CEO-board social ties and their effect on firm performance and CEO remuneration - Empirical evidence from listed firms in the Netherlands

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CEO-board social ties and their effect on

firm performance and CEO remuneration

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Empirical evidence from listed firms in the Netherlands

Niek Wiltjer Master's Thesis Finance University of Groningen Author: N. Wiltjer Programme: MSc Finance Student number: 1706802 Email: niek@wiltjer.net Address: [removed] City: Groningen

Submission date: February 22th, 2013 Course name: Master's Thesis Finance Course code: EBM866B20

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CEO-board social ties and their effect on

firm performance and CEO remuneration

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Empirical evidence from listed firms in the Netherlands

Niek Wiltjer Master's Thesis Finance University of Groningen

ABSTRACT

This paper investigates the effect of CEO-Board social ties on firm performance and the level of CEO remuneration. We expect social ties to have a negative effect on firm performance because of inefficient monitoring and poorer corporate governance, which can lead to excessive risk taking and individualistic decision-making by the CEO. Furthermore, based on self-categorization theory, we believe that supervisory board members might evaluate the CEO too positive if strong social ties exist, and therefore grant the CEO higher remuneration packages. Using a unique dataset we find evidence concerning two of our six proxies of social ties. Our results show that both shared nationality and shared alma maters, between CEOs and their supervisory board members, decrease firm performance.

Key words: social ties, board independence, supervisory boards, self-categorization theory, similarity-attraction theory, firm performance, executive compensation

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I. Introduction

We investigate whether social ties between CEOs and their supervisory board members (CEO-board) affect firm performance (i). And whether these CEO-board social ties affect the level of CEO remuneration (ii).

The main tasks of a supervisory board are monitoring and, if necessary, disciplining the firm‟s board of management, which carries out the daily management of the firm‟s operations. Furthermore, a supervisory board determines the level of CEO remuneration. Dutch law also specifies an advisory task for the supervisory board. In order to carry out the monitoring task well and to determine a CEO‟s remuneration objectively, a board has to be independent. If certain board members are not independent of the firm and/or the CEO, their actions or decisions might be influenced by personal interests. This in turn, might affect the firm‟s profitability in the short and long term, since less monitoring can lead to to excessive risk taking and individualistic decision-making by the CEO.

We believe that CEO-board social ties have a large impact on a supervisory board member‟s monitoring and discretionary power. Specifically, discretionary power, or in other words, his ability to discipline, might be affected if there are strong CEO-board social ties. Prior research shows that social ties affect economic outcomes (Westphal, Boivie and Chng, 2006; Cohen, Frazzini and Malloy, 2008). For instance, Uzzi (1996) finds that social ties promote cooperation and voluntary exchanges of assets and services between managers.

First, we predict that social ties between CEOs and their supervisory board members negatively affect the firm performance, because these CEO-board social ties affect the monitoring function and the use of discretionary power by supervisory board members, since people like others who are similar to themselves (Stets and Burke, 2000). These so-called similar others are likely to have a strong influence. Therefore we expect that supervisory board members with strong social ties monitor less and give too much freedom to a CEO, which negatively affects firm performance. Second, we predict that CEO-board social ties positively affect the level of CEO remuneration, mainly because experiments (e.g. Tajfel and Turner, 1986) show that members of the same social group tend to be evaluated more positively than people who are not part of this social group.

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sociology and economic literature we determine six variables which will be used as proxies of CEO-board social ties. Our proxies of CEO-board social ties are: age, nationality, being alumni of the same university (alma mater), shared academic discipline, mutual industry of employment and same regional origin.

Our study differs from current research in several ways. First, we contribute to the field of finance and corporate governance by demonstrating that there exists a statistically significant negative relation between social ties and firm performance for two (shared nationality and shared alma mater) of the six proxies we use. Although the results show evidence to support only two out of the twelve hypotheses, we believe that our paper can be used as a basis for further research. Furthermore, our study is based upon an extensive and unique dataset. Our handmade dataset includes all CEOs and supervisory board members of firms listed on Dutch exchanges during 2011.

The organization of this paper is as follows: Section II presents the theoretical background and hypotheses of this study. Section III discusses the methodology. Section IV contains our data discussion and section V presents our results. Section VI concludes.

II. Theoretical background

This section introduces underlying theories, discusses variables concerning social ties and presents expected effects on firm performance and CEO remuneration.

2.1 Research design

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influence the independence of this board. If supervisory boards are not independent, they cannot operate objectively and might handle according to personal interests or the interests of friends and acquaintances.

In our research, social ties are defined as non-familial, informal connections. As a result of these connections people may know each other, although this is not necessary. Recall that we operationalize social ties using mutual qualities and experiences. Through social ties, interactions are facilitated and personal relations are easily made. Our research is focused on social ties between a firm‟s CEO and its supervisory board members. Our assumptions are largely based on two theories, namely similarity/attraction theory and self-categorization theory. Both of them are explained in detail below.

Similarity-attraction theory suggests that people like (and are attracted to) others who are similar, rather than dissimilar, to themselves (Stets and Burke, 2000). Therefore, people may choose to associate with certain others because of their similar personalities. Homophily is the underlying concept and is defined as the attraction to people having one or more common social attributes. This concept leads to the principle that people tend to associate with, and be most influenced by others similar to them (McPherson, Smith-Lovin, and Cook, 2001). There are several reasons why people prefer the company of similar others. First, sharing similar attitudes provides confirmation that a person is not alone in his or her belief. Second, knowledge of similar attitudes may help people to predict others‟ future behaviours in the future. Third, the so-called likeness begets liking explanation; people assume that others who hold similar attitudes to themselves have a greater chance of being attracted to them.

In addition, self-categorization theory suggests that each person identifies with a particular social group, and categorizes themselves as a member of this group (Stainton Rogers, 2003). Furthermore, it proposes that people have a tendency to endorse the norms that distinguish their social group from other groups. Tajfel and Turner (1986) state that members of the same social group tend to be evaluated more positively than people who belong to different social groups1; this effect is called: in-group favouritism.

We expect CEO-board social ties to have a negative effect on firm performance because of excessive risk taking and individualistic decision-making by the CEO, which is made possible due to inefficient monitoring. Furthermore, based on similarity-attraction theory, we believe that supervisory board members may not discipline the management and, on top of this, overestimate the functioning and the capacities of the CEO due to in-group favouritism.

