Female directors and corporate risk taking in UK listed companies

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Female Directors and Corporate Risk Taking

in UK Listed Companies

University of Amsterdam, Amsterdam Business School

Master in International Finance

Master Thesis

By: Zunaira Javed

Thesis Supervisor: Prof. Chris Florackis

August, 2015

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1. Introduction

In the past decade, there has been a move worldwide to increase the representation of women in top leadership positions in all industries and professions.

This move has been particularly felt in the corporate world where the percentage of women in leadership and ownership positions is dismally low. Whereas more than 50% of the new graduates entering the workforce are female, women held just 18% of the non-executive board positions in the European Union’s largest companies in 2013, according to figures from the European Commission (Ernst and Young. Diversity

Report, 2014). Men held over 80% of the board seats in the S&P 500 companies and in the Asia Pacific region, the gap was even worse, with women holding less than 10% of the board seats in 2015 (Glinski, 2015).

The board of directors is an important component of the corporate structure

with the power to regulate the decisions a company makes and act in the best interests of the shareholders. Thus the importance of gender diversity on the board of directors has been highly emphasized in recent years, with many lobbying for laws requiring companies to have more gender diverse boards of directors. As lawmakers responded to demands by the public, some countries passed quota laws to increase female

representation on the boards of directors of companies while others have taken steps to incentivize voluntary increases by companies in the number of female directors on their boards. Two prominent examples are Norway and the UK. Norway passed a law on January 1, 2006 that required publicly listed companies in the country to increase the number of women to 40% of board membership. The UK, on the other hand, has not

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women on the board from 10.5% in 2010, to 20.17% in 2014 (Ernst and Young, Point of View Report, July 2014).

With such global changes on the horizon, there has been extensive research to

show that increasing gender diversity at the top affects the profitability of companies. A worldwide study conducted by Credit Suisse in 2012 that looks at returns on equity for companies for the period 2005-2011, shows that the average return on equity for companies with at least one woman on the board was four percentage points higher than the average return for companies with no women on the board (Credit Suisse

Research Institute, 2012). Similarly, research from McKinsey & Company shows that companies that had the most gender-diverse management teams had stock price growth that was 17 percentage points higher than the industry average, between 2005 and 2007. It also showed that the average operating profit was almost double the

industry average, between 2003 and 2005 (McKinsey & Company, 2007 and 2010).

Chapple and Humphrey (2014) look at stock portfolio performance to assess the impact of gender diversity on the board of directors and Smith and Verner (2006) did a panel study of 2500 Danish firms to assess the impact on profitability of women in top management.

There hasn’t been extensive research, however, on how gender diversity on the board affects the risk management of a company. The literature that exists on risk management focuses on CEO gender impact with a few studies on insolvency risk.

Faccio, Marchica, and Mura (2015) conducted a study researching the effects of the gender of leadership on risk management practices. They looked at CEO gender and its effect and found that firms with female CEO’s have lower leverage and less volatile earnings. Nick Wilson and Ali Altanlar (2009) analyzed data on over 900,000 limited

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companies in 2007-08 in the UK and found that companies with at least one woman as a director on the board reduce the risk of insolvency by 20%.

Since the enforcement of the quota in Norway, a whole body of research has

developed around the effect of the enforced quota on the performance of companies in Norway as well as the impact of female directors on the decision making of corporations. The enforcement of the quota, however, also adds other factors that impact the research being conducted, such as the changes in the efficiency of the boards where forced additions of women directors have negatively impacted the

decision-making abilities.

This paper adds to the existing literature by looking at gender diversity on the board of directors (BoD) and the impact that it has on risk management of the company. The unique aspect of this paper is that it looks specifically at the BoD and gauges the

impact on risk management by using leverage and volatility as proxies for corporate risk taking. This is an area that is relatively unexplored in risk management literature. Moreover, we focus on the UK, where companies have made voluntary changes to their board structure as changes due to quota add many other external variables that would have an effect on the company. The effect of female directors on the boards of UK

companies has not been studied in detail before even as the country is implementing new policies to ensure an increase in gender diversity on the boards. Thus, this paper will not only be looking at an unexplored area of research but the results can have major policy implications by helping policymakers decide how to increase gender diversity on boards.

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company?” Answering this question can help researchers further delve into the subject to see if psychological research conducted on men and women to gauge risk behavior applies to men and women in their professional financial decisions. This can also help

companies to see the effect of board diversification and thus improve the discussion on the methods to be used to provide equal representation to women.

We hypothesize that the gender diversity of a board of directors should not affect the risk management of a company since there should be no difference in the decision making of equally qualified men and women. The rest of the paper is structured as

follows: Section 2 presents a number of relevant studies relating to the psychological research on the nature of risk taking in men and women, the importance of the board of directors and the history of gender diversity on the boards in the UK. Section 3 provides the methodology and hypothesis of our analysis. Section 4 provides details on the data

collection process and the variables used in the analysis. Section 5 presents the main empirical analysis and results, discussing the possible theoretical research that can explain it while Section 6 concludes.

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2. Literature Review

We look at three interrelated areas of literature that may help to interpret our results; firstly, that there is a difference in risk preference between men and women, i.e.

women are more risk averse than men; second, that the board of directors has a direct impact on the risk management practices of company and improving the gender diversity has an influence on the efficiency of the board; third, we explore the changes in female representation of the board of directors, specifically for the UK.

Differences in Risk Preference:

Existing research on risk preference in men vs. women has conflicting results with some studies showing that women are more risk averse than men while others showing that there is no difference in risk preference between men and women; the perceived difference exists due to cultural biases and the methodologies that are used

to conduct the research.

Eckel and Grossman (2002) summarize studies conducted on the differences in economic decisions of men and women before 2002 and these indicate that women are more risk averse than men on average. Researchers have also looked specifically at pension assets to gauge the risk taking capacity of individuals by analyzing the

allocation of their pensions to stocks versus bonds. These studies have found that women are more risk averse than men when it comes to the allocation of their pension assets, allocating a larger percentage to bonds as compared to stocks (Bajtelsmit and Vanderhei 1996; Hinz, McCarthy, and Turner 1996; Bajtelsmit, Bernasek, and Jianakoplos 1999). Watson and McNaughton (2007) further affirm this hypothesis by

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preferences for both genders in Australia and finding that women prefer less risky investment strategies as compared to men. Abstract gambling experiments have also been conducted that show women to be more risk averse than men (Irwin P Levin et al,

1998).

