Board Leadership Structure of Publicly Traded Insurance Companies

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1 Board Leadership Structure of Publicly Traded Insurance Companies Steve Miller 1 and Tina Yang 2 Abstract: CEO duality is a contentious issue driving much debate amongst regulators and business leaders. It is also an aspect of corporate governance to which insurance companies have made significant changes in recent years. Despite its significance, we know little about the determinants of CEO duality in the insurance industry and its impact on firm performance. This paper addresses these research questions. We find strong evidence that CEO duality is a complex decision that insurance firms fine tune in response to their individual circumstances. Compared to other industries, the insurance industry is unique in that the costs and benefits of CEO duality vary more with firm size. We find no evidence that CEO duality is detrimental to firm performance. If anything, the valuation impact of CEO duality appears to be positive for large insurers. Our results have important policy implications. Evidence suggests that regulatory initiatives targeting CEO duality of insurance firms should pay close attention to the role of firm size. It may also be desirable to promote regulations that can provide insurance companies decision flexibility in adjusting their leadership structures to competitive environments. [Key words: corporate governance, CEO duality, insurance industry.] JEL classification: G34; G38; K22. W INTRODUCTION hether the Chief Executive Officer can also chair the board (CEO duality) has been one of the most contentious governance issues in the past two decades. Advocates for splitting the roles include regulators and governance activists. For example, between , reports sponsored by national governments and/or major stock exchanges in at least 16 1 Corresponding author. Haub School of Business, Saint Joseph s University, Philadelphia PA 19131, USA; steve.miller@sju.edu; phone (610) School of Business, Villanova University, Villanova, PA USA; tina.yang@villanova.edu; phone (610) Journal of Insurance Issues, 2015, 38 (2): Copyright 2015 by the Western Risk and Insurance Association. All rights reserved.

2 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 185 countries recommended splitting the CEO and chairman roles. In 2009, the U.S. Congress introduced several proposals that called for the titles to be split. Pushing back against this movement toward separate titles are corporations and some large investors. Recent battles fought at companies like JP Morgan Chase and Netflix further showcase the intensity and unresolved nature of this debate. 3 We aim to contribute to the debate by conducting an in depth analysis of the determinants of CEO duality using publicly traded insurance companies. Although there is a vast literature on this subject, little empirical work exists on the determinants of CEO duality of insurance firms. Therefore, the insurance industry represents a unique opportunity for an out ofsample test for existing theories on CEO duality and hence has the potential to provide new insights for the on going debate on CEO duality. Further, by focusing on a single industry, we reduce the likelihood that our results are due to a spurious correlation caused by unobserved heterogeneities (He and Sommer, 2010; Adams, Hermalin, and Weisbach, 2010). As our objective is to provide new insights for the continuing debate regarding CEO duality, we conduct three auxiliary analyses to help us better understand a firm s decision concerning CEO duality. First, we compare and contrast for the first time in the literature the determinants of observed and structural duality. Observed duality is a firm s duality status that we observe on a daily basis. (Henceforth, we use duality and observed duality interchangeably.) By structural duality, we refer to a firm s longterm duality status after accounting for transient changes. To give an example of a transient change in duality status, a firm can temporarily separate the positions of the CEO and the Chairman of the Board (COB) when it transitions from one leadership to the next. During this process, the old CEO retains the COB title and only relinquishes the COB title to the new CEO after the new CEO successfully passes the probation period, which can span several years. 4 Because we focus on one industry, we are able to manually identify transient duality changes and distinguish between observed and structural duality. Second, we study the economic factors that drive insurance firms to structurally change their leadership models (i.e., move to structurally combine or split the dual roles as opposed to keeping the leadership model unchanged). Lastly, we investigate the relation between CEO duality and firm performance. 3 Bloomberg, May 21, 2013, by Dawn Kopecki, JPMorgan Shareholders Reject CEO Chairman Split in Win for Dimon. New York Times, June 9, 2014, by Michael J. De La Merced, Proposal to Split Netflix s Chairman and C.E.O. Roles Fails. 4 Appendix I gives a detailed real world example of a transient change in duality status and illustrates the distinction between observed and structural duality.

3 186 MILLER AND YANG Using a sample of 119 insurance companies from 1996 to 2012, we find limited support for the prevailing duality theories. Few economic factors significantly impact the probability of an insurance firm having CEO duality. As the literature emphasizes the role of firm size in the determinants of board structure, we also separately examine duality determinants for large, medium, and small insurance firms. We find overwhelming support for duality theories for large insurers, some support for small insurers, and the weakest support for medium insurers. Our results are in contrast with the evidence on banks and industrial firms, which shows similar determinants across different sized firms (Grinstein and Valles, 2008; Pathan and Skully, 2010). Therefore, our evidence suggests that the insurance industry may be unique in that the costs and benefits of CEO duality in this industry vary more with firm size than in other industries. We take a two pronged approach to compare and contrast the determinants of observed and structural duality. First, we juxtapose the determinants of observed duality when transient duality changes are included in the sample versus excluded from the sample. In both cases, we find similar results for the full sample and for medium and small insurers. However, when transient duality changes are excluded from the sample, fewer economic factors have explanatory power for the duality decisions of large insurers. Our interpretation of this result is that transient duality decisions matter more for large insurers. As most of the transient duality changes are due to CEO succession planning, our results are consistent with the notion that succession planning matters more for complex firms, because those firms incur greater costs in transferring firm specific knowledge and expertise to an outsider (Naveen, 2006). Next, we analyze the determinants of structural duality. We find the strongest support for duality theories in the subsample of small insurers. (Recall that when analyzing the determinants of observed duality, we find the strongest support for duality theories in the subsample of large insurers.) The probability of small insurers practicing structural CEO duality is positively related to past performance, CEO tenure and age, and CEO pay relative to the industry, but is negatively related to the number of outside boards on which the CEO sits. Taken together, the evidence again highlights the importance of accounting for the effect of firm size in studying CEO duality in the insurance industry. We conduct an exploratory study of an insurer s decision to structurally change its leadership model. The analysis is exploratory in nature because to the best of our knowledge, no study has directly addressed this question and thus we lack theory guidance. We find that different factors drive an insurer s decision to practice CEO duality rather than to structurally change its leadership model. For example, during our earlier analysis

4 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 187 of the determinants of CEO duality, we find an overall insignificant relation between the probability of CEO duality and the percent of independent directors on the board for the full sample, but a significantly positive relation for large firms. We interpret the results as somewhat supportive of the vigilant board hypothesis that firms with more independent boards are better positioned to maximize the benefits of CEO duality while keeping the agency costs of dual CEOs in check. However, in the analysis of which economic factors drive an insurer to structurally change its leadership model, we find that the percent of independent directors has one of the largest impacts on the likelihood of insurers structurally moving toward separate titles, consistent with the perception that more independent boards may be more sensitive to external pressure to split the roles. We also find that economic factors have an asymmetric effect on an insurer s decision to structurally move to combine versus to split the roles, with one exception CEO ownership significantly affects both decisions. Shareholder pressure exerts no influence on the decision to structurally split the titles, but substantially deters the decision to structurally combine them. Overall, we interpret the evidence as consistent with the notion that CEO duality is a complex decision that insurers fine tune in response to their individual circumstances. Lastly, we analyze the relation between CEO duality and firm performance. We use the instrumental variable (IV) approach and the simultaneous equation model (SEM) to mitigate the endogeneity concern and find no evidence that CEO duality is detrimental to firm performance. If anything, CEO duality seems to enhance large insurers performance. As Appendix II shows, the practice of CEO duality in the insurance industry has undergone significant changes in the past decade. Yet, we know little about the determinants of CEO duality of insurance companies, a knowledge gap that is in stark contrast to the rich empirical evidence on the determinants of board size and composition of insurance companies. 5 Although a few studies include insurance firms as part of their samples in analyzing CEO duality, the large number of firms from other industries likely obscures the unique economic underpinning of CEO duality in the insurance industry. Indeed, we find that firm size seems to play a more important role in the insurance industry compared to other industries, 5 Mayers, Shivdasani, and Smith (1997) and He and Sommer (2010) provide rigorous analyses of the determinants of board composition and size. But neither paper examines the determinants of CEO duality. To the best of our knowledge, He, Miller, and Yang (2012) is the only study that investigates the determinants of CEO duality in the insurance industry. But their focus is the impact of the Sarbanes Oxley Act on the overall board structure of insurance companies. Their sample spans

5 188 MILLER AND YANG in terms of the determinants of CEO duality and its impact on firm performance. We also find strong evidence that insurance companies make complex duality decisions that are consistent with cost benefit tradeoffs specific to their individual circumstances. Therefore, our results have important policy implications. While it has been argued that one size fitsall governance mandates can be harmful to firms (Coles, Daniel, and Naveen, 2008), this argument may be particularly true for the subject of CEO duality in the insurance industry. The reminder of the paper is organized as follows: Section 2 reviews the related literature and develops hypotheses; Section 3 discusses the empirical model that we use to analyze the determinants of CEO duality; Section 4 describes the sample, data sources, and summary statistics; Section 5 presents the empirical results; and Section 6 concludes. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT Related Literature In the early 1990s, shareholder activists and regulators increased the pressure on listed firms to separate the CEO and COB positions. Spurred by this push to abolish CEO duality, a number of studies emerged to analyze the relation between firm performance and board leadership structure (e.g., Rechner and Dalton, 1991; Pi and Timme, 1993; Baliga, Moyer, and Rao, 1996). Brickley, Coles, and Jarrell (1997) synthesize the emerging evidence and provide the first systematic evaluation of the costs and benefits of CEO duality. They use a sample of 661 U.S. firms in the 1989 Forbes compensation survey and analyze various aspects of board leadership structure, including COB characteristics, the relation between CEO duality and succession planning, the performance consequences of different leadership structures, and the market reactions to changes in leadership structures. They conclude that CEO duality has benefits as well as costs. Further, the benefits of CEO duality are greater for larger firms. Using one year of data from 1995 for 1,883 firms, Faleye (2007) provides the first large sample evidence on the determinants of CEO duality. He finds that complex firms (proxied by total assets; the ratio of net property, plant, and equipment to total assets; and sales growth) are more likely to vest the two positions in one person. Further, firms perform better under a dual leadership if their organization is more complex. Faleye also finds that the likelihood of combined titles is positively related to CEO ownership and reputation (as proxied by the number of press articles in which the CEO s name appears).

