Gender diversity in the boardroom and firm performance : evidence from US S&P500 firms

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BACHELOR THESIS

Gender diversity in the boardroom and firm

performance: Evidence from US S&P500 firms

Luyuan Shao, Economics and business, University of Amsterdam

2018/6/20

Advisor: Patrick Stastra

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I would like to sincerely thank my advisor Patrick for his helpful comments and patience.

Abstract: Using the sample consisting of 138 S&P 500 US firms between 2010 and 2012, this paper

tested whether gender diversity on board influence company performance or not. We selected the

presence of female directors and proportion of female directors in the boardroom as our

explanatory variables and return on asset as well as cash flow from operations to total asset were

chosen as dependent variables. By employing Ordinary Least Squares and Random Effect Model,

this study indicates that gender diverse issue in the boardroom may have no significant influence

on performance of companies.

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Statement of Originality

This document is written by Student [Luyuan Shao] who declares to take full responsibility for the

contents of this document.

I declare that the text and the work presented in this document are original and that no sources other

than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of completion of the

work, not for the contents.

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1. Introduction

The outbreak of financial crisis in 2008 has revealed severe problems in corporations, which results a rising

attention among the policy makers toward how to reach an effective corporate governance structure. Lacks of

monitoring in the company is a major cause for corporate failure according to the agency theory (CĂRĂUȘU,

2015). This has led many regulators as well as scholars to focus on the composition of the board regarding to

gender, ethnicity, percentage of independent board members and the background of directors, etc. Gender

diversity, as one of the widely discussed topic by the general public, has been included in guidelines and

legislations in many countries. David et al. (2010) state regardless of how much a vital role that corporate

governance plays in a company, many companies fail to manage it and are not alert to its importance.

Consequently, the US government introduced a massive piece of laws named Sarbanes-Oxley Act of 2002 to

regulate the companies. Other countries like Norway amended the Public Limited Companies Act in 2005,

stated that if there are more than nine directors in the boardroom, the proportion of female should be no less

than 40%. Similarly, In the Law of Equality passed by the Spanish Parliament in 2007, it requires 40% of all

board seats should be occupied by females for listed firms. In 2011, Lord Davies set a number of targets for the top performance companies in the UK to recognize the beneficial result of gender diversity in boardrooms.

Nevertheless, in some developing countries, like China and Thailand, currently there is no requirement for

gender quota on company board. This may be caused by the lack of attention on feminist consciousness,

which results in an automatic impression that women are expected to stay at home and take responsibility of

the housework duties rather than going out for searching a job.

These regulations and policies give rise to at least two arguments for gender diversity in companies. One

argument is corresponding to the agency theory, which states that enforcing a certain female board director

quota through enhanced corporate monitoring would have a positive impact on the performance of

organizations. According to Singh and Vinicombe (2004), Females in general are more serious when making

decisions than males. Additionally, gender diversity of directors might result in more diverse perspectives

toward business issues, less information bias when making decisions as well as more creativity within the firm.

This argument has also been documented by a large amount of empirical studies in different countries (Daniel

et al. 2015; Gul et al. 2011; Vafaei et al. 2015; Ararat et al. 2015; Carter et al. 2003). On the other hand,

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Ferreria (2009), and Smith, Smith and Verner (2006) are consistent with the social psychology theory. They

state that more women directors might give rise to more conflict in the boardroom, thus leading to the

decision-making process less efficient.

Consequently, empirical findings from prior studies are still inconclusive. It remains a question on whether it

is a wise choice for countries to demand more gender quotas for the company boardrooms. However, any

relationship should be set based on existing evidences. If the quota policy intervention improves the

performance of companies, then it serves as an efficient monitoring device for firms and proves

gender-diverse firms perform better than homogenous firms. Whereas, if there is no such effects, then these

quota put forward by policy makers may simply help social fairness.

The main purpose of this research is to examine whether a gender diverse board enhances organization

performance or not by using a sample consists of 414 observations (138 firms) from the US S&P 500 index

during 2010-2012. Although, there are other studies also explore the US S&P 500 firms, the improvement of

this study is that we use data from a more recent year period, and employ different measurements of

performance (ROA and CFO/TA). We implement the OLS regression both with and without control of the

industry dummies and year dummies. Besides, we use the Random Effect methodology as well, which was

rarely in previous studies. For the purpose of reliability, we also do robustness checks for both OLS

methodology and REM methodology. We failed to identify any significant relation between gender diversity

and the performance of companies. This result does not support the requirements for female quota set by

many countries that were aiming at improve the performance of the firms.

This research is organized as follows: section 2 provides some existing literatures and theories related to

gender diverse and performance of company topic as well as explanation of the hypothesis development based

on empirical findings. Section 3 illustrates the research method including sample collection, variables used in

the analysis, descriptive statistics and regression models. Furthermore, section 4 discusses the results of the

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2. Literature Review and the Development of Hypothesis

2.1 Empirical Research

The notice of the importance of board monitoring has attracted public attentions on board diversity. The board

composition has been considered vital in approaching the interest of stakeholders in organizations, providing

information to managers as well as monitoring to ensure making decisions efficiently (Marinova, Plantenga

and Remery, 2010). Gender diversity, average age of directors, board size as well as independent board

members are main compositions of board diversity, which were used frequently in previous literatures by

scholars to discover how the boardroom composition affects the decision-making process and, hence,

influencing the company performance. Gender diversity is one of the most crucial variables of board

composition. Studies on this topic have been prompted by the fact that female is underrepresented in

organization boards (Yap, Chan and Zainudin, 2017).Although there are already numerous studies in this area,

Marinova et al. (2010) conclude that the empirical result regarding to whether diversity in the boardroom

affect organization performance in an optimistic or passive way is still inconclusive.

Research on gender diversity in the boardroom and organization performance generally has reached two

conclusions. One line of studies state that more females on the corporation’s board leading to a good business

performance. Daniel et al. (2015), using firms from four different Asian countries, and employing Return on

Equity (ROE) as measurement of performance, reported a positive link between gender diversity and ROE.

