RELATIVE PAY FOR RELATIVE PERFORMANCE

Ivan E. Bricka, Darius Paliaa, and Chia-Jane Wangb

This version: August 12, 2013

Abstract

Using a large panel data set, this paper examines industry effects in CEO pay levels

and pay-performance sensitivities. We find the following results. First, we find

significant industry effects in both CEO pay levels and pay-performance sensitivities.

Second, we find strong evidence that CEO compensation relative to other firms in the

industry is positively correlated to that firm’s relative performance within that industry.

Third, we find a stronger relative pay for relative performance relationship in industries

where more industry-specific talent is required. Fourth, our results are robust to both

OLS and dynamic GMM (which controls for dynamic endogeneity of the relationships)

and to using two alternative definitions of the pay-performance sensitivities. These results

suggest that managers are both compensated and evaluated on their talent relative to other

firms in their industry.

______________________________________________________________________________________ aRutgers Business School, and

bManhattan College, respectively. We thank Patrick Bolton, Martijn

Cremers, Valentin Dimitrov, Sirdar Dinc, Mike Fishman, Yuna Heo, Simi Kedia, Oded Palmon, Maya

Vaisman, Kam-Ming Wan and seminar participants at Rutgers, Villanova, the 2012 Triple Crown

Conference and the 2013 City University of Hong Kong International Conference on Corporate Finance

and Financial Markets for helpful comments. Brick (Palia) thanks the David Whitcomb Center for

Financial Services (the Thomas A. Renyi Chair) for partial financial support. All errors remain our

responsibility. Corresponding author: Darius Palia; dpalia@rci.rutgers.edu

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1. Introduction

This paper examines whether CEO’s are paid and evaluated relative to their

industry peers. For example, CEOs of a bank would be paid relative to other banks,

whereas CEOs of a manufacturing company would be paid relative to other

manufacturing companies. They would be a corresponding relative performance

evaluation between that bank and other banks, and a manufacturing company and other

manufacturing companies. We call this hypothesis the relative pay for relative

performance hypothesis.

For the relative pay for relative performance hypothesis to hold as an optimal

equilibrium outcome, one needs two assumptions to hold in the standard principal-agent

model control (Holmstrom, 1979, Holmstrom and Milgrom, 1987, 1992, and Jewitt,

1988). In this model, risk neutral shareholders (or on their behalf, the board of directors),

design a compensation structure based on the manager’s incentive compatible and

participation constraints. The first assumption is that there are varying levels of skill that

affects the reservation utility of managers in the participation constraint. Such an

argument suggests that in order to attract or retain talented CEOs, shareholders have to

offer higher pay (see, for example, Oyer (2004), Murphy and Zabojnik (2007), Gabaix

and Landier (2008), Edmans and Gabaix (2009), Edmans, Gabaix, and Landier (2009))1.

Extending this argument to the type of talent, it seems reasonable that different industries

employ CEOs who have both industry-specific human capital and general management

skills. For example, the CEO of a bank must have general management skills reflective

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of a CEO of any firm. In addition, the bank CEO must have specific skills such as

managing the loan portfolio and credit risk that are particularly unique to the banking

industry. In order to attract the appropriate managerial talent, firms have to offer these

executives wages that are commensurate with their ability. If the level of managerial

skills varies by industry, we would expect to find an industry effect in the determination

of CEO compensation. Therefore, boards might optimally design a relative compensation

contract for the CEO.

The second assumption that is that the optimal sharing rule (i.e., the manager’s

pay-performance sensitivity) should reduce the noise in performance measures consistent

with enhanced signal extraction. That is, the manager should be paid based upon the

environment s/he can control. Hence, under these models, industry performance which is

not under the control of a specific CEO should be removed from her performance

evaluation. Therefore boards optimally design a relative performance contract for the

CEO.

Note that we are not arguing that some industries have more talented CEOs than

others. Instead we suggest that some industries have more industry-specific talent which

makes it costly for outsiders to enter and/or harder for insiders to leave. We would also

expect that the relative pay for relative performance sensitivity would depend on the

informativeness of the relative performance measures. Accordingly, the more difficult it

is for the board to identify the source of firm success is due to the CEO’s skill or luck, the

less sensitive is the CEO’s relative pay to relative performance. Parrino (1997) argues

1 See Section 2 for a detailed explanation of these and other related papers.

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that industries that tend to hire from within are easier to monitor and better able to filter

the effects of industry shocks. He finds that firms are more likely to fire CEOs for poor

relative performance in industries that tend to hire CEOs from within. This suggests the

importance of industry-specific talent. That is, the more important the industry-specific

knowledge for a CEO to be successful, the more difficult it is for boards of directors to

hire from outside (Parrino 1997, Murphy and Zabojnik 2007). This suggests that we

should examine whether industries with high insider hiring have a higher sensitivity of

relative pay to relative performance compared to industries that hire CEOs from outside.

Using a large panel data set of US CEO compensation data for the years 1994 to

2008, we examine both CEO pay levels and pay-performance sensitivities and test for

empirical support of the relative pay for relative performance hypothesis. We find the

following results. First, we find significant industry effects in both CEO pay levels and

pay-performance sensitivities.2 Second, we find strong evidence that CEO compensation

relative to other firms in the industry is positively correlated to that firm’s relative

performance within that industry, namely, support for the relative pay for relative

performance hypothesis. This result holds when we use both OLS with standard errors

clustering at the firm level and the dynamic GMM (Arellano and Bover, 1995) method

which controls for unobservable heterogeneity and the dynamic endogeniety of the

2 Evidence for significant industry effects has been found in the capital structure, dividend, and labor wage

literature. Evidence for significant industry effect in capital structure has been found by Bradley, Jarrell and

Kim (1984), Smith and Watts (1992), MacKay and Phillips (2005), Frank and Goyal (2007) and Leary and

Roberts (2010). Lintner (1953), Michel (1979), McCabe (1979), and Dempsey, Laber and Rozeff (1993)

find significant industry effects in dividend policies. See Section 2 for papers on industry wages.

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relationships.3 Third, we find a stronger relative pay for relative performance relationship

in industries wherein more industry-specific talent is required. Fourth, consistent with

the prior literature, we find no evidence in support for the relative performance evaluation

(RPE) hypothesis that links absolute pay with relative performance. Finally, the above

results are robust to using two measure of the pay-performance relationship, namely, pay-

performance sensitivity (defined as the increase in CEO wealth for a one-percent increase

in shareholder wealth (Core and Guay 2002)), or the scaled wealth-performance

sensitivity (defined as the dollar change in CEO wealth for a 100-percent change in firm

value divided by annual compensation (Edmans, Gabaix, and Landier 2009)). The latter

definition is independent of firm-size and is derived from a model where effort has a

multiplicative effect on firm value and utility.

Our paper is related to two strands of the literature. First, is the literature that

examines the absolute change in CEO compensation to relative performance, which the

literature denotes as relative performance evaluation (RPE). The empirical papers testing

relative performance evaluation implicitly ignore any industry human capital required in

the participation constraint. We are breaking this assumption in the participation

constraint, by allowing the CEO’s reservation utility to be industry specific. Many

empirical papers4 have found little support for RPE. In order to ensure that there is

3 See Section 3.1 for further details.

4 Antle and Smith (1986), Gibbons and Murphy (1990), Jensen and Murphy (1990), Barro and Barro

(1990), Janakiraman, Lambert and Larker (1992), Aggarwal and Samwick (1999a,b), Joh (1999) and

Murphy (1999) fail to find support for the relative performance evaluation. Albuquerque (1999) and

Lewellen (2013) find support for the relative performance evaluation. However, Lewellen (2013) does not

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nothing strange about our sample, we also performed regressions analogous to these

papers that test the relative performance evaluation hypothesis. We also find no evidence

for the RPE hypothesis. We differ from this literature by focusing on the relative change

in pay, rather than the absolute change in pay.

Our paper is also related to the literature that examines benchmarking in CEO

compensation. On the one hand, Bizjak, Lemmon and Naveen (2008), Bizjack, Lemmon

and Nguyen (2011)5

and Albuquerque, De Franco and Verdi (2012) find that

benchmarking is undertaken for retention of talented human capital. On the other hand,

Faulkender and Yang (2010, 2012) find that benchmarking is undertaken for

opportunistic reasons and increased disclosure has not reduced this behavior. These

papers examine whether CEO pay in one firm is related to the CEO pay of its peers,

which they call relative pay. But none of these papers examine whether relative pay is

correlated with the firm’s relative performance. That said, our results are generally

consistent with those in Bizjak, Lemmon and Naveen (2008), Bizjack, Lemmon and

Nguyen (2011), and Albuquerque, De Franco and Verdi (2012).

