Do Women Directors Impact the Risk and Return of Indian Banks?

Abstract

The purpose of the paper is to investigate whether women directors impact the risk and return of Indian banks. This study employs panel data models for a sample of 29 Indian banks that form part of the National Stock Exchange 500 index for the period 2009–2016. This paper concludes that women directors influence the accounting returns (measured through Return on Assets) of Indian banks. However, it was found that women directors did not affect the risks (measured through Equity Beta and gross NPA to Total Assets) of the sample banks. This paper contributes to the literature and practitioners in several ways. To the best of the knowledge of the authors, no study has examined the impact of women directors on the risk and return of banks operating in India. Hence, the findings of this article have substantial implications both to academia and practitioners.

Keywords

Indian banks women directors risks of banks returns of banks

Introduction

The mechanism employed by the investors to monitor the performance of managers of firms is termed as corporate governance (Clarke, 1998; Dong, Girardone, & Kuo, 2017). The effectiveness of corporate governance is measured by using a set of board characteristics (Andres & de Vallelado, 2008; Johnson, 2007). Researchers have used the proportion of non-executive directors (Armstrong, Core, & Guay, 2014), board size (Johl, Kaur, & Cooper, 2015), CEO duality (Jizi, Salama, Dixon, & Stratling, 2013), the proportion of women directors (Abdullah. Ismail, & Nachum, 2016), annual remuneration per board member (Tremblay, Côté, & Balkin, 2003), number of board meetings (Jizi et al., 2013), the average number of meetings attended per director (Chou Chung, & Yin., 2013) and multiple directorships (Barros, Boubaker, & Hamrouni, 2013) as the proxies for board characteristics in their efforts to quantify the impact of board characteristics on the performance of the firm.

According to a recent report of the Asian Development Bank (ADB) on women directors, several governments insist on a mandatory minimum proportion of women directors in the boards of the companies that operate in their countries. According to the report, the minimum proportion of women directors on boards is 25 per cent in the U.K,40 per cent in Norway and 30 per cent in Germany. The report cites that ‘90% of top 500 firms in the US have at least one woman in their board’. Further, the report states that the boardroom gender diversity is improving in Asian countries with female representation at 7.5 per cent in 2012.

The relationship between female board representation and accounting returns is positive and significant in countries that have greater shareholder protection (Byron & Post, 2016). The presence of female directors on the board of firms may better reveal the values of society, besides that of the investors of the firms (Zahra & Pearce, 1989). The representation of female directors in the boards of firms results in higher returns on assets (Adler, 2001), higher return on equity (Adler, 2001; Schwartz-Ziv, 2013), higher return on sales (Adler, 2001; Schwartz-Ziv, 2013), higher Tobin’s Q (Carter, Simkins, & Simpson, 2003), higher earnings quality (Srinidhi, Gul, & Tsui, 2011), higher firm value (Campbell & Minguez-Vra, 2008) and lower variability of stock market returns (Jane Lenard, Yu, Anne York, & Wu, 2014). However, some studies have indicated that firms with women directors on their boards have reported lower shareholder returns (Adams & Ferreira, 2009). Francoeur, Labelle, and Sinclair-Desgagné (2008) and Rhode and Packel (2012) have revealed that women directorship does not have any effect on the financial performance of firms. The extant literature on women directors consists of many studies that describe the relationship between women directors and the performance of firms operating in developed markets.

The steep decline in the asset quality of the Indian public sector banks over recent time periods (as indicated by the inferior financial performance, accelerating stressed assets and declining market share) would call for the recapitalization of these banks and thereby adding to the fiscal deficit of the government (Dhal & Mishra, 2014. If the governance of these banks continues to be weak, it would precipitate fiscal instability and economic slowdown, especially with the prospects of an emerging-market economy such as India, as it depends on the financial soundness of the banking sector and the overdependence of businesses on banks for meeting their long-term and short-term capital requirements, due to their lack of access to capital markets (Rawlin, Sharan, & Lakshmipathy, 2012). In this context, there is a need to investigate the impact of board characteristics on the risk and return of Indian banks. The Securities and Exchanges Board of India (SEBI), in compliance with the provisions of the Indian Companies Act, 2013, has made it mandatory for Indian listed firms to have a minimum of one woman director with effect from October 2014.

Therefore, it is imperative to investigate the impact of women directors on the financial performance (risk and return) of Indian banks. However, the authors could not come across any research work that empirically investigates the impact of women directors on the performance of banks operating in India. Therefore, the research questions that come to the fore are

Do women directors impact the accounting returns (proxied by return on assets) of the Indian banks?

Do women directors enhance the market returns (proxied by Tobin’s Q) of the Indian banks?

Does female representation on boards impact the systematic equity risk (measured through equity beta) of the Indian banks?

Do women directors improve the asset quality (proxied by the proportion of gross non-performing assets (NPAs) to total advances) of the Indian banks?

Hence, the paper has the following objectives:

To examine the impact of women directors on the accounting returns (measured through return on assets) of the Indian banks;

To investigate the impact of women directors on the market returns (proxied by Tobin’s Q) of the Indian banks;

To examine the impact of women directors on the systematic equity risk (measured through beta) of the Indian banks; and

To explore the impact of women directors on the asset quality (measured through the ratio of gross NPAs to total advances) of the Indian banks.

This study uses panel data method for a sample of 29 Indian banks that form part of the National Stock Exchange 500 index over a period of eight years. The panel estimation method employed is capable of handling unobservable heterogeneity and possible endogeneity. The sample banks are heterogeneous and the usage of panel data helps us avoid the risk of arriving at biased results (Hsiao, 1985). The proportion of women directors in the board of banks is collected from the annual reports of the sample banks, while the accounting performance measure of Return on Assets (ROA) and market performance measure of Tobin’s Q are considered as the proxies for returns of banks and equity beta and the proportion of gross NPAs to total advances of the banks are considered as the proxies for systematic risk and asset quality of the banks respectively. The data on the dependent variables (ROA, Tobin’s Q, Equity Beta and proportion of gross NPA to total advances) are collected from Ace Equity (the database that reports financial data of Indian firms). The study has employed control variables, namely bank age, bank size, growth rate in advances, logarithm of provisions for NPAs, logarithm of deposits, capital adequacy ratio, board size, CEO duality, number of board meetings, average number of board meetings attended, average number of boards served and the public sector bank dummy. The paper concludes that women directors influence the accounting returns (measured through ROA) of Indian banks, while it is found that women directors do not impact the market returns of Indian banks. The gender diversity in boards does not have an impact on both the accounting risk (ratio of gross NPAs to total advances) and market risk (equity beta) of Indian banks.

The study contributes to the body of knowledge on women directors and practitioners in many ways. First, the findings of the study may provide new theoretical insights to the literature on women directors, which may be useful for future research. Second, the banks may enhance their financial performance by making use of the findings of the study. Third, investors may make use of the findings of the study in deciding on whether to invest in a bank’s stock based on the presence of women directors on the boards of banks. The uniqueness of the study is that it employs the data pertaining to the membership of women directors on the boards of banks, which is extracted from the annual reports of the sample banks.

The remainder of the study is organized as follows: the second section deals with the literature review and the development of the theoretical framework. The third section presents the methodology conveying information about the sample selection, variables used and the model specification. Analysis and discussion of the results are presented in the fourth section. The final section concludes the study and spells out the scope for further research.

