Is Chief Executive Officer Power Bad? - Stephen M. Ross School of

Is Chief Executive Officer Power Bad?*

E. Han Kim**Ross School of Business, University of Michigan

Yao LuSchool of Economics and Management, Tsinghua University

Received 6 December 2010; Accepted 23 May 2011

Abstract

This paper focuses on abnormal chief executive officer (CEO) structural power over top

executives and examines its impacts on CEO pay for performance sensitivity and firm perfor-

mance. We find that greater abnormal power is associated with weaker firm performance, but

the relation is significant only when monitoring by external shareholders is weak. We also iden-

tify a channel through which the power adversely impacts firm performance: CEOs’ capture of

the compensation process. Greater abnormal CEO power lowers CEOs’ pay for performance

sensitivity, but again the relation is driven by observations under weak external monitoring.

External monitoring is measured by institutional ownership concentration; the abnormal power,

by residuals of a regression relating CEO structural power to its likely determinants. The nega-

tive impact of the abnormal power on firm performance is robust to potential reverse causality.

Keywords Chief executive officer power; Pay for performance sensitivity; Firm performance;

Institutional investor concentration

JEL Classification: G30, G34, J33, L25, M52, G32

1. Introduction

The influence of chief executive officers (CEOs) on corporate policies and perfor-

mance has been well documented. Bertrand and Schoar (2003) find that CEO fixed

effects matter for a wide range of firm policies; Bennedsen et al. (2006) document

that CEO deaths are strongly negatively correlated with firm profitability and

*Acknowledgments: We have benefitted from helpful comments and suggestions from Sugato

Bhattacharyya, J. B. Chay, Amy Dittmar, Luo Jiang, Jin-Mo Kim, John McConnell, Adair Morse,

Amiyatosh Purnannandam, and participants of finance seminars at Rutgers University and the

State University of New York at Buffalo, the University of Michigan, the 2008 China Interna-

tional Conference in Finance, a corporate strategy seminar at the University of Michigan, and

the Fifth International Conference on Asia-Pacific Financial Markets. We acknowledge financial

support from the Mitsui Life Financial Research Center at the University of Michigan.

**Corresponding author: E. Han Kim, Ross School of Business, University of Michigan, Ann

Arbor, MI 48109, USA. Tel: 734-764-2282, Fax: 734-936-6631, email: [email protected].

Asia-Pacific Journal of Financial Studies (2011) 40, 495–516 doi:10.1111/j.2041-6156.2011.01047.x

� 2011 Korean Securities Association 495

growth; Cronqvist et al. (2009) show that differences in corporate financial leverage

can be traced to CEOs’ personal leverage; and Jenter and Lewellen (2011) provide

evidence that CEO age approaching retirement has an important impact on the

likelihood of their firms being taken over and on the takeover premiums that their

shareholders receive.

These CEO impacts should be greater when CEOs have more power, where

power is defined as the capacity to exert one’s own will on corporate decisions.

Recent evidence on CEO power suggests that powerful CEOs are bad news for share-

holders. Bebchuk et al. (2011) argue that concentration of power in the CEO reduces

firm performance. Landier et al. (2008) show that strong CEO power over top execu-

tives hurts firm performance. Bertrand and Mullainathan (2000) demonstrate that in

the absence of adequate monitoring by blockholders, CEOs manipulate the compen-

sation process to pay themselves what they can. Bebchuk and Fried (2004) argue that

powerful CEOs reduce the linkage between CEO compensation and firm perfor-

mance. Morse et al. (2010) show that powerful CEOs rig incentive contracts.

With all these negative impacts of CEO power, why do firms grant power to CEOs?

In an ideal world, shareholders would grant an optimal level of power, weighing

various costs and benefits specific to a firm’s characteristics. For some firms, concen-

tration of power in the CEO office helps to expedite decision-making processes, result-

ing in more timely and efficient reaction to internal and external problems or

pro-action to anticipated changes in market conditions. Such benefits are evident in

Adams et al. (2005), who find that powerful CEOs are associated with the best and the

worst performances. We argue that the deleterious effects of CEO power arise from

deviations from the optimal level. However, the deviation, abnormal CEO power,

might not be bad; its desirability depends on how the power is used and whether its

use is properly monitored and guided to protect and enhance shareholder value.

In the present paper, we focus on the deviation; namely, the abnormal power

beyond the customary CEO power given CEO and firm characteristics. Specifically,

we measure the abnormal CEO power by residuals of a regression relating a power

measure to its likely determinants. This measure of abnormal power is related to a

channel through which power might impact firm performance: CEOs’ pay for per-

formance sensitivity (PPS). Our purpose is to examine whether power is used to

capture the compensation process. We also consider whether the likelihood of the

capture is affected by the intensity of monitoring by external shareholders. Because

any capture of the compensation process is likely to adversely affect shareholder

value, the final phase of our investigation focuses on the relation between the

abnormal CEO power and firm performance, which is measured by Tobin’s Q or

return on assets (ROA).

We find that abnormal CEO power is negatively related to PPS, and the negative

relation is driven by observations under weak external monitoring (EM). The inten-

sity of monitoring by external shareholders is measured by institutional ownership

concentration (IOC). When IOC is high, CEO power has no effect on PPS or firm

performance. Only when monitoring by the external shareholders is weak does the

E. H. Kim and Y. Lu

496 � 2011 Korean Securities Association

abnormal power reduce PPS and firm performance. When IOC is below the sample

median, the median PPS for CEOs with the most abnormal power is lower by

$0.164 per $1000 change in shareholder value than the median PPS for the CEO

with the least abnormal power. Although $0.164 seems small in magnitude, it repre-

sents a 33% decrease in PPS relative to the PPS for the CEO with the least abnor-

mal power. As for firm performance, when IOC is below the sample median, a one

standard deviation increase in CEO abnormal power leads to a 0.342% decrease in

Tobin’s Q and a 1.216% decrease in ROA. The sample mean Q and ROA are 2.148

and 4.098%, respectively.

These relations concerning firm performance and the abnormal power are sub-

ject to reverse causality, which we address by estimating three-stage least square

regressions. Although the estimation results do not rule out the possibility that our

measures of firm performance affect the abnormal power or IOC, our main conclu-

sion is robust; namely, abnormal CEO power is bad for shareholder value when

firms are subject to weak monitoring by external shareholders.

This study adds to the emerging literature on CEO impact and power. We use the

measure of abnormal CEO structural power of Landier et al. (2008) and identify a

negative relation between the power and firm performance that is robust to firm fixed

characteristics and possible reverse causality. Furthermore, we demonstrate that the

negative relation is at work only when monitoring by external shareholders is weak.

When monitoring is strong, the abnormal CEO power seems benign. In addition, we

reveal that CEOs’ PPS is a channel through which the power affects performance.

The rest of the paper is organized as follows. Section 2 develops hypotheses and

defines the abnormal CEO power and EM. Section 3 describes our empirical design

and data. Section 4 relates the abnormal power to PPS; and Section 5, to firm

performance. Section 6 addresses endogeneity issues. Section 7 concludes.

2. Hypotheses Development

This section presents hypotheses on how abnormal CEO power affects CEO pay for

performance sensitivity (PPS) and firm performance. It also describes our measures

of the abnormal power and the strength of monitoring by external shareholders.

We begin with two non-mutually exclusive views of CEO power, the contracting

and the capturing view.

2.1. Contracting and Capturing Views

In the contracting view, shareholders determine the optimal level of CEO power by

trading off efficiency gains from having a more centralized CEO office against risks

of abusing the power for private benefits. Shareholders also hold CEOs accountable

for their actions through incentive contracts linking compensation to performance.