Some paper have suggested that CEO pay is not optimally set and is the outcome

of powerful and entrenched CEOs and their crony boards (see, for example, Bertrand and

Mullainathan, 2001, Bebchuk and Freid, 2004, Brick, Palmon and Wald 2006, and

Morse, Nanda, and Seru 2011). We do not suggest that these effects are not present. We

find relative performance evaluation in the sample period covered by Albuquerque (1999), but finds

relative performance evaluation when industries are defined by their primary product market competitors. 5 Bizjack, Lemmon and Nguyen (2011) find an upward bias in CEO pay which is reduced with disclosure.

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only suggest that some part of CEO pay is based as an optimal equilibrium outcome

under the relative pay for relative performance hypothesis.

This paper proceeds as follows. Section 2 explains the related literature whereas

Section 3 describes our data, variables, and empirical methodology. Our empirical

results are reported in Section 4, and conclusions presented in Section 5.

2. Related literature

3.1 Literature for CEO talent

The standard principal-agent model of Holmstrom (1979), Holmstrom and

Milgrom (1987, 1992) did not have a role for CEO ability or talent. The growing

literature that does include CEO talent has its roots in seminal papers by Lucas (1978)

and Rosen (1981). In these papers, compensation for the most talented managers is high

as they manage large firms where the manager’s marginal product is high. In a

competitive assignment model, Gabaix and Landier (2008) and Tervio (2008) suggest

that more talented CEOs are matched to firms with larger market values and therefore

have to be compensated with a higher level of pay. In these models there is no moral

hazard and therefore no incentive effect of CEO pay.

In a moral hazard framework, Himmelberg and Hubbard (2000) find that CEOs of

large firms (namely, more talented CEOs) have a positive relationship with aggregate

stock market returns. This is because the supply of these CEOs is relatively inelastic, and

aggregate productivity shocks will simultaneously raise firm value and the marginal

value of CEO services.

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Inserting moral hazard into a talent assignment model, Edmans, Gabaix, and

Landier (2009)) derive CEO pay levels and pay-performance sensitivity wherein effort

has a multiplicative effect on firm value and utility. With this multiplicative production

function, effort has a multiplicative effect on firm value resulting in their scaled pay-

performance sensitivity measure (defined as the dollar change in CEO wealth for a 100-

percent change in firm value divided by annual compensation). Such talented CEOs have

a higher level of CEO pay. Endogenizing for firm size, Baranchuk, MacDonald, and

Yang (2011) employ a ‘superstars’ model wherein each firm chooses its size, the

manager and her compensation. In equilibrium, they suggest that higher-ability CEOs

will be matched to larger firms with higher pay levels and pay-performance sensitivities.

Bertrand and Mullainathan (2001) find that CEO pay varies with shocks that are

not under the CEO’s control, a phenomenon they call ‘pay for luck’. However, Oyer

(2004), Falato, Li, and Milbourn (2009), and Eisfeldt, and Kuhnen (2012) show that

industry or market shocks can be correlated with the value of the outside option to CEO

talent. In order to retain talented CEOs within the firm, one would have to offer a higher

pay.

Murphy and Zabojnik (2003, 2007) find that CEO salary and bonus is higher

when CEOs are hired from the outside and for industries where outside hiring is more

prevalent. They attribute this finding to the trend of firms valuing general skills more

than firm-specific skills. Frydman (2007) finds that executives with more general skills

are more likely to have a higher level of pay in the 1990s than in the 1960s than

executives with firm-specific skills. Using detailed biographical data, Custodio, Ferreira,

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and Matos (2011) find that CEOs with generalist skills (defined as the first principal

component of number of industries she worked for, was a prior CEO before, and had a

general business degree) earned higher pay levels and incentive pay than CEO who have

specialist skills. Cremers and Grinstein (2011) find that benchmarking is more prevalent

and CEO pay is more related to industry performance when successor CEOs come from

outside their industry. They find no evidence for a weaker relationship between

compensation and firm size in industries with more firm-specific skills, a finding contrary

to firm size proxying for talent (as in Gabaix and Landier 2008). Note that these papers

examine either the level of pay or change in pay but not how pay varies within the

industry and how it changes with relative performance.

2.2 Literature on industry compensation

There is a large literature in labor economics that has shown that different

industries pay their employees differently. Specifically, they regress the logarithm of the

wage rate for each employee on individual characteristics such as age, education,

occupation, gender, race, union membership, and so on. When they include industry

dummies, they find them to be strongly statistically significant. These inter-industry wage

differentials were first found by Slichter (1950) during 1923 and 1946. More recent

evidence for inter-industry wage differentials has been found by Krueger and Summers

(1987, 1988), Murphy and Toppel (1987), Thaler (1989), Gibbons and Katz (1992), and

Goux and Maurin (1999). Interestingly, Krueger and Summers (1987) find that the high

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wage industries in the earlier period correlate highly with the high wage industries in the

1980s.

Some studies have examined whether CEOs with different skills and talent are

compensated differently. Due to political constraints on CEO pay, Joskow, Rose and

Shepard (1993) and Joskow, Rose and Wolfram (1996) find that regulated utilities have

low pay-performance sensitivities and levels. Palia (2000) finds that regulated utilities

attract CEOs with a lower-quality education (proxied by the ranking of educational

institution s/he graduated from) and are offered lower pay levels and sensitivities than

CEOs of manufacturing firms.

3. Methodology, data and variables

3.1 Methodology

In this section we describe the methodology employed to ascertain the

relationship between pay and performance. We begin by testing the relative pay for

relative performance hypothesis using the following empirical specification for CEOs:

(firm pay – industry pay)it = α + β(firm perf.– industry perf.)it + ΩXit + µt + εit (1)

where Xit are a comprehensive set of control variables and µt are year-dummies to control

for general macroeconomic factors. We regress the difference between CEO pay and the

average industry pay against the difference between the firm’s performance and industry

performance. We estimate (1) above using two measures of CEO pay, namely, CEO pay

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levels and CEO pay-performance sensitivities. If one has to find support for the relative

for relative pay for relative performance to hold in (1) above, one should find a positive

and statistically significant relationship on β.

In contrast, the relative performance hypothesis (RPE) examines the absolute

change in CEO compensation to relative performance. More specifically,

∆(firm pay) it = α + β(firm perf.)it + (industry perf.)it + ΩXit + µt + εit (2)

where Xit are the same set of control variables as in (1) and µt the year-dummies. If one

has to find support for the relative performance hypothesis in (2) above, one should find a

negative and statistically significant relationship for .

Equation (1) is first estimated using OLS. The standard errors are corrected for

clustering at the firm-level so as to take into account that the observations within firms

maybe non-independent.

Given that we have panel data, we use panel data estimation techniques to avoid

any unobserved omitted variable problem. Since we cannot be sure whether the

unobserved effects are correlated with the explanatory variables, firm-level fixed effects

estimation is chosen over random effects estimation.6 But it is possible that certain

unobservable firm and/or CEO characteristics are omitted and cause a spurious

correlation between pay and performance. The fixed effects methodology has the

6A Hausman and Taylor (1981) specification test shows that the fixed effects estimator is preferred over the

random effects estimator in our sample.

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advantage that it captures any unobservable firm-level heterogeneity that was shown to

be important in Murphy (1985) and Himmelberg, Hubbard, and Palia (1999).7

But it is possible that the relationship between CEO pay and firm performance

suffers from a simultaneity or endogeneity bias (Roberts and Whited forthcoming). For

example, the CEO’s risk aversion parameter should enter her tolerance for risk and would

adversely affect the amount of incentive pay she could bear. The normal way to solve

such omitted variable or endogeniety concerns is to find an instrumental variable. But in

this context, all potential instrumental variables are unable to satisfy the exclusion

restriction for instrumental variable identification as they are likely to be related to both

firm performance and CEO pay.

The simultaneity bias affecting the regression coefficients could be due to static

endogeneity (Palia 2001) and/or dynamic endogeneity (Wintoki, Linck, and Netter 2012).

In static endogeneity, we do not know whether CEO pay in this fiscal year causes firm

performance to rise in the same fiscal year, or vice versa. In dynamic endogeneity, past

values of CEO pay might have caused current firm performance to rise, or vice versa.

One way to check the robustness of our OLS results is to use lagged values of CEO pay

using the dynamic GMM method of Arellano and Bover (1995).8

Arellano and Bover (1995) first check which lags are uncorrelated with the first-

differenced residuals under the null hypothesis of no serial correlation. Consistent with

7Murphy (1985) argues that the firm-level fixed effects model is the optimal estimation methodology.