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Additionally, we predict CEO-board social ties to positively affect CEO remuneration, because supervisory board members with social ties might believe that their CEO deserves a higher pay, since similarity-attraction theory suggests that they can identify themselves easier with their CEO. They might be similar to their CEO in some way and, therefore, are better able to understand their CEO‟s actions or responses in certain situations and therefore are more likely to evaluate the CEO more positively. In order to investigate these two topics we formulated the following research questions:

Are CEO-board social ties negatively related to firm performance? (i) Are CEO-board social ties positively related to the level of CEO remuneration? (ii)

Based on sociology and economic literature we determine six variables which are used as proxies of CEO-board social ties. These variables are part of the larger concept: social ties, and their effects will be separately tested in 12 hypotheses in order to answer the research questions presented above.

2.2 Age

Age is determining for a person‟s traits and attitude (Howe and Strauss, 2007). Directors, who are not of the same age, are more likely to have different views and standards, what makes them feel like being part of a different social group. Self-categorization theory suggests that these different views and standards might affect the decision-making process of a supervisory board, since the CEO is due to this less likely to be a member of the same social group and non-members tend to be evaluated less positively than members. We presume that effects resulting from age differences only occur when there is a large difference in age. Although, we realize that this is quite likely to occur in our dataset, since in our dataset, supervisory board members seem to be older than CEOs on average.

Based on self-categorization theory, we assume that there will be more affinity towards the CEO if supervisory board members and their CEO have only a small age gap, since it is more likely they feel being part of the same perceived social group. This leads to less monitoring and more (misplaced) confidence in their CEO‟s capabilities. Therefore, we expect age difference to positively affect firm performance. Furthermore, we expect similar age to have a negative effect on CEO remuneration, since supervisory board members are able to identify themselves better with the CEO (similarity-attraction theory), and thus grant the CEO a higher salary.

H1: Age difference is positively related to firm performance.

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2.3 Nationality

Nationality is the status of belonging to a particular nation. Anderson (1991) argues that nations are 'imagined communities'; systems of cultural representation whereby people come to imagine a shared experience of belonging to a particular community. Furthermore, Hofstede (1980) defines national culture as the set of collective beliefs and values that distinguish people of one nationality from those of another. On average the degree of power distance, individualism, uncertainty avoidance, masculinity and long term orientation is expected to be equal for CEOs and supervisory board members with the same nationality (Hofstede and Minkov, 2010).

Thus, we expect nationality to be an important variable to determine CEO-board social ties, as a result of in-group favouritism. Since this paper takes as its base that the monitoring function of a supervisory board is affected if its members share certain social ties with the CEO, we expect shared nationality to have a negative effect on firm performance. Furthermore, since CEO remuneration is determined by the supervisory board as well, we expect supervisory boards, who have more shared nationalities with the CEO, to grant more favourable (and thus higher) compensation packages.

H3: Shared nationality is negatively related to firm performance.

H4: Shared nationality is positively related to CEO remuneration.

2.4 Alma mater

Alma mater refers to the university that one has attended. Connections forged through a mutual alma mater enjoy enhanced interaction via in-jokes, shared traditions, and a sense of group belonging (Hwang and Kim, 2009). Cohen, Frazzini and Malloy (2008) report that having a common alma mater facilitates information sharing between boards of directors and mutual fund managers and between executives of a firm and analysts following the firm‟s stock. In addition to this, Chidambaran, Kedia and Prabhala (2011) find that commonalities in educational alma mater between CEO and directors tend to elevate fraud probability. Social-psychological literature is consistent with these results. Wilder (1986) shows that social group members are generally allocated more rewards in relation to non-group members. This effect is explained by similarity-attraction theory, which states that due to homophily, people who are similar to one have a stronger influence.

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reasoning, we believethat this leads to a higher CEO remuneration as well, since social group members are evaluated more positively.

H5: Shared alma mater is negatively related to firm performance.

H6: Shared alma mater is positively related to CEO remuneration.

2.5 Academic discipline

Academic discipline or the field of study is defined as the branch of knowledge that is taught at college or university level. We predict that CEOs and supervisory board members with equal academic disciplines to have similar opinions and knowledge about processes.

As a result of this, we expect directors to approve CEO‟s actions more often if they have a mutual academic discipline, since they more positively evaluate this CEO because he is member of the same social group. Subsequently, we predict that firms with a higher number of mutual academic disciplines have a lower performance, for the very reason that their monitoring function could be affected, since they may give too much freedom to CEOs who are similar to themselves (similarity-attraction theory). Following the same reasoning for CEO remuneration, we expect supervisory board members with mutual academic backgrounds to overestimate the CEO‟s functioning and, for that reason, wrongfully grant higher compensation packages, as a result of in-group favouritism.

H7: Shared academic discipline is negatively related to firm performance.

H8: Shared academic discipline is positively related to CEO remuneration.

2.6 Main industry of employment

The main industry of employment is the industry in which one worked the longest or which is in other means central in one‟s career. We expect similarities to exist, for CEO‟s and supervisory board members working in a mutual industry through shared interests and common experiences. These supervisory board members may better understand the CEO‟s reasoning and its response to certain situations. Hwang and Kim (2009) argue that mutual industries signify additional similarities through shared interests and common experiences, providing further points of contact.

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H9: Shared main industry of employment is negatively related to firm performance.

H10: Shared main industry of employment is positively related to CEO remuneration.

2.7 Regional origin

There are unique regional qualities which vary within the Netherlands and vary between countries. These traits could be appreciated if directors have them in common. Examples are sober-mindedness for people from the Groningen province or individualistic for people from the USA. These traits may very well contribute to an affinity for others with the same regional origin.

If the CEO and one or more supervisory board member have the same regional origin, this affinity for others can lead to less stricter applying of rules and more resistance to disagree with the CEO during meetings and votes. Recall that board independence is essential for monitoring and that it can affect firm performance due to excessive risk-taking and individualistic decision-making. So, we predict a negative effect of shared regional origin on firm performance. Furthermore, we hypothesize that a affinity for others leads to a higher pay if the CEO and one or more supervisory board members share the same regional origin, based on self-categorization theory which states that people belonging to the same perceived category are evaluated more positively.

H11: Shared regional origin is negatively related to firm performance.

H12: Shared regional origin is positively related to CEO remuneration.

III. Methodology

This section discusses the methodology of our study, it describes which regressions we run and explains the two main models we use. Furthermore, it presents methods to verify and validate our results.

3.1 Research design

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The social tie proxy of age („YEAR‟) is measured in a different way compared to the other five proxies. Instead of similarity, the basic principle here is dissimilarity, since age differences are ratio data instead of (dichotomous) ordinal data. This is an important point which affects the hypotheses and therefore the expected signs. Subsection 4.4 discusses the measurement of social ties in more detail.

Our research is divided into two models; namely the firm performance- and the CEO remuneration-model. We start with the initial tests of CEO-board social ties on firm performance. Subsequently, we perform three robustness tests using different measurements of firm performance. Next, an additional test is performed using a modified sample, which only contains the supervisory board members who are chairman of the board.

