Board of Directors’ Size and Performance in the Banking Industry1

Mohamed Belkhir

UAE University, College of Business and Economics,

Department of Economics and Finance

July 2008

forthcoming in the International Journal of Managerial Finance

Abstract This paper investigates the relationship between board size and performance in a sample of 174 bank and savings-and-loan holding companies, over the period 1995-2002. Using panel data techniques, we find that contrary to theories predicting that smaller boards of directors are more effective, increasing the number of directors in banking firms does not undermine performance. In contrast, the evidence is in favor of a positive relationship between board size and performance, as measured by Tobin’s Q and the return on assets. We investigate whether this positive association is due to the fact that banks reduce the number of their directors in the aftermath of poor performance by testing for the relationship between board size and performance. The findings show that the number of directors leaving the board and the number of those joining the board for the first time increase following a poor performance, but the net change in board size is not affected by past performance. We conclude that the board size-performance relationship goes from board size to performance and that the calls to reduce the number of directors in banks might have adverse effects on performance. Key words: Corporate governance, board size, performance, banking

JEL classification: G21, G34

1 This paper was written while I was a Fulbright visiting scholar at the Finance department of the University of North Texas (UNT). I have benefited from helpful comments and suggestions from Imre Karafiath, Mazhar Siddiqi, and Patrick Brandt (at UNT), and from Christophe Hurlin, Jean-Paul Pollin and other participants at the “Wednesday Seminar” at the Laboratoire d’Economie d’Orleans. I am grateful to Jean-Paul Pollin for his guidance and support. I would like also to thank Mickael Braswell (Finance Department Chair at UNT), James Conover and other faculty and staff for their hospitality and helpfulness. My thanks also go to an anonymous referee and to David Michayluk (the editor) for their helpful comments. Naturally, any remaining omissions are my own. UAE University, College of Business and Economics, Department of Economics and Finance, P.O.Box : 17555 – Al Ain – UAE. e-mail: m.belkhir@uaeu.ac.ae

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1. Introduction Recent corporate governance literature has seen the emergence of an extensive body

of empirical work on the effectiveness of boards of directors in monitoring managers and on their effect on firm performance. The topics covered include the relation between performance and the proportion of outside directors (Hermalin and Weisbach, 1991; Bhagat and Black, 2002), board size (Yermack, 1996; Eisenberg et al., 1998), insiders’ ownership (Morck et al., 1988; McConnell and Servaes, 1990), and leadership structure (Brickley et al., 1997). Most previous studies in this area have focused on unregulated firms in general, and industrial firms in particular. Boards of directors in banking firms and their effectiveness have received only a limited attention. One possible explanation for this is the assumption that regulation limits the role of directors in controlling management. While regulation still distinguishes the banking industry from many others, it is, nowadays, no longer a major factor when it comes to limiting the scope of competition on the product market or the role of the market for corporate control2. In a business environment characterized by an increased competition and higher threats from the market for corporate control, the effectiveness of internal governance systems in protecting shareholders’ interests has become more relevant than ever before3. For this reason, governance structures in banking organizations are supposed to evolve toward the governance system used by unregulated firms, and likely to have similar effects on performance4.

This paper focuses on a particular aspect of boards of directors in banking. It

investigates the relationship between board size and performance in U.S. banking organizations during the period 1995-2002. Indeed, many academics, business leaders and observers advocate that a limited number of directors enhances the effectiveness of the board, and improves performance. We hypothesize that if this prediction holds for all industries an empirical investigation should reveal a negative correlation between board size and performance in banking firms.

Besides getting interested in an important aspect of the board of directors of banks,

i.e., board size, this study is distinguished from the existing literature with respect to many aspects. First, in addition to bank holding companies (BHCs), the sample is extended to cover savings-and-loan holding companies (SLHCs). Deregulation has affected commercial banking as well as the thrift industry by increasing the role of the

2 Deregulation that started in the 1980s, and continued in the middle of the 1990s with the Riegle-Neal Interstate Banking and Branching, made banks less protected by expanding the market for corporate control. The Gramm-Leach-Bliley Act which became effective in 2000 increased competition in the product market and expanded the banking opportunity set by broadening the range of activities banks can engage in. 3 Smith and Watts (1992) suggest that agency conflicts increase with the increase of the firm’s opportunity set. Lorsch and MacIver (1989) find that directors exercise more power when their firms face external threats. 4 Kole and Lehn (1999) examine the effect of deregulation on governance structures of airline companies, and find that after deregulation, those structures have evolved towards those of unregulated firms. Their results show that after deregulation, equity ownership is more concentrated, CEO pay increases, stock option grants to CEOs increase, and board size decreases.

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market for corporate control and unifying the financial services market. Savings institutions are competing on the same market and are under the same “threats” as commercial banks. Governance structures in SLHCs are thus supposed to be similar to those in BHCs and to have similar effects on performance. Second, unlike several past studies on governance in BHCs, our sample includes a larger number of small BHCs and SLHCs and is more representative of the typical banking organization in the U.S. Third, this study goes a step further by conducting an explicit test of the relationship of board size to past performance and the change in bank size.

Using a panel data set of nearly 1,150 bank-years, over the period 1995-2002, our

analysis reveals the following results: 1. Contrary to the argument and some empirical findings that smaller boards are

more effective, and induce an increase in performance, we find that there is no such evidence in banking organizations. In contrast, the evidence is in favor of a positive relation between board size and measures of performance (Tobin’s Q and return on assets). The results are robust to controls for bank size, board leadership structure, CEO tenure, insiders’ stock ownership, and board independence.

2. The empirical relation between board size and performance is similar in both BHCs and SLHCs, suggesting that governance structures may affect performance in the same way in the savings and commercial banking industries.

3. There is no evidence of board size being adjusted following past poor performance. A past poor performance induces an increase in the number of directors leaving and joining the board, but with no effect on the net change in board size. We also find that while past performance does not affect the change in board size, a change in the bank’s size yields a change in the number of directors sitting on the board.

The remainder of this paper is organized as follows: section 2 reviews the literature on board size and banking corporate governance. Section 3 describes the data. Section 4 provides results of univariate tests. Section 5 presents the regression analysis and the results for the relationship between board size and performance. Section 6 investigates the question of causation of board size to past performance. Finally section 7 provides a summary and conclusions.