“Absent a theory indicating the relevant variables, and data on these variables, these cross-sectional models

are inherently subject to a serious omitted variables problem.” (page 12) 8 The dynamic GMM method has also been used by Wintoki, Linck, and Netter (2012) to examine the

relationship between board structure and firm performance.

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their paper, for each regression specification we conduct an autoregression test and find

that three-year or four-year lags are what we need as valid instrument variables. Further,

the Arellano and Bover method uses these instrumental variables in both levels and

differences and uses GMM to estimate the pay-performance relationship.

3.2 Data and variables used

We use a large panel data set of US CEO compensation data obtained from

ExecuComp for the years 1994 to 2008. We look at both CEO compensation levels and

CEO pay-performance sensitivities. We use data from CRSP to obtain individual stock

returns and accounting data from Standard and Poor’s Compustat. Table 1 defines each

of the variables used in our study.

*** Table 1***

We use ExecuComp’s tdc1 variable as our proxy for CEO pay levels; defined as

the sum of salary, bonus, value of restricted stock granted, total value of stock options

granted (using Black-Scholes), and long-term incentive payouts. To control for skewness,

we take the natural logarithm of tdc1 as our variable of interest. We denote this variable

as ltdc1.

We use two measures to examine the CEO’s pay-performance relationship. The

first measure follows the procedure by Core and Guay (2002) to calculate pay-for-

performance sensitivity, which we denote as totpps. It is defined as the change in the

CEO’s wealth for a one-percent change in equity value. Consistent with the prior

literature, we use this measure of totpps because it is the change in total wealth invested

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in the firm that incentivizes the CEO. The procedure we use to estimate totpps sums the

pay-for-performance measures of current option grants, options outstanding that were

granted prior to the current fiscal year, and shares of stock that are held by the CEO. We

exclude any years in which the CEO only served for part of the fiscal year as we want to

match firm performance to the particular compensation package of one individual.

To calculate the performance sensitivity of current option granted, let numsecur

denote the number of newly options granted to the CEO with a given maturity m and

exercise price X. Then the performance sensitivity of options granted is given by pps =

e-dm

(N(D1))0.01*P* numsecur,where d is the dividend yield of the stock, P is the price of

the stock at the end of the fiscal year, and N(.) is the standard normal cumulative density

function. D1 is given by D1 = {log(P/X) + m[(r-d) + (2/2)]}/m0.5

where r is the yield

to maturity of the seven-year treasury security at the fiscal year end and is the Black-

Scholes volatility measure of the underlying stock as given by ExecuComp. In a given

fiscal year the firm may give the CEO several grants of options with different m and X.

We evaluate each grant and sum the values to obtain the performance sensitivity for all

newly granted options in that fiscal year.

We also calculate the performance sensitivity of previously granted options. We

assume the maturity of previously granted options to be three years less than the average

maturity of the current option grants if these options are immediately exercisable. We

further assume that the maturity of previously granted options that are not immediately

exercisable is one year less than the average maturity of the current option granted. In

both cases, we use the yield to maturity of a five-year treasury security prevailing at the

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fiscal year end. We define the exercise price for the previously granted options that are

exercisable to be equal to the current stock price less the ratio of the intrinsic value of

unexercised but exercisable previously granted options (inmonex) to the number of

previously granted unexercised but exercisable options (uexnumex). We define the

exercise price for the previously granted options that are not exercisable to be equal to the

current stock price less the ratio of the intrinsic value of unexercisable granted options

(inmonun) less the intrinsic value of the current options granted to the number of

previously granted unexercised and unexercisable options (uexnumun - soptgrnt).

Finally, by definition the pps for stock held by the CEO equals one-percent of the value

of equity held by the CEO. To account for possible non-linearities we take the logarithm

of totpps as our variable of interest. We denote this variable as lpps.

The second measure is the scaled wealth-performance sensitivity of Edmans,

Gabaix, and Landier (2009). These authors derive an optimal sharing rule in a principal-

agent model with talent assignment by considering multiplicative specifications for the

CEO’s utility and production function. The optimal incentive structure is derived as the

dollar change in CEO wealth for a 100-percent change in firm value divided by annual

compensation. Two advantages of this definition are that it is independent of firm-size

and is more effective at solving agency problems that have multiplicative impacts on firm

value. We obtain this measure from http://finance.wharton.upenn.edu/~aedmans/

data.html.

To examine if firms generally reward CEO for relative performance, we need to

calculate the relative pay of the CEO against the relative performance of the firm. The

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relative pay in year t is defined as the difference between firm i’s CEO compensation

variable (tdc1, ltdc1, pps and lpps) of fiscal year t and the equal-weighted average of all

firms in the industry for that fiscal year. We denote relative pay as dtdc1, dltdc1, dpps

and dlpps, respectively. The relative performance of firm j of industry i is defined as the

difference between firm i’s performance variable (tobinq, roa, and crawret12) and the

equal-weighted average industry performance (mtobinq, indret, and mroa). The equal

industry returns are obtained from Kenneth French’s website http://mba.tuck.dartmouth

.edu/ pages/faculty/ken.french/data_library.html). In order to ensure that we do not have

any spurious correlation between the dependent variable of relative pay and independent

variable of relative performance, we adjust the average of industry performance by

excluding the own firm’s contribution to the industry average. We denote the relative

performance as dtobinq, droa, and dcrawret12.

We use three performance measures of the firm. The first proxy we use is

Tobin’s Q (tobinq) and is the one generally used by a large part of the previous literature.

As in Smith and Watts (1992), Shin and Stulz (1998), and Palia (2001), Tobin’s Q is

calculated as the market value of equity (price of common shares times the number of

shares outstanding), minus the book value of equity, plus the book value of assets, over

book value of assets. The second proxy is the firm’s return-on-assets (roa) and is

calculated as earnings before interest, taxes, and depreciation over book value of assets.

The performance proxy roa has also been used by Gompers, Ishii, Metrick (2003) and

Core, Guay, and Rusticus (2006) as alternative measures of firm performance. The third

proxy is the holding period return of firm’s equity for the fiscal year, denoted as

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crawret12, used by Antle and Smith (1986), Murphy (1999) and Gong, Li and Shin

(2011).

We include several control variables in our regressions. We use R&D expenses

scaled by total assets (Berger, Ofek, Yermack 1997, Titman and Wessels 1988, Jung,

Kim, and Stulz 1996) to proxy for firm growth. We denote this variable by rd. Given

that the transparency of managerial actions will decline in growth opportunities, thereby

reducing the effectiveness of monitoring by board and large blockholders, the optimal

pay and sensitivity should be higher to encourage taking valuable risk-increasing projects

(Guay 1999). We set rd equal to zero .when Compustat records a missing value. We also

use Tobin’s Q to proxy for the present value of growth opportunities. We expect

compensation may vary with firm size. For this reason, size is included to avoid the

possibility that our explanatory variables might proxy for the size. Size is measure by the

log of sales, denoted by lsales. Since there may be a non-linear relationship between size

and compensation, we include the square term of lsales, which we denote as lsalessq.

We further expect that pay is related to leverage as shown by John and John (1993).

Consequently, we include leverage, denoted as lev as a control variable, and lev is

defined as total debt divided by total assets. Dividend payments may be used to reduce

manager’s control of resources and subject the firm to the external monitoring by the

market (Jensen 1986). To control for the impact of dividends, we include a control

variable, divyield, defined as the ratio of the total dividends paid to the common stock

capitalization at the end of the fiscal year. Finally, we include two CEO characteristics

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that should impact compensation, the number of years the CEO has served in office

(tenure), and the age of the CEO (age).

Table 2 provides summary statistics for each of these variables. We have

complete compensation information to calculate total dollar compensation for 13,251

firm-year observations. The mean tdc1 is $4.51 million while the median level of

compensation is $2.39 million. To control for extreme values affecting our results, we

take the natural logarithm of pay levels (ltdc1) which has a mean value of 7.84 and a

median value of 7.78. The mean (median) totpps for our sample is $766,542 ($223,090).

To control for extreme values affecting our pay-performance results, we also take the

natural logarithm of pay-performance sensitivities denoted as lpps. The mean (median)

lpps for our sample is 12.36 (12.32), suggesting a much more normal distribution than

when we use totpps.

We now examine the relative pay measures dltdc1, dtotpps, and dlpps. The mean

(median) of each of these variables are -0.014 (-0.031), $6,536.5 ($-221,909), and -0.002

(-0.038), respectively. It seems like the distribution of relative pay to the industry is

skewed to the left. The natural logarithm helps mitigate this problem.