Subsequently, the initial tests of the CEO remuneration-model are performed. As a robustness test for the CEO remuneration model we repeat our analysis using an extended sample consisting of all CEOs who were in office during 2011. As a second robustness test, severance pays are excluded from total CEO pay using, once more, the extended sample. Three additional tests are performed for this model. We start by using the modified sample, which contains solely chairmen. Next, an additional test is performed using a new modified sample consisting of only remuneration committee members. We conclude with an additional test of the effects of the proxy variables on variable pay using the initial sample. Summaries of all tests and results are given in tables XII(page 35) and XIII (page 35/36).

We cannot apply panel regression techniques, as a consequence of only using 2011 data. Panel regression helps to mitigate problems of multicollinearity and it can remove the impact of certain forms of omitted variables bias in regression results (Brooks, 2008). Especially the latter might affect our results, although we include three control variables in both models. Besides, with panel regression techniques we can use methods like accounting for a firm fixed and time fixed effect to resolve endogeneity issues. We minimize endogenity issues by including control variables in our regressions. Furthermore, literature shows no evidence that the used dependent variables affect the independent variables. Hence, the effects are one-way and no simultaneity occurs in our models.

3.2 Specification of firm performance-model

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(1) (2) (3) (4) (5) (6) in which the subscript i denotes the firm observation;

„PERF‟ is a firm‟s performance measured by the Price/Book ratio;

‟YEAR‟ is the natural logarithm of the total difference in age between a firm‟s supervisory board members and its CEO, divided by the firm‟s total number of supervisory board members;

„NAT‟ is the number of a firm‟s supervisory board members who have one or more equal nationalities as its CEO, divided by the firm‟s total number of supervisory board members; „ALMA‟ is the number of a firm‟s supervisory board members who attended one or more equal universities as its CEO, divided by the firm‟s total number of supervisory board members;

„DISC‟ is the number of a firm‟s supervisory board members who specialized in one or more equal academic disciplines as its CEO, divided by the firm‟s total number of supervisory board members;

„INDU‟ is the number of a firm‟s supervisory board members who have the same main industry of employment as its CEO, divided by the firm‟s total number of supervisory board members;

„REGION‟ is the number a firm‟s of supervisory board members who have same place of birth (inside the Netherlands) or country of birth (outside the Netherlands) as its CEO, divided by the firm‟s total number of supervisory board members;

is the random disturbance term; are parameters to be estimated.

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using these equations, the effect could be a result of (one or more) control variables. The expanded equations, (7) to (12), are formulated as follows:

(7) (8) (9) (10) (11) (12)

in which, besides the variables explained above, ‟C_FIRM SIZE‟ is the natural logarithm of a firm‟s size measured by its assets in thousands of Euros; ‟C_LEVERAGE‟ is a firm‟s financial leverage calculated as: total debt divided by the sum of total debt and total equity. ‟C_FIRM AGE is the natural logarithm of a firm‟s age measured in years; 3.3 Specification of CEO remuneration-model The effect of social ties on CEO remuneration is tested in the same way as firm performance. First equations (13) to (18) are estimated, in their simple forms, using the OLS regression technique. Again, each equation contains a different proxy of CEO-board social ties. Subsequently, three control variables are added. Regression equations (19) to (24) reflect these additions. (13) (14) (15) (16) (17) (18)

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in which, besides the variables explained above, „C_PERF‟ is a firm‟s performance measured by the Price/Book ratio;

‟C_CEO TENURE‟ is the natural logarithm of the number of years that a specific director is CEO of a firm.

Table I provides a summary of the hypotheses and the variables used as proxies of social ties. The hypotheses are based on the theories described in section II.

Table I

Summary of hypotheses.

This table presents a summary of the hypotheses and the variable name of the used proxy of social ties.

Number Hypothesis Proxy of social ties

H1 Age difference is positively related to firm performance. „YEAR‟

H2 Age difference is negatively related to CEO remuneration. „YEAR‟

H3 Shared nationality is negatively related to firm performance. „NAT‟

H4 Shared nationality is positively related to CEO remuneration. „NAT‟

H5 Shared alma mater is negatively related to firm performance. „ALMA‟

H6 Shared alma mater is positively related to CEO remuneration. „ALMA‟

H7 Shared academic discipline is negatively related to firm performance. „DISC‟

H8 Shared academic discipline is positively related to CEO remuneration. „DISC‟

H9 Shared main industry of employment is negatively related to firm performance. „INDU‟

H10 Shared main industry of employment is positively related to CEO remuneration. „INDU‟

H11 Shared region or country of birth is negatively related to firm performance. „REGION‟

H12 Shared region or country of birth is positively related to CEO remuneration. „REGION‟

3.4 Robustness tests

First, by means of robustness testing, regression equations (7) to (12) are retested using alternative measures of firm performance. Both alternative market-based and accounting-based measures of firm performance are used.

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3.5 Additional tests

Besides the initial- and robustness tests, we perform several additional tests. The results of these additional tests do not directly help us answering our research questions; nevertheless they do address closely related issues.

First, we argue that chairmen of supervisory boards have greater influence compared to regular members, since the chairman of a board has more power and often has close contact with the CEO. Furthermore, the chairman has a casting vote in case of a tie. For these reasons, we investigate the effect of social ties between CEOs and chairmen of supervisory boards on firm performance and CEO remuneration. Hence, both regression equations (7) to

(12) and (19) to (24) will be retested using social ties between CEOs and chairmen of their supervisory boards.

Second, following the reasoning of greater influence, our attention is drawn to remuneration committees. Remuneration committees have a large influence on CEO remuneration, since they draw up the compensation propositions (Anderson and Bizjak, 2003; Ogden and Watson, 2012). Therefore, regression equations (19) to (24) will be retested using solely social ties between CEOs and members of the remuneration committee.

Third, since supervisory board members might be unable to significantly affect the base salary of CEOs, we perform an additional test about their influence on variable pay. We reason that the potential effect of social ties on CEO remuneration is observed stronger in variable pay, because, on average, base salaries do not differ strongly per year and are set by referring to external benchmark data. To adjust for firm size and job difficulty we divide variable pay by the total pay. The testing of this so-called Variable-to-Total-Pay ratio is performed using a smaller sample, since several firms do not have variable pay plans or did not grant variable pays to their CEOs in 2011. Assuming that CEOs are risk-averse and prefer a higher base salary instead of variable pay, we predict negative effects of social ties on variable pay. Therefore, regression equations (19) to (24) will be retested using „PAY_RATIO‟ instead of „PAY_TOTAL‟ as the dependent variable.

