CHIEF EXECUTIVE OFFICER (CEO) RESPONSES TO CEO COMPENSATION EQUITY By ERIC ALAN FONG A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2004
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CHIEF EXECUTIVE OFFICER (CEO) RESPONSES TO CEO COMPENSATION EQUITY
By
ERIC ALAN FONG
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2004
Copyright 2004
by
Eric Alan Fong
To my wife, Mary
ACKNOWLEDGMENTS
First, I would like to thank my advisor, Henry Tosi, for believing in my ideas and
providing me with the guidance necessary to complete this task. Without the effort he put
forth as my chair, this dissertation would have never taken shape. Additionally,
throughout this dissertation process Henry has taught me how to be both an academic and
a professional. For these things, and much more, I am very grateful.
Next, I would like to extend my gratitude to my committee--James Algina, Jason
Colquitt, Wei Shen, and Rodney Lacey--as well as Heather Elms for their support and
advice. Their expertise allowed this dissertation to reach its utmost potential as an
extension of managerial science.
Finally, I would like to thank my wife, Mary, who is the most important individual
in my life. Mary has provided me with love, caring, and support throughout my
adventures as a doctoral student. Mary carried me through the worst times and shared in
my joy in the best of times. I could not be more fortunate in finding a person, friend, and
lover to share my life experiences.
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TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................................................................................. iv
LIST OF TABLES........................................................................................................... viii
LIST OF FIGURES ........................................................................................................... ix
CHAPTER 1 INTRODUCTION and LITERATURE REVIEW ........................................................1
Background...................................................................................................................4 Framework of CEO Pay ........................................................................................4 The Role of Equity ................................................................................................7
Equity reactions and distributive justice ........................................................8 CEO compensation inequity and comparisons.............................................14
The Bases for CEO Compensation Differences ..................................................18 Agency theory and managerial capitalism ...................................................20 Bases for CEO power ...................................................................................23
CEO Reactions to Inequity .........................................................................................32 Organizational Growth ........................................................................................32 Diversification .....................................................................................................35 CEO Withdrawal .................................................................................................38 Organizational Performance................................................................................40
Summary.....................................................................................................................42 3 RESEARCH DESIGN.................................................................................................43
Sample and Data Collection .......................................................................................43 Analytical Methods.....................................................................................................43 Variable Descriptions and Operationalizations ..........................................................44
Independent Variables .........................................................................................44 CEO compensation inequity.........................................................................44 CEO power ...................................................................................................50
Control Variables.................................................................................................54 Industry.........................................................................................................54 Firm performance.........................................................................................55 CEO age .......................................................................................................55
Results of Analyses.....................................................................................................57 Change in Organizational Growth.......................................................................57 Change in Diversification....................................................................................58 CEO Withdrawal .................................................................................................59 Change in Organizational Performance...............................................................60
Discussion...................................................................................................................63 Theoretical Implications and Future Research ...........................................................64 Managerial Implications .............................................................................................67 Limitations..................................................................................................................69 Conclusion ..................................................................................................................70
APPENDIX A MODEL SPECIFICATIONS.......................................................................................71
Model Specification of CEO Inequity ........................................................................71 Variables..............................................................................................................72 Models .................................................................................................................72
Parameter Interpretations.....................................................................................73 Model Specification of Tests of Non-Withdrawal Hypotheses..................................76
Parameter Interpretations.....................................................................................77 Model Specification of Tests of Withdrawal Hypotheses ..........................................78
Parameter Interpretations.....................................................................................80 B VARIABLE CORRELATION MATRIX FOR CEO INEQUITY VARIABLES......82
C VARIABLE CORRELATION MATRIX FOR VARIABLES USED TO TEST HYPOTHESES............................................................................................................88
LIST OF REFERENCES...................................................................................................91
Table page 1 CEO Inequity on Change in Organizational Size ........................................................58
2 CEO Inequity on Change in Diversification................................................................58
3 CEO Inequity on CEO Withdrawal .............................................................................59
4 CEO Inequity on Change in Organizational Performance...........................................61
5 Simple Correlation Matrix of Variables in the Operationalization of CEO Inequity ..83
6 Simple Correlation Matrix of Variables Used to Test the Hypotheses........................88
viii
LIST OF FIGURES
Figure page 1 A General Framework for CEO Compensation.............................................................6
2 CEO Inequity (ROA) X Ownership Structure on CEO Withdrawal ...........................60
3 CEO Inequity (Shareholder Return) X Ownership Structure on CEO Withdrawal ....60
4 CEO Inequity (ROA) X Ownership Structure on Change in Organizational Performance .................................................................................................................61
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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
CHIEF EXECUTIVE OFFICER (CEO) RESPONSES TO CEO COMPENSATION EQUITY
By
Eric Alan Fong
May, 2004
Chair: Henry L. Tosi Major Department: Management
This dissertation examines the interactive effects of CEO under- and overpayment
inequity and CEO power (outsider ratio, duality, and ownership structure) on changes in
firm size, changes in firm performance, changes in diversification strategy, and CEO
withdrawal. Using variables from both the CEO compensation literature as well as the
equity theory literature, I develop a measure of CEO under- and overpayment inequity
and use this measure to test hypotheses concerning the above independent variables.
