Corporate governance, compensation consultants, and CEO pay levels

Corporate governance, compensation consultants,and CEO pay levels

Christopher S. Armstrong • Christopher D. Ittner •

David F. Larcker

Published online: 9 March 2012

� Springer Science+Business Media, LLC 2012

Abstract This study investigates the relation between corporate governance and

CEO pay levels and the extent to which the higher pay found in firms using com-

pensation consultants is related to governance differences. Using proxy statement

disclosures from 2,110 companies, we find that CEO pay is higher in firms with

weaker governance and that firms with weaker governance are more likely to use

compensation consultants. CEO pay remains higher in clients of consulting firms

even after controlling for economic determinants of compensation. However, when

consultant users and non-users are matched on both economic and governance

characteristics, differences in pay levels are not statistically significant, indicating

that governance differences explain much of the higher pay in clients of compen-

sation consultants. We find no support for claims that CEO pay is higher in

potentially ‘‘conflicted’’ consultants that also offer additional non-compensation-

related services.

Keywords Equity incentives � Corporate governance � Executive compensation �Compensation consultants

JEL Classification G30 � G34 � M12 � M52 � M55

C. S. Armstrong � C. D. Ittner (&)

The Wharton School, University of Pennsylvania, 1300 Steinberg-Dietrich Hall, Philadelphia,

PA 9104, USA

e-mail: [email protected]

C. S. Armstrong


D. F. Larcker

Graduate School of Business, Rock Center for Corporate Governance, Stanford University,

655 Knight Way, Stanford, CA 94305, USA


123

Rev Account Stud (2012) 17:322–351

DOI 10.1007/s11142-012-9182-y

1 Introduction

The relation between chief executive officer (CEO) pay levels and corporate

governance has received considerable attention. Critics charge that CEOs of firms

with weak corporate governance can extract pay in excess of that justified by the

firms’ economic characteristics because they control the pay-setting process and

have board members without the knowledge or incentives needed to reject excessive

pay proposals (for example, Bebchuk and Fried 2003, 2004; Morgenson 2006a;

Anderson et al. 2007; U.S. House of Representatives 2007). These claims are

supported by empirical studies finding that CEO pay is greater than predicted by

economic determinants when the CEO is more powerful and corporate governance

is weak (for example, Lambert et al. 1993; Borokhovich et al. 1997; Conyon and

Peck 1998; Daily et al. 1998; Core et al. 1999; Cyert et al. 2002; Faleye 2007).

A key question raised by these results is how CEOs justify ‘‘excess’’ pay levels to

their boards of directors, shareholders, and outside observers. One potential

mechanism is through the use of compensation consultants. A wide range of

business leaders, academics, and politicians charge that CEOs of companies with

weak governance use compensation consultants, who are beholden to clients for

current and future business, to design and justify excessive pay packages (for

example, Crystal 1991; Bebchuk and Fried 2004; Buffett 2007; U.S. House of

Representatives 2007). These claims have prompted increased compensation

disclosure requirements and political investigations. The Securities and Exchange

Commission, for example, requires proxy statements filed on or after Dec. 15, 2006,

to disclose which, if any, consultants provide compensation advice to the company.1

A number of studies provide evidence that CEO pay levels are higher than

predicted by economic determinants in firms that use compensation consultants (for

example, Corporate Library 2007; Murphy and Sandino 2008; Conyon et al. 2009;

Cadman et al. 2010). However, these studies provide relatively little evidence

regarding why pay is higher in consulting clients. Although consultants can provide

advice on executive pay packages, the ultimate responsibility for setting and

approving pay levels rests with the board of directors. In the absence of weak

governance structures that allow CEOs to extract ‘‘excess’’ pay, there is no clear

reason why pay levels should be higher in consulting clients. Moreover, if firms

with weak governance are more likely to use compensation consultants, and

governance characteristics are not sufficiently incorporated into the analyses, then

any conclusions about what is driving the excess pay levels may be inappropriate.

We conduct an analysis of the influence of compensation consultants on the link

between corporate governance and CEO pay using proxy disclosures by a diverse

sample of 2,110 companies. Consistent with prior studies, we find that executive pay

1 Regulation S-X 407 (e) (3) (iii) states that companies are required to provide a ‘‘narrative description’’

of ‘‘any role of compensation consultants in determining or recommending the amount or form of

executive and director compensation, identifying such consultants, stating whether such consultants are

engaged directly by the compensation committee (or persons performing the equivalent functions) or any

other person, describing the nature and scope of their assignment, and the material elements of the

instructions or directions given to the consultants with respect to the performance of their duties under the

engagement’’.

Corporate governance 323

123

levels are higher than predicted by economic characteristics in firms with weaker

governance and in firms using compensation consultants. We also find that firms

with weaker governance are more likely to use consultants. When we employ

propensity score matched pair analyses to match firms on both economic andgovernance characteristics, we find no significant difference in total pay levels

between consultant users and non-users.2 This evidence indicates that two

economically similar firms with equally weak governance structures are both likely

to exhibit similar higher-than-predicted pay levels, regardless of their use or non-use

of consultants. These findings suggest that governance differences account for much

of the unexplained pay differences between consultant users and non-users. Finally,

we find no evidence that our results are driven by differences between clients of

potentially ‘‘conflicted’’ consultants who offer a broad range of advisory services

relative to clients of specialized, ‘‘non-conflicted’’ compensation consulting firms,

providing no support for claims that consultants with potential conflicts of interest

are more likely to facilitate the extraction of excess CEO pay in firms with weak

governance.

The remainder of the study is organized as follows. Sect. ‘‘2’’ reviews the prior

literature on governance arguments for differences in total CEO pay levels in

companies using or not using compensation consultants. Sect. ‘‘3’’ discusses our

sample and variables. Sect. ‘‘4’’ provides results. Sect. ‘‘5’’ concludes.

2 Literature review

2.1 CEO pay, governance, and compensation consultants

Economic theory (along with the pay justifications included in the majority of

annual proxy statements) suggests that executive pay packages are designed to

efficiently achieve attraction, retention, and incentive objectives. This ‘‘efficient

contracting’’ view of compensation plan design maintains that any differences in

pay levels across firms are due to differences in economic characteristics that affect

these objectives. Consistent with this view, empirical studies have identified a wide

variety of economic factors that are associated with pay differentials (see Murphy

1999 and Prendergast 1999 for reviews).

In contrast, critics of the pay-setting process charge that CEO pay packages are

not always in shareholders’ best interests. ‘‘Managerial power’’ theories contend

that CEOs have a great deal of power or control over the board of directors and that

CEOs use this power to extract ‘‘excess’’ pay levels that are larger than economic

characteristics justify (for example, Allen 1981; Bebchuk and Fried 2003, 2004). A

number of factors are claimed to influence the CEO’s power over the board,

2 Propensity score matching is robust to misspecification of the functional form linking CEO

compensation to selected determinants and allows us to assess the impact of the endogenous choice of

compensation consultants on the results. See Rosenbaum (2002) and Rosenbaum and Rubin (1983) for

theoretical background and Armstrong et al. (2010) for a detailed explanation of propensity score

matching in compensation research and an application examining whether equity incentives motivate

managers to engage in accounting manipulations.

324 C. S. Armstrong et al.

123

including the executive’s ability to support the nomination and retention of

directors, the presence of company employees (or ‘‘insiders’’) on the board, and

joint dealings between the CEO and the directors’ firms, among others (for example,

Crystal 1991; Bebchuk and Fried 2003, 2004). These factors, together with many

directors’ lack of incentives, skills, or available time to adequately scrutinize

proposed CEO pay packages, give CEOs in firms with weak governance

considerable influence over the pay-setting process and compensation outcomes.

Consistent with these managerial power theories, empirical studies have found a

variety of governance characteristics (such as the CEO’s appointment of board

members, board of directors characteristics and rules, and shareholder rights) related

to CEO pay levels, with weaker governance associated with greater excess pay (for

example, Lambert et al. 1993; Borokhovich et al. 1997; Conyon and Peck 1998;

Daily et al. 1998; Core et al. 1999; Cyert et al. 2002; Faleye 2007).

Bebchuk and Fried (2003, 2004) argue that one constraint on powerful CEOs’ pay

levels is the ability to implement pay packages that can plausibly be defended and

justified to board members, shareholders, and outside observers, thereby avoiding the

‘‘outrage costs’’ associated with pay levels that are deemed to be too high. Numerous

executive compensation critics claim that compensation consultants provide an

important mechanism for this justification (for example, Crystal 1991; Bebchuk and

Fried 2003, 2004; Morgenson 2006a; Anderson et al. 2007; U.S. House of

Representatives 2007). The majority of large U.S. companies engage compensation

consultants to provide assistance in the design of executive compensation contracts.

This assistance can range from the simple provision of benchmarking data on pay

practices in other companies to advice on the structure and level of executive

compensation and the tax, legal, and accounting implications of pay packages.

Although some consultants focus solely on the provision of compensation advice,

others offer a broad range of services such as pension and benefit administration,

actuarial services, and advice on other human resources management practices.