Another research, Gul et al. (2011) measured firm performance by stock price informativeness (public

information disclosure), concluded that gender diversity on corporate board has a significant and positive

influence on organization’s stock price informativeness by enhancing information disclosure for large firms

and providing incentives for gathering private information in small firms. In Australia, Vafaei et al. (2015)

documented a significant and positive connection between board diversity with respect to gender and business

performance using top 500 listed Australian companies. Moreover, Ararat et al. (2015) examined the emerging

markets with data collected from the BIST-100 index, they observed a non-linear but positive link between

multiple diversity and firm’s performance measured by ROE and MTB (Market-to-book ratio). Earlier studies,

Carter et al. (2003) reported that the percentage of female directors is positively connected to organization’s

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Another line of studies state that there is no impact or adverse impact of gender diversity on business

performance. David et al. (2010) examined the effect of the diversity pertaining to gender and ethnicity on

organization performance of US S&P500 index companies for the period of 1998 to 2002. They observed that

more women directors in the boardroom do not contribute to a good performance of organizations. This is

consistent with the study conducted by Siantar (2016), their work examined listed India organizations on the

National Stock Exchange (NSE) using data from 2006 to 2015. Similarly, although three different measures of

gender diversity were used by Yap, Chan and Zainudin (2017), they did not find any relationships between the

diverse of the gender in the boardroom and Tobin’s Q listed on the top 100 FTSE Bursa Malaysia between

2009 and 2013. In addition, Rose (2007) did not conclude that board gender diversity has a significant link

with Tobin’s Q by using samples from the Danish companies. In addition, using the data collected from US

firms, Adam and Ferreria (2009) reported that the percentage of female in the boardroom does not contribute

to the increase in Tobin’s Q. On the contrary, Smith, Smith and Verner (2006) employed gross value added

measure as the first quartile turnover rate of firm relative to the turnover rate of the industry, a new method to

measure business performance which has not been employed by other scholars before. They documented

gender diversity has negative impacts on the gross value added for Danish companies.

The implication is difficult to deduce based on these limited 11 previous studies, since they investigated in

various areas, countries, used different variables and models etc. In order to get a better insight of the causal

relation between the diverse boards and organization performance, except for just focusing on the empirical

studies, two well-established theories related to this topic are also taken into account in this paper.

2.2 Theoretical Perspective 2.2.1 Agency Theory

Agency theory is a commonly used theory in explaining how to reach an effective corporate governance

structure. It provides a fundamental concept that the functions of corporate board are monitoring and

controlling (David et al., 2010). According to Gallego et al. (2009) a more diverse boardroom may improve

monitoring of managers so that the independence of board can be enhanced. For example, board with female

directors might provide a fresher view on complex managerial decisions than the board only contains male

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Vinicombe (2004) highlighted that females perform more seriously in their roles than males, which enhances

the governance of the corporation. However, not all evidence shows that female directors have a positive

effect on board monitoring, the results are still mixed. When analyzing the commercial banks of ASEAN-5,

Ramly et al. (2015) observed that independent directors serve as a monitoring device for the banks and thus

increase bank efficiency. Nonetheless, this positive impact weakens when the directors are female.

Concluding, research on agency theory lists some evidences that encourage firms to include more female

directors in the corporations, but it does not rule out the probability that a diverse board might have an adverse

effect.

2.2.2 Social Psychology Theory

Social psychology theory analyzes social behaviors of individuals as well as groups (Cremer & Murnighan,

2011). It is applied widely by organizations to manage its employees. Social psychology theory considers how

individual might behave when he/she has a majority status (e.g., male representatives) versus when he/she

might have a minority status (e.g., female representatives) (Mogbogu, 2016). While agency theory may prefer

gender diversity on the board, social psychology theory presumes that gender diverse in the boardrooms may

adversely affect the performance of the firm. In the hiring decision process within an organization, stereotype

toward female persist although some studies show that female get slightly higher scores than male on job

performance estimate (Robbins and Judge, 2016). Even when males and females have an equally level of

abilities, men is more likely to be chosen as a leader in an organization than women (Robbins and Judge,

2016). Besides the stereotype toward female, another reason most organizations may prefer a less diverse

board is that gender diversity may generates more conflicts among employees. Campbell and Minguez-Vera

(2008) argue that including more females on board induces various ideas on a single issue, causing the

decision-making procedure more complex and potentially more time-consuming. It is also argued that social

psychological procedure plays a crucial role in the cooperativeness of the boardroom. Members tend to be

more homogeneous in a group are often more productive with less emotional conflicts (Williams and O’Reilly,

1998). However, there are still arguments that support diversity of gender in organizations. For instance,

Siantar (2016) indicated that firm performance can be enhanced by heterogeneous boards with more creative

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research on the social psychology theory provides results that are generally opposite from the agency theory

on the board gender diversity issue.

2.3 Development of Hypothesis

In conclusion, the outcomes of both empirical research and theoretical evidence are mixed. Five empirical

studies provided a positive impact of diversify of gender in the boardroom on organization performance. Six

empirical studies indicated neither causal relationship nor negative relations were found between the gender

diversity on the board and organization performance. Even though the agency theory support for board gender

diversity to some extent, the social psychology theory does not prefer a more diverse board. Therefore, since

the limited evidence does not show a clear path towards the connection between board gender diversity and

organization performance, the fundamental hypothesis of this research is stated as follow:

Board gender diversity does not affect organization performance.

3. Research method

3.1 Sample Collection

The sample for this thesis consists of a balanced panel of companies listed in the S&P500 index over the

three-year period of 2010-2012. The identities of board directors were directly obtained from the Institutional

shareholder services (ISS), which is one of the vendors of Wharton Research Data Service (WRDS).

Financial data to compute the cash flow from operation to total assets ratio, return on asset and leverage ratio

were extracted from the Compustat-Capital IQ database, a vendor of WRDS as well. Due to the missing

corporate governance data and firm financial data, the balanced panel sample contains 414 firm-year

observations.

