Dutch board diversity and firm performance

Rick Homan1

University of Groningen, Faculty of Economics and Business, Groningen, The Netherlands

Abstract

This study extends previous research on the effects of executive board diversity by

examining the relationship between age-, gender- and nationality diversity on firm

performance in the Netherlands. Based on a sample of 79 Dutch listed firms studied over

the period 2010-2015, this study reports a positive link between board diversity and firm

performance. Firm performance is, hereby, estimated using a forward-looking market

performance measure (Tobin’s Q) and a backward-looking accounting measure (ROA).

Keywords: Corporate governance, executive board diversity, firm performance, gender, age,

nationality

JEL classification: G34, J16, J15, and L25.

1 Supervisor: Dr. V. Purice (v.purice@rug.nl), University of Groningen, Faculty of Economics and Business

2

“There are not more than five musical notes, yet the combinations of these five give rise to more

melodies than can ever be heard.

There are not more than five primary colours, yet in combination they produce more hues than can

ever be seen.

There are not more than five cardinal tastes, yet combinations of them yield more flavours than can

ever be tasted.”

― Sun Tzu (544bc - 496bc), The Art of War

1. Introduction

In today’s business corporations, employees and top management not only become increasingly diverse

in terms of gender, age, and nationality, but also in terms of tenure, experience, educational background

and socioeconomic status (Nielsen and Nielsen, 2012; Ruigrok, Peck, and Tacheva, 2007). A reason for

this increased importance of diversification can be found in the area of corporate governance, stressing

the importance of gender diversity (Adams and Ferreira, 2009). Nowadays, stakeholders such as

shareholders, investors and inquisitive readers will almost certainly find the heading ‘corporate

governance’ in every annual report. According to the Organisation for Economic Co-operation and

Development (OECD, 2015), corporate governance “involves a set of relationships between a

company’s management, its board, its shareholders and other stakeholders.(..)”. Political, social and

business entities are increasingly calling for demographically diversified boards (Van Veen and

Marsman, 2008). This caused developed countries like the United States, Australia and many European

countries to take measures in order to influence corporate governance, focusing mainly on increasing

diversity of (e.g.) the board (Adams and Kirchmaier, 2016; Adams and Ferreira, 2009). One prime

example is the implementation of gender quota laws for boards by governments in order to change the

level of diversity (Adams and Kirchmaier, 2016; Terjesen, Aguilera, and Lorenz, 2015). In 2003,

Norway became the first country in the world to introduce a gender quota that at least 40% of the

company board be composed of women (Ahern and Dittmar, 2012). Recently , Germany imposed a

new law that requires women to hold 30% of the top board seats as of January 2016. The Netherlands

has a introduced a ‘target quota’ of 30%. This quota has no legal basis, but the Dutch government plays

an active role in achieving this aim.

Another reason that provides for an increasing degree of diversity is globalisation. Globalisation causes

firms to become transnational and detached from their national origin (Barlett and Goshall, 1989). In a

more globalised world firms have to accommodate a higher degree of diversity among their employees

at different managerial levels, especially at the top of the organization (Beck, 2008; Gupta and

Govindarajan, 2002). This has led to increasingly diversified corporations, which is also reflected in the

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board composition. A longitudinal study by Staples (2007) shows that boards of multinational firms are

becoming increasingly diversified and that the social forces spurring this trend are unlikely to disappear

anytime soon. There is a growing preference for female or ethnic minority board directors (Netter,

Poulsen and Stegemoller, 2009). As director characteristics could affect the effectiveness and their

influence in boards, it is likely that differences in nationality and gender will affect firm performance.

Therefore, studying diversity can help understand the effects of group composition on board

effectiveness and firm performance (Adams, Hermalin, and Weisbach, 2010).

The subject of board diversity has attracted researchers from various disciplines who have tried to link

diversity with other aspects of a firm. Some recent examples are board diversity and its impact on

corporate governance (Adams and Ferreira, 2009), organizational innovation (Chen, Leung, and Evans,

2015) and corporate social responsibility (Harjoto, Laksmana, and Lee, 2015). Gonzalez and

Hagendorff (2016) researched the diversities effect on corporate risk-taking. An increasing amount of

studies in the field of corporate governance and finance literature researched the relationship between

board diversity on firm performance. One of the first studies in this area was done by Carter, Simkins,

and Simpson (2003). They find that the proportion of women and minorities on US boards has a positive

relationship with firm value. Giannetti and Zhoa (2016) did a similar study covering US firms in 2001-

2011. They find that more diverse boards have greater stock return and volatility.

There is limited research on this subject regarding board diversity in Dutch boards. Using empirical

data on 186 listed firms in 2007, Marinova, Plantenga, and Remery (2016) find no relationship between

board gender diversity and firm performance. Their study suggests that there is a need for more

empirical research due to the limited European evidence on the effect of board diversity on firm

performance. Because other studies did find a relationship between board gender diversity and firm

performance, it would be a cause for more research, taking into account the limitation of previous

studies (see, e.g., Vafaei, Amed, and Mather, 2015; Boubaker, Dang, and Nguyen, 2014; Liu, Wei, and

Xie, 2014). Marinova et al. (2016) use a market-based performance indicator (Tobin’s Q) and suggest

that future studies should also use an accounting-based measure. In addition, the use of panel data is

preferred. Other research by Estélyi and Nisar (2016) argues that there is a need for board diversity

study, especially when it comes to nationality diversity among boards.

This study will investigate the influence of Dutch board diversity on firm performance. This research

is relevant for several reasons. First of all, it addresses the limitations and recommendations of previous

research done by (e.g.) Marinova et al. (2016) and Estélyi and Nisar (2016). Secondly, the outcomes of

this study are of particular interest to the company and their management because it analyses the

performance impact of the firm when the diversity of a board changes. Companies can take this into

account when hiring personnel and adopt policies. Thirdly, the Netherlands introduced a target number

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of 30% women on boards in January 2013. However, the effects of gender diversity in firms are not yet

been fully studied and conclusions are mixed (Marinova et al., 2016). More research into these effects

is therefore of interest to policy makers in order to predict the results of their policy.

The results represented in this paper indicate a positive relationship between age, gender and nationality

diversity and firm performance. However, these results were found not significant and are potentially

suffering from reverse causality. Lastly, it could not be proven that age, gender and nationality have a

joint significant impact on firm performance.

The remainder of this paper is structured as follows. Several theories on board diversity are introduced

in section 2 and are used to develop three hypotheses that are tested in this study. Section 3 explain the

conceptual model and the data collection. In addition, the regression models are introduced, potential

problems of endogeneity are addressed, and robustness checks are explained. Section 4, presents the

descriptive statistics and shows the results of the OLS- and fixed effects regression models. Concluding

section 5 sums up the findings and presents limitations and suggestions for future research.

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2. Literature review

This section examines the relevant theories and empirical evidence that explain how board diversity can

affect firm performance. Based on this three hypotheses are formulated.

2.1 Corporate governance

The performance of a firm is significantly impacted by corporate governance (Al-Matari, Al-Swidi and

Fadzil, 2014). Corporate governance and the role of a company’s board of directors is of utmost

importance in economics (Adams et al., 2010). The role of the board of directors is to monitor the

activities of a firm (agency perspective) and set forth the corporate strategy that influences firm

performance (Adams et al., 2010; Petrovic, 2008). Postma and Van Ees (2000) also note the boards

have an interlocking function (resource dependency perspective). The subtasks will vary per firm and

are allocated across several directors, depending on the board system. Two board types can be depicted:

one-tier (unitary) and two-tier (dual) boards. One-tier systems are more common in Anglo-Saxon

countries whereas two-tier systems are more common in continental Europe (Dehaene, De Vuyst,

Ooghe, 2001). Two-tier boards have executive directors (management board) that are responsible for

the daily operations of the company, and non-executive directors (supervisory) who supervise the

executive directors (Jungmann, 2006).

2.2 Theories on board diversity

Board diversity and the effect on firm performance has been researched by many scholars with mixed

results. For example, Carter et al. (2003; 2010) argue that directors with varying characteristics are able

to understand the complexities of the environment to a further extent. In turn, the firm is provided with

timely and valuable information resulting in lower transaction costs of dealing with unforeseen

contingencies (Hillman and Dalziel, 2003). Horwitz and Horwitz (2007) researched the effects of team

diversity and showed that more diverse teams provide a broader range of knowledge, information, and

resources as to their homogeneous counterparts. However, diversity can also come at a cost because of

increased group conflicts and reduced workforce cohesion (Li and Wu, 2014). This causes individuals

to become reluctant in sharing information with other. To theoretically substantiate the results, several

theories are used and sometimes being combined. In line with the scope of this study, the economic

theories are discussed.

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Upper echelons theory

The upper echelons theory suggests that top executive directors’ experiences, values, and personalities

affect their choices and subsequently organizational performance. This theory is based on the bounded

rationality concept, arguing that due to limited information, biased perception and filtering, individuals

do not act objectively in decision-making decisions (Hambrick, 2007, Hambrick & Mason, 1984). The

board of directors is viewed as an important decision-making body, and its composition has an impact

on firm performance since decision-making is jointly determined by individual board members

(Carpenter, Geletkanycz, and Sanders, 2014; Finkelstein, Hambrick, and Cannella, 2009; Finkelstein &

Hambrick, 1996). Diversity between these individual board members refers to observable

(demographic) and less visible (cognitive) characteristics (Mahadeo, Soobaroyen, and Hanuman, 2012;

Marimuthu, 2008). Board diversity is generally investigated under the assumption that analysing these

characteristics can help to understand the effect of board diversity on performance (Adams et al., 2010).

Recent research studying the role of board diversity and top management teams used the upper echelons

perspective in combination with several alternative theories to find the answer to the question if board

diversity contributes to firm strategy and firm performance since no single theory is able to explain the

correlation between board diversity and firm performance. (Nielsen, 2010; Lynall, Golden, and

Hillman, 2003).

