the relationship between board gender diversity and firm ... · the upper echelons theory (uet) is...

The Relationship between Board Gender Diversity and Firm Performance: A

Comprehensive Evaluation

J.D Jayaraman, Ph.D.

Associate Professor

Department of Finance

New Jersey City University

Prathibha Amand.

Verisk Analytics, Inc.

Address for correspondence:

J.D Jayaraman, Ph.D.

Department of Finance

School of Business

New Jersey City University

Harborside 2, Jersey City, NJ 07311

Email: [email protected]

Phone: 917-514-4277

Prathibha Amand

Verisk Analytics, Inc.

545 Washington Blvd, Jersey City, NJ 07310

[email protected]

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The Relationship between Board Gender Diversity and Firm Performance: A

Comprehensive Evaluation

Abstract

In the recent past board gender diversity has garnered significant academic and media interest.

Whether increasing gender diversity on boards leads to better firm financial performance is still

an open question. We investigated the relationship between board gender diversity (BGD) and

firm performance (FP) using a large sample of the firms in the Russell 3000, S&P Europe 350,

and S&P Asia 50 indices, over the most recent 11 years (2008 – 2018). To identify the effect of

BGD on firm performance we used a dynamic panel model that accounts for the endogeneity

biases that are inherent in the BGD-FP relationship. We do not find evidence of any relationship

between BGD-FP. We also investigated the existence of a critical mass of 30% women directors

beyond which firm performance begins to improve, suggesting a “U” shaped relationship

between BGD and FP. We found no evidence of a critical mass nor a “U” shaped relationship.

Our results were robust and held up under extensive robustness checks.

Keywords: Board gender diversity, corporate governance, women on board, critical mass

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Introduction

The composition of a company’s board of directors has long been an important area of

corporate governance research. Corporate boards make important decisions that impact the lives

of not only the company’s employees, but also the community, country and the global market.

In the recent past there has been increased focus on gender diversity in corporate boards and a

move towards increasing the proportion of women on corporate boards. Many countries (e.g.

Norway, Spain, Belgium, and India) have imposed quotas for women on the board. In this

current environment of heightened interest in board gender diversification, whether gender

diversity on boards impacts firm performance becomes an important research question.

Although there is a fairly large body of literature on board gender diversity (BGD) and

firm performance (FP), the debate on whether higher board gender diversity leads to better firm

performance is anything but settled. Empirical evidence for the relationship between BGD and

FP is inconsistent and inconclusive. One set of studies suggest that women directors add value

and have a positive impact on the firm’s financial performance (Nguyen & Faff, 2012; Singh,

Vinnicombe, & Johnson, 2001; Erhardt, Werbel, & Shrader, 2003, Campbell & Mínguez-Vera,

2008; Carter, Simkins, & Simpson, 2003; Francoeur, Labelle, & Sinclair-Desgagné, 2008) while

another stream of literature finds exactly the opposite - a negative relationship between BGD and

FP (Darmadi, 2011; Minguez-Vera & Martin, 2011; Bøhren & Strøm, 2010; Adams & Ferreira,

2009; Haslam, Ryan, Kulich, Trojanowski, & Atkins, 2010). A third stream of literature finds no

relationship between BGD and FP (Carter, D'Souza, Simkins, & Simpson, 2010; Rose, 2007;

Shrader, Blackburn, & Iles, 1997). Thus, the impact of board gender diversity on firm

performance is still an open question that warrants further research.

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The other question that has been less extensively explored in the literature is whether

there is a critical mass of women directors beyond which there is a positive impact of BGD on

FP. The argument is that there is a threshold number of women directors that needs to be

reached to move from tokenism, break gender barriers and reap the benefits of gender diversity.

The current empirical evidence seems to suggest that there is a critical mass of women directors

somewhere around 30% (Wiley & Monllor-Tormos, 2018; Cohen, Broschak, & Haveman, 1998;

Joecks et al., 2013; Kramer et al., 2006). But, these studies use small restricted samples over

short and older time periods (e.g. Joecks et al, 2013 uses a sample of 151 firms in Germany from

2000 to 2005) and most do not account for endogeneity issues that peril empirical investigations

of such relationships. Thus, the existence of a critical mass of women directors is still an open

question and warrants further investigation.

Our study contributes to the BGD – FP relationship literature by investigating the

relationship between board gender diversity and firm performance using a large sample of all the

firms in the Russell 3000 for a period of 11 years (2008 – 2018). The Russell 3000 represents

about 98% of all U.S. incorporated stocks. We also investigate the BGD – FP relationship in

non-U.S. companies using firms in the S&P Europe 350, and S&P Asia 50 indices. We

contribute to the critical mass literature by investigating the existence of a critical mass of

women directors in the large sample described above. We employ a robust statistical

methodology (Dynamic Panel System Generalized Method of Moments) that takes into account

endogeneity biases that makes results questionable if not accounted for (a majority of the extant

literature on BGD and FP is plagued by endogeneity issues). To the best of our knowledge ours

is one of the largest and most comprehensive sample in which BGD – FP relationship and the

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existence of a critical mass has been investigated using a robust statistical methodology that

accounts for endogeneity issues.

The rest of the paper is organized as follows: We present an overview of the literature

which is followed by the development of the research questions and hypotheses. We then

describe the sample and methodology. Next we present the results and discuss them. Finally we

conclude by discussing some limitations and suggestions for further research.

Literature Review

Empirical evidence of the relationship between board gender diversity and firm financial

performance is ambiguous and inconclusive. All three possibilities – positive, negative, none –

have been found in the literature. We present a brief overview of the key theories that have been

put forth to explain the BGD-FP relationship and then discuss the three strands of literature on

the relationship between BGD and FP.

Board Gender Diversity Theories

The Agency Theory asserts that the separation of ownership and control creates agency

costs due to managers acting in their own interest rather than in the interest of the shareholders.

Boards are one way to mitigate this agency cost. Based on agency theory, researchers have

proposed that greater diversity in the board will lead to greater monitoring of managers and thus

improve firm performance (Carter et al. (2003); Hillman & Dalziel, 2003; Pfeffer & Salancik,

1978). Thus the agency theory suggests a positive relationship between BGD and FP with

increased gender diversity in boards leading to better firm performance. There is some empirical

support for the positive effect of women on the monitoring responsibilities of the board. Women

directors and culturally diverse board members could put forth unique questions (Campbell &

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Mínguez-Vera, 2008) and introduce new perspectives (Rosener, 1990) that can add value to the

decision making process and increase board independence (Carter et al., 2003).

The Resource Dependence Theory (RDT) is another theory that has been used to explain

the BGD-FP relationship. The crux of this theory is that companies must effectively manage

uncertainity in their environement for them to be successful. Board members help the company

in obtaining resources that may not be otherwise available, through their contacts and

networking. Women on boards help with access to a broader range of stakeholders (Siciliano,

1996). Women board directors increase the legitimacy of an organization (Hillman & Dalziel,

2003) and increase information exchange (Larcker, So, & Wang, 2013), which, in turn, enhances

customer and employee relations (Hillman, Shropshire, & Cannella, 2007) and ultimatley leads

to increased firm performance. Thus, RDT also suggests a positive relationship between BGD

and FP.

The Upper Echelons Theory (UET) is yet another theory that focuses on upper

management and has been used to explain the relationship between BGD and FP. According to

UET, directors have different cognitive frames, and these cognitive frames (information-seeking

and evaluation processes) impact firm performance (Hambrick, 2007). Observable

characteristics of directors such as their race or gender have been used as proxies for cognitive

frames (Dezsö & Ross, 2012; Krishnan & Park, 2005). Female directors are likely to bring

different cognitive frames to a board due to differences in experiences and knowledge. For

example, female directors tend to have more university degrees and are more likely to hold

advanced degrees than male directors (Carter et al., 2010; Hillman, Cannella, & Paetzold, 2000;

Hillman, Cannella, & Harris, 2002) and are stronger in marketing and sales (Groysberg & Bell,

2013). Women board directors are less likely to have been CEOs or COOs and predominantly

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come from non-business backgrounds (Hillman et al., 2002; Singh et al., 2008). Female

directors are likely to have new and different understandings of consumer markets (Bilimoria &

Wheeler, 2000; Campbell & Minguez-Vera, 2008; Carter, Simkins, & Simpson, 2003) and thus

can enrich the board. Thus, the UET posits a positive relationship between BGD and FP.

There are several theories that suggest a negative relationship between BGD and FP.

