Inside The Black Box: The Role And Composition Of

Inside the Black Box: The Role and Composition of Compensation Peer Groups*

Michael Faulkender † Olin School of Business Washington University in St. Louis

Jun Yang ‡ Kelley School of Business Indiana University

*

We would like to thank Richard Mahoney (retired CEO from Monsanto Co.) and seminar participants at Washington University in St. Louis. We also thank Cassandra Marshall and Raj Mistry for their research assistance. All errors are ours. † Email: [email protected] ‡ Email: [email protected]

Inside the Black Box: The Role and Composition of Compensation Peer Groups

Abstract This paper documents the features of compensation peer groups and demonstrates that they play a significant role in determining CEO compensation. Anecdotally, we know that compensation peer groups have had a growing role in determining executive compensation but only recently have firms begun voluntarily disclosing the members of these peer groups. To empirically test their role, we hand-collect a sample of 83 of the S&P 500 firms that provided explicit lists of compensation peer firms in their fiscal 2005 disclosures. Results show that inclusion of the group’s median compensation more than triples the portion of the variation in CEO cash compensation that can be explained, dominating measures such as size and firm performance. The average peer group has more than eleven firms in it with just over half of them coming from the same 3-digit SIC as the firm. Univariate analysis suggests that firms forego lower paid potential peers in their same industry in favor of higher paid peers outside of their industry when constructing the peer groups. In multivariate regression analysis, this result carries through as we find that even after controlling for industry and relative size, peer group composition is significantly affected by the level of compensation of the potential peers. Firms appear to select high paid peers as a mechanism to increase CEO compensation and this effect is strongest in firms with low GIM index values, low E-scores, and low blockholder ownership. We conclude that in firms with weak internal governance, CEOs are most able to create benchmarks (compensation peer group compositions) that help generate higher compensation for themselves. Given that disclosure of peer group composition had until recently been voluntary, our results are likely to underestimate the extent to which peers are selected by characteristics seemingly unrelated to managerial performance or their reservation wage.

1. Introduction Recent growth in CEO compensation, especially the dramatic increases for top paid CEOs, have led many to question whether CEOs have too much influence over their own compensation. The pay package of $187 million for former New York Stock Exchange Chairman Richard Grasso, and the $210 million golden parachute for the ousted, and arguably mediocre performing, former Home Depot CEO generated notable press coverage and have led us to wonder who sets CEO pay. The academic literature on this issue is exploding, but has not reached a consensus. Many view the pay increases as a sign of CEOs' abuse of power, 1 but others argue that the compensation simply reflects market equilibrium where the board optimally sets up CEO pay. 2 Theoretically, the pay setting process is quite transparent in the US. For a publicly traded company, the initial pay recommendations typically come from the company’s human resources department, often working in conjunction with outside compensation consultants. If accepted by the compensation committee, the recommendations are then passed to the full board of directors (BOD) for approval. This process seems at least to provide the management with opportunities to influence CEO pay via the initial recommendations. 3 To address this potential influence, the NYSE instituted a new rule that took effect in 2003 that bans CEOs of NYSE-listed firms from sitting on compensation committees as well as from choosing external consultants. However, even with this new rule in place and independent directors who “approach their jobs with 1

See, for example, Bebchuk and Fried (2003, 2004), Bertrand and Mullanaithan (2001). See, for example, Murphy and Zabojnik (2004), Oyer (2004), Baranchuk, MacDonald and Yang (2006), and Gabaix and Landier (2006). 3 The empirical evidence on whether CEOs influence their pay setting is somewhat mixed. Focusing on the role of compensation committees, O’Reilly, Main, and Crystal (1988), Main, O’Reilly, and Wade (1995), and Newman and Mozes (1997) suggest the existence of the influence; while Anderson and Bizjak (2003) suggest the opposite. 2

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diligence, intelligence, and integrity”, we are still likely to observe board actions that tend to favor the CEO given a range of market data on competitive pay levels (see Murphy 1999). To further enable compensation transparency, the SEC has issued a new requirement that comes into effect for fiscal years ending on or after December 31, 2006: “The Compensation Committee report should disclose the nature of the group with which the Committee is comparing the registrant's compensation (e.g., Fortune 100 companies), the extent to which it differs from the peer group and where in the range established by that comparison the issuer targets its compensation (e.g., high, median, or low end of the range). Where different competitive standards are used for different components of the pay package, that should be made clear.” In this study, we seek to further our understanding of executive compensation by examining the role and composition of these compensation peer groups. Using handcollected data from SEC filings of the list of these peer groups for S&P 500 firms in 2005, we begin by documenting the extent to which the level of compensation of the peer group members does indeed explain observed compensation amounts. 4 When added to our traditional measures of compensation, how much incremental explanatory power does peer group compensation have? The second goal is to document the characteristics of the peer group. We would expect that firms in the same industry and of similar size would be obvious peer group members and this paper examines whether indeed that is the case. Or, are there factors aside from relative size and industrial focus that explain peer group membership, and therefore influence overall managerial compensation?

