The effect of CEO power on bond ratings and yields

Journal of Empirical Finance 17 (2010) 744–762

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Journal of Empirical Finance j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / j e m p f i n

The effect of CEO power on bond ratings and yields☆ Yixin Liu a,⁎, Pornsit Jiraporn b,c,1 a b c

Department of Accounting and Finance, Whittemore School of Business and Economics, University of New Hampshire, Durham, NH 03824, United States Great Valley School of Graduate Professional Studies, Pennsylvania State University, Malvern, PA 19355, United States Thammasat University, Bangkok, Thailand

a r t i c l e

i n f o

Article history: Received 4 April 2009 Received in revised form 9 February 2010 Accepted 23 March 2010 Available online 29 March 2010 JEL classification: G32 G34 G38

a b s t r a c t We argue that executives can affect firm outcomes only if they have influence over crucial decisions. This study explores the impact of CEO power or CEO dominance on bond ratings and yield spreads. We find that credit ratings are lower and yield spreads higher for firms whose CEOs have more decision-making power. To further investigate why bondholders are concerned about CEO power, we show that powerful CEOs tend to maintain an opaque information environment. Bondholders demand higher yields because it is difficult for them to monitor managers in firms with powerful CEOs. Taken together, the results suggest that bondholders perceive CEO power as a critical determinant of the cost of bond financing. © 2010 Elsevier B.V. All rights reserved.

Keywords: CEO power Cost of bond financing Agency theory Bondholders

1. Introduction Existing literature has examined intensively how firm-, industry- and market-level characteristics explain corporate performance. But the influence of individual managers in shaping these outcomes has been largely ignored. This is surprising given that CEOs and other top executives are typically perceived as key factors in making investment, financing and other strategic decisions. As such, their views of the firm clearly have a profound impact on corporate practices and outcomes. Several recent studies attempt to fill this void and ask the important question: do individual managers matter to firm behavior? Rotemberg and Saloner (2000) and Van den Steen (2005), for example, explicitly incorporate the vision of the CEO in their model of firm policy. Bertrand and Schoar (2003) report strong evidence of manager fixed effects for a wide range of corporate decisions. Malmendier and Tate (2008) argue that firms whose CEOs achieved superstar status subsequently underperform the benchmarks. Overall, the empirical evidence so far supports the notion that manager-level characteristics affect firm outcomes. An important dimension of the top management team characteristics is the distribution of decision-making power. When a firm's decision-making power is more concentrated in the hands of the CEO, he would have more discretion to influence decisions and correspondingly have his opinions reflected more directly in corporate outcomes. This has both positive and negative implications for stakeholders, as CEOs could use this dominant role to either better adjust firm policy or to advance their own objectives. Bebchuk et al. (2009b) empirically study how the relative significance of CEO in the top management team affects firm value. They present strong evidence that having a dominant CEO is associated with declining firm value. ☆ The authors would like to thank seminar participants at the University of New Hampshire. ⁎ Corresponding author. E-mail addresses: [email protected] (Y. Liu), [email protected] (P. Jiraporn). 1 Tel.: + 1 610 725 5342. 0927-5398/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jempfin.2010.03.003

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In this study, we examine the relation between CEO power and the cost of bond financing.2 To measure CEO power, we employ the novel approach developed by Bebchuk et al. (2009b). Using data on executive compensation, Bebchuk et al. (2009a,b) examine the CEO's pay slice (CPS), defined as the fraction of the aggregate top-five total compensation paid to the CEO. They argue that CPS reflects the relative significance of the CEO among the top executives and thus can be used as a surrogate for CEO centrality or CEO dominance. We then relate CPS to the credit ratings and yield spreads of new corporate straight bond issues and investigate whether CEO power matters to bondholders. Theoretically, it is not clear how potent CEO power should affect bondholders. We propose four hypotheses. First, the “risk aversion” hypothesis suggests that CEO power may exacerbate excessive managerial risk aversion. As an influential decisionmaker in the company, a powerful CEO is more likely to have his risk-reducing projects such as diversifying acquisitions taken. Grinstein and Hribar (2004) provide evidence consistent with this view. They find that CEOs with more power tend to engage in larger merger deals and the market responds more negatively to their acquisition announcements. While potent CEOs' endeavor to decrease risk is suboptimal for well-diversified shareholders, it is beneficial for bondholders. According to this view, bondholders should benefit as CEO power strengthens.3 Second, the “reputation hypothesis” assumes that powerful CEOs are more likely to have longer tenure. Longer tenure implies that external parties including bondholders are more likely to deal with the same CEO for a longer period of time and therefore could anticipate similar actions from the CEO. If the CEO cares about his reputation and his relation with these external parties, he may choose to shun away from actions that hurt bondholders. The “reputation hypothesis” thus also predicts a negative relation between CEO power and yield spreads. On the contrary, it can be argued that, in firms where CEOs are powerful and dominate most major decisions, the risk arising from judgment errors is not well-diversified, resulting in more extreme decisions and higher variance of firm performance (Adams et al., 2005). The “lack of opinion diversification” hypothesis posits that such increased variability may worsen risk-shifting, thereby hurting bondholders. Finally, the fourth hypothesis we propose is based on an agency argument. The “self-interest” hypothesis contends that strong CEO power may give CEOs greater leeway for perquisite consumption or overcompensation. For example, Core et al. (1999) find that CEOs with greater power or that are more entrenched earn greater compensation. Perk consumption or excess compensation reduce firm cash flows. Both shareholders and bondholders are harmed as a result.4 CEO power may also facilitate self-dealing in the form of keeping an opaque information environment for self-serving purposes. Both the “lack of opinion diversification” hypothesis and the “self-interest” hypothesis suggest that bondholders stand to lose as CEO power increases. Our empirical evidence reveals a positive association between CEO power and the cost of new debt, as measured by at-issue yield spreads. It appears that bondholders perceive powerful CEOs as detrimental to their wealth and, as a result, demand higher yields from firms with strong CEO power. To further confirm the results, we examine the impact of CEO dominance on firms' credit ratings. Credit-rating agencies have access not only to firms' public information but also to nonpublic information such as minutes of board meetings and profit breakdown by product (Jiang, 2008). Therefore, credit ratings reveal rich information about how rating agencies perceive the effect of CEO dominance. We find that the results based on credit ratings are consistent with those based on yield spreads, i.e. firms with stronger CEO power receive lower credit ratings. To further corroborate the results, we construct a composite index of CEO power using several definitions of CEO power in the literature, such as whether or not the CEO is also Chairman of the Board and whether or not the CEO is the company founder (Morck et al., 1989; Adams et al., 2005). Our results remain consistent when alternative measures of CEO power are utilized in place of the CEO's pay slice. Our findings suggest that bondholders exhibit a negative perception when the issuing firms' CEOs have greater power. We suggest one possible mechanism that explains the negative perception. We show that powerful CEOs appear to maintain an opaque information environment, as measured by the bid–ask spread. Information opaqueness makes it hard for bondholders (as well as shareholders) to determine firm value and verify managerial actions. To protect their investment, bondholders correspondingly demand higher yields. Our study makes several important contributions to the extant literature on the cost of debt and on agency theory. First, our study enriches the literature that examines the effect of CEO power on firm outcomes (Bebchuk et al., 2009b; Adams et al., 2005). In the management literature, there is an extensive debate over whether top executives matter. The early literature argues that managers do not matter (Lieberson and O'Connor, 1972; Finkelstein and Hambrick, 1996; Pfeffer, 1997). On the contrary, several studies argue and present evidence that executives do matter (Child, 1972; Hambrick and Mason, 1984; Tushman and Romanelli, 1985; Weiner and Mahoney, 1981). In economics and finance, a large number of studies address related questions (Hermalin and Weisbach, 1988; Agrawal and Knoeber, 2001; Denis and Denis, 1995; Parrino, 1997; Huson et al., 2004; Malmendier and Tate, 2008; Bertrand and Schoar, 2003). To the best of our knowledge, ours is the first study to directly address the influence of manager characteristics such as CEO power on the cost of bond financing.

