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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 atWashington University in St. Louis. We also thank Cassandra Marshall and Raj Mistry for their researchassistance. All errors are ours. Email: [email protected] Email: [email protected]
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Inside the Black Box: The Role and Composition of
Compensation Peer Groups
Abstract
This paper documents the features of compensation peer groups and demonstratesthat they play a significant role in determining CEO compensation. Anecdotally, weknow that compensation peer groups have had a growing role in determining executivecompensation 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 theS&P 500 firms that provided explicit lists of compensation peer firms in their fiscal 2005disclosures. Results show that inclusion of the groups median compensation more thantriples 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 SICas the firm. Univariate analysis suggests that firms forego lower paid potential peers intheir same industry in favor of higher paid peers outside of their industry whenconstructing the peer groups. In multivariate regression analysis, this result carriesthrough as we find that even after controlling for industry and relative size, peer groupcomposition 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 compensationand 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, CEOsare most able to create benchmarks (compensation peer group compositions) that helpgenerate higher compensation for themselves. Given that disclosure of peer group
composition had until recently been voluntary, our results are likely to underestimate theextent to which peers are selected by characteristics seemingly unrelated to managerial performance or their reservation wage.
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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 reflectsmarket 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 companys
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).2 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 therole of compensation committees, OReilly, Main, and Crystal (1988), Main, OReilly, and Wade (1995),and Newman and Mozes (1997) suggest the existence of the influence; while Anderson and Bizjak (2003)suggest the opposite.
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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 thegroup with which the Committee is comparing the registrant'scompensation (e.g., Fortune 100 companies), the extent to which it differsfrom the peer group and where in the range established by that comparisonthe issuer targets its compensation (e.g., high, median, or low end of therange). 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 hand-
collected 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?
4 Some firms began voluntarily disclosing their compensation peer groups as early as 2004 but not until2005 were there a sufficient number of them reporting such that an empirical examination of these groupscan be conducted. Because such disclosure is voluntary, we will necessarily need to address potentialsample selection issues. We elaborate in the Data Description section.
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We find that the median and 75 th 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 CEOs
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
5 Possibly to justify selection of such firms as members of the compensation peer group, firms often statethat they choose firms with which they compete for managerial talents.
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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 theBOD in setting up CEO pay and the impact of potential takeovers. In equilibrium, internal governance bythe 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 corporategovernance (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 thenames of the compensation peer group. Specifically, we follow Bizjak, Lemmon and
Naveen (2007) and run the following regression:
CEO Compensation t = + 1*log(sales t) + 2*ROA t + 3*ROA t-1
+ 4*StockRet t + 5*StockRet t-1 + 6*Volatility +
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 firms 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 firms stock over the fiscal year (ExecuComp item TRS1YR) and
the volatility of the firms 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 75 th 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 measures 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 firmsselected 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 firms 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 CEOs 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, arguingthat errors in estimating peer group inclusion are likely to be correlated for a particular firm.
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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 firms 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 independentcompensation consultant's analysis of the proxy data for our peer companies, provide a comprehensive compensation competitivenessevaluation. 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 HealthcareEquipment & Services Index, the same peer group used for totalshareholder 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
firms 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 unreportedresults, 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. Gompers, Ishii, and Metrick (2003)
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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 E-
index ) 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 onthe 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 75 th 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 75 th percentile of peer compensation, the corresponding
CEOs pay increases by $0.72.
We repeat our analysis looking instead at just salary and bonus (total cashcompensation) 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 CEOs 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 75 th 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 75 th 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 requiresuncovering 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 negativeskew. 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.
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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 firms 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 industrymeasurement 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 firms
GIM index increases. So, we see that the potential peers 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 declinesas 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 peers
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 75 th 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
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working paper, Washington University in St. Louis.
