Do female CEOs make a difference? Evidence in Vietnam

December 15, 2018

Abstract

Literature on gender in corporate leadership in developing countries is very rare. This paper contributes to the literature by examining the effect of female CEO choice on firm performance and risk taking for Vietnamese listed firms in the period from 2007 to 2015. While the firms with female CEOs consist of around 4.80% of large public firms in the U.S., this figure in Vietnam is around 6.18% on average. We also show that firms with female CEOs are mainly in agriculture and service industries. Moreover, we find that firms with female CEO generate higher profitability and face less risk than firms with male CEOs. These results are consistent under different regression specifications. Our results suggest the difference in gender gap in corporate operations.

JEL Classification: O15, J71, G32, M51, D22 Keywords: CEO, female, gender, leadership, performance, risk

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1. Introduction

Although women have increasingly participated in top corporate leadership, the gender

disparity is still remarkable. In 1998, only one female CEO led a Fortune 500 company. This

figure has substantially increased over the last two decades and reached 4.80% in 2018. However,

with the headcount of only 24 out of 500, female CEOs remain a minority in the U.S. largest

corporations1. This situation is also prevalent in other parts of the world. For example, in 2014,

only 3% of the largest 145 Scandinavian companies had female CEOs2. In China, female CEOs

consisted of around 4.4% in the period of 2000-2008 (Lam, McGuinness, and Vieito, 2013).

In addition to the gender imbalance in chief executive positions, the role of female CEOs

in firm operations is still mixed. On the one hand, several studies in the U.S. or European countries

show that firms with female CEOs tend to perform better (Erhardt, Werbel, and Shrader, 2003;

Kotiranta, Kovalainen and Rouvinen, 2007; Francoeur, Labelle, and Sinclair-Desgagne, 2008; Khan

and Vieito, 2013; and Lam et al., 2013, among others), and face less risk (Martin, Nishikawa, and

Williams, 2009; Huang and Kisgen, 2013; Faccio, Marchica, and Mura, 2016; and Niessen and

Ruenzi, 2017, among others). On the other hand, Sabarwal and Terrell (2008) use firm level data

from 26 post-socialist economies in Eastern and Central Europe and find that female entrepreneurs

have a significantly smaller scale of operations and are less efficient in terms of total factor

productivity. Similarly, using the data from Sri Lanka, de Mel, McKenzie and Woodruff (2008)

show that female–run enterprises tend to have low returns to capital than their counterparts.

This paper adds to current literature by examining the role of female CEOs on firm

performance and risk taking in a developing country with high growth speed, Vietnam. Different

1 https://www.cnbc.com/2018/05/21/2018s-fortune-500-companies-have-just-24-female-ceos.html 2 Wall Street Journal, May 21, 2014, “Even Scandinavia Has a CEO Gender Gap.”

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from almost all developing countries over the world, Vietnam has just changed its economy from

centrally planned to market oriented in 1986. To promote the development of non-state owned

sectors, the Assembly of Vietnam issued the Company Act of 1990 and the Private Enterprise Act

of 1990 as well as pushed the privatization of state-owned enterprises which dominated the whole

economy. Vietnamese stock markets were initiated in 2000 with only two stocks traded in Ho Chi

Minh Stock Exchange (HOSE). This number has significantly increased over the last decade and

reached 731 firms in 2017 with the total market capitalization of 148.17 billion USD, consisting

of 74.6% of the country’s gross domestic products (GDP)3.

Although the stock markets have developed very fast over last 10 years, literature on CEO

gender and firm performance in Vietnam is still neglected. Using the data of listed firms on the

Vietnamese stock markets (consisting of Ho Chi Minh City Stock Exchange and Hanoi Stock

Exchange) in the period of 2007-2015, our paper shows that 6.18% of which are run by female

CEOs. This number is higher than that in many countries such as China (around 4.4%) and the

U.S. (around 4.8%). However, the proportion of female CEOs varies over the period. It reached

the highest in 2007 with the ratio of 9.86% and the lowest in 2011 with the ratio of 4.95%.

Previous studies (e.g. Amin and Islam, 2014) show that women and men are different in

job preferences: women prefer to work in retail sectors or service industries while men dominate

manufacturing businesses. Consistent with this argument, we find that firms with female CEOs

are mainly in agriculture and service industries. For example, female CEOs consist of 28.60% in

inland transportation services, 25.00% in agriculture and 22.20% in financial service industries. In

3https://english.vietnamnet.vn/fms/business/192879/market-capitalisation-hits-74-6-percent-of-vietnam-s-gdp.html

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our data, the industries with all male CEOs are cargo inland shipping, computer services, oil and

gas.

Different from the evidence in the U.S. (Khan and Vieito, 2013), our paper demonstrates

that on average, female-led firms are larger. However, these firms tend to use less debt, less

tangible assets and are younger. Female-led firms also have more cash holding, more equity ratio

and more stock liquidity, consistent with Zeng and Wang (2015) and Adhikari (2018). Consistent with

the findings by Khan and Vieito (2013), we show that firms with female CEO tend to have high

profitability and low risk.

Why do firms with female CEOs tend to have higher profits and face lower risk? Literature

on the leadership theory suggests the importance of nurturing communication, being more

inclusive, and creating alliances (Stogdill, 1974). In addition, as Hillman, Cannella and Harris

(2002) highlight, women offer unique perspectives, experiences and styles of work compared to

their male counterparts, leading to differences in leadership effectiveness between women and

men. Female leaders are more democratic (Johnson and Eagly 1990), more collaborative (Eagly

and Carli 2003), and better at create good workplace management practices (Melero, 2011),

resulting in the fact that they receive more valuable advice from board of directors as well as other

stakeholders. In addition, literature on psychology and sociology demonstrates that men are more

overconfident than women and women tend to be risk averse (Croson and Cneezy, 2009). More

overconfidence leads male CEOs to invest in negative NPV projects which turn out to be losses in

the future, causing firm profit to drop (Huang and Kisgen, 2013). Because of risk averse, women

invest less in risky assets than men do (e.g. Sunden and Surette, 1998).

To further examine the role of female CEOs in firm operations, we run regression models

of measures of firm performance and risk taking on CEO gender and other firm characteristics.

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The results show that firms with female CEOs tend to perform better in the future and face less

risk. Specifically, female-led firms earn 1.10% higher return on assets, 2.1% higher return on

equity and 0.06 higher Tobin’s Q. In contrast, total risk measured by total return volatility drops

by 0.15% if firms have female CEOs.

Because the proportion of female CEOs is small comparing with that of male CEOs, we

robustly test our results by matching female-led firms with their counterparts based on firm

characteristics. Employing the propensity score matching method, for each female-led firm, we

choose one firm with a male CEO in the same industry and year having the nearest propensity

score. Using this matching sample, we run regressions of measures of firm performance and risk

taking on CEO gender control variables. Consistent with our previous results, we find that firms

with female CEOs tend to perform better.

Although we robustly test our results using the matched sample, the endogeneity problem

may exist due to the omission of unobservable factors affecting both firm performance or risk and

CEO gender. To deal with this issue, we follow Huang and Kisgen (2013) by employing the 2SLS

approach with instrumental variable. We use the ratio of the number of female CEOs to the number

of total CEOs in a certain industry as an external instrument for firm’s female CEO dummy. This

ratio can be an instrument because it meets two conditions: (1) it is significantly related to firm’s

female CEO dummy; and (2) it impacts firm performance only through the firm’s female CEO

choice. Because the proportion of industry female CEOs to total CEOs mainly measures the

position of industry female CEOs in the market, it is difficult to believe that its impact on firm is

through different channels rather than firm CEO gender. Our results are consistent under this

approach.