IV. Data

In this section we discuss our sample, describe the data gathering and explain how we operationalize these variables. Furthermore, we present summary statistics of our sample and its specific variables.

4.1 Sample selection

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2011. The firms are selected based on their listing on the Amsterdam Exchange Index (AEX), Amsterdam Midkap Index (AMX) and the Amsterdam Small Cap Index (AScX). All Dutch firms have a two-tier board system as they are obliged to according to Dutch law. However, our sample contains several non-Dutch firms of which some have a one-tier board structure. We do not consider this to be problematic in our analysis as tasks and responsibilities of supervisory board members and non-executive directors are similar. We constructed an initial sample of 74 firms which were listed most of the 2011 period.2 Though, the final sample is 69 firms, since several observations are excluded from our final dataset for various reasons.3,4

For eachfirm, data is gathered on CEOs and supervisory board members. We focus on 2011,

since it is the most recent year for which data is relatively easy accessible and fully available. Furthermore, we perform several (not reported) checks in order to examine if differences between industries influence our results significantly. Ratios (i.e. Price/Book, Tobin‟s q, ROA and ROE) are grouped per industry, no strong industry effects occur. In addition, financial firms are excluded; yet again this exclusion does not affect the results significantly.

4.2 Firm performance

Firm performance is measured using the Price/Book (P/B) ratio. The P/B ratio is also known as the Price-Equity ratio and is calculated as: Stock Price / (Total assets - Liabilities). The P/B ratio is a market-based measure of firm performance and is widely used (Cheung and Wei, 2006; Duffhues and Kabir, 2008; Bhagat and Bolton, 2008). The P/B ratio is considered to indicate long term performance, since market expectations are included in the market prices (Murray, 2007). For the P/B ratio we do not use the logarithmic function, since this would make the economic meaning of this dependent variable less easy to interpret and, important to mention, using the logarithmic function does not change our findings.

Robustness test are performed by repeating the regressions using different measures of firm performance. First, using the Tobin‟s q ratio; another market-based measure. Following Aggarwal, Erel, Williamson and Stulz (2009), Tobin‟s q is defined as: (total assets

+ market value of equity – total common equity – deferred taxes) / total assets. Second, using

an accounting-based measure namely Return on Equity (ROE), which is defined as operating

earnings / book value of total equity. In order to transform wide-ranging quantities to smaller

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We experimented with excluding financial firms, because ROE (and ROA) can vary substantially between the financial industry and other industries. Furthermore we experimented with excluding non-Dutch firms. Neither of these changes in the sample matters empirically.

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A full list of the firms in our sample is provided in appendix A.

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scopes we use the cube root of ROE denoted as (Jordan, Ross and Murphy, 2002). We prefer the cube root over the natural logarithm, since it preserves the positive or negative sign. ROE is a better indicator of firm performance than simple measurement variables as Earnings per Share, since ROE includes the balance sheet (Stern, Shiely and Ross, 2004). Third, using the cube root of Return on Assets (ROA), another accounting-based measure, which is defined as: operating earnings / book value of total assets. Data to calculate Tobin‟s

q and the P/B ratio are obtained from „DataStream‟. ROA and ROE are derived directly from

the „Orbis‟ database. All four performance measures are expressed as yearly percentages and can, as a result of this, be compared easily with other firms.

4.3 CEO remuneration

CEO remuneration is the CEO‟s total remuneration measured in thousands of Euros. It is the sum of the base salary, paid cash bonuses, granted shares/options, pensions, severance pays and other benefits granted by the firm to its CEO (Goergen and Renneboog, 2011).5 The values of these components are obtained directly from the 2011 annual reports.6 For several CEOs who were appointed or succeeded in 2011, this number is first converted to an annual number.

4.4 Social ties

As stated above, the following variables are used as proxies for social ties: year of birth, nationality, alma mater, academic discipline, main industry of employment and regional origin. These characteristics are gathered for CEOs as well as supervisory board members. For firms whose CEO is replaced during 2011, an unweighted average of both the new and old CEOs‟ score is used, as it is not clear how much effect each CEO has on the firm‟s performance.7

Year of birth. CEO-board social ties regarding age are defined as the average of

difference in age between the CEO and its supervisory board members. The natural logarithm of this average is used in our regressions in order to transform wide-ranging quantities to smaller scopes. Year of birth is retrieved from the annual reports for most directors. Missing years of birth are obtained from the Dutch Chamber of Commerce.

5

For two firms this number is converted from U.S. Dollars to Euros using the exchange rate: 77.2857 $/€ (12/31/2011) per $100. For one firm it is converted from British Pounds to Euros using the exchange rate 119.7175 £/€ (12/31/2011) per £100.

6

Dutch firms are obliged to publish this according to the Dutch Corporate Governance Code (Monitoring Commissie Corporate Governance Code, 2008).

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CEO-board social ties regarding nationality, alma mater, academic discipline, main industry of employment and regional origin are identified using an identical method. This method is in line with research on CEO-director connections by Chidambaran, Kedia and Prabhala (2010). The number of similarities between the CEO and its supervisory board members is determined for each variable. Hence, this is the number of supervisory board members who have the same nationality, alma mater, academic discipline, industry or regional origin as the CEO. Consequently, this number is divided by the number of supervisory board members to determine a score per firm. For example: the CEO of Koninklijke BAM Groep studied at the Delft University of Technology. Three of the six supervisory board members attended this particular university as well. Hence, Koninklijke BAM Groep‟s score regarding alma mater is: 3 / 6 = 0.500. Directors for whom we cannot retrieve certain data are excluded in the calculation of this specific social tie score. However, these directors are included in the calculation of the other social tie scores. The values on academic disciplines and main industries of employment are first categorized before identification of the similarities takes place. We manually match the names of alma maters, taking care to identify multiple versions of the names (e.g. Harvard University and Harvard).

Nationality. We obtain the directors` nationalities from each firm‟s annual report of

2011. This is sufficient for CEOs as well as supervisory board members, since the Dutch Corporate Governance Code (Monitoring Commissie Corporate Governance Code, 2008) requires firms to publish this information.

Alma mater. Directors‟ alma maters are mainly retrieved from „Orbis‟ and

„ManagementScope‟. For several firms this information is obtained from annual reports, since we believe this information is more reliable, as it is directly published by the firm. Details on departments and/or faculties are omitted. Different locations of one institution are combined into one category, since we believe it is the overarching name that is binding the alumni rather than the physical locations. For many directors we obtain multiple alma mater values, as many directors attended two or more universities. Therefore we use every university one attended in determining the CEO-board social tie per firm.8

Academic discipline. The academic disciplines of directors are mainly obtained from

„Orbis‟ and „ManagementScope‟. We manually hand-collect remaining information which is not available in either of the above sources. Sources used to retrieve missing information are among others: „Times‟, „Wikipedia‟, „Forbes‟.9

To categorize the academic disciplines in our dataset we use a list based on the „Classification of Instructional Programs‟ (CIP)

8

The alma maters listed in our dataset may be the undergraduate or graduate school attended. Some directors attended the institution, but did not graduate.

