|||-0.918*|-1.053*|-1.023*|-1.002*|-0.977*|
|N:0|||(0.524)|(0.557)|(0.556)|(0.557)|(0.558)|
|Power Outages (days)||||-0.166**|-0.150**|-0.153**|-0.153**|
|||||(0.070)|(0.067)|(0.067)|(0.068)|
|Courts Obstacle (cell average)||||-0.134|-0.592|-0.510|-0.547|
|||||(0.767)|(0.739)|(0.736)|(0.738)|
|Crime Losses Y:1 N:0||||-0.603|-0.665|-0.677|-0.699|
|||||(0.669)|(0.670)|(0.673)|(0.675)|
|Firm Inspected Y:1 N:0||||-0.767|-0.800|-0.801|-0.772|
|||||(0.602)|(0.602)|(0.603)|(0.603)|
|How Much Of An Obstacle: Tax||||-0.228|-0.257|-0.250|-0.265|
|Rates||||||||
|||||(0.230)|(0.229)|(0.228)|(0.228)|

80
|How Much Of An Obstacle: Labor|||||0.029|0.037|0.054|0.060|
|---|---|---|---|---|---|---|---|---|
|Regulations?|||||(0.301)|(0.301)|(0.300)|(0.300)|
|Population (logs)||||||-33.804***|-33.512***|-33.549***|
|||||||(10.927)|(10.895)|(11.034)|
|Urbanization (%)||||||1.818***|1.838***|1.784***|
|||||||(0.399)|(0.401)|(0.391)|
|WBL Index||||||17.410**|17.254**|29.623***|
|||||||(8.464)|(8.391)|(11.185)|
|Ratio of Female to Male Population||||||3.297***|3.401***|3.342***|
|||||||(0.673)|(0.671)|(0.686)|
|Fertility Rate||||||-15.450***|-15.138***|-15.024***|
|||||||(3.310)|(3.289)|(3.240)|
|Starting a Business (DTF)||||||0.056|0.056|0.053|
|||||||(0.039)|(0.039)|(0.039)|
|Inflation (%)||||||0.067|0.066|0.073|
|||||||(0.062)|(0.062)|(0.063)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|36.532***|-93.580**|-94.242**|-118.901***|-120.447**|276.010|275.380|273.203|
||(2.923)|(41.992)|(43.394)|(43.635)|(46.979)|(174.742)|(175.621)|(178.676)|
|Number of observations|44,539|44,539|43,616|41,444|36,944|36,944|36,944|36,944|
|R-squared|0.477|0.478|0.484|0.502|0.511|0.514|0.515|0.515|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