The means and medians of our performance variables are as follows. The mean

(median) Tobin’s Q is 1.989 (1.501). The mean (median) roa is 0.13 (0.13), and the

mean (median) annual stock returns is 15.3% (8.61%). The average size as measured by

annual sales is $4.7 billion, and firms have a mean leverage ratio of 22%. Our sample has

an average dividend yield of 1.3%. The average tenure of a CEO is 7.77 years and the

average age of the CEO is 56 years.

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***Table 2***

4. Empirical Results

4.1 Are there industry effects in CEO pay?

We begin by examining if different industries have different compensation levels

and sensitivities. It seems reasonable that different industries employ CEOs who have

both industry-relevant human capital and general management skills. In order to attract

appropriate managerial talent, firms have to offer these executives wages that are

commensurate with their ability. If the level of managerial skills varies by industry, we

would expect to find an industry effect in the determination of CEO compensation. Table

3 reports the mean values of our four compensation variables, tdc1, ltdc1, pps and lpps,

of firms grouped by the 48 Fama-French industry definitions. To be included in the

analysis, we required that an industry included firms with pay data for at least four firms

in any given year. The dollar level of total compensation, tdc1, ranges from a low of

$1.19 million (Fabricated Products) to a high of $17.13 million (Beer). The pay-for-

performance sensitivity, totpps, range from a low of $0.84 million (Fabricated Products)

to a high of $2.70 million (Beer). To test the statistical significance of the observed

differences in the mean compensation variables, we perform an analysis of variance

(ANOVA) test across industries. Both the F-test and Chi-square statistic indicate that the

distributions of these variables across industries are not identical, providing evidence of

an industry effect for managerial compensation.

*** Table 3***

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4.2 Is mean industry pay related to mean industry performance?

While above section shows that different industries have significantly different

mean pay, we now examine whether this mean industry pay is correlated with mean

industry performance. We use equally weighted portfolios, so as to not let our results be

biased towards the largest firms. Table 4 shows these regression results with year

dummies and control variables which not presented for brevity. In all three specifications,

we find industry pay and industry performance is correlated. It is reasonable to assume

that firm performance is partially driven by the overall industry performance and

managerial skill. To retain this managerial talent, the successful CEO should be paid

more than her peers in the industry, but only to the extent that firm performance is not

driven by industry performance. That is the optimal sharing rule should reduce the noise

in performance measures consistent with enhanced signal extraction.9 Consequently, we

would expect a positive relationship between relative pay and relative performance.

*** Table 4***

4.3 Testing the relative pay for relative performance hypothesis using OLS

If managerial skills vary with industry then we would expect that CEO of a firm

in industry i should be evaluated relative to CEOs of other firms in that industry i.

Therefore, we would expect that the relative pay of the CEO of firm in industry i should

9 In a dynamic principal-agent model, Demarzo, Fishman, He and Wang (2012) suggest that the managerial

compensation contract may depend on risks that are beyond the manager’s control.

21

be correlated with the relative performance of her firm. We use OLS with standard errors

clustered at the firm level.

Table 5 provides the results for the pooled panel regressions of CEO’s log of the

relative total compensation, dltdc1, on relative firm performance. The table presents six

specifications. Specifications (1) and (2) use dtobinq as the proxy for relative firm

performance. Specifications (3) and (4) uses droa as the proxy for relative firm

performance and specifications (5) and (6) uses dcrawret12 as the proxy for relative firm

performance. Specifications (1), (3) and (5) do not include control variables whiles

specifications (2), (4) and (6) include our control variables. All estimations include year

dummy variables (not reported), and the t-statistics are computed based on robust

standard errors that incorporate firm-level clustering. Note that for each specification,

there is a positive and statistically significant relationship between relative total

compensation and relative firm performance.10

*** Table 5***

Tables 6 and 7 are analogous to that of Table 5 except that Table 6 uses CEO’s

relative pay performance sensitivity, dtotpps, as the dependent variable while Table 7

uses the logarithm of the relative pay performance sensitivity, dlpps, as the dependent

variable. For both tables, we see a statistically significant relationship between relative

pay performance sensitivity and relative firm performance, except when we use dtotpps

as the dependent variable and droa as a proxy for firm performance. The results are

supportive of the relative pay for relative performance hypothesis. All three tables

22

indicate that the sensitivity to relative performance decreases with dividend yield. This

result is consistent with the notion that dividends play a role in reducing the agency costs

of free cash flows (Jensen, 1986) and consequently the role of compensation as a means

of reducing agency costs can be diminished. In all three tables, size as proxied by the

level of firm sales is also positively related to the sensitivity of compensation to firm

relative performance.

*** Tables 6 and 7***

4.4 Testing which industries have different relative pay for relative performance

sensitivities

We would expect that the relative pay for relative performance sensitivity would

depend on the informativeness of the relative performance measures. We acknowledge

that the hiring and retention of CEOs should be directly related to CEO talent for all

industries. However, the more difficult it is for the board to identify the source of firm

success is due to the CEO’s skill or luck, the less sensitive is the CEO’s relative pay to

relative performance. Parrino (1997) argues that industries that tend to hire from within

are easier to monitor and better able to filter the effects of industry shocks. He finds that

firms are more likely to fire CEOs for poor relative performance in industries that tend to

hire CEOs from within. This suggests the importance of industry-specific talent. That is,

the more important the industry-specific knowledge for a CEO to be successful, the more

difficult it is for boards of directors to hire from outside (Parrino 1997, Murphy and

Zabojnik 2007). This suggests that we should examine whether industries with high

10

Our results do not change when we remove the industry means from each of our control variables.

23

insider hiring have a higher sensitivity of relative pay to relative performance compared

to industries that hire CEOs from outside.

We proxy for industry-specific talent by using the percentage of insider CEOs in

the industry in which the firm operates (also used by Parrino 1997, and Cremers and

Grinstein 2011). They suggest that industries with a lot of insider CEOs have a lot of

industry- and firm-specific capital for which they are more highly compensated. Using

the same logic we would expect that industries with lot of inside hires would have higher

relative pay sensitivity to relative performance than industries with more outside hires.

We obtain the percentage of insider CEOs in the industry from Table 3 of Cremers and

Grinstein (2011). We split our sample into terciles of this variable. We classify industries

with the lowest (highest) tercile percentage of insider CEOs as those with the lowest

(highest) level of industry-specific talent. We rerun our regressions on the relative pay for

relative performance on these two extreme terciles, the results of which are given in

Table 8. For brevity we do not present the results on year dummies and the control

variables. In five of the six specifications, we find a significantly greater sensitivity of

relative pay to relative performance in industries where there is more insider hiring than

in industries where there is more outsider hiring. For example, banking (one of the

industries with high insider hiring) has a much greater sensitivity of relative pay to

relative performance than food products (one of the industries with low insider hiring).

These results are consistent with the informativeness of performance measures and our

relative pay for relative performance hypothesis.

*** Table 8***

24

Aggarwal and Samick (1999) suggest that competitive industries will exhibit a

greater pay for relative performance sensitivity than less competitive industries. We first

test for differences in competitiveness between the industries with more insider hiring

and industries with less insider hiring. We calculate the Herfindahl Index (HI), defined as

the sum of the squared market share of each firm in the industry for each fiscal year. The

median HI for industries with more insider hiring is greater than the HI for industries

with less insider hiring, a finding opposite of what their hypothesis requires. We also

reran the regressions in Tables 5-7 while including HI and the interaction of HI and the

relative performance variables. If competitive industries exhibit a greater pay for relative

performance sensitivity than less competitive industries, we would expect the interaction

term to be negative. None of the specification shows such a negative relation (results not

reported).

4.5 Testing the relative pay for relative performance hypothesis using GMM

Tables 9, 10 and 11 report the results from two-step GMM panel regression to

control for dynamic endogeneity. Once again, all estimations include year dummies, the

results of which are not reported. Table 9 summarizes the results when the dependent

variable is dltdc1. Note that for this regression, the relative pay is not positively

associated only if we use dcrawret12 as our independent variable. Table 10 reports the

results when dtopps is the dependent variable. In this case, the relative pay performance

sensitivity is positively related to all of the relative performance measures. Finally, Table

11 reports the results when dltopps is the dependent variable. Once again, there is a

25

positive association between relative pay and relative performance for each of our

performance proxies. Note that in these tables both size and dividend yield play a

diminished role in impacting the sensitivity of compensation to relative firm performance

as compared to the pooled regression results.

*** Tables 9, 10 and 11***

In summary, the above results show strong evidence in support for the relative

pay for relative performance hypothesis using both OLS (Tables 6-8) and dynamic GMM

(Tables 9-11).