2

We use CEO power, CEO centrality and CEO dominance interchangeably in this paper. There has been abundant research on the excessive risk aversion of management due to the underdiversified nature of their portfolios. Fama (1980) reports that managers with an underdiversified human capital want to have lower leverage than optimal to reduce their firm-specific risk. Masulis (1988) argues that managers will prefer less leverage relative to diversified shareholders to reduce the risk emanating from their underdiversified investment in the firm they operate. 4 CEO perk consumption may or may not be shared by other executives. An anecdotal example is former Tyco International CEO Dennis Kozlowski. Tyco picked up half the tab for a $2.1 million trip to the Italian island of Sardinia, where the central event was a 40th birthday party for Kozlowski's wife, Karen. 3

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Second, several recent important studies attempt to ascertain the impact of corporate governance on the cost of debt (Ashbaugh-Skaife et al., 2006; Anderson et al., 2004).5 Bebchuk et al. (2009a,b) argue that CEO power constitutes an important governance mechanism that affects agency costs. We contribute to the literature in this area by providing evidence that CEO power does indeed have a palpable impact on the cost of debt and thus appears to represent a relevant corporate governance mechanism as suggested by Bebchuk et al. (2009a,b). Finally, our paper adds to the recent debate about the relation between the agency cost of debt and the agency cost of equity (Ortiz-Molina, 2006; Billett and Liu, 2007; Klock et al., 2005). Our results indicate that the cost of debt increases with CEO power. Bebchuk et al. (2009a,b) report lower firm value as CEO power increases. Combined with the results from Bebchuk et al. (2009a,b), our findings suggest that dominant CEOs may exacerbate both types of agency costs. We organize the remainder of the paper as follows. In Section 2, we review the previous literature. Section 3 describes the sample selection and the methodology. We present the empirical results in Section 4 and conclude the paper in Section 5. 2. Prior literature and hypothesis development Motivated by agency theory, we contend that the degree of CEO power likely influences the severity of the agency costs of debt, which in turn affect how debt securities are priced. Thus, our central hypothesis is that CEO power is related to the cost of bond financing. In this section, we review the related literature and develop our hypotheses. 2.1. CEO power CEO dominance indicates how much decision-making power is concentrated in the hands of the CEO. There are multiple dimensions to the concept of “power”, some of which are not easily observable. Finkelstein (1992) identifies four sources of power: structural power, ownership power, expert power, and prestige power. Structural power is the most commonly cited in the literature and is based on formal organizational structure and hierarchical authority (Brass, 1984; Hambrick, 1981; Perrow, 1970; Tushman and Romanelli, 1985). Like Adams et al. (2005), our study focuses on structural power, especially the power of the CEO over the top executive team. We do not argue that all forms of CEO power should affect the cost of bond financing. 2.2. The role of CEO power on corporate outcomes The notion that variation in senior executives' choices is crucial to the understanding of firm behavior is behind the management and organizational behavior literature on managerial discretion. Finkelstein and Hambrick (1996) offer an exhaustive review on this important topic. This issue is part of an interesting debate over whether managers “matter” for corporate decisions and outcomes. Hannan and Freeman (1977) play down the impact of managerial discretion on corporate performance because of organization and environmental constraints that limit the scope of managerial actions. By contrast, Hambrick and Mason (1984) and Tushman and Romanelli (1985) contend that executive leadership is a basic driving force behind the evolution of organizations. The literature on this topic is rich and varied and also spans several areas of research, including management, economics, and finance. For conciseness, we discuss only the most recent studies that provide direct empirical evidence on this debate. Recent empirical evidence demonstrates that strong CEO dominance appears to exacerbate shareholder–manager agency costs and has an adverse impact on firm performance. In a recent crucial study, Bebchuk, Cremers, and Peyer (2009b) report that strong CEO dominance is associated with lower firm value as measured by Tobin's q and with poorer accounting profitability.6 They argue that the poor performance may be attributed to the agency conflict because strong CEO power is also related to several instances of shareholder–manager agency-related outcomes. In particular, strong CEO power is related to higher odds of the CEO receiving a “lucky” option grant at the lowest price of the month and a higher tendency to reward the CEO for luck in the form of positive industry-wide shocks. In addition, firms with powerful CEOs show a lower likelihood of CEO turnover controlling for prior performance. The rich results in Bebchuk et al. (2009a,b) constitute a solid piece of evidence that CEO dominance is a critical variable that affects several important corporate outcomes. Moreover, the mechanism through which CEO power influences these outcomes seems to be related to shareholder–manager agency costs. Specifically, the evidence suggests that strong CEO power allows the CEO to act in manners advantageous to himself but not necessarily to shareholders, thereby worsening the agency conflict between shareholders and managers. In a similar vein, Adams et al. (2005) investigate how CEO power influences performance variability. They hypothesize that powerful CEOs are less likely to have to compromise with other top executives, resulting in more extreme decisions, either beneficial or deleterious to the firm. The evidence corroborates this hypothesis, suggesting that variability in firm performance increases with the degree of CEO influence because fewer moderate decisions are more likely to be taken when the CEO is more dominant. 5

Additional studies in this area include Bhojraj and Sengupta (2003), Sengupta (1998), and Duru et al. (2005). A related debate focuses on the effect of CEO power on firm performance in family firms. For example, Fahlenbrach (2008) indicates that family firms (whose CEOs often have concentrated power) are associated with superior firm performance. In contrast, Holdernewss and Sheelan (1988) find that family ownership reduces firm value. Recently Villalonga and Amit (2006) point out that family firms create value only when the founders serve as CEO or as Chairman with a hired CEO. However, firm value is reduced when descendants serve as CEOs. 6

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2.3. Do powerful CEOs reduce the cost of bond financing? Strong CEO power may reduce the cost of bond financing for, at least, two reasons. First, the literature has documented that CEOs tend to exhibit strong risk aversion due to their inability to diversify their wealth.7 CEOs have incentives to reduce firm risk to a level suboptimal to shareholders. As the dominant person in the firm, a powerful CEO has more discretion to influence decisions and correspondingly is more likely to have his risk-reducing projects implemented.8 Pathan (2009) finds evidence in support of this view. He reports that CEO power negatively affects bank risk-taking in his sample of large US bank holding companies. If strong CEO power is associated with excessive risk avoidance, the agency cost of debt in the form of risk-shifting may be alleviated. As a result, companies with powerful CEOs may enjoy lower costs of debt, holding everything else constant. We call this hypothesis the “risk aversion” hypothesis. Second, CEO power is likely related to the length of CEO tenure. More powerful CEOs may have more influence over directors and are probably able to stay on the job longer than would be the case if the CEO were less powerful. Consequently, powerful CEOs may face the reputation concerns associated with their sustained presence in the firm and its effect on third parties. Longer tenure implies that external parties, such as bondholders, are more likely to deal with the same chief executive and his corporate policies for longer periods in firms with powerful CEOs than in firms with weak CEOs. For instance, banks and other parties frequently develop personal and well-informed relationships with company executives, suggesting that the presence of a powerful CEO with long tenure allows these relationships to build over an extended period of time.9 As a result, an exploitative action on the part of the CEO is likely to lead bondholders to anticipate similar actions in the future as long as the same CEO remains on the job (Anderson et al., 2004). Therefore, due to the reputation concern, powerful CEOs are less likely to act against bondholders. This weaker tendency to expropriate wealth from bondholders should reduce the agency cost of debt and, hence, lead to lower debt yields. We label this hypothesis the “reputation hypothesis”. 2.4. Do powerful CEOs increase the cost of bond financing? It can be argued that the cost of bond financing should increase as CEO power strengthens. It is well-documented in the literature that CEOs can expropriate wealth from shareholders through various means (for instance, excessive compensation and perquisite consumption, wasteful acquisitions etc.), which likely reduces the anticipated cash flows of the firm and ultimately its value.10 Powerful CEOs may be in a better position to exert influence within a firm in order to advance their own interests. When this happens, both shareholders and bondholders are worse off.11 An anecdote in support of this argument is from Adelphia Communications Corporation. In 2002, Adelphia's CEO, the larger-than-life John Rigas, was forced to step down after being charged with the personal misuse of corporate funds and with hiding $2.3 billion in liabilities from investors. Furious bondholders took the cable company to court. According to this view, if power exacerbates CEOs' self-dealing behavior, stronger CEO power is likely associated with higher costs of debt. We call this hypothesis the “self-interest” hypothesis. Another argument can be made as to why powerful CEOs may increase the cost of bond financing. Powerful CEOs are less likely to have to compromise with directors or other decision makers in the firm. Such lack of compromises may lead to decisions that are more extreme, either good or bad. With less CEO power, more moderate decisions should be taken as the CEO has to compromise with other members of the top management team when they disagree with him (Adams et al., 2005). Sah and Stiglitz (1986, 1991) develop a theoretical model on group decision making that entails a “diversification of opinions” effect. The final group decision represents a compromise that reflects the different opinions of the group members. Adams et al. (2005) report that firms where the CEO is more powerful experience more variability in firm performance. According to this view, strong CEO power is associated with wider variance in future cash flows and thus exacerbates the asset substitution problem (or risk-shifting). As a consequence, bondholders demand higher debt yields from firms with more powerful CEOs. This hypothesis is labeled the “lack of opinion diversification” hypothesis. 2.5. Our research focus Grounded in agency theory, our central research question is the impact of CEO power on the cost of bond financing. Theoretical arguments and some empirical evidence suggest that CEO power may have a material impact on the cost of new bond issues. These arguments, however, do not resolve whether powerful CEOs tend to exacerbate or alleviate the agency cost of debt. If powerful 7 Unlike a typical shareholder who holds diversified portfolios, CEOs have their human capital as well as a significant portion of their wealth tied up in the firm and are therefore exposed to non-systematic (firm-specific) risk (Abdel-Khalik, 2007). 8 Risk-reducing projects may include acquiring other firms with relatively safe cash flows, diversifying into new lines of business, bringing down the amount of borrowing at the firm, etc. 9 Consistent with this view, Adams (2005) reports evidence that bondholders react negatively to CEO turnovers. 10 Consistent with this view, Bebchuk, Cremers, and Peyer (2009b) provide strong evidence that firms with powerful CEOs experience significantly lower firm value as proxied by Tobin's q. Their findings suggest that strong CEO power imposes greater agency costs on shareholders, ultimately resulting in a loss in firm value. 11 There is other anecdotal evidence consistent with this view. For example, Pinch Sulzberger at the New York Times Company is an example of a deeply entrenched CEO under whose management shareholders have lost 60% of the market value. While shareholders appear to have little or no recourse to remove Pinch Sulzberger due to a special two-class voting structure of the firm, bondholders have become much aggressive and demanded a premium for the company's debt, thereby putting pressure on Pinch Sulzberger. See ‘Pinch Sulzberger's Achilles Heel: The Bondholders’ (the American Thinker, September 17th, 2007, http:// www.freerepublic.com/focus/f-news/1898281/posts).