However, asymmetric information and transaction costs in the market for CEOs

might lead to deviations from optimal contracting. Some CEO characteristics are

unknown to their employers at the time of hiring. When subsequent realizations

Is Chief Executive Officer Power Bad?


reveal that the power granted to a CEO exceeds the optimal level, re-contracting is

costly because the CEO is likely to resist relinquishing the abnormal power and

replacing a CEO is expensive. These re-contracting costs arise because of invest-

ments required to acquire firm-specific CEO skills, uncertainty about the quality

and availability of other suitable CEO candidates, and search costs, among others.

It is possible to reduce these costs ex-ante by initially giving a CEO a very low

level of power and later adjusting it when his or her true characteristics are revealed.

However, this strategy imposes other costs: interim efficiency losses during the adjust-

ment process. When a firm is run by a CEO with abnormally weak influence, it might

function like a firm run by committees. Such firms might not be able to efficiently

react to or proactively manage changes in internal and external business environments

in a timely manner. Furthermore, a qualified candidate with other comparable

employment opportunities will not choose a CEO position that comes with unreason-

ably weak power, making the ex-ante solution untenable. Therefore, asymmetric

information, re-contracting costs, interim efficiency, and competition between firms

may lead to situations in which a CEO’s power deviates from the optimal level.

The positive deviations from the optimal level of CEO power allow for the cap-

turing view, which is an extension of the skimming view articulated by Bertrand and

Mullainathan (2000) and others (e.g. Crystal, 1991; Milbourn, 2003; Morse et al.,

2010). The skimming view posits that when ownership is diffuse and without effec-

tive oversight by shareholders, powerful CEOs capture the compensation process and

set their own pay. This view assumes that most CEOs would rather tilt the compen-

sation process in their favor at the expense of firm performance. At any given point

in time, the PPS reflects what remains after CEOs capture the governance process.

We define power as the capacity to exert one’s own will, as in Finkelstein

(1992), who defines four dimensions of managerial power: structural power, owner-

ship power, expert power, and prestige power. Structural power is based on organi-

zational structure and hierarchical authority, both formal and informal.1 The

aforementioned studies demonstrating risks of CEO power focus on this dimension

of power.2 The focus of the present paper is also on CEO structural power.

1Ownership power stems from equity ownership. In another paper, we analyze how CEO

ownership affects firm performance and risk-taking (Kim and Lu, 2011). Expert power arises

from the ability to contribute to organizational success by influencing a particular strategic

choice through functional expertise. Prestige power represents personal prestige, status of rep-

utation, and others’ perception of CEO influence through contacts and qualifications. Expert

and prestige power are similar in that both arise from personal abilities to make contribu-

tions to firm performance. These ability-based powers are unlikely to harm firm performance.

In an attempt to measure ability-based powers, Milbourn (2003) and Rajgopal et al. (2006)

use the number of times a CEO is mentioned in the press as a proxy for CEO reputation

and outside employment opportunity.2Managerial power has been a subject of much research in the management literature. See

Adams et al. (2005) for a brief review from the finance ⁄ governance perspective.

E. H. Kim and Y. Lu


2.2. Hypotheses

Our predictions are about how abnormal CEO structural power affects PPS and

firm performance. We assume that CEOs prefer low PPS: they want more compen-

sation for stronger performance and want to maintain the high level of compensa-

tion when performance is weak. To achieve this, CEOs might rig incentive contracts

by altering the benchmark when performance is weak (Morse et al., 2010). Tweak-

ing the performance benchmark would require other top executives’ support, which

is easier to obtain when a CEO has strong structural power over his or her top

executives. Hence, we predict:

Hypothesis 1. Abnormal CEO power is negatively related to PPS.

We interpret the negative relation between abnormal CEO power and PPS as

evidence of the abnormal power helping CEOs capture the governing process. When

governance is captured by CEOs, their private benefits will receive higher priority

than shareholder value maximization. Hence, we hypothesize:

Hypothesis 2. Abnormal CEO power is negatively related to firm performance.

We also predict that the effects of CEO power on both PPS and firm performance

depend on the strength of monitoring by external shareholders. Strong EM helps to

curb the negative effects of abnormal CEO power. In our paper on CEO ownership

(Kim and Lu, 2011), we find that both the benefits and deleterious effects of CEO

ownership power are prevalent only when EM is weak. Therefore, we predict:

Hypothesis 3. Effects of abnormal CEO power on PPS and firm performance are

most pronounced when EM is too weak to preempt or restrain the negative effects

of CEO power. Conversely, when EM is strong, the effects on PPS and performance

are less noticeable.

3. Empirical Design and Data

3.1. Proxy for Chief Executive Power

We measure the abnormal CEO structural power by the excess fraction of top exec-

utives hired by a CEO, as in Landier et al. (2008). CEOs are heavily involved in

recruiting their top lieutenants; consequently, top executives during a CEO’s tenure

are more likely to share similar preferences and to be more loyal to the CEO. These

executives are less likely to dissent, whereas executives accustomed to working with

a previous CEO are more likely to challenge orders received from a new CEO.

Therefore, we assume the greater the fraction of top executives hired by a CEO, the

greater the CEO’s informal hierarchical authority and, hence, the stronger is his or

her structural power over the executives.

The fraction of top executives hired during a CEO’s tenure tends to be related

to a number of factors, such as the tenure of the CEO and of other top executives

and whether the CEO is recruited from outside. Therefore, we follow Landier et al.



and measure the abnormal fraction hired by CEOs with residuals of the following

regression:

Fracit ¼ a0 þ a1CEOTENit þ a2EXECSENit þ a3OUTSIDEit þ a4KNOWNit

þ a5FRAC 1Yit þ Yeart þ eit : ð1Þ

Here, Frac is the fraction of executives hired by the CEO; CEOTEN, CEO’s tenure

(in years);3 EXECSEN, average non-CEO executive tenure; OUTSIDE, an indicator

variable equal to one if the CEO comes from outside the firm; KNOWN, the fraction

of executives for which tenure is reported in the data; and FRAC_1Y, the fraction of

executives who arrived within a year of the CEO’s nomination. The regression also

controls for year fixed effects to account for macroeconomic factors affecting top

executive hiring decisions. Table 1 contains definitions of all variables.

The residuals in the regression may be viewed as abnormal fractions of execu-

tives hired by CEOs. This is our proxy for abnormal CEO structural power over

other top executives.4 Table 2 shows the regression result. Consistent with Landier

et al. (2008), the fraction of executives appointed during a CEO’s tenure is posi-

tively related to the length of CEO tenure and negatively related to the average

non-CEO executive tenure. A substantial fraction of new hiring seems to be done

during a CEO’s first year in office, and a CEO hired from outside tends to hire

fewer top executives during his or her tenure.

3.2. Strength of External Monitoring

Our proxy for the strength of EM is IOC. Previous researchers demonstrate the

important monitoring role of institutional investors and block holders in shaping

corporate governance (e.g. Shleifer and Vishny, 1986; Bertrand and Mullainathan,

3Previous studies relying on ExecuComp to obtain CEO tenure report non-trivial numbers of

negative CEO tenure. We trace the negative tenure to the practice of ExecuComp reporting a

CEO starting year only for the latest appointment. Thus, if a CEO leaves the position and

returns later, relying on an ExecuComp start date will give a negative tenure. We correct for

this problem by backtracking the previous appointment year using the CEO name.4Other proxies used to estimate CEOs’ structural power include CEO centrality as measured

by the pay gap between CEO and other top executives (Bebchuk et al., 2008; Morse et al.,

2010) and CEO being the only inside member of the board (Adams et al., 2005; Bebchuk

et al., 2008). We do not use these proxies because they are multidimensional. For CEO cen-

trality, the contracting view suggests that compensation differences between a CEO and his

or her top executives should reflect not only the concentration of his or her structural power

but also differences in expert and prestige power, which is likely to have a favorable impact

on firm performance. As for the CEO being the only member of the board, it has a duality:

the CEO might have more structural power over other executives, but he might have less

influence over the board because it has more outside directors. Finally, CEO tenure is not

used as a proxy for CEO power because it contains both structural power obtained over time

and ability-based power as reflected by the ability to hold on to the job.