2. Related Literature The largely shared wisdom regarding the optimal board size is that the higher the

number of directors sitting on the board the less performance. This leans on the idea that communication, coordination of tasks, and decision making effectiveness among a large group of people is harder and costlier than it is in smaller groups. The costs overwhelm the advantages gained from having more people to draw on. Jensen (1993) states that “Keeping boards small can help improve their performance. When boards get beyond seven or eight people they are less likely to function effectively and are easier for the CEO to control.” (p. 865) Lipton and Lorsch (1992) also call for the adoption of small boards, and recommend that board size be limited to seven or eight members. Based on these theoretical arguments firms with larger boards of directors could experience lower performance.

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Several studies empirically investigate the link between board size and performance in non-regulated firms. For a sample of large public U.S. corporations, Yermack (1996) finds a statistically significant negative relationship between board size and firm performance as measured by Tobin’s Q. He confirms the negative relationship with several accounting measures of performance. Eisenberg et al., (1998) report a similar negative relationship between board size and return on assets for a sample of small and midsize Finnish firms. However, Bhagat and Black (2002) find that the negative relationship between board size and performance is not robust to the change of the performance measure. For a sample of companies listed on the Stock Exchange of Singapore, Mak and Li (2001) find that the sign and significance of the relationship between board size and performance, is sensitive to the estimation method. They conclude that this is evidence that board characteristics are endogenous and that failing to take endogeneity into account may yield a significant relationship with performance that does not really exist. Internal governance systems in banking firms and their effects on performance have received less attention than in industrial firms. One strain of the empirical literature investigates the adjustment of governance systems to the deregulation of the banking activity. This includes the investigation of the effect of deregulation on pay-performance sensitivity of bank CEOs (Hubbard and Palia, 1995, Crawford et al., 1995, and Brewer III et al., 2003), and of bank outside directors (Becher et al., 2005). These studies find that pay-performance sensitivity of CEOs as well as that of outside directors adjusts to the shift in the business environment generated by deregulation. In a more competitive environment and in an expanding market for corporate control, banks adjust their compensation contracts by relying more on equity based compensation.

Another strain of empirical work, which is also the focus of this paper, looks at the effect of board leadership and structure on performance and shareholders’ value of banking firms. Brook et al. (2000) study the determinants of a bank becoming a target for an acquisition by another bank. They find that outside directors’ equity ownership enhances enforcing managers to act in the interest of shareholders by accepting a takeover bid. Becher et al. (2005) report that banks perform better when there is more equity in their directors’ compensation packages. In contrast, Brewer III et al. (2000) find that bid premiums offered for target banks increase with the proportion of independent outside directors. However, Pi and Timme (1993) and Adams and Mehran (2004) find that the proportion of outside directors is not related to performance measures in banking.

3. Data This study focuses on the relationship between board size and performance in

banking. The analysis is conducted on a sample of bank holding companies (SIC 6021 and 6022) and savings and loan holding companies (SIC 6035 and 6036) available in the Research Insight database of Standard & Poor’s. The sample period starts in 1995 and ends in 2002. To be included in the sample the bank has to have a total assets book value

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of $1 billion, for at least four years, during the sample period. This size requirement yields a total of 192 BHCs and SLHCs. To make the panel data technique usable, we require that, to be included in the final sample, each bank has to have at least four consecutive years of data in the Center for Research in Securities Prices (CRSP) database, in the Research Insight database and on the Securities and Exchange Commission (SEC) website. This yields a final sample of 174 banks, 119 of which are BHCs and 55 are SLHCs.

Table 2 reports descriptive statistics for variables used to investigate the

relationship between board size and performance in banking. Following past studies (Yermack, 1996, Eisenberg et al., 1998) that focused on the relationship between performance and board size we have adopted two measures of performance: A proxy for Tobin’s Q and the Return on assets (ROA). Tobin’s Q is calculated as the book value of total assets minus the book value of common equity plus the market value of common equity divided by the book value of total assets. ROA is collected from Research Insight, and is defined as the net income before extraordinary items, divided by the book value of total assets. Board size is the number of directors sitting on the board at the annual shareholders meeting. Definitions of the other variables are provided in table 1.

Insert table 1 here. Insert table 2 here.

Table 2 shows that the average sample bank has total assets of $22 billions (median:

$4.183 billions). The average Tobin’s Q and ROA in the sample are 1.09 and 1.07%, respectively. The average bank in the sample has a tier 1 leverage ratio equal to 8.02%, and a stock price volatility of 8%. The governance structure of the average bank is characterized by a board of approximately 13 directors (median: 12), of which 67% (approximately 9 directors) are outsiders and independent from management5. The CEO is also the chairman of the board in 65% of the cases, has held his position for 9.5 years, and owns 5% of the company’s common equity. All directors and top executive officers (except the CEO) of the average company own, together, 8.4% of the common equity.

4. Univariate analysis This section presents the results of a univariate analysis conducted in order to gain a

preliminary insight on the nature of the relationship between board size and performance in banking.

Correlation Analysis

5 See the appendix for the classification of directors into affiliated outside directors, independent outside directors and inside directors. The average board size is much smaller than the average board size reported by Booth et al. (2002) and Adams and Mehran (2004). Booth et al. report an average board size of 16.37 for a sample of the largest 100 BHCs in 1999, and Adams and Mehran report an average board size of 18 directors. Booth et al (2002) report that in 1999, 80% of the 100 largest BHCs’ CEOs also held the title of chairman of the board.

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Table 2 reports Pearson correlation coefficients between board size and other variables, and between Tobin’s Q and other variables, as well as their statistical significance. It shows a positive and statistically significant correlation between the two measures of performance (Tobin’s Q and ROA) and board size6. Board size is also positively and statistically correlated with total assets and board independence, suggesting that larger banking companies have more directors on their boards, and that the more the directors, the more are those who are non-executives and independent from management.

The positive correlation between board size and CEO-Chairman duality suggests

that CEOs of companies with smaller boards are less likely to be also the chairman. Taking for granted the idea that separating CEO and chairman titles enhances control by the board, this may be interpreted as evidence of effectiveness of smaller boards. The negative correlation between board size and CEO ownership may be interpreted in two different ways. Smaller boards are more effective in aligning CEOs interests on those of shareholders, possibly by relying on more stock units and stock options in compensating the CEO (therefore, increasing CEO ownership). The alternative interpretation is that companies with less CEO ownership rely on more directors. In this case, larger boards may be seen as more effective in monitoring the CEO because they remedy for the lack of control due to low CEO stock ownership.

Performance sorted by board size In contrast to Yermack (1996) and Eisenberg et al. (1998), figure 1 shows an

increasing trend of performance, measured by Tobin’s Q, as board size increases. Up to a certain size, larger boards are not matching with poor performance. Figure 1 shows that there is an increase in Tobin’s Q when board size expands to approximately 19 members. Beyond 19 members Tobin’s Q starts a decreasing movement as board size increases. Plotting means and medians of ROA observations sorted by board size yields qualitatively the same curve as in figure 1.