Literature Review, Theoretical Framework and Development of Hypotheses

Women Directorship

The performance of firms gets influenced by the composition and the characteristics of the board (Velte, 2017). Previous studies had emphasized board diversity and they concluded that the dimensions of nationality, gender, education and age were the factors contributing to the enhancement of the overall effectiveness of the board (Byron & Post, 2016; Velte, 2017). Pande and Ford (2012) highlighted that the mandatory norms on the appointment of the minimum number of directors resulted in more female representation in the boards of firms.

Figure 1.

Theoretical Framework

Women Directors and Returns of Firms

Campbell and Minguez-Vera (2008) performed their study on sixty-eight Spanish companies during the period 1995–2010 and concluded that a higher firm value was associated with an increase in gender equity. Barua, Davidson, Rama, and Thiruvadi (2010) related the gender of the CFO with better accrual quality for US firms based on a sample of 2599 firms in the year 2005. Srinidhi et al. (2011), in their examination of the US boards for 13,848 firm-years during the period 2001–2007, related female participation with a better quality of earnings. A positive linkage was established between the number of female board members and the cash holdings of the Tunisian firms over the period 1997–2010 (Loukil & Yousfi, 2016). Liu, Wei, and Xie (2014) analysed a sample of China’s listed firms during the period 1999–2011 and reported a positive and significant association between gender diversity in the boards and the performance of firms. Terjesen, Couto and Francisco (2016), in their study of 3876 listed firms from 47 countries in 2010, revealed a positive linkage between women directorship and market and accounting performance measures. They also established a positive linkage between women directorship and accounting performance measures (measured through ROA). They also found that the independent directors on the board did not contribute in the absence of women directors. Low, Roberts, and Whiting (2015), based on 5503 firm-years for the period 2012–2013, showed that an increase in the number of women directors positively influenced the performance of companies in East Asia (measured through ROA). Kilic and Kuzey (2016), based on a set of Turkish listed firms for the period 2008–2012, reported a positive association between women members in the board and the performance of firms measured through ROA, Return on Equity (ROE) and Return on Sales (ROS). Based on 125 Spanish listed non-financial firms during the period 2005–2009, Reguera-Alverado, de Fuentes, and Laffarga (2017) established a positive relationship between female directors and firms’ financial performance (measured by Tobin’s Q). Bennouri, Chtioui, Nagati, and Nekhili (2018), employing a sample of 394 French firms, empirically tested the linkages between women directors and the firms’ accounting and market performance. They revealed that women directors had a positive influence on ROA as well as ROE of the sample firms even though women directorship led to a decrease in the market performance.

Women Directors and Risk of Firms

Martin, Nishikawa, and Williams (2009), using the data on announcements of 70 CEO (female) appointment, concluded that women CEOs were primarily perceived to be risk-averse by the market. However, in another study, the CEO gender was studied by employing 9000 firm-year observations to examine the differences in the risk aversion level between firms led by male and female CEOs and it was confirmed that men and women leaders were equally risk-averse (McGuiness, Lam, & Vieito, 2013). Araysi, Dah, and Jizi (2016), based on a group of firms listed in the FTSE-350 index between the years 2007 and 2012 found that women directorship reduces firms’ equity risk. Sila, Gonzalez, and Hagendorff (2016), based on data on 1960 American firms for the period 1996–2010, stated that female presence on the board had no impact on the equity risk of the firms. A study based on quarterly data relating to 30 publicly listed Indonesian firms for the period 2009–2015 found that women CEOs had managed to lower the company’s risk. Further, the number of women directors and the presence of a woman CFO had a considerable influence on the risk of the firms (Fauzi, Basyith, & Ho, 2017).

Women Directors and Risk and Return of Banks

We can, therefore, say that there is enough literature that describes the relationship (positive or negative or no association) between women directorship and risk and return of firms (see above). However, very few researchers have examined the influence of women directorship on the risk and return of banks. Sahay et al. (2017), in their examination of 800 banks in 72 countries, for the period 2001–2013, reported that in low- and middle-income countries, female board membership in banks was higher as compared to the advanced economies. Pathan and Faff (2013), based on their study on US bank holding companies, concluded that women directorship in the pre-SOX period resulted in the better financial performance of banks (proxied by accounting measures). Garcia-Meca, García-Sánchez, and Martínez-Ferrero (2015), using a sample of 159 banks in nine countries between 2004 and 2010, established that diversity in terms of gender had a positive impact on banks’ performance (proxied by Tobin’s Q and ROA). Skala and Weill (2018), using a set of 365 Polish cooperative banks from 2008–2012, established that banks with women CEOs were found to be less risky, having higher capital adequacy ratio and higher equity to asset ratio.

Research Gap and Theoretical Framework

The extant literature consists of several studies that have examined the impact of female directors on the performance of banks (risk and return) in developed nations. However, few studies have examined the effect of female directorship on the performance of banks in developing countries. To the best of the knowledge of the authors, the literature is silent on the examination of the relation between female directorship and the risk and return of the banks operating in India. This study aims to answer the following research questions.

Do women directors impact the accounting returns (proxied by ROA) of the Indian banks?

Do women directors enhance the market returns (proxied by Tobin’s Q) of the Indian banks?

Does female representation on boards impact the systematic equity risk (measured through the equity beta) of the Indian banks?

Do women directors improve the asset quality (proxied by the proportion of gross non- performing assets to total advances) of the Indian banks?

The following is the theoretical framework adopted by the study to obtain the answers to the above-stated research questions.

Hypotheses Development

Based on the above discussion, and on the findings of the previous studies on the impact of women directors on the financial performance of firms, we could formulate the following hypotheses by presuming that women directors have a positive influence on the accounting and the market returns of the Indian banks while they have a negative impact on the accounting and market risks of Indian banks.

H1: Women directorship positively affects the accounting returns of the Indian banks.

H2: Women directorship positively affects the market returns of the Indian banks.

H3: Women directorship reduces the systematic equity risk (proxied by the equity risk) of the Indian banks.

H4: Women directorship leads to improvement in the asset quality (proxied by the proportion of gross non-performing loans to total advances) of the Indian banks.

Data and Methodology

Data

The study investigates the influence of women directorship on the performance of Indian banks during the period from 2009 to 2016 using data from the Ace Equity database and annual reports of the sample banks. The initial sample comprises of banks that form part of the National Stock Exchange 500 index. During the sample period, certain banks did not have annual reports available hence they are excluded from the study. The final data pertains to 29 Indian banks (refer Appendix 1 for the list of sample banks) corresponding to the eight years, leading to 232 bank-year observations. The data relating to women directors has been obtained from the annual reports of the banks that are readily available on their websites. The data on equity beta, the proportion of gross NPAs to total advances, return on assets (ROA) and Tobin’s Q are obtained from the Ace Equity database. The data for the control variables used by the study are collected from the annual reports and the Database for the Indian Economy (DBIE).

Description of Variables

Dependent variable

The dependent variables used by the study and their measurements are presented below.

Return of Banks is measured using the following proxies

Return on assets: It is defined as the ratio of profit after tax and interest to total assets (Bennouri et al., 2018; Kilic & Kuzey, 2016).

Tobin’s Q: It is computed as the ratio of the market value of a firm to its replacement costs. It was difficult to obtain the replacement costs for the sample banks, hence the ratio is computed by dividing the market value of a bank by its book value (Carter, Simkins, & Simpson, 2003; Garcia–Meca et al., 2015).

Risk of Banks is proxied by

Systematic equity risk: It is proxied by equity beta, which is defined as the market risk of the bank that is affected by the non-diversifiable sources of risk. It is computed by regressing the daily stock prices of a bank against the market indices for a period of one year.