Critics charge that consultants have strong incentives to help inflate CEO pay to

ensure future business. According to the chairman of the New Jersey Investment

Council: ‘‘The theoretical role of the compensation consultant is to make an

independent assessment of what senior executives are supposed to be paid. The

business model of being a compensation consultant is based on satisfying the

interests of the people about whom they’re supposed to be making that independent

judgment.’’3 For example, consultants who are beholden to powerful CEOs for

business (either ongoing compensation advice or other services) can use proprietary

compensation surveys to justify high CEO pay levels by selectively choosing the

peer group for benchmarking purposes (Crystal 1991; Buffett 2007).4 As one SEC

3 Journal of Corporate Law (2005). Beginning in 2004, NYSE and NASDAQ rules required all or nearly

all members of the board’s compensation committee to be ‘‘independent’’ (that is, not an officer or

employee of the company or a family member of such a director). However, if these ‘‘independent’’

directors owe their positions, future opportunities, or pay levels to the CEO, their compensation decisions

may still be influenced by the CEO’s power over the board (Bebchuk and Fried 2004; Campos 2007).4 See Conyon et al. (2009), Cadman et al. (2010), and Murphy and Sandino (2010) for studies examining

the association between CEO pay practices and the provision of additional (noncompensation) services by

consultants, and Goh and Gupta (2010) for research on compensation consultant opinion-shopping.


123

commissioner noted, ‘‘It is extremely difficult to avoid using high comparables, and

consultants can pretty much find high comparable income data to support paying a

high amount to the CEO. This is the case even if the consultant reports directly to

the board’’ (Campos 2007). Proponents of managerial power theories argue that

consulting practices such as these provide a mechanism for CEOs with significant

influence over the board of directors to justify excessive pay packages.

A primary assumption in this literature is that ‘‘weak’’ corporate governance and

‘‘excess’’ CEO pay are not in shareholders’ best interests. However, lower

governance costs may offset higher compensation costs. Recent theoretical research

suggests that strict corporate governance can be suboptimal because of the tradeoffs

between the board’s dual roles as advisors and monitors. If monitoring is too strict,

CEOs may not share information that would be beneficial in the board’s advisory

role in an effort to avoid more intensive monitoring (Adams and Ferreira 2007),

may avoid investments that may be perceived as manager-specific even if the

investment is in the shareholders’ interests (Almazan and Suarez 2003), and may

withhold private information on their abilities that the board needs in deciding

whether a CEO should be replaced (Laux 2008). Consequently, in some

circumstances it may be in shareholders’ best interest to employ ‘‘weaker’’ boards

that lack some independence. A similar argument can be made in the current

setting—namely, that weaker corporate governance, which may be optimal, reduces

the board’s ability to internally generate a benchmark for evaluating CEO

performance and compensation. This situation creates a demand in these firms for

compensation consultants to establish a suitable benchmark.

Given the potential interdependencies among governance practices, compensa-

tion policies, and the use of compensation consultants, we make no assumptions

regarding the optimality or sub optimality of these choices. Instead, we focus on the

more basic questions of whether corporate governance plays any role in the

observed relation between compensation consultants and CEO pay levels.

2.2 Related research

Despite the growing popular, legal, and political backlash against CEO pay levels

and perceived corporate governance failures, along with criticisms of compensation

consultants’ role in setting and justifying executive pay packages (for example,

Morgenson 2006a, b, 2008; Frank 2007; Piore 2007; U.S. House of Representatives

2007), relatively little evidence exists on the extent to which the higher pay levels

observed in compensation consultant clients are a manifestation of weaker

governance. What evidence that does exist is mixed. Consistent with claims that

consultants are used strategically to justify high pay, Wade et al. (1997) find that

companies with larger CEO salaries and bonuses are more likely to cite consultants

when rationalizing pay levels to shareholders, but the authors do not examine

whether these results are influenced by governance characteristics. Bizjak et al.

(2008) and Faulkender and Yang (2010) examine whether peer groups are

selectively chosen by companies with weak governance to extract excess pay. While

Bizjak et al. (2008) conclude that peer-group benchmarking is related more to

economic factors than to weak governance, Faulkender and Yang (2010) conclude


123

that CEOs of companies with weak governance choose peer groups that generate

higher compensation. Neither study examines the role of compensation consultants

in the choice of peer groups.

In the studies most closely related to ours, Conyon et al. (2009), Cadman et al.

(2010), and Murphy and Sandino (2010) examine the association between

compensation consultants and executive pay. Both Conyon et al. (2009) and

Cadman et al. (2010) find that total CEO pay is higher than predicted by economic

determinants in consulting clients but find no evidence that the higher pay is

associated with their limited set of proxies for corporate governance (CEO tenure

and average board member tenure). Murphy and Sandino (2010), in turn, examine

CEO pay levels in a sample of compensation consultant users. Two of their three

‘‘CEO influence’’ variables (an indicator for whether the CEO is also chairman of

the board and the percentage of directors the CEO appointed but not the percentage

of non-independent directors) are positively associated with CEO pay in U.S. firms

but not Canadian firms. However, they find higher CEO pay when the compensation

consultant works for the board rather than for management, which is inconsistent

with their prediction that consultants working for the board are more independent

and will recommend lower pay levels. Murphy and Sandino (2010) do not examine

differences between compensation consultant users and non-users.

Given this limited evidence and the increasing attention on the influence of

corporate governance and compensation consultants on CEO pay levels, we extend

prior compensation and governance studies to examine whether compensation

consultant use is associated with weaker governance and, if so, whether weaker

governance explains the observed relation between consultant usage and higher

CEO pay.

3 Sample and variable measurement

Our sample consists of 2,110 publicly traded companies with (1) fiscal years ending

on or after Dec. 31, 2006 (thereby falling under the new disclosure requirements),

that filed their annual proxy statements (DEF 14A) as of Dec. 14, 2007, and (2)

available data for the variables used in our analyses. These companies are primarily

in the Russell 3000, but we also include select smaller companies meeting these

criteria. This sample is broadly representative of corporations in the economy and

consists of considerably more firms than the samples in other compensation

consultant studies.

3.1 Compensation consultant use

Data on the use and identity of compensation consultants are collected from the

compensation committee report in the companies’ first proxy statement following

the introduction of the new disclosure rules. In most cases, the primary consultant is

clearly identified in the text of the proxy. If no consultant is discussed, we classify

the company as not using a consultant. In instances in which multiple consultants


123

are listed, we code the consultant the company used for senior executive-

compensation advice.5

In our sample, 87.60% use consultants for compensation advice, and 95 different

consulting firms are employed, with nine consultants engaged by more than 40

companies. The most frequently used consultant is Towers Perrin (12.24% of the

sample), followed by Mercer (11.07%), Frederic Cook (8.96%), Hewitt (8.11%),

Watson Wyatt (5.42%), Pearl Meyer (4.80%), Radford (2.64%), Compensia

(2.47%), and Hay (2.33%). The remaining companies use a wide variety of different

consulting firms, most of which are small boutique compensation consultants.

3.2 Compensation

Given the focus on total pay levels in recent debates over executive compensation,

we examine the CEO’s total annual compensation. Total annual compensation is

defined as the sum of salary, actual bonus, target long-term incentive plan payments,

pension contributions and other perquisites, the Black–Scholes value of stock option

grants, and the market value of restricted and unrestricted stock grants. As is

common in executive compensation studies (for example, Core and Guay 1999), we

apply a time discount of 0.70 to option maturity when calculating the option’s

Black–Scholes value to account for the prevalence of early option exercises. Annual

compensation in our sample ranges from $0 to $91,375,384, with a mean (median)

of $4,974,377 ($2,703,304). Similar to most executive compensation studies, we use

the natural logarithm of this number in our analyses because of the highly (right)

skewed distribution of pay.

The level of expected compensation may be influenced by the mix of pay between

relatively fixed components such as salary, benefits, and short-term bonuses and

riskier long-term variable pay components such as stock options, restricted stock, and

performance units. Economic theory indicates that expected pay levels must be higher

when pay is riskier to compensate the executive for the additional risk. Similar to Core

et al. (1999) and Murphy and Sandino (2008), we control for the risk-premium due to

additional compensation risk by including Pay Mix as a control variable in our tests.

This variable is calculated as the ratio of long-term variable pay (stock options,

restricted stock, and performance units) to total compensation.6

5 A common example of multiple consultants is an instance in which one consulting firm provides

compensation advice for senior executives and another provides advice for lower-level management.6 Our results are similar if we include bonuses in the numerator to get a more inclusive measure of

variable pay. As noted above, we examine total CEO pay levels because they have been the primary focus

of recent CEO compensation debates. However, if Pay Mix is capturing differences in consultant use and

governance, its inclusion will bias us against finding pay level differences due to these two factors. To

examine this possibility, we repeated our analyses using Pay Mix as the dependent variable in place of

CEO pay level. We find mixed governance results in a regression of Pay Mix on the economic and

governance variables discussed later in the paper (model adjusted R2 = 18.7%). Pay Mix is positively

associated with the percentage of old board members and negatively associated with the percentages of

busy board members and board members who are outsiders. The busy board result is inconsistent with

higher Pay Mix in firms with weaker governance. In propensity scoring matched pair analysis, we find

significantly higher Pay Mix in consultant users when only economic characteristics are included in the

analyses but no significant pay differences when both economic and governance characteristics are

included.


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3.3 Economic determinants

Consistent with prior theoretical and empirical compensation research, we include

variables to capture potential economic determinants of CEO pay. Studies indicate

that CEO compensation levels should increase with firm size, operating and stock

price performance, and investment opportunities (for example, Murphy 1999;

Lambert and Larcker 1987; Smith and Watts 1992; Core and Guay 1999). We

measure firm size using Log (Market Cap), which equals the natural logarithm of

the firm’s market capitalization at the beginning of the fiscal year.

Several variables are employed to capture operating and stock price performance.