3.2 Dependent Variables

A recent research by David et al. (2010) on gender and ethnic diversity and organization performance in US

S&P500 firms used Tobin’s Q and Return on Asset (ROA) as measurement of financial performance. They

found that the relationship is mixed when they employing different response variables. In order to enhance the

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(ROA) and cash flow from operation to total asset (CFO/TA) respectively. According to Vafaei et al. (2015),

ROA is accounting-based measurement, which is a useful tool to assess how well the company has performed

in the previous years. The ROA of a firm, measured by dividing firm’s net income by total assets, is a good

indicator of how firm’s total assets have been generalized to make income for its stakeholders (Yap, Chan and

Zainudin, 2017). The higher the ratio, the more effective the company is. This is because the firm is utilizing

more earnings with less investment (Christopher, Siantar and Mahajan, 2016). On the other hand, CFO/TA is

an economic-based measurement, which indicates how efficiency the firm is in using assets (Smallbusiness,

2018). Vafaei et al. (2015) calculated CFO/TA as cash flow from operation activities divided by the firm’s

total assets.

3.3 Independent variables

This paper employed two proxies as independent variable of interest to measure gender diversity. Yap, Chan,

and Zainudin (2017) suggested that the mixed measures attributed to more comprehensive examinations of the

representations of female directors in the boardrooms. Therefore, gender diversity is measured by a dummy

variable (fem) indicating the presence of women directors in the boardroom and also measured by the

percentage of female board directors (propfem), which is calculated by dividing total amount of women

directors on the board by the total amount of board directors.

3.4 Control Variables

Omitted-variable bias occurs if one or more variables are left out. But in fact may have a relation with either

dependent variables or independent variables. In line with previous researches, six control variables are

included in this research to reduce the omitted variable bias. These are Board size (bsize), proportion of

independent board directors (propib), average age of board directors (aveage), leverage of the firm (leverage),

industry dummy (indusdum) and year dummy (yeardum) respectively.

Adams and Ferreira (2009) together with Carter et al. (2010) and Christopher, Siantar and Mahajan (2016)

suggest that board size need to be controlled in the regression model. However, there are mixed conclusions

between the board size and company performance based on previous researches. Jackling and Johl (2009)

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(2015) stated a strong relationship between board size and ROA &ROE, while they did not find any

relationships using Tobin’s Q and CFO/TA as measurements. Moreover, based on the agency theory, Yermak

(1996) argued that board size has a negative impact on financial performance. Hence, in order to reach a more

accurate result, board size were controlled in the regression model.

Independent board directors are defined by the Institutional shareholder services (ISS) as directors of a board

who have no direct links with the company. The causality between independent board directors and

organization performance has already been investigated by several studies, reaching different conclusions. In

their study, Elgaied and Rachdi (2008) presented that independent board directors play a significant role in the

analysis of organization performance measured by ROA. Min and Verhoeven (2013) suggest that independent

board members enhance firm performance through Tobin’s Q and firm productivity. However, Prabowo and

Simpson (2011) found no significant connection when investigating the share of independent board directors

and organization performance. Consistent with Carter et al. (2010), who also examined the US S&P500 firms,

the independent board directors is treated as a control variable in this paper.

In addition to the independent board directors, the third control variable is the average age of board directors,

which is defined by the sum of ages of all board directors divided by the sum of the number of total directors.

According to Vroom and Pahl (1971), older managers are more likely to avoid risk-taking investment. While

younger managers are more willing to seek for risky project with higher return, therefore firms with younger

managers will result in a higher growth in performance than firms with relatively older managers (Hambrik

and Mason, 1984). In Germany, in their study using 149 German firms during the period of 2009-2011,

Eulerich et al. (2014) pointed out that board age is expected to have a negative influence on organization

performance. Furthermore, Nakano and Nguyen (2011) demonstrated that board age has a significantly

negative influence on company performance in Japan. Both of the results suggest that older board may

decrease firm’s performance. In this research, the sample of the average age of board directors for the 138 US

S&P500 firms is roughly sixty-three years old. Therefore board average age is expected to have a negative

relationship with firm’s performance based on the previous literatures discussed above.

The impact from the firm’s financial leverage is another major area of interest in numerous studies. Ilyukhin

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showed a passive effect of financial leverage on ROA, ROE and operating margin. Besides, Ruland and Zhou

(2005) and Robb and Robinson (2009) also pointed out that financial leverage indeed can help to improve

firm performance. Additionally, Ibhagui (2018) figured out a significant negative influence of using debt on

financial performance when a sample is consisting of small-sized companies. However this effect diminishes

as the size of the firm increases. Finally, they induced a positive relation between leverage and Tobin’s Q for

Nigeria listed companies. Consequently, leverage of the firm (leverage) is included as a control variable, and

it is defined by dividing long-term debt by market value to equity.

Industry dummies are included as control variables. Since the data indicate industry sector for each firm in

WRDS is presented as a code number, it cannot be directly verified which industry each firm belongs to.

Hence, information for industry is hand-collected from the Wikipedia website corresponding with companies

using the Global Industry Classification Standards (GICS). This is an industry classification developed by

MSCI Inc. and Standard& Poor’s (S&P) in 1999 and is used as a benchmark for S&P and MSCI. Therefore,

following GICS, the sample consists of 10 main industry sectors shown in chart 1 below.

Chart 1 Sample Composition by Industry

Sample composition by industry

Industrials Health care Energy Consumer discretionary Consumer staples Materials Information technology Financial Telecommunication services Real estate

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13 3.5 Descriptive Statistics

The statistical characteristics regarding to the distribution of female directors on the board between 2010 and

2012 are presented in table I in the Appendix. Since the data for this paper is balanced, the number of

companies for the sample years is constant. The distribution of women board directors is stable, from 8.6014%

in 2010 to 8.6631% in 2012. Although more and more countries introduced regulations for improving the

female representatives on the board in the recent years, male directors still take up most positions in the

boardroom.