The proportion of gross NPAs to total advances: The gross NPA to total advances ratio is determined by dividing the gross NPAs of banks by their total advances in a year. This ratio reflects the asset quality of the banks (Caiazza, Cotugno, Fiordelisi, & Stefanelli, 2018).

The Z-score: It is computed by dividing the sum of ROA and Equity to Assets by the standard deviation of the ROA for the sample banks (Bertay, Demirgüç-Kunt, & Huizinga, 2013; Laeven & Levine, 2009).

All the dependent variables were measured at time t+1 to capture the impact of independent and control variables of the sample banks at time t_0.

Independent variable

The proportion of women directors: This study has considered the proportion of the women directors as the only independent variable. The proportion of women directors is computed as the ratio of the number of women members to the total number of the board of directors of the bank (Srinidhi et al., 2011).

Control variables

This study has employed the following control variables: bank age, bank size, growth rate in advances logarithm of provisions for NPAs, logarithm of deposits, capital adequacy ratio, board size, CEO duality, number of board meetings in an year, average number of board meetings attended, average number of boards served and the public sector bank dummy. Bank age is determined as the total number of years since the inception of the bank (Anderson & Eshima, 2013). Bank size is computed as the natural logarithm of the total assets of the bank (Caiazza, Cotugno, Fiordelisi, & Stefanelli, 2018). The growth rate in advances is computed as (Current Year Advances − Previous Year Advances) ÷ Previous Year Advances.

The logarithm of the provisions for NPAs is calculated by taking the natural logarithm of the provisions for NPAs of the sample banks (Bawa, Goyal, Mitra, & Basu, 2019). The logarithm of deposits, on the other hand, is computed by taking the natural logarithm of the deposits of the sample banks (Stern and Feldman, 2004). The capital adequacy ratio (CAR) is the magnitude of the available capital of a bank as a percentage of the bank’s risk-weighted assets. The board size refers to the strength of the board of directors of banks (Johl, Kaur, & Cooper, 2015). The CEO duality, on the other hand, is a measure of the difference in the roles of the chairman and CEO and a dummy value of 1 is assigned if the chairman and chief executive officer positions are separated and 0 in case the positions are merged (Jizi et al., 2013). The total number of board meetings indicates the aggregate number of board meetings conducted in a year (Jizi et al., 2013). The average number of meetings attended is taken as the ratio of the total number of meetings attended by directors on the board to the total number of board members (Chou, Chun, & Yin, 2013). The average number of boards served is representative of the multiple directorship aspect of the board of the bank. It is computed as the ratio of the number of boards an individual director serves on to the strength of the board of the bank (Barros, Boubaker, & Hamrouni, 2013). The public sector dummy is defined as an interaction of PSB_dummy and the proportion of women directors in the board of banks where the PSB_Dummy is assigned a value of one in case the bank is a public sector bank and a value of zero in the case the bank is a private bank. The public sector dummy attempts to capture the heterogeneity in the bank types for the sample banks in our study.

Method

The current work makes use of the panel data methodology which is extensively used in the corporate governance literature (McKnight and Weir, 2009; Bokpin, 2013; Jizi et al., 2013; Barros et al., 2013). Panel data has both time-series and cross-sectional elements, which allows us to construct and investigate complicated behavioural models, as compared to the pure cross-section or pure time-series models (Baltagi, 2005). Our study employs balanced data procedures because our sample consists of data across banks over a period and hence there are cross-sectional effects.

The endogeneity problem arises in multiple linear regression when the exogeneity assumption is not followed. Further, the causes of endogeneity include, among others, omitted correlated variable bias, simultaneous determination of the dependent and independent variables and measurement error for one of the variables (Moore, 1989). The panel data has been created by employing the dependent variable at t₀+1 time and the corresponding independent variable and control variables at time t₀. In addition to this, 12 control variables were used in the model to reduce the ‘omitted correlated variable bias’. We believe that by addressing the above stated two issues, that is, simultaneous determination of dependent and independent variables and omitted variable bias, we can reduce the endogeneity problem to a considerable extent such that the predictability of the model is enhanced.

Model Specification

To examine the effect of female directors on the bank performance (risk and return), we formulate the following models.

Model 1

ROA_it=α + β₁PWD_it + β₂Bank Size_it + β₃Bank Age_it + β₄Growth_Adv_it + β₅L_dep_it+ β₆L_prov_it + β₇CAR_it + β₈Bsize_it4 + β₉CEO_duality_it + β₁₀NBME_it + β₁₁ANMA_it + β₁₂ANBS_it + β₁₃PSB_Dummy_it + β₁₃SIB_Dummy_it + ε_it

where

ROA refers to the return on assets of the bank

PWD refers to the proportion of women directors on the board of the bank

Bank Size refers to the natural logarithm of the assets of the bank

Bank Age refers to the age of the bank from the year of inception

Growth_Adv refers to the growth rate in advances of the bank

L_Prov refers to the logarithm of the provisions of the bank

L_Dep refers to the logarithm of deposits of the bank

Bsize refers to the strength of the board of directors of banks

CEO_duality refers to the distinction between the roles of the chairman and CEO

NBME indicates the total number of board meetings in a year

ANMA is defined as the ratio of the total of meetings attended by directors to the total number of board member

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board

PSB_dummy is defined as a dummy variable employed in order to capture the ownership of the bank

SIB_dummy is defined as a dummy variable employed in order to capture a systemically important bank

Model 2

Q Ratio_it=α +β₁PWD_it + β₂Bank Size_it + β₃Bank Age_it + β₄Growth_Adv_it+ β₅L_dep_it + β₆L_prov_it + β₇CAR_it + β₈Bsize_it4 + β₉CEO_duality_it + β₁₀NBME_it + β₁₁ANMA_it + β₁₂ANBS_it + β₁₃PSB_Dummy_it + β₁₃SIB_Dummy_it + ε_it

where

Q Ratio refers to the Tobin’s Q Ratio of the bank

PWD refers to the proportion of women directors on the board of the bank

Bank Size refers to the natural logarithm of the assets of the bank

Bank Age refers to the age of the bank from the year of inception

Growth_Adv refers to the growth rate in advances of the bank

L_Prov refers to the logarithm of the provisions of the bank

L_Dep refers to the logarithm of deposits of the bank

Bsize refers to the strength of the board of directors of banks

CEO_duality refers to the distinction between the roles of the chairman and CEO

NBME indicates the total number of board meetings in a year

ANMA is defined as the ratio of the total of meetings attended by directors to the total number of board member

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board

PSB_dummy is defined as a dummy variable employed in order to capture the ownership of the bank

SIB_dummy is defined as a dummy variable employed in order to capture a systemically important bank

Model 3

Beta_it=α + β₁PWD_it + β₂Bank Size_it + β₃Bank Age_it + β₄Growth_Adv_it + β₅L_dep_it + β₆L_prov_it + β₇CAR_it + β₈Bsize_it4 + β₉CEO_duality_it + β₁₀NBME_it + β₁₁ANMA_it + β₁₂ANBS_it + β₁₃PSB_Dummy_it + β₁₃SIB_Dummy_it + ε_it

where

Beta refers to the Equity Beta of the bank

PWD refers to the proportion of women directors on the board of the bank

Bank Size refers to the natural logarithm of the assets of the bank

Bank Age refers to the age of the bank from the year of inception

Growth_Adv refers to the growth rate in advances of the bank

L_Prov refers to the logarithm of the provisions of the bank

L_Dep refers to the logarithm of deposits of the bank

Bsize refers to the strength of the board of directors of banks

CEO_duality refers to the distinction between the roles of the chairman and CEO

NBME indicates the total number of board meetings in a year

ANMA is defined as the ratio of the total of meetings attended by directors to the total number of board member