Previous studies suggest that two elements of operating performance matter in

assessing executive pay levels. First, operating performance in the previous year

provides a reasonable estimate of expected performance in the current year, which is

likely to influence the target pay levels companies set. Second, changes in operating

performance during the period determine actual bonus payouts and other variable pay

elements that are based on the period’s operating performance. We therefore use two

variables to measure operating performance. Return on Assets is the company’s net

income in the prior year (Compustat element NI), scaled by the average of beginning-

and end-of-year total assets (average of Compustat element AT for the two dates).

Change in Return on Assets is the change in Return on Assets in the current year. We

also include stock price performance over the prior two prior years, denoted PriorReturn (-1) and Prior Return (-2), respectively. The two return measures allow for

the possibility that boards exhibit lags in their CEO pay decisions.

Similar to prior studies, we use the Book-to-Market ratio as an inverse measure of

the company’s investment opportunities. Book-to-Market equals the book value of

the firm’s total assets scaled by market capitalization, both measured at the

beginning of the fiscal year. Lower book-to-market ratios are expected to reflect

greater investment opportunities, since much of the market’s valuation of the

company (along with future cash flows) reflects factors that are not captured in the

book value of the company’s assets.

CEO-specific characteristics may also influence compensation levels. For

example, pay is likely to be higher for more experienced CEOs. We use three

variables to capture CEO experience: CEO Tenure (number of years the CEO has

held the title of chief executive officer), CEO Age (age of the CEO in years), and

New CEO (an indicator equal to one if the CEO was appointed to this position

during the fiscal year). The New CEO variable is included because firms often

provide relatively large compensation packages in the first year of a CEO’s tenure to

establish a certain level of equity incentives and to make the CEO whole with

respect to any compensation forfeited from his or her prior employer.7

7 New CEOs may also use talent agents or executive placement firms to help them negotiate new pay

packages. For example, using a propensity score matched pair research design, Rajgopal et al. (2011) find

that CEOs who use talent agents receive initial pay that is higher than predicted by either economic or

governance characteristics, a result that does not persist after the initial year. Given the potential

differences in the pay-setting practices used for new CEOs, we also conducted our tests after dropping the

252 firms with new CEOs in our sample. The only significant difference was that a variable capturing the

use of dual-class voting shares became insignificant in a regression of pay levels on economic and


123

CEOs may have other economic incentives that substitute for annual pay. To

control for the CEO’s existing equity incentives, we include the variable PortfolioIV, which equals the natural logarithm of one plus the intrinsic value of the CEO’s

equity portfolio of stock, restricted stock, and option holdings (both vested and

unvested). There are two possible pay outcomes associated with equity incentives. If

the CEO’s existing equity incentives and total wealth are high, there may be little

reason to provide additional incentives using annual compensation, and compen-

sation levels may be lower. In contrast, if the equity incentives provide the CEO

with considerable power over the board, we may observe higher annual

compensation. Finally, we include Founder CEO (an indicator that equals one if

the current CEO is one of the company’s founders) since company founders may

have incentives other than maximizing pay.

3.4 Governance measures

Prior studies linking corporate governance to compensation practices have typically

examined two broad categories of governance constructs: (1) board of director

characteristics and (2) board charter rules. We proxy for these constructs using

board of directors data from the Equilar analysis of proxy statements and board rules

data from FactSet SharkRepellent.Consistent with earlier studies, we use six variables to capture board character-

istics. Yermack (1996) and Coles et al. (2008) find that board size negatively

influences managerial and board decision-making. We therefore measure Number ofDirectors as the natural logarithm of the number of directors on the board, which

accounts for skewness in the number of directors. Our next five variables assess

board characteristics that Core et al. (1999), Fich and Shivdasani (2006), and others

find associated with excess CEO compensation. (1) % Outside Directors is the

percentage of board members classified as outsiders.8 (2) % Board Old is the

percentage of board members who are at least 69 years old. (3) % Board Busy is the

percentage of board members who serve on at least two boards of directors. (4)

Outside Chairman is an indicator that equals one if the chairman of the board is

classified as an outsider and zero otherwise (where outsiders are not involved in the

management of the company and have no substantial business dealings with the

company). And (5) % Outsider Apptd. by CEO is the percentage of board members

classified as outsiders who were appointed after the current CEO’s term began.

Following Core et al. (1999) and other governance studies, we expect stronger board

governance to be positively related to Outside Chairman and % Outside Directorsand negatively related to the other board characteristic variables.

Footnote 7 continued

governance variables. However, the model’s adjusted R2 remained nearly identical. Our propensity

scoring models and matched pairs tests of pay levels between consultant users and non-users exhibit no

significant changes when these observations are removed, suggesting that the inclusion of new CEOs is

not responsible for our results.8 The definitions of inside and outside board members are consistent with the NYSE definitions used for

listing requirements. These definitions are used in proxy statements and are adopted in our measures.


123

We include two additional variables for board charter rules that activist

shareholder groups and governance research suggest are important indicators of

governance effectiveness. The first is an indicator for whether the company’s board

members are all elected annually or are elected to staggered, multiyear terms

(denoted as Staggered Board). Activist shareholders argue that staggered terms

impede shareholders’ monitoring of the board by making it harder for them to alter

the board’s composition over a short period (for example, Daines and Klausner

2001; Gompers et al. 2003). Second, Dual Class equals one if the company has

multiple classes of shares with unequal voting rights. These shares are argued to be

an indicator of weaker governance (Gompers et al. 2003; Wang et al. 2010).

3.5 Industry

In addition to the preceding economic and governance factors, the compensation

literature emphasizes the importance of industry membership for labor market

benchmarking purposes. We follow prior literature and use industry fixed effects to

capture industry-specific differences in compensation levels. These fixed effects

indicators are based on two-digit SIC codes unless there are fewer than 25

observations in the industry, in which case we use one-digit SIC codes.9

3.6 Descriptive statistics

Descriptive statistics for CEO compensation and the economic and governance

variables are presented in Table 1. The table reports tests for differences in means

(one-way ANOVA) and medians (Kruskal–Wallis) for two groups of firms: those

that employ compensation consultants and those that do not. Firms using consultants

tend to have higher market capitalization and higher total pay, as well as a higher

proportion of riskier compensation. CEOs in firms without compensation consul-

tants are more likely to be firm founders and generally have longer tenure. However,

comparisons of the other economic variables are not consistently significant in the

two types of tests.

In contrast, the majority of the governance variables differ across the two

groups. Firms using consultants have more directors, busier board members, and

staggered boards, all of which are claimed to be elements of weaker governance.

Firms not using compensation consultants tend to have older boards and a larger

percentage of outside directors appointed by the CEO, purported to be indicators

of weaker governance. However, the chairmen of their boards and board members

are more likely to be outsiders, which prior studies suggest are signs of stronger

governance.

9 Albuquerque (2009) argues that a combination of industry and size quartile is a stronger peer group for

benchmarking executive compensation than is industry alone. As a robustness check, we conducted the

tests using the Albuquerque measure rather than our industry fixed effects. The results were virtually

identical, with any significant (insignificant) results remaining significant (insignificant) and estimated

coefficients changing little.


123

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123

Ta

ble

1co

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Con

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dev

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ean

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Th

ista

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pre

sen

tsd

escr

ipti

ve

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cs(m

ean

and

med

ian

)fo

rsa

mp

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acco

rdin

gto

wh

ether

aco

mp

ensa

tio

nco

nsu

ltan

tw

asu

sed

du

rin

gth

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rep

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are

the

pv

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on

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and

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test

sac

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Use

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OV

Ate

sts

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join

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ual

ity

of

mea

ns

of

each

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acro

ssca

tegori

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sum

esth

eco

var

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enden

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ally

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alv

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he

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skal

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way

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fv

aria

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isa

no

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amet

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test

of

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join

teq

ual

ity

of

med

ian

sac

ross

cate

go

ries

.T

ota

la

nn

ual

com

pen

sati

on

is

the

tota

lco

mp

ensa

tio

no

fth

eC

EO

for

the

fisc

aly

ear,

wh

ich

isd

efined

asth

esu

mo

fsa

lary

,ac

tual

bo

nus,

targ

etlo

ng

-ter

min

centi

ve

pla

np

aym

ents

,p

ensi

on

con

trib

uti

on

s

and

oth

erp

erq

uis

ites

,th

eB

lack

–S

cho

les

val

ue

of

sto

cko

pti

on

gra

nts

(usi

ng

70

%o

fth

eo

pti

on

’sli

fe),

and

the

mar

ket

val

ue

of

rest

rict

edan

du

nre

stri

cted

sto

ckg

ran

ts.

Pa

yM

ixis

the

rati

oo

flo

ng

-ter

mv

aria

ble

pay

(sto

cko

pti

ons,

rest

rict

edst

ock

,an

dp

erfo

rman

ceu

nit

s)to

tota

lco

mp

ensa

tio

n.L

og

(ma

rket

cap

ita

liza

tio

n)is

the

nat

ura

llo

gar

ith

m

of

the

firm

’sm

arket

capit

aliz

atio

nat

the

beg

innin

gof

the

fisc

alyea

rin

mil

lions

of

doll

ars.

Ret

urn

on

ass

ets

isth

efi

rm’s

net

inco

me

scal

edb

yth

eav

erag

eo

fth

eb

egin

nin

g-

and

end

-of-

yea

rto

tal

asse

ts.

