metadata: authors: 0: author: Mohammad Amin
1: author: Gomez Caceres,Norma Janeth
count: World
docna: 0: docna: Does Financial Development Reduce Gender
            Disparity in Top Manager Positions in Manufacturing SMEs in
            Developing Countries ?
docty: Policy Research Working Paper
abstracts: cdata!: Women often face more hurdles than men
            in obtaining finance. This is especially so when credit
            supply is limited and financial markets are less developed.
            As a result, owners of firms may prefer men over women as
            top managers of their firms, widening the gender gap in top
            manager positions. This paper tests this idea using
            firm-level survey data for small and medium-size formal
            manufacturing enterprises in 47 developing countries. The
            results confirm a positive relationship between credit
            supply and the likelihood of having a woman versus a man as
            the top manager. This positive relationship is much stronger
            in industries that are more dependent on external sources of
            finance for technological reasons. It is also stronger in
            countries with poor coverage by credit bureaus and low
            competition between banks, which is consistent with
            &#x201c;statistical&#x201d; and
            &#x201c;taste-based&#x201d; discrimination against
            women borrowers. The main result is robust to several
            endogeneity checks, sample alterations, and alternative
            measures of credit supply and financial development.
display_title: Does Financial Development Reduce
            Gender Disparity in Top Manager Positions in Manufacturing
            SMEs in Developing Countries ?
listing_relative_url: /research/2024/02/34266428/financial-development-reduce-gender-disparity-top-manager-positions-manufacturing-smes-developing-countries
topicv3: Access to Finance for Women m1326289 1251,Gender and Employment m1326451 618,Women and Corporate Leadership m1504513 2899
publishtoextweb_dt: 2024-02-26T00:00:00Z
dois: 10.1596/1813-9450-10711
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 69
author_keys: 0: author_key: NA
1: author_key: 1157557
model_extractions: Policy Research Working Paper 10711 Does Financial Development Reduce Gender Disparity in Top Manager Positions in Manufacturing SMEs in Developing Countries?

_Mohammad Amin Norma Gomez_ Development Economics Global Indicators Group February 2024
Policy Research Working Paper 10711

# **Abstract**

Women often face more hurdles than men in obtaining finance. This is especially so when credit supply is limited and financial markets are less developed. As a result, owners of firms may prefer men over women as top managers of their firms, widening the gender gap in top manager positions. This paper tests this idea using firm-level survey data for small and medium-size formal manufacturing enterprises in 47 developing countries. The results confirm a positive relationship between credit supply and the likelihood of having a woman versus a man as the top manager.

This positive relationship is much stronger in industries that are more dependent on external sources of finance for technological reasons. It is also stronger in countries with poor coverage by credit bureaus and low competition between banks, which is consistent with “statistical” and “taste-based” discrimination against women borrowers. The main result is robust to several endogeneity checks, sample alterations, and alternative measures of credit supply and financial development.

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
# **Does Financial Development Reduce Gender Disparity in Top Manager Positions in Manufacturing SMEs in Developing Countries?**

**By: Mohammad Amin* and Norma Gomez****

_Keywords_ : Credit, gender, top managers, SMEs _JEL_ Codes: E50, G10, J13, J16

> * Corresponding author. Senior Economist, Enterprise Analysis Unit, DECEA, World Bank, Washington, DC. Email: <u>mamin@worldbank.org. ORCID: https://orcid.org/0000-0002-94513629</u>

** Economist, Enterprise Analysis Unit, DECEA, World Bank, Washington, DC. Email: <u>ngomez1@worldbank.org. ORCID: https://orcid.org/0000-0002-9934-4123</u>

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.

<mark>We thank the Enterprise Analysis Unit of the Development Economics Global Indicators Department of the World Bank Group for making the data available. We also thank Jorge Luis Rodriguez Meza for useful comments. All remaining errors are our own.</mark>
# **1. Introduction**

Women often face greater hurdles in obtaining finance than men. This is especially so when credit supply is limited and financial markets are not well developed. As a result, firm owners may prefer men over women as top managers of their firms. Alternatively put, easier credit availability increases the likelihood of a woman versus a man being the top manager in a firm and thereby narrows the gender gap in top management positions. The present paper empirically confirms this insight for a sample of manufacturing SMEs in 47 developing countries. The positive relationship between credit supply and the likelihood of having a woman top manager that we find is much stronger in industries that are more dependent on external sources of finance. It is also stronger where credit bureaus have poor coverage and competition between banks is low, which is consistent with women experiencing “statistical” and “taste-based” discrimination in the financial markets.

Access to finance is important because firms need cash up front and before sales are realized. Cash is needed up front to pay workers and suppliers (Chodorow-Reich 2014, Bacchetta et al. 2019), maintain inventories (Blinder and Maccini 1991, Deloof 2003, Aktas et al. 2015), advance credit to customers (Brennan et al. 1988, Aktas et al. 2015), and make long-term investments (Fazzari et al. 1988, Blalock et al. 2008, Levine and Warusawitharana 2021).

Ensuring a smooth flow of cash throughout the production cycle is an important task for firm managers. Women may be disadvantaged in conducting this task relative to men because of discrimination in the financial markets (Section 2 reviews the literature). This is especially likely when credit supply is limited. That is, when credit supply contracts and lenders prefer men over women borrowers, there is lesser credit left for women borrowers. There are similar examples of greater gender bias against women following a resource crunch. For example, Muravyev et al.

1
(2009) find that female-led firms are more likely to be financially constrained than male-led firms, and this gap is bigger in financially less developed countries. Also see Maccini and Yang (2009), Feeny et al. (2021), Jayachandran and Pande (2017), and Jayachandran and Kuziemko (2011) on how adverse shocks to household incomes increase the disparity between boys and girls in the allocation of nutritional and educational expenses. Regarding the mechanisms, several studies indicate the presence of “statistical” and/or “taste-based” discrimination by lenders against women borrowers (Section 2 reviews the literature). We explore these pathways below.

Our empirical results confirm a positive relationship between credit supply and the likelihood of having a woman top manager (figures 1 and 2). A one standard deviation increase in the bank credit to GDP ratio is associated with an increase in the odds of having a woman vs. man top manager by 0.35 to 0.48 log points. The corresponding increase in the probability of having a woman top manager is 4.4 to 4.8 percentage points against 18 percent women managers in the full sample. Consistent with our theoretical predictions, the positive relationship between the bank credit to GDP ratio and the likelihood of having a woman top manager is significantly higher in sectors that are more dependent on external sources of finance, in countries where it is difficult for women to signal their creditworthiness due to absence of credit bureaus, and in countries where competition in the banking industry is low.

We contribute to the literature in several ways. First, to the best of our knowledge, our paper is the first to explore the finance channel for explaining the gender gap in top management positions. Second, we uncover several heterogeneities in the credit supply and gender of the top manager relationship. The exercise enriches our understanding of the possible mechanisms at play. It also allows for targeted policy intervention. Third, we pay due attention to endogeneity concerns. We control for all time invariant country specific factors (country fixed effects), which are a major 2
source of omitted variable bias. Further, following the strategy of Rajan and Zingales (1998) for guarding against endogeneity concerns, we test for some theoretical predictions about how credit supply affects the choice of woman vs. man top manager. Fourth, we focus on developing countries, which is a novelty in the literature (see Section 2). Fifth, we use firm-level survey data which allows us to account for several potential omitted variables such as firm size and productivity, industry, and gender composition of workers and firm owners.

# **2. Conceptual framework and literature review**

# 2.1 _Gender gap in top management_

Gender gaps favoring men over women exist in most countries and in diverse areas like labor market outcomes, health, education, and political empowerment (see Klasen 2020, World Bank 2011). Likewise, the share of women among top managers of firms is also low when compared to their share in total population and in lower-level jobs. See Fernandez and Campero (2017), European Union (2016), Coric (2018), Peng and She (2020), Islam and Amin (2016), Islam et al. (2019), and Sekkat et al. (2015). For example, Coric (2018) for 45 countries in Europe and Islam and Amin (2016) for 73 developing countries report that only 26 percent and 19 percent of the top management positions, respectively, are held by women.

Direct evidence on the drivers of the gender gap in top management positions is limited. However, the broader literature on gender gaps offers several potential explanations. First, studies report that compared to men, women are more risk averse (see Croson and Gneezy 2009, Dohmen et al. 2011), and prefer less competitive environments (see Niederle and Vesterlund 2007, Gneezy et al. 2003) and more flexible work conditions (see Goldin 2014, Bertrand 2018). Thus, women may shy away from managerial work that is more risky, competitive, and requires a long and 3
inflexible work schedule. Second, greater absence from the labor market due to children and less education among women than men (Bertocchi and Bozzano 2020, Bertrand 2018) implies that fewer women than men have the necessary skills and experience to become top managers. Third, there is discrimination against women prospective managers stemming from cultural beliefs and norms about gender roles (see Gao et al. 2016, Bertrand and Hallock 2001, Gorman and Kmec 2009, Powell et al. 2002, Paris and Decker 2012).

# 2.2 _Access to finance and gender_

Several studies show that women and women-led firms face greater difficulty in obtaining finance than their men counterparts. Relative to men-led firms, women-led firms are more likely to report access to finance as a major or bigger obstacle (Asiedu et al. 2013), less likely to apply for a loan because of anticipated rejection (Moro et al. 2017, Cavalluzzo et al. 2002), face higher rejection rates on loan applications (Muravyev et al. 2009, Cavalluzzo et al. 2002, Belluci et al. 2010), less likely to have a loan (Chaudhuri et al. 2018, Aristei and Gallo 2016), pay higher interest rates and/or other borrowing costs (Mascia and Rossi 2017, Muravyev et al. 2009, Alesina et al. 2013), and face stricter collateral requirements (Belluci et al. 2010). To provide an example, Muravyev et al. (2009) consider firms that have a single majority shareholder who is also its manager in 34 countries in Central Asia and Europe. They find that the probability of receiving a bank loan is about 5 percent lower for women-managed firms than men-managed firms. The former also pay 0.5 percentage points higher interest on bank loans.

Poorer access to finance for women as highlighted in the above studies is not a forgone conclusion. Other studies show no significant gender gap in loan applications (Pham and Talavera 2018), lower likelihood of women-led firms being financially constrained (Wellalage and Locke 2017), and lower interest rates charged to women-owned firms than male-owned firms (Pham and 4
Talavera 2018). Also, see Asiedu et al. (2012) and Blanchflower et al. (2003). Thus, more empirical analysis is needed on the nexus between gender and finance.