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CEOs attenuate the agency cost of debt, stronger CEO power should be associated with a lower cost of bond financing. On the contrary, if powerful CEOs make the agency cost of debt more severe, then, there is expected to be a positive association between CEO power and the cost of bond financing. The critical objective of this study is to explore this issue. 3. Sample formation and data description 3.1. Sample selection We obtain non-convertible bond issues by U.S. firms from the SDC New Issues database. SDC is the primary data source for a large number of papers exploring the economic determinants of debt costs (Francis et al., 2009; Ortiz-Molina, 2006; Mansi, Maxwell, Wald, 2009). We focus on new issues because argue that direct transaction prices from SDC are more accurate than matrix prices taken from secondary data.12 SDC collects information from sources including regulatory filings, news sources, company releases and prospectus. For each bond issue, SDC reports detailed information including the issue date, yield-to-maturity, proceeds and ratings. If a firm has more than one bond issue in a given year, to avoid spurious correlation, we follow Anderson et al. (2004) and Mansi et al. (2006) and construct a single observation by taking proceeds-weighted average of all the issues. We then match the bond issue data with Standard and Poor's EXECUCOMP database. The EXECUCOMP database provides detailed executive compensation data on publicly-traded S&P 500, MidCap, and SmallCap firms. To be included in the sample, we impose the following criteria: 1) The issuing company is covered by both Compustat and CRSP 2) The company is not a regulated utility or financial institution (SIC code 6000–6999, 4900–4999) 3) The issue must have non-missing Yield Spread from SDC and non-missing CPS (CEO's Pay Slice) from EXECUCOMP. Since EXECUCOMP coverage starts in 1992, our sample period for debt issues goes from 1993 to 2006.13 Our final sample consists of 1453 nonconvertible bond issues by 515 unique U.S. firms. Table 1 provides the time distribution of our final sample. 3.2. Measuring CEO power using CEO Pay Slice (CPS) Because CEO dominance is not directly observable, it is necessary to construct a variable that empirically captures CEO dominance. The measurement of power has been a major stumbling block in investigations of various phenomena in the literature (Pfeffer, 1981). One of the serious problems has been an overreliance on perceptual indicators of power and a lack of objectivity in the resulting measures (Finkelstein, 1992).14 Recognizing the potentially unreliable nature of the perceptual measures of power, several studies argue in favor of more objective power indicators (Pfeffer, 1981; Pfeffer and Moore, 1980; Salancik and Pfeffer, 1974; Provan, 1980).15 One way to capture CEO power more objectively is to examine his relative compensation among top executives (Finkelstein, 1992; Bebchuk et al., 2009a,b). Bebchuk et al. (2009b) argue that the CEO's pay slice (CPS) captures the relative significance of the CEO in terms of abilities, contribution or power. As such, CPS provides a useful proxy for the relative centrality of the CEO in the top management team. This particular measure of CEO power is especially interesting because Bebchuk et al. (2009a,b) find that CPS has strong explanatory power for a rich set of critical corporate outcomes, including firm value as measured by Tobin's q, accounting profitability, and stock market reactions to acquisition announcements. We follow their approach and define CPS (i.e., CEO's pay slice) as the CEO's total compensation as a fraction of the combined total compensation of the top-five executives (including the CEO) in a given company. Total compensation includes salary, bonus, other annual pay, long-term incentive payouts, the total value of restricted stock granted that year, the Black–Scholes value of stock options granted that year, and all other total compensation (EXECUCOMP item TDC1). We measure CPS at the fiscal year end prior to the bond issue to ensure that CPS is public information to bondholders at the time of the new issue. 3.3. CEO Pay Slice versus other indicators Previous studies have used a number of indicators of power such as the number of titles captured by the CEO and CEO duality– where one person jointly serves as CEO and chairman of the board– (Harrison et al., 1988; Davidson et. al., 2004; Finkelstein, 1992, for instance). Bebchuk et al. (2009a,b) point out that, relative to other measures of power, CPS is more advantageous for at least two reasons. First, because CPS is likely the product of many observable and unobservable dimensions of the firm's top executives and management model, it enables researchers to capture dimensions of the CEO's role in the top executive team beyond the ones captured by formal and easily observed variables such as whether the CEO also chairs the board. Second, because CPS is computed 12 We only study public straight bond offerings. Since public straight bond offerings are very different from private debt issues, our results may not be generalizable to private debt offerings. 13 This is because we measure CPS at the fiscal year end prior to the bond issue. 14 Studies using perceptual measures of power include (Pfeffer, 1981; Perrow, 1970; Hinings et al., 1974; Pfeffer and Salancik, 1974; Hambrick, 1981; and Tushman and Romanelli, 1983). 15 For instance, Pfeffer (1981) asserts that perceptual measures assume that social actors are knowledgeable about power within their organizations; informants are willing to divulge what they know about power distribution; and such a questioning process will not itself create the phenomenon under study.

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Table 1 Sample distribution by year. The final sample consists of 1453 bond issues from 1993 to 2006. This table reports the sample distribution by year. Year

# of firms

% of firms in sample (%)

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Total

72 70 130 128 139 170 131 70 118 114 104 62 58 87 1453

4.96 4.82 8.95 8.81 9.57 11.70 9.02 4.82 8.12 7.85 7.16 4.27 3.99 5.99 100.00

based on compensation information from executives who are all at the same firm, it controls for any firm-specific characteristics that affect the average level of compensation in the firm's top executive team. We believe that CPS, as a continuous measure, captures the gradation and nuances of CEO power better than the dichotomous variables used in prior literature.16 Nevertheless, for robustness, we also construct a composite measure of CEO power with dichotomous variables borrowed from prior literature (Morck et al., 1989; Adams et al., 2005). We examine whether the CEO is also the President, whether the CEO chairs the board, whether the CEO has the status of a founder, whether the CEO is the only insider on the board and whether the CEO is the only person signing the letter to shareholders in the annual report.17 3.4. Measuring the cost of bond financing We employ two alternative measures to gauge the cost of bond financing. 3.4.1. Credit ratings We follow Klock et al. (2005) and compute bond ratings using a conversion process in which AAA-rated bonds are assigned a value of 22 and D-rated bonds a value of 1. For example, a firm with an A+ rating from S&P would receive a score of 18. We focus on S&P credit ratings because Litov (2005) argues that the S&P ratings reflect the overall creditworthiness of the firm. In a few cases where the S&P ratings are missing but Moody's ratings are available, we use Moody's ratings (Bhojraj and Swaminathan, 2003).18 Again, we use the average proceeds-weighted credit ratings if the bond issuer issues more than one bond in a year. Table 2 shows our bond rating conversion. 3.4.2. Yield spread Our direct measure of the cost of bond financing is Yield Spread, defined as the difference between the at-issue yield spread of the bond and a U.S. Treasury bond with comparable maturity, measured in basis points. This measure has been widely used in the literature to capture the ex-ante cost of debt (Ortiz-Molina (2006), Bhojraj and Sengupta (2003) and Jiang (2008), Ortiz-Molina, 2006; Mansi, Maxwell, Wald, 2009). In this paper, we attempt to investigate how bondholders assess the quality of the bond issues given that they can observe the power structure of the issuing firms. Therefore, the ex-ante Yield Spread measure is more appropriate for our study.19 3.5. Summary statistics Table 3 exhibits the summary statistics. Panel A shows the descriptive statistics for firm characteristics, including sales, total assets, ROA, total leverage, long-term leverage, market-to-book ratio, the annualized standard deviation of daily stock returns in the year prior to the bond issue, capital intensity and coverage ratio. Panel B reports the characteristics of the bond issues. The average yield spread is 139.22 basis points. The average credit rating is 15.40, which corresponds to BBB+. The average bond issue has a maturity of 11.04 years (10 median). On average, the sample firms raised $831 million in proceeds from issuing bonds. Panel C shows the univariate statistics for CPS. The average CPS is 38.66%. The average CPS reported by Bebchuk et al. (2009b) is 34.31%. So, bond issuers in our sample appear to have a slightly higher average CPS than those in their sample firms. 16