6 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 189 Linck, Netter, and Yang (2008) study the determinants of board structure board size, board composition, and CEO duality of 6,931 nonregulated firms (i.e., excluding financials and utilities) from 1990 to While noting that unlike board size and independence, there is limited theoretical work modeling the determinants of board leadership, they find that the likelihood of CEO duality is driven by firm size, CEO age, and CEO tenure. Pathan and Skully (2010) study the determinants of board structure for 212 U.S. banks from 1997 to They find that the same economic factors that drive the board structure of industrial firms also drive that of banks, with one exception CEO power does not impact board composition. Similarly to Faleye (2007) and Linck, Netter, and Yang (2008), they find that the probability of CEO duality is positively related to banks scope of operation, growth opportunities, CEO tenure and age, and the ownership of officers and directors excluding that of the CEO. Grinstein and Valles (2008) study firms decisions to separate the dual roles before and after the 2002 Sarbanes Oxley Act. Using 995 S&P 1500 firms in 2000, they find that the likelihood of a separate leadership is positively associated with board ownership excluding that of the CEO, but is negatively associated with leverage, CEO age, CEO tenure, CEO ownership, the number of outside directorships of the CEO, and the percent of independent directors on the board. Comparing 804 firms in 2004 to the same set of firms in 2000, they find that the determinants of separation did not change significantly over the four year span. However, there seems to be evidence that in 2000, small firms were more likely to split the roles than were medium and large firms, but large and medium firms were more inclined to split in Dey, Engel, and Liu (2011) shed light on firms board leadership decisions via the lens of market reactions and the consequences of the decisions. They focus on 232 firms that changed leadership structures during Of the firms that chose to change their leadership models, those that separated the CEO and COB titles due to investor pressure had significantly lower announcement returns, poorer accounting performance for up to two years following the change, and lower market value of investments made after the change. The performance outcomes were more negative for firms with higher predicted probabilities of CEO duality based on a model of the economic determinants of board leadership structure. To summarize, the existing literature provides evidence consistent with the notion that board leadership structure is important to firms and that firms choose board leadership structure in response to their operating and governance environments.

7 190 MILLER AND YANG Hypotheses development We view board leadership structure, as most corporate decisions (e.g., ownership structure, compensation arrangement, merger and acquisition decision), an outcome of tradeoff decisions made by firms in response to their competitive environments. We argue that firms choose CEO duality when the benefits of such a leadership model outweigh the costs. In this section, we motivate our hypotheses based on this economic principle and make specific predictions linking the probability of CEO duality to the underlying economic determinants. Table 1 summarizes the hypotheses, the empirical proxies, and the predicted relations. As our goal is to synthesize the many arguments developed over years either in favor of, or in opposition to, CEO duality, we focus Table 1 on the determinants and their respective proxies that the existing literature has found to be important in explaining CEO duality. However, we also explore the potential usefulness of other determinants and their respective proxies in robustness tests (Section Robustness Check Controlling for Other Potential Determinants). Benefits of CEO Duality One benefit of CEO duality comes from unity of command. Scholars utilize organization theory to argue that consolidation of the CEO and COB positions the two most senior leadership positions establishes clear lines of authority and responsibility within a firm, which helps avoid confusion among top managers and outside stakeholders as to the strategic direction of the firm, thereby facilitating effective decision making. A separate leadership model, on the other hand, creates multiple authority relationships and weakens the legitimacy and effectiveness of leadership, which is detrimental to firm survival especially in times of crisis (Finkelstein and D Aveni, 1994; Dalton, Daily, Ellstrand, and Johnson, 1998). Companies frequently cite these arguments to support their decisions of CEO duality. For example, in their opposition to a shareholder proposal calling for separate titles filed at the 2010 annual shareholder meeting, Goldman Sachs reasoned that [t]he most effective leadership model for our firm at this time is to have the roles of CEO and Chairman combined This structure helps to ensure clarity regarding leadership of the firm, allows the firm to speak with one voice and provides for efficient coordination of Board action, particularly in times of market turmoil or crisis. Another benefit of CEO duality derives from the unparalleled firmspecific knowledge that the CEO possesses. A CEO accumulates the most intimate knowledge of the firm and its competitive environment through the daily running of the firm (Jensen and Meckling, 1995). Therefore,

8 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 191 Table 1. Hypotheses, Proxies, and Related Studies a Hypotheses Proxies Predicted relations Studies that find the predicted relation between duality and the proxies Benefits of duality Costs of duality Organizational complexity Decision speed Firm size + Faleye (2007) Linck, Netter, and Yang (2008) Grinstein and Valles (2008) Pathan and Skully (2010) Dey, Engel, and Liu (2011) Leverage + Grinstein and Valles (2008) Pathan and Skully (2010) Growth opportunities + Faleye (2007) Pathan and Skully (2010) Dey, Engel, and Liu (2011) CEO ability ROA t 1 + Dey, Engel, and Liu (2011) CEO tenure + Linck, Netter, and Yang (2008) Pathan and Skully (2010) Dey, Engel, and Liu (2011) Succession planning Vigilant board Incentive alignment CEO reputation CEO age + Faleye (2007) Linck, Netter, and Yang (2008) Grinstein and Valles (2008) Pathan and Skully (2010) Dey, Engel, and Liu (2011) %Independent directors CEO ownership CEO relative pay CEO outside board membership + Grinstein and Valles (2008) Dey, Engel, and Liu (2011) + Faleye (2007) Grinstein and Valles (2008) Pathan and Skully (2010) Dey, Engel, and Liu (2011) + Dey, Engel, and Liu (2011) + Grinstein and Valles (2008) Dey, Engel, and Liu (2011) a This table presents the hypotheses that we test in the paper regarding the determinants of CEO duality. Overall, we predict that the probability of an insurance company practicing CEO duality should increase in the benefits of the insurer having CEO duality and decrease in the costs of the insurer having CEO duality. The table also presents the empirical proxies, the predicated relations between the proxies and the probability of CEO duality, and related prior studies.

9 192 MILLER AND YANG duality firms should enjoy significant cost savings in terms of information acquisition, processing, and transmission (Yang and Zhao, 2014). In their arguments for CEO duality, firms often cite the information advantage of the CEO. For example, responding to a shareholder proposal calling for a separate leadership filed at the 2012 annual shareholder meeting, Prudential reasoned: The Board believes this structure [CEO duality] provides the optimum benefit of having our CEO, the individual most familiar with the Company s day to day operations, chair regular Board meetings as we discuss key business and strategic issues. We hypothesize that the benefits of unity of command and CEOspecific information increase as the firm s operations become more expansive and complex. Following the literature (e.g., Faleye, 2007; Grinstein and Valles, 2008), we use firm size and leverage as proxies of organizational complexity. Hypothesis 1 Organizational complexity: CEO duality is positively related to firm size and leverage. CEO duality enhances decision speed. A single leader promotes responsiveness to the environment and gives the firm the competitive advantage of nimbleness and flexibility (Pfeffer and Salancik, 1978; Faleye, 2007). These organizational qualities are particularly important for firms that compete in rapidly changing environments, because in those environments, information becomes obsolete at a faster rate and the consequences of lost market opportunities become more severe. Consistent with these arguments, Yang and Zhao (2014) find that duality firms outperform nonduality firms after an exogenous shock brings about increased competition and new market opportunities. We hypothesize that the benefits of speedy decision making are more important to firms with higher growth potential. Similar to Faleye (2007), we use the average sales growth rate over the past five years to proxy for a firm s investment opportunity set. Hypothesis 2 Decision speed: CEO duality is positively related to growth opportunities. The benefits of a strong, single leader can only come to fruition if the leader has high ability. Adams, Almeida, and Ferreira (2005) find that firms whose CEOs have more decision making authority experience greater variability in performance. Therefore, the likelihood of CEO duality should increase with CEO ability to ensure that the shareholders can reap the benefits of CEO duality while curtailing as much as possible the left tail variability in firm performance. Empirical evidence supports this argument as firms reward high performing CEOs with the additional title of the COB (Brickley, Coles, and Jarrell, 1997). Following the literature (e.g.,

