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The effect of board diversity on firm

capital structure and performance

Nijmegen school of management

Master Economics, specialization Corporate Finance & Control Master Thesis: final version

Student: Ingrid Meijer (s1030994) Supervisor: J. Qiu

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Abstract

This paper examines the effect of board diversity on capital structure and firm performance. Board diversity is measured as gender diversity and the number of foreigners on the board. This effect has not been widely examined. Therefore, the analysis is performed on 5,269 European firms from 44 countries. Looking at a period of 2010-2018. Using different methods to perform the analysis, the change of reporting biased results is limited. The robust and significant results indicate that more females on the board lead to safer forms of financing and a more stable performance. Foreigners on the board, on the other hand, lead to riskier forms of financing and a more volatile performance. Additionally, some moderating effects between board diversity and corporate governance measures are found for the different forms of financing.

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Table of content

Abstract _____________________________________________________________________ II 1 Introduction ______________________________________________________________ 4 2 Literature review __________________________________________________________ 5 2.1 Capital structure theories _______________________________________________ 6 2.2 Board of directors’ influence on capital structure ____________________________ 7 2.3 The effect of board diversity on capital structure _____________________________ 7 2.4 Board diversity and its effect on firm performance ___________________________ 9 2.5 Hypothesis development ________________________________________________ 9 3 Methodological approach __________________________________________________ 10 3.1 Data and variables ____________________________________________________ 10 3.2 Model specification ___________________________________________________ 12 4 Results _________________________________________________________________ 14 4.1 Descriptive results ____________________________________________________ 14 4.2 Empirical results _____________________________________________________ 15 4.3 Robustness check ____________________________________________________ 21 5 Discussion and conclusion __________________________________________________ 22 References __________________________________________________________________ 24 Appendix ___________________________________________________________________ 28 A. Specification per country_________________________________________________ 28 B. Fixed effects___________________________________________________________ 29 C. Pooled OLS ____________________________________________________________ 31 D. Random effects ________________________________________________________ 34 E. Firm performance ______________________________________________________ 38

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1 Introduction

In 2012 the European Commission submitted a proposal, Directive on gender balance among non-executive directors of companies listed on stock exchange, to deal with the imbalance of women on the board of directors of firms. This proposal stated that the number of women fulfilling a non-executive function within the board of directors should be 40% by 2020. By 2019, on average, this was 26.4% in the EU (Rankin, 2020). Hence, the target of 40% is unlikely to be reached. This proposal stresses the importance given by politics within Europe for more diverse boards of directors.

Empirical evidence shows that females tend to be more risk averse than males (Byrnes et al., 1999; Weber et al., 2002). That women are more risk averse will affect their decision making (Eckel & Grossman, 2008). For example, it is found that female executive managers issue less debt then their male equivalent (Huang & Kisgen, 2013). For capital structure of firms, this could mean that more gender diverse boards could lead to a lower leverage, since the board of directors makes the decisions regarding the firm’s capital structure (Ferreira, 2010). The topic of capital structures in the prior literature addresses quite some theories, like the trade-off theory and the pecking order theory, explaining how firms choose there capital structure (Myers, 1984). The trade-off theory explains that firms choose a target debt-to-value ratio and make a trade-off between tax shields and bankruptcy costs. The pecking order theory explains that firms prefer to first use internal equity (retained earnings) then debt followed by external equity. Within debt firms prefer to use short-term debt over long-term debt.

Prior literature shows that the board of directors has a significant influence on the capital structure of firms (Al‐Najjar & Hussainey, 2011; Alves, Couto, & Francisco, 2015; Heng, Azrbaijani, & San, 2012). Alves et al. (2015) argue that more independent and diversified boards reduce information asymmetry and therefore could use more external equity. Board diversity can be measured in numerous ways. Common measures are gender and ethnic diversity. Even though the way firms are financed is important, the effect of board diversity on capital structure is not widely examined. The only research examining the effect of board diversity on the leverage of firms are the research of Alves et al. (2015), Elmagrhi, Ntim, Malagila, Fosu, & Tunyi, (2018) and Adusei and Obeng (2019). Alves et al. (2015) find a negative relation for between the board gender diversity and the capital structure for mainly Japanese, American and Indian firms. The same negative relation was found by Elmagrhi et al. (2018), but this study is limited to charities located in the United Kingdom. The findings of Adusei and Obeng (2019) about microfinance institutions also suggest a negative relation. With these prior studies a literature gap is left for European firms. In the end firms must safeguard their survival and need to have good firm performance to survive. It is widely examined how the board diversity effects firm

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performance. However, the results found with respect to this effect are contradicting. Therefore, this study will address the following question:

How does board diversity influence the capital structures and firm performance of European listed firms?

This study is performed on 5,269 European firms from 44 countries, looking at a time frame from 2010 until 2018. The methods used to perform the analysis are threefold; fixed effects, pooled OLS, and random effects. Board diversity is measured as gender diversity and nationality diversity. Firm performance is measured as the standard deviation of return on assets, so the stability is measured. The capital structure is divided in internal equity, external equity, short-term debt, and long-term debt to test whether the pecking order theory holds. Additionally, it is also examined whether corporate governance measures have an interaction effect with board diversity.

The findings show a robust and significant relation between board diversity and capital structure. The results indicate that females on the board lead to less risky decisions and foreigners increase the amount of riskier forms of financing.

Examining the effect of board diversity on capital structure within European firms can be of importance for the target set for more gender diverse boards. It would be interesting for policy makers and European firms to know the impact board diversity potentially has on the capital structure of firms so they can incorporate this in future legislation and in the appointing of new board members. From a scientific point of view, this study can contribute to the existing literature by providing more evidence to the study of the effect of board diversity on capital structure.

This study is structured as follows. The next chapter will discuss the existing literature relevant to this study. Then the data and methodology will be explained in chapter 3. Followed by the results section. The study finishes with a discussion and conclusion.

2 Literature review

Board diversity is widely examined in the literature, as is capital structure. However, the effect of board diversity on capital structure has not been researched often. To understand the effects of a (diversified) board of directors it can be important to understand the underlying capital structure theories explaining how firms choose their financing behavior, which will first be discussed in this chapter. Second, literature on the effect the board of directors has on capital structure will be discussed in this chapter to examine whether the board of directors is important for the capital structure of firms. Thereafter, the literature that is available about the effect that board diversity has on capital structure is reviewed. Since there is broad literature on the effect that board diversity has

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on firm performance, this is examined at last to give a broader view of the potential effect that board diversity can have.