Insert figure 1 here.

Mean and Median difference in performance between subsamples divided according to board size In order to check if banks with smaller boards outperform their counterparts with

larger boards, the sample is divided into two subsamples according to board size. The cut-off board size is the sample median. Observations of Tobin’s Q for which board size is equal or below the sample median form a group called Tobin’s Qbelow. Observations for which board size is above the sample median form a group called Tobin’s Qabove. If the theoretical view that smaller boards improve performance holds in banking, one would expect a significant difference of means and medians between Tobin’s Qbelow and Tobin’s Qabove in favor of the first group. We test for the mean difference using the paired t-test and for the median difference using the Wilcoxon Signed Ranks test. The analysis is conducted for the total sample and for each individual year. Table 3 shows that Tobin’s Qabove exhibits a higher mean and median on the total sample and in each individual year, 6 In contrast, Eisenberg et al. (1998, table 2) report a negative correlation between the ROA and board size.

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except in 1995. The mean and median difference is statistically significant for the total sample and for each individual year, except for 1995.

Insert table 3 here.

The univariate analysis conducted above does not show evidence of smaller boards

beating large boards in terms of performance. In contrast, it shows that larger boards match with higher performance.

5. Regression Analysis 5.1. Model Specification The corporate governance literature states that as the number of directors increases

the effectiveness of the board in monitoring management decreases. The result would be a fall in performance. If this argument is valid across industries, one would expect to find a negative relationship between board size and performance in banking. We investigated this theoretical hypothesis by regressing measures of performance on board size and other control variables thought to have an effect on performance, either directly, or through shaping the governance structure of the bank. Based on the concave curve in figure 1, we test for a log-linear relationship between board size and performance.

Among the governance control variables we include a dummy variable that takes the value of 1 if the CEO is also the Chairman of the board and 0 otherwise (CEO-Chairman duality). It is argued that when the CEO is also the chairman of the board, she gets too much influence on the board to make it perform properly its functions (e.g. Jensen, 1993). The result is a weak internal system of control that may negatively affect performance7. We also include a variable called CEO Tenure in the regression equation in order to control for the potential effect of the number of years spent by the CEO in her position on performance. The corporate governance literature suggests that a CEO may become entrenched as the number of years during which she has held her position within the company increases. CEO entrenchment may adversely affect the effectiveness of the board of directors in the control of management. This could result in a decline in performance. Some authors contend that the presence of independent outside directors enhances the effectiveness of the board in monitoring top managers. We control for the effect of outside independent directors on performance by including a variable called Board independence. Stock ownership is considered among the governance mechanisms that help in aligning top managers and directors’ (insiders) interests with those of shareholders. The more stock insiders own the more they are interested in maximizing the value of the firm (Jensen and Meckling, 1976). Therefore, regression equations include two ownership variables: CEO ownership and insiders’ ownership.

The regression equations also include a set of financial variables that may have an

effect on performance. The natural logarithm of total assets controls for bank size. The

7 Pi and Timme (1993) report a negative relationship between bank performance and the combination of the CEO and Chairman titles in one person’s hands. In contrast, Brickley et al., (1997) find that there is no negative relationship between the CEO-Chairman duality and different measures of performance.

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effect of bank size on performance could result from the presence of economies of scale or scope. In the presence of economies of scale and/or scope, large banks outperform smaller banks. In contrast, in the presence of diseconomies of scale, bank size would be negatively associated with performance. Recent studies using data from the 1990s tend to reveal the presence of economies of scale in the banking industry (Berger and Mester, 1997; Hughes and Mester, 1998; Stiroh, 2000). The tier 1 leverage ratio is included as a control for financial leverage. In financial theory, a high debt level could reduce agency costs due to the separation of ownership and control, by imposing an additional discipline on managers and by pressuring them to allocate firm resources efficiently (Jensen, 1986). These arguments suggest that financial leverage has a positive effect on performance. However, in the case of banks, a high financial leverage (i.e., a low tier 1 capital ratio) can be considered by investors as a signal of a low capitalization, and therefore a source of pressure by the regulatory authorities requiring the capital ratio to be higher than a minimum level8. Finally, the standard deviation of the stock return is included in the regression equations as a control for the uncertainty of the cash flows generated by the bank. Theoretically, a high uncertainty of cash flows negatively affects shareholders’ value because it increases the expected costs of distress and under-investment (Smith and Stulz, 1985; Demarzo and Duffie, 1995; Mello and Persons, 2000; Haushalter et al., 2002). These costs materialize when the generated cash flows turn out to be insufficient to allow the firm to pay back its debt and to undertake positive NPV projects that maximize its value. The expected value of equity decreases with the increase of the uncertainty of cash flows. Haushalter et al. (2002) show empirically that the negative sensitivity of the equity value to the uncertainty of cash flows increases with the firm’s debt ratio. Firms having high debt-to-equity ratios have the highest expected costs of financial distress because for a given cash flow their probability of financial distress is higher, ceteris paribus. Banks have high debt-to-equity ratios. The effect of cash flows’ uncertainty on bank performance should therefore be a strong one.

All the regression equations include year dummy variables and firm fixed effects9.

The inclusion of year dummy variables allows controlling for the possible change in the average bank performance from one year to another. The inclusion of fixed effects allows for a different intercept for each entity in the sample. Hausman and Taylor (1981) suggest that the fixed-effects framework represents an unbiased method of controlling for omitted variables in a panel data set. We also test for heteroskedasticity in the data. The test shows evidence of heteroskedasticity of the standard errors. The standard errors are, therefore, adjusted for heteroskedasticity in all the regressions. This yields more robust estimates (White, 1980).

8 A very low level of the capital ratio could trigger the intervention of regulatory authorities. Such actions can lower the latitude of bank managers and shareholders in decision making. For instance, some positive NPV investments can be rejected because the regulatory authorities judge that they are very risky. Such situations can be negatively perceived by investors, and the ex ante performance of the bank negatively affected. Based on this argument, a high capital ratio is better seen by investors, and should be positively related to Tobin’s Q. The net effect of the capital ratio on performance should result from the relative importance of the two effects discussed above. 9 To check which of the random or fixed effects fits best the data, we conduct Hausman test stating that the random effects model is better, for the data, than the fixed effects model. A strong rejection of the random effects model is observed.

