1
Gender Diversity and Firm Performance: The
Influence of Legal Quotas
Abstract: I study the effect of boards’ gender diversity on firm performance in 20 developed countries with and without legal quotas for gender diversity in boards to understand the effect of mandatory quotas on the relationship between women’s presence in the board and firm performance. I find positive relationships between women and firm performance and a negative influence of quotas on this relationship. Furthermore the role of country-level gender equality on the relationship between women in boards and firm performance is explored by comparing two countries with a mandatory gender quota with different levels of gender equality. The effect of quotas on the diversity-performance may be different based on the country level gender (in)equality.
University of Groningen – Faculty of Economics and Business
Student number: s2778505
Name: Tom Stalman
Study programme: Msc. International Financial Management Supervisor: Melsa Ararat
Co-assessor: Adri de Ridder
2 1. Introduction
The purpose of this research is to investigate whether the effect of board gender diversity on firm performance differs between quota and non-quota countries. Literature on the topic of gender diversity and firm performance is ample, however, no research has to my knowledge yet examined the different outcomes by looking at the effect of quotas. Prior research has focused on the effects of board gender diversity on firm performance in different countries. Carter, D’Souza, and Simkins (2010) examined major US corporations and did not find a significant relationship between board gender diversity and firm performance. Chapple and Humphrey (2013) did not find a significant relationship between board gender diversity and firm performance for Australian firms. Marinova, Plantenga, and Remery (2016) find no evidence for this relationship regarding Dutch and Danish listed firms, however, their research is based on a small set of observations. Research by Joecks, Pull, and Vetter (2012) finds a positive relationship between firm performance and board gender diversity for German firms, but they report that this relationship holds at higher levels of female representation, indicating that there is a so called ‘magic number’ of women in the boardroom. Finally, Solakoglu and Demir (2015) only find weak evidence that gender diversity affects firm performance in Turkish firms. So, it is still a debated subject with no clear answer. The results from these previous researches may be explained by contextual factors and what motivates the companies to appoint female directors. Presence of a quota and the level of enforcement may be important. Mandatory quotas may lead to selecting females for the board from a symbolic point of view or female directors may not be qualified if there isn’t enough supply. Also, national institutional factors may influence the effect of gender diversity on firm performance.
3 and firm performance examine the relationship based on the monitoring efficiency (agency theory) outcome of diversity and access to different resources (resource dependence theory) by different genders which might enhance firm performance. A third theory which applies to this relationship is the human capital theory, which is deemed to be an extension to the resource dependency theory. The human capital theory is derived from the work of Becker (1964). Human capital refers to the knowledge, habits, social and personal traits of an individual.
The logic of a quota comes from the fact that the gender diversity in boards have a positive effect beyond the firm’s financial performance. They transform public attitudes about gender equality, politics, democracy, and society in general (Huse and Seierstad, 2013). Furthermore, they could correct the market failure between the demand and supply for female directors. Stemming from conscious or unconscious biases or board entrenchment as the percentage of female directors are much lower than the percentage of women in the workforce. Women represent only 10% of board director positions worldwide (Terjesen, Aguilera, and Lorenz, 2015). Legal quotas can therefore be seen as a governmental response to a market failure. Research by Marinova, Plantenga, and Remery (2016) actually raises the question whether the negative effects of gender diversity are only temporal, as the negative relation was observed over one year only. Moreover, in their study of 3000 U.S. firms between 2007 and 2014, Conyon and He (2016) indeed found that there exists a significant positive relationship between gender diversity and firm performance. There is thus reason to believe that the negative effects are only temporal, implying that female representation on boards is beneficial over time. The immediate negative effect which was found may appear due to the fact that a currently working board is being broken up.
4 2. Literature review
2.1. Board gender diversity
Diversity among board directors improves the chances that different types of knowledge, perspectives and ideas will be considered in the decision-making process (Post, Rahman, and Rubow, 2011). It is generally accepted that diversity in an organisation favours problem-solving, increases leadership effectiveness and promotes global relations in a more efficient manner (Robinson and Dechant, 1997). Gender composition of the board is an important corporate governance mechanism. Women differ from men in terms of personality, communication style, educational background, and career experience and expertise (Buss, 2005; Feingold, 1994). Farrell and Hersch (2005) discuss board gender diversity as a market mechanism, based on supply and demand. The supply side is driven by available women candidates and the demand side is the demand for women directors from the firms. They argue that there is an increasing demand and it is due to the role of positive effects resulting from gender diversity. Since the article is written, market interventions effected the supply and demand. For instance, in Germany the number of women in a board is determined by law as in some other European countries, therefore; taking the proposed idea from Farrell and Hersch (2005) there is sufficient evidence for the demand side. The most well-known and impactful market intervention is a gender quota (hereafter quotas), mandated by law. This controversial governmental solution addresses the ethical issue of gender inequality in boards. The first announced quota was in Norway, in November 2002. This quota required at least 40% of directors to be female. From here on the trend is set and fourteen countries followed Norway with mandatory quotas. These countries are: Spain, Finland, Québec (Canada), Israel, Iceland, Kenya, France, Malaysia, Italy, Belgium, UAE (United Arab Emirates), India, Greenland, and Germany. Including Norway this makes up for 15 countries. The Norwegian quota is described as a “snowball” (Machold, Huse, Hansen, and Brogi, 2013) which has led to ideological and political debates (Huse and Seierstad, 2013) and consequentially led to a changing global landscape with regards to quotas (Seierstad, Warner-Søderholm, Torchia, and Huse, 2015).
5 Simpson, 2010). This is overlapping with the justice perspective which focusses on the individual equality and social equality. This perspective and rationale argues for quotas that all demographic groups should be equally represented on the board (Dahlerup, 2002). An argument supporting the ethical basis of quotas is that “quotas symbolize equality, signify justice, reflect the value of equal representations of both sexes” (Terjesen and Sealy, 2016: 33). An argument opposing quotas from an equality perspective is that the most qualified people should be selected as board members no matter what demographic group they are from (Terjesen and Sealy, 2016). Hence, the equality rationale gives opportunity to promote equal opportunities for women across the entire firm, industry or the workforce as a whole. So, the equality rationale places an emphasis on the rights of women and that they are to be equal to those of men (Terjesen and Sealy, 2016).