Furthermore, when looking at the distribution of women directors in 10 industry sectors (see table II in the

Appendix). Real estate industry has the highest proportion of female directors, with approximately 10.34% of

women directors sitting on the board. In contrast, the energy industry sector is the lowest with only 6.40% of

female directors sitting on the board. This gap for the proportion of women directors in each industry sector

indicates that we should control for industries in the regression model. Otherwise the coefficient of our main

explanatory variable may be biased.

Table 1 below provides key information on variables for board and financial performance of the sample. The

second column illustrates that within the three-year period, there are 414 firm-year observations in the sample.

The mean of the two main performance variables ROA and CFO/TA are 0.2258 and 0.4036 respectively,

which indicates every dollar that firm invested in asset generated approximately 23 cents in income and 40

cents in operation cash flow. The standard deviation of ROA and CFO/TA are 0.2016 and 0.3237, which

means there are no significant outliers among 414 firm-year observations. The percentage of women board

directors ranges from 0% to 55.56%, with an average of 15.94%. The proportion of independent directors

ranges from 44.44% to 100%, with an average of 83.08%. Out of 100 directors, 83 directors are independent.

This number is not surprising in the US. According to Baum (2017) nearly 85% board director members were

independent in 2013 by analyzing the US public firms. Finally, almost every firm has long-term debt to

finance capital. The minimum and maximum leverage ratio is 0 and 2.8908, indicates that liabilities are 0%

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14 Table 1 Description of firm characteristics (2010-2012)

Variable N Mean Median Standard deviation min max

ROA 414 .2258 .2027 .2016 -.9142 1.9677 CFO/TA 414 .4036 .3255 .3237 -.0581 2.2036 fem 414 .8937 1 .3086 0 1 propfem 414 .1594 .1429 .0988 0 .5556 bsize 414 10.3164 10 2.5348 6 32 propib 414 .8308 .8571 .0944 .4444 1 aveage 414 63.0390 63 3.1000 54 74.1429 leverage 414 .2832 .1946 .3315 .0000 2.8908

Table III (see Appendix) reports the correlation coefficients of the various variables. Severe multicollinearity

will influence the regression result when there is an obvious relationship between two explanatory variables.

According to (Studenmund, 2011), it is problematic if the value of the correlation coefficient is larger than

0.80. From the data shown below, it seems that the severe multicollinearity does not occur since all the values

of coefficients are very low.

3.6 Regression Models

In order to well-examine the causality of gender diversity and organization performance, two proxies of board

gender diversity (fem and propfem) are used as independent variables, two measures of firm performance

(ROA and CFO/TA) are used as dependent variables. The fundamental model is:

=α + + + + + + 2+ 3+

=α + + + + + + +

=α + + + + + + + +

222_{Notes: indusdum variable includes Information Technology dummy (ITdum), Consumer Staples (CSdum), Energy dummy (Energydum), Materials}

dummy (Materialsdum), Telecommunication Services (TSdum), Consumer Discretionary dummy (CDdum), Industrials dummy (Industrailsdum), Financial dummy (Financialdum) and Real Estate dummy (REdum). Industry dummy equals 1 if the observation is belongs to a specific industry, equals 0 otherwise. To be specifically, we have 10 different industries in our sample, which means we use 9 industry dummies, and 1 reference industry. The results of these 9 industry dummies are all comparing with the reference industry. In our sample, we use the Health Care industry as the reference industry.

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Based on our hypothesis in the literature research, the following hypotheses are tested:

H0: = 0 H1: 0

Where is the coefficient of gender diversity (fem and propfem)

Firstly, we employ the ordinary least squares (OLS) regression model, and then employ the Hausman test to

decide whether to apply the Fixed Effects Regression Model (FEM) or the Random Effects Regression Model

(REM). This is for checking if the results are consistent with OLS. Next, we run robustness check for both

OLS methodology and REM methodology. The list of variables and their definitions are shown in the table IV

in the Appendix.

4. Regression Results and Findings

4.1. OLS Regression Results

The preliminary OLS regression output without being controlled for the industry dummy variable and year

dummy variable is summarized in Table 2. We can notice that all of our regression models present a negative

relationship between our main explanatory variables and dependent variables. Model 1, model 3 and model 4

all have a significantly negative coefficient, meaning that gender diversity result in a significant negative

impact on firm’s performance at 90%, 99% and 95% level respectively. Moreover, for the control variables,

propib is significantly as well as positively associated with the ROA for model 1 and model 2 respectively.

However, this significant relationship disappeared when using CFO/TA as the performance measure. Leverage

however, seems to have a negative impact on the performance measure, with the significant coefficients of

-0.2190(0.0280) and -0.2200(0.0282) for model 1 and model 2. The relationship is negative since debt has the

potential to decrease the revenues as more cash is used to service that debt. For other control variables, there is

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Table2. OLS Regression for Gender Diversity and Firm’s Performance without Including Industry Dummies and Year Dummies.

Notes: In parentheses: standard error. *, **, *** denotes p < 0.1, p < 0.05, p <0.01 respectively.

4.2 OLS Regression Results after Adding More Control Variables and Random Effect Model Results

For the purpose of enhancing reliability and accuracy of the results, we apply two methods to check our

results. The correlation between independent variables and unobserved variables may disconcert both

explanatory variables and dependent variables. This will cause omitted variable bias of our output. As

illustrated in section 3, the percentage of women directors varies a lot across different industries. Hence, we

add industry dummies and year dummies in the regression model as our control variables as well as use

