Amsterdam Business School
MSc Business Economics, Finance Track Master Thesis
Firm Performance and the Characteristics of Board
Appointees: An Empirical Analysis
Author: Jim Leusink
Student number: 10047824 Date: July 2014
2 Abstract
This paper studies whether (recent) firm performance affects the type of directors (i.e. experience and reputation) being appointed, when new directors are being hired. The results provide indications that when firms hire new directors, poor performance leads to appointing older (i.e. more experienced) insiders and older outsiders. Moreover, there are indications that for inside director appointments this effect increases with firm size, and decreases with firm age. On the contrary, for outside director appointments this effect increases with firm age. It does not appear that poor performance induces firms to appoint directors with a better reputation. There is weak evidence that during an economic crisis the effect of poor performance is smaller for inside director appointments (compared to a non-crisis period), and larger for outside director appointments (compared to a non-non-crisis period). Moreover, there are also indications that the effect of poor performance is non-linear, i.e. the worst performing firms appear to respond at least less heavily to poor performance than better performing firms.
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Table of Contents
Abstract 2
Table of Contents 3
I. Introduction 4
II. Literature Review 6
A. The Board of Directors 6
B. The Determinants of Board Composition 7
C. Firm Performance and the Appointment of New Directors 8 D. Firm Performance, the Appointment of New Directors, and 10 the Business Cycle
III. Hypotheses and Empirical Method 12
A. Hypotheses 12
B. Empirical Method 14
IV. Data and Descriptive Statistics 17
A. Data 17
B. Descriptive Statistics 18
V. Empirical Results 20
A. Firm Performance and the Type of New Directors Being Appointed 21 B. Firm Performance, the Type of New Directors Being Appointed, 27 and the Business Cycle
VI. Robustness Checks 31
A. Using Non-Linear Regression Specifications 31
B. Using Additional Performance Measures 35
VII. Conclusion and Discussion 37
4 I. Introduction
In theory, well governed firms should perform better than poorly governed firms. An important part of (corporate) governance is the board of directors (Hermalin and Weisbach, 2003). A lot of research has been done in order to find out whether certain board characteristics (size, independence, diversity, etc.) affect firm performance (a.o. Eisenberg et al., 1998; Hermalin & Weisbach, 1991). However, less focus has been placed on the board determinants, and as a result the market for directors is not well understood. This questions what factors affect the composition of the board of directors. Since understanding board determinants is key to understanding corporate governance; the directors who get appointed affect how effectively the board will perform their tasks (Hermalin and Weisbach, 1988), the aim of this study it to improve upon the existing knowledge of board determinants. This can for example be helpful in evaluating the current board selection process (Hermalin and Weisbach, 1988).
This study focusses on the market for directors, more specifically, it is questioned if firm performance affects the type of director(s) being hired. In difficult times, boards may take less risk, or it may be simply optimal to make other (hiring) decisions. Therefore, poor performance may change the importance of the experience and the reputation of the newly appointed directors. So, the research question is: “Does (recent) firm performance affect the type of directors (i.e. experience and reputation) being appointed, when new directors are being hired?” This is questioned separately for both insiders and outsiders. Moreover, it is studied if the possible effect differs across firms (with respect to firm size and firm age). Finally, it is studied whether the effect differs during an economic crisis. This all has not been previously studied, therefore this study sheds new light on the market for directors.
In order to achieve this, several hypotheses are tested. The first hypothesis is that when hiring new directors, poorly performing firms appoint directors (insiders and outsiders) who are more experienced, and who have a better reputation, than better performing firms. Hypothesis 2A and 2B state the effect of poor performance on the type of directors being hired (i.e. more experienced directors, who have a better reputation) increases with firm size, and decrease with firm age respectively. The third hypothesis is that the effect of poor performance on the type of directors being hired (i.e. more experienced directors, who have a better reputation) is larger during a period of an economic crisis. These hypotheses are tested with OLS regressions and panel regressions, using data on director appointments
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from Risk Metrics, which contains board data of S&P 1500 companies. These regressions include time fixed effects and firm fixed effects, and additional control variables, in order to try to obtain unbiased estimates.
The results at least provide some support for the hypotheses. Firstly, it appears that poorly performing firms (measured by lagged Tobin’s Q) do appoint older insiders and older outsiders, which is in favour of hypothesis 1. However, it doesn’t appear that poorly performing firms appoint directors who have a better reputation. Regarding insiders, the coefficients of interest with respect to the number of other board seats are actually significantly negative, however they seem to have low economic meaning. Possibly the advantages of hiring directors with a good reputation (i.e. they are likely to be good directors) are balanced by the disadvantages (i.e. a possible lack of time available).
Secondly, there is weak evidence that larger firms respond more heavily to poor performance (measured by lagged Tobin’s Q) than smaller firms, which supports hypothesis 2A. Moreover, in line with hypothesis 2B, the negative effect of firm performance (measured by lagged Tobin’s Q) on the average age/experience of the appointed insiders seems to become less negative as firm age increases. However, the opposite holds for outsiders; there are indications that the effect of poor performance on the average age/experience of directors being hired increases as firm age increases. This last finding might be caused by younger firms facing more difficulties in hiring experienced directors after poor performance. Note that there are no indications that the effect of firm performance on the average reputation of the appointed directors is also heterogeneous.
Moreover, the results at least provide indications that during an economic crisis the effect of poor performance on the type of directors being hired is smaller for insiders (compared to a non-crisis period), and larger for outsiders (compared to a non-crisis period). The first part of these findings contradicts with hypothesis 3, it’s likely that the fact that it’s hard for firms to know the quality of other directors during a crisis (Schoar and Zuo, 2011) induces firms to sooner internally promote insiders after poor performance during a crisis. The second part of the findings (i.e. the larger effect for outsiders) is in line with hypothesis 3, meaning that firms appoint “safer” outside directors and/or have a higher need for experience and/or conservatism inside the board, most likely in order to enhance the board’s monitoring role. Finally, from the robustness check(s) it appears that the effect of firm performance on the type of directors being hired might be non-linear. Although it seems a bit counterintuitive, it
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appears that the best 50% performing firms respond more heavily to poor performance (when hiring insiders) than the worst 50% performing firms. Likely, the worst performing firms face troubles in finding experienced directors who are willing to work for them, which might causes them to appoint less experienced directors than they prefer.
The rest of this paper is organized as follows. In section II the related literature regarding the board of directors, and its determinants, is discussed. Moreover, based on previous studies, it is discussed how firm performance might affect the type of directors being hired, and how this might differ during an economic crisis. In section III is discussed which hypotheses are tested, and what empirical method is used to test them. Section IV discusses the data, and provides the descriptive statistics of the 6232 firm/year observations which are used in this study. Section V provides the main empirical results, and section VI provides robustness checks. Finally, section VII concludes and discusses the main limitations of this study, as well as suggestions for future research.
II. Literature Review
In this section the board of directors itself is firstly discussed, which sheds light on how important the board is, and thereby also how important understanding the determinants is. Since boards essentially decide who will be appointed, it also gives an impression on which board characteristics are important and can influence the hiring decisions. Secondly, existing evidence regarding board determinants is discussed. Thirdly, based on previous studies, the possible effects of recent firm performance on the type of directors being hired are discussed. Finally, it is discussed how these effects might differ during an economic crisis.