10 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 193 Linck, Netter, and Yang, 2008), we use CEO tenure and firm past performance (prior year s return on assets, ROA t 1 ) to proxy for CEO ability. Hypothesis 3 CEO ability: CEO duality is positively related to CEO ability. Vancil (1987) argues that the succession planning process is widely practiced in the U.S. and provides detailed case studies of this practice. During this process, the retiring CEO retains the COB title and the new CEO typically holds additional operating titles such as President or Chief Operating Officer. If he successfully passes the training test, the new CEO earns the additional COB title and the old COB resigns from the board. Compared to having a separate leadership, this process provides several benefits to a firm. As only high performing CEOs will be promoted to the chairmanship, the prospect of the additional COB title provides strong incentives to the new CEO. It also serves as an effective tool in mitigating the horizon problem of the retiring CEO. For example, the retiring CEO may work less hard as he is close to retirement. This process also eases the transition from active duty to retirement for an aging CEO, making it less likely that the CEO will attempt to hold on to his position too long (Brickley, Coles, and Jarrell, 1997; Brickley, Linck, and Coles, 1999). Following the literature (e.g., Linck, Netter, and Yang, 2008), we use CEO age to proxy the closeness of the CEO to retirement, and hypothesize that the likelihood of CEO duality is positively related to CEO age. Hypothesis 4 Succession planning: CEO duality is positively related to CEO age. Costs of CEO Duality The arguments against CEO duality are largely predicated on agency theory. Managers of modern corporations have decision rights but not control rights over shareholder capital, and therefore it cannot well be expected, that they should watch over it with the same anxious vigilance (as owners). Negligence and profusion, therefore, must always prevail (Smith, [1776] 1937: 700). Agency theory predicts that the likelihood of CEO duality increases in firm and CEO characteristics that are associated with a lower propensity of CEO self dealing. In this section, we discuss three of these characteristics vigilant boards, CEO incentive alignment, and CEO reputation. The board of directors is the first line of defense against self dealing managers (Fama and Jensen, 1983; Weisbach, 1988). A vigilant board, which can effectively monitor and constrain the agency conflicts between the manager and the shareholders, allows a firm to take full advantage of the

11 194 MILLER AND YANG benefits of CEO duality while keeping the agency costs in check (Finkelstein and D Aveni, 1994). Consistent with this argument, McWilliams and Sen (1997) find that stock price reaction to antitakeover amendments is more negative when the board is dominated by non independent directors. The reaction becomes increasingly negative for duality firms when non independent directors increase their equity ownership and proportional representation on the board. In contrast, board composition and ownership structure have no explanatory power for stock price reaction when the CEO does not chair the board. Supporting the vigilant board hypothesis, Grinstein and Valles (2008) and Dey, Engel, and Liu (2011) find that the percent of independent directors is an important determinant of board leadership structure. Interestingly, prior studies have examined the potential monitoring effect of other governance controls. Few of these other controls have been found to be important in explaining board leadership structure. 6 Therefore, we hypothesize that the likelihood of CEO duality is positively related to the percent of independent directors on the board. Hypothesis 5 Vigilant board: CEO duality is positively related to the percent of independent directors on the board. Agency costs arise because managers are not the owners of the firm. When a CEO owns a larger equity stake in the firm, he internalizes more of the agency costs generated by his own opportunistic behaviors such as perquisite consumption and quiet life, and therefore has a greater incentive to act in the best interest of shareholders. Hence, when the incentives of the CEO and shareholders are better aligned, firms enjoy lower agency costs of CEO duality, leading to our next hypothesis: Hypothesis 6 Incentive alignment: CEO duality is positively related to CEO ownership. Faleye (2007) argues that reputational capital builds up gradually over time but can be easily destroyed as a result of few bad acts. Therefore, a reputable CEO is less likely to engage in self serving activities, reducing the agency costs of CEO duality. Following the literature (e.g., Grinstein and Valles, 2008; Dey, Engel, and Liu, 2011), we use a CEO s pay relative to 6 Faleye (2007) examine board size and the ownership by outside blockholders. Grinstein and Valles (2008) study the ownership by directors excluding that of the CEO. Pathan and Skully (2010) study the Entrenchment Index (Bebchuk, Cohen, and Ferrell, 2009), the ownership by outside blockholders, and the ownership by officers and directors minus that of the CEO. With the exception of the ownership by officers and directors minus that of the CEO, none of the variables have been found to significantly impact board leadership structure.

12 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 195 his peers and outside board memberships (i.e., the number of other firms boards on which the CEO sits) to proxy for his reputational capital. Hypothesis 7 CEO reputation: CEO duality is positively related to CEO reputational capital. The Role of Firm Size The existing literature has argued for a special role of firm size regarding CEO duality. Juxtaposed views have been proposed, and the empirical evidence is mixed. One school of thought predicts that large firms benefit more from a separate leadership model (Palmon and Wald, 2002; Grinstein and Valles, 2008). As firms grow in size, agency conflicts arising from the separation of ownership and control become more severe. Additionally, large firms have more complex operations than small firms, selling more varied products across more expansive geographic areas, engaging in more M&A activities, employing more sophisticated financial instruments, and so forth (Lehn, Patro, and Zhao, 2009). Complex operations are more opaque and give managers more decision latitude, which also induces higher agency costs. Consistent with these arguments, Palmon and Wald (2002) find that a change from duality to non duality induces negative abnormal returns for small firms, and negligible returns for medium firms, but positive abnormal returns for large firms. Other scholars present an alternative view, arguing that CEO duality is more beneficial to large firms (Brickley, Coles, and Jarrell, 1997). Large firms have more complete institutional arrangements and more advanced governance controls to combat agency conflicts (Fama and Jensen, 1983). While large firms may face higher agency costs of CEO duality, Brickley, Coles, and Jarrell (1997: 194) argue that in the large complex company no one on the board has greater reputational and financial capital at risk in the future performance of the organization than does the CEO. Lastly, large firms may also incur higher monitoring costs than small firms due to their complex operations. Consistent with these arguments, Faleye (2007) finds that CEO duality enhances the performance of complex firms, but hurts the performance of non complex firms. Linck, Netter, and Yang (2008) find that about 70% of large firms, compared to about 50% of small firms, have a unitary leadership. Large firms employ a substantially larger number of independent directors, consistent with the notion that governance controls work as a system, one control being counter balanced by another. Pathan and Skully (2010) find similar patterns for banks. Prior studies have also compared and contrasted board determinants across firm sizes. Linck, Netter, and Yang (2008) find that fewer factors appear to be important in explaining board size and board composition for

13 196 MILLER AND YANG small firms than for medium and large firms. They do not study the determinants of CEO duality across firm sizes. In contrast, Pathan and Skully (2010) find that the economic determinants of board size, board composition, and CEO duality are similar across large, medium, and small banks. In view of the existing literature, we treat the role of firm size as an empirical question for our sample of insurance firms and make no prediction regarding whether firm size moderates the relations between CEO duality and the underlying economic determinants. EMPIRICAL MODEL To test the hypotheses, we estimate the probability of a firm having CEO duality by fitting the following logistic model (henceforth, the determinants model): logit(p=duality it ) = α + β 1 *size it + β 2 *leverage it + β 3 *sales_growth it + β 4 *ROA i,t 1 + β 5 *CEO_tenure it + β 6 *CEO_age it + β 7 *%outsider it + β 8 *CEO_own it + β 9 *Relative_pay it + β 10 *#outside boards it + d t + d j + ε it (1) The dependent variable (duality it ) is a binary variable that takes the value of one if Firm i combines the CEO and COB positions in year t, zero if the two positions are held by separate individuals. α is the constant. Under the category of the benefits of CEO duality, size is firm size, measured as the logarithm of total book assets. leverage is the ratio of long term debt over total book assets. size and leverage measure organizational complexity and according to the organizational complexity hypothesis (H1) should be positively related to duality (β 1 >0, β 2 >0). sales_growth is the average sales growth rate over the past five years. The decision speed hypothesis (H2) predicts that growth is positively related to duality (β 3 >0). ROA t 1 is firm past performance, calculated as the prior year earnings before interest and taxes over total book assets measured at the start of the prior year. CEO_tenure is the logarithm of the number of years a CEO has been in office. ROA t 1 and CEO_tenure measure CEO ability. The CEOability hypothesis (H3) predicts that the likelihood of CEO duality increases in CEO ability (β 4 >0, β 5 >0). CEO_age tests the succession planning hypothesis (H4) and should be positively related to duality (β 6 >0). Since CEO_tenure and CEO_age have large, positive numbers, we take the log form of the two variables to achieve a more normal distribution.

14 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 197 Under the category of the costs of duality, %outsider is the percent of independent directors on the board. The vigilant board hypothesis (H5) predicts that %outsider is positively related to duality (β 7 >0). CEO_own is the percent of common stocks held by the CEO. The incentive alignment hypothesis (H6) predicts that CEO_own is positively related to duality (β 8 >0). Relative_pay is the total compensation received by a CEO relative to the industry average in year t. 7 We calculate the industry average for Firm i in Year t by summing the total compensation of all the CEOs in the 2 digit SIC code industry, excluding that of Firm i, divided by the total number of insurance firms in that 2 digit SIC code industry minus one. #outside boards is the number of board memberships held by the CEO in other publiclytraded firms. The CEO reputation hypothesis (H7) predicts that Relative_pay and #outside boards are positively related to duality (β 9 >0, β 10 >0). Similar to prior studies that analyze the determinants of board leadership structure, we control for year and industry fixed effects by using year dummies (d t ) and industry dummies (d j ). Industry dummies are created using the first three digits of the SIC code. Following the literature norm in constructing the error term (ε it ), we use Huber White robust standard errors that incorporate firm level clustering (Linck, Netter, and Yang, 2008; Pathan and Skully, 2010). Data SAMPLE We start the sample collection process with all publicly traded insurance companies in the U.S. in the merged CRSP/COMPUSTAT database (i.e., firms with the Standard Industry Classification Code between 6311 and 6399). We then merge the financial data from CRSP/COMPUSTAT with the Risk Metrics database to obtain board data, including whether a CEO is the COB. RiskMetrics provides annual coverage of board data for S&P 1500 firms starting in We obtain pay data and CEO characteristics including CEO age, CEO tenure, and CEO ownership from the EXECU COMP database. The final sample with the requisite data consists of 946 firm year observations, or 119 unique firms, from 1996 to As we discuss later, we obtain compensation data from EXECUCOMP. Total compensation is the TDC1 variable, which includes salary, bonus, other annual (e.g., perquisites and tax reimbursement), restricted stocks, stock options, long term incentive pay, and total of all other forms of compensation (e.g., severance payments, debt forgiveness, and payment for unused vacation).

