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The effect of gender diversity on firm performance

in the banking industry.

Chrisje van Hapert

10737693

Bsc Economics and Business

Specialisation: Finance and Organization Supervisor: Patrick Stastra

June 2018 Words: 5568

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Statement of own work

I, Chrisje van Hapert, hereby declare that I have written this thesis myself and that I take full responsibility for its contents. I confirm that the text and work presented in this thesis is original and that I have not used any sources other than those mentioned in the text and in the references. The Faculty of Economics and Business is only responsible for the submission of the thesis, not for the content.

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Abstract

This paper aims to investigate the effect of gender diversity in the boardroom on firm performance in the financial industry. Previous research on gender diversity and firm performance in general is ambiguous, meaning that positive, negative and no effects were found in the different studies. Because of a lack of research on this topic in the financial industry, this paper hypothesize that there is no effect of female board members on firm performance. The sample consists of the top 250 financial firms in the Netherlands, for the year 2016. Firm performance is measured by return on equity and return on assets. Gender diversity is measured by the percentage of female board directors. The OLS regression results imply no relation between gender diversity and firm performance. Therefore, this research cannot provide evidence that female board directors improve or decrease firm performance.

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Table of content

1. Introduction p. 5

2. Theoretical framework p. 6

3. Methodology p. 9

3.1. Variables and model p. 9

3.2. Data p. 11

4. Results p. 13

4.1. Parametric assumptions testing p. 13

4.2. Impact of gender diversity on Return on Equity (ROE) p. 13 4.3. Impact of gender diversity on Return on Assets (ROA) p. 15

5. Discussion and concluding remarks p. 16

Bibliography

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1. Introduction

This paper studies the effect of gender diversity in the board on firm performance. Gender diversity is a worldwide frequently discussed topic in corporate governance. In the past years, politicians and investors are putting pressure on companies for gender equality (Mooney, 2018). In a report by Deloitte (2017), the global situation of women on board is examined. The dataset contains information on gender diversity in the boards of 7,000 companies in 64 different countries around the world. The initiatives, quotas, the overall numbers, the committees and industry specific numbers of all countries are reported. Norway for example was the first country to implement legislation focused on gender diversity in 2005 and it has one of the highest percentages of female board members with 42.0%. In France, a 40.0% legislative quota is used to increase gender diversity and appears to be successful. The United States has no quotas for woman on board and this leads to an overall percentage of 14.2% female board directors. They do have different initiatives to speed up the increase of female directors on board. In the Netherlands, the Dutch Management and Supervisory Act is active since 2013, which strives for at least 30.0% gender representation in the boards. The Dutch Corporate Governance Code (NCCG, 2016) also demands for more gender equality in the boards. The result of this quota and initiative is an overall percentage of 21.4% board seats held by women.

The report by Deloitte (2017) also states the percentage of woman representation in the different industries. In every continent, except for Africa, the financial industry is in the top three industries with highest percentage of women on board. This corresponds with the report by PwC (2017). They examined the board composition of different industries. In the report, PwC finds that with 26.0% the banking and capital markets have the most women on the board. This result is surprisingly, since the financial industry is traditionally known as a male dominated industry (Booysen & van Wyk, 2007).

Previous studies have been done on gender diversity and the effect on firm performance, but none of these studies have measured the topic specifically for the financial industry. This gap in the literature and the surprising results of the reports by Deloitte (2017) and PwC (2017), leads to the following research question: what is the effect of gender diversity on firm performance in the financial industry?

The literature on this topic is inconsistent, meaning that a positive relationship, a negative relationship and no relationship are found in studies on gender diversity and firm performance. Therefore, the hypothesis in this paper states that there is no effect of gender

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diversity on firm performance. The model used is as follows. The dependent variable is firm performance, measured by return on equity (ROE) and return on assets (ROA). The independent variable is gender diversity, measured by the percentage of female board members. The data is retrieved from Orbis (Bureau van Dijk, 2018) and contains the top 250 finance and insurance companies in the Netherlands. The data is taken for the year 2016, since this was the most complete recent year. The ordinary least squares (OLS) regression is done in Stata. There is no significant effect found from gender diversity on both ROE and ROA. This finding is consistent with previous studies (Pasiouras and Kosmidou (2007), Setiyono and Tarazi (2014), de Cabo et al. (2011)). Since the data is limited to one country (the Netherlands), one year (2016) and the model includes a limited number of variables, this result should be interpreted with caution. However, it does contribute to the lack of research on gender diversity and firm performance in the financial sector and gives reasoning for further research.