2.1 Capital structure theories

Modigliani and Miller (1958) were the first to examine how firms are financed. They assumed perfect markets and argued that the way firms are financed is not important since it does not influence the value of the firm. However, later research developed other theories explaining how firms determine the way they finance. Here two of these theories will be explained. First, there is the trade-off theory. Myers (1984) explains that within this theory, firms set a target debt to value ratio. To determine the optimal ratio for the firm, a tradeoff is made between the interest tax shields and costs of bankruptcy. The equity and debt within the firm are substitutes of one another until the maximum firm value is reached. Second, the pecking order theory explains how information asymmetry is of importance. This theory explains that external financing leads to information asymmetry because the shareholders or debtholders do not have all the information the firm has. Therefore, firms prefer to finance the firm with internal resources, e.g. retained earnings. When there is not enough internal equity, firms will use external financing preferring debt over equity. This is because debt is less risky than equity (Myers, 2001).

2.1.1 Capital structure and firm performance

Firms choose their leverage considering the outcome it has on the firm’s performance. Therefore, capital structure theories have certain relations to firm performance. The pecking order theory, for instance, states that firms prefer internal debt over external debt, and debt over equity financing. Firms, therefore, will try to obtain higher free cash flow from, for example, retained earnings, leading to lower leverage (Myers, 1984). This suggest a negative relation between the level of debt within a firm and its financial performance. Evidence that the capital structure negatively impacts firm performance and therefore supports the pecking order theory is found by Hasan, Ahsan, Rahaman, Alam (2014).

However, there are two other theories explaining the relation between capital structure and firm performance as a positive relation. This would mean that the relation found by Elmagrhi et al. (2018) is contradicting these two theories. The first theory, the agency costs hypothesis suggests that, on the one hand, higher debt levels can encourage managers to behave in the interest of the shareholders by obtaining a higher leverage. This can result in lower agency costs and therefore increase firm value (Berger & Bonaccorsi di Patti, 2006). This effect can be generated through the threat of bankruptcy (Grossman & Hart, 1982) and because more debt leads to higher interest payments, which require the managers to retain more free cash flow (Jensen, 1986). In contrast, too much debt can lead to higher agency costs and thus decrease firm performance. This is explained by the increased conflict between

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debt holders and shareholders. More debt can lead to more risks for the debt holders for which they want higher interest expenses. This negative relation is also found by Gleason et al. (2000), suggesting that this effect can be explained by increasing agency problems leading to firms obtaining more debt than is appropriate, which in turn leads to lower firm performance. This theory thus suggests that debt can be beneficial of firm performance, however, within limits. If board diversity leads to less debt it could cause agency problems to occur.

The other theory suggesting that higher debt is good firm performance is signaling theory. This theory suggests that higher level of debts can give a signal to outside investors that the firm is confident to be able to pay the increased interest expenses suggesting a good future financial performance. This has a positive effect on firm performance (Elmagrhi et al., 2018). However, board diversity could have a negative influence on the signal giving to outside investors.

These contradicting theories suggest that board diversity is important to examine since it is shown that board diversity can have an influence on capital structure and because capital structure influence firm performance, board diversity can be important for firms. The reason behind this being that in the end firms need a healthy financial performance to exist.

2.2 Board of directors’ influence on capital structure

The board of directors is responsible for the approval of major strategic and financial decisions. The financial decisions also include decision making over changes in capital structures (Ferreira, 2010). Prior research on the effect of the board of directors on the capital structure of firms finds that more independent board members can lead to more long-term debt financing (Alves et al., 2015). They argue that more independent and diversified boards of directors reduces information asymmetry which enables the firm to use a higher fraction of external equity financing. These findings suggest support for the pecking order theory. Besides that, Heng et al. (2012) find that the size of the board and the percentage of independent non-executive board members have a significant influence on the capital structures of Malaysian firms. Similarly, within the UK, Al Najjar & Hussainy (2011) find that among other characteristics, the board size and outside directorship have an important influence on the way firms are financed.

2.3 The effect of board diversity on capital structure

The existing literature on the effect of board diversity on capital structure is limited. Elmagrhi et al. (2018) find evidence for a negative relation between gender diversity on boards of directors and capital structure. This result is explained by suggesting that more gender diverse boards look at problems from different perspectives leading to better monitoring over management and therefore reducing agency problems which were created by the presence of large free cash flows. This result is,

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however, limited to a maximum of three females in the board of directors. This negative relation suggest it is in line with the pecking order theory as explained in section 2.1.1.

However, they do not find any evidence that ethnic diversity is related to short term debt, long term debt or total debt. This might be caused by the little amount of ethnic diversity on the boards of UK charities. Gyapong et al. (2016) suggest that firms only nominate ethnic minority members for symbolic reasons not because they see the value of diverse boards. However, they do find evidence for a positive relation of ethnic board diversity and total debt when they control for interactions of corporate governance mechanisms, like the size of the board, audit firm size, existence of a separate independent corporate governance committee, and board meetings. This also compliments the suggestion that ethnic minority board members have less influence on the board and therefore that managers can increase their power over board decisions (Gyapong et al., 2016).

In the research of Adusei and Obeng (2019) a similar negative effect is found between board gender diversity and the capital structure of microfinance institutions. They explain this result by the risk aversion of females and that equity becomes less risky due to the disclosure effect of women in boards of directors. With the disclosure effect they refer to the research of Ahmed, Monem, Delaney, and Ng (2017), where they find evidence that a higher fraction of female directors on the board of directors leads to more frequently and more in depth disclosure of firm information.

Alves et al. (2015) also examined the effect of gender diverse boards and firms capital structure. They find that more gender diverse boards are more likely to be more independent and efficient. However, the results are only significant for two (external equity and short-term debt) of the eight models analyzed. They explain the low significance by the possibility that gender diversity is highly correlated to with the number of independent board members leading to multicollinearity problems. Despite the results, they argue that firms with more gender diverse board tend obtain more market external equity and less short-term debt. This result is, therefore, in line with the research of Elmagrhi et al. (2018).