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Our paper is related to previous studies on the role of female CEOs in firm operations and

risk. For example, Khan and Vieito (2013) show that female CEOs of the U.S. firms tend to earn

higher profits than their counterparts and take less risk. Smith, Smith, and Verner (2006) find that a

higher proportion of females in a company has a positive impact on the overall performance of Danish

firms. Martin-Ugedo, Minguez-Vera and Palma-Martos (2018), using a sample of Spanish firms in

the publishing industry, show that firms with female CEOs have larger return on assets, larger

returns on equity and lower financial leverage..

We make several contributions to the current literature on the impact of CEO gender. First,

our paper is the first to investigate the role of female CEOs in firm operations in Vietnam. We

show that Vietnamese female-led firms tend to perform better and face less risk than their

counterparts. Second, we demonstrate that the role of female CEOs in firm operations in Vietnam

may be different from that in other developing countries such as India or Sri Lanka. Finally, our

results support the hypothesis on gender differences in preferences.

The remainder of the paper is organized as follows. Section 2 discusses relevant prior

literature and develops hypotheses. Section 3 describes data selection, variable measurement, and

provides the descriptive statistics. Section 4 presents the univariate analysis. Section 5 presents the

multivariate analysis. Section 6 concludes.

2. Literature Review and Hypothesis Development

Traditional finance theory proposes agency and asymmetry information as ways in which a

manager’s preferences and characteristics may play a role in a firm’s investment selection, thus affecting

its performance and risk. However, upper echelons theory, formalized by Hambrick and Mason (1984),

dictates that firm behaviors and performance are also reflective of observable, stable demographic

characteristics of managers, such as age, education, and gender, managerial traits and experiences. Over

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the past decade, a substantial body of research has accumulated that supports the theory. Bertrand and

Schoar (2003), for example, report that manager fixed effects are important for various corporate decisions

such as investment, financial, and organizational practices. Adams, Almeida, and Ferreira (2005) examine

powerful CEOs who have greater influence on firm decisions and find that these firms have higher

performance variability. Bennedsen, Perez-Gonzalez, and Wolfenzon (2010) find that CEOs do matter for

firm performance since their deaths or distractions from immediate family deaths negatively impact firm

performance. More recent studies such as Kaplan, Klebanov, and Sorensen (2012) and Graham, Harvey,

and Puri (2013) examine managerial behavioral traits more comprehensively from detailed assessments of

candidates for the CEO positions and surveys of U.S. and non-U.S. CEOs. Kaplan et al. (2012) find that

subsequent performance of corporates involved in buy-out and venture capitalist transactions depend

positively on the CEO’s general ability and execution skills. Graham et al. (2013) report that the CEO’s

optimism and risk-aversion affect corporate financial decisions and acquisition. Thus, the literature

highlights the importance of CEO traits in decision making.

2.1 CEO Gender and Firm Performance

Leadership theory suggests the importance of nurturing communication, being more inclusive, and

creating alliances (Stogdill, 1974). In addition, as Hillman et al. (2002) highlight, women offer unique

perspectives, experiences and styles of work compared to their male counterparts. Thus, gender emerges as

an important trait that affects behavior, leading to differences in leadership effectiveness between women

and men. Ford and Richardson (1994) find that female managers normally extract less personal benefits

from the company and thus make more ethical decisions in the workplace than men. Johnson and Eagly

(1990) find that women leaders are more democratic and participative and less autocratic than male leaders.

Eagly and Carli (2003) extend this to show that female leaders are less hierarchical and more cooperative

and collaborative. Melero (2011) show that women leaders create good workplace management practices

through more interpersonal channels of communication and more employee participation in decision

making. Tate and Yang (2015) find that female CEOs cultivate a more female-friendly workplace

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environment and pay more equal wages to newly hired workers. This literature suggests that female leaders

foster a corporate environment that is conducive to increasing firm value, impacting its performance.

In contrast to the leadership theory that present positive arguments for women in executive

positions, resource dependency theory presents arguments that may discourage the appointment of women

to these posts. Resource dependency theory view firms as operating in an open system and needing to

exchange and acquire certain resources in order to survive, creating a dependency between the firm and

external units and environment (Pfeffer and Salancik, 1978). The theory also recognizes that managers can

act to reduce environmental uncertainty and dependence. Zelechowski and Bilimoria (2004) argue that

women relate less with managers in other companies. Kesner (1988) argues that women are less likely to

do business. More recently, Inci, Narayanan and Seyhun (2017) show that women have a disadvantage

relative to males in access to inside information, probably due to their smaller informal networks. As a

consequence, based on the resource dependency theory, these arguments suggest that female CEOs do not

add value to firms.

Finally, Adams and Ferreira (2009) point to the possibility that women in top management positions

in companies will have no influence on a firm’s performance. They argue that female managers reject

feminine stereotypes and values and, as a result, behave like male managers.

Empirical evidence on the impact of gender on firm performance tends to support a positive

relationship. Erhardt et al. (2003), based on Fortune 500 firms, find evidence that firms with a higher

number of female executives have higher profitability relative to their average sector profitability.

Francoeur et al. (2008) find that firms operating in complex environments generate positive and significant

abnormal returns when they have a high proportion of females in top management. Krishnan and Parsons

(2008) find that firms with gender diversity in senior management are associated with higher earnings

quality. They also find that, after the IPO process, firms with a higher number of women in senior

management are more profitable and have higher stock returns than firms with fewer women in

management ranks. Jurkus, Park and Woodard (2011) find that firms with a larger percentage of female

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officers have smaller agency costs, contributing to firm performance. Khan and Vieito (2013) find that

firms managed by female CEOs are associated with better performance compared to those managed by

male CEOs.

In international settings, the evidence proves somewhat inconclusive. On the one hand, Smith et al.

(2006) find that a higher proportion of females in a company has a positive impact on the overall

performance of Danish firms. Kotiranta et al. (2007) report that Finnish firms with female versus male

CEOs earn higher profits, possibly reflecting the contribution of female leadership to the firms’ overall

cultural diversity and multidimensionality, and good governance and management practices. Lam et al.

(2013) report that female CEOs have a better performance compared to their male counterparts in Chinese

firms. Martin-Ugedo et al. (2018), using a sample of Spanish firms in the publishing industry, show that

firms with female CEOs have larger return on assets, larger returns on equity and lower financial leverage.

On the other hand, Sabarwal and Terrell (2008) use firm level data from 26 post-socialist economies

in Eastern and Central Europe and find that female entrepreneurs have a significantly smaller scale

of operations and are less efficient in terms of total factor productivity. Similarly, using the data

from Sri Lanka, de Mel et al. (2008) show that female–run enterprises tend to have low returns to

capital than their counterparts.

2.2 CEO Gender and Firm Risk

There is considerable evidence showing that women are more risk averse than men (see, e.g. Croson

and Gneezy, 2009). This gender difference in risk preferences has been documented in various situations.

For example, women smoke less, wear seat belts more often, and are less likely to use illegal drugs (see,

e.g. Hersch, 1996; Pacula, 1997). In the labor market, women prefer to work in safer industries, while in

the same industry they choose more secure jobs. (Hersch, 1998).

In the area of financial risk, gender differences are also clear. Olsen and Cox (2001) investigate

gender differences in attitudes towards risk for investors with a professional background and find that

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female investors weigh up risk attributes such as the possibility of loss and ambiguity to a greater extent

than their male counterparts. Sunden and Surette (1998) show that women allocate retirement plans in a

more conservative way. Considering risky assets, they hold approximately equal proportions of stocks and

bonds, while men invest twice as much of their wealth in stocks. Similarly, Barber and Odean (2001)

examine the account data for 35 thousand U.S. households during 1991-1997 and find that men invest in

risky assets more often than women do. Women also spend more time researching before making an

investment decision, and more willing to ask for advice once they are in trouble. The difference in risk

tolerance is also reflected in mutual fund investing, where female fund managers seem to take less

unsystematic risk and opt for more stable investments than male fund managers (Niessen and Ruenzi, 2017).