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Some firms began voluntarily disclosing their compensation peer groups as early as 2004 but not until 2005 were there a sufficient number of them reporting such that an empirical examination of these groups can be conducted. Because such disclosure is voluntary, we will necessarily need to address potential sample selection issues. We elaborate in the Data Description section. 2

We find that the median and 75th percentile of compensation for the peer group do generate significant incremental explanatory power in understanding cross-sectional variation in observed CEO pay. The inclusion of this measure in our regression of total cash compensation (salary and bonus payment) eliminates the statistical significance of the estimated coefficient on our measure of firm size and the adjusted R-squared more than triples after adding these variables to the specification. Examining the composition of these peer groups in light of this economically significant role that these peer groups play, we see that firms in the same industry and generally larger in size are more likely to be included in the compensation peer group. However, we also find that even after controlling for industry and relative size, the level of compensation of the potential peer is also statistically significant, suggesting that factors seemingly unrelated to the CEO’s reservation wage appear to also affect peer group composition. 5

In other words,

compensation committees seem to be endorsing compensation peer groups that include unrelated firms because such firms would potentially ratchet up the level of pay for the CEOs. This result complements the results of Bizjak, Lemmon and Naveen (2007) that CEOs whose pay is below the median pay level of their counterparts in firms of similar size and industry receive raises that are larger in both percentage and dollar terms. Moreover, we examine whether corporate governance affects the make-up of the compensation peer group. There is an extensive literature on measures of corporate governance (notably Gompers, Ishii, and Metrick (2003) and Bebchuk, Cohen, and Ferell (2004)) and the influence of governance on firm valuation and stock performance. However, people tend to use the GIM-index and E-index in a more general context as

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Possibly to justify selection of such firms as members of the compensation peer group, firms often state that they choose firms with which they “compete for managerial talents.” 3

proxies for corporate governance. We find that as the external governance worsens (when the GIM-index and E-index get higher), firms tend to select compensation peers with lower pay, after controlling for the effects of industry and size. This is in line with the notion that a weaker market for corporate control may be associated with stronger boards, and it is the compensation committee of the board which ultimately sets CEO pay. 6 Using large block holdings in place of these measures of external governance, we find that in the presence of greater incentives to monitor, resulting from larger block holdings, there is less sensitivity of peer group formation to the compensation of the CEOs at the potential peer firms. The rest of the paper is organized as follows. Section 2 details the empirical strategy that we will follow in exploring the role of compensation peer groups. The data that we use is outlined in Section 3. The role of compensation peer groups in explaining observed CEO pay is covered in Section 4 while the factors determining the composition of these peer groups are discussed in Section 5. Section 6 concludes. 2. Empirical Strategy We proceed in three steps. The first is to provide basic statistics on peer group composition and to analyze the potential self-selection bias inherent in the fact that compensation peer group disclosure was voluntary in 2005. Because only 83 of the 498 firms in the S&P 500 disclose their compensation peer group, how representative are the firms that disclose the compensation peer group relative to those that choose not to? 6

Cyert, Kang, and Kumar (2002) examine, both theoretically and empirically, the strategic role of the BOD in setting up CEO pay and the impact of potential takeovers. In equilibrium, internal governance by the BOD and the external takeover threat act as substitutes in constraining the management's profligacy of awarding equity-based compensation to itself. In a more recent empirical study, Gillan, Hartzell, and Starks (2007) present evidence that internal corporate governance (via board monitoring) and external corporate governance (via takeovers) appear to serve as substitutes in disciplining managers.

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Recognize though that because disclosure is voluntary, we would expect that firms that select compensation peer groups that would be more difficult to explain to shareholders would be the firms less likely to disclose the members of the peer group. As a result, we likely underestimate the extent to which factors seemingly unrelated to CEO performance actually impact CEO compensation. The second step is to examine the incremental power generated from including peer compensation in a regression of CEO compensation on firm characteristics previously documented in the literature as explaining observed compensation. We begin by running a baseline specification on the 83 CEOs’ compensation for which we have the names of the compensation peer group. Specifically, we follow Bizjak, Lemmon and Naveen (2007) and run the following regression: CEO Compensationt = α + β1*log(salest) + β2*ROAt + β3*ROAt-1 + β4*StockRett + β5*StockRett-1 + β6*Volatility + ε where our measures of compensation will separately be total compensation (TDC1) and total cash compensation (TCC). As a measure of the size of the firm, we take the natural log of the firm’s sales (COMPUSTAT Item #12) in the corresponding fiscal year and ROA is defined as EBIT (item #13) divided by book assets (item #6). Also included is the performance of the firm’s stock over the fiscal year (ExecuComp item TRS1YR) and the volatility of the firm’s stock over the previous 60 months (ExecuComp item BS_VOLAT). Once we have the initial values estimated, we then add a variable containing either the median or 75th percentile compensation of the peer group in the previous fiscal year (consistent with practice and to ensure availability at the time compensation is

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determined) into the specification. Our interest in the results extends beyond merely interpreting the coefficient, but also to looking at the increase in R-squared that results from this measure’s inclusion as well as the impact it has on the other coefficients in the regression. Given the large impact the peer compensation has, and therefore recognizing that the important economic question is the composition of the compensation peer group, our focus turns to an examination of the determinants of membership in that group. What are the factors that determine whether or not a firm is included in the compensation peer group? To conduct such an examination, one has to not only have the list of firms selected for peer group membership but also those not chosen. While there are more than 5000 firms listed on COMPUSTAT in 2005 that are arguably potential peers, we limit ourselves to the firms in the S&P 500 during 2005, as these are the potential firms that are of similar size and visibility, as well as the fact that we have compensation data for this subset of potential peer group members. We then run a probit regression of whether the potential peer is indeed included in the corresponding firm’s compensation peer group on a baseline set of controls that have been previously documented to explain cross-sectional variation in compensation. Specifically, we include whether the potential peer is in the same industry as the underlying firm and the relative size differences between the potential peer and the firm. 7 We then add variables that are unlikely related to the extent to which the potential firms are particularly comparable but that are related to potential malfeasance. If the firm wanted to raise the CEO’s compensation but still justify that the CEO is making the