E. H. Kim and Y. Lu


2000, 2001; Hartzell and Starks, 2003; Cremers and Nair, 2005; Del Guercio et al.,

2008; Edmans, 2009). We follow Hartzell and Starks (2003) and estimate IOC by

the percentage of institutional holdings by top five institutions. The results are

robust to measuring IOC by the Herfindahl Index of institutional ownership. Insti-

tutional ownership data is obtained from the CAS Spectrum database.

Table 1 Definitions of variables used in performance and pay-for-performance-sensitivity

analyses

Variable

Panel A: Variables used in performance analyses

Power Abnormal fraction of executives hired by the firm during the

current CEO’s tenure. The internal rank of executives is based

on the sum of salaries and bonuses

Tobin’s Q The market value of common equity plus the book value of

total liabilities divided by the book value of total assets

ROA Return on total assets

FirmAge Log of one plus the listing age of a firm measured by the number

of years from the firm’s initial public offering as reported in

CRSP or the number of years since its first appearance in CRSP

LNS Log (Sales)

K ⁄ S The ratio of property, plant, and equipment to sales

I ⁄ K The ratio of capital expenditures to property, plant, and

equipment

Female Indicator equal to one if a CEO is female, and zero otherwise

CEOAge Log(CEO age)

CEO_Chair Indicator equal to one if a CEO also chairs the board, and zero

otherwise

IOC The sum of percentage share ownership of the top five institutional

investors

IOC_IY()i) The mean value of IOC of all firms in the same industry in a given

year, excluding firm i itself

Panel B: Variables used in pay-for-performance-sensitivity analyses

Ch_Flow_CEO_

Com ($K)

The change in total direct CEO compensation flow (including

salaries, bonuses, options, stock grants, and other compensation)

in 2000 dollars from year t ) 1 to year t

Ch_sv ($MM) The change in shareholder value, measured by the product of

shareholder value in 2000 dollars at year t ) 2 and the geometric

mean of shareholder rate of returns from t ) 2 to t

CDF(Power) The cumulative density function of the power variable

CDF(CEO Age) The cumulative density function of the current age of the CEO

CDF(Firm Size) The cumulative density function of log total assets

CDF(Risk) The cumulative density function of the variance of daily stock

returns during the current year



3.3. Data and Summary Statistics

We use panel data from 1994 through 2006, constructed by merging the executive data

in ExecuComp with accounting data in Compustat and stock return data in CRSP.

We drop firm-year observations in which a new CEO’s first year in office overlaps with

the last year of the previous CEO. Our total sample consists of 11 474 firm-year obser-

vations associated with 1397 unique firms over the period 1994–2006. Table 3 shows

the number of observations by year for the full sample, and separately for high IOC

and low IOC. High IOC (HIOC) and Low IOC (LIOC) are defined as those with

above and below the sample median IOC.5 The number of LIOC observations is

greater (smaller) than HIOC observations in the earlier (later) years because of the

steady increase in IOC over time. Sample size for individual regressions varies depend-

ing on the availability of data to construct dependent and independent variables.

Table 4 provides summary statistics for key variables. The average fraction of

top four executives hired during a CEO’s tenure is 15.6%. Importantly, the mean

and median of our proxy for abnormal CEO power are close to zero. A number of

variables, especially changes in CEO compensation flow and shareholder value, indi-

cate the presence of large outliers even after winsorizing them at 1 and 99%. We

are mindful of these outliers and design our estimation of PPS to mitigate the

Table 2 Regression to construct abnormal fraction (Frac) of top-executives hired during a

chief executive officer’s (CEO’s) tenure

This table reports regression estimates to construct the abnormal fraction (Frac) of top executives hired

during a CEO’s tenure. Power variable is the residual of the regression in which the dependent variable

is the percentage of top executives hired by the firm during a CEO’s tenure. The regression controls for

year fixed effects. Robust standard errors are in parentheses. Coefficients marked with ***, **, and * are

significant at 1, 5, and 10%, respectively.

Frac

(1)

CEOTEN 0.002*** (0.000)

EXECSEN )0.001*** (0.000)

OUTSIDE )0.011*** (0.002)

KNOWN )0.007* (0.004)

FRAC_1Y 0.835*** (0.005)

Constant )0.012*** (0.003)

Year fixed effects Y

Observations 20 730

Adjusted R2 0.83

5We use a pooled sample median as the demarcation point rather than the yearly median

because of the time trend. IOC has been increasing steadily over time: the mean IOC is 18%

in 1992, 24% in 1999, and 30% in 2006. Therefore, classifying 2006 firm observations into

high and low IOC by the 2006 median might wrongly classify a firm-year into low IOC when

it has a high IOC relative to the entire sample.

E. H. Kim and Y. Lu


outlier problems. The difference in the number of observations across variables is

due to missing variables.6

4. Pay for Performance Sensitivity

In this section, we examine whether abnormal CEO power helps CEOs capture the

compensation process by relating changes in total CEO compensation flows to

changes in shareholder value. Our specification closely resembles those of Hartzell

and Starks (2003), Milbourn (2003), and Rajgopal et al. (2006). We estimate:

DTotal Compensationit ¼ b0 þ b1DShareholder Valueit þ b2DShareholder Valueit

� CEO Powerit�1 þ b3CEO Powerit�1

þX6

j¼4

bjDShareholder Valueit � Control Variablesijt�1

þX9

j¼7

bjControl Variablesijt�1 þ Firmi ðor IndustryiÞ

þ Yeart þ eit : ð2Þ

Table 3 Number of observations by year

This table shows the number of observations by year. Column (2) reports the number of firms in the full

sample by year. Columns (3) and (4) report the number of firms in the HIOC and LIOC sample, respec-

tively. IOC is defined as the sum of percentage share ownership of the top 5 institutional investors.

HIOC and LIOC are defined as above and below the sample median IOC.

Year

(1)

Full

(2)

HIOC

(3)

LIOC

(4)

1994 577 154 283

1995 610 189 293

1996 645 191 327

1997 692 253 309

1998 765 286 343

1999 867 339 382

2000 930 389 398

2001 928 406 401

2002 963 463 398

2003 1031 457 458

2004 1082 614 361

2005 1130 691 336

2006 1254 755 307

Total 11 474 5187 4596

6We also exclude observations with negative market-to-book value ratios.



Here, DTotal Compensationit is the change in total direct compensation flow

(including salaries, bonuses, value of new stocks and stock options granted, and

other compensation) to the CEO of firm i in 2000 dollars from year t ) 1 to year t.

It is winsorized at the 1 and 99 percentile. DShareholder Valueit is changes in share-

holder value from t ) 2 to t, equal to shareholder value in 2000 dollars at t ) 2

multiplied by the geometric mean rates of returns from t ) 2 to t. We use the mean

shareholder returns over 2 years to allow CEO compensation to reflect performance

not only during the concurrent year but also during the prior year.

Control variables include: firm size, as measured by log of total assets, to

account for the well-documented relation between firm size and PPS (e.g. Schaefer,

1998; Baker and Hall, 2004); risk measured by the variance of stock return to

account for a possible correlation between the uncertainty surrounding the firm and

output based pay (Lafontaine and Bhattacharyya, 1995; Aggarwal and Samwick,

Table 4 Summary statistics for all variables used in performance and pay-for-performance-

sensitivity analyses

Definitions of the variables are given in Table 1. Panel A reports the summary statistics for the variables

used in performance analyses. Panel B reports the summary statistics for the variables used in pay-for-

performance sensitivity analyses.