In the second part, a theoretical framework is given in where the existing literature on the topics relevant for this paper are examined. In the third part, the hypothesis of my research question is defined. In the fourth part, the variables, the model and the data used will be explained. In the fifth part, the results of the regression are discussed and analysed. In the last part, the conclusion and discussion are presented.

2. Theoretical framework

Since the financial crisis of 2008, it became clear how important corporate governance policy and practice is. All corporate entities need some sort of corporate governance code that covers the system by which the entity is directed and ensures that it is moving towards the long-term goals of the entity (Tricker, 2015). One of the aspects of corporate governance is board diversity. Board diversity is a broad term that includes a number of different characteristics. In the research of Harjoto et al. (2014) it is measured by gender, race, age, outside directorship, tenure, power and expertise. They argue that board diversity brings new resources, skills and perspectives to the group that can enhance board performance. There are several studies on the effect of gender diversity on firm performance, however the outcomes are ambiguous.

The research by Carter et al. (2003) was the one of the first researches on this topic. They measured what the effect of women and ethnic minority directors on the board for Fortune 1000 firms was on firm value, measured by Tobin’s Q. They found an overall

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positive effect of board diversity on firm value. Carter et al (2010) did a similar study using a different sample, namely major US corporations of the S&P 500 index. For the firm performance measure, Tobin’s Q and Return on Assets (ROA) were used. Between the ROA and board diversity, a positive and significant relation was found. Between Tobin’s Q and board diversity, there is neither a positive nor a negative relation. Campbell and Minguez-Vera (2008) found a positive effect of gender diversity on firm value, also with Tobin’s Q as the performance measure.

According to Terjesen et al. (2015), female directors improve the effectiveness of boards. They also argue that the public relates a gender diverse board to good ethical behaviour of the firm. Their results also suggest that firms with more female directors have higher firm performance, measured by Tobin’s Q and return on assets. In the research of Ali et al. (2013) a positive linear relation was found between gender diversity and employee productivity. They argue that the benefits of a gender-balanced board can improve the employee productivity and therefore the operating revenue of the firm.

There are several reasons for the positive impact female board members can have on board performance. Adams and Ferreira (2009) suggest that female board directors can enhance board performance, because it leads to greater participation of directors in the decision-making process, better monitoring of the board and CEO, and more alignment with the interests of shareholders. Robinson and Duchant (1997) claim that they also improve the understanding of the marketplace and increase creativity and innovation. Another argument is that if a firm considers only male potential candidates, they could miss out on female candidates that are more qualified (Smith et al., 2006). Additionally, female board members can be a role model for women at lower levels in the firm. This could enhance the productivity of the other women and thus supports the previous mentioned theory of Ali et al. (2013).

Contrary to the papers above, the following studies found a negative effect of female board members on firm performance. Adams and Ferreira (2009) found in their research that the average effect of gender diversity on firm performance is negative. They used Tobin’s Q and ROA as performance measures. Dobbin and Jung (2011) measured the effect of gender diverse boards on stock performance. Their results suggest that relation between female board directors and stock value is negative and that there is no relation between female board directors and the profits of the firm. The study of Wang and Clift (2009) also implies that gender diversity has no significant influence on firm performance, measured by return on equity, return on assets and shareholder return.

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According to Adams and Ferreira (2009), the reasons why gender diversity can be beneficial can also be the reason why it has a negative effect on firm value. For instance, too much monitoring leads to lower shareholder value. A diverse board has more opinions and perspectives, which can be a hinder in the decision process (Smith et al., 2006). It is also more difficult to coordinate a diverse board and it can result in more conflicts between the directors. Pletzer et al. (2015) also mention several reasons in their study why female board members can hinder performance. When the board is male dominated, a female director could have difficulty with participating and maintain their standing and they could experience role conflict. Some companies use female directors as tokens to satisfy the public’s norms. These female directors could therefore not be taken serious and this results in negative board performance.