81
||**Ta**|**ble A13: Inclu**|**ding all interac**|**tion terms simu**|**ltaneously**||||
|---|---|---|---|---|---|---|---|---|
|Dependent variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Exports (%, cell|0.768|0.887*|1.037*|1.160**|1.073**|0.685*|0.656*|0.629*|
|average)*Herfindahl-Hirschman<br>Index|(0.491)|(0.493)|(0.539)|(0.501)|(0.513)|(0.360)|(0.367)|(0.374)|
|Exports (%, cell average)*Courts|-0.076*|-0.089**|-0.074*|-0.070*|-0.096**|-0.075*|-0.077*|-0.077*|
|<br>Obstacle (cell average)|(0.045)|(0.044)|(0.044)|(0.042)|(0.044)|(0.045)|(0.045)|(0.045)|
|Exports (%, cell average)*Women|0.163*|0.156*|0.146|0.149|0.136|0.186**|0.191**|0.192**|
|<br>can work the same night hours as<br>men Y:1 N:0|(0.098)|(0.093)|(0.093)|(0.091)|(0.090)|(0.090)|(0.092)|(0.092)|
|Exports (%, cell average)*Women|0.115|0.157|0.146|0.157|0.166*|0.200**|0.194*|0.201**|
|<br>can work in the same industries as|(0.101)|(0.098)|(0.098)|(0.097)|(0.100)|(0.099)|(0.100)|(0.096)|
|men Y:1 N:0|||||||||
|Exports (%, cell average)|-0.548|-0.313|-0.425|-0.603|-0.707|-0.760|-0.735|-0.755|
||(0.625)|(0.614)|(0.631)|(0.525)|(0.532)|(0.496)|(0.491)|(0.499)|
|Exports (%, cell average)*GDP|0.059|0.034|0.044|0.061|0.078|0.075|0.072|0.079|
|per capita (logs)|(0.069)|(0.068)|(0.070)|(0.058)|(0.059)|(0.055)|(0.055)|(0.071)|
|Herfindahl-Hirschman Index|-9.772**|-11.543***|-12.715***|-13.130***|-15.763***|-10.768***|7.869|8.584|
||(4.495)|(4.053)|(4.110)|(3.871)|(4.275)|(3.803)|(7.985)|(7.882)|
|Courts Obstacle (cell average)|0.000|0.151|-0.049|0.456|0.589|0.120|0.043|-0.040|
||(0.932)|(0.903)|(0.843)|(0.794)|(0.833)|(0.827)|(1.426)|(1.421)|
|Women can work the same night|<br>-8.005***|<br>-7.419***|<br>-7.169***|<br>-5.233*|<br>-5.004*|<br>2.012|<br>0.334|<br>-0.669|
|<br>hours as men Y:1 N:0|||||||||
||(2.570)|(2.591)|(2.572)|(2.726)|(2.785)|(3.065)|(3.966)|(3.982)|
|Women can work in the same<br>industries as men Y:1 N:0|<br>-3.586|<br>-3.368|<br>-3.268|<br>-2.308|<br>-1.489|<br>-3.658|<br>-2.687|<br>-3.203|
||(2.218)|(2.074)|(2.064)|(2.051)|(2.340)|(2.412)|(3.123)|(3.067)|

82
Exports (%, cell average)*WBL Index No. of Workers (logs)*WBL Index No. of Workers (logs)*GDP per capita (logs) No. of workers (logs)*HerfindahlHirschman Index No. of Workers (logs)*Courts Obstacle (cell average) No. of Workers (logs)*Women can work the same night hours as men Y:1 N:0 No. of Workers (logs)*Women can work in the same industries as men Y:1 N:0 No. of Workers (logs) GDP per capita (logs) Age of Firm (logs)

|||||||-0.076|
|---|---|---|---|---|---|---|
|||||||(0.374)<br>-3.269*|
||||||-0.547*|(1.850)<br>-0.344|
||||||(0.279)|(0.290)|
||||||-5.813***|-5.886***|
||||||(2.040)|(2.029)|
||||||0.012|0.028|
||||||(0.365)|(0.361)|
||||||0.454|0.739|
||||||(0.690)|(0.698)|
||||||-0.317|-0.177|
||||||(0.545)|(0.540)|
|0.561**|0.711***|0.763***|0.908***|0.907***|5.948**|6.143**|
|(0.245)|(0.258)|(0.282)|(0.289)|(0.288)|(2.594)|(2.596)|
|22.277***|22.327***|24.001***|19.137***|8.615|10.374*|9.511*|
|(5.020)|(5.085)|(5.007)|(5.240)|(5.330)|(5.426)|(5.447)|
||-0.433|-0.887***|-0.693*|-0.824**|-0.819**|-0.804**|

