Abstract
Are powerful CEOs better at responding to industry-wide downturns? Concentrating decisionmaking power in the CEO may facilitate a rapid response. However, a powerful CEO is less
likely to receive independent advice or to have decisions scrutinized, potentially diminishing the
quality of decisions made. We investigate the relation between CEO power and decision-making
under pressure by examining firm performance when industry conditions deteriorate. We focus
on industry downturns since these represent an exogenous ‘shock’ to the firm’s environment. We
identify three settings where the net effect of CEO power is likely magnified: when the firm is
innovative, when the industry is characterized by high managerial discretion and when the
industry is competitive. In these settings powerful CEOs perform significantly worse than other
CEOs during industry downturns.
1
INTRODUCTION
There is considerable heterogeneity in the decision-making authority of CEOs across firms.
Attributes such as tenure, status as founder or as a significant owner and formal positions such as
board chairman help determine the level of CEO power. The power a CEO wields can have
significant consequences for firms, which raises the question of how much decision-making
power should be vested in a CEO. The answer to this is likely to be contextual: the level of CEO
power that is appropriate may depend on factors such as the nature of the firm’s industry,
investment opportunities and competition. A potential advantage of concentrating decisionmaking power is that the CEO may be able to make quicker decisions. On the other hand a
powerful CEO may act more unilaterally with less input from the board or other managers,
resulting in lower quality decisions. We explore the impact of CEO power on firm performance
by focusing on settings in which CEO power is likely more consequential and relatively rapid
decisions may be required. Overall, our finding is that in such settings the benefits of a dispersed
power base outweigh the costs.
No single definition exists for CEO power but a common thread in the literature is the
CEO’s ability to overcome resistance and consistently influence key decisions within a firm
(Haleblian and Finkelstein, 1993; Pfeffer, 1997; Adams, Almeida, and Ferreira, 2005). The
determination and evolution of a CEO’s power is recognized to be an endogenous process. For
instance, the CEO’s ability relative to alternative managers that the firm could hire likely impacts
the bargaining process between the CEO and the board (Hermalin and Weisbach, 1998). It is also
possible that prior firm performance impacts the level of a CEO’s power such that if a firm
performs well under the CEO, the firm grants the CEO greater power (Daily and Johnson, 1997).
Endogeneity thus makes it difficult to distinguish the impact of CEO power from the
2
characteristics of CEOs that may enable them to acquire power in the first place. To circumvent
this, we focus on ‘shocks’ to an industry (i.e. significant industry downturns). The notion is that
such downturns are outside the control of any one firm or CEO. This allows us to draw
conclusions relating to the impact of CEO power on firm value, at least under conditions of
economic stress. How well powerful CEOs fare is an empirical question: while the need for
urgent decisions could play to the strength of powerful CEOs, the lack of independent advice
may diminish the quality of those decisions.
We focus on three separate settings in which the actions of CEOs are especially likely to
affect firm outcomes. The first setting is innovative firms. CEOs have unparalleled firm-specific
knowledge and this may be particularly important to innovative firms.1 The second setting we
focus on is industries with greater managerial discretion. The characteristics of a firm’s
environment affect the level of managerial discretion and thus the influence managers have on
firm outcomes (Finkelstein and Hambrick, 1996). The third setting we investigate is competitive
industries. The consequences of CEO decisions are likely more severe in such a setting as
competition ensures any missteps by CEOs are penalized.
The impact of CEO power on firm value has seen much empirical study and the findings
have been mixed, possibly reflecting the endogeneity of CEO power and firm value. Using board
independence as a measure of power, some studies find a negative relation between board
independence and firm value or no relation at all.2,3 While other studies find that independent
1
Brickley, Coles, and Linck (1999) make a similar argument for information-sensitive firms. Fama and Jensen
(1983) argue that specific information is detailed information that is costly to transfer and the literature has argued
that innovative firms and R&D-intensive firms have more specific information unknown to outsiders (see Harris and
Raviv, 1991; Graham and Harvey, 2001).
2
In general, independent directors have two functions, monitoring and advising. Monitoring guards against harmful
behavior, and advising provides input on strategy. Maug (1997) finds it is not optimal for firms with high
information asymmetry to invite monitoring from independent directors because it is costly for the firms to transfer
firm-specific information to outsiders.