Agency theory

The agency theory is closely connected with firm performance with regard to the board’s monitoring

capabilities, associated costs and management pursuing their own interest at the expense of

shareholders’ interest (Hillman and Dalziel, 2003). The theory is used in conjunction with the upper

echelons to explain why some executives would choose firm strategies for personal benefits rather than

act in the best interest of the principal (see, e.g., Wright, Moynihan, and Pandey, 2012). The impact of

these incentives is largely disregarded in upper echelons theory (Ryan and Wiggings, 2004; Hambrick

and Jackson, 2000). According to the agency theory (Jensen and Meckling, 1976), the board’s main

responsibility is monitoring. In the case of conflicts between managers and shareholders, the board

should intervene and resolve. If a manager (agent) acts as a utility maximizer, there are reasons to

believe that the manager will not always act in the best interest of the shareholder (principal). To solve

this, the board can limit divergences by establishing incentives, monitor its behaviour and replace

managers that do not act in the shareholders’ interest. The agency view assumes that board members

cherish their reputation as expert monitors and will not collude with insiders of the firm to subvert

shareholder interest (Carter et al., 2003). A board should consist of enough independent members to

effectively perform the monitoring role. Scholars suggest that greater diversity enhances the ability of

the board to control and monitor managers (Adams and Ferreira; 2009; Carter et al., 2003; Erhardt,

Werbel, and Shrader, 2003). Therefore it has been argued that the ultimate independent directors on

boards are people with a different gender, nationality or cultural background.

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Resource dependency theory

Among others, Hillman, Cannella, & Patzold (2000) combined the upper echelons theory with the

resource dependency theory to reason how board composition influences firm performance. The

resource dependence theory asserts that boards provide firms benefits through linkages to external

organisations, providing firms access to resources that would not be available otherwise (Pugliese,

Minichilli, and Zattoni, 2014). Pfeffer and Salancik (1978) point to four benefits of these linkages: (1)

providing information resources and expertise, (2) provision of communication channels between the

firm and network that are important to the firm, (3) access to support from outside organisations

(monetary or reputational commitments), and (4) legitimacy for firms. In short, the theory reasons that

by selecting directors with diverse backgrounds and various characteristics, a firm is better equipped to

have access to these four benefits, resulting is higher firm performance (Hillman et al., 2000).

Hillman and Dalziel (2003), argue that the theory is useful when applied to characteristics such as

experience, and social- and political connections but is limited when trying to understand the role of

other demographic characteristics of the board such as gender or age. Although the resource dependency

is used by many scholars because of its importance in explaining the behaviour of firms with regard to

its external environment, recent research scrutinises the theory because it cannot be used to directly

explain a firm’s performance (Sharif & Yeoh, 2014; Drees and Heugens, 2013; Davis and Cobb, 2010;

Hillman, Withers, and Collins, 2009).

Human capital

As noted some scholars argue that the resource dependency theory does not explain firm performance.

Instead, the human capital theory is used as a complementary theory to the resource dependency theory,

to explain differences in the financial performance of firms (Carter, D’Souza, Simkins, and Simpson,

2010; Singh, 2007). Human capital can be described as the value that board members provide through

the use of their skills, know-how, expertise, and experience. Moreover, human capital is inherent to

people and is not owned by the organisation (Maddocks and Beaney, 2002). Carter et al. (2010) argue

that human capital explains firm performance and is influenced by the amount of diversity (human

capital) in the board. The effect can both positively and negatively impact firm performance (Carter et

al., 2010). Terjesen, Sealy, and Singh (2009) use the human capital theory to explain why boards that a

more diversified potentially benefit from an increased variety of input during the decision-making

process. Would a board be conventionally composed chances are certain decisions are less contested

and challenged which is in line with the arguments of the human capital theory.

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2.3 Age diversity

One aspect of this study is board age diversity, a phenomenon of which the effect on firm performance

is not yet fully understood (Sila, Gonzalez, and Hagendorff, 2016; Kunce, Boehm, and Bruch, 2013).

Early research argues that people of different ages possess unique types of knowledge and cognitive

abilities (Horn & Cattell, 1967). Young individuals often have more knowledge of recent technologies

and a greater mental capacity (i.e. intellectual speed and efficiency in learning) to adapt to new situations

(Bugg, Zook, DeLosh, Davalos, & Davis, 2006; Skirbekk, 2004; Horn & Cattell, 1967). However, when

people get older they acquire mental skills that are assumed to improve with experience and learning.

Examples are in-depth knowledge and better problem-solving skills (Grund and Westergaard-Nielsen,

2008; Skirbekk, 2004). The human capital theory is used to explain that older board members, having

developed more knowledge, skills and experiences, are useful to the organisation (Shore et al., 2009).

Age diversity can be categorized into two different types of age-related differences: (1) differences in

values and (2) knowledge. Values differ between people of different ages. Historical experiences,

training and interacting with other individuals, affect the values and the way of thinking about work and

life (Parry and Urwin, 2011; Wagner, 2007). As people get older they find themselves in different stages

of their lives (e.g. young professional versus older people with grandchildren) with corresponding

values. For example, young people tend to ‘work to live’, spending more time with friends and value

social interaction (Sullivan, Forret, Carraher, and Mainiero, 2009). While older people value the belief

of hard work, and are said to “live to work”.

Age-related knowledge is shared, combined and integrated within groups and potentially improve the

quality of decision-making, creativity, efficiency, problem-solving, and finally labour productivity

(Carton & Cummings, 2012; Harrison & Klein, 2007; Horwitz & Horwitz, 2007). According to Grund

and Westergaard-Nielsen (2008), firms can benefit from synergies when age-based knowledge and

competencies are mixed within a firm. However, less effective communication, cooperation and

cohesion within groups with varying ages might lead to conflicts (Bell, Villado, Lukasik, Belau, and

Briggs, 2011). In turn, potential synergies are not likely to occur and age diversity might even negatively

impact performance (Kunce, Boehm, and Bruch, 2013; Klein, Knight, Ziegert, Lim & Saltz, 2011;

Grund & Westergaard-Nielsen, 2008).

Empirical evidence

Recent research based on the upper echelons theory did not find a relationship between age diversity

and firm performance (Ferrero-Ferreo, Fernández-Izquierdo, and Munoz-Torres, 2015). One of the

conclusions of the study by Ferrero-Ferrero et al. (2015) is that: “theoretical pluralism rather than one

dominant theory better captures the complexity of the behaviour of boards of directors”. Engelen, Van

der Laan, and Van den Berg (2012) used the human capital perspective and found a partly positive

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relationship between age diversity and firm performance in the Netherlands. This relationship turned

out to be hyperbolic: Age diversity does increase firm performance but up to a certain point. After this

point, an increase in age diversity will have a negative impact on firm performance. However, Engelen

et al. (2012) focused on whether board age diversity matters during times of crisis. According to Francis,

Hasan & Wu (2012) director age is positively related to firm performance during crises, because

experience might be a valuable resource in times of crises. Contrasting results are found by Waelchli

and Zeller (2013) consistent with the agency theory. On average the board of directors impose their

own life cycle on the firms they lead causing a negative effect on firm performance. A similar research

by Taljaard, Ward and Muller (2015) find strong associations of board age diversity with decreased

financial performance. Firms did not benefit from increased or accumulated knowledge and experience

as assumed by the human capital theory.

Hypothesis 1

Hypothesis 1 is constructed based on the study of Ferrero-Ferrero et al. (2015) and the theories

introduced in the previous section. It is expected that increased board diversity has a positive impact on

firm performance because age comes with more knowledge and experience as suggested by the human

capital theory and older board of directors are assumed to have increased linkages (resource

dependency). The following hypothesis was formulated:

𝐻1: 𝐵𝑜𝑎𝑟𝑑 𝑎𝑔𝑒 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 ℎ𝑎𝑠 𝑎 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑛 𝑎 𝑓𝑖𝑟𝑚′𝑠 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒

2.4 Gender diversity

The gender of the board members is one of many characteristics of diversity within a board (Van der

Walt & Ingley, 2003). Recent quota-legislation (e.g. Norway, Spain, France and the Netherlands) is a

result of the (normative) debate focussing on gender in the boardroom and argued to a matter of interest

to scholars (Lückerath-Rovers, 2013). Whether the appointment of women to the board fosters greater

firm performance because of improved corporate governance is largely dependent on the question what

governance should achieve. Supporters of more gender diversity rely on two types of arguments to

convince the public of their stance: the ethical or business case for diversity (Robinson & Dechant,

1997). Supporters with an ethical business case do not aim to increase firm performance, but rather

argue that greater female representation to represent ‘the real world’ is a reason by itself for more

women on a board. Women should be considered for leadership positions for equality reasons. The

latter, business case argument for diversity holds that more diversity could be related to better firm

performance. Brown et al. (2002) note that if the appointment of women to the board does not result in

increased firm performance it has merely a symbolic value. Previous research indicates that admitting

women to the board is used by firms as a sign to signal a certain quality of the firm (Miller and Triana,

2009).

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Females are also said to value their responsibilities as directors more, corresponding with an increase

in effective corporate governance. By appointing more women to the board of a company they serve as

a linkage instrument, using the resource dependency theory as a basis, that can provide advantages to

firms due to improved linking with their stakeholders (Hillman, Shropshire, and Cannella, 2007).

Diversity is in general positive for organizations by providing wider and better connections with

stakeholders, in turn lowering market uncertainty and dependency (Miller & del Carmen, 2009). Carter

et al. (2003), argue that female board members are more inclined to ask questions that would not be

asked by their male counterpart. In addition, they are not intertwined in ‘old boys’ networks (Adams &

Ferreira, 2009; Campbell and Minguez-Vera, 2008). That is why evidence suggests that women are

better monitors and are more able to alleviate value-decreasing stakeholder conflicts (Post and Byron,

2015; Adams & Ferreira, 2009).