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5.2. Board size and Tobin’s Q Table 4 presents parameter estimates for the regression of Tobin’s Q on log (board

size) and control variables. The first column (model 1) presents results of the regression of Tobin’s Q on log (board size) and financial control variables for the full sample. Model 2 adds to model 1 by including other governance variables. Columns 3 and 4 present results for Tobin’s Q regressions on log (board size) and control variables for the subsamples of BHCs and SLHCs, respectively. The separation between the two subsamples is conducted in order to make sure that our conclusions about the board size-performance relationship are not driven by the inclusion of the two types of banks (savings and commercial) into the same sample.

Insert table 4 here.

Contrary to our expectations and to what is suggested in most of the corporate

governance literature, the coefficient estimate on our variable of interest, log (board size), is positive across all specifications. This coefficient estimate is statistically significantly different from zero at the 5% level in model 1 and in the model including only SLHCs. This coefficient estimate is statistically significant at the 10% level in model 2. Accordingly, contrary to the findings of Yermack (1996) for Fortune 500 industrial firms, and of Eisenberg et al. (1998) for the small and midsize Finnish firms, our results indicate that capital markets do not value banking companies with small boards higher. In contrast, banks with larger boards seem to achieve higher market value. We also test for a quadratic and a piecewise linear relationships between board size and Tobin’s Q, but find no statistically significant relation10.

Consistent with several past studies11, board independence, as measured by the

proportion of outside independent directors, does not have any positive effect on performance. The coefficient estimate on CEO-chairman duality is positive and statistically significant at the 10% level in the model including only SLHCs. This result suggests that, for SLHCs, having a CEO who is also the chairman of the board improves performance. The coefficient estimate on CEO tenure is negative and statistically significant at the 5% level for the SLHCs sample. This may be evidence of the entrenchment of CEOs when they have been within their companies for a long time.

10 In order to check the robustness of our results to the exclusion of observations with very small and very large numbers, we re-run all the regressions while excluding observations with 4 and 31 board members. The sign and statistical significance of the coefficient on our variable of interest (board size) remain unchanged. We thank the referee for raising this issue. 11Hermalin and Weisbach (1991) report that there is no relationship between board composition and performance. More recently, Bhagat and Black (2002) find that companies tend to increase the proportion of independent directors following weak performance, but this strategy does not necessarily lead to a performance improvement. Adams and Mehran (2004) find that an increase in the proportion of independent outside directors does not increase performance of BHCs. However, BHCs with majority independent outside directors perform better than those with boards dominated by inside and affiliated directors.

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For the two ownership variables, only the coefficient estimates on Insider ownership are positive across the four models presented in table 4. Nevertheless, only the coefficient estimate on Insider ownership in the full sample is statistically significant at better than the 1% level. This result seems to be driven by the presence of SLHCs. In the latter sample, the coefficient estimate on Insider ownership is positive and statistically significant at the 10% level12.

The sign and significance of coefficient estimates on financial control variables

vary with the specification and the type of the bank (commercial or savings). Bank size appears to be negatively associated with performance, but this seems to be driven by the presence of BHCs. The coefficient estimate on log (total assets) is negative and statistically significant in models 1, 2, and for the BHCs subsample. The uncertainty of cash flows, as measured by the volatility of stock returns, is negatively and statistically significantly related to Tobin’s Q in the full sample and in the BHCs subsample.

5.3. Board size and return on assets In this paragraph the ROA is used as a measure of performance in order to check

the robustness of the previous results regarding the board size-performance relation. Table 5 reports the results for regressions of ROA on log (board size) and control variables. The regression equations include the same control variables used to investigate the relation between board size and Tobin’s Q.

Insert table 5 here.

Consistent with the results in table 4, the coefficient estimate on log (board size) is

positive across all columns. The difference, however, is that these coefficient estimates are statistically significant at better than the 5% level in the full sample model as well as in the BHCs and SLHCs subsamples. The regression of ROA on board size and control variables confirms the unexpected result of a non-negative relationship between board size and performance in banks. Indeed, these results suggest that adding more directors to the board increases the return on assets of a bank. The positive and statistically significant association between performance and board size in SLHCs appears to be robust to the performance measure. This suggests that, contrary to the shared thought of more effectiveness of small boards, SLHCs may gain value by increasing the number of their directors. As with Tobin’s Q, testing for a quadratic and piecewise linear relationship between board size and the ROA does not yield statistically significant estimates.

12 We have also estimated the regression equations of performance by including only one variable of ownership structure measured as the sum of CEO ownership and Insider ownership (Ownership = CEO ownership + Insider ownership). The results regarding the coefficient estimate on log (Board size) are unchanged. The coefficient estimate on the variable Ownership is positive and statistically significant in model 2 (at the 1% level) and in the SLHCs model (at the 5% level). We also test for the presence of a quadratic relationship between the variable Ownership and Tobin’s Q, by adding another variable (Ownership square) in the regression equation. Only the variable Ownership appears with a positive and statistically significant coefficient at the 5% level in model 2 and at the 10% level in the SLHCs model.

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The coefficient estimates on other governance variables tend to confirm the findings when performance is measured by Tobin’s Q. The coefficient estimates on board independence are negative across all specifications. Board independence is negatively and statistically significantly correlated with the ROA in the BHCs subsample. Increasing the proportion of outside independent directors at banking companies does not improve performance and it may negatively affect performance. The positive association between CEO-Chairman duality and performance in SLHCs is robust to the change in the performance measure. In fact, the coefficient estimate on the CEO-Chairman duality variable is positive and statistically significant at the 5% level in the SLHCs subsample, suggesting that performance is improved when the CEO also holds the title of Chairman of the board of directors. The negative relationship between CEO tenure and performance in SLHCs is also robust to the change in the performance measure. As when performance is measured by the Tobin’s Q, the coefficient estimate on CEO tenure in the SLHCs subsample is negative and statistically significant at the 5% level in the ROA regression equation. According to these results, CEOs who have held their positions for a long period are harmful for SLHCs performance. These results are therefore consistent with the entrenchment hypothesis. Regarding the ownership structure variables, only the CEO ownership variable appears with a positive and statistically significant coefficient estimate in the full sample (at the 10% level) and in the SLHCs subsample (at the 1% level)13.

6. Past performance and Board size The above analysis is further evidence in favor of banks with larger boards

achieving higher performance than their counterparts with small boards. However, we might interpret these findings in two ways: larger boards could contribute to better performance, or banks might adjust board size in response to past performance. If larger boards achieve better performance, the hypotheses of Lipton and Lorsch (1992) and Jensen (1993), and the empirical findings by Yermack (1996), and Eisenberg et al. (1998) are just not valid in the banking industry, and the many calls for the reduction of board size might yield adverse effects on bank performance. However, if bankers “believe” in the idea that smaller boards achieve better performance, and shrink their boards in the aftermath of poor performance, the causation of the board-size performance relation goes in the opposite direction from the Lipton-Lorsch and Jensen hypotheses. That is it goes from performance to board size.