In previous research, one of the claims is that differences between men and women are the reason for the lack of female board members. In the paper by Croson and Gneezy (2009), these gender differences are identified in three sections: risk preferences, social preferences and competitive preferences. One of the results of the study indicates that women are more risk averse than men. The suggested reasons for this result are that women react emotionally different to uncertain situations and men are more confident and thus see risk more as a challenge instead of a threat. Although female managers and entrepreneurs do not differ in risk taking than men. The result regarding social preferences suggest that women’s reactions are more context dependent than men. Women tend to be more sensitive to social signals than men and this leads to higher variability in female behaviour. In terms of preference for a competitive environment, women’s preference is lower than men’s. The difference in competition comes both from nature as nurture. With this in mind, the financial industry is taught of as a risky and competitive industry and thus a men-dominated industry. But this study focused on all women in the population.

There are several studies on women in the finance industry as well. The research done by Sapienza et al. (2009) supports the findings of Croson and Gneezy (2009) about risk taking by finding that women who choose risky careers in finance to be less risk averse than women entering other sectors. Deaves et al. (2008) found no gender difference in overconfidence. They argue that perhaps women in a ‘male’ area such as finance are more overconfident than the general women population. From these findings could be concluded that female board directors in the financial industry are different than female board directors from other industries. That could be an explanation for the higher percentage of gender diversity in the financial industry from the reports by PwC (2017) and Deloitte (2017).

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The existing literature on the effect of gender diversity on firm performance is ambiguous. There are both benefits and costs from having female board members; it is difficult to say which outweighs the other. Besides, there is limited research on the effect of gender diversity in the financial sector. Therefore, the following hypothesis is formed:

Hypothesis: gender diversity in the boards has no effect on firm performance in the financial industry.

3. Methodology

This section describes the research method used to test the effect of gender diversity in the board of firms in the Dutch financial industry on firm performance. In the first part, the variables and the structure of the model are explained. In the second part, the data that is used for the regression is covered.

4.1. Variables and model

In this research, a regression model tests the effect of gender diversity in the board on firm performance. This model will consist different variables related to firm performance and board composition.

𝐹𝑖𝑟𝑚  𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒

=   𝛽!+ 𝛽!𝑃𝑒𝑟𝑐𝑒𝑛𝑡𝑎𝑔𝑒𝐹𝑒𝑚𝑎𝑙𝑒 + 𝛽!ln 𝑏𝑜𝑎𝑟𝑑𝑠𝑖𝑧𝑒 + 𝛽!ln 𝑡𝑜𝑡𝑎𝑙  𝑎𝑠𝑠𝑒𝑡𝑠 + 𝛽!𝑀𝑖𝑑𝑑𝑙𝑒𝐴𝑔𝑒𝑑 + 𝛽!𝑂𝑙𝑑 + 𝛽!𝐶𝐸𝑂𝑇𝑒𝑛𝑢𝑟𝑒 + 𝛽!𝐿𝑖𝑠𝑡𝑒𝑑 +  𝛽!𝐵𝑎𝑛𝑘𝑑𝑢𝑚𝑚𝑦

+ 𝛽!"𝐴𝑣𝑒𝑟𝑎𝑔𝑒𝐴𝑔𝑒𝐷𝑖𝑟𝑒𝑐𝑡𝑜𝑟𝑠 + 𝜀  

The dependent variable is firm performance. Investors use different financial ratios to evaluate firms and compare similar firms to each other, such as Tobin’s Q and EBIT. Firm performance has been measured by return on equity (ROE) and return on assets (ROA). These ratios are often used, and are consistent with other studies on firm performance (Adler (2001); Erhardt et al. (2003); Catalyst (2004); Low et al. (2015)). When the return on equity (ROE) is high, it may imply that the firm has made investments that are very profitable. Return on assets (ROA) includes interest expense in its calculation and is therefore less sensitive to

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leverage than return on equity (Berk & DeMarzo, 2014). They are calculated as follows, based on the profit and loss statement:

Return on Equity   ROE =  𝑃𝑟𝑜𝑓𝑖𝑡  𝑜𝑟  𝐿𝑜𝑠𝑠  𝑏𝑒𝑓𝑜𝑟𝑒  𝑡𝑎𝑥𝐵𝑜𝑜𝑘  𝑉𝑎𝑙𝑢𝑒  𝑜𝑓  𝐸𝑞𝑢𝑖𝑡𝑦

Return on Assets   ROA =  𝑃𝑟𝑜𝑓𝑖𝑡  𝑜𝑟  𝐿𝑜𝑠𝑠  𝑏𝑒𝑓𝑜𝑟𝑒  𝑡𝑎𝑥 + 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡  𝐸 𝑝𝑒𝑛𝑠𝑒 𝐵𝑜𝑜𝑘  𝑉𝑎𝑙𝑢𝑒  𝑜𝑓  𝐴𝑠𝑠𝑒𝑡𝑠

To measure gender diversity in the board, the independent variable, the percentage of female board members (PercentageFemale) is used. This is number of female board members divided by the total number of board members. Previous literature used a dummy for the presence of women on board, but this gives less information than the percentage, as it just indicates that a woman in present and not which stake in the decision making process the women have.