Empirical evidence

In the past, studies focussing on gender diversity and the effect on firm performance turn out to be

positive, negative or nonsignificant (Webber & Donahue, 2001). Marinova et al. (2016) researched the

effect of gender diversity on firm performance in the Netherlands and Denmark. Their results show no

relation between the share/presence of women on boards and firm performance2. Rose, Munch-Madsen,

and Funch (2013) researched the impact of female board representation on corporate performance on a

sample in Nordic countries as well as Germany. Their study was similar to this study in the sense that

they used the same theoretical foundation. No support was found for any performance impact due to

changes in female board representation. Similarly, Engelen et al. (2012) based their research on the

human capital theory and found no effect of board gender diversity on firm performance during times

of crisis. In their research on women directors on boards, Terjesen et al. (2009) note that the agency-,

resource dependency- and human capital theories are not mutually exclusive and all have a different

impact on board diversity and firm performance.

Adams and Ferreira (2009) find that women are found to be more active in their monitoring role within

the boardroom and therefore provide increased firm performance. This corresponds to what is expected

by the agency theory and is also empirically supported by Taljaard et al. (2015) and Carter et al. (2003).

However, Adams and Ferreira (2009) also find that more gender diverse boards can suffer from over-

monitoring when shareholder protection is strong, causing a negative effect on firm performance.

Opposite results are found when shareholders are weakly protected. Proposing that gender diversity is

positive depending on the level of shareholder’s protection (Ujunwa, Okoyeuzu, and Nwakoby, 2012;

Adams and Ferreira, 2009; Okike, 2007).

2 It needs to be noted that their research only used one year (2007) of observations which limits the probative

value.

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Hypothesis 2

Although the literature is ambiguous on the point whether gender diversity has an effect on firm

performance, this study draws the hypothesis based on the literature by (i.a.) Adams and Ferreira (2009)

and Cartel et al. (2003) supported by the foundations of the presented theory. It is assumed that higher

percentage of female representation on the board has a positive effect on a firm’s financial performance

due to better monitoring. The following hypothesis was formulated:

𝐻2: 𝐵𝑜𝑎𝑟𝑑 𝑔𝑒𝑛𝑑𝑒𝑟 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 ℎ𝑎𝑠 𝑎 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑛 𝑎 𝑓𝑖𝑟𝑚′𝑠 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒

2.5 Nationality diversity

Estélyi and Nisar (2016) suggest that director nationality could be an important factor in how the interest

of various stakeholders are determined in the corporate world. They argue that foreign board members

can influence the quality of a board’s decision-making. Increasing globalisation and operations

worldwide require firms having knowledge of international markets as well as experience with different

regulatory regimes and it is argued to be crucial knowledge to have within a firm (Rose, et al. 2013).

The increased exposure of internationalisation and globalisation by foreign nationals on the board

reduces the potential for managerial entrancement and subsequently has the potential to increase firm

performance. However, a recent study by Masulis, Wang and Xie (2012) found that board members

from foreign countries are weak monitors and cause weaker corporate governance and higher agency

costs. As previously indicated, poorer monitoring could lead to worse firm performance (Adams and

Ferreira, 2009; Carter et al., 2003). Nationality diversity among board members can increase chances

of cross-cultural communication problems (Lehman and Dufrene, 2009) and interpersonal conflicts

(Martin, 2014). Other studies have used the agency theory to underpin their finding that nationality

diversity causes competitive advantages in terms of increased international networks and commitments

to shareholders rights (Oxelheim, Gregorič, Randøy, and Thomsen, 2013; Oxelheim and Randøy,

2003).

Empirical evidence

As with gender diversity, studies on nationality have produced mixed results when it comes to board

diversity and firm performance. Estélyi and Nisar (2016) explain that national differences can occur

due to various reasons such as socio-economic conditions, culture and personality. Foreign nationals

could be better able to understand the needs of stakeholders and the market in which the firm does

business. Foreign nationals cause increased activity within the board based on increased levels of board

meeting attendance, committee assignments and corporate governance. Results show a positive and

significant relationship between nationality diversity and firm performance. Miletkov, Moskalev, and

Wintoki (2015) conducted a cross-country study and examine the hypothesis if the impact of foreign

12

directors is different in countries outside the U.S. The results show that firm performance is positively

influenced by foreign directors following cross-border acquisitions and with directors coming from a

country with a strong legal protection of shareholders rights. Their findings are in line with that of

Adams and Ferreira (2009) and Carter et al. (2003).

In their study, Masulis, et al. (2012) researched the costs and benefits of foreign independent directors.

Their results a show significantly poorer performance of foreign directors on firm performance. These

findings are confirmed by Frijns, Dodd and Cimerova (2016) arguing that foreign independent directors

are detrimental to firm performance. Frijns et al. (2016) argue this is primarily due to cultural differences

whereas Masulis et al. (2012) argue that foreign directors are less effective in monitoring activities and

therefore causing higher agency costs. When high-quality legal institutions are present in countries, it

is expected that foreign directors cause a poorer firm performance (Miletkov, Moskalev, and Wintoki,

2015).

Rose (2007) finds no significant results on the proportion of foreign nationals on the board and market-

based performance. A Nordic study by Randøy, Thomsen, and Oxelheim (2006) analysing the 500

largest companies from Denmark, Norway and Sweden found no significant effect of gender, age and

nationality on stock market performance and return on assets (ROA). They conclude that if increased

diversity is attractive for a firm (e.g. political preference) it can be achieved without an impact to the

market value. Put differently, the market does not view an increase board nationality diversity as a

method to increase (neither decrease) e.g. monitoring capabilities (agency perspective), or expand

linkages (resource dependency) or non-nationals as being more qualified (human capital).

Hypothesis 3

Based on Estélyi and Nisar (2016) and Miletkov et al. (2015), the hypothesis is drawn that nationality

diversity caused by the presence of foreign nationals on the board has a positive effect on a firm’s

financial performance.

𝐻3: 𝐵𝑜𝑎𝑟𝑑 𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑡𝑦 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 ℎ𝑎𝑠 𝑎 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑒𝑓𝑓𝑒𝑐𝑡 𝑜𝑛 𝑎 𝑓𝑖𝑟𝑚′𝑠 𝑓𝑖𝑛𝑎𝑛𝑐𝑖𝑎𝑙 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒

Summarizing, several theories have been introduced and reviewed. Next, previous research with regard

to age-, gender- and nationality diversity have been discussed and their relation to the theories is

demonstrated. It is shown that there are reasons to believe that board diversity and the underlying

determinants: age, gender, and nationality have an impact on firm performance. In order to test these

assertions, three hypotheses have been drawn.

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3. Research Method

This section describes the research method that is used in this study. First, the conceptual model is

shown and discussed briefly. In the second part, the variables used in this study are discussed and linked

to theory. Third, the collection and used data are presented. Fourth, robustness checks are introduced

and potential problems with endogeneity are addressed and explained. The last section also presents the

used regression equations.

3.1 Conceptual model

The concept focuses on board diversity constituted by age, gender and nationality. The goal is to test

whether diversity of these factors impact the financial performance of a firm. This study uses the board

directors as units of observation. Since there is no widely accepted definition of board diversity, and as

meaningful composite measures are difficult to construct, this research tests age, nationality and

diversity as proxy measures of board diversity one at a time, using regression analysis. The conceptual

model used in this research is schematically shown in appendix C. The basic model that is tested:

𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 𝛼 + 𝛽1𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 + 𝛽2𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒 + 𝛽3𝑏𝑜𝑎𝑟𝑑 𝑠𝑖𝑧𝑒 + 𝛽5𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒 + 𝜀

Performance is the financial performance of a firm, measured by either Tobin’s Q, ROA and ROE.

diversity is a measure of either age, gender or nationality diversity of the board. Firm size is the natural

log of the total assets of a firm, valued at the end of the year. Board size takes into account the size of

the board. Leverage is added to take into account financial risk. 𝜀 is the error term. The choice of

variables and their link with the theory is explained in more details in the next section. The model is

used in several statistical estimations further explained in section 3.4.

3.2 Variables

Dependent variable

The dependent variable is firm performance. When analysing the performance of a firm, one of the

issues is choosing a financial performance measure (or indicator). Performance measures can be

categorized into two categories. On the one hand, there are accounting measures with traditional

measures such as return on assets (ROA), return on equity (ROE), return on investment (ROI) and

turnover. Alternatively, performance can be measured by looking at cash flow returns on investments

and the economic added value. On the other hand, one can measure the impact of a variable on

performance with the help of market data. An example of market-based financial performance measures

is Tobin’s Q. Many reference studies use Tobin’s Q as a proxy for a firm’s financial performance (see,

14

e.g., Bohren and Strom, 2010; Adams and Ferreira, 2009; Campbell and Minguez-Vera, 2007).

However, alternatives like the market-to-book ratio and market-adjusted stock market returns can also

be used.

Tobin’s Q is a forward-looking measure that captures the value of a firm as a whole rather than a sum

of its parts. Put differently, it is forward-looking because it includes the expected future cash flow of a

firm (i.e. the combined market value of firm’s debt and equity). Tobin’s Q is a ratio of the firm’s market

value to its book value. Any value greater than ‘1’ suggests that the firm has intangible assets associated

with future growth opportunities. According to Montgomery and Wernerfelt (1988), Tobin’s Q is

widely used as a proxy for a firm’s ability to generate shareholder value and is a good proxy for a firm´s

competitive advantage. A limitation of Tobin’s Q is the limited availability of the market value of debt.

Since market data is (in general) not available for private non-listed firms, only listed firms (of which

market data is available) can be used.

