metadata: authors: 0: author: Fang,Sheng
1: author: Shamseldin,Heba M.M.
2: author: Xu,L. Colin
count: World
docna: 0: docna: Foreign Direct Investment and Female Entrepreneurship
docty: Policy Research Working Paper
subtopic: Private Sector Economics,Private Sector Development Law,Marketing,Labor Markets,Gender and Poverty,Technology Industry,Technology Innovation,Gender and Economic Policy,Economics and Gender,Gender and Economics,Gender and Development,Educational Sciences
teratopic: Law and Development,Private Sector Development,Industry,Education,Social Protections and Labor,Macroeconomics and Economic Growth,Science and Technology Development,Gender
abstracts: cdata!: Using World Bank Enterprise Survey data
            around the world, this paper examines how foreign direct
            investment is associated with female entrepreneurship (that
            is, a firm being managed and at least partly owned by
            women), along with other factors such as business
            environment and female empowerment, and their interactions
            with foreign direct investment. Female entrepreneurship
            rises with foreign direct investment inflow, lower entry
            barriers for women, women's better access to finance,
            higher female labor force participation, and women's
            better education. The positive association of foreign direct
            investment inflow and female entrepreneurship is stronger
            for firms in the service sectors and small firms. The
            horizontal competition effects of intra-industry foreign
            direct investment for female entrepreneurship are reduced
            when women face lower entry barriers for starting a business
            and have a higher labor force participation rate, and the
            effects do not depend on women's access to finance or
            their level of education.
repnme: repnme: Foreign Direct Investment and Female Entrepreneurship
display_title: Foreign Direct Investment and Female Entrepreneurship
listing_relative_url: /research/2019/12/31642928/foreign-direct-investment-female-entrepreneurship
topicv3: Private Sector Development m1326060 1921,Education m1327048 470,Gender m1327621 615,Female and Youth Entrepreneurship m1327623 398,Entrepreneurship m1582903 4424
publishtoextweb_dt: 2019-12-16T00:00:00Z
historic_topic: Law and Development,Private Sector Development,Industry,Education,Social Protections and Labor,Macroeconomics and Economic Growth,Science and Technology Development,Gender
origu: DECRG: Finance & Priv Sec Devt (DECFP)
no_of_pages: 36
author_keys: 0: author_key: 1599574
1: author_key: 125549
model_extractions: Policy Research Working Paper 9083 Foreign Direct Investment and Female Entrepreneurship _Sheng Fang Heba M.M. Shamseldin L. Colin Xu_ Development Economics Development Research Group December 2019
Policy Research Working Paper 9083

# **Abstract**

Using World Bank Enterprise Survey data around the world, this paper examines how foreign direct investment is associated with female entrepreneurship (that is, a firm being managed and at least partly owned by women), along with other factors such as business environment and female empowerment, and their interactions with foreign direct investment. Female entrepreneurship rises with foreign direct investment inflow, lower entry barriers for women, women’s better access to finance, higher female labor force participation, and women’s better education. The positive association of foreign direct investment inflow and female entrepreneurship is stronger for firms in the service sectors and small firms. The horizontal competition effects of intra-industry foreign direct investment for female entrepreneurship are reduced when women face lower entry barriers for starting a business and have a higher labor force participation rate, and the effects do not depend on women’s access to finance or their level of education.

This paper is a product of the Development Research 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 lxu1@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
# **Foreign Direct Investment and Female Entrepreneurship**<sup>**1**</sup>

## **Sheng Fang**

## **Heba M.M. Shamseldin**

## **L. Colin Xu**

JEL codes: J7, O1, Z1, L6, L8.

> 1 We are grateful to comments and help of Caroline Freund, Jingyu Gao, Nadia Nocha, Christine Qiang, Alexandros Ragoussis, Deborah E. Winkler, and Trang Thu Tran. The views reflect the authors’ own, and do not implicate the World Bank or the countries it represents.
# **1. Introduction**

The past decades have witnessed remarkable progresses for women on most fronts. According to the World Development Indicators for 187 countries (World Bank, 2019), while the labor participation rate for males has declined from 76.2 percent in 1990 to 72.3 percent in 2018, that for females has increased from 48.5 to 51.8 percent. In 2016, the average share of females (over the age of 25) with a bachelor’s degree or above was 19.7 percent, surpassing that for males (i.e., 18.3 percent). Similarly, based on harmonized learning outcome (HLO), the latest cross-country indicator of comparable human capital from the World Bank, girls out-perform boys on average in the world (Angrist et al., 2019). The advancement of women, however, is not uniform. In leadership jobs, women continue to lag significantly behind (Goldin et al., 2006; Blau and Kahn, 2017; Fairlie et al. 2017; OECD 2019). In this paper, we focus on the determinants of female leadership in businesses, and in particular, the role played by foreign direct investment (FDI), around the world.

Understanding female entrepreneurship is important. Entrepreneurship, by turning ideas into business ventures and new products, is a key driver of economic growth (Schumpeter, 2017). The body of research on entrepreneurship, however, does not say much about what determines female entrepreneurship. With increasing participation of women in economic life, and with their relatively faster growth in human capital, commonly viewed by economists as the driver of growth (Lucas, 1988; Romer, 1990), understanding female entrepreneurship around the world gains urgency, and could be a key area to facilitate development, especially among countries where women traditionally do not play important roles in working in and/or running formal firms.

FDI could be an important determinant of female entrepreneurship. In less than two decades, FDI has dramatically risen in importance: the relative stock of FDI increased three-fold from 8 percent of world GDP in 1990 to 72 percent in 2017 (OECD, 2008, 2018). Increasing international trade and capital flow have fundamentally changed the world, by facilitating economic growth (Alfaro, 2016), raising the income level of poorer countries and possibly spreading the norms of richer investing countries around the world (Dollar and Kraay, 2004). The existing literature on the effects of FDI has largely focused on economic outcomes such as the spillover effects on domestic firms’ performance and the 2
reallocation effects of FDI on market composition (Alfaro, 2016), but FDI’s effects on social development are largely ignored. In this paper, we investigate how FDI inflow affects an important aspect of social changes, female entrepreneurship, in the world, along with the interaction between FDI inflow and various economic barriers for female entrepreneurship, and of female empowerment. Female employment and leadership have important ramifications for development of poor countries. More work and leadership opportunities for women would raise the return to women’s time and human capital investment, induce women to emphasize the quality of children at the expense of the quantity of children (Becker and Lewis 1973; Galor 2005), reduce fertility rates and facilitate the critical transition from the Malthusian regime to the modern growth regime (Galor and Weil 2000; Galor 2005).

In this paper, we study whether and how FDI affects female entrepreneurship, and how the FDI effects interact with gender-specific barriers and empowerment. We mainly use the World Bank Enterprise Survey data, and supplement it with complementary indicators of barriers to female entrepreneurship and women’s empowerment. We allow the FDI effects to have two components: the country-level FDI level, and the countryindustry level FDI (i.e., country-specific measure of FDI intensity in an industry, intraindustry FDI for short). The overall FDI effect captures the comprehensive effect of FDI across all industries, and the intra-industry FDI allows for within-industry spillover and/or “crowding out” effects. We define female entrepreneurship by a firm having both women participation in firm ownership _and_ a female being the top manager of the firm, capturing both ownership and control by women for the firm (Atrido and Hallward-Dreimeier, 2011). By this measure, female entrepreneurship varies from zero in some countries to close to 40 percent in Mongolia.

FDI might affect female entrepreneurship via several channels. Via FDI, there can be social norms transmitted from investing countries to host countries. There can be knowledge spillover, both across and within industries, spurred by the new knowledge stock brought by FDI firms (Acs _et al_ ., 2009, 2011). There can be crowding out effects on female entrepreneurship due to women’s lack of access to key resources to compete with FDI firms within FDI-intensive industries. It is thus an empirical question as to whether and how FDI affects female entrepreneurship. With a data set of firms in around 130 3
countries, we can allow the FDI effects to depend on barriers to women doing business, and on women’s empowerment, issues that have not been examined in the literature.