[3] Bebchuk, L. and J. Fried, 2003, Executive Compensation as an Agency Problem, Jour-
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Table 1: Disclosing Firms
SIC Gvkey Name SIC Gvkey NAME
1311 8068 OCCIDENTAL PETROLEUM CORP 1311 11923 ANADARKO PETROLEUM CORP1311 15084 BURLINGTON RESOURCES INC 1311 16478 EOG RESOURCES INC1389 22794 BJ SERVICES CO 1531 2845 CENTEX CORP1531 8823 PULTE HOMES INC 2030 5568 HEINZ (H J) CO2085 2435 BROWN-FORMAN -CL B 2086 12756 COCA-COLA ENTERPRISES INC2111 8543 ALTRIA GROUP INC 2600 6104 INTL PAPER CO
2631 10426 TEMPLE-INLAND INC 2711 6475 KNIGHT-RIDDER INC2820 4087 DU PONT (E I) DE NEMOURS 2834 1478 WYETH2834 2403 BRISTOL-MYERS SQUIBB CO 2834 6266 JOHNSON & JOHNSON2834 6730 LILLY (ELI) & CO 2834 7257 MERCK & CO2834 8530 PFIZER INC 2834 9459 SCHERING-PLOUGH2836 9699 SIGMA-ALDRICH CORP 2840 8762 PROCTER & GAMBLE CO2844 1920 AVON PRODUCTS 2911 2991 CHEVRON CORP2911 7017 MARATHON OIL CORP 2911 8549 CONOCOPHILLIPS2911 10156 SUNOCO INC 2911 15247 VALERO ENERGY CORP3011 5234 GOODYEAR TIRE & RUBBER CO 3420 10016 STANLEY WORKS3531 2817 CATERPILLAR INC 3570 5606 HEWLETT-PACKARD CO3577 11636 XEROX CORP 3640 3497 COOPER INDUSTRIES LTD3663 7585 MOTOROLA INC 3663 24800 QUALCOMM INC3674 157858 FREESCALE SEMICONDUCTOR INC 3711 8253 PACCAR INC3760 6774 LOCKHEED MARTIN CORP 3812 7985 NORTHROP GRUMMAN CORP3841 2111 BECTON DICKINSON & CO 4011 7923 NORFOLK SOUTHERN CORP4813 2146 BELLSOUTH CORP 4841 3226 COMCAST CORP4911 9846 EDISON INTERNATIONAL 4922 4242 EL PASO CORP4924 8470 PEOPLES ENERGY CORP 4931 5742 CENTERPOINT ENERGY INC5331 4016 DOLLAR GENERAL CORP 5399 29028 COSTCO WHOLESALE CORP5411 6502 KROGER CO 5651 7922 NORDSTROM INC5731 2184 BEST BUY CO INC 5812 11366 WENDYS INTERNATIONAL INC5940 14624 OFFICE DEPOT INC 6020 2019 BANK OF NEW YORK CO INC6020 4737 FIRST HORIZON NATIONAL CORP 6020 7238 MELLON FINANCIAL CORP6020 7647 BANK OF AMERICA CORP 6020 7711 NATIONAL CITY CORP6020 7982 NORTHERN TRUST CORP 6020 8007 WELLS FARGO & CO6020 8245 PNC FINANCIAL SVCS GROUP INC 6020 10187 SUNTRUST BANKS INC6020 11856 BB&T CORP 6035 5216 GOLDEN WEST FINANCIAL CORP6111 10121 SLM CORP 6111 15208 FEDERAL HOME LOAN MORTG CORP6211 7267 MERRILL LYNCH & CO INC 6211 12124 MORGAN STANLEY & CO. Inc6211 30128 LEHMAN BROTHERS HOLDINGS INC 6211 114628 GOLDMAN SACHS GROUP INC6311 1487 AMERICAN INTERNATIONAL GROUP 6311 6742 LINCOLN NATIONAL CORP6311 143356 PRUDENTIAL FINANCIAL INC 6331 9351 SAFECO CORP6351 24287 AMBAC FINANCIAL GP 7320 4423 EQUIFAX INC8011 23877 COVENTRY HEALTH CARE INC 8071 64166 QUEST DIAGNOSTICS INC9997 5047 GENERAL ELECTRIC CO
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Table 2: Summary Statistics
The descriptive statistics are based on 490 of S&P500 rms that ExecuComp provides CEO compensa-tion data in 2005. T CC is the cash compensation (salary and bonus payment). T DC 1 is the CEO totalcompensation including option grants during the year (in $million). Sales is rm sales (in $million);ROA is the return on assets; Return is one year stock return ( T rs 1yr in ExecuComp ). Volatility is the60-month volatility used for option valuation ( BS V OLAT in ExecuComp ). The 5% ownership is thetotal ownership by block holders (with ownership 5%), provided by Compact Disclosure . Disclosingrms are rms that disclose explicit lists of compensation peers in their SEC lings: DEF-14A in 2005.*, ** and *** indicate the difference of variable between the two groups of rms is signicant at the10%, 5% and 1% levels, respectively.