The question of causation has been examined by estimating regression models of

the association between past performance, as measured by the prior year ROA, and changes in board size. Following Hermalin and Weisbach (1988), Eisenberg et al. (1998) and Yermack (1996) we estimate count-based maximum likelihood Poisson regression 13 As for the Tobin’s Q regression equations, we have also estimated the regression equations of ROA by including one variable of ownership structure (Ownership) which is equal to the sum of CEO ownership and Insider ownership. The results regarding the coefficient estimate on log (Board size) are unchanged. The Ownership variable appears with a positive and statistically significant coefficient estimate in the full sample (at the 1% level) and in the SLHCs sample (at the 10% level). We conclude that there is a robust positive and statistically significant relationship between equity ownership by managers and directors and performance in SLHCs.

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models of the number of directors leaving and joining the company’s board each year. We also estimate an ordinary least squares regression model in which the dependent variable is the total annual change in board size (director appointments minus director departures). The explanatory variables for all models are the “prior year ROA” (a measure of past performance), “∆ log (total assets) prior year”, “New CEO”, “Board size (prior year)”, and “Dummy for savings institutions”. The variable “∆ log (total assets) prior year” is included in the regression equations in order to control for the potential effect of the change in bank size on the number of directors joining and leaving the board and on the net change in board size. It is expected that an increase in bank size is followed by an increase in the number of directors sitting on the board, especially that our sample covers a period of intense consolidation in the banking industry. Bank asset growth in the 1990s and around the turn of the century was mainly due to mergers and acquisitions (M&As). There are, at least, two reasons to believe that M&As induce an increase in the number of directors sitting on the board of the surviving banks. First, consolidation increases the number and complexity of the bank’s operations requiring, therefore, more expertise and possibly more directors to rely on. Second, banking M&As are in most of the cases friendly, which implies that some of the directors, either officers or outsiders, of the target bank join the board of the surviving bank (see for example, Wulf, 2004)14. “New CEO” is a dummy variable that takes on 1 if the CEO is newly hired to the bank and 0 otherwise. A CEO is considered to be newly hired if his tenure within the bank is equal to 1, 2, 3, or 4 years. “Board size (prior year)” is the number of directors sitting on the board during the last year. “Dummy for savings institutions” is a dummy variable that takes on 1 if the bank belongs to the category of savings institutions and 0 if it is a commercial bank.

Insert table 6 here.

Table 6 reports the results. Similar to Eisenberg et al., Yermack, and Hermalin and

Weisbach, we find that poor performance is associated with higher levels of both director appointments and departures. In the Director Appointments regression equation, the coefficient estimate on “ROA (prior year)” is negative and statistically significant at the 5% level suggesting that a higher number of directors join the board following a low performance. In the Director Departures regression equation, the coefficient estimate on “ROA (prior year)” is negative and statistically significant at the 1% level implying that the number of directors leaving the board increases following a poor performance year. The coefficient estimates on “ROA (prior year)” in the two regression equations (Director Appointments and Director Departures) are the same (– 0.209 and – 0.205) suggesting

14 The change in bank size is measured as the difference between the natural logarithm of total assets at the end of the prior year and the natural logarithm of total assets two years before. That is, for instance, when board size change is measured in 1999, the growth in total assets is measured as the natural logarithm of total assets at fiscal year end 1998 minus the natural logarithm of total assets at fiscal year end 1997. This is dictated by the hypothesized relationship stating that the adjustment in board size follows the change in bank size. Assume a bank acquires another bank in 1998. Bank size has thus increased by the difference between total assets at the fiscal year end 1998 and 1997. Given the way board data are collected, the adjustment in board size, if any, is observed only in the proxy statements filed with the SEC in early 1999Unless we assume that the merger or acquisition occurred before the surviving company files its proxy statements with the SEC (that is approximately in the three first months).

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that the effect of poor past performance on directors’ appointments and departures is the same. These results suggest that more directors are replaced when banks perform poorly, but that the total number of directors sitting on the board does not change. The OLS models (with and without fixed effects) of the net annual change in board size lead to the same conclusion that board size is insensitive to past poor performance. Based on these results, it can be said that causation does not go from performance to board size. The positive association between board size and bank performance cannot be explained by the fact that banks reduce their board size following a poor performance.

Table 6 shows that an increase in bank size is associated with higher levels of

director appointments and departures. However, the effect is higher on director appointments (a coefficient estimate of 2.278) than on departures (a coefficient estimate of 0.628), suggesting that following an increase in bank size the number of appointed directors is greater than the number of leaving directors. This yields an increase in the total number of directors sitting on the board. The same conclusion emerges from both pooled and fixed effects OLS models of the net annual change in board size. The coefficient estimate on “∆ log (total assets) prior year” is positive and statistically significant at the 1% level. The OLS models show that boards expand in response to increases in bank size. The Poisson and OLS models show that the net change in board size is negatively and statistically significantly correlated with past board size. Banks with larger boards tend to reduce the number of their directors15. The findings of this section suggest that changes in bank size, but not past performance, influence current choices of board size.

7. Summary and conclusions Extant corporate governance literature predicts that larger boards reduce

performance. Lipton and Lorsch (1992) and Jensen (1993) have criticized the performance of large boards, arguing that problems of poor communication, coordination of tasks, and decision making undermine the effectiveness of such groups. The validity of this hypothesis in banking firms has been tested but no evidence to support it has been detected. Using regression models with data from 1995-2002 for 174 bank and savings-and-loan holding companies, we find no evidence of a negative association between board size and performance. Indeed the results push towards a conclusion in favor of a positive relationship. The basic result proves robust to a variety of controls for bank size, board leadership structure, CEO tenure, board independence, and ownership structure.

The positive association between board size and performance emerges from

subsamples of bank and savings-and-loan holding companies, suggesting that board size is related to performance in the same way in both savings and commercial banking industries. The alternative explanation for the documented positive correlation between board size and performance in banking is that the effect goes from performance to board size and not the opposite (board size affects performance). Our test for the relationship between board size and past performance indicates no evidence that past performance

15 However, the net effect of the change in bank size and past board size is in favor of the first. A joint F-test yields a strong rejection of the null hypothesis that the effect of the two factors is equal to zero. In the presence of fixed effects the F-statistic is equal to 20.87 with a p-value of 0.00.