Social Identity Theory (Tajfel, 1978; Ashforth & Mael, 1989) suggests that people maintain a

positive identity by associating themselves with similar people, and this can lead to in-group vs

out-of-group friction. Another theory called the Similarity Attraction Theory (Byrne, 1971)

posits that birds of a feather flock together and thus tensions can arise between groups. Yet

another theory called the Self-Categorization Theory (Turner et al., 1987) suggests that people

create social categories based on external characteristics like gender, which leads to inter group

conflicts. The negative group dynamics described by these theories hinders communication

between male and female directors and generates distrust (Jehn, Northcraft, & Neale, 1999;

Milliken & Martins, 1996). Thus, these theories suggest that firm performance will be

negatively impacted with more women on the board.

The Critical Mass Theory (CMT) tries to reconile between the poitive and negative

theories and suggests that a critical mass of women directors are needed before there is a positive

impact on firm performance. For example Konrad et al. (2008) posit that with three or more

women on the board, the focus changes from gender to talent, thus reducing any bias they may

feel (Torchia et al., 2011). Female directors could be perceived as “tokens” and not trusted or

respected when there are only a few of them (Torchia et al., 2011). Thus, the CMT implies a

“U” shaped relationship between BGD and FP.

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Having summarized the popular theories explaining the BGD-FP relationship, we

proceed to provide an overview of the literature that has found empirical evidence of a positive,

negative or no realtionship between BGD-FP.

Empirical evidence

There have been a plethora of studies that have examined the empirical evidence for the

relationship between BGD and FP. Burke (1997), was one of the first researchers who found

that companies with women on their board had a significant competitive advantage, which

resulted in higher sales. Singh & Vinnicombe (2004) analyzed UK companies and found that

companies with greater BGD had better governance and higher market capitalization. De Luis

Carnicer et al. (2008) found a positive relationship between BGD and accounting returns using a

sample of 2,000 Spanish companies.

Other studies have found a positive relationship between BGD and improved profitability

(Erhardt, Werbel, & Shrader, 2003), higher firm value (Campbell & Mínguez-Vera, 2008;

Carter, Simkins, & Simpson, 2003), and higher abnormal returns (Francoeur, Labelle, &

Sinclair-Desgagné, 2008).

On the other hand there have been several studies that have found a negative relationship

between BGD and FP. Adams and Ferreira (2009) found a negative relationship between BGD

and FP (as measured by Tobin’s Q and ROA) in a sample of U.S.. firms from 1996 to 2003.

Similarly, Haslam, Ryan, Kulich, Trojanowski, and Atkins (2010), in a study of the FTSE 100

companies between 2001 to 2005, found that BGD had a negative influence on ROA and ROE.

Watson and Robinson (2003) found a negative relationship between BGD and FP and attributed

it to women being more risk averse.

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Contrary to all the above findings many studies found no relationship between BGD and

FP. Carter et al. (2010) studied companies in the S&P 500 from 1998 to 2002 and found no

evedence of a relationship between BGD and FP. Rose (2007) analyzed Danish companies,

excluding banks and insurance companies found no relationship between BGD and Tobin’s Q.

Shrader, Blackburn, & Iles (1997) found that BGD is unrelated to ROI, ROE and ROA.

A succinct summary of the literature from the Oxford Handbook of Strategy

Implementation (Hitt et al., 2017) is shown in Table 1.

---------------------------------

Insert Table 1 about here

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There have been a couple of meta analysis conducted to investigate the relationship

between BGD and FP found in the extant literature. Post and Byron (2015) analyzed the

findings from 140 studies of board gender diversity with a combined sample of more than 90,000

firms from more than 30 countries and found weak evidence of a positive relationship between

BGD and accounting returns. The strength of the relationship was found to be close to zero.

Pletzer, Nikolova, Kedzior, and Voelpel (2015) conducted a meta analysis on 20 studies with

3057 companies and found no relationship between BGD and FP. Thus, the meta analysis

studies, which combine the conflicting findings in the literature to provide an overall effect,

conclude that the relationship between BGD and FP is very weak at best and close to zero.

Now we turn to the literature on the existence of a critical mass in the BGD-FP

relationship. Kramer, Konrad, Erkut, and Hooper (2006) carried out a qualitative study

by interviewing 50 women directors, 12 CEOs, and seven corporate secretaries from Fortune

1000 companies and found that a critical mass of three or more women could enhance corporate

governance. Strydom et al. (2016), in an analysis of Australian firms from 2005 to 2013,

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suggested the existence of a U-shaped relationship between BGD and earnings and indicated

30% as the tipping point. Joecks et al. (2013) studied 151 German companies over 2000 to 2005

and found that boards with three or more women directors are associated with higher ROE.

Wiley & Monllor-Tormos (2018) found a “U” shaped relationship between number of female

directors and firm performance measured by Tobin’s Q, in a sample of Fortune 500 firms in the

science, technology and finance firms. They also found evidence of critical mass at 30%.

Research Questions and Hypotheses

Although Agency Theory, Resource Dependence Theory, Social Identity Theory,

Similarity Attraction Theory and Self-Categorization Theory have been used to explain the

relationship between BGD and FP, the Upper Echelons Theory (UET) provides a strong and

clear theoretical foundation for connecting board gender diversity and firm performance (Post &

Byron, 2015) and hence we use the UET as the theoretical framework for our hypothesis

development.

One of the research questions that this study attempts to answer is whether there is any

relationship between board gender diversity and firm performance. Based on the Upper

Echelons Theory we propose that women on boards of directors, due to their different cognitive

frames and differences in terms of knowledge, experience, and values, shape both the content

and process of board decision-making and board activities that have a positive impact on firm

financial performance. This leads to our first hypothesis:

Hypothesis 1: Board gender diversity is positively related to firm financial performance. More

specifically, the number of female board directors is positively related to market performance as

measured by Tobin’s Q and accounting returns as measured by ROA and ROE.

We investigate this hypothesis in three regions of the world – U.S., Europe and Asia.

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The second research question that this study attempts to answer is whether there exists a

critical mass of female directors beyond which there is a positive effect of increased board

gender diversity on firm financial performance. We draw on the Critical Mass Theory (CMT) to

formulate our second hypothesis. CMT suggests that there is a quadratic “U” shaped

relationship between BGD and FP and the empirical evidence suggests that the critical mass is at

30% female directors. This leads us to our second hypothesis:

Hypothesis 2: There exists a quadratic “U” shaped relationship between BGD and FP, with a

critical mass at 30%. More specifically, there exists a quadratic relationship between the

number of female directors on a board and market performance as measured by Tobin’s Q and

accounting returns as measured by ROA and ROE and this relationship is moderated by a

critical mass of 30% female board directors.

Again, we test this hypothesis in three regions of the world – U.S., Europe, Asia.

Methodology

Data

Extant literature on the relationship between BGD and FP has used sample sizes

anywhere from 112 firms over one year (Erhardt et al.,2003) to 1939 firms over eight years

(Adams & Ferriera, 2009). Our study uses all the firms in the Russell 3000 index for the U.S.,

S&P Europe 350 for Europe, S&P Asia 50 for Asia. The choice of time period for the analysis is

important to the results of the study. Many studies in the extant literature use older time frames

that may not be all that relevant in the current environment. The 2008 financial crisis brought

into focus the predominantly male dominated boards of U.S. companies and sparked a debate on

whether greater gender diversity could have prevented certain masculine behavior that allegedly

contributed to the financial crisis (McDowell, 2011; Van Staveren, 2014). There is some

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evidence that since 2008 the male dominated boardroom culture has begun to change and

become more inclusive, female directors are more likely to be accepted and boardroom gender

bias has decreased (Jonsdottir, Singh, Terjesen, & Vinnicombe, 2015; Ryan & Haslam, 2007;

Sun, Zhu, & Ye, 2015). Hence, we chose a time period of the most recent eleven years from

2008 to 2018 for our study. Corporate governance data and financial performance data were

obtained from 2008 to 2018 from Bloomberg. Thus, our study is one of the most comprehensive

in terms of sample size and most recent in terms of time frame.

Variables

Dependent variables

The dependent variable is firm performance. Firm financial performance can be

measured by accounting returns and market performance. Accounting returns refers to how well

a firm uses its assets and investments to generate returns while market performance refers to the

behavior of a security in the market and reflects the market participant’s perception of the

financial soundness and future growth potential of the company. In this study, following extant

literature, we use Tobin’s Q as a market based measure of frim performance (Adams & Ferreira,

2009; Carter et al., 2003; Carter et al., 2007; Carter et al., 2010; Nguyen et al., 2015). Tobin’s Q

which is defined as the ratio of the market value of the company’s assets to the replacement cost

or book value of the company’s assets is widely considered as the best measure of a firm’s

market value (Dobbin & Jung, 2011). We take the natural log of Tobin’s Q to mitigate the

effects of outliers (Nguyen et al. 2015). We use the accounting measures Return on Assets

(Dobbin & Jung, 2011; Farrell & Hersch, 2005; Shrader et al., 1997) and Return on Equity

(Miller & Triana, 2009; Zahra & Stanton, 1988) for robustness checks.