Using two different specifications, we obtain several findings. Female entrepreneurship increases with higher FDI inflow, lower entry barriers for women, women’s better access to finance, higher female labor participation, and women’s education. The positive association of FDI inflow and female entrepreneurship is stronger for firms in the service sectors and for small-sized firms. The horizontal competition effects of intra-industry FDI for female entrepreneurship is reduced when women face lower entry barriers for starting businesses and have higher labor participation rate, and do not depend on women’s access to finance and their education level.

Our paper contributes to the limited literature on female entrepreneurship and entrepreneurship. The economic literature on gender is large, as aptly surveyed in Croson and Gneezy (2009), Bertrand (2011), Niederle and Vesterlund (2011), and Blau and Kahn (2017). However, the literature on female entrepreneurship and leadership is thin, and focuses on developed countries. The literature has documented a gender gap in top corporate jobs (Bertrand and Hallock, 2001), examined the impact of female corporate leadership or entrepreneurship (in corporate boards or being CEO) on firm performance or evaluation (del Mel et al., 2008, 2014; Ahern and Dittmar, 2012; Bennouri et al., 2018, Amore and Garofalo, 2016, Calderon et al. 2016; Hanousek et al., 2017), access to finance (De Mel et al. 2009), risk taking (Berger, Kick and Schaeck, 2014; Huang and Kisgen, 2013; Faccio et al., 2016), merger and acquisition (Levi, Li and Zhang, 2014), gender equity (Tate and Yang, 2015), and corporate responsibility (Lara et al., 2017; Matsa and Miller, 2013). Relatively little is known about the determinants of female entrepreneurship. Some researchers have documented key characteristics of female-led firms (especially microfirms) in various regions (Bruhn, 2009; Campos et al. 2014, 2019), or gender differences in entrepreneurial propensity (Koellinger et al 2013). Guiso and Rustichini (2011) and Bonte and Piegeler (2013) examine the role of biology, competitiveness, and culture in explaining female entrepreneurship. Klapper and Parker (2010) offer survey evidence on characteristics of female-entrepreneur firms, and suggestive evidence of the importance of access to finance as a reason that limits female entrepreneurship. Ashraf, Delfino and Glaeser (2019) offer theory and evidence on how the rule of law and gender 4
equality affect female entrepreneurship. We add to the literature on female entrepreneurship by examining how FDI affects female entrepreneurship around the world, and how the FDI effects interact with the underlying sectoral structure and the business and social environments.

In the rest of this paper, we first offer a conceptual framework to think about the effect of FDI on female entrepreneurship. We then discuss the data, the empirical methods, and our findings. The last section concludes.

# **2. Conceptual framework and hypotheses**

To consider FDI’s gender-specific entrepreneurship effect, we begin with two opposite aspects of FDI effects. On the one hand, according to the knowledge spillover theory of entrepreneurship (Acs et al., 2009, 2013), FDI has spillover effects by spurring entrepreneurial ideas for starting new businesses. The new ideas would stem from the rising knowledge stock embedded in FDI firms, or from derivative demand for FDI services and inputs. The spillover effects could be intra-industry or inter-industry, but likely stronger for technologically-related firms, and likely stronger for intra-industry firms. On the other hand, FDI also has horizontal competition (or market-stealing) effects, taking away market shares and other key resources of domestic firms (Alfaro, 2016). Here the FDI competition effects are mainly intra-industry, and especially strong for intra-industry domestic firms that directly compete with foreign firms.

A specific type of spillover effects of FDI likely have gender implications: the diffusion of social norms. Besides economic effects, FDI may be accompanied by changing social norms that emphasize gender equality, which could affect the tendency of women to start and manage businesses. _The FDI-norm view_ would thus imply a positive effect of FDI in general on female entrepreneurship.

FDI competition effects could affect female entrepreneurship. With better access to technology and finance, FDI firms’ entry into a specific industry would intensify competition in _that_ industry (Alfaro, 2016). Such powerful competition from FDI firms is likely to disadvantage particularly female-led firms. In other words, FDI entails a resource bias effect on female entrepreneurship. Because FDI firms tend to be much larger, more 5
capital-intensive and productive, their domestic competitors would be better able to confront rising competition when their resources prove to be closer matches to the FDI competitors. Since female entrepreneurs tend to have worse access to finance and to political power (necessary for obtaining licenses, land, among others), and femaleentrepreneur firms tend to be smaller, more service-concentrated, more skilled, and they tend to be less productive, as confirmed by our data and the recent literature on female entrepreneurship (Aterido and Hallward-Dreimeier, 2011; Estrin and Mickiewicz, 2011; Brohn and Love, 2011; Bardasi et al., 2011), FDI competition in the FDI industries likely engenders _resource bias_ in favor of male entrepreneurs. Since a key ingredient facilitating entrepreneurship is access to scarce and valuable resources such as financial capital, social capital (Parker, 2009), the resource bias against women implies that there is a negative intra-industry FDI effect on female entrepreneurship.

Where women entrepreneurs face stronger barriers to compete with strong formal firms—such as when facing higher entry barriers, or when access to finance is worse--it would be even harder to compete with FDI firms in the industry. Here the horizontal competition effect of FDI firms would be greater, and we expect lower female entrepreneurship. We thus expect on more negative intra-industry FDI effects (due to horizontal competition) for women’s entrepreneurship in countries with stronger barriers such as higher entry barriers and worse access to finance for women entrepreneurs. We call this the _FDI competition and resource bias hypothesis._ Female labor participation would affect female entrepreneurship both directly and via FDI. Female labor participation increases the number of women participating in economic life and facilitates their exposure to new ideas and demand posed by FDI. This by itself should increase female entrepreneurship. When women have lower participation in the labor market, the stock of managerial and business talent from women is reduced. Women entrepreneurs would find it harder to compete directly with FDI firms. The intraindustry FDI competition effects would be stronger, resulting in lower possibility of women’s entrepreneurship. We thus expect a more pronounced negative intra-industry FDI effect (due to horizontal competition) for women’s entrepreneurship in countries with lower female labor participation rate.

Female education increases females’ ability to deal with disequilibria brought about 6
by FDI inflow (Schultz, 1975), thus enabling female entrepreneurs. Better female education should therefore have the direct effect of increasing women’s entrepreneurship. However, education itself does not directly affect women’s access to critical resources to compete with FDI—as demonstrated by rapid female advances in education but the lack of advances in top jobs that are mentioned in the introduction. Female education thus likely has no significant interactions with FDI in determining female entrepreneurship.

The FDI effects likely differ among firms in manufacturing and in services. Femaleled firms have comparative advantage over male-led firms in service industries due to the relative importance of interpersonal relationships, and the relative lack of importance of brawn (vs brain) (Pitt et al., 2012; Zhang and Xu 2016). Furthermore, since entry into the service sectors likely requires less capital input, female entrepreneurs, facing stronger financial constraints, are more likely to enter service sectors. Thus, _the FDI-sectoral-bias hypothesis_ is that the FDI effect on female entrepreneurship in service sectors should be positive, or at least more positive than in manufacturing sectors.

The FDI effects should also differ among firms of different sizes. Perhaps due to the lack of access to critical inputs, female-led firms tend to be smaller. Since FDI firms tend to have stronger spillover effects on smaller firms that serve FDI firms in various capacity, and there are relatively more female-led ones among small firms, we expect FDI inflow in general to have stronger spillover effects on female entrepreneurship among the small firms than among the medium-and-large firms. The intra-industry FDI effects on female entrepreneurship among small firms (relative to other larger firms) is ambiguous: the spillover effect is positive, but the horizontal competition effect is negative due to female-led firms’ lack of competitiveness with respect to larger FDI firms. The _FDI-sizebias hypothesis_ is thus the following: on female entrepreneurship, FDI should have a larger general spillover effects among small firms than among medium-and-large firms, but an ambiguous intra-industry effect when comparing the effects among the two types of firms.