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board

PSB_dummy is defined as a dummy variable employed in order to capture the ownership of the bank

SiB_dummy is defined as dummy variable employed in order to capture a systemically important bank

Model 4

GNPA_it=α + β₁PWD_it+ β₂Bank Size_it + β₃Bank Age _it + β₄Growth_Adv_it + β₅L_dep_it + β₆L_prov_it + β₇CAR_it + β₈Bsize_it4 + β₉CEO_duality_it + β₁₀NBME_it + β₁₁ANMA_it + β₁₂ANBS_it + β₁₃PSB_Dummy_it + β₁₃SIB_Dummy_it +ε_it

where

Gross NPA/Total Advances refers to the ratio of gross non-performing assets to total advances of the bank

PWD refers to the proportion of women directors on the board of the bank

Bank Size refers to the natural logarithm of the assets of the bank

Bank age refers to the age of the bank from the year of inception

Growth_Adv refers to the growth rate in advances of the bank

L_Prov refers to the logarithm of the provision of the bank

L_Dep refers to the logarithm of deposits of the bank

Bsize refers to the strength of the board of directors of banks

CEO duality refers to the distinction between the roles of the chairman and CEO

NBME indicates the total number of board meetings in a year

ANMA is defined as the ratio of the total of meetings attended by directors to the total number of board member

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board

PSB_dummy is defined as a dummy variable employed in order to capture the ownership of the bank

SIB_dummy is defined as dummy variable employed in order to capture a systemically important bank

Results and Discussion

This section presents the results and describes the inferences of the empirical findings.

Descriptive Statistics

Table 1.1 presents the descriptive statistics of the dependent, independent and control variables of the sample banks. We can observe from the table that the mean proportion of women directors of the sample banks is 0.067 (around 7 women directors per 100 total directors) and the standard deviation for the same is 0.06, the mean ROA, Q Ratio, Equity Beta and proportion of gross NPA to total advances of the Indian banks were 0.008, 1.433, 1.156 and 2.973 respectively, while the sample banks’ standard deviation for these four variables were 0.006, 1.259, 0.406 and 2.615 respectively.

Table 1.1

Descriptive Statistics for the Independent Variable Dependent Variables and Control Variables

Variable	Mean	Standard Deviation	Minimum	Maximum
ANMA	0.871255	0.077955	0.52	1
ANBS	1.756626	1.583273	0	7.3
B_AGE	73.94397	35.2634		151
BETA	1.156767	0.40567	0	2.3
B_SIZE	11.06897	2.009605	7	19
CDS	0.517241	0.500783	0	1
D_PSB	0.040392	0.055848	0	0.2
GNPA_TA	2.97306	2.615404	0	17.4
L_DEPOSITS	5.043594	0.460631	3.866931	6.197775
L_PROV	2.705552	0.855011	0	4.51727
NBME	12.69828	4.304875	4	28
PWD	0.067601	0.062386	0	0.25
Q_RATIO	1.432543	1.259398	0.26	8.45
ROA	0.00785	0.006255	−0.019901	0.017591

Notes: ROA refers to the return on assets of the bank.

Q Ratio refers to the Tobin’s Q Ratio of the bank.

Beta refers to the equity beta of the bank.

Gross NPA/Total Advances refers to the ratio of gross non-performing assets to total advances of the bank.

PWD refers to the proportion of women directors on the board of the bank.

Bank Size refers to the natural logarithm of the assets of the bank.

Bank age refers to the age of the bank from the year of inception.

Growth_Adv refers to the growth rate in advances of the bank.

L_Prov refers to the logarithm of provision of assets of the bank.

L_Dep refers to the logarithm of deposits of the bank.

Bsize refers to the strength of the board of directors of banks.

CEO duality refers to the distinction between the roles of the chairman and CEO,

NBME indicates the total number of board meetings in a year.

ANMA is defined as the ratio of the total of meetings attended by directors to the total number of board member.

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board.

PSB_dummy is defined as dummy variable in order to capture the ownership of the bank.

SiB_dummy is defined as dummy variable in order to capture a systemically important bank.

Correlation Analysis

The Pearson pairwise correlations were computed for the dependent, independent and control variables of the study. From Table 1.2, it can be observed that the proportion of women directors is positively correlated with the equity beta (at 5% significance level). However, it does not have a significant association with other three dependent variables. This indicates that the presence of women directors on the boards of Indian banks results in increased systematic equity risk for them. Further, women directorship is found to be having a positive and significant relation with five control variables of the study, namely the logarithm of provisions, the logarithm of deposits, annual number of board meetings, the average number of boards served and public sector bank dummy. Bank age has a negative and significant relationship with the women directorship of the sample banks.

Table 1.2

Correlation Analysis for the Independent Variable Dependent Variables and Control Variables

R Sq	L_Prov	L_depo	CAR	GNPA	Beta	QRat	ROA	B_Age	B_size	CDS	NBME	ANMA	ANBS	D_PSB	PWD
L_Prov	1	0.816**	−0.186**	0.352**	0.268**	−0.333**	−0.313**	0.01	0.23**	−0.024	−0.25*	−0.06	0.252**	0.323**	0.206**
L_deposits	0.816**	1	−0.149*	0.202**	0.316**	−0.178**	−0.252**	0.013	0.291**	−0.16*	−0.074	−0.100	0.245**	0.325**	0.249**
CAR	−0.186**	−0.149*	1	−0.415**	−0.126	0.435**	0.492**	0.335**	−0.081	0.175**	−0.432**	−0.263**	0.457**	−0.335**	−0.003
GNPA	0.352**	0.202**	−0.415**	1	0.287**	−0.309**	−0.267**	0.084	−0.041	−0.106	0.136*	−0.005	−0.173**	0.195**	−0.034
Beta	0.268**	0.316**	−0.126	0.287**	1	−0.169**	−0.221**	−0.114	0.050	−0.148*	−0.088	−0.169*	0.203**	0.015	0.142*
Q Ratio	−0.333**	−0.178**	0.435**	−0.309**	−0.169**	1	0.469**	−0.161*	−0.110	0.172**	−0.150*	−0.071	0.284**	−0.305**	0.021
ROA	−0.313**	−0.252**	0.492**	−0.267**	−0.221**	0.469**	1	−0.174**	−0.165*	0.206**	−0.32**	−0.142*	0.392**	−0.362**	0.017
Bank_age	0.013	0.013	−0.335**	0.084	−0.114	−0.161*	−0.174**	1	−0.095	−0.053	0.376**	0.049	−0.412**	0.155*	−0.210**
Board_size	0.230**	0.291**	−0.081	−0.041	0.050	−0.110	−0.165*	−0.095	1	−0.049	0.195**	−0.081	0.106	0.116	0.107
CDS	−0.024	−0.163*	0.175**	−0.106	−0.148*	0.172**	0.206**	−0.053	−0.053	−0.049	1	−0.206**	0.025	0.055	−0.142*
NBME	−0.247**	−0.074	−0.432**	0.136*	−0.088	−0.150*	−0.324**	0.376**	0.195**	−0.206**	1	0.039	−0.538**	0.243**	−0.164*
ANMA	−0.055	−0.100	−0.263**	−0.005	−0.169*	−0.071	−0.142*	0.049	−0.081	0.025	0.039	1	−0.265**	0.157*	0.088
ANBS	0.252**	0.245**	0.457**	−0.173**	0.203**	0.284**	0.392**	−0.412**	0.106	0.055	−0.538**	−0.265**	1	−0.267**	0.134*
D_PSB	0.323**	0.325**	−0.335**	0.195**	0.015	−0.305**	−0.362**	0.155*	0.116	−0.142*	0.243**	0.157*	−0.267**	1	0.578**
PWD	0.206**	0.249**	−0.003	−0.034	0.142*	0.021	0.017	−0.210**	0.107	−0.026	−0.164*	0.088	0.134*	0.578**	1

Notes: *. Correlation is significant at the 0.05 level (2-tailed).