3
boards add value in certain circumstances.4 For example, Coles, Daniel, and Naveen (2008) find
a positive relation for larger, more diversified firms, the idea being that outside directors provide
valuable advice to the CEO and management team. For entrepreneurial firms, Daily and Dalton
(1992, 1993) find a positive relation between board independence and financial performance. We
focus on shocks to an industry to deal with endogeneity and focus on settings where the
consequences of CEO power are greater in order to better understand the impact of CEO power
on firm value.
The literature has measured CEO power in other ways. Bebchuk, Cremers, and Peyer
(2011) argue that the CEO’s pay relative to other top executives within the firm reflects the
relative importance of the CEO as well as the extent to which the CEO is able to extract rents.
They find this measure of power is associated with lower firm value. Using the CEO’s
concentration of titles and founder status as measures of power, Adams, Almeida, and Ferreira
(2005) document stock returns are more volatile for firms run by powerful CEOs. However, the
empirical evidence of founder impact on firm value is mixed. For example, Fahlenbrach (2009),
Amit and Villalonga (2006) and Adams, Almeida, and Ferreira (2005) find that foundermanaged firms have higher values whilst Yermack (1996) finds that such firms have lower
values. Using the framework of Finkelstein (1992), we construct a composite measure of CEO
power that incorporates each of the above variables along with ownership, tenure and board
independence.
3
Agrawal and Knoeber (1996), Yermack (1996), Rosenstein and Wyatt (1997), Klein (1998), Bhagat and Black
(2002), Adams (2009), amongst others, find a negative relation between board independence and firm value.
Baysinger and Butler (1985), Hermalin and Weisbach (1991) and Mehran (1995) find no relation at all. See Dalton,
Daily, Ellstrand and Johnson (1998) for a meta-analytic review.
4
See, amongst others, Weisbach (1988), Borokhovich, Parrino, and Trapani (1996), Brickley, Coles, and Terry
(1994), Byrd and Hickman (1992) and Cotter, Shivdasani, and Zenner (1997).
4
Several studies of CEO power focus on computer-related industries and the results are
mixed. Haleblian and Finkelstein (1993) find firms with dominant CEOs performed worse in the
computer industry than in the natural gas distribution industry compared with firms that have a
more balanced power distribution. Their finding is consistent with Eisenhardt and Bourgeois
(1988), who find firms with dominant CEOs contributed to poor performance in the microcomputer industry (Eisenhardt and Bourgeois’ sample comprised eight firms). In contrast, a
recent study by Dowell, Shackell, and Stuart (2011) argues CEO power is beneficial for firms
facing a crisis. They focus on the survival rate of internet firms during the upheaval period
spanning 2000-2002 and find that more independent and smaller boards increase a firm’s
probability of survival when the firm’s level of financial distress is high. However, when using a
broader measure of CEO power they find weaker (insignificant) results. A drawback of such
studies is that the results may not generalize since the studies mainly focus on a relatively small
number of firms from one or two industries. We employ a more general setting and explore how
CEO power impacts firms during an industry-wide downturn.
We find, in each of the settings we explore, that CEO power has a negative effect on firm
value and performance when the industry it operates in experiences a negative shock. As a
prelude, Figure 1 focuses on two particular industries where one might expect the impact of CEO
power to differ substantially, Computer and Petroleum and Natural Gas (Haleblian and
Finkelstein, 1993). We plot the impact on firm value and performance indicated by the average
change in the Market-to-Book ratio (ΔM/B) and the average change in the Return-on-Assets ratio
(ΔROA) during shock years for firms in these industries. Before computing the average change,
we demean the variables for comparison purposes. Figure 1 plots the average change for
5
powerful versus non-powerful CEOs for each industry.5 As is evident, firms with powerful CEOs
appear to perform much worse in the Computer industry during shock years relative to less
powerful CEOs. On the other hand, for the Petroleum and Natural Gas industry where
managerial discretion is less, the performance of powerful CEOs is similar to that of other CEOs.
As we shall see shortly, this finding extends to a much more general setting.
‘Insert Figure 1 here’
THEORY AND HYPOTHESES
Whether CEO power matters for firm performance is an open question given there exist both
costs and benefits. From an agency cost perspective, if power allows CEOs to become
entrenched then power can have a negative effect on performance. Even without agency costs, a
potential downside of CEO power is that the decision-making process can be sub-optimal.