# **3. Data set**

The main data source is the World Bank’s Enterprise Surveys (WBES) in 709 cities of 128 countries. The WBES data are collected by the World Bank to benchmark the business 7
climate in (mostly) developing countries and to understand the determinants of firm performance. The richest country in the sample is Sweden with a GDP per capita of US$46,000, and the poorest countries (i.e., Burundi, Liberia, Malawi, Ethiopia) have a GDP per capita around US$200. In each country, the survey is based on the universe of eligible non-agricultural firms obtained from the country’s statistical office with stratified random sampling with replacement, and the result is a representative sample of the nonagricultural private economy in the country.<sup>2</sup> Stratification is based on two criteria: the sector of activity and firm size. Typically, the stratified sampling yields between 100 and 1,000 firms per country, with 108 firms for the median city. Industries range from manufacturing and construction to services and retail and wholesale trade,<sup>3</sup> and some industries such as mining and financial services are excluded in the survey. Each survey is largely a cross-sectional data set.

We include data collected after 2006, although some enterprise surveys were conducted earlier. Prior to that year, there was considerable heterogeneity across countries in terms of the questionnaire format, sectors covered, and sampling methodology. Moreover, the samples for surveys conducted before 2006 were not generally representative. After dropping observations that have missing values for whether the firm is headed by a female manager, our final sample consists of more than 80,000 firm-year observations covering 567 cities in 117 countries.<sup>4</sup> A complete list of variables and data sources is shown in Table 2; the summary statistics for our key variables used in regressions are in Table 3.

# **4. Basic patterns for the relationship between FDI and female corporate leadership**

How do we define female entrepreneurship? Ideally, we want to capture firms with women being the primary owner and running the firm; that is, women act as both owners and chief executives. Female-owned firms may not be a good indicator for entrepreneurs since family ownership is prevalent and important in both developed and developing countries

> 2 Thus, wholly state-owned firms are not in the sample.

> 3 See http:\\www.enterprisesurveys.org for a more detailed description of the WBES.

> 4 Because of the missing values of other variables, for each dependent variable in the regressions, the number of observations differs. In the baseline regressions, the number of observations when the dependent variable is female-manager and female-entrepreneur are 50,472 and 47,702 respectively.

8
(Anderson and Reeb, 2003), and in the case of women inheriting family firms but without effective control, it does not make sense to view the firm as a female entrepreneur firm. Furthermore, the previous literature using the firm sample of several African countries in the WBES data has documented carefully that using the definition of women managers tends to make more intuitive sense than that of women’s participation in firm ownership (Aterido and Hallward-Driemeier, 2011). We thus try to be conservative in defining female-entrepreneur firms and rely on two variables in the WBES data set to capture aspects of female entrepreneurship: women’s participation in ownership (“whether women have some ownership of the firm”), and whether the top manager is a woman. We define female entrepreneurship as the firm’s top manager is a woman _and_ women have ownership share in the firm. We impose the requirement of women being top managers because nonprincipal ownership by itself does not imply female entrepreneurship.

The extent of female entrepreneurship varies greatly in the world. Before presenting the statistics, we must acknowledge that the country-specific samples are not large, some industries are not covered, as mentioned earlier, and as a consequence, we must view the statistics about the extent of female entrepreneurship within a country with caution. This being said, within the sample, on average 10.9 percent of firms are headed by female entrepreneurs, 14% of firms are female-top-managed, and 31% of firms have female ownership participation (Table 3). The sample share of female-entrepreneur firms differs greatly by country (see Table 1), ranging from below 1 percent in Morocco, the Arab Republic of Egypt, Jordan, and Iraq; to 1 to 5 percent in Israel, Pakistan, India, Mauritania, and Bangladesh; to 5 to 10 percent in Sweden, Argentina, Turkey, Chile, Nepal, Ethiopia, Mexico, Tanzania, several large central Asian countries (such as Uzbekistan and Tajikstan), Senegal, Nigeria, Sri Lanka, and Peru; to between 10 and 15 percent in China, Brazil, Czech Republic, Malawi, and Colombia; to 15 to 25 percent in Ghana, Slovak Republic, Honduras, Romania, Hungary, Indonesia, Vietnam, Poland, Kyrgyz Republic, and Ukraine; to more than 25 percent in Namibia, the Philippines, the Lao People’s Democratic Republic, and Mongolia (36%, the highest in our sample). We should point out that the WBES data are not a random sample of all firms (especially informal firms) of a country, and the country average is informative but should not be taken at face value.

How is the presence of FDI firms related to female ownership of domestic firms?

9
To proceed, we first plot the country-average of foreign ownership of firms with the share of domestic female-entrepreneur firms. Domestic firms are defined to be non-FDI firms, with FDI firms being defined to be firms with foreign ownership exceeding 10 percent.<sup>5</sup> In general, FDI is associated with a higher share of domestic female-entrepreneur firms, as Figure 1 shows.

Sample female-entrepreneur firms differ from sample male-entrepreneur firms in many areas (see Table 5). Female-entrepreneur firms, relative to male-entrepreneur firms, tend to be significantly smaller (in terms of the number of employees, by about 36 percent), significantly more likely in services (versus manufacturing, by 7 percentage points, with the full-sample mean being around 0.50), younger, less likely to export, have significantly _higher_ share of skilled workers in all employees (74.4% vs 68.9), in general are located in cities with lower access to bank finance or to inter-firm trade credit, and with stronger competition from informal firms. From Table 1, it seems that a higher share of femaleowned firms is likely strongly correlated with the level of development of a country, which is why we control for the level of development of the country in our empirical specifications.

Since female entrepreneurship is affected by both spillover and competition from FDI firms, it is useful to know how FDI firms differ from domestic firms in basic characteristics.<sup>6</sup> Table 6 conducts basic t-tests for the differences of FDI firms and domestic firms in basic characteristics. FDI firms tend to have higher capital intensity, are much larger (by 90 log points in the number of employees), and have higher productivity. Thus, FDI firms exploit their comparative advantage of better access to capital to support technology of higher capital intensity and firms of larger sizes. It is also clear that foreignowned firms are not necessarily more skill-intensive, as shown by their lower share of skilled workers in all employees relative to that of domestic firms. This is consistent with what is found in the literature that FDI tends to go to sectors with lower skill intensity (OECD 2019, p. 18). Thus, relative to domestic female-entrepreneur firms, foreign-owned

> 5 The 10 percent threshold is adopted by convention in the literature. A minimum threshold is imposed to ensure that foreign owners have substantial influence for the operation of the firm. We have alternatively tried using the threshold of 100 percent, and the results are robust .

> 6 See Lejarraga and Ragoussis (2018) for how FDI firms and domestic firms differ in other key outcomes based on WBES.

10
firms are somewhat more similar to male entrepreneur firms in being larger and having a lower share of skilled workers. With larger sizes and better access to finance, male entrepreneur firms are likely to have better chance to compete with larger FDI firms. Maleled firms presumably would also have stronger political connections and power (Goldstein and Udry, 2008; Miao et al., 2019), and they are likely better able to deal with mounting competition of large foreign-owned firms in the same industry.

To shed light on how industry-level FDI premium is related to female entrepreneurship, we compute the average of firms’ foreign ownership share and the share of female entrepreneur firms for each country-industry-year cell, with both subtracting the countrylevel mean values. De-meaning both variables has the effect of controlling for the country fixed effects. We plot the country-industry level correlations between foreign ownership and the share of female entrepreneurship. As Figure 2 shows, the relationship between industry-level foreign ownership (relative to the country) and female entrepreneurship is not systematically positive or negative. Whether the results hold once we control for confounding variables, we shall leave it to multivariate regressions, to which we now turn.