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Independent variables

The primary independent variable in our study is board gender diversity. BGD can be

measured by the number of female directors or the proportion of female directors or sometimes

just the presence of female directors on the board. In our study we use the number of female

directors as the primary independent variable (Carter et al., 2010; Dobbin & Jung, 2011). We

also conduct robustness checks using the percentage of female directors as the independent

variable (Adams & Ferreira, 2009; Nguyen et al., 2015).

Control Variables

We used several variables to control for firm characteristics, board characteristics and

macroeconomic factors that could impact firm performance (the dependent variable). The first

set of variables were to control for firm characteristics. These included the number of

employees, number of females in the workforce, number of female executives, and industry type.

The second set of variables were governance variables that involve the characteristics of

the board and its members. These include CEO related variables - CEO pay, CEO duality

(whether or not the CEO is also the chair of the board) , percentage of shares owned by the CEO,

CEO tenure and CEO age (Bhagat & Bolton, 2008; Holm & Scholer, 2010; Singla, George, &

Veliyath, 2010); board related variables - board size, board age, board average tenure (Rose,

Munch- Madsen, & Funch, 2013; Sanders & Carpenter, 1998; Zahra, Priem, & Rasheed, 2007),

independent directorship (Carter et al., 2010), directors who sit on the audit committee (Alderfer,

1986; Carlsson & Karlsson, 1970; Masulis, Wang, & Xie, 2007; Vroom & Pahl, 1971).

Finally, the third set of variables were the macro economic measures - Economic Policy

Uncertainty (EPU) index, which measures policy related economic uncertainty (Baker, Bloom &

Davis, 2015) and the Aruoba-Diebold-Scotti Business Conditions Index, which tracks real

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business conditions (Aruoba, Diebold & Scotti, 2009). A vast majority of the studies in the

literature do not control for macroeconomic factors that might impact firm performance.

Moderating Variables

Research has indicated that 30% is the critical mass required to unlock the positive

impact of BGD on FP (Joecks et al., 2013). Other research studies have used number of female

directors rather than proportion and indicated that 3 women on the board is required for critical

mass (Konrad et al., 2008; Post et al., 2011). We find the proportion of women on the board to

be more meaningful when it comes to critical mass and hence use a dummy variable that takes a

value of 1 if there are 30% or more women on the board, and 0 otherwise, as our primary

moderating variable. But, we also conduct robustness checks with the absolute number of three

women on the board for critical mass. We also try various other critical mass percentages (20%,

40%, 50%) and number of women on the board (4, 5, 6, 7).

Table 2 provides a list of all the variables used in the study and their definitions.

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Analysis

The relationship between BGD and FP is prone to endogeneity bias (Hermalin &

Weisbach, 2001). High performing companies may be more inclined to bring women on to the

board (Farrell & Hersch, 2005). Board characteristics are also not exogenous random variables

and are chosen endogenously by firms based on their operating environment (Adams & Ferreira,

2007; Coles et al., 2008; Harris & Raviv, 2008). Certain unobservable firm characteristics that

are omitted, referred to as Omitted Variable Bias (OVB), can impact the relationship between

BGD and FP. One such example is corporate attitude towards Corporate Social Responsibility

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(CSR), which is unobservable and can impact BGD. It is practically impossible for an empirical

model to capture all possible variables that have an impact on BGD and FP. Moreover when

endogenous explanatory variables are used in the model there is the possibility of reverse

causality, where rather than BGD affecting FP, FP might affect BGD. Another issue in such

studies is that the endogeneity is dynamic, in that there may be intertemporal effects between

governance variables and firm performance.

One common technique used to address endogeneity is the use of instrument variables.

One could identify an instrument variable that explains BGD but is exogenous to firm

performance, but it is a challenge to identify such a variable. Number of female connections of

male directors is one such instrument variable that has been commonly used in the literature, but,

studies have shown that this instrument variable is truly not exogenous (Sila, Gonzalez &

Hagendorff, 2016).

Thus, studies investigating the relationship between BGD and FP should control for the

endogenous nature of the BGD-FP relationship in order to obtain reliable estimates (Adams &

Ferreira, 2009; Carter et al., 2010; Dezsö & Ross, 2012; Hermalin & Weisbach, 2001). But,

much of the extant literature does not address endogeneity bias. Studies such as Zahra & Stanton

(1988), Erhardt et al. (2003), Shrader et al. (1997), Farrell & Hersch (2005), and Desvaux,

Devillard- Hoellinger, & Baumgarten (2007) use univariate analysis and do not address any

aspect of endogeneity, while studies such as Adams & Ferreira (2009), Anderson, Reeb,

Upadhyay, & Zhao (2011), Carter et al. (2010), Carter et al. (2003), and Sabatier (2015) only

attempted to control for two endogeneity biases: omitted variable bias and reverse causality.

In our study we employ a Dynamic Panel System Generalized Method of Moments

(DPS-GMM) model proposed by Arellano & Bover (1995) and Blundell & Bond (1998) which

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allows us to simultaneously address all the endogeneity issues that impact the BGD-FP

relationship. The DPS-GMM estimator addresses reverse causality by instrumenting the

endogenous explanatory variables through their lagged values (Blundell & Bond, 1998). It

controls for dynamic panel bias by adding lags of each dependent variable (Roodman, 2009;

Wintoki et al., 2012). It mitigates omitted variable bias through a fixed effects approach

(Nguyen et al., 2015). Also, the GMM estimator has proven to be the best-performing estimator

for dealing with endogeneity in the field of corporate governance, especially with panel data

(Chapple & Humphrey, 2014; Nguyen et al., 2015; Sila et al., 2016; Wintoki et al., 2012). The

DPS-GMM technique has been used in recent research investigating the relationship between

board composition and firm outcomes (Wintoki et al, 2012; Sila, Gonzalez & Hagendorff, 2015;

Wiley & Monllor-Tormos, 2018).

Following prior research our model specification is below:

𝐹𝑃𝑖𝑡 = 𝛼 + ∑ 𝛾𝑝𝐹𝑃𝑖𝑡−𝑝

𝑝

+ 𝛽𝐶𝐺𝑖𝑡 + 𝛿𝐹𝐶𝑖𝑡 + 𝑦𝑒𝑎𝑟𝑖 + 𝜂𝑖 + 𝜖𝑖𝑡 𝑝 = 1, … . . , 𝑠

The index i in our model specification refers to each firm and the index t refers to the year. FP

are the firm performance variables. CG are the corporate governance variables – the independent

variable of interest (number of female directors) and the other control variables (see table 2) .

FC are the firm characteristics control variables (see table 2). 𝜂𝑖 is a firm-specific time invariant

effect representing unobserved characteristics of the firm influencing its performance, and εit is

the error term. yeari are the year dummy variables.

Results

We first present some descriptive statistics of our sample and then proceed to present the

results from our United States sample, which is the main thrust of our study, followed by the

results from our European and Asian sample.

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Descriptive Statistics

Table 3 shows the descriptive statistics for our U.S., Europe and Asian samples. About

71% of the firms in our U.S. sample, 94% of the firms in our European sample and 54% of the

firms in our Asia sample, had at least one female director. The maximum number of female

directors in our U.S. sample was 8 while in our European sample it was 9 and in our Asian

sample it was 6. The average female board room representation in Europe was double (24%)

that of the US (12.5%) and was lowest in Asia (8%). The average board size was higher in

Europe and Asia (11) than in the U.S. (9). The mean number of independent directors is similar

between Europe and the U.S. (7) but lower in Asia (5). We also observe large variation in the

financial performance measures especially ROA and ROE. These descriptive statistics are in

line with prior research (e.g. Sila et al, 2016).

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Tables 4 - 6 shows some key statistics by number of female directors for the U.S., Europe

and Asian samples. We find that there are on an average more female directors on larger and

more independent boards in the U.S. and Europe, but the relationship is not very clear in Asia.

This suggests that companies that are more mature might tend to appoint more female directors.

We do not see a clear monotonically increasing relationship between number of female directors

and financial performance measures in any of the regions. Interestingly in the U.S. sample there

seems to be spike in the performance measures when there are 7 female directors on the board,

but there seems to be a decline in performance measures at around 5 female directors in our

European and Asian samples.