Variable

Observation

(1)

Mean

(2)

Median

(3)

SD

(4)

Minimum

(5)

Maximum

(6)

Panel A: Variables used in performance analyses

FRAC 11 474 0.156 0.000 0.248 0.000 1.000

Power 11 479 )0.003 0.010 0.106 )0.840 0.191

Tobin’s Q 11 479 2.148 1.548 2.560 0.398 105.090

ROA 10 788 4.098 4.896 16.825 )577.850 233.264

FirmAge 11 423 2.880 2.996 0.897 0.000 4.407

LNS 10 783 7.312 7.222 1.564 )2.279 12.578

K ⁄ S 10 468 0.883 0.451 2.923 0.000 244.333

I ⁄ K 10 343 0.125 0.098 0.103 0.000 4.302

Female 11 479 0.014 0.000 0.119 0.000 1.000

CEOAge 10 920 4.014 4.025 0.138 3.367 4.511

CEO_Chair 11 479 0.646 1.000 0.478 0.000 1.000

IOC 9783 0.259 0.251 0.104 0.000 0.972

IOC_IY()i) 9565 0.261 0.263 0.041 0.107 0.415

Panel B: Variables used in pay-for-performance sensitivity analyses

Ch_Flow_CEO_Com ($K) 10 740 226.424 91.250 2194.579 )9492.284 9514.891

Ch_sv ($MM) 13 091 444.756 113.294 1611.402 )7118.041 13 868.280

CDF(Power) 8593 0.503 0.504 0.293 0.000 1.000

CDF(CEO Age) 13 170 0.551 0.600 0.284 0.000 1.000

CDF(Firm Size) 13 960 0.533 0.544 0.279 0.000 1.000

CDF(Risk) 14 724 0.488 0.481 0.285 0.000 1.000

E. H. Kim and Y. Lu


1999; Prendergast, 2002); and CEO age because it is shown to be related to PPS

(Gibbons and Murphy, 1992; Milbourn, 2003). Power and control variables are

lagged by 1 year. Because DTotal Compensation is calculated based on the change in

total direct compensation flow of the CEO from t ) 1 to year t, we drop firm-year

observations in which a new CEO’s first year in office overlaps with the last year of

the previous CEO’s tenure.

We follow Aggarwal and Samwick (1999) and Milbourn (2003) and use cumula-

tive density functions for variables interacted with shareholder value change. Using

cumulative density functions reduces the importance of outliers by normalizing the

variables to the unit interval. It also helps to interpret the estimated coefficients b1

and b2 in an economically meaningful way, revealing how the power affects PPS.

We estimate two regressions: equation (1), a median regression with industry

fixed effects to account for industry differences in PPS (Murphy, 1999), where

industry is defined by the Fama–French 48 industry groupings; and equation (2),

an OLS regression with firm fixed effects.7 Both regressions include year fixed

effects. Median regressions are also used by Hall and Liebman (1998), Milbourn

(2003), and Rajgopal et al. (2006). They are more robust to the presence of large

outliers than mean regressions. This robustness is important because summary sta-

tistics in Table 4 indicate the presence of large outliers in changes of CEO compen-

sation flow and of shareholder value even after winsorizing. The precision of

estimates from a median regression is higher, because the median regression is a

more robust estimate of central tendency than the mean regression.8

4.1. Full Sample results

Table 5 reports the regression estimates relating abnormal CEO power to PPS. The

coefficients of interest are those on the interaction terms, especially the interaction

between shareholder value changes and the power variable. A positive coefficient

means abnormal CEO power increases PPS; a negative coefficient means abnormal

power decreases PPS.

Column (1), using the median regression, indicates that abnormal power has a

negative and significant impact on PPS. The OLS estimation result in Column (2)

also shows a negative but insignificant coefficient. The overall evidence suggests that

abnormal power helps CEOs capture the compensation process; however, the statis-

tical significance is sensitive to the choice of model specification.

Results on control variables are consistent with previous findings: PPS is

negatively related to firm size (Schaefer, 1998; Baker and Hall, 2004), and the med-

ian regression shows a positive relation between PPS and risk, which is consistent

with Rajgopal et al. (2006) and Morse et al. (2010). According to Lafontaine and

7Stata does not allow estimation of median regressions with firm fixed effects. OLS mean

regressions are estimated to check the robustness to firm fixed effects.8See Koenker and Hallock (2001) for more in-depth discussion on quantile regression.



Bhattacharyya (1995) and Prendergast (2002), firms with low uncertainty are easier

to monitor and, hence, are less reliant on pay incentives.

4.2. Interactive Effects with External Monitoring

The results based on the full sample mask important heterogeneity. The relation

between abnormal CEO power and PPS might be stronger for firms under weaker

EM, which makes it easier for CEOs to use their power to influence the compensa-

tion process. Strong EM, in contrast, might make it difficult to capture the compen-

sation process. To test this hypothesis, we separate firm-year observations into

strong and weak EM subsamples. An observation is considered to be under strong

(weak) EM if its IOC is above (below) the sample median. Separate estimation for

each subsample allows for coefficients of the independent variables and fixed effects

to vary across strong and weak EM regimes.

Table 6 reports the subsample results. The results reveal no relation between

abnormal power and PPS when EM is strong (odd numbered columns), but a sig-

nificant negative relation when EM is weak (even numbered columns). This is true

regardless of whether we use the median regression specification or OLS with firm

Table 5 Relation between chief executive officer (CEO) power and pay-for-performance

sensitivity

Regression in column (1) is estimated by using the median regressions with year and industry fixed

effects. Industries are defined as in Fama and French (1997). The regression in column (2) is estimated

by using the OLS regression with year and firm fixed effects. Definitions of all variables are given in

Table 1. The sample is constructed by excluding the year of CEO turnover and the following year. Stan-

dard errors are reported in parentheses. Coefficients marked with ***, **, and * are significant at 1, 5,

and 10%, respectively.

Ch_Flow_CEO_Com ($K)

Median regression

(1)

OLS regression

(2)

Ch_sv ($MM) 0.694*** (0.047) 0.870*** (0.178)

Ch_sv ($MM) · CDF(Power)t)1 )0.040* (0.021) )0.056 (0.082)

CDF(Power)t)1 46.861* (27.017) )78.968 (133.716)

Ch_sv ($MM) · CDF(CEO Age)t)1 )0.039* (0.023) )0.135 (0.093)

CDF(CEO Age)t)1 )63.310** (29.178) )138.121 (158.593)

Ch_sv ($MM) · CDF(Firm Size)t)1 )0.597*** (0.045) )0.637*** (0.171)

CDF(Firm Size)t)1 194.600*** (37.328) )1282.532*** (436.178)

Ch_sv ($MM) · CDF(Risk)t)1 0.164*** (0.026) )0.017 (0.097)

CDF(Risk)t)1 66.017 (40.592) 88.457 (243.197)

Constant )312.524 (265.055) 1031.348*** (315.396)

Firm fixed effects and year fixed effects N Y

Industry fixed effects and year fixed effects Y N

Observations 9649 9649

Adjusted R2 (Pseudo R2) (0.0226) )0.06

E. H. Kim and Y. Lu


Tab

le6

Rel

atio

nb

etw

een

chie

fex

ecu

tive

offi

cer

(CE

O)

po

wer

and

pay

-fo

r-p

erfo

rman

cese

nsi

tivi

ty:

hig

han

dlo

win

stit

uti

on

alo

wn

ersh

ipco

nce

ntr

atio

n

Th

ista

ble

rep

ort

sre

gres

sio

nes

tim

ates

sep

arat

ely

for

firm

year

ob

serv

atio

ns

wit

hh

igh

and

low

inst

itu

tio

nal

ow

ner

ship

con

cen

trat

ion

(IO

C).