# **5. Multivariate regression analysis**

We allow female entrepreneurship ( _Female Entrep_ ) of domestic firms _i_ of country _c_ and industry _j_ to be affected by FDI through two different channels. The first is the overall level of FDI inflow to the corporate sector, as captured by the average foreign ownership of all sample firms in country _c_ , and we call this . The second component is the industry FDI ( c ), as captured by subtracting from the average of corporate foreign ownership of cc FF firms in country _c_ and industry _j_ . We allow FDI to have industry component to take into cc FF FF account intra-industry spillover effects and the potential crowding out effects. Our first specification is the following: F F F E E c c (1) iic ii 2cc cc 1cc cc iic ii cc ii iic ii Here, is country-level control variables including the GDP per capita, education (as FFEE = ββ ZZ + ββ FF + δδ FF + XX ββ+ θθ + γγ + εε measured by the index of higher education and training, from World Economic Forum), cc ZZ and the urbanization rate. is a vector of firm-level controls and local business environment. Firm-level controls include the ownership share of the largest owner (as a iic ii XX 11
proxy of family ownership), size indicators of being small (i.e., with fewer than 50 permanent employees three years ago) and being medium and large (i.e., with 50+ employees three years ago), indicators of firm age categories (i.e., being 6 to 10 years old, being more than 10 years old). Since the business environment is often critical for entrepreneurship and firm growth (Xu, 2011), following Reyes et al. (2017), we control for indicators of local business environment that includes city-level indicators of access to bank finance, to trade credit (as a proxy of informal finance and inter-firm linkage), intensity of informal competition, electricity outage, access to internet, and capacity agglomeration (i.e., the share of firms that employ more than 50 employees, which has been found to be a key indicator of local firm capacity, see Clarke et al. 2016).<sup>7</sup> In our second specification, we have better control of country-level heterogeneity by directly controlling for country fixed effects, which would encompass factors such as the level of development, its culture and institutions, among others. With this specification, we cannot examine the relationship between _Female Entrep_ and , which is absorbed in the estimate of the country fixed effect. However, we can examine the effect of cc c . It FF would reduce our concern for the omitted variable bias if our estimate of the effect of c FF is similar for specifications 1 and 2. FF

# **Baseline results**

Using the sample of all _domestic_ firms, columns (2) and (4) of Table 7 present our baseline regressions for the relationship between FDI and _Female Entrep_ with specifications 1 and 2. To gauge whether the results on female entrepreneurship is robust when using indicators other than our main _Female Entrep_ , we also use the dummy variable of the top manager being female as the dependent variable in columns (1) and (3). Overall the results are similar with the dependent variable being _Female Entrep_ or female top manager. In light

> 7 In general, we compute city-level average of firm’s response on objective and/or subjective indicator of the business environment (with the average excluding the answering firm to avoid endogeneity) as the measurement of the local business environment. This way of measuring the business environment has the benefit of not relying on individual choices, which are likely endogenous for the typical outcomes in which we are interested,such as firm performances or specific firm choices. It is still subject to potential endogeneity in being potentially correlated with omitted variables at the city level (see Xu (2011) for a discussion of the pros and cons of this way of measuring the business environment, and related literature that opts for this way). Further support on why capacity agglomeration might matter is implied by Acs and Audretsch (1988), who suggest that the innovation tendency differs systematically by the agglomeration of large firms in an industry.

12
of the robustness, and most importantly, _Female Entrep_ better captures our notion of female entrepreneurship, our discussion centers on _Female Entrep_ in columns (2) and (4).

Female entrepreneurship is higher in countries with higher FDI level. Based on column (2), increasing the average share of foreign firms in a country by one standard deviation (i.e., 0.078) is associated with increasing _Female Entrep_ by 0.04, or 13.4% of one standard deviation of _Female Entrep_ . Thus, FDI has an overall positive association with female entrepreneurship, consistent with our earlier discussion that FDI has positive spillover effects on female entrepreneurship. This positive correlation is not due to omitting the level of economic development, which is already controlled for by GDP per capita, the education level, and the urbanization level.

Other findings are also interesting. Local prevalence of internet is positively and weakly associated with _Female Entrep_ . Local prevalence of trade credit, an indicator of the importance of business social network, tends to be associated with lower female entrepreneurship, perhaps indicating females’ disadvantage in business social network. Interestingly, the country’s income level, after conditioning on education, is negatively associated with _Female Entrep_ , while the country’s education achievement is positively associated with _Female Entrep_ . The importance of education for female entrepreneurship is consistent with Schultz (1975) on education’s critical importance for handling disequilibria and facilitating entrepreneurship.

Based on both specifications 1 and 2, high FDI industries tend to exhibit lower _Fem Entrep_ , but the coefficients are all statistically insignificant. Thus, FDI on average does not have a “crowding out” effect for female entrepreneurship within the heavy-FDI-presence industries. This finding suggests that there are forces such as knowledge spillover and transmission of social norms countering the forces associated with the FDI competition and resource bias effects, according to which FDI competition likely results in a withinindustry FDI crowding out effect.

# **Firm size and FDI effects on female entrepreneurship**

How do firms of various sizes respond differently to FDI in terms of facilitating female entrepreneurship? As discussed earlier, female entrepreneur firms tend to be smaller in size, and the findings on FDI’s impact for smaller firms are thus more relevant for understanding 13
female entrepreneurship. The government and/or international organizations could use the more nuanced findings to target firms of specific sizes if they aim at enabling women entrepreneurs. To shed light on this question, in Table 8, we separate the sample firms into two groups: those hiring 50 or fewer permanent employees three years ago (i.e., three years before the firm survey); and the other larger firms.

The general FDI effects on _Female Entrep_ are much larger for small firms than for medium-and-large firms. The coefficient of is more than twice larger for small firms than for medium-and-large firms. Interestingly, the within-industry FDI crowding out cc FF effect on _Female Entrep_ does not differ significantly between these two types of firms. These results support the _FDI-size-bias hypothesis._

# **Firm sector affiliation and FDI effects on female entrepreneurship**

Given women’s general lack of comparative advantage (relative to men) in brawn, and women’s advantage in handling interpersonal relationships, it is generally believed that women tend to do better in service-sector jobs than manufacturing jobs (Buera et al., 2013; Ngai and Petrongolo, 2017). Indeed, women’s job growth in service sectors tends to outpace that in other sectors (Lan and Shepherd, 2018). Given the higher share of women being employed in services, and the consistency between gender norms and job requirements in services than in other sectors, we now examine whether the FDI effects on female entrepreneurship differ by sectors. To this end, in Table 9 we allow the FDI effects to differ by sectors.

The FDI spillover effects are 70 percent larger for firms in services than in manufacturing (i.e., 0.70 vs 0.41). Furthermore, once controlling for country fixed effects, the within-industry FDI premium has further positive spillover effects only in service sectors. In contrast, though statistically insignificant, the negative sign of c in manufacturing is consistent with the existence of within-industry FDI crowding out effects FF on female entrepreneurship in manufacturing sectors. The results support the FDI-sectoralbias hypothesis.

# **Entry barriers for women and FDI effects on female entrepreneurship**

How do barriers to female entrepreneurship shape the overall FDI spillover effect and the intra-industry crowding out effect? If there is an intra-industry FDI crowding out effect, 14
we should find it in countries where women face higher entry barriers. We thus allow the two FDI terms to depend on two available measures of barriers to female entrepreneurship: the number of days required to start a business for female entrepreneurs, which measures entry costs for female entrepreneurs; the share of females age 15 and older with financial institution account, which measures financial access.<sup>8</sup> Table 10 reports the results.

While higher entry cost for women, by itself, is not significantly associated with female entrepreneurship,<sup>9</sup> it is associated with a more pronounced within-industry FDI crowding out effect. The interaction of c and the entry barrier indicator for females is statistically significant for both specifications. By column (1), the c effect on _Female_ FF _Entrep_ is -0.09 at the mean of the entry costs for females (i.e., 22.1), but -0.17 when the FF entry costs for females increase by one standard deviation (i.e., 26.5) from the mean, which implies that increasing the average share of foreign firms in an industry by one standard deviation (0.082) is associated with lower _Female Entrep_ by 0.014, or 4.5% of one standard deviation of _Female Entrep_ (0.312). Higher entry cost for females is associated with relatively higher effects. This is consistent with the general equilibrium reallocation of female entrepreneurship across cc FF industries: when the entry barrier is high for females, in high-FDI industries, women face even harsher conditions for competition relative to men, and opt to reallocate or start their businesses to other industries. The findings in this paragraph and the last one, when combined, are suggestive that where entry costs for females are high, women entrepreneurs have a strong tendency to shift away from FDI industries to non-FDI industries.