**. Correlation is significant at the 0.01 level (2-tailed).

ROA refers to the return on assets of the bank.

Q Rat refers to the Tobin’s Q Ratio of the bank.

Beta refers to the equity beta of the bank.

Gross NPA/Total Advances refers to the ratio of gross non-performing assets to total advances of the bank.

PWD refers to the proportion of women directors on the board of the bank.

Bank Size refers to the natural logarithm of the assets of the bank.

Bank age refers to the age of the bank from the year of inception.

Growth_Adv refers to the growth rate in advances of the bank.

L_Prov refers to the logarithm of provision of assets of the bank.

L_Dep refers to the logarithm of deposits of the bank.

Bsize refers to the strength of the board of directors of banks.

CEO duality refers to the distinction between the roles of the chairman and CEO,

NBME indicates the total number of board meetings in a year.

ANMA is defined as the ratio of the total number of meetings attended by directors to the total number of board members.

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board.

PSB_dummy is defined as dummy variable in order to capture the ownership of the bank.

SiB_dummy is defined as dummy variable in order to capture a systemically important bank.

Regression Analysis Panel Data Estimation Results

Model 1

We have employed the year fixed effects method. Table 2 presents the results of Model 1 using the year fixed effects method. The proportion of women directors has a positive and significant relationship with the accounting returns of Indian banks measured through ROA. This indicates that women directorship positively impacts the accounting performance of Indian banks. The results are in support of the findings of Terjesen et al. (2016), Kilic and Kuzey (2016) and Bennouri et al. (2018). The control variable logarithm of provisions has a negative and significant impact on the accounting performance of Indian banks. This reflects that the banks with lower provisions have better accounting returns. Hence, we accept hypothesis H1 and conclude that women directorship positively impacts the accounting performance of Indian banks. The overall model is found to be statistically significant at a 1% significance level.

Table 2.

Year Fixed Effects Estimation Results for the Model 1

R Squared	0.3381
Prob(F)	0.000
ROA	Coef.	Std. Err.	t	P>\|t\|
PWD	0.0190858	0087899	2.17	0.031
CAR	−0.0001616	.0001854	−0.87	0.385
L_DEPOSITS	0.0025976	0.0029702	0.87	0.383
L_PROV	−0.0024948	0.0011923	−2.09	0.038
D_PSB	−0.0187887	0.0116821	−1.61	0.109
Growth_adv	−0.0020624	0.0024503	−0.84	0.401
bank_age	0.0001023	0.0004662	0.22	0.827
bank_size	−0.0014281	−0.0057995	−0.25	0.806
d_sib	0.003494	−0.0031849	1.10	0.274
board_size	−0.0001637	0.0001854	−0.88	0.378
CEODuality_score	−0.0001486	−0.0023809	−0.06	0.950
NBME	0.0000639	0.0001538	0.42	0.678
ANMA	−0.0010391	0.0043245	−0.24	0.810
ANBS	−0.0004046	0.0004101	−0.99	0.325
2010	0.0003142	0.001388	0.23	0.821
2011	−0.0003833	0.0015768	−0.24	0.808
2012	−0.0022592	0.0019124	−1.18	0.239
2013	−0.0026495	0.0023807	−1.11	0.267
2014	−.0063598	0.0027663	−2.30	0.023
2015	−0.0060827	0.0031505	−1.93	0.055
2016	−0.0003199	0.0035955	−0.09	0.929
Cons	0.0087839	0.0360568	0.24	0.808

Notes: ROA refers to the return on assets of the bank.

PWD refers to the proportion of women directors on the board of the bank.

Bank Size refers to the natural logarithm of the assets of the bank.

Bank age refers to the age of the bank from the year of inception.

Growth_Adv refers to the growth rate in advances of the bank.

L_Prov refers to the logarithm of provision of assets of the bank.

L_Dep refers to the logarithm of deposits of the bank.

Bsize refers to the strength of the board of directors of banks.

CEO duality refers to the distinction between the roles of the chairman and CEO.

NBME indicates the total number of board meetings in a year.

ANMA is defined as the ratio of the total number of meetings attended by directors to the total number of board member.

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board.

PSB_dummy is defined as a dummy variable in order to capture the ownership of the bank.

SiB_dummy is defined as a dummy variable in order to capture a systemically important bank.

Model 2

We conducted the fixed effect estimation for Model 2. Table 3 presents the results of the Model 2 using the year fixed effects method. The proportion of women directors has a positive and insignificant relationship with the market performance of the Indian banks. The results indicate that women directorship does not impact the market performance of the Indian banks measured through Q Ratio. This contrasts with the findings of Garcia-Meca et al. (2015) and Reguera-Alverado et al. (2017). The control variables of capital adequacy ratio and the size of the banks had a positive and significant impact on market performance. This reflects that larger banks and banks with better capital adequacy compliance had better market performance. Hence, we reject hypothesis H2 and concluded that women directorship did not impact the market performance of the Indian banks. The overall model is found to be statistically significant at a 1% significance level.

Table 3.

Year Fixed Effects Estimation Results for the Model 2

R Squared	0.3381
Prob(F)	0.000
Q Ratio	Coef.	Std. Err.	t	P>\|t\|
PWD	0.68678	1.79944	0.38	0.703
CAR	−0.0799441	.0379588	−2.11	0.037
L_DEPOSITS	−0.378588	−.6080429	−0.62	0.534
L_PROV	0.2195727	0.2440809	0.90	0.370
D_PSB	0.5937163	2.391536	00.25	0.804
Growth_adv	0.3829708	.5016162	0.76	0.446
bank_age	−0.1032507	.095446	−1.08	0.281
bank_size	2.19111	1.187248	1.85	0.067
d_sib	−0.3024174	0.6520064	−0.46	0.643
board_size	0.0000748	0.0379603	0.00	0.998
CEODuality	−0.5800062	0.4874139	−1.19	−0.236
NBME	−0.0272621	0.0314949	−0.87	0.388
ANMA	−0.1166883	0.8853091	−0.13	0.895
ANBS	0.0351242	0.0839542	0.42	0.676
2010	0.302244 .	0.2841431	0.1.06	0.289
2011	−0.5832594	0.3227884	−1.81	0.072
2012	−0.1717055	−0.3915095	−0.44	0.661
2013	−0.7315663	0.4873609	−1.50	0.135
2014	−.1514974	.5663057	−0.27	0.789
2015	−0.4783566	0.6449567	−0.74	0.459
2016	.5106274 .	7360613	−0.69	0.489
Cons	−1.504583	7.381458	−0.20	0.839

Notes: Q Ratio refers to the Tobin’s Q Ratio of the bank.

PWD refers to the proportion of women directors on the board of the bank.

Bank Size refers to the natural logarithm of the assets of the bank.