# 2.3 _Industry dependence on external finance_

Our claim is that firms prefer men over women managers because the latter face greater difficulty in obtaining finance. Naturally, the preference for men over women managers will be stronger in industries that depend more on external finance. Rajan and Zingales (1998) estimate industries’ dependence on external finance arising from technological reasons, such as initial project scale, gestation period, cash harvest period, requirement for ongoing investment, and available growth opportunities. As Rajan and Zingales (1998) among others argue, showing that the impact of credit supply is stronger in industries more dependent on external finance can serve as a “smoking gun” in the debate on causality. Buccirossi et al. (2013) nicely summarize the point in estimating the impact of competition policy on the total factor productivity (TFP) of firms. They note that (page 1327): “We search for situations where we expect competition policy to have a differential effect on productivity as compared to other omitted factors or policies. If we were to observe this kind of behavior in the data, this would enhance our confidence that the estimated nexus between the quality of a competition policy regime and TFP growth can be interpreted in a causal way.” Likewise, if the positive relationship that we find between credit supply and the likelihood of having a woman top manager is driven by omitted factors, measurement errors, or reverse causality, then there is no reason for the stated relationship to be stronger in those industries that Rajan and Zingales (1998) identify as more dependent on external finance for technological reasons. We summarize this heterogeneity-based check against endogeneity concerns as follows: 5
_Hypothesis 1_ : The positive relationship between the bank credit to GDP ratio and the likelihood of having a woman vs. man top manager is much stronger (more positive) in industries that rely more on external sources of finance for technological reasons.

# 2.4 _Credit bureaus and competition between banks_

We explore two other sources of heterogeneity involving “statistical” and “taste-based” discrimination against women in the financial market. “Statistical” discrimination (Phelps 1972) occurs when lenders do not have perfect information about the borrowers’ creditworthiness. Thus, lenders may proxy a borrower’s creditworthiness by an easily observable demographic characteristic, such as gender, which is believed to be correlated with creditworthiness. Discrimination occurs if lenders believe that women-led firms have on average lower creditworthiness than men-led firms. Note that being a manifestation of information-related frictions, “statistical” discrimination is likely to be much less, and therefore the nexus between credit supply and gender of the top manager much weaker, when more information becomes available through for example, expansion of credit reporting agencies like credit bureaus in the country. Such a result is unlikely or less likely if the credit supply and gender of the top manager relationship that we uncover suffers from omitted variable bias, measurement errors, or reverse causality. Thus, in the spirit of Rajan and Zingales (1998) as detailed above, the proposed prediction can raise our confidence against endogeneity concerns. We summarize this heterogeneity-based test against endogeneity concerns as follows: 6
_Hypothesis 2_ : The positive relationship between the bank credit to GDP ratio and the likelihood of having a woman vs. man top manager is much weaker (less positive) when credit bureaus provide better coverage.

“Taste-based” discrimination (Becker 1957) arises because of prejudicial preferences of lenders about gender roles and norms. Such discrimination is characterized by lack of adherence to objective criteria in formulating a judgement about individuals. However, such discrimination is costly to lenders because it involves foregoing profitable lending opportunities. The higher is the cost of discrimination, the lesser is the severity of “taste-based” discrimination, and therefore the weaker is the nexus between credit supply and the gender of the top manager. One factor that raises the cost of “taste-based” discrimination is more competition between banks (see Becker 1957, Cooke et al. 2019, Blau and Kahn 2017). Thus, we predict the following: _Hypothesis 3_ : The positive relationship between the bank credit to GDP ratio and the likelihood of having a woman vs. man top manager is much weaker (less positive) when there is more competition in the banking industry.

This hypothesis is based on the specific ways in which credit supply impacts women’s chances of becoming top managers. It is unlikely or less likely to hold if the credit supply and gender of the top manager relationship suffers from omitted variable bias, measurement errors, or reverse causality. Thus, in the spirit of Rajan and Zingales (1998), _Hypothesis 3_ is a useful test against endogeneity concerns.

7
# **3. Data description and main variables**

# 3.1 _Data sources_

Our primary data source consists of firm-level survey data collected by the World Bank's Enterprise Surveys (WBES). The WBES focus on formal or registered private firms with five or more full-time permanent workers. The survey incorporates the entire manufacturing sector but excludes agriculture, extractive industries, and certain service industries like finance and health care. The WBES is nationally representative of the targeted private sector. All the surveys use a common sampling methodology, stratified random sampling, and a common questionnaire. The sampling stratification is done on firm size, industry, and location within the country. Sampling weights provided by the WBES are used in all the regressions.<sup>1</sup> Our baseline sample includes all SMEs in the manufacturing sector. Following the definition used by WBES for stratification purposes, SMEs are all firms with fewer than 100 fulltime permanent workers. Large firms (100 or more workers) have very different financing needs and access than SMEs and therefore they are analyzed separately. The focus on the manufacturing sector is to keep the sample relatively homogenous. Our empirical strategy is based on change over time in credit supply in a country. Thus, our sample includes all developing and emerging countries with multiple rounds of WBES administered across years. We note that our sample is a repeated cross-section and not a firm-panel. The WBES tracks firms over time but the observed attrition rates are quite high to generate sizeable panels. Our baseline sample includes 15,460 manufacturing SMEs in 47 countries. Table A1 in the Appendix provides the list of countries covered along with the survey years.

> 1 The firm-level WBES data that we use are publicly available at <u>https://www.enterprisesurveys.org/en/enterprisesurveys. All the other data sources that we use are also publicly</u> available.

8
We complement the WBES with other data sources. These include World Development Indicators (WDI), World Bank; Global Financial Development Database (GFDD), World Bank; Inter Parliamentary Union (IPU); Doing Business, World Bank; and Women Business and Law (WBL), World Bank.

# 3.2 _Estimation methodology_

Our baseline results are based on the following logistic equation: where the subscript _i_ denotes the firm, _j_ the industry (at the 2-digit ISIC Rev. 3.1 level), _k_ the country where the firm operates, and _t_ the year the WBES was administered in the country. p(.) is the probability of success. _Y_ is a dummy variable equal to 1 if the top manager of the firm is a woman and 0 otherwise. _X_ is bank credit to GDP ratio, our main explanatory variable that varies across countries and time. CFE, YFE, and IFE denote country fixed effects, year fixed effects, and industry fixed effects, respectively. _Firm controls_ and _Country controls_ include various controls for firm and country characteristics, and _u_ is the error term. The parameters in equation (1) are estimated by applying maximum likelihood estimation to the following transformed log odds equation: 9
The heterogeneities in _Hypotheses 1-3_ are analyzed using the following equation: Equation (3) differs from equation (2) because it includes the interaction term between the bank credit to GDP ratio ( _X_ ) and industry/country-year characteristics of interest captured by _Z_ . It also includes as controls interaction terms between the bank credit to GDP ratio and country and firm characteristics (defined below). Equation (3) is estimated using the maximum likelihood estimation method. All regressions use Huber-White robust standard errors clustered on the country-year. In the Appendix, a formal definition of all the variables is provided in table A2 and summary statistics are provided in table A3.

- 3.3 _Dependent variable_

10
The dependent variable is a dummy equal to 1 if the top manager of the firm is a woman and 0 otherwise ( _Woman top manager_ ). The data source is WBES. The mean value of the variable is about 0.18 or 18 percent and the standard deviation is 0.38.

# 3.4 _Main explanatory variable_

Our main explanatory variable is total domestic credit provided by banks to the private sector as a ratio of GDP ( _Bank credit_ ). The variable is lagged by 1 year from the date of WBES in the countryyear.<sup>2</sup> We focus on bank credit because it is one of the most important sources of credit for private SMEs in the developing world (see Berger and Udell 1998, Muravyev et al. 2009). In our sample, the mean value of _Bank credit_ equals 0.41 and the standard deviation is 0.18. The data source is WDI, World Bank.

For robustness, we show that our main result holds for alternative measures of credit availability and financial development (all lagged by 1 year). These include total credit by banks and non-bank financial intermediaries provided to the private sector as a ratio of GDP ( _Total credit to private sector_ ), log of number of ATMs per 100,000 adults, and log of number of bank branches per 100,000 adults. The data source for all these variables is WDI, World Bank. We also use total bank credit to the private sector as a ratio of total bank deposits ( _Bank credit to bank deposit ratio_ ) taken from GFDD, World Bank. This variable captures the quality of financial intermediation and therefore the availability of credit (see Beck et al. 2007, Dutta and Sobel 2018).

# 3.5 _Controls_

> 2 Average values of the variable are used where the average is computed over one year lagged values of the variable for the years the WBES round under consideration was administered in the country and for which data are available. Unless specified otherwise, the same holds for all the other country-year level variables.

11
We control for several firm and country characteristics to further raise our confidence against omitted variable bias problem. For this, we draw on the broader literature on gender gaps in the labor markets. We provide a brief description of the variables below and a more detailed description is provided in table A2 in the Appendix.

# 3.5.1 _Baseline_ c _ontrols_

We begin with controlling for all time invariant country specific factors such as social norms and culture and slow-moving institutions. We do so by using a set of dummy variables indicating the country where the firm operates ( _Country fixed effects_ ). Industry specific factors and global annual shocks to the dependent variable are accounted for by dummy variables indicating the industry (2digit ISIC Rev. 3.1) of the firm ( _Industry fixed effects_ ) and by dummy variables for the year the WBES round was administered ( _Year fixed effects_ ). The data source is WBES.

At the macro-level, gender gaps in the labor market are found to be correlated with income levels or GDP per capita (see Coric 2018, Gaddis and Klasen 2014), gender parity in education (see Klasen et al. 2021, Bussemakers et al. 2017, Islam and Amin 2016), and macroeconomic growth (see Klasen 2019). Country size and demographics may also matter. Smaller countries tend to rely more on international markets that are more competitive. This incentivizes cost cutting via less discrimination against women. Demographics matter as ceteris paribus, more women relative to men in the age group relevant for managers implies proportionately more women managers. We account for all these factors using the following controls (all lagged by 1 year): log of GDP per capita (PPP adjusted and at constant 2017 int’l dollars); ratio of gross primary enrollment rate of women to men ( _Primary enrollment gender parity index_ ); log of total population in the country; 12
growth rate of GDP per capita (percent, annual); and the percentage of women in the 35-64 years age group relevant for managers. The data source for all these variables is WDI, World Bank.

We control for several firm characteristics that may be correlated with the gender of the top manager and vary systematically with credit supply. Direct evidence on differences between women- and men-led firms is limited to a few firm characteristics. These include labor productivity and growth rate of sales and employment (see Allison et al. 2023, Islam et al. 2020), firm size (see Fairlie and Robb 2009), age of firm (see Blum et al. 1994, Islam et al. 2020), exposure to international markets (see Marques 2015, Haddoud et al. 2021), gender composition of the owners (Sekkat et al. 2015), and gender composition of the workforce (Hurley and Choudhary 2016). We go beyond and account for additional factors such as, quality of the regulatory and business environment that may have gendered effects on firm functioning (see Vershinina et al. 2022. Hanousek et al. 2019), and therefore may affect the choice of woman vs. man top manager.