Experimental evidence suggests that groups can outperform individuals in decision-making (see
Bainbridge, 2002, for a review). Having input from outside directors can also be beneficial. For
example, Coles, Daniel, and Naveen (2008) find that the advisory role of outside directors is
relatively more important for diversified firms. On the other hand, to the extent that firms with
powerful CEOs can act relatively quickly, CEO power can have a positive impact on
performance (Finkelstein and D’Aveni, 1994; Boyd, 1995; Harris and Helfat, 1998; Coles,
Daniel, and Naveen, 2008). Thus, the net effect of CEO power on firm performance is not clear.
The impact of CEO power may be most prevalent during times of industry turmoil. In
normal times, it is possible the net effect is insignificant but in turbulent times the benefit (or
5
The definitions of shock years, M/B, ROA and powerful CEOs will be discussed in the RESEARCH METHOD
section.
6
cost) of a dispersed power base may become important.6 As Haleblian and Finkelstein (1993)
point out, firms in turbulent environments can benefit more from increases in top managerial
inputs than firms in stable environments due to a balanced power distribution facilitating
information sharing and idea exchange (see also Eisenhardt and Bourgeois, 1988). We focus on
industry downturns as these are exogenous in the sense of being outside the control of any one
firm or CEO. Within these downturns we investigate three settings where the net effect of CEO
power is likely magnified. The first is innovative firms, the second is industries characterized by
managerial discretion and the third is competitive industries. In each case the decisions of the
CEO are pivotal as the impact on firm value and performance is presumably greater in such
settings.
CEO power, innovativeness and shocks
The actions of CEOs are especially likely to affect firm outcomes in innovative firms (see Daily,
McDougall, Covin, and Dalton, 2002, who make a similar argument for entrepreneurial firms).
Ranft and O’Neill (2001) cite examples where CEOs of innovative firms fail to see reasonable
threats in their competitive environments until too late, isolating themselves from the advice of
others and becoming myopic in their views. This is potentially compounded when the CEO has
greater power. For innovative firms facing a market downturn, the benefits of a dispersed power
base may become vital. For example, outside directors may be able to provide expertise and
experience that complements the CEO’s knowledge of the firm, which can be especially
important during industry-wide downturns. This leads to the following prediction:
6
As Coles et al. (2008) point out, changing CEO power involves significant costs resulting in CEO power being
somewhat persistent.
7
Hypothesis 1: CEO power will have a negative effect on firm value and performance for
innovative firms when the industry it operates in experiences a negative shock.
The impact of managerial discretion
Managerial discretion, or latitude of action, varies from industry to industry as the characteristics
of a firm’s environment affect the level of managerial discretion and thus how much influence
managers have on firm outcomes (Finkelstein and Hambrick, 1996).7 To the degree that power
grants CEOs the ability to act unchallenged (i.e. without necessarily consulting with or taking
advice from others), the consequences of CEO power is magnified in environments where the
impact of any decision is greater. Thus, as with innovative firms, we expect the effect of CEO
power on firm value during shock years to be more pronounced in industries where managerial
decision-making faces fewer constraints. To the extent that decision-making is sub-optimal in
such situations, we have the following prediction:
Hypothesis 2: CEO power will have a negative effect on firm value and performance for
firms in industries characterized by greater managerial discretion, when the industry it
operates in experiences a negative shock.
The impact of competition
Aldrich (1979) proposes that strategic decisions are made more frequently in high complexity
(competitive) environments relative to low complexity (concentrated) environments. While CEO
power enables quicker decisions, it does not necessarily follow that CEO power enables better
7
Hambrick and Finkelstein (1987) specify seven environmental forces that determine the degree of managerial
discretion within an industry and Hambrick and Abrahamson (1995) construct an industry discretion rating from
these factors.
8
quality decisions. In a competitive market, the consequences of managerial decisions are more
severe than in a concentrated market as there is less room for error. For example, product
differentiation or barriers to entry insulate a firm in a concentrated industry relative to a
competitive industry thus enabling firms to be somewhat shielded from poor decisions. To the
extent that decision-making is sub-optimal in such situations, we have the following prediction:
Hypothesis 3: CEO power will have a negative effect on firm value and performance for
firms in competitive industries when the industry it operates in experiences a negative
shock.