A. The Board of Directors
Before focusing on the board determinants, the board of directors itself is discussed. The board is a mechanism that in theory mitigates the agency problem(s) (Hermalin and Weisbach, 2003). The board’s main role is to align the incentives of the managers with those of the shareholders. This should result in management that handles in the best interest of the firm, instead of management that handles in their own best interest.
Since boards are an important part of corporate governance, they are often studied. Mostly is studied how board characteristics (independence, size, diversity, etc.) affect firm performance/value (Hermalin and Weisbach, 2003). For example, Eisenberg et al. (1998)
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found that a larger board decreases the profitability of small firms, and Hermalin and Weisbach (1991) report evidence of a statistically small negative relationship between board independence and firm performance. In their study on board diversity, Carter et al. (2003) found that board diversity (age, gender, ethnicity, etc.) appears to increase firm value. Using exogenous changes in the composition of the board of directors, Fracassi and Tate (2012) report empirical evidence that CEO-director ties reduce firm value. Finally, Morck et al. (1987) found empirical evidence of a non-linear relationship (positive at first, and negative thereafter) between managerial ownership and firm performance. At low ownership levels higher ownership appears to align managerial incentives with those of the firm, whereas higher ownership facilitates managerial entrenchment at (too) high ownership levels.
Besides the effect on firm performance, several studies focus on the effect of certain board characteristics on observable actions of the board of directors. For example, Weisbach (1988) found evidence that suggests that when boards are dominated by outside directors, poorly performing CEOs are more likely to be replaced, i.e. CEO turnover is more sensitive to performance. Yermack (1996) found empirical evidence that poorly performing CEOs are also more likely to be replaced if the board of directors consists of less directors. Moreover, Yermack (1996) found evidence that smaller boards adopt a higher pay-for-performance sensitivity. Finally, in their study, Byrd and Hickman (1992) found that firms with independent boards make better (or at least less worse) acquisition decisions, which is interpreted as shareholders benefiting from independent boards. However, when the board becomes too independent these results do no longer appear to hold.
B. The Determinants of Board Composition
Compared to the effect of certain board characteristics on firm value (or observable board actions), board determinants are less well understood, whereas understanding board determinants is actually key to understanding corporate governance (Hermalin and Weisbach, 1988). However, multiple studies did empirically found evidence regarding several determinants of the board of directors. The findings of these studies are discussed next. Lehn et al. (2009) argue that board size is determined by a trade-off between the incremental information that hiring additional directors brings, and the additional coordination problems that it brings. So, “one size fits all” does not appear to hold. Firms which are growing into new product lines, or entering new geographical markets, hire more
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board members (Coles et al., 2008; Lehn et al., 2009), since these firms are likely to require more knowledge. Additionally, because of higher agency problems (which is caused by a greater flexibility in investment decisions) these firms also require more outside directors (Lehn et al., 2009). Finally, Boone et al. (2007) found empirical evidence regarding several board determinants. For example, more diversified firms have larger boards, with a higher fraction of independent directors. Moreover, boards are also larger for firms in which managers’ opportunities to handle in their own best interest are large, or in which the cost of monitoring is small. Their final result is that a stronger managerial influence, with low constraints on this influence, leads to a lower fraction of independent board members. On overall, their findings suggest that the number of directors and the composition of the board of directors does vary across firms.
Besides the above discussed board determinants, several studies report empirical evidence that (recent) firm performance is also a determinant of board composition, although they all focused on different effects of firm performance than this study does. For example, Huson et al. (2004) empirically studied the determinants of CEO turnover. They report evidence that accounting measures of performance worsen prior to CEO turnover, compared to other firms. These results indicate that the board of directors punishes poorly performing CEOs by firing them. Moreover, Hermalin and Weisbach (1988) report evidence that insiders are more likely to leave and outsiders are more likely to join after poor performance. Most likely, insiders are held responsible for poor performance, and board independence is increased in order to enhance corporate monitoring.
C. Firm Performance and the Appointment of New Directors
Based on previous studies, it appears that firm performance is a board determinant (Hermalin and Weisbach, 1988; Huson et al., 2004). However, besides the discussed effects, firm performance could also affect the type of director(s) being appointed (i.e. in terms of experience and reputation), when a company hires director(s) (insiders and/or outsiders). The main contribution of this paper is to shed light on this topic, since no previous studies have done this before. Multiple previous studies do actually give a reason to argue that such effects might exist. These studies, and the implications for this study, are discussed next. According to Carter et al. (2003) it could be argued that younger directors are less risk averse and more creative than older directors, who are more conservative. The findings of
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Bertrand and Schoar (2003) confirm this theory, since they empirically found that older directors behave more conservative. Moreover, these older directors are also more experienced, since they are in business for a longer time (age is a proxy for experience). Therefore, poor performance might affect the importance of the experience of directors (both insiders and outsiders) being hired, for two main reasons. Firstly, poor performance could induce the board of directors to take less risk when hiring new directors, since the firm is closer to default, and making wrong decisions is therefore costly (i.e. the firm may default and board members could lose their jobs, etc.). From a theoretical perspective, this might incline boards to nominate experienced directors, to decrease the probability of unintendedly hiring bad directors. Moreover, these experienced directors are more conservative (Bertrand and Schoar, 2003), and they may have dealt with poor performance earlier in their career, and therefore it is likely that they know better how to deal with the current poor performance. Secondly, as poor performance is likely to signal poor management (Hermalin and Weisbach, 1988), it might also signal poor decision-making and/or corporate monitoring by the board of directors. So, poor performance might signal the need for more knowledge and experience inside the board. This is in order to improve the boards’ decision-making process and the effectiveness of corporate monitoring.
However, age (i.e. experience) could have negative effects on firm performance as well, which therefore might affect (poorly performing) firms’ hiring decisions. Acharya et al. (2011) theoretically argue that hiring younger subordinates (i.e. insiders) limits the self-serving actions of the CEO, which is important if the CEO does not have the same long-term interests as the firm. Younger subordinates often do have a long-term horizon, because they are possible candidates to replace the current CEO, and therefore they will exert a high amount of effort to possibly get such a promotion. However, this only holds if the utility of the CEO depends on the effort of his subordinates. Otherwise, the CEO will subtract value out of the firm even if the subordinates exert a high amount of effort, which reduces the incentives of the subordinates to exert effort. Moreover, hiring older directors (insiders and outsiders) might lead to directors inside the board who have a more short-term view, which induces them to act in their own interest, instead of acting in the long-term interest of the company/shareholders (Acharya et al., 2011). Therefore, it’s puzzling what hiring decisions firms (should) make after poor performance, since from a theoretical perspective hiring experienced directors has positive effects, but it could also have negative implications on the
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Besides age, reputation could also matter when hiring directors after poor performance. Amongst others, Fich and Shivdasani (2006) argue that the number of multiple directorships is a proxy for director reputation. Since hiring a director with multiple directorships has a positive effect on firm value (and vice versa), it appears that reputation matters when hiring a new director (Ferris et al., 2003). Therefore, besides a possible effect on director age/experience, another effect of recent performance could be that when poorly performing companies hire new directors, they choose to appoint directors who have a good reputation. This may be because it is less risky compared to hiring a director without a good reputation. It may also be caused by a higher need for “busy” directors which are considered to be helpful in improving performance (Fich and Shivdasani, 2006), since these “busy” directors are often good directors. Moreover, Perry and Peyer (2005) report empirical evidence that outside directorships for executives (which are insiders) can also increase firm value through learning or networking. Therefore, besides signalling that you are a good director, a good reputation possibly also leads to more learning opportunities and more networking opportunities for directors, which can enhance firm value.
