Generalists versus Specialists:
Managerial Skills and CEO Pay*
Cláudia Custódio†
Arizona State University – W. P. Carey School of Business
Miguel A. Ferreira‡
Universidade Nova de Lisboa – Faculdade de Economia
Pedro Matos§
University of Southern California – Marshall School of Business
This Version: March 2011
Abstract
We show that compensation is higher for CEOs with more general managerial skills
using detailed biographic information on CEOs of the S&P 1,500 firms in 1993-2007.
This relation is driven by managers’ industry mobility and past experience as a top
manager and is robust to the inclusion of firm- and CEO-level controls, including fixed
effects. Comparing the actual CEO’s pay to the pay of a portfolio of CEOs who are
specialists in the industries that match the CEO’s past industry experience implies a 10%
pay premium to generalists. Furthermore, we provide evidence that pay increases the
most when firms hire a new CEO from outside the firm and switch from a specialist to a
generalist manager. Our findings suggest that an increase in the importance of general
human capital relative to firm-specific human capital helps to explain the increase in
executive pay.
JEL Classification: G34, J24, J33
Keywords: Executive compensation, Market for executives, Human capital, CEO
*
We thank Ilona Babenko and Oguzhan Ozbas for helpful comments. This research is supported by a research grant
from the Fundação para a Ciência e Tecnologia (FCT/POCI 2010).
†
E-mail: claudia.custodio@asu.edu
‡
E-mail: miguel.ferreira@fe.unl.pt
§
E-mail: pmatos@usc.edu
1. Introduction
The rapid rise in the level of U.S. executive pay in the last decades has sparked a heated debate
over the causes of this trend (Frydman and Jenter (2010)). These causes, however, are not
completely understood, particularly the role of the managerial labor market. In fact, we can also
observe a trend in the market for executives, as CEOs are now more likely to be hired from
outside the firm than internally promoted. Murphy and Zabojnik (2004) and Frydman (2009)
argue that general managerial skills gained importance over time relative to firm-specific skills,
which leads to more external hires and an increase in equilibrium average pay for CEOs.1 In this
paper, we consider the hypothesis that general managerial skills are paid at a premium relative to
firm-specific skills.
Using the panel of S&P 1,500 firms' CEOs in the period of 1993-2007, we test whether
compensation is higher for generalists than specialists CEOs. We use full biographic information
of the CEOs including age, educational background and, most importantly, detailed information
on all current and past positions in his professional career. This detailed biographic information
is drawn from the BoardEx and includes not only past positions in S&P 1,500 firms but also
positions outside S&P 1,500 firms and non-board positions. Our sample includes more than
4,000 CEOs, whose resumes include more than 32,000 different past positions.
In order to measure the level of general managerial skills we focus on industry mobility,
experience as top executive, and education. For each CEO in each year, we observe the number
of industries in which he worked (past number of industries), whether he held a CEO position at
a different company (past CEO dummy), and whether he has a business degree (general
education dummy). We construct an index of general managerial ability, as given by the first
factor of the principal component analysis of the three individual measures of general ability.
This index summarizes information on each manager’s general human capital and allows us to
classify a CEO as a generalist or a specialist. We find that the average general managerial ability
1
Ferreira and Sah (2010) propose a model where more generalist managers tend to occupy the top of the hierarchy
as the complexity of the environment and the quality of communication in the organization increases.
1
index has increased over the last 15 years, consistent with the notion that general skills have
become more important relative to firm-specific skills.
We find that the general managerial ability index is positively and significantly associated with
total CEO pay. The effect is economically meaningful. A one-standard deviation in the index of
general managerial ability is associated with an additional 6% in total pay, which indicates that a
generalist CEO earns a quarter of a million dollars more per year than a specialist CEO.
Furthermore, we find that the positive relation between pay and general managerial ability is
driven by industry mobility and past experience as a top manager and holds for both cash and
equity-based pay. The results are robust to the inclusion of many firm and CEO characteristics as
control variables, including firm and CEO fixed effects.
To measure the magnitude of the premium that generalist CEOs command over their specialist
peers, we develop a new measure of CEO excess compensation inspired by the measures of
excess value used in the corporate diversification literature (e.g., Berger and Ofek (1995). We
compare the market wage of a CEO with multi-industry experience with the average market
wage of a matched portfolio of CEOs with single-industry experience. For each CEO-year, the
excess compensation is the difference between the total compensation awarded to the CEO and
his imputed compensation, calculated as the average CEO compensation of the portfolio of
industries where the CEO worked up to a given year. The industry-level compensation is the
median compensation of CEOs that worked only in a single industry up to a given year. We find
that CEOs with a multi-industry background excess compensation is 10% higher than that of
single-industry CEOs. Furthermore, the average excess compensation of multi-industry CEOs
has increased over the last 15 years and is closely linked to the evolution of the total CEO pay.
We then examine whether the market wage premium of generalist CEOs is heterogeneous across
different types of firms. We find that the general managerial ability wage premium is pervasive
across small and large firms. Interestingly, the premium is more prevalent in diversified firms
(i.e., conglomerates) than in stand-alone firms. We investigate this issues in more detail and find
that conglomerates pay more to executives that have moved across industries in the past. This
result is in line with Rose and Shepard (1997) and Anderson, Bates, Bizjak, and Lemmon (1998)
2
who also find that conglomerates pay more to CEOs in order to compensate for their managerial
ability.
Finally, we test whether CEOs with more transferable skills across firms and industries capture
higher market wages when they change jobs. We examine cases of CEO turnover and classify
them into an internal or an external hire. We further classify an external hire into a switch from a
specialist to a generalist or a switch from a generalist to a specialist. We find a stronger increase
in CEO pay when there is a switch from a specialist to a generalist CEO through an external hire.
This is direct evidence that general managerial human capital does command a premium in the
CEO labor market and that the premium arises when a generalist CEO is hired from outside the
firm.
Overall, we conclude that managerial attributes are important to understanding CEO pay and that
general managerial human capital does command a premium in the CEO labor market. Our
findings are consistent with Graham, Li, and Qiu (2009) that find that manager fixed effects
explain the majority of the variation in executive pay. Furthermore, the evidence is consistent
with the view that CEO pay is determined in a competitive labor market between firms and
CEOs, and that pay has increased because conditions in the market have changed. In particular,
the evidence is consistent with the view that a change in the CEO job function toward
emphasizing general skills helps to explain the rise in CEO compensation over the last decades
(Murphy and Zabojnik (2004, 2008) and Frydman (2009)). This change could be a result of
product market changes due to industry deregulation (Hubbard and Palia (1995), and Cunat and
Guadalupe (2009a)), foreign competition (Cunat and Guadalupe (2009b)), or skill-biased
technological change (Garicano and Rossi-Hansberg (2006)).
Other authors have alternative explanations of the rise in CEO compensation. Gabaix and
Landier (2006) argue that the CEO job has not changed but that the increase in pay can be mostly
attributed to the growth in firm size and to the assertive matching of top talent to large firms.
Kaplan and Rauh (2010) argue that greater firm scale and superstars are more likely to explain
the increased skewness at the top of the income distribution. Bebchuk, Fried, and Walker (2002)
argue that the surge in CEO pay is due to rent extraction by powerful CEOs that award
themselves excessive pay packages.
3
Our analysis is most closely related to Frydman (2009) who finds that the rise in executive pay is
explained by the growing importance of general managerial skills in the second half of the
twentieth century using a sample of the top 50 firms. Frydman (2009) measures the generality of
human capital by educational background and occupational mobility within a firm (i.e., the
number of organizational areas an executive worked such as production, sales, or human
resources). There are several ways in which our analysis is different from hers. First, our
measure of general managerial skills focuses on external mobility across industries and firms,
rather than internal mobility within a firm, and past experience as a top manager. Second, we use
a comprehensive data set that covers the sample of S&P 1,500 firms in recent decades. Finally,
we directly test the hypothesis that generalist CEOs are paid more than specialist CEOs by
developing a new measure of excess compensation.
Our analysis extends the work of Murphy and Zabojnik (2008) and Eisfeldt and Kuhnen (2010)
by providing evidence on the effect of firm-specific and general managerial skills on CEO
turnover. These models predict a compensation premium not only for externally hired CEOs, but
also for hiring industry outsiders, particularly after industry shocks. Cremers and Grinstein (2010)
show that the external market for CEOs tends to respond more to industry shocks. Kaplan,
Klebanov, and Sorensen (2010) provide evidence that firms involved in leverage buyouts do
trade-off general versus firm-specific skills of CEOs while performing their hiring decisions.
The remainder of the paper is organized as follows. In the next section, we present the sample
and variables used in this study. In Section 3 we present the empirical results on the relation
between CEO compensation and general managerial skills. In Section 4 we investigate the
specific case of diversified firms or conglomerates. Section 5 concludes.
2. Sample and Data Description
4
Our initial sample consists of a panel of 25,562 CEO-firm-years in the 1993-2007 period drawn
from the Execucomp database. 2,3 We manually match the executives in Execucomp that are
identified as CEOs in a specific year with their Boardex profiles in order to get data on their
characteristics including education and professional experience in terms of industries, firms, and
positions. The final sample contains 21,142 CEO-firm-years observations and 4,264 different
CEOs. The number of Execucomp CEO-firms in our sample per year is provided in the last
column of Table 1. We could not find a match in Boardex for 1,211 CEOs contained in our
initial sample. There is some survivorship bias in BoardEx, which affects primarily the match in
the start of the sample period. The percentage of CEO in Execucomp whose profile is in
BoardEx grows from 67% in 1993 to 80% in 1998 and then to more than 90% in the 2000-2007
period.4 The primary findings in the paper are robust when we use the 2000-2007 sample period.
We perform an additional data match between firms in Boardex and Compustat (or alternatively
Datastream for international firms) in order to obtain the industry classification of past positions
of the CEOs. Because Compustat and Datastream only include publicly-traded firms, our
analysis is restricted to past positions in publicly-listed firms. The sample of past positions
contains 32,500 observations.
2.1. Measuring CEO Compensation and General Managerial Ability
Figure 1 and Table 1 report the time series of the average CEO total compensation in the 19932007 period. Total compensation consists of salary, bonus, value of restricted stock granted,
value of options granted, long term incentive payout and other compensation (Execucomp item
TDC1). In some tests we consider cash compensation and equity-based compensation separately.
We develop a new measure of excess compensation that aims to capture the compensation
premium (or discount) of a generalist or multi-industry CEO (i.e., an executive who worked in
2
Although Execucomp contains data starting in 1992, we drop this first year due to the small number of
observations (only 433 CEO-firms).
3
Cadman, Klasa, and Matsunaga (2009) and Gao, Li, and Lemmon (2010) argue that there are systematic
differences in CEO compensation, both in terms of levels and structure, across private and public firms and for
Execucomp and non-Execucomp firms.