Male fund managers trade more frequently, reflecting a significantly higher turnover ratio compared to

female managers.

While it is well documented that women are less risk tolerant than men in general, there may not

necessarily be a difference between males and females among top executives, given the specific and rare

combination of skills needed to ascend to a high management position. Adams and Funk (2012), using a

large survey of directors in Sweden, show that in contrast to findings for the population, female directors

are more open to change, less conservation-oriented, and more risk loving than male directors. Adams and

Ragunathan (2015) show that banks with more female directors did not have lower risk than other banks

during the financial crisis.

Differences in the structure of compensation and incentives may also explain the documented

association between gender and risk-taking. In particular, low-risk firms may be more likely to offer fixed

pay contracts and may be more likely to attract female executives. Consistent with this type of matching,

in Bandiera, Guiso, Prat, and Sadun’s (2015) model, more risk-averse and less talented managers match

with firms offering low-powered incentives – a prediction that they confirm empirically using survey data

on Italian managers combined with longitudinal data from administrative records. Carter, Franco and Gine

(2017) find that risk-taking incentives, measured by portfolio delta, option delta and option vega, are

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significantly lower for women, which could be interpreted as women’s acceptance of pay packages with

less incentive pay.

Empirically, Martin et al. (2009) report that changes in both firm total risk and idiosyncratic risk

following a female CEO appointment are significantly smaller than following a male CEO appointment.

Khan and Vieto (2013) find that when the CEO is female, the firm risk level is smaller than when the CEO

is a male. Cole (2013), in a sample of privately owned U.S. firms, reports cross-sectional evidence that

female-owned firms have lower leverage than male-owned firms. Ho, Li, Tam and Zhang (2015)

demonstrate that companies with female CEOs report more conservative earnings. This relationship is

stronger in firms exposed to high rather than low litigation and takeover risks. Adhikari (2018) find that

firms led by female top executives hold more cash, partly due to precautionary motives. Huang and Kisgen

(2013) document that male executives carry out more acquisitions and issue more debt than their female

counterparts, consistent with men being more overconfident than women.

In international settings, Zeng and Wang (2015) find that female CEOs are associated with a higher

level of corporate cash holdings compared with their male counterparts in Chinese listed firms, suggesting

that they are more conservative through the precautionary motive of cash. Liu, Wei and Xie (2016) find

that female CFOs engage in less earnings management than male CFOs in Chinese firms. Faccio et al.

(2016), using a large sample of privately held and publicly traded European companies, document that firms

run by female CEOs have lower leverage, less volatile earnings, and a higher chance of survival than male

CEO firms.

2.3 Hypothesis Development

Previous literature suggests that there are differences between male and female leaders in terms of

management style, risk aversion, investment strategies, and financial decision making. In addition, Ford

and Richardson (1994) state that women make more ethical decisions in the workplace than men.

Furthermore, Jurkus et al (2011) note that women, in addition to being more risk averse and not as

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overconfident, worry more about the way the company is spent and normally extract less personal benefits

from the company than men. More overconfidence leads male CEOs to invest in negative NPV projects,

which turn out to be losses in the future.

These evidence suggest that gender representation on top executive roles influence the decision

making process and firm risk taking. As Perryman, Fernando and Tripathy (2016) argue, with respect to

firm performance and firm risk, the influence of female managers may indirectly result in firms have less

large returns (i.e. taking less risk) while simultaneously having few huge losses (i.e., having more stable

performance increases). Such risk-return paradox maybe the result of differences in management

capabilities, brought on through the differential gender characteristics.

Based on the above discussion, we posit that the gender of CEOs reflect capabilities that influence

both firm performance and risk. Stated formally, we hypothesize that

Hypothesis 1: Firms managed by female CEOs, on average, perform better than firms managed by

male CEOs.

Hypothesis 2: Firms managed by female CEOs, on average, have a lower risk level than firms

managed by male CEOs.

3. Sample Selection, Variable Measurement and Descriptive Statistics

3.1. Sample Selection and Variable Measurement

Vietnamese stock market has recently been developing. In 2000, the first stock exchange, Ho

Chi Minh Stock Exchange, was inaugurated with only two stocks. Six years later, the second stock

exchange, Hanoi Stock Exchange, was established. However, during that period of time, only few

firms were listed on the stock market. To encourage firms issuing securities to finance their

operations as well as to develop the stock market, Vietnam Congress passed the Law of Securities

in 2006 which took effect in 1st January, 2007. In 2007, the Circular 38/2007/TT-BTC, was issued

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with the purpose of enhancing fair and transparent trading in the stock market. Therefore, we start

our sample in 2007 and end the sample in 2015 when we can obtain data for our paper.

We collect financial data from the Center for Economics and Finance Research at Ton Duc

Thang University in Vietnam. We use this database to compute return on assets, return on equity,

Tobin’s Q, logarithm of total assets, book-to-market, tangible assets, cash holding, long-term debt,

equity and dividend ratios. Tobin’s Q is defined as the ratio of market value of equity plus total

debt and book value of total assets. Book-to-market ratio is the fraction of book value of equity to

its market value.

We use the trading and holding databases from Tai Viet Corporation (Vietstock) to calculate

stock liquidity and managerial ownership. Following previous literature on stock liquidity (e.g.

Ginglinger and Hamon, 2012), we use effective bid-ask spread to proxy for stock liquidity. This

measure is computed as follow:

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝐸𝐸𝐸𝐸𝐸𝐸𝑅𝑅𝐸𝐸𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑆𝑆𝑆𝑆𝑆𝑆𝑅𝑅𝑅𝑅𝑆𝑆𝑖𝑖,𝑡𝑡 = 2 �𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑇𝑇𝑇𝑇 𝑃𝑃𝑇𝑇𝑖𝑖𝑃𝑃𝑃𝑃𝑖𝑖,𝑡𝑡−𝑀𝑀𝑖𝑖𝑇𝑇𝑀𝑀𝑀𝑀𝑖𝑖𝑇𝑇𝑡𝑡𝑖𝑖,𝑡𝑡𝑀𝑀𝑖𝑖𝑇𝑇𝑀𝑀𝑀𝑀𝑖𝑖𝑇𝑇𝑡𝑡𝑖𝑖,𝑡𝑡

�

where 𝑀𝑀𝑅𝑅𝑆𝑆𝑆𝑆𝑀𝑀𝑅𝑅𝑀𝑀𝑅𝑅 = 12� (𝐴𝐴𝐴𝐴𝐴𝐴 𝑆𝑆𝑆𝑆𝑅𝑅𝐸𝐸𝑅𝑅 + 𝐵𝐵𝑅𝑅𝑆𝑆 𝑆𝑆𝑆𝑆𝑅𝑅𝐸𝐸𝑅𝑅), i stands for firm i, and t stands for trading day.

Our main independent variable is female CEO dummy. We define this dummy variable to

be equal to 1 if a firm’s CEO is female and 0 otherwise.

We use trading data to calculate two types of risk: total risk and symmetric risk. Total risk

is defined as the standard deviation of daily stock returns for each firm in each year while

symmetric risk (beta) is the beta coefficient from the CAPM model estimation using daily stock

returns over a year. We mainly use stock returns to measure firm’s risk because of two important

reasons. First, as documented in the literature, stock returns reflect all information about firm

performance and its expectation in the future, which include firm’s profits, firm’s value of both

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assets in place and growth opportunities. Second, because the time period of the data in Vietnam

is short, the risk measure based on firms’ accounting information may not be accurately estimated.