I tested the hypotheses using a sample of 1342 CEOs from 800 U.S. publicly traded
corporations from 30 industries for the years 1990-1999. A multi-level methodology is
utilized that allows for the specification of models that include factors at the time-, CEO-,
and industry-levels necessary to test the hypotheses.
Results show that CEO under- and overpayment are related to changes in firm
performance and CEO withdrawal and that CEO reactions are dependent upon the level
of discretion they possess. This dissertation reveals that CEOs may react to compensation
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relative to other CEOs similar to themselves by attempting to affect their situation to
make their compensation more equitable. Also, CEOs with less discretion reacted in ways
that are more beneficial to shareholder interests, which is they improve the performance
of the organization; therefore suggesting that good governance matters.
xi
CHAPTER 1 INTRODUCTION AND LITERATURE REVIEW
The compensation of top executives, specifically the compensation of the chief
executive officer (CEO), has attracted a significant amount of attention from both the
academic literature and the popular press. On one hand, much of popular press has
focused on the excessive income that executives receive on the whole and in comparison
to other employees in the organization, suggesting that executive compensation is unfair
(e.g., Colvin, 2001; Reingold, 2000). On the other hand, the academic literature on CEO
compensation has focused on how CEO compensation motivates CEOs to affect
organizational performance and shareholder returns. However, both the popular press and
academics alike have recognized a weak relationship between CEO compensation and
Specifically, the results suggest that agency theory, in the discussion of incentive
alignment, should consider equity comparisons. One reason the previous results
concerning compensation and performance may have been equivocal is that equity
considerations have not been extensively studied in the CEO compensation literature. It
may be that CEOs are not only reacting to incentive alignment, but also reacting to under-
or overpayment in comparison to their peers.
Porac and colleagues (1999) provide evidence that boards anchor comparability
judgments concerning organizational performance and CEO compensation to other
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organizations within the focal organization’s primary industry. The conclusion drawn
from this industry anchoring is that boards may be considering CEO compensation equity
when they anchor their definitions of comparable organizations to the primary industry
that the organization competes. However, defining equity is a complex issue that involves
more than just focusing on organizations in a similar industry or similar in size as well as
performance, which are structural variables that may represent CEO effort (i.e., greater
size leading to greater managerial effort). Adams (1965) notes that equity comparisons
revolve around not only effort, but also human capital such as age and experience, both
of which were variables in this dissertation. Therefore, this dissertation focuses on equity
from a different standpoint that includes not only the structural variables used by Porac
and colleagues (1999), but also contingency variables such as power and human capital
variables. This distinction is important because it is clear that CEOs react to overpayment
and underpayment when contingency and human capital variables are considered.
Therefore, the results of this dissertation suggest that future CEO compensation studies
should take into account more than just industry when considering CEO compensation,
future research should also include contingency and human capital variables as well as
location.
This dissertation also has theoretical implications for equity theory. The findings
are consistent with equity theory, which suggests that individuals will attempt to change
their outcome/input ratio when their ratio is different from that of their comparison
others. In this case, the comparison others are CEOs in the same industries, within close
proximity of each other, facing similar contingency variables as well as having similar
human capital such as age, tenure, and experience. The previous equity theory literature
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has focused on lower levels of organizations as opposed to top management and has
examined equity from an intra-organizational perspective, individuals are generally
considered to make equity comparisons with others within the organization. For example,
Greenberg’s (1988, 1990) studies on managerial office space and performance as well as
compensation reduction and withdrawal have focused on the respective comparisons
between lower-level managers and factory workers within the same organizations.
According to equity theory, equity comparisons are made between individuals who are
similar; however, similarity does not have to be limited by the boundaries of the
organization, nor is it restricted to lower level employees. The results of this dissertation
suggest that equity comparisons occur not only at the highest level of the organization,
but also can be inter-organizational in nature. CEOs make inter-organizational
comparisons because there is no one with similar responsibilities or status intra-
organizationally.
Future research on CEO reactions to inequitable compensation can focus on
reactions that CEOs may be able to take to restore equity that were not examined in this
dissertation. For example, research examining top executive compensation has found that
the CEO’s salary is dependent upon the salaries of the compensation committee (Ezzamel
& Watson, 1998; Main, O’Reilly, & Wade, 1995; O’Reilly et al., 1988). The CEOs
feeling underpayment inequity may attempt to change their outcomes by influencing the
membership to the compensation committee. Equity may be achieved by replacing lower
paid outside members of the board with that of higher paid outside members. Thus, the
compensation committee would be made up of higher paid members, which in turn
should lead to higher pay for the CEO due to the social comparisons occurring.