Co

lum

ns

(1)

and

(3)

rep

ort

the

resu

lts

esti

mat

edfo

rth

eH

IOC

sub

sam

ple

,co

lum

ns

(2)

and

(4)

rep

ort

the

resu

lts

esti

mat

edfo

rth

eL

IOC

sub

sam

ple

.H

IOC

and

LIO

Car

ed

efin

edas

abo

ve

and

bel

ow

the

sam

ple

med

ian

IOC

.R

egre

ssio

ns

inco

lum

ns

(1)

and

(2)

are

esti

mat

edb

yu

sin

gm

edia

nre

gres

sio

ns

wit

hye

aran

din

du

stry

fixe

def

fect

s.In

du

stri

esar

e

defi

ned

asin

Fam

aan

dF

ren

ch(1

997)

.R

egre

ssio

ns

inco

lum

ns

(3)

and

(4)

are

esti

mat

edb

yu

sin

gth

eO

LS

regr

essi

on

wit

hye

aran

dfi

rmfi

xed

effe

cts.

Defi

nit

ion

so

f

all

vari

able

sar

egi

ven

inT

able

1.A

llre

gres

sio

ns

con

tro

lfo

rfi

rman

dye

arfi

xed

effe

cts.

Ro

bu

stst

and

ard

erro

rsar

ere

po

rted

inp

aren

thes

es.

Co

effi

cien

tsm

arke

dw

ith

***,

**,

and

*ar

esi

gnifi

can

tat

1,5,

and

10%

,re

spec

tive

ly.

Ch

_F

low

_C

EO

_C

om($

K)

Med

ian

regr

essi

on

OL

Sre

gres

sio

n

HIO

C

(1)

LIO

C

(2)

HIO

C

(3)

LIO

C

(4)

Ch

_sv

($M

M)

0.93

1***

(0.1

11)

0.49

7***

(0.0

69)

1.50

6***

(0.3

47)

0.62

6**

(0.2

55)

Ch

_sv

($M

M)

·C

DF

(Po

wer

) t)

10.

008

(0.0

58)

)0.

164*

**(0

.030

))

0.07

4(0

.186

))

0.25

0**

(0.1

10)

CD

F(P

ower

) t)

1)

21.0

81(5

1.72

0)62

.916

(48.

225)

)22

8.73

6(2

23.5

68)

)64

.690

(240

.737

)

Ch

_sv

($M

M)

·C

DF

(CE

OA

ge) t

)1

)0.

215*

**(0

.061

)0.

043

(0.0

33)

)0.

413*

*(0

.186

))

0.08

0(0

.137

)

CD

F(C

EO

Age

) t)

1)

83.3

89(5

7.35

6)3.

837

(50.

594)

)60

7.14

8**

(278

.996

)31

.904

(270

.143

)

Ch

_sv

($M

M)

·C

DF

(Fir

mSi

ze) t

)1

)0.

783*

**(0

.102

))

0.33

9***

(0.0

64)

)1.

150*

**(0

.323

))

0.27

5(0

.246

)

CD

F(F

irm

Size

) t)

163

.311

(74.

107)

193.

368*

**(6

5.17

9))

1808

.376

**(7

49.6

24)

)12

80.0

67(8

36.9

75)

Ch

_sv

($M

M)

·C

DF

(Ris

k)t)

10.

375*

**(0

.069

))

0.01

3(0

.036

)0.

100

(0.2

08)

)0.

262*

(0.1

36)

CD

F(R

isk)

t)1

79.6

88(7

9.37

1))

45.8

11(7

3.14

8)59

2.50

2(4

11.8

06)

)25

.631

(458

.024

)

Co

nst

ant

)62

2.87

4(4

73.3

33)

156.

463

(343

.789

)68

7.20

1(4

27.0

38)

572.

442

(497

.382

)

Fir

mfi

xed

effe

cts

and

year

fixe

def

fect

sN

NY

Y

Ind

ust

ryfi

xed

effe

cts

and

year

fixe

def

fect

sY

YN

N

Ob

serv

atio

ns

4281

3608

4281

3608

Ad

just

edR

2(P

seu

do

R2)

(0.0

344)

(0.0

233)

)0.

05)

0.00



fixed effects. The negative relation between abnormal power and PPS is driven by

observations under weak EM.

The median regression estimation result in Column (2) implies that when EM is

weak, the median pay sensitivity for the CEO with the most abnormal power is

lower than that for the CEO with the least abnormal power by $0.164 per $1000

change in shareholder value. Considering that the PPS for the CEO with the least

power is $0.497 per $1000 change in shareholder value, the $0.164 decrease repre-

sents a 33% decrease in PPS.

Our estimated pay sensitivities are smaller than those reported by Milbourn

(2003) and Rajgopal et al. (2006), who use similar empirical methodology. The dif-

ference is in the dependent variable. Their dependent variable includes changes in

the value of all stocks and stock options held by CEOs and, hence, measures CEOs’

firm-related wealth change, whereas our dependent variable includes changes only

in compensation flows. When Hartzell and Starks (2003) estimate executives’ PPS

of salaries and bonuses, they show only $0.032 per $1000 change in shareholder

value. Our estimates are much greater than theirs because we include the value of

new grants of stocks and stock options in our measure of CEO compensation flows.

5. Firm Performance

How does the effect of abnormal CEO power on the compensation process show

up on the bottom line? If a CEO uses the power to capture the compensation

process and lowers his or her PPS, the alignment of his or her incentive to share-

holder value is reduced to below the optimal level. Such deviations from optimal

incentive contracts might hurt firm performance. In this section we test this

hypothesis by examining how firm performance is related to abnormal CEO

power.

The relation between firm performance and abnormal CEO power is estimated

with the following specification:

Performit ¼ b0þb1CEO Powerit þX8

j¼2

bjControl Variablesijt þ FirmiþYeart þ eit ; ð3Þ

where Performit is measured by either Tobin’s Q or ROA. Tobin’s Q is equal to the

book value of assets plus the market value of common stock minus the sum of

book value of common stock divided by the book value of assets. ROA is return on

assets. Summary statistics on Q and ROA are reported in Table 4.

The control variables include the standard controls used in Q regressions (e.g.

Himmelberg et al., 1999; Kim and Lu, 2011), plus variables measuring CEO charac-

teristics. We control for firm size, measured by the log of sales (LNS);9 growth and

9We do not measure firm size by total assets in performance regressions because the calcula-

tions of both Tobin’s Q and ROA include total assets.

E. H. Kim and Y. Lu


discretionary investment opportunities, measured by the ratio of capital expendi-

tures to property, plant, and equipment (I ⁄ K); tangibility of assets, measured by the

ratio of property, plant, and equipment to sales (K ⁄ S); firm age, measured as the

log of one plus the number of years from a firm’s initial public offerings as reported

in CRSP or the number of years since its first appearance in CRSP (FirmAge); an

indicator for CEO gender (Female); CEO age (CEOAge); and an indicator for a

CEO chairing the board (CEO_Chair). In addition, we control for firm and year

fixed effects. All regressions are estimated using pooled panel data with robust stan-

dard errors.

5.1. Full Sample Results

Table 7 reports regression estimates for Tobin’s Q and ROA. The results are consis-

tent with those for PPS. Firm performance, whether measured by Tobin’s Q or

ROA, is negatively related to the measure of abnormal CEO power. The coefficients

on Power are negative and significant for both Q and ROA. The coefficients imply

that a one standard deviation increase in the abnormal power is associated with a

0.136% lower Tobin’s Q and a 0.485% lower ROA.