Studies supporting the hypothesis of women being more risk averse than men cite various reasons for this difference. The risk preferences between the two genders can be different due to the nature of the genders or because of nurture i.e. the way that men and women are brought up and the effect that society’s expectations has on them.

Dohmen et al (2012) show that differences in risk aversion can be inherited from parents, with children showing similar risk preferences as parents regardless of societal norms. They also show, using the German Socioeconomic Panel (GEOSP), that the education level of the parents has a direct effect on the risk preference of their

offspring, with individuals who have highly educated parents being significantly more likely to choose risky outcomes. Men can be seen to make more risky choices as compared to women due to overconfidence as well. Barber and Odean (2001) use trading data for over 35,000 households to test the psychological research that men are more overconfident than women, in areas such as finance. They look at the trading data

and find that the average turnover of common stocks for men is nearly one and a half times that of women. Thus, overconfidence leads men to increase their trading activity, reducing returns. This same overconfidence can also lead them to make more risky decisions than women.

Other research points to the fact that an individual’s environment and social

learning plays a big role in the risk preferences that they demonstrate. A study conducted by Booth and Nolen (2012) looks at the individual risk preferences of girls

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when making decisions in a single sex vs. co-ed environment. The results show that girls in single sex environment are less risk averse than girls in co-ed environments and are just as likely to take risks as boys in co-ed environments. This result points to the fact

that the environment that a female is exposed to plays a major role in her risk taking preference. Since we are looking at risk management decisions in the boardroom, where women have to participate in co-ed environments, it can be inferred that women in these settings would be more risk averse than men.

However, there are studies that dispute the evidence of women being more risk

averse than men even in contextual settings. Women being more risk averse than men in financial settings is seen as a stereotype that is actually biasing the studies that are being conducted to test the hypothesis. It is also seen as a cause for fewer women to be able to make risky decisions since they are not allowed the opportunities or support to

do so (Johnnie E.V. Johnson and Philip L. Powell, 1994). Nelson (2012) conducts a review of the empirical literature that exists on the topic of differences in risk preference between men and women and concludes that the statement “women are more risk averse than men” is prevalent in the literature due to confirmation bias and taking a closer look at the data casts doubt on the validity of that assertion. The results

are shown to be more inconclusive when the data is analyzed and factors such as cultural biases and sample size are taken into account. Schubert et al (1999) conducted an experiment including abstract gambling decisions as well as financially motivated risky decisions in an investment or insurance context. They concluded that there was no difference in he risk preference of women as compared to men when they were making

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Taking into account the conflicting results from research in the past, this paper will try to assess whether female board membership affects the risk appetite of a company.

Impact of Board of Directors:

The board of directors is one of the most important components of corporate governance, overseeing the functioning of a company and ensuring that management acts in the best interests of the shareholders. They are responsible for making major strategic and financial decisions including the risk management decisions of a company.

The board typically meets regularly to address any business that requires its attention. Fama and Jensen (1983) show that boards of directors have a direct influence on a firm by monitoring the CEO. Adams and Ferreira (2007), however, point out that the efficiency of the decisions that a board makes is highly dependent on the quality of the

information that the management provides to it. Thus how much influence a board can have on a company’s performance is also directly linked to the information that it receives. While the efficacy and quality of the decision making of a board of directors can differ, it has a direct influence on the decisions that a company makes. The importance of corporate boards is evident by the extensive literature on the topic as

well as the regulatory measures that are in place to monitor them.

The composition of the board is an important factor that influences the efficiency and decision making of a board. With increased pressure to increase the gender diversity on corporate boards, the two methods used by legislators were enforced quotas and voluntary increases. Two prominent examples mentioned earlier in the

paper are those of the Norway and UK. While Norway enforced a quota of 40% female representation on the board of directors to be met by publicly listed companies, the UK

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opted for voluntary targets that companies were encouraged to meet. The enforced quotas led to a decrease in productivity in Norwegian companies, due to the introduction of external factors that decreased the efficiency of the board (Nielsen and

M. Huse, 2010). Boards are selected by companies to maximize efficiency, however these boards are no longer efficient when forced to implement a change. The pool of women to choose from is not large enough and thus the qualifications of the board change as well according to Ahern, K. R., and Dittmar A. (2012). Thus to take these changes into account, this paper focuses on the UK and precludes these factors from

affecting the empirical research.

While the composition of a board is an important factor in determining its impact on the company, the research is ambivalent on whether having female directors on the board leads to better monitoring or not. Most corporate governance research

that looks at gender diversity on boards takes the agency theory approach (Terjesen et al. 2009). According to the agency theory, the major role of the board is to advise and monitor management and ensure that conflicts of interest that exist between the management of a company and its shareholders are resolved (Hart, 1995). It is important, therefore, to have qualified individuals on the board who are independent

and can therefore serve as effective monitors for management. According to Nielsen and Huse (2010), if the agency theory is being used, then the gender of a board member should not affect their ability to perform board tasks. However, indirect arguments for gender diversity on a board increasing its monitoring efficiency have been made. Adams and Ferreira (2009) suggest that women on the board leads to an increased level of

independent decision making which is valued under the agency theory. This is because they are not a part of the ‘old boy’s club’ and so would not be influenced the same way

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that a man in their position might be. However, this same reasoning could also imply that women would be less effective in getting their opinions heard in a board setting if the other members do not see them as an integral part of the board (Carter et al, 2003).

Other studies suggest that women add value to the board since they facilitate communication in decision-making (Bilimoria, 2000). Research pointing to women being more risk averse than men is also cited as an advantage in making a board more balanced with the addition of women to it.

Diversity on the board is not only desired as a measure to enhance performance,

but it also has positive effects on the corporate social performance of a company (Hafsi and Turgut, 2013). Corporate social performance is an important facet of a company’s performance since it impacts communities, employees and consumers and ensures longevity of the company through sustainable relationships with its stakeholders.