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Insert Tables 4 - 6 about here

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Tables 7 - 9 shows some key measures by time for the U.S., Europe and Asian samples.

We find that the total number of female directors rises steadily from 2008 to 2018 in all our

samples. The increase is highest in Europe where we see close to a 100 fold increase from 2008

to 2018, while in Asia there is close to a 15 fold increase and in the U.S. the number of female

directors have tripled from 2008 to 2018. But, the mean percentage of female directors seems to

have remained more or less constant in the U.S. and Asia with an uptick only in 2017 and 2018,

while in Europe we see a fivefold increase from 2008 to 2018. The mean percentage of female

executives, mean board size, and mean number of independent directors all remained constant

throughout the 10 year period in the U.S. sample. But, we do see large increases in number of

female CEOs, number of female board chairpersons and CEO duality from 2008 to 2018 in

Europe and U.S. It is interesting to note that the Asian sample seems to indicate that the female

director being a chairperson is a rarity.

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Insert Tables 7 - 9 about here

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The descriptive statistics provide clear evidence of companies increasingly appointing

female directors to the board and increase in women CEOs and board chairpersons in the past

eleven years. This broad trend is in line with numerous research findings and articles in the

popular press. But, caution should be exercised in interpreting our comparative evidence across

regions, as our Asian and European sample sizes are much smaller than our U.S. sample size.

Nevertheless, the descriptive statistics discussed above provide many interesting trends in board

gender diversity and firm characteristics and performance across the U.S., Europe and Asia.

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Is there a positive relationship between board gender diversity and firm performance in

U.S. firms?

Table 10 presents the results of fitting the DPS-GMM model to our U.S. sample. We use

two lags of the firm performance measures (the dependent variable) in estimating our model. All

independent variables are treated as endogenous, except the year dummies, and are instrumented

by two of their past (lagged) values. We find no evidence of a positive relationship between

number of female board directors and firm performance as measured by Tobin’s Q. The

coefficient is in fact very small and negative, but not statistically significant, thus indicating that

there is no relationship between BGD and FP. We conduct robustness checks using the

accounting measures of ROA and ROE as proxies for firm performance and again find no

evidence of any relationship between BGD and FP.

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Another interesting result to note is that the percentage of female executives and the

presence of a female CEO are statistically significant (p < 0.05) for the market based measure of

performance (Tobin’s Q) but not for the accounting based measures of performance (ROA,

ROE). It is also interesting to note that the coefficient for female CEO is negative, indicating

that firm performance as measured by Tobin’s Q goes down if the company has a female CEO.

We note this finding just as an interesting aside that is not related to our main research question

and caution that further research needs to be conducted before any conclusions can be drawn

regarding the relationship between female CEOs and firm performance.

The results of the Sargan test of overidentifying restrictions, with the null hypothesis that

all the instruments variables are exogenous, shows that the null hypothesis cannot be rejected. In

20

other words the two past values of the independent variables, which were used as instrument

variables, are exogenous. The Arellano-Bond test for autocorrelation shows evidence of strong

first order autocorrelation but not second order autocorrelation in the residuals.

We conduct a robustness check of our results using the percentage of female directors

instead of the absolute number of female directors as our main independent variable. The results

of this analysis is presented in table 11. We again find no evidence of a positive relationship

between percentage of female directors and firm financial performance (Tobin’s Q, ROA, ROE).

Again the coefficient for percentage of female directors is very small, negative and not

statistically significant, indicating no relationship between BGD and FP. We note similar

statistically significant results for percentage of female executives and female CEO as described

earlier.

---------------------------------


---------------------------------

Is there a critical mass effect at 30% female directors in the relationship between BGD and

FP in U.S. firms?

To address this question we added a dummy independent variable for 30% critical mass

to our model. We also added an interaction variable between number of female directors and

critical mass. Since some prior research studies (e.g. Wiley & Monllorr-Tormos, 2018) have

reported a “U” shaped nonlinear quadratic relationship between BGD and FP we included the

square of the number of female directors as an independent variable to test for a quadratic

relationship.

Table 12 shows the results of the DPS-GMM model with the above described variables

added. We do not find any evidence of the existence of a critical mass at 30%. The critical mass

21

dummy and the interaction variable are not statistically significant. We do find weak evidence (p

< 0.1) of a nonlinear quadratic relationship between number of female directors and Tobin’s Q.

---------------------------------


---------------------------------

We conducted robustness checks using the ROA and ROE as dependent variables and

find similar results (see table 12). The nonlinear relationship is not evidenced with ROA, but

only with Tobin’s Q and ROE and is only weakly significant. We conduct further robustness

checks (table 13) by replacing number of female directors with percentage of female directors

and find exactly the same results. We further tested various levels of critical mass (20%, 40%,

50%) and found no evidence of a critical mass at any of those levels. As discussed previously, in

our descriptive statistics we found an uptick in performance when the number of female directors

was 7, so, we wanted to test whether there is evidence of critical mass at 7 female directors. We

fit our model with dummies for 3,4,5,6, and 7 female directors respectively and found no

evidence of critical mass at any of these levels. For the sake of brevity we have not reported the

results in tabular form for the various levels of critical mass, but, the results are available on

request.

---------------------------------


---------------------------------

Thus, we find no evidence of the existence of a critical mass in the BGD-FP relationship,

but we do find weak evidence of a quadratic relationship between BGD and FP.

The analysis of our U.S. sample leads us to reject our hypotheses that a positive

relationship exists between BGD and FP and also that there exists a critical mass at 30% that

22

moderates the BGD-FP relationship. Now we proceed to present the results of our analysis for

the European and Asian samples.


European firms?

Table 14 shows the results of fitting the DPS-GMM model described above to our

European sample. Similar to the U.S. we find no evidence of a positive relationship between

BGD and FP. The coefficient (relationship) is positive, unlike in the U.S. sample, but very small

and not statistically significant, indicating no relationship between BGD and FP. The robustness

checks using the accounting measures of ROA and ROE as proxies for firm performance again

find no evidence of a positive relationship between BGD and FP. The coefficients are again

small and not statistically significant indicating that there is no relationship between BGD and

FP. Unlike the U.S. sample we do not find any statistically significant association between

percentage of female executives and female CEO on firm performance.

---------------------------------


---------------------------------

The results of the Sargan test of overidentifying restrictions shows that the instrument

variables are exogenous. The Arellano-Bond test for autocorrelation shows evidence of first

order autocorrelation but not as strong as in the U.S. sample.

Robustness checks using percentage of female directors instead of the number of female

directors confirm our finding of no evidence of any relationship between BGD and FP. For

brevity, we do not present a table with the results of this robustness check, but, the results are

available on request.

23


FP in European firms?

Table 15 presents the results of fitting the critical mass model which includes a dummy

variable for critical mass 30%, an interaction variable between critical mass and BGD and a

number of female directors squared variable. We do not find evidence of a 30% critical mass nor

a “U” shaped relationship between BGD and FP as measured by Tobin’s Q.

---------------------------------


---------------------------------

Robustness checks with ROA and ROE as measures of firm performance show no

evidence of critical mass and a quadratic relationship when using ROA as a proxy for firm

performance, but show weak evidence (p < 0.1) of the existence of a critical mass at 30% and a

nonlinear quadratic relationship between BGD and ROE (see table 15).

We conducted further robustness checks by replacing number of female directors with

percentage of female directors and find similar results. We further tested various levels of

critical mass (20%, 40%, 50%) and critical mass of number of female directors (3, 4, 5, 6, 7) and

found no evidence of a critical mass at any of those levels. The results of these robustness

checks are not tabulated but are available on request.

Thus, the results from analyzing our European sample lead us to reject our hypotheses

that a positive relationship exists between BGD and FP and also that there exists a critical mass

at 30% that moderates the BGD-FP relationship. The evidence shows that there is in fact no

relationship between BGD and FP.

24


Asian firms?

Table 16 presents the results of fitting our model to the Asian sample. Similar to U.S.

and Europe we find no evidence of a positive relationship between BGD as proxied by number

of female directors and FP as measured by Tobin’s Q. The coefficient of BGD is negative but

not statistically significant, thus indicating no association between BGD and FP.

---------------------------------


---------------------------------

Similar to the other samples, the Sargan test confirms that the variables used as

instruments in our model are exogenous and thus valid. Similar to the European sample the first

order autocorrelations are mild and there is no evidence of second order autocorrelation.

The robustness checks using ROA and ROE as proxies for firm performance again find

no evidence of any relationship between BGD and FP (see table 16). In line with the other

samples, robustness checks using percentage of female directors shows very similar results and

confirms our finding of no statistically significant association between BGD and FP in our Asian

sample.