However, as for director experience, better director reputation might also lower firm performance. This holds especially for outside directors, since too many directorships might lower the effectiveness of corporate monitoring (Core et al., 1999; Shivdasani and Yermack, 1999). This holds, because additional directorships may reduce directors’ monitoring capabilities, since it leads to less time available (Shivdasani and Yermack, 1999). Moreover, Core et al. (1999) found that busy directors seem to overpay CEOs, which could be seen as a confirmation that the monitoring capabilities are reduced. However, monitoring might actually become more important in difficult times, since poor performance could signal an agency problem (Hermalin and Weisbach, 1988). Since outside directors are often the most important for corporate monitoring (Shivdasani and Yermack, 1999), poor performance could therefore incline boards to hire outsiders who are less busy (i.e. have a lower reputation), in order to enhance corporate monitoring.
D. Firm Performance, the Appointment of New Directors, and the Business Cycle
In the previous section is discussed how firm performance might affect the type of directors being appointed. However, these possible effects might be heterogeneous. During
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economic busts people face more (systematic) uncertainty (i.e. more risk). As they are risk averse, they are reluctant to take risk during economic busts, since more uncertainty is associated with a lower attractiveness of investments (March and Shapira, 1987). Moreover, it could be that people also become more risk averse due to an economic bust, which thereby further increases their reluctance to take risk (Malmendier and Nagel, 2011).
This both holds, because (excessive) risk-taking during a period with high uncertainty could destroy a high amount of value. Erkens et al. (2012) show that firms who took the most risk prior to the financial crisis, had to bear the largest losses during the crisis. They argue that corporate governance had a major impact on firm performance during the global financial crisis, because of firms’ excessive risk-taking. Based on these findings, from a theoretical perspective, it could be argued that more (systematic) uncertainty during a crisis, and are larger probability of default, leads to firms becoming reluctant to take risk. So, there might be a need for more conservatism in the board, to deal with the risk and to prevent firms from excessive risk-taking. Moreover, making the wrong (risky) decisions during an economic crisis could also be very costly (i.e. firms could go bankrupt, or destroy a high amount of firm value), so boards of directors might also be willing to take less risky decisions, compared to a non-crisis period.
There is little empirical evidence regarding the effect of the business cycle on the risk taking/decision making of the board. However, Schoar and Zuo (2011) report empirical evidence that CEOs who started their career during a recession adopt other management styles, compared to CEOs who started during a non-recession. CEOs who started their career during a recession are on overall more conservative, and this is a long-lasting effect. They invest less in capital expenditures and R&D, and adopt a significantly lower leverage policy. Moreover, they are also more diversified across different business segments, in order to diversify their risk. On overall, it appears that those starting CEOs persistently behave more risk averse than CEOs who started their career during a non-recession period.
These results are an indication that during a recession boards are willing to take less risk and/or make more conservative decisions (which was not necessarily caused by their willingness to take less risk). Note that Schoar and Zuo (2011) found that starting recession CEOs are actually 1,5 years younger than non-recession CEOs. However, according to them this is due to firms sooner internally promoting CEOs during a recession, because it is difficult to separate the quality of managers/directors from other firms from the overall
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(bad) market conditions (i.e. it’s hard to know which directors are suitable for the job, because most of the companies are performing poorly).
III. Hypotheses and Empirical Method
Based on economic theory, and the in the previous section discussed related literature, several hypotheses are formulated. The hypotheses, and the used methodology to test them, are discussed in this section.
A. Hypotheses
As argued in section C of the literature review, firm performance might affect the appointment of new directors. This leads to the following hypothesis:
H1: When hiring new directors (insiders and outsiders), poorly performing firms appoint
more experienced directors, who have a better reputation, than better performing firms.
This is partially expected because boards might want to take less risk after poor performance, and therefore nominate “safer” directors. Moreover, it could also be that poor performance signals the need for better directors (Hermalin and Weisbach, 1988), since this poor performance is likely to be (at least partially) caused by poor functioning of the board. Therefore experience and reputation might become more important, since an experienced director, with a good reputation, is likely to be a good director (Ferris et al., 2003) who can improve the functioning of the board and thereby improve firm performance. However, especially regarding insiders, this effect can be somewhat offset by the fact that hiring older insiders leads to insiders who have a more short term view. This could lead to insiders who act in their own interest. Moreover, this could also lead to CEOs who act more in their own interest, because older subordinates less effectively limit the self-serving actions of CEOs (Acharya et al., 2011).
Moreover, according to existing literature firm characteristics are board determinants as well (Coles et al., 2008; Lehn et al., 2009). So, the effect of poor performance on the type of director(s) being hired could differ between firms (i.e. could be heterogeneous), this leads to the following hypotheses:
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H2A: The effect of poor firm performance on the type of directors being hired (i.e. older/
more experienced directors, who have a better reputation) increases with firm size.
H2B: The effect of poor performance on the type of directors being hired (i.e. older/more experienced directors, who have a better reputation) decreases with firm age.
Hypothesis 2A is expected to hold, because larger firms are often engaged in more diverse activities than smaller firms (such as operating in different product and geographical markets), and therefore have a higher need for knowledge/experience (Lehn et al., 2009). Moreover, according to Lehn et al. (2009) these firms also have a higher need for effective corporate monitoring, because of their higher agency costs. Therefore, for larger firms poor performance might signal the need for good experienced directors the most, since poor performance likely signals that they lack the required additional knowledge/experience and are ineffective in corporate monitoring (i.e. they might fail at solving the additional agency problems which they have to deal with).
Hypothesis 2B is expected to hold, because poor performance is likely to harm younger firms the most. Most likely, these firms are put the closest to default after poor performance, and therefore younger firms might be willing to take the least amount of risk. So, these firms might be the most inclined to hire “safer” directors after poor performance. Moreover, as previously discussed, good experienced directors might be the most suitable to deal with poor firm performance. Since poor firm performance likely harms younger firms the most, for these firms it is the most crucial to improve their performance in order to remain viable and to continue as a going concern. Therefore, after poor performance younger firms are likely to be more inclined to hire experienced directors, who have a better reputation, than older firms are.
Finally, as discussed in section D of the literature review, the discussed effect of poor performance on the type of director being hired might also depend on the business cycle, which leads to the following hypothesis:
H3: The effect of poor performance on the type of directors being hired (i.e. older/more
experienced directors, who have a better reputation) is larger in a period of an economic crisis.
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This is expected to hold, because boards might make different decisions during an economic crisis. Based on Schoar and Zuo (2011) it appears that the business cycle does affect corporate governance. More specifically, it’s likely that during a crisis boards make more conservative decisions, because there is more (systematic) uncertainty. Therefore, after poor performance, when hiring directors firms/boards might be inclined to hire more experienced directors, who have a better reputation. Hiring such directors is less risky than hiring unexperienced directors, without such a good reputation (i.e. with less other board seats); these directors could more likely turn out to be bad directors. Moreover, older (i.e. more experienced) directors are more conservative (Bertrand and Schoar, 2003), and during an economic crisis the need for conservative and good directors increases, since firms should be prevented from taking excessive risk (Erkens et al., 2012). However, regarding inside directors, the effect might be offset by the possibility that firms sooner internally promote insiders, since it’s harder to know the quality of the directors of other firms during bad market conditions (Schoar and Zuo, 2011). If firms are willing to take less risk, they might actually internally promote (younger) directors, since they do not want to face the risk of unintendedly hiring bad insiders who are at that time working at other firms.