Based on the discussion above, the baseline firm-level controls include the following (all taken from WBES): firm size proxied by log of number of workers employed at the firm 3 fiscal years ago (lagged) and a dummy variable equal to 1 if the firm is part of a larger establishment and 0 otherwise ( _Multi establishment firm_ ); log of age of the firm; labor productivity equal to log of the ratio of annual sales (in 2009 USD) to the number of workers three fiscal years ago; firm growth proxied by the average annual growth rate of employment (log difference) at the firm over the last 3 fiscal years; dummy variable equal to 1 if the firm purchased fixed assets during the last fiscal year and 0 otherwise; dummy variable equal to 1 if the firm has one or more women owners and 0 otherwise ( _Women owners_ ); share of women workers in the workforce at the firm at the end of the last fiscal year ( _Women workers_ ); exposure to international markets proxied by the share of firms’ sales in the last fiscal year that were made directly abroad ( _Exports_ ), proportion of firms’ 13
ownership that is with foreign individuals, entities, and organizations ( _Foreign ownership_ ), and a dummy variable equal to 1 if the firm has internationally recognized quality certification and 0 otherwise ( _Quality certification_ ); access to financial services captured by a dummy variable equal to 1 if the firm has overdraft facility and 0 otherwise<sup>3</sup> ; financial transparency proxied by a dummy variable equal to 1 if the firm had its financial statements audited externally in the last fiscal year and 0 otherwise ( _Firm audited_ ), and a dummy variable equal to 1 if the firm was registered with the relevant authority when it started operations ( _Registered when started)_ ; a set of dummy variables indicating the current legal status of the firm (see table A2 for details); quality of the business environment proxied by a dummy variable equal to 1 if obtaining licenses and permits is a major or bigger obstacle for the firm and 0 if it is a lesser obstacle, losses from power outages in the last fiscal year as a ratio of total sales in the last fiscal year, and a dummy variable equal to 1 if the firm was visited or inspected by tax officials in the last year and 0 otherwise ( _Visited by tax officials_ ).

# 3.5.2 _Robustness_ c _ontrols_

We use additional controls for robustness purposes. Some of these controls involve a noticeable loss in sample due to missing data. We first check if replacing gender parity in primary education with that in tertiary education (data source is WDI, World Bank) affects our main result. Next, we add country-year level controls. The data source is WDI or as stated in brackets. The controls include the percentage of seats in the lower house of the parliament that are held by women (taken from IPU); fertility rate; percentage of women in total population; cost of starting a business as a

> 3 Results are unchanged if we use whether the firm has a checking or savings account instead of an overdraft facility. Having a checking/savings account is included as robustness control (see section 3.4.2).

14
percentage of GNI (Doing Business, World Bank); and a dummy variable equal to 1 if the law prohibits discrimination in hiring practices based on gender (WBL, World Bank).

Additional firm-level controls taken from WBES include a dummy variable equal to 1 if the firm currently uses technology licensed from a foreign-owned firm and 0 otherwise; a dummy equal to 1 if the firm spent on R&D activity during the last fiscal year and 0 otherwise; a dummy variable equal to 1 if the firm has a checking and/or savings account and 0 otherwise; a dummy variable equal to 1 if the firm competes against unregistered or informal businesses and 0 otherwise; number of times either inspected or required to meet with tax officials during the last 12 months; proportion of the firm’s senior management time spent in dealing with government regulations in a typical week over the last 12 months ( _Time tax_ ); a dummy variable equal to 1 if the firm suffered losses due to crime during the last fiscal year and 0 otherwise; and a dummy variable equal to 1 if the firm reports that typically firms make bribe payments to government officials to “get things done” and 0 otherwise.

# 3.6 _Interaction variables_

For testing _Hypothesis 1_ , we use interaction term between bank credit and industries’ dependence on external finance ( _Financial dependence_ ) as reported in Rajan and Zingales (1998). For robustness, we also use the percentile ranks of _Financial dependence_ . For testing _Hypothesis 2_ , we use interaction term between bank credit and (one year lagged values of) a dummy variable equal to 1 if credit bureaus are present in the country-year and 0 otherwise. For robustness, we show that our results with (one year lagged values of) the percentage of the total population in the country-year that is covered by credit bureaus ( _Credit bureau coverage_ ). The data source for the credit bureau dummy and coverage is Doing Business, World Bank. For _Hypothesis 3_ , we use the 15
interaction term between bank credit and a proxy measure of the level of competition in the banking industry which equals the assets of the five largest banks in the country-year as a share of total commercial banking assets ( _5-bank asset concentration_ ). For robustness, we also report results using a similar index for the three largest banks (3 _-bank asset concentration_ ). Higher values of the concentration indices imply less competition in the banking industry. The data source is GFDD, World Bank.

In all the heterogeneity estimations, we include as controls interaction terms between bank credit and the following variables (defined above): log of GDP per capita, log of number of workers at the firm (firm size), log of age of the firm., and labor productivity of the firm (logs). These controls guard against the possibility that our main interaction term (as stated in Hypotheses 1-3) is spuriously picking up the differential effect of bank credit in countries with high vs. low GDP per capita, and firms of different size, age, and labor productivity.

# **4. Base regression results**

Base regression results are provided in table 1. Panel A in table 1 contains the estimated log odds ratios while Panel B contains the estimated marginal effects of the bank credit to GDP ratio.<sup>4</sup> Table 1 shows that regardless of the controls, the relationship between the bank credit to GDP ratio and the likelihood (log odds ratios and marginal effect) of a having a woman top manager is positive and statistically significant at the 1 percent level. Without any controls (except for country, year, and industry fixed effects), the odds of having a woman top manager increase by 2.0 log points due a unit increase in the bank credit to GDP ratio (column 1, Panel A). The corresponding increase with various baseline controls included (columns 2-5) is larger, equaling 2.7 log points for the final

> 4 Unless stated otherwise, all marginal effects throughout the paper are evaluated at the mean value of the various explanatory variables.

16
specification (column 5). The marginal effects reveal a similar pattern. That is, without any controls (except for country, year, and industry fixed effects), a unit increase in the bank credit to GDP ratio increases the probability of having a woman top manager by 26.4 percentage points (column 1, Panel B). This marginal effect increases but only slightly when the remaining controls are added to the specification, equaling 27.1 percentage points for the final baseline specification (column 5, Panel B). Alternatively, across the different specifications in table 1, a one standard deviation increase in the bank credit to GDP ratio is associated with an increase of 4.4 to 4.8 percentage points in the probability (marginal effect) of having a woman top manager. The corresponding increase for the final baseline specification is 4.8 percentage points. This is an economically large increase against the share of 18 percent of women managed firms in the sample.

Some of the controls are significantly correlated with the dependent variable. Focusing on log odds ratios (Panel A), the odds of having a woman top manager are significantly lower for large firms (number of workers) and in countries with higher primary education enrollment rate among women relative to men. The odds are significantly higher for firms that have proportionately more women workers, firms that have women owners, firms that face higher losses due to power outages, and in countries that have a higher proportion of women in the 35-64 years age group. Given the counter-intuitive result for primary education, we confirm that our main result for the bank credit to GDP ratio continues to hold, and slightly more strongly, if we do not control for primary education gender parity index or replace it with tertiary education gender parity index.<sup>5</sup>

> 5 That is, for the final baseline specification and without controlling for primary education gender parity index, the estimated coefficient value of the bank credit to GDP ratio (log odds ratio) equals 2.83 (significant at 1 percent level). The corresponding figure with controlling for primary education gender parity index is roughly the same, equaling 2.71 (column 5, Panel A, table 1). Results replacing the primary education gender parity index with the one for tertiary education are discussed in the section 5.1.

17
# **5. Robustness**

# 5.1 _Additional controls_

We check if the results discussed above hold if we replace gender parity in primary education with gender parity in tertiary education. The results are provided in column 1 in table A4 in the Appendix. We find that the relationship between the bank credit to GDP ratio and the chances (log odds and marginal effect) of having a woman top manager survives. It remains positive and significant (at the 1 percent level) and is quantitatively larger than in the baseline model (column 5, table 1).

Next, starting with the final baseline specification (column 5, table 1), we include the additional controls that were discussed above. Regression results are provided in columns 2-4 in table A4 in the Appendix. These results show that the positive relationship between the bank credit to GDP ratio and the chances of having a woman top manager survives. In fact, the relationship is quantitatively larger than in the baseline model. For instance, with all the additional controls included in the specification, the probability of having a woman top manager increases by 0.38 percentage points for each percentage point increase in the bank credit to GDP ratio (see column 4, Panel B, table A4). The corresponding increase in the final baseline specification was lower at 0.27 percentage points (see column 5, Panel B, table 1).

# 5.2 _Alternative measures of financial development_

Regression results using alternative measures of credit supply or financial development are provided in table 2. For brevity, the results are shown with no controls (except for country, industry, and year fixed effects) and with all the baseline controls. Columns 1 and 2 in table 2 18
contain the results for total credit to the private sector, columns 3 and 4 contain the results for bank credit to bank deposit ratio, columns 5 and 6 contain the results for number of ATMs per 100,000 adults, and columns 7 and 8 contain the results for number of bank branches per 100,000 adults. Our main result of a positive and statistically significant relationship between credit supply or financial development and the likelihood (odds ratio and marginal effect) of having a woman top manager holds for all the measures considered. The relationship is significant at close to the 5 percent level in one specification (p-value of 0.051, column 4) and at less than the 5 percent or 1 percent level in the remaining specifications. To provide an example, a one standard deviation increase in bank credit to bank deposit ratio is associated with an increase in the probability of having a woman top manager (marginal effect) by 3.9 percentage points with all the baseline controls included in the specification (column 4, table 2).

# 5.3 _Dropping one country at a time_

We checked that our main result is not driven by any single country. To this end, we ran the baseline regressions with and without the controls dropping one country at a time from the sample. The estimated coefficient value of the bank credit to GDP ratio remained positive and statistically significant at the 1 percent level regardless of which country was dropped. For example, for the final baseline specification, the estimated coefficient value of the bank credit to GDP ratio (log odds ratio) ranged between 2.07 (when the Slovak Republic was dropped) and 3.4 (when Slovenia was dropped).

# **6. Heterogeneity**

# 6.1 _Financial dependence of sectors_

19
Above, we argued that a stronger (more positive) relationship between financial development and the likelihood of having a woman top manager in sectors that are more dependent on external finance will bolster our confidence against endogeneity concerns with our main result ( _Hypothesis 1_ ). To test this, we repeat the baseline regressions after adding the interaction term between the bank credit to GDP ratio and the financial dependence of sectors on external finance in the specification. The results are provided in table 3. Panel A in the table contains the estimated log odds ratios while the mean marginal effect of the interaction term is provided in Panel B.

Focusing on the log odds ratio, the interaction term between the bank credit to GDP ratio and the dependence of sectors on external finance is positive and statistically significant at less than the 1 percent or 5 percent level. That is, the odds of having a woman top manager increases with the level of financial development in the country but much more so in sectors that are more dependent on external sources of finance. In fact, for sectors with very low dependence on external finance, there is no significant relationship between financial development and the odds of having a woman top manager. For instance, consider the final baseline specification (column 5 in table 3). For this specification, a one standard deviation increase in the bank credit to GDP ratio leads to an increase in the odds of having a woman top manager by 0.92 log points (significant at the 1 percent level) in sectors most dependent on external finance. In contrast, the increase is a mere 0.11 log points (insignificant at the 10 percent level) in sectors that are least dependent on external finance. Like the log odds ratios, the marginal effect of the interaction term is also positive, large, and statistically significant at the 1 percent level across all the specifications (see Panel B, table 3).

20
For robustness, we replace the absolute measure of sectors’ dependence on external finance with its percentile rank. The regression results are provided in table A5 in the Appendix. These are qualitatively like the ones discussed in the previous paragraph.