Looking at empirical research on the impact of a board and the impact of changes in its composition, helps us conclude that a change in the composition of the board would cause changes in the decision making and risk management policies of the company if these individuals held different opinions on these aspects. Looking at the case of the UK, the changes in the composition of the boards and their impact can thus

be studied by looking at the risk proxies for the companies being studied.

Female Directors on Boards in the UK:

Equality of decision-making is one of the fundamental values that the European Union promotes and to ensure that women would be able to achieve this equality, there has been a push to increase the number of women on the boards of corporations (Mills

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recommended for countries as early as 2008, with the European Parliament voting in favor of draft legislation to impose 40% quota for female non executive directors on the boards of large publicly listed companies by 2020, and state owned companies by 2018,

in November 2013 (EY Diversity Report 2014). Many countries have imposed these quotas with Norway taking the lead in 2008 and Belgium, France, Iceland, Italy, the Netherlands and Spain following later on. However, other countries chose to encourage companies to voluntarily increase women on the boards of directors to avoid the negative repercussions of quota impositions.

Companies in the UK lobbied for voluntary targets as compared to quota enforcement and have been making good progress since the movement started in 2010. Private groups and the UK government have been working together to ensure that policy changes and social pressure is combined to ensure that the voluntary targets are

met and the percentage of women in UK boardrooms increases with time. The 30% club was formed in 2010 for this exact purpose. The club consists of CEO’s and chairs of corporate boards and has the goal of achieving 30% women on the board of FTSE 100 companies by end 2015. In the same year, the revised UK Corporate Governance Code came into effect, which included for the first time, a principle recognizing the value of

diversity in the boardroom, specifically mentioning gender diversity. Supporting Principle B.2 of the code states that “the search for board candidates should be conducted, and appointments made, on merit, against objective criteria and with due regard for the benefits of diversity on the board, including gender”. The UK Government also commissioned Lord Mervyn Davies of Abersoch to undertake a review

of gender diversity on the boards of listed companies and to make recommendations regarding changes that would help overcome barriers to increasing the appointment of

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women to these boards. Lord Davies’ report, the Women on Boards review, was published in February 2011 and made several recommendations while highlighting the advantages of increasing female representation on UK boards to encourage companies

to follow the recommendations that it presented to meet voluntary targets and avoid quota implementations by the government. One of these recommendations was that the Financial Reporting Council of the UK should require listed companies to establish a policy regarding boardroom diversity and enforce reporting of said policy as well as progress made annually in implementing the policy and achieving the objectives set

forth in it. The Financial Reporting Council amended the UK Corporate Governance Code in 2012 to include this recommendation. Companies were thus expected to set out their policy on boardroom diversity in their annual reports and to report any progress on measurable objectives that they set for themselves. The Developments in Corporate

Governance and Stewardship 2014 report of the FRC reports that there has been progress in the implementation of this policy since 2012 with 85% of FTSE 100 companies stating a clear policy on boardroom diversity and 78% specifically mentioning gender in their policy in 2014. However, only 58% of the companies set measurable objectives to achieve the goals set forth in their policies so there is more

progress to be made on that front.

The private and public sector working together have been instrumental in increasing the percentage of women directors in UK boards. Lord Davies report set forth a goal of 25% female directors on FTSE 100 boards and the FRC and 30% club working together have resulted in the UK almost achieving that goal already. The

percentage of women directors in FTSE 100 companies is up to 24.7%, from 12.5% in 2010. The number of all male boards has gone down to 0, from 21 in 2010. For FTSE

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250 companies, the percentage of women directors has gone up to 18.7%, from 7.8% in 2010. The percentage of all male boards stands at 8%, down from 52.4% in 2010 (BoardWatch, May 2015).

The figure below shows the percentage of female directors on the FTSE 100 boards from 1995-2015 (BoardWatch, 2015).

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3. Methodology and Hypothesis

The paper looks at the percentage of female directors on the boards of companies in the UK and uses two risk proxies, leverage and volatility, to analyze the

effect of the presence of female directors on the board of a company on the risk management practices of the company. Looking at the conflicting research present for the difference in risk preferences between men and women, we hypothesize that the gender diversity of a board of directors should not affect the risk management of a company since there should be no difference in the decision making of equally qualified

men and women. Thus, our empirical research is conducted with the following hypothesis.

Hypothesis: Gender diversity on a board of directors has no effect on the risk taking practices of a company.

Alternate: Gender diversity on a board of directors affects the risk taking practices of a company.

We test this hypothesis by investigating the relationship between the percentage of women on a board of directors and the leverage and volatility of the company, separately. The first step in the process was to arrange the data in a panel to observe

the different variables for each company over time. This allows us to control for individual heterogeneity across companies. Next, we had to decide whether to use fixed effects or random effects model to run our regressions. The fixed effects model controls for time-invariant characteristics within an entity, the entity in our case being the company. Fixed effects regression is thus the model to be used when you want to

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you use the changes in the variables over time to estimate the effects of the independent variables on your dependent variable, and is the main technique used for analysis of panel data (Stock and Watson, 2007). Using a fixed effects model ensures that the

results of our regression are not biased due to omitted time-invariant characteristics of the dataset. However, even though fixed effects always gives consistent results statistically, it might not be the most efficient model to run in all cases. An important assumption in this model is that the time-invariant characteristics being studied are not correlated with other entities and thus it would be more efficient to run the random

effects model if we believe that there might be omitted variables that are random across time and entities. The random effects model, assumes that the differences across entities, due to the time-invariant characteristics, are random and could possibly have an effect on the dependent variable being studied. We use the Hausman Test to decide

between the fixed effects and random effects model for our regressions. The Hausman test tests the null hypothesis that the coefficients estimated by the efficient random effects estimator are the same as the ones estimated by the consistent fixed effects estimator, and thus the random effects model is the preferred model. The alternate is that the fixed effects model is the preferred model.

We use two separate estimates of leverage to ensure the robustness of our results, total debt to common equity ratio (Leverage 1) and total debt to total assets ratio (Leverage 2) and use the standard deviation of return on assets as our estimate of volatility. For the regression of Leverage 2 and Volatility, we get significant p-values after running the Hausman Test and thus reject the null and use the fixed effects

estimator. For Leverage 1, we get an insignificant p-value of 0.1629 and thus use the random effects model.