RESEARCH METHOD
Sample
We begin with the universe of firms contained in the S&P Execucomp database spanning the
years 1992 to 2009. As is standard in the literature, we exclude regulated industries such as
utilities and financials (SIC codes 4900-4949 and 6000-6999). We identify CEOs using the
‘CEOANN’ variable in Execucomp. For firm financial information, we intersect the Execucomp
dataset with Compustat and for firm share price information, we intersect the dataset with CRSP.
To obtain information on the board of directors we intersect our sample with the Investor
Responsibility Research Center (IRRC) and RiskMetrics databases. Our sample consists of 3,724
CEOs in 2,097 unique firms during the period 1992 to 2009, covering a total of approximately
17,600 firm-years.
Model and dependent variable
9
We have a panel dataset and since unobservable firm characteristics are likely to affect firm
value and performance, we estimate firm fixed-effect models using each firm’s change in market
value or performance as the dependent variable. Fixed-effect models are an unbiased method of
controlling for omitted variables in panel datasets (Hausman and Taylor, 1981). We also include
year dummies in our models and importantly, we use industry shocks as our setting for
investigating the impact of CEO power on value, thus reducing concerns regarding endogeneity.
Moreover, the change in performance can be more directly related to the shock as absolute
measures of post shock performance more likely reflect enduring performance effects carried
over from the pre-shock period (Datta and Rajagopalan, 1998). As we discuss later, our results
are robust to using industry-adjusted measures and to various econometric techniques.
Change in market-to-book ratio (∆M/B)
Our primary measure of valuation impact is the change in Market-to-Book ratio, ∆M/B, where
the Market-to-Book ratio (M/B) is defined as the market value of common equity plus the book
value of assets minus the book value of common equity, all divided by the book value of assets. 8
The Market-to-Book ratio is a common valuation metric used in the literature (see, for example,
Bebchuk, Cremers, and Peyer, 2011; Crossland and Hambrick, 2011).
Change in return on assets (∆ROA)
As a secondary valuation impact measure, we use the change in Return on Assets, ∆ROA, where
Return on Assets (ROA) is computed as earnings before interest, taxes, depreciation and
amortization (EBITDA) divided by the book value of assets. Although this is a common
We compute the change in the Market-to-Book ratio, ∆M/B, yearly as M/Bt – M/Bt-1 where the ‘t’ subscript
represents the year the ratio is computed.
8
10
performance metric used in the management literature, we note that it is also one more prone to
manipulation by a firm’s executives.9 In contrast, it is much more difficult for a CEO to
manipulate the firm’s market price. Thus, we treat the ∆ROA measure as a secondary measure to
the ∆M/B measure.
Main independent variables
CEO power
We utilize the framework of Finkelstein (1992) to measure CEO Power.10 We construct a
summary index of CEO power based on seven variables: CEO Pay Slice (CPS), Duality, Triality,
Tenure, Ownership, Dependent Directors and Founder. Each variable is described more fully
below. We use CEO Pay Slice (CPS), Duality, Triality and Dependent Directors to measure
structural power, Ownership and Founder to measure ownership power and Tenure to measure
expert power. For each continuous variable we construct an indicator variable that takes the
value one if it is above the sample median and zero otherwise. For each discrete variable
(Duality, Triality, Founder), an indicator variable takes the value one if the associated
characteristic is met and zero otherwise. The power index, CEO Power Index, is the sum of each
of the indicator variables and thus ranges from 0 to 7. A higher index value indicates greater
CEO power.
CEO Pay Slice (CPS): Bebchuk et al. (2011) construct a variable they term the CEO Pay
Slice (or CPS for short) defined as the CEO’s total compensation as a fraction of the total
9
There is a large accounting literature documenting earnings management. See Dechow, Ge, and Schrand (2010) for
a recent review.
10
Finkelstein (1992) argues that CEO power has four dimensions: structural, ownership, expert and prestige. The
last of these dimensions is often measured using the CEO’s educational background. This information is not easily
accessible given our sample size so we deviate from Finkelstein by excluding this dimension. Tang, Crossan, and
Rowe (2011) also drop this dimension arguing it is not a proximal measure of executive power when compared with
the other dimensions.
11
compensation for the firm’s top five executives.11 Bebchuk et al. argue that the CPS could reflect
the relative importance of the CEO as well as the extent to which the CEO is able to extract
rents. We construct CPS in a similar manner; greater values of CPS indicate greater CEO power.
We create an indicator variable that takes the value one if CPS is above the sample median.