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an

ge

inR

OA

isth

ech

ang

ein

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firm

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asse

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sp

rev

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sly

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ned

)d

uri

ng

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fisc

aly

ear.

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ok-

to-m

ark

etis

the

bo

ok

val

ue

of

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firm

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tal

asse

tssc

aled

by

mar

ket

cap

ital

izat

ion

,b

oth

mea

sure

dat

the

fisc

al-y

ear

end

.P

rio

rre

turn

(-2

)is

the

sto

ckp

rice

retu

rno

ver

the

pre

ced

ing

yea

r.P

rio

rre

turn

(-1

)is

the

sto

ckp

rice

retu

rno

ver

the

pri

or

yea

r.C

EO

Ten

ure

isth

en

um

ber

of

yea

rsth

ecu

rren

tC

EO

has

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dth

ech

ief

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uti

ve

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cer

titl

e.C

EO

Ag

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age

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ent

CE

Oin

yea

rs.P

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of

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CE

O’s

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,an

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on

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dan

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nv

este

d.N

ewC

EO

is

anin

dic

ato

req

ual

too

ne

ifth

eC

EO

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efi

scal

-yea

ren

db

ecam

eth

eC

EO

du

ring

the

fisc

aly

ear.

Fo

und

erC

EO

isan

ind

icat

or

that

tak

esa

val

ue

of

on

eif

the

curr

ent

CE

O

isone

of

the

founder

sof

the

firm

and

zero

oth

erw

ise.

Num

ber

of

dir

ecto

rsis

the

nu

mb

ero

fd

irec

tors

on

the

bo

ard

.%

Ou

tsid

ed

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tors

isth

eper

centa

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of

the

dir

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rs

who

are

clas

sifi

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ider

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ract

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Bo

ard

Old

isth

eper

centa

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of

the

mem

ber

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the

boar

do

fdir

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ho

are

atle

ast

69

yea

rsold

.F

ract

ion

of

bo

ard

bu

syis

the

per

cen

tag

eo

fth

em

emb

ers

of

the

bo

ard

of

dir

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rsw

ho

serv

eo

nat

leas

ttw

ob

oar

ds

of

dir

ecto

rs.O

uts

ide

lea

dd

irec

tor

isan

ind

icat

or

that

equ

als

on

eif

the

lead

dir

ecto

ris

clas

sifi

edas

ano

uts

ider

and

zero

oth

erw

ise.

Fra

ctio

nO

uts

ider

sapptd

.by

CE

Ois

the

frac

tion

of

the

mem

ber

so

fth

eboar

dof

dir

ecto

rsw

ho

are

clas

sifi

edas

outs

ider

sw

ho

wer

eap

po

inte

dsi

nce

the

CE

Oto

ok

offi

ce.S

tag

ger

edb

oa

rdis

anin

dic

ato

rv

aria

ble

that

tak

esa

val

ue

of

on

eif

the

firm

’sb

oar

do

fd

irec

tors

isst

agger

edan

dze

roo

ther

wis

e.

Du

al

cla

ssis

anin

dic

ato

rv

aria

ble

that

tak

esa

val

ue

of

on

eif

the

firm

has

mu

ltip

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asse

so

fsh

ares

wit

hd

iffe

ren

tial

vo

tin

gri

gh

tsan

dze

roo

ther

wis

e


123

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

(17)

(18)

(19)

(20)

(21)

(22)

(1)

Tot

al A

nnua

l Com

pens

atio

n0.

380.

700.

160.

020.

010.

130.

040.

020.

110.

41-0

.08

-0.0

70.

350.

190.

010.

41-0

.20

0.00

-0.0

90.

020.

16

(2)

Pay

Mix

0.55

0.38

0.01

-0.0

30.

050.

02-0

.08

-0.1

1-0

.14

0.11

-0.0

6-0

.07

0.03

0.16

-0.1

50.

36-0

.15

-0.0

50.

00-0

.01

0.16

(3)

Log(

Mar

ket C

apit

aliz

atio

n)0.

780.

390.

37-0

.01

0.08

0.21

-0.0

4-0

.02

0.07

0.42

-0.0

3-0

.10

0.46

0.21

-0.0

20.

47-0

.29

0.00

-0.0

60.

020.

21

(4)

Ret

urn

on A

sset

s0.

230.

100.

37-0

.21

0.06

0.43

0.08

0.09

0.09

0.18

-0.0

8-0

.08

0.12

0.04

0.05

0.05

-0.1

30.

090.

010.

020.

04

(5)

Cha

nge

in R

OA

0.09

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50.

02-0

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-0.0

10.

050.

170.

000.

010.

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10.

000.

020.

010.

04-0

.04

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4-0

.03

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10.

04

(6)

Boo

k-to

-Mar

ket

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2-0

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9-0

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-0.0

10.

07-0

.03

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0.02

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0.00

0.02

-0.0

30.

00-0

.05

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10.

05-0

.02

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2-0

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(7)

Pri

or R

etur

n (-

2)0.

220.

030.

230.

370.

14-0

.19

0.44

0.09

0.07

0.16

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3-0

.02

0.03

0.03

0.07

0.03

-0.0

40.

10-0

.02

-0.0

40.

00

(8)

Pri

or R

etur

n (-

1)0.

13-0

.08

0.01

0.01

0.33

0.10

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0.00

0.01

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90.

000.

03-0

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0.01

0.02

-0.0

20.

01-0

.02

0.00

0.00

(9)

CE

O T

enur

e0.

00-0

.07

-0.0

20.

10-0

.05

-0.0

70.

140.

030.

330.

29-0

.38

0.38

-0.0

8-0

.10

0.13

-0.1

4-0

.03

0.43

0.00

0.09

-0.1

6

(10)

CE

O A

ge0.

09-0

.12

0.08

0.07

-0.0

10.

060.

050.

020.

320.

15-0

.14

0.03

0.12

-0.0

10.

210.

01-0

.05

0.12

-0.0

40.

05-0

.03

(11)

Log(

Por

tfol

io I

V)

0.57

0.28

0.63

0.34

0.03

-0.1

90.

340.

180.

410.

15-0

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0.18

0.13

-0.0

50.

080.

13-0

.08

0.21

-0.0

50.

10-0

.02

(12)

New

CE

O-0

.11

-0.0

7-0

.03

-0.0

6-0

.04

0.05

-0.1

4-0

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6-0

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0-0

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3-0

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0.05

-0.2

80.

020.

010.

02

(13)

Fou

nder

CE

O-0

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-0.0

6-0

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1-0

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9-0

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10.

330.

010.

19-0

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0.03

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(14)

Num

ber

of D

irec

tors

0.37

0.06

0.48

-0.0

20.

030.

120.

050.

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-0.2

20.

050.

010.

15-0

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50.

040.

020.

21

(15)

% O

utsi

de D

irec

tors

0.26

0.17

0.22

0.04

0.05

-0.0

10.

040.

01-0

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1-0

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0.11

-0.0

90.

23-0

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90.

06-0

.14

0.18

(16)

% B

oard

Old

-0.0

2-0

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10.

05-0

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0.01

0.06

0.03

0.12

0.15

0.06

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3-0

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9-0

.14

0.04

0.01

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90.

05-0

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(17)

% B

oard

Bus

y0.

510.

370.

470.

120.

09-0

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0.03

-0.1

20.

030.

270.

00-0

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0.17

0.27

-0.1

1-0

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0-0

.02

-0.0

40.

18

(18)

Out

side

Cha

irm

an-0

.29

-0.1

4-0

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-0.1

1-0

.04

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1-0

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-0.0

4-0

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-0.0

6-0

.23

0.05

0.03

-0.1

7-0

.18

0.05

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0-0

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50.

00-0

.11

(19)

% O

utsi

der

App

td. b

y C

EO

-0.0

4-0

.05

-0.0

20.

11-0

.08

-0.0

60.

07-0

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0.11

0.29

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40.

29-0

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40.

01-0

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9

(20)

Stag

gere

d B

oard

-0.0

4-0

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-0.0

4-0

.01

-0.0

20.

03-0

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-0.0

20.

02-0

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50.

020.

030.

050.

06-0

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-0.0

2-0

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2-0

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0.08

(21)

Dua

l Cla

ss0.

050.

000.

03-0

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-0.0

10.

01-0

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0.00

0.05

0.04

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10.

01-0

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30.

000.

12-0

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3

(22)

Con

sult

ant U

sed

0.25

0.16

0.22

0.01

0.00

0.01

0.00

0.04

-0.1

1-0

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0.03

0.02

-0.1

10.

220.

18-0

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0.18

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1-0

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0.08

-0.0

3

Ta

ble

2C

orr

elat

ion

mat

rix

Th

ista

ble

pre

sen

tsa

corr

elat

ion

mat

rix

of

the

pri

mar

yv

aria

ble

s.P

ears

on

pro

duct

-mo

men

tco

rrel

atio

ns

are

rep

ort

edab

ov

eth

ed

iago

nal

,an

dS

pea

rman

rank

-ord

er

corr

elat

ion

sar

ere

po

rted

bel

ow

the

dia

go

nal

.C

orr

elat

ion

coef

fici

ents

that

are

stat

isti

call

ysi

gn

ifica

nt

atth

e1

%le

vel

or

less

are

repo

rted

inb

old

.C

onsu

lta

nt

use

dis

an

ind

icat

or

that

tak

esa

val

ue

of

on

eif

aco

mp

ensa

tio

nco

nsu

ltan

tis

use

dan

dze

roo

ther

wis

e.A

llre

mai

nin

gv

aria

ble

sar

ed

efin

edin

the

cap

tio

no

fT

able

1


123

3.7 Correlations

Table 2 reports correlations between our variables. Total annual compensation is

most highly correlated with firm size (market capitalization), followed by the CEO’s

existing portfolio value and the ratio of long-term variable pay to total

compensation (Pay Mix). Pay levels are significantly correlated (p \ 0.01) with a

number of the governance variables, including positive associations with the

number of directors, and the percentage of busy board members, and have a

significant negative association with chairmen who are outsiders. These correlations

are consistent with higher CEO pay in firms with weaker boards. Consultant use is

positively correlated with total annual compensation (Pearson r = 0.16, Spearman

r = 0.25) and with all of the governance variables except for dual class shares, with

the signs of the correlations generally suggesting that compensation consultants are

employed in firms with weaker governance.