FP in Asian firms?

Table 17 shows the results of fitting the critical mass model to the Asian sample. We do

not find any evidence of a 30% critical mass nor a “U” shaped relationship between BGD and FP

as measured by Tobin’s Q.

---------------------------------


---------------------------------

25

Robustness checks with ROA, ROE, percentage of female directors and various values

for critical mass, all, confirm our finding of no evidence of critical mass nor a “U” shaped

relationship between BGD and FP.

Thus, in summary, we find no evidence of any relationship between BGD and FP across

our U.S., European and Asian samples. We also find no evidence of the existence of a critical

mass at any level in the relationship between BGD and FP. Furthermore we do not find any

evidence of a nonlinear quadratic (U shaped) relationship between BGD and FP.

Discussion

Our study uses one of the largest and the most recent sample that we are aware of in the

literature, to investigate the relationship between board gender diversity and firm performance.

We also use a robust statistical methodology that takes into account endogeneity biases that can

cast doubt on results if not properly accounted for. Our results lead us to reject our hypothesis

that there is a positive relationship between BGD and FP and that the relationship is quadratic

with a moderating critical mass of 30%. We do not find any relationship between BGD and FP

and thus fall into the “neutral” category of the empirical literature on the relationship between

BGD and FP and are consistent with studies such as Carter et al. (2010), Rose (2007), Shrader,

Blackburn, & Iles (1997). We note that our sample size is much larger and more comprehensive

than all of the studies in the “neutral” category of the literature and arguably our methodology is

more robust. Our findings are also in line with the meta-analysis (Post & Byron, 2015; Pletzer,

Nikolova, Kedzior, and Voelpel, 2015) that found close to zero or no relationship between BGD

and FP. Thus, we add to the empirical literature on BGD and FP with a comprehensive analysis

employing robust methodology and using one of the largest, most current samples spanning three

regions of the world. We contribute to the yet unsettled debate on whether increased board

26

gender diversity leads to increased firm performance and bring a more current and

comprehensive answer to this question.

We also add to the less abundant literature on the existence of a critical mass in board

gender diversity. Our finding of no evidence of critical mass contradicts extant literature (Wiley

& Monllor-Tormos, 2018; Strydom et al., 2016; Joecks et al., 2013). All of these studies used

very small sample sizes (in the hundreds) and were also regional or restricted to certain sectors

(one in Germany, another in Australia, another restricted to technology and finance sectors).

These studies also do not use recent data (the most recent in these studies is 2013). To the best

of our knowledge, our study is the largest study to date that address the question of the existence

of a critical mass using the most recent data. Thus we make a significant contribution to the

literature on the existence of a critical mass in the relationship between board gender diversity

and firm performance.

We caution against a broad interpretation of our results as board gender diversity being

undesirable to a company as it does not affect firm financial performance. There are numerous

other non-financial reasons why gender diversity in boards is highly desirable. Moreover, we do

not capture any of the psychological impacts of greater diversity such as employee morale, etc.

in our model. Thus, our study simply points out that there is no evidence of any impact of

increased board gender diversity on firm financial performance based on our methodology in our

large recent sample and does not take any position on whether board gender diversity is desirable

or not.

Limitations and Work in Progress

We discuss some of the limitations of the current study as presented above and outline

the work in progress to address some of these limitations. Our Asian sample and European

27

samples are not as large as our American sample and hence the results we have presented for

Europe and Asia may not be as strong as the ones for the U.S. We are currently in the process of

gathering more data for Asia and Europe to increase our sample size. Though we have included

many control variables (more than most studies in the literature), there is always scope for

improving and expanding the list of control variables. We are working on including other

macroeconomic and firm characteristics variables such as capital expenditure, R&D expenditure

etc. We are also working on extending our analysis to include exploring the relationship

between BGD and FP in various different industries segments and more regions of the world.

Conclusion

In the past decade there has been increasing focus on board gender diversity all over the

world and in particular in the U.S. Many U.S. firms are under increasing pressure to appoint

more female board members and improve gender diversity on boards. Some countries, such as

Norway, have imposed quotas for female directors. Although there presently is no mandatory

gender quota in the US, public opinion, pressure from the media, shareholders and other

stakeholders, along with SEC disclosure rules are likely to drive US firms to continue to increase

board gender diversity. The extant literature provides inconsistent evidence regarding the impact

of increased board gender diversity on firm performance. With a large sample of over 3000

firms and a recent time frame of the past eleven years (2008 – 2018) our comprehensive study

contributes to this debate by investigating the relationship between boardroom gender diversity

and firm financial performance in the U.S., Europe and Asia using a robust statistical method

that takes into account endogeneity bias. We find no evidence of any relationship between board

gender diversity and firm financial performance in any of the three regions. Thus, we conclude

that in our sample there is no relationship between the number of female directors on the board

28

and firm financial performance. Moreover we find no evidence of a critical mass of female

directors beyond which firm performance starts to improve. We stress that our finding of no

empirical evidence of a positive impact on firm performance from increased board gender

diversity does not mean that board gender diversity is undesirable. The case for greater board

gender diversity should also rest on fairness and gender equality rather than on pure economic

and financial considerations.

29

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36

Table 1

Summary of literature

Source: The Oxford Handbook of Strategy Implementation by Michael A. Hitt, Susan E. Jackson, Salvador

Carmona, Leonard Bierman, Christina E. Shalley, Mike Wright (2017)

Table 2

Variable definitions

Dependent Variables Tobin’s Q Ratio of the market value of the company’s assets to

the replacement cost or book value of the company’s

assets. ROA Net income divided by the book value of total assets ROE Net income divided by shareholders equity

Independent Variables No of female directors Number of female directors on the board Percentage of female directors Percentage of female directors on the board

Control Variables Percentage of female executives Percentage of female executives in the company No of female executives Number of female executives in the company Percentage of females in the workforce Percentage of women in the entire workforce Board size Size of the board

Number Research Study Year Region

Research

Period No_of_org

Mean

Proportion

Of Women

on Board TobinQ ROA

1 Shrader, Blackburn & Iles 1997 US 1992-1993 200 0.08 non log - negative -n.s

2

Carter, D' Souza, Simkins &

Simpson 2003 US

1998-2002 641

0.12 positive, p- n.s positive, p<0.05

3 Carter, Simkins & Simpson 2003 US 1997 638 0.1 positive, p<0.05

4 Erhardt et al. 2003 Us 1997-1998 112 0.24 positive, p<.001

5 Bonn 2004 Japan 1998-1999 169 0 negative, p= n.s

6 Campbell & Minguez- Vera 2008 Spain 1995-2000 68 0.03 positive, p<0.05

7 Adams & Ferreira 2009 US 1996-2003 1939 0.08 negative, p<.10 negative, p<.10

8 Miller & del Carmen Triana 2009 US 2003-2005 326-432 0.13 ROI+ROS = positive,ns

9 Bohren & Strom 2010 Norway 1989-2002 229 0.05 negative, p<0.05

10 Haslam et al 2010 UK 2001-2005 126 0.07-0.11 negative , p--n.s negative, n.s

11 Dobbin & Jung 2011 US 1997-2006 432 NA negative, p<0.05 positive, p-n.s

12 He & Huang 2011 US 2001-2007 530 0.16 negative, p<.10

13 Ahern & Dittmar 2012 Norway 2001-2009 248 0.04-.43 negative p< .01

14

Mahadeo,Soobaroyen &

Hanuman 2012 Mauritius

2007 42

0.03 positive, p<0.01

15 Darmadi 2013 Indonesia 2007 354 0.12 negative, p<0.05 negative, p-n.s

16 Joecks, Pull & Vetter 2013 Germany 2000-2005 151 0.08 positive, curlinear, p<.05

17 Ali, Ng&Kulik 2014 Australia 2011-2012 288 0.08 positive, p ns

18

Carolyn Wiley, Mireia Monllor-

Tormos 2018 US

2007-2013 1357

positive, p<0.05

37

Board meeting per year Number of board meetings per year Board average tenure Average tenure of board members in years Board average age Average age of board members Number of independent directors Number of independent directors Number of independent directors in the

audit committee

Number of independent directors in the audit committee

CEO Duality Is CEO also the chair of the board? CEO Tenure Tenure of the CEO in years Stock owned by CEO Percentage of stock owned by the CEO CEO Age Age of CEO CEO Compensation CEO total compensation Female board chairperson Is the board chairperson female? Female CEO Is the CEO female? Number of employees Total number of employees Industry Dummy variable for type of industry Aruoba-Diebold-Scotti Business