15 198 MILLER AND YANG Univariate Statistics Table 2 reports summary statistics for key variables, with Panel A covering the variables in Equation (1) and Panel B covering additional variables used in this study. Because of our interest in the role of firm size, we also report the summary statistics separately for large, medium, and small firms. We identify size groups by ranking firms into terciles based on book value of total assets in a given year. Due to data availability, our sample consists mainly of S&P 1500 insurance companies. Therefore, the sample firms are large, with a mean value of $ billion in total book assets for the full sample. Most statistics are consistent with expectations. For example, larger firms are older, have more independent directors on the board, and have a lower level of CEO ownership. However, several sample statistics diverge from the norm of the larger population of S&P 1500 firms. First, larger insurers have lower leverage, a slower sales growth rate, lower ROA, and lower valuation (Tobin s Q). This pattern is in contrast to Grinstein and Valles (2008), whose sample covers all S&P 1500 firms. Grinstein and Valles find that larger firms have more debt, higher valuation, and higher ROA. Second, a well documented relation in the literature is the percent of firms having CEO duality increasing monotonically from small firms to medium firms to large firms (Linck, Netter, and Yang, 2008; Grinstein and Valles, 2008). However, we find that in our sample, large and small firms are more inclined to deploy CEO duality than medium firms. Table 3 reports correlation between the main variables of interest. No serious correlation is detected among the independent variables. Only four coefficients have an absolute value larger than 0.3. In untabulated results, we also perform a collinearity test on the independent variables in Equation (1). Firm size has the largest value of the variance inflation factor (VIF), VIF being VIFs of the rest of the independent variables are all less than 8 Firms disclose board leadership structure at each annual shareholder meeting. Most firms (94.2% in the case of our sample) hold the meeting within five months of fiscal year end. When merging financial data with board data, which RiskMetrics collect from the proxy statement, we use the financial data that are as of the fiscal year ending immediately before the shareholder meeting date. When merging board data with data on CEO characteristics, we match based on CEO name in the same calendar year. Risk Metrics report the number of board memberships held by a CEO in other publicly traded companies. The data coverage on this variable starts in To preserve sample size, we assume for the same CEO the 1998 values for 1996 and 1997 values. We then set missing data for the variable, CEO outside board membership, to zero (7.29% of the firm years). Our results hold if we exclude those observations or if we include as an additional control variable a dummy that equals one if the variable, CEO outside board memberships, is missing.

16 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 199 Table 2. Summary Statistics a Mean Std. dev. Mean Full sample Large firms Medium firms Small firms Panel A: N Duality Structural duality Firm size (total assets, in $MM) 48, , , , , Leverage Sales growth rate ROA t CEO tenure CEO age %independent directors 70.57% 17.17% 75.24% 69.28% 67.23% CEO ownership 3.32% 8.80% 1.65% 3.13% 5.25% CEO relative pay CEO outside board memberships Panel B: Tobin s Q Firm age Stock return volatility Shareholder pressure CEO change dummy

17 200 MILLER AND YANG a This table reports the summary statistics for the main variables of interest in this study. The sample consists of 119 insurance companies with the Standard Industry Classification Code (SIC) between 6311 and 6399 from 1996 to We identify size groups by ranking firms into terciles based on their total book assets in a given year. Panel A reports the summary statistics of the variables used in Equation (1). Duality is a dichotomy variable that takes the value of one if a firm combines the titles of the CEO and the Chairman of the Board (COB), zero otherwise. Structural duality accounts for temporary changes in duality status due to succession planning and other transient events. See Appendix I for illustration of duality, structural duality, and transient duality change. Firm size is book value of total assets. Leverage is long-term debt over total book assets. Sales growth rate is the average sales growth rate over the past five years. ROA t-1 is the prior-year earnings before interest and taxes over total book assets at the beginning of the prior year. CEO tenure is the number of years a CEO in office. %independent directors is the percent of independent directors on the board. CEO ownership is the percent of common stock held by the CEO. CEO relative pay is the total compensation (TDC1 from EXECUCOMP) received by a CEO for the year relative to the industry average. CEO outside board memberships is the number of board memberships held by the CEO in other publicly-traded firms. Panel B reports the summary statistics of other key variables used in this study. Tobin s Q is the ratio of the sum of book value of total assets and the market value of common equity, minus the book value of common equity, over the book value of total assets. Firm age is the number of years since the firm first appeared in Compustat. Stock return volatility is the annualized standard deviation of daily stock returns. Shareholder pressure is an indicator variable that equals one for years after a firm received a shareholder proposal calling for splitting the CEO and COB titles, zero otherwise. CEO_change is a dummy indicating whether a firm turned over the CEO in a given year.

18 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 201 Table 3. Correlation Table a Duality 1 2 Firm size Leverage Sales growth rate ROA t CEO tenure CEO age %independent directors CEO ownership CEO relative pay CEO outside board memberships a This table reports Pearson correlation coefficients for the main variables of interests. Firm size, CEO tenure, CEO age, and CEO relative pay are in logarithm form. In bold are coefficients with significance levels at 5% or better. The sample comprises 946 firm-year observations. See Table 2 for variable description.

19 202 MILLER AND YANG two. The rule of thumb is that VIFs exceeding ten are signs of serious multicollinearity (O Brien, 2007). Therefore, multicollinearity is not a serious concern for our sample. EMPIRICAL RESULTS Determinants of CEO Duality Table 4 reports regression results from estimating the determinants model, Equation (1), using the full sample and then partitioning the sample into large, medium, and small firms to analyze the role of firm size. Columns (1.a) (4.a), with the heading Coeff, give the estimated coefficients from the logit model (i.e., the change in the log odds of CEO duality for one unit increase in the explanatory variable). To better understand the relative importance of the regressors, we report standardized odds ratios (Stdz. odds) next to the logit coefficients in Columns (1.b) (4.b). 9 Columns (1.a) and (1.b) report the estimation results for the full sample. Only CEO tenure, CEO age, and CEO relative pay are significantly related to the probability of CEO duality. CEO tenure has the largest standardized odds ratio, suggesting that this independent variable has the greatest impact on the probability of a firm having CEO duality one standard deviation increase in the logarithm of CEO tenure increases the odds of CEO duality by 183.8% ( ). Columns (2) (4) report the estimation results when we re run Equation (1) separately for large, medium, and small firms, identified on the basis of the firms book value of total assets in a given year. Several patterns are worth noting. First, firm size appears to play an important role in explaining CEO duality. Similarly to Grinstein and Valles (2008), we find that firm size is insignificant in the full sample and in the subsamples of large, medium, and small firms. However, a considerably larger number of explanatory variables enter the regressions with significant signs for large and small firms than for the full sample. Further, McFadden s pseudo R2 is larger for each subsample of different sized firms than for the full sample. These results are in contrast to the existing literature. Grinstein and Valles (2008) compare the probability of separate roles for large, medium, and small firms in the S&P 1500 index, including insurance firms, from 2000 to Pathan and Skully (2010) compare the probability of CEO 9 Standardized odds ratio gives the change in the odds of an insurance firm having CEO duality for one standard deviation change in the explanatory variable, when all the explanatory variables are normalized to have a mean of zero and a standard deviation of one.

20 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 203 duality for large, medium, and small banks. Both studies find that independent variables have comparable explanatory power across different size cohorts. Additionally, Linck, Netter, and Yang (2008) compare the determinants of board size and board composition for a large sample of industrial firms. They also find similar results across groups of different sized firms. Therefore, insurance firms appear to be unique in that the costs and benefits of CEO duality vary more with firm size than they do for banks and industrial firms. Second, most explanatory variables carry the predicted signs, except for two. Contrary to H2, which predicts that growth opportunities should be positively related to the probability of CEO duality, sales growth rate has a significantly negative standardized odds ratio of for large firms, suggesting that a one standard deviation increase in sales growth rate reduces by 45% the probability of large firms having CEO duality. Interestingly, the coefficient of sales growth rate is negative and insignificant for medium firms, but turns positive albeit still insignificant for small firms. Recall, Table 2 shows that large firms have the lowest growth rate of all the size cohorts. We find partial support for H7, which predicts a positive relation between CEO duality and CEO reputation. Specifically, consistent with H7, CEO relative pay is significantly positive for large and small firms, but the number of board seats held by the CEO in other publicly traded companies has a significantly negative coefficient for small firms. In untabulated robustness tests, we use two alternative classification schemes to divide firms into different size groups. In one classification scheme, we divide firms into large, medium, and small based on whether an insurance company is a member of the S&P 500 Largecap Index, the S&P 400 Midcap Index, or the S&P 600 Smallcap Index. 10 In the other classification scheme, we rank firms into quartiles based on total book assets in a given year. For both classification schemes, we find qualitatively similar results. For example, more explanatory variables enter the regression of large firms with significant signs regardless of the classification scheme. ROA t 1 is significantly and positively related to CEO duality for S&P 600 Smallcap firms (stdz. odds = 7.10) and for the bottom quartile firms (stdz. odds = 1.99). To summarize, the overall results are generally consistent with the hypotheses and the existing evidence on banks and industrial firms, particularly for large and small firms. The evidence also suggests that it is important to consider the role of firm size in analyzing the determinants of CEO duality for insurance companies. 10 Of the 946 firm years for our sample, 215 are S&P 500, 345 are S&P 400, and 200 are S&P 600.