Before robust After robust

Model 1 ROA Model 2 ROA Model 3 CFO/TA Model 4 CFO/TA Model 1 ROA Model 2 ROA Model 3 CFO/TA Model 4 CFO/TA fem -.701** (.0318) - -.2355*** (.0539) - -.0701 ( .0525) - -.2355*** (.0857) - propfem - -.0090 (.1010) - -.3188* (.1736) - -.0090 (.1167) - -.3188 (.2078) bsize .0021 (.0037) .0005 (.0037) .0009 (.0063) -.0036 (.0064) .0021 (.0033) .0005 (.0034) .0009 (.0052) -.0036 (.0051) propib .2691*** (.1013) .2185** (.1030) .1153 (.1720) .0265 (.1771) .2691*** (.0938) .2185** (.0904) .1153 (.1626) .0265 (.1551) aveAGE .0014 (.0031) .0027 (.0032) -.0027 (.0053) -.0008 (.0055) .0014 (.0032) .0027 (.0035) -.0027 (.0049) -.0008 (.0046) leverage -.2190*** (.0280) -.2200*** (.0282) -.0159 (.0475) -.0241 (.0485) -.2190*** (.0232) -.2200*** (.0237) -.0159 (.0346) -.0241 (.0359) constant .0153 (.2194) -.0654 (.2209) .6830* (.3723) .5253 (.3797) .0153 (.2172) -.0654 (.2313) .6830* (.3586) .5253 (.3441) Industry dummy NO NO NO NO NO NO NO NO Year dummy NO NO NO NO NO NO NO NO N 414 414 414 414 414 414 414 414 R-square .1465 .1363 .0466 .0102 .1465 .1363 .0466 .0102 Adjusted R-square .1360 .1257 .0349 -.0019 - - - -

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17 Random Effect Regression to diminish omitted variable bias.

Table 3 below depicts that including industry dummies and year dummies weakens the significance of the

relationship between the fem and ROA, propfem and CFO/TA. The coefficient of propfem turns into

insignificant while the coefficient for fem still remains significant compared to the result in table 2 when there

is no industry dummies and year dummies included. Concerning the control variables, there is no major

change except for the coefficient of leverage. The relationship for leverage and CFO/TA convert to significant

compared to table2, which indicates that a high leverage ratio will lead to a decrease in company performance.

As for the dummy variables, all of the industry dummies listed in table 3 are comparing with a randomly

selected reference industry (health care industry). For instance, as illustrated in table 3, all the coefficients for

consumer staples industry dummy, telecommunication services industry dummy and real estate industry

dummy are positive and significant, which reveals that firms in those industries performed better than firms in

health care industry. Regarding to the year dummies, there is no significant evidence showing that firm

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Table3. OLS Regression for Gender Diversity and Firm’s ROA and CFO/TA Including Industry Dummies and Year Dummies.

Model 1 ROA Model 2 ROA Model 3 CFO/TA Model 4 CFO/TA Model 1 ROA Model 2 ROA Model 3 CFO/TA Model 4 CFO/TA fem -.0611** ( .0316) - -.1579*** (.0478) - -.0611 ( .0521) - -.1579** (.0718) - propfem - -.0489 (.1029) - -.1627 (.1572) - -.0489 (.1075) - -.1627 (.1662) bsize -.0031 ( .0036) -.0044 (.0036) -.0026 (.0055) -.0058 (.0054) -.0031 ( .0042) -.0044 (.0046) -.0026 (.0047) -.0058 (.0045) propib .2586*** ( .10) .2344** (.1023) .0692 (.1514) .0164 (.1562) .2586*** ( .0902) .2344*** (.0927) .0692 (.1350) .0164 (.1409) aveAGE .0034 ( .0031) .0039 (.0031) -.0076 (.0046) -.0064 (.0047) .0034 ( .0036) .0039 (.0038) -.0076* (.0040) -.0064* (.0038) leverage -.2375*** ( .0276) -.2389*** (.0278) -.1425*** (.0418) -.1466*** (.0424) -.2375*** ( .0272) -.2389*** (.0272) -.1425*** (.0480) -.1466*** (.0492) ITdum -.0113 ( .0389) -.0048 (.0390) -.0447 (.0590) -.0288 (.0595) -.0113 ( .0255) -.0048 (.0247) -.0447 (.0403) -.0288 (.0391) CSdum .1463*** ( .0350) .1484*** (.0357) .0934* (.0530) .1012* (.0546) .1463*** ( .0275) .1484*** (.0290) .0934** (.0378) .1012*** (.0387) Energydum .0469 ( .0343) .0601* (.034) .3460*** (.0519) .3777*** (.0520) .0469 ( .0380) .0601* (.0366) .3460*** (.0579) .3777*** (.0633) Materialsdum -.0468 (.0353) -.0470 (.0356) -.0462 (.0535) -.0490 (.0543) -.0468** (.0196) -.0470** (.0198) -.0462 (.0287) -.0490* (.0281) TSdum .1468*** (.0521) .1456*** ( .0523) .7157*** (.0789) .7130*** (.0799) .1468* (.0767) .1456** ( .0769) .7157*** (.1518) .7130*** (.1528) CDdum -.0014 (.033) -.0006 (.0332) -.0044 (.0501) -.0021 (.0507) -.0014 (.0253) -.0006 (.0255) -.0044 (.0389) -.0021 (.0382) industrialsdum -.0366 (.0304) -.0367 (.0306) -.0454 (.0460) -.0462 (.0467) -.0366* (.0203) -.0367* (.0209) -.0454 (.0342) -.0462 (.0351) Financialdum .1728*** (.0453) .1780*** (.0459) .0562 (.0686) .0670 (.0700) .1728** (.0836) .1780** (.0870) .0562 (.0706) .0670 (.0600) REdum .1694** (.0757) .1626** (.0763) .4334*** (.1146) .4133*** (.1165) .1694** (.0691) .1626** (.0691) .4334*** (.0896) .4133*** (.0891) 2011dum .0051 (.0213) .0056 (.0213) .0179 (.0322) .0193 (.0326) .0051 (.0231) .0056 (.0231) .0179 (.0318) .0193 (.0325) 2012dum .0007 (.0213) -.0000 (.0214) .0260 (.0323) .0246 (.0327) .0007 (.0233) -.0000 (.0236) .0260 (.0320) .0246 (.0324) constant -.0759 (.2154) -.1239 (.2157) .9402*** (.3266) .8250** (.3295) -.0759 (.2154) -.1239 (.2337) .9402*** (.3266) .8250** (.2922) N 414 414 414 414 414 414 414 414 R-square 0.2675 0.2610 0.3477 0.3316 0.2675 0.2610 0.3477 0.3316 Adjusted R-square 0.2380 0.2312 0.3214 0.3047 - - - -

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Random Effects Regression Model (REM) and Fixed Effects Regression Model (FEM) are both efficient

analytical tools in dealing with panel data as well as help to control the hidden heterogeneity. FEM is used

when the mean of groups are fixed. Besides, it allows missing information on the hidden individual effects to

correlate with the independent variables. Opposite from FEM, the mean of groups in REM are random. When

individual effects have no significant correlation with the parameters in the model, REM is more efficient

(Gallego et al., 2009).