Disclosing Firms (A) Non-dscl. Firms (B) Difference (A-B)
TCC ($million) Mean 4.205 2.872 1.334***Median 2.950 2.352Std Dev 4.224 2.868
TDC1($million) Mean 13.207 9.401 3.806***Median 8.975 6.852Std Dev 11.604 8.723
Sales($million) Mean 27,054 14,083 12,970***Median 14,057 6,523.7Std Dev 35,414 12,970
ROA Mean 6.647% 6.279% 0.368%
Median 6.063% 5.259%Std Dev 5.243% 6.814%
Return Mean 18.09% 9.941% 8.151%***Median 6.063% 5.259%Std Dev 10.11% 5.981%
Volatility Mean 0.2838 0.3440 -0.0600***Median 0.2560 0.2890Std Dev 0.1216 0.1932
GIM index Mean 9.901 9.649 -0.252Median 10 10Std Dev 2.370 0.1932
E index Mean 2.432 2.441 0.0087Median 2 3Std Dev 1.322 1.314
Block ownership Mean 21.86% 26.85% -4.99%**Median 19.35% 24.92%Std Dev 15.57% 18.43%
# of Obs. 83 407
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Table 3: Effect of Peer Total CompensationThe dependent variable is CEO total compensation (including option grants): T DC 1/ 1000 inthe ExecuComp database. The 50% peer pay is the median total compensation of a rms peergroup, and the 75% peer pay is the 75th percentile of the total compensation of a rms peers.Other variables are dened in Table . The P-value of a coefficient is given in the parenthesesunder the coefficient, and *, ** and *** indicate the coefficient is signicant at the 10%, 5%and 1% levels, respectively.
Dependent Variable: Total Compensation T DC 1 ($million)
Independent Variables Benchmark 50% Peer Pay 75% Peer Pay
Intercept -21.26** -4.533 -7.400(0.04) (0.64) (0.47)
log(sales) 3.776*** 0.590 1.041(0.00) (0.58) (0.37)
ROA 0.401 0.686 0.618(0.42) (0.12) (0.18)
Lagged ROA -0.598 -0.752* -0.791*
(0.24) (0.09) (0.09)Stock return 0.115*** 0.133*** 0.128***
(0.01) (0.00) (0.00)
Lagged stock return 0.035 0.029 0.010(0.54) (0.56) (0.85)
Volatility -12.47 -11.89 -10.05(0.21) (0.17) (0.27)
50% peer pay 1.143***(0.00)
75% peer pay 0.721***(0.00)
# of Obs 82 82 82
Adj. R2 0.2356 0.4278 0.3579
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Table 4: Effect of Peer Cash CompensationThe dependent variable is CEO total cash compensation: TCC/ 1000 in the ExecuComp data-base. The 50% peer pay is the median cash compensation of a rms peer group, and the 75%peer pay is the 75th percentile of the cash compensation of a rms peers. Other variablesare dened in Table . The P-value of a coefficient is given in the parentheses under the coef-cient, and *, ** and *** indicate the coefficient is signicant at the 10%, 5% and 1% levels,respectively.
Dependent Variable: Cash Compensation T CC ($million)
Independent Variables Benchmark 50% Peer Pay 75% Peer Pay
Intercept -3.597 -0.273 -1.543(0.39) (0.94) (0.67)
log(sales) 0.976** 0.219 0.256(0.02) (0.58) (0.50)
ROA -0.171 -0.089 -0.102(0.39) (0.61) (0.56)
Lagged ROA -0.014 -0.052 -0.016
(0.95) (0.77) (0.93)Stock return 0.011 0.015 0.013
(0.53) (0.33) (0.39)
Lagged stock return -0.014 -0.012 -0.013(0.53) (0.54) (0.50)
Volatility -0.705 -0.435 0.779(0.86) (0.90) (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
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Table 5: Summary Statistics on Peer and Potential Peer Firms
Same industry is determined using the 3-digit SIC code. Mean sales (peer-rm) is the the average
sales of the rms in the peer group less the sales of the rm, both in the previous year. Total pay isT DC 1/ 1000 and cash pay is TCC/ 1000 in the Execucomp.