7

In estimating the standard errors, we follow Petersen (2006) and cluster them at the firm level, arguing that errors in estimating peer group inclusion are likely to be correlated for a particular firm. 6

median of his peers, the solution is to select peers that have relatively high compensation themselves. Therefore, we add a variable capturing the potential peer firm’s previous year compensation. If the coefficient corresponding to such a variable were found to be significantly positive, it would suggest that even after controlling for size and industry, some peers are chosen because they would raise the median for the group, justifying higher CEO pay. Finally, we interact this sensitivity to potential peer pay with measures of governance and CEO power to see whether or not such peer group selection is more acute in exactly the firms where the CEO is more likely able to successfully extract rents for himself.

3. Data Description Our primary dataset was generated by hand-collecting the names of the compensation peer groups for the members of the S&P 500 in 2005 off of their SEC DEF-14A filings that are available on EDGAR. Of those 498 firms (Fannie Mae does not have an available CIK number and Apollo Group Inc. does not provide a DEF-14A statement), 76 of them gave a detailed list of the firms that are in the compensation peer group. For example, Dynegy Inc. stated: “We believe that these surveys, together with our independent compensation consultant's analysis of the proxy data for our peer companies, provide a comprehensive compensation competitiveness evaluation. Our peer group for the fiscal year ended December 31, 2005, which we refer to as the ‘2005 Peer Group,’ comprises AES Corporation; Calpine Corporation; Duke Energy Corporation; El Paso Corporation; NRG Energy, Inc.; and Reliant Energy, Inc.” Another 7 stated that their peer group was comprised of exactly the firms that made up a particular index. For example, Quest Diagnostics, Inc. stated:

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“In 2005, the Committee evaluated the competitiveness of senior management's total compensation relative to the pay of executives at a peer group comprising of the Standard & Poors' 500 Healthcare Equipment & Services Index, the same peer group used for total shareholder return comparison purposes in the performance graph shown on page 35.” In other words, 83 of the 498 S&P 500 firms provided an explanation that enabled us to determine exactly which firms were in the peer group, and that list of the 83 firms is provided in Table 1. The disclosures for the other 415 ranged, for example, from providing no information to stating that “they are firms of similar size” to stating that some of them belonged to a particular index but provided no information on the rest of the firms, to giving an incomplete list. Due to the variation in disclosures, we need to estimate the extent to which sample selection may bias the results of our examination. We therefore merge our hand-collected data with both ExecuComp and COMPUSTAT to retrieve information on executive compensation as well as various accounting variables for both the firms themselves and the peers. There are some significant differences between those that provide full disclosure and those that do not, as can be seen by the summary statistics provided in Table 2. Disclosing firms are larger in size and earned higher stock returns during fiscal year 2005 so not surprisingly, the CEOs of these firms were paid an average of $1.33 million more in salary and bonus and $3.81 million more in total compensation (including option grants) than non-disclosers. There does not appear to be significant differences in the GIM-Index and E-index measures of governance for reporting versus non-reporting firms, although non-disclosing firms do appear to have greater block-holder ownership than firms that report their compensation peer groups. In terms of industry representation,

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commercial banks (SIC 6020), pharmaceutical preparations (SIC 2834), and petroleum refining (SIC 2911) have the highest numbers of disclosing firms. To determine whether there is a significant difference in compensation between those disclosing peer group representation and those not disclosing, we ran a regression of CEO compensation on size, the Fama-French 12 industry classifications, and the firm’s stock return plus a dummy variable for whether or not full disclosure occurred. The economic interpretation of the coefficient corresponding to the full disclosure dummy variable is the incremental compensation associated with having provided complete information on the members of the compensation peer group. In unreported results, we find that the estimated coefficient is positive but statistically insignificant, indicating that the disclosing group may be reasonably representative of the entire S&P 500. Our prior is that if anything, those fully disclosing are the ones least likely to be manipulating the compensation process since it is easier to question the process of determining CEO pay when more information is provided. The firms most likely to be excessively paying their chief executives would likely be the firms least willing to provide the names of the compensation peer group and then be in a position to have to justify such a group. We therefore believe that our results likely extend beyond the 83 firms for which we have complete data and if anything, under-estimate the extent to which compensation practices may be manipulated through the selection or compensation peer groups. Our evaluations of the effects of governance on peer group composition utilize three common metrics of corporate governance, the GIM index, the E-index, and the percentage of shares owned by blockholders.

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Gompers, Ishii, and Metrick (2003)

construct the GIM index that incorporates 24 provisions of takeover defenses followed by IRRC whereas Bebchuk, Cohen and Farrell (2004) limit their entrenchment index (the Eindex) to six provisions that seem to be relevant for valuation and stock returns. Blockholder data is acquired from Compact Disclosure and a blockholder is defined as a shareholder who owns at least five percent of the outstanding shares. The ownership shares are then aggregated for all of the blockholders.

4. Role of Peer Compensation on CEO Pay Our primary objective is to understand the role of compensation peer groups on the observed level of CEO pay so we begin with a baseline estimation of the determinants of the level of compensation for CEOs among the firms for which we have peer group information. As can be seen by the results located in the first column of Table 3, larger firms that generate high stock returns have higher total compensation, consistent with results previously documented in the literature.