The economic rationale argues in favour of quotas that the argument for promoting gender diverse boards is better understood by those who lead the workforce, the managers, when it is put into economic terms (Sawer, 2000). This argument is a complement to the equality rationale. Another question that arises is that of the motivation to add women to the board, is this a consideration regarding integrity or just compliance? This is especially focused on firms, in such a sense that if firms try to reach the goal by simple compliance, or if the firm adds women from an integrity perspective. It is also observed on a country level. For instance, the Norwegian quota was installed to utilize female talent with the hope of increasing the participation of women in the workforce, but then other European countries followed with quotas as a compliance to gain legitimacy (Terjesen and Sealy, 2016). Firms are found to use four different strategies to tackle the institutional demands regarding quotas. These four strategies are: acquiesce, defy, avoid, and manipulate (Pache and Santos, 2010). In Norway and Iceland, the most common strategy is acquiesce and comply, after the quota implementation in Norway we however witnessed the avoid strategy as well. When looking at the defy strategy, almost half of the Spanish firms defy the law by just ignoring it (Ahern and Dittmar, 2012; Bøhren and Staubo, 2014). The avoid strategy is illustrated well by the UK government, which avoids the threat of an EU law by addressing the issue beforehand in the UK. In France, firms appoint female directors to manipulate the law (Branson, 2012).
2.2.3. Board gender diversity – resource dependence theory
6 an individual to a board, it expects the individual will come to support the organization, will concern himself with its problems, will variably present it to others, and will try to aid it”. They mention that a board can bring four benefits to the firm; advice and counsel, legitimacy, channels for communicating information between the firm and organizations outside the firm, and access commitments and support from elements outside the firm. In light of gender diversity within the board the resource dependency theory is applicable. Gender diversity brings more or different resources to the firm since women differ from men in terms of personality, communication style, educational background, and career experience and expertise (e.g. Buss, 2005; Feingold, 1994). First taking career experience and expertise into account, female directors may have a different network which gives access to new resources. Robinson and Dechant (1997) found that diversity in a board promotes global relations in a more efficient manner. To specify the provision of resources more understandable Hillman and Daziel (2003) mention that board capital is the primary antecedent of a board’s provision of resources. Arguing that previous research had not yet looked into board capital and rather payed attention to human and relational capital, Hillman and Daziel (2003) combine human and relational capital, into board capital. Board capital is then presented as a combination of directors’ expertise, experience, knowledge, reputation and skills (Becker, 1964; Coleman, 1988) and also “the sum of actual and potential resources embedded within, available through, and derived from the network of relationships possessed by an individual or social unit” (Nahapiet and Ghoshal, 1998). Where the first part refers to the term human capital and the second part to relational capital. Women, by bringing different expertise, experience, and knowledge to the board, increase the human capital. It may be also possible that their networks are more extensive thus enlarging the relational capital and the board capital as a whole. When their networks aren’t more extensive they can be expected to differ from those of men therefore their relational capital can positively affect the board capital as a whole. Finally, board gender diversity links to performance with regard to the resource dependence theory because a board’s resources are directly related to performance (Hillman, Dalziel, 2003). These resources reduce dependency between the firm and external parties (Pfeffer and Salancik, 1978), reduce uncertainty for the firm (Pfeffer, 1972), and lower transaction costs (Williamson, 1984).
2.2. Board gender diversity – Agency theory
7 a principal-agent relationship. Because of information asymmetry between the owner, being shareholders, and the agents, being managers, creates a problem. Agency theory proposes a set of mechanisms that can mitigate this problem. Mechanisms include incentives such as equity payment to managers to align their interests with the interests of the owners. Another mechanism is the board of directors with a monitoring and controlling function, to guard the interests of the shareholders (Jensen, 1986). By drawing from research of Buss (2005) and Feingold (1994) that men and women differ on a many different aspects such as experience, knowledge, personality and education, because of this, female directors can possibly contribute to the board composition and affecting the monitoring and controlling function in a positive way. This is already proposed in earlier research that a more diverse board results in better control due to the different perspectives, viewpoints, and ideas. Gender diversity therefore can be a mechanism to reduce agency problems (Chen, Crossland, and Huang, 2014; Adams and Ferreira, 2009; Carter, D’Souza, and Simkins, 2003).
2.3. Board gender diversity – firm performance
The board of directors is the primary monitoring and advising body, it plays an important role in protecting shareholders’ interests. The board of directors’ tasks are among others guiding the firms’ decisions and influencing firm performance (Finkelstein, Hambrick, and Cannella, 2009; Hambrick, 2007; Hambrick and Mason, 1984). The presence of women on these boards can affect the boards’ tasks and the outcomes of these tasks. There is ample research on the benefits that women on the board can generate, for example some benefits are arising from the difference between human and social capital and the decision-making style differs between genders (e.g. Burgess and Tharenou, 2002; Carter, D’Souza, Simkins, and Simpson, 2010; Terjesen, Aguilera, and Lorenz, 2015). They also differ in their core values, risk attitudes and perspective (Buss, 2005; Feingold, 1994; Adams and Funk, 2012; Perryman, Fernando, and Tripathy, 2016; Carter, D’Souza, Simkins, and Simpson, 2010)
8 (Janis, 1972). Female director’s role in this counter groupthink idea is that they value different opinions, they want to retrieve information from all board members and adopt a more cooperative way of decision-making (Post and Byron, 2015). Thus, a gender diverse board can possibly improve the decision-making quality. Research by Miller and Del Carmen Triana (2009) indicated that as a result from improved decision-making quality board gender diversity can be linked to more corporate innovation, enhanced firm capability to utilize resources and investments (Triana, Miller, and Trzebiatowski, 2013), better understanding of the market and the stakeholders (Carter, Simkins, and Simpson, 2003), more effective board control and more strict monitoring (Nielsen and Huse, 2010; Adams and Ferreira, 2009). These factors all seem to improve firm performance or at least give the opportunity to excel as a firm opposed to competitors. Prior literature has found that gender diversity can possibly result in superior firm performance as well (Campbell and Minguez-Vera, 2008; Carter, Simkins, and Simpson, 2003). Potential costs of board gender diversity are due to high dissimilarity, the board might lack unity and this resulting in an increase in conflicts within the board. This then can lower the speed and the quality of decision making (Pelled, Eisenhardt, and Xin, 1999). Thus, this cost is the opposite of the potential benefit gender diversity can create, being better quality decision making. Within the board diversity may create in-groups and out-groups, as to that female directors and male directors will identify with their gender equals and in this way socially categorize the board. This is also known as the social categorization perspective, which indicates that differences and similarities in a group form the basis for categorizing members into groups themselves. This social categorization may disrupt boards functioning (Harrison and Klein, 2007; van Knippenberg, Dawson, West, and Homan, 2010; Williams and O’Reilly, 1998). So, given the aforementioned, board gender diversity could possibly enhance firm performance, or it might destroy firm performance. Thus, it is important to find out to what extent the benefits outweigh the costs. This leads to the following hypothesis:
Hypothesis 1. Board gender diversity enhances firm performance.