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Additionally, future research can look into other theories that may help explain
equity comparisons and possible changes of referent for the CEO, a limitation discussed
later in this dissertation. For example, tournament theory suggests that executive salary
structures can be related to a series of tournaments among top executives (Lazear &
Rosen, 1981; Rosen, 1986). Lazear and Rosen (1981), in an attempt to explain top
executive salary differentials, suggested that it is possible to view the top executives in an
organization vying to become the CEO as competing in a tournament. In the tournament
process to become the CEO, the top executives give up some of their earnings to increase
the compensation of the CEO. In essence, the excess from the top executives’ forfeiture
of compensation becomes part of a lottery prize realized in the CEO’s salary. This theory
gives a theoretical justification for the very large differences between the CEO salaries
and those of the other top executives in the organization. A CEO who attempts to restore
equity through changing his or her referent may choose other top executives in his or her
organization rather than another CEO because of the tournament comparisons that are
already occurring. Although the expectation should be that the CEO receives a greater
salary, greater or lesser differentials between the CEO and his or her top managers may
lead to feelings of inequity for the CEO.
Managerial Implications
This dissertation has implications for managers as well. The results suggest that
CEO pay equity matters to the low power CEO as well as to the firm and therefore should
be an important consideration in the determination of low power CEO pay. In
determining the CEO’s compensation, the compensation committee must keep in mind
that CEOs are aware of and will react to the compensation levels of other CEOs similar to
themselves by affecting organizational performance when the CEO has low power.
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Additionally, in determining the level of pay of CEOs, compensation committees should
realize that underpaid CEOs will be more likely to withdraw from the organization.
This dissertation also adds insight into the debate as to whether CEOs are or are not
overpaid for their effort. Previous studies have suggested that CEOs are either taking
advantage of their position by influencing the board to award excessive salaries (e.g.,
Finkelstein & Hambrick, 1989; Tosi & Gomez-Mejia, 1989) or that a CEO’s high
compensation is justified by their impact on their organization (e.g., Coughlin & Schmidt,
1985; Jensen & Murphy, 1990). Both of these assessments may be correct. According to
the data, low power CEOs who are overpaid attempt to make-up for their overpayment by
increasing their inputs. Therefore, the data suggests that CEOs in low power situations,
where they are less able to influence their compensation, may not be overpaid for their
effort because they work harder when they receive higher compensation. However, this
does not hold true for high power CEOs who seem not to react to overpayment or
underpayment. It may be that high power CEOs do not react to overpayment because
they feel the overpayment is justified due to their influence on the setting of their own
pay. Therefore, by taking advantage of their high power during the process of setting
their pay and with the knowledge that they can affect their compensation when their
contract is to be renewed, high power CEOs have little incentive to affect their
compensation by growth, withdrawal, or performance.
Additionally, an interesting conclusion can be drawn from the results of this
dissertation that may help explain some of the extremely large and continuing increases
in CEO compensation over the past two decades. That is, CEOs who are underpaid in
comparison to their peers may be attempting to increase their compensation by seeking
69
positions elsewhere to find other, more lucrative, opportunities. In turn, these reactions
may leave other CEOs inequitably underpaid; thus, creating a cycle of increasing
compensation as CEOs react to underpayment.
Limitations
A limitation of this study is that the equity theory literature suggests that
individuals do not react to inequity by just influencing their inputs and outcomes.
Individuals can also rationalize their inputs and outcomes through psychological
distortion, act against their referent(s), and/or change the referent (Weick & Nesset,
1968). Although it would be unlikely that a CEO would act against his or her referent
because of the difficulty of affecting another CEO’s inputs or outcomes, a CEO may
psychologically distort his or her inputs and outcomes to restore equity or change their
referent. For example, Greenberg (1989) found that workers who were forced to take a
pay cut and remained on the job elevated the perceived importance of the work
environment features as contributors to their overall payment equity. Thus, they
cognitively distorted their inputs to restore equity. By changing their referent, the CEO
could select CEOs that make himself or herself feel more equitably compensated or,
even, focus on the pay of other top executives inside their organization. This dissertation
cannot address the issue of cognitive distortion as a means of restoring equity because the
data used for this study was archival and did not address the feelings of inequity
experienced by the CEOs. Additionally, it was not feasible to question the CEO
concerning his or her referent(s) and therefore could not measure the CEO’s changes in
referents.
70
Conclusion
The dominant view in the literature on CEO compensation, based largely on agency
theory, is that by aligning compensation with performance a CEO should guide the firm
towards increased performance. One assumption made by this theoretical link between
pay and performance is that pay should be viewed in the absolute sense; that is, the
alignment of this compensation with performance should be enough to motivate CEOs to
increase performance. This limited view of compensation places constraints on the CEO
compensation research. In this framework, a CEO who has his or her compensation tied
to organizational performance should increase performance even if exceptional
performance leads to less compensation than their counterparts who may or may not have
their compensation aligned with performance. The result is a view that alignment should
increase performance despite what other CEOs receive as compensation because in
absolute terms the compensation is high, while in actuality the relative compensation may
be low.