21 204 MILLER AND YANG Table 4. Determinants of CEO Duality* All firms Large firms Medium firms Small firms Coeff. Stdz. odds Coeff. Stdz. odds Coeff. Stdz. odds Coeff. Stdz. odds (1.a) (1.b) (2.a) (2.b) (3.a) (3.b) (4.a) (4.b) Firm size (0.565) (0.259) (0.652) (0.707) Leverage c b (0.550) (0.061) (0.447) (0.046) Sales growth rate a (0.392) (0.004) (0.470) (0.776) ROA t i b (0.384) (0.847) (0.483) (0.043) CEO tenure a a a a (0.000) (0.000) (0.001) (0.000) CEO age a a (0.007) (0.009) (0.122) (0.228) %independent directors c (0.912) (0.084) (0.134) (0.415) CEO ownership b (0.219) (0.036) (0.518) (0.549) CEO relative pay a a c (0.008) (0.001) (0.295) (0.059) CEO outside board memberships a (0.276) (0.269) (0.208) (0.005) Year fixed effects YES YES YES YES

22 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 205 Industry fixed effects YES YES YES YES #Observations Model p value Pseudo R *This table reports the estimation results from fitting the logit model, Equation (1). The dependent variable is a dichotomous variable that equals one if the firm combines the CEO and COB positions and zero otherwise. See Table 2 for the description of other variables. Column (1) reports the estimation results for the full sample. Columns (2) (4) report the estimation results when we re run the logit model separately for large, medium, and small firms. Columns with the heading Coeff. give the estimated coefficients from the logit model, while those with Stdz. odds give the corresponding standardized odds ratios. Firm size, CEO tenure, CEO age, and CEO relative pay are in logarithm form. p values are reported in parentheses below the coefficient estimates and are computed using robust standard errors with firm level clustering. a, b, and c denote significance at the 1%, 5%, and 10% level, respectively.

23 206 MILLER AND YANG Robustness Check Excluding Observations Associated With Transient Duality Changes The literature shows that many changes in duality status arise from CEO succession planning (Brickley, Coles, and Jarrell, 1997). As discussed in Section 2, succession planning refers to a process that firms use to transition from the old CEO to the new one, also known as passing thebaton (Vancil, 1987). During this process, the retiring CEO retains the COB title, and the new CEO is awarded the additional COB title only if he or she successfully passes the probationary period, upon which time the old COB resigns from the board. To the best of our knowledge, no study has explicitly compared and contrasted the determinants of CEO duality when those events are included in the sample versus when those events are excluded from the sample. Some studies address the potential confounding effect of succession planning on the determinants of CEO duality by excluding transient changes in duality status from the sample. As an example, Faleye (2007) excludes non dual observations up to five years (year 0 to year 4) if the firm has a dual CEO in the immediate preceding year (year 1). Some studies, like Linck, Netter, and Yang (2008), Grinstein and Valles (2008), and Pathan and Skully (2010), include transient duality changes in the sample and model succession planning by using additional regressors (i.e., CEO age). Therefore, the literature seems to implicitly assume that the explanatory power of independent variables for the determinants of CEO duality remain unchanged regardless of how transient duality changes are treated by excluding those events from the sample or by including them but adding additional control variables like CEO age. We argue that this implicit assumption warrants investigation. We postulate that duality changes due to succession planning are procedural and temporary changes in board leadership structure necessitated by another corporate event CEO turnover. (There are also a few instances in our sample in which a firm temporarily switched from a separate model to a dual model because of a turnover in the Chairman office. 11 ) Different economic factors likely drive these transient duality decisions than the long term or structural duality decisions. Therefore, it is instructive to compare and contrast the determinants of CEO duality when the firm year observations associated with transient duality changes are included in the 11 As an example, Pacificare Health Systems appointed Alan R. Hoops as the CEO and Terry O. Hartshorn as the COB in April Mr. Hoops became the COB in Jan when Mr. Hartshorn left the office of COB. Mr. Hoops served as COB until Nov when David A. Reed was appointed as the new COB.

24 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 207 determinants test versus when those observations are excluded from the test. To identify transient duality changes, we read corporate proxy statements stored in the Securities and Exchange Commission (SEC) s online Edgar database, whenever there is a change in duality status. In Appendix I, we give an example of how we classify transient duality changes. We identify 64 firm year observations as transient. The 64 observations are distributed across 21 large, 19 medium, and 24 small firms. For three firm years, we are unable to determine whether the change in duality status is permanent or transient, because the firm stopped filing proxy statements due to either M&A (one large firm and one small firm) or financial distress (one small firm). To ensure the robustness of the test (i.e., a strict classification of structual duality decisions), we exclude the 67 observations from the robustness test. The estimation results of the robustness test are reported in Table 5. As Table 5 Columns (1), (3), and (4) show, excluding firm years associated with transient duality changes has a minimal effect on the estimation results for the full sample and medium firms. However, for large firms, notable differences emerge when transient years are excluded (i.e., results in Table 5 Column (2)), compared to when they are included (i.e., results in Table 4 Column (2)). Specifically, leverage, CEO age, and the percent of independent directors lose explanatory power once we exclude the transient years from the subsample of large firms. In contrast, similar to Table 4 Column (2), we still find comparable support for the incentive alignment hypothesis, as evidenced in the significant and positive coefficients of CEO ownership. We again find partial support for the CEO reputation and the vigilant board hypotheses in the variable of CEO relative pay and the percent of independent directors on the board, respectively. Sales growth rate remains significantly negative, corroborating the results in Table 4 Column (2). Additionally, leverage loses significance in the regression for small firms. To summarize, we obtain similar results whether we include or exclude transient duality events in the sample, except for large firms. The finding that transient duality decisions matter more for large firms is consistent with Naveen (2006), who finds that succession planning matters more for complex firms, because those firms incur greater costs in transferring firm specific knowledge and expertise to an outsider. Robustness Check After Reclassifying Transient Duality to Structural Duality For more robustness, instead of excluding the 67 firm year observations associated with transient duality changes, we include those

25 208 MILLER AND YANG Table 5. Determinants of CEO Duality, Excluding Observations Associated with Transient Duality Change* All firms Large firms Medium firms Small firms Coeff. Stdz. odds Coeff. Stdz. odds Coeff. Stdz. odds Coeff. Stdz odds (1.a) (1.b) (2.a) (2.b) (3.a) (3.b) (4.a) (4.b) Firm size (0.817) (0.371) (0.747) (0.501) Leverage (0.751) (0.564) (0.312) (0.146) Sales growth b (0.363) (0.026) (0.308) (0.547) ROA t i b (0.172) (0.728) (0.745) (0.039) CEO tenure a c a a (0.000) (0.065) (0.003) (0.001) CEO age b (0.016) (0.130) (0.119) (0.152) %independent directors (0.884) (0.138) (0.209) (0.372) CEO ownership b (0.206) (0.046) (0.525) (0.593) CEO relative pay a a a (0.004) (0.004) (0.317) (0.008) CEO outside board memberships a (0.208) (0.341) (0.359) (0.013)

26 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 209 Year fixed effects YES YES YES YES Industry fixed effects YES YES YES YES #Observations Model p value Pseudo R *This table reports the estimation results from fitting the logit model, Equation (1), after excluding firm years associated with transient duality changes. Transient duality changes are temporary changes in board leadership structure, which occurred because if a change of personnel in either the CEO or the Chairman. Appendix I provides an example of how we classify transient duality change and define structural duality. The dependent variable is a dichotomous variable that equals one if the firm combines the CEO and COB positions and zero otherwise, as illustrated in Appendix I Column (3). See Table 2 for the description of other variables. Firm size, CEO tenure, CEO age, and CEO relative pay are in logarithm form. Column (1) reports the estimation results for the full sample. Columns (2) (4) report the estimation results when we re run the logit model separately for large, medium, and small firms. Columns with the heading Coeff. give the estimated coefficients from the logit model, while those with Stdz. odds give the corresponding standardized odds ratios. p values are reported in parentheses below the coefficient estimates and are computed using robust standard errors with firm level clustering. a, b, and c denote significance at the 1%, 5%, and 10% level, respectively.

27 210 MILLER AND YANG Table 6. Determinants of CEO Duality, after Reclassifying Transient Duality to Structural Duality* All firms Large firms Medium firms Small firms Coeff. Stdz. odds Coeff. Stdz. odds Coeff. Stdz. odds Coeff. Stdz. odds (1.a) (1.b) (2.a) (2.b) (3.a) (3.b) (4.a) (4.b) Firm size (0.996) (0.491) (0.764) (0.636) Leverage (0.188) (0.175) (0.212) (0.344) Sales growth b (0.362) (0.031) (0.152) (0.546) ROA t a (0.161) (0.706) (0.718) (0.004) CEO tenure a a a (0.006) (0.298) (0.014) (0.004) CBO age a c (0.015) (0.113) (0.107) (0.090) %independent directors c (0.634) (0.229) (0.088) (0.468) CFOownership b (0.218) (0.042) (0.692) (0.401) CEO relative pay a a b (0.003) (0.001) (0.364) (0.023) CEO outside board memberships b (0.148) (0.261) (0.723) (0.048)

28 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 211 Year fixed effects YES YES YES YES Industry fixed effects YES YES YES YES #Observations Model p-value Pseudo R *This table reports the estimation results from fitting the logit model, Equation (1), after reclassifying firm years associated with transient duality change to structural duality. Transient duality changes are temporary changes in board leadership structure, which occurred because of a change of personnel in either the CEO or the Chairman. Appendix I provides an example of how we classify transient duality change and define structural duality. The dependent variable is a dichotomous variable that equals one if the firm combines the CEO and COB positions and zero otherwise, as illustrated in Appendix I Column (5). See Table 2 for the description of other variables. Firm size, CEO tenure, CEO age, and CEO relative pay are in logarithm form. Column (1) reports the estimation results for the full sample. Columns (2) (4) report the estimation results when we re run the logit model separately for large, medium, and small firms. Columns with the heading Coeff. give the estimated coefficients from the logit model, while those with Stdz. odds give the corresponding standardized odds ratios. p values are reported in parentheses below the coefficient estimates and are computed using robust standard errors with firm level clustering. a, b, and c denote significance at the 1%, 5%, and 10% level, respectively.