We first apply the Hausman Test to check whether Random Effects Regression is more suitable than Fixed

Effects Regression. From the Hausman test, the results show that the p-values of our four regression models

are 0.3368, 0.3638, 0.8648 and 0.8917 respectively. Due to the p-values are all larger than 0.05 at the 95%

level (see Appendix V), REM is more appropriate than FEM for our sample.

Table 4 displays the output from REM, the result seems in conformity with the result in table 3. The data

suggest that the presence of women significantly lowers the performance of the company, while the

percentage of the women directors has no significant influence on performance of the company. Furthermore,

by applying REM, we get significantly higher R-squares for all models compared to the OLS with industry

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Table4. Random Effects Regression Model for Gender Diversity and Firm’s ROA and CFO/TA

REM 1 ROA REM 2 ROA REM 3 CFO/TA REM 4 CFO/TA REM 1 ROA REM 2 ROA REM 3 CFO/TA REM 4 CFO/TA fem -.0645* ( .0340) - -.1228*** (.0385) - -.0645 ( .0334) - -.1228 (.0824) - propfem - -.0274 (.1186) - -.1978 (.1487) - -.0274 (.1285) - -.1978 (.1779) bsize -.0027 ( .0045) -.0040 (.0045) .0021 (.0065) .0005 (.0065) -.0027 ( .0053) -.0040 (.0052) .0021 (.0075) .0005 (.0070) propib .2365** ( .1134) .2164* (.1157) -.0018 (.1388) .0107 (.1410) .2365** ( .1053) .2164** (.1102) -.0018 (.1288) .0107 (.1323) aveAGE .0003 ( .0037) .0010 (.0037) -.0107** (.0051) -.0105* (.0052) .0003 ( .0029) .0010 (.0031) -.0107** (.0052) -.0105* (.0054) leverage -.2228*** ( .0341) -.2247*** (.0343) -.0893* (.0486) -.0949* (.0491) -.2228*** ( .0328) -.2247*** (.0327) -.0893** (.0448) -.0949** (.0459) ITdum -.0147 ( .0467) -.0059 (.0515) -.0447 (.0932) -.0299 (.0944) -.0147 ( .0339) -.0059 (.0335) -.0447 (.0576) -.0299 (.0564) CSdum .1418** ( .0467) .1429*** (.0474) .0822 (.0860) .0928 (.0875) .1418*** ( .0426) .1429*** (.0435) .0822 (.0599) .0928 (.0597) Energydum .0491 ( .0452) .0652 (.0451) .3532*** (.0819) .3758*** (.0828) .0491 ( .0458) .0652 (.0454) .3532*** (.0953) .3758*** (.0990) Materialsdum -.0497 (.0472) -.0490 (.0475) -.0553 (.0873) -.0584 (.0886) -.0497* (.0280) -.0490* (.0275) -.0553 (.0418) -.0584 (.0415) TSdum .1401** (.0694) .1390** ( .0697) .6897*** (.1275) .6894*** (.1291) .1401 (.0921) .1390 ( .0919) .6897*** (.2348) .6894*** (.2383) CDdum -.0040 (.0441) -.0029 (.0443) -.0130 (.0815) -.0092 (.0825) -.0040 (.0389) -.0029 (.0394) -.0130 (.0618) -.0092 (.0604) industrialsdum -.0344 (.0406) -.0338 (.0408) -.0421 (.0751) -.0423 (.0761) -.0344 (.0310) -.0338 (.0318) -.0421 (.0543) -.0423 (.0556) Financialdum .1705*** (.0604) .1778*** (.0610) .0448 (.1112) .0477 (.1130) .1705** (.0737) .1778** (.0782) .0448 (.1141) .0477 (.1091) REdum .1659* (.1011) .1609** (.1019) .4184** (.1873) .4002** (.1901) .1659** (.0694) .1609** (.0683) .4184*** (.1204) .4002*** (.1197) 2011dum .0049 (.0171) .0053 (.0172) .0165 (.0157) .0182 (.0159) .0049 (.0207) .0053 (.0207) .0165 (.0167) .0182 (.0178) 2012dum .0016 (.0172) -.0006 (.0173) .0247 (.0159) .0246 (.0161) .0016 (.0191) -.0006 (.0195) .0247 (.0165) .0246 (.0166) constant .1328 (.2583 -.0615 (.2595) 1.1044*** (.3445) 1.0166*** (.3528) .1328 (.2056) -.0615 (.2595) 1.1044*** (.3445) 1.0166*** (.3363) N 414 414 414 414 414 414 414 414 R-square 0.2653 0.3818 0.3784 0.3631 0.2653 0.2589 0.3416 0.3259 Wald chi2 84.00 79.80 87.00 76.61 97.96 100.43 51.00 49.95

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21 4.3 Robustness Check

Non-robust estimator is calculated by using the sample mean, which is useful for symmetric distributions.

While robust estimator is calculated by the sample median and is helpful when there are extreme observations.

From table 1, we can see that the spreads of ROA and CFO/TA are extremely large, with the lowest value of

-91.42% and -5.81%, the highest value of 196.77%, 220.36% for ROA and CFO/TA respectively. Additionally,

the difference of the mean and median for ROA, CFO/TA and fem from table 1 also suggests that there might

be some outliers. This indicates that the answer from OLS and REM might be misleading in our sample. To

further ensure the reliability of the relationship concluded from OLS and REM, we also conduct robustness

checks (see the right four columns in table 2, table 3 and table 4) for each model. It is clear that after

concluding robustness checks, all the coefficients are presented insignificant in the random effect model and

only one estimator is shown to be significant in OLS model. On the whole, based on the robustness checks

together with the preliminary OLS model without industry dummies and year dummies, OLS model with

industry dummies and year dummies as well as random effect model, we fail to find any significant link for

gender diversity and organization performance.