Approximately 24% of the cross-

sectional variation in pay is explained by the baseline model. The results from adding the median level of compensation for the members of the peer group to the regression specification, located in column 2, show that indeed peer compensation is an important consideration in understanding the level of CEO pay. The coefficient itself is highly significant, statistically at better than the one percent level, and economically it suggests that the CEO of the corresponding firm earns an extra $1.14 for each dollar increase in the median compensation among the peers, all else equal. In addition, notice that the adjusted R-squared of the regression nearly doubles to 42.8% and that the estimated coefficient on size is no longer statistically significant and has fallen in

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magnitude by 84.4%. Obviously that does not mean that size is unimportant in CEO pay since it will certainly be a factor in choosing the peers. However, what the results to indicate is that size does not play a role once its effect on choosing members of the compensation peer group has been controlled for. In column 3, we repeat the analysis using the 75th percentile of CEO compensation for the peer group and find results similar to for the median. The adjusted R-squared is a little smaller and the coefficient suggests that for a $1 increase in the 75th percentile of peer compensation, the corresponding CEO’s pay increases by $0.72. We repeat our analysis looking instead at just salary and bonus (total cash compensation) instead of total compensation and report these results in Table 4. The baseline specification indicates that size seems to play the dominant role with stock return having a statistically insignificant effect on salary and bonus. Given the lack of significance of the variables included in the specification, it is not surprising to see that only 8.3% of the cross-sectional variation is explained. However, when we add the median salary and bonus from the previous fiscal year for the compensation peer group, we see that variable again being strongly significant, both statistically and economically. The coefficient indicates that for an additional $1 of median salary and bonus in the peer group, the corresponding CEO’s pay increases by nearly $0.90.

Statistically, the

coefficient is significant at better than one percent and the adjusted R-squared of the regression more than triples, with 27.6% of the cross-sectional variation now explained. In addition, the one variable, size, that was significant in the baseline specification has declined in magnitude by more than 77% and is no longer statistically significant. Using the 75th percentile of lagged salary and bonus for the peer group, we find very similar

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results statistically and a coefficient consistent with CEO salary and bonus rising by $0.78 for a $1 increase in the 75th percentile of total cash compensation for the peer group. Overall, the results indicate that firms are indeed following a compensation policy in which a set of peers are chosen and then the board determines CEO compensation based upon the observed salary and bonus of that peer group.

When included in

regressions explaining compensation, these variables dominate all of the other controls that have previously been used to explain CEO pay and the explanatory power of the model increases dramatically.

Therefore, explaining CEO compensation requires

uncovering the factors that determine the selection of the compensation peer group. That is what we explore next.

5. Selection of Compensation Peer Groups Given the large role that these peer groups have in understanding observed executive compensation, we begin with some summary statistics on the composition of these peer groups. As provided in Table 5, the average peer group is comprised of more than eleven firms, just over half of them in the same 3-digit SIC as the firm itself. On average, firms choose peers that are larger than themselves, though the data actually has a negative skew as seen by the median being larger than the mean. 8 Since size and industry have previously been documented to predict compensation, as well as the theoretical argument that the outside opportunity for a CEO would likely be a senior position in a firm of similar size and probably in the same industry, it is not surprising to see that these 8

General Electric is one of the 83 firms in the sample and the largest contributor in our data to the negative skew. There are few firms that have sales larger than GE, meaning that its peers will necessarily be smaller and some of them are particularly small, hence the large negative value for that measure. 12

are important elements to examine when evaluating the make-up of these groups. The median total compensation for the average peer group is $10.77 million, with $3.765 million of that in the form of cash compensation. Looking at some univariate results for characteristics of firms chosen to be in the compensation peer group, we break up the potential peers into four categories based upon two measures: whether or not they are selected for the peer compensation group and whether or not the potential peer is in the same 3-digit SIC as the firm. As demonstrated by the results in panel B, we examine 41,549 potential firm-peer pairs. Since there are nearly 500 potential peers in the sample and the average peer group has eleven firms, most of the potential peers will not end up being peers. Consistent with the earlier results, a large fraction of the firms chosen (44%) are in the same industry and 37% of the time, another S&P 500 firm in the same industry is chosen to be part of the compensation peer group. Aside from the industry break-down though, there are some interesting patterns that emerge with regard to the compensation at the potential peers in the previous year. The table provides mean and median total compensation for each of the four categories in panel B with the same statistics for cash compensation in panel C. If we look at the potential peers outside of the firm’s industry that are selected as peers (upper right quadrant), these firms have the highest compensation when measured at both the mean and median values. In contrast, firms in the same industry that were not chosen as peers (lower right quadrant) have the lowest total compensation of the four categories and second lowest cash compensation. In other words, at least based upon univariate analysis, the selection of potential firms for the compensation peer group seems to favor higher

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paid firms outside of their industry over lower paid potential peers that belong to the same industry. To determine whether these univariate results are robust to other controls, we proceed by conducting multivariate regression analysis, starting with a baseline specification containing just size and whether the firm and potential peer are in the same industry. As shown by the results in Table 6, firms with the same three-digit SIC code and larger firms are the ones most likely to be chosen for the peer compensation group. We also examined industry classification at the two- and four- digit levels, which were also statistically significant at better than one percent, but found that three-digit industry measurement had the strongest statistical results. This specification also includes the differences in the natural log of the size of the potential peer and the firm, but the results hold when we instead use the raw difference (without logs) as well as just the overall level of peer size. So, even though firm size does not seem to have a large effect on compensation once we control for the pay level of the peer group, the size of the potential peer relative to the size of the firm does play an important role in the choice of which firms are chosen for the peer group. We now turn to an examination of another factor that may influence observed CEO compensation that is arguably more related to “rent-seeking” rather than the reservation wage of the CEO. Because compensation is so related to the level for the peer group, the way to increase CEO pay is to select firms for the peer group based upon the level of compensation those firms pay their CEOs. Therefore, we add to the baseline specification a variable measuring the total compensation paid in the previous fiscal year to the CEO at the potential peer.