Duality and Triality: There is a large literature that uses the concentration of titles vested
in the CEO as a measure of CEO power (Adams et al., 2005; Pathan, 2009; Morse, Nanda, and
Seru, 2011, amongst others). We create two indicator variables to reflect the concentration of
titles. Duality is an indicator variable that takes the value one if the CEO is also the Chair of the
company’s board of directors. Triality is an indicator variable that takes the value one if in
addition to the CEO serving as Chair of the company’s board of directors the CEO also has the
title of President of the firm. Both Duality and Triality indicate that the CEO has greater power.
Tenure: CEO tenure increases the CEO’s influence over the board and thus increases
CEO power (Linck, Netter, and Yang, 2008). We determine CEO tenure by utilizing the
‘BECAMECEO’ variable in Execucomp. We create an indicator variable that takes the value one
if CEO tenure is above the sample median.
Ownership: Greater CEO stock ownership reduces the influence of the board and enables
the CEO to exercise more discretion in making decisions, thus increasing CEO power
(Finkelstein, 1992; Fischer and Pollock, 2004). Using Execucomp, we determine the CEO’s
stock ownership and construct an indicator variable that takes the value one if the CEO’s
ownership is above the sample median
11
Total compensation comprises the following: Salary, Bonus, Other Annual, Total Value of Restricted Stock
Granted, Total Value of Stock Options Granted (using Black-Scholes), Long-Term Incentive Payouts, and All Other
Total in Execucomp’s 1992 reporting format and Salary, Bonus, Non-Equity Incentive Plan Compensation, GrantDate Fair Value of Option Awards, Grant-Date Fair Value of Stock Awards, Deferred Compensation Earnings
Reported as Compensation, and Other Compensation in Execucomp’s 2006 new reporting format.
12
Founder: CEOs that are also founders are likely to be more powerful (Adams et al.,
2005; Morse et al., 2011, amongst others). We consider a CEO as a founder of the company if
the CEO is a founder, a descendant of the founder, or served as CEO from inception. 12 We
construct an indicator variable, Founder, that takes the value one if the CEO is also the founder.
Dependent Directors: The higher the proportion of dependent directors on the board, the
more likely the CEO can exert influence over the board (Morse et al., 2011). To identify board
members, we use the IRRC database from 1996 to 2006 and the RiskMetrics database from 2007
to 2009. As is common when using these databases, in years not covered by either database we
backfill the data using the closest year following the missing observation (Gompers, Ishii, and
Metrick, 2003; Bebchuk, Cohen, and Ferrell, 2009). We construct an indicator variable that takes
the value one if the proportion of dependent directors for the firm is above the sample median
CEO Power: The CEO Power Index is the sum of each of the indicator variables listed
above. We construct an indicator variable, CEO Power, that takes the value one if the CEO
Power Index is above the sample median and zero otherwise.
Industry shock identification
Using each firm’s primary four digit SIC code, we assign firms to one of 49 Fama and French
(1997) industry classifications.13 We define an industry shock as a five percent or greater
12
We thank Murali Jagannathan for providing this data.
The Fama and French (1997) industry classifications were developed to address some of the problems with SIC
codes caused by changes in the variety and growth of products and services, shifts in technology and the makeup of
businesses (Clarke, 1989). The Fama and French industry classifications sort four-digit SIC codes into industry
groups that are more likely to share common risk characteristics (Bhojraj, Lee, and Oler, 2003). The original
classification scheme sorted SIC codes into 48 industry groups but was later extended to 49 industry groups. The
Fama and French industry classifications are widely used in the financial economics literature (see Chan,
Lakonishok, and Swaminathan, 2007, for a partial list).
13
13
decrease in aggregate industry sales.14 Our definition of an industry shock is similar to that of
Mitchell and Mulherin (1996) in that industry shocks are based on industry sales activities. We
also try measures based upon industry market returns and industry market excess returns and find
similar results. With a five percent cut-off, we identify a total of 60 industry shock years
distributed across 30 industries over our sample period (see Table I – Panel D). Not surprisingly,
there are a number of industry shocks during the years 2001, 2002 and 2009 when the overall
economy was doing poorly. Importantly, our results are robust to excluding these years.