Control variables indicate that Q is negatively correlated with size, firm age, tan-

gibility of assets, and CEO age, but positively correlated with I ⁄ K. All of these

results are consistent with previous studies (e.g., Himmelberg et al., 1999; Kim and

Lu, 2011). ROA is also negatively related to firm age, but, unlike Q, is positively

related to size.

Table 7 Relation between chief executive officer (CEO) power and firm performance

Regression in column (1) estimates the relation CEO power and Tobin’s Q with the full sample over the

entire sample period. Regression in column (2) estimates the relation between CEO power and ROA.

Definitions of all variables are given in Table 1. All regressions control for firm- and year fixed effects.

Robust standard errors are reported in parentheses. Coefficients marked with ***, **, and * are signifi-

cant at 1, 5, and 10%, respectively.

Tobin’s Q

(1)

ROA

(2)

Power )1.286*** (0.302) )4.575** (1.800)

FirmAge )0.521*** (0.076) )2.293*** (0.454)

LNS )0.917*** (0.061) 2.738*** (0.367)

K ⁄ S )0.033*** (0.009) 0.045 (0.053)

I ⁄ K 5.716*** (0.291) 0.792 (1.738)

Female 0.188 (0.328) )1.735 (1.956)

CEOAge )0.592** (0.258) )0.680 (1.541)

CEO_Chair 0.165* (0.087) 0.542 (0.519)

Constant 11.100*** (1.074) )4.432 (6.410)

Firm fixed effects and year fixed effects Y Y

Observations 9855 9855

Adjusted R2 0.39 0.40



5.2. Interactive Effects with External Monitoring

Because the effects of abnormal CEO power on PPS depend on the strength of EM,

if abnormal power indeed affects firm performance, the performance effect should

also depend on EM. That is, the negative effects on firm performance should be

most pronounced when EM is too weak to deter the negative effects of abnormal

power. (See Durnev and Kim (2005) for interactive effects of internal and external

governance in a cross-country context.) To test this hypothesis, we estimate the

interactive effects of EM and abnormal power on firm performance.

Table 8 reports the estimation results. Columns (1), (2), (4), and (5) contain

separate estimation results for strong and weak EM subsamples for Q or ROA. The

results are remarkably consistent with the PPS results. Abnormal power and firm

performance are unrelated for observations under strong EM. The negative relation

for the full sample is driven by observations under weak EM. This is true regardless

of whether performance is measured by Tobin’s Q (Column 2) or ROA (Column 5).

The estimated coefficients for the low IOC sample indicate that one standard devia-

tion increase in the abnormal CEO power is associated with a 0.342% decrease in

Tobin’s Q and a 1.216% decrease in ROA.

Columns (3) and (6) utilize the full sample with an interaction term between

the abnormal power and IOC. This approach has the benefit of fully utilizing the

information in the IOC variable, but at the cost of forcing the coefficients of

other independent variables to be the same for observations under both strong

and weak EM. The estimation result is robust to this alternative specification. The

interaction term is positive and significant, regardless of whether performance is

measured by Q or ROA, implying that stronger monitoring by institutional inves-

tors negates the negative effects that abnormal CEO power has on firm perfor-

mance.10

6. Robustness

In this section we address endogenous issues. There are alternative explanations for

our results. For one, poorly performing firms might give their CEOs’ more abnor-

mal power to make it easier to implement changes, causing a negative correlation

between the power variable and firm performance. However, this does not explain

why the negative relation is observed only when EM is weak. When EM is strong,

the external monitors are more likely to be aware of the need for stronger leader-

ship to implement changes, predicting a stronger negative correlation, which is con-

tradicted by our evidence of no relation under strong EM.

10We do not use this interaction term approach for the PPS analysis because it would require

several triple interaction terms in a single regression, causing a rather severe multicollinearity

problem. It would also make it difficult to interpret the coefficients.

E. H. Kim and Y. Lu


Tab

le8

Rel

atio

nb

etw

een

chie

fex

ecu

tive

offi

cer

(CE

O)

po

wer

and

firm

per

form

ance

:h

igh

and

low

inst

itu

tio

nal

ow

ner

ship

con

cen

trat

ion

Th

ista

ble

rep

ort

sre

gres

sio

nes

tim

ates

sep

arat

ely

for

firm

year

ob

serv

atio

ns

wit

hh

igh

and

low

inst

itu

tio

nal

ow

ner

ship

con

cen

trat

ion

(IO

C).

Co

lum

ns

(1)

and

(4)

rep

ort

resu

lts

esti

mat

edfo

rth

eH

IOC

sub

sam

ple

,co

lum

ns

(2)

and

(5)

rep

ort

resu

lts

esti

mat

edfo

rth

eL

IOC

sub

sam

ple

.C

olu

mn

s(3

)an

d(6

)re

po

rtth

ere

sult

ses

ti-

mat

edfo

rth

efu

llsa

mp

lew

ith

the

inte

ract

ion

term

bet

wee

nIO

Can

dC

EO

po

wer

.H

IOC

and

LIO

Car

ed

efin

edas

abo

vean

db

elo

wth

esa

mp

lem

edia

nIO

C.

Th

e

dep

end

ent

vari

able

isT

ob

in’s

Qin

colu

mn

s(1

)–(3

)o

rR

OA

inco

lum

ns

(4)–

(6).

Defi

nit

ion

so

fal

lva

riab

les

are

give

nin

Tab

le1.

All

regr

essi

on

sco

ntr

ol

for

firm

-an

d

year

fixe

def

fect

s.R

ob

ust

stan

dar

der

rors

are

rep

ort

edin

par

enth

eses

.C

oef

fici

ents

mar

ked

wit

h**

*,**

,an

d*

are

sign

ifica

nt

at1,

5,an

d10

%,

resp

ecti

vely

.

To

bin

’sQ

RO

A

HIO

C

(1)

LIO

C

(2)

Fu

ll

(3)

HIO

C

(4)

LIO

C

(5)

Fu

ll

(6)

Pow

er0.

122

(0.2

83)

)3.

222*

**(0

.695

))

6.73

0***

(0.7

80)

)1.

620

(1.6

10)

)11

.467

***

(4.0

57)

)17

.036

***

(4.5

21)

Pow

er·

IOC

20.2

96**

*(2

.725

)50

.308

***

(15.

804)

IOC

)1.

743*

**(0

.380

)0.

367

(2.2

03)

Fir

mA

ge)

0.18

9**

(0.0

81)

)1.

815*

**(0

.235

))

0.66

2***

(0.0

95)

0.52

5(0

.459

))

1.52

7(1

.372

))

1.30

4**

(0.5

50)

LN

S)

0.19

6***

(0.0

68)

)1.

860*

**(0

.147

))

1.05

1***

(0.0

71)

2.80

5***

(0.3

86)

2.21

6***

(0.8

58)

2.85

1***

(0.4

13)

K⁄S

)0.

272*

**(0

.074

))

0.06

2***

(0.0

13)

)0.

041*

**(0

.010

))

3.50

6***

(0.4

21)

0.31

6***

(0.0

76)

0.28

4***

(0.0

55)

I⁄K

4.10

5***

(0.3

35)

6.07

5***

(0.5

27)

5.84

7***

(0.3

12)

9.16

0***

(1.9

08)

)5.

996*

(3.0

78)

0.65

8(1

.806

)

Fem

ale

)0.

121

(0.3

05)

0.59

4(0

.964

)0.

177

(0.3

87)

)0.

498

(1.7

33)

)1.

047

(5.6

27)

)0.

623

(2.2

46)

CE

OA

ge)

0.50

3**

(0.2

50)

)0.

472

(0.5

83)

)0.