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4. Data

Data:

The primary data sources used in this paper are BoardEx and DataStream.

BoardEx is a proprietary institutional platform that employs analysts to ensure that the data it provides is accurate and timely with no duplicate or uncorrelated information. BoardEx gathers biographical data on the boards of directors of companies. Thus, this database was used to get information on all listed companies in the UK and their boards of directors, from 1999-2009. Once this gender information was gathered, the

percentage of female directors for each company was calculated for each year.

The next step was to use DataStream to gather data on the risk proxies for each company as well as the control variables to be used in the regression. Since we are using listed companies, reporting standards require them to report accounting data on an

annual basis and thus we were able to get information for a large sample of data. We divided the data into two parts to conduct our analysis using the two risk proxies, leverage and volatility, separately. Leverage data was collected for the years 1999-2009. To calculate the volatility for companies at overlapping five-year periods, return on assets data was collected starting in 1995. Since this data was not available for all

companies, the sample size for analyzing volatility is smaller than that for leverage.

We thus obtain a sample of 910 companies for leverage, with 5602 observations and a sample of 651 companies for volatility, with 3255 observations.

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Females on the Board:

Data collected from BoardEx specified the gender of each board member for companies per year. Using this information, the percentage of females on the board of

directors of a company was calculated for each year and this information was used to calculate the independent variable, percentage of females on the board.

Across the companies in our sample, for the analysis of leverage, there are 62% companies with no females on the board and 38% of companies that have at least one female board member. Across the companies in our sample for the analysis of volatility,

there are 57% companies with no females on the board and 43% of companies that have at least one female board member. To get a more comprehensive understanding of the differences in risk variables with changes in the percentage of females on the board, samples of companies with greater than 20% females on the board and greater than

10% females on the board are also used.

Risk Variables:

To conduct a comparison between different companies and analyze their risk taking profiles, the paper looks at two outcome variables that relate to corporate risk taking. The two proxies that will be used will be leverage and volatility. The empirical

analysis is conducted over a broad range of companies and thus, these two proxies are used because they are directly impacted by management risk taking decisions to clearly form a link between cause and effect.

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Leverage

Leverage is a measure of the riskiness of corporate financing choices. When deciding between how to finance the operations of a company, the percentage of debt

that a company takes brings tax benefits but it also has a direct effect on the risk of bankruptcy of the company. This is because any negative shock to the company’s underlying business conditions will result in a greater shock to the profitability of the company, the greater the leverage of the company. The most obvious risk of leverage, thus, is that it multiplies losses. Due to financial leverage's effect on solvency, a

company that borrows too much money might face bankruptcy during a business downturn, while a less-leveraged company may avoid bankruptcy due to higher liquidity. We use two measures of leverage in our analysis, to ensure the robustness of our results. Firstly we use total debt to common equity (Leverage 1) and secondly, total

debt to total assets (Leverage 2).

Leverage 1 is defined as the ratio of total debt to common equity, where total debt represents all interest bearing and capitalized lease obligations. It is the sum of long and short-term debt and common equity represents common shareholders' investment

in a company.

Leverage 2 is defined as the ratio of total debt to total assets, where total debt is the same as above and total assets represent the sum of total current assets, long term receivables, investment in unconsolidated subsidiaries, other investments, net property

plant and equipment and other assets.

To account for outliers in the data that would bias our results, we look at the total debt to assets ratio and remove any values less than 0% and greater than 100%. These outliers are

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removed from our analysis of volatility as well to ensure that our results are not skewed due to outliers. We also exclude financial companies from our analysis of leverage since the debt structure of financial companies is fundamentally different from that of other corporations and thus would not present a valid comparison.

Across the companies in our sample, the average leverage ratio for Leverage 1 is 27.70% while it is 11.99% for Leverage 2. For companies with no females on the board of directors, this ratio is 26.20% and 11.46% for Leverage 1 and Leverage 2, respectively and it is 30.19% and 12.88% for companies with at least one female on the board of directors for Leverage 1 and Leverage 2, respectively. The difference in Leverage 1 between companies with no females on the board and those with at least one female on the board is statistically significant with the p-value less than 0.01. The difference in Leverage 2 between companies with no females on the board and those with at least one female on the board is also statistically significant with the p-value being less than 0.01 for the difference. Thus, these results imply that companies with at least one female on the board have higher leverage as compared to those with no female directors on the board.

Volatility

The volatility of a company’s operating return on assets, σ(ROA), is a measure of the riskiness of outcomes of the company. This is a standard proxy for risk in the financial economics literature (Faccio, Marchica and Mura, 2015). It signifies higher risk as management is not taking measures to manage volatility. At any given point in time, the risk of default and loss of profitable investments is higher with higher volatility.

Return on assets is a profitability ratio, defined as the ratio of earnings before interest and taxes to total assets. Depending on the availability of data for each company, we calculate the standard deviation of return on assets over five-year overlapping windows

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(1995-1999, 1996-2000, 1997-2001, 1998-2002, 1999-2003, 2000-2004, 2001-2005, 2002-2006, 2003-2007, 2004-2008 and 2005-2009).

Across the companies in our sample, the average σ(ROA) is 9.34%. For companies

with no females on the board of directors, the average is 9.58% while it is 9.03% for companies with at least one female on the board of directors. The difference in means is not significant, however (the p-value of the difference between the two is more than

0.05).

Control Variables:

A number of company level control variables are used in our model. Sales growth

is used to control for the difference in growth levels between the companies and is defined as net sales to revenues to get the annual rate of growth of sales. A size variable is used to control for the differences in sizes of the company with ln(Size) being used and differences in industry type are also controlled.

Sales Growth

Sales Growth is calculated using the formula (Current Year's Net Sales or Revenues / Last Year's Total Net Sales or Revenues - 1) * 100.

Size

Total Assets is used as a measure of the size of a company where total assets represent the sum of total current assets, long-term receivables, investment in

unconsolidated subsidiaries, other investments, net property plant and equipment and other assets. For all companies in the dataset, the total assets information was gathered from DataStream. Most of the data for companies was reported in British Pounds (£).

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For companies that reported the data in other currencies, Oanda Currency Converter was used to find the historical currency conversion rate and convert the amount to British Pounds (£).