Three Settings: Innovative Firms, Managerial Discretion and Industry Competition
Innovative firms
Our first measure of innovation is the Research and Development (R&D) ratio, RD, defined as
the ratio of R&D expenses to the lagged value of total assets. As is common in the literature, if
R&D expense is missing we set RD to zero and set a RD Missing dummy to one. Our second
measure is the number of patents a firm registers in a given year.15
Managerial discretion
We use Hambrick and Abrahamson’s (1995) industry discretion ratings to classify industries into
high- and low-discretion categories. In order to increase the matches with our data we follow
Adams, Almeida, and Ferreira (2005) and average Hambrick and Abrahamson’s measures by
two-digit SIC code. As an alternative measure, we also perform our tests on two industries the
14
When determining industry sales, we ensure there are at least five firms within an industry. We also try a 10
percent cut-off and find our results are robust.
15
We obtain data on patents from the NBER data patent project through Bronwyn H. Hall’s website
(http://elsa.berkeley.edu/users/bhhall/pub/data/).
14
literature has argued are at polar ends of managerial discretion, Computer and Petroleum and
Natural Gas (Haleblian and Finkelstein, 1993).
Industry competition
For our measure of competition we use the Herfindahl Index, defined as the sum of the squared
market shares for all firms in an industry group. The Herfindahl Index ranges from 0 to 1 where
a score approaching zero reflects pure competition and a score approaching 1 reflects only a few
competitors in an industry (a concentrated industry). We also use the market share captured by
the largest four firms in an industry as a measure of competition, the smaller the share the more
intense the competition.
Control variables
We control for the following firm-specific variables the prior literature finds important for
determining valuation and performance: Firm Size is the log of the firm’s market capitalization
(share price times shares outstanding); Capital Expenditure (CapEx) is the ratio of capital
expenditures to total assets; Leverage is the ratio of long-term debt to total assets and Volatility is
the standard deviation of a firm’s daily stock returns over the previous year. We also include
year dummies as well as time-invariant firm heterogeneity using firm fixed-effects.
RESULTS
Table 1 presents descriptive statistics for our variables. In Panel A we report our sample firm
characteristics. The average firm size is almost $6 billion (median $1.4 billion) since Execucomp
generally tracks the largest 1,500 listed firms. Our sample period covers two major market-wide
15
downturns (sub-periods 2000-2002 and 2008-2009) and this is reflected in the average change in
ROA being slightly negative (median is zero). In more than half our firm years there is some
level of R&D reported. With our shock definition of a five percent or greater decrease in
aggregate industry sales, seven percent or our firm years are identified as shock years. In Panel B
we report our sample CEO characteristics. The CEO is also the chairman 63 percent of the time,
the founder 24 percent of the time, has an average tenure of 8 years and owns a little more than
two percent of the firm (median ownership is 0.4%). The 38 percent average CEO Pay Slice we
report is similar to that reported in Bebchuk et al. (2011). In Panel C we report the descriptive
statistics for our CEO Power Index. The index ranges from 0 to 7 with a mean slightly above 3
(median is 3). Finally, in Panel D we report the industries that experienced shocks along with the
frequency of shocks for each industry. As is evident, a broad cross-section of industries
experience shocks during our sample period.
‘Insert Table 1 here’
In Table 2 we report the correlations between each of our variables. From Panel A we see
that among the independent variables, most of the correlations are less than 20 percent in
absolute terms with the largest correlation of -42.5 percent occurring between RD and RD
Missing. Thus, multicollinearity does not appear to present a significant problem for our analysis.
In Panel B we report the correlations among the individual components that comprise our CEO
Power Index. While almost all the components are significantly correlated with one another,
16
most correlations are relatively small.16 Overall, it appears our individual components are
detecting different aspects of CEO Power.
‘Insert Table 2 here’
Table 3 presents the first set of regression results. Panel A reports regression results using
the ∆M/B as the dependent variable. Model (1) contains the results incorporating the control
variables and the direct effects of Shock and CEO Power. Not surprisingly, a Shock has a
negative effect on valuation. It appears that our aggregate measure of power, CEO Power, does
not impact changes in valuation on average. Hypothesis 1 predicts that CEO power will have a
negative effect on firm value for innovative firms when the industry it operates in experiences a
negative shock. To test this hypothesis we split the sample into two sub-samples based on
whether the firm’s RD is above (Model 2) or below (Model 3) the sample median of firms with
non-missing RD and add the interaction between Shock and CEO Power. The coefficient on this
interaction term is negative and significant at the 10 percent level for Model 2 (p-value=5.8%),
the sub-sample where RD is above the sample median. The coefficient on this interaction term is
not significant in Model 3 where RD is below the sample median. These results are consistent
with Hypothesis 1. In Model (4) we combine the two sub-samples and test Hypothesis 1 by
including the triple interaction of Shock, CEO Power and RD. The coefficient on this triple
interaction term is negative and significant at the one percent level (p-value<0.1%), again
consistent with Hypothesis 1. In Model 5 we re-run Model (4) but replace ∆M/B with ∆ROA as
the dependent variable. The coefficient on the triple interaction term is negative and significant
16
The majority of correlations are less than 20 percent in absolute terms. Not surprisingly, the largest significant
correlation is between Founder and Ownership (r = 50.7%).