In contrast to CPS, dichotomous variables classify a given CEO as either ‘powerful’ or ‘not powerful’ and nothing in between. Results using this measure are reported in Section 4.5. 18 There are only 2 cases in the sample where Moody's ratings are used. 19 It can be argued that bond prices from the Lehman Brothers Fixed Income Database may provide a more dynamic, though not ex-ante, measure of debt costs (Cremers et al., 2007). Unfortunately we do not have access to this database. 17

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Table 2 Bond rating conversion table. This table provides bond rating conversion codes for S&P ratings and Moody's ratings used in the analysis. Conversion number

S&P rating

Moody's ratings

22 21 20 19 18 17 16 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1

AAA AA+ AA AA− A+ A A− BBB+ BBB BBB− BB+ BB BB− B+ B B− CCC+ CCC CCC− CC C D

Aaa Aa1 Aa2 Aa3 A1 A2 A3 Baa1 Baa2 Baa3 Ba1 Ba2 Ba3 B1 B2 B3 Caa1 Caa2 Caa3 Ca C D

4. Empirical results The objective of our research is to ascertain the impact of CEO power on the cost of bond financing. Therefore, we conduct regression analyses where the cost of new bonds is the dependent variable. To account for other factors that affect the cost of newly issued bonds, we include a number of control variables based on prior literature (e.g. Ziebart and Reiter, 1992; Lamy and Thompson, 1988; Ashbaugh-Skaife et al., 2006). Firm size, as measured by the logarithm of total assets, is included as larger firms tend to be less risky, and thus are expected to enjoy a lower cost of bond financing (Ashbaugh-Skaife et al., 2006; Klock et al., 2005; Cremers et al., 2007). The accounting-based ratios of debt-to-assets (Totallev), return-on-assets (ROA), and interest coverage (CoverageRatio) are used to proxy for firms' default risk. We also control for differences in firms' debt structure by including Subord, which is coded one if the firm has subordinated debt (Ashbaugh-Skaife et al., 2006). The debt structure of a firm with subordinated debt is considered to be more risky due to the differential claims to assets by debt providers. In addition, we include firms' capital intensity (CAP_INTEN) to account for differences in firms' asset structure, where firms with greater capital intensity present lower risk to debt providers, and thus are expected to have a lower cost of bond financing (Ashbaugh-Skaife et al., 2006). We further control for firm risk by measuring the annualized standard deviation of stock returns in the year prior to the debt issue (Retstd) (Anderson et al., 2004). Firms with more volatile stock returns are expected to be more risky and thus bear a higher cost of bond financing. We also control for two issue-specific characteristics, namely issue size (Log Proceeds) and issue maturity (lnMaturity). Finally, we account for differences in default risk using credit ratings in the yield spread regressions. 4.1. CEO power and credit ratings One of the most important factors influencing the cost of bond financing is the firm's credit ratings. We begin by asking whether credit-rating agencies incorporate CEO power in bond ratings. We employ an ordered Probit model because the categories of credit ratings convey ordinal risk assessments.20 Given the numerical values assigned to the ratings, a negative coefficient would indicate that the variable is associated with lower credit ratings. Table 4 presents the results. In model 1, the CEO's pay slice (CPS) exhibits a negative and highly significant coefficient. The evidence in Table 4 indicates that firms where CEO power is stronger have lower credit ratings, and thus experience a higher cost of bond financing. The other control variables generally have signs consistent with prior literature. For instance, firm size is found to be significantly positively related to credit ratings. Higher debt ratios and greater stock volatility lower credit ratings. We also see

20

See Bhojraj and Sengupta (2003) for a list of papers using an ordered probit model for this purpose.

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Table 3 Descriptive statistics. This table contains descriptive statistics for the sample firms used in the study. Sales is the net sales in million dollars (Compustat data12); Assets is the total book value of assets in million dollars (data6); ROA is calculated as income before extraordinary item over total assets (data18/data6); Totallev is total book debt over total assets (data9 + data34)/data6; Ltlev is the long-term debt over total assets (data9/data6); M_B is the market value of assets divided by the book value of assets (data6 − data60 + data25 ⁎ data199)/data6. CAP_INTEN is gross PPE scaled by total assets (data7/data6). CoverageRatio is operating income before depreciation divided by interest expenses (data13/data15). All firm characteristic variables are measured at the fiscal year end prior to the bond issues. Retstd is the annualized standard deviation of daily returns over the fiscal year prior to bond issuance. Yield Spread is the number of basis points of the issue's yield spread over the comparable maturity treasury. Rating refers to the bond's raw credit rating. Maturity is the number of years bonds are outstanding. Proceeds from the bond issue are measured in million dollars. The bond characteristics variables are proceeds-weighted if a firm has multiple issues in a given year. CPS (CEO's pay slice) is the ratio of CEO total compensation to the sum of all top-five executives' total compensation. Total compensation is the ‘TDC1’ item from EXECUCOMP including salary, bonus, other annual compensation, the total value of restricted stock granted, the Black–Scholes value of stock options granted, long-term incentive payouts, and all other total incentive payouts. Mean

Standard deviation

25th percentile

Median

75th percentile

Firm characteristics Sales Assets ROA Totallev Ltlev M_B Retstd CAP_INTEN CoverageRatio

13,210.15 15,377.89 0.06 0.31 0.25 1.89 0.32 0.70 14.56

25,037.53 36,640.18 0.05 0.15 0.14 1.04 0.12 0.40 93.16

2330.29 2604.70 0.03 0.21 0.15 1.26 0.24 0.41 4.86

5624.50 5896.45 0.05 0.30 0.24 1.58 0.30 0.65 7.78

13,274.15 14,885.00 0.08 0.39 0.33 2.14 0.38 0.96 12.54

Issue characteristics Yield Spread Rating Maturity Proceeds

139.22 15.40 11.04 831.14

106.27 2.88 6.65 6132.78

69.32 14 7 198

102.56 15 10 300

172.73 17 11.52 657.7

Pay structure variables CPS

38.66%

12.45%

31.33%

38.64%

45.21%

from the log likelihood that the overall model specifications are significant. In conclusion, the Probit results based on credit ratings provide evidence that stronger CEO power is associated with a higher cost of bond financing. Finally, to ensure that our results are not driven only by companies that issue bonds, we execute a regression analysis where we include all firms on COMPUSTAT with available credit ratings and CPS. The results remain similar, suggesting that a sample selection bias is unlikely (results not shown but available upon request). 4.2. CEO power and yield spreads We now directly examine the relation between CEO power and yield spreads, measured by the at-issue bond yield in excess of the Treasury yield with comparable maturity. Our main variable of interest is the CEO's pay slice (CPS), which represents CEO power or CEO centrality. Given the evidence in Bebchuk et al. (2009a,b) that CPS tends to be persistent over time, we report heteroscedasticity-robust standard errors clustered by firm to avoid the problem of inflated t-statistics due to serial correlation. A potential problem with raw credit ratings in the yield spread regressions is that credit ratings may have already incorporated the information from some of the control variables.21 To avoid potential collinearity problems, we use an estimate of the bond credit ratings instead of the raw measures (Liu et al., 2009). Specifically, we estimate a model for credit ratings with CPS, Log Assets, Totallev, ROA, CoverageRatio, Subord, CAP_INTEN, Retstd, Log Proceeds and lnMaturity. The error term from this regression contains rating information net of the impact of these control variables. We then label the error term as the credit-rating variable in our yield spread regressions. Table 4 displays the results of the regression analysis. As shown in model 1, the coefficient of CPS is positive and highly significant at the 1% level, suggesting that stronger CEO power is associated with a higher cost of bond financing. Note that, since we control for many firm characteristics in the regression, the coefficient of CPS in the regression reflects its impact on yield spreads above and beyond the effects of other firm characteristics. This evidence is consistent with our tests on credit ratings, which also show that higher CEO power increases the cost of bond financing. As far as other control variables are concerned, their coefficients are consistent with general expectations.

21

Our credit rating results also support this conjecture.