When women’s access to finance, as measured by adult women with financial institutions accounts, is better, _Female Entrep_ is higher, as indicated by the total effect of this variable evaluated at the means of the FDI interaction variables. Increasing this variable by one standard deviation (i.e., 25.1) is associated with higher _Female Entrep_ by 5 percentage points, almost half of the mean level, indicating a huge effect. Interestingly, the FDI variables do not interact with women’s access to finance in influencing _Female_

> 8 The original data sources are Doing Business for “time required to start a business for female entrepreneurs,” and Global Findex of World Bank, both available from World Development Indicators.

> 9 This is based on evaluating the effect of the “barriers indicator” at the mean of the interaction terms.

15
_Entrep_ , suggesting that access to finance itself directly and powerfully affects _Female Entrep_ , but its effect does not hinge on the FDI level and its distribution across industries. In some sense then, access to finance by women is a robust facilitator of female entrepreneurship.

# **Women’s empowerment and FDI effects on female entrepreneurship**

Women’s empowerment is supposed to facilitate women’s entrepreneurship. Is this true in our comprehensive data? How do FDI and female empowerment interact with each other in influencing female entrepreneurship? To answer these questions, we include two aspects of female empowerment: female labor participation rate, and female education (as proxied by the completion rate of primary schools among adult females).<sup>10</sup> It is useful to note that these two measures capture distinct aspects of female empowerment: the former captures general economic participation, and the latter captures improvement in human capital. Table 11 reports the results.<sup>11</sup> When female labor participation is higher, women tend to face lower discrimination and have better access to scarce resources. Not surprisingly, this empowerment measure itself is associated with higher female entrepreneurship. Increasing it by one standard deviation (i.e., 16) is associated with higher _Female Entrep_ by 3.2 percentage points (when evaluated at the mean of the interacting FDI variables), a big effect.

Interestingly, when a country’s female labor participation rate is higher, FDI’s within-industry crowding out effect is more limited, perhaps because women face less serious obstacles in access to scarce resources (such as power and finance), and the market stealing effect is thus not strongly biased against women entrepreneurs. Based on the estimates in column (1), the coefficient of c is -0.096 (0.064) at the mean (one SD above the mean) of female labor participation rate. Thus, higher female labor force participation FF thus facilitates female entrepreneurship, and could change the intra-industry FDI effects from crowding out to crowding in female entrepreneurship **_._** In contrast, better female education is associated with higher female

> 10 Both variables come from the World Development Indicators.

> 11 To the extent that the rule of law may also level the playing field, especially for women, we have also examined whether the FDI effects depend on a country’s level of rule of law. We have not found it to matter much.

16
entrepreneurship, but its effect does not hinge on FDI penetration. Increasing the female primary completion rate by one standard deviation (i.e., 20.4) is associated with increasing _Female Entrep_ by 2 percentage points (when evaluated at the mean of the interacting FDI variables), or 20% of the mean. Improving female education thus raises female entrepreneurship, but in an FDI-neutral way. Female education is thus also a robust facilitator of female entrepreneurship.

# **6. Conclusions**

Using a comprehensive cross-country, firm-level data set, we examine how FDI affects female entrepreneurship around the world. We find that female entrepreneurship varies greatly across countries. Overall, FDI is positively associated with female entrepreneurship. The overall FDI effect on female entrepreneurship is stronger for small firms and for service-sector firms. Reducing barriers for female entrepreneurs (by improving access to finance) and empowering women (by improving education and by facilitating female labor participation) are all associated with higher female entrepreneurship. The association between female entrepreneurship and industry FDI is on average insignificant, indicating no intra-industry FDI crowding out effects for female entrepreneurship. The intra-industry FDI crowding out effects become pronounced when women face high entry costs and when the female labor participation rate is low, but do not hinge on women’s access to finance and their education. Interestingly, in service sectors, the within-industry FDI effect is positive, and FDI entry in service sectors is thus particularly female-entrepreneurshipfriendly.

Our results thus indicate that female entrepreneurship likely increases with FDI inflow, improvement in the business environment for women, and in women’s empowerment. There is some support for the resource bias view of FDI crowding out female entrepreneurship where the environment is not female-entrepreneurs-friendly, as indicated by pronounced crowding out effects on female entrepreneurship when women face higher entry barriers and participate in the labor market to a lesser extent.

Our research is subject to an important caveat. Due to the nature of our data, all our findings—though often couched in terms of “effects” for ease in exposition but not in terms 17
of real causality--represent associations and not causality. Nevertheless, our findings do represent a novel and thorough investigation of the relationship between female entrepreneurship and FDI around the developing world, and the findings seem to form a coherent narrative of how FDI might have affected female entrepreneurship, and what roles entry barriers for women and female empowerment might have in making FDI inflow work better for facilitating female entrepreneurship.

18
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21
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**Table 1. The sample share of female entrepreneur firms among domestic firms across countries**

|Country|Share|Country|Share|Country|Share|Country|Share|
|---|---|---|---|---|---|---|---|
|Morocco|0.00%|Tanzania|8.68%|Brazil|13.57%|Bolivia|19.26%|
|Jordan|0.23%|Uzbekistan|9.10%|Venezuela, RB|13.95%|Georgia|19.76%|
|Lebanon|0.27%|Tajikistan|9.22%|Czech Republic|14.18%|Myanmar|20.20%|
|Egypt, Arab Rep.|0.37%|Kenya|9.24%|Uruguay|14.23%|Bulgaria|20.35%|
|Yemen, Rep.|0.40%|Senegal|9.45%|Trinidad and<br>Tobago|14.29%|Lesotho|20.69%|
|West Bank and<br>Gaza|0.53%|Central African<br>Republic|9.52%|<br>Malawi|14.50%|Croatia|21.05%|
|Djibouti|0.59%|<br>Nigeria|9.57%|Paraguay|14.89%|Indonesia|21.49%|
|Iraq|0.81%|Sri Lanka|9.78%|Colombia|14.94%|Moldova|21.70%|
|Israel|1.02%|Peru|9.83%|Burundi|14.96%|Vietnam|21.73%|
|Afghanistan|1.27%|Albania|10.00%|Jamaica|15.06%|Poland|21.78%|
|Tunisia|1.60%|China|10.14%|Barbados|15.24%|Grenada|22.58%|
|Sudan|1.75%|Dominican Republic|10.41%|Ghana|15.29%|Kyrgyz Republic|23.36%|
|Pakistan|2.30%|Costa Rica|10.55%|Uganda|15.49%|Estonia|23.54%|
|Azerbaijan|2.66%|Antigua and Barbuda|10.61%|Lithuania|15.71%|Ukraine|24.04%|
|India|3.09%|Panama|10.64%|Rwanda|15.88%|Nicaragua|25.00%|
|Mauritania|3.85%|Belize|10.69%|Bhutan|15.97%|Belarus|26.02%|
|Bangladesh|3.87%|Bosnia and<br>Herzegovina|10.94%|Slovak Republic|16.22%|Namibia|26.18%|
|Liberia|5.56%|<br>Ecuador|11.04%|Gabon|16.67%|Bahamas, The|27.27%|
|Suriname|6.29%|El Salvador|11.19%|Slovenia|16.89%|Congo, Rep.|28.57%|
|Sweden|6.39%|Armenia|11.88%|Russian<br>Federation|16.92%|Philippines|28.64%|
|Argentina|6.56%|Guatemala|12.53%|St. Lucia|17.21%|Latvia|29.41%|
|Turkey|6.64%|Congo, Dem. Rep.|12.53%|Honduras|17.35%|Lao PDR|34.75%|
|Chile|7.79%|Zimbabwe|12.90%|Romania|17.49%|St. Vincent and the<br>Grenadines|35.25%|
|Nepal|8.12%|North Macedonia|13.09%|Hungary|17.60%|Mongolia|35.75%|
|Ethiopia|8.45%|St. Kitts and Nevis|13.16%|Dominica|18.26%|||
|Mexico|8.56%|Guyana|13.22%|Kazakhstan|19.09%|||

Note: Based on non-FDI firms, i.e. we delete FDI firms with foreign ownership being 10% or more.