4 Results

4.1 Economic and governance determinants of CEO pay levels

The univariate tests in Tables 1, 2 suggest that the higher CEO pay levels observed

in consultant users may actually reflect differences in corporate governance rather

than simply being due to compensation consultant use. We begin analyzing this

possibility by investigating whether CEO pay levels in our sample are related to

differences in economic and governance factors, independent of the use of

compensation consultants. If weak governance leads to pay levels that are greater

than economic factors justify, we should first observe higher-than-predicted pay

when governance is weaker (for example, Lambert et al. 1993; Borokhovich et al.

1997; Conyon and Peck 1998; Daily et al. 1998; Core et al. 1999; Cyert et al. 2002;

Faleye 2007).

Similar to prior compensation studies, our initial tests in Table 3 estimate

ordinary least squares models with the natural logarithm of total annual CEO

compensation as the dependent variable. We first estimate the model including only

the economic variables and then add the governance variables to assess their

incremental explanatory power. Both of the models include industry fixed effects,

with t-statistics based on robust standard errors that are clustered at the one-digit

SIC industry level.

The majority of the explanatory power is provided by the economic variables.

When the models are estimated with the economic variables alone, the adjusted R2

is 71.0%.10 Coefficient signs on most of the economic determinants are consistent

with expectations. Pay tends to be higher in larger companies, in those with higher

stock returns in the prior year, and for CEOs who are older and have larger existing

10 Although these results indicate that a riskier compensation mix has a significant influence on pay

levels, the exclusion of Pay Mix has little effect on our other results. The economic variable that explains

the most of the variation in pay levels is firm size (the natural logarithm of market capitalization), which

has an adjusted R2 of 58.0% when included alone in an untabulated model.


123

Table 3 Economic and

governance determinants of

annual compensation

This table presents the estimatedcoefficients and t-statistics of theregression where the natural

logarithm of Total annualcompensation is the dependentvariable. All of the remainingvariables are as defined in Tables 1,

2. Industry fixed effects (based ontwo-digit SIC unless there arefewer than 25 observations, inwhich case based on one-digit SIC)

are included but not reported in thetable. t-statistics are based onrobust standard errors that areclustered at the one-digit SIC

industry level. Model F-statistic isthe F-statistic from an F-test of thestatistical significance of the model.F-statistic for DR-squared is the F-

statistic for the change in R2 whenthe variables in addition to those inColumn (1) are added to the model.Statistical significance at the 10, 5,

and 1% levels (two-sided) isdenoted by *, **, and ***,respectively

(1) (2)

Intercept 9.968***

(37.93)

9.593***

(28.46)

Pay mix 1.519***

(10.88)

1.455***

(11.23)

Log (market cap) 0.393***

(31.87)

0.339***

(19)

Return on Assets -0.363*

(-1.85)

-0.294

(-1.52)

Change in ROA 0.104*

(1.85)

0.086

(1.48)

Book-to-market -0.016***

(-3.63)

-0.013***

(-2.88)

Prior return (-2) -0.087*

(-1.82)

-0.069

(-1.5)

Prior return (-1) 0.34***

(5.36)

0.312***

(5.02)

CEO tenure -0.025

(-1.1)

-0.011

(-0.43)

CEO age 0.011***

(5.74)

0.009***

(4.46)

Log (portfolio IV) 0.05***

(2.62)

0.055***

(3.04)

New CEO -0.126***

(-2.83)

-0.102**

(-2.11)

Founder CEO -0.167**

(-2.32)

-0.118*

(-1.88)

Number of directors 0.194

(1.11)

% Outside directors 0.326***

(3.65)

% Board old 0.226

(1.47)

% Board busy 0.379***

(4.77)

Outside lead director -0.065**

(-2.46)

% Outsider apptd. by CEO -0.028

(-0.72)

Staggered board -0.021

(-0.97)

Dual class 0.134***

(2.88)

Adj. R-squared (%) 71.0 72.3

Model F-statistic 431.69*** 273.30***

F-statistic for DR-squared 11.00***

Nobs 2,110 2,110


123

equity holdings. Pay tends to be lower when Book-to-Market is lower and the CEO

is either new to the position or a company founder. Contrary to our predictions, we

find that total pay is decreasing in Return on Assets in this model.11

When the governance variables are added, the model’s adjusted R2 increases to

72.3% (1.3% greater than the model without governance variables). An F-test for

the change in R2 from the addition of the governance variables is highly significant

(p \ 0.001), indicating that these variables as a group add statistically significant

(though modest economic) explanatory power. Four of the eight governance

variables are statistically significant (p \ 0.05, two-tailed), and all of the significant

variables have the predicted signs. Consistent with the claims of compensation

critics, total pay levels tend to be higher when board members are busier, the lead

director is an insider, and the firm has dual class voting shares. The governance

results in Table 3 support prior studies’ findings that CEO pay levels are higher than

predicted by economic characteristics when corporate governance is weaker.

4.2 Matched pair tests of pay levels and the use of compensation consultants

We next examine whether the higher total pay levels for consulting clients seen in

Tables 1, 2 (as well as in prior studies by Murphy and Sandino 2008; Conyon et al.

2009; and Cadman et al. 2010) are driven by firms with weaker corporate

governance rather than by the provision of consulting services. If firms using

consultants also have weaker governance, then the omission of governance

characteristics from the model leads to potential correlated omitted variable

problems and misinterpretation of the results.

In these tests, we employ a propensity score matched pair research design

(Rosenbaum and Rubin 1983), which requires a model for the conditional

probability of using a compensation consultant given observable features of the

company’s contracting environment. We assume that the choice to use a consultant

is based on the economic and governance variables (or ‘‘covariates’’) discussed

above, and we estimate a series of multivariate logistic models where the dependent

variable in each model equals one if a compensation consultant is used and zero

otherwise.

After estimating the conditional probability that a company uses a given

consultant, we match a firm that uses a consultant with a firm having a similar

probability of using a consultant but, in fact, does not. We employ a nonbipartite

matching algorithm suggested by Derigs (1988), which is an ‘‘optimal’’ algorithm in

the sense that it considers the potential distances between other matched pairs when

forming a particular matched pair. If pay levels in the matched pairs remain

significantly higher after matching on the economic covariates alone, but the

differences disappear after matching on both the economic and governance

covariates, the higher pay levels in the consultant users can be attributed to

governance differences rather than to the use of a consultant. For example, consider

11 In unreported analyses, we also include an indicator for whether Return on Assets is negative to allow

for a different level of compensation for loss firms. This indicator variable is negative but not statistically

significant, and the statistical significance of the other variables in the model is unchanged.


123

two pairs of matched firms. The matched firms in the first pair (one a consultant user

and the other a non-user) have similar economic characteristics, similar stronggovernance characteristics, and similar pay levels. The matched firms in the second

pair (one a consultant user and the other a non-user) also have similar economic

characteristics, similar weak governance characteristics, and similar pay levels. If

this were the case, it would be impossible to conclude that consultant use is driving

pay differentials.

Propensity score matching has several advantages for these tests. First, the use or

non-use of a compensation consultant provides natural dichotomous ‘‘treatment’’

(consultant) and ‘‘control’’ (no consultant) groups, the type of setting this statistical

method was designed to evaluate. Second, propensity score matching alleviates a

number of econometric problems found in typical regression models. In particular,

propensity score matching does not rely on a linear functional form linking the

outcome variable of interest (the level of CEO pay) with both the independent

variable of interest (compensation consultant) and the other predictor variables or

‘‘covariates’’ (for example, firm size, industry, and governance practices). In

addition, propensity score matching provides some insight into the extent to which

the confounding effects of correlated omitted variables are likely to affect the

parameter estimates derived from a linear model.

The matched pair approach relaxes the strict functional form of the relation

between total pay levels and the compensation consultant treatment and other

covariates implicit in a linear regression model. With a matched pair research

design, the matched pairs are formed from observations that differ in the variable of

interest (that is, the use of a compensation consultant in this study) but are otherwise

similar along other relevant variables (that is, economic and governance charac-

teristics). Thus, any differences in CEO compensation levels can be attributed to

differences in the use of a compensation consultant rather than to differences in the

other covariates, regardless of the underlying structural form.

Our matched pair research design also explicitly acknowledges that compensa-

tion consultant use or non-use is not a result of random or exogenous assignment

and attempts to model the matching of firms’ consultant use on the basis of

observable variables (for example, economic and governance characteristics).12

After matched pairs have been formed on the basis of observable characteristics and

statistical tests for differences in compensation conducted, we can assess the

sensitivity of our significant results to unobservable, correlated omitted variables (or

hidden bias). Specifically, we can determine the magnitude of the correlated omitted

variable bias that is necessary to cause any statistically significant differences

between matched pairs to become insignificant. This computation enables us to

provide insight into whether the observed results are statistically robust.