B. Empirical Method
In order to test the discussed hypotheses, OLS regressions are used. These regressions are done using the (average) age of the appointed insiders/outsiders and the (average) number of other board seats of the appointed insiders/outsiders as dependent variables. Besides these regressions, panel regression are also used. Such regressions allow for controlling for firm fixed effects and year fixed effects, and thereby mitigate omitted variable bias. Note that including firm fixed effects is important because hiring decisions are likely be influenced by for example company culture, which also likely influences firm performance. Moreover, the year fixed effects could be especially important during crisis years, since a crisis likely influences both firms’ hiring decisions and firm performance. However, these panel regressions could lead to more imprecise estimates if the variation in the dependent variables is mostly cross-sectional (instead of mostly within firm variation). This holds because including firm fixed effects leads to only using the within firm variation to estimate all the regression coefficients, and using less variation leads to more imprecise estimations (i.e. larger standard errors of the estimates).
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Additionally, control variables are used to further mitigate omitted variable bias. The average age of the other directors, the %independent other directors, and the number of other directors are used since these variables are crucial board related characteristics. They are likely to be related with the type of director(s) being hired, for example because the board decides on which directors to nominate, and thereby strongly influences which directors are being appointed. Often the shareholders don’t even have a choice, meaning that the same number of directors are nominated as there are vacancies, so the board essentially hires their new members. The discussed crucial board characteristics are also related with firm performance/value (Carter et al., 2003; Eisenberg et al., 1998; Hermalin and Weisbach, 1991), and these variables are therefore included as control variables.
Moreover, a CEO can strongly influence the selection process, and this influence increases with CEO tenure and %CEO-ownership (Shivdasani and Yermack, 1999). CEOs who are longer with the firm and have a higher ownership are likely to be more powerful. Such powerful CEOs are more willingly to hire directors who are not too critical about them, i.e. for example directors who spend less time on effective corporate monitoring (Shivdasani and Yermack, 1999), which is in order to enhance the self-fulfilling actions of CEOs. Since Morck et al. (1987) found empirical evidence of a non-linear relationship between managerial ownership and firm performance, and since CEO tenure is also likely to impact firm performance, not including these variables could lead to omitted variable bias (i.e. endogenous results).
Finally, firm size and firm age are controlled for, a.o. because larger firms require a different board composition than smaller firms (Lehn et al., 2009). Moreover, according to Lehn et al. (2009), this also holds for firms with different growth opportunities/prospects, which holds for a.o. younger versus more mature firms. Since Dhawan (2001) found that smaller/younger firms are likely to be more productive and more profitable than larger firms, firm size and firm age are used as additional control variables to mitigate omitted variable bias. Moreover, these variables are also likely to influence the ability of firms to hire certain types of directors (i.e. good experienced directors might be able to earn more at larger/older firms, and it might be more prestigious for them to work at such firms).
The discussed dependent variables, the independent variables of interest, and the control variables all lead to the following main models (which are also further enhanced, as discussed later in this section):
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M1: Age Of Appointed Directorsit = β1*Performanceit-1 + β2*Age Of Other Directorsit +
β3*%Indep. Other Directorsit + β4*# Of Other Directorsit + β5*%CEO-Ownershipit +
β6*CEO Tenureit + β7*Firm Sizeit + β8*Firm Ageit + αi + λt + uit
M2: # Of Other Board Seats Of Appointed Directorsit = β1*Performanceit-1 + β2*Age Of
Other Directorsit + β3*%Indep. Other Directorsit + β4*# Of Other Directorsit +
β5*%CEO-Ownershipit + β6*CEO Tenureit + β7*Firm Sizeit + β8*Firm Ageit + αi +
λt + uit
Note that age of appointed directors is equal to the average age of the appointed insiders/outsiders and the number of other board seats is equal to the average number of other board seats of the appointed insiders/outsiders. Moreover, αi and λt are the firm fixed
effects and the year fixed effects respectively.
In this setting simultaneous causality will not be problematic, since the effect of previous year’s performance (Tobin’s Q and ROA) on the type of director being hired is studied, and the hiring decisions made this year will not affect the performance of the previous year. However, omitted variable bias might remain problematic, since perhaps more variables are correlated with both the hiring decisions of companies and recent firm performance (however both the hiring decisions, and therefore the market for directors, are still nowadays not completely understood). Therefore, although the attempt is to deal with endogeneity as good as possible, some cautiousness is required in interpreting the results. In order to test hypothesis 1, the sign and significance of β1 in both equations is
interpreted. A negative (and significant) sign for these β1’s(in both equations) would lead to
confirm the first hypothesis in this setting. This holds, since that would mean that there is a significant reason to belief that a lower performance leads to appointing older (i.e. more experienced) directors, who have more other board seats (i.e. a better reputation).
Additionally, interaction terms are included to both models in order to test hypothesis 2A and hypothesis 2B. These terms are interactions between lagged performance and firm size, and lagged performance and firm age respectively. The sign and significance of the coefficients of these interaction terms are interpreted. It is expected that the coefficients on the interaction terms between lagged performance and firm size are statistically significant and negative; this would mean that effect of poor performance on the type of directors
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being hired (i.e. more experienced directors, who have a better reputation) increases with firm size. The coefficients of the interaction terms between lagged performance and firm age are expected to be statistically significant and positive; this would mean that effect of poor performance on the type of directors being hired (i.e. more experienced directors, who have a better reputation) decreases with firm age.
In order to test hypothesis 3, a dummy variable is included in both models, which states whether the economy was in a crisis in that year (1 for 2001, 2002, and year 2008 or later; 0 otherwise). Moreover, interaction terms between this dummy variable and the performance of the previous year are included in both models. The sign and significance of the coefficients of these interaction terms are interpreted. Significantly negative coefficients are expected, since that would mean that during a crisis the effect of poor performance on the type of directors being hired is larger.
IV. Data and Descriptive Statistics A. Data
In order to test the hypotheses, director appointments from 1997-2012 are studied, and each of these appointments is classified as either an inside director appointment or an outside director appointment. Only the firms which appointed (inside and/or outside) directors in at least two different years are included, since than the firm fixed effects (for example company culture) can be controlled for. Data is collected for the age of the directors being hired (used as a proxy for experience), and the number of other board seats (used as a proxy for reputation). For each firm/year combination, the average age of the newly appointed directors, and the average number of other board seats of the newly appointed directors is determined (separately done for insiders and outsiders). These variables are used as the dependent variables. All the data on director appointments is obtained from the Risk Metrics database, which contains director data from the S&P 1500 companies. Director appointments for which the director is firstly recorded in Risk Metrics more than one year later than the appointment are excluded. This data is excluded to assure that the used data reflects the true age and the true number of multiple directorships of the directors at the time that they were appointed. Note that if the time difference is equal to one year, the age of the director at his/her appointment is defined as the age at the first time he/she was recorded in Risk Metrics minus one year.