We exclude firm-year observations having total assets or sales volume less than 1 million

VND. To avoid effects from outliers, we winsorize all variable at the 1st and 99th percentile. Our

final sample consists of 4,253 observations from 764 firms from 2007 to 2015.

Appendix 1 describes the variables used in our analyses in more details.

3.2. Descriptive Statistics

Panel A of Table 1 reports the descriptive statistics of variables used in our paper. In our

sample, on average, the proportion of female CEOs is 6.20% of the total sample, which is much

higher than in many developed countries such as the U.S. or northern European countries.

However, the median and the 75th percentile values of female CEO dummy are still equal to 0. On

average, firms in our sample have return on assets of 5.20%, return on equity of 8.40% and Tobin’s

Q of 1.472. Return on assets is ranging from -20.80% to 30.10% while return on equity is ranging

from -134.00% to 49.40%. Tobin’s Q of firms in our sample ranges from 0.506 to 4.016.

***** Insert Table 1 here*****

The results also show that the mean of systematic risk is 0.912 while the mean of total risk

is 3.30%. The 25th percentile and 75th percentile values of systematic risk are 0. 342 and 1.246,

respectively, while those of total risk are respectively 2.65% and 3.83%. The average of logarithm

of total assets is 13.019, which is equivalent to 1,485,449 million VND (or around 67 million

USD). The logarithms of total assets range from 9.904 to 16.796, with standard deviation of 1.432.

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The mean of tangible assets ratios is 0.193, meaning that on average the tangible assets of

firms in our sample consists of 19.30% of firm assets. The total debt ratio ranges from 0.046 to

0.941, with the mean of 51.60% of total assets. The 25th percentile and 75th percentile values of

total debt ratios are respectively 33.90% and 69.20%. The average book equity to total assets ratio

is 0.475, meaning that 45.70% of total assets are financed by equity. On average, Vietnamese firms

use more debt than firms in other developing countries such as Argentina, India, and Korea (de

Jong, Kabir, and Nguyen, 2008). These results are reasonable because Vietnamese stock market

has just recently developed.

The mean of dividend yield in our sample is 2.60%, with a standard deviation of 3.8%. The

ratio of cash flow to total assets, measured by cash flow from operations to total assets, averages

9.40%. On average, firms in our sample are 22 years old.

To further to investigate the important role of female CEOs, we compute the ratio of female

CEOs to the total number of CEOs over the period from 2007 to 2015. The results in Panel B show

that the proportion of female CEOs is from 4.95% to 9.86%, which reached the highest in 2007

and lowest in 2011. On average, the ratio of female CEOs is 6.18% for the full sample, which is

higher than that in the U.S. and Europe (e.g. Huang and Kisgen, 2013; Faccio et al., 2016). This

means that the gender disparity in corporate leadership in Vietnam is less severe than in other

countries.

4. Univariate Analysis

Our paper investigates the effect of CEO gender on firm performance and risk taking;

therefore, we mainly focus on CEO gender, three measures of firm performance and two risk

measures. Because both CEO gender and firm performance may depend on the industry in which

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firms operate, we separate firms by their industries and calculate the average of these measures. In

our sample, firms with female CEOs are mainly in service, food, pharmacy and farming industries.

The list of ten industries having the highest female CEO ratios is reported in Table 2. In contrast,

some industries do not have any firm with female CEOs, which are cargo inland shipping,

computer services, oil and gas.

***** Insert Table 2 here*****

Table 2 shows that the ratio of firms with female CEOs is highest in inland transportation

services. The mean of the female CEO dummy is 0.286, meaning that around one third of firms in

this industry have female CEOs. The next industries having highest female CEO ratios are

agriculture, financial services, and repairing and guarantee services. One fourth of firms in

agricultural industry have female CEOs, while this figure for firms in financial services is 22.20%.

Of the ten industries with the highest female CEO ratios, four have return on assets lower

than the average and three have return on equity lower than the average of the sample. In contrast,

only two industries have mean systematic risk greater than the mean systematic risk of our sample,

but none of these industries have higher total risks than the sample average. These results may be

affected by the industries in which firms operate. Therefore, to examine the impact of CEO gender

on firm performance and risk, we need to control for industry effects, which will be discussed in

the next sections.

Table 3 presents the descriptive statistics for firms with female and male CEOs in our

sample. On average, firms with female CEOs have higher profitability ratios and lower risk. For

example, firms with female CEOs have 7.80% return on assets on average, while this figure for

firms with male CEOs is 5.10%. The difference is significant at 1% level. Similarly, return on

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equity and Tobin’s Q of firms with female CEOs are 12.50% and 1.645, respectively, while these

numbers of firms with male CEOs are respectively 8.50% and 1.463.

***** Insert Table 3 here*****

In addition, the results in Table 3 show that firms with female CEOs have lower risk. The

systematic risk and total risk of these firms are 0.807 and 3.028%, while these figures of firms

with male CEOs are 0.912 and 3.315%, respectively. Both differences are negatively significant

at 1% level.

The characteristics of these two types of firms are also significantly different. Firms with

female CEOs are on average larger and have higher equity, more cash holding, more liquidity and

more managerial ownership as well as pay higher dividend than firms with male CEOs. However,

they have less tangible assets, borrow less debt, and are younger. This evidence suggests that firms

with female CEOs tend to perform better and take less risk than their counterparts.

To further examine the relation between CEO gender and firm performance, we conduct a

correlation matrix of female CEO dummy variable and three measures of firm performance and

two risk measures. The results in Panel A of Table 4 show that female CEO dummy variable is

positively and significantly correlated with return on assets, return on equity and Tobin’s Q. It

means that firms with female CEOs tend to perform better than firms with male CEOs.

***** Insert Table 4 here*****

In contrast, the correlations between female CEO dummy variable and systematic risk and

total risk are significantly negative. These results imply that firms with female CEOs tend to have

less risk than firms with male CEOs, consistent with the results in Table 3 and in previous studies

(e.g. Faccio et al., 2016).

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Panel B of Table 4 presents the paired correlations between CEO gender and other firm

characteristics. Consistent with the results in Table 3, the correlation between female CEO dummy

and total assets, equity ratio, cash holding ratio, dividend yield and managerial ownership are

significantly positive. In contrast, firms with female CEOs tend to use less debt and have tangible

assets ratio. The correlations between female CEO dummy and total debt and tangible assets ratios

are -9.7% and -6.70%, respectively, with p-values of 0.00, significant at 1% level.

5. Multivariate Analysis

In this section, we investigate the effects of CEO gender on firm performance and risk

taking behaviors by using two different approaches. First, following Huang and Kisgen (2013), we

employ the regression model to examine these effects for the whole sample. The benefit of this

approach is that all firms can be considered, including firms with female-to-female or male-to-

female transition. However, because the number of firms with female CEOs is much smaller than

the number of firms with male CEOs, we use another approach to study these effects by employing

the propensity score matching method which matches female-led firms with male-led firms. We

then study the impacts of CEO gender on firm performance and risk based on this matching

sample.

5.1. Baseline Regression Model

To investigate the effect of CEO gender on firm performance and risk taking behavior, we

run the following multivariate regression model:

PERFORM/RISKi,t+1 = β0 + β1FCEOi,t + β2FIRM-CHARi,t + β3INDUSTRY-DUMMYj,t +

β4YEAR-DUMMYt + ԑi,t (1)

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where PERFORM is one of three measures of firm performance which are return on assets, return

on equity, or Tobin’s Q. RISK is one of two risk measures: systematic and total risk. FCEO is a

dummy variable which is equal to 1 if firm’s CEO is female and 0 otherwise. FIRM-CHAR is a

set of firm characteristics, INDUSTRY-DUMMY is industry dummy variables and YEAR-

DUMMY is year dummy variables.