5. Conclusion

The failures of corporate governance in recent decades have led politicians and scholars to focus on the

composition of boardrooms in organizations. Among different researches examining the corporate governance

topics, gender diversity in the boardroom stands out particularly due to the trend that more and more countries

implemented regulations requiring female director quotas in organizations. However, it still remains a

question that whether it can enhance organization performance or not through requiring female director quotas.

There are mainly two arguments from the existing literatures. Agency theory states that more gender diverse

board leads to a better firm performance through the function of board monitoring. Social psychology theory

claims that gender diversity can result in more conflicts between board members thus has a negative impact on

performance of companies. Moreover, results from empirical researches are inconclusive as well. Drawing on

the mixed evidence from both theoretical perspective and empirical perspective, the hypothesis of this study is

that requiring a more gender diverse board will not result in a better performance of companies.

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results from OLS, we ran another model named REM. It showed that the result is consistent with the results

from OLS. We also did robustness checks for the outputs from OLS and REM. It gave us more precise results

than non-robust results since robustness checks using the median of the sample, which exclude large outliers.

Overall, from our regression methodology using the presence of female directors and proportion of female

directors as main independent variables, ROA and CFO/TA as dependent variables, we conclude that there do

not appear any changes in firm performance that are resulted from the perspective with and without gender

diversity of the board. A board with more gender diversity does not lead to a better performance of the

company.

Accordingly, the regulations and policies that requiring a certain quota for the amount of women directors in

organizations might not be directly associated with performance of corporations. Instead, the trend toward

female quotas in the boardrooms might be pushed by publics. In their study, Marinova, Plantenga and Remery

(2010) state that the balance representation of female in top management is connected with social justice

rather than merely a matter of firm performance. On the other hand, the imbalance between the labor force

and the low employment rate might be another reason that was considered by the governments on introducing

female quotas policies. In conclusion, when countries introduce policies to mandate a certain female quota for

companies, they should take into account the impact of the policy on the economic benefits of the company

seriously.

This paper contributes a better insight of the gender diversity topic in corporate governance. First of all, we

use a combination of OLS regression and REM regression as our methodologies, which is more rigorous than

previous papers with just one regression methodology. Moreover, we included 9 industry dummies to reduce

the omitted variables bias. In most of the previous studies, although 2SLS or OLS regression model were

conducted, the industry dummies were hardly employed as control variables, which might lead to a biased

result.

One limitation of this research is that we only considered the US S&P 500 companies. For different countries

with different cultural and policies, the result might vary. Another restriction is that the impact of gender

diversity on company performance might be lagged (She, Sainter and Mahajan, 2016). Which means the fact

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research should use lagged firm’s performance variables to check for the result. Additionally, listed firms may

be more likely to hire more female board members. For future research, it seems more persuasive if non-listed

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6. Reference List

Adams, R., & Ferreria, D. (2009). Women in the Boardroom and Their Impact of Governance and Performance.

Journal of Financial Economics, (94) , pp.291-309.

Ararat,M., Aksu, M., & Cetin,A. T. (2015). How Board Diversity Affects Firm Performance in Emerging Markets: Evidence on Channels in Controlled Firms. Corporate Governance: An International Review, 2015, 23(2): 83-103.

Baum, H. (2017). The Rise of the Independent Director: A Historical and Comparative Perspective. Max Planck

Private Law Research Paper, No.16/20.

Campbell, K. & Minguez-Vera, A. (2008). Gender Diversity in the Boardroom and Firm Financial Performance.

Journal of Business Ethics, 83: 435-451.

CĂRĂUȘU, D.N. (2015). Monitor and Control in Companies: An Agency Theory Approach. Journal of Public

Administration, Finance and Law, Special Issue 2/2015.

Daniel, C.M.L., Roberts, H., Rosalind H. W. (2015). Board Gender Diversity and Firm Performance: Empirical Evidence from Hong Kong, South Korea, Malaysia and Singapore. Pacific-Basin Finance Journal, 35(2015) 381-401.

De Cremer, D., Van Dick, R., & Murnighan, J. K. (2011). Social Psychology and Organizations. Taylor and

Francis. DOI: 10.4324/9780203846957.

Elgaied, M. & Rachdi, H. (2008). Composition, Structure of Board of Directors and Performance: The Case of American Firms.

Eulerich, M., Velte, P., & Uum, C. V. (2014). The impact of management board diversity on corporate performance - an empirical analysis for the German two-tier system Problems and Perspectives in Management, 12(1), 25-39.

Gul, F. A., Srinidhi, B., & Ng, A. C. (2011). Does Board Gender Diversity Improve the Informativeness of Stock Prices? Journal of Accounting and Economics, 51(2011) 314-338.

Hambrick, D. C., & Mason, P. A. (1984). Upper Echelons: The Organization as a Reflection of Its Top Managers.

Academy of Management Review, 9(2), 193-206.

Ibhagui, O.W., (2018). Leverage and Firm Performance: New Evidence on the Role of Firm Size. North American Journal of Economics and Finance (2018).

Ilyukhin, E. (2015). The Impact on Financial Leverage on Firm Performance: Evidence from Russian. Journal of

Corporate Finance Research, Vol. 9, No. 2, pp. 24-36, 2015.

Jackling, B. J. & Johl, S. (2009). Board Structure and Firm Performance: Evidence from India’s Top Companies.

Corporate Governance: An International Review, 17: 492-509.

Kang, H., Cheng, M., & Gray, S. J. (2007). Corporate Governance and Board Composition: Diversity and Independence of Australian Boards. Corporate Governance: An International Review, 15(2), 194–207.

Marinova, J., Plantenga, J. & Remery, C. (2010). Gender Diversity and Firm Performance: Evidence from Dutch and Danish Boardrooms. Tjalling C. Koopmans Research Institute, discussion paper series nr: 10-03.