Besides Tobin’s Q, many studies us an accounting measure as a proxy for performance. The accounting

rate of returns such as ‘ROA’ and ‘ROE’ are backward-looking. According to Hagel et al. (2013), ROA

is not a perfect measure but it is one of the most effective and broadly available financial measures to

assess the financial performance of a firm. It captures the performance of a firm in a comprehensive

way, looking at both the income statement performance as well as the assets within a business. Other

metrics used to measure performance are more vulnerable to financial engineering (e.g. ROE, return to

shareholders and the use of debt leverage). Moreover, ROA is less vulnerable to short-term gaming

(tampering) because assets such as property, plant, and equipment, and intangibles involve long-term

decisions that are hard to tamper with in the short run (Hagel, Brown, Samoylova, and Lui, 2013). ROA

can be calculated by dividing the operating income before depreciation by total assets. Both Tobin’s Q

and ROA are relatively objective, and most predominantly used indicators of financial performance

(Marinova et al., 2016; Sanan, 2016; Pletzer, Nikolova, Kedzior and Voelpel, 2015). In similar earlier

diversity studies researchers have used these measures (see, e.g., Marinova et al., 2016; Carter et al.,

2010; Adams and Ferreira, 2009). In this research, both measures are used as a proxy for firm

performance. An advantage going with both measures is that they complement each other well. That is

because, the study is able to track historical financial performance by using ROA, while Tobin’s Q is

the market expectation predictor for future financial performance. An overview of the variables is given

in appendix A.

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Independent variables

In order to achieve a reliable and measurable definition of board diversity, three independent variables

are used. Because two-tier boards are common in the Netherlands all regressions are run on the

executive board. Non-executives generally have a lower impact on the organizational strategy,

consequently reducing the effect of board diversity.

The first independent variable is the board age diversity. As mentioned previously, the units of

observation are directors so in order to properly analyse the complete board each director’s age is used

to calculate the mean age of a board given a specific year (see similar methodology, e.g. McIntyre,

Murphy, and Mitchell, 2007). For example, if the board of a given company and year (i.e. 2015) has

three directors with the ages 50, 60 and 70, the average age of the board is 60. The database contains

information about the average board of directors age.

Second, board gender diversity is used as an independent variable. Board gender diversity is calculated

as proportionate female directors on the board (Sanan, 2016; Dutta and Bose, 2006).

The third independent variable is board nationality diversity. This study uses the Blau heterogeneity

indicator (Blau, 1977), designed to measure the level of diversity between individuals (Van Ees,

Hooghiemstra, Van der Laan, & Veltrop, 2007). The measure is commonly employed in diversity

studies (see, e.g., Triana, Miller and Trzebiatowski, 2013; Campbell & Minguez-Vera, 2008). The Blau

indicator measures the proportion of a board that belongs to a specific category (such as nationality).

The diversity can be calculated as follows (Engelen et al., 2012):

𝐵 = 1 − ∑ (𝑛𝑘

𝑛) ²

𝐾

𝑘=1

In which a board with n members, out whom 𝑛𝑘 are from one specific category k. The outcome B can

vary between 0 and 1, in which 0 is not diversified at all, and 1 is fully diversified.

Control variables

Consistent with previous empirical research (Ferrero-Ferrero et al., 2015; Miller and Triana, 2009;

Cheng, 2008), several firm-specific variables that could potentially affect firm performance are taken

into account as control variable.

First of all, in previous literature argues that larger firms are more vulnerable to agency problems (Lehn,

Patro, and Zhao, 2009). Firms that have grown substantially over the years often have a wider scope

and more complex form of operations requiring more monitoring (increased agency costs). As firms

grow, they will develop an internal labour market that facilitates in matching human capital to specific

tasks and positions. Larger organisations have developed a more intricate and dense division of labour

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and a growing number of hierarchical positions (De Meulenaere, 2016; Pfeffer & Cohen, 1984). In

addition, larger firms are also more likely to have international activities and corresponding

complexities that ask for diversity (Oxelheim, 2013). Furthermore, firm size is related to both market

returns (Fama and French, 1992) and when firm size is measured by asset size it is also related to

Tobin’s Q (Faleye, 2007). Taking the above into account, firm size is added as a control variable. Firm

size is measured by the value of the natural log of total assets in the regression measured at year-end.

Second, the size of the board is also included in the analysis. Yermack (1996), using the agency theory

as the theoretical basis, finds that Tobin’s Q and board size are inversely related. Similar papers such

as Ahern & Dittmar (2012) and Nygaard (2011) find that board size negatively influences returns of a

firm. Cheng (2008) shows that firms with larger boards have less variability in firm performance.

Smaller board tend to take more risks (Pathan, 2009; Cheng, 2008). Some scholars argued that smaller

boards of directors, have fewer problems with communication and decision-making, reducing agency

cost. Guest (2009) finds that larger boards have a strong negative impact on a firm’s profitability

because their effectiveness is lower compared to smaller boards. Contradicting results have been found

by Jackling and Johl (2009), arguing that larger boards bring better information and have more

knowledge from more directors. This positive association stem from the resource dependency theory

arguing that an increase in directors provides for a larger information pool subsequently leading to better

firm performance (Carter et al., 2010). Board size is measured by the number of directors on the board.

Earlier studies have used this as a control variable due to its impact on firm performance (Sanan, 2016;

Campbell & Minguez-Vera, 2008; Cheng, 2008).

Third, this research takes firm-, industry- and time fixed effects into account. This prevents the results

to capture differential time-, firm- or industry trends. Behavioural and sociological research deem

organizational factors and their fit with the environment as a major factor of success (Hansen, &

Wernerfelt, 1989). In addition, it is known that companies operating in the natural resources and mining;

manufacturing; and financial activities sectors are underrepresented (Adams and Kirchmaier, 2016).

Furthermore Randøy et al. (2006) notes that board diversity is influenced by industry effects. Hence, an

industry dummy in combination with a year dummy is added in the OLS regressions to rule out these

and similar industry and time-bound effects (Ferrero-Ferrero et al., 2015). In similar fashion, the use of

panel data makes it possible to use firm- and year fixed effects in the fixed effects model regressions

(see, e.g., Van Ees, Postma and Sterken, 2003).

Fourth, previous studies (Sanan, 2016; Ferrero-Ferrero et al., 2015; Campbell & Minguez-Vera, 2008)

used a leverage ratio to include the financial risk of a firm. Leverage could also be related to the

performance of a firm because of reduced agency cost, an increased tax shield, or increased cost of

17

financial distress (Loderer and Waelchli, 2010). This study uses the ratio of debt to total assets to

measure leverage. High leverage firms are associated with larger boards (Wang, Tsai & Lin, 2013).

3.3 Data collection

A six-year sample, from 2010 to 2015, is selected based on all listed companies on Dutch stock

exchanges (AEX, AMX, and AScX). This is not the first study to make use of panel data, so it is in line

with similar studies (Carter et al., 2010; Rose, 2007). The use of listed firms is necessary because

Tobin’s Q is calculated by means of the market capitalisation which is only available for listed firms

(and is frequently updated because of the listing on one of the Dutch exchanges). In addition, listed

firms are obliged to publish extensive annual statements that contain more detailed information than

unlisted firms. Because panel data is used, it is possible to measure the potential effects of diversity

over several years (also strategic decision-making often takes some time to take effect).

Two sources are used to retrieve the relevant data. First of all, all board and director characteristics are

gathered using the BoardEx database. For each company, data is gathered about the director’s

characteristics: age, gender and nationality. Moreover, data is retrieved about the size of the executive

board. In the case of missing data, the cells are left blank. BoardEx was unable to retrieve the nationality

of 166 entries which have been manually completed by consulting public resources (i.e. annual report,

Bloomberg and management scope, see appendix B).

The financial data is gathered using Bureau van Dijk’s Orbis database. This database contains

information about a firm’s financial information need for measuring firm performance. From this

sample, all firms are eliminated that are not covered by either the BoardEx database or Orbis database.

After deleting the firms which have missing values, there remains a final sample covering 79 unique

companies and consisting of 3,905 observations. All companies in the sample have a two-tier board

system (which is prevalent in the Netherlands) and the executive board is, therefore, most likely to

affect firm performance and is used as the main unit of analysis.

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3.4 Analysis and regressions

All equations used in this study are based on the conceptual model but are adjusted to test for either the

independent variable 'age', 'gender' or nationality', corresponding with the three hypotheses. Both OLS

regression models and fixed effects models are applied. This section will first the used robustness

checks and potential endogeneity. Followed by a description of the OLS models (1-3), fixed effects

models (4-6) and fixed effects models with lags (7-9).

Robustness check

As a robustness check, all models are estimated by using ROE as an alternative proxy for firm

performance. Previous studies (Lu & White, 2014; Joecks, Pull, and Vetter, 2012) used return on equity

as an alternative measure of historic performance and can be used alongside with ROA. The use of a

third proxy for firm performance decreases the likelihood that results are based on coincidence due to

the choice of a specific proxy (Joecks et al., 2012). The return on equity is calculated by dividing

net income before taxes by shareholder’s equity.

Endogeneity

Prior research (see, e.g., Marinova et al., 2016; Carter et al., 2010; Jackling & Johl, 2009) suggest that

well-performing firms attract more diverse board members. This could suggest that board

characteristics and firm performance are jointly endogenous (Hermalin & Weisbach, 2003; Field &

Keys, 2003). Adams and Ferreira (2009) suggest that problem with endogeneity occur because of

variables that are omitted and affect both firm performance as well as the selection of diverse directors.