As mentioned in previous studies (e.g. Huang and Kisgen, 2013; Faccio et al.2016), both

firm performance/risk and CEO gender are affected by many variables such as sales, leverage,

dividend. Therefore, in our models, we need to control for these variables. Specifically, we control

for firm size measured by logarithm of total assets, tangible assets, total debt ratios and dividend

yield. We also control for equity to total assets ratio, cash holding, and firm age.

Because the stock market provides a channel to monitor firm managers, which can affect

firm’s managerial decision, to capture the information from the stock market, we add stock

liquidity measure into our models. We further control for managerial ownership because it can be

proxied for managerial incentives, which play an important role in firm operations and CEO gender

choice.

We also control for industry fixed-effects because firm performance and CEO gender

might be different for different industries. We control for time effects because firm performance

is highly related to business cycles, as documented previous studies (e.g. Huang and Kisgen, 2013).

Finally, to mitigate the endogeneity problem that both firm performance and CEO gender are

driven by omitted variables, we include current performance measures and risk measures in our

regression models for performance.

5.2. Whole Sample

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Table 5 reports the results from the regression models of firm performance on CEO gender

and other control variables. The first column shows that the coefficient of the female CEO dummy

on ROA is 0.011 with the p-value of 0.002, significant at 1% level. This means that firms with

female CEOs earn 1.10% ROA higher than their counterparts after controlling for other firm

characteristics. The second column shows that firms with female CEOs have 2.10% ROE higher

than firms with male CEOs. The p-value of this coefficient is 0.030, significant at 5% level.

Similarly, when the CEO of a firm is female, its Tobin’s Q is 0.06 higher than when the CEO is

male, as shown in column 3.

***** Insert Table 5 here*****

The effects of other firm characteristics on firm performance are also presented in Table 5.

Current performance measures are positively correlated with corresponding future performance

measures. Similarly, firms with high dividend yield or cash holding perform better in the future.

The p-values of these coefficients are 0.00, significant at 1% level. The ratio of equity to total

assets is negatively correlated with future return on assets and return on equity, while stock

illiquidity is positively correlated with Tobin’s Q. Table 5 also shows that old firms tend to perform

better than young ones.

In addition to the impact of CEO gender on firm performance, it also affects firm risk. To

investigate this effect, we use model 1 to run regression of firm risk measures on CEO gender and

other control variables. The results are reported in Table 6.

***** Insert Table 6 here*****

Consistent with previous studies (e.g. Huang and Kisgen, 2013; Faccio et al., 2016), Table

6 shows that firms with female CEOs tend to take less risk than firms with male CEOs.

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Specifically, when a firm chooses a female CEO, its systematic risk will reduce by 0.092 and its

total risk will drop by 0.15% next year. Table 6 also shows that old firms or firms with high cash

holding and dividend yield tend to experience less risk, while large firms tend to have low total

risk but high systematic risk.

5.3. Matching Sample

To further examine the effect of CEO gender on firm performance and risk, we employ the

propensity score matching method to match a firm with a female CEO to a similar firm with a

male CEO in the same industry and year. This method begins with a probit regression model of

the female CEO dummy on firm characteristics. Following the previous studies (e.g. Huang and

Kisgen, 2013), we use the set of control variables from the baseline regression specification (model

1), including industry and year dummies. The inclusion of these variables not only ensures that

firms with female and male CEOs share statistically the same firm characteristics such as size,

profitability, dividend yield, cash holding, age, managerial ownership, total debt, equity ratio, and

tangible assets but also ensures that the coefficient estimators are not driven by the differences in

any industry and time.

Column 1 of Panel A of Table 7 reports the results from the probit regression model. The

results show that the model specification can explain a significant variability in the female CEO

dummy, as captured by pseudo R square of 12.37% and p-value from the test of fitness of overall

model less than 0.0001. From this column, we estimate the predicted probability, or propensity

score, for each firm-year observation. We then match firms with female CEOs (treatment group)

with firms with firms with male CEOs (control group) having the nearest propensity score. We

exclude any industry having only one firms with a female CEO. We end up with 184 paired firms

or 368 firm- year observations.

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Because the propensity score matching approach is valid only when two groups of firms

are not statistically different in predicting CEO gender, we conduct a diagnostic test to verify this

assumption. We rerun regression of female CEO dummy on firm characteristics and report the

results into column 2 of Panel A of Table 7. None of the independent variables are statistically

significantly correlated with female CEO dummy, which implies that no firm characteristics of

two groups makes the CEO gender prediction different. Moreover, the pseudo R square

significantly drops to 0.72% and the Chi-square test for the model fitness shows that the null

hypothesis of all coefficients of 0 cannot be rejected because its p-value is 99.99%.

To further examine whether firms in two groups have different characteristics or not, we

report the means of all control variables used in our regression specification (model 1) for each

group and compute the difference between them. The results are reported in Panel B of Table 7.

***** Insert Table 7 here*****

Overall, there are not statistically significant differences in firm characteristics of two

groups. For example, the mean of logarithm of total assets of firms in two groups are 13.269,

virtually the same. The differences between two means of other firm characteristics are not

significantly different from 0 because the p-values of all t-tests with the null hypothesis that two

groups have the same firm characteristics are greater than 45.0%, suggesting that firms with female

CEOs have the same characteristics with firms with male CEOs. These results verify the validity

of the assumption of the propensity score matching method.

Table 8 provides the results from the regressions of firm performance and risk taking

behaviors on CEO gender and other control variables for firms in the matching sample. Consistent

with the results in Table 5, it shows that female CEO dummy is significantly positively related to

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three measures of firm performance but negatively correlated with two proxies for risk taking.

Specifically, when a female CEO is hired, return on assets increases by 1.20% and return on equity

goes up by 1.90% next year. Comparing with the results in Table 5, these coefficients are nearly

double, implying that the benefits of female CEOs are more pronounced when we focus on the

matched sample. Similarly, the coefficient of female CEO dummy on Tobin’s Q is 0.037, which

is higher than that coefficient in Table 5.

***** Insert Table 8 here*****

The effect of female CEO dummy on risk is also stronger in the matched sample.

Comparing with the results in Table 6, the coefficients of this variable on firm total risk and

systematic risk are negatively smaller, implying that firms with female CEOs tend to face much

less risk than their counterparts.

5.4. 2SLS Method with Instrumental Variable

An important issue in examining the effect of CEO gender on firm performance and risk is

endogeneity. That is the correlation between them may simply reflect unobservable characteristics

which influence both the CEO gender choice and firm performance and risk taking. This omission

may lead to incorrectly attribute the impact of the CEO gender choice on firm operations. In our

baseline regression specification, we partially address this issue by including current performance

measures and risk measures into the model. However, the endogeneity may still exit. In this

section, we deal with this issue by employing 2SLS method with an instrumental variable.

We use the ratio of the number of female CEOs to the number of total CEOs in a certain

industry as an external instrument for firm’s female CEO dummy. To be an instrumental variable,

the ratio of industry female CEOs to total CEOs must satisfy two conditions: (1) it must be

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significantly related to firm’s female CEO dummy, and (2) it impacts firm performance only

through firm’s female CEO choice. Because the proportion of industry female CEOs to total CEOs

mainly measures the position of industry female CEOs in the markets, it is difficult to believe that

its impact on firm performance is not through firm’s CEO choice. Therefore, we focus on the first

condition by examining the relation between this ratio and firm’s CEO gender.

The first column of Table 9 presents the first-stage regression results of female CEO

dummy on the ratio of industry female CEOs to total CEOs and other control variables used in our

baseline regression model (1). The results show that the ratio of industry female CEOs to total

CEOs is significantly positively correlated with firm’s female CEO dummy. This coefficient is

12.498 with p-values is 0.000, significant at 1% level. We then apply Cragg-Donald Wald F-

statistic and Stock and Yogo tests for weak instrument. The results (not reported) show that the

null hypothesis of weak instrument is statistically rejected, suggesting that the ratio of industry

female CEOs is a valid instrument for firm’s female CEO choice.