# 6.2 _Credit bureaus_

We argued above that a weaker (less positive) relationship between financial development and the likelihood of having a woman top manager in countries where credit bureaus are more prevalent will bolster our confidence against endogeneity concerns with our main result ( _Hypothesis 2_ ). To test this, in table 4, we provide the regression results for the interaction term between bank credit to GDP ratio and a dummy variable equal to 1 if credit bureaus are present in the country and 0 otherwise. Panel A in table 4 contains the estimated log odds ratios and Panel B contains the estimated marginal effects of the interaction term.

As evident from table 4 _Hypothesis 2_ is easily passed. The interaction term the between bank credit to GDP ratio and the presence of credit bureaus is negative and statistically significant at the 1 percent or 5 percent level. This holds for the estimated log odds ratio (Panel A) and the marginal effect (Panel B) of the interaction term. In other words, likelihood of having a woman top manager increases with financial development much more in countries with no credit bureaus than in countries with credit bureaus. We note that for all the baseline specifications, the impact of a given increase in bank credit to GDP ratio on the odds of having a woman top manager is positive and statistically significant (at the 5 percent level or less) in countries that have a credit bureau as well as in countries that do not have a credit bureau, but the latter impact is much smaller in magnitude. To get a sense of the magnitudes involved, consider the final baseline specification (column 5, table 4). For this specification, a one standard deviation increase in the bank credit to 21
GDP ratio increases the odds of having a woman top manager by 1.21 log points (significant at the 1 percent level) in countries with no credit bureaus. The corresponding increase in countries with credit bureaus is much smaller, equaling 0.36 log points (significant at the 1 percent level).

For robustness, we check if the results discussed in the previous paragraph hold if we replace the credit bureau dummy with the percentage of the total population in the country that is covered by credit bureaus. Regression results for the interaction term between the bank credit to GDP ratio and credit bureau coverage are provided in table A6 in the Appendix. The interaction term is negative (log odds ratios and marginal effects). The estimated log odds ratio for the interaction term is statistically significant at the 10 percent level without any controls (except for country, year, and industry fixed effects) and with the controls for GDP per capita and firm size (see columns 1 and 2, Panel A, table A6). It is significant at 1 percent level in the remaining specifications including the final baseline specification.<sup>6</sup>

# 6.3 _Bank concentration_

Our last heterogeneity check is for the nexus between financial development and the level of concentration in the banking industry ( _Hypothesis 3_ ). Regression results for the interaction term between the bank credit to GDP ratio and 5-bank asset concentration index are provided in table 5. The interaction term is positive and statistically significant at the 1 percent level in most specifications considered and at the 5 percent level in the remaining specifications. This holds for log odds ratios and marginal effect. Thus, _Hypothesis 3_ is easily passed. Briefly, financial

> 6 The average marginal effect of the interaction term is insignificant at the 10 percent level without any controls and with the control for GDP per capita and firm size (see columns 1 and 2, Panel B, table A6). It is significant at the 1 percent level in the remaining specifications including the final baseline specification (see columns 3-6, Panel B, table A6).

22
development improves women’s likelihood or chances of becoming top managers much more in countries with a heavier concentration of banking activity. Focusing on the log odds ratios, for all the specifications in table 5, a given increase in the bank credit to GDP ratio is associated with a statistically significant increase in the odds of having a woman top manager when bank concentration is above a critical threshold level, and no significant change (at the 10 percent level or less) at sufficiently low levels of bank concentration. To provide an example, consider the final baseline specification (column 5, table 5). For this specification, a one standard deviation increase in the bank credit to GDP ratio is associated with an increase in the likelihood of having a woman top manager by 0.93 log points (significant at the 1 percent level) when bank concentration is highest (100 percent). The corresponding change when bank concentration is at its lowest level in our sample (36.2 percent) is a decrease in the odds but only by a mere 0.128 points (insignificant at the 10 percent level or less).

For robustness, we repeat the regression exercise in the previous paragraph after replacing the 5-bank asset concentration index with the 3-bank asset concentration index. The results are provided in table A7 in the Appendix, and they are qualitatively like the ones discussed in the previous paragraph.

# 6.4 _Adding all interaction terms simultaneously_

Regression results with all the interaction terms discussed above included simultaneously are provided in table 6. The interaction terms are those between the bank credit to GDP ratio and the following variables: financial dependence of sectors, presence of credit bureaus, and the 5-bank asset concentration index. As evidence from table 6, all the three interaction terms maintain their 23
signs as above and are statistically significant. Thus, the interaction terms results discussed above are independent of each other.

# 6.5 _Falsification test_

Large firms tend to have better access to finance in part because there is more information available about their creditworthiness. As a result, the gender of the top manager is less important in securing finance for large firms when compared to SMEs. If such a result is observed in the data (falsification test), it will eliminate several potential omitted factors from affecting our results that have similar effects on SMEs and large firms.

Regression results for the large firm sample are provided in table A8 in the Appendix. The relationship between the bank credit to GDP ratio and the likelihood of having a woman top manager is much weaker for the large firm sample than for the SME sample above. In fact, for the large firm sample, the relationship is negative in most specifications and positive in only the final specification (column 5). Regardless, it is statistically insignificant (at the 10 percent level or less) in all the specifications considered. Thus, the falsification test is passed.

# **7. Conclusion**

Understanding the gender gap in top management positions is important for various reasons. Women in top positions may help reduce gender inequality by undermining beliefs about competence difference. They could improve work–life balance for women and reduce the gender wage gap at lower hierarchical levels. If innate talent is equally distributed between women and men, it follows that economic efficiency can be improved if women had the same odds as men to 24
reach top management positions (see Hsieh et al. 2019). Further, diversity in leadership may improve firm efficiency (see Bertrand 2018).

The existing literature attributes the low presence of women in top manager positions to factors such as less education and job experience among women, discrimination by firm owners and directors against women, time devoted to providing care in the family by women, and greater aversion to risk and competition among women compared to men. The present paper contributes to this literature by highlighting another factor, which is credit supply in the country. Our empirical results reveal that easier credit supply increases women’s chances of becoming top managers of private manufacturing SMEs in the developing world.

Our main result of a positive link between credit supply and the likelihood of a woman vs. man top manager is important to policy makers for several reasons. First, reducing prejudice and discrimination against women in top management positions may take several years or even generations. Our results suggest an alternative policy option, which is financial development. Second, our results suggest that different aspects of financial development should be considered in tandem with each other. That is, expanding credit supply is effective in reducing gender gaps at the top level but less so when credit bureaus are well-functioning and/or competition in the banking industry is high. Third, several countries have implemented policies to promote SMEs in part because SMEs encourage inclusive growth. Our results suggest some caution here as ceteris paribus, large firms are likely to have better gender parity in top management positions in countries at low levels of financial development than SMEs.

Our results offer several avenues for future researchers. We provide a few examples here. First, our results suggest that the broader business environment may be a factor driving gender gaps at the high end of the income and wage distribution. For instance, it is possible that women 25
may have weaker political connections, and poorer access government institutions, government programs, and physical infrastructure facilities. Thus, like credit supply, these factors may magnify the gender gap in top management positions. Future research can explore this issue. Second, the positive link between gender of the top manager and credit supply uncovered above suggests that findings in the existing studies that treat gender of the top manager as exogenous to the firm may suffer from omitted variable bias and a selection bias. The former bias may arise because the gender of the top manager is correlated with credit supply as shown above. The latter bias may exist because to compensate for poorer access to finance, women who become top managers are likely to be superior to men in other areas such as education, job experience, and innate talent. The implication of these biases remains to be properly analyzed. Third, to keep the sample relatively homogenous, we focused on manufacturing SMEs alone. Future research can extend the analysis to services industries such as retail, hospitality, and health care that are important providers of jobs for women. Fourth, data and space limitations did not allow us to explore the impact of the gender of the top manager on how firms structure their finances. For instance, are firms led by women more likely to use microfinance institutions and trade credit vs. bank finance than firms led by men? What about the choice between debt and equity? Last, apart from gender, there is evidence of racial disparities in obtaining finance. It will be interesting to extend the above analysis to racial disparity in top management positions.

26
27
# **References**

<mark>Aktas, Nihat, Ettore Croci, and Dimitris Petmezas (2015). “Is working capital management valueenhancing? Evidence from firm performance and investments.”</mark> _<mark>Journal of Corporate Finance</mark>_ <mark>30(Feb): 98-113.</mark> https://doi.org/10.1016/j.jcorpfin.2014.12.008 Alesina, Alberto F., Francesca Lotti, and Paolo Emilio Mistrulli (2013). “Do women pay more for credit? evidence from Italy.” _Journal of the European Economic Association_ , 11(suppl 1): 45–66.

<mark>Allison, Lee, Yu Liu, Samuele Murtinu, and Zuobao Wei (2023). “Gender and firm performance around the world: The roles of finance, technology and labor.”</mark> _<mark>Journal of Business Research</mark>_ <mark>, 154: 113322.</mark> Aristei, D. and M. Gallo (2016). “Does gender matter for firms’ access to credit? Evidence from international data.” _Finance Research Letters_ 18: 67–75.

<mark>Asiedu, Elizabeth, James A. Freeman, and Akwasi Nti-Addae (2012). "Access to credit by small businesses: How relevant are race, ethnicity, and gender?"</mark> _<mark>The American Economic Review</mark>_ <mark>102(3): 532-537</mark> _<mark>.</mark>_ <mark>Asiedu, Elizabeth, Kanyama, Isaac, Ndikumana, Leonce and Nti-Addae, Akwasi (2013). “Access to Credit by Firms in Sub-Saharan Africa: How Relevant Is Gender?”</mark> _<mark>The American Economic Review (Papers and Proceedings)</mark>_ <mark>103(3): 293-97.</mark> <mark>Bacchetta, P., K. Benhima, and C. Poilly (2019). “Corporate Cash and Employment”</mark> _<mark>American Economic Journal: Macroeconomics</mark>_ <mark>11(3): 30-66.</mark> <mark>Beck, Thorsten, Asli Demirgüç-Kunt, and Ross Levine (2007). “Finance, inequality and the poor.”</mark> _<mark>Journal of economic growth</mark>_ <mark>12: 27-49.</mark> Becker, Gary S. (1957). **The Economics of Discrimination.** Chicago: University of Chicago Press.

Bellucci, Andrea, Alexander Borisov, and Alberto Zazzaro (2010). “Does gender matter in bank– firm relationships? Evidence from small business lending.” _Journal of Banking & Finance_ , 34(12): 2968–2984.

Berger, A. N. and G. F. Udell (1998). “The economics of small business finance: The roles of private equity and debt markets in the financial growth cycle.” _Journal of Banking and Finance_ 22(6–8): 613–673.

Bertocchi, G., and M. Bozzano (2020). “Gender gaps in education”, in Klaus F Zimmermann (ed.), **Handbook of Labor, Human Resources and Population Economics** , 1-31. Available at <u>https://link.springer.com/content/pdf/10.1007/978-3-319-57365-6_26-1?pdf=chapter%20toc</u> <mark>Bertrand, Marianne (2018). “Coase Lecture – The Glass Ceiling”</mark> _<mark>Economica</mark>_ <mark>, 85(338): 205-231.</mark> 28
<mark>Bertrand, Marianne, and Kevin F. Hallock (2001). "The gender gap in top corporate jobs."</mark> _<mark>ILR Review</mark>_ <mark>55 (1): 3-21.</mark> Blalock, Garrick, Paul J. Gertler, David I. Levine (2008). “Financial constraints on investment in an emerging market crisis” _Journal of Monetary Economics_ , 55(3): 568-591, <mark>Blanchflower, David, Levine, Phillip and Zimmerman, David (2003). “Discrimination in the Small-Business Credit Market”,</mark> _<mark>The Review of Economics and Statistics</mark>_ <mark>, 85(4): 930-943.</mark> Blau, F.D., and L. M. Kahn (2017). “The Gender Wage Gap: Extent, Trends, and Explanations,” _Journal of Economic Literature_ , 55(3): 789–865.