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In this study one year lagged Tobin’s Q and one year lagged ROA are used as firm performance measures, which are the independent variables of interest. Similar to Schoar and Zuo (2011) Tobin’s Q is measured by the book value of total assets minus the sum of the book value of common equity and balance sheet deferred taxes plus the market value of common equity all divided by the book value of total assets. ROA is measured by dividing EBITDA by the book value of total assets. This performance data is obtained from the Compustat database, and ranges from 1996-2011, since it is lagged. Several control variables are used in order to mitigate the problem of omitted variable bias. Note that the size of the firm is measured by the natural logarithm of the market value of common equity (share price times the number of common shares outstanding), and that the age is measured by the number of years since the initial public offering. These measures are similar to the once that Boone et al. (2007) use. Data for the control variables (which ranges from 1997-2012) is obtained from Risk Metrics (data on board specific control variables), Compustat (data on firm specific control variables), and Execucomp (data on CEO specific control variables).
B. Descriptive Statistics
Descriptive statistics for all the variables are presented in table I. The average age of the appointed insiders in the used sample is 51,754 years old, with a standard deviation of 6,216 years. The average age of the appointed outsiders in this sample is somewhat older, namely 56,024 years old (the standard deviation is 6,783 years). Although the appointed outsiders on average had more other board seats than the insiders (the averages are 0,851 and 0,293 respectively, with standard deviations of 1,026 and 0,603 respectively), the number of other board seats of the appointed directors appears to be low for both outsiders and insiders. Regarding firm performance, note that both Tobin’s Q and ROA are winsorized at a 0,1% level in order to treat outliers. The resulting average value of previous years’ Tobin’s Q is 1,966, with a standard deviation of 1,443. The resulting average value of previous years’ return on assets is 13,981%, with a standard deviation of 9,673%.
The average board of the firms in the sample consists of 8,897 other directors (i.e. directors already in place at time of the appointment of the new director(s)), of which 72,586% are considered to be independent. The other members of the boards are on average 60,669 years old (with a standard deviation of 4,054 years). For some rare observations the age of the directors was below 20 (mostly 1), these observations are not
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included in the sample (and also excluded as observations for new director appointments). The CEOs are on average 6,380 years with their firm (with a standard deviation of 6,629 years). %CEO-ownership (winsorized at a 0,1% level) is on average 1,483% of the total common shares outstanding (with a standard deviation of 4,361%).
Table I: Descriptive Statistics
The table provides descriptive statistics for characteristics of the sample. Director appointments are included if the company appointed directors in at least two different years (but excluded if the director is included in the Risk Metrics database more than a year later than his/her appointment). The table presents the number of observations, the mean, the median, the standard deviation, the minimum and the maximum value of the variables. Age equals the average age of the appointed insiders/outsiders. # Of Other Board Seats equals the average number of other board seats of the appointed insiders/outsiders. Lagged Tobin’s Q is the one year lagged value of Tobin’s Q. Lagged ROA is the one year lagged return on assets. Age Of Other Directors is the average age of all the board members who were already with the firm. %Indep. Other
Directors equals the fraction of independent board members who were already with the firm. # Of Other Directors equals
the number of board members who were already with the firm. CEO Tenure is measured by the number of years that the CEO is with the firm. %CEO-Ownership is the percentage of total common shares outstanding that the CEO owns. Firm Size is the natural logarithm of the market value of common equity. Firm Age is the number of years since the IPO.
The average size of the firms is 7,938, which equals the average natural logarithm of the market value of common equity (it has a standard deviation of 1,595). The age of the firms is measured by the number of years since their initial public offering (IPO), where the first year with non-missing stock price data in Compustat is considered to be the year of the IPO. Using this measure for age, the firms included in this sample are on average 25,326 years old, with
Variable Number of
Observations
Mean Median Standard
Deviation
Minimum Maximum
Appointed Insiders: Age
# Of Other Board Seats
729 729 51,754 0,293 52 0 6,216 0,603 32 0 76 5 Appointed Outsiders: Age 5854 56,024 56,500 6,783 25 85
# Of Other Board Seats 5854 0,851 0,500 1,026 0 9
Firm Performance:
Lagged Tobin’s Q 6232 1,966 1,503 1,443 0,591 14,573
Lagged ROA 6232 13,981% 13,100% 9,673% -38,638% 65,454%
Board Characteristics:
Age Of Other Directors 6232 60,669 60,875 4,054 42,111 77,833
%Indep. Other Directors 6232 72,586% 75% 16,043% 0% 100%
# Of Other Directors 6232 8,897 9 2,505 1 32 CEO Characteristics: CEO Tenure 6232 6,380 4 6,629 0 57 %CEO-Ownership 6232 1,483% 0,231% 4,361% 0% 49,670% Firm Characteristics: Firm Size 6232 7,938 7,763 1,595 3,410 13,139 Firm Age 6232 25,326 24 14,424 1 53
20 a standard deviation of 14,424 years.
Additionally, table II shows the cross-correlations between all the mentioned explanatory variables. These cross-correlations are useful for identifying possible problems caused by (im)perfect multicollinearity between the explanatory variables (i.e. linear correlations between explanatory variables which lead to possible imprecise estimation results).
Table II: Correlation Matrix
The table provides the cross-correlations between all the explanatory variables. Lagged Tobin’s Q is the one year lagged value of Tobin’s Q. Lagged ROA is the one year lagged return on assets. Age Of Other Directors is the average age of all the board members who were already with the firm. %Indep. Other Directors equals the fraction of independent board members who were already with the firm. # Of Other Directors equals the number of board members who were already with the firm. CEO Tenure is measured by the number of years that the CEO is with the firm. %CEO-Ownership is the percentage of total common shares outstanding that the CEO owns. Firm Size is the natural logarithm of the market value of common equity. Firm Age is the number of years since the IPO.
From table II can be seen that multicollinearity is not problematic in this study, since all correlations (in absolute terms) between the explanatory variables are below 0,481 (except for the correlation between Lagged Tobin’s Q and Lagged ROA). Therefore, any potential imprecise estimates are not likely to be caused by too high (imperfect) multicollinearity.
V. Empirical Results
This section discusses the main empirical results. Firstly the results concerning hypothesis 1, and hypothesis 2A and 2B, are discussed in part A. Finally, the results regarding hypothesis 3 are discussed in part B.
Lagged Tobin’s Q Lagged ROA Age Of Other Directors %Indep. Other Directors # Of Other Directors CEO Tenure %CEO- Ownership Firm Size Lagged ROA 0,506
Age Of Other Directors -0,187 -0,081
%Indep. Other Directors -0,121 -0,096 0,201
# Of Other Directors -0,152 -0,061 0,171 0,086
CEO Tenure 0,038 0,023 0,116 -0,160 -0,069
%CEO-Ownership 0,077 0,065 -0,032 -0,255 -0,132 0,410
Firm Size 0,163 0,151 0,138 0,139 0,481 -0,065 -0,149
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A. Firm Performance and the Type of New Directors Being Appointed
Table III provides the regression results regarding hypothesis 1. Panel A shows the coefficients for the appointed insiders, whereas panel B shows the coefficients for the appointed outsiders. From the table can be seen that there are indications (but no strong statistical evidence) that poor performance (when measured by lagged Tobin’s Q) leads to firms appointing older insiders and older outsiders when hiring new directors, and insiders who have a better reputation (which has a low economic meaning). This provides at least some support for hypothesis 1, meaning that poorly performing companies either choose to hire somewhat “safer” directors (i.e. make less risky hiring decisions), that poorly performing companies have a higher need for good experienced directors to enhance the functioning of the board of directors, or that both these theories hold.