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(http://nces.ed.gov/ipeds/cipcode), which consists of 46 categories.10 The directors in our sample are distributed among 35 of the 46 discipline categories. In line with the alma mater variable, we examine social ties for each academic discipline if a director followed multiple study programs.

Main industry of employment. We use hand-collected employment data from

„ManagementScope‟ to determine a main industry of employment per director. To determine this main industry we take into consideration: the most recent employment, the length of the employment period as well as the estimated hours per week devoted to the job. Based on these three dimensions we determine one main industry of employment per director. In order to categorize the industries of main employment in our dataset we use a list based on the

„Industry Classification Benchmark‟ (ICB) (http://www.icbenchmark.com/

ICBDocs/Structure_Defs_English.pdf), which consists of eleven categories.11 Directors are distributed among each of the eleven categories

Regional origin. Determining where a director lived during his childhood is difficult.

Simply because families move to other places and considering that this information is not publicly available on most directors. Therefore we use the common proxy: place of birth, since this is readily available and is an easily defined measure, although we understand the implications of this choice (e.g. cases in which directors are born in a hospital located in another city). Place and country of birth are provided by the Dutch Chamber of Commerce for Dutch firms, since these firms are obliged by Dutch law to provide this information on their directors. For foreign firms listed at the Dutch stock exchange, we are unable to retrieve the regional origin of their directors. As a result of this, seven firms are excluded from our analysis regarding the regional origin tie.12 Furthermore, we categorize places outside the Netherlands according to their countries, because using finer regions results in much sparser partitions. Most directors are born in the Netherlands, as our sample consists of firms listed on Dutch stock exchanges. The directors in our sample are distributed among 41 regions, namely 29 countries (39.64%) and 12 provinces (60.36%) inside the Netherlands.

4.5 Control variables

Firm size. Firm size is the size of the total firm and measured as the natural logarithmic of its

total assets. Fama and French (1992) found firm size to be related to market returns. Since the firm size is used as a control variable in many studies related to firm performance (Wallsten, 2000; Brick, Palmon and Wald, 2006; Topak, 2011). Furthermore, firm size is positively

10

A list of the used academic discipline categories is provided in appendix B.

11

A list of the used industry categories is provided in appendix C.

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linked to CEO remuneration (Baker, Jensen and Murphy, 1988; Murphy, 1999). Hence, firm size will be included as a control variable in both regressions equations for firm performance and CEO remuneration.

Firm age. The natural logarithm of the firm‟s age measured in years. Due to the

effects of learning curve and survival bias, older firms are likely to be more efficient than younger ones (Ang, Cole and Lin, 2000). Therefore, it is a common control variable in many firm performance models (Brown and Caylor, 2006). Firm age will be used as a control variable for the regressions on firm performance.

Leverage. We define leverage as total debt divided by the sum of total debt and total

equity. Agency theory conjectures that debt financing is more effective than equity. It is believed that it controls managers' incentive from wasting free cash flows and, consequently, it enhances the CEO's motivation in improving the firm performance (Jensen, 1986). Following this reasoning, we use leverage as a control variable to estimate firm performance.

Firm performance. We measure firm performance, in line with our model on the

effects of social ties on firm performance, using the P/B ratio. The statistical link between CEO remuneration and firm performance is well established (Jensen and Murphy, 1990; Wallsten, 2000; Brick, Palmon and Wald, 2006). Firm performance will be applied as a control variable in estimating CEO remuneration.

CEO tenure. CEO is the number of years that this specific director is CEO of the firm.

Hermalin and Weisbach (1998) argue that CEO tenure is a proxy of CEO quality since, a long tenure indicates that a CEO is worth keeping. In line with Hwang and Kim (2009) we use CEO tenure as a control variable in estimating CEO remuneration.

4.6 Descriptive statistics

Our dataset contains 520 director observations of social tie data, and 74 firm observations of financial data for 2011. In addition to this, we obtained the 2011 compensation data for 79 CEOs.13 After the exclusion of Fornix BioSciences, Pharming Group, Kardan, Antonov and Aperam, a final sample of 69 firms remains. Table II shows extensive descriptive statistics on our final sample. No problems are found regarding autocorrelation, heteroscedasticity or multicollinearity.14

First and foremost, alma mater („ALMA‟) and regional origin („REGION‟) have the most striking descriptive statistics, since observations for most directors do not indicate social ties, which lead to low scores for these variables. Hence, this results in extremely low means

13

The number of CEOs is higher than the number of firms as a result of seven CEO successions during 2011 in various firms of our sample.

14

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and medians of 0.000. We must take these statistics into account in our conclusions. In addition, nationality („NAT‟) has a relatively high mean. This is a result of the high number of Dutch directors in our sample, which obviously is the consequence of focussing on listed firms in the Netherlands. This is considered not to be problematic, since it does not influence our results according to a (non-reported) test in which we exclude all non-Dutch directors.

Table II

Descriptive statistics

This table presents descriptive statistics at firm-level of our final sample.

Variable Obs. Mean Median Std. Dev. Min Max Social ties: ‘YEAR’ (LN) [69] 2.099 2.197 .427 1.100 3.000 ‘NAT’ [69] .581 .625 .344 .000 1.000 ‘ALMA’ [69] .076 .000 .156 .000 .600 ‘DISC’ [69] .419 .429 .330 .000 1.000 ‘INDU’ [69] .278 .250 .208 .000 1.000 ‘REGION [62] .095 .000 .121 .000 .500 Performance: ‘PERF’ [PRICE-BOOK] [69] 1.632 1.296 1.283 .051 6.218 ‘PERF’ [TOBIN] [69] 1.234 1.074 .630 .337 4.903 ‘PERF’ [ROA] ( ) [69] .860 1.533 1.646 -2.905 3.209 ‘PERF’ [ROE] ( ) [69] 1.145 2.164 2.305 -4.418 3.937 Remuneration: ‘PAY_TOTAL’ ‘PAY_RATIO’ [69] [46] 1,821.750 294 1,251,000 .311 1,680.902 .186 315 .014 11,422 .834 Control variables: ‘C_FIRM SIZE’ (LN) [69] 14.634 14.474 1.813 11.201 19.627 ‘C_FIRM AGE’ (LN) [69] 3.596 3.638 .995 .693 .5165 ‘C_LEVERAGE’ [69] 2.110 .417 7.874 .031 46.144 ‘C_CEO TENURE’ (LN) [69] 1.445 1.609 .976 0 .358

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Firms with a one-tier board structure do not have a supervisory board. However, several members of their board of directors only have a monitoring and advising role. Hence, these non-executive directors can be seen as supervisory board members. To keep things simple we refer to both types of directors as supervisory board members.