Blinder, A. S. and L. J. Maccini (1991). “The Resurgence of Inventory Research: What Have We Learned?” _Journal of Economic Surveys_ 5(4): 291–328.

Blum, T. C., D. L. Fields, and J. S. Goodman (1994). “Organizational-level determinants of women in management.” _The Academy of Management Journal_ , 37(2) :241–268.

<mark>Brennan, M., V. Maksimovic, and J. Zechner (1988). “Vendor Financing,”</mark> _<mark>Journal of Finance</mark>_ <mark>43(5): 1127-1141.</mark> <mark>Buccirossi, P., L. Ciari, T. Duso, G. Spagnolo, and C. Vitale (2013). Competition Policy and Productivity Growth: An Empirical Assessment,</mark> _<mark>The Review of Economics and Statistics</mark>_ <mark>, 95(4): 1324-1336. https://doi.org/10.1162/REST_a_00304</mark> <mark>Bussemakers, Carlijn, Kars van Oosterhout, Gerbert Kraaykamp, and Niels Spierings (2017). Women’s worldwide education–employment connection: A multilevel analysis of the moderating impact of economic, political, and cultural contexts.</mark> _<mark>World Development</mark>_ <mark>99: 28-41.</mark> <mark>Cavalluzzo, Ken S., Linda C. Cavalluzzo, and John D. Wolken (2002). “Competition, Small Business Financing, and Discrimination: Evidence from a New Survey.”</mark> _<mark>The Journal of Business</mark>_ <mark>75(4): 641–79.</mark> <u><mark>https://doi.org/10.1086/341638.</mark></u> Chaudhuri, K., S. Sasidharan, and R. S. N. Raj (2018). “Gender, small firm ownership, and credit access: Some insights from India.” _Small Business Economics_ 54: 1165-1181. https:// doi.org/10.1007/s11187-018-0124-3.

Chodorow-Reich, G. (2014). “The Employment Effects of Credit Market Disruptions: Firm-level Evidence from the 2008–09 Financial Crisis.” _Quarterly Journal of Economics_ 129(1): 1–59.

Cooke, Dudley, Ana P. Fernandes, Priscila Ferreira (2019), “Product Market Competition and Gender Discrimination.” _Journal of Economic Behavior & Organization_ 157(Jan): 496-522.

<mark>Coric, Bruno (2018). “The glass ceiling puzzle, legal institutions, and the shadow economy.”</mark> _<mark>Feminist Economics</mark>_ <mark>24(4): 56-82. DOI: 10.1080/13545701.2018.1456672</mark> 29
Croson, R. and Gneezy, U. (2009). “Gender differences in preferences.” _Journal of Economic Literature_ 47(2): 448–74.

Deloof, M. (2003). “Does Working Capital Management Affect Profitability of Belgian Firms?” _Journal of Business Finance and Accounting_ 30(3-4): 573–588.

Dohmen, T., Falk, A., Huffman, D., Sunde, U., Schupp, J. and Wagner, G. G. (2011). “Individual risk attitudes: measurement, determinants, and behavioral consequences.” _Journal of the European Economic Association_ 9(3): 522–50.

<mark>Dutta, Nabamita, and Russell S. Sobel (2018). “Entrepreneurship and human capital: The role of financial development.”</mark> _<mark>International Review of Economics & Finance</mark>_ <mark>57: 319-332.</mark> European Union (2016). “Gender balance on corporate boards: Europe is cracking the glass ceiling.” _Directorate-General for Justice and Consumers_ , Fact sheet, July.

<mark>Fairlie, Robert W., and Alicia M. Robb (2009). “Gender differences in business performance: evidence from the Characteristics of Business Owners survey.”</mark> _<mark>Small Business Economics</mark>_ <mark>33: 375-395.</mark> <mark>Fazzari, Steven M., R. Glenn Hubbard, Bruce C. Petersen, Alan S. Blinder, and James M. Poterba (1988). “Financing Constraints and Corporate Investment.”</mark> _<mark>Brookings Papers on Economic Activity</mark>_ <mark>1: 141–206.</mark> <u><mark>https://doi.org/10.2307/2534426.</mark></u> <mark>Feeny, Simon, Ankita Mishra, Trong-Anh Trinh, Longfeng Ye, and Anna Zhu (2021). “Early-Life exposure to rainfall shocks and gender gaps in employment: Findings from Vietnam.”</mark> _<mark>Journal of Economic Behavior & Organization</mark>_ <mark>183: 533-554</mark> <mark>Fernandez, Roberto M. and Santiago Campero (2017). “Gender Sorting and the Glass Ceiling in High-Tech Firms”</mark> _<mark>ILR Review</mark>_ <mark>70(1): 73-104.</mark> <mark>Gaddis, I., and S. Klasen (2014). Economic development, structural change, and women’s labor force participation.</mark> _<mark>Journal of Population Economics</mark>_ <mark>27: 639–681.</mark> <mark>Gao, Huasheng, Yaheng Lin, and Yujing Ma (2016). “Sex Discrimination and Female Top Managers: Evidence from China”</mark> _<mark>Journal of Business Ethics</mark>_ <mark>138(4): 683-702.</mark> Gneezy, U., M. Niederle, and A. Rustichini (2003). “Performance in competitive environments: gender differences.” _Quarterly Journal of Economics_ , 118(3): 1049–74.

Goldin, C. (2014). “A grand gender convergence: its last chapter.” _<mark>The A</mark> merican Economic Review_ 104(4): 1091–119.

<mark>Gorman, Elizabeth H., and Julie A. Kmec (2009). “Hierarchical Rank and Women’s Organizational Mobility: Glass Ceilings in Corporate Law Firms.”</mark> _<mark>American Journal of Sociology</mark>_ <mark>114(5): 1428–74.</mark> <u><mark>https://doi.org/10.1086/595950.</mark></u> 30
Haddoud, M. Y., A. K. E. Onjewu, W. Nowiński, and P. Jones (2021). “The determinants of SMEs’ export entry: A systematic review of the literature.” _Journal of Business Research_ 125: 262–278.

<mark>Hanousek, Jan, Anastasiya Shamshur, and Jiri Tresl (2019). “Firm efficiency, foreign ownership and CEO gender in corrupt environments.”</mark> _<mark>Journal of Corporate Finance</mark>_ <mark>59: 344-360.</mark> <mark>Hsieh, Chang‐Tai, Erik Hurst, Charles Jones, and Peter J. Klenow (2019), The allocation of talent and U.S. economic growth.”</mark> _<mark>Econometrica</mark>_ <mark>87(5): 1439-1474.</mark> <mark>Hurley, D. and A. Choudhary (2016). “Factors influencing attainment of CEO position for women.”</mark> _<mark>G</mark> ender in Management_ <mark>31(4): 250-265. https://doi.org/10.1108/GM-01-2016-0004</mark> <mark>Islam, Asif and Mohammad Amin (2016). “Women managers and the gender-based gap in access to education: Evidence from firm-level data in developing countries.”</mark> _<mark>Feminist Economics</mark>_ <mark>22(3): 127-153. DOI: 10.1080/13545701.2015.1081705</mark> Islam, Asif, Isis Gaddis, Amparo Palacios López, and Mohammad Amin (2020). “The Labor Productivity Gap between Formal Businesses Run by Women and Men.” _Feminist Economics_ 26(4): 228—258.

<mark>Islam, Asif, Silvia Muzi, and Mohammad Amin (2019). “Unequal Laws and the Disempowerment of Women in the Labour Market: Evidence from Firm-Level Data.”</mark> _<mark>The Journal of Development Studies</mark>_ <mark>55(5): 822-844. DOI: 10.1080/00220388.2018.1487055</mark> Jayachandran, Seema, and Ilyana Kuziemko (2011). “Why do mothers breastfeed girls less than boys? Evidence and implications for child health in India.” _Quarterly Journal of Economics_ 26(3): 1485–1538.

Jayachandran, Seema and Rohini Pande (2017). “Why are Indian children so short? The role of birth order and son preference.” _American Economic Review_ 107 (9): 2600–2629.

<mark>Klasen, S. (2020). “From ‘MeToo’ to Boko Haram: A survey of levels and trends of gender inequality in the World.”</mark> _<mark>World Development</mark>_ <mark>128: 1-10.</mark> <mark>Klasen, Stephan (2019). What explains uneven female labor force participation levels and trends in developing countries?</mark> _<mark>The World Bank Research Observer</mark>_ <mark>34(2): 161–197.</mark> <mark>Klasen, Stephan, TU THI NGOC Le, Janneke Pieters, and Manuel Santos Silva (2021). What drives female labour force participation? Comparable micro-level evidence from eight developing and emerging economies.</mark> _<mark>The Journal of Development Studies</mark>_ <mark>57(3): 417-442.</mark> <mark>Levine, O. and M. Warusawitharana (2021). “Finance and productivity growth: Firm-level evidence.”</mark> _<mark>Journal of Monetary Economics</mark>_ <mark>117: 91–107.</mark> 31
<mark>Maccini, Sharon, and Dean Yang (2009). “Under the weather: Health, schooling, and economic consequences of early-life rainfall.”</mark> _<mark>The American Economic Review</mark>_ <mark>99(3): 1006-1026.</mark> Marques, H. (2015). “Does the gender of top managers and owners matter for firm exports?” _Feminist Economics_ 21(4): 89–117.

<mark>Mascia, Danilo V. and Stefania P.S. Rossi (2017). “Is there a gender effect on the cost of bank financing?”</mark> _<mark>Journal of Financial Stability</mark>_ <mark>31: 136-153.</mark> <mark>Moro, Andrea, Tomasz Piotr Wisniewski, and Guido Massimiliano Mantovani, (2017). “Does a manager's gender matter when accessing credit?” Evidence from European data.”</mark> _<mark>Journal of Banking & Finance</mark>_ <mark>80: 119-134.</mark> <mark>Muravyev, Alexander, Oleksandr Talavera, and Dorothea Schäfer (2009). Entrepreneurs' gender and financial constraints: Evidence from international data.”</mark> _<mark>Journal of Comparative Economics</mark>_ <mark>37(2): 270-286.</mark> Niederle, M. and L. Vesterlund (2007). “Do women shy away from competition? Do men compete too much?” _Quarterly Journal of Economics_ 122(3): 1067–101.

Paris, Lori D. and Diane L. Decker (2012). “Sex Role Stereotypes: Does Business Education Make a Difference?” _Gender in Management: An International Journal_ 27(1): 36–50.

Pham, T., and O. Talavera (2018). “Discrimination, social capital, and financial constraints: The case of Viet Nam.” _World Development_ 102: 228–242.