9 the equal access for the different genders to resources and opportunities such as education, economic participation, employment and political empowerment (Hausmann, Tyson, and Zahidi, 2012). Thus, it would seem that countries with high gender parity will have diverse boards that benefit firm performance. Having higher gender parity enlarges the pool of candidates to choose from as possible board members. The pool will contain more females who have larger human and/or relational capital. So, when gender parity in a country is low the pool of females will be smaller and have a lower degree of human and/or relational capital. The idea of a larger pool comes from the aforementioned work by Farrell and Hersch (2005) that the board gender diversity could be seen as a market mechanism. However, the gender parity in a country can be assumed to increase more capable women candidates, there are other influences as well. One influence and maybe the most important one is the gender quota. The quota as mentioned before is a regulation that sets a minimum percentage or number of female board members. It is likely that this will affect the board gender diversity on firm performance relationship. How this affects the relationship is interesting to see and can give an answer to the question of the need for quotas. It is likely to assume that the gender quota in countries with high gender parity will lead to higher firm performance as compared to countries with low gender parity. However, this is a relationship that has not yet been examined. So, this research will focus on the effect of board gender diversity and firm performance and the difference in this relationship between quota countries and non-quota countries. Further, two quota countries will be compared with respect to country differences such as the gap in equality between genders.
2.4. Board gender quotas
10 (2014) then mentions is that in prior research, the interpretation of how good the quota implementation worked is up for debate based on the time frame used. The second issue addressed is the control group, because the law is applicable to all listed Norwegian firms. The third issue is the sample because the firms that did not comply choose for delisting, which was almost 50% (Bøhren and Staubo, 2014). Fourthly, the issue that there is a multitude of effects, such as reforms, around the time of the introduction of gender quotas and this comes back to the fact that the timing around the quota implementation is so important. The final issue is that different research suggests different effects from the same case study on women in the board, so to say the reasoning is inconclusive. The question still remains what the effect of quotas is on the relationship between board gender diversity and firm performance, therefore it can be important to look at differences between non-quota countries and quota countries and compare the different quota countries based on their individual differences. This gives us the following hypotheses.
Hypothesis 2. A legislated mandatory quota weakens the positive relationship between board gender diversity and firm performance.
Although there are arguments in favour and opposed of legislated quotas, the effect on the relationship between gender diversity and firm performance is subject to many external factors (Post and Byron, 2015). Where the effect may be negative, research has shown that over a longer time period the negative effect diminishes (Conyon and He, 2016). Therefore, I state the following hypothesis that the expected negative interaction effect from the legislated quotas is only temporary and over time the addition of female representation on the board will indeed enhance firm performance.
Hypothesis 3. A legislated mandatory quota will strengthen the relationship between diversity and performance over time
11 and firm performance. Gender parity can explain different outcomes on the country level between the quota countries and the effect of a mandated quota, I therefore conduct some exploratory analysis to understand the differences between low and high gender equality contexts.
3. Methodology 3.1. Sample
12 Table 1. List of countries with observations
Country Observations Non-quota countries Australia 1737 China 374 Great Britain 2261 Hong Kong 904 Japan 2416 Netherlands 257 New Zealand 84 South Korea 488 Sweden 270 Switzerland 383
United States of America 6904
Quota countries Belgium 110 Finland 246 France 664 Germany 446 India 398 Italy 151 Malaysia 181 Spain 195 NO 138 Total 18607 3.2.Variables 3.2.1. Dependent variables
13 said to measure wealth and Return on assets to measure income (Carter, D’Souza, Simkins, and Simpson, 2010).
3.2.2. Independent explanatory variables
To measure for board gender diversity, the percentage of women on the board will be taken. This measure is used in prior research (e.g. Liao, Luo, and Tang, 2015). To measure if a country has a quota a dummy variable will be used. If the country has an imposed quota in a certain year it will be assigned the value 1 and otherwise the value 0, so a country that has incorporated a quota will not have a 1 assigned for every year only for the years the quota is active. Finally a quota moderator will be introduced, this is measured by multiplying the dummy for quotas times the variable for board gender diversity. This way it measures the effect of the board gender diversity in quota countries. Appendix A shows the countries which adopted a quota, and the set quota for those countries.
3.2.3. Independent control variables
14 law countries. For the industry control I use the Fama-French 49 industry classification based on SIC codes. These account for industry fixed effects. Further, country fixed effects are controlled for with the use of a dummy the same accounts to year fixed effects.
3.3.Method and models
Table 2. Description of function variables
Firm performance Firm performance measured by Tobin’s Q and ROA
ROA The return on assets, measured as net income divided by total
assets
Tobin’s Q Measured as market value divided by book value
Ln(Firmsize) The natural logarithm of the total assets
Leverage Measured as the debt to assets ratio
Boardstructure
Measured with a dummy assigning 1 for one-tier and 0 for two-tier boards.
CEO duality
CEO duality, if a person is both chairmen of the board and CEO of a company. Measured as a dummy assigning 1 for duality and 0 for a divide between functions
BoardInd
Board independence, measured as the percentage of independent board members
Boardsize The total number of directors sitting on the board
Legalorigin
The legal origin of a country, measured as a dummy assigning 1 to common law countries and 0 to civil law countries
Industry A control variable for industry fixed effects.
Country A control variable for industry fixed effects
Year A control variable for year fixed effects.
%women The percentage of female representation on the board of directors
Quota
If a country has a legally mandated quota, measured as a dummy assigning 1 to a country with a quota and 0 if there is no quota present.
Quota mode
The quota moderator, measures the interaction effect between female board representation (%women) times the quota dummy
The research will be conducted using several regressions. All independent variables are lagged by t-1. The first regression (equation 1) will look into the effects of board gender diversity, measured as the percentage of women present in the board, on firm performance. The research will look at it through two dependent variables, being Tobin’s Q and return on assets (ROA).