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The results from adding that additional variable, contained in column 2 of Table 6, indicate that the level of CEO compensation at the potential peer company does indeed have a significantly positive effect on the likelihood of including that firm in the peer group. Interestingly, the results are stronger when we use the raw dollar amount of the potential peer compensation rather than its natural log (regressions unreported). Normally, the benefit of taking logs is a reduction in the outliers in the data, which often generates better fit. In this case, the firm would find it optimal to choose outliers as they are the ones that would move up the peer measure of compensation the most (assuming that enough of them were chosen). Therefore, it does appear that allowing for greater skew to remain in the data actually better captures the economic outcome that being in the upper tail of the distribution increases the likelihood of being selected for the peer group. Considering that even after controlling for size and industry that CEO pay at the potential peer firm is significant, we now turn to the characteristics of the firms in which such sensitivity is higher by interacting the effect of potential peer compensation with measures of governance.

The idea is that if indeed CEOs are using peer group

membership to boost his own pay, we would expect that to most be the case in firms in which the CEO is most entrenched or where the CEO has the most power. We begin with the GIM index and again find that potential peer compensation is statistically significant but that the sensitivity to potential peer compensation declines as the firm’s GIM index increases. So, we see that the potential peer’s CEO compensation is more likely to influence membership in the peer group at the democracies rather than the dictatorships. This result initially appears to be contrary to the role we might have

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anticipated that governance would take. However, there is some recent work such as Cyert, Kang, and Kumar (2002) and Gillan, Hartzell, and Starks (2007) arguing that some firms use external governance whereas other firms choose internal governance to monitor and discipline managers. If that is the case, we might actually see firms with stronger internal governance (and therefore those with higher GIM values) being the ones that are less beneficial for CEOs when it comes to constructing the compensation peer group, consistent with our results.

Similar results emerge when we use the E-Index

(Bebchuck, Cohen, and Ferrell (2004)) or block ownership in that CEO pay at the potential peer has a significant role in constructing the peer group but the effect declines as the incentive to monitor increases. We also use total cash compensation rather than total compensation for the potential peers and also generate similar results. As indicated by the results in Table 7, higher cash compensation at the potential peer makes it more likely that the firm will be chosen to be part of the reference group, but the sensitivity to the potential peer’s compensation declines as internal governance improves. It is not clear a priori from the earlier results whether total compensation or just total cash compensation has a larger influence on which firms are chosen for the peer group but the results appear to be robust to using either measure.

6. Conclusion Numerous firms have stated that they follow a process of basing CEO compensation on an analysis of similar companies, but only recently has this process become more transparent with greater disclosure of peer group membership. We believe

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our work is the first to document that compensation peer groups do indeed play an important role in determining CEO compensation. Inclusion of measures of the median or 75th percentile of compensation for the group dominates other characteristics that have traditionally been used to explain cross-sectional variation in executive pay. Additionally, we document a number of summary statistics regarding compensation peer groups and analyze the determinants of group composition. We find that while industry and size are important in explaining the composition of these compensation peer groups, the level of compensation at the potential peer firms also plays a significant role. This effect is particularly strong for firms that are likely to have weak internal governance. Until recently, disclosure of this information was voluntary leading us to believe that we have likely underestimated the effects that we have documented. However, beginning just recently, firms are now required to provide this information in their annual SEC filings. Such increased transparency should lead to greater analysis by shareholders, as well as other firm stakeholders, of how potential firms are selected as members of the compensation peer group. It will be interesting to observe whether this additional scrutiny will alter the patterns that we have documented here.

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References [1] Anderson, R. and J. Bizjak, 2003, “An Empirical Examination of the Role of the CEO and the Compensation Committee in Structuring Executive Pay”, Journal of Banking and Finance, 27(7), pp. 1323-1348. [2] Baranchuk, N., G. MacDonald, and J. Yang, 2006, “The Economics and Super Managers”, working paper, Washington University in St. Louis. [3] Bebchuk, L. and J. Fried, 2003, “Executive Compensation as an Agency Problem”, Journal of Economic Perspectives, 17(3), pp. 71-92. [4] Bebchuk, L. and J. Fried, 2004, “Pay without Performance: The Unfulfilled Promise of Executive Compensation”, Cambridge, MA: Harvard University Press. [5] Bebchuk, L., A. Cohen, and A. Ferrell, 2004,“What Matters in Corporate Governance”, Harvard Law School John M. Olin Center Discussion Paper, No. 491. [6] Bertrand, M. and S. Mullainathan, 2001, “Are CEOs Rewarded for Luck? The Ones Without Principals Are.” Quarterly Journal of Economics, 116 (3), pp. 901-932. [7] Bizjak, J., M. Lemmon, and L. Naveen, 2007, “Has the Use of Peer Groups Contributed to Higher Levels of Executive Compensation?” revise and resubmit Journal of Financial Economics. [8] Cyert, R. M., S. Kang, and P. Kumar, 2002, “Corporate Governance, Takeovers, and Top-Management Compensation: Theory and Evidence”, Management Science, 48(4), pp. 453-469. [9] Gabaix, X. and A. Landier, 2006, “Why Has CEO Pay Increased So Much?” working paper, Princeton and NYU. [10] Gillan, S., J. Hartzell, and L. Starks, 2007, “Explaining Corporate Governance: Boards, Bylaws, and Charter Provisions”, the AFA Chicago meeting paper. [11] Gompers, P., J. Ishii, and A. Metrick, 2003, “Corporate Governance and Equity Prices”, Quarterly Journal of Economics, 118, pp. 107-155.