4
The CEO profiles missing in BoardEx are mainly from executives that have retired or deceased before 2000.
5
several different industries) when matched to an equivalent portfolio of specialist or singleindustry CEOs. This measure is inspired by the excess value measure commonly used in the
corporate diversification literature (e.g., Berger and Ofek (1995)). It is computed as the
difference between the CEO total compensation and its imputed compensation. The imputed
compensation is the average CEO compensation of the portfolio of industries where the CEO
worked based on his past work experience. The industry level compensation is given by the
median compensation of CEOs that worked only in that industry (single-industry CEOs). The
industry match is done at the four-digit SIC code level when there are five or more singleindustry CEOs or at the highest SIC level where there are at least five single-industry CEOs. In
the case of a single-industry CEO, the excess compensation measure is simply the difference
between the CEO total compensation and the median compensation of single-industry CEOs in
his industry.
Figure 1 and columns (3)-(4) of Table 1 show a significant increase in both average total
compensation and average excess compensation, as well as a positive correlation between the
two.5 Because the average excess compensation is always positive over time, we conclude that
multi-industry CEOs are paid at a premium. Moreover, this evidence suggests that the increase in
total CEO compensation is linked to an increase in the compensation premium of generalist
CEOs over specialists working in similar industries.
We investigate the importance of general human capital of CEOs is related to the increase in
CEO pay over the recent years. To do so, we create an index of the generality of the CEO’s
human capital (general managerial ability index) based on the CEO’s education and past work
experience (prior to the current position) in publicly-listed firms. We consider the following
proxies of general managerial ability:
Past number of industries: number of industries at the four-digit SIC level where a CEO has
worked in publicly-listed firms, prior to the current position.
5
The analysis of the average excess compensation over time in Table 1 and Figure 1 is restricted the sample to
multi-industry CEOs. This helps us to better interpret the generalist CEO excess compensation since the measure is
simply capturing how much more (or less) a generalist is paid when compared to its equivalent portfolio of
specialists.
6
Past CEO dummy: dummy variable that equals one if a CEO held a CEO position at another
publicly-listed firm, prior to the current position.
General education dummy: dummy variable that equals one if the CEO has a business degree,
irrespective of its level (undergraduate, master and PhD).
In some tests and in the robustness section we use some variations of the prior three variables:
Past multi-industry experience: dummy variable that equals one if a CEO worked in more than
one industry (four-digit SIC), prior to the current position
Past number of firms: number of publicly-listed firms a CEO worked, prior to the current
position.
We use principal components to transform the proxy variables into a smaller number of factors
that contain the same information as the original proxy variables. The first principal component is
the linear combination of the three variables (past number of industries, past CEO dummy, and
general education dummy) used to proxy for general managerial ability. Following the usual
practice, we use all principal components whose eigenvalues exceed one (i.e., that have more
explanatory power than any one of the original proxies by itself). The results of this analysis are
presented in Table 2 and they give us a single factor for the general managerial ability index with
a eigenvalue of 1.14. As expected, the factor loadings are positive on all three proxies. The
factor gives about equal weight to the past number of industries and general education dummy,
and a much lower weight to the past CEO dummy. Thus, higher levels of general human capital
are reflected in a higher value of the index. We standardize the index to have zero mean and a
standard deviation of one.
Our measure is related to the index of generality of human capital defined in Frydman (2009).
Frydman’s measure takes into account the CEO's education and his occupational mobility within
the current firm (i.e., in different areas of the firm such as sales and production). However, it
does not look at the industry and top manager experience of a CEO. So we focus on the role of
external mobility rather that internal mobility in the CEO labor market.
7
We find an increase in the importance of the general managerial skills of CEOs that is consistent
with the findings in Frydman (2009). Figure 1 and Table 1 show that that there is a shift in the
relative importance of general versus firm-specific managerial skills with the index of general
managerial ability steadily increasing over time. This is consistent with executives running firms
in more recent years having more general skills, which are transferable across firms and
industries. 6
Figure 2 shows the cross sectional distribution of the general index scores over the 1993-2000
and 2001-2007 periods. The figure shows a shift in the distribution of the index to the right,
which is consistent with an increase in the importance of general managerial skills during our
sample period.
2.2. Cross-Industry Variation
Table 3 shows the average total and excess compensation and the average general managerial
ability index by industry. As we have argued before, industry level phenomena, such as
technological shocks, changes in product market competition and regulation might determine the
need for top executives with more general skills. We find significant variation across industries
in terms of the index of general managerial ability as well as in terms of the differences in pay
between generalists and specialists CEOs.
The telecom industry has the highest average level of generality of CEO human capital (0.35)
and at the same time is also the industry where the CEOs get the highest average total pay ($6.9
million). Moreover, generalist CEOs (defined as the CEOs with a general managerial ability
index above the median) in this industry are the ones paid the highest amount. When looking at
our excess compensation measure we find that CEOs of telecom firms with resumes that include
positions in other industries receive on average $5.3 million more than CEOs that have spent all
their careers only in the telecom sector (single-industry CEOs). In fact, during the 1990s, the
telecom industry changed rapidly not only in terms of technological innovation (cell phones,
6
The individual measures that are part of the index are also increasing over time, particularly during the first half of
our sample period. The average past number of industries steadily increases from 1 in 1994 to 1.2 in 2002. In 1993,
40% of CEOs have general education, while after 2002 this figure increases to more than 50%. The average past
CEO dummy increases from 34% in 1993 to 50% in 2002.
8
internet, etc), but also in terms of regulation as a result of the Telecommunications Act in 1996
(Laffont and Tirole (2000)). The rapid increase in competition associated with deregulation and
the need to rapidly adjust to technological innovation could increase the demand for managers
who can respond to this type of environment, namely managers with strong general skills.
The industry with the lowest general managerial skills index is Oil, Gas and Coal (-0.142). We
still find a premium in generalists' compensation in this industry. However, the difference in
average total compensation between generalists and specialists is less than 1 million dollars.
Except for CEOs in the Consumer Durables industry, we find a positive and statistically
significant generalist compensation premium across all industries, both when comparing the
average total compensation of generalist to specialists as well as when using the excess
compensation measure.
2.3. Summary Statistics of CEO and Firm Characteristics
Table 4 shows summary statistics for compensation, CEO characteristics and firm characteristics.
The average CEO in our sample receives $4 million of total compensation, which corresponds to
$1.2 million in cash and $2.1 million in equity incentives. The mean excess compensation is $1.4
million.
Besides the CEO characteristics that are included in the general ability index already described
above (past number of industries, past CEO dummy, general education dummy), we measure the
following additional CEO attributes:
CEO tenure: number of years a CEO has held the top executive position in the firm. We expect a
negative relation between the number of years in the CEO position and compensation, as more
recently hired CEOs are expected to have their compensation adjusted to recent market
conditions.
External hire dummy: dummy variable that equals one if the CEO is hired from outside the firm,
and zero otherwise (i.e., promoted internally). We expect a premium to exist for externally hired
CEO, since the company has to face labor market wages to hire him.
9
CEO age: the age of the CEO in years.
A CEO in our sample has worked in one other industry than his current industry and two other
firms, on average. 17% of the CEOs have previous experience as a top executive and 48% have a
business degree. They are 55 years old and spend 8 years in a given CEO position on average,
and about 22% of the CEOs are hired from outside the firm.
In our tests we control for the following firm characteristics:
Size: total sales in US$ millions. Gabaix and Landier (2005) argue that size is the main
determinant of CEO compensation, because CEO talent is matched to firm size.
Leverage: total debt divided by total assets.
Tobin’s Q: sum of total assets plus market value of equity minus book value of equity divided by
total assets. Tobin’s Q aims to capture the growth opportunities of the firm and tends to be
positively associated to CEO compensation.
ROA: earnings before interest, taxes, depreciation and amortization divided by total assets. This
is a measure of accounting performance which is expected to be positively correlated with
compensation. We include both contemporaneous and lagged ROA as control variables.
Volatility: annualized standard deviation of monthly stock returns.
Diversification dummy: dummy variable that equals one if a firm operated in more than one
business segment. Rose and Shepard (1997) and Anderson et.al (1998) find a positive premium
in compensation associated with diversified firms. In some tests we use the number of business
segments rather than the dummy.
Stock return: annual stock return. We expect a positive relation between stock market
performance and compensation. We include both contemporaneous and lagged stock return as
control variables.
Cash: sum of cash and short-term investments divided by total assets.
R&D: research and development expenses divided by total assets.
10
CAPEX: capital expenditures divided by total assets.
Firm age: number of years since a firm’s shares have been publicly listed.
The Appendix table provides definitions and data sources for all variables. All variables are
winsorized at the 5th and 95th percentile values.
2.4. Firm Characteristics and General Managerial Ability Index
We look at the match between firms and general managerial skills using univariate tests. Table 5
presents correlation coefficients between the general ability index and firm characteristics. The
table also presents mean firm characteristics for “generalist” and “specialist” CEOs and test for
the difference in firm characteristics between the two types of CEOs. A generalist is defined as a
CEO with general managerial ability index above the yearly median, while a specialist has an
index below the yearly median. As expected, we find that firms with generalist CEOs are bigger,
older and more diversified. We also find that firms with generalist CEOs have slightly higher
leverage and cash holdings, and lower stock return volatility. We do not find statistically
significant differences in terms of accounting and stock market performance (ROA and stock
returns) between generalists and specialists. The differences in Tobin’s Q, CAPEX and R&D are
not economically meaningful, although they are statistically significant.
The results in Table 5 suggest that firm size and diversification are relevant firm characteristics
in explaining the relation between the generality of CEO human capital and compensation. With
respect to firm size, for instance, at this stage we cannot reject the possibility that the positive
correlation between compensation and the generality of human capital is due to the fact that
bigger firms may tend to appoint generalist CEOs. A similar reasoning can be applied to
diversified firms. We further investigate these issues in multivariate tests in Sections 3 and 4.
With respect to CEO characteristics, we find that generalist CEOs tend to have shorter tenure and
to be more frequently hired from outside the firm. We expect this to be the case, since generalist
CEOs are more likely to have worked in different firms.
11
3. Do Generalist CEOs Get Paid More?
We investigate the relation between CEO compensation and the generality of managerial skills.
We first use panel regression tests where the dependent variables are CEO total, cash, or equity
compensation. We also use a measure of CEO excess compensation based on the CEO’s industry
experience. We then examine the relation between CEO compensation and the generality of
managerial skills in a sample of large versus small firms and diversified versus standalone firms.
We also investigate CEO compensation around CEO turnovers and switches of CEO type.
Finally, we conduct several robustness checks on our primary results.