(1) 𝐹𝑖𝑟𝑚 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 𝛼𝑖,𝑡−1+ 𝛽1(ln(𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒))𝑖,𝑡−1+ 𝛽2(𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒)𝑖,𝑡−1+ 𝛽3(𝐵𝑜𝑎𝑟𝑑𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒)𝑖,𝑡−1+
𝛽4(𝐶𝐸𝑂 𝑑𝑢𝑎𝑙𝑖𝑡𝑦)𝑖,𝑡−1+ 𝛽5(𝐵𝑜𝑎𝑟𝑑𝐼𝑛𝑑)𝑖,𝑡−1+ 𝛽6(𝐵𝑜𝑎𝑟𝑑𝑠𝑖𝑧𝑒)𝑖,𝑡−1+ 𝛽7(𝐿𝑒𝑔𝑎𝑙𝑜𝑟𝑖𝑔𝑖𝑛)𝑖,𝑡−1+
𝛽8(%𝑤𝑜𝑚𝑒𝑛)𝑖,𝑡−1+ 𝛽9(𝑄𝑢𝑜𝑡𝑎)𝑖,𝑡−1+ 𝛽10(𝑄𝑢𝑜𝑡𝑎𝑚𝑜𝑑)𝑖,𝑡−1+ 𝜀𝑖
15 (2) 𝐹𝑖𝑟𝑚 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 𝛼𝑖,𝑡−1+ 𝛽1(ln(𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒))𝑖,𝑡−1+ 𝛽2(𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒)𝑖,𝑡−1+ 𝛽3(𝐵𝑜𝑎𝑟𝑑𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒)𝑖,𝑡−1+
𝛽4(𝐶𝐸𝑂 𝑑𝑢𝑎𝑙𝑖𝑡𝑦)𝑖,𝑡−1+ 𝛽5(𝐵𝑜𝑎𝑟𝑑𝐼𝑛𝑑)𝑖,𝑡−1+ 𝛽6(𝐵𝑜𝑎𝑟𝑑𝑠𝑖𝑧𝑒)𝑖,𝑡−1+ 𝛽7(𝐿𝑒𝑔𝑎𝑙𝑜𝑟𝑖𝑔𝑖𝑛)𝑖,𝑡−1+
𝛽8(%𝑤𝑜𝑚𝑒𝑛)𝑖,𝑡−1+ 𝛽9(𝑄𝑢𝑜𝑡𝑎)𝑖,𝑡−1+ 𝛽10(𝑄𝑢𝑜𝑡𝑎𝑚𝑜𝑑)𝑖,𝑡−1+ 𝛽11(𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦)𝑖,𝑡−1+ 𝛽12(𝐶𝑜𝑢𝑛𝑡𝑟𝑦)𝑖,𝑡−1+
𝛽13(𝑌𝑒𝑎𝑟)𝑖,𝑡−1+ 𝜀𝑖
The following regression (equation 3) looks into the effect of gender diversity on firm performance in quota countries individually. The outcomes of these different regressions will indicate outcomes regarding individual country effects and can be attributed to different factors per country. The country outcomes will be compared together with the corresponding gender gap scores which indicate the equality of men and women. These scores indicate the lower the gap the more equal a country is. For the first regressions the following functions are used. The gender gap scores are used to compare the individual quota countries, the examination of these scores compared to the values in the regressions is somewhat arbitrary. It is a check to see if the assumptions from prior research can be derived from these scores.
(3) 𝐹𝑖𝑟𝑚 𝑃𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒𝑡+3= 𝛼 + 𝛽1(ln(𝐹𝑖𝑟𝑚𝑠𝑖𝑧𝑒))i,t−1+ 𝛽2(𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒)i,t−1+ 𝛽3(𝐵𝑜𝑎𝑟𝑑𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒)i,t−1+
𝛽4(𝐶𝐸𝑂 𝑑𝑢𝑎𝑙𝑖𝑡𝑦)i,t−1+ 𝛽5(𝐵𝑜𝑎𝑟𝑑𝐼𝑛𝑑)i,t−1+ 𝛽6(𝐵𝑜𝑎𝑟𝑑𝑠𝑖𝑧𝑒)i,t−1+ 𝛽7(𝐿𝑒𝑔𝑎𝑙𝑜𝑟𝑖𝑔𝑖𝑛)i,t−1+
𝛽8(%𝑤𝑜𝑚𝑒𝑛)i,t−1+ 𝛽9(𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦)i,t−1+ 𝛽10(𝐶𝑜𝑢𝑛𝑡𝑟𝑦)i,t−1+ 𝛽11(𝑌𝑒𝑎𝑟)i,t−1+ 𝜀𝑖
In all models the dependent variable is firm performance measured by Tobin’s Q and ROA. Table 2 shows the list of all used variables and their definition. Measurements for the variables are given in the section regarding the variables.
4. Descriptive statistics
16 structure has an average of 0.724, this is a dummy variable for one tier and two tier board structures for firms. This means that overall approximately 72.4% of the firms have a one tier structure. CEO duality shows an average of 0.449 which indicates that 44.9% of the firms have CEO duality. The average for board independence shows an average of 0.526 or 52.6% which means that on an average 52.6% of the members of a board are independent. The max is 0.953 or 95.5% and a minimum of 0.006 or 0.6%. Board size has an average of 9.822, so almost 10 members per board. Common law has an average of 0.660 or 66%, this indicates that approximately 66% of the sample is originated in a common law country. Legal origin, board structure, CEO duality and Quota all have a maximum of 1 and a minimum of 0, this is due because they are measured as dummy variables. The sample size consists of 18607 firm year observations spread over 20 countries.