Phelps, Edmund S. (1972). “The statistical theory of racism and sexism.” _The American Economic Review_ 62(4): 659–61.

Powell, Gary N., D. Anthony Butterfield, and Jane D. Parent (2002). “Gender and managerial stereotypes: Have the times changed?” _Journal of Management_ 28(2): 177–93.

<mark>Peng, Congmin and Po-Wen She (2020). “Are women less likely to be managers in the UK labour market?”</mark> _<mark>Economic Modelling</mark>_ <mark>85: 317-324.</mark> Rajan, R. and L. Zingales (1998). “Financial dependence and growth.” _<mark>The A</mark> merican Economic Review_ 88(3): 559–586.

<mark>Sekkat, Khalid, Ariane Szafarz, and Ilan Tojerow (2015). “</mark> Women at the Top in Developing Countries: Evidence from Firm-Level Data” _IZA Discussion Papers_ <mark>9537, Institute of Labor Economics (IZA).</mark> <mark>Vershinina, Natalia, Gideon Markman, Liang Han, Peter Rodgers, John Kitching, Nigar Hashimzade, and Rowena Barrett (2022). “Gendered regulations and SME performance in transition economies.”</mark> _<mark>Small Business Economics</mark>_ <mark>58(2): 1113-1130.</mark> 32
<mark>Wellalage, Nirosha and Stuart Locke (2017). “Access to credit by SMEs in South Asia: Do women entrepreneurs face discrimination.”</mark> _<mark>Research in International Business and Finance</mark>_ <mark>, 41 (October): 336-346</mark> <mark>World Bank (2011).</mark> **<mark>World development report 2012: Gender equality and development</mark>** <mark>.</mark> _<mark>The World Bank</mark>_ <mark>.</mark> 33
# **Figure 1: Share of women-managed firms is higher in countries that have higher bank**

# **credit to GDP ratio**

<!-- Start of picture text --> 19.2%<br>18.4%<br>14.7%<br>Bottom one-third Intermediate one-third Highest one-third<br>Quantiles of Bank credit to GDP ratio (at the country-year level)<br>years)<br>Percentage of firms that have a woman top manager (average over all country-<br><!-- End of picture text --> 34
**Figure 2: Proportion of women-managed firms increases with bank credit to GDP ratio**

<!-- Start of picture text -->
-.2 -.1 0 .1 .2<br>Bank credit to GDP ratio (residuals)<br>.1<br>.05<br>0<br>-.05<br>Proportion of women-managed firms (residuals)<br>-.1<br><!-- End of picture text -->

Note: The figure is a partial scatter plot obtained of country-year level variables obtained after controlling for country dummies. The line of fit shown is statistically significant at the 1 percent level.

35
|**Ta**|**ble 1: Base r**|**egression re**|**sults**|||
|---|---|---|---|---|---|
|Dependent variable: Woman top|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|
|manager Y:1 N:0||||||
||**Panel A: L**|**og odds ratio**|**s**|||
|Bank credit (ratio of GDP)|2.007***|2.027***|2.680***|2.480***|2.712***|
||(0.352)|(0.401)|(0.598)|(0.646)|(0.636)|
|GDP per capita (logs)||-0.181|-0.739|-0.499|-1.259|
|||(0.716)|(0.998)|(0.971)|(1.045)|
|Number of workers (logs)||-0.208***|-0.229***|-0.193***|-0.189***|
|||(0.048)|(0.062)|(0.062)|(0.062)|
|Age of firm (logs)|||-0.084|-0.103|-0.110|
||||(0.108)|(0.112)|(0.113)|
|Labor productivity (logs)|||-0.012|0.011|-0.003|
||||(0.046)|(0.048)|(0.045)|
|Women owners Y:1 N:0|||2.564***|2.664***|2.667***|
||||(0.136)|(0.150)|(0.151)|
|Women workers (proportion)|||1.492***|1.497***|1.532***|
||||(0.209)|(0.206)|(0.208)|
|Exports (direct, share of sales)|||-0.121|-0.121|-0.130|
||||(0.246)|(0.242)|(0.241)|
|Foreign ownership|||<br>0.280|<br>0.318|<br>0.330|
||||(0.301)|(0.303)|(0.303)|
|Quality certification Y:1 N:0|||0.095|0.119|0.118|
||||(0.139)|(0.138)|(0.137)|
|Overdraft Y:1 N:0||||-0.096|-0.103|
|||||(0.128)|(0.128)|
|Multi establishment firm Y:1 N:0||||0.112|0.075|
|||||(0.171)|(0.174)|
|Obtaining license is a major||||0.066|0.070|
|obstacle Y:1 N:0||||(0.185)|(0.186)|
|Losses from power outages||||1.528*|1.653**|
|||||(0.793)|(0.772)|
|Firm audited Y:1 N:0||||-0.082|-0.085|
|||||(0.115)|(0.116)|
|Registered when started Y:1 N:0||||0.048|0.038|
|||||(0.208)|(0.208)|
|Legal form fixed effects||||Yes|Yes|
|<br>Visited by tax officials Y:1 N:0||||-0.039|-0.036|
|||||(0.106)|(0.106)|
|Employment growth rate (per||||-0.248|-0.244|
|annum, log difference)||||(0.301)|(0.300)|

36
|Bought assets last year Y:1 N:0||||0.127|0.128|
|---|---|---|---|---|---|
|||||(0.121)|(0.121)|
|GDP per capita growth rate (%,<br>annual)|||||0.003<br>(0.030)|
|Primary enrollment gender parity|||||-0.146**|
|index|||||(0.070)|
|% of women in 35-64 years age|||||0.540***|
|group|||||(0.195)|
|Total population (logs)|||||-2.230<br>(1.390)|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|
|Number of observations|15,460|15,460|15,460|15,460|15,460|
||**Panel B: M**<br>|**arginal effe**<br>|**ct**<br>|||
|Bank credit (ratio of GDP)|0.264***|0.265***|0.272***|0.248***|0.271***|
||(0.046)|(0.052)|(0.059)|(0.063)|(0.062)|
|Controls(as above)|Yes|Yes|Yes|Yes|Yes|
|All coefficient values are log odds<br>standard errors clustered on countr|ratios obtaine<br>y in brackets.|d from logit<br>*** (1%), *|estimation.<br>* (5%), * (1|Huber-White<br>0%).|robust|

37
||**Table 2: Altern**<br>**Total credit to private**<br>**sector**|**ative measures of financia**<br>**Bank credit to bank**<br>**deposit ratio**|**l development**<br>**ATMs per 100,000**<br>**adults**|**Bank br**<br>**100,0**|**anches per**<br>**00 adults**|
|---|---|---|---|---|---|
|Dependent variable:|**(1)**<br>**(2)**|**(3)**<br>**(4)**|**(5)**<br>**(6)**|**(7)**|**(8)**|
|Woman top manager||||||
|Y:1 N:0||||||
|||**Panel A: Log odds ratios**||||
|Total credit to private|1.940***<br>2.539***|||||
|sector (ratio of GDP)|(0.350)<br>(0.679)|||||
|Bank credit to bank||0.509***<br>0.462*||||
|deposit ratio||(0.165)<br>(0.237)||||
|<br>ATMs per 100,000||<br>|0.391***<br>0.620***|||
|adults (logs)|||(0.089)<br>(0.187)|||
|Bank branches per||||0.522***|0.547**|
|<br>100,000 adults (logs)||||(0.156)|(0.245)|
|GDP per capita (logs)|-0.429|-0.120|-0.877||-0.347|
||(1.039)|(1.019)|(0.979)||(1.004)|
|Number of workers|-0.185***|-0.182***|-0.181***||-0.186***|
|(logs)|(0.062)|(0.062)|(0.063)||(0.063)|
|Age of firm (logs)|-0.098|-0.111|-0.113||-0.108|
||(0.113)|(0.114)|(0.117)||(0.114)|
|Labor productivity|-0.005|-0.004|-0.002||-0.005|
|(logs)|(0.045)|(0.045)|(0.047)||(0.045)|
|Women owners Y:1|2.645***|2.654***|2.629***||2.655***|
|N:0|(0.153)|(0.149)|(0.149)||(0.150)|
|Women workers|1.524***|1.548***|1.618***||1.548***|
|(proportion)|(0.209)|(0.208)|(0.209)||(0.209)|
|<br>Exports (direct, share|<br>-0.091|<br>-0.125|<br>-0.098||<br>-0.120|
|of sales)|(0.240)|(0.241)|(0.241)||(0.242)|
|Foreign ownership|0.314|0.317|0.261||0.314|
||(0.303)|(0.306)|(0.304)||(0.305)|
|Quality certification|<br>0.095|<br>0.120|<br>0.100||<br>0.122|
|Y:1 N:0|(0.138)|(0.137)|(0.138)||(0.138)|
|Overdraft Y:1 N:0|-0.091|-0.110|-0.095||-0.108|
||(0.129)|(0.127)|(0.127)||(0.128)|
|Multi establishment|<br>0.075|<br>0.087|<br>0.116||<br>0.079|
|firm Y:1 N:0|(0.176)|(0.176)|(0.177)||(0.176)|
|Obtaining license is a|0.100|0.070|0.088||0.069|
|<br>major obstacle Y:1 N:0|(0.187)|(0.186)|(0.188)||(0.186)|
|<br>Losses from power|<br>1.593**|<br>1.664**|<br>1.018||<br>1.631**|
|outages|(0.779)|(0.773)|(0.781)||(0.775)|
|Firm audited Y:1 N:0|-0.082|-0.085|-0.054||-0.080|
||(0.117)|(0.115)|(0.114)||(0.115)|

38
|Registered when started||0.040||0.047||0.003||0.039|
|---|---|---|---|---|---|---|---|---|
|Y:1 N:0||(0.209)||(0.209)||(0.206)||(0.208)|
|Legal form fixed<br>||Yes||Yes||Yes||Yes|
|effects|||||||||
|Visited by tax officials||-0.014||-0.031||-0.048||-0.040|
|Y:1 N:0||(0.105)||(0.106)||(0.109)||(0.106)|
|Employment growth||-0.216||-0.257||-0.241||-0.251|
|rate (per annum, log||(0.304)||(0.298)||(0.304)||(0.298)|
|difference)|||||||||
|Bought assets last year||0.131||0.116||0.115||0.117|
|Y:1 N:0||(0.124)||(0.121)||(0.123)||(0.121)|
|GDP per capita growth||0.030||-0.007||0.004||-0.021|
|rate (%, annual)||(0.030)||(0.030)||(0.031)||(0.032)|
|Primary enrollment||-0.179**||-0.193**||-0.173**||-0.175**|
|gender parity index||(0.070)||(0.081)||(0.076)||(0.080)|
|% of women in 35-64||0.335*||0.627***||0.436**||0.526**|
|years age group||(0.188)||(0.209)||(0.214)||(0.229)|
|Total population (logs)||-1.763||-0.511||-1.244||-1.283|
|||(1.298)||(1.349)||(1.448)||(1.486)|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of|15,132|15,132|15,460|15,460|15,219|15,219|15,460|15,460|
|observations|||||||||
||||**Panel B: Ma**|**rginal effect**|||||
||**Total cred**<br>**sec**|**it to private**<br>**tor**|**Bank cre**<br>**depos**|**dit to bank**<br>**it ratio**|**ATMs p**<br>**ad**|**er 100,000**<br>**ults**|**Bank br**<br>**100,00**|**anches per**<br>**0 adults**|
|Financial development|0.255***|0.254***|0.067***|0.046*|0.051***|0.062***|0.069***|0.054**|
|measure as above|(0.045)|(0.067)|(0.022)|(0.024)|(0.011)|(0.018)|(0.020)|(0.024)|
|Controls(as above)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

All coefficient values are log odds ratios obtained from logit estimation. Huber-White robust standard errors clustered on country in brackets. *** (1%), ** (5%), * (10%).