29 212 MILLER AND YANG observations after reclassifying them to structural duality. More precisely, we re run Equation (1) using duality as coded in Appendix I Column (5); results are reported in Table 6. For comparison, Table 4 reports estimation results of Equation (1) using duality coded as in Appendix I Column (3), while Table 5 reports estimation results of Equation (1) using duality coded as in Appendix I Column (3) but excluding the 67 transient duality changes as coded in Appendix I Column (4). As Table 6 shows, we obtain qualitatively similar results as those reported in Table 5, with a few exceptions. CEO tenure loses significance in the regression for large insurers, while the percent of independent directors and CEO age become marginally significant in the regressions for medium and small insurers, respectively. To summarize, the robustness checks reaffirm that our results in Table 4 hold for the full sample regardless of whether we include or exclude transient duality changes, or reclassify transient duality changes to structural duality status. In addition, the collective evidence suggests that the cost benefit tradeoffs of CEO duality vary with firm size. Further, in corroboration with the results in Table 5, Table 6 results show that transient changes seem to have disproportionate effect on insurers of different sizes. In terms of the determinants of structural duality, we find the strongest support for our hypotheses in the subsample of small firms. Robustness Check Controlling for Other Potential Determinants As we discussed in the hypotheses development section, one main objective of this paper is to verify whether the determinants that have been found to explain duality decisions of banks and industrial firms are also important in explaining the leadership structure of insurance companies. Therefore, our focus has so far been on testing the predictive power of empirical proxies that prior studies have found to be important determinants of CEO duality. In untabulated robustness tests, we explore the possibilities of whether other factors also matter for the duality decisions of insurance firms and whether our results hold when considering these additional factors. Specifically, we consider the following additional controls: the number of business segments in which an insurer operates, whether an insurer has international operations, firm age, stock return volatility, board size, and equity ownership by institutional investors. Adding these additional controls does not alter our main results. For example, when including all the additional controls in Equation (1), CEO tenure and CEO age continue to be significantly positive for the full sample. We continue to find broad support for most of the hypotheses for the subsample of large firms except for the decision speed hypothesis. CEO tenure continues to have the largest explanatory power for the subsample of medium firms. Leverage, CEO tenure, CEO relative pay, and CEO outside

30 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 213 board memberships enter the regression of small firms with similar sign and significance as in Table 4 Column (4) after we control for the additional controls. Further, the additional controls lack explanatory power for the probability of CEO duality when added to our model. Impetus to Structurally Change CEO Duality We have so far focused our analysis on understanding the economic factors that drive insurance companies to choose one board leadership model versus another. In an ealier section, we introduce and examine the determinants of structural CEO duality, namely, the economic factors that drive insurance firms to strategically choose CEO duality over the long run. One of the main findings from the determinants analysis is that firms configure their leadership structure in response to their underlying operating environments. In this section, we build on this knowledge and take our analysis one step further by examining an insurer s decision to structurally change its leadership model (henceforth the change model). Specifically, we ask the question: When the competitive environments change, which factors drive the firm to make the strategic decision to alter the structure of its leadership model? To the best of our knowledge, no study has examined this dimension of a firm s decision regarding CEO duality. Therefore, given the lack of literature precedence, our analysis of this subject is exploratory in nature. Specifically, we focus on two research questions. First, we test whether any explanatory variable in the determinants model is important in explaining an insurer s decision to structurally change its leadership model, and whether we find similar patterns of relative importance in the explanatory variables between the determinants model and the change model. Second, compared to the determinants model, in which the dependent variable has two outcomes the probability of duality versus the probability of nonduality the dependent variable in the change model has three outcomes: a firm moves from non dual to dual (combine), a firm moves from dual to non dual (split), and a firm stays the course (no change). Therefore, the change model provides an opportunity to test whether the same economic determinant may have an asymmetric effect on an insurer s decision to change to combine the titles versus change to split the titles. To address these two research questions, we estimate the following multinomial logit model (i.e., the change model): mlogit(p it,j /p it,no_change ) = α j + β j *ΔX it + γ 1j *shareholder_pressure it + γ 2j *SOX + γ 3j *Dodd Frank + γ 4j *CEO_turnover it + γ 5j *COB_turnover it, j = combine, split (2)

31 214 MILLER AND YANG where p it,j is the probability of Firm i in Year t changing to either structurally combine or structurally split the CEO and COB roles. no_change is the reference category, in which case the firm did not structurally change duality status. Using the example in Appendix I to illustrate the concept of a structural change in duality status, Lincoln National Corp. did not structurally change its leadership model for and ; the firm moved to a structural non dual status in 2008, i.e., p it,j = split. α is the constant. β is the vector of regression coefficients. X is the vector of yearover year changes in the same variables, proxying for firm and CEO characteristics, as used in Equation (1). We use three variables, Shareholder_pressure, SOX, and Dodd Frank, to control for the potential effect of shareholder activism, the 2002 Sarbanes Oxley Act (SOX), and the 2010 Dodd Frank Act on an insurer s decision to structurally change its board leadership model. Using a comprehensive sample of U.S. shareholder proposals filed between 2000 and 2006, Buchanan, Netter, Poulsen, and Yang (2012) find a dramatic increase in the number of shareholder proposals calling for separate roles post Dey, Engel, and Liu (2011) document that of the 232 firms switching away from duality, 41 firms disclosed that outside pressure was a primary motivation for the change. To capture the potential effect of shareholder activism, we use shareholder_pressure, an indicator variable that equals one for years after a firm received a shareholder proposal calling for a separate leadership and zero otherwise. Therefore, we assume once a firm comes under the scrutiny of investors, the pressure to switch to, or continue to practice, a separate leadership stays in place. 12 We use the RiskMetrics database to identify whether an insurance company was targeted for a shareholder proposal during Of the 119 firms in our sample, six firms received nine proposals. SOX presents the most significant reform to corporate governance since the Great Depression (Linck, Netter, and Yang, 2009). He, Miller, and Yang (2012) study the impact of SOX on the board structure of insurance companies from 2000 to 2004 and document a trend of decreasing incidences of CEO duality post SOX. The U.S. Congress debated the merits of CEO duality in the aftermath of the financial crisis. The Dodd 12 Our assumption is consistent with the anecdotal evidence. For example, under shareholder pressure, Walt Disney stripped then CEO Michael Eisner of his COB title in Robert Iger was appointed CEO in 2005 and awarded the additional title of COB in A shareholder proposal calling for the separation of the CEO and COB positions was filed at the 2013 annual shareholder meeting. Disney reached a compromise with shareholders, saying it would split the roles in the future under normal business circumstances. At the time of the 2014 shareholder meeting, Robert Iger still held both titles and no shareholder proposal calling for split roles was filed at that year s shareholder meeting.

32 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 215 Fig. 1. This figure shows the frequency of structural changes in duality status from 1997 to 2012 for the sample of 824 firm year observations in our study. We use structural to distinguish changes in CEO duality that are long term vs. those that are transient or temporary in nature. In Appendix I, we give an example to illustrate the concepts of structural duality status and transient changes in CEO duality status. Frank Act stopped short of mandating the separation of CEO and COB roles, but required the SEC to issue rules mandating that listed firms disclose the reasoning behind their board leadership structures (Yang and Zhao, 2014). Fier and Liebenberg (2013) also find that the market perceived the Dodd Frank Act to have a large and negative impact on the insurance industry. Therefore, to capture the potential effects of SOX and the Dodd Frank Act, we use two dummy variables, SOX and Dodd Frank, which equal one for years post 2002 and post 2010, respectively. Lastly, as turnover in the CEO office defaults in a leadership change, we control for the potential mechanical relation between a change in CEO and a structural change in CEO duality using an indicator variable, CEO_change, which equals one if Firm i turned over the CEO in Year t. The number of firm year observations used to estimate the change model is 824, compared to 946 for the determinants model, because we lose observations by computing year over year changes. To conduct a stricter test of structural duality change, we also exclude the three observations for which we are unable to determine whether the change in duality status is transient. Because of the low rate of structural duality change (3% of the 824 firm years), we do not estimate the change model separately for large, medium, and small firms. Figure 1 shows the frequency of structural duality changes from 1997 to Consistent with the general perception and the existing empirical