4.6 Testing the relative performance evaluation (RPE) hypothesis

In order to ensure that our results are not being driven by the relative performance

evaluation hypothesis (RPE), we also conducted regressions analogous to those

performed by Gibbons and Murphy (1990), Janakiraman, Lambert and Larker (1992),

Barro and Barro (1990), John (1999), and Antle and Smith (1986). These regressions are

reported in Tables 12 and 13. Each table reports the pooled panel regressions and each

table has two panels. In Table 12, the first panel reports the results when the dependent

variable is ∆tdc1 and the second panel reports when the dependent variable is ∆ltdc1.

When we examine all three performance measures we find that the industry effect is

insignificantly related in four of the six specifications (mtobinq and indret) and only

negatively related in the profitability measure (mroa). Table 12 reports the results when

the dependent variable is ∆pps and ∆lpps. In five of the six specifications, the industry

performance measure is not negatively related to pay levels and pay-performance

26

sensitivities. Consistent with the prior literature of Antle and Smith (1986), Gibbons and

Murphy (1990), Barro and Barro (1990), Janakiraman, Lambert and Larker (1992), John

(1999), and Murphy (1999), the results of Tables 12 and 13 are generally not supportive

of the relative performance evaluation hypothesis.

*** Tables 12 and 13***

4.7 Testing the relative pay for relative performance hypothesis using the scaled wealth-performance sensitivity measure

We now examine whether our support for the relative pay for relative

performance hypothesis is dependent on the definition of the pay-performance sensitivity.

Accordingly, we repeat our regressions using the scaled wealth-performance sensitivity

(wpps) of Edmans, Gabaix, and Landier (2009). Note that the optimal incentive structure

here is derived as the dollar change in CEO wealth for a 100-percent change in firm value

divided by annual compensation. We find the mean value of the wpps 25.34 and a median

value of 7.59. We also used the natural logarithms of this scaled wealth-performance

sensitivity (lwpps) which has a mean value of 2.16 and a median value of 2.03. One

again, we calculate the relative scaled wealth-performance sensitivity to the industry

mean (with industry defined as firms not including that firm) for each of these measures.

We call these dependent variables dwpps and dlpps. The results of such an analysis are

given in Tables 14 and 15.11

In examining the regression coefficients across these tables,

we find a positive and statistically significant relationship in 10 out of 12 specifications.

This shows strong support for the relative pay for relative performance hypothesis and

11

We find similar results (not reported) when we use dynamic GMM.

27

suggests that our previous results are not dependent on the definition of pay-performance

sensitivity.

*** Tables 14 and 15***

In summary, we find that the change in relative compensation is positively related

to the relative performance of the industry. These results suggest that managers are both

compensated and evaluated on their talent relative to other firms in their industry.

5. Conclusions

It seems reasonable that different industries employ CEOs who have both

industry-relevant human capital and general management skills. In order to attract

appropriate managerial talent, firms have to offer these executives wages that are

commensurate with their ability. Accordingly, we would expect that the CEO of a bank

should be evaluated relative to CEOs of other banks. If the level of managerial skills

varies by industry, we would expect to find an industry effect in the determination of

CEO compensation. More specifically, managerial pay levels and pay-performance

relationships should vary by industry. In addition, the optimal sharing rule should reduce

the noise in performance measures consistent with enhanced signal extraction. That is,

the manager should be paid based upon the environment s/he can control. Hence, under

these models, industry performance which is not under the control of a specific CEO

should be removed from her performance evaluation. Using these two arguments, this

paper examines the relative pay for relative performance hypothesis, wherein one would

28

expect that the relative pay of the CEO should be correlated with the relative performance

of her firm.

Using a large panel data set of US CEO compensation data for the years 1994 to

2008 this paper tests the relative pay for relative performance hypothesis. We look at

both CEO total compensation levels and CEO pay-performance sensitivities. We find

significant evidence for an industry effect in both CEO pay levels and CEO pay

performance sensitivity. We also find strong evidence in support of the relative pay for

performance hypothesis, using OLS with standard errors clustering at the firm level and

dynamic GMM (Arellano and Bover, 1995) which controls for dynamic endogeneity of

the relationships. We find a stronger relative pay for relative performance relationship in

industries where the performance measures are more informative. We follow Parrino

(1997) in identifying such industries where more industry-specific talent is important.

Finally, the above results are robust to using two measures of the pay-performance

relationship, namely, pay-performance sensitivity or the scaled wealth-performance

sensitivity. These results suggest that managers are both compensated and evaluated on

their talent relative to other firms in their industry.

29

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34

Table 1: Variable definitions

Variable Definition

Dependent Variables:

tdc1 CEO’s total compensation: salary, bonus and other annual payouts including

granted options, restricted stock and long-term incentive payouts

dltdc1 Relative total compensation: the natural log of tdc1- the industry mean log of tdc1

totpps Pay-performance sensitivity: the dollar change in CEO’s wealth in outstanding

options and stock for one percent change in stock price (Core and Guay 1999)

dtotpps Relative pay-performance sensitivity: totpps - the industry mean totpps

lpps Natural log of the pay-performance sensitivity

dlpps Relative natural log of the pay-performance sensitivity: lpps - the industry mean

lpps

Independent Variables

tobinq Tobin’s Q: the market value of assets over book value of assets

mtobinq Industry mean Tobin's Q

dtobinq Tobin's Q - the industry mean Tobin's Q

crawret12 Stock return: the holding period return for 12 months of the current fiscal year

indret Industry mean stock return: the equal-weighted holding return for the industry for

a given calendar year and month

dcrawret12 Relative stock return: crawret12- indret

roa Return on assets: earnings before interest, tax, depreciation and amortization over

total assets

mroa Industry mean ROA

droa Relative ROA: roa - mroa

rd R&D expense over total assets

lsales Natural log of sales

lev Total debt divided by total assets

divyield Current fiscal year dividend yield

tenure The number of years the CEO is in office

age The age of the CEO

35

Table 2: Descriptive statistics

Variable N Mean Median Std. Dev. Min Max

tdc1a 13,251 $4,507.94 $2,393.33 $8,299.78 $0.00 $369,888.00

ltdc1 13,250 7.839 7.781 1.034 -6.908 12.821

dltdc1 13,250 -0.014 -0.031 0.987 -14.910 4.564

totpps 13,142 $766,541.7 $223,089.6 $3,014,890.0 $331.77 $108,000,778.4

lpps 13,142 12.357 12.315 1.436 5.804 18.498

dtotpps 13,142 $6,536.48 -$221,909.7 $3,053,746 -$6,339,666 $107,000,000

dlpps 13,142 -0.002 -0.038 1.364 -6.601 6.062

tobinq 13,251 1.989 1.501 1.825 0.298 78.565

dtobinq 13,619 0.006 -0.164 1.675 -5.142 73.775

roa 12,826 0.131 0.133 0.112 -2.671 0.807

droa 12,826 0.118 0.090 0.165 -2.360 2.255

crawret12 12,909 1.152875 1.086107 0.6166814 0.0215827 27.19421

dcrawret12 12,909 0.0011802 -0.0375959 0.5608874 -2.385436 24.74578

Salesb 13,038 $4,692.33 $1,239.9 $13,295.2 $0 $402,298

lsales 13,029 7.194 7.125 1.582 -2.364 12.905

lev 12,989 0.220 0.211 0.167 0.000 0.959

divyield 13,034 0.013 0.005 0.027 0.000 1.445

tenure 12,403 7.772 5.671 7.078 0.033 55.033

age 12,758 55.518 56 7.118 29 90

a $thousands, and

b $millions, respectively.

36

Table 3: Analysis of Variance for CEO compensation across industry This table reports the analysis of variance for CEO compensation for 2,113 sample firms over the period 1994-

2008. The four CEO compensation variables, TDC1, LTDC1, TOTPPS and LPPS are as defined in Table 1.

tdc1 ltdc1 totpps lpps

Industry ff Mean Std. Dev. Mean Std.

Dev. Mean Std. Dev. Mean

Std.

Dev.