In the research of Elmagrhi et al. (2018) it is also tested whether there is a moderating effect of certain corporate governance practices on the relation between capital structure and board diversity. They look at the interaction of the effect of the size of the board of directors, the size of the audit firm, whether there is a separate independent corporate governance committee present in the firm and the frequency of the board meetings with the amount of women present in the board of directors and the with the amount of ethnic minorities within the board. The interactions effect between gender diversity all the corporate governance mechanisms have a significant effect on the capital structure. However, the presence of a separate independent corporate governance committee does not have a significant effect. About the ethnic diversity of the boards only the interaction effect with the size of the board of directors has a significant effect on capital structure. However, this can also be due to the

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fact in their sample there is a small number of ethnic minorities present in the board of directors of charities.

2.4 Board diversity and its effect on firm performance

For firms it is of course importance to have good performance to safeguard their survival. Therefore, the effect of board diversity on firm performance is widely examined. The results found are mainly positive. Terjesen, Couto, & Francisco (2016) find that firms with a gender diverse board of directors have a higher value of Tobin’s Q and return on assets. They also find that more female directors on the board positively influences the board’s effectiveness. This study used data from 3,876 public firms within 47 countries, making it generally applicable. Similarly, Carter et al. (2003) find that the fraction of women and minorities on board of directors on Fortune 1000 firms influence the firm values positively. In another research Carter et al. (2008) they separate gender and ethnic minorities. They make the distinction between three functions of the board (audit, executive compensation, and director nomination) and examine trough which of these functions board diversity influences firm performance. They state that gender diversity influences firm performance positively through the audit function of the firm and that ethnic diversity influences trough all three functions. Not only gender diversity, also nationality diversity is examined. Estélyi and Nisar (2016) examined why firms would elect foreigners on their boards. One of these reasons that was found is that more nationalities on the board has a positive effect on the firm performance. Another positive effect is found by Ujunwa, Okoyeuzu, and Nwakoby (2012) who examined the effect of Nigerian firms on firm performance. Other positive relations between board diversity and firm performance are also found by Gyapong, Monem, & Hu (2016), Erhardt et al. (2003), Miller & del Carmen Triana (2009), and Arat et al. (2015).

In contrast, some negative relations have been found as well. Adams and Ferreira (2009) find that gender diversity, on average, negatively affects firm performance. However, this research is only limited to US firms. This negative relation is also found by Shehata et al. (2017), who examined the effect of board diversity on firm performance for small- and medium-sized enterprises within the UK, while most research on this topic focuses on large firms, which could explain the negative results found. The research of Frijns et al. (2016) also finds a negative relation. However, this study only examines the effect of culture within boards on firm performance and find that this negative effect of cultural differences mainly occurs among the independent board members.

2.5 Hypothesis development

The prior literature thus far suggested that a more diverse board of directors leads to obtaining more risky assets (Adusei & Obeng, 2019; Alves et al., 2015; Elmagrhi et al., 2018). Besides, most of the literature examining the effect of board diversity is positive, suggesting that board diversity can be of importance for firms. Therefore, since there is no evidence suggesting otherwise, the effect of

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European listed firms board diversity has the same effect as prior literature, which leads to the development of the following hypothesis:

Hypothesis 1: Board diversity allows firms to use riskier forms of financing.

Additionally, prior research shows that leverage is important for firms considering firm performance. The literature finds evidence for an effect of board diversity on firm performance (Adams & Ferreira, 2009; Ararat et al., 2015; Carter et al., 2003, 2008; Erhardt et al., 2003; Estélyi & Nisar, 2016; Frijns et al., 2016; Gyapong et al., 2016; Miller & del Carmen Triana, 2009; Shehata et al., 2017; Terjesen et al., 2016; Ujunwa et al., 2012), either positive or negative. However, most of the research suggests there is a positive relation between board diversity and firm performance. Therefore, the following hypothesis is developed:

Hypothesis 2: Board diversity has a positive effect on a firm’s performance.

3 Methodological approach

3.1 Data and variables

The data will be obtained from two databases. First, financial data about the capital structures of firms, as well as part of the control variables will be collected from Eikon. Second, the information on the boards of European firms is gathered for the BoardEx database. A total of 5,269 European firms over a period from 2010-2018 is examined, including firms from the United Kingdom. In Table 5 the specification per country can be found. In total, data of 44 different countries is collected. By far the most firms are from the UK. This is because BoardEx has two separate databases, one for European companies and one for UK companies. The data used is unbalanced, meaning that for a fraction of the firms the data is unavailable for each year.

3.1.1 Dependent variables

Consistent with prior research, the main dependent variables include various measures of equity ratios, debt ratios, return on assets. The capital structure is the financing of the firm, which consists of equity and debt. Since there is evidence in previous research that the pecking order theory holds, the capital structure of firms is segregated into internal equity, external equity, short term debt and long-term debt. In the pecking order theory internal equity is at the top, followed by short long-term debt, long term debt and lastly external equity. Following the research of Alves et al. (2015), internal equity (IE) is measured by the book value of retained earnings divided by the total assets of f the firms. To measure the external equity (EE) the book value of retained earning is subtracted from the book value of total equity and divided by the total assets . Short term debt (STD) is measured as the book value of

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current liabilities minus the accounts payable divided by the total assets. The long-term debt (LTD) is measured as the book value of non-current liabilities divided by the total assts. The measures for debt are also in line with other previously executed research (Elmagrhi et al., 2018; Hasan et al., 2014).

A common measure for firm performance is return on assets (ROA) and is used often in the prior literature about the relation between board diversity and firm performance (Ararat et al., 2015; Carter et al., 2003; Erhardt et al., 2003; Estélyi & Nisar, 2016; Terjesen et al., 2016). ROA is preferred as a measure of firms performance because according to Alves et al. (2015) the operating profit of companies has influence on the amount of retained earnings. This is of importance for the way the firm is financed, especially if the firm, according to the pecking order theory, prefers to use retained earnings as the largest fraction of the capital structure. Firm performance is also linked with operational risk, which can be captured by looking at the standard deviation of ROA. The standard deviation measures the volatility of the ROA over a couple of years, it therefore measures the stability of the firm. This will represent the firm performance over a longer period. Therefore, to measure firm performance, the sigma of ROA is taken as the dependent variable in the analysis between board diversity and firm performance.