Industry Type

To control for the type of industry that a company belongs to, general industry classification information was gathered from DataStream. This classifies companies into six groups:

1. Industrial

2. Utility

3. Transportation 4. Bank/Savings & Loan 5. Insurance

6. Other Financial

For the analysis of leverage, therefore, only industrial, utility and transportation companies were used. All six classifications were used for the analysis of volatility. Table 1. below provides a description for all variables used in the analysis. Summary information for all variables is reported in the next section.

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Table 1. Variables

The table contains a description of each variable being used in the analysis

Independent Variable

Females on the Board Percentage of female board members in a company’s board of directors.

Risk Proxies

Leverage 1 Ratio of Total Debt to Common Equity,

where total debt is the sum of long and short term debt and common equity represents common shareholders' investment in a company. This ratio is calculated for five year periods ending at the date of reporting.

Leverage 2 Ratio of Total Debt to Total Assets where total debt is the sum of long and short term debt and total assets is the sum of total current assets, long term receivables, investment in unconsolidated subsidiaries, other investments, net property plant and equipment and other assets. This ratio is calculated for five year periods ending at the date of reporting.

Volatility Standard deviation of the company’s

Return on Assets, where RoA is the ratio of earnings before interest and taxes to total assets.

Control Variables

Sales Growth Annual rate of growth of sales. It is calculated using the formula (Current Year's Net Sales or Revenues / Last Year's Total Net Sales or Revenues - 1) * 100.

Size ln(Total Assets), where Total Assets is the

sum of total current assets, long term receivables, investment in

unconsolidated subsidiaries, other investments, net property plant and equipment and other assets.

Industry Type Classification of companies into six

different industry types: industrial, utility, transportation, bank/savings & loans, insurance and other financial.

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Descriptive Statistics:

For the purposes of analysis, data was collated into different groups. The first group being companies with greater than 20% females on the board, second being

companies with greater than 10% females on the board, third being companies with one or more females on the board and the fourth one being companies with no females on the board. Since the datasets for the analysis of the two risk proxies are separate, separate descriptive statistics are reported for leverage and for volatility.

Leverage

Table 2 below, shows that from 1999-2009, the percentage of females on the board of directors of companies has gone up steadily with 40% of companies having at least one female director on the board in 2009, as compared to 31% in 1999 and 12% of companies having greater than 20% female directors on the board in 2009, as

compared to only 5% in 1999. Thus, the trend of increasing female representation on corporate boards is represented in the dataset.

Only non-financial companies were used to analyze the effect of females on the board of directors on the leverage of a company since the debt structure of financial companies cannot be compared to that of non-financial companies. Table 3 shows the

breakdown of observations by industry type, with a majority of the observations falling under the industrial category with 4% of the total observations in the utility and

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Table 2. Percentage of Females on the Board (Leverage)

The table shows the percentage of females on the board of directors in the sample of companies used to analyze the effect of female representation on the board on leverage.

Companies with 0% females on the board. Companies with >0% females on the board. Companies with >10% females on the board. Companies with >20% females on the board. 1999 69% 31% 24% 5% 2000 65% 35% 28% 7% 2001 65% 35% 29% 8% 2002 63% 37% 30% 10% 2003 61% 39% 32% 12% 2004 63% 37% 31% 10% 2005 61% 39% 34% 12% 2006 63% 37% 32% 11% 2007 62% 38% 35% 11% 2008 62% 38% 35% 12% 2009 60% 40% 36% 12% 1999-2009 62% 38% 33% 11%

Table 3. Industry Type (Leverage)

This table shows the number of observations in each industry type and the percentage of observations per industry type in the dataset used to analyze the effect of female representation on the board on leverage. The total number of observations is 5602 in this dataset.

Industry Type Frequency Percentage

Industrial 5384 96.11%

Utility 104 1.86%

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Table 4. Descriptive Statistics Leverage 1

The table shows descriptive statistics for the dataset used to analyze the effect of female corporate board membership on the leverage of a company. The averages for each year as well as across all years for the variables, leverage, sales growth and size are reported. Leverage is the ratio of total debt to common equity. Sales growth is the annual rate of growth of sales. Size is the natural logarithm of the total assets of a company. The leverage and sales growth are reported in percentage points. The mean differences in leverage for companies with no females on the board of directors and those with at least one female on the board, greater than 10% females on the board and greater than 20% females on the board respectively are also reported. *, **, and *** indicate significance at 10%, 5%, and 1%, respectively.

Companies with 0% females on the

board. Companies with >0% females on the board. Companies with >10% females on the board. Companies with >20% females on the board.

Year Leverage Growth Sales Size Leverage Growth Sales Size Leverage Growth Sales Size Leverage Growth Sales Size

1999 36.38% 13.22% 12.37 34.27% 46.54% 14.34 31.62% 57.20% 13.97 35.51% 57.69% 13.88 2000 33.06% 254.24% 12.33 30.96% 44.04% 14.10 29.02% 47.56% 13.89 33.40% 31.91% 13.51 2001 30.24% 41.37% 12.08 31.59% 73.09% 13.94 29.88% 84.10% 13.61 31.63% 6.59% 13.89 2002 29.92% 12.74% 11.86 33.31% 13.60% 13.86 31.93% 16.34% 13.59 33.50% 5.75% 13.35 2003 28.69% 131.27% 11.49 31.00% 173.29% 13.73 29.59% 115.18% 13.36 28.50% 97.24% 13.33 2004 25.61% 175.03% 11.29 26.87% 187.56% 13.50 24.52% 162.43% 13.11 23.88% 193.22% 13.36 2005 22.09% 193.40% 11.10 26.99% 220.34% 13.20 25.12% 190.11% 12.82 21.29% 304.25% 12.45 2006 23.07% 923.19% 11.05 27.52% 153.32% 13.22 26.44% 173.78% 12.89 23.26% 43.78% 12.52 2007 22.89% 114.68% 11.18 30.37% 60.40% 13.23 30.07% 65.55% 13.01 26.18% 33.06% 12.87 2008 24.96% 71.77% 11.33 32.79% 215.56% 13.32 32.61% 230.71% 13.08 31.49% 624.19% 12.96 2009 25.45% 17.49% 11.48 31.07% 5.22% 13.65 29.62% 5.76% 13.38 29.40% 14.64% 13.40 1999-2009 26.20% 208.11% 11.46 30.19% 120.77% 13.52 28.93% 117.47% 13.21 27.51% 164.21% 13.06

Mean Difference in leverage as compared to companies with 0% females on the board.