5. Correlation matrix
Table 3 provides the correlation matrix, the matrix shows if the independent and dependent variables are significantly correlated with each other. It also shows if there is an issue with multi-collinearity. It can be seen that all independent variables are significantly correlated with the dependent variable Tobin’s Q on a 1% level, except for Quota. Almost all independent variables are significantly correlated on a 1% level with the dependent variable Return on assets (ROA) except for Quota and Board structure. There is a positive correlation between the percentage of women and Tobin’s Q, this also goes for ROA with a slight difference. Firm size is both significantly correlated with ROA and Tobin’s Q however, it is positive with ROA; 0.070 and negative with Tobin’s Q; -0.263. This difference can be explained because of the measurement differences between Tobin’s Q and ROA, as Tobin’s Q is a market based Table 3. Descriptive statistics (T=15)
Variables Tobin's Q ROA % Women Quota Firm size Leverage Board structure CEO duality Board independence Board size Legal origin Mean 1.288 0.047 0.124 0.136 7.105 0.233 0.724 0.449 0.526 9.822 0.660 Maximum 7.864 0.325 0.750 1.000 9.985 0.892 1.000 1.000 0.953 30.000 1.000 Minimum 0.081 -0.882 0.000 0.000 3.929 0.000 0.000 0.000 0.006 1.000 0.000 Median 0.904 0.053 0.111 0.000 6.872 0.219 1.000 0.000 0.571 9.000 1.000 Standard deviation 1.240 0.112 0.116 0.343 1.118 0.172 0.447 0.497 0.302 3.091 0.474 25th percentile 0.515 0.021 0.000 0.000 6.352 0.104 0.000 0.000 0.252 8.000 0.000 75th percentile 1.586 0.092 0.200 0.000 7.741 0.332 1.000 1.000 0.814 11.000 1.000 N 18607 18607 18607 18607 18607 18607 18607 18607 18607 18607 18607
17 performance measure which takes the market value of the firm into account and ROA is an accounting based performance measure. The high correlation of 0.738 that occurs between legal origin and board structure can be explained by the fact that they are both dummy variables. This can be justified because legal origin influences the board structure in some countries as it can be mandatory. There is no concern for multicollinearity, as they are not predictors in the model but only control variables.
6. Results
Table 5 shows the results of the first regressions. Model 1 and model 5 measure the effect of the control variables on the dependent variables Tobin’s Q and ROA respectively. Models 2, 3, and 4 add an independent explanatory variable. Model 2 adds the independent variable for board gender diversity, model 3 adds the independent variable for quotas and model 4 adds the interaction between the variable gender diversity and the quota. The same structure goes for the models including ROA as the dependent variable.
6.1.Gender gap
The gender gap is a score assigned to countries by the World Economic Forum. Based on multiple categories all countries get assigned scores which give them an overall ranking. This
18 rank shows how a countries’ gender gap is in relation to other countries. When a country has a high rank it indicates that there is a smaller gender gap than countries which score fairly low.
6.2.Tobin’s Q
Table 5 shows the results of the first regression. To control for possible endogeneity issues all variables are lagged by 1 year. The results for model 1 up until 4 where Tobin’s Q is the dependent variable for firm performance are all significant at a 1% level. However, the first observation to be made from this model is the adjusted R-squared which is fairly low being 0.151 in the complete model. I do observe that the interaction effect when a country has a quota with the relationship between the percentage of women on the board and firm performance is
Table 5. Regression, results of the effects of women on corporate boards on firm performance through ROA and Tobin's Q. All independent variables are lagged one period. Fixed effects are not included in the model. The first number in each cell is
the regression coefficient and the value in the parentheses is the associated t-value. ***, **, * indicates p<0.01, p<0.05, and p<0.1 respectively
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Tobin's Q Tobin's Q Tobin's Q Tobin's Q ROA ROA ROA ROA
Firm size -0.242*** -0.233*** -0.224*** -0.227*** 0.005*** 0.006*** 0.007*** 0.006*** [0.010] [0.011] [0.011] [0.011] [0.001] [0.001] [0.001] [0.001] Leverage -1.808*** -1.821*** -1.828*** -1.835*** -0.063*** -0.065*** -0.065*** -0.066*** [0.063] [0.063] [0.063] [0.063] [0.006] [0.006] [0.005] [0.006] Board structure -0.257*** -0.263*** -0.244*** -0.238*** -0.004** -0.005** -0.005* -0.005** [0.035] [0.034] [0.033] [0.033] [0.002] [0.002] [0.003] [0.002] CEO duality 0.171*** 0.173*** 0.173*** 0.176*** 0.008*** 0.008*** 0.008*** 0.008*** [0.018] [0.018] [0.018] [0.018] [0.002] [0.002] [0.002] [0.002] Board independence 0.179*** 0.135*** 0.127*** 0.119*** 0.023*** 0.017*** 0.017*** 0.016*** [0.034] [0.036] [0.036] [0.036] [0.004] [0.004] [0.003] [0.004] Board size -0.012*** -0.015*** -0.016*** -0.017*** 0.003*** 0.002*** 0.002*** 0.002*** [0.002] [0.002] [0.002] [0.003] [0.000] [0.000] [0.000] [0.000] Legal origin 0.270*** 0.280*** 0.312*** 0.273*** 0.009*** 0.011*** 0.011*** 0.009*** [0.034] [0.034] [0.037] [0.036] [0.002] [0.002] [0.003] [0.002] % women 0.405*** 0.318*** 0.614*** 0.054*** 0.053*** 0.068*** [0.084] [0.090] [0.094] [0.009] [0.008] [0.010] Quota 0.154*** 0.577*** 0.001 0.022*** [0.045] [0.079] [0.003] [0.005] Quota*%women -2.366*** -0.119*** [0.283] [0.020] Intercept 3.369*** 3.310*** 3.230*** 3.253*** -0.021** -0.029*** -0.029*** -0.028** [0.090] [0.091] [0.095] [0.094] [0.010] [0.011] [0.008] [0.011] Adjusted R-squared 0.145 0.146 0.147 0.151 0.021 0.023 0.023 0.024 N 18607 18607 18607 18607 18607 18607 18607 18607
Industry fixed effects NO NO NO NO NO NO NO NO
Year fixed effects NO NO NO NO NO NO NO NO
19 negative and significant at a 1% level. The second observations to be made is the positive effect of women on the board on firm performance, which is significant at a 1% level as well.
6.3.ROA
For the ROA the adjusted R-squared is 0.024 for the complete model, which suggest very low explanatory power of the model. In models 5 up till 8 all variables are lagged by 1 year as is the case for the models 1 up till 4 including Tobin’s Q as dependent variable. The effect of women on the board is positive and significant at a 1% level. As is the interaction effect from the quotas on the relationship between women on the board and firm performance, also on a 1% significance level. Which indicates that a quota has a negative interaction effect on the relationship between gender diversity in the board and firm performance.