83
||(0.367)|(0.341)|(0.357)|(0.353)|(0.353)|(0.353)|
|---|---|---|---|---|---|---|
|Foreign Ownership (proportion)|-0.655|0.159|0.175|0.201|0.191|0.277|
||(0.866)|(0.901)|(0.967)|(0.960)|(0.954)|(0.955)|
|Female Ownership Y:1 N:0||6.156***|5.979***|5.931***|5.956***|5.981***|
|||(0.624)|(0.654)|(0.654)|(0.653)|(0.653)|
|Skills Obstacle (0-4)||0.582**|0.627**|0.615**|0.629**|0.630**|
|||(0.230)|(0.267)|(0.268)|(0.267)|(0.267)|
|How Much Of An Obstacle:||-0.005|0.010|-0.013|-0.018|-0.027|
|Transport?|||||||
|||(0.196)|(0.208)|(0.208)|(0.208)|(0.209)|
|Firm Purchased Fixed Assets Y:1||-0.969*|-0.995*|-0.981*|-0.917|-0.909|
|N:0|||||||
|||(0.523)|(0.556)|(0.556)|(0.557)|(0.558)|
|Power Outages (days)|||-0.150**|-0.143**|-0.146**|-0.144**|
||||(0.068)|(0.067)|(0.067)|(0.067)|
|Crime Losses Y:1 N:0|||-0.661|-0.701|-0.694|-0.711|
||||(0.671)|(0.671)|(0.673)|(0.673)|
|Firm Inspected Y:1 N:0|||-0.767|-0.804|-0.774|-0.768|
||||(0.604)|(0.603)|(0.598)|(0.599)|
|How Much Of An Obstacle: Tax|||-0.220|-0.248|-0.254|-0.262|
|Rates|||||||
||||(0.229)|(0.228)|(0.227)|(0.227)|
|How Much Of An Obstacle:|||0.053|0.059|0.049|0.047|
|Labor Regulations?|||||||
||||(0.300)|(0.300)|(0.299)|(0.298)|
|Population (logs)||||-33.168***|-31.558***|-31.448***|
|||||(10.906)|(10.966)|(11.101)|
|Urbanization (%)||||1.805***|1.735***|1.709***|
|||||(0.390)|(0.391)|(0.386)|
|WBL Index||||15.274*|15.788*|26.692**|

84
|||||||(8.287)|(8.297)|(11.230)|
|---|---|---|---|---|---|---|---|---|
|Ratio of Female to Male||||||3.169***|3.181***|3.165***|
|Population|||||||||
|||||||(0.670)|(0.670)|(0.688)|
|Fertility Rate||||||-13.356***|-13.350***|-13.294***|
|||||||(2.845)|(2.856)|(2.909)|
|Starting a Business (DTF)||||||0.059|0.055|0.053|
|||||||(0.039)|(0.040)|(0.040)|
|Inflation (%)||||||0.048|0.053|0.057|
|||||||(0.061)|(0.061)|(0.063)|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Country-Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|35.126***|-157.297***|-156.807***|-177.871***|-135.539***|257.768|218.589|220.035|
||(3.049)|(42.919)|(43.453)|(42.896)|(45.089)|(174.899)|(177.039)|(180.347)|
|Number of observations|43,198|43,198|42,299|40,237|36,944|36,944|36,944|36,944|
|R-squared|0.481|0.482|0.488|0.506|0.512|0.515|0.516|0.516|

Standard errors in brackets. All standard errors are Huber-White robust and clustered on country-times-industry. Sample size varies due to missing data.