3.1.2 Independent variables

The board diversity is measured in twofold, gender diversity and nationality diversity. T

he literature examining board diversity and capital structure showed that gender diversity influences the level of debt within a firm. Therefore, it is also taken as a measure for board diversity in this research. Board gender diversity is measured as the percentage of women present on the board of directors, which is in line with former research (Alves et al., 2015; Carter et al., 2008; Elmagrhi et al., 2018; Gyapong et al., 2016). Even though there is no research done to the effect of board nationality diversity on capital structure, prior literature showed that this does have a positive effect on firm performance (Estélyi & Nisar, 2016; Ujunwa et al., 2012). Therefore, the board nationality diversity is chosen as the other part of the board diversity analyzed in this research. The nationality diversity is as measured as percentage of members present on the board that have a different nationality than the country the firm is located in.

3.1.3 Control variables

In the model, various control variables are used which, in previous research, had an influence on the results of examining capital structure in relation to board diversity. In the research of Elmagrhi et al. (2018) they examine whether the size of the board of directors and whether the audit firm of the firms is one of big four have an interaction effect with board diversity on the relation of board diversity with capital structure. These two variables show, in their research, a significant effect, therefore these variables will also be used as control variables in this research. The board size is in the literature often

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linked in research examining the effect of the board of directors on capital structure (Adams & Ferreira, 2009; Al‐Najjar & Hussainey, 2011; Alves et al., 2015; Carter et al., 2008; Heng et al., 2012; Zainal et al., 2013). The board size (Board size) will be simply be measured as the number of the total amount of persons present in the board of directors. Whether the firm has one of the big four audit firms (Audit firm) will be measured as a dummy variable with 1 if the audit firm is one of the big four and 0 otherwise.

According to Modigliani and Miller (1958) taxes are important determinants in the decision of how to divide the financing. Therefore, Alves et al. (2015) use the effective tax rate as one of their control variables. This shows to have a significant effect on most of the models they used, therefore the effective tax rate is also used as a control variable here. The effective tax rate is measured as the total income tax paid divided by the pre-tax profit (Tax rate).

Firm size, when looking at board diversity, also has proved to be important in prior literature (Adams & Ferreira, 2009; Zainal et al., 2013). Alves et al. (2015) also found that firm size has an influence on the relation between board diversity and capital structure. Larger firms are more likely to diversify their finances and therefore issue more debt than smaller firms will. The size of the firm is measured as the logarithm of the sales because the information that firms issue is more complex and it can be harder for the investor to understand this information, as is similar to the research of Adams and Ferreira (2009) and Alves et al. (2015).

Another control variable, only used when testing the first hypothesis, is the operating profitability. Which is the same as the dependent variable for testing the second hypothesis. ROA is measured in the same way as mentioned before, so EBTIDA divided by total assets. Additionally, Alves et al. (2015) follow the research of Frank and Goyal (2009) and add the standard deviation of the ROA (Sigma ROA) to measure the volatility of the profitability. Firms with a more volatile profitability are more likely to default because there is more operating risk involved (Frank & Goyal, 2009).

Lastly, a country specific control variable is added collected from the World Bank database. This variable is the logarithm of the GPD per country, following the research of Adusei and Obeng (2019).

3.2 Model specification

To test whether board gender diversity and board nationality diversity have an influence on the capital structure of firms, the following baseline model is used:

(𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑠𝑡𝑟𝑢𝑐𝑡𝑢𝑟𝑒)𝑖𝑡 = 𝛽0+ 𝛽1(𝑏𝑜𝑎𝑟𝑑 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦)𝑖,𝑡+ ∑ 𝛽𝑗(𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑗)𝑖,𝑡 9

𝑗=1 + 𝜀𝑖,𝑡

Equation 1 Baseline model leverage

In this baseline model the board diversity will be examined where both gender and nationality diversity are included. The capital structure examined will be one of the dependent variables as mentioned in section 3.1.1. This means that four models are run.

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To test which panel estimation technique is the best one to use on the data, several tests are conducted. First, the Breusch-Pagan Lagrange multiplier is used to test whether random effects or pooled OLS is the preferred method. This test is conducted on all the models run for this examination. The results show that for all models the probability is 0.0000, which indicates that random effects are preferred over pooled OLS. Next, the Hausman test is used to examine whether fixed effects or random effect is preferred. For these tests as well, the results show a probability of 0.0000 for all models. This indicates that fixed effects are preferred over random effects. However, a restriction for fixed effects is that the variables vary over time. This is not the case for the variable audit firm; therefore, this variable must be dropped to do the analysis. If, however, random effects were used as a technique, it is assumed that the time invariant variables do not correlate with the firm-specific effects that are not observed. If this assumption has failed then the results might be biased and inconsistent (Adusei & Obeng, 2019). With this in mind, Adusei and Obeng (2019) choose to use all three estimations techniques to report unbiased results in the relation of board diversity and capital structure. Therefore, all three of these techniques will be used in this analysis as well, to limit the chance of reporting biased results.

The data is corrected for heteroskedasticity and autocorrelation in the error term, by estimating the t-statistics of the coefficients with error terms that are clustered by firm and corrected for heteroskedasticity.

To test whether board gender diversity and board nationality diversity have an influence on firm performance, the following baseline model is used:

(𝐹𝑖𝑟𝑚 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒)𝑖𝑡 = 𝛽0+ 𝛽1(𝑏𝑜𝑎𝑟𝑑 𝑑𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦)𝑖,𝑡+ ∑ 𝛽𝑗(𝐶𝑜𝑛𝑡𝑟𝑜𝑙 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒𝑗)𝑖,𝑡 9

𝑗=𝑡

+ 𝜀𝑖,𝑡

Equation 2 Baseline model firm performance

In this baseline model board diversity will be examined by means of cross-sectional data. This means that for each firm there is one observation. To measure the independent and control variables the averages of over the years are taken.

The models are expanded with the addition interaction terms between the board diversity and the control variables to see if not board diversity but in effect with another variable has a stronger effect on determining capital structure. As described in section 2.3, Elmagrhi et al. (2018) checks for interaction between board diversity and different corporate governance mechanisms and its effect on capital structure. In this research the interaction effect of board size and board diversity and whether the audit firm is one of the big four and board diversity is also examined to check if these moderating effects apply for European listed firms.

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4 Results

4.1 Descriptive results

In Table 1Fout! Verwijzingsbron niet gevonden. the descriptive results can be found. On average, firms have an internal equity of -1.41%, an external equity of 50.23%, a short term debt of 5.49%, and a long-term debt of 14.35%. Alves et al. (2015) also find that external equity has on average the largest fraction, followed by long-term debt. The return on assets is, on average, 105.16%. For the independent variables, on average, 14.87% of the board of directors is woman and 16.90% has another nationality than the country the firm is located in. On average, the board size consists of 8 persons, firms pay 22.97% income taxes, have a log sale of 5.34, about 59% of the firms has one of the big four as an audit firm, and a ROA volatility of 8.76.