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Table 4 Credit ratings/yield spread and CPS. This table reports the effect of CPS on credit ratings and Yield Spread. For model 1, the dependent variable is the bond's raw credit rating (Rating). For model 2, the dependent variable is the number of basis points of the issue's yield spread over the comparable maturity treasury (Yield Spread). Subord is a dummy equal to one if the firm has subordinated debt and zero otherwise. Credit Rating in model 2 is the raw numerical credit ratings orthogonalized to other control variables in the regression. All other control variables are defined the same as in Table 3. Coefficients for intercepts and industry dummies (based on 2-digit SIC codes) are omitted to conserve space. Chi-square statistics are reported in the parenthesis in model 1. t-Statistics are reported in the parenthesis in model 2 with heteroscedasticityrobust standard errors clustered by firm. Significance at the 5% and 1% levels is indicated by ** and ***, respectively.

CPS Log Assets Totallev ROA CoverageRatio Subord CAP_INTEN Retstd Log Proceeds lnMaturity Credit Rating Log likelihood R2

Model 1

Model 2

Ordered probit

OLS

Dependent variable = raw credit rating

Dependent variable = yield spread

(Chi-squares)

(t-statistic)

− 0.932 0.590 − 2.316 8.839 − 0.001 − 0.645 0.139 − 0.998 − 0.193 0.134

(15.43)*** (292.00)*** (92.09)*** (190.33)*** (4.40)** (43.31)*** (1.63) (12.49)*** (24.19)*** (6.42)**

44.23 (2.69)*** − 20.742 (− 8.40)*** 129.424 (6.39)*** − 391.408 (− 6.84)*** 0.010 (1.51) 35.418 (4.83)*** 13.292 (1.33) 381.843 (14.20)*** 12.934 (5.08)*** − 2.910 (− 0.84) − 21.824 (− 13.28)***

− 3156.36 63.60%

Overall, the evidence we document in Tables 4 and 5 shows that CEO power has a significant yield-increasing effect on the cost of bond financing. This effect is above and beyond any impact CEO power has on other aspects of the firm. Table 5 Two-stage least squares (2SLS) estimation. This table reports the simultaneous 2SLS estimation of Yield Spread and CPS. Industry Median CPS is calculated using two-digit SIC codes. The dependent variable is Yield Spread in the first model and CPS in the second model. All other control variables are defined the same as in Tables 3 and 4. Regular t-statistics are reported in the parenthesis. Significance at the 10%, 5% and 1% levels is indicated by *, ** and ***, respectively. Dependent variable

Intercept (t-statistic) Industry Median CPS (t-statistic) CEO age (t-statistic) Predicted CPS (t-statistic) Log Assets (t-statistic) Totallev (t-statistic) ROA (t-statistic) CoverageRatio (t-statistic) Subord (t-statistic) CAP_INTEN (t-statistic) Retstd (t-statistic) Log Proceeds (t-statistic) lnMaturity (t-statistic) Credit Rating (t-statistic) F-Statistics R2 Sargan's (1958) statistics P-value

First stage

Second stage

CPS

Yield

0.147 (1.85) 0.709*** (5.40) − 1.75* (− 1.85) – 0.006 (1.24) − 0.020 (− 0.54) 0.147** (2.06) − 0.000** (− 2.35) − 0.028* (− 1.87) − 0.014 (− 1.19) 0.084** (2.14) − 0.002 (− 0.31) − 0.004 (− 0.49) − 0.001 (− 0.63) 4.47*** 7.43% – –

36.617 (0.80) – – 233.618** (2.20) − 23.435*** (− 7.91) 93.704*** (4.10) − 302.161*** (− 6.39) 0.004 (0.16) 47.738*** (5.02) 11.983 (1.56) 379.524*** (15.12) 11.190*** (3.12) 0.289 (0.06) − 19.059*** (− 13.02) 73.82*** 52.99% 0.252 (0.62)

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4.3. Exploring endogeneity Thus far, the empirical evidence shows a positive association between CEO power and the cost of bond financing. We argue that stronger CEO power exacerbates managerial self-interest behavior at the expense of outside stakeholders and, as a consequence, raises the cost of issuing new bonds. However, it is conceivable that the direction of causality runs in the other direction, i.e. firms with a higher cost of bond financing choose to have CEO's with stronger power. This reverse causality, however, is less plausible, given our research design. In our empirical tests, our proxy for CEO power is based on compensation data in the year preceding the bond issue. CEO power in the earlier period could not have resulted from the cost of bond financing in the subsequent period. In any event, to alleviate concerns for endogeneity, we execute an analysis using the two-stage least squares (2SLS) estimation. This method requires instrumental variables that are related to CPS but cannot be correlated with bond yields except through CPS. We consult the literature and identify two instrumental variables. First, several recent studies employ industry-level governance as an instrumental variable (John and Knyazeva, 2006; Knyazeva, 2009; John and Kadyrzhanova, 2008). We thus select industry-median CPS as our first instrument. The logic is as follows. Due to possible reverse causality, bond yields of a given firm might influence the CPS of that particular firm. However, firm-level bond yields are unlikely related to industry-level CPS. Managers may have influence over their own firms' policies, but they should have little, if any, influence over other firms. As a result, industry-level variables are more likely to be exogenous. Second, the literature in CEO succession suggests that older CEOs closer to retirement may diminish their role as they prepare to hand over the company's rein to their successors. We thus hypothesize that CEO age is related to CPS and use CEO age as our second instrumental variable. Table 5 shows the two-stage least squares (2SLS) regression results. In the first stage, the dependent is CPS. Our first instrument, industry-median CPS, exhibits a positive and significant coefficient. As expected, industry-level CPS significantly explains firm-level CPS. The coefficient of CEO age, our second instrument, is significantly negative, consistent with the notion that older CEOs closer to retirement tend to diminish their role. The F-statistics for the first-stage regression are significant and thus reject the null hypothesis that the coefficients on the instruments are jointly zero. In the second-stage regression, we replace CPS with predicted CPS from the first-stage regression. The coefficient of predicted CPS is significantly positive, corroborating our previous findings. To ensure that our instruments are valid, we perform Sargan's (1958) test of over-identifying restrictions. The Sargan statistics are not significant. We are thus unable to reject the null hypothesis that our instrumental variables are uncorrelated with the residuals in the second-stage regression. In other words, our instruments are acceptable. The 2SLS method explicitly takes into account possible endogeneity and still produces consistent results, i.e. firms with stronger CEO power experience higher bond yields.22 There are two types of possible endogeneity: the first type caused by simultaneity in the variables, the second type brought about by omitted variables. We have mitigated the concerns for the first type of endogeneity by using simultaneous equations. The second type of endogeneity can be alleviated by employing a fixed-effects regression analysis, which controls for firm characteristics that may be omitted in the model. The results generated by the fixed-effects approach are similar, still indicating a higher cost of bond financing for firms with more powerful CEOs. 4.4. Controlling for CEO characteristics and corporate governance Ortiz-Molina (2006) argues that managerial ownership matters to bondholders because managerial incentive structures affect a firm's future risk choices. In addition to managerial ownership, Bebchuk et al. (2009a,b) also include two CEO-related variables to control for potentially unobserved heterogeneity. These CEO-related variables are CEOchair dum, which is a dummy variable equal to one if the CEO also chairs the board, and CEO only Dir Dum, which is a dummy variable equal to one if the CEO is the only insider director on the board. To investigate whether our findings so far are robust to the inclusion of CEO characteristics variables, we follow Bebchuk et al. (2009a,b) and manually construct the two CEO dummies based on information from the proxy statements. Results are reported in model 1 and 2 in Table 6. As evident, controlling for these CEO characteristics, CPS continues to exhibit a negative effect on credit ratings and positive effect on yield spreads. Klock et al. (2005) report that firms' anti-takeover provisions affect bond yields. Ashbaugh-Skaife et al. (2006) and Bhojraj and Sengupta (2003) find that institutional ownership is an important determinant of credit ratings and yield spreads. In Table 6 model 3 and 4, we include institutional ownership, measured by the percentage of outstanding shares held by all institutions, and the entrenchment index (Bebchuk et al., 2009a). We still find a significant negative (positive) relation between CPS and credit ratings (yield spreads). Overall, Table 6 presents evidence that the effect of CPS on credit ratings and yield spreads is distinct and quite robust.

22 Please note that, it is hard, if not impossible, to eliminate endogeneity completely. We do not claim that our empirical tests rule out endogeneity entirely. We simply make a modest claim that our tests improve the odds that causality runs from CEO power to the cost of bond financing.