Conditions Index

Aruoba-Diebold-Scotti Business Conditions Index

Economic Policy Uncertainty (EPU) index Economic Policy Uncertainty (EPU) index

Moderating Variables Critical Mass 20% Critical mass at 20% of women directors Critical Mass 30% Critical mass at 30% of women directors Critical Mass 40% Critical mass at 40% of women directors Critical Mass 50% Critical mass at 50% of women directors Critical number of female directors 3 Critical mass at 3 women directors Critical number of female directors 4 Critical mass at 4 women directors Critical number of female directors 5 Critical mass at 5 women directors Critical number of female directors 6 Critical mass at 6 women directors Critical number of female directors 7 Critical mass at 7 women directors

Table 3

Descriptive statistics

USA Europe Asia

Mean SD Mean SD Mean SD

No of female directors 1.222 1.085 2.79 1.663 1.002 1.25

Percentage of female directors 12.53 10.6 24.18 12.54 7.881 9.058

Percentage of female executives 13.55 13.6

No of female executives 1.16 1.379 2.627 7.244

Percentage of females in the workforce 37.2 4.301 27.78 21.77

Board size 9.223 2.527 11.38 3.057 11.3 3.35

Board meeting per year 8.16 3.916 9.26 3.554 8.392 4.641

38

Board average tenure 5.72 2.041 5.157 4.024

Board average age 58.82 3.275 56.54 8.144

Number of independent directors 7.302 2.448 7.55 2.587 5.198 2.324

Number of independent directors in the audit

committee

3.836 1.129 3.52 1.145 3.442 0.9908

CEO Tenure 7.962 7.46 3.623 4.776

Stock owned by CEO 1.819 5.787 0.3832 1.5

CEO Age 57.27 7.238

Number of employees 15541 57505 70903 85578 77261 55211

Tobin’s Q 1.959 1.723 1.79 1.227 1.717 1.396

ROA 1.878 16.59 17.94 20 15.72 9.208

ROE 6.351 45.01 5.86 6.81 7.742 7.407

Aruoba-Diebold-Scotti Business Conditions

Index

-0.393 0.989 N/A N/A N/A N/A

Economic Policy Uncertainty (EPU) index 125.4 26.13 197.50 38.86 145.3 29.76

Note: The greyed out cells indicate that data was not available on the Bloomberg

Table 4

Descriptive statistics by number of females on the board (U.S. sample)

Number of female

Directors

Board

Size

No of Independent

directors

No of

Employees

Tobin’s

Q

ROA ROE

0 8 6 6212 2.05 -0.06 0.29

1 9 7 9501 1.97 1.68 4.76

2 10 8 20854 1.87 3.24 10.87

3 11 9 39030 1.85 4.33 16.91

4 12 10 66548 1.83 4.95 17.46

5 12 11 61038 1.94 5.61 18.40

6 14 11 64150 1.83 5.53 21.80

7 14 10 32414 3.26 10.28 29.74

8 17 11 46000 4.80 9.18 24.43

Table 5

Descriptive statistics by number of females on the board (Europe sample)

Number of

female

Directors

Board

Size

No of

Independent

directors

No of Employees Tobin’s Q ROA ROE

0 9 6 44136 1.89 5.97 14.41

39

1 10 7 65635 1.86 6.32 21.42

2 10 7 52763 1.77 5.63 17.14

3 11 8 68633 1.77 6.20 19.26

4 13 8 80779 1.82 6.16 17.89

5 13 8 106655 1.94 6.23 16.96

6 16 10 131082 1.45 3.41 13.03

7 15 8 96992 1.64 4.83 14.67

8 18 9 175841 1.19 1.23 9.54

9 21 6 71308 0.99 0.24 6.26

Table 6

Descriptive statistics by number of females on the board (Asia sample)

Number of

female

Directors

Board

Size

No of

Independent

directors

No of Employees Tobin’s Q ROA ROE

0 10 5 71957 1.79 7.87 13.28

1 11 5 50136 1.95 9.10 20.07

2 13 5 109757 1.53 6.61 15.74

3 14 6 139875 1.17 5.22 15.40

4 16 7 92739 1.05 6.82 14.68

5 16 8 72644 1.26 3.07 14.27

6 17 6 296584 1.05 1.20 13.32

Table 7

Descriptive statistics by time (U.S. sample)

Year No of

female

directors

Mean

Percentage

female

directors

Mean

percentage

of female

executives

Mean

board

size

Mean

indepen

dent

directors

No of

CEO

duality

No of

female

chairperson

No of

female

CEOs

2008 1004 11.6 14.9 10.1 7.9 454 9 10

2009 1205 11.2 14.4 9.8 7.7 556 11 11

2010 1295 11.0 14.1 9.6 7.5 596 15 16

2011 1865 10.2 13.4 9.2 7.2 871 36 41

2012 2028 10.6 13.3 9.1 7.2 881 38 49

2013 2191 11.0 13.3 9.1 7.2 905 51 58

2014 2456 11.7 13.4 9.1 7.2 919 54 72

2015 2813 12.7 13.5 9.1 7.2 947 67 86

2016 3180 13.7 13.5 9.1 7.2 952 76 95

2017 3443 14.8 13.5 9.1 7.3 930 82 105

2018 3778 15.8 13.5 9.1 7.3 911 84 111

40

Table 8

Descriptive statistics by time (Europe sample)

Year No of

female

directors

Mean

Percentage

female

directors

No of

female

executives

Mean

board

size

Mean

indepen

dent

directors

No of

CEO

duality

No of

female

chairperson

No of

female

CEOs

2008 6 6.10 2 11 8 0 0 0

2009 17 9.38 6 11 7 0 0 1

2010 34 10.70 19 10 7 3 0 2

2011 164 14.47 69 11 7 9 1 4

2012 281 18.28 113 11 7 19 2 4

2013 341 20.14 151 11 7 23 4 5

2014 382 22.95 151 12 8 25 4 3

2015 450 26.23 183 12 8 24 5 4

2016 522 29.18 217 11 8 22 5 7

2017 553 30.45 232 11 8 21 8 6

2018 607 31.55 258 12 8 22 10 7

Table 9

Descriptive statistics by time (Asia sample)

Year No of

female

directors

Mean

Percentage

female

directors

No of

female

executives

Mean

board

size

Mean

indepen

dent

directors

No of

CEO

duality

No of

female

chairperson

No of

female

CEOs

2008 4 3.8 3 11 5 6 0 0

2009 9 5.4 5 12 6 8 0 0

2010 15 3.9 32 11 5 9 0 1

2011 39 6.9 40 12 5 8 1 1

2012 45 7.4 43 12 5 11 0 2

2013 48 7.5 61 12 5 14 0 2

2014 54 8.5 151 12 5 13 0 3

2015 50 8.2 194 11 5 16 0 3

2016 57 9.3 193 11 5 17 0 3

2017 54 8.9 200 11 5 17 0 3

2018 60 9.6 218 11 5 16 0 3

41

Table 10

Relationship between board gender diversity and firm performance (U.S. sample)

This table reports two-step dynamic panel system GMM estimations of the natural log of Tobin’s Q, ROA and ROE

on the number of female directors and control variables. All models include year dummy variables. All independent

variables are treated as endogenous except year dummy variables. Endogenous variables are instrumented by two of

their past values. In parentheses are finite-sample robust standard errors (Windmeijer, 2005). The null hypothesis for

the Sargan test of overidentification is that all instruments are exogenous. AR(1) and AR(2) are test statistics for the

null hypothesis that there is no serial correlation of orders 1 and 2 in the first-difference residuals. *, ** and ***

denote statistical significance at 10%, 5% and 1% respectively.