39
||**Table 3: Fina**|**ncial depend**|**ence of secto**|**rs**|||
|---|---|---|---|---|---|---|
|Dependent variable: Woman top<br>manager Y:1 N:0|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Bank credit (ratio of|3.030***|3.106***|2.971**|2.682**|2.725**|2.806**|
|GDP)*Financial dependence|(1.080)|(1.097)|(1.275)|(1.310)|(1.310)|(1.303)|
|Bank credit (ratio of GDP)|1.155**|1.098**|1.794**|1.707**|1.853**|24.394*|
||(0.487)|(0.537)|(0.741)|(0.792)|(0.797)|(13.903)|
|Financial dependence|0.044|0.041|0.339|0.478|0.498|0.459|
||(0.495)|(0.504)|(0.689)|(0.718)|(0.729)|(0.719)|
|GDP per capita (logs)||0.168|-0.454|-0.262|-1.131|-1.300|
|||(0.751)|(1.043)|(1.021)|(1.069)|(1.052)|
|Number of workers (logs)||-0.208***|-0.228***|-0.194***|-0.189***|-0.000|
|||(0.049)|(0.062)|(0.061)|(0.061)|(0.153)|
|Age of firm (logs)|||-0.096|-0.124|-0.131|-0.327|
||||(0.110)|(0.115)|(0.116)|(0.307)|
|Labor productivity (logs)|||-0.006|0.019|0.004|0.135|
||||(0.047)|(0.048)|(0.045)|(0.093)|
|Women owners Y:1 N:0|||2.600***|2.700***|2.703***|2.715***|
||||(0.134)|(0.148)|(0.149)|(0.150)|
|Women workers (proportion)|||1.444***|1.446***|1.482***|1.497***|
||||(0.205)|(0.202)|(0.205)|(0.207)|
|Exports (direct, share of sales)|||-0.125|-0.122|-0.133|-0.096|
||||(0.252)|(0.248)|(0.248)|(0.245)|
|Foreign ownership|||0.259|0.298|0.314|0.299|
||||(0.297)|(0.296)|(0.296)|(0.291)|
|Quality certification Y:1 N:0|||0.082|0.108|0.107|0.117|
||||(0.142)|(0.142)|(0.141)|(0.141)|
|Overdraft Y:1 N:0||||-0.095|-0.102|-0.105|
|||||(0.126)|(0.126)|(0.125)|
|Multi establishment firm Y:1 N:0||||0.127|0.091|0.105|
|||||(0.178)|(0.181)|(0.179)|
|Obtaining license is a major||||0.055|0.059|0.055|
|obstacle Y:1 N:0||||(0.185)|(0.186)|(0.186)|
|Losses from power outages||||1.538*|1.671**|1.841**|
|||||(0.802)|(0.779)|(0.799)|
|Firm audited Y:1 N:0||||<br>-0.084|<br>-0.087|<br>-0.076|
|||||(0.119)|(0.119)|(0.119)|
|Registered when started Y:1 N:0||||<br>-0.019|<br>-0.028|<br>-0.065|
|||||<br>(0.214)|<br>(0.214)|<br>(0.210)|
|Publicly listed company Y:1 N:0||||0.607|0.564|0.627|
|||||(0.585)|(0.595)|(0.597)|
|Privately held Limited Liability||||0.853*|0.838|0.878*|
|<br>company Y:1 N:0|||||||

40
|||||(0.512)|(0.521)|(0.526)|
|---|---|---|---|---|---|---|
|Sole proprietorship Y:1 N:0||||1.464***|1.425***|1.473***|
|||||(0.524)|(0.532)|(0.539)|
|Partnership company Y:1 N:0||||0.805|0.772|0.823|
|||||(0.548)|(0.557)|(0.559)|
|Limited partnership company Y:1||||1.143**|1.102**|1.155**|
|N:0||||(0.519)|(0.526)|(0.530)|
|Visited by tax officials Y:1 N:0||||-0.035|-0.031|-0.041|
|||||(0.105)|(0.104)|(0.104)|
|Employment growth rate (per||||-0.343|-0.339|-0.312|
|annum, log difference)||||(0.303)|(0.301)|(0.302)|
|Bought assets last year Y:1 N:0||||0.112|0.113|0.111|
|||||(0.120)|(0.121)|(0.121)|
|GDP per capita growth rate (%,|||||-0.006|-0.040|
|annual)|||||(0.031)|(0.037)|
|Primary enrollment gender parity|||||-0.164**|-0.150**|
|index|||||(0.068)|(0.064)|
|% of women in 35-64 years age|||||0.611***|0.326|
|group|||||(0.187)|(0.218)|
|Total population (logs)|||||-2.299|-3.014*|
||||||(1.410)|(1.754)|
|Bank credit (ratio of GDP)*GDP||||||-1.909|
|per capita (logs)||||||(1.438)|
|Bank credit (ratio of||||||-0.472|
|GDP)*Number of workers (logs)||||||(0.330)|
|Bank credit (ratio of GDP)*Age of||||||0.494|
|firm (logs)||||||(0.662)|
|Bank credit (ratio of GDP)*Labor||||||-0.338*|
|productivity (logs)||||||(0.193)|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|15,299|15,299|15,299|15,299|15,299|15,299|
||**Pane**|**l B: Margina**|**l effect**||||
|Bank credit (ratio of|0.578***|0.585***|0.440***|0.413***|0.413***|0.473***|
|GDP)*Financial dependence|(0.171)|(0.171)|(0.148)|(0.143)|(0.143)|(0.147)|
|Controls(as above)|Yes|Yes|Yes|Yes|Yes|Yes|

All coefficient values are log odds ratios obtained from logit estimation. Huber-White robust standard errors clustered on country in brackets. *** (1%), ** (5%), * (10%).

41
|**Table 4: I**|**nteraction w**|**ith theprese**|**nce of credi**|**t bureaus**|||
|---|---|---|---|---|---|---|
|Dependent variable: Woman top|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|manager Y:1 N:0|||||||
||**Panel**|**A: Log odds**|**ratios**||||
|Bank credit (ratio of GDP)*Credit|-2.603**|-2.536**|-4.121***|-3.985***|-4.693***|-4.082***|
|bureau present Y:1 N:0|(1.154)|(1.141)|(1.152)|(1.147)|(1.248)|(1.305)|
|Bank credit (ratio of GDP)|4.111***|4.045***|5.708***|5.387***|6.695***|32.956|
||(0.975)|(1.013)|(0.896)|(0.867)|(1.283)|(20.064)|
|Credit bureau present Y:1 N:0|1.817**|1.789**|2.537***|2.394***|3.001***|2.551***|
||(0.843)|(0.840)|(0.740)|(0.743)|(0.853)|(0.863)|
|GDP per capita (logs)||<br>-0.086|<br>-0.439|<br>-0.230|<br>-1.146|<br>-1.513*|
|||(0.651)|(0.965)|(0.900)|(0.932)|(0.913)|
|Number of workers (logs)||<br>-0.200***|<br>-0.231***|<br>-0.200***|<br>-0.197***|<br>0.021|
|||(0.050)|(0.065)|(0.064)|(0.064)|(0.155)|
|Age of firm (logs)|||-0.084|-0.107|-0.112|-0.363|
||||(0.111)|(0.116)|(0.116)|(0.307)|
|Labor productivity (logs)|||-0.017|0.007|-0.009|0.136|
||||(0.048)|(0.049)|(0.047)|(0.102)|
|Women owners Y:1 N:0|||<br>2.560***|<br>2.662***|<br>2.664***|<br>2.675***|
||||(0.140)|(0.155)|(0.155)|(0.156)|
|Women workers (proportion)|||<br>1.491***|<br>1.498***|<br>1.534***|<br>1.552***|
||||(0.215)|(0.211)|(0.214)|(0.216)|
|Exports (direct, share of sales)|||-0.108|-0.108|-0.117|-0.084|
||||(0.245)|(0.240)|(0.240)|(0.237)|
|Foreign ownership|||<br>0.291|<br>0.332|<br>0.338|<br>0.324|
||||(0.304)|(0.307)|(0.308)|(0.304)|
|Quality certification Y:1 N:0|||0.104|0.128|0.128|0.139|
||||(0.139)|(0.138)|(0.138)|(0.138)|
|Overdraft Y:1 N:0||||-0.086|-0.092|-0.099|
|||||(0.128)|(0.128)|(0.127)|
|Multi establishment firm Y:1 N:0||||<br>0.113|<br>0.068|<br>0.088|
|||||(0.174)|(0.176)|(0.175)|
|Obtaining license is a major||||<br>0.088|<br>0.093|<br>0.091|
|<br>obstacle Y:1 N:0||||(0.187)|(0.188)|(0.188)|
|Losses from power outages||||<br>1.493*|<br>1.625**|<br>1.834**|
|||||(0.800)|(0.781)|(0.805)|
|Firm audited Y:1 N:0||||<br>-0.073|<br>-0.075|<br>-0.068|
|||||(0.118)|(0.118)|(0.118)|
|Registered when started Y:1 N:0||||<br>0.035|<br>0.024|<br>-0.004|
|||||(0.216)|(0.216)|(0.213)|
|Legal form fixed effects||||Yes|Yes|Yes|
|Visited by tax officials Y:1 N:0||||-0.019|-0.010|-0.018|

42
|Employment growth rate (per||||(0.106)<br>-0.321|(0.106)<br>-0.309|(0.106)<br>-0.299|
|---|---|---|---|---|---|---|
|annum, log difference)||||(0.313)|(0.313)|(0.310)|
|Bought assets last year Y:1 N:0||||0.129<br>|0.131<br>|0.128<br>|
|||||(0.123)|(0.124)|(0.125)|
|GDP per capita growth rate (%,<br>|||||0.014<br>|-0.029<br>|
|annual)|||||(0.027)|(0.040)|
|Primary enrollment gender parity|||||-0.167**|-0.149**|
|index|||||(0.071)|(0.067)|
|% of women in 35-64 years age|||||0.322|0.066|
|group|||||(0.216)|(0.239)|
|Total population (logs)|||||-3.198**|-4.096**|
||||||(1.421)|(2.062)|
|Bank credit (ratio of GDP)*GDP<br>||||||-2.316<br>|
|per capita (logs)||||||(2.020)|
|Bank credit (ratio of||||||-0.527|
|GDP)*Number of workers (logs)||||||(0.327)|
|Bank credit (ratio of GDP)*Age of||||||0.626|
|firm (logs)||||||(0.657)|
|Bank credit (ratio of GDP)*Labor<br>productivity (logs)||||||-0.364*<br>(0.204)|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|14,275|14,275|14,275|14,275|14,275|14,275|
||**Panel B**|**: Marginal**|**effects**||||
|Bank credit (ratio of GDP)*Credit|-0.247**|-0.239**|-0.356***|-0.351***|-0.375***|-0.267**|
|bureau present Y:1 N:0|(0.122)|(0.120)|(0.111)|(0.112)|(0.105)|(0.106)|
|Controls(as above)|Yes|Yes|Yes|Yes|Yes|Yes|

All coefficient values are log odds ratios obtained from logit estimation. Huber-White robust standard errors clustered on country in brackets. *** (1%), ** (5%), * (10%).