In both cases the coefficients in regression 1 and 2 (in panel A and panel B) are statistically significant. In economic terms, the coefficients from regression 2 (in panel A and B) mean that a one standard deviation decrease in lagged Tobin’s Q would lead to hiring insiders who are 5,215 months older, and outsiders who are 2,128 months older. For example, when hiring two new insiders this would lead to 10,430 months of additional experience (compared to a firm hiring two insiders after performing one standard deviation better), when hiring two outsiders this number equals 4,256 months. The coefficients from regression 3 (in panel A and B) have somewhat more economic meaning, i.e. a one standard deviation decrease in Tobin’s Q would to hiring insiders who are 9,704 months older, and outsiders who are 3,085 months older. However, since most variation in lagged Tobin’s Q in this sample is cross-sectional, the coefficients in regression 3 are rather imprecise and no longer statically significant (because of the higher standard errors when using only the within firm variation). When performance is measured by lagged ROA the discussed results only remain for outsiders; although in contrary to regressions 4 and 5 the coefficient is no longer statistically significant, the results of regression 6 in panel B suggest that a one standard deviation decrease in ROA leads to hiring outsiders who are 1,373 months older. With respect to the reputation of the newly appointed directors, there are only some economically insignificant indications that poorly performing firms (measured by lagged Tobin’s Q) appoint insiders who have a better reputation. The coefficients on lagged Tobin’s Q in regression 7, 8 and 9 in panel A are all statistically significant. However, the economic meaning is that a one standard deviation decrease in Tobin’s Q would lead to appointing
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Table III: Tests of Hypothesis 1
Age Of Appointed Directors # Of Other Board Seats Of Appointed Directors
1 2 3 4 5 6 7 8 9 10 11 12 Panel A: Appointed Insiders
Lagged Tobin’s Q Lagged ROA (in %)
-0,373** (0,165) -0,301* (0,172) -0,560 (0,370) -0,028 (0,028) -0,021 (0,029) 0,008 (0,061) -0,029** (0,014) -0,037** (0,014) -0,047* (0,028) -0,002 (0,002) -0,003 (0,002) -0,008 (0,007) Age Of Other Directors 0,299*** (0,067) 0,275*** (0,072) -0,073 (0,159) 0,251*** (0,066) 0,285*** (0,071) -0,025 (0,157) -0,007 (0,006) -0,006 (0,007) -0,016 (0,017) -0,006 (0,006) -0,005 (0,007) -0,017 (0,017) %Indep. Other Directors 0,019 (0,016) 0,014 (0,017) 0,015 (0,034) 0,022 (0,016) 0,016 (0,017) 0,021 (0,034) -0,002 (0,002) -0,001 (0,002) -0,007* (0,004) -0,001 (0,002) -0,001 (0,002) -0,007* (0,004) # Of Other Directors 0,210** (0,097) 0,249** (0,098) 0,099 (0,227) 0,251*** (0,095) 0,285*** (0,095) 0,179 (0,224) 0,003 (0,011) -0,003 (0,011) 0,002 (0,024) 0,007 (0,011) 0,001 (0,011) 0,004 (0,023) CEO Tenure -0,103*** (0,035) -0,098*** (0,034) -0,015 (0,084) -0,103*** (0,035) -0,097*** (0,034) -0,014 (0,085) -0,003 (0,003) -0,003 (0,003) -0,002 (0,009) -0,003 (0,003) -0,003 (0,003) -0,001 (0,009) %CEO-Ownership -0,036 (0,061) -0,033 (0,060) -0,044 (0,224) -0,045 (0,062) -0,038 (0,060) -0,066 (0,224) -0,000 (0,006) -0,001 (0,006) 0,002 (0,002) -0,001 (0,006) -0,002 (0,006) 0,019 (0,020) Firm Size 0,331** (0,161) 0,285* (0,167) 0,267 (0,727) 0,229 (0,155) 0,197 (0,159) -0,122 (0,760) 0,077*** (0,018) 0,084 (0,019) 0,072 (0,064) 0,069*** (0,017) 0,075*** (0,017) 0,079 (0,061) Firm Age 0,023 (0,018) 0,023 (0,018) -0,020 (0,177) 0,029 (0,018) 0,027 (0,018) -0,008 (0,180) 0,001 (0,001) 0,001 (0,002) -0,012 (0,064) 0,001 (0,002) 0,001 (0,002) -0,013 (0,023) Constant 28,102*** (3,850) 29,412*** (4,306) 52,833*** (10,959) 27,216*** (3,824) 28,886*** 4,275 50,411*** 10,813 0,221 (0,601) 0,371 (0,442) 1,683 (1,293) 0,154 (0,379) 0,330 (0,450) 1,694 (1,313)
Firm Fixed Effects No No Yes No No Yes No No Yes No No Yes
Year Fixed Effects No Yes Yes No Yes Yes No Yes Yes No Yes Yes
Observations Adjusted R-Squared 729 0,116 729 0,122 729 0,132 729 0,111 729 0,119 729 0,124 729 0,041 729 0,050 729 0,151 729 0,038 729 0,047 729 0,151
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The table provides the estimated coefficients for all models which are used to test hypothesis 1. The dependent variables in panel A are the (Average) Age Of Appointed Insiders and the (Average) # Of Other Board Seats Of Appointed Insiders. The dependent variables in panel B are the (Average) Age Of Appointed Outsiders and the (Average) # Of Other Board Seats Of Appointed Outsiders. Clustered standard errors are shown in parentheses. Statistical significance at the ten, five and one percent level is indicated by *, ** and ***.