***** Insert Table 9 here*****

The results from second stage regressions are reported in columns 2-6. Columns 2 - 4 show

that female CEO dummy is positively correlated with three measures of firm performance.

Consistent with the results in Table 5 and 8, these results imply that firms with female CEOs tend

to perform better than firms with male CEOs.

In contrast to the positive association between CEO choice and firm performance, the effect

of female CEO dummy on firm risk taking is negative. Columns 5 and 6 demonstrate that firms

with female CEOs tend to face less both systematic risk and total risk in the future. These results

are consistent with results in Table 6 and 8. The effects of other firm characteristics on firm

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performance and risk are also consistent with the results in the previous tables. For example, firms

with high return on equity, tangible assets, dividend yield or cash holding tend to perform better

and experience less risk.

6. Conclusion

This paper investigates the role of female CEOs in firm operations in Vietnam. Using data

of Vietnamese listed companies from 2007 to 2015, we show that 6.18% of which are run by

female CEOs. We further demonstrate that firms with female CEOs tend to cluster in agriculture

and service industries such as inland transportation, financial and publishing services. Different

from male-led firms, firms with female CEOs tend to be larger and have higher cash holding, stock

liquidity and profitability.

We find that firms with female CEOs tend to generate higher profitability and face less risk

than firms with male CEOs. This finding is consistent when we either focus on matched sample

only, or employ 2SLS method with instrumental variable.

Our findings support the hypothesis that women offer unique perspectives, experiences and

styles of work compared to their male counterparts, leading to differences in leadership

effectiveness between women and men. In addition, men are more overconfident and women are

more risk averse and more collaborative, which allow firms with female CEOs avoid many

negative NPV projects and receive valuable advice from board of directors and other stakeholders.

Paper #720314

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Appendix 1: Variable Definition

All variables are at firm level for each current year (t) except otherwise noted.

Variable Definition

CEO Variables FCEO Dummy variable which is equal to 1 if firm’s CEO is female and 0 otherwise

Firm performance ROA Return on assets ROE Return on equity Q The Tobin’s Q, which is the market value of equity plus total debt to book

value of total assets BETA Systematic risk which is estimated from CAPM model of firm’s stock returns

on market portfolio returns over a year VOL Total risk which is standard deviation of firm’s daily stock returns over a

year Firm characteristics LAT The natural logarithm of total assets TANG The ratio of tangible assets to total assets TDEBT The ratio of total debt to total assets BEAT The ratio of book equity to total assets DIV The ratio of cash dividend to total assets ILLIQ The relative effective spread, which is computed as the ratio of the difference

in trading price and midpoint to midpoint MOWN The ratio of managerial ownership LAGE The natural logarithm of firm age until 2015

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Table 1: Descriptive Statistics

This table reports the descriptive statistics for the main variables for firms in our sample. All variables are defined in Appendix 1. Sample includes all Vietnamese listed firms from 2007-2015 and excludes firms having total assets or sales volume less than 1 million VND. N is the number of observations. All variables are winsorized at 1st and 99th percentiles.

Panel A: Descriptive Statistics

Variable N Mean Std Dev Min 25th

Pctl Median 75th Pctl Max

CEO Gender

FCEO 4,253 0.062 0.241 0.000 0.000 0.000 0.000 1.000

Firm Performance

ROA 4,253 0.052 0.077 -0.208 0.011 0.043 0.089 0.301 ROE 4,253 0.086 0.226 -1.340 0.033 0.109 0.183 0.494 Q 4,253 1.472 0.531 0.506 1.158 1.423 1.671 4.016

Firm Risk Measure

BETA 4,253 0.912 0.788 0.008 0.342 0.746 1.246 4.463 VOL 4,253 3.300 0.929 1.499 2.650 3.212 3.827 6.523

Firm Characteristics

LAT 4,253 13.019 1.432 9.904 12.097 12.948 13.955 16.796 TANG 4,253 0.193 0.191 0.000 0.050 0.129 0.270 0.828 TDEBT 4,253 0.516 0.225 0.046 0.339 0.542 0.692 0.941 BEAT 4,253 0.475 0.226 0.001 0.295 0.451 0.651 0.998 DIV 4,253 0.026 0.038 0.000 0.000 0.013 0.039 0.554 CASH 4,253 0.094 0.105 0.001 0.019 0.055 0.132 0.499 ILLIQ 4,253 3.704 2.241 0.906 2.276 3.083 4.510 15.552 MOWN 4,253 0.053 0.097 0.000 0.001 0.011 0.055 0.515 LAGE 4,253 3.129 0.529 2.079 2.708 3.135 3.584 4.111

Panel B: Female CEOs through time

Year #Firms #FCEO Ratio 2007 213 21 9.86% 2009 408 27 6.62% 2010 444 29 6.53% 2011 646 32 4.95% 2012 650 35 5.38% 2013 644 38 5.90% 2014 637 41 6.44% 2015 611 40 6.55% Total 4,253 263 6.18%

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Table 2: Female CEOs in Different Industries

This table presents the descriptive statistics for CEO gender and dependent variables in 10 industries with highest female CEO ratios. Sample includes all Vietnamese listed firms in these industries from 2007-2015 and excludes firms having total assets or sales volume less than 1 million VND. All variables are winsorized at 1st and 99th percentiles.

Industry Obs FCEO ROA ROE Q BETA VOL 1 Inland transportation

services 28 0.286 0.038 0.021 1.072 0.829 3.321

2 Agriculture 8 0.250 0.107 0.147 1.171 0.694 3.246 3 Financial services 27 0.222 0.041 0.032 1.253 1.128 3.236 4 Repairing and guarantee

services 5 0.200 0.009 0.133 1.923 0.710 3.225

5 Publishing services 118 0.178 0.083 0.134 1.269 0.635 3.437 6 Pharmacy 152 0.178 0.108 0.158 1.690 0.677 2.907 7 Food and beverage 308 0.123 0.060 0.084 1.632 0.835 3.040 8 Farming 67 0.119 0.129 0.190 1.657 0.718 2.526 9 Telecommunication 45 0.111 0.064 0.120 1.380 0.750 3.206 10 Real estates 361 0.105 0.034 0.061 1.288 1.172 3.242 Sample average 4,253 0.062 0.052 0.086 1.472 0.912 3.300

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Table 3: Female CEOs and Firm Characteristics

This table shows the descriptive statistics for firms with female (FCEO) and male CEOs (MCEO). Sample includes all Vietnamese listed firms from 2007-2015 and excludes firms having total assets or sales volume less than 1 million VND. All variables are winsorized at 1st and 99th percentiles. *, ** and *** denote statistical significance at 10%, 5% and 1%, respectively.

Variable FCEO MCEO Mean difference

Firm performance

ROA 0.078 0.051 0.028*** ROE 0.125 0.085 0.040*** Q 1.645 1.463 0.182***

Risk measures

BETA 0.807 0.912 -0.106*** VOL 3.028 3.315 -0.287***

Firm characteristics

LAT 13.338 13.050 0.289*** TANG 0.143 0.168 -0.026*** TDEBT 0.431 0.523 -0.092*** BEAT 0.561 0.468 0.092*** DIV 0.033 0.025 0.008*** CASH 0.109 0.092 0.017*** ILLIQ 3.311 3.739 -0.428*** MOWN 0.079 0.056 0.023*** LAGE 3.138 3.150 -0.012*** Obs 263 2,901

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Table 4: Correlation Matrix

This table reports the paired correlations between variables used in our paper. Sample includes all Vietnamese listed firms from 2007-2015 and excludes firms having total assets or sales volume less than 1 million VND. All variables are winsorized at 1st and 99th percentiles. *, ** and *** denote statistical significance at 10%, 5% and 1%, respectively.