In our final sample we have 100% coverage in terms of age, 100% coverage in terms of nationality, 89.76% coverage in terms of alma mater, 88.15% coverage in terms of academic discipline and 100% coverage in terms of industry. Furthermore, we have 100% coverage in terms of regional origin for Dutch firms and 87.15% of all firms in our sample. Social ties are less accurate for some firms due to missing data points, as a result of excluding directors, for whom we cannot retrieve certain data points, in the calculation of specific social tie scores. However, we do not believe this is problematic since it concerns random missing data points without any pattern.

The industrial makeup of the final sample is as follows: 5.71% oil & gas; 8.57% basic materials; 27.14% industrials; 14.29% consumer goods; 2.86% health care; 10.00% consumer services; 1.43% telecommunications; financials 12.86%; and 17.14% Technology firms. It follows that, industrials are overrepresented and telecommunications are underrepresented in our sample.15

V. Results

In this section we present our results regarding the effect of social ties on firm performance (subsections 5.1 to 5.3) and their effects on CEO remuneration (subsections 5.4 to 5.6). For both models, we start by presenting our main regression results. Subsequently, we present results of robustness tests. Furthermore, we present results of additional tests, in which we take into account the chairmen, remuneration committees and variable pay.We conclude with the discussion of our results and possible explanations for unexpected findings.

5.1 Regressions results of the firm performance-model

In table III, we estimate regression equations (1) to (6). Each equation contains one social ties proxy (i.e. ‟YEAR‟, ‟NAT‟, ‟ALMA‟, ‟DISC‟, ‟INDU‟ and ‟REGION‟). As in table IV, these equations are expanded with control variables. Equations (7) to (12) reflect these additions.

Age. We find no evidence for the proposed positive effect of „YEAR‟ on „PERF‟

(hypothesis H1). In other words, our results do not show that, if directors differ more in age

with respect to the CEO, the firm performance is necessarily better. In fact, estimated

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coefficients of both the regression equation without control variables (1) and the regression equation with control variables (7) are negative, whereas we expected positive coefficients.

Nationality. In line with hypothesis H3, our results show a significant negative effect

of „NAT‟ on „PERF‟, that is, more shared nationalities between CEO and its supervisory board members lead to a lower firm performance. First, as showed in table III, we find a coefficient of -0.925 for regression equation (2). Despite that this effect is relatively weak the effect is statistically significant (p = 0.094) using a significance level of 10%. Subsequently, when we add control variables (8) this effect becomes more statistically significant (p = 0.058) , while the coefficient‟s sign remains to be negative (coef. = -1.111).

Alma mater. Initially we find significant evidence for the negative effect of „ALMA‟

on „PERF‟; i.e. the number of shared attended universities between the CEO and its supervisory board members, and firm performance (hypothesis H5). Estimates of regression

(3) show a coefficient of -1.407 and a p-value of 0.049. Thus, this indicates a significant strong negative effect. However, when we add control variables (regression equation (9)), this effect becomes insignificant (p = 0.166) and remains to be negative (coef. = -1.092).

Academic discipline. Our results show no signs of a negative effect of „DISC‟ on

„PERF‟ (H7). The effect of shared academic disciplines (measured by types of universities

degrees) on firm performance is not observed in our sample. In fact, the signs of the estimated coefficients are a positive for regression equations (4) and (10). However, neither is statistically significant.

Industry of main employment. We find no statistically significant evidence for the

proposed negative effect (hypothesis H9) of „INDU‟ on „PERF‟. Although the signs indicate

that firm performance is better when directors and their CEO have worked in different industries, the results are not significant (without control variables: p = 0.308 and with control variables: p = 0.438).

Region. Based on regression equation (6) we find significant evidence for the negative

effect of „REGION‟ on „PERF‟; i.e. the shared regions of origins between the CEO and its supervisory board members, and firm performance (hypothesis H11). Estimates of regression

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Table III

Regression results social ties on performance (P/B ratio)

This table presents estimations of the following six regression equations:

(1) (2) (3) (4) (5) (6)

in which „PERF‟ is a firm‟s performance measured by the Price/Book ratio; ‟YEAR‟ is the number of CEO-board connections regarding the year of birth divided by the total number of supervisory board members; „NAT‟ is the number of CEO-board connections regarding nationality divided by the total number of supervisory board members; „ALMA‟ is the number of CEO-board connections regarding attended universities divided by the total number of supervisory board members; „DISC‟ is the number of CEO-board connections regarding the academic discipline divided by the total number of supervisory board members; „INDU‟ is the number of CEO-board connections regarding the main industries of employment divided by the total number of supervisory board members; and „REGION‟ is the number of CEO-board connections regarding places/countries of birth divided by the total number of supervisory board members. For each regression equation coefficients and standard errors (between brackets) are presented. Furthermore, ***, **, and * indicate statistical significance at respectively the 1%, 5%, and 10% levels. The used sample is based on connections between all supervisory board members and their CEOs who were in office between 01/01/2011 and 12/31/2011.

Coefficient (standard errors)

Variable Expected sign (1) (2) (3) (4) (5) (6)

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Table IV

Regression results social ties on performance (P/B ratio)

(7) (8) (9) (10) (11) (12)

in which „PERF‟ is a firm‟s performance measured by the Price/Book ratio; ‟YEAR‟ is the number of CEO-board connections regarding the year of birth divided by the total number of supervisory board members; „NAT‟ is the number of CEO-board connections regarding nationality divided by the total number of supervisory board members; „ALMA‟ is the number of CEO-board connections regarding attended universities divided by the total number of supervisory board members; „DISC‟ is the number of CEO-board connections regarding the academic discipline divided by the total number of supervisory board members; „INDU‟ is the number of CEO-board connections regarding the main industries of employment divided by the total number of supervisory board members; „REGION‟ is the number of CEO-board connections regarding places/countries of birth divided by the total number of supervisory board members; ‟C_FIRM SIZE‟ is the natural logarithm of firm size measured by assets in thousands of Euros; ‟C_LEVERAGE‟ is a firm‟s financial leverage calculated as: total debt divided by the sum of total debt and total equity; and ‟C_FIRM AGE is the age of a firm measured in years. For each regression equation coefficients and standard errors (between brackets) are presented. Furthermore, ***, **, and * indicate statistical significance at respectively the 1%, 5%, and 10% levels. The used sample is based on connections between all supervisory board members and their CEOs who were in office between 01/01/2011 and 12/31/2011.