17
at the one percent level (p-value=0.8%), again consistent with Hypothesis 1. Thus, the results
from Models 2, 3, 4 and 5 are all supportive of Hypothesis 1.
In Panel B we repeat the regressions from Panel A but now use the number of Patents as
our measure of firm innovativeness.17 Model (1) contains the results incorporating the control
variables and the direct effects of Shock and CEO Power and again it appears CEO Power does
not impact valuation on average. We split the sample into two sub-samples based on whether the
firm’s Patents is above (Model 2) or below (Model 3) the sample median of firms with nonmissing Patents and add the interaction between Shock and CEO Power. The coefficient on this
interaction term is negative and significant at the one percent level for Model 2 (p-value<0.1%)
but not significant in Model 3, again consistent with Hypothesis 1. In Model (4) we combine the
two sub-samples and test Hypothesis 1 by including the triple interaction of Shock, CEO Power
and Patents. The coefficient on this interaction term is negative and significant at the one percent
level (p-value<0.1%), consistent with Hypothesis 1. In Model 5, we re-run Model (4) but replace
∆M/B with ∆ROA as the dependent variable. Here the coefficient on the triple interaction term is
not significant. Overall, the results from Panel B are supportive of Hypothesis 1.
‘Insert Table 3 here’
Table 4 reports the next set of regression results. Hypothesis 2 predicts the effect of CEO
power on firm value during shock years to be more pronounced in industries where managerial
decision-making faces fewer constraints. As mentioned earlier, to classify industries into highand low-discretion categories we use Hambrick and Abrahamson’s (1995) industry discretion
17
Consistent with the literature, whenever a firm is not reported in the patents dataset, we set the number of patents
to zero and a Patents Missing dummy to one. As the patents dataset ends in 2006, the sample size decreases by over
3,000 observations.
18
ratings. We average Hambrick and Abrahamson’s measures by two-digit SIC code to classify the
industries associated with our sample firms into high (above median) and low (below median)
discretion categories.18 In Model (1) of Panel A we repeat the corresponding model from Table 3
but add the dummy variable Industry Discretion which takes the value one when the industry
discretion rating is above the sample median. Neither CEO Power nor Industry Discretion load
significantly.
To test the effect of CEO power on firm value during shock years for high-discretion
industries, we split the sample into two sub-samples based on whether the firm’s industry
discretion rating is above (Model 2 of Panel A) or below (Model 3 of Panel A) the sample
median and add the interaction between Shock and CEO Power. The coefficient on this
interaction term is significantly negative at the five percent level for Model 2 (p-value=2.6%),
the sub-sample where the industry discretion is above the sample median. This interaction term
is not significant in Model 3 where the industry discretion is below the sample median. These
results are consistent with Hypothesis 2. In Model (4) we combine the two sub-samples and
include the triple interaction of Shock, CEO Power and Industry Discretion. The coefficient on
this triple interaction term is significantly negative (p-value=5.4%), again consistent with a more
negative effect of CEO power on firm value during shock years in industries in which managers
have greater discretion. Model (5) repeats Model (4) but replaces ∆M/B with ∆ROA as the
dependent variable. The negative sign of the coefficient on the triple interaction term is
consistent with Hypothesis 2 but is not significant.
In Panel B of Table 4 we focus on two industries at opposite ends of Hambrick and
Abrahamson’s (1995) industry discretion ratings, Computer and Petroleum and Natural Gas. We
18
Hambrick and Abrahamson (1995) report their measure for 71 industries based on four digit SIC codes. Even
though we average Hambrick and Abrahamson’s measures by two-digit SIC code in order to increase the matches
with our sample, the sample size reduces by almost 3,000 observations when compared with Panel A from Table 3.