43
||**Table**|**5: Bank con**|**centration**||||
|---|---|---|---|---|---|---|
|Dependent variable: Woman top|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|manager Y:1 N:0|**Pan**|**el A: Log od**|**ds ratios**||||
|Bank credit (ratio of GDP)*5-|0.061***|0.063***|0.078***|0.094***|0.067**|0.067**|
|bank asset concentration|(0.016)|(0.015)|(0.028)|(0.029)|(0.032)|(0.033)|
|Bank credit (ratio of GDP)|-2.541*|-2.457*|-2.561|-4.124*|-1.606|28.846**|
||(1.339)|(1.264)|(2.333)|(2.483)|(2.542)|(13.169)|
|5-bank asset concentration|-0.019**|-0.017**|-0.011|-0.022|0.002|0.007|
||(0.009)|(0.008)|(0.013)|(0.015)|(0.019)|(0.020)|
|GDP per capita (logs)||-0.855|-1.854*|-1.460|-1.690|-2.029*|
|||(0.792)|(1.064)|(1.112)|(1.145)|(1.187)|
|Number of workers (logs)||-0.205***|-0.230***|-0.198***|-0.195***|0.007|
|||(0.050)|(0.064)|(0.063)|(0.064)|(0.154)|
|Age of firm (logs)|||-0.066|-0.088|-0.088|-0.334|
||||(0.110)|(0.115)|(0.115)|(0.310)|
|Labor productivity (logs)|||-0.024|0.001|-0.023|0.089|
||||(0.047)|(0.049)|(0.046)|(0.097)|
|Women owners Y:1 N:0|||2.595***|2.692***|2.698***|2.714***|
||||(0.140)|(0.153)|(0.154)|(0.156)|
|Women workers (proportion)|||1.472***|1.474***|1.511***|1.523***|
||||(0.216)|(0.214)|(0.217)|(0.218)|
|Exports (direct, share of sales)|||-0.104|-0.118|-0.118|-0.089|
||||(0.252)|(0.249)|(0.248)|(0.247)|
|Foreign ownership|||0.097|0.132|0.151|0.145|
||||(0.257)|(0.255)|(0.255)|(0.253)|
|Quality certification Y:1 N:0|||0.055|0.085|0.084|0.095|
||||(0.139)|(0.139)|(0.138)|(0.138)|
|Overdraft Y:1 N:0||||-0.131|-0.133|-0.136|
|||||(0.127)|(0.127)|(0.126)|
|Multi establishment firm Y:1 N:0||||0.082|0.072|0.069|
|||||(0.181)|(0.183)|(0.181)|
|Obtaining license is a major||||<br>0.074|<br>0.068|<br>0.069|
|obstacle Y:1 N:0||||(0.195)|(0.197)|(0.197)|
|Losses from power outages||||1.453*|1.638**|1.789**|
|||||(0.822)|(0.796)|(0.812)|
|Firm audited Y:1 N:0||||<br>-0.091|<br>-0.094|<br>-0.085|
|||||(0.120)|(0.121)|(0.120)|
|Registered when started Y:1 N:0||||0.051|0.035|-0.005|
|||||(0.217)|(0.217)|(0.214)|
|Legal form fixed effects||||Yes|Yes|Yes|
|Visited by tax officials Y:1 N:0||||-0.043|-0.043|-0.050|

44
|||||(0.108)|(0.108)|(0.108)|
|---|---|---|---|---|---|---|
|Employment growth rate (per||||-0.335|-0.317|-0.296|
|annum, log difference)||||(0.302)|(0.301)|(0.301)|
|Bought assets last year Y:1 N:0||||0.180*|0.188*|0.184*|
|||||(0.108)|(0.108)|(0.109)|
|GDP per capita growth rate (%,|||||0.008|-0.037|
|annual)|||||(0.030)|(0.036)|
|Primary enrollment gender parity|||||-0.259***|-0.230***|
|index|||||(0.081)|(0.078)|
|% of women in 35-64 years age|||||0.606***|0.338|
|group|||||(0.235)|(0.265)|
|Total population (logs)|||||-1.090|-2.217|
||||||(1.260)|(1.532)|
|Bank credit (ratio of GDP)*GDP||||||-2.757**|
|per capita (logs)||||||(1.334)|
|Bank credit (ratio of||||||-0.504|
|GDP)*Number of workers (logs)||||||(0.330)|
|Bank credit (ratio of GDP)*Age||||||0.611|
|of firm (logs)||||||(0.667)|
|Bank credit (ratio of GDP)*Labor<br>productivity (logs)||||||-0.290<br>(0.202)|
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|14,727|14,727|14,727|14,727|14,727|14,727|
||**Pan**|**el B: Margin**|**al effect**||||
|Bank credit (ratio of GDP)*5-|0.009***|0.010***|0.010***|0.011***|0.010***|0.012***|
|bank asset concentration|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.004)|
|Controls(as above)|Yes|Yes|Yes|Yes|Yes|Yes|

All coefficient values are log odds ratios obtained from logit estimation. Huber-White robust standard errors clustered on country in brackets. *** (1%), ** (5%), * (10%).

45
|**Table 6: All i**|**nteraction te**|**rms included**|**simultaneou**|**sly (Log odds**|**ratios)**||
|---|---|---|---|---|---|---|
|Dependent variable: Woman<br>top manager Y:1 N:0|**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Bank credit (ratio of|3.384***|3.461***|3.492***|3.213**|3.234**|3.306**|
|<br>GDP)*Financial dependence|(1.120)|(1.132)|(1.270)|(1.314)|(1.306)|(1.303)|
|Bank credit (ratio of|-3.945***|-3.895***|-6.302***|-6.238***|-7.564***|-6.950***|
|GDP)*Credit bureau present<br>Y:1 N:0|(1.111)|(1.080)|(1.033)|(1.028)|(1.251)|(1.324)|
|<br>Bank credit (ratio of GDP)*5-|0.084***|0.087***|0.128***|0.149***|0.151***|0.140***|
|<br>bank asset concentration|(0.019)|(0.018)|(0.025)|(0.027)|(0.035)|(0.040)|
|Financial dependence|-0.411|-0.436|-0.444|-0.302|-0.307|-0.348|
||(0.515)|(0.523)|(0.574)|(0.623)|(0.618)|(0.623)|
|Credit bureau present Y:1 N:0|2.538***|2.516***|3.757***|3.614***|4.719***|4.254***|
||(0.797)|(0.785)|(0.648)|(0.642)|(0.813)|(0.891)|
|5-bank asset concentration|-0.031***|-0.030***|-0.031***|-0.043***|-0.050**|-0.043|
||(0.009)|(0.008)|(0.011)|(0.014)|(0.021)|(0.026)|
|GDP per capita (logs)||-0.502|-1.684|-1.249|-2.034|-2.447*|
|||(0.750)|(1.149)|(1.170)|(1.254)|(1.278)|
|Number of workers (logs)||-0.197***|-0.226***|-0.202***|-0.198***|0.037|
|||(0.052)|(0.067)|(0.065)|(0.066)|(0.161)|
|Age of firm (logs)|||-0.080|-0.116|-0.117|-0.359|
||||(0.115)|(0.122)|(0.122)|(0.327)|
|Labor productivity (logs)|||-0.026|0.002|-0.014|0.092|
||||(0.049)|(0.050)|(0.049)|(0.099)|
|Women owners Y:1 N:0|||<br>2.622***|<br>2.720***|<br>2.719***|<br>2.728***|
||||(0.146)|(0.160)|(0.160)|(0.161)|
|Women workers (proportion)|||1.451***|1.453***|1.496***|1.511***|
||||(0.220)|(0.215)|(0.220)|(0.221)|
|Exports (direct, share of sales)|||-0.100|-0.114|-0.119|-0.090|
||||(0.257)|(0.254)|(0.253)|(0.251)|
|Foreign ownership|||0.093|0.139|0.137|0.128|
||||(0.270)|(0.267)|(0.266)|(0.264)|
|Quality certification Y:1 N:0|||0.044|0.077|0.071|0.081|
||||(0.143)|(0.143)|(0.143)|(0.143)|
|Overdraft Y:1 N:0||||<br>-0.117|<br>-0.120|<br>-0.126|
|||||(0.126)|(0.127)|(0.125)|
|Multi establishment firm Y:1||||<br>0.065|<br>0.044|<br>0.053|
|N:0||||(0.189)|(0.190)|(0.187)|
|Obtaining license is a major||||0.094|0.094|0.093|
|obstacle Y:1 N:0||||(0.196)|(0.198)|(0.198)|

46
|Losses from power outages||||1.420*|1.538*|1.691**|
|---|---|---|---|---|---|---|
|||||(0.834)|(0.813)|(0.838)|
|Firm audited Y:1 N:0||||-0.090|-0.086|-0.082|
|||||(0.126)|(0.127)|(0.126)|
|Registered when started Y:1||||-0.028|-0.044|-0.071|
|N:0||||(0.231)|(0.232)|(0.230)|
|Legal form fixed effects||||Yes|Yes|Yes|
|Visited by tax officials Y:1||||-0.021|-0.013|-0.018|
|N:0||||(0.107)|(0.108)|(0.108)|
|Employment growth rate (per||||-0.527*|-0.495|-0.484|
|annum, log difference)||||(0.320)|(0.319)|(0.316)|
|Bought assets last year Y:1||||0.167|0.171|0.171|
|N:0||||(0.112)|(0.112)|(0.113)|
|GDP per capita growth rate|||||-0.002|-0.033|
|(%, annual)|||||(0.030)|(0.043)|
|% of women in 35-64 years|||||0.042|-0.078|
|age group|||||(0.272)|(0.291)|
|Primary enrollment gender|||||-0.211***|-0.179**|
|parity index|||||(0.080)|(0.081)|
|Total population (logs)|||||-4.505***|-4.789**|
||||||(1.661)|(2.011)|
|Bank credit (ratio of<br>||||||-1.702<br>|
|GDP)*GDP per capita (logs)||||||(2.280)|
|Bank credit (ratio of||||||-0.567*|
|GDP)*Number of workers<br>||||||(0.344)|
|(logs)|||||||
|Bank credit (ratio of||||||0.605|
|GDP)*Age of firm (logs)||||||(0.704)|
|Bank credit (ratio of||||||-0.273|
|GDP)*Labor productivity||||||(0.199)|
|(logs)|||||||
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Year fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Industry fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|13,399|13,399|13,399|13,399|13,399|13,399|

All coefficient values are log odds ratios obtained from logit estimation. Huber-White robust standard errors clustered on country in brackets. *** (1%), ** (5%), * (10%).

47