12 Endogeneity can be addressed in OLS models using two-stage instrumental variable procedures.

However, this approach requires identifying appropriate instruments that are correlated with the

independent variable of interest but uncorrelated with the error term in the model, which can be difficult

in a study such as this. In addition, two-stage least squares continues to rely on linearity assumptions. As a

result, Larcker and Rusticus (2010) show that only under very restrictive conditions will two-stage least

squares reduce endogeneity problems in OLS.


123

4.2.1 Propensity scoring models of compensation consultant use

Table 4 presents results from our logistic propensity scoring models for consultant

use. Column 1 estimates the model using only the economic covariates. The model

is statistically significant with a pseudo-adjusted R2 of 15.3%. The probability of

using a compensation consultant is increasing in market capitalization and

decreasing in (1) the prior year’s stock return, (2) CEO tenure, (3) whether the

CEO is new to the position, and (4) the value of the CEO’s equity portfolio. The

negative results for CEO tenure and equity portfolio are consistent with the

predictions of Cadman et al. (2010), although only CEO equity ownership is

significant in their consultant prediction model.

The addition of the governance variables in Column 2 increases the adjusted R2

to 21.8%. The significant coefficients on the governance variables are consistent

with claims that firms with weaker governance are more likely to employ

consultants to justify their pay levels.13 In particular, the probability of consultant

use increases if the firm has a larger board, a larger percentage of busy board

members, and a staggered board. All of these characteristics are assumed to be

indicators of weak governance. Companies with a larger percentage of old board

members are less likely to engage consultants, consistent with the univariate tests in

Tables 1, 2 that indicated that non-users tend to have older boards. Overall, the

evidence in Table 4 indicates that firms with weaker governance are more likely to

use compensation consultants, even after taking economic factors into account.

4.2.2 Covariate balance tests

The preceding propensity matching models are used to match firms that have similar

economic and governance characteristics but differ in their use of consultants.

Matching is done without replacement and using an ‘‘optimal’’ rather than a

‘‘greedy’’ algorithm so that each matched pair is formed considering the effect of

the removal of those observations (since matching is without replacement) on

subsequent matches. The resulting sample size in these analyses is 263 matched

pairs (526 firms), since this is the number of firms in our sample that do not employ

compensation consultants and are therefore available for matching.

13 One of the necessary conditions to implement a propensity score research design is known as the

overlapping or common support assumption (Rosenbaum and Rubin 1983). This requirement rules out the

possibility that the propensity score model perfectly classifies observations into either the treatment or

control groups conditional on the observable covariates, X (that is, 0 \ Pr (Treatment = 1|X) \ 1). This

condition is necessary to ensure that for each observation, there is a potential match that has a similar

probability of receiving the treatment. In other words, it ensures that observations with the same

observables (that is, X) have a positive probability of both receiving the treatment and not receiving the

treatment. A related concern is a propensity score model with a low degree of explanatory power. If the

explanatory power of the model is too low, the propensity score research design essentially forms

matched pairs at random, which leaves a large scope for hidden bias to confound the results. In our case,

the propensity score models of the conditional probability of using a compensation consultant have

relatively high degrees of explanatory power (that is, 15.3% when only the economic variables are

included and 21.8% when the economic and individual governance variables are included), suggesting

that the matched pairs that we form in our next analysis are relatively similar on the basis of their

observable covariates.


123

Table 4 Determinants of

consultant usage

This table presents the estimatedcoefficients and z-statistics of alogistic regression whereConsultant usage is thedependent variable, which takes avalue of one if the firm uses acompensation consultant and zerootherwise. All of the remainingvariables are as defined inTables 1, 2. Industry fixed effects(based on two-digit SIC unlessthere are fewer than 25observations, in which case basedon one-digit SIC) are included butnot reported in the table. Z-statistics are based on robuststandard errors that are clusteredat the one-digit SIC industrylevel. Statistical significance atthe 10, 5, and 1% levels (two-sided) is denoted by *, **, and***, respectively

(1) (2)

Intercept 2.444***

(2.68)

-2.849**

(-2.37)

Pay mix 1.246***

(3.73)

0.999***

(2.90)


(7.63)

0.257***

(3.14)

Return on assets 0.340

(0.69)

0.571

(1.17)

Change in ROA 0.511*

(1.88)

0.518*

(1.95)

Book-to-market -0.013

(-0.64)

-0.002

(-0.09)

Prior return (-2) -0.427***

(-3.69)

-0.287**

(-2.33)

Prior return (-1) 0.250

(1.27)

0.074

(0.37)

CEO tenure -0.329**

(-2.44)

-0.287**

(-2.03)

CEO age 0.000

(0.01)

0.001

(0.15)

Log (portfolio IV) -0.234***

(-3.58)

-0.131**

(-1.96)

New CEO -0.556*

(-1.82)

-0.462

(-1.48)

Founder CEO -0.054

(-0.26)

0.124

(0.56)

Number of directors 1.865***

(5.47)

% Outside directors 2.133***

(4.70)

% Board old -1.430***

(-2.90)

% Board busy 0.733**

(2.06)

Outside lead director -0.197

(-1.17)

% Outsider apptd. by CEO -0.277

(-1.12)

Staggered board 0.288**

(1.97)

Dual class -0.076

(-0.31)

Pseudo adj. R-squared (%) 15.3 21.8

Nobs 2,110 2,110


123

We evaluate the efficacy of our matching algorithm by examining the covariate

balance (that is, the similarity of the covariate distributions) between the matched

pairs within each consultant category. Covariate balance is achieved if the two

matched groups appear similar with respect to their observable contracting

environments (that is, the economic and/or governance covariates in the propensity

score equation). Lack of balance across certain covariates highlights an identifi-

cation problem that complicates separation of the treatment effect (that is,

compensation consultant use) and the effect of the unbalanced covariate (for

example, firm size or board structure).14

To assess covariate balance between the treatment and control groups, we

conduct tests of differences in means using parametric t-tests and differences in

medians using nonparametric Kolmogorov–Smirnov (KS) tests. The results are

reported in Table 5. Table 5 presents comparisons when only the economic

variables are used in the propensity score model. With the exception of median

(though not mean) differences in Return on Assets, none of differences in economic

characteristics are statistically significant in the treatment (consultant) and control

(no consultant) groups (p \ 0.10, two-tailed). Thus, the algorithm effectively

matches on these factors. However, the treatment and control groups continue to

exhibit significant differences in governance characteristics after matching on the

economic characteristics. In particular, the treatment group has more board

members and larger percentages of outsiders on their board and busy board

members, while the control group has a larger percentage of old board members. In

contrast, when the firms are matched on both economic and governance

characteristics (Table 6), they are not significantly different on any of the

characteristics, with the exception of median (but not mean) Pay Mix and CEOAge. Again, this evidence suggests that the matching algorithm was effective in

identifying firms that are similar along the variables included in the propensity

matching model (though not on variables that are not included) but are in different

compensation consultant groups.

4.2.3 Matched pair pay level results

Table 7 presents the matched pair pay level results. We conduct parametric and

nonparametric tests of pay level differentials using t-tests and Wilcoxon signed-rank

tests. The results indicate that companies using compensation consultants continue

to exhibit statistically higher CEO pay (p \ 0.01, two-tail) than do their matched

counterparts with similar observable economic (but not governance) dimensions.

The mean ($529,618) and median ($300,425) differences are consistent with prior

14 To illustrate this point in the current context, suppose consulting firms consult only to relatively large

companies. Further, suppose that it is not possible to form matched pairs of firms that use consultants with

non-users of a similar size. In that case, there would be a lack of balance across the matched pairs with

respect to firm size. Consequently, any difference in the level of total compensation across the matched

pairs cannot be unambiguously attributed to using a compensation consultant, because the difference

could also be attributable to differences in firm size. Examining the covariate balance across the matched

pairs is therefore crucial for highlighting any identification problems that might exist.


123

studies’ findings that consulting clients pay their CEOs more than economic

characteristics predict.