Panel A: The Correlations between CEO Gender and Firm Performance

FCEO ROA ROE Q BETA VOL FCEO 1.000

ROA 0.085*** 1.000

(0.00)

ROE 0.044*** 0.763*** 1.000

(0.00) (0.00)

Q 0.084*** 0.317*** 0.182*** 1.000

(0.00) (0.00) (0.00)

BETA -0.034** -0.037** -0.025 0.083*** 1.000

(0.03) (0.02) (0.11) (0.00)

VOL -0.075*** -0.130*** -0.095*** 0.082*** 0.283*** 1.000 (0.00) (0.00) (0.00) (0.00) (0.00)

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Panel B: The Correlations between CEO Gender and Firm Characteristics

FCEO LAT TANG TDEBT BEAT DIV CASH ILLIQ MOWN LAGE FCEO 1.000

LAT 0.057*** 1.000

(0.00)

TANG -0.067*** -0.013 1.000

(0.00) (0.40)

TDEBT -0.097*** 0.318*** 0.004 1.000

(0.00) (0.00) 0.79

BEAT 0.097*** -0.338*** 0.001 -0.989*** 1.000

0.00 0.00 0.94 0.00

DIV 0.047*** -0.120*** 0.032* -0.383*** 0.390*** 1.000

0.00 0.00 0.04 0.00 0.00

CASH 0.036** -0.084*** -0.125*** -0.307*** 0.306*** 0.362*** 1.000

0.02 0.00 0.00 0.00 0.00 0.00

ILLIQ -0.045*** -0.360*** -0.022 0.030* -0.019 -0.079*** 0.010 1.000

0.00 0.00 0.15 0.05 0.22 0.00 0.53

MOWN 0.069*** 0.028* -0.076*** 0.052*** -0.058*** -0.085*** -0.099*** 0.025 1.000

0.00 0.07 0.00 0.00 0.00 0.00 0.00 0.10

LAGE 0.005 0.018 0.106*** 0.078*** -0.075*** 0.080*** 0.066*** -0.001 -0.063*** 1.000 0.76 0.25 0.00 0.00 0.00 0.00 0.00 0.97 0.00

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Table 5: CEO Gender and Firm Performance

This table reports the results from the regression of firm performance on female CEO dummy: PERFORMi,t+1 = β0 + β1FCEOi,t + β2FIRM-CHARi,t + Β3Industry-Dummyj,t + β4Year-Dummyt + ԑi,t

Where PERFORM is one of three measures of firm performance which are return on assets and return on equity ratios, or Tobin’s Q. FIRM-CHAR is a set of firm characteristics. All variables are defined in Appendix 1. Sample includes all Vietnamese listed firms from 2007-2015 and excludes firms having total assets or sales volume less than 1 million VND. All variables are winsorized at 1st and 99th percentiles. Standard errors are clustered by firm and p-values are reported in parentheses. *, ** and *** denote statistical significance at 10%, 5% and 1%, respectively.

(1) (2) (3) ROAi,t+1 ROEi,t+1 Qi,t+1

FCEOi,t 0.011*** 0.021** 0.060** (0.002) (0.030) (0.049) LATi,t 0.000 0.001 0.004 (0.766) (0.758) (0.364) TANGi,t 0.015** 0.039 0.008 (0.015) (0.115) (0.800) TDEBTi,t -0.069** -0.234** -0.018 (0.031) (0.019) (0.930) BEATi,t -0.065** -0.212** -0.271 (0.041) (0.035) (0.184) DIVi,t 0.374*** 0.679*** 1.366*** (0.000) (0.000) (0.000) CASHi,t 0.043*** 0.137*** 0.130* (0.000) (0.000) (0.055) ILLIQi,t -0.001 -0.002 0.010** (0.586) (0.633) (0.020) MOWNi,t -0.007 -0.011 0.075 (0.445) (0.754) (0.173) LAGEi,t 0.004** 0.011 0.013 (0.039) (0.218) (0.172) VOLi,t -0.002 -0.008 -0.021** (0.171) (0.201) (0.011) ROAi,t 0.535*** (0.000) ROEi,t 0.522*** (0.000) Qi,t 0.709*** (0.000) Intercept 0.064 0.212* 0.295 (0.103) (0.091) (0.202) Ind. FE Yes Yes Yes Time FE Yes Yes Yes N 3,174 3,174 3,174 Adj. R2 0.5672 0.3228 0.6769

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Table 6: CEO Gender and Firm Risk Taking

This table reports the results from the regressions of firm performance on female CEO dummy: RISKi,t+1 = β0 + β1FCEOi,t + β2FIRM-CHARi,t + Β3Industry-Dummyj,t + β4Year-Dummyt + ԑi,t

Where RISK is either systematic risk or total risk. FIRM-CHAR is a set of firm characteristics. All variables are defined in Appendix 1. Sample includes all Vietnamese listed firms from 2007-2015 and excludes firms having total assets or sales volume less than 1 million VND. All variables are winsorized at 1st and 99th percentiles. Standard errors are clustered by firm and p-values are reported in parentheses. *, ** and *** denote statistical significance at 10%, 5% and 1%, respectively.

(1) (2) (3) (4) BETA i,t+1 BETA i,t+1 VOL i,t+1 VOL i,t+1 FCEOi,t -0.092* -0.048 -0.150** -0.073 (0.096) (0.354) (0.016) (0.172) LATi,t 0.051*** 0.037*** -0.141*** -0.109*** (0.000) (0.003) (0.000) (0.000) TANGi,t -0.080 0.004 -0.338*** -0.194** (0.280) (0.960) (0.000) (0.030) TDEBTi,t -0.790** -0.691** -0.250 0.073 (0.034) (0.029) (0.550) (0.822) BEATi,t -0.706* -0.616* -0.753* -0.336 (0.059) (0.055) (0.074) (0.304) DIVi,t -1.902*** -1.622*** -2.647*** -1.901*** (0.000) (0.000) (0.000) (0.000) CASHi,t -0.472*** -0.302** -0.497*** -0.209 (0.000) (0.011) (0.008) (0.216) ILLIQi,t 0.012 0.012 0.167*** 0.096*** (0.227) (0.220) (0.000) (0.000) MOWNi,t -0.041 -0.003 -0.184 -0.099 (0.766) (0.979) (0.325) (0.545) LAGEi,t -0.076*** -0.036 -0.117*** -0.071** (0.007) (0.179) (0.001) (0.015) ROEi,t -0.117 -0.088 -0.512*** -0.476*** (0.169) (0.278) (0.000) (0.000) BETAi,t 0.125*** (0.000) VOLi,t 0.355*** (0.000) Intercept 1.866*** 1.624*** 5.769*** 3.602*** (0.000) (0.000) (0.000) (0.000) Ind. FE No Yes No Yes Time FE Yes Yes Yes Yes N 3,174 3,174 3,174 3,174 Adj. R2 0.1598 0.1940 0.3001 0.3727

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Table 7: Matching Sample

Panel A of this table presents the diagnostics and results from the regression of female CEO dummy on firm characteristics: P(FCEOi,t =1⃓X) = Ф(XTβ), where FCEO is a dummy variable which is equal to 1 if firm’s CEO is female and 0 otherwise. X is a set of firm characteristics shown in appendix 1. All variables are winsorized at 1st and 99th percentiles. Standard errors are clustered by firm and p-values are reported in parentheses. *, ** and *** denote statistical significance at 10%, 5% and 1%, respectively. Panel B shows the descriptive statistics for firms in both treatment and control groups. Panel A: Propensity score matching