Ln Tobin’s Q ROA ROE

No of female directors -0.002 (0.004) -0.042 (0.215) 0.261 (0.598)

Percentage of female executives 0.002 (0.001)** 0.021 (0.053) 0.076 (0.145)

Percentage of females in the workforce 0.001 (0.003) -0.115 (0.211) 0.265 (0.435)

Board size -0.002 (0.003) 0.004 (0.185) -0.17 (0.606)

Board average tenure -0.002 (0.001)** 0.045 (0.062) 0.267 (0.142)*

Board meeting per year -0.003 (0.001)*** -0.096 (0.03)*** -0.218 (0.089)**

Number of independent directors 0.003 (0.003) 0.107 (0.203) 0.721 (0.597)

Number of independent directors in the

audit committee 0.002 (0.002) 0.113 (0.149) 0.037 (0.381)

CEO Duality -0.009 (0.006) 0.064 (0.393) 1.841 (1.107)*

CEO Tenure 0.002 (0.002) 0.137 (0.104) 0.182 (0.303)

CEO Compensation -0.065 (0.031)** -1.351 (1.076) -0.888 (3.057)

Stock owned by CEO 0.007 (0.003)** -0.102 (0.246) -0.392 (0.672)

CEO Age -0.003 (0.002) -0.042 (0.137) -0.249 (0.321)

Female board chairperson 0.007 (0.02) -0.771 (1.021) 0.202 (3.25)

Female CEO -0.043 (0.02)** -0.714 (1.214) -1.456 (2.673)

Industry 0.016 (0.005)*** 0.631 (0.318)** -0.355 (0.726)

Number of employees -0.005 (0.003)* 0.566 (0.202)*** 1.836 (0.455)***

Aruoba-Diebold-Scotti Business

Conditions Index 0.026 (0.065) 1.19 (5.685) 3.789 (12.492)

Economic Policy Uncertainty (EPU)

index -0.001 (0.001)* -0.03 (0.039) -0.139 (0.11)

Firm performance measure lag 1 0.709 (0.031) 0.422 (0.039)*** 0.387 (0.042)***

Firm performance measure lag 2 0.012 (0.018) 0.019 (0.013) -0.032 (0.013)**

42

Observations 14896 14896 14896

Sargan Test (df = 346) 367.76 355.98 319.24

AR(1) -13.1257*** -8.94714*** -6.34417***

AR(2) -1.0369 -0.337735 0.808977

Table 11

Relationship between BGD and FP – robustness check using percentage of female directors (U.S.

sample)


on the percentage of female directors and control variables. All models include year dummy variables. All

independent variables are treated as endogenous except year dummy variables. Endogenous variables are

instrumented by two of their past values. In parentheses are finite-sample robust standard errors (Windmeijer, 2005).

The null hypothesis for the Sargan test of overidentification is that all instruments are exogenous. AR(1) and AR(2)

are test statistics for the null hypothesis that there is no serial correlation of orders 1 and 2 in the first-difference

residuals. *, ** and *** denote statistical significance at 10%, 5% and 1% respectively.


Percentage of female directors -0.0004 (-0.0004) -0.013 (0.022) -0.018 (0.054)

Percentage of female executives 0.002 (0.001)** 0.026 (0.055) 0.109 (0.125)

Percentage of females in the workforce 0.001 (0.003) -0.113 (0.21) 0.33 (0.447)

Board size -0.002 (0.003) 0.019 (0.186) -0.234 (0.633)

Board average tenure -0.002 (0.001)** 0.043 (0.062) 0.254 (0.147)*

Board meeting per year

-0.003 (0.001)***

-0.097

(0.029)*** -0.219 (0.089)**



audit committee 0.002 (0.002) 0.114 (0.15) 0.038 (0.379)

CEO Duality -0.008 (0.006) 0.081 (0.391) 1.894 (1.124)*

CEO Tenure 0.002 (0.002) 0.142 (0.107) 0.174 (0.276)

CEO Compensation -0.065 (0.031)** -1.277 (1.04) -1.001 (2.678)

Stock owned by CEO 0.006 (0.003)* -0.118 (0.235) -0.485 (0.682)

CEO Age -0.003 (0.002) -0.04 (0.138) -0.24 (0.303)


Female CEO -0.042 (0.02)** -0.621 (1.214) -1.334 (2.624)

Industry 0.016 (0.005)*** 0.657 (0.33)** -0.27 (0.832)

43

Number of employees -0.005 (0.003)* 0.551 (0.204)*** 1.861 (0.45)***


Conditions Index 0.016 (0.064) 1.571 (6.654) 5.191 (12.551)


index -0.001 (0.001)** -0.03 (0.041) -0.131 (0.108)

Firm performance measure lag 1 0.71 (0.03)*** 0.421 (0.039)*** 0.387 (0.042)***

Firm performance measure lag 2 0.013 (0.018) 0.018 (0.013) -0.032 (0.013)**


Sargan Test (df = 346) 360.228 354.56 318.37

AR(1) -13.1291*** -8.933*** -6.340***

AR(2) -1.07636 -0.339 0.805

Table 12

Critical mass of female directors (U.S. sample) This table reports two-step dynamic panel system GMM estimations of the natural log of Tobin’s Q, ROA and ROE

on the number of female directors, number of female directors squared, critical mass 30% dummy, interaction

between critical mass and number of female directors, and control variables. All models include year dummy

variables. All independent variables are treated as endogenous except year dummy variables. Endogenous variables

are instrumented by two of their past values. In parentheses are finite-sample robust standard errors (Windmeijer,

2005). The null hypothesis for the Sargan test of overidentification is that all instruments are exogenous. AR(1) and

AR(2) are test statistics for the null hypothesis that there is no serial correlation of orders 1 and 2 in the first-

difference residuals. *, ** and *** denote statistical significance at 10%, 5% and 1% respectively.


No of female directors -0.016 (0.008)* -0.604 (0.485) -1.964 (1.206)

Percentage of female executives 0.001 (0.001)* -0.006 (0.044) -0.01 (0.15)

Percentage of females in the

workforce 0.001 (0.002) -0.086 (0.125) -0.089 (0.434)

Board size -0.001 (0.003) -0.005 (0.186) -0.098 (0.593)

Board average tenure -0.002 (0.001)** 0.017 (0.059) 0.205 (0.152)

Board meeting per year -0.003 (0.001)*** -0.104 (0.031)*** -0.251 (0.09)***


Number of independent directors in

the audit committee 0.003 (0.002) 0.076 (0.15) 0.09 (0.371)

CEO Duality -0.006 (0.006) 0.141 (0.395) 1.555 (1.141)

CEO Tenure 0.001 (0.002) 0.094 (0.09) 0.214 (0.311)

44

CEO Compensation -0.04 (0.023)* -0.902 (0.729) -1.657 (2.188)


CEO Age -0.002 (0.002) 0.008 (0.116) -0.172 (0.352)

Female board chairperson 0.004 (0.018) -1.373 (1.253) -1.854 (2.875)

Female CEO -0.03 (0.016)* -0.303 (1.035) -0.684 (2.777)

Industry 0.015 (0.004)*** 0.567 (0.294)* -0.362 (1.28)

Number of employees -0.005 (0.003)** 0.555 (0.193)*** 1.924 (0.555)***


Conditions Index 0.02 (0.056) 3.429 (5.345) -3.965 (13.26)


index -0.001 (0.001)** -0.016 (0.041) -0.127 (0.094)

Critical Mass 30% 0.016 (0.038) 0.956 (2.529) -1.996 (6.434)

No of female directors^2 0.005 (0.002)* 0.195 (0.135) 0.794 (0.382)*

No of female directors * Critical

Mass 30% -0.01 (0.011) -0.327 (0.747) 0.608 (2.017)


Firm performance measure lag 2 0.022 (0.017) 0.017 (0.013) -0.034 (0.013)***

Observations 14896

14896 14896

Sargan Test (df = 418) 412.49

438.94 383.88

AR(1) -13.481***

-8.869*** -6.352***

AR(2) -1.291***

-0.346 0.824

Table 13

Critical mass of female directors – Robustness check using percentage of female directors (U.S.

sample) This table reports two-step dynamic panel system GMM estimations of the natural log of Tobin’s Q, ROA and ROE

on the percentage of female directors, percentage of female directors squared, critical mass 30% dummy, interaction








Percentage of female directors -0.001 (0.001) -0.044 (0.036) -0.155 (0.088)*

45

Percentage of female executives 0.001 (0.001)* 0.006 (0.045) -0.009 (0.143)

Percentage of females in the

workforce 0.001 (0.002) -0.089 (0.135) -0.067 (0.435)

Board size -0.002 (0.003) -0.026 (0.186) -0.263 (0.597)

Board average tenure -0.002 (0.001)** 0.014 (0.058) 0.21 (0.145)

Board meeting per year -0.003 (0.001)*** -0.101 (0.03)*** -0.243 (0.089)***


Number of independent directors in

the audit committee 0.002 (0.002) 0.067 (0.148) 0.062 (0.37)

CEO Duality -0.007 (0.006) 0.101 (0.389) 1.613 (1.142)

CEO Tenure 0.001 (0.002) 0.084 (0.096) 0.179 (0.3)

CEO Compensation -0.041 (0.023)* -0.972 (0.716) -1.828 (2.25)


CEO Age -0.001 (0.002) 0.058 (0.124) -0.144 (0.367)

Female board chairperson 0.005 (0.018) -1.048 (1.131) -1.497 (3.01)

Female CEO -0.033 (0.017)** -0.407 (1.045) -0.768 (2.879)