584*

*(0

.285

)1.

864

(1.4

21)

)3.

958

(3.4

04)

)0.

506

(1.6

53)

CE

O_

Ch

air

0.12

6(0

.084

))

0.05

4(0

.197

)0.

144

(0.0

95)

1.54

4***

(0.4

78)

)1.

092

(1.1

49)

0.51

9(0

.553

)

Co

nst

ant

5.38

5***

(1.0

59)

21.4

64**

*(2

.404

)12

.758

***

(1.1

86)

)22

.016

***

(6.0

20)

11.4

71(1

4.03

5))

8.76

1(6

.879

)

Fir

mfi

xed

effe

cts

and

year

fixe

def

fect

s

YY

YY

YY

Ob

serv

atio

ns

4757

4221

8978

4757

4221

8978

Ad

just

edR

20.

520.

360.

400.

430.

430.

43



Another possible scenario is that when a firm performs poorly, more top exec-

utives leave, opening more positions for the remaining CEO to fill, thereby

increasing the abnormal fraction of executives hired during the current CEO’s

tenure.11 This story also does not explain why it does not work for observations

under strong EM. If anything, external monitors under strong EM are more likely

to demand a shakeup in executive suites, leading to more replacement and more

new hiring.

Finally, IOC might not be completely exogenous. It could be affected by the

abnormal CEO power, Q, or ROA. For example, institutional investors might be

more attracted to firms with an appropriate level of CEO power, better performing

firms, or firms undervalued by the market. To address these potential issues, we

estimate three-stage least squares simultaneous regressions as follows:

Performit ¼ a0 þ a1Powerit þ a2Powerit � IOCit þ a3IOCit þ a4FirmAgeit þ a5LNSit

þ a6K=Sit þ a7I=Kit þ a8Femaleit þ a9CEOAgeit þ a10CEO Chairit

þ gt þ hi þ eit ; ð4Þ

Powerit ¼ b0 þ b1IOCit þ b2Performit þ b3LNSit þ b4FirmAgeit þ b5Femaleit

þ b6CEOAgeit þ b7CEO Chairit þ gt þ hi þ eit ; and ð5Þ

IOCit ¼ c0 þ c1Powerit þ c2Performit þ c3FirmAgeit þ c4LNSit

þ c5K=Sit þ c6I=Kit þ c7IOC IYð�iÞit þ hi þ eit :ð6Þ

In this system of equations, the performance variables, the power variable, and

IOC are all treated as endogenous. We estimate the system of equations with the

full sample using the interaction terms of the power variable and IOC to estimate

the interactive effects of the power and the strength of EM. In the performance

equation, we include the power variable, IOC, and all control variables measuring

firm characteristics and CEO characteristics used in equation (3). In the CEO power

equation, independent variables include IOC, the performance variable, FirmAgeit,

Femaleit, CEOAgeit, and CEO_Chairit, but not K ⁄ S and I ⁄ K, because the abnormal

CEO power might be related to CEO characteristics and some basic firm character-

istics, such as firm size and firm age, but might be unrelated to the tangibility of

assets or growth and discretionary investment opportunities. In the IOC equation,

independent variables include CEO power, a performance variable, FirmAgeit, K ⁄ Sit,

and I ⁄ Kit, but not Femaleit, CEOAgeit, and CEO_Chair. Although IOC might be

affected by firm characteristics, it is unlikely to be affected by CEO characteristics.

Additionally, we include a predictor of IOC in the IOC equation: IOC of peer firms,

11This possibility was raised by Landier et al. (2008) but was rejected as an explanation for a

negative relation between the abnormal power and Q.

E. H. Kim and Y. Lu


Tab

le9

Th

ree-

stag

ele

ast

squ

ares

esti

mat

ion

of

firm

per

form

ance

,ch

ief

exec

uti

veo

ffice

r(C

EO

)p

ow

er,

and

inst

itu

tio

nal

ow

ner

ship

con

cen

trat

ion

Reg

ress

ion

sin

colu

mn

s(1

)–(3

)es

tim

ate

the

equ

atio

ns

for

To

bin

’sQ

,C

EO

po

wer

,an

dIO

C.

Reg

ress

ion

sin

colu

mn

s(4

)–(6

)es

tim

ate

the

equ

atio

ns

for

RO

A,

CE

O

po

wer

,an

dIO

C.

Defi

nit

ion

so

fal

lva

riab

les

are

give

nin

Tab

le1.

All

regr

essi

on

sco

ntr

ol

for

ind

ust

ry-

and

year

fixe

def

fect

s.In

du

stri

esar

ed

efin

edas

inF

ama

and

Fre

nch

(199

7).

Ro

bu

stst

and

ard

erro

rsar

ere

po

rted

inp

aren

thes

es.

Co

effi

cien

tsm

arke

dw

ith

***,

**,

and

*ar

esi

gnifi

can

tat

1,5,

and

10%

,re

spec

tive

ly.

3SL

So

fT

ob

in’s

Qre

gres

sio

ns

3SL

So

fR

OA

regr

essi

on

s

To

bin

’sQ

(1)

Po

wer

(2)

IOC

(3)

RO

A

(4)

Po

wer

(5)

IOC

(6)

Pow

er)

4.07

9***

(0.7

18)

)0.

0112

(0.0

0937

))

6.87

8(4

.586

))

0.00

379

(0.0

0941

)

Pow

er·

IOC

11.5

2***

(2.5

64)

19.6

1(1

6.38

)

IOC

)5.

802*

**(0

.282

))

0.01

77(0

.012

1)19

.59*

**(1

.799

))

0.00

764

(0.0

121)

Tob

in’s

Q)

0.00

177*

**(0

.000

441)

)0.

0082

0***

(0.0

0039

4)

RO

A)

8.47

e-05

(7.1

2e-0

5)0.

0006

72**

*(6

.30e

-05)

Fir

mA

ge)

0.23

6***

(0.0

369)

0.00

173

(0.0

0155

))

0.01

03**

*(0

.001

37)

)0.

0777

(0.2

35)

0.00

242

(0.0

0154

))

0.00

854*

**(0

.001

38)

LN

S)

0.10

4***

(0.0

205)

)0.

0015

5*(0

.000

865)

)0.

0073

9***

(0.0

0075

9)2.

065*

**(0

.130

))

0.00

115

(0.0

0087

9))

0.00

813*

**(0

.000

772)

K⁄S

)0.

0038

6(0

.008

77)

)0.

0011

1***

(0.0

0032

9))

0.64

3***

(0.0

558)

)0.

0006

68**

(0.0

0033

3)

I⁄K

6.81

4***

(0.2

90)

0.00

325

(0.0

112)

3.88

4**

(1.8

43)

)0.

0575

***

(0.0

109)

Fem

ale

1.07

6***

(0.2

41)

0.00

598

(0.0

104)

3.82

9**

(1.5

42)

0.00

438

(0.0

104)

CE

OA

ge)

0.75

2***

(0.2

06)

)0.

0391

***

(0.0

0882

)5.

237*

**(1

.314

))

0.03

68**

*(0

.008

82)

CE

O_

Ch

air

0.13

6**

(0.0

590)

)0.

0002

29(0

.002

54)

)0.

278

(0.3

77)

)0.

0005

09(0

.002

54)

IOC

_IY

()i)

)0.

333*

**(0

.060

6))

0.33

1***

(0.0

612)

Co

nst

ant

0.15

8***

(0.0

536)

)33

.47*

**(8

.085

)0.

0600

(0.0

541)

Ob

serv

atio

ns

8774

8774

8774

8774

8774

8774

Ind

ust

ryfi

xed

effe

cts

and

fixe

def

fect

s

YY

YY

YY

v296

61.9

419

1.65

6929

2.46

997.