(1) (2) Whole sample Matched sample FCEOi,t FCEOi,t LATi,t 0.115*** 0.013 (0.004) (0.810) ROEi,t 0.369 0.425 (0.279) (0.431) TANGi,t -1.149 0.293 (0.001) (0.550) TDEBTi,t 2.282 0.291 (0.178) (0.914) BEATi,t 3.282* 0.480 (0.052) (0.857) DIVi,t 0.047 -2.553 (0.972) (0.195) CASHi,t -0.447 0.678 (0.319) (0.380) ILLIQi,t -0.060* 0.017 (0.082) (0.769) MOWNi,t 1.189 0.486 (0.001) (0.366) LAGEi,t 0.166 -0.003 (0.064) (0.980) Intercept -10.005 -0.714 (0.958) (0.813) Ind. FE Yes Yes Time FE Yes Yes N 2,782 368 Pseudo R2 0.1237 0.0072

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Panel B: Firm characteristics of two groups

FCEO MCEO Difference (P-value) LAT 13.269 13.269 0.000 (0.999) ROE 0.132 0.126 0.006 (0.690) TANG 0.132 0.126 0.006 (0.685) TDEBT 0.432 0.436 -0.004 (0.838) BEAT 0.558 0.554 0.004 (0.842) DIV 0.035 0.039 -0.003 (0.457) CASH 0.106 0.100 0.006 (0.534) ILLIQ 2.949 2.895 0.054 (0.708) MOWN 0.083 0.071 0.012 (0.346) LAGE 3.132 3.125 0.007 (0.893) Obs 183 183

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Table 8: Firm Performance and Risk Taking in Matched Sample This table reports the results from the regressions of firm performance and risk on female CEO dummy: PERFORM/RISKi,t+1 = β0 + β1FCEOi,t + β2FIRM-CHARi,t + Β3Industry-Dummyj,t + β4Year-Dummyt + ԑi,t

Where PERFORM is one of three measures of firm performance which are return on assets and return on equity ratios, or Tobin’s Q. RISK is either systematic or total risk. FIRM-CHAR is a set of firm characteristics. All variables are defined in Appendix 1. All variables are winsorized at 1st and 99th percentiles. Standard errors are clustered by firm and p-values are reported in parentheses. *, ** and *** denote statistical significance at 10%, 5% and 1%, respectively.

(1) (2) (3) (4) (5) ROAi,t+1 ROE,t+1 Q,t+1 BETA,t+1 VOL,t+1 FCEOi,t 0.012** 0.019* 0.037 -0.065 -0.155** (0.013) (0.960) (0.254) (0.340) (0.035) LATi,t 0.004 0.012* -0.016 -0.014 -0.177*** (0.144) (0.076) (0.365) (0.679) (0.000) TANGi,t 0.039 0.124 -0.169 -0.317 -0.245 (0.145) (0.104) (0.476) (0.302) (0.582) TDEBTi,t 0.050 0.016 0.262 -1.486 2.760* (0.686) (0.958) (0.733) (0.437) (0.092) BEATi,t 0.074 0.060 -0.010 -1.820 2.113 (0.553) (0.836) (0.990) (0.339) (0.195) DIVi,t 0.207* 0.372 1.344* 0.163 -2.230 (0.097) (0.176) (0.079) (0.878) (0.201) CASHi,t 0.066** 0.115* 0.288 -0.014 0.117 (0.042) (0.058) (0.159) (0.969) (0.823) ILLIQi,t 0.005** 0.015** 0.037** 0.016 0.073 (0.028) (0.018) (0.032) (0.540) (0.103) MOWNi,t -0.002 -0.018 0.164 -0.164 -0.067 (0.939) (0.787) (0.183) (0.593) (0.796) LAGEi,t 0.001 -0.012 0.019 -0.075 -0.119 (0.908) (0.421) (0.640) (0.350) (0.219) ROAi,t 0.639*** (0.000) VOLi,t -0.005 -0.011 -0.093*** 0.249*** (0.195) (0.225) (0.008) (0.001) ROEi,t 0.650*** 0.006 -0.494* (0.000) (0.985) (0.100) Qi,t 0.869*** (0.000) BETAi,t 0.135*** (0.001) Intercept -0.122 -0.187 0.196 3.200 2.542 (0.400) (0.571) (0.837) (0.121) (0.173) Ind. FE Yes Yes Yes Yes Yes Time FE Yes Yes Yes Yes Yes N 372 372 372 372 372 Adj. R2 0.6556 0.3424 0.7708 0.1843 0.3337

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Table 9: CEO Gender and Firm Performance – IV approach This table reports the results from the first and second stages of the 2SLS regression of firm performance and risk taking on female CEO dummy and other control variables. The external instrumental variable is the ratio of number of industry female CEOs to total CEOs. All other variables are defined in Appendix 1. Sample includes all Vietnamese listed firms from 2007-2015 and excludes firms having total assets or sales volume less than 1 million VND. All variables are winsorized at 1st and 99th percentiles. Standard errors are clustered by firm and p-values are reported in parentheses. *, ** and *** denote statistical significance at 10%, 5% and 1%, respectively.

(1) (2) (3) (4) (5) (6) FCEOi,t ROAi,t+1 ROE,t+1 Q,t+1 BETA,t+1 VOL,t+1 IV 12.498*** (0.000) FCEOi,t 0.133** 0.396* 0.308 -1.268 -1.563* (0.027) (0.079) (0.161) (0.135) (0.069) LATi,t 0.014*** -0.001 -0.004 0.003 0.085*** -0.089*** (0.001) (0.378) (0.476) (0.528) (0.000) (0.000) TANGi,t -0.076*** 0.025*** 0.068** 0.025 -0.115 -0.310*** (0.008) (0.003) (0.031) (0.452) (0.331) (0.008) TDEBTi,t 0.169 -0.087** -0.285** -0.003 -0.650 0.284 (0.235) (0.019) (0.037) (0.983) (0.206) (0.568) BEATi,t 0.272* -0.095** -0.301** -0.290* -0.447 0.028 (0.057) (0.015) (0.039) (0.066) (0.415) (0.958) DIVi,t 0.103 0.381*** 0.654*** 1.437*** -0.858* -1.793*** (0.469) (0.000) (0.000) (0.000) (0.088) (0.000) CASHi,t -0.028 0.049*** 0.151*** 0.155*** -0.372** -0.265 (0.559) (0.000) (0.001) (0.003) (0.025) (0.103) ILLIQi,t -0.005* -0.000 -0.001 0.007* -0.048*** 0.093*** (0.100) (0.745) (0.677) (0.054) (0.000) (0.000) MOWNi,t 0.163*** -0.026* -0.070 0.042 0.002 0.135 (0.000) (0.070) (0.197) (0.465) (0.992) (0.498) LAGEi,t 0.013 0.003 0.007 0.012 -0.062* -0.055* (0.132) (0.262) (0.421) (0.209) (0.050) (0.075) ROEi,t 0.016 0.519*** -0.009 -0.465*** (0.519) (0.000) (0.918) (0.000) ROAi,t 0.518*** (0.000)

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Qi,t 0.694*** (0.000) BETAi,t 0.098*** (0.000) VOLi,t 0.329*** (0.000) Intercept -0.482** 0.112** 0.353* 0.297 0.849 2.932*** (0.012) (0.044) (0.088) (0.186) (0.276) (0.000) Ind. FE Yes Yes Yes Yes Yes Yes Time FE Yes Yes Yes Yes Yes Yes N 3,174 3,174 3,174 3,174 3,174 3,174 Adj. R2 0.0572 0.4360 0.1912 0.6577 0.1236 0.2245

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