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5.2 Robustness tests of the firm performance-model

In appendix E, we present tables containing results from a range of robustness tests using alternative indicators of firm performance („PERF‟). First, table E-I indicate the regression results of equations (7) to (12), using the Tobin‟s q ratio instead of the P/B ratio to measure firm performance. The signs of the coefficients confirm earlier results, although the effects are weaker for all six variables. Furthermore, still using Tobin‟s q, alma mater‟s effect on firm performance is significant (p = 0.031), instead of nationality, as is the case when using the P/B ratio. Hence, on the whole, the results using Tobin‟s q confirm the results of the test in which we use the P/B ratio as measure of performance.

Next, we perform a second robustness test using an accounting-based measure, instead of a market-based measure of firm performance, namely the cube root of Return-on-Assets (ROA). Table E-II presents the regression results of equations (7) to (12) using ROA. Once more, the results show negative coefficients for „NAT‟, „ALMA‟ and „INDU‟. This time, none of the six social tie proxies show statistically significant estimates.

Our last robustness test is performed using the cube root of Return-on-Equity (ROE), another accounting-based measure. These results show, as presented in table E-III, indications of a negative effect of both nationality and alma mater on firm performance. Once more, none of the six social tie proxies show statistically significant estimates.

To sum up the results of the robustness tests: first, initial results show statistically significant evidence for the effect of shared nationality on firm performance. This is not confirmed by the robustness tests, although coefficients of all three robustness tests indicate a negative effect. Second, the effect of shared alma mater on firm performance is not statistically significant in our initial results. However, it is statistically significant when using Tobin‟s q. Furthermore, coefficients of all four tests regarding alma mater indicate a negative effect. Third, signs of the negative effect of shared industries on firm performance are confirmed by two out of the three robustness checks.

5.3 Additional test regarding the firm performance-model

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Table V

Regression results social ties on performance - chairmen only

(7) (8) (9) (10) (11) (12)

in which „PERF‟ is a firm‟s performance measured by the Price/Book ratio; ‟YEAR‟ is the number of CEO-board connections regarding the year of birth divided by the total number of supervisory board members; „NAT‟ is the number of CEO-board connections regarding nationality divided by the total number of supervisory board members; „ALMA‟ is the number of CEO-board connections regarding attended universities divided by the total number of supervisory board members; „DISC‟ is the number of CEO-board connections regarding the academic discipline divided by the total number of supervisory board members; „INDU‟ is the number of CEO-board connections regarding the main industries of employment divided by the total number of supervisory board members; „REGION‟ is the number of CEO-board connections regarding places/countries of birth divided by the total number of supervisory board members; ‟C_FIRM SIZE‟ is the natural logarithm of firm size measured by assets in thousands of Euros; ‟C_LEVERAGE‟ is the financial leverage calculated as: total debt

divided by total debt and total equity; and ‟ C_FIRM AGE is the age of a firm measured in years. For each regression

equation coefficients and standard errors (between brackets) are presented. Furthermore, ***, **, and * indicate statistical significance at respectively the 1%, 5%, and 10% levels. The used sample is based on connections between all supervisory board members and their CEOs who were in office between 01/01/2011 and 12/31/2011.

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5.4 Regressions results of the CEO remuneration-model

In table VI, we estimate regressions equations (13) to (18). These regressions do not contain control variables. Each equation contains one social tie proxy (i.e. ‟YEAR‟, ‟NAT‟, ‟ALMA‟, ‟DISC‟, ‟INDU‟ and ‟REGION‟). In table VII, these equations are expanded with control variables. Equations (19) to (24) reflect these additions.

Age. We find no statistically significant evidence for the proposed negative effect of

„YEAR‟ on „PAY_TOTAL‟, that is, the effect of shared or close birth years on the CEO‟s total remuneration. Signs of the coefficients, as shown in tables VI and VII, might indicate a small negative effect, which is in line with hypothesis H2. Nevertheless, this effect is

statistically insignificant for both regression equations (13) and (19).

Nationality. Our results show no signs and/or evidence for a positive effect of „NAT‟

on „PAY_TOTAL‟ (H4). The positive effect of shared nationalities on the CEO‟s total

remuneration is not observed in our sample. In fact, we find significant evidence (p = 0.001) for a negative relation (coef. = -2345.471), even when we add control variables (coef. = -1050.307 and p = 0.024).

Alma mater. We find no evidence for the positive effect of „ALMA‟ on

„PAY_TOTAL‟ (H6); i.e. the relation between the number of shared attended universities of

directors and their CEO, and the CEO‟s total remuneration. Initially we find a significant negative effect (p = 0.056), however, its statistical significance disappears when we add control variables (regression equation (9), p = 0.523). Adding the control variables positively influences the R-squared value, which indicates that second regression better fits the data in our sample.

Academic discipline. We do not find significant evidence that „DISC‟ has a positive

effect on „PAY_TOTAL‟. In other words, our results do not confirm that a high number of shared academic disciplines (between the CEO and its supervisory board members) increases the total CEO remuneration (H8). However, signs of coefficients of both regressions equation

(16) (without control variables) and equation (22) (with control variables) are positive, which might indicate that a positive effect exists, but clearly neither is statistically significant.

Industry of main employment. Our results show no evidence for the positive effect of

„INDU‟ and „PAY_TOTAL‟ as defined in hypothesis H10. Hence, our results do not indicate

that the total CEO remuneration is higher when a CEO and its supervisory board members have mainly worked in the same industries. Estimates of equation (17) show a coefficient of -1794.912 and a p-value of 0.029. Thus, this indicates a significant strong negative effect, which is the opposite of what we expected based on literature and earlier research.

Region. Based on regression equation (18) and (24) we find no significant evidence

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between a CEO and its supervisory board members, and CEO remuneration (hypothesis H12).

Coefficients for both equations have different signs and neither is significant. In closing we must remark that we are unable to obtain regional origin of the directors for the seven foreign firms in our sample

Table VI

Regression results social ties on CEO remuneration - total pay

(13) (14) (15) (16) (17) (18)

in which ‟PAY_TOTAL‟ is a CEO‟s total remuneration measured in thousands of Euros; ‟YEAR‟ is the number of CEO-board connections regarding the year of birth divided by the total number of supervisory CEO-board members; „NAT‟ is the number of CEO-board connections regarding nationality divided by the total number of supervisory board members; „ALMA‟ is the number of CEO-board connections regarding attended universities divided by the total number of supervisory board members; „DISC‟ is the number of CEO-board connections regarding the academic discipline divided by the total number of supervisory board members; „INDU‟ is the number of CEO-board connections regarding the main industries of employment divided by the total number of supervisory board members; and „REGION‟ is the number of CEO-board connections regarding places/countries of birth divided by the total number of supervisory CEO-board members. For each regression equation coefficients and standard errors (between brackets) are presented. Furthermore, ***, **, and * indicate statistical significance at respectively the 1%, 5%, and 10% levels. The used sample is based on connections between all directors and their CEOs who were in office at 12/31/2011.