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[12] Main, G., C. OReilly, and J. Wade, 1995, “The CEO, the Board of Directors, and Executive Compensation: Economic and Psychological Perspectives”, Industrial and Corporate Change, 11, pp. 606-628. [13] Murphy, K., 1999, “Executive Compensation”, Orley Ashenfelter and David Card (eds.), Handbook of Labor Economics, Vol. 3, North Holland. [14] Murphy, K. and J. Zabojnik, 2004, “CEO Pay and Appointments: A Market-Based Explanation for Recent Trends”, American Economic Review Papers and Proceedings. [15] Newman, H. and H. Mozes, 1999, “Compensation Committee Composition and its Influence on CEO Compensation Practices”, Financial Management, Autumn, 1999. [16] OReilly, C., B. Main, and G. Crystal, 1988, “CEO Compensation as Tournament and Social Comparison: A Tale of Two Theories”, Administrative Science Quarterly, 33, pp. 257-274. [17] Oyer, P., 2004, “Why Do Firms Use Incentives that Have No Incentive Effects?” Journal of Finance, 59, pp. 1619-1649.

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Table 1: Disclosing Firms

SIC

Gvkey

Name

SIC

Gvkey

NAME

1311 1311 1389 1531 2085 2111 2631 2820 2834 2834 2834 2836 2844 2911 2911 3011 3531 3577 3663 3674 3760 3841 4813 4911 4924 5331 5411 5731 5940 6020 6020 6020 6020 6020 6111 6211 6211 6311 6311 6351 8011 9997

8068 15084 22794 8823 2435 8543 10426 4087 2403 6730 8530 9699 1920 7017 10156 5234 2817 11636 7585 157858 6774 2111 2146 9846 8470 4016 6502 2184 14624 4737 7647 7982 8245 11856 10121 7267 30128 1487 143356 24287 23877 5047

OCCIDENTAL PETROLEUM CORP BURLINGTON RESOURCES INC BJ SERVICES CO PULTE HOMES INC BROWN-FORMAN -CL B ALTRIA GROUP INC TEMPLE-INLAND INC DU PONT (E I) DE NEMOURS BRISTOL-MYERS SQUIBB CO LILLY (ELI) & CO PFIZER INC SIGMA-ALDRICH CORP AVON PRODUCTS MARATHON OIL CORP SUNOCO INC GOODYEAR TIRE & RUBBER CO CATERPILLAR INC XEROX CORP MOTOROLA INC FREESCALE SEMICONDUCTOR INC LOCKHEED MARTIN CORP BECTON DICKINSON & CO BELLSOUTH CORP EDISON INTERNATIONAL PEOPLES ENERGY CORP DOLLAR GENERAL CORP KROGER CO BEST BUY CO INC OFFICE DEPOT INC FIRST HORIZON NATIONAL CORP BANK OF AMERICA CORP NORTHERN TRUST CORP PNC FINANCIAL SVCS GROUP INC BB&T CORP SLM CORP MERRILL LYNCH & CO INC LEHMAN BROTHERS HOLDINGS INC AMERICAN INTERNATIONAL GROUP PRUDENTIAL FINANCIAL INC AMBAC FINANCIAL GP COVENTRY HEALTH CARE INC GENERAL ELECTRIC CO

1311 1311 1531 2030 2086 2600 2711 2834 2834 2834 2834 2840 2911 2911 2911 3420 3570 3640 3663 3711 3812 4011 4841 4922 4931 5399 5651 5812 6020 6020 6020 6020 6020 6035 6111 6211 6211 6311 6331 7320 8071

11923 16478 2845 5568 12756 6104 6475 1478 6266 7257 9459 8762 2991 8549 15247 10016 5606 3497 24800 8253 7985 7923 3226 4242 5742 29028 7922 11366 2019 7238 7711 8007 10187 5216 15208 12124 114628 6742 9351 4423 64166

ANADARKO PETROLEUM CORP EOG RESOURCES INC CENTEX CORP HEINZ (H J) CO COCA-COLA ENTERPRISES INC INTL PAPER CO KNIGHT-RIDDER INC WYETH JOHNSON & JOHNSON MERCK & CO SCHERING-PLOUGH PROCTER & GAMBLE CO CHEVRON CORP CONOCOPHILLIPS VALERO ENERGY CORP STANLEY WORKS HEWLETT-PACKARD CO COOPER INDUSTRIES LTD QUALCOMM INC PACCAR INC NORTHROP GRUMMAN CORP NORFOLK SOUTHERN CORP COMCAST CORP EL PASO CORP CENTERPOINT ENERGY INC COSTCO WHOLESALE CORP NORDSTROM INC WENDY’S INTERNATIONAL INC BANK OF NEW YORK CO INC MELLON FINANCIAL CORP NATIONAL CITY CORP WELLS FARGO & CO SUNTRUST BANKS INC GOLDEN WEST FINANCIAL CORP FEDERAL HOME LOAN MORTG CORP MORGAN STANLEY & CO. Inc GOLDMAN SACHS GROUP INC LINCOLN NATIONAL CORP SAFECO CORP EQUIFAX INC QUEST DIAGNOSTICS INC