As discussed earlier, one explanation for the statistically significant differences in

pay between consultant users and non-users is that corporate governance differences

are correlated omitted variables that are responsible for the significant consultant

results. Propensity score matching mitigates overt bias by balancing the relevant

covariates across the two categories of interest (in this case, firms that use

consultants and their matched counterparts that do not). However, the results are

susceptible to ‘‘hidden bias’’ as a result of correlated omitted variables that are not

balanced across the two categories (as seen for some of the governance variables in

the covariate balance tests in Table 5). Rosenbaum (2002, 2007) develops a

bounding approach for assessing the sensitivity of the matched pair results to hidden

Table 5 Matched pair analysis: economic matched pair covariate balance

Mean

treatment

Mean

control

Median

treatment

Median

control

t test

difference

p value

KS bootstrap

difference

p value

Pay mix 0.634 0.658 0.623 0.662 0.273 0.128

Log (market capitalization) 6.243 6.150 6.225 5.991 0.480 0.291

Return on assets -0.003 -0.001 0.026 0.034 0.878 0.017

Change in ROA -0.013 -0.024 0.000 0.001 0.707 0.901

Book-to-market 0.291 0.341 0.400 0.380 0.778 0.495

Prior return (-2) 0.177 0.201 0.045 0.080 0.689 0.695

Prior return (-1) 0.145 0.159 0.106 0.087 0.704 0.965

CEO tenure 1.995 2.017 2.054 2.079 0.762 0.390

CEO age 55.103 54.795 55.000 54.000 0.679 0.214

Log (portfolio IV) 16.926 16.736 16.811 16.793 0.234 0.419

New CEO 0.106 0.103 0.000 0.000 0.887 0.943

Founder CEO 0.183 0.179 0.000 0.000 0.910 0.960

Number of directors 2.184 2.138 2.197 2.079 0.028 0.039

% Outside directors 0.671 0.626 0.700 0.625 0.001 0.011

% Board old 0.127 0.160 0.111 0.125 0.020 0.022

% Board busy 0.310 0.265 0.286 0.222 0.019 0.021

Outside lead director 0.692 0.753 1.000 1.000 0.120 0.129

% Outsider apptd. by CEO 0.715 0.726 0.800 1.000 0.713 0.036

Staggered board 0.471 0.414 0.000 0.000 0.189 0.189

Dual class 0.114 0.103 0.000 0.000 0.675 0.705

This panel presents the mean and median values of the variables across the treatment and control groups

where the treatment is whether a compensation consultant was used and firms are matched according to

the economic variables Pay mix, Log (market capitalization), Return on assets, Change in ROA, Book-to-

market, Prior return (-2), Prior return (-1), CEO tenure, CEO age, Log (portfolio IV), New CEO, and

Founder CEO. The last two columns present the p values of a paired t test and Kolmogorov–Smirnov test

across the treatment and control samples. All of the variables are defined in Tables 1, 2


123

bias. In our context, hidden bias exists if two companies (denoted i and j) have the

same observed economic covariates but different probabilities (denoted p) of being

assigned to a consultant category (user or non-user). The odds that each company is

assigned to the relevant consultant category are denoted pi/(1-pi) and pj/(1-pj),

respectively. If the odds ratio, denoted by U, does not equal one, then the two

companies have an unequal probability of being assigned to a consultant category

and hidden bias exists. Rosenbaum (2002) shows that relaxing the assumption that

U = 1 allows computation of the amount of hidden bias needed to alter the

significant inferences. Smaller values indicate statistically significant results that are

more sensitive to hidden bias.

When we assess the sensitivity of our significant results when firms are matched

only on economic covariates, the significant (negative) difference in median pay

between the companies that do not use consultants and their matched counterparts

Table 6 Economic and governance matched pair covariate balance

Mean

treatment

Mean

control

Median

treatment

Median

control

t test

difference

p value

KS bootstrap

difference

p value

Pay mix 0.654 0.658 0.650 0.662 0.856 0.091

Log (market capitalization) 6.168 6.150 5.993 5.991 0.888 0.631

Return on assets -0.009 -0.001 0.030 0.034 0.623 0.631

Change in ROA -0.004 -0.024 0.001 0.001 0.488 0.409

Book-to-market 0.438 0.341 0.390 0.380 0.341 0.791

Prior return (-2) 0.173 0.201 0.042 0.080 0.631 0.717

Prior return (-1) 0.156 0.159 0.114 0.087 0.920 0.373

CEO tenure 1.970 2.017 2.054 2.079 0.522 0.320

CEO Age 54.544 54.795 54.000 54.000 0.729 0.077

Log (portfolio IV) 16.714 16.736 16.659 16.793 0.895 0.836

New CEO 0.118 0.103 0.000 0.000 0.578 0.604

Founder CEO 0.179 0.179 0.000 0.000 1.000 1.000

Number of directors 2.134 2.138 2.079 2.079 0.845 0.428

% Outside directors 0.622 0.626 0.625 0.625 0.767 0.811

% Board old 0.154 0.160 0.143 0.125 0.705 0.699

% Board busy 0.269 0.265 0.286 0.222 0.824 0.455

Outside lead director 0.757 0.753 1.000 1.000 0.919 0.954

% Outsider apptd. by CEO 0.711 0.726 0.875 1.000 0.619 0.509

Staggered board 0.395 0.414 0.000 0.000 0.658 0.694

Dual class 0.118 0.103 0.000 0.000 0.578 0.625

This panel presents the mean and median values of the variables across the treatment and control groups

where the treatment is whether a compensation consultant was used and firms are matched according to

the economic variables Pay mix, Log (market capitalization), Return on assets, Change in ROA, Book-to-

market, Prior return (-2), Prior return (-1), CEO tenure, CEO age, Log (portfolio IV), New CEO, and

Founder CEO and the governance variables Number of directors, % Outside directors, % Board old,

% Board busy, Outside lead directors, % Outsider apptd. by CEO, Staggered board, and Dual class. The

last two columns present the p values of a paired t test and Kolmogorov–Smirnov test across the treatment

and control samples. All of the variables are defined in Tables 1, 2


123

would become insignificant if U = 1.24. The U of 1.24 means that a correlated

omitted variable (or variables) would only have to shift the assumed equal (50%/

50%) probability of being assigned to the no consultant category to a 54.4%/

44.6% (= 1.24) probability for the significant result to become insignificant at the

10% level. Thus, omitting a relevant variable that is only mildly correlated with

both being in the no consultant group and pay levels could produce the significant

results. The small magnitude of U in these analyses indicates that the results using

only economic covariates are sensitive to hidden bias due to correlated omitted

variables.

Given this limitation, we extend our matched pair pay level analysis to include

both economic and governance covariates. When matched on both sets of

covariates (Table 7), the pay level differences in the matched pairs are no longer

statistically significant (p [ 0.10, two-tailed). The lack of a significant difference

after matching on governance implies that these governance characteristics are

important correlated omitted variables in tests that include economic character-

istics alone. More importantly, the results are consistent with the attribution of pay

level differences in consultant users and non-users to governance differences

rather than with anything inherent in the use or non-use of compensation

consultants. The strong governance effect is consistent with prior studies that find

governance factors important in explaining executive compensation levels (for

example, Conyon and Peck 1998; Daily et al. 1998; Core et al. 1999). The results

are also consistent with the consulting clients with weak governance using

consultant’s advice to facilitate or justify pay packages in excess of what

economic characteristics alone would predict, while clients with stronger

governance use consultants’ advice to design efficient compensation plans

consistent with their economic characteristics.

Table 7 Matched pair compensation differences

Matching variables Mean

compensation

difference

Median

compensation

difference

Wilcoxon

statistic

p value t-statistic p value

Economic $529,618 $300,425 3.179 0.001 1.719 0.087

Economic and governance $51,185 $74,539 1.318 0.187 1.087 0.279

This panel presents the results of the comparison of the level of Total Annual Compensation between the

firms that use a compensation consultant and do not use a compensation consultant. In the first row, firms

are matched according to the economic variables Pay mix, Log (market capitalization), Return on assets,

Change in ROA, Book-to-market, Prior return (-2), Prior return (-1), CEO Tenure, CEO age, Log(portfolio IV), New CEO, and Founder CEO. In the second row, firms are matched according to the

economic variables and the governance variables Number of directors, % Outside directors, % Board old,

% Board busy, Outside lead directors, % Outsider apptd. by CEO, Staggered board, and Dual class. The

first two columns present the mean and median differences in the level of total annual compensation

between the firms that use a compensation consultant and their matched counterparts. The third and fourth

columns present a Wilcoxon statistic of the rank-sum difference in the median compensation between the

firms that use a compensation consultant and their matched counterparts and the corresponding p value

(two-sided). The fifth and sixth columns present a t-statistic for a test of the difference in the mean

compensation between the firms that use a compensation consultant and their matched counterparts and

the associated p value (two-sided)


123

4.3 Comparison with OLS results

As discussed earlier, our propensity score matched pair research design has

advantages in our research setting relative to traditional linear regression approaches

used in related studies. These advantages include relaxing the strict linear functional

form linking pay levels to consultant use and the other covariates and minimizing

correlated omitted variables problems that are likely to affect the parameter

estimates derived from a linear model. However, matching without replacement

limits our sample size in the propensity matching tests to 263 matched pairs (526

firms), which represents the number of firms in our sample that do not use

compensation consultants and are available for matching. Consequently, our

inability to detect any consultant effects on pay levels after matching on governance

characteristics may be due to the smaller sample size or sample composition.

To provide some evidence on whether the differences between our results and

prior studies are due to differences in research methods or to the smaller sample

used in the matched pairs tests, we estimate linear OLS models of pay levels on the

economic and governance variables and a compensation consultant indicator for

both the full sample and matched pair subsample. If our results are simply due to the

smaller matched pair sample, the consultant indicator should be significant in the

full sample and insignificant in the smaller matched pair subsample. We report the

results in Table 8. Consistent with prior studies, we find that when the consultant

indicator is included in the model with the economic variables alone, the coefficient

on the indicator is positive and highly significant (p \ 0.01, two-tailed) using either

sample. The coefficient on the consultant indicator remains positive and significant

in both samples when the governance variables are added, indicating that the

observed consultant effect does not disappear in the smaller sample in linear

regression tests. This evidence suggests that the insignificant consultant effects in

our propensity matched pair analyses are due to this method’s econometric

advantages rather than to the smaller matched sample used in these tests.