23
# **Table 2. Variable definitions**

|Variables|Definition|
|---|---|
|F_c<br>F_cj|The average of foreign ownership shares of all sample firms in the country.<br>The average foreign ownership share of the country-industry-year cell minus that of the<br>country cell.|
|Female-Manager|Equals to 1 if the top manager is a female, and 0 otherwise.|
|Female entrepreneur<br>Small3<br>Large3|Equals to 1 if both the top manager and any of the owner are female, and 0 otherwise.<br>The firm’s number of permanent employees three years ago was less than 20.<br>The firm’s number of permanent employees three years ago was more than 100.|
|age6_10<br>age10plus|The firm’s number of employees three years ago was 20-100 (more than 100).<br>The firm’s age is between 6 and 10 years (or 10 or more years).|
|Capital/Labor ratio|Equals to K/L, and K is replacement costs of land and machine (million dollar), L is<br>number of labors.|
|Skilled workers’ share|The ratio of skilled workers / (skilled workers + unskilled workers)|
|Corruption|City-industry-level average of the firm’s answer on whether corruption constitutes an<br>obstacle, ranking from 0 to 1.|
|Formal Finance<br>Trade Credit|City-industry share of firms having a line of credit or loan from a financial institution.<br>City-industry average of the proportion of total annual sales of goods and services that<br>are paid for after delivery.|
|Inf. Competition|<br>City-industry share of firms who say that they directly compete with informal firms.|
|Outage|City-industry share of firms that experienced a power outage in the survey year.|
|Web|City-industry share of firms that answer that they use websites to conduct business.|
|Cap. Agglomeration|The share of firms whose number of employees exceeding 50, as a proxy of capacity<br>agglomeration.|
|Lngdppc<br>Education|Log of GDP per capita, in constant USD, from World Development Indicators.<br>5th pillar: Higher education and training, from World Economic Forum (WEF).|
|Urbanization<br>Barrier: time required to start a<br>business for females (in days)|Urban population (% of total), from World Development Indicators.<br>Time required to start a business, female (days), from Doing Business for “time required<br>to start a business for female entrepreneurs”.|
|Barrier: financial institution<br>account, female (% for age<br>15+)|Financial institution account, female (% age 15+), from Global Findex of World Bank.|
|Empowerment indicator:<br>Fem. labor participation rate|Labor<br>force<br>participation<br>rate,<br>female<br>(%<br>of<br>female<br>population<br>above ages 15), from World Development Indicators.|
|Empowerment indicator:<br>Fem. Primary completion rate|Primary completion rate, female (% of relevant age group), from World Development<br>Indicators.|

24
Table 3. Summary statistics for key variables

||N|mean|sd|p10|p25|p50|p75|p90|
|---|---|---|---|---|---|---|---|---|
|Female-Manager|50472|0.140|0.347|0.000|0.000|0.000|0.000|1.000|
|Female entrepreneur|47702|0.109|0.312|0.000|0.000|0.000|0.000|1.000|
|OwnLargest|57574|0.792|0.265|0.400|0.500|1.000|1.000|1.000|
|Small|57574|0.553|0.497|0.000|0.000|1.000|1.000|1.000|
|Large|57574|0.139|0.346|0.000|0.000|0.000|0.000|1.000|
|Age 6-10|57574|0.229|0.420|0.000|0.000|0.000|0.000|1.000|
|Age 10+|57574|0.672|0.469|0.000|0.000|1.000|1.000|1.000|
|**City level**|||||||||
|Corruption obstacle|613|0.486|0.277|0.088|0.271|0.482|0.695|0.875|
|Overdraft facility|611|0.375|0.263|0.070|0.140|0.323|0.584|0.774|
|Trade credit|613|0.431|0.198|0.181|0.276|0.418|0.579|0.707|
|Inf competition|608|0.498|0.209|0.246|0.337|0.478|0.660|0.783|
|Outage|613|0.576|0.269|0.219|0.344|0.578|0.835|0.937|
|Web|617|0.443|0.239|0.114|0.244|0.454|0.618|0.768|
|Capacity agglo.|621|0.267|0.148|0.076|0.159|0.259|0.355|0.456|
|**Country-Industry or Country Level**|||||||||
|F_cj|1423|0.008|0.082|-0.075|-0.035|-0.004|0.034|0.102|
|F_c|122|0.105|0.078|0.025|0.057|0.096|0.130|0.186|
|Lngdppc|114|7.606|1.235|5.923|6.648|7.701|8.627|9.276|
|Education|122|3.589|0.695|2.642|3.069|3.685|4.066|4.407|
|Urbanization|116|48.718|21.196|21.934|32.213|48.938|65.252|77.186|
|Time required to start a business for females (in days)|116|22.140|26.476|5.500|8.200|14.000|25.750|48.000|
|Financial institution account, female (% for age 15+)|92|43.647|25.142|13.568|26.023|38.156|65.563|78.585|
|Fem. labor participation rate|111|53.161|15.975|31.006|45.486|52.800|62.440|75.324|
|Fem.primarycompletion rate|112|86.328|20.431|56.622|74.492|92.982|100.797|104.641|

Notes: For firm-level variables, we delete FDI firms with foreign ownership being 10% and more.

25
Table 4 Sample distribution of female owner and female manager

||Female|Owner|Male|Owner|
|---|---|---|---|---|
||Freq.|Per.|Freq.|Per.|
|Female<br>Manager|8,289|11.07%|2,477|3.31%|
|Male Manager|15,073|20.12%|49,060|65.50%|

26
**<u>Table 5: T test for key variables (Female entrepreneur is defined as the firm has both female owner and female manager)</u>**

||<br>Ma<br>|<br>le entrepren|<br>eur|<br>Fem<br>|<br>ale entrepre|<br>neur|<br>Difference<br>|<br>diffse|
|---|---|---|---|---|---|---|---|---|
|Var|Obs.|Mean|SD.|Obs.|Mean|SD.|(female – male)|_|
|**lnL**|60365|3.351|1.333|7774|2.914|1.256|-0.437***|0.016|
|**lnS**|47089|13.077|2.270|5723|12.383|2.253|-0.694***|0.032|
|**Small**|60380|0.504|0.500|7777|0.650|0.477|0.146***|0.006|
|**Large**|60380|0.163|0.370|7777|0.097|0.297|-0.066***|0.004|
|ServiceInd|60380|0.457|0.498|7777|0.531|0.499|0.074***|0.006|
|Foreign share|60380|0.000|0.002|7777|0.000|0.002|0.000|0.000|
|OwnLargest|58297|0.797|0.264|7471|0.808|0.261|0.011***|0.003|
|Age|59607|18.266|15.326|7676|16.210|13.580|-2.056***|0.184|
|Exports|59860|0.087|0.234|7708|0.078|0.230|-0.009***|0.003|
|**Capital/Labor ratio**|20064|0.137|2.883|2035|0.059|0.387|-0.077|0.064|
|**Skilled workers’ share**|31228|0.689|0.304|3168|0.744|0.307|0.055***|0.006|
|Corruption obstacle|54691|0.485|0.289|6923|0.455|0.254|-0.031***|0.004|
|Overdraft facility|53017|0.429|0.282|6789|0.387|0.265|-0.042***|0.004|
|Trade credit|52988|0.457|0.221|6852|0.441|0.222|-0.016***|0.003|
|Inf competition|53508|0.493|0.234|6722|0.501|0.231|0.008***|0.003|
|Outage|55182|0.576|0.282|7159|0.523|0.266|-0.053***|0.004|
|Web|55776|0.501|0.258|7203|0.481|0.243|-0.020***|0.003|
|Capacity agglo.|60346|0.308|0.139|7777|0.295|0.126|-0.012***|0.002|
|TFP|18084|-0.083|1.377|1753|-0.187|1.398|-0.104***|0.034|
|lnLP|46653|9.632|1.529|5669|9.383|1.542|-0.248***|0.022|
|Lgrow|55752|0.033|0.137|7151|0.030|0.139|-0.004**|0.002|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels.

lnL is the log of estimated total employees at survey year. lnS3 is the log of total sales at survey year.