20

Table 2: Summary Statistics The descriptive statistics are based on 490 of S&P500 firms that ExecuComp provides CEO compensation data in 2005. T CC is the cash compensation (salary and bonus payment). T DC1 is the CEO total compensation including option grants during the year (in $million). Sales is firm sales (in $million); ROA is the return on assets; Return is one year stock return (T rs1yr in ExecuComp). Volatility is the 60-month volatility used for option valuation (BS V OLAT in ExecuComp). The 5% ownership is the total ownership by block holders (with ownership ≥ 5%), provided by Compact Disclosure. Disclosing firms are firms that disclose explicit lists of compensation peers in their SEC filings: DEF-14A in 2005. *, ** and *** indicate the difference of variable between the two groups of firms is significant at the 10%, 5% and 1% levels, respectively.

Disclosing Firms (A)

Non-dscl. Firms (B)

Difference (A-B)

TCC ($million)

Mean Median Std Dev

4.205 2.950 4.224

2.872 2.352 2.868

1.334***

TDC1($million)

Mean Median Std Dev

13.207 8.975 11.604

9.401 6.852 8.723

3.806***

Sales($million)

Mean Median Std Dev

27,054 14,057 35,414

14,083 6,523.7 12,970

12,970***

ROA

Mean Median Std Dev

6.647% 6.063% 5.243%

6.279% 5.259% 6.814%

0.368%

Return

Mean Median Std Dev

18.09% 6.063% 10.11%

9.941% 5.259% 5.981%

8.151%***

Volatility

Mean Median Std Dev

0.2838 0.2560 0.1216

0.3440 0.2890 0.1932

-0.0600***

GIM index

Mean Median Std Dev

9.901 10 2.370

9.649 10 0.1932

-0.252

E index

Mean Median Std Dev

2.432 2 1.322

2.441 3 1.314

0.0087

Block ownership

Mean Median Std Dev

21.86% 19.35% 15.57%

26.85% 24.92% 18.43%

83

407

# of Obs.

21

-4.99%**

Table 3: Effect of Peer Total Compensation The dependent variable is CEO total compensation (including option grants): T DC1/1000 in the ExecuComp database. The 50% peer pay is the median total compensation of a firm’s peer group, and the 75% peer pay is the 75th percentile of the total compensation of a firm’s peers. Other variables are defined in Table . The P-value of a coefficient is given in the parentheses under the coefficient, and *, ** and *** indicate the coefficient is significant at the 10%, 5% and 1% levels, respectively.

Dependent Variable: Total Compensation T DC1 ($million) Independent Variables

Benchmark

50% Peer Pay

75% Peer Pay

Intercept

-21.26** (0.04)

-4.533 (0.64)

-7.400 (0.47)

log(sales)

3.776*** (0.00)

0.590 (0.58)

1.041 (0.37)

ROA

0.401 (0.42)

0.686 (0.12)

0.618 (0.18)

Lagged ROA

-0.598 (0.24)

-0.752* (0.09)

-0.791* (0.09)

Stock return

0.115*** (0.01)

0.133*** (0.00)

0.128*** (0.00)

Lagged stock return

0.035 (0.54)

0.029 (0.56)

0.010 (0.85)

Volatility

-12.47 (0.21)

-11.89 (0.17)

-10.05 (0.27)

50% peer pay

1.143*** (0.00)

75% peer pay

0.721*** (0.00)

# of Obs

82

82

82

Adj. R2

0.2356

0.4278

0.3579

22

Table 4: Effect of Peer Cash Compensation The dependent variable is CEO total cash compensation: T CC/1000 in the ExecuComp database. The 50% peer pay is the median cash compensation of a firm’s peer group, and the 75% peer pay is the 75th percentile of the cash compensation of a firm’s peers. Other variables are defined in Table . The P-value of a coefficient is given in the parentheses under the coefficient, and *, ** and *** indicate the coefficient is significant at the 10%, 5% and 1% levels, respectively. Dependent Variable: Cash Compensation T CC ($million) Independent Variables

Benchmark

50% Peer Pay

75% Peer Pay

Intercept

-3.597 (0.39)

-0.273 (0.94)

-1.543 (0.67)

log(sales)

0.976** (0.02)

0.219 (0.58)

0.256 (0.50)

ROA

-0.171 (0.39)

-0.089 (0.61)

-0.102 (0.56)

Lagged ROA

-0.014 (0.95)

-0.052 (0.77)

-0.016 (0.93)

Stock return

0.011 (0.53)

0.015 (0.33)

0.013 (0.39)

Lagged stock return

-0.014 (0.53)

-0.012 (0.54)

-0.013 (0.50)

Volatility

-0.705 (0.86)

-0.435 (0.90)

0.779 (0.82)

50% peer pay

0.897*** (0.00)

75% peer pay

0.784*** (0.00)

# of Obs

82

82

82

Adj. R2

0.0837

0.2758

0.2966

23

Table 5: Summary Statistics on Peer and Potential Peer Firms Same industry is determined using the 3-digit SIC code. Mean sales (peer-firm) is the the average sales of the firms in the peer group less the sales of the firm, both in the previous year. Total pay is T DC1/1000 and cash pay is T CC/1000 in the Execucomp.