85
||**Ta**|**ble A14: Inclu**|**ding all interact**|**ion terms simu**|**ltaneously**||||
|---|---|---|---|---|---|---|---|---|
|Dependent variable:_Female_<br>_Workers_|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|**(8)**|
|Exports (%, cell average)*Starting<br>a Business (DTF)|-0.007***<br>(0.002)|-0.006***<br>(0.002)|-0.006***<br>(0.002)|-0.006***<br>(0.002)|-0.006***<br>(0.002)|-0.006***<br>(0.001)|-0.006***<br>(0.002)|-0.006***<br>(0.002)|
|Exports (%, cell average)*Courts<br>Obstacle (cell average)|-0.105**<br>(0.044)|-0.115***<br>(0.044)|-0.105**<br>(0.044)|-0.100**<br>(0.041)|-0.126***<br>(0.044)|-0.099**<br>(0.043)|-0.101**<br>(0.043)|-0.101**<br>(0.044)|
|Exports (%, cell average)*Women<br>can work the same night hours as<br>men Y:1 N:0|0.150*<br>(0.087)|0.153*<br>(0.083)|0.132<br>(0.083)|0.153*<br>(0.087)|0.136<br>(0.086)|0.173**<br>(0.086)|0.176**<br>(0.087)|0.174**<br>(0.088)|
|Exports (%, cell average)*Women|0.201**|0.237**|0.238**|0.249**|0.258**|0.266***|0.261***|0.264***|
|<br>can work in the same industries as<br>men Y:1 N:0|(0.101)|(0.101)|(0.104)|(0.102)|(0.103)|(0.097)|(0.097)|(0.095)|
|Exports (%, cell average)|-0.316|-0.072|-0.125|-0.121|-0.266|-0.460|-0.453|-0.460|
||(0.516)|(0.511)|(0.519)|(0.497)|(0.498)|(0.489)|(0.486)|(0.510)|
|Starting a Business (DTF)|0.069*|0.059|0.075**|0.056|0.064|0.128***|0.128**|0.113*|
||(0.038)|(0.036)|(0.037)|(0.036)|(0.041)|(0.043)|(0.064)|(0.065)|
|Courts Obstacle (cell average)|0.196<br>(0.923)|0.335<br>(0.899)|0.226<br>(0.843)|0.620<br>(0.816)|0.752<br>(0.851)|0.226<br>(0.824)|0.140<br>(1.418)|0.066<br>(1.413)|
|Women can work the same night|-7.745***|-7.342***|-6.951***|-5.394*|-4.993*|2.104|0.706|-0.363|
|<br>hours as men Y:1 N:0|(2.599)|(2.631)|(2.588)|(2.761)|(2.827)|(3.024)|(3.956)|(3.976)|
|Women can work in the same|-4.351**|-4.157**|-4.032*|-3.281|-2.670|-4.272*|-3.550|-3.929|
|industries as men Y:1 N:0|(2.210)|(2.110)|(2.117)|(2.083)|(2.359)|(2.422)|(3.153)|(3.116)|
|Exports (%, cell average)*GDP|0.091*|0.060|0.067|0.062|0.086|0.092*|0.092*|0.090|
|per capita (logs)|(0.055)|(0.056)|(0.057)|(0.056)|(0.056)|(0.054)|(0.054)|(0.070)|

86
|No. of workers (logs)*Herfindahl-||||||-6.125***|-6.144***|
|---|---|---|---|---|---|---|---|
|Hirschman Index||||||(1.981)|(1.978)|
|No. of Workers (logs)*GDP per<br>||||||-0.549*<br>|-0.371<br>|
|capita (logs)||||||(0.320)|(0.319)|
|No. of Workers (logs)*Starting a||||||-0.001|0.004|
|Business (DTF)||||||(0.016)|(0.016)|
|No. of Workers (logs)*Courts||||||0.014|0.028|
|Obstacle (cell average)||||||(0.363)|(0.359)|
|No. of Workers (logs)*Women<br>||||||0.376<br>|0.687<br>|
|can work the same night hours as||||||(0.696)|(0.704)|
|men Y:1 N:0||||||||
|No. of Workers (logs)*Women<br>||||||-0.233<br>|-0.119<br>|
|can work in the same industries as||||||(0.564)|(0.555)|
|men**Y:1 N:0**||||||||
|Exports (%, cell average)*WBL|||||||0.043|
|Index|||||||(0.363)|
|No. of Workers (logs)*WBL|||||||-3.295*|
|Index|||||||(1.880)|
|No. of Workers (logs)|0.518**|0.665***|0.716**|0.861***|0.869***|6.010**|6.143**|
||(0.245)|(0.258)|(0.282)|(0.289)|(0.288)|(2.527)|(2.532)|
|GDP per capita (logs)|19.350***|19.859***|21.294***|16.245***|6.741|8.508|7.740|
||(5.010)|(5.226)|(5.237)|(5.461)|(5.343)|(5.484)|(5.494)|
|Herfindahl-Hirschman Index||-6.955*|-6.569*|-9.727***|-6.613**|12.845*|13.158*|
|||(3.557)|(3.446)|(3.747)|(3.327)|(7.587)|(7.568)|
|Age of Firm (logs)||-0.470|-0.900***|-0.712**|-0.844**|-0.835**|-0.815**|
|||(0.362)|(0.342)|(0.357)|(0.355)|(0.354)|(0.354)|
|Foreign Ownership (proportion)||-0.791|-0.002|0.019|0.085|0.081|0.169|