14

directly affects current board size. However, the evidence suggests that boards expand in response to an increase in bank total assets.

Overall, our results suggest that contrary to what is suggested in the corporate

governance literature, banks with smaller boards of directors do not outperform their counterparts with larger boards. In contrast, there is robust evidence that larger boards achieve a higher performance. Therefore, reducing the number of bank directors may have adverse effects on performance.

However, we recognize that a number of factors which are not controlled for in the

performance regression equations could cause this positive association between board size and performance. The analysis conducted in section 6 shows that boards expand in response to an increase in bank size. If this size increase is mainly due to mergers and acquisitions (M&As) and if these operations have also a positive effect on performance, the board size-performance positive association could only be a correlation due to the positive effect of M&As on both board size and performance. Further research devoted to the relation between board size and performance in banking and using methods other than the regression analysis could improve our outstanding of this relation. Event studies assessing the market reactions to banks increasing or reducing the number of their directors could supplement the results of the present study. Moreover, future empirical studies interested in the effectiveness of boards of directors in the banking sector could use X-efficiency estimates as dependent variables. References Adams, R., Mehran, H., 2004. Board structure and Banking Firm Performance. Working paper, Federal Reserve Bank of New York. Becher, D.A., Campbell II, T.L., Frye, M.B., 2005. Incentive compensation for bank directors: The impact of deregulation. The Journal of Business, forthcoming. Berger, A.N., Mester, L.J. 1997. Inside the black box: What explains differences in the efficiencies of financial institutions. Journal of Banking and Finance 21, 895-947. Bhagat, S., Black, B., 2002. The non-correlation between board independence and long-term performance. Journal of Corporation Law, vol 27, Issue 2, 231-43. Booth, J.R., Cornett, M., M., Tehranian, H., 2002. Boards of directors, ownership, and regulation, Journal of Banking and Finance 26, 1973-96. Brewer III, E.,Hunter, W.C., Jackson III, W., 2003. Deregulation and the Relationship Between Bank CEO Compensation and Risk-Taking, Federal Reserve Bank of Chicago, Working Paper 2003-32.

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Brewer III, E., Jackson III, W., Jagtiani, J., 2000. Impact of Independent directors and the regulatory environment on bank merger prices: evidence from takeover activity in the 1990s. working paper 2000-31, Federal Reserve Bank of Chicago. Brickley, J.A., Coles, J.L., Jarrell, G., 1997. Leadership Structure: Separating the CEO and Chairman of the Board. Journal of Corporate Finance 3, 189-220. Brook, Y., Hendershott, R.J., and Lee D., 2000. Corporate governance and recent consolidation in the banking industry. Journal of Corporate Finance 6, 141-164. Crawford, A., Ezzell, J.R., Miles, J.A., 1995. Bank CEO pay-performance relations and the effects of deregulation. Journal of Business 68, 231-256. Demarso, P., Duffie, D. 1995. Corporate incentives for hedging and hedge accounting. Review of Financial Studies 8, 743-771. Eisenberg, T., Sundgren, S., Wells, M.T., 1998. Larger board size and decreasing firm value in small firms. Journal of Financial Economics 48, 35-54. Haushalter, G.D., Heron, R.A., Lie, E. 2002. Price uncertainty and corporate value. Journal of Corporate Finance 8, 271-286. Hausman, J.A., Taylor, W.E., 1981. Panel data and unobservable individual effects. Econometrica 49, 1377-1398. Hermalin, B., Weisbach, M., 1988. The effects of board composition and direct incentives on firm performance. Financial Management 20, no.4, 101-112. Hermalin, B., Weisbach, M., 1991. The effects of board composition and direct incentives in firm performance. Financial Management 20, 101-112. Hubbard, G., Palia, D., 1995. Executive Pay and Performance : Evidence from the U.S. Banking Industry, Journal of Financial Economics 39, 105-130. Hughes, J.P., Mester, L.J. 1998. Bank capitalization and cost: Evidence of scale economies in risk management and signalling. The Review of Economics and Statistics 80, 314-325. Jensen, M.C., 1993. The modern industrial revolution, Exit, and the failure of internal control systems. The Journal of Finance Vol. 48, No3, 831-880. Jensen, M.C., Meckling., W.H., 1976. Theory of the firm: managerial behavior, agency costs and ownership structure. Journal of Financial Economics 3, 305-360. Kole, S.R., Lehn, K.M., 1999. Deregulation and the adaptation of governance structure: the case of the U.S. airline industry. Journal of Financial Economics 52, 79-117.

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Lipton, M., Lorsch, J., 1992. A modest proposal for improved corporate governance. Business Lawyer 48, 59-77. Lorsch, J.W., MacIver, E., 1989. Pawns and Potentates: The reality of America’s Corporate Boards. Boston: Harvard Business School Press. Mak, Y.T., Li, Y., 2001. Determinants of corporate ownership and board structure: evidence from Singapore. Journal of Corporate Finance 7, 235-256. McConnell, J., Servaes, H., 1990. Additional evidence on equity ownership and corporate value. Journal of Financial Economics 27, 595-612. Mello, A.S., Persons, J.E., 2000. Hedging and liquidity. Review of Financial Studies 13, 127-153. Morck, R., Shleifer, A., Vishny, R., 1988. Management ownership and Market Valuation: An Empirical Analysis. Journal of Financial Economics 20, 293-315. Murphy, K.J., Zimmerman, J.L., 1993. Financial Performance surrounding CEO turnover. Journal of Accounting and Economics 16, 273-315. Pi, L., Timme, S., 1993. Corporate Control and bank efficiency. Journal of Banking and Finance 17, 515-30. Smith Jr., C.W., Stulz, R. 1985. The determinants of firms’ hedgeing policies. Journal of Financial and Quantitative Analysis 20, 391-405. Smith, C., Watts, R., 1992. The Investment Opportunity Set and Corporate Financing, Dividends, and Compensation Policies, Journal of Financial Economics 32, 263-292. Stiroh, K., 2000. How did bank holding companies prosper in the 1990s?, Journal of Banking and Finance 24, 263-92. White, H., 1980. A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48, 817-838. Wulf, J., 2004. Do CEOs in mergers trade power for premium? Evidence from “mergers of equals”. Journal of Law, Economics and Organization 20, 60 - 101. Yermack, D., 1996. Higher market valuation of companies with small board of directors. Journal of Financial Economics 40, 185-211.