3.1. CEO Compensation Tests
Table 6 presents our main test of whether CEOs with higher general managerial ability receive
higher compensation. We run both ordinary least squares (OLS) and firm fixed effects panel
regressions, where the dependent variable is the logarithm of CEO total compensation (columns
(1)-(4)), the logarithm of CEO cash (salary plus bonus) compensation (columns (5)-(6)) and the
logarithm of equity (restricted stock plus option awards) compensation (columns (7)-(8)). Our
explanatory variable of interest is the general managerial ability index. The OLS regressions
include industry (two-digit SIC) and year dummies. The firm fixed effects specification controls
for unobserved sources of firm heterogeneity and also include year dummies. 7 Fixed-effects
methods solve “joint determination” problems in which an unobserved time-invariant variable
simultaneously determines both total compensation and the general managerial ability index. It is
equivalent to looking only at within-firm changes in the general managerial ability index.
Because only the effects of within-firm changes in total compensation are taken into account,
firm-specific omitted variables cannot explain the observed relationship between CEO
compensation
and
managerial
attributes.
All
reported
t-statistics
are
adjusted
for
heteroskedasticity and within-firm correlation using firm-level clustered standard errors.
Columns (1)-(2) present the coefficients of a regression of total compensation on firm and CEO
attributes but not including the general managerial ability index. This is the base case regression.
We find that firm size is positively and significantly associated with total compensation, which is
7
We obtain consistent findings using CEO fixed effects regressions.
12
consistent with the findings in Gabaix and Landier (2005) and others. 8 We consider other
commonly used firm-level factors affecting executive pay and find that total pay is positively and
significantly associated with growth opportunities (Tobin’s Q) and firm performance as
measured by ROA and stock returns. The only CEO attribute that significantly and consistently
impacts total pay is the external hire dummy. We find that external CEO appointments carry a
positive and significant wage premium, which is consistent with Murphy and Zabojnik (2004,
2008).
Columns (3)-(4) present the coefficients of a regression of total compensation on the general
managerial ability index as well as firm and CEO attributes controls. There is strong evidence of
a positive and statistically significant relation between total pay and the index. The coefficient on
the general managerial ability index variable is positive and statistically significant. This is
consistent with CEOs with more general managerial skills carrying a wage premium. It is also
economically significant. Using the specification in column (3), a one-standard deviation
increase in the index is associated with an increase in total compensation of about 6%. This
corresponds to approximately a quarter of a million dollars of extra pay per year.
With respect to the other explanatory variables, the inclusion of the general managerial ability
index does not significantly affect the coefficients of the control variables in columns (1)-(2),
although the magnitude of the effects is slightly lower in some cases. We still see that firm size
and performance are positively associated with total pay and that external CEO appointments
carry a significant premium.
Columns (5)-(6) present estimates of similar regressions using cash compensation as the
dependent variable, while columns (7)-(8) use equity incentives as the dependent variable. In all
specifications we find a positive and significant relation between compensation and the general
managerial ability index. The magnitude of the effect, however, is stronger in the case of equity
compensation. Using the specifications in columns (5) and (7), a one-standard deviation increase
in the index is associated with an increase of 1.8% and 8.6% in cash and equity compensation,
respectively. In unreported regression, we find that general managerial ability index is positively
8
Our results are robust when we use the market value of assets or equity as proxies for firm size.
13
associated with equity compensation, if we run a Tobit specification for the ratio of equity
compensation to total compensation.
We have documented a positive relation between CEO total pay and an index that captures the
degree of generality of managerial skills. The index includes three components related with the
external mobility or mobility across industries of executives (past number of industries), past
experience as a CEO (past CEO dummy), and educational background (general education
dummy). Table 7 presents the estimates of the same regressions as those in Table 6 but now
using as main explanatory variables the individual components of the index rather than the
aggregate index. We find that the past number of industries and the past CEO dummy are
positively and significantly associated with total compensation, while the general education
dummy coefficient is insignificant. Thus, the evidence supports the hypothesis that both high
external mobility across industries and a past appointment as a CEO carry positive pay premiums.
A CEO that worked in different industries in the past receives an additional pay of 6% in terms
of total compensation, while a CEO that has a past CEO appointment receives 5% more. This
represents an increase in average annual total compensation of approximately $240,000 and
$200,000, respectively. The fixed effects specifications show consistent results. Having an
MBA-type education does not have a significant effect, perhaps reflecting the fact that MBAtype education is nowadays widespread not only among executives, but also among nonexecutive professionals. Indeed, if both executives and non-executives invest in a more general
education, this might explain the rise in executive compensation, but not the increasing gap
between executive and non-executive compensation. Our results challenge some of the findings
of Frydman (2009) on the role of business education, de per se, explaining the rise in CEO pay
over the last decades.
3.2. CEO Excess Compensation Tests
We have documented in the previous subsection that CEOs with more general skills earn a
significant pay premium. In this section, to examine whether general managerial skills is
associated with higher or lower CEO total pay, we measure the percentage difference between a
CEO’s total pay and the imputed pay given its past industry experience measured by the pay of
single-industry CEOs (i.e., the pay of CEOs that worked only in one industry over their career).
14
We measure excess compensation as the log of the ratio of a CEO’s total compensation to its
imputed compensation - i.e. the premium or discount in pay resulting from industry mobility.
This measures allows us to do a better match between the compensation of a CEO with more
general skills (generalists) and the CEOs with less general skills (specialists) in terms of their
industry experience. The measure of CEO compensation is inspired by the measures of excess
value used in the corporate diversification literature (e.g., Berger and Ofek (1995)).
In the regressions of Table 8 the dependent variable is the CEO excess compensation. We control
for other factors that could affect excess compensation. Firm size, performance, growth
opportunities and other firm and CEO characteristics are taken into account using the same set of
control variables used in Table 6. The main explanatory variables of interest are the past multiindustry dummy and the general ability index. We use the past multi-industry dummy as
explanatory variable because it matches naturally the definition of the dependent variable in this
section. Columns (1)-(2) present the coefficients of the regression of CEO excess compensation
on the past multi-industry dummy as well as control variables. The coefficient on the past multiindustry dummy captures the percentage difference in average excess compensation between
CEOs with a career path across more than one industry (multi-industry) and single-industry
CEOs (single-industry). The pay premium for multi-industry CEOs is more than 10% in the OLS
specification in column (1).
Columns (3)-(4) present the coefficients of a regression of CEO excess compensation on the
general managerial ability index as well as control variables. There is strong evidence of a
positive and statistically significant relation. A one-standard deviation increase in the index is
associated with 4.5% more CEO excess compensation using specification (3).
In summary, the findings using the excess compensation measure are consistent with those using
total compensation as the dependent variable. The magnitude of the effects is also similar. The
results so far support the notion that CEOs with more general managerial skills carry a
significant wage premium in the labor market. In particular, CEO compensation is higher for
executives that have not made their entire career in a single industry and therefore there is
evidence of an industry mobility wage premium.
15
3.3. Effect of Firm Size and Diversification
In the previous subsections, we find evidence of a positive relation between CEO compensation
and proxies of CEO general managerial human capital and industry mobility. In this subsection,
we investigate whether the relation between general managerial attributes and executive
compensation is heterogeneous across different types of firms. We first study firm size because
the size of a firm’s operations has been shown to be an important determinant of CEO
compensation (e.g., Gabaix and Landier (2006)). Second, we study corporate diversification as
measured by the diversification dummy (i.e., a firm operates in more than one business segment).
Both firm size and the number of business segments have been used as proxies of the scope and
complexity of the firm’s operations (e.g., Boone, Field, Karpoff, and Raheja (2007), and Coles,
Daniel, and Naveen (2008)).
Table 9 presents the estimates of regressions of CEO pay for groups of firms based on their size
and whether they are diversified. The specifications and the set of control variables are the same
as the ones used in Table 6. Our explanatory variable of interest is the general managerial ability
index.
Columns (1)-(4) of Table 9 present results by splitting the sample into small and large firms
based on whether firm size is below the yearly median (small firms in columns (1)-(2)) or above
the yearly median (large firms in columns (3)-(4)). We find a positive and statistically significant
relation between total compensation and the general managerial ability index in both small and
large firms. The magnitude of the effects is also similar in both groups of firms. We conclude
that our primary finding is not driven only by large firms. Smaller firms seem also to be willing
to pay a similar premium for general managerial skills.
Columns (5)-(8) of Table 9 present results by splitting the sample into with a single business
segment (standalone firms in columns (5)-(6)) and diversified firms (multi-business segments
firms in columns (7)-(8)). Interestingly, we find a positive and statistically significant coefficient
only for the diversified firms sample in the firm fixed effects specifications (column (8)). This
finding is consistent with the hypothesis that diversified firms are the ones that are in more need
and pay a higher premium for executives with general managerial skills. In other words, a
16
generalist manager is more valuable when matched to a generalist firm, such as conglomerates. 9
We explore this issue in more detail in Section 4 below.
3.4. Evidence from CEO Turnovers
Our evidence so far is based on firm-year panel regression estimates. In this subsection we
examine changes in compensation around CEO turnover, in particular when there is a switch of
CEO type. We expect to find an increase in CEO compensation when a firm switches from a
CEO with less general skills (specialist) to a CEO with more general skills (generalist). We
classify CEOs as generalists if their general managerial ability index is above the yearly median,
and we classify them as specialists if their index below the median. Furthermore, the effect is
likely to be more pronounced when the new CEO is hired from outside the firm as in this case
the firm has to pay to access the market (Murphy and Zabojnik (2004, 2008)).
We run regression tests where the dependent variable is total, cash, equity or excess
compensation. We Use the following explanatory variables to measure the effect on pay from a
CEO turnover: (1) no switch of CEO type, which is a dummy that equals one if there is a CEO
turnover at time t but there is no switch of CEO type; (2) switch to generalist – external hire,
which is a dummy that equals one if there is a CEO turnover at time t and a specialist CEO is
replaced by a generalist CEO hired from outside the firm; (3) switch to specialist – external hire,
which is a dummy that equals one if there is a CEO turnover at time t and a generalist CEO is
replaced by a specialist CEO hired from outside the firm; and (4) switch – internal hire, which is
a dummy that equals one if there is a CEO turnover at time t and the new CEO is internally
appointed. We include these four variables as regressors and therefore the intercept in the
regression captures the benchmark case of no CEO turnover at time t. Table 10 presents the
results.
Column (1) presents the coefficients of a regression of total compensation on the four
explanatory variables of interest and control variables (same set of variables used in Table 6).
The coefficient on switch to generalist – external hire is positive and significant, while the
9
Xuan (2009) finds that appointing specialist CEOs in multi-division firms might lead to inefficient capital
allocation decisions, particularly if the CEOs was previously linked to a division of the firm.
17
coefficient on switch to specialist – external hire is insignificant at the 5% level. The effect on
CEO total compensation of switching from a specialist to a generalist CEO by external
appointment is substantial at about 26% extra pay.