Food 2 $4,655 $4,489 7.96 1.04 $8,588 $25,391 12.72 1.28

Beer 4 $17,131 $21,267 9.19 1.09 $26,683 $15,166 14.59 0.77

Toys 6 $4,994 $6,170 8.06 0.92 $13,283 $42,691 12.68 1.52

Fun 7 $5,623 $8,095 7.83 1.51 $10,755 $17,795 12.91 1.52

Books 8 $4,213 $3,270 8.06 0.79 $6,440 $9,090 12.63 1.33

Hshld 9 $4,894 $6,174 8.01 0.99 $6,323 $13,312 12.41 1.42

Clths 10 $4,474 $6,339 7.84 1.03 $6,648 $11,262 12.26 1.54

Hlth 11 $4,452 $5,172 7.88 1.03 $6,532 $8,767 12.73 1.16

MedEq 12 $3,066 $4,231 7.58 0.90 $7,674 $19,761 12.54 1.29

Drugs 13 $6,047 $6,842 8.08 1.19 $8,068 $11,643 12.79 1.41

Chems 14 $3,518 $2,872 7.85 0.82 $3,369 $8,019 11.96 1.19

Rubbr 15 $2,524 $3,503 7.41 0.88 $2,672 $2,406 12.17 0.84

Txtls 16 $1,317 $977 7.00 0.59 $6,203 $21,358 11.17 1.84

BldMt 17 $3,348 $3,948 7.68 0.90 $5,010 $10,539 12.05 1.37

Cnstr 18 $6,841 $8,083 8.26 1.10 $10,242 $19,025 12.59 1.62

Steel 19 $2,536 $2,471 7.50 0.80 $1,989 $3,154 11.54 1.18

FabPr 20 $1,187 $645 6.96 0.50 $838 $831 11.08 0.66

Mach 21 $3,239 $3,135 7.73 0.84 $2,842 $3,788 11.98 1.09

ElcEq 22 $4,250 $10,822 7.69 0.98 $4,560 $8,794 12.11 1.25

Autos 23 $3,842 $4,650 7.78 0.95 $4,039 $5,430 12.08 1.36

Aero 24 $6,193 $6,191 8.28 0.99 $8,074 $14,917 12.55 1.42

Ships 25 $3,173 $1,436 7.97 0.46 $2,490 $2,184 12.14 0.74

Guns 26 $4,091 $4,347 7.73 1.19 $3,535 $3,256 12.23 1.20

Gold 27 $1,942 $1,766 7.29 0.71 $1,138 $1,869 10.78 1.33

Mines 28 $5,848 $5,902 8.17 1.08 $3,238 $4,564 12.08 1.09

Oil 30 $4,084 $7,045 7.76 0.99 $3,526 $5,978 12.04 1.22

Util 31 $2,727 $3,098 7.50 0.89 $1,753 $2,708 11.24 1.32

Telcm 32 $10,098 $20,209 8.31 1.31 $19,315 $64,419 12.82 1.67

PerSv 33 $4,906 $11,259 7.73 1.03 $8,310 $15,651 12.59 1.44

BusSv 34 $3,801 $4,762 7.83 0.89 $6,002 $10,049 12.53 1.21

Hardw 35 $6,454 $20,328 8.02 1.22 $11,415 $53,593 12.39 1.60

Softw 36 $6,824 $18,503 7.93 1.32 $10,363 $34,377 12.68 1.36

Chips 37 $4,582 $6,026 7.86 1.07 $7,512 $18,708 12.49 1.38

LabEq 38 $3,182 $4,746 7.61 0.90 $4,624 $7,118 12.32 1.20

Paper 39 $2,616 $2,720 7.50 0.84 $2,109 $2,636 11.66 1.14

Boxes 40 $3,254 $1,639 7.92 0.66 $4,157 $6,612 12.25 1.19

Trans 41 $3,059 $3,412 7.60 0.90 $17,949 $101,103 12.28 1.46

Whlsl 42 $2,670 $2,433 7.58 0.80 $3,787 $6,254 11.92 1.42

Rtail 43 $4,753 $7,432 7.87 1.09 $8,833 $21,669 12.50 1.57

Meals 44 $3,942 $4,552 7.73 1.09 $9,776 $25,288 12.58 1.57

Banks 45 $4,916 $6,406 7.99 0.97 $7,152 $15,413 12.61 1.28

Insur 46 $5,216 $5,411 8.15 0.91 $12,861 $36,297 12.88 1.41

Fin 48 $7,949 $10,464 8.32 1.19 $26,506 $86,204 13.19 1.66

37

Other 49 $5,553 $11,387 7.61 1.32 $12,177 $27,548 12.19 2.28

Total

$4,508 $8,300 7.84 1.03 $7,665 $30,149 12.36 1.44

F-

Statistic 11.30a

15.54a

7.8a

30.49a

Bartlett's

Test for

10,000b

650b

2,000b

412b

Equal

Variances

aAll F-statistics are statistically significant at the 1% level.

bAll Chi-Square tests are with 43 degrees of freedom and are statistically significant at the 1% level.

38

Table 4: Pooled panel regressions of mean industry CEO compensation on mean industry

performance

This table reports the regression results when regressing mean industry compensation variables against mean

performance variables over the period 1994-2008. All estimations include year effects and t-statistics are

computed based on robust standard errors that incorporate firm-level clustering. Control variables are included

but not reported. a, b and c denotes significance at 1%, 5% and 10% level, respectively.

Industry mean log of total compensation

(ltdc1)

Industry mean log of pay-performance

sensitivity (lpps)

Model (1) (2) (3)

(4) (5) (6)

Intercept 8150.573a 8121.950

a 8724.849

a 12.753

a 12.758

a 12.352

a

(10.69) (10.64) (11.25) (96.47) (97.17) (95.83)

Industry performance measure:

Mean Q 59.650b 0.014

b

(2.48) (2.48)

Mean ROA -2728.502a -0.440

(-6.39) (-0.450)

Mean return 623.892a 0.586

a

(4.10) (30.20)

control variables Yes Yes Yes Yes Yes Yes

R2 0.26 0.27 0.25 0.50 0.50 0.39

39

Table 5: Pooled panel regressions of CEO’s relative total compensation on relative firm

performance

This table reports the panel regression results for sample firms over the period 1994-2008. The dependent

variable is dltdc1, the natural log of total compensation relative to the industry mean. All estimations include

year effects and t-statistics are computed based on robust standard errors that incorporate firm-level clustering.

a, b and c denotes significance at 1%, 5% and 10% level, respectively.

Dependent variable: relative total compensation (dltdc1)

Model (1) (2) (3) (4) (5) (6)

Intercept -0.010 -2.951a

-0.080c

-2.769a -0.003 -2.845

a

(-0.26) (-10.04) (-1.93) (-9.55) (-0.07) (-10.38)

Relative performance measure:

dtobinq 0.065a 0.078

a

(6.25) (8.54)

droa 0.607a 0.646

a

(5.27) (8.04)

dcrawret12 0.081a 0.112

a

(3.67) (3.71)

Control Variables:

rd 1.960a 1.933

a 2.089

a

(5.66) (7.48) (5.96)

lsales 0.355a 0.278

a 0.350

a

(5.20) (3.85) (5.61)

lsalessq 0.003 0.008c 0.003

(0.70) (1.72) (0.82)

divyield -2.004a -1.838

a -1.872

a

(-3.32) (-3.15) (-3.17)

lev 0.115 0.092 0.006

(1.38) (1.11) (0.07)

tenure -0.001 -0.001 -0.001

(-0.31) (-0.31) (-0.23)

age 0.000 0.000 -0.002

(-0.14) (-0.05) (-0.65)

R2 0.01 0.38 0.01 0.37 <0.01 0.37

N 13,033 11,702 12,825 11,520 12,908 11,425

40

Table 6: Pooled panel regressions of CEO’s relative pay-performance

sensitivity on relative firm performance

This table reports the panel regression results for sample firms over the period 1994-2008. The dependent

variable is dtotpps, the CEO’s pay-performance sensitivity relative to the industry mean. All estimations include

year effects and t-statistics are computed based on robust standard errors that incorporate firm-level clustering.

All coefficients are expressed as a fraction of 106. a, b and c denotes significance at 1%, 5% and 10% level,

respectively.

Dependent variable: relative pay-performance sensitivity (dtotpps)

Model (1) (2) (3) (4) (5) (6)

Intercept 0.008 -2.441a -0.035 -2.000

a 0.004 -2.241

a

(0.20) (-3.62) (-0.58) (-3.05) (-0.10) (-3.39)

Relative performance measure:

dtobinq 0.253a

0.274a

(5.80) (6.18)

droa 0.342 0.450

(0.87) (1.46)

dcrawret12 0.313a 0.304

a

(5.44) (5.42)

Control Variables:

rd 0.380 0.584 0.856c

(0.76) (1.33) (1.75)

lsales 0.090 -0.003 0.068

(0.51) (-0.02) (0.40)

lsalessq 0.018 0.024c

0.020

(1.27) (1.83) (1.45)

lev -2.256b

-2.069b -2.320

b

(-2.24) (-2.05) (-2.28)

divyield -0.331 -0.668b -0.699

a

(-1.34) (-2.45) (-2.75)

tenure 0.073a 0.073

a 0.074

a

(3.07) (3.01) (3.06)

age 0.004 0.002 0.002

(0.25) (0.12) (0.13)

R2

0.02 0.09 0.00 0.06 <0.01 0.06

N 12,945 11,630 12,737 11,448 12,898 11,415

41

Table 7: Pooled panel regressions of log of CEO’s relative pay-performance

sensitivity on relative firm performance

This table reports the panel regression results for sample firms over the period 1994-2008. The dependent

variable is dlpps, the log of CEO’s pay-performance sensitivity relative to the industry mean. All estimations

include year effects and t-statistics are computed based on robust standard errors that incorporate firm-level

clustering. a, b and c denotes significance at 1%, 5% and 10% level, respectively.