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Table 5. Descriptive Statistics Leverage 2

The table shows descriptive statistics for the dataset used to analyze the effect of female corporate board membership on the leverage of a company. The averages for each year as well as across all years for the variables, leverage, sales growth and size are reported. Leverage is the ratio of total debt to total assets. Sales growth is the annual rate of growth of sales. Size is the natural logarithm of the total assets of a company. The leverage and sales growth are reported in percentage points. The mean differences in leverage for companies with no females on the board of directors and those with at least one female on the board, greater than 10% females on the board and greater than 20% females on the board respectively are also reported. *, **, and *** indicate significance at 10%, 5%, and 1%, respectively.

Year Leverage Growth Sales Size Leverage Growth Sales Size Leverage Growth Sales Size Leverage Growth Sales Size

1999 14.88% 13.22% 12.37 13.87% 46.54% 14.34 13.22% 57.20% 13.97 13.28% 57.69% 13.88 2000 14.17% 254.24% 12.33 13.16% 44.04% 14.10 12.27% 47.56% 13.89 13.67% 31.91% 13.51 2001 13.36% 41.37% 12.08 13.86% 73.09% 13.94 12.95% 84.10% 13.61 12.79% 6.59% 13.89 2002 13.33% 12.74% 11.86 13.72% 13.60% 13.86 12.88% 16.34% 13.59 12.74% 5.75% 13.35 2003 12.32% 131.27% 11.49 13.24% 173.29% 13.73 12.58% 115.18% 13.36 11.81% 97.24% 13.33 2004 10.98% 175.03% 11.29 11.90% 187.56% 13.50 11.15% 162.43% 13.11 10.55% 193.22% 13.36 2005 9.45% 193.40% 11.10 11.63% 220.34% 13.20 11.05% 190.11% 12.82 9.50% 304.25% 12.45 2006 10.04% 923.19% 11.05 11.98% 153.32% 13.22 11.47% 173.78% 12.89 10.60% 43.78% 12.52 2007 10.23% 114.68% 11.18 13.17% 60.40% 13.23 13.19% 65.55% 13.01 12.18% 33.06% 12.87 2008 11.27% 71.77% 11.33 13.52% 215.56% 13.32 13.55% 230.71% 13.08 13.12% 624.19% 12.96 2009 11.40% 17.49% 11.48 13.09% 5.22% 13.65 12.64% 5.76% 13.38 12.43% 14.64% 13.40 1999-2009 11.46% 208.11% 11.46 12.88% 120.77% 13.52 12.41% 117.47% 13.21 11.77% 164.21% 13.06

Mean Difference in leverage as compared to companies with 0% females on the board. Mean

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Volatility

The data for the sample of companies used for volatility analysis shows a similar

pattern to that of the data used for leverage. From 1999-2009, the percentage of females on the board of directors of companies has gone up steadily with 45% of companies having at least one female director on the board in 2009, as compared to 30% in 1999 and 14% of companies having greater than 20% female directors on the board in 2009, as compared to only 4% in 1999.

Table 6. Percentage of Females on the Board (Volatility)

The table shows the percentage of females on the board of directors in the sample of companies used to analyze the effect of female representation on the board on volatility.

Companies with 0% females on the board. Companies with >0% females on the board. Companies with >10% females on the board. Companies with >20% females on the board. 1999 70% 30% 23% 4% 2000 72% 28% 20% 3% 2001 66% 34% 25% 6% 2002 64% 36% 26% 8% 2003 59% 41% 30% 10% 2004 52% 48% 38% 12% 2005 52% 48% 39% 12% 2006 52% 48% 40% 12% 2007 54% 46% 39% 11% 2008 55% 45% 40% 12% 2009 55% 45% 40% 14% 1999-2009 57% 43% 35% 11%

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Table 7. Industry Type (Volatility)

This table shows the number of observations in each industry type and the percentage of observations per industry type in the dataset used to analyze the effect of female representation on the board on volatility. The total number of observations is 3255 in this dataset.

Industry Type Frequency Percentage

Industrial 2841 87.28%

Utility 45 1.38%

Transportation 62 1.90%

Bank/Savings & Loan 3 0.09%

Insurance 57 1.75%

Other Financial 247 7.59%

For the analysis of the effect of female representation on the board of directors on the volatility of a company, financial and non-financial companies were both used.

Thus, Table 6 shows the breakdown of observations by industry type. The majority of the observations fall under the industrial category here as well, with 87.28% of the observations. The second largest group is companies that fall under the category of other financial with 7.59% of the dataset.

The mean volatility for the companies with no females on the board of directors

in our sample is 9.58%. The mean volatility for companies with greater than 20% females on the board is 9.72%, the mean difference as compared to companies with no females on the board being 0.14%. This difference, however, is not statistically significant. Table 8 shows that the mean difference in volatility between companies

with at least one female on the board and those with no females on the board as well as companies with greater than 10% females on the board and no females on the board is statistically insignificant as well.

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Table 8. Descriptive Statistics Volatility

The table shows descriptive statistics for the dataset used to analyze the effect of female corporate board membership on the volatility of a company. The averages for each year as well as across all years for the variables, leverage, sales growth and size are reported. Volatility is the standard deviation of the company’s annual return on assets. Sales growth is the annual rate of growth of sales. Size is the natural logarithm of the total assets of a company. The leverage and sales growth are reported in percentage points. The mean differences in volatility for

companies with no females on the board of directors and those with at least one female on the board, greater than 10% females on the board and greater than 20% females on the board respectively are also reported. *, **, and *** indicate significance at 10%, 5%, and 1%, respectively.