Columns (2) and (3) present estimates of similar regressions but using the logarithm of CEO
cash and equity compensation as dependent variables. The results show that the increase in
compensation associated with a switch from a specialist to a generalist CEO is driven by equity
incentives, rather than by increases in salary or bonus. The results are also consistent with a
significant impact on CEO excess compensation of a switch from a specialist to a generalist CEO
(column (4)).
Overall, there is evidence of an increase in CEO pay when a firm switches from a specialist to a
new generalist CEO via an external hire. This is direct evidence that general managerial human
capital does command a premium in the CEO labor market.
3.5. Robustness
In this subsection we perform several robustness checks of our primary findings. Table 11
presents robustness checks with respect to the general managerial ability index. We alternatively
construct the general ability index using the past CEO dummy, the general education dummy,
but now using the past number of firms rather than the past number of industries (columns (1)(2)). The past number of firms measures the number of different firms where an executive
worked in the past. The intuition is that a generalist CEO should possess skills that are
transferable across firms (and not only industries) rather “firm-specific human capital” that it is
valuable only within a particular organization. The second variation of the general managerial
ability index is to count the past number of industries based on two-digit SIC codes rather than
four-digit SIC codes (columns (3)-(4)). The third variation of the index is to count the past
number of CEO positions rather than using the past CEO dummy variable (columns (5)-(6)). We
find strong evidence of a positive relation between CEO total pay and the alternative definitions
of the general managerial ability index in all specifications. Similar to the results in Table 6, the
effect of the index of general managerial attributes on CEO total pay is always economically and
statistically significant.
18
We also perform robustness checks on the measure of excess compensation. We calculate the
measure of excess compensation by performing the match between the multi-industry CEO and
the portfolio of single-industry CEOs using two-digit SIC codes instead of the four-digit SIC
codes. Using this coarser industry classification has two main implications: first, we ensure that
the industries are significantly different from each other, and second, that we have more singleindustry CEOs to use as a benchmark. In untabulated regressions (similar to those in Table 8),
we find the results to be robust to this alternative definition of the measure of excess
compensation. There is strong evidence of a positive and statistically significant relation between
excess compensation and the multi-industry dummy.
3.6. Discussion
We go one step further in our analysis and we look at whether there is evidence that higher pay
levels by generalist CEOs is consistent with an efficient functioning of the CEO labor market.
An alternative hypothesis is that generalist CEOs accumulate their multi-industry experience
because they are just “serial CEOs” that engage in “job hopping” (Giannetti (2010)).
Additionally, generalist CEOs may feature more prominently in databases of executive search
firms and have an easier time being recruited. Dasgupta and Ding (2010) emphasize the
enhanced role of executive search firms in the last decades. We investigate the relation between
generalist CEOs and firm performance to test this alternative explanation of our findings.
Specifically, the alternative hypothesis predicts that firms hiring generalist CEOs would see
performance and shareholder returns negatively affected. In untabulated regerssions, we find
evidence of a positive relation between shareholder value (Tobin’s Q) and the general ability
index using an OLS specification, although the relation is not statistically significant when we
use a firm fixed effects specification. We also look at whether generalist CEOs exhibit higher
turnover or “job hopping”. In unreported tests, we do not find evidence consistent with this
prediction. The relation between CEO turnover and the general ability index is statistically
insignificant and even negative in some specifications. Taken together, the findings do not
support the existence of market inefficiencies in the setting of CEO pay. Overall, the results are
consistent with an efficient market-based explanation of the significant wage premium awarded
to CEOs with general managerial skills.
19
4. A Detailed Look at Conglomerate Firms
In sub-section 3.3 we provide evidence that conglomerates pay more, on average, to CEOs with
multi-industry career backgrounds. This is not a mechanic effect that large and more diversified
firms pay their CEOs more. Interestingly, in our basic tests in Table 6 we do not find evidence
that conglomerates per se pay more to their CEOs irrespective of their level of general
managerial ability.
In order to perform a more direct test of the relation between firm diversification and CEO
compensation we define a new measure of excess compensation: conglomerates excess
compensation. While the excess compensation measure helps us to answer the question on
whether generalists are paid at a premium or a discount when compared to specialists, the
conglomerates excess compensation measure answers a different question: whether diversified
firms pay an additional premium to their CEOs. We use Compustat segment-level data to
construct the conglomerates excess compensation. We compare the actual CEO compensation of
a diversified firm to their imputed standalone compensation for individual business segments.
The imputed compensation is the average compensation of the portfolio of industries that
matches the business segments of the diversified firm where the industry-level compensation is
the median of across standalone firms in a industry. The industry match is done at the four-digit
SIC code level where there are five or more stand-alone firms or at the highest SIC level when
there are at least five stand-alone firms. In the case of standalone firms, the excess compensation
measure is simply the difference between the CEO total compensation and the median
compensation of standalone firms in its industry. The conglomerates excess compensation is the
log of the ratio of a CEO’s total compensation to its imputed compensation. This measure is
similar in spirit to the excess value measure used to estimate the value effect of diversification
(e.g., Berger and Ofek (1995)). The main difference is that we compare the compensation of a
conglomerate with the compensation of an equivalent portfolio of standalone firms in the same
industries instead of comparing firm valuation.
20
Rose and Shepard (1997) and Anderson et al. (1998) find a significant compensation premium in
diversified firms. However, while controlling for industry effects they take only into account the
core business of the conglomerate, ignoring the industries of the secondary business segments.
By using our measure of conglomerate excess compensation we make sure to match the
conglomerate’s compensation to a benchmark that includes all the industries where the
conglomerate operates in.
Table 12 presents the estimates of regressions where the dependent variable is the new measure
of conglomerates excess compensation. Our explanatory variables of interest are alternatively the
diversification dummy (equals one if the firm has more than one business segment) and the
firm’s number of segments.
The positive and significant coefficient of the diversification dummy in columns (1)-(2) is
consistent with CEOs of diversified firms earning higher wages relative to standalone firms. The
conglomerates CEO pay premium is substantial at 26% using the specification in column (1).
Columns (3)-(4) present the estimates using the number of segments as main explanatory
variable. We find a positive and significant relation between the number of business segments
and CEO total compensation.
Overall, using our measure of conglomerates excess compensation, we find evidence consistent
with Rose and Shepard (1997) who argue that diversified firms are more complex organizations,
and more difficult to manage, and therefore CEOs in diversified firms should be paid at a
premium relative to stand-alone firms. Anderson et al. (1998) argue that the compensation
premium in conglomerates is not necessarily associated with poorer corporate governance in
these firms. Our findings also suggest that conglomerates are only willing to pay a premium to
CEOs to compensate them for their general skills. In fact, Laux (2001) suggests that it is cheaper
to provide incentives in conglomerates that standalones.
An alternative view is that diversifications destroy value due to greater agency costs, which
would be associated with higher rent extraction by managers (Jensen (1986)). There is a wide
literature on the existence of a diversification discount (Berger and Ofek (1995), Lang and Stulz
(1994), Rajan, Servaes and Zingales (2000) among others) which could be consistent with higher
21
agency costs, however, there is also contradictory evidence that diversification de per se causes a
loss of firm value (Villalonga (2004)).
Finally, we find a positive and significant relation between the conglomerates excess
compensation and the index of general managerial ability in columns (5)-(6). This suggests that
not only conglomerates pay a premium to CEOs relative to standalone firms but that the
premium for general managerial skills is greater in conglomerates. In untabulated results, we also
find that that the coefficient of the interaction variable between the diversification dummy and
the general ability index is positive and significant.
We check for the robustness of the results using a finer measure of conglomerate excess
compensation, where the imputed compensation is now calculated imposing the requirement that
standalone firms should have single-industry CEOs (i.e., executives that have always worked in
the same industry). In untabulated results, we still find a compensation premium in diversified
firms when we use this finer measure of conglomerate excess compensation.
5. Conclusion
This paper investigates the hypothesis that general managerial skills are paid at premium relative
to firm-specific skills. We construct a new measure of the generality of human capital based on a
CEO’s external mobility across industries, experience as top executive, and education. We find a
positive and significant relation between the index of general managerial ability and CEO
compensation using the sample of S&P 1500 firms in the 1993-2007 period. The results are
robust to the inclusion of many firm and CEO characteristics as control variables, including firm
and CEO fixed effects.
We measure the compensation premium to generalist CEOs using a new measure of excess
compensation that compares the total compensation of a CEO that has worked in many industries
with the average compensation of a matched portfolio of single-industry CEOs. We find the
excess annual compensation premium is as high as 10%.
Our paper provides direct evidence of the growing importance of general managerial skills
versus firm-specific skills. This trend is likely to increase the outside opportunities of CEOs with
22
general skills and therefore lead to higher levels of CEO pay. Our findings highlight the
importance of the inner workings of the CEO labor market to understand the evolution of CEO
pay in recent decades.
23
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26
Table 1
CEO Compensation and General Managerial Ability Index by Year
This table presents average score of the general managerial ability index and average CEO total compensation per
year from 1993 to 2007. The index of general managerial ability is the first factor of applying the principal
components method to three proxies of general managerial ability (past number of industries, past CEO dummy, and
general education dummy). The sample consists of Execucomp firms for which CEO profile data are available from
BoardEx. Average CEO excess compensation for the sample of CEOs that worked in more than one four-digit SIC
industry (multi-industry CEOs) are also shown. All variables are winsorized at the 5th and 95th percentile values.
Variable definitions and data sources are provided in the Appendix.
Year
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Average general
ability index
-0.107
-0.155
-0.110
-0.104
-0.075
-0.045
-0.026
-0.004
0.022
0.058
0.051
0.073
0.076
0.075
0.067
Average total
compensation
($ thousands)
2,131
2,270
2,415
2,994
3,546
3,734
4,227
4,577
4,783
4,400
4,191
4,672
4,769
4,952
5,054
Total
0.000
4,092
27
Average excess
compensation of
multi-industry CEOs
($ thousands)
744
852
1,149
1,330
1,728
1,691
2,088
2,598
2,588
2,239
2,142
2,315
2,282
2,279
2,191
2,000
Obs.
774
1,011
1,092
1,184
1,272
1,392
1,515
1,595
1,548
1,572
1,642
1,653
1,670
1,652
1,570
21,142
Table 2
General Managerial Ability Index - Principal Component Analysis Results
This table presents the results of the principal component analysis of three proxies of general managerial ability
(past number of industries, past CEO dummy, and general education dummy). Factor loadings on each proxy
general managerial ability, eigenvalue, and proportion of variation explained by the first factor are shown. Variable
definitions and data sources are provided in the Appendix.