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Table 6 Credit ratings/yield spread and CPS — controlling for CEO characteristics and anti-takeover provisions. This table reports the effects of CPS on credit ratings and yield spreads controlling for CEO characteristics and anti-takeover provisions. For model 1, 3, 5, the dependent variable is the bond's raw credit rating (Rating). For model 2, 4, 6, the dependent variable is the number of basis points of the issue's yield spread over the comparable maturity treasury (Yield Spread). CEO ownership is the percentage of outstanding shares held by the CEO. CEOchair dum is a dummy variable equal to one if the CEO is also chairs the board, zero otherwise. CEO only Dir dum is a dummy variable equal to one if the CEO is the only insider on the board, zero otherwise. Eindex is the entrenchment index based on six antitakeover provisions: staggered boards, limits to shareholder bylaw amendments, poison pills, golden parachutes and supermajority requirements for mergers and charter amendments. Institutional Ownership is measured as the number of shares held by institutions divided by the total number of shares outstanding. All control variables are defined the same as in Tables 3 and 4. Coefficients for intercepts and industry dummies (based on 2-digit SIC codes) are omitted to conserve space. Significance at the 10%, 5% and 1% levels is indicated by *, ** and ***, respectively. Model 1

CPS Log Assets Totallev ROA CoverageRatio Subord CAP_INTEN Retstd Log Proceeds lnMaturity

CEOchair dum CEO only Dir dum

Model 4

OLS

Ordered probit

OLS

Dependent variable = raw credit rating

Dependent variable = yield spread

Dependent variable = raw credit rating

Dependent variable = yield spread

(Chi-squares)

(t-statistic)

(Chi-squares)

(t-statistic)

− 1.081 (15.87)*** 0.602 (249.06)*** − 2.979 (108.46)*** 7.397 (124.36)*** − 0.001 (6.28)** − 0.579 (25.84)*** 0.105 (0.71) − 0.610 (3.93)** − 0.171 (16.12)*** 0.155 (7.07)***

− 1.010 (1.39) 0.179 (4.90)** − 0.142 (3.98)**

45.922 (2.21)** − 19.507 (− 6.59)*** 166.528 (6.39)*** − 322.415 (− 4.64)*** 0.017 (2.37)** 36.497 (4.00)*** 21.935 (1.90)* 335.324 (10.33)*** 12.992 (− 11.21)*** − 3.440 (− 0.88) − 22.924 (− 11.21)*** 65.406 (0.89) − 10.312 (− 1.81)* 14.779 (2.76)***

Eindex Institutional Ownership Log likelihood R2 N

Model 3

Ordered probit

Credit Rating CEO ownership

Model 2

− 2646.41 1100

61.55% 1100

− 0.849 (9.25)*** 0.486 (150.95)*** − 3.101 (103.58)*** 6.575 (97.37)*** − 0.001 (6.27)** − 0.715 (33.97)*** − 0.139 (1.20) − 1.533 (21.64)*** −0.037 (0.70) 0.096 (2.55)

53.579 (2.80)*** − 12.160 (− 3.71)*** 152.229 (6.05)*** − 221.351 (− 3.52)*** 0.019 (2.77)*** 35.999 (3.68)*** 22.473 (1.97)** 365.403 (10.38)*** 4.742 (1.52) 2.371 (0.60) − 20.579 (− 8.70)***

0.038 (1.98) − 2.094 (72.64)*** − 2385.32

− 2.485 (− 1.06) 106.398 (5.35)***

1006

59.71% 1006

4.5. Bid–ask spreads and CPS Our findings suggest that firms with dominant CEOs tend to have lower credit ratings and incur higher debt costs. Our findings are consistent with both the “self-interest” hypothesis and the “lack of opinion diversification” hypothesis. In this section, we attempt to shed some light on which effect drives our results. Is it managerial self-dealing behavior, which stems from the agency conflict, or the lack of opinion diversification, which does not necessarily imply managerial opportunistic actions? We address this question by studying firms' bid–ask spreads, a measure of firm information opaqueness.23 Numerous studies have documented that self-interested managers may desire an opaque information environment in an attempt to

23 In addition, we explore earnings quality as measured by discretionary current accruals (DCA) from the Jones model. Firms with high DCA arguably experience more information asymmetry. Unreported, we find that firms in the top tertile based on CPS exhibit higher DCA than those in the bottom tertile, consistent with the view that firms with more powerful CEOs are less transparent.

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Table 7 Bid–ask spreads and CEO power. This table reports the OLS regression results of bid–ask spread on CEO power. E_P is earnings per share. Loss is a dummy equal to one if E_P is negative and zero otherwise. M_B is the market value of assets over book value of assets. Retstd is the annualized standard deviation of daily returns over the fiscal year prior to bond issuance. VOL is equal to trading volume divided by shares outstanding, both measured at the fiscal year end. PRC is the trading price measured at the fiscal year end. The dependent variable in model 1 is the average bid–ask spread expressed relative to the mid-point of the bid–ask prices. The dependent variable in model 2 is the average bid–ask spread expressed relative to the trading price. Coefficients for intercepts and industry dummies (based on 2-digit SIC codes) are omitted to conserve space. t-Statistics are based standard errors clustered by firm. Significance at the 10%, 5% and 1% levels is indicated by *, ** and ***, respectively.

CPS (t-statistic) Log Assets (*104) (t-statistic) E_P(*105) (t-statistic) Loss(*104) (t-statistic) M_B(*104) (t-statistic) Retstd (t-statistic) VOL(*104) (t-statistic) PRC(*105) (t-statistic) R2

Model 1

Model 2

Dependent variable: |ask–bid|/midpoint

Dependent variable: |ask–bid|/price

0.001 (2.11)** 5.36 (6.05)*** 1.28 (0.46) − 2.61 (− 0.80) 1.07 (1.12) 1.17 (70.80)*** 1.22 (4.59)*** − 0.76 (− 2.27)** 91.97%

0.001 (2.10)** 5.38 (6.06)*** 1.32 (0.47) − 2.78 (− 0.85) 1.00 (1.05) 1.17 (70.08)*** 1.22 (4.59)*** − 0.75 (− 2.26)** 92.00%

avoid monitoring (Demsetz and Lehn, 1985), to maintain control (Zhao et al., 2002), to profit from their private information (Aboody and Lev, 2000), and to increase their bargaining power within the firm (Stoughton and Talmor, 1999). If bondholders perceive that powerful CEOs are likely to withhold information as in the case of Adelphia Communications, which makes it harder for bondholders to assess firm value and verify corporate actions, bondholders may charge a higher rate to protect their investments. A positive association between bid–ask spreads and CPS would be consistent with the “self-interest” hypothesis. In our tests, we directly control for firm risk (Retstd). This is important because the “lack of opinion diversification” hypothesis implies that firm risk is higher when CEOs are dominant and less likely to compromise with other executives. By directly controlling for firm risk, we hold the risk effect or the “lack of opinion diversification” effect constant. Thus, if we continue to find a positive association between bid–ask spreads and CPS, this would suggest that the higher yield spreads bondholders demand are attributable, at least in part, to the more opaque information environment that powerful CEOs attempt to maintain. We use two measures of bid–ask spreads: the average bid–ask spread normalized by the mid-point of the bid and ask prices, and one normalized by price. Kyle (1985) demonstrates that bid–ask spreads will increase as firm transparency declines. Table 7 contains the results.24 Consistent with our conjecture, CPS is found to have a significant positive coefficient in both model 1 and 2. This evidence suggests that firms with dominant CEOs are more likely to have greater bid–ask spreads, i.e. less transparency. Thus the opaque information environment in CEO dominating firms provides one explanation as to why powerful CEOs are perceived negatively by bondholders. Our evidence on bid–ask spreads provides support for the CEO self-interest hypothesis. Consistent with this notion, Bebchuk et al. (2009a,b) document that stronger CEO power is associated with several agency-related instances such as the tendency to expand the firm beyond its optimal size through unnecessary acquisitions, higher probability for opportunistic timing of option grants, and greater likelihood for the CEO to be reward for luck as a result of an industry-wide shock. Our empirical evidence, when viewed in conjunction with the findings in Bebchuk et al. (2009a,b), seems to strongly suggest that the CEO is more likely to act in self-interest when given more power. 4.6. Controlling for information asymmetry In the preceding section, we show that firms with more powerful CEOs experience more information asymmetry, as measured by wider bid–ask spreads. It could be argued that information asymmetry should be directly controlled for in

24

Our control variables come from Garfinkel and Nimalendran (2003) and Flannery et al. (2004).