The variables board size, firm size, firm age, CEO tenure, listed and average age of directors are used as control variables. Board size is measured by the natural logarithm of the total number of directors, as do previous studies on gender diversity and firm performance (e.g. Campbell & Minguez-Vera, 2007). A larger board size might lead to more female positions and is therefore included as a control variable (Adams and Ferreira, 2009). Firm size is added, because larger firms get more public and government attention and therefore feel more pressure to diversify their boards (Adams and Ferreira, 2004). It is measured by the natural logarithm of total assets of the firm, as do previous studies on gender diversity and firm performance (e.g. Campbell & Minguez-Vera, 2007). The age of a firm may have a negative effect on firm performance and is therefore included as a dummy (Loderer and Waelchli, 2010). Three categories for the age of the firm are identified, namely young (0 – 35 years), middle-aged (35 – 70 years) and old (70+ years). The age is calculated starting from the day of incorporation. The dummy Young is left out the regression, to prevent multicollinearity. The CEO tenure is measured by the number of years the CEO of the firm holds his or her position. Research shows that CEO tenure leads to benefits, for example gaining experience, and costs, for example resistance to change (Limbach et al., 2015). These costs and benefits of CEO tenure have an impact on firm value. The variable listed is included as a dummy. When the firm is publicly listed and stock traded, it equals “1” and when it is not listed, its value is zero. The average age of directors is measured by the sum of age of all the board members divided by the number of board members. In the study of Taljaard et al. (2015), a positive relationship between gender and age diversity on firm performance was found. A young board with more female board members may result in higher firm performance. Thus average age of directors is included in the model to control for this effect.

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In other studies on gender diversity and firm performance, the independence of the board is also used as a variable. Independent directors are directors that come from outside the firm and do not have ties with the firm. Since most Dutch companies have a two-tier system and therefore have independent supervisory directors, this variable is not included in the model (NCCG, 2016).

Stata is used for the ordinary least squares (OLS) regression with performance measures return on equity (ROE) and return on assets (ROA).

Firms in the Netherlands use the two-tier corporate board structure, meaning that the board consists of an executive board and a supervisory board. No distinction has been made if the women were part of the executive or the supervisory board.

4.2.Data

The financial and director characteristics data for the regression are retrieved from the database Orbis (Bureau van Dijk, 2018). This database contains information on both listed and non-listed companies around the world. The data is taken from the year 2016, since this was the most complete recent year. The top 250 firms in both the financial and insurance sector in the Netherlands are used, these include Dutch banks, insurers, traded funds and international financial institutions that are active on the Dutch market. Firms with missing data or large outliers are excluded from the sample. This resulted in 207 firms for the regression on return on equity and 215 firms for the regression on return on assets. If necessary, data from the annual reports of the firms, financial news platforms and business social media platform LinkedIn were also used.

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Table 1 - Descriptive statistics

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VARIABLES N mean

sd min max median

listeddummy 250 0.0320 0.176 0 1 0 Bankdummy 250 0.0960 0.295 0 1 0 ROEPL2016 207 11.01 25.65 -91.04 150.4 5.868 ROAPL2016 215 1.153 3.164 -23.39 14.46 0.623 perc_fem 250 0.145 0.171 0 1 0.121 CEO_tenure 250 83.23 93.51 0 454 51.50 mt_age 250 49.40 18.19 0 91 55.24 lnboard 250 1.839 0.971 0 3.497 2.079 lnfirmsize16 250 15.61 1.341 14.06 20.55 15.28 old 250 0.180 0.385 0 1 0 middleaged 250 0.220 0.415 0 1 0

In table 1, the descriptive statistics of the sample are reported. The first two variables are the measurements for firm performance, return on equity and return on assets used for the year 2016. The average return on equity for 207 observations is 11.01% and the average return on assets for 215 observations is 1.15%. The third variable is the main predictor variable, which is the percentage of female board directors. The firms in this sample have an average of 14.5% female board directors. As the size of the boards differs, in some cases the amount of woman on the board is 100%. The remaining variables are the control variables. CEO tenure is expressed in the number of months that the CEO has been in function. The top 250 of companies is based on asset value, so a considerable portion of the sample consists of pension funds. Board member of these funds can be quite old, which would explain the maximal MT age of 91 years.