Bank age refers to the age of the bank from the year of inception.

Growth_Adv refers to the growth rate in advances of the bank.

L_Prov refers to the logarithm of provision of assets of the bank.

L_Dep refers to the logarithm of the deposits of the bank.

Bsize refers to the strength of the board of directors of banks.

CEO duality refers to the distinction between the roles of the chairman and CEO.

NBME indicates the total number of board meetings in a year.

ANMA is defined as the ratio of the total number of meetings attended by directors to the total number of board member.

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board.

PSB_dummy is defined as a dummy variable in order to capture the ownership of the bank.

SIB_dummy is defined as a dummy variable in order to capture a systemically important bank.

Model 3

We employed the fixed effects method to gauge the relationship between the presence of women directors and the market risks of banks. Table 4 presents the results of Model 3 using year fixed effects regression. The proportion of women directors has a negative and insignificant relationship with the systematic equity risk (proxied with equity beta) of Indian banks. The results indicate that women directorship does not impact the systematic equity risk of Indian banks. This is in support of the findings of Sila, Gonzalez, & Hagendorff (2016). As far as control variables are concerned, bank size was found to be having a negative and significant impact on the equity beta of Indian banks. This indicates that smaller banks had a greater exposure to market risk compared to their bigger competitors. Hence, we reject hypothesis H3 and conclude that women directorship does not impact the equity beta of the Indian banks. The overall model is found to be statistically significant at a 1% significance level.

Table 4.

Year Fixed Effects Estimation Results for the Model 3

R Squared	0.4453
Prob(F)	0.000
Beta	Coef.	Std. Err.	t	P>\|t\|
PWD	−0.2234921	.5356407	−0.42	0.677
CAR	0.0036547	0.0112992	0.32	0.747
L_DEPOSITS	−0.0028079	0.1809966	−0.02	0.988
L_PROV	0.0947385	0.0726557	1.30	0.194
D_PSB	0.6009223	0.7118905	0.84	0.400
Growth_adv	0.1032049	0.1493165	0.69	0.490
bank_age	0.0169369	0.0284115	0.60	0.552
bank_size	−1.033686	0.3534089	−2.92	0.004
d_sib	0.1107204	0.1940832	0.57	0.569
board_size	−0.0103682	0.0112997	− 0.92	0.360
CEO Duality_score	0.0508906	0.1450889	0.35	0.726
NBME	−0.0068598	0.0093751	−0.73	0.465
ANMA	−0.0491969	0.2635306	−0.19	0.852
ANBS	0.0147015	0.0249907	0.59	0.557
2010	0.1662496	0.0845811	1.97	0.051
2011	0.2525829	0.0960847	2.63	0.009
2012	0.3398224	0.1165409	2.92	0.004
2013	0.5344736	0.1450731	3.68	0.000
2014	0.6978676	0.1685727	4.14	0.000
2015	0.7831854	0.1919848	4.08	0.000
2016	0.5677406	0.2191039	2.59	0.010
Cons	5.756055	2.197244	2.62	0.010

Notes: Beta refers to the equity beta of the bank.

PWD refers to the proportion of women directors on the board of the bank.

Bank Size refers to the natural logarithm of the assets of the bank.

Bank age refers to the age of the bank from the year of inception.

Growth_Adv refers to the growth rate in advances of the bank.

L_Prov refers to the logarithm of provision of assets of the bank.

L_Dep refers to the logarithm of deposits of the bank.

Bsize refers to the strength of the board of directors of banks.

CEO duality refers to the distinction between the roles of the chairman and CEO.

NBME indicates the total number of board meetings in a year.

ANMA is defined as the ratio of the total number of meetings attended by directors to the total number of board member.

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board.

PSB_dummy is defined as a dummy variable in order to capture the ownership of the bank.

SiB_dummy is defined as a dummy variable in order to capture a systemically important bank.

Model 4

We employed the year fixed effects method. Table 5 presents the results of Model 4 using panel fixed effects regression. The proportion of women directors has a negative and insignificant relationship with the gross NPA to total advances ratio of the Indian banks. The results indicate that women directorship had no impact on the asset quality of the Indian banks. This contrasts with the findings of Skala and Weill (2018), who concluded that there was a positive relationship between women directors and bank risk. The control variable logarithm of provisions had a positive impact on the gross NPA of Indian banks. This indicates that banks with higher provisions had a negative impact on the asset quality of banks. Hence, we reject hypothesis H4 and conclude that women directorship does not affect the asset quality of Indian banks. The overall model is found to be statistically significant at a 1% significance level.

Table 5.

Year Fixed Effects Estimation Results for Model 4

R Squared	0.5085
Adj R Squared
Prob(F)	0.000
GNPA	Coef.	Std. Err.	t	P>\|t\|
PWD	−2.487067	3.711703	−0.67	0.504
CAR	−0.0517669	.0802484	−0.65	0.520
L_DEPOSITS	−1.354942	1.281966	−1.06	0.292
L_PROV	1.434574	0.4987907	2.88	0.005
D_PSB	3.635553	4.930865	0.74	0.462
Growth_adv	−1.395014	1.069047	−1.30	0.194
bank_age	.478944	.5396325	0.89	0.376
bank_size	2.766278	2.543343	1.09	0.278
d_sib	−0.528935	1.368601	−0.39	0.700
board_size	−0.0853647	0.0789126	−1.08	0.281
CEODuality_score	−0.3986599	1.037738	−0.38	0.701
NBME	0.0227793	0.0661755	0.34	0.731
ANMA	−2.027242	1.866059	−1.09	0.279
ANBS	−0.0377418	0.1755519	−0.21	0.830
2010	−0.7859845	0.8256961	−0.95	0.342
2011	−1.741579	1.331296	−1.31	0.192
2012	−2.079356	1.90215	−1.09	0.276
2013	−2.433643	2.519677	−0.97	−0.335
2014	2.45685	3.024743	−0.81	−0.418
2015	−2.597874	3.586087	−0.72	0.470
2016	−0.8713776	4.126652	−0.21	0.833
Cons	−41.43769	43.33566	−0.96	0.340

Notes: GNPA refers to the equity beta of the bank.

PWD refers to the proportion of women directors on the board of the bank.

Bank Size refers to the natural logarithm of the assets of the bank.

Bank age refers to the age of the bank from the year of inception.

Growth_Adv refers to the growth rate in advances of the bank.

L_Prov refers to the logarithm of provision of assets of the bank.

L_Dep refers to the logarithm of deposits of the bank.

Bsize refers to the strength of the board of directors of banks.

CEO duality refers to the distinction between the roles of the chairman and CEO.

NBME indicates the total number of board meetings in a year.

ANMA is defined as the ratio of the total number of meetings attended by directors to the total number of board members.

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board.

PSB_dummy is defined as a dummy variable in order to capture the ownership of the bank.

SiB_dummy is defined as a dummy variable in order to capture a systemically important bank.

Robustness Checks

In addition to the empirical test carried out in the study, we have also conducted the robustness checks, as stated below. First, we have introduced Z-score as an alternate proxy for the equity risk of banks by replacing Equity Beta in Model 3. Second, we have employed the lagged dependent variable in each of the four models to find out whether there is any significant effect or impact of the lagged dependent variable on the results established by us in the previous section.