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the regression analysis. Information asymmetry may affect the ease with which bondholders can monitor management and should be reflected in credit ratings and yield spreads. As a result, we execute a regression analysis where we include bid–ask spreads as a control variable. The results show that CPS remains significant both in the credit rating and yield spread regressions. Thus, the inclusion of information asymmetry as a control variable does not change our conclusion. Incidentally, the regression results demonstrate that firms with wider bid–ask spreads experience lower credit ratings and higher yield spreads, consistent with general expectations. Bondholders demand higher yields from more opaque firms as it is harder to monitor them. This evidence is in agreement with the findings of several recent studies such as Moreman (2009) and Qi et al. (2010). 4.7. Alternative CEO power measure Our analysis has proceeded so far with the CEO dominance measure developed by Bebchuk et al. (2009b). An advantage of CPS is that it is an objective proxy based on compensation data readily available to investors. As such it offers investors a window into the workings of a firm's top management team. But due to the unobservable nature of CEO power, CPS is not a perfect proxy. One main concern about CPS is that the pay distribution among executives may also capture the tournament incentives for lower ranking executives. This interpretation, however, is not very plausible in our framework as it is unclear how the tournament effect would matter to bondholders. Nevertheless, in this section, we construct an alternative noncompensation based measures for CEO power and examine whether our previous findings on CEO power and credit ratings/ yield spreads continue to hold. Two important earlier studies have used different measures for the same concept. Morck et al. (1989) define a CEO as powerful if no other person holds the title of President or Chairman, and if the CEO is the only person who signs the letter to shareholders in the annual report. In a more recent paper, Adams et al. (2005) assume that a CEO is more powerful if he serves as chair of the board, if he is the only insider on the board and if he has the status of a founder. To ensure that our results are robust to these

Table 8 Results based on CEO Power Score. This table reports regression results based on an alternative CEO power measure on credit ratings and Yield Spread. To capture the various aspects of CEO power, we construct a comprehensive CEO Power Score based on the following variables. Chairmandummy is 1 if the CEO also serves as chair of the board, zero otherwise. Presidentdummy is 1 if the CEO also holds the title of President, zero otherwise. Founder is 1 if the CEO has the status of a founder, zero otherwise. Insider is 1 if the CEO is the only insider on the board, zero otherwise. Signer is 1 if the CEO is the only person who signs the letter to shareholders in the annual report, zero otherwise. CEO Power Score is then calculated as the sum of these dummy variables. All other control variables are defined the same as in Tables 3 and 4. For model 1, the dependent variable is the bond's raw credit rating (Rating). For model 2, the dependent variable is the number of basis points of the issue's yield spread over the comparable maturity treasury (Yield Spread). Coefficients for intercepts and industry dummies (based on 2-digit SIC codes) are omitted to conserve space. t-Statistics are based on heteroscedasticity-robust standard errors clustered by firm. Significance at the 10%, 5% and 1% levels is indicated by *, ** and ***, respectively.

CEO Power Score Log Assets Totallev ROA CoverageRatio Subord CAP_INTEN Retstd Log Proceeds lnMaturity

Model 1

Model 2

Ordered probit

OLS

Dependent variable = raw credit rating

Dependent variable = yield

(Chi-squares)

(t-statistic)

− 0.060 (3.86)** 0.637 (277.99)*** − 2.639 (90.16)*** 8.770 (156.57)*** − 0.001 (4.42)** − 0.657 (37.96)*** 0.165 (1.90) − 0.932 (9.36)*** − 0.210 (24.60)*** 0.164 (8.09)***

Credit Rating Log likelihood R2

4.794 (2.20)** − 22.139 (8.04)*** 148.843 (6.44)*** − 397.685 (− 6.04)*** 0.010 (1.48) 34.386 (4.14)*** 15.327 (1.41) 373.692 (12.63)*** 14.423 (5.22)*** − 4.113 (− 1.09) − 22.091 (− 11.87)***

− 2732.86 62.53%

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alternative measures, we hand collect information from proxy statements. We then construct a comprehensive CEO Power Score by adding 1 to the Score when each of the following criteria is met: the CEO is chair of the board; the CEO is the President; the CEO has the status of a founder; the CEO is the only insider on board; and CEO is the only person who signs the letter to shareholders in the annual report. The results based on the CEO Power Score are reported in Table 8. Consistent with our previous findings, the CEO Power Score has a negative impact on credit ratings and positive effect on Yield Spread. As such, our results are not sensitive to different measures of CEO power. 5. Concluding remarks In light of recent research on how manager-level characteristics affect corporate decisions, we focus on an important dimension of the top management team-CEO power or CEO dominance, and study how it affects the cost of bond financing. The empirical evidence reveals a positive association between CEO power and the cost of newly issued bonds. Furthermore, we explore a mechanism behind the association between CEO power and the cost of bond financing. The evidence reveals that firms with more powerful CEOs tend to have a more opaque information environment. With less transparency, it is more difficult for bondholders to monitor managers. As a result, bondholders expect higher yields. Our findings are consistent with the notion that bondholders perceive powerful CEOs as detrimental to their investments and consequently demand higher yields from firms with powerful CEOs. Appendix A. Variable definition

Variable name

Definition

CPS

The ratio of CEO total compensation to the sum of all top-five executives' total compensation. tdc1/sum of tdc1 for top 5 executives Constructed by adding 1 if one the following criteria is met: the CEO serves as chair of the board; the CEO also holds the title of President; the CEO has the status of a founder; the CEO is the only insider on the board; or the CEO is the only person who signs the letter to shareholders in the annual report. log of the total book value of assets data6 Income before extraordinary item over total assets data18/data6 Total book debt over total assets (data9 + data34)/data6 Gross PPE scaled by total assets data7/data6 Operating income before depreciation divided by interest expenses data13/data15

CEO Power Score

Log Assets ROA Totallev CAP_INTEN Coverage ratio Retstd Subord Yield Spread

Rating Credit rating

Log Proceeds lnMaturity CEO age Industry Median CPS CEO ownership CEOchair dum CEO only Dir dum Eindex

Institutional ownership

Compustat/EXECUCOMP items

Annualized standard deviation of daily returns over the fiscal year prior to bond issuance Dummy equal to one if the firm has subordinated debt and zero otherwise. data80 The number of basis points of the issue's yield spread over the comparable maturity treasury. If Spread_to_benchmark a firm issues multiple bonds in a year, this variable is calculated as the proceeds-weighted yield spread. Raw credit ratings converted to numerical values using a conversion process in which AAA- Standard_and_Poor's Rating rated bonds are assigned a value of 22 and D-rated bonds a value of 1. Residuals from regressing raw numerical credit ratings on CPS, Log Assets, Totallev, ROA, CoverageRatio, Subord, CAP_INTEN, Retstd, Log Proceeds and lnMaturity.

Data sources EXECUCOMP Proxy statements

Compustat Compustat Compustat Compustat Compustat CRSP Compustat SDC

SDC

SDC, Compustat, CRSP log of total issue proceeds. If a firm has more than one bond issue, this variable is the log of the Proceeds_amt_in_this_market SDC sum of all proceeds. log of issue maturity. If a firm has more than one bond issue, this variable is the log of the Maturity year-issue year SDC proceeds-weighted issue maturity. Age of the CEO EXECUCOMP Median CPS of all firms in the same industry (with the same 2-digit SIC codes) The percentage of outstanding shares held by the CEO A dummy variable equal to one if the CEO is also chairs the board, zero otherwise. A dummy variable equal to one if the CEO is the only insider on the board, zero otherwise. The entrenchment index based on six anti-takeover provisions: staggered boards, limits to shareholder bylaw amendments, poison pills, golden parachutes and supermajority requirements for mergers and charter amendments. The number of shares held by institutions divided by the total number of shares outstanding.

SHROWN_EXCL_OPTS/ (1000 ⁎ shrsout)

EXECUCOMP Proxy Statements Proxy Statements IRRC

Proxy Statements

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Appendix B. Compustat credit ratings and CPS This table reports the ordered Probit regression results of Compustat credit ratings on CPS. The sample includes all EXECUCOMP firms from 1992 to 2006 satisfying the following criteria: 1) The company is covered by both Compustat and CRSP; 2) The company is not a regulated utility or financial institution (SIC code 6000–6999, 4900–4999); 3) the firm must have non-missing CPS from EXECUCOMP and non-missing credit ratings from Compustat. Because Compustat assigns higher values to lower ratings, to facilitate interpretation, we use the inverse ratings as the dependent variable. The inverse ratings are equal to 27 (the maximal rating value in our sample) minus the Compustat credit ratings. The higher the inverse rating values now correspond to better credit ratings. Coefficients for intercepts and industry dummies (based on 2-digit SIC codes) are omitted to conserve space. Chisquare statistics are reported in the parenthesis. Significance at the 5% and 1% levels is indicated by ** and ***, respectively.

Model 1 CPS (Chi-square) Log Assets (Chi-square) Totallev (Chi-square) ROA (Chi-square) CoverageRatio (Chi-square) Subord (Chi-square) CAP_INTEN (Chi-square) Retstd (Chi-square) Log likelihood N

− 0.204 (4.72)** 0.384 (1615.60)*** − 1.156 (197.10)*** 2.812 (300.87)*** − 0.000 (0.45) − 0.346 (91.83)*** 0.149 (11.21)*** − 0.888 (173.47)*** − 18,371.32 8120

Appendix C. Yield spreads and CPS — firm fixed effects This table reports the OLS regression results of Yield Spread on CPS controlling for firm fix effects. The dependent variable is the number of basis points of the issue's yield spread over the comparable maturity Treasury (Yield Spread). Coefficients for firm dummies are omitted to conserve space. t-Statistics from regular OLS estimations are reported in the parenthesis. Significance at the 1% level is indicated by ***, respectively.