The current results suggest that the agency framework overlooks relative
compensation. The results strongly suggest the CEO literature should not assume that
absolute compensation is the only motivator of CEO action and that relative
compensation motivates the CEO. Where the CEO is inequitably paid relative to his or
her peers, the CEO reacts accordingly to correct for this inequity. Furthermore, the results
show that reactions to relative pay only occur when the CEO has low power, which
suggests that low power CEOs lack the ability to affect their pay in the pay setting
process unlike their high power counterparts. The present findings warrant the inclusion
of relative pay into the framework that guides the CEO compensation research.
APPENDIX A MODEL SPECIFICATIONS
The operationalization CEO inequity and tests of the hypotheses are multi-level in
nature. The independent variables are at the CEO- and industry-levels for the
operationalization of CEO inequity. In addition, Murphy (1985) points out that in the
case of the analysis of CEO compensation, cross-sectional models are subject to an
omitted variables problem, which can be a serious problem because they cannot
accommodate variables that should enter the analysis in a lagged manner consistent with
their underlying theoretical framework. Thus, to test hypotheses concerning CEO
compensation, a three-level model consisting of time nested in CEOs nested in industries
is required.
HLM provides a statistical methodology that allows for a simultaneous test of both
the CEO- and industry levels for the operationalization of CEO inequity and HLM
provides the ability to simultaneously test time-, CEO-, and industry levels. The
incorporation of information from all levels leads to a more efficient estimation of the
regression coefficient standard errors (Burstein, 1980). The major advantage of HLM is
that it provides unbiased and efficient estimates for the regression coefficients and their
standard errors, regardless of the magnitude of the dependence among individual
responses, due to the nested nature of the data (Bryk & Raudenbush, 1989).
Model Specification of CEO Inequity
For illustration purposes location will be represented by one variable; however, as
specified in Chapter 3, location is made up of 17 dummy variables (Washington D.C.,
71
72
Dallas, Houston, Philadelphia, New York, Las Vegas, St. Louis, Minneapolis, Ann
Arbor, Boston, Chicago, Atlanta, Miami, Denver, Los Angels, San Francisco, and San
Diego). Once again, 65% of the CEOs fell within one of these locations.
Variables
Y – CEO pay
S – Organizational size at time t
P – Organizational performance
OM – Ownership structure (Owner Managed)
MC – Ownership structure (Manager Controlled)
OR – Outsider ratio of the board of directors
D – CEO duality
A – CEO age
T – CEO tenure
I – Insider (previously held a position inside the organization)
PP – Previous position rating
L – Location
Models
The indices i and j denote CEOs and industries where there are
i = 1, 2, ……, ij CEOs within industry j; and
j = 1, 2, ……, J industries.
73
Level – 1 (CEO)
ijjijjjijjjijj
jijjjijjjijjjijj
jijjjijjjijjjijjjij
rLLPPPPII
MCMCTTAADD
OROROMOMPPSSY
+−+−+−
+−+−+−+−
+−+−+−+−+=
)()()(
)()()()(
)()()()(
.11.10.9
.8.7.6.5
.4.3.2.10
βββ
ββββ
βββββ
Level – 2 (Industry)
joj 000 υγβ += ),0(~ 000 τυ Nj
jj 1101 υγβ += ),0(~ 111 τυ Nj
jj 2202 υγβ += ),0(~ 222 τυ Nj
jj 3303 υγβ += ),0(~ 333 τυ Nj
jj 4404 υγβ += ),0(~ 444 τυ Nj
jj 5505 υγβ += ),0(~ 555 τυ Nj
jj 6606 υγβ += ),0(~ 666 τυ Nj
jj 7707 υγβ += ),0(~ 777 τυ Nj
jj 8808 υγβ += ),0(~ 888 τυ Nj
jj 9909 υγβ += ),0(~ 999 τυ Nj
jj 1010010 υγβ += ),0(~ 101010 τυ Nj
jj 1111011 υγβ += ),0(~ 111111 τυ Nj
Parameter Interpretations
j0β -- Intercept for industry j of the level–1 relationship between CEO pay and all
the independent variables. Because of group mean centering, ojβ is an industry mean for
74
CEO pay when the CEO is in an owner-controlled organization, does not hold the
position of chairman of the board, previous position was outside of the organization, and
does not exist in one of the locations specified.
j1β -- Slope for industry j of the level–1 relationship between CEO pay and
organizational size.
j2β -- Slope for industry j of the level–1 relationship between CEO pay and
organizational performance.
j3β -- Slope for industry j of the level–1 relationship between CEO pay and an
owner-managed organization.
j4β -- Slope for industry j of the level–1 relationship between CEO pay and a
manager-controlled organization.
j5β -- Slope for industry j of the level–1 relationship between CEO pay and
outsider ratio on the board of directors.
j6β -- Slope for industry j of the level–1 relationship between CEO pay and CEO
duality.
j7β -- Slope for industry j of the level–1 relationship between CEO pay and CEO
age.
j8β -- Slope for industry j of the level–1 relationship between CEO pay and CEO
tenure.
j9β -- Slope for industry j of the level–1 relationship between CEO pay and
whether their previous position was inside the organization.