87
|(0.868)|(0.900)|(0.969)|(0.961)|(0.954)|(0.954)|
|---|---|---|---|---|---|
|Female Ownership Y:1 N:0|6.213***|6.036***|5.981***|6.010***|6.028***|
||(0.622)|(0.652)|(0.653)|(0.652)|(0.652)|
|Skills Obstacle (0-4)|0.576**|0.627**|0.608**|0.624**|0.626**|
||(0.231)|(0.268)|(0.269)|(0.267)|(0.267)|
|How Much Of An Obstacle:|-0.015|0.009|-0.014|-0.019|-0.029|
|Transport?|(0.195)|(0.208)|(0.208)|(0.208)|(0.209)|
|Firm Purchased Fixed Assets Y:1|-1.001*|-1.016*|-0.991*|-0.924*|-0.923*|
|N:0|(0.526)|(0.558)|(0.557)|(0.557)|(0.557)|
|Power Outages (days)||-0.161**|-0.146**|-0.148**|-0.146**|
|||(0.069)|(0.067)|(0.067)|(0.067)|
|Crime Losses Y:1 N:0||-0.606|-0.659|-0.650|-0.665|
|||(0.669)|(0.671)|(0.672)|(0.673)|
|Firm Inspected Y:1 N:0||-0.753|-0.799|-0.770|-0.764|
|||(0.601)|(0.600)|(0.595)|(0.596)|
|How Much Of An Obstacle: Tax||-0.245|-0.270|-0.276|-0.284|
|Rates||(0.227)|(0.227)|(0.227)|(0.227)|
|How Much Of An Obstacle: Labor||0.041|0.053|0.040|0.039|
|Regulations?||(0.299)|(0.300)|(0.299)|(0.298)|
|Population (logs)|||-38.727***|-37.235***|-36.871***|
||||(10.834)|(10.889)|(10.963)|
|Urbanization (%)|||1.787***|1.711***|1.704***|
||||(0.382)|(0.383)|(0.378)|
|WBL Index|||14.329*|14.724*|24.534**|
||||(8.108)|(8.125)|(11.212)|
|Ratio of Female to Male|||3.289***|3.293***|3.301***|
|Population|||(0.682)|(0.682)|(0.705)|
|Fertility Rate|||-14.080***|-14.013***|-13.892***|
||||(2.943)|(2.952)|(2.966)|
|Inflation (%)|||0.054|0.057|0.059|
||||(0.060)|(0.060)|(0.062)|

88
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|---|---|---|---|---|---|---|---|---|
|Country-Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Constant|28.864***|-137.286***|-142.785***|-159.907***|-116.971**|356.014**|318.759*|314.341*|
||(5.496)|(41.975)|(43.934)|(43.977)|(45.647)|(174.004)|(176.062)|(178.129)|
|Number of observations|43,198|43,198|42,299|40,237|36,944|36,944|36,944|36,944|
|R-squared|0.481|0.483|0.488|0.506|0.512|0.515|0.516|0.517|
|Standard errors in brackets. Al|l standard errors a|re Huber-White|robust and clust|ered on country|-times-industry|. Sample size|varies due to m|issing data.|

89