Industry 0.015 (0.004)*** 0.582 (0.291)** -0.316 (1.336)

Number of employees -0.005 (0.003)** 0.528 (0.198)*** 1.908 (0.562)***


Conditions Index 0.031 (0.056) 3.756 (5.063) -1.09 (12.406)


index -0.001 (0.001)* -0.016 (0.041) -0.123 (0.095)

Critical Mass 30% 0.025 (0.036) 1.473 (2.465) -0.399 (6.382)

No of female directors^2 0.003 (0.002)* 0.138 (0.099) 0.662 (0.284)*

No of female directors * Critical

Mass 30% -0.011 (0.011) -0.44 (0.717) 0.259 (1.951)


Firm performance measure lag 2 0.023 (0.017) 0.018 (0.013) -0.035 (0.013)***

Observations 14896

14896 14896

Sargan Test (df = 418) 413.44

429.21 387.06

AR(1) -13.437***

-8.864*** -6.370***

46

AR(2) -1.336

-0.374 0.831

Table 14

Relationship between board gender diversity and firm performance (Europe sample)









No of female directors 0.001 (0.005) 0.015 (0.262) 1.033 (0.648)

No of female executives 0.002 (0.007) -0.375 (0.282) -0.073 (0.351)

Board size 0 (0.004) -0.252 (0.202) 0.012 (0.344)

Board average tenure -0.006 (0.005) -0.024 (0.171) -0.689 (0.555)

Board average age 0.003 (0.005) -0.202 (0.152) 0.197 (0.234)

Board meeting per year -0.001 (0.002) -0.133 (0.09) -0.11 (0.188)

Number of independent directors -0.01 (0.007) -0.05 (0.218) -0.715 (0.39)*


audit committee 0.003 (0.006) -0.134 (0.481) 0.688 (0.63)

CEO Duality -0.018 (0.017) 1.407 (0.967) -1.355 (1.997)

Female board chairperson -0.009 (0.04) -1.976 (1.529) -0.522 (3.742)

Female CEO -0.022 (0.035) 1.886 (2.001) -1.153 (2.496)

Industry 0.01 (0.004)** 0.055 (0.164) 0.89 (0.371)**

Number of employees -0.014 (0.012) -0.305 (0.469) -0.367 (0.792)


index -0.002 (0.002) 0.063 (0.122) -0.099 (0.113)


Firm performance measure lag 2 0.155 (0.086)* 0.126 (0.057)** 0.032 (0.059)


Sargan Test (df = 272) 133.99 133.56 130.60

AR(1) -1.274 -3.017*** -2.334**

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AR(2) 0.863 -1.462 -1.174

Table 15

Critical mass of female directors (Europe sample) This table reports two-step dynamic panel system GMM estimations of the natural log of Tobin’s Q, ROA and ROE









No of female directors -0.012 (0.016) -0.525 (0.715) 2.424 (1.339)*

No of female executives 0.003 (0.007) -0.23 (0.248) -0.227 (0.43)

Board size 0 (0.004) -0.242 (0.202) 0.074 (0.348)

Board average tenure -0.005 (0.005) 0.02 (0.214) -0.872 (0.467)*

Board average age 0.002 (0.004) -0.253 (0.132)* 0.117 (0.234)

Board meeting per year -0.001 (0.002) -0.18 (0.1)* -0.17 (0.176)

Number of independent directors -0.007 (0.007) 0.17 (0.172) -0.515 (0.413)


audit committee 0.003 (0.006) -0.319 (0.371) 0.208 (0.64)

CEO Duality -0.016 (0.021) 1.082 (0.923) -2.207 (1.92)


Female CEO -0.024 (0.04) -0.214 (1.906) -1.142 (2.613)

Industry 0.008 (0.004)** 0.025 (0.142) 0.692 (0.352)**

Number of employees -0.012 (0.01) -0.43 (0.485) 0.14 (0.675)


index -0.001 (0.001) 0.051 (0.124) -0.046 (0.078)

Critical Mass 30% -0.013 (0.063) 2.378 (3.097) -10.93 (6.072)*

No of female directors^2 0.002 (0.003) 0.098 (0.125) -0.501 (0.264)*

No of female directors * Critical Mass

30% -0.002 (0.019) -0.489 (0.785) 3.281 (1.719)*


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Firm performance measure lag 2 0.199 (0.102)* 0.151 (0.058)*** 0.024 (0.064)


Sargan Test (df = 272) 122.68 131.01 120.75

AR(1) -1.230 -3.012*** -2.377**

AR(2) 0.826 -1.545 -1.218

Table 16

Relationship between board gender diversity and firm performance (Asia sample)









No of female directors -0.019 (0.019) -0.541 (1.318) -0.732 (1.768)

No of female executives 0.002 (0.003) 0.003 (0.121) 0.002 (0.111)

Percentage of females in the workforce 0 (0.001) -0.001 (0.034) -0.002 (0.023)

Board size -0.015 (0.014) 0.057 (0.405) 0.089 (0.550)

Board average tenure 0.007 (0.011) -0.506 (0.481) -0.607 (0.682)

Board average age -0.003 (0.005) 0.101 (0.209) 0.011 (0.243)

Board meeting per year -0.002 (0.006) 0.131 (0.301) 0.242 (0.334)

Number of independent directors 0.012 (0.016) -0.957 (0.856) -0.998 (0.766)


audit committee 0.012 (0.028) 0.13 (1.457) 0.11 (1.950)

CEO Duality -0.04 (0.103) 2.291 (2.125) 1.654 (2.320)

CEO Tenure 0.003 (0.011) 0.85 (0.481)* 1.230 (0.391)*

Female CEO -0.046 (0.107) 1.122 (4.758) 1.164 (4.662)

Stock owned by CEO -0.128 (0.137) -0.297 (2.068) -0.343 (2.583)

Industry 0.058 (0.044) -0.281 (0.902) -0.347 (1.002)

Number of employees 0.011 (0.014) 0.14 (0.541) 0.34 (0.751)

Firm performance measure lag 1 0.763 (0.27)*** 0.386 (0.205)* 0.423 (0.195)*

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Firm performance measure lag 2 -0.239 (0.303) -0.021 (0.13) -0.034 (0.12)


Sargan Test (df = 282) 25.73 19.08 21.58

AR(1) -2.140** -1.432 -1.654

AR(2) 0.647 0.646 0.666

Table 17

Critical mass of female directors (Asia sample) This table reports two-step dynamic panel system GMM estimations of the natural log of Tobin’s Q, ROA and ROE









No of female directors 0.021 (0.069) -1.665 (3.507) -1.785 (2.907)

No of female executives -0.003 (0.003) 0.158 (0.168) 0.177 (0.155)

Percentage of females in the workforce 0 (0.002) 0.076 (0.037)** 0.033 (0.069)*

Board size -0.006 (0.012) 0.442 (0.52) 0.67 (0.51)

Board average tenure 0 (0.011) 0.019 (0.488) 0.009 (0.631)

Board average age -0.009 (0.005)** 0.075 (0.21) 0.066 (0.29)

Board meeting per year -0.008 (0.007) 0.475 (0.255)* 0.498 (0.335)*

Number of independent directors 0.005 (0.017) -0.595 (0.965) -0.239 (0.810)


audit committee 0.026 (0.033) 0.41 (1.335) 0.56 (1.081)

CEO Duality 0.007 (0.119) 2.441 (2.287) 1.987 (1.287)

CEO Tenure 0.013 (0.013) 0.993 (0.49)** 0.873 (0.449)*

Female CEO 0.152 (0.129) 6.027 (5.456) 5.821 (3.483)

Stock owned by CEO 0.081 (0.12) 0.829 (1.651) 0.962 (1.551)

Industry 0.01 (0.042) 0.334 (1.131) 0.291 (0.978)

Number of employees -0.008 (0.013) 0.431 (0.674) 0.503 (0.874)

50

Critical Mass 30% -0.498 (0.391) -8.937 (37.674) -7.342 (28.591)

No of female directors^2 -0.012 (0.015) 0.029 (1.082) 0.011 (1.140)

No of female directors * Critical Mass

30% 0.14 (0.114) 3.692 (10.558) 2.687 (9.328)

Firm performance measure lag 1 0.514 (0.264)* 0.493 (0.247)** 0.454 (0.337)**

Firm performance measure lag 2 -0.027 (0.328) 0.094 (0.109) 0.088 (0.124)


Sargan Test (df = 313) 21.65 15.73 16.54

AR(1) -1.996** -1.483 -1.663*

AR(2) 0.066 -0.339 -0.322

the relationship between board gender diversity and firm ... · the upper echelons theory (uet) is...

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