Loadings
Scores
Proportion explained
Eigenvalue
Past nr. of industries
0.756
0.662
28
Past CEO dummy
0.013
0.011
0.380
1.141
General education
dummy
0.755
0.662
Table 3
CEO Compensation and General Managerial Ability Index by Industry
This table presents average score for the CEO general ability index and average CEO total compensation for each of the Fama-French 12 industries. Average
CEO total compensation for the sub-samples of CEOs above (high index) and below (low index) the median yearly general ability index and the t-statistic of the
difference, and average CEO excess compensation for the sample of CEOs that worked in more than one four-digit SIC industry (multi-industry CEOs) and its tstatistic are also shown. The sample consists of Execucomp firms for which CEO profile data are available from BoardEx in the 1993-2007 period. All variables
are winsorized at the 5th and 95th percentile values. Variable definitions and data sources are provided in the Appendix.
Industry
Consumer nondurables
Consumer durables
Manufacturing
Oil, gas, and coal
Chemicals
Business equipment
Telephone and television
Utilities
Wholesale and retail
Healthcare and drugs
Finance
Other
Average total
Average
Average total
compensation general compensation ($ high general ability
ability index
thousands)
index ($ thousands)
0.011
4,101
4,992
0.077
3,587
3,514
0.144
3,354
4,072
-0.142
4,518
5,125
0.085
4,090
4,411
-0.037
4,192
4,873
0.350
6,907
8,211
0.113
2,746
3,146
-0.129
3,585
4,482
0.015
4,379
5,527
-0.066
5,182
6,058
-0.031
3,947
4,683
29
Average total
compensation low general ability
index ($ thousands)
3,482
3,650
2,689
4,167
3,845
3,748
5,536
2,387
3,033
3,456
4,648
3,486
Difference
t-stat
6.94
-0.48
10.92
2.99
2.18
7.29
4.79
4.56
9.37
9.50
7.60
6.82
Average excess
compensation of
multi-industry CEOs
($ thousands)
2,110
883
1,385
2,189
1,925
1,975
5,371
832
1,816
2,289
2,962
1,952
t-stat
13.98
5.57
16.77
10.58
12.71
20.39
15.20
7.86
16.13
15.65
22.66
16.11
Obs.
1,423
648
2,815
874
749
3,581
435
1,180
2,495
1,627
2,911
2,404
Table 4
Summary Statistics
This table presents the mean, median, standard deviation, minimum, maximum and number of observations for each
variable. All variables are winsorized at the 5th and 95th percentile values. The sample consists of Execucomp firms
for which CEO profile data are available from BoardEx in the 1993-2007 period. All variables are winsorized at the
5th and 95th percentile values. Variable definitions and data sources are provided in the Appendix.
Variable
Total compensation ($ thousands)
Cash compensation ($ thousands)
Equity compensation ($ thousands)
Excess compensation ($ thousands)
General ability index
Past nr. industries
Past CEO dummy
General education dummy
Past multi-industry dummy
Past nr. firms
CEO tenure
External hire dummy
CEO age
Sales ($ milions)
Leverage
Tobin's Q
ROA
Volatility
Diversification dummy
Stock return
Cash
R&D
CAPEX
Firm age
Mean
Median
Std. Dev.
Panel A: Compensation
4,092
2,407
4,274
1,217
946
858
2,124
865
2,973
1,430
0
3,917
Panel B: CEO characteristics
0.00
0.16
1.00
1.12
1.00
1.38
0.17
0.00
0.37
0.48
0.00
0.50
0.57
1.00
0.49
1.67
1.00
1.72
7.95
6.00
7.03
0.22
0.00
0.41
55.53
56.00
7.35
Panel C: Firm characteristics
3,619.20
1,316.70
5,261.24
0.22
0.22
0.17
1.91
1.52
1.04
0.13
0.13
0.08
0.11
0.09
0.05
0.57
1.00
0.49
0.13
0.10
0.37
0.13
0.06
0.15
0.02
0.00
0.04
0.05
0.04
0.04
23.15
18.00
19.02
30
Min
Max
Obs.
458
306
0
-2,609
16,490
3,500
11,013
12,938
21,142
21,142
18,199
21,119
-1.19
0.00
0.00
0.00
0.00
0.00
1.00
0.00
29.00
6.41
13.00
1.00
1.00
1.00
18.00
49.00
1.00
90.00
21,142
21,142
21,142
21,142
21,142
21,142
19,727
19,396
20,131
120.57
0.00
0.97
0.00
0.04
0.00
-0.50
0.00
0.00
0.00
0.00
20,051.42
0.55
4.83
0.29
0.24
1.00
0.97
0.52
0.14
0.16
82.00
21,113
21,038
21,078
20,740
20,938
18,529
18,746
21,111
21,117
20,084
20,982
Table 5
General Managerial Ability Index and CEO and Firm Characteristics
This table presents correlation coefficient of the general managerial ability index with firm characteristics, and
averages of firm characteristics for the samples of CEOs above (high index) and below (low index) the median
yearly general ability index and t-statistic of the difference. The sample consists of Execucomp firms for which CEO
profile data are available from BoardEx in the 1993-2007 period. All variables are winsorized at the 5th and 95th
percentile values. Variable definitions and data sources are provided in the Appendix.
CEO tenure
External hire dummy
CEO age
Sales ($ millions)
Leverage
Tobin's Q
ROA
Volatility
Diversification dummy
Stock return
Cash
R&D
CAPEX
Firm age
Correlation with Average - high Average - low
general ability general ability general ability Difference
index
index
index
t-stat
-0.080
7.443
8.319
-8.680
0.106
0.260
0.187
12.283
55.74
55.38
3.4085
0.062
0.197
6,326
4,131
10.891
0.054
0.233
0.217
7.128
-0.020
1.889
1.925
-2.496
-0.024
0.133
0.133
0.040
-0.093
0.101
0.109
-10.986
0.129
0.605
0.550
7.465
0.002
0.134
0.129
0.887
-0.071
0.117
0.133
-7.846
0.026
0.025
0.023
3.979
-0.098
0.052
0.055
-4.515
0.125
25.497
21.434
15.358
31
Table 6
CEO Compensation and General Managerial Ability Index
This table presents estimates of OLS and firm fixed effects panel regressions of the logarithm of CEO total, cash and
equity compensation on the general managerial ability index and other CEO- and firm-level control variables. The
sample consists of Execucomp firms for which CEO profile data are available from BoardEx in the 1993-2007
period. All variables are winsorized at the 5th and 95th percentile values. Variable definitions and data sources are
provided in the Appendix. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, ***
indicates significance at the 10%, 5% and 1% levels.
(1)
General ability index
CEO tenure
External hire dummy
CEO age
Sales (log)
Leverage
Tobin's Q
ROA
ROA (t-1)
Volatility
Diversification dummy
Stock return
Stock return (t-1)
Cash
R&D
CAPEX
Firm age
Constant
Observations
R-squared
Year dummies
Industry dummies
(2)
(3)
Total compensation (log)
Fixed
OLS
effects
OLS
0.063***
[5.00]
-0.007***
0.000 -0.006**
[-2.76]
[0.049]
[-2.41]
0.127*** 0.190*** 0.104***
[4.01]
[5.41]
[3.26]
-0.000
-0.002
-0.001
[-0.11]
[-0.87]
[-0.48]
0.435*** 0.297*** 0.427***
[32.5]
[11.0]
[31.9]
0.378*** -0.323*** 0.378***
[3.97]
[-3.27]
[3.97]
0.132*** 0.092*** 0.128***
[6.75]
[5.09]
[6.62]
-0.541**
0.384* -0.528**
[-2.47]
[1.77]
[-2.42]
-0.109
0.383**
-0.098
[-0.55]
[2.19]
[-0.49]
0.445*
0.252
0.511**
[1.77]
[1.18]
[2.04]
-0.015
-0.041
-0.019
[-0.53]
[-1.44]
[-0.69]
0.175*** 0.131*** 0.177***
[6.93]
[5.66]
[7.07]
0.251*** 0.184*** 0.254***
[11.9]
[9.62]
[12.0]
0.452***
0.210 0.492***
[3.19]
[1.63]
[3.50]
2.087*** -1.911** 1.983***
[4.02]
[-2.57]
[3.85]
-0.081
0.430
0.004
[-0.23]
[1.32]
[0.011]
0.000
-0.001
0.000
[0.19]
[-0.35]
[0.15]
3.260***
3.390***
[14.0]
[14.5]
11453
11453
11453
0.500
0.262
0.503
Yes
Yes
Yes
Yes
No
Yes
(4)
Fixed
effects
0.041***
[3.14]
0.000
[0.091]
0.172***
[4.90]
-0.003
[-1.05]
0.294***
[11.0]
-0.330***
[-3.34]
0.091***
[5.08]
0.386*
[1.78]
0.393**
[2.24]
0.264
[1.24]
-0.041
[-1.45]
0.133***
[5.74]
0.185***
[9.72]
0.209
[1.63]
-1.844**
[-2.47]
0.432
[1.33]
-0.001
[-0.42]
11453
0.263
Yes
No
32
(5)
(6)
Cash compensation (log)
OLS
Fixed effects
0.018**
0.019**
[2.20]
[2.01]
0.001
0.006***
[0.51]
[3.18]
0.051**
0.071***
[2.47]
[2.81]
0.005***
0.001
[3.49]
[0.78]
0.256***
0.164***
[30.6]
[9.18]
0.190***
-0.185***
[2.94]
[-3.31]
-0.003
-0.028***
[-0.23]
[-2.79]
1.011***
1.496***
[7.56]
[11.3]
-0.618***
-0.423***
[-4.92]
[-3.79]
-0.749***
-0.857***
[-4.77]
[-6.98]
0.013
-0.016
[0.75]
[-0.90]
0.237***
0.222***
[14.7]
[16.2]
0.155***
0.126***
[12.1]
[11.4]
0.150*
0.160**
[1.75]
[2.24]
0.496
-0.667
[1.59]
[-1.64]
-0.742***
-0.246
[-3.24]
[-1.26]
0.002***
0.002
[4.03]
[1.57]
4.142***
[16.0]
11427
11427
0.555
0.326
Yes
Yes
Yes
No
(7)
(8)
Equity compensation (log)
OLS
Fixed effects
0.086***
0.057**
[4.16]
[2.21]
-0.001
-0.005
[-0.24]
[-0.96]
0.286***
0.352***
[5.80]
[5.39]
-0.009***
-0.003
[-2.82]
[-0.81]
0.582***
0.455***
[33.6]
[9.09]
0.456***
-0.342*
[3.20]
[-1.95]
0.342***
0.233***
[12.1]
[7.76]
-2.360***
-1.089***
[-6.88]
[-2.92]
0.524*
0.796**
[1.67]
[2.56]
2.030***
0.756**
[5.11]
[2.01]
-0.068
-0.189***
[-1.56]
[-3.65]
-0.105**
-0.117***
[-2.51]
[-3.02]
0.285***
0.236***
[8.14]
[7.19]
0.989***
0.014
[5.55]
[0.066]
2.672***
-2.605*
[3.77]
[-1.87]
0.068
0.024
[0.11]
[0.042]
-0.004***
-0.002
[-3.44]
[-0.85]
1.566***
[3.68]
8010
8010
0.470
0.225
Yes
Yes
Yes
No
Table 7
CEO Compensation and General Managerial Ability Index Components
This table presents estimates of OLS and firm fixed effects panel regressions of the logarithm of CEO total, cash and
equity compensation on individual proxies of general managerial ability (past number of industries, past CEO
dummy, general education dummy) and other CEO- and firm-level control variables. The sample consists of
Execucomp firms for which CEO profile data are available from BoardEx in the 1993-2007 period. All variables are
winsorized at the 5th and 95th percentile values. Variable definitions and data sources are provided in the Appendix.
Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, *** indicates significance at the
10%, 5% and 1% levels.
Past nr. industries
Past CEO dummy
General education dummy
CEO tenure
External hire dummy
CEO age
Sales (log)
Leverage
Tobin's Q
ROA
ROA (t-1)
Volatility
Diversification dummy
Stock return
Stock return (t-1)
Cash
R&D
CAPEX
Firm age
Constant
Observations
R-squared
Year dummies
Industry dummies
(1)
(2)
Total compensation (log)
OLS
Fixed effects
0.059***
0.023**
[6.46]
[2.42]
0.052*
0.058***
[1.93]
[2.61]
0.007
0.041
[0.31]
[1.51]
-0.006**
0.000
[-2.26]
[0.18]
0.087***
0.162***
[2.70]
[4.44]
-0.003
-0.003
[-1.26]
[-1.17]
0.423***
0.292***
[31.6]
[10.9]
0.365***
-0.331***
[3.85]
[-3.35]
0.126***
0.092***
[6.58]
[5.15]
-0.527**
0.398*
[-2.42]
[1.83]
-0.075
0.391**
[-0.38]
[2.22]
0.476*
0.270
[1.89]
[1.27]
-0.021
-0.040
[-0.79]
[-1.40]
0.184***
0.132***
[7.35]
[5.71]
0.257***
0.185***
[12.2]
[9.72]
0.458***
0.207
[3.27]
[1.61]
1.995***
-1.872**
[3.93]
[-2.51]
0.039
0.426
[0.11]
[1.31]
0.000
-0.001
[0.26]
[-0.43]
3.455***
[14.0]
11453
11453
0.506
0.264
Yes
Yes
Yes
No
(3)
(4)
Cash compensation (log)
OLS
Fixed effects
0.021***
0.013**
[3.74]
[2.00]
0.002
-0.000
[0.12]
[-0.035]
-0.008
0.011
[-0.53]
[0.59]
0.001
0.006***
[0.58]
[3.18]
0.045**
0.067**
[2.18]
[2.58]
0.004***
0.001
[2.98]
[0.58]
0.254***
0.163***
[30.3]
[9.14]
0.187***
-0.184***
[2.89]
[-3.30]
-0.004
-0.028***
[-0.31]
[-2.78]
1.009***
1.501***
[7.55]
[11.3]
-0.608***
-0.421***
[-4.85]
[-3.77]
-0.758***
-0.856***
[-4.83]
[-6.97]
0.012
-0.016
[0.67]
[-0.89]
0.240***
0.222***
[14.8]
[16.3]
0.156***
0.126***
[12.2]
[11.4]
0.134
0.157**
[1.57]
[2.20]
0.504
-0.663
[1.62]
[-1.63]
-0.730***
-0.246
[-3.20]
[-1.26]
0.002***
0.002
[4.10]
[1.58]
4.184***
[15.8]
11427
11427
0.556
0.326
Yes
Yes
Yes
No
33
(5)
(6)
Equity compensation (log)
OLS
Fixed effects
0.068***
0.026*
[5.46]
[1.66]
0.128***
0.068*
[3.18]
[1.81]
-0.014
0.018
[-0.41]
[0.41]
0.000
-0.003
[0.044]
[-0.82]
0.237***
0.325***
[5.43]
[5.54]
-0.011***
-0.005
[-3.78]
[-1.23]
0.531***
0.400***
[35.3]
[8.82]
0.435***
-0.253*
[3.37]
[-1.72]
0.295***
0.195***
[12.2]
[7.29]
-1.868***
-0.732**
[-6.28]
[-2.31]
0.430
0.641***
[1.64]
[2.59]
1.531***
0.549*
[4.39]
[1.75]
-0.056
-0.148***
[-1.43]
[-3.39]
-0.090**
-0.099***
[-2.54]
[-2.97]
0.229***
0.192***
[7.75]
[6.80]
0.864***
0.116
[5.50]
[0.65]
2.949***
-1.450
[4.86]
[-1.33]
-0.107
0.003
[-0.20]
[0.0058]
-0.004***
-0.003
[-3.46]
[-1.33]
2.425***
[8.51]
8010
8010
0.493
0.246
Yes
Yes
Yes
No
Table 8
CEO Excess Compensation and General Managerial Ability
This table presents estimates of OLS and firm fixed effects panel regressions of the CEO excess compensation on a
dummy that takes the value of one if a CEO worked in more than one industry (past multi-industry dummy) and the
general ability index and other CEO- and firm-level control variables. The excess compensation is defined as the
logarithm of the ratio of the CEO total compensation to its imputed compensation from single-industry CEOs that
match the CEO’s past industry (four-digit SIC) experience. The sample consists of Execucomp firms for which CEO
profile data are available from BoardEx in the 1993-2007 period. All variables are winsorized at the 5th and 95th
percentile values. Variable definitions and data sources are provided in the Appendix. Robust t-statistics adjusted for
firm-level clustering are reported in brackets. *, **, *** indicates significance at the 10%, 5% and 1% levels.
Past multi-industry dummy
(1)
OLS
0.104***
[3.96]
(2)
Fixed effects
0.055**
[1.99]
General ability index
CEO tenure
External hire dummy
CEO age
Sales (log)
Leverage
Tobin's Q
ROA
ROA (t-1)
Volatility
Diversification dummy
Stock return
Stock return (t-1)
Cash
R&D
CAPEX
Firm age
Constant
Observations
R-squared
Year dummies
-0.005**
[-2.03]
0.074**
[2.28]
-0.001
[-0.43]
0.334***
[27.4]
0.351***
[3.90]
0.098***
[5.12]
-0.085
[-0.40]
-0.054
[-0.28]
0.520**
[2.01]
0.010
[0.37]
0.158***
[6.09]
0.184***
[8.39]
0.484***
[3.61]
2.470***
[6.37]
0.490
[1.46]
-0.000
[-0.13]
-2.807***
[-19.8]
11442
0.344
Yes
34
-0.001
[-0.25]
0.140***
[3.81]
-0.002
[-0.65]
0.233***
[9.31]
-0.201**
[-2.12]
0.066***
[3.64]
0.340
[1.64]
0.234
[1.31]
-0.064
[-0.30]
-0.021
[-0.73]
0.123***
[5.20]
0.135***
[6.87]
0.145
[1.18]
-1.484**
[-2.06]
0.202
[0.63]
0.000
[0.17]
11442
0.079
Yes
(3)
OLS
0.045***
[3.29]
-0.005*
[-1.93]
0.074**
[2.28]
-0.000
[-0.21]
0.335***
[27.6]
0.355***
[3.94]
0.098***
[5.09]
-0.086
[-0.41]
-0.079
[-0.40]
0.544**
[2.11]
0.009
[0.32]
0.158***
[6.12]
0.185***
[8.43]
0.488***
[3.66]
2.552***
[6.57]
0.488
[1.45]
-0.000
[-0.24]
-2.777***
[-19.7]
11442
0.343
Yes
(4)
Fixed effects
0.027**
[1.98]
-0.000
[-0.12]
0.139***
[3.81]
-0.001
[-0.54]
0.233***
[9.29]
-0.204**
[-2.15]
0.066***
[3.67]
0.333
[1.60]
0.229
[1.28]
-0.060
[-0.28]
-0.022
[-0.76]
0.123***
[5.23]
0.135***
[6.92]
0.148
[1.21]
-1.419*
[-1.96]
0.200
[0.62]
0.000
[0.16]
11442
0.079
Yes
Table 9
CEO Compensation and General Managerial Ability Index: Effect of Firm Size and
Diversification
This table presents estimates of OLS and firm fixed effects panel regressions of the logarithm of CEO total
compensation on the general managerial ability index and other CEO- and firm-level control variables. The small
(large) firms sample consists of those firms whose sales are below (above) the yearly median. The stand-alone
(diversified) firms sample consists of those firms with number of business segments equal to (above) one. The
sample consists of Execucomp firms for which CEO profile data are available from BoardEx in the 1993-2007
period. All variables are winsorized at the 5th and 95th percentile values. Variable definitions and data sources are
provided in the Appendix. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, ***
indicates significance at the 10%, 5% and 1% levels.