Z-score as an Alternative Proxy for Risks

We employ the Z-score as an alternate proxy for market risks of banks (following Nitoi, Clichici, & Moagăr-Poladian, 2019). We formulated and empirically tested the following model: _it₁_it₂_it₃_it₄_it₅_it₆_it₇_it₈_it4₉_it₁₀_it₁₁_it₁₂_it₁₃_it₁₃_it_it

where

Z-score is computed by dividing the sum of ROA and Equity to assets by the standard deviation of the ROA for the sample banks (Bertay et al., 2013; Laeven & Levine, 2009).

PWD refers to the proportion of women directors on the board of the bank

Bank Size refers to the natural logarithm of the assets of the bank

Bank Age refers to the age of the bank from the year of inception

Growth_Adv refers to the growth rate in advances of the bank

L_Prov refers to the logarithm of provisions of the bank

L_Dep refers to the logarithm of deposits of the bank

Bsize refers to the strength of the board of directors of banks

CEO_duality refers to the distinction between the roles of the chairman and CEO

NBME indicates the total number of board meetings in a year

ANMA is defined as the ratio of the total of meetings attended by directors to the total number of board member

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board

PSB_dummy is defined employed as a dummy variable in order to capture the ownership of the bank

SIB_dummy is defined as dummy variable employed in order to capture a systemically important bank

Table 6 presents the results of the analysis using panel fixed effects regression. The proportion of women directors has a positive and insignificant relationship with the Z-score of the Indian banks. Hence, we conclude that women directorship does not affect the market risks of Indian banks. The overall model is found to be statistically significant at a 1% significance level.

Impact of Lagged Dependent Variables on the Relationship Between Women Directors and Risk and Return of Indian Banks

This section aims to establish the robustness of the results obtained from the four models in the previous section by studying the relation between the risk and return variables and women directors on the board of banks along with the lagged term of risk and return measures of banks. From Table 7, we can observe that women directorship does not impact the accounting returns (ROA) of the sample banks when we incorporate the lag of ROA as a control variable in Model 1. This result contrasts with the findings presented in the previous section. There is no difference in the results of the other three models when the lag of the dependent variables are plugged in the models (see Tables 8–10).

(II) Robustness Results

Table 6.

Year Fixed Effects Estimation Results for Revised Model 3 with z Score as the Dependent Variable

R Squared	0.0943
Adj R Squared
Prob(F)	0.20
Z-score	Coef.	Std. Err.	t	P>\|t\|
PWD	12.70249	7.749625	1.64	0.103
CAR	0.0137813	0.1797156	0.08	0.939
B_SIZE	−1.233212	5.373104	−0.23	0.819
Growth_adv	2.841888	2.233002	1.27	0.205
L_PROV	−0.9365867	1.052289	−0.89	0.375
L_DEPOSITS	0.8416516	2.562716	0.33	0.743
D_PSB	−13.55237	10.28665	−1.32	0.189
bank_AGE	0.260572	1.154388	0.23	0.822
2010	0.0003142	0.4973732	1.769121	0.779
2011	0.460698	2.852332	0.16	0.872
2012	−0.2111952	4.056041	−0.05	0.959
2013	−0.7564432	5.385189	−0.14	0.888
2014	1.779074	6.454388	0.28	0.783
2015	0.8959871	7.652135	0.12	0.907
2016	1.297807	8.815228	0.15	0.883
Cons	−13.15523	91.86122	−0.14	0.886

Notes: Z-score refers to the ratio of gross non-performing assets to total advances of the bank.

PWD refers to the proportion of women directors on the board of the bank.

Bank Size refers to the natural logarithm of the assets of the bank.

Bank age refers to the age of the bank from the year of inception.

Growth_Adv refers to the growth rate in advances of the bank.

L_Prov refers to the logarithm of provision of assets of the bank.

L_Dep refers to the logarithm of deposits of the bank.

Bsize refers to the strength of the board of directors of banks.

CEO duality refers to the distinction between the roles of the chairman and CEO.

NBME indicates the total number of board meetings in a year.

ANMA is defined as the ratio of the total number of meetings attended by directors to the total number of board members.

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board.

PSB_dummy is defined as dummy variable in order to capture the ownership of the bank.

SiB_dummy is defined as dummy variable in order to capture a systemically important bank.

Table 7.

Year Fixed Effects Estimation Results for Revised Model 1 with Lagged Dependent Variable

R Squared	0.3217
Prob(F)	0.000
ROA	Coef.	Std. Err.	t	P>\|t\|
PWD	0.0118696	0.0095414	1.24	0.215
CAR	−0.0005654	0.0002726	−2.07	0.040
L_DEPOSITS	0.001282	0.0034045	0.38	0.707
L_PROV	−0.0024584	0.0018349	−1.34	0.182
D_PSB	−0.0083175	0.012568	−0.66	0.509
Growth_adv	−0.0023284	0.0027915	−0.83	0.405
bank_age	−0.0000484	0.000565	−0.09	0.932
bank_size	−0.0029288	0.0067386	−0.43	0.664
d_sib	0.0028426	0.0032887	0.86	0.389
L_ROA	0.0896508	0.0823887	1.09	0.278
2011	−0.0008251	0.0012512	−0.66	0.511
2012	−0.0019341	0.0015685	−1.23	0.219
2013	−0.0018874	0.0021631	−0.87	0.384
2014	−0.0052182	0.0026653	−1.96	0.052
2015	−0.004393	0.0032514	−1.35	0.179
2016	0.0014833	0.0038755	0.38	0.702
Cons	0.0361607	0.0487722	0.74	0.460

Notes: ROA refers to the Return on Assets of the bank.

PWD refers to the proportion of women directors on the board of the bank.

Bank Size refers to the natural logarithm of the assets of the bank.

Bank age refers to the age of the bank from the year of inception.

Growth_Adv refers to the growth rate in advances of the bank.

L_Prov refers to the logarithm of provision of assets of the bank.

L_Dep refers to the logarithm of deposits of the bank.

Bsize refers to the strength of the board of directors of banks.

CEO duality refers to the distinction between the roles of the chairman and CEO.

NBME indicates the total number of board meetings in a year.

ANMA is defined as the ratio of the total number of meetings attended by directors to the total number of board members.

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board.

PSB_dummy is defined as a dummy variable in order to capture the ownership of the bank.

SiB_dummy is defined as a dummy variable in order to capture a systemically important bank.

L_ROA refers to the lagged term of the dependent variable ROA.

Table 8.

Year Fixed Effects Estimation Results for Revised Model 2 with Lagged Dependent Variable

R Squared	0.1598
Adj R Squared
Prob(F)	0.0078
Q Ratio	Coef.	Std. Err.	t	P>\|t\|
PWD	1.210951	1.846741	0.66	0.513
CAR	−0.002088	0.0534028	−0.04	0.969
L_DEPOSITS	0.254005	0.6739601	0.38	0.707
L_PROV	−0.053952	0.3541251	−0.15	0.879
D_PSB	−0.7678403	2.475936	−0.31	0.757
Growth_adv	0.4022365	0.5386857	0.75	0.456
B_AGE	−0.072426	0.1113098	−0.65	0.516
B_SIZE	1.76035	1.337551	1.32	0.190
D_SIB	−0.2044115	0.6489564	−0.31	0.753
board_size	0.0188499	0.04083	0.46	0.645
NBME	−0.0257482	0.0327989	−0.79	0.434
ANMA	−0.0039962	0.9173438	−0.00	0.997
ANBS	−0.012098	0.0904324	−0.13	0.894
L_Qratio	0.026224	0.079964	0.33	0.743
2011	−0.8373258	0.2415347	−3.47	0.001
2012	−0.5254635	0.3068523	−1.71	0.089
2013	−1.075085	0.4133457	−2.60	0.010
2014	−0.5226777	0.5154288	−1.01	0.312
2015	−0.8497084	0.6180864	−1.37	0.171
2016	−0.7912874	0.7431973	−1.06	0.289
Cons	−4.620908	9.481312	−0.49	0.627

Notes: Q Ratio refers to the ratio of Tobin’s Q of the bank.