2617

6.61

6833

6.96

R2

0.18

90.

020

0.16

10.

091

0.02

00.

160



IOC_IY()i)it, the mean value of IOC of all firms in the same industry in the same

year, excluding the firm itself. A firm’s IOC might be affected by how much institu-

tional investors are attracted to other firms in the same industry during the same

year. All regressions in the system include firm and year fixed effects.

Table 9 presents the estimation results. They show no relation between power

and IOC; however, the results do not rule out the possibility that firm performance

affects the power measure or IOC. Nevertheless, column (1) demonstrates the

robustness of our conclusions regarding the relation between Q and power: CEO

power has a negative effect on firm value and the negative effect is deterred by high

IOC. The same cannot be said about the relation between ROA and power. ROA is

based on accounting numbers; as such, a CEO with powerful influence over his or

her top executives is in a stronger position to smooth out fluctuations in ROA. We

suspect the weak relation between power and ROA is partly due to earnings man-

agement. Because Q is less subject to such ‘‘management’’ and is a more direct mar-

ket-based measure of shareholder value, we conclude that abnormal CEO power is

bad for shareholder value when external monitoring is not strong enough to

restrain it.

7. Conclusions

This paper focuses on CEO structural power, as measured by an abnormal frac-

tion of top executive hired by the current CEO. The abnormal CEO power seems

to decrease CEOs’ pay for performance sensitivity and to reduce firm perfor-

mance. These results not only suggest that CEOs’ abnormal power is bad for firm

performance but also illustrate that using abnormal power to capture the com-

pensation process is a channel through which CEO power affects firm perfor-

mance. These results are robust to firm fixed characteristics and potential reverse

causality.

The negative effects of CEO power on CEO PPS and firm performance are

observed only when monitoring by institutional investors is weak. CEO power

seems benign when they are subject to close monitoring by institutional investors.

Proper monitoring and guidance by institutional investors appear effective in pre-

venting CEOs from using their power to capture the compensation process and

hurting firm performance. Although unchecked CEO power is dangerous, it can be

benign when sufficient checks and balances against potential abuse of the power

exist.

References

Adams, R. B., H. Almeida, and D. Ferreira, 2005, Powerful CEOs and their impact on

corporate performance, Review of Financial Studies 18, pp. 1403–1432.

Aggarwal, R. K., and A. A. Samwick, 1999, The other side of the trade-off: The impact of

risk on executive compensation, Journal of Political Economy 107, pp. 65–105.

E. H. Kim and Y. Lu


Baker, G. P., and B. J. Hall, 2004, CEO incentives and firm size, Journal of Labor Economics

22, pp. 767–798.

Bebchuk, L., and J. Fried, 2004, Pay without performance: The unfulfilled promise of

executive compensation (Harvard University Press, Cambridge, MA.).

Bebchuk, L. A., K. J. M. Cremers, and U. C. Peyer, 2011, The CEO pay slice. Journal of

Financial Economics (forthcoming).

Bennedsen, M., F. Perez-Gonzalez, and D. Wolfenzon, 2006, Do CEOs matter? Working

Paper, Columbia University, New York.

Bertrand, M., and S. Mullainathan, 2000, Agents with and without principals, American

Economics Review 90, pp. 203–208.

Bertrand, M., and S. Mullainathan, 2001, Are CEOs rewarded for luck? The ones without

principals are, Quarterly Journal of Economics 116, pp. 901–932.

Bertrand, M., and A. Schoar, 2003, Managing with style: The effect of managers on firm

policies, Quarterly Journal of Economics 118, pp. 1169–1208.

Cremers, K. J. M., and V. B. Nair, 2005, Governance mechanisms and equity prices, Journal

of Finance 60, pp. 2859–2894.

Cronqvist, H., A. K. Makhija, and S. E. Yonker, 2009, What does CEOs’ personal leverage

tell us about corporate leverage? Working Paper Series 2009-4, Ohio State University,

Charles A. Dice Center for Research in Financial Economics, Columbus, OH.

Crystal, G., 1991, In search of excess: The overcompensation of American executives

(W.W. Norton, New York).

Del Guercio, D., L. Seery, and T. Woidtke, 2008, Do boards pay attention when institutional

investor activists ‘‘just vote no’’? Journal of Financial Economics 90, pp. 84–103.

Durnev, A., and E. H. Kim, 2005, To steal or not to steal: Firm attributes, legal environment,

and valuation, Journal of Finance 60, pp. 1461–1495.

Edmans, A., 2009. Blockholder trading, market efficiency, and managerial myopia, Journal of

Finance 64, pp. 2481–2514.

Fama, E., and K. French, 1997, Industry costs of equity, Journal of Financial Economics 43,

pp. 153–193.

Finkelstein, S., 1992, Power in top management teams: Dimensions, measurement, and

validation, Academy of Management Journal 35, pp. 505–538.

Gibbons, R., and K. Murphy, 1992, Optimal incentive contracts in the presence of career

concerns: Theory and evidence, Journal of Political Economy 100, pp. 468–505.

Hall, B. J., and J. B. Liebman, 1998, Are CEOs really paid like bureaucrats? Quarterly Journal

of Economics 111, pp. 653–691.

Hartzell, J., and L. T. Starks, 2003, Institutional investors and executive compensation,

Journal of Finance 58, pp. 2351–2374.

Himmelberg, C. P., R. G. Hubbard, and P. Darius, 1999, Understanding the determinants of

managerial ownership and the link between ownership and performance, Journal of

Financial Economics 53, pp. 353–384.

Jenter, D., and K. Lewellen, 2011, CEO preferences and acquisitions, Working Paper,

Stanford University, Palo Alto, CA.

Kim, E. H., and Y. Lu, 2011, CEO ownership, external governance, and risk-taking, Journal of

Financial Economics (forthcoming).

Koenker, R., and K. F. Hallock, 2001, Quantile regression, Journal of Economic Perspectives

15, pp. 143–156.



Lafontaine, F., and S. Bhattacharyya, 1995, The role of risk in franchising, Journal of

Corporate Finance 2, pp. 39–74.

Landier, A., D. Sraer, and D. Thesmar, 2008, Bottom-up corporate governance, NYU

Working Paper No. FIN-05-011, New York, NY. Available at SSRN: http://ssrn.com/

abstract=1294147

Milbourn, T. T., 2003, CEO reputation and stock-based compensation, Journal of Financial

Economics 68, pp. 233–263.

Morse, A., V. Nanda, and A. Seru, 2010, Are incentive contracts rigged by powerful CEOs?

Journal of Finance (forthcoming).

Murphy, K. J., 1999, Executive compensation, In O. Ashenfelter, D. Card eds: Handbook of

labor economics, Chapter 38 (Elsevier Science B. V., Amsterdam), pp. 2485–2563.

Nguyen, B. D., and K. M. Nielsen, 2010, What death can tell: Are executives paid for their

contributions to firm value? Working Paper, Chinese University of Hong Kong,

Hong Kong.

Prendergast, C., 2002, The tenuous trade-off between risk and incentives, Journal of Political

Economy 110, pp. 1071–1102.

Rajgopal, S., T. Shevlin, and V. Zamora, 2006, CEOs’ outside employment opportunities and

the lack of relative performance evaluation in compensation contracts, Journal of Finance

61, pp. 1813–1844.

Schaefer, S., 1998, The dependence of pay-performance sensitivity on the size of the firm,

Review of Economics and Statistics 80, pp. 436–443.

Shleifer, A., and R. Vishny, 1986, Large shareholders and corporate control, Journal

of Political Economy 94, pp. 461–488.

E. H. Kim and Y. Lu


Documents

Is Chief Executive Officer Power Bad? - Stephen M. Ross School of