6.4.Endogeneity issues
A Hausman test is performed to observe the need to include fixed- or random effects in the models to further control for endogeneity effects. The Hausman test uses the null hypothesis that the preferred model is random effects, where random effects are unbiased and appropriate. The result of the test was a P-value of 0.000 therefore rejecting the null hypothesis at the 1% significance level. This indicates that for the models in table 5 a fixed effects model is the most appropriate one and needs to be included, whereas random effects are biased and inconsistent. The regression where firm performance is influenced by gender diversity there are a lot of other factors that influence firm performance. However, due to data unavailability some of these variables were deemed to be omitted. Endogeneity then occurs in the model because the error term and the other variables are actually correlated. Therefore the regressions will be performed again but this time including fixed effects. The models will include country, year and industry fixed effects to omit endogeneity as much as possible. In the following models fixed effects will be taken into account.
6.4.1 Tobin’s Q
20 firm the lower Tobin’s Q will be. This is because Tobin’s Q is measured by the market value divided by the book value. If the book value of the firm increases, the ratio between market value and book value will decrease and therefore the Tobin’s Q. Leverage also negatively impacts firm value with 99% certainty, which means that a higher leverage will result in a lower Tobin’s Q. The control variables’ coefficients almost do not change when adding different independent explanatory variables. In model 2 the independent variable for board gender diversity is added (%women), this is significant at a 1% level and positively affects firm performance. When the percentage of female directors increases with 1% the Tobin’s Q goes up with 0.474, which could imply that board gender diversity indeed enhances firm
Table 6. Regression, results of the effects of women on corporate boards on firm performance through ROA and Tobin's Q. All
independent variables are lagged one period. Robust standard errors are used to calculate t-statistics. The first number in each cell is the regression coefficient and the value in the parentheses is the associated t-value. ***, **, * indicates p<0.01, p<0.05, and p<0.1 respectively
Independent variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8
Dependent variable Dependent variable Dependent variable Dependent variable Dependent variable Dependent variable Dependent variable Dependent variable
Tobin's Q Tobin's Q Tobin's Q Tobin's Q ROA ROA ROA ROA
Firm size -0.629*** -0.642*** -0.642*** -0.644*** 0.011*** 0.010*** 0.010*** 0.010*** [0.019] [0.019] [0.019] [0.020] [0.002] [0.002] [0.002] [0.002] Leverage -1.474*** -1.467*** -1.467*** -1.468*** -0.071*** -0.070*** -0.070*** -0.070*** [0.058] [0.058] [0.058] [0.058] [0.006] [0.006] [0.006] [0.006] Board structure 0.026 0.032 0.032 0.028 -0.005 -0.005 -0.005 -0.005 [0.040] [0.040] [0.040] [0.040] [0.004] [0.004] [0.004] [0.004] CEO duality 0.047*** 0.044** 0.044** 0.044** 0.002 0.002 0.002 0.002 [0.018] [0.018] [0.018] [0.018] [0.002] [0.002] [0.002] [0.002] Board independence 0.229*** 0.208*** 0.209*** 0.204*** 0.013*** 0.011** 0.011*** 0.011** [0.044] [0.045] [0.045] [0.045] [0.005] [0.005] [0.005] [0.005] Board size 0.020*** 0.019*** 0.019*** 0.019*** 0 0 0 0 [0.003] [0.003] [0.003] [0.003] [0.000] [0.000] [0.000] [0.000] Legal origin -0.697*** -0.648*** -0.648*** -0.635*** -0.044*** -0.040*** -0.040*** -0.040*** [0.076] [0.075] [0.075] [0.075] [0.007] [0.007] [0.007] [0.007] % women 0.474*** 0.472*** 0.572*** 0.033*** 0.032*** 0.035*** [0.095] [0.097] [0.099] [0.010] [0.010] [0.011] Quota 0.007 0.177** 0.004 0.009* [0.049] [0.086] [0.003] [0.005] Quota*%women -0.928*** -0.029 [0.340] [0.021] Intercept 5.268*** 5.304*** 5.304*** 5.287*** -0.055** -0.052** -0.052** -0.052** [0.184] [0.184] [0.184] [0.183] [0.026] [0.026] [0.026] [0.026] Adjusted R-squared 0.336 0.337 0.337 0.337 0.120 0.121 0.121 0.121 N 18607 18607 18607 18607 18607 18607 18607 18607
Industry fixed effects YES YES YES YES YES YES YES YES
Year fixed effects YES YES YES YES YES YES YES YES
21 performance when measured with Tobin’s Q. This would mean that hypothesis 1 would be confirmed. Model 3 shows the effect of the quota variable; this is does not have a significant impact on its own on firm performance. Model 4 adds the variable for board gender diversity and the moderator for quotas on the relationship between board gender diversity and firm performance. The quota moderator has a coefficient of -0.928 and is significant at a 1% level. Therefore, the effect of a quota negatively affects the relationship between board gender diversity and firm performance. Women still have a significant positive impact on firm performance at a 1% level. However, the sample includes countries without a quota as well. The adjusted R-squared for the model 4 indicates that 33,7% of Tobin’s Q is explained by the model.
6.4.2. ROA
22 are tested. Hypothesis 1 is supported, the effect of women on the board increases firm performance. This is consistent with previous findings that women indeed enhance firm performance (e.g. Campbell & Minguez-Vera, 2008; Carter et al., 2003). Hypothesis 2 is supported when Tobin’s Q is the dependent variable, the relation is not significant when ROA is the dependent variable. However, the coefficient is negative and measurement differences could give reason why the relation is not significant.
6.5. Country results
The following regressions are on quota countries individually to see the effect of a quota over time. Therefore, the tables include 6 models per country which only include the independent explanatory variable %women and the dependent differs starting from t = quota enforcement year. The choice for France and India from the quota countries is because France is amongst the highest ranks in the global gender gap rank and India is the lowest. Next to France other countries such as Norway, Finland and Germany score very high as well, however, Norway and Finland have implemented quotas a long time before India. Norway has not been included due to the fact that there already has been a lot of research into the quota in Norway. Finland and Germany have been excluded because the German quota has been implemented in 2014 with a final compliance date of 2016 so the effect of the German quota would be harder to measure due to the time span. For the country results the regressions are first run as normal OLS (ordinary least square) regressions, these are tables 7 and 8. They are run with fixed effects taken into account in table 9. India is incorporated only once as an exploratory result.