Table 1 Description variables

Variable Description Obs. Mean Std. Dev. Min Max

Panel A: dependent variables

ie Internal equity 30,707 -3.1330 254.9428 -42,502.1700 10.8713

ie_w* Internal equity winsorized 30,707 -0.0141 0.7384 -2.6903 0.7751

Ee External equity 30,707 2.9346 182.2777 -13.6742 30,308.3300

ee_w* External equity winsorized 30,707 0.5023 0.7622 -0.0198 3.1818

std Short-term debt 29,248 0.1181 4.2710 0 637.8000 std_w* Short-term debt winsorized 29,248 0.0549 0.0685 0 0.2628 ltd Long-term debt 30,211 0.7270 72.0412 0 11,950.6700 ltd_w* Long-term debt winsorized 30,211 0.1435 0.1519 0 0.5128

sigmaroa Standard deviation of ROA 30,966 13.9044 114.4366 0 6,910.8740

sigmaroa_w*

Standard deviation of ROA

winsorized 30,966 8.7616 10.0160 0.5056 40.6936

Panel B: independent variables

women % of female board members 30,969 0.1487 0.1472 0 1 nationalit~s % of foreign board members 30,672 0.1690 0.2276 0 1

Panel C: control variables

board_size Board size 30,969 8.2006 4.0963 1 42

audit_firm Dummy for audit firm 30,969 0.5915 0.4916 0 1

tax_rate Tax rate 27,567 0.2297 1.0302 0 68.6103

logfirm_size Logarithm of sales 29,208 5.3393 1.2274 0 9

roa Return on assets 30,429 -1.0825 50.9369 -3,937.5400 3,072.1100

roa_w*

Return on assets

winsorized 30,429 1.0516 13.4607 -43.6100 22.6300

loggdp Logarithm of GDP 26,635 12.1440 0.5092 9.3741 12.5963

* These variables are winsorized at the 5% and 95% interval, because it is unlikely that, for example, a firms’ external equity is 30,308 times their total assets. Therefore, these variables are used in the analysis.

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4.2 Empirical results

4.2.1 Board diversity and capital structure

The main results of the analysis are reported in Table 2, showing the fixed effects. The results for the pooled OLS and random effects can be found in Table 9 and Table 14Table 10, in the appendix. Each table shows the analysis with the four dependent variables: internal equity, external equity, short-term debt, and long-term debt. When looking at the F-statistics or, in case of the random effect regression, the Wald statistic, it is observed that all the models have high explanatory power, meaning the fit of the models is good.

All three methods show a positive relation between internal equity and the percentage of women on the boards, and all three relations are highly significant. This result is in line with the pecking order theory; internal equity is the preferred source of financing (Myers, 1984). The positive relation could also indicate that women prefer the safest form of financing, which supports the evidence that, in general, women are more risk averse then males and show this in their decision making (Byrnes et al., 1999; Eckel & Grossman, 2008; Huang & Kisgen, 2013; Weber et al., 2002). The results for external equity are also in line with the pecking order theory, since all three methods show a highly significant negative effect. External equity, according to the pecking order theory is the least favored form of financing (Myers, 1984). These findings are contradicting to the research of Alves et al. (2015), who found a negative relation for internal equity and a positive relation for external equity. They expected that more females on the board of directors would lead to more efficient boards with less information asymmetry. This would then allow the firms to use more risky forms of financing, like external equity. However, the results they found were not significant.

The results found for the effect of gender diversity on short-term debt are a bit more contradicting. The fixed effects and random effects methods both find highly significant negative relations. The pooled OLS finds a positive relation, however insignificant. These findings are in line with the results of the previous studies (Adusei & Obeng, 2019; Alves et al., 2015; Elmagrhi et al., 2018). Looking at the long-term debt, all three coefficients found are positive. However, only for the pooled OLS method the relation is significant at the 10% level. These results show that females on the board increase the amount of long-term debt of the firms, which is contradicting with the existing literature (Adusei & Obeng, 2019; Alves et al., 2015; Elmagrhi et al., 2018). Overall, with respect to gender diversity the results found are not entirely supporting the hypothesis made. With regards to the equity the hypothesis is not supported; evidence is found that more gender diverse boards prefer to use the safer forms of equity. With respect to debt, the hypothesis can be partly supported; firms prefer to use less short-term debt. However, for the long-term debt no evidence is found to support the hypothesis because the results found are insignificant.

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Table 2 Results Fixed effects

Fixed effects VARIABLES IE EE STD LTD % Women 0.287*** -0.260*** -0.0190*** 0.00273 (0.0313) (0.0297) (0.00518) (0.00916) % Nationalities 0.00929 0.0178 3.73e-05 -0.00536 (0.0537) (0.0500) (0.00588) (0.0114) Board size 0.00763*** -0.00461** -0.000248 0.00137** (0.00208) (0.00191) (0.000322) (0.000591) Audit firm ROA 0.00789*** -0.00405*** -0.000852*** -0.00101*** (0.000565) (0.000549) (7.33e-05) (0.000126) Sigma(ROA) -0.0433 0.0431* -0.000570 0.00108 (0.0277) (0.0260) (0.00292) (0.00449)

Tax rate 5.90e-05 -0.00124 5.43e-05 0.000217

(0.00174) (0.00135) (0.000318) (0.000654) Log(Firm size) 0.116*** -0.163*** 0.00672*** 0.0226*** (0.0165) (0.0178) (0.00194) (0.00360) Log(GDP) -0.0103 0.00275 5.72e-05 0.00345* (0.00711) (0.00626) (0.00123) (0.00195) Constant -0.215 0.988*** 0.0286 -0.0316 (0.228) (0.212) (0.0261) (0.0442) Observations 22,078 22,078 21,394 22,154 R-squared 0.094 0.074 0.021 0.018 Adj-R^2 0.0938 0.0735 0.0211 0.0175 F-statistic/Wald 42.09 28.24 19.09 12.21 Number of isin1 3,977 3,977 3,916 3,982

Firm-fixed effects YES YES YES YES

Year-fixed effects YES YES YES YES

IE = internal equity, EE = external equity, STD = short-term debt, LTD = long-term debt Standard errors in parentheses

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

With regards to the effect of nationality diversity on firm leverage, the results indicate that firms with boards with members of different nationalities use less internal equity, more external equity, less short-term debt, and more long-term debt. The positive relation between long-term debt and nationality diversity is in line with the finding of Elmagrhi et al. (2018), who found a significant positive relation between board ethnic diversity and long-term debt. In the fixed effects analysis, none of the relations between nationality diversity and the dependent variables are significant. The findings do not seem to support the pecking order theory but, rather the agency cost hypothesis and signaling theory. These theories predict that more debt, within limits, can have a positive effect on the firm. Due to these findings, hypothesis 1 cannot be supported with regards to nationality diversity.