4.4 Specialized vs. nonspecialized consultants

Although we assume that all compensation consultants have similar incentives to

facilitate (or not facilitate) the extraction of ‘‘excess pay’’ by CEOs of firms with

weak governance, compensation critics contend that the incentives to facilitate

higher pay levels are greater in nonspecialist consultants who also provide advisory

services other than compensation advice, such as actuarial services or benefits

administration. In many cases, these other services are more lucrative than the

compensation consulting engagement (Crystal 1991; U.S. House of Representatives

2007). Even if a nonspecialized consulting firm that offers a broad range of services

does not currently provide any additional services to the client, it may not want to

jeopardize the possibility of obtaining ‘‘add-on’’ work in the future (Bebchuk and

Fried 2004; U.S. House of Representatives 2007). Claims that broad-based service

offerings lead to conflicts of interest between consultants and clients suggest that

excess CEO pay should be higher in companies using nonspecialized, potentially


123

Table 8 OLS models of the determinants of annual compensation for the full sample and the matched

pair subsample

(1) (2) (3) (4)

Intercept 9.784***

(38.03)

10.838***

(36.65)

9.53***

(28.05)

10.687***

(28.05)

Pay mix 1.487***

(9.7)

1.497***

(8.32)

1.43***

(10.38)

1.469***

(7.67)


(32.01)

0.359***

(18.53)

0.336***

(18.89)

0.327***

(11)

Return on Assets -0.376*

(-1.85)

-0.345***

(-2.91)

-0.309

(-1.53)

-0.261**

(-2.13)

Change in ROA 0.088

(1.3)

0.049

(1.63)

0.072

(1.07)

0.039

(1.26)

Book-to-market -0.016***

(-3.46)

-0.045

(-1.02)

-0.013***

(-2.83)

-0.046

(-1.1)

Prior return (-2) -0.075

(-1.54)

-0.127***

(-3.71)

-0.062

(-1.31)

-0.119***

(-3.29)

Prior return (-1) 0.335***

(5.12)

0.227***

(2.9)

0.311***

(4.85)

0.204**

(2.58)

CEO tenure -0.019

(-0.81)

-0.075*

(-1.68)

-0.006

(-0.25)

-0.08*

(-1.67)

CEO age 0.011***

(6.24)

0.008***

(2.75)

0.009***

(4.85)

0.006

(1.55)

Log (portfolio IV) 0.055***

(2.76)

0.018

(0.85)

0.058***

(3.11)

0.013

(0.56)

New CEO -0.116***

(-2.73)

-0.165

(-1.46)

-0.097**

(-2.05)

-0.151

(-1.27)

Founder CEO -0.164**

(-2.32)

-0(-0.03)

.003

(-1.93)

-0.119*

0.031

(0.39)

Consultant Used 0.197***

(2.95)

0.225***

(3.31)

0.165***

(3.06)

0.215***

(3.82)

Number of Directors 0.161

(0.92)

0.111

(0.76)

% Outside Directors 0.29***

(3.44)

0.283**

(2.27)

% Board Old 0.254*

(1.7)

0.240

(1.06)

% Board Busy 0.369***

(4.54)

0.32**

(2.13)

Outside Lead Director -0.063**

(-2.43)

-0.015

(-0.27)

% Outsider Apptd. by CEO -0.027

(-0.67)

0.100

(1.11)


123

‘‘conflicted’’ consultants than in those using specialized, ‘‘nonconflicted’’ compen-

sation consultants.15

We examine this issue by grouping the consulting firms in our sample into

nonspecialized firms that offer a broad range of services (Towers Perrin, Mercer,

Hewitt, Watson Wyatt, Radford, and Hay) and specialized firms that limit their

offerings to compensation advice (Frederic Cook, Pearl Meyer, Compensia, and the

remaining smaller, primarily boutique firms). We conduct a propensity score matched

pair analysis on the subsample of firms that use compensation consultants. Companies

that do not use consultants are excluded. We treat specialized firms as the treatment

group and nonspecialized (potentially conflicted) firms as the control group.

In the (untabulated) propensity scoring model, we find that firms are more likely

to use specialized consultants when they have a lower market capitalization, lower

returns in the two prior years, and a CEO who is a founder. With respect to the

governance variables, the percentage of board members who are outsiders and

the percentage of busy board members have significant negative associations with

the probability of using specialized consultants, who are claimed to have fewer

conflicts of interest. The latter significant governance variable is consistent with

claims that firms with weaker governance choose nonspecialized consultants who

Table 8 continued

(1) (2) (3) (4)

Staggered Board -0.027

(-1.25)

-0.121**

(-1.96)

Dual Class 0.136***

(2.86)

0.113

(1.17)

Adj. R-squared (%) 71.5 60.9 72.6 62.1

Model F-statistic 404.99*** 61.09*** 263.18*** 38.38***

F-statistic for DR-squared 11.80*** 3.96***

Nobs 2,110 526 2,110 526

This table presents the estimated coefficients and t-statistics of regressions where the natural logarithm of

Total annual compensation is the dependent variable using the full sample (Columns (1) and (3) and the

subsample of firms from the matched pair analysis (Columns (2) and (4)). All of the remaining variables

are defined in Tables 1, 2. Industry fixed effects (based on two-digit SIC unless there are fewer than 25

observations, in which case based on one-digit SIC) are included but not reported in the table. t-statistics

are based on robust standard errors that are clustered at the one-digit SIC industry level. Model F-statistic

is the F-statistic from an F-test of the statistical significance of the model. F-statistic for DR-squared is

the F-statistic for the change in R2 when the variables in addition to those in columns (1) and (2) are

added to the model. Statistical significance at the 10, 5, and 1% levels (two-sided) is denoted by *, **, and

***, respectively

15 The separation into potentially ‘‘conflicted’’ and ‘‘nonconflicted’’ consultants is consistent with that

used in U.S. government investigations (for example, U.S. House of Representatives 2007). Academic

research to date provides mixed support for the prediction that consultants providing additional services

are associated with higher pay levels, with Conyon et al. (2009) and Cadman et al. (2010) finding no

support for these claims and Murphy and Sandino (2010) finding some support depending upon the type

of additional service provided. However, these studies provide relatively little evidence on the extent to

which governance influences these results.


123

may be more likely to facilitate excess pay to protect their other work. However, the

majority of the governance variables are insignificant, and the explanatory power in

these propensity scoring models is quite low (adjusted pseudo R2s range from 3.3%

in the model including only economic variables to 4.1% in the model including both

economic and governance variables). Thus, in contrast to the use or non-use of

compensation consultants, neither the economic nor governance covariates is a

strong predictor of the use of specialized versus non-specialized consultants (given

the choice to use a consultant). The subsequent propensity matching results should

therefore be interpreted accordingly.

To achieve adequate covariate balance, we trimmed 10% of the observations.

The resulting (untabulated) comparisons of mean and median pay level differences

between the matched pairs revealed no significant differences after controlling for

economic characteristics alone or for both economic and governance characteristics

(p \ 0.10, two-tailed). These results provide no support for claims that pay levels

are higher in potentially ‘‘conflicted’’ consulting firms that offer a broad variety of

services.16

5 Conclusion

This study contributes to the debate over the influence of corporate governance on

CEO pay levels and the role compensation consultants play in this relation.

Although a number of prior studies provide evidence that CEO pay levels are higher

in companies using compensation consultants, they provide little evidence regarding

why this relation exists. Using a larger sample of firms than was used in previous

studies, we continue to find that CEO pay is higher than predicted by economic

determinants in firms using consultants. However, we also find that companies with

weaker corporate governance are more likely to use compensation consultants. After

using propensity scoring methods to match firms on both economic and governance

characteristics, we find no significant pay differences in consultant users and non-

users. These results imply that the higher observed pay levels in firms using

compensation consultants is largely driven by governance differences, with

economically similar firms with comparable governance characteristics having

similar pay levels, regardless of whether they engage a compensation consultant.

Moreover, these results do not vary between ‘‘specialized’’ and ‘‘nonspecialized’’

(potentially conflicted) consultants, providing no support for claims that consultants

who offer a broad range of services are more likely to facilitate excess pay levels

because of greater conflicts of interest.

Our results are subject to three primary limitations. First, we (and other studies

using U.S. proxy statement data) do not have information on the magnitude of other

16 Further analysis indicates that governance characteristics help explain pay levels even within clients of

individual consultants. This evidence indicates that it is not the use of specific consultants but governance

differences that explain the observed relation between consultant use and excess pay levels. Additional

analysis of the association between individual consultants and CEO pay levels is provided in an earlier

version of this study, titled ‘‘Economic Characteristics, Corporate Governance, and the Influence of

Compensation Consultants on Executive Pay Levels,’’ available at http://ssrn.com/abstract=1145548.


123

http://ssrn.com/abstract=1145548

noncompensation services consultants provide, nor do we have the potential to

obtain this information in the future, because U.S. companies are not required to

disclose it. This lack of information limits our ability to examine claims that

consultants with conflicts of interest contribute to rent extraction. Second, our data

analyses provide no way to determine whether or when consultants play an active

role in facilitating rent extraction by CEOs in firms with weak governance. Finally,

we do not have time series data on consultant usage and do not know whether

consultants were used before the new disclosure requirements or how changes in

compensation consultants influenced CEO pay levels. Future studies can extend our

analysis by examining evolving compensation consultant usage and pay levels in the

years following the new disclosure requirements.

Acknowledgments This paper has benefited greatly from many insightful discussions with Paul

Rosenbaum. We also thank Bo Lu for making available his code to perform the nonbipartite matching

algorithm used in this study, and two anonymous reviewers and Katherine Schipper (editor) for their

comments. Funding from the Dorinda and Mark Winkelman Distinguished Scholar Award (Armstrong)

and Ernst & Young (Ittner) is gratefully acknowledged.

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Corporate governance, compensation consultants, and CEO pay levels