6.5.1. France
23 performance and a stable insignificant effect on ROA. For Tobin’s Q the highest value for the adjusted R-squared is 0.365 and for ROA the highest value is 0.094. To conclude these results there is no consensus between the different models, as the models where ROA is the dependent variables show no significance and do not have a positive effect but negative and not significant. So, hypothesis 3 is accepted based on the regression on the sample for France for Tobin’s Q as measurement for firm performance. However, fixed effects are excluded and endogeneity is present. Hypothesis 3 cannot be accepted without including fixed effects first, if the results hold I can accept hypothesis 3. The years preceding the quota implementation result in a more positive relation between gender diversity and firm performance. The second year after the quota implementation is less positive when Tobin’s Q is the dependent variable but over the course of three years a possible trend is visible.
Table 7. France. Regression, Results of the effect of women on corporate boards on firm performance measured through ROA and Tobin's Q and fixed effects are excluded from the models. All independent variables are lagged one
period. All dependent variables are lagged forward one, two and three periods per country. The first number in each cell is the regression coefficient and the value in the parentheses is the associated t-value. ***, **, * indicates p<0.01, p<0.05, and p<0.1 respectively
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Tobin's Q t+1 Tobin's Q t+2 Tobin's Q t+3 ROA t+1 ROA t+2 ROA t+3
Firm size -0.667*** -0.609*** -0.634*** -0.022*** -0.015** -0.011 [0.080] [0.090] [0.112] [0.006] [0.006] [0.008] Leverage -1.425*** -1.455*** -1.532*** -0.038* -0.040* -0.042* [0.279] [0.320] [0.390] [0.022] [0.020] [0.025] Board structure 0.467*** 0.483*** 0.529*** 0.032*** 0.035*** 0.032*** [0.087] [0.100] [0.123] [0.007] [0.007] [0.009] CEO duality -0.244* -0.259 -0.283 -0.006 -0.006 -0.003 [0.136] [0.162] [0.190] [0.008] [0.009] [0.011] Board independence -0.763*** -0.966*** -1.089*** -0.024 -0.029 -0.034 [0.247] [0.296] [0.356] [0.016] [0.018] [0.022] Board size -0.033** -0.044*** -0.051*** -0.002* -0.002* -0.003* [0.013] [0.015] [0.019] [0.001] [0.001] [0.002] % women 1.475*** 1.395*** 1.473** 0.014 -0.013 -0.029 [0.467] [0.468] [0.591] [0.028] [0.032] [0.044] Intercept 6.392*** 6.277*** 6.629*** 0.229*** 0.194*** 0.178*** [0.713] [0.806] [0.984] [0.042] [0.046] [0.053] Adjusted R-squared 0.318 0.338 0.365 0.086 0.094 0.087 N 248 185 123 248 185 123 Industry fixed effects NO NO NO NO NO NO
Year fixed effects NO NO NO NO NO NO
Country fixed
24 6.5.2. India
Table 8 shows the results for India, observed is that for models 1 up till 3, where Tobin’s Q is the measure for firm performance, there is no persistent significant relationship between gender diversity in the board and firm performance. However looking at the coefficient for % women it shows that the negative effect gets more negative in the first year after the quota implementation and then becomes less negative in the preceding years. The same phenomenon occurs as for the ROA, the coefficient drops in the first lag to a more negative value and then increases again. However, for the ROA the coefficient drops in the second lag as well but in
the third lag it increases again. Thus, because of measurement differences between Tobin’s Q and ROA the negative effect can persist longer for ROA. Although the results are exploratory, they provide insights into a possible moderating effect with regards to time. This can however
Table 8. India. Regression, Results of the effect of women on corporate boards on firm performance measured through ROA and Tobin's Q and fixed effects are excluded from the models. All independent variables are lagged one period. All
dependent variables are lagged forward one, two and three periods per country. The first number in each cell is the regression coefficient and the value in the parentheses is the associated t-value. ***, **, * indicates p<0.01, p<0.05, and p<0.1 respectively
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Tobin's Q t+1 Tobin's Q t+2 Tobin's Q t+3 ROA t+1 ROA t+2 ROA t+3
Firm size -2.740*** -2.855*** -2.739*** -0.041*** -0.048*** -0.037* [0.261] [0.357] [0.583] [0.010] [0.013] [0.019] Leverage -4.062*** -3.263*** -2.657* -0.243*** -0.223*** -0.236*** [0.680] [0.889] [1.394] [0.029] [0.039] [0.058] CEO duality -0.135 0.032 0.229 0.008 0.009 0.007 [0.289] [0.403] [0.661] [0.010] [0.014] [0.022] Board independence 0.001 -0.203 -1.929 0.064** 0.071* 0.023 [0.735] [0.964] [1.931] [0.029] [0.039] [0.064] Board size 0.081* 0.076 0.026 0.003** 0.003 0 [0.046] [0.060] [0.102] [0.002] [0.002] [0.003] % women -4.930*** -3.995* -1.773 -0.103 -0.134 -0.025 [1.731] [2.242] [3.098] [0.068] [0.086] [0.113] Intercept 26.120*** 26.713*** 26.521*** 0.421*** 0.477*** 0.424*** [2.399] [3.202] [5.384] [0.080] [0.102] [0.157] Adjusted R-squared 0.515 0.469 0.352 0.46 0.413 0.358 N 177 112 52 177 112 52 Industry fixed effects NO NO NO NO NO NO
Year fixed effects NO NO NO NO NO NO
Country fixed
25 not be said with certainty as further analysis would be necessary to control for other time variant effects, it is not the focus of this research.