PWD refers to the proportion of women directors on the board of the bank.

Bank Size refers to the natural logarithm of the assets of the bank.

Bank age refers to the age of the bank from the year of inception.

Growth_Adv refers to the growth rate in advances of the bank.

L_Prov refers to the logarithm of provision of assets of the bank.

L_Dep refers to the logarithm of deposits of the bank.

Bsize refers to the strength of the board of directors of banks.

CEO duality refers to the distinction between the roles of the chairman and CEO.

NBME indicates the total number of board meetings in a year.

ANMA is defined as the ratio of the total number of meetings attended by directors to the total number of board members.

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board.

PSB_dummy is defined as a dummy variable in order to capture the ownership of the bank.

SiB_dummy is defined as a dummy variable in order to capture a systemically important bank.

L_QRatio refers to the lagged term of the dependent variable Q Ratio.

Table 9.

Year Fixed Effects Estimation Results for Revised Model 3 with Lagged Dependent Variable

R Squared	0.4330
Adj R Squared
Prob(F)	0.0000
Beta	Coef.	Std. Err.	t	P>\|t\|
PWD	−0.2650488	0.5142157	−0.527	0.607
CAR	0.0052205	0.0148693	0.35	0.726
L_DEPOSITS	−0.1424608	0.1862349	−0.76	0.445
L_PROV	.0976894	0.0988281	0.99	0.324
D_PSB	0.3557833	0.6888065	0.52	0.606
Growth_adv	0.2059143	0.1498619	1.37	0.171
bank_age	0.064512	0.0309245	2.09	0.039
bank_size	−0.5915396	0.3737139	−1.58	0.116
d_sib	0.0932264	0.1835391	0.51	0.612
Board_size	−0.0140972	0.0113821	−1.24	0.217
ANMA	0.1733025	0.2542858	0.68	0.497
ANBS	0.0216319	0.0252166	0.86	0.392
lBeta	0.0508772	0.0579068	0.88	0.381
2011	0.0180287	0.06782	0.27	0.791
2012	0.0320368	0.0861383	0.37	0.710
2013	0.1614772	0.1165553	1.39	0.168
2014	0.2679069	0.1474123	1.82	0.071
2015	0.2828849	0.1791102	1.58	0.116
2016	0.0071101	0.2143245	0.03	0.974
constant	0.2907945	2.698466	0.11	0.914

Notes: Beta refers to the equity beta of the bank.

PWD refers to the proportion of women directors on the board of the bank.

Bank Size refers to the natural logarithm of the assets of the bank.

Bank age refers to the age of the bank from the year of inception.

Growth_Adv refers to the growth rate in advances of the bank.

L_Prov refers to the logarithm of provision of assets of the bank.

L_Dep refers to the logarithm of deposits of the bank.

Bsize refers to the strength of the board of directors of banks.

CEO duality refers to the distinction between the roles of the chairman and CEO.

NBME indicates the total number of board meetings in a year.

ANMA is defined as the ratio of the total number of meetings attended by directors to the total number of board members.

ANBS refers to the multiple directorship aspect of the board of directors and is defined as the ratio of the number of boards each director serves on to the strength of the board.

PSB_dummy is defined as a dummy variable in order to capture the ownership of the bank.

SiB_dummy is defined as a dummy variable in order to capture a systemically important bank.

L_Beta refers to the lagged term of the dependent variable Beta.

Table 10.

Year Fixed Effects Estimation Results for Revised Model 4 with Lagged Dependent Variable

R Squared	0.6942
Adj R Squared
Prob(F)	0.000
GNPA	Coef.	Std. Err.	t	P>\|t\|
PWD	1.54099	3.280272	0.47	0.639
CAR	−0.1483427	.0943678	−1.57	0.118
L_DEPOSITS	−1.485418	1.184379	−1.25	0.212
L_PROV	1.97403	0.6299472	3.13	0.002
D_PSB	−0.3713934	4.372299	−0.08	0.932
Growth_adv	−0.2370093	0.9990228	−0.24	0.813
bank_age	0.4175333	0.4477174	0.93	0.352
bank_size	2.161467	2.478493	0.87	0.385
d_sib	−0.777072	1.150854	−0.68	0.501
L_GNPA	0.910583	0.0981648	9.28	0.000
2011	1.377625	0.6409059	−2.15	0.033
2012	−1.130755	1.091737	−1.04	0.302
2013	1.810441	1.610995	−1.12	0.263
2014	−2.156834	2.042969	−1.06	0.293
2015	−2.713089	2.524148	−1.07	0.284
2016	−1.428496	2.998742	−0.48	0.634
Cons	−38.40874	37.94234	−1.01	0.313

Notes: GNPA refers to the ratio of gross non-performing assets to total advances of the bank.

PWD refers to the proportion of women directors on the board of the bank.

Bank Size refers to the natural logarithm of the assets of the bank.

Bank age refers to the age of the bank from the year of inception.

Growth_Adv refers to the growth rate in advances of the bank.

L_Prov refers to the logarithm of the provisions of the bank.

L_Deposits refers to the logarithm of deposits of the bank.

PSB_dummy is defined as a dummy variable in order to capture the ownership of the bank.

SiB_dummy is defined as a dummy variable in order to capture a systemically important bank.

L_GNPA refers to the lagged term of the dependent variable GNPA.

Conclusion, Implications and Scope for Further Work

This study empirically examined the influence of women directorship on the risk and return of Indian banks. The proportion of women directors was considered as the proxy for women directorship. The returns of the sample banks were measured through the accounting profitability measure of return on assets (ROA) and the market performance measure of Tobin’s Q (Q Ratio). The market risk of the Indian banks was proxied by the systematic equity risk (measured through Equity Beta) and the accounting risk of the sample banks was measured through the asset quality (proxied by the ratio of gross NPAs to total advances). The study was based on data relating to 29 Indian banks corresponding to eight years, which resulted in 232 bank-year observations. The data on the proportion of women directors was collected manually from the annual reports of the sample banks while data on the dependent variables (ROA, Q Ratio, Equity Beta, gross NPA to total advances) and control variables were collected from Ace Equity and the RBI’s DBIE databases.

The current study, based on the results of the panel year fixed effects model, concludes that the presence of women directors on the board of Indian banks does have an impact on their accounting returns (measured through returns on assets). However, the findings of the study reveal that gender diversity does not influence the market returns for Indian banks during the sample period.

Our study contributes to the existing literature in many ways. Firstly, this is the first study on the impact of women directors on the risk and return of banks operating in India. Secondly, the findings of the study contribute new theoretical insights to the body of knowledge on women directors, which may be useful to future researchers. Thirdly, banks may enhance their financial performance by taking cognizance of the findings of this study. Lastly, equity investors may make use of the findings of this article in deciding on whether to invest in a bank’s stock based on the presence of women directors on the board of the bank.

Further research can be carried out on some of the following themes: researchers can extend the current study to explore the impact of women directors on the risk–return performance based on ownership (public sector banks, private sector banks and foreign banks). The study can be extended into other emerging markets and a comparative study can be made between developed and developing economies in terms of women directors’ impact.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Appendices

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