Put differently, firm performance can impact a firm’s consideration to change its board composition

(Bhagat and Black, 2001). The endogeneity problem makes it hard to study the effects of board diversity

(Ahern and Dittmar, 2012). To cope with the problem, Adams and Ferreira (2009) used firm fixed

effects and prove that the used has a significant impact on the results of their analysis. The use of fixed

effects estimations is important because they are able to mitigate omitted variables and help to address

unobserved changes of time (Carter et al., 2010). In addition, Liu et al. (2014) and Carter et al. (2010)

suggest the use of lagged variables to minimize potential problems with reverse causality. Jackling and

Johl (2009) explain: “Overall, prior results suggest that boards respond to poor performance by raising

their level of board activity, which in turn is associated with improved operating performance in the

following years, thus suggesting a lag effect.” One-year lags are used since theory does not predict what

length of time is needed for the effects to occur (Cartel et al., 2010; Farrell & Hersch, 2005).

Ordinary Least Squares models

To test for each of the hypothesized relationships, the independent variables board age diversity

(𝛽1𝑎𝑔𝑒), board gender diversity (𝛽1𝑔𝑒𝑛𝑑𝑒𝑟) , and board nationality diversity (𝛽1𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑡𝑦 are

19

regressed against both Tobin’s Q, ROA and ROE as proxies of firm performance. The moderating effect

of firm size, leverage and board size are also taken into account. The following regression functions is

used to answer hypothesis 1 to 3, and can be displayed as follows:

(1) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑗𝑡 = 𝛼𝑗𝑡 + 𝛽1𝑎𝑔𝑒𝑗𝑡 + 𝛽2𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑗𝑡 + 𝛽3𝑠𝑖𝑧𝑒𝑗𝑡 +𝛽4𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑗𝑡 + 𝛾𝑗 + 𝜆𝑡 +

𝜀𝑗𝑡

(2) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑗𝑡 = 𝛼𝑗𝑡 + 𝛽1𝑔𝑒𝑛𝑑𝑒𝑟𝑗𝑡 + 𝛽2𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑗𝑡 + 𝛽3𝑠𝑖𝑧𝑒𝑗𝑡 +𝛽4𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑗𝑡 + 𝛾𝑗 +

𝜆𝑡 + 𝜀𝑗𝑡

(3) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑗𝑡 = 𝛼𝑗𝑡 + 𝛽1𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑡𝑦𝑗𝑡 + 𝛽2𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑗𝑡 + 𝛽3𝑠𝑖𝑧𝑒𝑗𝑡 +𝛽4𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑗𝑡 +

𝛾𝑗 + 𝜆𝑡 + 𝜀𝑗𝑡

Subscript j in the above equation reflects the specific industry being analysed. This study applies

industry fixed effects, 𝛾𝑗, and time fixed effects, 𝜆𝑡, to control for differences in unobservable variables

across industries and time (Adams and Ferreira, 2009). 𝜀𝑗𝑡 represents the corresponding error terms.

Fixed effects models

Because board age-, gender and nationality diversity are continuous, independent variables it is possible

to apply a fixed effects estimator. Three fixed effects equations are used to test for a possible relationship

between the independent variables and the dependent variables. The equations tested can be displayed

as follows.

(4) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = 𝛼𝑖𝑡 + 𝛽1𝑎𝑔𝑒𝑖𝑡 + 𝛽2𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽3𝑠𝑖𝑧𝑒𝑖𝑡 +𝛽4𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡 + 𝜇𝑖 + 𝜆𝑡 +

𝜀𝑖𝑡

(5) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = 𝛼𝑖𝑡 + 𝛽1𝑔𝑒𝑛𝑑𝑒𝑟𝑖𝑡 + 𝛽2𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽3𝑠𝑖𝑧𝑒𝑖𝑡 +𝛽4𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡 + 𝜇𝑖 +

𝜆𝑡 + 𝜀𝑖𝑡

(6) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = 𝛼𝑖𝑡 + 𝛽1𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑡𝑦𝑖𝑡 + 𝛽2𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑖𝑡 + 𝛽3𝑠𝑖𝑧𝑒𝑖𝑡 +𝛽4𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡 +

𝜇𝑖 + 𝜆𝑡 + 𝜀𝑖𝑡

Again the independent variables age (𝛽1𝑎𝑔𝑒), gender (𝛽1𝑔𝑒𝑛𝑑𝑒𝑟) and nationality (𝛽1𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑡𝑦) are

regressed against the three proxies for performance Tobin's Q, ROA and ROE. However, since fixed

effects are now taken into account 𝜇𝑖 depicts firm fixed effects. 𝜆𝑡 is used to control for the time fixed

effects. 𝜀𝑖𝑡 represents the corresponding error terms. Results of the above equations are given in section

5, table 4 of this study.

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Fixed effects models including lags

To partially cope with reverse causality three other fixed effects models are tested with the inclusion of

independent variables that are lagged by one-year (𝑡 − 1).

(7) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = 𝛼𝑖𝑡 + 𝛽1𝑎𝑔𝑒𝑖𝑡−1 + 𝛽2𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑖𝑡−1 + 𝛽3𝑠𝑖𝑧𝑒𝑖𝑡−1 +𝛽4𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1 +

𝜇𝑖 + 𝜆𝑡 + 𝜀𝑖𝑡

(8) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = 𝛼𝑖𝑡 + 𝛽1𝑔𝑒𝑛𝑑𝑒𝑟𝑖𝑡−1 + 𝛽2𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑖𝑡−1 + 𝛽3𝑠𝑖𝑧𝑒𝑖𝑡−1 +

𝛽4𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1 + 𝜇𝑖 + 𝜆𝑡 + 𝜀𝑖𝑡

(9) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = 𝛼𝑖𝑡 + 𝛽1𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑡𝑦𝑖𝑡−1 + 𝛽2𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑖𝑡−1 + 𝛽3𝑠𝑖𝑧𝑒𝑖𝑡−1

+𝛽4𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1 + 𝜇𝑖 + 𝜆𝑡 + 𝜀𝑖𝑡

Results of the above-mentioned regressions are reported in table 6. Fixed effects are taken into account,

𝜇𝑖 depicts firm fixed effects. 𝜆𝑡 is used to control for time fixed effects. 𝜀𝑖𝑡 represents the corresponding

error terms.

Multiple regression: F-test

Since the variables of interest are not correlated with each other it is possible to regress them in one

regression. Despite looking at the individual significant of factors, an F-test is performed to test whether

the group of independent variables (age, gender and nationality) has an effect on the dependent

variables and if they are jointly significant. The unrestricted model is based on the previous OLS

equation regressions but now included all independent variables. This equation model is described as

follows:

(10) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑗𝑡 = 𝛼𝑗𝑡 + 𝛽1𝑎𝑔𝑒𝑗𝑡 + 𝛽2𝑔𝑒𝑛𝑑𝑒𝑟𝑗𝑡 + 𝛽3𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑡𝑦𝑗𝑡 + 𝛽4𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑗𝑡 +

𝛽4𝑠𝑖𝑧𝑒𝑗𝑡 +𝛽5𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑗𝑡 + 𝛾𝑗 + 𝜆𝑡 + 𝜀𝑗𝑡

To help to mitigate omitted variables and address observed changes over time the fixed effects model

is applied to the multiple regression too. The fixed effects model is drafted as follows:

(11) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = 𝛼𝑖𝑡 + 𝛽1𝑎𝑔𝑒𝑖𝑡 + 𝛽2𝑔𝑒𝑛𝑑𝑒𝑟𝑖𝑡 + 𝛽3𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑡𝑦𝑖𝑡 + 𝛽4𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑖𝑡 +

𝛽5𝑠𝑖𝑧𝑒𝑖𝑡 +𝛽6𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡 + 𝜇𝑖 + 𝜆𝑡 + 𝜀𝑖𝑡

To partially cope with reverse causality all independent variables are lagged by one-ear (t-1), leading

to the following regression equation:

(12) 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑖𝑡 = 𝛼𝑖𝑡 + 𝛽1𝑎𝑔𝑒𝑖𝑡−1 + 𝛽2𝑔𝑒𝑛𝑑𝑒𝑟𝑖𝑡−1 + 𝛽3𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑡𝑦𝑖𝑡−1 +

𝛽4𝑓𝑖𝑟𝑚 𝑠𝑖𝑧𝑒𝑖𝑡−1 + 𝛽5𝑠𝑖𝑧𝑒𝑖𝑡−1 +𝛽6𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑖𝑡−1 + 𝜇𝑖 + 𝜆𝑡 + 𝜀𝑖𝑡

The corresponding hypothesis for the F-test is if 𝐻0: 𝛽1 = 𝛽2 = 𝛽3 = 0. In case the explanatory

variables are jointly significant the null-hypothesis stating 𝐻1: 𝑎𝑛𝑦 𝑜𝑓 𝛽1, 𝛽2 𝑜𝑟 𝛽3 ≠ 0 is rejected.

21

4. Empirical results

Subsections 4.1 and 4.2 present the descriptive - and correlation results. Subsection 4.3 presents the

results of the ordinary least squares regression between board age diversity, board gender diversity and

board nationality diversity on firm performance. The results of the fixed effects models are presented

in subsection 4.4 and 4.5. Lastly, a joint significance test is performed and presented in table 4.6.

4.1 Descriptive statistics

Table 2 provides the descriptive statistics of the companies used in this study. The sample used to test

the hypotheses is reviewed. The mean of Tobin’s Q (0.839) suggests that the market value of the listed

companies during the sampling period is lower than the book value. However, the variation between

the minimum value (0.010) and the maximum value (9.160) is significant. Both ROA and ROE reveal

a positive mean and show that the firms in the sample have been financially successful on average,

according to the accounting measures (mean of 3.400 and 4.213 respectively). The average age of an

executive director was 52 while the oldest director was 81 and the youngest 38.

The average number of directors on an executive board amounts 3, with a minimum of 1 and a maximum

of 7 directors. These findings are similar to e.g. Adams et al. (2010) and within the threshold of no more

than eight board members as recommended by Jensen (1993). The executive board shows a mean of

0.221 with regard to nationality and is lower than previous research that found values of 0.31 (Kilic,

2015) and 0.35 (Nielsen and Nielsen, 2012). The mean of board gender diversity (0.044) and

corresponds with earlier findings of Lückerath-Rovers (2013), although a slightly lower percentage of

4.02% was found. This minor difference could be explained because Lückerath-Rovers (2013) uses a

time period 2005-2007 and more women have been appointed to the board since that time. A reason for

this increase is the introduction of the voluntary quota by the Dutch government. Previous research has

Variables Firm years Mean St.Dev. Min. Max.