Panel A: Descriptive Statistics of Chosen Peer FirmsMean Median
Number of peers 11.57 10% same industry 0.502 0.500Mean sales(peer-rm) ($million) 1,044 3,106Mean sales(peer-rm) (%) 0.732 0.39550% peer total pay ($million) 10.77 9.94275% peer total pay ($million) 15.92 14.1450% peer cash pay ($million) 3.765 3.21375% peer cash pay ($million) 4.943 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 9.585 12.20 2.613***Median 7.146 10.10# of Obs. 39,842 554
Same industry (D) Mean 9.380 10.48 1.100**Median 9.166 8.839# of Obs. 732 431
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 2.896 3.828 0.932***Median 2.377 3.075# of Obs. 39,850 554
Same industry (D) Mean 3.280 3.476 0.196Median 2.438 2.735# of Obs. 732 431
Difference (C-D) 0.352**
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Table 6: Peer Group Selection Peer Total Compensation
The dependent variable is 1 if a S&P500 rm is a compensation peer of the 83 disclosing
rms under consideration. Same industry is 1 if the rm and peer share the same 3-digit SICcode. Diff log (sales ) is log(sales ) of the peer minus log(sales ) of the rm in the previousyear. Peer pay is the CEO total compensation of the peer in the previous year ( T DC 1/ 1000in ExecuComp ). GIM index is the Gompers, Ishii, and Metrick (2003) corporate governanceindex, E index is the entrenchment index used by Bebchuk, Cohen, and Ferrell (2004) andBlock ownership is the total ownership of block holders (with ownership 5%). The P-valueof a coefficient is given in the parentheses under the coefficient, and *, ** and *** indicatethe coefficient is signicant at the 10%, 5% and 1% levels, respectively. Standard errors areclustered at the rm level.
Dependent Variable: Firm is Peer
Independent Variables Baseline Peer Pay GIM-Index E-index Block Own.
Intercept -2.185*** -2.277*** -2.541*** -2.321*** -2.362***(0.00) (0.00) (0.00) (0.00) (0.00)
Same industry 2.277*** 2.281*** 2.226*** 2.228*** 2.267***(0.00) (0.00) (0.00) (0.00) (0.00)
Diff log(sales) 0.113*** 0.102*** 0.104*** 0.109*** 0 .110***(0.00) (0.00) (0.00) (0.00) (0.00)
Peer pay 0.008*** 0.042*** 0.022*** 0.021***
(0.00) (0.00) (0.00) (0.00)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 R 2 0.2651 0.2674 0.2507 0.2518 0.2733
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Table 7: Peer Group Selection Peer Cash Compensation
The dependent variable is 1 if a S&P500 rm is a compensation peer of the 83 disclosing
rms under consideration. Same industry is 1 if the rm and peer share the same 3-digit SICcode. Diff log (sales ) is log(sales ) of the peer minus log(sales ) of the rm in the previousyear. Peer pay is the CEO cash compensation of the peer in the previous year ( TCC/ 1000in ExecuComp .) GIM index is the Gompers, Ishii, and Metrick (2003) corporate governanceindex, E index is the entrenchment index used by Bebchuk, Cohen, and Ferrell (2004) andBlock ownership is the total ownership of block holders (with ownership 5%). The P-valueof a coefficient is given in the parentheses under the coefficient, and *, ** and *** indicatethe coefficient is signicant at the 10%, 5% and 1% levels, respectively. Standard errors areclustered at the rm level.
Dependent Variable: Firm is Peer
Independent Variables Baseline Peer Pay GIM Index E index Block Own.
Intercept -2.185*** -2.307*** -2.484*** -2.348*** -2.364***(0.00) (0.00) (0.00) (0.00) (0.00)
Same industry 2.277*** 2.278*** 2.220*** 2.223*** 2.257***(0.00) (0.00) (0.00) (0.00) (0.00)
Diff log(sales) 0.113*** 0.095*** 0.097*** 0.102*** 0.103***(0.00) (0.00) (0.00) (0.00) (0.00)
Peer pay 0.036*** 0.042*** 0.077*** 0.067***(0.00) (0.00) (0.00) (0.00)
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 R 2 0.2651 0.2690 0.2514 0.2532 0.2737