33 216 MILLER AND YANG evidence (Grinstein and Valles, 2008; He, Miller, and Yang, 2012), Figure 1 depicts a higher frequency of splits post 2002, the year SOX was implemented. Interestingly, Figure 1 shows a higher frequency of combines after 2009, which accords well with a 2011 Deloitte report indicating a global trend of firms reversing the course of splitting the roles. In 1992, the U.K. regulators issued the Cadbury Report, calling on U.K. firms to separate the CEO and COB positions, which arguably started a global movement toward the separation of the dual roles (Yang and Zhao, 2014). However, Deloitte finds that since 2009, firms from each country in their study (Canada, France, Germany, the U.K., and the U.S.) started moving toward combining the CEO and COB titles. 13 Table 7 reports the regression results from estimating Equation (2). When compared to the results of the determinants model, two interesting patterns emerge. First, different factors seem to drive firms decisions to structurally change their leadership models. Second, most factors, apart from CEO ownership, have an asymmetric impact on combining decisions versus splitting decisions. In addition, two findings are worth noting. First, in the determinants model (i.e., from our prior analysis exhibited in Table 4), the coefficient of CEO outside board membership is negative and insignificant for the full sample, and is significantly negative for small firms, which is inconsistent with Hypothesis 7 that more reputable CEOs tend to hold the COB title. However, in the change model (i.e., the results exhibited in Table 7 Column (1)), an increase in CEO outside board membership significantly increases the likelihood of a firm switching to structural duality, which supports H7. The disparity in these results is consistent with the notion that the reputational capital of a CEO may matter more in times of flux when the firm is contemplating whether to structurally change its leadership model in order to meet the changing demand of competitive environments. By contrast, the reputational capital of a CEO is likely to be less important once the decision about the leadership model has been made and the business environment is relatively stable. Second, as Table 7 Column (2.b) shows, besides the CEO change dummy, the percent of independent directors on the board has the largest impact on the likelihood of firms moving toward a separate leadership model. This result contrasts with our earlier finding in Table 4 Column (2) and is incongruent with the styled fact that firms with more independent boards are more likely to combine the dual roles (see, e.g., Linck, Netter, and Yang, 2008; Pathan and Skully, 2010), but is consistent with the notion that independent boards 13 Deloitte, May 2011, Board leadership: A global perspective.

34 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 217 Table 7. Impetus to Change Structural CEO Duality* Combine Split Coeff. Stdz. odds Coeff. Stdz. odds Base group: No change (1.a) (1.b) (2.a) (2.b) Firm size (0.297) (0.754) Leverage c (0.834) (0.067) Sales growth rate (0.600) (0.441) ROA t (0.193) (0.372) CEO tenure c (0.301) (0.097) CEO age c (0.070) (0.219) %independent director a (0.712) (0.001) CEO ownership b a (0.029) (0.008) CEO relative pay (0.551) (0.600) #CEO outside board memberships a (0.002) (0.233) Shareholder pressure a (0.000) (0.285) SOX c (0.579) (0.083) Dodd Frank (0.205) (0.378) CEO change dummy a 2.75E+05 (0.665) (0.001) #Observations 824 Model p value Pseudo R *This table reports the estimation results from fitting the multinomial logit model, Equation (2). The dependent variable is a categorical variable with three outcomes: a firm (1) switching to structurally combine the CEO and COB roles (Combine), (2) switching to structurally split the dual roles (Split), and (3) staying the course (No_change, the reference category). SOX and Dodd Frank are dummy variables that equal one for years post 2002 and post 2010, respectively. See Table 2 for the description of other variables. Firm size, CEO tenure, CEO age, and CEO relative pay are in logarithm form. p values are reported in parentheses below the coefficient estimates and are computed using robust standard errors with firm level clustering. a, b, and c denote significance at the 1%, 5%, and 10% level, respectively.

35 218 MILLER AND YANG may be more sensitive to external pressure pushing for the separation of CEO and COB positions. We find that shareholder pressure has no effect on the decision to split, but has a significantly negative impact on the decision to combine. This finding is consistent with Renneboog and Szilagyi (2011), who fail to find a direct link between shareholder proposals and firms decisions to abolish CEO duality, and with the general perception that shareholder pressure has an impact on firms decisions to combine the titles (Dey, Engel, and Liu, 2011). Corroborating the time trend depicted in Appendix II and Figure 1, we find some evidence that post SOX, insurance firms are more likely to split the dual roles. We conduct several robustness checks, untabulated to conserve space. As all shareholder proposals occurred after 2002, we dropped shareholder pressure from Equation (2) to mitigate the concern of potential multicollinearity between this variable and SOX. Our results hold in this robustness check. For example, all the other variables retain similar signs and significance levels, while SOX is still marginally significant with a positive sign (p value = 0.094) in the regression comparing the outcome of Split to the outcome of No change. In another robustness check, we omitted SOX from Equation (2). Our results also hold in this robustness check. For example, all the other variables retain similar signs and significance levels, while shareholder pressure is positive at the 1% significance level in the regression comparing the outcome of Combine to the outcome of No change. In another robustness check, we add a dummy in Equation (2) to account for the fact that some transient duality changes coincide with a turnover of COB. Our results hold. To summarize, the evidence suggests that an insurer s decision to structurally change its leadership model differs from its decision of practicing CEO duality, which may reflect the possibility that the former is more an off equilibrium decision, while the latter is more an at equilibrium decision. Regardless of whether this conjecture is true, the evidence is consistent with the notion that CEO duality is a complex decision that firms fine tune in response to their individual circumstances. In their opposition against shareholder proposals calling for split roles, firms frequently cite the need to retain decision flexibility regarding CEO duality. For example, in the 2011 proxy statement, Wellpoint rejected a shareholder proposal to separate the dual roles, stating that [CEO duality] allows the Board the flexibility to determine whether the roles should be combined or separated based upon the Company s circumstances and needs at any given time.

36 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 219 Impact of CEO Duality on Firm Performance While the literature on the determinants of CEO duality is rich, the literature on the relation between CEO duality and firm performance is several orders of magnitude larger. 14 However, despite voluminous research, there is surprisingly little evidence linking CEO duality to firm performance. Indeed, Dalton and Dalton (2011: 408) conclude: [W]e are not aware of a body of literature in corporate governance or elsewhere where null results present with such consistency. Several explanations have been proffered to explain the absence of a statistically significant relation between CEO duality and firm performance, including endogeneity, the lack of variation in CEO duality as a dependent variable, and the idea that board leadership structure is an equilibrium solution to firms contracting problems and hence no empirical relation can be detected in cross section (Hermalin and Weisbach, 2003). Given the literature, we make no prediction regarding the duality performance relation of the insurance companies. As endogeneity is a serious concern in empirical corporate governance research, we employ the IV and SEM methods to address this challenge. We use CEO age and the local supply of potential directors as instruments for CEO duality. To qualify as a valid instrument, two conditions must be met: (1) a strong correlation with the instrumented regressor, and (2) orthogonality with the error term. As Table 1 shows, the existing literature documents a strong correlation between CEO duality and CEO age. Additionally, it is not obvious that CEO age directly influences firm performance (Krause and Semadeni, 2013). Prior studies (Knyazeva, Knyazeva, and Masulis, 2013; Miletkov, Poulsen, and Wintoki, 2014) show that the local supply of potential directors is a good instrument for the percent of independent directors on the board. Knyazeva, Knyazeva, and Masulis (2013) argue that since qualified directors are a scarce human resource, firms located near larger pools of qualified directors should find it easier and less costly to attract outside directors. The same logic should apply to CEO duality; firms located near larger pools of qualified directors should find it easier and less costly to find qualified directors to be the Chairman of the Board. We proxy for the local supply of potential directors of an insurance firm using the total number of directors in that firm s headquartered state. We use all the directors in the RiskMetrics database to build the director pool. We obtain headquarters information from 14 For reviews of this literature, please see Dalton, Daily, Ellstrand, and Johnson (1998), Rhoades, Rechner, and Sundaramurthy (2001), Yang and Zhao (2014), and Krause, Semadeni, and Cannella (2014).

37 220 MILLER AND YANG Execucomp. The suitability of our instruments is supported by the underidentification, weak identification, and Hansen s over identification tests. For example, as Table 8 Column (1) shows, under and weak identification tests indicate that the instruments correlate strongly with the endogenous regressor. The Hansen s over identification test suggests that the instruments perform well, leading us not to reject the null of exogenous instruments. Following the literature (e.g., Faleye, 2007; Hoyt and Liebenberg, 2011; Yang and Zhao, 2014), we use Tobin s Q to measure firm performance. Tobin s Q is calculated as the ratio of the sum of book value of total assets and the market value of common equity, minus the book value of common equity, over the book value of total assets. We include a customary set of control variables that the existing literature uses to predict Tobin s Q (e.g., Cheng, 2008; Bebchuk, Cohen, and Ferrell, 2009; Yang and Zhao, 2014), including firm size (the log of book value of total assets), firm age (the log of the number of years since the firm first appeared in Compustat), sales growth rate (the average sales growth rate over the past five years), stock return volatility (standardized annual stock returns in CRSP), leverage (long term debt over total book assets), current year returns of assets (earnings before interests and taxes over total book assets at the beginning of the year, ROA), and both one year and two year lagged ROA. Table 8 reports the IV results for the full sample and the subsample of large, medium, and small firms. The IV approach is implemented using two stage least squares (2SLS) estimation of panel data models. We control for year and firm fixed effects and correct standard errors for potential heteroskedasticity and firm level clustering. To conserve space, we only report estimation results from the second stage regression. As Columns (1), (3), and (4) show, we find insignificant effect of CEO duality on Tobin s Q, which is consistent with the existing evidence. However, we find a significantly positive coefficient of for CEO duality in the subsample of large firms, suggesting that CEO duality increases Tobin s Q by 5.4% for large firms. To the extent that larger firms are under greater pressure to separate the titles than medium and small firms (Renneboog and Szilagyi, 2011), the finding that CEO duality has a more positive valuation impact on larger insurers than on medium and small insurers is consistent with Dey, Engle, and Liu (2011). Table 9 reports the SEM results for the full sample and the subsample of large, medium, and small firms. SEM implements the two stage estimation method, described in Maddala (1983) and operationalized by Keshk (2003), for a simultaneous equations model in which one of the endogenous variables is continuous (Tobin s Q in our study) and the other endogenous variable is dichotomous (CEO duality in our study). We do not