Tobin’s Q 327 0.839 0.978 0.010 9.160

Return on assets (ROA) (%) 390 3.400 8.859 -38.93 43.97

Return on equity (ROE) (%) 382 4.213 58.04 -936.4 434

Board age diversity 380 52.25 5.420 38 81

Board gender diversity 429 0.044 0.117 0 0.500

Board nationality diversity 322 0.221 0.256 0 0.800

Natual log of firm total assets 393 15.00 2.218 7.460 21.23

Executive board size 385 2.842 1.245 1 7

Leverage ratio 338 0.518 0.162 0.099 1.061

Table 2 illustrated the descritpive statistics for all variables. The sample data is collected via the Orbis- and

Descriptive statistics

BoardEx database. Variable definitions can be found in appendix A, table 1.

22

shown an increase in the percentage of women on the board as result of such a quota (Ahern and Dittmar,

2012).

In terms of total assets, the mean amounts 15.00 which is similar to 45.1 million euro. On average the

leverage ratio amounts 0.518 with a minimum of 0.099 and a maximum of 1.061. The former value

indicates almost no leverage is used, while the latter indicates there is more debt used than equity.

4.2 Correlation

The variables have to fulfil the classic assumptions in order for ordinary least squares estimators to be

the best available (Brooks, 2008). To cope with non-normalities, total assets has been transformed by

their natural logarithm. This greatly improved their normality in terms of skewness and kurtosis. Since

the total database contains sufficient observations any remaining non-normality will not influence the

validity of the research. Moreover, the sample size allows for the use of robust standard errors that allow

for the presence of heteroscedasticity (Brooks, 2008). To test the assumptions of linearity between the

dependent and independent variables, a scatterplot of the residuals are made and these show a linear

relationship. The assumption of statistical independence is dealt with in the next section of this

paragraph. All variables are tested for multicollinearity and the results are shown in table 3 on the next

page. In the case variables fully correlate with another the value is either -1 or 1 (Brooks, 2008). The

closer the calculated value is to zero, the fewer variables correlate with another.

Previous research found that larger firms tend to be more diversified in terms of gender (Adams and

Ferreira, 2003). The descriptive statistics do not confirm this finding since no correlation was found

between firm size and board gender diversity (-0.00). Firm size does correspond in terms of a higher

nationality diversity within the board (0.32). This result corresponds with earlier empirical research of

Oxelheim (2013) that found that larger firms on average employ more foreigners on their board.

The negative relationship between leverage and the performance indicators (Tobin’s Q, ROE and ROA)

can be attributed to the capital structure theory3 (based on the agency costs of managerial discretion)

arguing that firms with high leverage cannot take advantage of growth opportunities and is consistent

with earlier empirical research (see, e.g., Yazdanfar and Öhman, 2015; Lang, Ofek and Stulz, 1996).

Fama and French (2002), argue that excessive debt causes higher agency costs which corresponds to a

negative relationship between debt and firm profitability.

Other independent variables show only low (<0.29) or no signs of correlation. According to Liu et al.

(2014), a correlation in variables only results in a multicollinearity problem when two variables

correlate at 0.7 (absolute value). A variance inflation test (VIF) is performed on the variables to test for

3 Capital structure theories such as: Modigliani and Miller theory (1958, 1963), Pecking order

theory/asymmetric information, and trade-off theory offer additional clarification and explanations.

23

correlation. The independent variables all score less than 1.83, yielding almost no multicollinearity

between the predictors. As a general rule of thumb, any value lower than 10 is regarded as a sign of no

severe or serious multicollinearity (O’brien, 2007). This result confirms the findings shown in table 3.

Variables (1) (2) (3) (4) (5) (6) (7) (8) (9)

(1) Tobin’s Q 1.00

(2) Return on assets (ROA) (%) 0.57 1.00

(3) Return on equity (ROE) (%) 0.15 0.47 1.00

(4) Board age diversity -0.04 -0.02 -0.02 1.00

(5) Board gender diversity -0.10 -0.09 0.01 -0.20 1.00

(6) Board nationality diversity 0.08 0.09 0.00 0.23 0.10 1.00

(7) Natual Log of firm total assets -0.27 -0.03 0.07 0.07 -0.00 0.32 1.00

(8) Leverage ratio -0.41 -0.17 -0.21 -0.12 0.15 0.07 0.19 1.00

(9) Executive board size -0.08 -0.02 0.06 -0.15 0.15 0.32 0.26 0.08 1.00

Table 3

Correlation Matrix

Table 3 illustrates the correlation matrix of all variables used. The sample data is collected via the Orbis-

and BoardEx database. Variable definitions can be found in appendix A, table 1.

24

4.3 Ordinary least squares

The first step in identifying the relationship between board diversity and firm performance is the use of

an OLS, and a fixed effects model (presented in section 4.3). This relationship is likely to be subject to

endogeneity, and thus both would produce biased estimates in the presence of omitted variables and

possible reverse causality. Therefore, a second fixed effects model (section 4.5) is applied to partially

take reverse causality into account. If the relationship between board diversity and firm performance is

determined by factors that are included in the regression, the OLS estimator would produce a significant

result once these factors are included. In contrast, if unobservable or omitted variables are determining

the relationship, these factors should be captured by the fixed effects estimator (Sila, et al. 2016).

Table 5 reports the standard ordinary least squares regression results that correspond with equation (1),

(2), and (3) presented in section 3.4. The equation is run three times to show the effects of the

independent variable on the three proxies of firm performance (Tobin’s Q, ROE and ROA). The

coefficient of primary interest is 𝛽1𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦 (measured by age) and 𝐻0: 𝛽1 = 0 and 𝐻𝑎: 𝛽1 ≠ 0. The

equation tests for the hypothesis that board age diversity positively impacts firm performance.

Using Tobin’s Q, as a proxy for performance reveals that the coefficient for the average age of the

executive board is only marginally different from zero. The explanatory variable is found not

significant, which means there is no evidence of a significant relation between firm performance and

board age diversity. The accounting measures ROA and ROE do have a positive coefficient different

from zero but no significant results are found. Results are similar to Ferrero-Ferrero (2015) who finds

that age diversity positively impact performance. The findings are not in line with Waelchli and Zeller

(2013) who find a negative relation between board age and firm performance. The contrasting results

could be because Waelchli and Zeller (2013) use a sample of unlisted firms whereas this study only

focuses on listed firms.

Second, equation (2) tests the hypothesis that board gender diversity has a positive effect on firm

performance. All coefficients of the performance indicators are negative, implicating that a board

gender diversity yields lower firm performance. The regression using Tobin’s Q shows significance at

a five (0.05) percent significant level. However, nor the ROA or ROE used in equation (2) are

significant. Since no significance can be found using the accounting measures it could be that more data

is needed or additional explanatory variables are needed in order to achieve significant results. The

difference in significance could also be caused by sampling differences or potential sampling errors.

25

Since there is only partial evidence of executive board gender diversity having a negative effect on firm

performance, the second null hypothesis cannot be rejected. Results are not in line with previous

research (see, e.g., Lückerath-Rovers, 2013; Carter et al., 2010; Campbell and Mínguez-Vera, 2008;

Erhard et al, 2003; Carter et al., 2003) who all report a positive relationship between gender diversified

boards and firm performance. Adams and Ferreira (2009) suggest that higher gender diversity can lead

to over-monitoring and increased agency costs but this contradicts the significant result when using

Tobin’s Q. Low, Roberts, and Whiting (2016) provide evidence that female director appointments and

mandating gender quotas can reduce firm performance with strong cultural resistance. This could

indicate economic theories are less relevant but cultural theories are more applicable to explain this

result.

Third, the estimations using regression (3) are testing hypothesis 3, whether there is a positive

interaction between board nationality diversity and firm performance. Results show that the coefficient

of board nationality diversity on Tobin’s Q is 0.050 different from zero and not significant. In addition,

ROA and ROE show a negative coefficient implicating exactly the opposite of hypothesis 3. Former

evidence reports a positive relationship between board nationality diversity and firm performance

(Carter et al., 2003; Oxelheim and Randøy, 2003). However, a more recent study of Carter et al. (2010)

reports no significant effect.

In all of the equations, the coefficient of firm size measured by the natural logarithm of a firm's assets

is positive. Only equation (1) and when using Tobin's Q as dependent variable, it shows a very small

negative relationship. These results show that larger firms have a higher performance, in line with

previous research (see, e.g., Pervan, and Višić, 2012; Lee, 2009). With the exception to equation (3)

when using ROE, leverage shows similar results to the correlation results presented in table 3. A

possible explanation for the positive coefficient when using ROE in equation (3) is given in section 4.5.

The coefficient of board size is positive in all OLS regressions, which is not in line with previous

research (Ahern and Dittmar, 2012; Nygaard 2011; Hermalin and Weisbach, 2003) who find a negative

coefficient for board size. Coles, Naveen and Naveen (2008) offer an explanation for the positive

findings of this study. According to their study, larger and complex firms (which is generally the case

with listed firms) benefit from larger boards because it increases the human capital (in terms of

experience and expertise). Moreover, the resource dependency theory predicts larger boards have a

larger network and thus increased linkages.