The board size has different effects and significance per method. For short-term debt none of the methods find a significant relation. On long-term debt, the size of the board seems to have a positive effect for all three methods, but only significantly so for the fixed and random effects. Board size has contradicting results for internal and external equity. The random effect does not find significant

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relations for both dependent variables. The pooled OLS finds a significant negative relation for internal equity and a significant positive relation between board size and external equity. However, the fixed effects regression finds opposing relations, so a positive relation for internal equity and a negative relation for external equity, both significant. The pooled OLS, in this case, could show biased coefficients because the fixed effects consider unobserved effects that are related to the independent variables. If a firm has an audit firm that is part of the big four, then the internal equity is significantly higher in comparison with firms who do not have a big four audit firm. This effect is significantly negative in relation with short-term debt. Both pooled OLS and random effects show these results. As mentioned before the audit firm variable is omitted from the fixed effects regression because this is a time invariant variable. With regards to the return on assets, all three methods show strong significant results for all four dependent variables. Meaning that the return on asset has an important influence on the capital structure of firms. The more return on assets, the more internal equity a firm has and the less debt or external financing. Therefore, the results are in line with the pecking order theory and previous literature. Firms with a more volatile return on assets tend to have less internal equity and both forms of debt. This is because a more volatile profitability creates a higher likelihood that a firm will default because the operating risk is higher. Therefore, the positive relations found with external equity is explained. These findings are similar to the research of Alves et al. (2015). The tax rate does not seem to have a significant effect on firms’ capital structure. Only in the pooled OLS regression tax rate has a significant negative effect on external equity. Firms size have a strong effect on firms’ leverage. The results show that firm who have more sales and are therefore larger prefer to use debt and retained earnings as a source of financing. The same results are found by Alves et al. (2015). Finally, the logarithm of the GDP only has a slightly significant positive effect on long-term debt within the fixed effect regression. The same complex relation between GDP and capital structure is found by Adusei and Obeng (2019), in their analysis they only find a significant positive effect on the debt-to-equity ratio, where debt is measured as long-term debt.

4.2.2 The moderating effect of corporate governance mechanisms

After this main part of the analysis, the same regressions are run again, however now with interaction terms to see if some effects have a stronger effect on the leverage of firms. Following the research of Elmagrhi et al. (2018), interaction terms are added for variables of corporate governance; board size and the audit firm. The results for the fixed effects can be found in Table 3. The two additional methods, pooled OLS and random effect, can be found in Table 10 andTable 15, respectively. Strong significant effects are found with respect to the interaction effect between gender diversity and board size on both internal, and external equity. For internal equity, this effect is negative and for external equity this is positive. For internal equity this, thus, means that an increase of 1% in women

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in a large board of directors with an already high fraction of women, the effect on the internal equity is less than it would be for smaller boards. Indicating that the effect that women on the board have on internal equity decreases when the board size is larger.

Table 3 Interaction effects fixed effects

Fixed effects VARIABLES IE EE STD LTD % Women 0.335*** -0.294*** -0.0236*** 0.0116 (0.0391) (0.0381) (0.00621) (0.0112) % Nationalities -0.0250 0.0973 -0.00501 -0.0250* (0.0632) (0.0595) (0.00726) (0.0150) Board size 0.00754*** -0.00440** -0.000260 0.00136** (0.00205) (0.00188) (0.000325) (0.000592) Audit firm ROA 0.00789*** -0.00404*** -0.000852*** -0.00101*** (0.000565) (0.000549) (7.33e-05) (0.000126) Sigma(ROA) -0.0437 0.0439* -0.000622 0.000917 (0.0276) (0.0259) (0.00292) (0.00451)

Tax rate 5.18e-05 -0.00124 5.39e-05 0.000208

(0.00175) (0.00137) (0.000319) (0.000656)

Log(Firm size) 0.115*** -0.162*** 0.00673*** 0.0226***

(0.0163) (0.0176) (0.00193) (0.00360)

Log(GDP) -0.0104 0.00270 8.58e-05 0.00347*

(0.00713) (0.00630) (0.00123) (0.00195)

Women x Board size -0.0259*** 0.0216*** 0.000636 -0.00253

(0.00653) (0.00616) (0.00111) (0.00193)

Nationalities x Board size -9.73e-06 -0.00381 -0.000175 0.000409

(0.00833) (0.00737) (0.00103) (0.00190)

Women x Audit firm -0.103 0.0618 0.0156 -0.0238

(0.0793) (0.0745) (0.0121) (0.0201)

Nationalities x Audit firm 0.0891 -0.208* 0.0131 0.0509**

(0.116) (0.107) (0.0126) (0.0232) Constant -0.205 0.968*** 0.0304 -0.0282 (0.227) (0.211) (0.0260) (0.0443) Observations 22,078 22,078 21,394 22,154 Adj-R^2 0.0953 0.0754 0.0213 0.0184 F-statistic/Wald 31.06 21.51 12.99 8.637

Firm-fixed effects YES YES YES YES

Year-fixed effects YES YES YES YES

IE = internal equity, EE = external equity, STD = short-term debt, LTD = long-term debt

The interaction effects are centered for women, nationalities, and the board size by subtracting the mean from each observation

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

For external equity, this effect is opposite. So, the effect of women on the board on external equity increases when the board size increases. Indicating that the effect of board gender diversity is stronger for larger firms than it is the case for smaller firms. Therefore, as a robustness check it will be checked how the results change when the boards pass a certain threshold. With respect to different nationalities on the boards, the board size does not seem to affect the effect on the capital structure of firms. Nor does the audit firm seem to interact with the percentage of women. If a firm is audited

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by one of the big four firms, the effect on external equity is larger for firms with a smaller percentage of foreign board members. The opposite interaction effect is found with respect to long-term debt. The effect on long-term debt is larger for firms audited by one of the big four if the percentage of foreign board members is larger.