General ability index
CEO tenure
External hire dummy
CEO age
Sales (log)
Leverage
Tobin's Q
ROA
ROA (t-1)
Volatility
Diversification dummy
Stock return
Stock return (t-1)
Cash
R&D
CAPEX
Firm age
Constant
Observations
R-squared
Year dummies
Industry dummies
(1)
(2)
Small firms
Fixed
OLS
effects
0.057***
0.053*
[2.78]
[1.96]
-0.007**
-0.007*
[-2.12]
[-1.69]
0.088** 0.187***
[2.12]
[3.20]
-0.001
0.000
[-0.47]
[0.059]
0.427*** 0.322***
[16.4]
[7.36]
0.327*** -0.423***
[3.00]
[-3.11]
0.146*** 0.122***
[6.14]
[4.52]
-0.871***
0.374
[-2.90]
[1.28]
-0.086
0.462*
[-0.33]
[1.92]
0.596*
0.332
[1.81]
[1.23]
-0.059
-0.026
[-1.59]
[-0.64]
0.136***
0.046
[3.99]
[1.39]
0.236*** 0.146***
[8.05]
[5.42]
0.817***
0.314**
[5.69]
[2.16]
1.304** -1.853**
[2.03]
[-2.02]
0.304
-0.382
[0.68]
[-0.90]
-0.003*
-0.002
[-1.72]
[-0.68]
3.469*** 5.447***
[14.2]
[15.6]
5146
5146
0.327
0.215
Yes
Yes
Yes
No
(3)
(4)
Large firms
Fixed
OLS
effects
0.060***
0.036**
[4.00]
[2.30]
-0.003
0.004
[-0.95]
[1.39]
0.130*** 0.177***
[3.13]
[4.02]
-0.001
-0.003
[-0.41]
[-0.91]
0.397*** 0.316***
[19.0]
[7.63]
0.315*
-0.181
[1.94]
[-1.28]
0.095*** 0.072***
[3.29]
[2.94]
0.086
0.513
[0.27]
[1.55]
-0.045
0.209
[-0.15]
[0.83]
0.373
0.240
[1.03]
[0.72]
0.031
-0.023
[0.88]
[-0.57]
0.222*** 0.218***
[6.26]
[6.38]
0.264*** 0.219***
[9.13]
[8.01]
-0.219
-0.088
[-0.85]
[-0.38]
2.845***
-1.237
[3.78]
[-1.02]
-0.159 1.380***
[-0.32]
[2.79]
0.000
-0.001
[0.48]
[-0.34]
3.560*** 4.926***
[11.1]
[12.4]
6307
6307
0.442
0.283
Yes
Yes
Yes
No
35
(5)
(6)
Standalone firms
Fixed
OLS
effects
0.061***
0.008
[2.98]
[0.25]
-0.007**
-0.007
[-2.00]
[-1.55]
0.158*** 0.249***
[3.19]
[3.64]
-0.002
-0.001
[-0.59]
[-0.34]
0.416*** 0.293***
[24.5]
[6.41]
0.186 -0.390**
[1.41]
[-2.53]
0.126*** 0.103***
[5.48]
[3.61]
-0.514
0.506
[-1.62]
[1.40]
-0.293
0.420
[-0.97]
[1.42]
0.817**
0.274
[2.06]
[0.75]
(7)
(8)
Diversified firms
Fixed
OLS
effects
0.064*** 0.047***
[4.45]
[3.27]
-0.006*
0.005*
[-1.73]
[1.67]
0.065* 0.149***
[1.71]
[3.54]
-0.001
-0.003
[-0.32]
[-1.17]
0.447*** 0.299***
[27.4]
[8.74]
0.473*** -0.321***
[3.80]
[-2.64]
0.125*** 0.074***
[4.44]
[3.07]
-0.499*
0.305
[-1.71]
[1.12]
0.029
0.399*
[0.11]
[1.75]
0.246
0.298
[0.85]
[1.18]
0.112***
0.051
[2.94]
[1.36]
0.234*** 0.167***
[7.13]
[5.49]
0.739***
0.222
[4.24]
[1.16]
2.440*** -4.133***
[3.84]
[-3.47]
0.023
0.042
[0.046]
[0.076]
-0.000
-0.002
[-0.088]
[-0.20]
3.490*** 5.837***
[11.6]
[12.8]
4325
4325
0.469
0.220
Yes
Yes
Yes
No
0.228***
[6.81]
0.263***
[9.27]
0.254
[1.29]
1.582**
[2.17]
0.013
[0.030]
-0.000
[-0.42]
3.305***
[13.5]
7128
0.535
Yes
Yes
0.203***
[6.75]
0.201***
[8.20]
0.145
[0.82]
0.011
[0.010]
0.591
[1.52]
-0.002
[-0.87]
5.037***
[16.1]
7128
0.264
Yes
No
Table 10
CEO Compensation and Turnover and Switch of CEO Type
This table presents estimates of OLS panel regressions of the logarithm of CEO total compensation on switch of
CEO type dummy variables and other CEO- and firm-level control variables. A CEO is classified as a generalist
(specialist) if he has general ability index above (below) the median. The switch of CEO type dummy variables are:
(1) if there is a CEO turnover but no switch of CEO type (no switch of CEO type); (2) if a specialist CEO is
replaced by a generalist CEO hired from outside the firm (switch to generalist – external hire); (3) if a generalist
CEO is replaced by a specialist CEO hired from outside the firm (switch to specialist – external hire); and (4) if the
new CEO is internally appointed (switch – internal hire). The sample consists of Execucomp firms for which CEO
profile data are available from BoardEx in the 1993-2007 period. All variables are winsorized at the 5th and 95th
percentile values. Variable definitions and data sources are provided in the Appendix. Robust t-statistics adjusted for
firm-level clustering are reported in brackets. *, **, *** indicates significance at the 10%, 5% and 1% levels.
No switch of CEO type
Switch to generalist - external hire
Switch to specialist - external hire
Switch - internal hire
CEO tenure
External hire dummy
CEO age
Sales (log)
Leverage
Tobin's Q
ROA
ROA (t-1)
Volatility
Diversification dummy
Stock return
Stock return (t-1)
Cash
R&D
CAPEX
Firm age
Constant
Observations
R-squared
Industry dummies
(1)
(2)
(3)
(4)
Total
Cash
Equity
Excess
compensation compensation compensation compensation
(log)
(log)
(log)
(log)
-0.036
-0.099***
0.097**
-0.052*
[-1.20]
[-5.25]
[2.15]
[-1.72]
0.262***
-0.059
0.375***
0.194**
[2.90]
[-1.18]
[2.84]
[2.14]
0.199*
0.029
0.179
0.211*
[1.73]
[0.47]
[1.00]
[1.72]
-0.129***
-0.153***
0.017
-0.117***
[-3.25]
[-6.32]
[0.30]
[-3.02]
-0.007***
-0.001
0.001
-0.006**
[-2.74]
[-0.49]
[0.31]
[-2.29]
0.107***
0.054***
0.265***
0.077**
[3.27]
[2.61]
[5.99]
[2.35]
-0.000
0.005***
-0.008***
-0.000
[-0.11]
[3.53]
[-2.70]
[-0.099]
0.434***
0.258***
0.541***
0.340***
[32.4]
[31.0]
[36.0]
[28.0]
0.368***
0.183***
0.441***
0.366***
[3.87]
[2.84]
[3.39]
[4.14]
0.131***
-0.002
0.298***
0.103***
[6.72]
[-0.18]
[12.1]
[5.36]
-0.532**
1.021***
-1.866***
-0.063
[-2.43]
[7.62]
[-6.22]
[-0.30]
-0.110
-0.631***
0.422
-0.118
[-0.55]
[-4.98]
[1.61]
[-0.60]
0.470*
-0.752***
1.554***
0.529**
[1.86]
[-4.80]
[4.45]
[2.04]
-0.014
0.015
-0.047
0.014
[-0.51]
[0.87]
[-1.19]
[0.51]
0.175***
0.235***
-0.097***
0.153***
[6.94]
[14.6]
[-2.73]
[5.94]
0.255***
0.150***
0.234***
0.177***
[12.0]
[11.7]
[7.79]
[8.05]
0.452***
0.145*
0.861***
0.463***
[3.19]
[1.70]
[5.37]
[3.43]
2.103***
0.491
3.090***
2.575***
[4.05]
[1.57]
[4.99]
[6.70]
-0.080
-0.771***
-0.251
0.382
[-0.23]
[-3.36]
[-0.46]
[1.14]
0.000
0.002***
-0.004***
-0.000
[0.24]
[4.12]
[-3.41]
[-0.36]
-0.033
0.018
-0.511***
-0.003
[-0.86]
[0.79]
[-5.60]
[-0.077]
11469
11443
8020
11457
0.500
0.558
0.487
0.343
Yes
Yes
Yes
No
36
Table 11
Robustness of CEO Compensation and General Managerial Ability Index
This table presents estimates of OLS and firm fixed effects panel regressions of the logarithm of CEO total
compensation on the general managerial ability index and other CEO- and firm-level control variables. Alternative
definitions of the general managerial ability index are used: index 1 uses past number of firms, past CEO dummy,
and general education dummy; index 2 uses past number of industries (two-digit SIC), past CEO dummy, and
general education dummy; and index 3 uses past number of industries (four-digit SIC), past number of CEO
positions, and general education dummy. The sample consists of Execucomp firms for which CEO profile data are
available from BoardEx in the 1993-2007 period. All variables are winsorized at the 5th and 95th percentile values.
Variable definitions and data sources are provided in the Appendix. Robust t-statistics adjusted for firm-level
clustering are reported in brackets. *, **, *** indicates significance at the 10%, 5% and 1% levels.
(1)
General ability index
CEO tenure
External hire dummy
CEO age
Sales (log)
Leverage
Tobin's Q
ROA
ROA (t-1)
Volatility
Diversification dummy
Stock return
Stock return (t-1)
Cash
R&D
CAPEX
Firm age
Constant
Observations
R-squared
Year dummies
Industry dummies
(2)
Index 1
OLS
Fixed effects
0.076***
0.037***
[5.93]
[3.08]
-0.006**
0.000
[-2.27]
[0.13]
0.089***
0.168***
[2.74]
[4.68]
-0.001
-0.003
[-0.64]
[-1.04]
0.426***
0.292***
[31.8]
[10.9]
0.349***
-0.333***
[3.66]
[-3.41]
0.128***
0.093***
[6.65]
[5.18]
-0.511**
0.380*
[-2.34]
[1.74]
-0.083
0.374**
[-0.42]
[2.14]
0.461*
0.289
[1.84]
[1.36]
-0.016
-0.042
[-0.60]
[-1.47]
0.180***
0.132***
[7.19]
[5.72]
0.257***
0.187***
[12.2]
[9.80]
0.475***
0.217*
[3.41]
[1.69]
1.917***
-1.951***
[3.74]
[-2.60]
0.006
0.418
[0.017]
[1.28]
0.000
-0.001
[0.36]
[-0.34]
3.477***
[14.5]
11469
11469
0.504
0.263
Yes
Yes
Yes
No
37
(3)
(4)
Index 2
OLS
Fixed effects
0.061***
0.040***
[4.78]
[3.12]
-0.006**
-0.000
[-2.44]
[-0.021]
0.106***
0.176***
[3.33]
[5.02]
-0.001
-0.002
[-0.38]
[-0.97]
0.427***
0.294***
[32.0]
[11.0]
0.372***
-0.339***
[3.91]
[-3.46]
0.128***
0.092***
[6.63]
[5.14]
-0.531**
0.369*
[-2.43]
[1.70]
-0.093
0.383**
[-0.47]
[2.19]
0.533**
0.288
[2.12]
[1.35]
-0.018
-0.042
[-0.68]
[-1.48]
0.178***
0.133***
[7.11]
[5.74]
0.255***
0.187***
[12.1]
[9.79]
0.489***
0.219*
[3.47]
[1.71]
2.011***
-1.952***
[3.91]
[-2.60]
-0.011
0.435
[-0.031]
[1.33]
0.000
-0.001
[0.16]
[-0.39]
3.416***
[4.78]
11469
11469
0.502
0.263
Yes
Yes
Yes
No
(5)
(6)
Index 3
OLS
Fixed effects
0.076***
0.037***
[5.93]
[3.08]
-0.006**
0.000
[-2.27]
[0.13]
0.089***
0.168***
[2.74]
[4.68]
-0.001
-0.003
[-0.64]
[-1.04]
0.426***
0.292***
[31.8]
[10.9]
0.349***
-0.333***
[3.66]
[-3.41]
0.128***
0.093***
[6.65]
[5.18]
-0.511**
0.380*
[-2.34]
[1.74]
-0.083
0.374**
[-0.42]
[2.14]
0.461*
0.289
[1.84]
[1.36]
-0.016
-0.042
[-0.60]
[-1.47]
0.180***
0.132***
[7.19]
[5.72]
0.257***
0.187***
[12.2]
[9.80]
0.475***
0.217*
[3.41]
[1.69]
1.917***
-1.951***
[3.74]
[-2.60]
0.006
0.418
[0.017]
[1.28]
0.000
-0.001
[0.36]
[-0.34]
3.477***
[14.5]
11469
11469
0.504
0.263
Yes
Yes
Yes
No
Table 12
Conglomerates Excess Compensation
This table presents estimates of OLS and firm fixed effects panel regressions of the conglomerates excess
compensation on the firm’s diversification dummy and number of business segments and other CEO- and firm-level
control variables. The excess compensation is defined as the logarithm of the ratio of the CEO total compensation to
its imputed compensation from standalone firms that match the firm’s current business segments. The sample
consists of Execucomp firms for which CEO profile data are available from BoardEx in the 1993-2007 period. All
variables are winsorized at the 5th and 95th percentile values. Variable definitions and data sources are provided in
the Appendix. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, *** indicates
significance at the 10%, 5% and 1% levels.