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Appendix Director classification Inside director:

• Member of the current management team of the bank or of one of its subsidiaries. Affiliated outside director: A currently non-employee of the bank who:

• is a former employee of the bank or of one of its subsidiaries (e.g. a retired CEO), • has a family relationship with an officer of the firm (first cousin, brother in law,…), • is part of an interlocking directorship (defined as an officer of a firm that has one of the

bank’s officers sitting on its board of directors), • is part of a partnership or firm that has provided in the past, or currently provides services

to the bank (e.g. law firm, consulting firm, etc), • is involved in a pecuniary relationship with the company (e.g. a non-CEO chairman, a

vice-chairman who is paid more than the annual retainer and meeting fees, for counseling services he provides for the bank.),

• is affiliated with a firm that has loans classified by the company as potential problem loans.

Independent Outside director • All other outside directors not classified as affiliated.

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Table1. Variable description Tobin’s Q

The ratio of the market value of the bank to the replacement value of its assets. It is calculated as the (Total assets book value – common equity book value + common equity market value)/Total assets book value.

ROA Return on Assets: it is taken from the Research Insight database, and is defined as the net income before extraordinary items divided by total assets.

Total assets Total assets book value at the fiscal year end, and it is from Research Insight. Total assets are in Millions of dollars.

Leverage ratio A measure of regulatory capital. It is equal to the ratio of tier I capital to average total assets. It is from annual 10-K reports filed with the SEC.

Volatility Standard deviation of monthly stock returns. Stock returns are from the Center for Research on Securities Prices (CRSP).

Board size Board size is the number of directors sitting on the board at the shareholders annual meeting.

Board Independence The proportion of outside directors with no business or family ties to management. It is calculated as the number of independent outside directors divided by board size (see the details about the classification of directors).

CEO tenure The number of years the CEO has held his position within the company.

CEO-Chairman duality

A dummy variable that takes on 1 if the CEO is also chairman of the board and zero otherwise.

CEO ownership The percentage of outstanding common stock held by the CEO at the beginning of the fiscal year.

Insider ownership The percentage of outstanding common stock held by all directors and executive officers, except the CEO, at the beginning of the fiscal year.

Director Appointments

The number of directors appointed to the board for the first time. Directors whose names do not appear in the prior year proxy statement, but appear in the current year’s proxy statement.

Director Departures The number of directors who left the board prior or at the annual meeting. Directors whose names appear in last year’s proxy statement but do not appear in the current year’s one.

∆ log (total assets) prior year

A proxy for comapny size variation between yeart-1 and yeart-2 = log (total assets)t-1 – log (total assets)t-2.

New CEO A dummy variable that takes on 1 if the tenure of the CEO is 1,2,3, or 4, and 0 otherwise.

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Table2. Summary Statistics for a sample of 174 BHCs and SLHCs during 1995-2002. Table 2 shows descriptive statistics for a set of financial and governance variables for a sample of 174 BHCs and SLHCs over the period 1995-2002. A company is included in the sample if it is reported in the Research Insight database of Standard & Poor’s and had a total assets book value of $1billion for, at least, four years during the eight-year sample period. The number of observations varies from one variable to another due to missing proxy statements on the SEC website and to missing observations in Research Insight. All data on Board characteristics, including CEO and board ownership, are from the proxy statements (DEF 14A) filed with the SEC at the beginning of each fiscal year. Table 1 contains description of variables used in the study. The last two columns report the sample Pearson correlation coefficients for each variable with Tobin’s Q and board size. Asterisks indicate significance at the (1%)***, (5%)**, and (10%)* levels.

N Mean Median Std. Deviation

Correlation with board size

Correlation with Tobin’s Q

Financial variables

Tobin’s Q 1,285 1.09 1.071 0.098 0.178*** 1 ROA (%) 1,321 1.076 1.082 0.648 0.163*** 0.55*** Total Assets ($Millions) 1,321 22,057 4,183.18 67,398.8 0.238*** 0.047* Leverage ratio (%) 1,296 8.026 7.63 2.091 -0.017 0.187*** Volatility 1,301 0.08 0.073 0.035 -0.138*** 0.058**

Governance variables

Board size 1,165 13.195 12 5.128 1 0.178*** Board independence 1,165 0.67 0.666 0.135 0.156*** 0.031 CEO tenure 1,165 9.559 8 6.877 -0.025 -0.059** CEO-Chairman duality 1,165 0.647 1 _ 0.078*** 0.116*** CEO ownership (%) 1,165 5.048 1.21 11.367 -0.169*** -0.155*** Insider ownership (%) 1,165 8.356 5.52 10.543 0.016 -0.127***

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0.8

0.85

0.9

0.95

1

1.05

1.1

1.15

1.2

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

Board size

Tobi

n's

QMeanMedian

Figure 1: Board size and Tobin’s Q: sample means and medians (1,148 observations).

Figure 1 shows sample means and medians of Tobin’s Q observations sorted by board size. The sample consists of annual observations for 174 BHCs and SLHCs, over the period 1995 and 2002. Only 1 observation has a board of 4 members and 1 observation has a board of 31 members. No bank in the sample has a board of 30 members and only 2 observations have a board of 29 members. The remaining observations have boards that vary from 5 to 28 members.

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Table3. Univariate tests for the difference in Tobin’s Q means and medians between subsamples divided according to board size.

This table reports results of tests for the difference in Tobin’s Q sample means and medians between subsamples divided according to board size. Test statistics are provided for the total sample (eight years) and for each individual year. Tobin’s Qbelow includes observations of Tobin’s Q for which board size is less or equal to the sample median. Tobin’s Qabove includes observations of Tobin’s Q for which the board size is above the sample median. t-statistics refer to the t-test for the difference between Tobin’s Qbelow and Tobin’s Qabove means. z-statistics refer to the Wilcoxon Signed Ranks test for the median difference between Tobin’s Qabove and Tobin’s Qbelow. Asterisks indicate significance at the (1%)***, (5%)**, and (10%)* levels for the null hypothesis of equality of means and medians (t-statistics and z-statistics, respectively).