Dependent Variable: Log of relative pay-performance sensitivity (dlpps)

Model (1) (2) (3) (4) (5) (6)

Intercept 0.032 -3.872a -0.119

c -3.431

a 0.015 -3.737

a

(0.54) (-10.45) (-1.90) (-9.39) (0.24) (-10.73)

Relative performance measure:

dtobinq 0.203a

0.215a

(6.33) (6.46)

droa 1.275a 1.237

a

(8.40) (9.87)

dcrawret12 0.356a 0.366

a

(8.20) (6.85)

Control variables:

rd 1.340a 1.345

a 1.748

a

(3.47) (4.68) (4.53)

lsales 0.492a 0.332

a 0.494

a

(5.85) (3.88) (6.50)

lsalessq -0.003 0.008 -0.003

(-0.56) (1.35) (-0.58)

lev -5.223a -5.101

a -5.105

a

(-4.87) (-4.69) (-4.85)

divyield -0.224c

-0.417a -0.503

a

(-1.65) (-3.09) (-3.69)

tenure 0.068a 0.068

a 0.069

a

(17.93) (17.00) (17.25)

age -0.001 -0.002 -0.004

(-0.41) (-0.40) (-1.01)

R2

0.06 0.41 0.02 0.36 0.02 0.36

N 12,945 11,630 12,737 11,448 12,898 11,415

42

Table 8: Differences in the relative pay for relative performance across industries with low and high insider hiring

This table reports the panel regression results for sample firms over the period 1994-2008. All estimations include year effects and t-statistics are computed based

on robust standard errors that incorporate firm-level clustering. Control variables are included but not reported. a, b and c denotes significance at 1%, 5% and

10% level, respectively.

Industries with low insider hiring

Industries with high insider hiring

t-statistics for differences

Model (1) (2) (3)

(4) (5) (6)

(1) - (4) (2) - (5) (3) - (6)

Q ROA Returns

Q ROA Returns

Q ROA Returns

Panel A: dltdc1 0.076a 0.615

a 0.038 0.117

a 1.017

a 0.179

a -0.041

c -0.402

c -0.141

a

(6.9) (4.56) (1.27) (5.43) (6.44) (5.34) (-1.72) (-1.96) (-3.13)

Control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes

R2 0.402 0.391 0.378 0.455 0.461 0.454

Panel B: dlpps 0.205a 1.256

a 0.271

a 0.392

a 1.329

a 0.476

a -0.187

a -0.073 -0.205

b

(7.88) (6.44) (4.17) (8.69) (4.84) (6.64) (-3.59) (-0.22) (-2.11)

Control variables Yes Yes Yes Yes Yes Yes Yes Yes Yes

R2 0.436 0.373 0.363 0.409 0.372 0.375

43

Table 9: Dynamic panel estimation of relative total compensation

This table reports the results from two-step GMM panel regression for sample firms over the period 1994-2008.

The dependent variable is dltdc1, the natural log of total compensation relative to the industry mean. All

estimations include year effects and Windmeijer (2005) bias-corrected robust estimator z-statistics are reported

in parentheses. a, b and c denotes significance at 1%, 5% and 10% level, respectively.

Dependent variable: relative total compensation (dltdc1)

(1) (2) (3)

dltdct-1 0.140a

0.143a 0.135

a

(3.40) (3.81) (3.71)

Relative performance measure:

dtobinq 0.088a

(3.15)

droa 1.385a

(5.70)

dcrawret12 -0.017

(-0.30)

Control variables:

rd 0.548c

0.183 0.623b

(1.89) (1.01) (2.07)

lsales -0.223a -0.285

a -0.190

a

(-5.49) (-6.87) (-5.01)

lsalessq 0.037a 0.041

a 0.034

a

(10.65) (11.94) (10.82)

lev -0.040 0.039 -0.199b

(-0.43) (0.43) (-2.23)

divyield -1.453b

-0.625 -1.490b

(-2.33) (-1.15) (-2.10)

tenure 0.002 0.000 0.003

(0.94) (0.15) (1.13)

age -0.009a -0.008

a -0.010

a

(-3.59) (-3.38) (-4.45)

AR(2) (Prob > z) 0.20 0.48 0.52

N 8,816 8,697 8,666

Note: 1. AR(2) is test for second-order serial correlation in the first-differenced residuals under the null

hypothesis of no serial correlation.

2. The instruments used are the explanatory variables lagged by three years and higher.

44

Table 10: Dynamic panel estimation of relative pay-performance sensitivity

This table reports the results from two-step GMM panel regression for sample firms over the period 1994-2008.

The dependent variable is dtotpps, the total pay-performance sensitivity relative to the industry mean. All

estimations include year effects and Windmeijer (2005) bias-corrected robust estimator z-statistics are reported

in parentheses. All coefficients are expressed as a fraction of 105. a, b and c denotes significance at 1%, 5% and

10% level, respectively.

Dependent Variable: relative pay-performance sensitivity (dtotpps)

(1) (2) (3)

dtotppst-1 0.000a

0.000a 0.000

a

(10.45) (17.55) (21.34)

Relative performance measure:

dtobinq 2.534a

(4.45)

droa 8.622b

(2.50)

dcrawret12 6.430a

(5.24)

Control variables:

rd -2.271 -1.295 0.950

(-1.16) (-0.64) (0.49)

lsales -1.383a -2.001

a -0.980

b

(-3.44) (-4.21) (-2.53)

lsalessq 0.165a 0.213

a 0.130

a

(5.34) (5.96) (4.67)

lev 0.119 -1.318 -1.130

(0.10) (-1.07) (-1.15)

divyield -4.959 -3.708 -1.450

(-1.01) (-0.80) (-0.40)

tenure 0.336a 0.292

a 0.310

a

(5.75) (6.54) (7.20)

age -0.020 -0.009 -0.040

(-0.65) (-0.32) (-1.30)

AR(2) (Prob > z) 0.30 0.29 0.33

N 8,788 8,669 8,660

Note: 1. AR(2) is test for second-order serial correlation in the first-differenced residuals under the null

hypothesis of no serial correlation.

2. The instruments used are the explanatory variables lagged by three years and higher.

45

Table 11: Dynamic panel estimation of relative log pay-performance sensitivity

This table reports the results from two-step GMM panel regression for sample firms over the period 1994-2008.

The dependent variable is dlpps, the log of pay-performance sensitivity relative to the industry mean. All

estimations include year effects and Windmeijer (2005) bias-corrected robust estimatorl. z-statistics are reported

in parentheses. a, b and c denotes significance at 1%, 5% and 10% level, respectively.

Dependent Variable: relative log of pay-performance sensitivity

(dlpps)

(1) (2) (3)

dlppst-1 0.371a

0.478a 0.482

a

(7.05) (9.25) (8.90)

Relative performance measure:

dtobinq 0.118a

(5.46)

droa 0.837a

(3.58)

dcrawret12 0.408a

(3.29)

Control variables:

rd -0.185 -0.283 -0.013

(-0.62) (-1.25) (-0.05)

lsales -0.201a -0.211

a -0.139

a

(-4.81) (-4.83) (-3.85)

lsalessq 0.030a 0.028

a 0.023

a

(8.28) (7.72) (7.24)

lev -0.195b

-0.190b -0.214

b

(-2.15) (-2.00) (-2.38)

divyield -3.491a -3.226

a -2.726

a

(-3.92) (-3.95) (-3.59)

tenure 0.043a 0.035

a 0.037

a

(9.51) (7.82) (7.75)

age -0.006b -0.005

c -0.008

a

(-2.35) (-1.78) (-3.23)

AR(2) (Prob > z) 0.22 0.16 0.28

N 8,788 8,669 8,660

Note: 1. AR(2) is test for second-order serial correlation in the first-differenced residuals under the null

hypothesis of no serial correlation.

2. The instruments used are the explanatory variables lagged by three years and higher.

46

Table 12: Tests of the Relative Performance Evaluation (RPE) hypothesis: pooled panel

regressions of change in total compensation on firm performance and industry performance

This table reports the pooled panel regression results for sample firms over the period 1994-2008. The

dependent variable in Panel A is total compensation: tdc1t- tdc1t-1, and the dependent variable in Panel B is

log of total compensation: ltdc1t- ltdc1t-1. All estimations include year effects and t-statistics are computed

based on robust standard errors that incorporate firm-level clustering. a, b and c denotes significance at 1%, 5%

and 10% level, respectively.