Min, B-S. & Verhoeven, P. (2013). Outsider Board Activity, Ownership Stucture and Firm Vlue: Evidence from Korea. International Review of Finance, 13:2, 2013: pp. 187-214.

Mogbogu, O. (2016). Women on the Board of Directors and their Impact on the Financial Performance of a Firm: An Empirical Investigation of Female Directors in the United States Technology Sector. These and

Dissertation.625.

Nakano, M., & Nguyen, P. (2011). Do older boards affect firm performance? An empirical analysis based on Japanese firms.

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25

Prabowo, M. & Simpson, J. (2011). Independent Directors and Firm Performance in Family Controlled Firms: Evidence from Indonesia. Asian Pacific Economic Literature, 25(1), 2011: pp. 121-132.

Ramly, Z., Chan, S-G., Mustapha, M. Z., & Sapiei, N. S. (2015). Gender Diversity, Board Monitoring and Bank Efficiency in ASEAN-5. South East Asia Journal of Contemporary Business, Economics and Law, Vol.7, Issue 1(Aug.) ISSN 2289-1560.

Rose, C. (2007). Does Female Board Representation influence Firm Performance? The Danish Evidence.

Corporate Governance: An International Review, 15: 404-413.

Smith, N., Smith, V., & Verner, M. (2006). Do Women in Top Management Affect Firm Performance? A Panel Study of 2500 Danish Firms. International Journal of Productivity and Performance Management, 55: 569-593. She, C., Sainter, D. & Mahajan, A. (2016). Effects of Board Gender Diversity on Firm Performance and Director

Compensation in India.

Vroom, V. H., & Pahl, B. (1971). Relationship between age and risk taking among managers. Journal of Applied

Psychology, 55(5), 399-405.

Williams, K. Y. & O’Reilly, C. A., III (1998). The Complexity of Diversity: A Review of Forty Years of Research. In E. Gruenfeld & M. Neale (Eds.), Research on Managing in Groups and Teams. Thousand Oaks, CA: Sage. Yap, I. L.-K, Chan S.-G.,& Zainudin,R. (2017). Gender Diversity and Firms’ Financial Performance in Malaysia.

Asian Academy of Management Journal of Accounting and Finance, 13(1), 41-62.

Yermack, D. (1996). Higher Market Valuation of Companies with a Small Board of Directors. Journal of Financial

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7. Appendix

Table I Gender diversity in each of the sample years

Year Number of companies

Number of female board dire tors

Total amount of board directors

Proportion of female board directors

2010 138 123 1430 8.6014%

2011 138 122 1418 8.6037%

2012 138 125 1423 8.6631%

Table II Directors data by industry sector during the period of 2010-2012

Industry sector: Total amount of female board directors

Total amount of board directors

Proportion of female directors

Real estate 6 58 10.3448% Telecommunication services 15 152 9.8684% Health care 61 633 9.6367% Industrials 69 722 9.5568% Materials 40 436 9.1743% Consumer discretionary 51 565 9.0265% Consumer staples 45 505 8.9109% Information technology 28 368 7.6087% Financial 18 254 7.0886% Energy 37 578 6.4014%

Table III Correlation coefficients

ROA CFO/TA fem propfem bsize propib aveage leverage ROA 1.0000 CFO/TA .04469 1.0000 fem -.0851 -.2166 1.0000 propfem .0293 -.0940 .5573 1.0000 bsize .0251 -.0325 .1669 .0590 1.0000 propib .0683 -.0170 .2183 .2604 -.0938 1.0000 aveage .0406 .0165 -.1868 -.2433 .0024 -.0173 1.0000 leverage -.3531 -.0173 .0157 .0455 -.0804 .0875 -.0039 1.0000

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27 Table IV: Summary of variables and definitions

Variable name Definitions

Dependent variables:

Return on asset (roa) Net income divided by total asset. Cash flow from operation to total

asset(cfo/ta)

Cash flow from operations divided by total asset.

Main explanatory variables:

Gender diversity dummy(fem) Equals 1 if at least one female in the firm’s boardroom, equals 0 otherwise. Proportion of female on

board(propfem)

Number of female directors on board divided by the total number of the firm’s board directors.

Control variables:

Board size (bsize) Total number of directors in the boardrooms. Proportion of independent

board(propib)

Number of independent board members divided by the total number of board directors.

Average age of board directors(aveage)

Sum of ages of all directors divided by the total number of board directors Leverage of the firm(leverage) Long-term debt divided by the market value of equity ratio

Information technology industry dummy (ITdum)

ITdum=1 indicating a firm is in the information technology industry sector, o otherwise.

Consumer staples industry dummy (CSdum)

CSdum=1 indicating a firm is in the consumer staples industry sector, 0 otherwise. Energy industry dummy

(Energydum)

Energydum=1 indicating a firm is in the energy industry sectors, 0 otherwise. Materials industry dummy

(Materialsdum)

Materialsdum=1 indicating a firm is in the materials industry sectors, 0 otherwise. Telecommunication services

dummy (TSdum)

TSdum=1 indicating a firm is in the telecommunication industry sectors, 0 otherwise.

Consumer discretionary industry dummy (CDdum)

CDdum=1 indicating a firm is in the consumer discretionary industry sector, 0 otherwise.

Industrials industry dummy (Industrialsdum)

Industrialsdum=1 indicating a firm is in the industrial industry sector, 0 otherwise. Financial industry dummy

(Financialdum)

Financialdum=1 indicating a firm is in the financial industry sector, 0 otherwise. Real estate industry dummy

(REdum)

REdum=1 indicating a firm is in the financial industry sector, 0 otherwise. Year 2011 dummy (2011dum) Equals 1 if the observation is in 2011, otherwise 0.

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Table V: Hausman test between Random Effect Model and Fixed Effect Model

Hausman test (REM vs FEM) ROA Female dummy ROA Proportion of female CFO/TA Female dummy CFO/TA Proportion of female Prob> Chi2 0.3368 0.3638 0.8648 0.8917

Gender diversity in the boardroom and firm performance : evidence from US S&P500 firms

BACHELOR THESIS