Panel A: Descriptive Statistics of Chosen Peer Firms Mean Median Number of peers % same industry Mean sales(peer-firm) ($million) Mean sales(peer-firm) (%) 50% peer total pay ($million) 75% peer total pay ($million) 50% peer cash pay ($million) 75% peer cash pay ($million)

11.57 0.502 1,044 0.732 10.77 15.92 3.765 4.943

10 0.500 3,106 0.395 9.942 14.14 3.213 4.252

Panel B: Descriptive Statistics on Total Pay of Non-Peer vs. Peer Firms Non-Peer (A)

Peer (B)

Difference (B-A)

Diff industry (C)

Mean Median # of Obs.

9.585 7.146 39,842

12.20 10.10 554

2.613***

Same industry (D)

Mean Median # of Obs.

9.380 9.166 732

10.48 8.839 431

1.100**

Difference (C-D)

1.714***

Panel C: Descriptive Statistics on Cash Pay of Non-Peer vs. Peer Firms Non-Peer(A)

Peer (B)

Difference (B-A)

Diff industry (C)

Mean Median # of Obs.

2.896 2.377 39,850

3.828 3.075 554

0.932***

Same industry (D)

Mean Median # of Obs.

3.280 2.438 732

3.476 2.735 431

0.196

Difference (C-D)

0.352**

24

Table 6: Peer Group Selection – Peer Total Compensation The dependent variable is 1 if a S&P500 firm is a compensation peer of the 83 disclosing firms under consideration. Same industry is 1 if the firm and peer share the same 3-digit SIC code. Dif f log(sales) is log(sales) of the peer minus log(sales) of the firm in the previous year. P eer pay is the CEO total compensation of the peer in the previous year (T DC1/1000 in ExecuComp). GIM index is the Gompers, Ishii, and Metrick (2003) corporate governance index, E index is the entrenchment index used by Bebchuk, Cohen, and Ferrell (2004) and Block ownership is the total ownership of block holders (with ownership ≥ 5%). The P-value of a coefficient is given in the parentheses under the coefficient, and *, ** and *** indicate the coefficient is significant at the 10%, 5% and 1% levels, respectively. Standard errors are clustered at the firm level. Dependent Variable: Firm is Peer Independent Variables

Baseline

Peer Pay

GIM-Index

E-index

Block Own.

Intercept

-2.185*** (0.00)

-2.277*** (0.00)

-2.541*** (0.00)

-2.321*** (0.00)

-2.362*** (0.00)

Same industry

2.277*** (0.00)

2.281*** (0.00)

2.226*** (0.00)

2.228*** (0.00)

2.267*** (0.00)

Diff log(sales)

0.113*** (0.00)

0.102*** (0.00)

0.104*** (0.00)

0.109*** (0.00)

0.110*** (0.00)

0.008*** (0.00)

0.042*** (0.00)

0.022*** (0.00)

0.021*** (0.00)

Peer pay GIM index

0.027 (0.18)

GIM index*Peer pay

-0.003*** (0.00)

E index

0.022 (0.52)

E index*Peer pay

-0.006*** (0.00)

Block ownership

0.004 (0.25)

Block own.*Peer pay

-0.001*** (0.00)

# of Obs.

40,419

40,411

39,412

39,412

36,961

# of Clusters

82

82

80

80

75

Pseudo R2

0.2651

0.2674

0.2507

0.2518

0.2733

25

Table 7: Peer Group Selection – Peer Cash Compensation The dependent variable is 1 if a S&P500 firm is a compensation peer of the 83 disclosing firms under consideration. Same industry is 1 if the firm and peer share the same 3-digit SIC code. Dif f log(sales) is log(sales) of the peer minus log(sales) of the firm in the previous year. P eer pay is the CEO cash compensation of the peer in the previous year (T CC/1000 in ExecuComp.) GIM index is the Gompers, Ishii, and Metrick (2003) corporate governance index, E index is the entrenchment index used by Bebchuk, Cohen, and Ferrell (2004) and Block ownership is the total ownership of block holders (with ownership ≥ 5%). The P-value of a coefficient is given in the parentheses under the coefficient, and *, ** and *** indicate the coefficient is significant at the 10%, 5% and 1% levels, respectively. Standard errors are clustered at the firm level. Dependent Variable: Firm is Peer Independent Variables

Baseline

Peer Pay

GIM Index

E index

Block Own.

Intercept

-2.185*** (0.00)

-2.307*** (0.00)

-2.484*** (0.00)

-2.348*** (0.00)

-2.364*** (0.00)

Same industry

2.277*** (0.00)

2.278*** (0.00)

2.220*** (0.00)

2.223*** (0.00)

2.257*** (0.00)

Diff log(sales)

0.113*** (0.00)

0.095*** (0.00)

0.097*** (0.00)

0.102*** (0.00)

0.103*** (0.00)

0.036*** (0.00)

0.042*** (0.00)

0.077*** (0.00)

0.067*** (0.00)

Peer pay GIM index

0.018 (0.41)

GIM index*Peer pay

-0.008*** (0.00)

E index

0.020 (0.56)

E index*Peer pay

-0.018*** (0.01)

Block ownership

0.003 (0.46)

Block own.*Peer pay

-0.002** (0.03)

# of Obs.

40,419

40,419

39,420

39,420

36,969

# of Clusters

82

82

80

80

75

Pseudo R2

0.2651

0.2690

0.2514

0.2532

0.2737

26