The pooled OLS and random effect give some different results. The random effects show less significant effects than the fixed effects, the only significant effects found, like the fixed effect, are the interaction terms between the percentage of women and the board size with respect to internal equity and external equity. The pooled OLS shows more significant interaction effects. With respect to internal and external equity it shows the same effects as the fixed effects for the interaction between the percentage of women on the board and board size. Indicating that across firms the effect is the same as within firms. This is not the case for the effect on external equity by the interaction between foreign board members and audit firm. The pooled OLS shows a positive interaction effect as opposed to a negative interaction effect found within the fixed effects method. This indicates that across firms the effect on external equity is larger for firms that are audited by one of the big four where there is a larger percentage of foreign members present on the board. Furthermore, the pooled OLS finds significant results for the interaction between the percentage of women on the board and board size with respect to long-term debt. With respect to short-term debt, the pooled OLS method finds negative results for the interaction effects between the fraction of foreign board members and board size and the percentage of female board members and the audit firm.

4.2.3 Board diversity and firm performance

The same analysis as above was done with respect to the relation of board diversity and firm performance. The results are presented in Table 4. The percentage of women on the board of directors has a negative effect on the sigma of ROA, which is significant at the 10% level. This means the more women there are on the board, the less volatile the firms’ profitability is. Therefore, the firms have a more stable firm performance, indicating that females on boards are more risk averse. This finding is similar to the research of Lenard, Yu, York and Wu (2014). They also found a significant negative effect of board gender diversity on the volatility of ROA. This finding means that hypothesis two can be accepted with respect to board gender diversity. The opposite result is found for nationality diversity. The higher the percentage of board members with other nationalities on the board the more volatile the ROA is. Giannetti and Zhao (2019) examined firm performance volatility with respect to stock return. They found significant positive relations between the volatility and ancestral diversity. Even though this is not the entirely the same measurement, it does indicate that there is a positive relation like the one found in this analysis. This finding means that hypothesis two cannot be accepted with respect to board nationality diversity.

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Now continuing with the analysis of the control variables. Only the logarithm of the sales has a significant effect on the standard deviation of ROA. This finding seems logical, because if there is more turnover in the beginning, it is more likely that at the end more money is left.

Table 4 Board diversity and firm performance

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VARIABLES Sigma(ROA) Sigma(ROA)

% Women -1.473* 1.204 (0.886) (1.153) % Nationalities 6.061*** 5.682*** (0.538) (0.746) Audit firm -0.121 -0.140 (0.245) (0.245) Board size 0.0287 0.0148 (0.0379) (0.0392) Tax rate -0.122 -0.137 (0.171) (0.171) Log(Firm size) -3.977*** -3.922*** (0.134) (0.136) Log(GDP) 0 0 (0) (0)

Women x Board size 0.347

(0.277)

Nationalities x Board size -0.0823

(0.166)

Women x Audit firm -5.929***

(1.765)

Nationalities x Audit firm 0.847

(1.085) Constant 27.56*** 26.96*** (0.613) (0.645) Observations 4,504 4,504 R-squared 0.278 0.281 Adj-R^2 0.277 0.279 F-statistic 247.3 159.3

Sigma(ROA) = standard deviation of return on assets.

The interaction effects are centered for women, nationalities, and the board size by subtracting the mean from each observation.

Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

For this part of the analysis the same regression with interaction terms was conducted. The results of this analysis can be found in

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The interaction term between the percentage of women and audit firm is negative and highly significant. This means that the lower the number of women on the board of directors, the higher the difference in effect is on the volatility between firm audited by the big four or not. However, the main effect for the percentage of women is not significant anymore. The other interaction effects do not have a significant effect on the performance volatility. The percentage of foreign directors is still significant and positive. With regards to the control variables, again only the firm size has a significant negative effect.

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4.3 Robustness check

4.3.1 Lagged effects

There is a possibility that the independent variables are correlated with the error term; endogenous. This could be caused by reserve causality, meaning that, for example, the internal equity can cause the percentage of female board members instead of the other way around as tested in this analysis. To test for this, the models are run with lagged independent variables as is also done in previous literature (Adusei & Obeng, 2019; Alves et al., 2015; Elmagrhi et al., 2018). For the analysis of firm performance, it is not possible to test this way because cross-sectional data is used instead of panel data. The results can be found in the appendix. The results do not show any sign changes on the significant coefficients for the independent variables. Therefore, the results seem robust.

4.3.2 Threshold for diversity

To test if the effect of women and foreigners on the board is stronger when there is a large percentage present, following Adusei and Obeng (2019), a dummy is created to indicate if there are more women or foreigners than the mean of the observations. For the percentage of women, the mean is 15% and for the foreigners it is 17%. The results are reported in the appendix. There are no changes of signs that are significant. Within the fixed and random effects methods there no changes in sign. In the pooled OLS with respect to gender diversity there are two changes in signs. The gender diversity now has a significant negative effect on short-term debt, in comparison to an insignificant positive effect in the main analysis. Next to that, the gender diversity has an insignificant negative relation to long-term debt, in contrast to a significant positive relation found in the main analysis.

With respect to the firm performance stability there are no changes in signs either, indicating that the results found are robust and it does not matter how many women there need to be present on the board to have an effect on the performance volatility.

4.3.3 Exclusion of UK firms

Since most of the firms in the data sample are from the UK it is possible that the results are significantly influenced by these UK firms and could therefore biased the results. Therefore, by means of a dummy variable indicating whether the firms are from the UK or not the models are run again to see how this affects the results. The results can again be found in the appendix. With respect to the analysis of the capital structure, there are some additional significant effects found. Furthermore, the coefficient shows the same signs. However, because of the large drop in observations and since it does not fundamentally change the results, the benefits of dropping the UK firms do not way up against the disadvantages.

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With respect to the firm performance stability, the percentage of females on the board is now strongly significant and positive. This would mean that for the performance volatility it does seem that the effects on volatility are stronger when UK firms are not considered.