In the literature, the hypothesis is stated. To test this, the beta of gender diversity is measured and tested if it is significant different from zero., This can be written as:

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𝐻!:  𝛽! = 0 𝐻!:  𝛽! ≠ 0

4. Results

In this section the results of the empirical study are presented. Before the regressions were run, the data was tested on the parametric assumptions. Then the models were run and the hypotheses tested. In the following paragraphs the data is presented, the results of regression models are shown and an interpretation is given.

4.1. Parametric assumptions testing

After the data was collected from Orbis (Bureau van Dijk, 2018), the data was tested and transformed in accordance with previous research. As there are hardly any outliers, the data have not been winsorized. Two variables, namely board size and firm size, were transformed with a log transformation to change the distribution of values, to lower the spread in distribution.

The data was tested for linearity, normal distribution, heteroscedasticity and independence of variables. In table 4 in the appendix, the correlation matrix is presented to check for multicollinearity. The variables in the model are not highly correlated, so there is little ground for multicollinearity, which means all variables will be used in the regression. As a precaution a VIF analysis was done of the regression model. Only the assumption of homoscedasticity was violated, The test for heteroscedasticity showed the existence of heterogeneity of variance . (Breusch-Pagan / Cook-Weisberg test for heteroscedasticity (chi2 = 61.37, df = 1, p < 0.01))

This leads to the OLS regressions being done using robust standard errors. The results of these regressions are presented below.

4.2. Impact of gender diversity on Return on Equity (ROE)

The models were run on the whole dataset and on subsets for the separate subgroups in the sample (i.e. banks, insurance companies and other financial institutions). Due to different regulations for each of these subgroups, the returns and equity base are influenced in a different way, this lead to the subgroups.

Column 1 in table 2 shows the results of the regression using return on equity on all firms. The model implies no impact of board diversity on financial performance. In addition,

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the R-squared of this regression is 0.090, which indicates that the model has a very low predictive value.

The results of the regression where the pension funds are excluded are presented in column 2. The R-squared is 0.135, which is higher the R-squared from column 1 and thus indicates that this model predicts the variance in return on equity better. In this model, the results suggest no effect of female board members on firm performance (β = -4.052; t = -0.53; p = 0.594).

In column 3 and 4, the regression is done once for only banks and once for insurance and lease firms. When the regression is done for banks, the effect of women on boards is -0.76% and not significant, meaning that there is possibly no impact of gender diversity on firm performance. The R-squared of this regression is the highest of the four regressions with 0.393. From this result can be concluded that the model is better in predicting firm value for banks than the rest of the financial type firms. When the regression is done for insurance and lease companies, the results also imply no effect of the percentage of female board directors on firm performance (β = -4.610; t = -0.46; p = 0.649).

The overall result of gender diversity on return on equity is thus possibly zero in this sample. This is in line with the research of Belhaj and Mateus (2016) who no significant impact of the presence of female board directors on bank performance measured by return on equity. Their sample consisted of 73 large European banks, with data from 2002-2011. According to them, this is due to the low proportion of women in the boards of European banks and therefore, they play a minor role in the board. Setiyono and Tarazi (2014) also found no significant effect of gender diversity on the performance of banks. They argue that the net effect of a gender-diverse board is likely to disappear. In addition to that, they state that female board directors are less decisive and are more risk-averse than male directors, which result in the negative effect of gender diversity during the financial crisis.

Of the control variables, only the average age of directors has a significant effect on firm performance in column 1, 2 and 3. This suggests that a board with older directors has a lower firm performance. This is consistent with the research of Wiersema and Bantel (1992). They argue that younger boards are more likely to adapt corporate strategy and bear more risks, which could pay out in higher returns. The same way of thinking can be used to explain the negative effect of firm age in the four regressions. Older firms are less likely to change their strategies and therefore show lower returns. For the regression models with only banks, only the firm size is significant (β = 1.519; t = 2.71; p<0.05). Banks with a larger asset base show a better return on equity in this sample. This is in contrast with the research of Pasiouras

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and Kosmidou (2007) who found a negative relation between firm size and bank’s performance in the European Union. They argue that the reason for this result could be economies of scale and scope for smaller financial institutions or diseconomies of scale for larger financial institutions.