26

(1) (2) (3) (4) (5) (6) (7) (8) (9)

VARIABLES AGEB TQ AGEB ROE AGEB ROA GBX TQ GBX ROE GBX ROA NBX TQ NBX ROE NBX ROA

Board age diversity -0.004 0.714 0.181

[0.011] [0.490] [0.123]

Board gender diversity -0.552** -1.154 -5.665

[0.235] [23.945] [4.326]

Board nationality diversity 0.050 -15.286 -1.814

[0.176] [14.031] [2.733]

Log total assets -0.068*** 2.858* 0.183 -0.068*** 2.914* 0.195 -0.072*** 1.525* 0.149

[0.025] [1.658] [0.264] [0.024] [1.664] [0.261] [0.025] [0.866] [0.307]

Leverage -1.207*** -71.196 -5.864 -1.154*** -73.721 -5.830 -1.155*** 9.474 -2.524

[0.207] [51.738] [4.598] [0.218] [52.963] [4.613] [0.256] [21.657] [5.300]

Board size 0.085*** 4.921 0.349 0.090*** 4.607 0.344 0.081** 2.938 0.418

[0.031] [3.418] [0.342] [0.031] [3.134] [0.347] [0.034] [3.060] [0.471]

Constant 1.945*** -35.904 -3.340 1.749*** -3.153 4.702 1.822*** -21.524 3.305

[0.612] [32.731] [7.862] [0.336] [16.341] [4.185] [0.354] [22.495] [4.737]

Observations 271 301 307 275 305 311 246 251 257

R-squared 0.402 0.123 0.162 0.410 0.121 0.161 0.385 0.104 0.105

Year dummies YES YES YES YES YES YES YES YES YES

Industry dummies YES YES YES YES YES YES YES YES YES

Robust standard errors in brackets

*** p<0.01, ** p<0.05, * p<0.1

Table 4 shows the ordinary least squares regression with Tobin's Q, return on equity (ROE) and return on assets (ROA) as dependent

variables. Age, gender and nationality board diversity are the independent variables. Total assets, leverage of the firm and executive

board size are used as control variables. Variable definitions can be found in appendix A, table 1.

Ordinary Least Squares: Relation between board diversity and firm performance

Table 4

27

4.4 Fixed effects

Table 5 reports the fixed effects models based on equations (4), (5) and (6). Equation (4) only report

significant variables for firm size and leverage. This suggests that larger firms, and firms having more

leverage, have a lower Tobin’s Q.

As shown in column (5), the negative relationship between board gender diversity and Tobin’s Q is no

longer significant and consistent with the result obtained by Carter et al. (2010). This indicates that the

significant negative relationship uncovered by the statics OLS equation (5) may be driven by omitted

variable biases. Note that one of the accounting measures’ coefficient is negative while the market

measure reports a positive coefficient this could be attributed to earlier research by Miller and Traina

(2009) arguing that the market views more gender diversity as a positive signal.

Equation (6) is used to test hypothesis 3. Since the coefficients are positive, this might indicate that

boards that are more diversified in terms of nationality show a higher performance. However, none of

the equations shows significance in the explanatory variable board nationality diversity. Therefore the

null hypothesis cannot be rejected. Moreover, the used fixed effects model does produce better

estimations but it does not take into account other potential sources of endogeneity, which are likely to

occur when researching board diversity and performance (Wintoki, Linck, and Netter, 2012). Findings

are in line with research of Randøy et al. (2006) who also did not find a significant effect.

With regards to control variable board size, results show that the positive relationship between board

size and firm performance uncovered by the OLS equations disappears when fixed-effects are applied.

The OLS could be hampered by omitted variables biases. Moreover, the significance found between

board size and Tobin’s Q no longer holds and also turned negative. Larger boards are likely to negatively

affect firm performance. The negative relationship between board size and performance is in line with

previous research by Ahern and Dittmar (2012) and Nygaard (2011).

28

(1) (2) (3) (4) (5) (6) (7) (8) (9)

VARIABLES AGEB TQ AGEB ROE AGEB ROA GBX TQ GBX ROE GBX ROA NBX TQ NBX ROE NBX ROA

Board age diversity 0.005 0.474 0.303

[0.010] [0.970] [0.253]

Board gender diversity 0.093 10.449 -2.507

[0.197] [17.646] [3.778]

Board nationality diversity 0.241 7.911 3.482

[0.214] [14.973] [5.717]

Log total assets -0.176* 11.752 0.840 -0.167* 11.927 1.142 -0.260*** -18.618 -0.526

[0.095] [19.750] [1.491] [0.091] [18.976] [1.475] [0.095] [20.432] [1.691]

Leverage -1.375*** -49.694 -6.657 -1.431*** -51.607 -7.552 -1.277*** 106.928 -1.083

[0.286] [149.947] [12.417] [0.282] [146.553] [12.656] [0.242] [123.388] [13.720]

Board size -0.001 -1.156 -0.048 -0.001 -1.189 -0.152 -0.014 -2.018 0.070

[0.021] [3.120] [0.698] [0.021] [3.104] [0.657] [0.020] [2.644] [0.897]

Constant 3.944** -162.225 -18.738 4.071*** -139.231 -6.731 5.386*** 239.707 13.417

[1.562] [177.934] [24.183] [1.371] [206.735] [19.844] [1.414] [239.280] [21.397]

Observations 271 301 307 275 305 311 246 251 257

R-squared 0.294 0.026 0.068 0.297 0.026 0.055 0.319 0.081 0.039

Number of firm 55 59 59 55 59 59 49 49 49

Firm FE YES YES YES YES YES YES YES YES YES

Year FE YES YES YES YES YES YES YES YES YES

Industry FE NO NO NO NO NO NO NO NO NO

Robust standard errors in brackets

*** p<0.01, ** p<0.05, * p<0.1

Table 5

Fixed Effect model: Relation between board diversity and firm performance

Table 5 shows the results of the fixed effect model with Tobin's Q, return on equity (ROE) and return on assets (ROA) as dependent

variables. Age, gender and nationality board diversity are the independent variables. Total assets, leverage of the firm and executive

board size are used as control variables. Variable definitions can be found in appendix A, table 1.

29

4.5 Fixed effects including lags

Despite the fact that the fixed effects method accounts for omitted variable bias, the possibility of

reverse causality still exists. By adding lagged variables, the possibility that firm’s performance affects

board diversity, and thereby the reverse causality is reduced as suggested by Liu et al. (2014) and Carter

et al. (2010). As a robustness check, the previous models have been estimated again but now with the

inclusion of one-year lags for the independent variables.

The estimation method does not change the main dependent variables of interest (Tobin’s Q and ROA)

in equation (7) and R² is somewhat smaller in comparison to the previous model. Equation (8) changed

the interaction effect of ROA to a negative one in comparison to the previous model although R² is

somewhat lower and as with the non-lagged model and no significant result is found. The ROE equation

reports a significant positive relationship in support of hypothesis 2. The coefficients of both Tobin’s

Q and ROE are again positive and in conformation with the theory and empirical evidence suggesting

that women are better monitors (Post and Byron, 2015; Adams and Ferreira, 2009).

Equation (9) reports positive coefficients for all performance variables. Including a lag turned the

coefficient of ROE to a positive one. No significant results are found.

Note that the coefficients of the ROE equation (7), ROA equation (8) and ROE equation (9) show

opposite results compared to the fixed effects model, indicating it is possible this relationship is driven

by reverse causality.

4.6 Multiple regression: F-test

Table 7 reports the multiple linear regression that was calculated to predict the joint effect of age, gender

and nationality. The coefficients of the independent variables are similar to the previous OLS model

when using Tobin’s Q and ROA as the dependent variable in equation (10). The coefficient of board

gender diversity changes from negative to positive when using ROE, which is in line with the presented

theories.

Results of the fixed effects equation (11) are similar to the previous fixed effects model. The control

variable total assets has increased its significance to the 1% significance level. With the exception of

board gender diversity in the ROA equation, all coefficients are positive and in conformation with the

presented theory.

Equation (12), uses lags to cope with possible reverse causality. The independent variable board gender

diversity is no longer significant when using ROE. Moreover, its coefficient changes from negative to

positive indicating the model suffers from potential reverse causality. This can cause significant under-

and over-estimation of the potential effects.

30

The results of the F-test reports no joint significance for the independent variables after controlling for

omitted and missing values. This means the variables age, gender and nationality are not able in

predicting the performance of a firm.

31

(7) (8) (9)

VARIABLES AGEB TQ AGEB ROE AGEB ROA GBX TQ GBX ROE GBX ROA NBX TQ NBX ROE NBX ROA

Board age diversity 0.011 -0.603 0.029

[0.008] [0.477] [0.122]

Board gender diversity 0.222 24.787** 1.928

[0.196] [10.060] [4.523]

Board nationality diversity 0.300 -8.127 0.291

[0.196] [10.347] [3.280]

Log total assets 0.003 -1.484 0.153 0.010 -2.239 0.008 -0.031 -0.660 0.086

[0.022] [1.424] [0.382] [0.021] [1.515] [0.446] [0.030] [1.172] [0.562]

Leverage -0.565** 11.748 -1.675 -0.626** 9.279 -2.596 -0.640** 4.139 -4.601

[0.235] [9.494] [8.632] [0.270] [10.166] [8.130] [0.288] [9.595] [9.836]

Board size -0.051** -0.652 0.228 -0.053** -0.702 0.189 -0.080*** -2.220** 0.085

[0.021] [0.986] [0.346] [0.021] [1.001] [0.321] [0.020] [1.006] [0.376]

Constant 0.580 37.198 2.785 1.047** 41.888* 7.000 1.734*** 29.792 7.584

[0.524] [22.914] [8.826] [0.428] [21.973] [6.315] [0.567] [18.018] [7.829]

Observations 250 276 282 254 280 286 227 233 239

R-squared 0.249 0.038 0.040 0.249 0.045 0.038 0.295 0.054 0.029

Number of firm 52 58 58 52 58 58 49 55 55

Firm FE YES YES YES YES YES YES YES YES YES

Year FE YES YES YES YES YES YES YES YES YES

Industry FE NO NO NO NO NO NO NO NO NO

All independent variables are lagged one period. Robust standard errors in brackets.