5 Discussion and conclusion

This analysis is conducted with 5,269 firms within 44 countries in Europe over a timespan of 9 years, from 2010 until 2018. The analyses are run with unbalanced panel data. Different methods are used to minimalize the change of reporting biased results. The methods used are pooled OLS, fixed effects and random effects, where fixed effects are the preferred method. The results show a positive relation between internal equity and gender diversity. Which is in line with the pecking order theory. Also in line with the pecking order theory is the relation between gender diversity and external equity, which is negative. Next to that, boards with more females present use less short-term debt and more long-term debt. These two findings are not in line with the pecking order theory since short-term debt is stated to be preferred over long-term debt. These results indicate that females prefer safer forms of financing and therefore, hypothesis one cannot be supported. With respect to nationality diversity, the findings show evidence that more foreign directors on the board leads the firms to use riskier forms of financing, like external equity. Nationality diverse boards use less internal equity and short-term debt, and more external equity and long-term debt.

To test the second hypothesis the standard deviation of the return on assets is regressed against board diversity and the control variables. The results indicate that more females on the board lead to less volatile profitability and a more stable performance. The opposite result is found for foreign directors on the board, which leads to more volatile ROA. This is, however, in line with the results found with respect to capital structure; females are safer, and nationality diverse boards lead to more risk.

Next to these main results, some moderating effects are found as well. Moderating effects were found between the percentage of female board members and the board size on the internal and the external equity of the firms. Between the percentage of foreign directors and whether the firm is audited by one of the big four firms, a moderating effect is found with respect to external equity and long-term debt. With respect to the profitability volatility, a moderating effect is found as well, between the percentage of women and the audit firm.

To test for robustness several additional analyses were conducted. First, by means of one year lagged independent variables it is tested if there is potentially endogeneity. This is not the case since there are no significant changes in signs. Second, it is tested whether the amount of diversity has an influence on the results. This was done by using a dummy variable indicating if the percentage of the females or foreigners exceed the means of 15% and 17%, respectively. Again, the results do not change

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fundamentally. The same accounts for the third check. Here, the analysis was conducted by excluding the UK firms, because they are a large part of the observations.

Theoretically, this study contributes to the literature about board diversity and its impact on the firms’ capital structure. Some of the results are consistent with previous literature, and other findings give new insights. Policy makers can consider the findings that females are more risk averse and nationality diversity can cause more risk taking. The same goes for the firms themselves, they can take this into consideration when appointing new board members.

Some limitations of this study are, to start with, that there is no distinction between the different kind of industries. For example, banks could have different capital structures then firms which produce commodities. This distinction could be interesting to further explore, to find more insight in the reasoning behind the effects found between board diversity and capital structure. Another limitation is that there could be a simultaneity problem. This same study could be conducted by means of the second stage lease square method, which makes use of an instrumental variable. Besides, there are other measures for firm performance which could be conducted as well, like independence, age, or CEO duality. Future research could explore these limitations to broaden the literature on the effect of board diversity on capital structure.

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Appendix

A. Specification per country

Table 5 Specification per country

Country Number Obs. IE EE STD LTD ROA

Austria 59 419 0.2523 0.1050 0.0678 0.1941 3.7471 Azerbaijan 1 10 0.3699 0.1885 0.0820 0.1078 7.8090 Belgium 114 848 0.0920 0.3722 0.0653 0.1790 2.8656 Bulgaria 1 2 0.0151 0.3419 0.0781 0.4679 -2.8700 Croatia 4 24 0.1273 0.4638 0.0660 0.0382 4.7613 Cyprus 27 167 0.0018 0.4479 0.0748 0.1604 3.2710 Czech Republic 6 41 0.2106 0.1521 0.0596 0.1721 5.8595 Denmark 70 465 0.2916 0.1738 0.0512 0.1408 4.8632 Faroe Islands 3 23 -0.2243 0.5514 0.0799 0.0994 0.7532 Finland 102 631 0.2629 0.1601 0.0682 0.1744 4.5279 France 561 3,882 0.0069 0.4046 0.0648 0.1641 0.6122 Georgia 1 1 0.1046 0.0701 0.1606 0.0440 4.6100 Germany 492 3,148 0.1446 0.2937 0.0542 0.1490 2.9267 Gibraltar 5 32 -0.3538 0.8678 0.0179 0.0758 -4.7029 Greece 44 331 -0.0127 0.3801 0.1048 0.2356 2.0219 Guernsey 126 782 0.0812 0.6267 0.0345 0.1286 0.5447 Hungary 11 70 0.3262 0.0917 0.0642 0.1791 5.1418 Iceland 5 36 0.2365 0.2402 0.0323 0.2208 6.0092 Isle of Man 61 362 -0.1880 0.8076 0.0513 0.0900 -4.9766 Italy 176 1,161 0.1400 0.1859 0.0850 0.1914 2.7090 Jersey 89 489 -0.3265 0.9844 0.0511 0.0993 -2.1111 Liechtenstein 2 20 0.0821 -0.0015 0.0448 0.0491 0.4120 Lithuania 1 8 0.0286 0.0663 0.0290 0.0350 1.4813 Luxembourg 64 389 0.1037 0.3653 0.0403 0.2356 4.3528 Malta 9 49 0.1506 0.3273 0.0258 0.1253 7.2817 Monaco 9 61 0.0991 0.2722 0.0463 0.3389 3.4561 Netherlands 144 938 -0.0738 0.4523 0.0574 0.1886 1.5049 Norway 138 843 0.0517 0.3559 0.0577 0.1980 0.9800 Poland 49 337 0.1949 0.1902 0.0609 0.1342 3.3984 Portugal 40 314 0.0320 0.2368 0.1177 0.2465 2.6477 Republic of Ireland 112 774 -0.2820 0.8112 0.0333 0.1753 -0.6244 Romania 7 28 0.2483 0.2143 0.0511 0.0725 5.5292 Russian Federation 66 486 0.2418 0.1776 0.0820 0.1960 7.3067 Slovakia 3 5 0.1181 0.0908 0.0445 0.0796 1.6260 Slovenia 1 10 0.6131 0.1411 0.0099 0.0076 9.8130 Spain 141 1,044 0.0643 0.2472 0.0857 0.2128 3.0748 Sweden 265 1,490 0.1193 0.3665 0.0557 0.1622 3.7813 Switzerland 255 1,636 0.2108 0.2446 0.0509 0.1500 3.1347 Turkey 51 316 0.1653 0.1256 0.1188 0.1705 5.8830 Ukraine 4 28 0.3874 0.1464 0.1093 0.1590 1.9654 UK - England 1815 12,113 -0.1532 0.7174 0.0420 0.1107 -1.1437

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