75
j10β -- Slope for industry j of the level–1 relationship between CEO pay and
CEO’s previous position.
j11β -- Slope for industry j of the level–1 relationship between CEO pay and
location.
00γ -- Grand mean for change in organizational size.
10γ -- Mean of the industry-specific slopes for the relationship between CEO pay
and organizational size.
20γ -- Mean of the industry-specific slopes for the relationship between CEO pay
and organizational performance.
30γ -- Mean of the industry-specific slopes for the relationship between CEO pay
and an owner-management organization.
40γ -- Mean of the industry-specific slopes for the relationship between CEO pay
and a manager-controlled organization.
50γ -- Mean of the industry-specific slopes for the relationship between CEO pay
and outsider ratio on the board of directors.
60γ -- Mean of the industry-specific slopes for the relationship between CEO pay
and CEO duality.
70γ -- Mean of the industry-specific slopes for the relationship between CEO pay
and CEO age.
80γ -- Mean of the industry-specific slopes for the relationship between CEO pay
and CEO tenure.
76
90γ -- Mean of the industry-specific slopes for the relationship between CEO pay
and whether their previous position was inside the organization.
100γ -- Mean of the industry-specific slopes for the relationship between CEO pay
and CEO’s previous position.
110γ -- Mean of the industry-specific slopes for the relationship between CEO pay
and location.
Model Specification of Tests of Non-Withdrawal Hypotheses
For illustration purposes, only models for change in organization size and CEO
withdrawal will be shown since the models concerning change in diversification and
change in performance are similar in nature to the change in organizational size model.
Variables
Y – Change in organizational size
CI – CEO Inequity
OS – Ownership Structure
Models
The indices t, i, and j denote time, CEOs, and industries where there are
t = 1, 2, ……, nij time periods withing CEO i in industry j;
i = 1, 2, ……, Ij CEOs within industry j; and
j = 1, 2, ……, J industries.
Level – 1 (Time)
tijjtijtijijtij eCICIY +−+= −− )( ).1()1(10 ππ
Level – 2 (CEO)
ijjijjjij rOSOS 0.01000 )( +−+= ββπ
77
ijjijjjij rOSOS 1.11101 )( +−+= ββπ
Level – 3 (Industry)
jj 0000000 υγβ +=
jj 0101001 υγβ +=
jj 1010010 υγβ +=
jj 1111011 υγβ +=
Parameter Interpretations
ij0π -- Intercept for CEO i within industry j of the level-1 relationship between
change in organizational size and CEO inequity. Because of group mean centering, ij0π
is a mean for change in organizational size for CEO i within industry j.
ij1π -- Slope for CEO i within industry j of the level-1 relationship between change
in organizational size and CEO inequity.
j00β -- Intercept for industry j of the level–2 relationship between change in
organizational size and ownership structure. Because of group mean centering, is an
industry mean for change in organizational size.
j00β
j01β -- Slope for industry j of the level–2 relationship between change in
organizational size and ownership structure.
j10β -- Intercept for industry j of the level–2 relationship between change in
organizational size and CEO inequity controlling for ownership structure.
j11β -- Slope for industry j of the level–2 relationship between change in
organizational size and the interaction of CEO inequity and ownership structure.
78
organizational size and the interaction of CEO inequity and an owner-managed
organization controlling for duality, outsider ratio, and manager-controlled organization.
000γ -- Grand mean for change in organizational size.
010γ -- Industry-specific mean for change in organizational size to industry-specific
mean of ownership structure.
100γ -- Grand mean for inequity.
110γ -- Industry-specific mean for inequity to industry-specific mean of ownership
structure.
Model Specification of Tests of Withdrawal Hypotheses
Because CEO withdrawal was tested using HGLM and required controlling for
both organizational performance and CEO age to isolate withdrawal, the model
specifications look different from those used to test change in size, change in
performance, and change in diversification.
Variables
Y – Withdrawal (1 = Withdrawal, 0 = No Withdrawal)
CI – CEO Inequity
P – Organizational Performance
A – CEO Age
OS – Ownership structure
MC – Ownership structure
Models
The indices t, i, and j denote time, CEOs, and industries where there are
t = 1, 2, ……, nij time periods withing CEO i in industry j;
79
i = 1, 2, ……, Ij CEOs within industry j; and
j = 1, 2, ……, J industries.