38 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 221 Table 8. The Impact of CEO Duality on Tobin s Q, Using the Instrumental Variable Approach* All Large Medium Small firms firms firms firms (1) (2) (3) (4) Duality b (0.170) (0.019) (0.734) (0.602) Firm size a a a (0.000) (0.000) (0.348) (0.006) Firm age (0.235) (0.923) (0.501) (0.320) Sales growth rate (0.429) (0.759) (0.236) (0.142) Stock return volatility b (0.990) (0.046) (0.135) (0.266) Leverage a b a (0.000) (0.023) (0.781) (0.000) ROA a a b a (0.000) (0.000) (0.026) (0.000) ROA t a (0.468) (0.003) (0.525) (0.395) ROA t a (0.015) (0.305) (0.503) (0.264) Underidentification test p value Weak identification test Kleibergen Paap rk Wald F statistic Underidentification test Hansen J statistic p value Year fixed effects YES YES YES YES Firm fixed effects YES YES YES YES Table continues

39 222 MILLER AND YANG Table 8. continued #Observations Model p value R *This table reports regression results from the second stage of two stage least squares (2SLS) estimation of panel data model using instrumental variables (IV). Also reported are statistics from tests of instrument validity. We instrument CEO duality using the logarithm of CEO age and the local supply of potential directors. The local supply of potential directors of an insurance firm is computed as the total number of directors in that firm s headquartered state. We use all the directors in the RiskMetrics database to build the director pool. We obtain headquarters information from Execucomp. Firm size, and Firm age are in logarithm form. See Table 2 for the description of other variables. p values are reported in parentheses below the coefficient estimates and are computed using robust standard errors with firm level clustering. a, b, and c denote significance at the 1%, 5%, and 10% level, respectively. control for time, industry, or firm dummies, because our sample lacks a sufficient number of observations to compute the corrected standard errors in the second stage regression. 15 To the extent that these effects are important, the SEM results should be interpreted with caution. Panel A reports the estimation results when the dependent variable is Tobin s Q. The estimated coefficient of CEO duality is significantly positive in the full sample and the subsample of large firms, but is insignificant in the subsamples of medium and small firms. Other regressors carry the expected signs. For example, we find that fast growth firms, low debt firms, and firms with better operating performance (higher ROA) have higher Tobin s Q. Panel B reports the estimation results when the dependent variable is the probability of CEO duality. Tobin s Q does not seem to correlate strongly with CEO duality. The rest of the regressors enter the regressions with similar signs as in Table 4. For example, in both Table 4 and Panel B of Table 9, the coefficient of CEO tenure is significantly positive for the regressions of the full sample and each size group. To summarize, we find no evidence that CEO duality is detrimental to firm performance, as measured by Tobin s Q. If anything, the valuation impact of CEO duality appears to be positive for large firms, which is consistent with one of our earlier results that insurance companies, 15 Standard errors in the second stage regressions are corrected so that they are based on the predicted values, not on the observed values, of Tobin s Q and the probability of CEO duality.

40 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 223 Table 9. The Impact of CEO Duality on Tobin s Q, Using the Simultaneous Equation Model (SEM)* Panel A: SEM Equation 1: Dep. Var. = log(tobin s Q) All firms Large firms Medium firms Small firms (1) (2) (3) (4) Duality a c (0.000) (0.072) (0.343) (0.834) Firm size a a (0.002) (0.594) (0.500) (0.000) Firm age (0.899) (0.522) (0.602) (0.935) Sales growth rate a a (0.000) (0.135) (0.118) (0.006) Stock return volatility (0.590) (0.574) (0.690) (0.921) Leverage a b (0.000) (0.042) (0.237) (0.284) ROA a b a a (0.000) (0.051) (0.000) (0.000) ROA t c a c (0.085) (0.010) (0.071) (0.974) ROA t a b (0.000) (0.306) (0.451) (0.021) #Observations Model p value Adj. R squared *This table reports regression results from the two stage estimation method of the simultaneous equations model as described in Maddala (1983) and operationalized by Keshk (2003). The dependent variable in the first equation is the logarithm of Tobin s Q. The dependent variable in the second probit equation is a dummy variable, Duality, that equals one if the CEO is also the COB. See Table 2 for the description of other variables. Firm size, CEO tenure, CEO age, and CEO relative pay are in logarithm form. p values are reported in parentheses below the coefficient estimates and are computed using robust standard errors with firmlevel clustering. a, b, and c denote significance at the 1%, 5%, and 10% level, respectively.

41 224 MILLER AND YANG Table 9. continued Panel B: SEM Equation 2: Dep. Var. = Duality All firms Large firms Medium firms Small firms (1) (2) (3) (4) Tobin s Q c Firm size Leverage Sales growth rate (0.483) (0.564) (0.090) (0.840) c (0.080) (0.416) (0.447) (0.969) b (0.738) (0.678) (0.030) (0.148) c a (0.610) (0.093) (0.000) (0.778) ROA t CEO tenure CEO age %independent directors CEO ownership CEO relative pay CEO outside board memberships #Observations Model p value Pseudo R2 (0.176) (0.324) (0.821) (0.205) a b a a (0.000) (0.053) (0.000) (0.000) a a a (0.000) (0.002) (0.004) (0.137) a a (0.007) (0.306) (0.000) (0.197) c (0.103) (0.477) (0.887) (0.475) a b (0.002) (0.208) (0.025) (0.582) a b (0.260) (0.777) (0.000) (0.037)

42 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 225 particularly large insurers, seem to be very responsive in adjusting their board leadership structures to changing business environments. CONCLUSION CEO duality is a highly controversial topic and an area of corporate governance that has undergone dramatic changes in recent years. Despite its importance, we know little about the determinants of CEO duality and its impact on firm performance in the insurance industry. We contribute to the literature by conducting an in depth analysis of the determinants of CEO duality using publicly traded insurance companies. In addition, we complement the determinants analysis with an analysis of the impact of CEO duality on firm performance. We find that duality theories, which have been tested primarily on banks and industrial firms, generally hold for the insurance firms, especially after we account for the effect of firm size. We find strong evidence that CEO duality is a complex decision that insurance firms fine tune in response to their individual circumstances. Our evidence suggests that compared to other industries, the insurance industry is unique in that the costs and benefits of CEO duality vary more with firm size in this industry. We find no evidence that CEO duality is detrimental to firm performance. If anything, the valuation impact of CEO duality appears to be positive for large insurers. Our results have important policy implications. Given our findings that the decision about board leadership structure is complex and has value consequences, it may be desirable to promote regulations that can provide insurance companies the flexibility to be more responsive in adjusting their leadership structures to their competitive environments. Our results also suggest that any regulatory initiative in CEO duality should pay close attention to the potential role of firm size. Our results highlight areas for future research. A long line of literature shows that insurance companies closely rely on a system of governance controls in solving their contracting problems (see, e.g., Mayers and Smith, 1981; Miller, 2011). The fact that board leadership structure has undergone dramatic changes in recent years behooves researchers to explore the ripple effect of the changes in this aspect of corporate governance on other governance mechanisms. We also find that more insurers seem to combine the CEO and COB titles after Future work is needed to help us better understand this surprising new trend.

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46 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 229 APPENDIX I. An Illustration of the Distinction Between Observed and Structural Duality In this table, we use Lincoln National Corporation as an example to illustrate how we classify and address transient changes in CEO duality status (Transient change). The goal is to distinguish the difference between duality status (Duality) we observe on a daily basis and structural duality, which is the long term duality status after accounting for transient duality changes. We obtain CEO names from EXECUCOMP and raw duality data from RiskMetrics. We manually identify transient changes in duality status by checking proxy statements on the SEC Edgar database. Of our sample of 946 firm year observations, 67 are classified as transient changes. (See following pages)

47 230 MILLER AND YANG Appendix I. An Illustration of the Distinction between Observed and Structural Duality (1) (2) (3) (4) (5) (6) Year CEO name Details Duality (raw data) Transient changes Structural duality (after reclassifying transient changes) 1996 Ian McKenzie Rolland Ian McKenzie Rolland Jon A. Boscia Jon A. Boscia Jon A. Boscia Jon A. Boscia Jon A. Boscia Jon A. Boscia Jon A. Boscia Jon A. Boscia Jon A. Boscia Jon A. Boscia Dennis R. Glass Dennis R. Glass 0 0 Mr. Rolland held both CEO and COB titles since Mr. Boscia became the CEO in June Mr. Rolland retired from the position of CEO, but retained the title of COB. In March 2001, Mr. Boscia was given the additional title of COB. In July 2007, Mr. Glass was appointed CEO, while J. Patrick Barrett, an independent director, was elected as COB. Mr. Barrett retired in May William H. Cunningham, an independent director, was appointed as the new COB.

48 BOARD LEADERSHIP STUCTURE OF PUBLICLY TRADED INSURANCE COMPANIES 231 Appendix I. continued 2010 Dennis R. Glass Dennis R. Glass Dennis R. Glass 0 0 According to the 2013 proxy statement the latest available proxy statement at the time of this analysis Mr. Glass is still the CEO and Mr. Cunningham is the COB.

49 232 MILLER AND YANG APPENDIX II Time Trends of CEO Duality, This figure plots the time trends of the percent of firms with the CEO as the Chairman of the Board for insurance firms (2 digit SIC codes = 63), national and state commercial banks (4 digit SIC codes = 6020, 6021, or 6022), and non financials (4 digit SIC codes outside the range of ) from 1996 to The data come from RiskMetrics.