(1)
Diversification dummy
OLS
0.258***
[7.48]
(2)
Fixed
effects
0.119***
[3.29]
Nr. segments
(3)
OLS
0.042***
[3.88]
(4)
Fixed
effects
External hire dummy
CEO age
Sales (log)
Leverage
Tobin's Q
ROA
ROA (t-1)
Volatility
Stock return
Stock return (t-1)
Cash
R&D
CAPEX
Firm age
Constant
Observations
R-squared
Year dummies
Industry dummies
-0.009***
0.002 -0.009***
0.002
[-2.96]
[0.83]
[-2.89]
[0.79]
0.106*** 0.198*** 0.104*** 0.199***
[2.90]
[5.06]
[2.83]
[5.10]
0.002
-0.005*
0.003
-0.005*
[0.98]
[-1.84]
[1.12]
[-1.78]
0.343*** 0.229*** 0.340*** 0.233***
[23.1]
[8.16]
[23.5]
[8.39]
0.515***
-0.204* 0.511***
-0.196*
[5.00]
[-1.78]
[4.90]
[-1.72]
0.122*** 0.107*** 0.112*** 0.106***
[5.52]
[5.01]
[5.14]
[4.94]
-0.227
0.191
-0.165
0.197
[-0.88]
[0.74]
[-0.64]
[0.76]
-0.422*
-0.021
-0.453*
-0.032
[-1.82]
[-0.098]
[-1.96]
[-0.15]
-0.169
-0.079
-0.205
-0.060
[-0.54]
[-0.31]
[-0.66]
[-0.24]
0.105***
0.059** 0.118***
0.063**
[3.47]
[2.13]
[3.93]
[2.27]
0.208*** 0.142*** 0.215*** 0.144***
[7.95]
[6.01]
[8.25]
[6.05]
0.366**
0.198
0.325**
0.193
[2.54]
[1.36]
[2.25]
[1.32]
0.964** -2.637***
0.898** -2.615***
[2.24]
[-3.14]
[2.11]
[-3.14]
-0.040
0.158
-0.242
0.140
[-0.097]
[0.39]
[-0.59]
[0.35]
0.002**
-0.002 0.003***
-0.002
[2.40]
[-0.62]
[2.60]
[-0.59]
-2.758*** -1.315*** -2.698*** -1.329***
[-15.6]
[-5.18]
[-15.1]
[-5.26]
9972
9972
9972
9972
0.367
0.112
0.361
0.111
Yes
Yes
Yes
Yes
Yes
No
Yes
No
38
OLS
0.249***
[7.30]
(6)
Fixed
effects
0.115***
[3.17]
0.017**
[2.24]
General ability index
CEO tenure
(5)
0.071***
0.040**
[4.29]
[2.31]
-0.009***
0.002
[-2.63]
[0.85]
0.083** 0.179***
[2.21]
[4.53]
0.002 -0.006**
[0.67]
[-2.00]
0.336*** 0.227***
[23.0]
[8.10]
0.516***
-0.208*
[5.03]
[-1.82]
0.118*** 0.107***
[5.40]
[4.99]
-0.224
0.192
[-0.87]
[0.74]
-0.405*
0.002
[-1.74] [0.0077]
-0.080
-0.098
[-0.26]
[-0.39]
0.249*** 0.115***
[7.30]
[3.17]
0.106***
0.062**
[3.51]
[2.25]
0.207*** 0.144***
[7.93]
[6.12]
0.411***
0.184
[2.86]
[1.26]
0.766* -2.489***
[1.78]
[-2.97]
0.096
0.127
[0.23]
[0.32]
-2.677*** -1.272***
[-15.4]
[-5.02]
9975
9975
0.373
0.113
Yes
Yes
Yes
No
Figure 1
CEO Compensation and General Managerial Ability Index
6,000
0.1
5,000
0.05
4,000
0
3,000
-0.05
2,000
-0.1
1,000
-0.15
0
-0.2
1993
1994
1995
1996
1997
Average general ability index
1998
1999
2000
Average total compensation
39
2001
2002
2003
2004
2005
2006
2007
Average excess compensation of multi-industry CEOs
Average General Ability Index
Average CEO Compensation ($ thousands)
This table presents average score of the general managerial ability index and average CEO total compensation per
year from 1993 to 2007.The sample consists of Execucomp firms for which CEO profile data are available from
BoardEx. Average CEO excess compensation for the sample of CEOs that worked in more than one four-digit SIC
industry (multi-industry CEOs) are also shown. All variables are winsorized at the 5th and 95th percentile values.
Variable definitions and data sources are provided in the Appendix.
Figure 2
Distribution of General Managerial Ability Index
This figure presents the cross-sectional distribution of the scores of the CEOs on the general managerial ability
index in 1993-2000 (Panel A) and 2001-2007 (Panel B). The index of general managerial ability is the first factor of
applying the principal components method to three proxies of general managerial ability (past number of industries,
past CEO dummy, and general education dummy). The sample consists of Execucomp firms for which CEO profile
data are available from BoardEx. Variable definitions and data sources are provided in the Appendix.
0
.1
Fraction
.2
.3
Panel A: 1993-2000
-2
0
2
Scores for factor 1
4
6
0
.1
Fraction
.2
.3
Panel B: 2001-2007
-2
0
2
Scores for factor 1
40
4
6
Appendix A: Definition of Variables and Data Sources
Variable
Total compensation
Cash compensation
Equity compensation
Excess compensation
Conglomerate excess
compensation
General ability index
Past number of industries
Past CEO dummy
General education dummy
CEO tenure
External hire dummy
CEO age
Past multi-industry dummy
Past number of firms
Description
Panel A: CEO Compensation
Total CEO Compensation in thousand $, which consists of salary, bonus, value of restricted stock granted, value of
options granted, long term incentive payout and other compensation (Execucomp TDC1).
Salary plus bonus in thousand $ (Execucomp TOTAL_CURR).
Value of restricted stock granted plus value of options granted in thousand $ (Execucomp RSTKGRNT +
OPTION_AWARDS_BLK_VALUE).
Difference between the CEO total compensation and its imputed compensation from single-industry CEOs that
match the CEO past industry experience. The imputed compensation is the average compensation of the portfolio of
industries where the CEO worked where the industry-level compensation is the median compensation of CEOs that
worked only in a industry up to given year (single-industry CEOs).
Difference between the CEO total compensation and its imputed compensation from standalone firms that match the
individual business segments where the firm currently operates. The imputed compensation is the average
compensation of the portfolio of industries based on firm’s current business segments where the industry-level
compensation is given by the median compensation of CEOs in stand-alone firms.
Panel B: CEO Characteristics
First factor of applying the principal components method to three proxies of general managerial ability: past number
of industries, past CEO dummy, and general education dummy.
Number of industries (four-digit SIC) a CEO worked based on his work experience in publicly-listed firms prior to
the current position.
Dummy variable that takes the value of one if a CEO held a CEO position at another company based on his work
experience in publicly-listed firms prior to the current position.
Dummy variable that takes the value of one if a CEO has a business degree (undergraduate, master, MBA or PhD).
Number of years as CEO in the current position.
Dummy variable that takes the value of one if the CEO was hired from outside of the firm.
Age of CEO in years.
Dummy variable that takes the value of one if the number of industries (four-digit SIC) where a CEO worked based
on his work experience in publicly-listed firms prior to the current position is greater than one.
Number of firms a CEO worked based on his past work experience (prior to the current position) in publicly-listed
firms.
41
Appendix (continued)
No switch of CEO type
Switch to generalist - external
hire
Switch to specialist - external
hire
Switch - internal hire
Sales
Leverage
Tobin’s Q
ROA
Volatility
Diversification dummy
Stock return
Cash
R&D
CAPEX
Firm age
Number of segments
Panel C: CEO Turnover
Dummy variable that takes the value of one if there is a CEO turnover and the firm does not switch of CEO type
(Execucomp).
Dummy variable that takes the value of one if the firm switches of CEO type from a specialist (i.e., CEO with
general ability index below the yearly median) to a generalist (i.e., CEO with general ability index above the yearly
median) and the CEO is hired from outside the firm (Execucomp).
Dummy variable that takes the value of one if there the firm switches of CEO type from a generalist (i.e., CEO with
general ability index above the yearly median) to a specialist (i.e., CEO with general ability index below the yearly
median) and the CEO is hired from outside the firm (Execucomp).
Dummy variable that takes the value of one if the firm switches of CEO type by internal promotion (Execucomp).
Panel D: Firm Characteristics
Log of sales in thousands of US$ (Compustat SALE).
Total debt divided by total assets (Compustat TD/AT) .
Sum of total assets plus market value of equity minus book value of equity divided by total assets (Compustat
(AT+CSHOxPRCC_F-CEQ)/AT)).
Earnings before interest, taxes, depreciation and amortization divided by total assets (Compustat EBITDA/AT)
Annualized standard deviation of monthly stock returns (CRSP).
Dummy variable that takes the value of one if a firm has more than one business segment (Compustat).
Annual stock return (Compustat (PRCC_F(t)/AJEX(t)+DVPSX_F(t)/AJEX(t))/(PRCC_F(t-1)/AJEX_F(t-1))).
Cash and short-term investments divided by total assets (Compustat CHE/AT).
R&D expenses divided by total assets (Compustat XRD/AT).
Capital expenditures divided by total assets (CAPX/AT).
Number of years since a firm has listed its shares (CRSP).
Number of business segments (Compustat).
42