Table 2 – Regression results using Return on Equity (ROE) as performance measure

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VARIABLES Total No pension funds Banks Insurance + Lease

perc_fem -4.052 3.746 -0.760 4.611 (7.590) (9.038) (9.068) (10.10) lnboard 0.364 2.539 0.668 2.510 (2.373) (2.988) (2.018) (3.342) lnfirmsize16 1.113 0.463 1.519** 0.0807 (1.536) (1.189) (0.561) (1.788) old -4.130 -5.226 -0.584 -5.599 (3.083) (3.882) (3.229) (4.387) middleaged -4.010 -4.110 4.655 -7.596 (4.988) (4.325) (3.211) (5.965) CEO_tenure 0.0437 0.0694 -0.0120 0.0821 (0.0338) (0.0491) (0.0118) (0.0555) mt_age -0.419*** -0.587*** -0.984 -0.600*** (0.151) (0.186) (0.661) (0.196) listeddummy 1.426 -0.152 -0.723 -0.845 (3.970) (4.121) (2.456) (7.094) Bankdummy 0.759 0.681 (3.542) (3.552) Constant 12.02 23.93 36.10 29.78 (23.97) (18.49) (38.14) (27.33) Observations 207 151 24 127 R-squared 0.090 0.135 0.393 0.141

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1

4.3. Impact of gender diversity on Return on Assets (ROA)

The results of the regression on return on assets are presented in table 3. Compared to the model with return on equity as a dependent variable, this model on the whole shows a lower predictive value. In column 1, 2 and 4, the R-squared is below 0.05. This means that the variance in return on assets is explained by the model for less than 5% in most cases, except for banks, where the R-squared is 0.399. None of the variables shows a significant coefficient. The results imply possibly no effect between female board directors and firm performance. This is in line with the research from de Cabo et al. (2011). They found no significant difference in the number of female directors on the board of European Union banks related to the return on average assets (ROAA).

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Table 3 – Regression results using Return on Assets (ROA) as performance measure

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VARIABLES total no pension funds banks insurance + lease

perc_fem 1.074 0.125 1.202 0.321 (1.371) (2.092) (1.690) (2.351) lnboard -0.313 -0.219 -0.354 -0.270 (0.449) (0.603) (0.397) (0.700) lnfirmsize16 -0.172 -0.102 -0.0291 -0.179 (0.180) (0.244) (0.0929) (0.364) old -0.286 -0.253 0.347 -0.299 (0.506) (0.671) (0.414) (0.812) middleaged -0.482 1.107 0.715 1.698 (0.533) (1.011) (0.430) (1.588) CEO_tenure 0.000688 0.00213 -0.00250 0.00227 (0.00236) (0.00305) (0.00143) (0.00359) mt_age -0.0110 -0.0167 -0.141 -0.0157 (0.0172) (0.0222) (0.0830) (0.0240) listeddummy 0.429 0.278 -0.530 0.916 (0.446) (0.467) (0.401) (0.688) Bankdummy 0.140 -0.410 (0.418) (0.517) Constant 4.931* 3.948 9.870* 5.107 (2.829) (3.799) (5.023) (5.613) Observations 215 155 24 131 R-squared 0.035 0.037 0.399 0.038

Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1 5. Discussion and concluding remarks

In this paper, the effect of gender diversity in the boards of financial firms on firm performance is studied. Previous research on this topic was ambiguous. There are papers that found a positive effect between female board directors and firm value (Carter et al. (2003), Carter et al. (2010), Campbell and Minguez-Vera (2008), Terjesen et al. (2015), Ali et al. (2014)) and there are papers that found a negative effect (Adams and Ferreira (2009), Dobbin and Jung (2011), Wang and Clift (2009)). There are several arguments in favour for gender diversity. For instance, it adds new skills and resources to a board, which leads to different perspectives that can enhance the decision and problem-solving processes. Adams and Ferreira (2009) argue that the potential benefits of a gender diverse board can result in a negative effect on firm value if they are too much.