Year Volatility Growth Sales Size Volatility Growth Sales Size Volatility Growth Sales Size Volatility Growth Sales Size 1999 4.72% 10.78% 12.7172 4.69% 15.90% 14.7470 5.43% 17.34% 14.4597 5.27% 15.38% 14.4711 2000 5.42% 19.64% 12.7522 5.62% 18.56% 14.9038 6.55% 13.80% 14.5544 2.58% 12.66% 15.2345 2001 5.14% 12.77% 12.7238 7.56% 4.76% 14.5182 7.97% 7.96% 14.1492 6.17% 9.71% 14.7535 2002 5.78% 5.34% 12.6852 6.84% 3.04% 14.4110 7.65% 3.19% 14.1668 9.47% 0.39% 14.1603 2003 5.48% 203.17% 12.6797 6.04% 146.33% 14.3243 7.00% 134.02% 14.0063 3.91% 66.44% 14.7053 2004 8.61% 228.32% 12.2843 6.03% 252.51% 14.1469 6.46% 220.46% 13.8259 5.93% 270.19% 14.2378 2005 8.11% 228.05% 11.9487 6.39% 256.70% 14.0603 6.41% 216.35% 13.7032 6.73% 345.95% 13.9966 2006 7.77% 1816.24% 12.0241 6.14% 17.95% 13.9944 5.94% 18.54% 13.6986 5.82% 19.19% 13.8937 2007 8.25% 25.71% 11.9342 5.46% 17.45% 13.9631 5.60% 18.55% 13.7527 6.67% 15.80% 13.7535 2008 16.52% 22.97% 11.7660 15.50% 211.96% 13.6809 15.85% 239.40% 13.4077 16.07% 734.26% 13.2321 2009 16.49% 13.09% 11.7185 16.53% 0.91% 13.7920 16.78% 1.73% 13.5705 16.45% 8.12% 13.3913 1999-2009 9.58% 254.68% 12.1643 9.03% 101.72% 14.0534 9.53% 98.83% 13.7539 9.72% 199.13% 13.8310

Mean Difference in leverage as compared to companies with 0% females on the board.

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5. Empirical Research

Leverage:

To test our hypothesis, we take our first risk proxy, leverage, and regress it on

the percentage of female directors on a board and our control variables, sales growth and size, while controlling for the industry type of a company, so as to avoid any spurious correlations. To control for the industry type, we have to create dummy variables for two out of the three classification groups to avoid the dummy variable trap. We do the regressions for Leverage 1 (total debt to common equity) and Leverage

2 (total debt to total assets) separately. The regression for Leverage 1 is a random effects GLS regression while the regression for Leverage 2 is a fixed effects regression. These models are selected by using the Hausman Test, as explained earlier in the Methodology and the results are reported in Table 9 below.

The results indicate that there is no statistically significant relationship between the leverage of a company and the percentage of females on the board of a company in our dataset, both our regressions returning insignificant p-values for the co-efficient for the percentage of females on the board of directors. There is a strong positive relationship between the size of a company and the amount of leverage that it has. This

result is intuitive as the larger a company is, the more stable it is and the more security it has to be able to increase its leverage. Access to debt for larger, more established companies is greater than that for smaller companies and their risk of bankruptcy is lower as compared to smaller companies with equal amounts of leverage.

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Regression equation for Leverage 1: Leverage 1= β1(Females on the board) + β2(Sales Growth) + β3(ln Size) + β4(Dummy Variable 1) +

β5(Dummy Variable 2) + αi + eit where β are the coefficients, α is the constant term and e are the

errors.

Regression equation for Leverage 2: Leverage 2= β1(Females on the board) + β2(Sales Growth) + β3(ln Size) + αi + eit where β are the

coefficients, α is the constant term and e are the errors.

Table 9. Regression Results for Leverage

This table shows the regression results with leverage as the dependent variable to analyze the effect of board diversity on leverage in companies. There are two measures of leverage that are used, Leverage 1 and Leverage 2. Leverage 1 is the ratio of total debt to common equity and Leverage 2 is the ratio of total debt to total assets. Females on the board represents the percentage of female board members for the company. Sales growth is the annual rate of growth of sales. ln Size is the natural logarithm of the total assets of a company. Dummy variables 1-2 are created to take into account the three different industry types that the companies are grouped into. The dummy variables are for industrial, utility and transportation, respectively. The regressions use annual data from 2001 to 2009. Constants were included in the regressions but are not reported. The regression for Leverage 1 is a GLS random effects regression while that for Leverage 2 is a fixed effects regression and therefore dummy variables are omitted. *, **, and *** indicate significance at 10%, 5%, and 1%, respectively.

Dependent Variable (Leverage 1) Dependent Variable (Leverage 2) Females on

the Board Sales Growth ln Size Variable 1 Dummy Variable 2 Dummy Females on the Board Sales Growth ln Size Co-efficient _-0.0636 _-0.0000 _0.0513*** _-0.1169** _-0.0081 _-0.0304 _-0.0000 _0.0247*** Standard Error _0.0443 _0.0000 _0.0029 _0.0586 _0.0757 _0.0184 _0.0000 _0.0017 p-value _0.151 _0.598 _0.000 _0.046 _0.915 _0.097 _0.650 _0.000 R-squared within _0.0327 _0.0411 between _0.1720 _0.1417 overall _0.1198 _0.1084

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Volatility:

To test our second risk proxy, we regress our measure of volatility on the percentage of female directors on the board and our control variables, using dummy

variables to control for industry type again. In this regression, all independent variables are measured at the end of the five-year sample period over which the standard deviation of the return on assets is calculated. So, for example, a σ(ROA) calculated from 2005-2009 is reported for year 2009 and the percentage of female directors of the company in 2009 is used while annual sales growth and size reported in 2009 is used as

well. The regression is a fixed effects regression and the results are reported in Table 10 below.

Table 10. Regression Results for Volatility

This table shows the regression results with volatility as the dependent variable to analyze the effect of board diversity on volatility of companies. Volatility is the standard deviation of the company’s annual return on assets. Females on the board represents the percentage of female board members for the company. Sales growth is the annual rate of growth of sales. ln Size is the natural logarithm of the total assets of a company. Dummy variables 1-5 are created to take into account the six different industry types that the companies are grouped into. The dummy variables are for industrial, utility, transportation, bank/savings & loan and insurance,

respectively. The dummy variables were omitted in the fixed effects regression and the results are not reported. The regression uses annual data from 2001 to 2009. Constants were included in the regression but are not reported. *, **, and *** indicate significance at 10%, 5%, and 1%, respectively.