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Table VII

Regression results social ties on CEO remuneration - total pay

(19) (20) (21) (22) (23) (24)

in which ‟PAY_TOTAL‟ is a CEO‟s total remuneration measured in thousands of Euros; ‟YEAR‟ is the number of CEO-board connections regarding the year of birth divided by the total number of supervisory CEO-board members; „NAT‟ is the number of CEO-board connections regarding nationality divided by the total number of supervisory board members; „ALMA‟ is the number of CEO-board connections regarding attended universities divided by the total number of supervisory board members; „DISC‟ is the number of CEO-board connections regarding the academic discipline divided by the total number of supervisory board members; „INDU‟ is the number of CEO-board connections regarding the main industries of employment divided by the total number of supervisory board members; „REGION‟ is the number of CEO-board connections regarding places/countries of birth divided by the total number of supervisory CEO-board members; ‟C_FIRM SIZE‟ is the natural logarithm of a firm‟s size measured by assets in thousands of Euros; „C_PERF‟ is a firm‟s performance measured by the Price/Book ratio; and ‟C_CEO TENURE‟ is the natural logarithm of the number of years that a specific director is CEO of the firm. For each regression equation coefficients and standard errors (between brackets) are presented. Furthermore, ***, **, and * indicate statistical significance at respectively the 1%, 5%, and 10% levels. The used sample is based on connections between all directors and their CEOs who were in office at 12/31/2011.

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5.5 Robustness tests of the CEO remuneration-model

In appendix F, we present the results of two robustness tests for which we use adjusted samples. First, table F-I shows the estimations of regressions equations (19) to (24). This time, the sample consists of all CEOs who were in office during 2011. Hence, several firms had two CEOs. For these particular firms we use a weighted average of CEO remuneration.16 The results of this robustness test shows no large differences compared to our initial results. In line with the initial results, the estimate of „NAT‟ is statistically significant. R-squared values of all six regression equations are lower.

Second, once more, we estimate the regression equations (19) to (24) using a sample consisting of all CEOs in office during 2011. The results are shown in table F-II. This time, an extra adjustment is made, namely the exclusion of severance pay from the total remuneration („PAY_TOTAL), because we realize that total pay of the seven succeeded CEOs is largely driven by severance pays, as discussed in subsection 3.4. Although results differ, we find no significant evidence to support our hypotheses. Furthermore, most R-squared values differ slightly. An important difference is the increased negative effect of „NAT‟ on „PAY_TOTAL‟. This statistically significant effect is not in line with the positive effect predicted in hypothesis (H4), but it is observed in our initial results and results of both

the robustness tests.

5.6 Additional tests regarding the CEO remuneration-model

First, table IX presents results of an additional test in which only the connections between the chairmen of the supervisory board and their CEOs are taken into account. The results show statistically significant evidence (coef. = 745.354 and p = 0.085) for hypothesis H8 („DISC‟)

applying a significance level of 10%. In addition to this, results suggest a negative effect of shared age on CEO remuneration (H2) and shared alma mater on CEO remuneration (H6). In

spite of this, both effects are not statistically significant. In contrast to this, negative effects regarding shared nationality and shared main industry of employment are significant. Conflicting with hypotheses H4 and H10, which both forecast positive effects on CEO

remuneration.

Second, another additional test is performed using a sample which contains merely connections between remuneration committee members and their CEOs. Using this modified sample we estimate regression equations (19) to (24) once more. The results are shown in table X. We find two significant results, nationality and alma mater, both not in line with our

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Table IX

Regression results social ties on CEO remuneration – chairmen only

(19) (20) (21) (22) (23) (24)

in which ‟PAY_TOTAL‟ is a CEO‟s total remuneration measured in thousands of Euros; ‟YEAR‟ is the number of CEO-board connections regarding the year of birth divided by the total number of supervisory CEO-board members; „NAT‟ is the number of CEO-board connections regarding nationality divided by the total number of supervisory board members; „ALMA‟ is the number of CEO-board connections regarding attended universities divided by the total number of supervisory board members; „DISC‟ is the number of CEO-board connections regarding the academic discipline divided by the total number of supervisory board members; „INDU‟ is the number of CEO-board connections regarding the main industries of employment divided by the total number of supervisory board members; „REGION‟ is the number of CEO-board connections regarding places/countries of birth divided by the total number of supervisory CEO-board members; ‟C_FIRM SIZE‟ is the natural logarithm of a firm‟s size measured by assets in thousands of Euros; „C_PERF‟ is a firm‟s performance measured by the Price/Book ratio; and ‟C_CEO TENURE‟ is the natural logarithm of the number of years that a specific director is CEO of the firm. For each regression equation coefficients and standard errors (between brackets) are presented. Furthermore, ***, **, and * indicate statistical significance at respectively the 1%, 5%, and 10% levels. The used sample is based on connections between chairmen of supervisory committees and their CEOs who were in office at 12/31/2011.

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Table X

Regression results social ties on CEO remuneration – remuneration committee only

(19) (20) (21) (22) (23) (24)

in which ‟PAY_TOTAL‟ is a CEO‟s total remuneration measured in thousands of Euros; ‟YEAR‟ is the number of CEO-board connections regarding the year of birth divided by the total number of supervisory CEO-board members; „NAT‟ is the number of CEO-board connections regarding nationality divided by the total number of supervisory board members; „ALMA‟ is the number of CEO-board connections regarding attended universities divided by the total number of supervisory board members; „DISC‟ is the number of CEO-board connections regarding the academic discipline divided by the total number of supervisory board members; „INDU‟ is the number of CEO-board connections regarding the main industries of employment divided by the total number of supervisory board members; „REGION‟ is the number of CEO-board connections regarding places/countries of birth divided by the total number of supervisory CEO-board members; ‟C_FIRM SIZE‟ is the natural logarithm of a firm‟s size measured by assets in thousands of Euros; „C_PERF‟ is a firm‟s performance measured by the Price/Book ratio; and ‟C_CEO TENURE‟ is the natural logarithm of the number of years that a specific director is CEO of the firm. For each regression equation coefficients and standard errors (between brackets) are presented. Furthermore, ***, **, and * indicate statistical significance at respectively the 1%, 5%, and 10% levels. The used sample is based on connections between remuneration committee members and their CEOs who were in office at 12/31/2011.