The data used for the regression in this paper consists of the top 250 financial firms in the Netherlands. Firm performance is measured by the return on assets (ROA) and the return

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on equity (ROE). Gender diversity is measured by the percentage female directors on a board. The conclusion of the regression results is that gender diversity has no significant effect on firm performance and this is in line with the hypothesis. This research thus can not give additional motivation to add or increase female board directors in the financial industry, but it also suggests that female board directors do not decrease firm performance.

The results from this paper should be interpreted with caution, as there are limitations. First of all, it is limited to a small sample, namely the top 250 financial firms in the Netherlands, the sample itself also shows a large diversity of types of organisations. This research could possibly be relevant for other similar countries in terms of culture, politics and economics (e.g. Belgium and Denmark), but it is external validity is not high. To have a more general view of gender diversity and firm performance in the financial industry, the research should be conducted in more different countries (e.g. US and China). In the second place, the data period used is from the year 2016, but this could give a less strong picture. Carter et al. (2010) hypothesize that the influence of board diversity on performance develops over time and Smith et al. (2006) conclude that firm performance is influenced by a number of factors that are unobservable in a study. For these reasons, it is advised to look at the effect of gender diversity on firm performance for the same sample of firms based on several years of observation.

Next, only a limited number of control variables could be taken into account due to data restrictions. Firm performance is affected by more factors than the variables used in the model from this research. Future research should therefore include more variables. For example, it could be interesting to include growth orientation as a variable since the research from de Cabo et al. (2011) shows a positive relation between gender diversity and growth orientation. Furthermore, for the firm performance measure, return on equity and return on assets were used. These are just two of the numerous ways to measure the performance of a firm. Other measures, such as Tobin’s Q, could give different outcomes.

This research is an addition to the studies done on gender diversity and firm performance in the financial industry. Since the financial crisis, it became clear that it is crucial to have knowledge on what enhances board and firm performance of financial institutions. Board diversity, such as female directors, is an important aspect. There have been done a few studies on this topic, but not yet using a sample with firms from the Netherlands. With this paper, a contribution is made to the understanding and knowledge of how financial institutions perform. Although in this research and several other researches (Pasiouras and Kosmidou (2007), Setiyono and Tarazi (2014), de Cabo et al. (2011)) no correlation was

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found between female board directors and firm performance, there are also researches that found a positive effect. Therefore, additional research is necessary to better understand the real impact female board directors have on firm performance. It is recommended that this future research includes more variables and is conducted in different countries using data from multiple years. Also, additional research on the factors that influence bank performance is essential. It is questionable that almost none of the variables used in this model were significant. This could be due to various reasons, such as the reliability of the data.

As final remark, the average percentage of female board directors in our sample is 14.5%. Since the Dutch Management and Supervision Act strives for at least 30% of female directors, the financial industry in the Netherlands should take an active role in striving for a more gender balanced board. Although this paper did not provide evidence that female board directors enhance firm performance, there are more arguments for more gender equality in the boardroom, such as social justice and anti-discrimination.

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Appendix

Table 4 – Correlation matrix

 

  ROEPL2016   ROAPL2016   lnboard   lnfirmsize16   old   middleaged   perc_fem   CEO_tenure   mt_age   listeddummy   Bankdummy  

ROEPL2016   1                       ROAPL2016   0.3150*   1                     lnboard   -­‐0.1442*   -­‐0.1508*   1                   lnfirmsize16   0.0120   -­‐0.1001   0.3669*   1                 old   -­‐0.0657   -­‐0.0518   0.2533*   0.2590*   1               middleaged   -­‐0.0696   -­‐0.0668   0.1357*   -­‐0.0445   -­‐0.2488*   1             perc_fem   -­‐0.0784   0.0007   0.2339*   0.1608*   0.2143*   0.0493   1           CEO_tenure   0.0386   -­‐0.0516   0.3756*   -­‐0.0058   0.0840   0.2518*   0.0940   1         mt_age   -­‐0.2489*   -­‐0.1371*   0.6202*   0.1227   0.1323*   0.1714*   0.1742*   0.3395*   1       listeddummy   0.0182   -­‐0.0047   0.1877*   0.2567*   0.0331   -­‐0.0417   0.0756   0.0519   0.0622   1     Bankdummy   -­‐0.0347   -­‐0.0470   0.3275*   0.3551*   0.0240   0.0892   0.1375*   -­‐0.0957   0.1321*   0.1722*   1   * Significant at 0.05

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