Model 1 CPS (t-statistic) Log Assets (t-statistic) Totallev (t-statistic) ROA (t-statistic) CoverageRatio (t-statistic) Subord (t-statistic) CAP_INTEN (t-statistic) Retstd (t-statistic) Log Proceeds (t-statistic) lnMaturity (t-statistic) Credit Rating (t-statistic) R2 N

49.436 (2.85)*** − 5.879 (− 1.24) 151.59 (5.74)*** − 385.79 (− 6.77)*** − 0.042 (− 0.35) 35.335 (3.70)*** 10.707 (0.59) 389.50 (18.31)*** 13.936 (5.38)*** 1.018 (0.29) − 20.723 (− 13.53)*** 85.30% 1311

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759

Appendix D. Yield spreads and CPS — controlling for types of institutional investors This table reports the effects of CPS on credit ratings and yield spreads controlling for types of institutional ownership. INST_TRA is the equity ownership by transient institutions. INST_QIX is the holdings by quasi-indexer institutions. INST_DED is the holdings by dedicated institutions. Classification of transient, quasi-indexer and dedicated institutions follows Bushee (1998). Bank Holding is the equity ownership by banks. Insurance Holding is the equity ownership by insurance companies. Investment Firm Holding is the equity ownership by investment companies and their managers. Independent Advisor Holding is the equity ownership by independent investment advisors. Block is a dummy variable equal to one if there is an institutional block holder (with at least 5% ownership) and zero otherwise. All control variables are defined the same as in Table 3. For model 1, the dependent variable is the bond's raw credit rating (Rating). For model 2, the dependent variable is the number of basis points of the issue's yield spread over the comparable maturity treasury (Yield Spread). Coefficients for intercepts and industry dummies (based on 2-digit SIC codes) are omitted to conserve space. t-Statistics are based on heteroscedasticity-robust standard errors clustered by firm. Significance at the 10%, 5% and 1% levels is indicated by *, ** and ***, respectively.

CPS INST_TRA INST_QIX INST_DED

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Ordered probit

OLS

Ordered probit

OLS

Ordered probit

OLS

Dependent variable = raw credit rating

Dependent variable = yield

Dependent variable = raw credit rating

Dependent variable = yield

Dependent variable = raw credit rating

Dependent variable = yield

(Chi-squares)

(t-statistic)

(Chi-squares)

(t-statistic)

(Chi-squares)

(t-statistic)

− 0.818 (9.30)***

39.506 (2.16)**

− 0.898 (14.60)***

44.823 (2.70)***

− 0.150 (6.07)** 0.538 (259.13)*** − 2.461 (108.17)*** 7.420 (153.39)*** − 0.001 (5.55)** − 0.624 (40.84)*** 0.144 (1.77) − 0.730 (6.85)*** − 0.166 (18.52)*** 0.115 (4.84)**

16.401 (3.75)*** − 17.196 (− 6.50)*** 136.023 (6.49)*** − 302.795 (− 4.97)*** 0.018 (2.46)** 37.208 (5.01)*** 15.138 (1.50) 357.082 (12.15)*** 11.533 (4.24)*** − 2.065 (− 0.59) − 22.328 (− 12.71)***

− 0.779 (8.61)*** − 2285 (45.85)*** − 1.068 (12.13)*** − 1.445 (13.82)***

42.371 (2.23)** 116.798 (4.14)*** 36.212 (1.80)* 20.669 (0.68)

Bank Holding

5.717 (4.52)*** 0.136 (0.07) 0.189 (0.18) − 1.110 (− 1.30)

Insurance Holding Investment Firm Holding Independent Advisor Holding

− 253.318 (− 5.50)*** − 114.772 (− 1.43) − 187.663 (− 5.60)*** − 49.278 (− 1.68)*

Block Log Assets Totallev ROA CoverageRatio Subord CAP_INTEN Retstd Log Proceeds lnMaturity

0.525 (187.51)*** − 2.381 (79.08)*** 7.403 (127.96)*** − 0.001 (5.29)** − 0.781 (49.47)*** − 0.017 (0.02) − 0.761 (5.50)** − 0.124 (8.42)*** 0.131 (4.81)**

Credit Rating Log likelihood R2 N

− 16.864 (− 5.24)*** 122.344 (5.71)*** − 316.319 (− 4.42)*** 0.015 (2.11)** 38.206 (4.41)*** 16.512 (1.46) 324.031 (9.46)*** 9.445 (3.00)*** − 1.597 (− 0.40) − 21.523 (− 10.67)***

− 2568.86 1090

0.512 (160.28)*** − 2.537 (83.85)*** 7.957 (118.73)*** − 0.006 (8.94)*** − 0.751 (45.03)*** − 0.021 (0.03) − 0.694 (4.29)** − 0.137 (9.09) 0.147 (5.11)**

− 19.400 (− 5.73)*** 138.198 (5.57)*** − 375.198 (5.57)*** 0.312 (2.00)** 39.501 (4.84)*** 21.039 (1.93)* 284.974 (8.24)*** 5.966 (1.84)* 4.393 (1.10) − 21.441 (− 10.59)***

− 2490.56 62.29% 1090

1048

− 3229.24 66.15% 1048

1330

63.11% 1330

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Appendix E. Bond maturity, issue size and CPS This table reports the mean values of log issue proceeds and log maturity by quartiles of CPS.

CPS quartile 1 CPS quartile 2 CPS quartile 3 CPS quartile 4

lnMaturity

Log Proceeds

2.226 (N = 356) 2.301 (N = 350) 2.288 (N = 353) 2.154 (N = 352)

5.905 (N = 363) 5.741 (N = 363) 5.855 (N = 364) 6.004 (N = 363)

Appendix F. Mean/median values of CPS for bond issuers versus non-issuers This table reports the mean/median values of CPS for bond issuers in our sample versus other firms from EXECUCOMP with valid CPS but do not issue any bonds.

Mean CPS Median CPS

Bond issuers (N = 1453)

Non-issuers (N = 20,591)

0.38 0.38

0.37 0.36

Appendix G. Current discretionary accruals and CPS

Current discretionary accruals from the Jones model

Bottom tercile of CPS (N = 480)

Top tercile of CPS (N = 475)

0.48%

0.72%

Note: High CPS is associated with greater earnings management.

Appendix H. Credit ratings/yield spread and CPS —controlling for bid–ask spreads This table reports the effect of CPS and Yield Spread controlling for bid–ask spreads. For model 1, the dependent variable is the bond's raw credit rating (Rating). The dependent variable is the number of basis points of the issue's yield spread over the comparable maturity treasury (Yield Spread). Subord is a dummy equal to one if the firm has subordinated debt and zero otherwise. Credit Rating is the raw numerical credit ratings orthogonalized to other control variables in the regression. Bid–Ask is the average bid–ask spread expressed relative to the mid-point of the bid–ask prices. All other control variables are defined the same as in Table 3. Coefficients for intercepts and industry dummies (based on 2-digit SIC codes) are omitted to conserve space. Chi-square statistics are reported in the parenthesis in model 1. t-Statistics are reported in the parenthesis in model 2 with heteroscedasticity-robust standard errors clustered by firm. Significance at the 5% and 1% levels is indicated by ** and ***, respectively.

CPS Log Assets Totallev ROA

Model 1

Model 2

Ordered probit model

OLS

Dependent variable = raw credit rating

Dependent variable = yield spread

(Chi-squares)

(t-statistic)

− 0.900 (14.35)*** 0.596 (296.13)*** − 2.338 (92.80)*** 8.810 (188.74)***

38.867 (2.34)** − 21.461 (− 8.55)*** 131.793 (6.29)*** − 388.214 (− 6.31)*** (continued on next page)

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Appendix (continued) H (continued)

CoverageRatio Subord CAP_INTEN Retstd Log Proceeds lnMaturity

Model 1

Model 2

Ordered probit model

OLS

Dependent variable = raw credit rating

Dependent variable = yield spread

(Chi-squares)

(t-statistic)

− 0.001 (4.05)** − 0.661 (45.29)*** 0.139 (1.61) 0.563 (0.97) − 0.178 (20.07)*** 0.124 (5.48)**

Credit Rating Bid–Ask Log likelihood R2

− 22.572 (10.00)*** − 3141.43

0.007 (0.94) 36.024 (5.04)*** 14.187 (1.41) 148.152 (3.20)*** 11.034 (4.28)*** − 1.256 (− 0.36) − 21.373 (− 12.55)*** 3316.403 (5.22)*** 64.36%

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