Panel B: Appointed Outsiders
Lagged Tobin’s Q -0,172** (0,071) -0,123* (0,073) -0,178 (0,126) 0,013 (0,013) -0,001 (0,013) -0,009 (0,021)
Lagged ROA (in %) -0,027**
(0,011) -0,022** (0,011) -0,012 (0,021) -0,000 (0,002) -0,002 (0,002) -0,005 (0,003) Age Of Other Directors 0,351*** (0,025) 0,303*** (0,027) -0,379*** (0,056) 0,356*** (0,025) 0,306*** (0,027) -0,374*** (0,057) 0,001 (0,003) 0,007* (0,004) -0,005 (0,007) 0,001 (0,004) 0,008* (0,004) -0,005 (0,008) %Indep. Other Directors 0,017** (0,007) 0,002 (0,007) -0,003 (0,012) 0,016** (0,007) 0,001 (0,007) -0,003 (0,012) 0,000 (0,001) 0,002** (0,001) -0,002 (0,002) 0,000 (0,001) 0,002** (0,001) -0,002 (0,002) # Of Other Directors -0,065 (0,043) -0,030 (0,044) -0,048 (0,079) -0,059 (0,043) -0,027 (0,044) -0,039 (0,079) -0,002 (0,008) -0,009 (0,008) 0,010 (0,013) -0,004 (0,008) -0,011 (0,008) 0,010 (0,013) CEO Tenure 0,013 (0,017) 0,015 (0,017) 0,041 (0,027) 0,012 (0,017) 0,014 (0,017) 0,041 (0,027) -0,003 (0,002) -0,002 (0,002) 0,004 (0,003) -0,003 (0,002) -0,003 (0,002) 0,004 (0,004) %CEO-Ownership -0,004 (0,002) -0,009 (0,024) -0,018 (0,052) -0,003 (0,024) -0,008 (0,025) -0,019 (0,052) -0,007** (0,003) -0,007** (0,003) -0,017** (0,008) -0,007** (0,003) -0,007** (0,002) -0,017** (0,008) Firm Size 0,176*** (0,068) 0,133* (0,069) 0,113 (0,253) 0,163** (0,066) 0,128* (0,067) 0,044 (0,252) 0,126*** (0,011) 0,137*** (0,011) 0,112*** (0,038) 0,131*** (0,011) 0,140*** (0,011) 0,124*** (0,040) Firm Age -0,018*** (0,007) -0,015** (0,007) 0,330*** (0,059) -0,016** (0,007) -0,013* (0,007) 0,336*** (0,059) -0,001 (0,001) -0,001 (0,001) -0,023** (0,008) -0,001 (0,001) -0,002 (0,001) -0,024** (0,009) Constant 33,397*** (1,600) 35,628*** (1,734) 70,180*** (3,809) 33,185*** (1,589) 35,621*** (1,730) 70,027*** 3,821 -0,199 (0,243) -0,459* (0,254) 0,879 (0,543) -0,143 (0,235) -0,433* (0,246) 0,876 (0,542)
Firm Fixed Effects No No
Yes
Yes No No Yes No No Yes No No Yes
Year Fixed Effects No Yes No Yes Yes No Yes Yes No Yes Yes
Observations 5854 5854 5854 5854 5854 5854 5854 5854 5854 5854 5854 5854
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inside directors who have 0,068 more other board seats (using -0,047 as the value of the coefficient of interest). This number appears to be low, and if compared to the average of 0,293 other board seats of the appointed inside directors it has some minor meaning at most. Moreover, these statistically significant results no longer hold when using ROA as the performance measure, nor for the newly appointed outside directors. So, poorly performing firms do not seem to hire directors with a better reputation. This could be because a better reputation has advantages (a.o. more likely to be a good director) and disadvantages (i.e. less time available) as well.
Finally, note that the coefficient of the Age of Other Directors changes drastically when including firm fixed effects in regressions 3 and 6 (in both panel A and B) compared to regression 1 and 2, and 4 and 5 respectively. Without the usage of firm fixed effects, these coefficients are positive, suggesting that older directors hire older new directors. However, with the usage of firm fixed effects, these coefficients turn out to be negative, suggesting that older directors hire younger new directors. This is likely to be caused by certain firms always appointing older directors compared to other firms (i.e. firm fixed effects), but when the current directors are older these firms appoint younger directors in order to not get a too old board. Not including firm fixed effects would result in omitted variable bias, since then it would appear that the age of the other board members causes to hire older directors, which actually does not seem to be the case.
Next, table IV provides the results regarding possible heterogeneity (i.e. with the usage of interaction terms between lagged performance and firm characteristics). The results in table IV provide weak evidence in support for hypothesis 2A and 2B, however some of the results actually contradict with the hypotheses. There are indications that poor performance inclines larger firms more to appoint older (i.e. more experienced) directors than smaller firms, and that this also holds for youngers versus more mature firms. However, this no longer holds when using ROA as the performance measure nor when studying outside director appointments. Moreover, it actually appears that the effect of poor performance on the age/experience of the outside directors being appointed increases with firm age, which contradicts with hypothesis 2B. Since the results from regressions 7-12 in panel A and panel B are either (statistically and/or economically) insignificant or strongly contradict when using the other lagged performance measure (i.e. Lagged Tobin’s Q versus Lagged ROA), there are no strong nor weak indications that the effect of poor performance on the reputation of the
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Table IV: Tests of Hypothesis 2A and 2B
Age Of Appointed Directors # Of Other Board Seats Of Appointed Directors
1 2 3 4 5 6 7 8 9 10 11 12 Panel A: Appointed Insiders
Lagged Tobin’s Q
Lagged ROA (in %)
Lagged Tobin’s Q * Firm Size Lagged Tobin’s Q * Firm Age
Lagged ROA (in %) * Firm Size
Lagged ROA (in %) * Firm Age -0,094 (0,876) -0,056 (0,101) 0,013 (0,013) -0,360 (0,907) -0,015 (0,104) 0,011 (0,013) 0,211 (1,769) -0,107 (0,199) 0,016 (0,029) -0,021 (0,112) -0,000 (0,014) -0,000 (0,002) -0,045 (0,113) 0,004 (0,014) -0,000 (0,002) 0,071 (0,262) 0,001 (0,003) -0,003 (0,004) 0,006 (0,073) -0,004 (0,009) 0,000 (0,001) 0,019 (0,075) -0,006 (0,009) -0,000 (0,001) -0,037 (0,181) -0,001 (0,021) -0,000 (0,002) -0,020* (0,010) 0,002 (0,001) 0,000 (0,000) -0,019* (0,010) 0,002 (0,001) 0,000 (0,000) -0,031 (0,024) 0,003 (0,003) -0,008 (0,038) Age Of Other Directors 0,295*** (0,067) 0,270*** (0,073) -0,062 (0,161) 0,310*** (0,066) 0,282*** (0,071) -0,026 (0,156) -0,007 (0,006) -0,005 (0,007) -0,016 (0,017) -0,006 (0,006) -0,006 (0,007) -0,017 (0,017) %Indep. Other Directors 0,019 (0,016) 0,014 (0,018) 0,016 (0,034) 0,022 (0,016) 0,016 (0,017) 0,002 (0,003) -0,002 (0,002) -0,001 (0,002) -0,007* (0,004) -0,001 (0,002) -0,001 (0,002) -0,007* (0,004) # Of Other Directors 0,214** (0,097) 0,252** (0,098) 0,103 (0,227) 0,251*** (0,097) 0,288*** (0,098) 0,182 (0,228) 0,004 (0,011) -0,002 (0,012) 0,002 (0,024) 0,008 (0,011) 0,003 (0,011) 0,007 (0,024) CEO Tenure -0,105*** (0,035) -0,098*** (0,034) -0,017 (0,084) -0,103*** (0,036) -0,096*** (0,035) -0,008 (0,085) -0,003 (0,003) -0,004 (0,003) -0,002 (0,009) -0,002 (0,003) -0,003 (0,003) -0,001 (0,010) %CEO-Ownership -0,037 (0,058) -0,034 (0,058) -0,048 (0,229) -0,044 (0,062) -0,040 (0,061) -0,068 (0,222) -0,000 (0,006) -0,001 (0,006) 0,021 (0,021) -0,002 (0,006) -0,003 (0,006) 0,018 (0,020) Firm Size 0,398 (0,244) 0,280 (0,253) 0,332 (0,766) 0,237 (0,281) 0,144 (0,289) -0,087 (0,765) 0,084*** (0023) 0,096*** (0,023) 0,074 (0,064) 0,037 (0,024) 0,046* (0,024) 0,048 (0,066) Firm Age 0,001 (0,029) 0,003 (0,028) -0,040 (0,179) 0,032 (0,031) 0,030 (0,032) 0,025 (0,180) 0,001 (0,002) 0,001 (0,002) -0,012 (0,023) -0,000 (0,003) -0,000 (0,003) -0,011 (0,024) Constant 28,073*** (4,149) 30,077*** (4,671) 51,572*** (11,408) 27,055*** (4,202) 29,207*** (4,679) 49,425*** (11,007) 0,155 (0,397) 0,251 (0,471) 1,671 (1,301) 0,453 (0,402) 0,611 (0,477) 1,871 (1,311)
Firm Fixed Effects No No Yes No No Yes No No Yes No No Yes
Year Fixed Effects No Yes Yes No Yes Yes No Yes Yes No Yes Yes
Observations Adjusted R-Squared 729 0,115 729 0,120 729 0,128 729 0,109 729 0,116 729 0,122 729 0,039 729 0,048 729 0,147 729 0,039 729 0,047 729 0,151
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The table provides results for all models which are used to test hypothesis 2A and 2B. The dependent variables in panel A are the (Average) Age Of Appointed Insiders and the (Average) # Of Other Board Seats Of Appointed Insiders. The dependent variables in panel B are the (Average) Age Of Appointed Outsiders and the (Average) # Of Other Board Seats Of Appointed Outsiders. Clustered standard errors are shown in parentheses. Statistical significance at the ten, five and one percent level is indicated by *, ** and ***.