Level – 1 (Time)
))()(( ).1()1(2).1()1(1011)1(
jtijtijjtijtijij PPCICItije
Yprob−−−− −+−+−+
==πππ
Level – 2 (CEO)
ijjijjjijjjij rAAOSOS 0.02.01000 )()( +−+−+= βββπ
ijjijjjijjjij rAAOSOS 0.12.11101 )()( +−+−+= βββπ
ijjijjjijjjij rAAOSOS 0.22.21202 )()( +−+−+= βββπ
Level – 3 (Industry)
jj 0000000 υγβ +=
jj 0101001 υγβ +=
jj 0202002 υγβ +=
jj 1010010 υγβ +=
jj 1111011 υγβ +=
jj 1212012 υγβ +=
jj 2020020 υγβ +=
jj 2121021 υγβ +=
jj 2222022 υγβ +=
80
Parameter Interpretations
ij0π -- The log odds of withdrawal for CEO i with mean performance in industry j.
ij1π -- Slope of the log odds of withdrawal for CEO i within industry j of the level-
1 relationship when a CEO faces inequity.
ij2π -- Slope of the log odds of withdrawal for CEO i within industry j of the level-
1 relationship with organizational performance.
j00β -- The log odds of withdrawal for industry j of the level–2 relationship for
ownership structure and CEO age.
j01β -- Slope of the log odds for industry j of the level–2 relationship between
withdrawal and ownership structure controlling for CEO age.
j02β -- Slope of the log odds for industry j of the level–2 relationship between
withdrawal and CEO age controlling for ownership structure.
j10β -- The log odds for industry j of the level–2 relationship between withdrawal
and CEO inequity controlling for ownership structure and CEO age.
j11β -- Slope of the log odds for industry j of the level–2 relationship between
withdrawal and the interaction of CEO inequity and ownership structure controlling for
CEO age.
j12β -- Slope of the log odds for industry j of the level–2 relationship between
withdrawal and the interaction of CEO inequity and CEO age controlling for ownership
structure.
j20β -- The log odds for industry j of the level–2 relationship between withdrawal
and organizational performance ownership structure and CEO age.
81
j21β -- Slope of the log odds for industry j of the level–2 relationship between
withdrawal and the interaction of organizational performance and ownership structure
controlling for CEO age.
j22β -- Slope of the log odds for industry j of the level–2 relationship between
withdrawal and the interaction of organizational performance and CEO age controlling
for ownership structure.
000γ -- The average of log odds for withdrawal.
010γ -- Industry-specific log odds for withdrawal to industry-specific mean of
ownership structure controlling for CEO age.
020γ -- Industry-specific log odds for withdrawal to industry-specific mean of CEO
age controlling for ownership structure.
100γ -- The average of the log odds for inequity.
110γ -- Industry-specific log odds for inequity to industry-specific mean of
ownership structure controlling for CEO age.
120γ -- Industry-specific log odds for inequity to industry-specific mean of CEO
age controlling for ownership structure.
200γ -- The average of the log odds for organizational performance.
210γ -- Industry-specific log odds for organizational performance to industry-
specific mean of outsider ownership structure controlling for CEO age.
220γ -- Industry-specific log odds for organizational performance to industry-
specific mean of CEO age controlling for ownership structure.
APPENDIX B VARIABLE CORRELATION MATRIX FOR CEO INEQUITY VARIABLES
A simple correlation matrix of variables used in the creation of the CEO inequity
variable along with the means and standard deviations of those variables are reported in
Table 5. This matrix represents the cross-sectional relationships at the CEO-level.