Small means that the firm’s number of permanent employees at survey year was less than 20. Large means that the firm’s number of permanent employees at survey year was more than 100. ServiceInd is an indicator for industry, service is 1 and manufacturing is 0.

Capital/Labor ratio equals to K/L, and K is replacement costs of land and machine (million dollar), L is number of labors.

Skilled workers’ share is the ratio of skilled workers / (skilled workers + unskilled workers) We delete FDI firms with foreign ownership being 10% and more.

27
**Table 6. How does FDI firms and sectors differ from domestic firms and sectors**

||**D**|Panel A:<br>**omestic fir**|domestic f<br>**ms**|irms VS F|DI firms<br>**FDI firms**||||
|---|---|---|---|---|---|---|---|---|
||<br>**(i.e., fore**<br>|<br>**ign owners**<br>|<br>**hip <0.1)**<br>|**(i.e., fore**<br>|<br>**ign owners**<br>|<br>**hip > =0.1)**<br>|Difference|diff_se|
|Var|Obs.|Mean|SD.|Obs.|Mean|SD.|||
|Capital/Labor ratio|32717|0.109|2.445|3529|0.316|6.248|0.207***|0.054|
|lnL3|87071|3.054|1.366|9324|3.954|1.611|0.900***|0.015|
|TFP|29777|-0.162|1.339|3238|0.151|1.414|0.313***|0.025|
|lnLP|76658|9.481|1.530|8120|10.033|1.656|0.553***|0.018|
|Skilled workers’share|47350|0.689|0.310|4851|0.672|0.322|-0.017***|0.005|
|||Panel B: F|DI-heavy s|ector VS o|ther sectors||||
||FR<br>|Nckt <=me<br>|dian<br>|F<br>F<br>|RNckt > med<br>DI-heavy se<br>|ian:<br>ctor<br>|Difference|diff_se|
|Var|Obs.|Mean|SD.|Obs.|Mean|SD.|||
|Capital/Labor ratio|15795|0.058|0.794|16649|0.159|3.338|0.101***|0.027|
|lnL3|45468|3.053|1.371|40885|3.055|1.361|0.002|0.009|
|TFP|14341|-0.217|1.338|15187|-0.106|1.337|0.111***|0.016|
|lnLP|40866|9.354|1.509|35232|9.632|1.541|0.278***|0.011|
|Skilled workers’share|25344|0.699|0.294|21549|0.675|0.327|-0.024***|0.003|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels.

Capital/Labor ratio equals to K/L, and K is replacement costs of land and machine (million dollar), L is number of labors.

Skilled workers’ share is the ratio of skilled workers / (skilled workers + unskilled workers) In panel A, we define foreign-owned firms to be those with ownership being 10% and more.

In panel B, we delete FDI firms with foreign ownership being 10% and more.

28
Table 7. Baseline results: the determinants of female manager and of female entrepreneur

||Panel A: Witho|ut CountryFE<br>|Panel B: Control|for CountryFE<br>|
|---|---|---|---|---|
|Dependent Var.|**Female-Manager**<br>(1)|**Female**<br>**entrepreneur**<br>(2)|**Female-Manager**|**Female**<br>**entrepreneur**|
|**F_c**|0.492***|0.537***|||
||(0.084)|(0.093)|||
|**F_cj**|-0.087|-0.089|-0.023|-0.053|
||(0.073)|(0.074)|(0.057)|(0.059)|
|OwnLargest|0.001|-0.019***|-0.002|-0.019***|
||(0.007)|(0.007)|(0.007)|(0.007)|
|Small3|0.035***|0.041***|0.035***|0.041***|
||(0.005)|(0.004)|(0.005)|(0.004)|
|Large3|-0.004|-0.011**|-0.004|-0.012**|
||(0.006)|(0.005)|(0.006)|(0.005)|
|Age6_10|-0.001|0.001|-0.001|-0.002|
||(0.007)|(0.006)|(0.006)|(0.006)|
|Age10plus|-0.016**|-0.011**|-0.012**|-0.009*|
||(0.007)|(0.006)|(0.006)|(0.005)|
|Corruption|0.000|-0.011|0.036**|0.017|
||(0.013)|(0.013)|(0.015)|(0.012)|
|Overdraft facility|-0.011|-0.008|0.024|0.016|
||(0.014)|(0.012)|(0.018)|(0.014)|
|Trade Credit|-0.078***|-0.041**|-0.044**|-0.004|
||(0.022)|(0.020)|(0.018)|(0.015)|
|Inf. Competition|-0.006|0.007|0.003|0.007|
||(0.013)|(0.011)|(0.010)|(0.009)|
|Outage|-0.021|-0.017|0.019*|0.013|
||(0.014)|(0.012)|(0.011)|(0.009)|
|Web|0.040***|0.020|0.039***|0.014|
||(0.015)|(0.013)|(0.013)|(0.012)|
|Capacity Agglo.|-0.031|-0.047*|0.011|-0.022|
||(0.029)|(0.025)|(0.028)|(0.021)|
|Lngdppc|-0.015*|-0.015*|||
||(0.009)|(0.009)|||
|Education|0.053***|0.039***|||
||(0.013)|(0.012)|||
|Urbanization|0.000|0.000|||
||(0.000)|(0.000)|||
|Country FE|<br>No|<br>No|Yes|Yes|
|Industry, Year FE|Yes|Yes|Yes|Yes|
|Observations|47,993|45,596|50,472|47,702|
|Adjusted R<sup>2</sup>|0.044|0.055|0.074|0.086|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country-industry level in columns. OLS are used here. We delete FDI firms with 10% or more foreign ownership.

29
**Table 8. SMEs or medium-and-large firms**

|Dependent Var.||Female en|trepreneur||
|---|---|---|---|---|
||SM|Es|Medium and|large firms|
||(number of em<br>|ployees three<br>|(number of em<br>|ployees three<br>|
||years ago:|0 or fewer)|years ago:|above 50 )|
||(1)|(2)|(3)|(4)|
|F_c|0.624***||0.264**||
||(0.108)||(0.105)||
|F_cj|-0.051|-0.018|-0.122|-0.099|
||(0.085)|(0.072)|(0.080)|(0.062)|
|OwnLargest|-0.004|-0.002|-0.049***|-0.054***|
||(0.009)|(0.008)|(0.010)|(0.011)|
|Small3|0.032***|0.033***|||
||(0.004)|(0.004)|||
|Large3|||-0.004|-0.004|
||||(0.005)|(0.005)|
|Age6_10|0.001|-0.002|0.004|0.003|
||(0.007)|(0.007)|(0.012)|(0.012)|
|Age10plus|-0.011*|-0.009|0.000|0.003|
||(0.006)|(0.006)|(0.012)|(0.013)|
|Corruption|-0.012|0.011|-0.009|0.036**|
||(0.014)|(0.013)|(0.014)|(0.018)|
|Overdraft facility|-0.015|0.011|-0.000|0.007|
||(0.016)|(0.016)|(0.012)|(0.017)|
|Trade Credit|-0.047*|-0.011|-0.005|0.018|
||(0.024)|(0.018)|(0.021)|(0.021)|
|Inf. Competition|0.007|0.006|0.008|0.005|
||(0.013)|(0.010)|(0.014)|(0.015)|
|Outage|-0.024*|0.015|-0.012|-0.004|
||(0.014)|(0.010)|(0.012)|(0.014)|
|Web|0.019|0.006|0.019|0.021|
||(0.015)|(0.014)|(0.015)|(0.016)|
|Capacity Agglo.|-0.070**|-0.047*|0.011|0.018|
||(0.030)|(0.026)|(0.024)|(0.028)|
|Lngdppc|-0.014<br>||-0.015*<br>||
||(0.010)||(0.008)||
|Education|0.043***||0.008||
||(0.014)||(0.012)||
|Urbanization|0.000||-0.000||
||(0.000)||(0.000)||
|**Country FE**|**No**|**Yes**|**No**|**Yes**|
|Industry, Year FE|Yes|Yes|Yes|Yes|
|Observations|33,637|35,398|11,959|12,304|
|AdjustedR<sup>2</sup>|0.063|0.097|0.027|0.047|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country-industry level in columns. OLS are used here. Based on non-FDI firms, i.e. we delete FDI firms with 10% or more foreign ownership.