Panel B: Appointed Outsiders Lagged Tobin’s Q
Lagged ROA (in %) -0,022 (0,341) -0,135 (0,337) -0,437 (0,548) 0,129** (0,054) 0,114** (0,053) 0,125 (0,085) -0,089* (0,050) -0,068 (0,049) -0,172** (0,079) -0,016** (0,007) -0,013* (0,007) -0,013 (0,012) Lagged Tobin’s Q * Firm Size Lagged Tobin’s Q * Firm Age Lagged ROA (in %) * Firm Size Lagged ROA (in %) * Firm Age 0,003 (0,038) -0,012*** (0,005) 0,020 (0,038) -0,011** (0,005) 0,034 (0,063) -0,003 (0,010) -0,019*** (0,007) -0,000 (0,001) -0,018** (0,007) -0,000 (0,001) -0,022* (0,012) 0,001 (0,002) 0,010* (0,006) 0,001 (0,001) 0,006 (0,006) 0,001 (0,001) 0,017* (0,009) 0,001 (0,002) 0,002** (0,001) 0,000 (0,000) 0,002* (0,001) -0,001 (0,001) 0,001 (0,002) 0,000 (0,000) Age Of Other Directors 0,355*** (0,025) 0,307*** (0,027) -0,379*** (0,057) 0,355*** (0,025) 0,306*** (0,027) -0,376*** (0,056) 0,001 (0,004) 0,007* (0,004) -0,006 (0,008) 0,001 (0,004) 0,008* (0,004) -0,005 (0,008) %Indep. Other Directors 0,016** (0,007) 0,001 (0,007) -0,003 (0,012) 0,017** (0,007) 0,002 (0,007) -0,003 (0,012) 0,001 (0,001) 0,002** (0,001) -0,002 (0,002) 0,000 (0,001) 0,002** (0,001) -0,002 (0,002) # Of Other Directors -0,068 (0,043) -0,033 (0,044) -0,047 (0,079) -0,072* (0,043) -0,040 (0,044) -0,049 (0,078) -0,001 (0,008) -0,010 (0,008) 0,010 (0,013) -0,002 (0,008) -0,010 (0,008) 0,010 (0,014) CEO Tenure 0,014 (0,017) 0,016 (0,017) 0,041 (0,027) 0,013 (0,017) 0,014 (0,017) 0,041 (0,027) -0,003 (0,002) -0,003 (0,002) 0,004 (0,004) -0,003 (0,002) -0,003 (0,002) 0,004 (0,004) %CEO-Ownership -0,004 (0,024) -0,009 (0,024) -0,017 (0,052) -0,004 (0,024) -0,009 (0,024) -0,020 (0,052) -0,007** (0,003) -0,007** (0,003) -0,016* (0,008) -0,007** (0,003) -0,007** (0,003) -0,017** (0,008) Firm Size 0,196* (0,100) 0,117 (0,101) 0,064 (0,280) 0,448*** (0,116) 0,384*** (0,116) 0,324 (0,301) 0,104*** (0,016) 0,123*** (0,016) 0,074* (0,041) 0,103*** (0,018) 0,118*** (0,017) 0,112** (0,046) Firm Age 0,002 (0,011) 0,003 (0,011) 0,334*** (0,061) -0,011 (0,013) -0,013 (0,013) 0,319*** (0,061) -0,003 (0,002) -0,003 (0,002) -0,024** (0,010) -0,002 (0,002) -0,001 (0,002) -0,024** (0,010) Constant 32,791*** (1,727) 35,366*** (1,850) 70,583*** (4,038) 31,064*** (1,736) 33,765*** (1,859) 68,518*** (3,966) 0,043 (0,262) -0,294 (0,277) 1,277** (0,576) 0,065 (0,250) -0,288 (0,262) 0,987* (0,574)
Firm Fixed Effects No No
Yes
Yes No No Yes No No Yes No No Yes
Year Fixed Effects No Yes No Yes Yes No Yes Yes No Yes Yes
Observations 5854 5854 5854 5854 5854 5854 5854 5854 5854 5854 5854 5854
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board appointees differs in a particular way across firms.
Using the coefficients from regression 3 in panel A, when firm size is at the 75th percentile level (i.e. 8,968), the slope of the line relating firm performance (in this case lagged Tobin’s Q) and the average age of the appointed insiders is estimated to be 0,129 more negative than when firm size is as the median level (i.e. 7,763). This suggests that larger firms respond more heavily to poor performance than smaller firms, which supports the idea that poor performance signals the need for good experienced directors the most for larger firms. However, since the coefficient (i.e. -0,107) is not statistically significant, and the results no longer hold when using ROA as the performance measure, there is no strong evidence for this. Regarding outside director appointments, the regression results strongly contradict when using the two different lagged performance measures, therefore it’s hard to draw a conclusion about possible heterogeneity (with respect to firm size) of firm performance on the average age of the appointed outside directors. However, this does not necessarily mean that it is more likely that the effect of firm performance on the type of the appointed outside directors is equal across firms with different sizes.
Although statistically insignificant, the results from regression 3 in panel A provide some indications that poor performance inclines younger firms more to appoint insiders who are more experienced, than older firms. When firm age is at the 75th percentile level (i.e. 39 years old), the slope of the line relating firm performance (i.e. Lagged Tobin’s Q) and the average age of the appointed insiders is estimated to be 0,233 more positive than when firm age is at the median level (i.e. 24 years old). However, from regression 2 in panel B it appears that when hiring outsiders, older firms respond more heavily to poor performance than younger firms. When firm age is at the 75th percentile level (i.e. 39 years old), the slope of the line relating firm performance (i.e. Lagged Tobin’s Q) and the average age of the appointed outsiders is estimated to be 0,160 more negative than when firm age is at the median level (i.e. 24 years old). The coefficient of interest (i.e. -0,011) is statistically significant; this result contradicts with hypothesis 2B. However, this could be explained by the possibility that younger firms face more difficulties in hiring experienced directors.
B. Firm Performance, the Type Of New Directors Being Appointed, and the Business Cycle Section A discusses the effect of firm performance on the type of directors being hired, and possible heterogeneity between firms (with respect to firm size and firm age). However,