Dependent variable Panel A: Change in total compensation Panel B: Change in Log of total compensation

tdc1

ltdc1

Intercept

-847.506 432.115 -841.235 -0.286b

-0.132 -0.376a

(-1.06) (0.48) (-1.02) (-2.90) (-1.38) (-3.74)

Performance measure:

Tobinq

406.314a

0.030a

(3.13)

(3.55)

Mtobinq

51.632

0.004

(1.29)

(0.80)

Roa

2031.091a

0.403a

(3.12)

(5.62)

Mroa

-936.412b

-0.087b

(-2.07)

(-1.93)

crawret12 995.047c

0.159b

(1.85) (2.16)

Indret 406.517 0.102c

(0.96) (1.74)

Control variables:

Rd

-4355.187a -1247.372 -1750.022

c -0.509

a -0.200

c -0.374

a

(-2.66) (-1.24) (-1.86) (-3.95) (-1.72) (-3.49)

Lsales

-106.263 -278.556 -91.997 0.029 -0.013 0.026

(-0.48) (-1.24) (-0.44) (1.40) (-0.58) (1.23)

Lsalessq

12.654 23.758 12.010 -0.001 0.001 -0.001

(0.83) (1.55) (0.81) (-1.04) (0.92) (-0.89)

Lev 668.363 76.677 42.360 -0.015 -0.035 -0.039

(1.63) (0.23) (0.13) (-0.36) (-0.84) (-0.96)

Divyield

1040.717 85.340 1423.564 -0.353 -0.393 -0.128

(0.29) (0.02) (0.39) (-1.11) (-1.18) (-0.47)

Tenure

4.803 5.577 5.402 -0.002a -0.002

a -0.002

a

(0.69) (0.81) (0.78) (-3.34) (-3.25) (-3.31)

Age

-5.842 -9.042 -10.322 0.001 0.001 0.001

(-0.75) (-1.11) (-1.29) (1.59) (1.50) (1.29)

R

2

0.02 0.01 0.02 0.02 0.02 0.02

N

8,817 8,698 8,816 8,817 8,697 8,816

47

Table 13: Tests of the Relative Performance Evaluation (RPE) hypothesis:

pooled panel regressions of change in total compensation on firm performance

and industry performance

This table reports the pooled panel regression results for sample firms over the period 1994-2008. The

dependent variable in Panel A is pay for performance sensitivity: ppst-ppst-1, and the dependent variable in

Panel B is log of pay-performance sensitivity: lppst-lppst-1. All estimations include year effects and t-statistics

are computed based on robust standard errors that incorporate firm-level clustering. The coefficients for pps

are expressed as a fraction of 105. a, b and c denotes significance at 1%, 5% and 10% level, respectively.

Dependent variable Panel A: Change in total compensation Panel B: Change in Log of total compensation

pps

lpps

Intercept

-8.428a

-4.051 -8.686a -1.043

a -0.786

a -1.320

a

(-2.87) (-1.47) (-3.32) (-8.97) (-7.25) (-11.89)

Performance measure:

Tobinq

1.733a

0.063a

(6.31)

(5.88)

Mtobinq

-0.041

-0.003c

(-1.26)

(-1.77)

Roa

3.125a

0.543a

(2.77)

(6.53)

Mroa

0.755

-0.062

(0.84)

(-0.95)

crawret12 4.762a 0.460

a

(6.66) (4.04)

Indret 1.251c

0.222a

(1.80) (3.11)

Control variables:

Rd

-10.220a 2.404 -0.997 -0.539

a 0.004 -0.374

a

(-3.57) (1.38) (-0.61) (-3.86) (0.02) (-3.75)

Lsales

0.824 0.647 0.831 0.080a 0.025 0.067

a

(1.19) (1.06) (1.31) (3.16) (1.02) (3.03)

Lsalessq

-0.055 -0.043 -0.054 -0.004b -0.001 -0.003

b

(-1.15) (-0.99) (-1.20) (-2.49) (-0.41) (-2.28)

Lev 3.091a 0.101 0.456 0.050 -0.015 0.020

(3.46) (0.14) (0.65) (1.16) (-0.36) (0.55)

Divyield

-3.582 -9.375b -1.803 -2.218

a -2.438

a -1.670

a

(-1.18) (-2.59) (-0.66) (-5.26) (-5.26) (-4.61)

Tenure

0.103b

0.109b 0.106

b 0.001 0.002

c 0.001

c

(2.25) (2.37) (2.32) (1.47) (1.77) (1.70)

Age

-0.023 -0.045 -0.043 0.002c

0.002 0.001

(-0.50) (-0.96) (-0.93) (1.99) (1.40) (1.48)

R

2

0.06 0.02 0.06 0.14 0.12 0.30

N

8,788 8,669 8,660 8,788 8,669 8,660

48

Table 14: Pooled panel regressions of CEO’s relative wealth scaled pay-

performance sensitivity on relative firm performance

This table reports the panel regression results for sample firms over the period 1994-2008. The dependent

variable is dwpps, the wealth scaled CEO’s pay-performance sensitivity relative to the industry mean. All

estimations include year effects and t-statistics are computed based on robust standard errors that incorporate

firm-level clustering. a, b and c denotes significance at 1%, 5% and 10% level, respectively.

Dependent variable: Relative wealth scaled pay-performance sensitivity (dwpps)

Model (1) (2) (3) (4) (5) (6)

Intercept 0.522 -42.001c

-1.030 -34.569 0.423 -38.775

(0.40) (-1.72) (-0.60) (-1.46) (0.33) (-1.60)

Relative performance measure:

Dtobinq 5.477a

5.040a

(3.18) (3.01)

Droa 13.571 7.929

(1.45) (0.90)

dcrawret12 7.098a 6.793

b

(2.59) (2.32)

Control variables:

Rd -18.828 -14.647 -8.932

(-0.77) (-0.59) (-0.35)

Lsales 8.958c 7.403 8.638

c

(1.84) (1.63) (1.80)

Lsalessq -0.428 -0.323 -0.394

(-1.39) (-1.15) (-1.31)

Lev -38.713c -34.079 -38.545

c

(-1.78) (-1.56) (-1.69)

Divyield -25.896a -33.275

a -32.546

a

(-2.66) (-3.14) (-3.05)

Tenure 2.788a 2.786

a 2.826

a

(5.14) (5.06) (5.10)

Age -0.254 -0.286 -0.292

(-0.92) (-1.01) (-1.03)

R2

0.01 0.05 <0.01 0.04 <0.01 0.05

N 13,032 11,702 12,822 11,520 12,903 11,425

49

Table 15: Pooled panel regressions of log of CEO’s wealth scaled relative pay-

performance sensitivity on relative firm performance

This table reports the panel regression results for sample firms over the period 1994-2008. The dependent

variable is dlwpps, the log of wealth scaled CEO’s pay-performance sensitivity relative to the industry mean.

All estimations include year effects and t-statistics are computed based on robust standard errors that

incorporate firm-level clustering. a, b and c denotes significance at 1%, 5% and 10% level, respectively.

Dependent Variable: Log of relative wealth scaled pay-performance sensitivity

(dlwpps)

Model (1) (2) (3) (4) (5) (6)

Intercept 0.071 -1.045a -0.787

a 0.044 -0.932

a -0.932

a

(1.21) (-3.65) (-2.63) (0.74) (-3.20) (-3.20)

Relative performance measure:

dtobinq 0.144a

0.140a

(5.08) (4.88)

droa 0.748a 0.655

a

(6.35) (5.65)

dcrawret12 0.287a 0.266

a

(9.29) (8.38)

Control variables:

rd -0.482c

-0.458 -0.239

(-1.80) (-1.51) (-0.90)

lsales 0.164a 0.078 0.155

a

(2.83) (1.27) (2.64)

lsalessq -0.007c -0.001 -0.006

c

(-1.91) (-0.31) (-1.65)

lev -2.888a -2.921

a -2.814

a

(-3.27) (-3.16) (-3.22)

divyield -0.363a -0.535

a -0.535

a

(-3.00) (-4.36) (-4.38)

tenure 0.069a 0.068

a 0.070

a

(16.88) (16.27) (16.47)

age -0.001 -0.001 -0.002

(-0.28) (-0.30) (-0.66)

R2 0.04 0.23 0.01 0.20 0.02 0.20

N 13,032 11,702 12,822 11,520 12,903 11,425