Coefficient Standard Error p-value Females on the Board 0.1219*** 0.0351 0.001 Sales Growth -0.0000 0.0000 0.533 ln Size 0.0096** 0.0042 0.023 R-squared within 0.0075 between 0.0531 overall 0.0262

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Regression equation for Volatility: Volatility = β1(Females on the board) + β2(Sales

Growth) + β3(ln Size) + αi + eit where β are the

coefficients, α is the constant term and e are the errors.

The results indicate that there is a statistically significant relationship between the volatility of a company and the percentage of female directors on the board of the company for our sample (the p-value is less than 0.01). This relationship is positive,

with a unit increase in the percentage of females on the board causing a 0.1219 unit increase in the volatility of the company. The size of a company also has a significant positive relationship with the volatility of the company.

Our results for the two risk proxies used are conflicting. While there is a

significant difference in the mean leverage values of companies with no females on the board as compared to companies with at least one female on the board, there is no significant relationship shown in the regression analysis between leverage and the percentage of female directors on the board, for either one of the leverage proxies used. The mean volatility values, on the other hand, are not significantly different across the

four different groups used for analysis, namely companies with no females on the board, companies with at least one female on the board, companies with greater than 10% females on the board and companies with greater than 20% females on the board. The regression analysis of volatility shows there to be a positive relationship between volatility and the percentage of females on the board of a company in contrast to the

mean differences for volatility across the different groups.

Thus, looking at the conflicting evidence in front of us, we cannot reject our null hypothesis that the gender diversity of a board of directors has no effect on the risk

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taking practices of a company. There isn’t economically and statistically significant data to support our rejection of the null hypothesis.

Analysis:

There can be many reasons for the results that we have obtained that explain the statistical significance obtained via the regressions for volatility but not leverage and therefore, why a causal relationship cannot be established from the obtained results. Firstly, the results can be due to discrepancies in the data sample that we are using for testing the causal relationship. There can be survivorship bias, as companies that are

listed are the ones that have moved forward with the times and employed more female directors and there is a sample of companies that is excluded because they have ceased to exist. This will skew the results in the favor of the surviving companies. Unobservable factors can be present as well that could affect the data and thus cause unobserved

heterogeneity that cannot be captured by the regression methods that we have used. To be able to correct for this, we could further carry on the research and conduct a difference in difference analysis. The results can also be affected by selection bias and reporting bias since we had to remove companies from the analysis that had not provided reporting on the independent, dependent and control variables that we

needed for our analysis.

Apart from data related reasons, there can be economic and social reasons for the results that we have from our analysis. There has been extensive psychological research to show that women are more risk averse than men and there have been many studies refuting this claim over time as well. We have tried to analyze the effect of

female corporate leadership on the risk taking of a company to see whether there are any differences that would thus point to different risk taking preferences amongst

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women, as compared to men. However, we are looking at a sample of data from the UK where voluntary targets to increase women on the boards of companies were set in 2010 and the UK Corporate Governance Code only recommended reporting on the

implementation of these goals in 2012. Our sample covers data from 1999-2009 and so companies had not felt the major impact of the reforms yet and we believe that maybe the women who had been appointed on boards had not been in the position for a sufficient enough time. It takes time for the board to start functioning efficiently after changes are implemented and for new board members to ensure that their voices are

reflected in the decisions of the board (Nielsen and M.Huse, 2010). Thus, conducting this study again in another five to ten years, when the changes being implemented have resulted in stable gender diverse boards over a period of time, could yield different results as women would have had the time to truly impact board decision making.

Our data showing no statistical difference in the risk taking practices of boards with female directors and, in fact, showing a mean leverage for boards with at least one female present that is significantly higher than the mean leverage for boards with no female directors can be explained by research that shows no difference in risk preference amongst men and women in top executive positions, given the level of skill

and expertise that is required to reach that level (Adams and Funk, 2012, Adams and Ragunathan, 2013).

Implications:

The research conducted in this paper has policy implications for the future for not only the UK market but the global market at large that is shifting from a male

dominated corporate workforce to a more gender balanced workforce. The major questions to be asked are whether the psychological studies that have been conducted

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to show that women are more risk averse than men (Hudgens and Fatkin (1985), Bruce and Johnson (1994), Johnson and Powell (1994), Sundén and Surette (1998) and Bernasek and Shwiff (2001)) apply to women in corporate settings, especially at top

management and decision making positions? Or does the educational background of women and their work experience ensures that any personal risk preferences do not affect the risk taking decisions that they make for companies as qualified directors? This research complements studies conducted by Adams and Funk, 2012 that show that studies conducted to gauge personal risk preferences do not take into account the

individual characteristics that are needed to reach the top of the corporate ladder and thus cannot explain decision making by individuals with that background and skills. The findings can also be used to develop more targeted ways to ensure that women are educated in the societal and gender biases that they are subjected to and thus learn to

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6. Conclusion

We investigate how gender diversity on a board of directors affects the risk taking choices of a company by looking at two risk proxies, leverage and volatility. We document that there is no statistically significant difference in the leverage of firms with female directors on the board as compared to those without female directors on the

board. This finding holds through our robustness test of using two separate measurements of leverage, total debt to common equity and total debt to total assets. Looking at the mean difference in leverage between companies with no females on the board and those with at least one female on the board, however, gives a statistically insignificant difference between the two. For our second risk proxy of volatility, we

document a statistically significant positive relationship between the presence of females on the board of directors of a company and its volatility. Thus companies with females on the board of directors are shown to have higher volatility than those without any females on the board of directors. Our descriptive data from the sample used, however, shows that there is no statistically significant difference in the mean volatility

of companies without any females on the board of directors and companies with at least one female on the board of directors. Thus, with conflicting results from our regression analysis, we conclude that there is not enough statistical evidence to reject our null hypothesis of gender diversity on a board having no effect on the risk taking choices of a

company. We find that there is sufficient economical as well as psychological research to support our hypothesis.

The research was conducted over data from the years 1999-2009. With the rapidly changing landscape of female corporate leadership, women are becoming a more permanent feature on corporate boards. As they get more time to have their

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there is further work that needs to be done to study the impact of the voluntary changes to boards that are being made by UK companies. This will be able to guide further policy in how to increase women on boards in companies and what effect that will have.

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