*** p<0.01, ** p<0.05, * p<0.1

Table 6

Lagged Fixed Effect model: Relation between board diversity and firm performance

Table 6 shows the results of the fixed effect model with Tobin's Q, return on equity (ROE) and return on assets (ROA) as dependent

variables. Age, gender and nationality board diversity and performance are the one-year lagged independent variables. Total assets,

leverage of the firm and executive board size are used as control variables. Variable definitions can be found in appendix A, table 1.

32

(10) (11) (12)

VARIABLES OLS TQ OLS ROE OLS ROA FE TQ FE ROE FE ROA FE w/ lag TQ FE w/ lag ROE FE w/ lag ROA

Board age diversity -0.013 0.747 0.166 0.007 1.964 0.322 0.011 -0.203 0.066

[0.012] [0.681] [0.137] [0.012] [1.375] [0.307] [0.010] [0.649] [0.177]

Board gender diversity -0.733** 3.339 -2.923 0.052 10.624 -3.192 0.075 20.583 -0.322

[0.300] [24.347] [5.350] [0.261] [19.100] [5.250] [0.225] [18.419] [7.329]

Board nationality diversity 0.200 -18.919 -2.153 0.210 0.527 2.329 0.266 -7.915 0.090

[0.183] [18.580] [2.862] [0.248] [17.401] [5.904] [0.217] [10.688] [3.536]

Log total assets -0.074*** 1.554* 0.146 -0.272*** -23.709 -1.288 -0.050 0.080 -0.019

[0.025] [0.879] [0.312] [0.092] [22.607] [1.675] [0.032] [1.331] [0.513]

Leverage -1.160*** 12.676 -1.736 -1.271*** 115.808 0.057 -0.623*** 4.473 -3.377

[0.258] [24.128] [5.396] [0.233] [124.985] [13.399] [0.213] [11.294] [9.215]

Board size 0.077** 3.289 0.511 -0.013 -1.751 0.021 -0.058** -1.717 0.140

[0.036] [3.257] [0.485] [0.021] [2.778] [0.930] [0.026] [1.262] [0.330]

Constant 2.436*** -57.377 -4.484 5.201*** 209.770 8.005 1.496* 26.966 4.457

[0.706] [52.793] [9.146] [1.535] [216.320] [22.641] [0.806] [34.380] [14.334]

Observations 250 276 282 254 280 286 227 233 239

R-squared 0.249 0.038 0.040 0.249 0.045 0.038 0.295 0.054 0.029

F-statistic 0.1044 0.5725 0.5398 0.4939 0.5058 0.5410 0.1068 0.2349 0.9685

[2.07] [0.67] [0.72] [0.81] [0.79] [0.73] [2.15] [1.46] [0.08]

Number of firm 49 49 49 49 55 55

Firm FE NO NO NO YES YES YES YES YES YES

Year FE YES YES YES YES YES YES YES YES YES

Industry FE YES YES YES NO NO NO NO NO NO

All independent variables are lagged one period. Robust standard errors in brackets. F-statistic value in brackets.

*** p<0.01, ** p<0.05, * p<0.1

Table 7

Multiple regression: Joint significance testing

Table 7 shows the results of the OLS- (10), fixed effects (11) and fixed effects with lags (12) models with Tobin's Q, return on equity (ROE) and

return on assets (ROA) as dependent variables. Age, gender and nationality board diversity are jointly regressed on performance. One-year

lagged independent variables are used in regression (10). The reported F-statistic tests for the joint significance of age, gender and nationality.

Total assets, leverage of the firm and executive board size are used as control variables. Variable definitions can be found in appendix A, table 1.

33

5. Conclusion

Over the past few years, diversity on the board of directors has become a much debated topic. The

purpose of this paper is to present empirical evidence on the effect of board diversity on firm

performance and complement the existing literature. Firm performance was defined as financial

performance and measured by one market-measure (Tobin’s Q) and two accounting measures (ROE

and ROA). The scope of this study consisted of a sample of Dutch listed firms, which were examined

over the period 2010 to 2015. The combination of the upper echelons theory , agency theory, resource

dependency theory, and recent empirical literature have not been able to clearly demonstrate a

relationship between board diversity and firm performance. Panel data was used to estimate both OLS

and fixed effects models and take partially take into account the potential endogeneity issues.

First of all, the results of the OLS regressions show a positive- but non-significant relationship between

the board age diversity and firm performance. When Tobin's Q was used as a measure of financial

performance, the coefficient of the explanatory variable is only marginally different from zero and not

significant. To take care of endogeneity, fixed effects models have been employed. The results from the

fixed effects regression equations indicate a positive but again non-significant relationship between a

board average age and firm performance. An explanation for this positive coefficient is provided by the

human capital theory arguing that age comes with greater knowledge and experience. At the same time,

the resource dependency theory predicts that on average older boards have increased linkages to other

stakeholders. These findings are in line with previous resource by Ferrero-Ferrero et al. (2015), Engelen

et al. (2012), and Hasan and Wu (2012).

Secondly, the results of the second set of OLS equations testing for a potential positive link between

board gender diversity and firm performance is significant at the 5% level when Tobin's Q was used as

a measure for financial performance. All three OLS regression models have negative coefficients with

regard to the explanatory variable board gender diversity, indicating a negative link between a firm's

board gender diversity and firm performance. However, when the same regression is run using a fixed

effects model, the coefficients of the main dependent variable (Tobin's Q) and ROE (used as robustness

check) turn positive, while ROA still shows a negative coefficient. Also the lagged model with fixed

effects reports a significant and positive link between board gender diversity and firm performance.

The results correspond with Adams and Ferreira (2009) who also find evidence of positive changes in

performance due to board gender diversity. The agency theory provides an explanation for this positive

link, theorizing that women are better monitors and thus reduce agency cost (Post and Byron, 2015;

Adams & Ferreira, 2009).

34

Finally, the third hypothesis arguing that the presence of foreign nationals has a positive effect on firm

performance was tested. The OLS regression result shows only a coefficients marginally different from

zero when it is tested on the forward-looking performance indicator Tobin's Q. However, both ROA

and ROE indicate that the presence of foreign nationals has a negative impact on firm performance. All

OLS estimations are non-significant. Contrary results were found when using fixed effects models

which could indicate the OLS model suffers from the omitted variable bias. The fixed effects model

coefficients indicate higher board nationality diversity comes with better firm performance. The

resource dependency theory argues that differences in nationality diversity increases international

network (Oxelheim et al., 2013; Oxelheim, 2003).Lastly, the joint significance of the independent

variables (age, gender and nationality) could not be proven. However, the individual t-statistics of the

joint regression model including age, gender and nationality reported similar results to the other

models. As a last note, it is possible that the used models suffer from reverse causality as is reported by

the fixed effect model with lags.

Overall, the positive coefficient results can, by and large, be attributed to the theories introduced.

Increased diversity bolsters independence and lessens agency problems within the firm. In addition,

higher levels of diversity expand boards’ linkages taking into account stakeholders’ needs and limit a

firms’ dependence on strategic resources. Lastly, the human capital theory predicts an increase in

different skills and experiences as diversity increases. The differences between the coefficients of the

forward- and backward-looking dependent variables can be attributed to signals to the market (Miller

and Triana, 2009). Increased diversity may lead companies towards higher performance and

competitiveness. Shareholders and its management can therefore not only benefit from greater diversity

but at the same time act in line with moral and societal norms.

This study has some limitations. First of all, results can suffer from contingencies because this study

was conducted on firms listed on the Dutch stock exchanges. Other countries have different rules and

regulations, business culture, and potentially other factors that have an influence on board diversity and

firm performance. Conducting this study across multiple countries and with a larger sample of firms

would enhance the understanding of board diversity and its potential effect on firm performance. In

addition, it is advised to take into account the survivorship bias which is not addressed in this study.

Second, the scope of this research was purely on economic theories. Since no significant results could

be found it can be that a possible link between board diversity must be sought not only in economic

theory but jointly with (e.g.) social theories.

Lastly, the choice of gender, age and nationality diversity are much-debated topics in the Netherlands

and many other western countries. At the same time, diversity on the basis of religion, language or the

level of integration can be important constructs too.

35

Appendix

Appendix A: Variables overview

Table 1

Variable Definitions

Financial performance (dependent variables) Definition

Tobin’s Q Calculated by dividing book value of total assets by market

capitalisation at the end of a year.

Return on assets (ROA) (%) Calculated by dividing total assets by net income at the end of the

year.

Return on equity (ROE) (%) Calculated by dividing shareholders equity by net income at the end

of the year.

Diversity (independent variable)

Board age diversity Sum of the ages of all executive board directors divided by total size

of executive board.

Board gender diversity Gender diversity of the executive board. Calculated as a percentage

of total board members of a firm.

Board nationality diversity Nationality diversity of the executive board. Calculated using the

Blau-index.

Control variables

Natual log of firm total assets Natural logarithm of total assets measured by the book value at year-

end.

Board size Number of directors on the executive board.

Leverage ratio Leverage ratio calculated by; (short-term debt + long-term debt)

divided by total assets.

36

Appendix B: Manually adjusted or corrected data

Some of the data have been manually adjusted, supplemented or adjusted in order to take care of missing

values or increase the total amount of observations.

With regard to 168 positions (directors) data was missing with regard to their nationality and/or gender.

Via the use of public sources such as annual reports, management scope and company websites this

information was added. In addition, some companies were added as a whole because the information

was not available in either Orbis or BoardEx. These companies are:

● Value8

● Amsterdam Commodities

● Stern Group NV

● Ballast Nedam NV

● Corio NV

● Exact Holding

● Grontmij NV

● Hunter Douglas NV

● Kardan NV

● Macintosh Retail Group NV

● Nutreco NV

● Pharming S.A.

● TKH Group N.V.

● Unit4

● USG People NV

Appendix C: Conceptual model

37

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