6.5.3. France
Table 9 shows the results for France with fixed effects incorporated in the model. Models 4 up till 6 show the regressions where ROA is the dependent variable and models 1 up till 3 show the regressions where Tobin’s Q is the dependent variable. Models 4 up till 6 show that gender diversity has no significant effect on firm performance. The R-squared however, has increased to 0.494 in model 5, this is an increase from 0.094. Therefore, the models have gained more explanatory power when fixed effects are added in. The variable of interest is % women. The coefficients for gender diversity, although not significant, are positive in comparison to the
models without fixed effects. Models 1 up till 3 show a significant relationship between gender diversity and firm performance at a 1% significance level. The coefficient for gender diversity moves in a certain trend through the years. This indicates that over time the influence of women
Table 9. France. Regression, Results of the effect of women on corporate boards on firm performance measured through ROA and Tobin's Q. All independent variables are lagged one period. All dependent variables are lagged forward one, two and
three periods per country. The first number in each cell is the regression coefficient and the value in the parentheses is the associated t-value. ***, **, * indicates p<0.01, p<0.05, and p<0.1 respectively
Independent variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
France France France France France France
Tobin's Q t+1 Tobin's Q t+2 Tobin's Q t+3 ROA t+1 ROA t+2 ROA t+3
Firm size -0.576*** -0.533*** -0.587*** -0.022** -0.021** -0.017 [0.100] [0.119] [0.151] [0.009] [0.010] [0.013] Leverage -0.563*** -0.567*** -0.678*** 0.012 0.009 0 [0.188] [0.184] [0.228] [0.016] [0.013] [0.013] Board structure 0.502*** 0.512*** 0.537*** 0.031*** 0.034*** 0.030*** [0.112] [0.133] [0.176] [0.007] [0.008] [0.010] CEO duality -0.280** -0.251* -0.27 -0.002 -0.002 0.005 [0.115] [0.148] [0.190] [0.008] [0.008] [0.012] Board independence -0.105 -0.27 -0.326 0.006 0.014 0.011 [0.173] [0.205] [0.273] [0.015] [0.015] [0.018] Board size -0.048*** -0.057*** -0.060** -0.002** -0.002* -0.003* [0.016] [0.019] [0.025] [0.001] [0.001] [0.001] % women 2.019*** 2.246*** 2.541*** 0.044 0.021 0.017 [0.494] [0.571] [0.817] [0.032] [0.033] [0.046] Intercept 5.919*** 5.749*** 6.121*** 0.233*** 0.217*** 0.193** [0.756] [0.898] [1.199] [0.063] [0.071] [0.093] Adjusted R-squared 0.598 0.6 0.586 0.442 0.494 0.479 N 248 185 123 248 185 123
Industry fixed effects YES YES YES YES YES YES
Year fixed effects YES YES YES YES YES YES
26 in the board on firm performance gets stronger when a quota is implemented. As the coefficient increases from t+1 to t+3. Now fixed effects are included the model has more explanatory power and hypothesis 3 is accepted.
6.6. Gender gap scores
The Global Gender Gap report for the years 2012 up till 2016 are of use for the two regressions for France and India. Table 10 shows the scores per year and the global ranking of both countries. A high score for the gender gap index indicates that there is more equality between men and women. When the results for France are observed, seen is an overall positive effect of women on the board. Even in the subsequent years after the quota implementation. When I compare these results with the gender gap scores of those years I observe a rise as well, which indicates that women and men are becoming more equal in France as 1 would be a perfect score. The growth in the effect of women is largest in t+1 for both dependent variables, this indicates from year 2011 to 2012 and the gender gap score decreases in this period for France. However, I observe that indeed firms in a country with a high gender gap score (indicating that there is more equality) may experience a less negative effect from the quota implementation. The positive effect in France can be due to the fact that women and men are already very much equal in the workforce, and by a quota implementation the effect was still positive but less positive than thereafter due to the breaking up of an existing board for instance. Looking at the results from India the effect of women in the board decreases the firms’ performance in the first period from 2013, which is the year of quota adoption, and then in the subsequent year the effect gets more negative. However, in the following two years the effect gets less negative when looking at Tobin’s Q. In India the quota can also be implemented to encourage more women to participate in the workforce and not to enhance firm performance. These outcomes are only arbitrary, however further research can examine the relationship more thoroughly and see how diversity and gender gap scores interact.
Table 10. Gender gap rankings and scores per year. The number in the parentheses
stands for the global scores
2011 2012 2013 2014 2015 2016
France 48 57 45 16 15 17
(0.701) (0.698) (0.709) (0.759) (0.761) (0.755)
India 113 105 101 114 108 87
(0.619) (0.644) (0.655) (0.645) (0.664) (0.683)
27 7. Conclusion
28 8. Limitations
I acknowledge several limitations to this study. As for the measure of firm performance, there are multiple measures for this and not one is sufficient enough to exclude others. However by taking a market based and an accounting based measure I have tried to cover this as much as possible. My results show that different measures give different outcomes. The difference in outcomes between the measures can be due to the fact that Tobin’s Q uses market value in such a way that it looks at future earnings. So, it could be said that investors value diversity as a long-term value factor, as the return on assets is a measure for immediate results therefore not fully incorporating the long-term effect of diversity in the outcome. The same accounts for the effect of women on the board on firm performance, when looking at quota countries individually there are multiple factors which influence this. Such as personality, network, and work ethic these are all aspects that I was not able to measure given the extensive list of countries used. Both of these limitations are due to data unavailability in this research. The Asset4 database that was used to gather all board data did not provide all board data for all firms. The solution to this could be to use another more extensive database that does cover all companies. A limitation regarding the timeframe is that the research was conducted over the years 2003-2017 which includes the financial crisis, in future research including a dummy for the effect of the crisis could be a solution. A final limitation regarding this research is the fact that there can be multiple reasons why a government incorporates a gender quota. As this research focusses on the relationship to firm performance, governments may impose the quotas to encourage women to join the workforce or even to create a common feeling of equality. This research has looked into two countries separately where India was used for exploratory purposes.
9. Contribution
29 diversity and firm performance in a quota environment. Future research could take a smaller set of countries and firms, thereby giving more room to extensively look into the differences between female and male directors. Finally, Future research can, as mentioned in the gender gap section, further examine this relationship by incorporating different aspects of these gender gap scores or by incorporating the gender gap score as a whole. The possible outcomes can show light on the fact that equal opportunities within a country to education and other necessities is more important for a government to influence than a quota. The ‘market’ could then correct itself. As the results from India have shown, the reasons behind a quota might differ between countries. These potential differences in reasoning why quotas are implemented are a focus where future research can look into. They could centralise all data on quota years and include all quota countries in the regression to see this possible relationship. Finally, the research looks into the effect of a quota on the relationship between gender diversity and firm performance up till 3 years after the implementation date, however to better understand the impact at the date of implementation future research should look into the effect of gender diversity one year prior to the implementation date.
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Appendix A. Quota Countries
Countries with Gender Quotas
Country Quota Passage Date
Norway 40% December, 2003 Spain 40% March, 2007 Finland 40% June,2005 France 40% January, 2011 Malaysia 30% June, 2011 Italy 33% June, 2011 Belgium 33% June, 2011 India 1 FBD* August, 2013 Germany 30% December, 2014