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83
Table 5. Simple Correlation Matrix of Variables in the Operationalization of CEO Inequity
n Mean s.d. 1 2 3 4 1. Log Total Pay 1340 6.92 1.12 1.00 2. Outsider Ratio 1270 .72 .13 .24** 1.00 3. Duality 1311 .66 .43 .15** -.07* 1.00 4. CEO Age 1308 53.68 8.30 .00 -.10** .18** 1.00 5. CEO Tenure 1306 7.81 7.21 -.05 -.22** .26** .37** 6. Insider 1286 .72 .42 -.04 -.02 .08** .01 7. Org. Size 1329 -.02 1.75 .50** .21** .17** .13** 8. Previous Position 1254 1.71 .56 -.03 -.02 -.03 -.02 9. Man-controlled 1342 .05 .21 .11** -.01 .06* .02 10. Owner-managed 1342 .23 .42 -.23** -.29** .20** .07** 11. ROA Perf. 1342 .01 .19 .09** -.05 .05 .08** 12. S.H. Return Perf. 1314 .35 2.24 .02 .01 -.04 -.05 13. Washington D.C. 1342 .04 .20 .01 .04 .04 -.02 14. Dallas 1342 .04 .19 .01 -.01 .01 .00 15. Houston 1342 .05 .22 .04 .02 .09** .05* 16. Philadelphia 1342 .01 .12 .00 .02 .00 -.03 17. New York 1342 .08 .28 .17** -.03 .03 .12** 18. Las Vegas 1342 .02 .13 .07** -.03 .07* .02 19. St. Louis 1342 .01 .10 -.03 .01 .01 .03 20. Minneapolis 1342 .02 .15 -.02 .05 -.01 -.05 21. Ann Arbor 1342 .01 .11 .02 .04 .03 .02 22. Boston 1342 .04 .21 -.05 .01 -.10** .02 23. Chicago 1342 .03 .17 .11** .01 .04 .01 24. Atlanta 1342 .02 .13 -.01 .04 -.04 -.07* 25. Miami 1342 .01 .12 -.04 -.06* .00 .03 26. Denver 1342 .02 .13 -.03 .03 .03 -.01 27. Los Angeles 1342 .05 .23 -.03 -.02 -.05 .05 28. San Francisco 1342 .10 .30 .11** .04 -.04 -.13** 29. San Diego 1342 .02 .15 -.03 .04 -.07* -.07** ** significant at p < 0.01, two-tailed test * significant at p < 0.05, two-tailed test
84
Table 5. (Continued) 5 6 7 8 9 10 11 1. Log Total Pay 2. Outsider Ratio 3. Duality 4. CEO Age 5. CEO Tenure 1.00 6. Insider .08** 1.00 7. Org. Size .03 .14** 1.00 8. Previous Position .10** -.02 .02 1.00 9. Man-controlled .00 .05 .24** .10** 1.00 10. Owner-managed .34** .08** -.14** -.07* -.09** 1.00 11. ROA Perf. .12** .13** .23** .01 .03 .08** 1.00 12. S.H. Return Perf. -.04 -.03 -.03 -.03 -.01 -.01 -.02 13. Washington D.C. .02 .03 .00 .00 .03 .04 -.07** 14. Dallas .03 .05 .02 .03 .01 -.04 .07** 15. Houston .05 .05 -.01 -.02 -.04 -.09** .00 16. Philadelphia -.05 .02 -.02 -.01 -.03 .01 -.06* 17. New York .00 .01 .14** .00 .09** -.01 .01 18. Las Vegas -.01 -.09** .02 -.05 -.03 .07* .01 19. St. Louis .00 .00 -.01 -.03 -.02 -.04 .00 20. Minneapolis -.06* -.01 -.03 .04 .04 -.03 .03 21. Ann Arbor -.05 -.02 .03 -.01 -.03 .02 -.01 22. Boston .02 .00 -.14** -.01 -.05 -.04 -.11** 23. Chicago -.04 .00 .19** -.03 .07* -.05* .05 24. Atlanta -.05 -.07* -.01 -.03 .00 -.03 .02 25. Miami -.03 -.01 -.05* -.01 -.03 .11** .01 26. Denver -.06* -.02 -.05 -.06* .03 -.02 -.03 27. Los Angeles -.06* .03 -.02 .01 -.01 .06* -.01 28. San Francisco -.02 -.02 -.05 -.03 -.07** -.04 .02 29. San Diego .01 -.03 -.09** .06* .06* -.01 -.06* ** significant at p < 0.01, two-tailed test * significant at p < 0.05, two-tailed test
85
Table 5. (Continued) 12 13 14 15 16 17 18 1. Log Total Pay 2. Outsider Ratio 3. Duality 4. CEO Age 5. CEO Tenure 6. Insider 7. Org. Size 8. Previous Position 9. Man-controlled 10. Owner-managed 11. ROA Perf. 12. S.H. Return Perf. 1.00 13. Washington D.C. .00 1.00 14. Dallas -.01 -.04 1.00 15. Houston -.02 -.05 -.05 1.00 16. Philadelphia -.01 -.03 -.02 -.03 1.00 17. New York -.01 -.06* -.06* -.07** -.04 1.00 18. Las Vegas .20** -.03 -.03 -.03 -.02 -.04 1.00 19. St. Louis -.01 .02 -.02 -.03 -.01 -.03 -.01 20. Minneapolis -.01 -.03 -.03 -.03 -.02 -.05 -.02 21. Ann Arbor -.01 -.02 -.02 -.03 -.01 -.04 -.02 22. Boston .00 -.04 -.04 -.05 -.03 -.07* -.03 23. Chicago -.01 -.04 -.04 -.04 -.02 -.05 -.02 24. Atlanta .00 -.03 -.03 -.03 -.02 -.04 -.02 25. Miami -.01 -.02 -.02 -.03 -.01 -.04 -.02 26. Denver -.02 -.03 -.03 -.03 -.02 -.04 -.02 27. Los Angeles -.01 -.05 -.05 -.06* -.03 -.07** -.03 28. San Francisco .02 -.07* -.07* -.08** -.04 -.10** -.04 29. San Diego .01 -.03 -.03 -.04 -.02 -.05 -.02 ** significant at p < 0.01, two-tailed test * significant at p < 0.05, two-tailed test