30
**Table 9. Service VS. manufacturing sample**

|Dependent Var.||Female en|trepreneur||
|---|---|---|---|---|
||Panel A: serv|ice sample|Panel B: manuf|acturing sample|
||(1)|(2)|(3)|(4)|
|**F_c**|0.699***||0.412***||
||(0.155)||(0.114)||
|**F_cj**|-0.074|0.206**|-0.087|-0.081|
||(0.144)|(0.094)|(0.081)|(0.068)|
|Firm controls|Yes|Yes|Yes|Yes|
|City BE controls|Yes|Yes|Yes|Yes|
|Country controls|Yes|No|Yes|No|
|Country FE|No|Yes|No|Yes|
|Industry, Year FE|Yes|Yes|Yes|Yes|
|Observations|20274|21684|25322|26018|
|Adjusted R<sup>2</sup>|0.049|0.086|0.061|0.084|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country-industry level in columns. OLS are used here. Based on non-FDI firms, i.e. we delete FDI firms with 10% or more foreign ownership.

31
**Table 10. Interactions: Barriers to female entrepreneurship**

|Dependent variable||Female entrepr<br>|eneur<br>||
|---|---|---|---|---|
||Barrier: time required<br>for females|to start a business<br>(in days)|Barrier: finan<br>account, female|cial institution<br>(% for age 15+)|
||(1)|(2)|(3)|(4)|
|F_cj|-0.027|-0.011|0.083|-0.029|
||(0.086)|(0.065)|(0.189)|(0.142)|
|F_cj * Barriers|-0.003**|-0.002*|-0.004|-0.001|
||(0.001)|(0.001)|(0.003)|(0.003)|
|F_c|0.240<br>(0.155)||0.584***<br>(0.183)||
|F_c * Barriers|0.012***<br>(0.004)||0.000<br>(0.004)||
|Barriers indicator|-0.001**<br>(0.000)||0.002***<br>(0.000)||
|Firm controls|Yes|Yes|Yes|Yes|
|City BE controls|Yes|Yes|Yes|Yes|
|Country controls|Yes|No|Yes|No|
|Country FE|No|Yes|No|Yes|
|Industry, Year FE|Yes|Yes|Yes|Yes|
|Observations<br>|45,596|46,150|43,219|43,579|
|AdjustedR<sup>2</sup>|0.056|0.084|0.063|0.085|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country-industry level in columns. OLS are used here. Based on non-FDI firms, i.e. we delete FDI firms with 10% or more foreign ownership.

32
**Table 11. Interactions: female empowerment**

|Dependent variable||Female en<br>|trepreneur<br>||
|---|---|---|---|---|
||Empowerme<br>Fem. labor part|nt indicator:<br>icipation rate|Empowerme<br>Fem. Primary c|nt indicator:<br>ompletion rate|
||(1)|(2)|(3)|(4)|
|F_cj|-0.628**|-0.571***|-0.218|0.152|
||(0.254)|(0.183)|(0.413)|(0.384)|
|F_cj * Empowerment indicator|0.010**|0.010***|0.001|-0.002|
||(0.005)|(0.003)|(0.005)|(0.004)|
|F_c|0.476||0.462||
||(0.299)||(0.359)||
|F_c * Empowerment indicator|-0.002<br>(0.006)||0.001<br>(0.004)||
|Empowerment indicator|0.002***<br>(0.000)||0.001***<br>(0.000)||
|Firm controls|Yes|Yes|Yes|Yes|
|City BE controls|Yes|Yes|Yes|Yes|
|Country controls|Yes|No|Yes|No|
|Country FE|No|Yes|No|Yes|
|Industry, Year FE|Yes|Yes|Yes|Yes|
|Observations<br>|45,182|45,736|44,069|44,623|
|Adjusted R<sup>2</sup>|0.060|0.085|0.057|0.086|

Notes: *, **, *** represent statistical significance at the 10, 5, and 1 percent levels. Heteroskedasticitycorrected standard errors clustered at the country-industry level in columns. OLS are used here. Based on non-FDI firms, i.e. we delete FDI firms with 10% or more foreign ownership.

33
# **Appendix.**

Table A1. The share of female corporate leadership **(female manager** ) of **domestic firms** in sample countries

|Country|Share|Country|Share|Country|Share|Country|Share|
|---|---|---|---|---|---|---|---|
|Yemen, Rep.|0.90%|Togo|11.65%|Zimbabwe|16.80%|Moldova|23.34%|
|Iraq|1.08%|Tanzania|11.67%|Ghana|16.87%|Bulgaria|23.66%|
|Jordan|1.78%|Senegal|11.72%|Cabo Verde|17.07%|Grenada|24.19%|
|Afghanistan|2.42%|Côte d'Ivoire|12.00%|Guyana|17.36%|Venezuela, RB|24.33%|
|West Bank<br>and Gaza|2.43%|Burkina Faso|12.58%|Belize|17.56%|St. Lucia|24.59%|
|Azerbaijan|3.61%|Albania|12.67%|Malawi|17.74%|Georgia|24.70%|
|Sudan|3.65%|Ethiopia|12.68%|Paraguay|17.80%|Kyrgyz Republic|24.82%|
|Lebanon|4.19%|Peru|12.69%|North<br>Macedonia|17.87%|Poland|25.06%|
|Pakistan|4.58%|Cameroon|12.79%|<br>Czech<br>Republic|18.04%|Dominica|25.22%|
|Mauritania|4.81%|Nigeria|12.88%|<br>Colombia|18.31%|Croatia|25.70%|
|Morocco|5.36%|Costa Rica|13.21%|Hungary|18.35%|Indonesia|25.76%|
|Bangladesh|5.44%|Suriname|13.29%|Congo,<br>Rep.|18.42%|Lesotho|26.09%|
|Egypt, Arab<br>Rep.|5.52%|Antigua and<br>Barbuda|13.64%|<br>Uganda|18.88%|Madagascar|26.73%|
|Chad|7.07%|Congo, Dem.<br>Rep.|13.70%|Uruguay|19.13%|Ukraine|27.12%|
|Israel|7.17%|<br>Djibouti|13.86%|Slovenia|19.20%|Nicaragua|27.72%|
|Tunisia|7.34%|Benin|13.93%|Romania|19.48%|Estonia|28.13%|
|Argentina|7.42%|Mali|14.23%|Jamaica|19.70%|Liberia|29.41%|
|India|7.57%|Armenia|14.51%|Lithuania|19.74%|Sierra Leone|30.00%|
|Turkey|7.77%|Trinidad and<br>|14.51%|Barbados|20.00%|Myanmar|30.51%|
|||Tobago||||||
|Sweden|8.73%|<br>El Salvador|14.58%|Russian<br>Federation|20.49%|Bahamas, The|30.91%|
|Chile|9.20%|Guatemala|14.65%|Botswana|20.74%|Philippines|31.61%|
|Eritrea|9.49%|Dominican<br>Republic|14.81%|Gabon|21.05%|Latvia|32.92%|
|Kenya|10.14%|<br>Mauritius|15.17%|Bhutan|21.43%|Belarus|34.59%|
|China|10.82%|Brazil|15.22%|Rwanda|21.43%|St. Vincent and the<br>Grenadines|35.77%|
|Tajikistan|10.87%|Bosnia and<br>Herzegovina|15.29%|Slovak<br>Republic|21.48%|<br>Namibia|35.85%|
|Uzbekistan|10.89%|<br>Central African<br>Republic|15.48%|<br>Panama|21.48%|Lao PDR|37.39%|
|Niger|11.30%|<br>St. Kitts and<br>Nevis|16.24%|Vietnam|22.61%|Mongolia|40.21%|
|Sri Lanka|11.43%|Burundi|16.54%|Bolivia|23.15%|||
|Nepal|11.52%|Angola|16.67%|Kazakhstan|23.26%|||
|Mexico|11.52%|Ecuador|16.67%|Honduras|23.31%|||

Note: Based on non-FDI firms, i.e. we delete FDI firms with foreign ownership being 10% or more.

34