Total sample 1995 1996 1997 1998 1999 2000 2001 2002

Median (Board size) 12 14 13 11 12 12 12 12 12

Mean 1.071 1.06 1.064 1.111 1.096 1.060 1.061 1.061 1.064 Tobin’s Qbelow Median 1.053 1.055 1.068 1.093 1.062 1.032 1.033 1.044 1.051

Mean 1.115 1.059 1.09 1.172 1.153 1.101 1.121 1.11 1.087 Tobin’s Qabove Median 1.096 1.056 1.084 1.16 1.126 1.088 1.096 1.096 1.08

t-statistic -7.183*** 0.059 -2.884*** -4.159*** -2.173** -2.701*** -3.108*** -4.615*** -2.324**

z-statistic -10.011*** 0.118 -2.746*** -4.223*** -4.162*** -4.457*** -4.117*** -4.64*** -2.262**

Observations 1,148 64 105 140 154 171 172 174 168

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Table 4. Board Size and Tobin’s Q: Fixed Effects Regressions This table reports fixed effects estimates of the relationship between Tobin’s Q and board size. The sample consists of annual observations for 174 BHCs and SLHCs, over the period 1995-2002. Regression models include variables of board independence, CEO-chairman duality, CEO tenure, CEO ownership, insider ownership, log (total assets), leverage ratio, and volatility. Description of calculation of all variables is provided in table 1 above. Columns 1 and 2 present estimates for the full sample. Column 3 presents estimates for the subsample of BHCs. Column 4 presents estimates for the subsample of SLHCs. All regressions equations include firm fixed effects and year dummy variables. Standard errors are corrected for heteroskedasticity. Robust t-statistics are in parentheses. Asterisks indicate significance at the (1%)***, (5%)**, and (10%)* levels. Dependent variable: Tobin’s Q Independent variables Model 1 Model 2 BHCs SLHCs

log (Board size) 0.032** (1.98)

0.030* (1.75)

0.029 (1.41)

0.046** (2.12)

Board independence -0.011 (-0.57)

-0.013 (-0.43)

-0.023 (-0.56)

CEO-Chairman duality 0.011 (1.29)

0.013 (1.34)

0.032* (1.95)

CEO tenure -0.0001 (-0.31)

-0.0003 (-0.73)

-0.002** (-2.12)

CEO ownership 0.001 (1.26)

-0.0003 (-0.20)

0.004 (1.55)

Insider ownership 0.002*** (3.96)

0.0009 (1.00)

0.001* (1.76)

log (total assets) -0.036** (-2.42)

-0.030* (-1.93)

-0.043** (-2.24)

0.008 (0.33)

Leverage ratio -0.002 (-1.47)

-0.001 (-1.05)

0.00007 (0.03)

-0.0006 (-0.34)

Volatility -0.302* (-1.76)

-0.309* (-1.79)

-0.559** (-2.10)

0.023 (0.46)

Intercept 1.296*** (10.84)

1.224*** (8.86)

1.376*** (8.14)

0.831*** (3.38)

Sample size 1,148 1,148 827 321 Adjusted R-square 0.6966 0.6997 0.6902 0.6438 F-statistic 34.76*** 26.66*** 23.36*** 7.08***

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Table 5. Board Size and ROA: Fixed Effects Regressions Table 5 reports fixed effects estimates of the relationship between ROA and board size. The sample consists of annual observations for 174 BHCs and SLHCs, over the period 1995-2002. Regression models include variables of board independence, CEO-chairman duality, CEO tenure, CEO ownership, insider ownership, log (total assets), leverage ratio, and volatility. Description of calculation of all variables is provided in table 1 above. Columns 1 and 2 present estimates for the full sample. Column 3 presents estimates for the subsample of BHCs. Column 4 presents estimates for the subsample of SLHCs. All regressions equations include firm fixed effects and year dummy variables. Standard errors are corrected for heteroskedasticity. Robust t-statistics are in parentheses. Asterisks indicate significance at the (1%)***, (5%)**, and (10%)* levels. Dependent variable: ROA Independent variables Model 1 Model 2 BHCs SLHCs

log (Board size) 0.273** (2.36)

0.306*** (2.44)

0.242** (2.12)

1.274*** (2.83)

Board independence -0.052 (-0.18)

-0.289* (-1.69)

-0.122 (-0.15)

CEO-Chairman duality 0.039 (0.70)

0.032 (0.64)

0.600** (1.98)

CEO tenure -0.001 (-0.37)

0.005* (1.78)

-0.043** (-2.42)

CEO ownership 0.036* (1.71)

-0.01 (-1.21)

0.112*** (4.01)

Insider ownership 0.008 (1.03)

0.002 (0.61)

0.007 (0.43)

log (total assets) 0.114 (0.65)

0.153 (0.78)

-0.239*** (-3.02)

1.381*** (5.39)

Leverage ratio 0.03 (1.57)

0.035* (1.95)

0.052*** (3.62)

0.055** (1.98)

Volatility -2.487*** (-3.36)

-2.522*** (-3.43)

-3.17*** (-4.09)

-2.238 (-1.47)

Intercept -0.687 (-0.50)

-1.375 (-0.77)

2.457*** (3.59)

-13.687*** (-5.64)

Sample size 1,148 1,148 827 321 Adjusted R-square 0.4850 0.4904 0.6376 0.3894 F-statistic 3.31*** 2.72*** 5.57*** 3.75***

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Table 6: Past performance, director appointments, director departures and the change in board size. This table shows estimates for the relationship between bank past performance and director appointments, departures, and the change in board size. The first column presents estimates for the poisson maximum-likelihood regression of the number of director appointments on the ROA (prior year), the change in bank size, and control variables. The second column presents estimates for the poisson maximum-likelihood regression of the number of leaving directors on the ROA (prior year), the change in bank size, and control variables. The third and fourth columns present OLS regressions of the change of board size on the ROA (prior year), the change in bank size, and control variables. The sample consists of annual observations for 174 BHCs and SLHCs over the period 1995-2002. Standard errors are corrected for heteroskedasticity. Robust t-statistics are in parentheses. Asterisks indicate significance at the (1%)***, (5%)**, and (10%)* levels.

Dependent variable Director Appointments

Director Departures Change in board size

Estimation Poisson ML Poisson ML Pooled OLS Fixed Effects OLS

ROA (prior year) -0.209** (-2.52)

-0.205*** (-3.29)

-0.555 (-0.61)

0.064 (0.59)

∆ ln (total assets) prior year 2.278*** (10.83)

0.628** (2.16)

2.883*** (5.34)

3.074*** (5.77)

New CEO 0.118 (1.18)

0.224*** (2.66)

-0.063 (-0.51)

0.054 (0.29)

Board size (prior year) 0.033*** (4.00)

0.096*** (11.76)

-0.096*** (-5.45)

-0.587*** (-8.47)

Dummy for savings institutions

-0.627*** (-4.70)

-0.326*** (-2.62)

-0.455*** (-3.60)

Intercept -0.438** (-2.34)

-1.155*** (-7.35)

1.054*** (4.23)

7.22*** (7.75)

Sample size 902 902 902 902 F-statistic 12.84*** 29.71*** R-square/Pseudo R-square 0.1123 0.1387 0.1178 0.4192 Wald Chi-square 181.22*** 228.07***