86
Table 5. (Continued) 19 20 21 22 23 24 25 1. Log Total Pay 2. Outsider Ratio 3. Duality 4. CEO Age 5. CEO Tenure 6. Insider 7. Org. Size 8. Previous Position 9. Man-controlled 10. Owner-managed 11. ROA Perf. 12. S.H. Return Perf. 13. Washington D.C. 14. Dallas 15. Houston 16. Philadelphia 17. New York 18. Las Vegas 19. St. Louis 1.00 20. Minneapolis -.02 1.00 21. Ann Arbor -.01 -.02 1.00 22. Boston -.02 -.03 -.02 1.00 23. Chicago -.02 -.03 -.02 -.04 1.00 24. Atlanta -.01 -.02 -.02 -.03 -.02 1.00 25. Miami -.01 -.02 -.01 -.03 -.02 -.02 1.00 26. Denver -.01 -.02 -.02 -.03 -.02 -.02 -.02 27. Los Angeles -.03 -.04 -.03 -.05 -.04 -.03 -.03 28. San Francisco -.04 -.05 -.04 -.07* -.06* -.04 -.04 29. San Diego -.02 -.02 -.02 -.03 -.03 -.02 -.02 ** significant at p < 0.01, two-tailed test * significant at p < 0.05, two-tailed test
87
Table 5. (Continued) 26 27 28 29 1. Log Total Pay 2. Outsider Ratio 3. Duality 4. CEO Age 5. CEO Tenure 6. Insider 7. Org. Size 8. Previous Position 9. Man-controlled 10. Owner-managed 11. ROA Perf. 12. S.H. Return Perf. 13. Washington D.C. 14. Dallas 15. Houston 16. Philadelphia 17. New York 18. Las Vegas 19. St. Louis 20. Minneapolis 21. Ann Arbor 22. Boston 23. Chicago 24. Atlanta 25. Miami 26. Denver 1.00 27. Los Angeles -.03 1.00 28. San Francisco -.04 -.08** 1.00 29. San Diego -.02 -.04 -.05 1.00 ** significant at p < 0.01, two-tailed test * significant at p < 0.05, two-tailed test
APPENDIX C VARIABLE CORRELATION MATRIX FOR VARIABLES USED TO TEST
HYPOTHESES
A simple correlation matrix of variables used in the analysis of the hypotheses as
well as the means and standard deviations of those variables are reported in Table 6. This
matrix represents the cross-sectional relationships at the CEO-level—the CEO average
was used for those variables that varied over time.
Table 6. Simple Correlation Matrix of Variables Used to Test the Hypotheses n Mean s.d. 1 2
Table 6. (Continued) 3 4 5 6 7 1. InequityROA 2. InequitySR 3. Growth 1.00 4. Change ROA .01 1.00 5. Change SR .00 .17* 1.00 6. Change Diversification .03 -.05 .04 1.00 7. Turnover .00 .02 .02 .02 1.00 8. CEO Age .02 .03 .00 -.03 -.05 9. ROAt-1 -.03 -.28** -.20** .00 -.06 10. SRt-1 .00 -.16** -.99** .00 -.02 11. Ownership Concentration -.01 -.04 .02 .00 -.06* ** significant at p < 0.01, two-tailed test * significant at p < 0.05, two-tailed test
90
Table 6. (Continued) 8 9 10 1. InequityROA 2. InequitySR 3. Growth 4. Change ROA 5. Change SR 6. Change Diversification 7. Turnover 8. CEO Age 1.00 9. ROAt-1 .03 1.00 10. SRt-1 -.01 .20** 1.00 11. Ownership Concentration .07* .11** -.02 ** significant at p < 0.01, two-tailed test * significant at p < 0.05, two-tailed test
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BIOGRAPHICAL SKETCH
Born in Miami, Florida, and the only son of Chinese immigrants Clement and Lee
Loo Fong, I attended public schools and graduated from Palmetto Senior High School in
1994. Prior to attending the University of Florida to obtain my Doctor of Philosophy, I
graduated with honors from the University of Florida earning a Bachelor of Science in
both management and psychology in 1998.
While at the University of Florida earning my doctorate I met my wife, Mary, with
whom I spend my free time. We enjoy exercising, cultural events, and spending time
taking care of our cat, Samantha, and lovebird, Lexi. I also enjoy working on home
improvement projects, cars, and I am a very avid freshwater aquarium hobbyist.
However, my favorite pastime is to sit down, with Mary, on Sunday mornings to read the
Gainesville Sun, drink a cup of coffee, and listen to the Top 40 Countdown.