19
do so in order to investigate whether the results hold in a much narrower setting. The Computer
industry, which is often used in the literature as a setting for an industry in flux (for instance,
Haleblian and Finkelstein, 1993; Eisenhardt and Bourgeois, 1988), is an example of a high
managerial discretion industry whereas Petroleum and Natural Gas is an example of a low
managerial discretion industry.
Model (1) contains the results of incorporating the levels of each variable for the
Computer industry. CEO Power does not load significantly. To test the impact of CEO power on
firm value during shock years, in Model (2) of Panel B we add the interaction between Shock and
CEO Power. The coefficient on this interaction term is significantly negative at the 10 percent
level (p-value=7.5%). Models (3) and (4) repeat Models (1) and (2) but use the ∆ROA as the
dependent variable for our measure of firm performance. The sign on the coefficient of the
interaction term in Model (4) is negative as in Model (2) but not significant at conventional
levels (p-value=12.7%). Models (5) through (8) repeat the analysis of Models (1) through (4) but
focus on the Petroleum and Natural Gas industry. The coefficient on the interaction term is not
significant in Models (6) and (8). Overall, the results from Panel B indicate the effect of CEO
power on firm value during shock years is more negative for the Computer industry (a high
managerial discretion industry) relative to the Petroleum and Natural Gas industry (a low
managerial discretion industry). Thus, despite the significantly reduced degrees of freedom, the
results are consistent with the results from Panel A.
‘Insert Table 4 here’
20
Hypothesis 3 predicts the effect of CEO power on firm value during shock years is more
pronounced in competitive industries. In Panel A of Table 5, we use the Herfindahl Index as our
measure of competition. In Model (1) of Panel A we repeat the corresponding model from Table
3 but add the dummy variable Competitive which takes the value one when the Herfindahl Index
is below the sample median. Neither CEO Power nor Competitive load significantly. To test the
effect of CEO power on firm value during shock years for competitive industries, we split the
sample into two sub-samples, Competitive industry (Model 2) or Concentrated industry (Model
3) based on the sample median of the Herfindahl Index and add the interaction between Shock
and CEO Power. The coefficient on this interaction term is significantly negative at the five
percent level for Model 2 (p-value=3.3%), the Competitive industry sub-sample. This interaction
term is not significant in Model 3, the Concentrated industry sub-sample. These results are
consistent with Hypothesis 3. In Model (4) we combine the two sub-samples and include the
triple interaction of Shock, CEO Power and Competitive. The coefficient on this interaction term
is negative, but not significant. Exploring this further, we find this triple interaction term is
highly correlated with the interaction of Shock and Competitive. When we re-run the regression
without this double interaction, the triple interaction becomes significant at the one percent level.
Model (5) repeats Models (4) but replaces ∆M/B with ∆ROA as the dependent variable. The
coefficient on the triple interaction term is negative and significant at the one percent level (pvalue=0.3%), consistent with Hypothesis 3.
In Panel B of Table 5 we repeat the analysis of Panel A but use the market share captured
by the largest four firms in an industry (Top 4) as our measure of competition. We define an
industry as Competitive when the proportion of the industry sales captured by the largest four
firms (Top 4) is below the sample median. Again, splitting the sample into two sub-samples we
21
find the coefficient on the interaction between Shock and CEO Power is significantly negative at
the one percent level for Model 2 (p-value=2.5%), the Competitive industry sub-sample but not
significant in Model 3, the Concentrated industry sub-sample. These results are consistent with
Hypothesis 3. In Model (4) we combine the two sub-samples and include the triple interaction of
Shock, CEO Power and Competitive. The coefficient on this interaction term is negative, but not
significant. The triple interaction becomes significant at the one percent level when we re-run the
regression without the interaction between Shock and Competitive (the triple interaction term is
highly correlated with the interaction of Shock and Competitive). Model (5) repeats Models (4)
but replaces ∆M/B with ∆ROA as the dependent variable. As in Panel A, the coefficient on the
triple interaction term is negative and significant at the one percent level (p-value=0.3%), again
consistent with Hypothesis 3.
‘Insert Table 5 here’
DISCUSSION AND CONCLUSIONS
We investigate the relation between CEO power and firm value by examining changes in
valuation caused by shocks to an industry. We identify three settings where the net effect of CEO
power is likely magnified: when the firm is innovative, when the industry is characterized by
high managerial discretion and when the industry is more competitive. We find CEO power has a
negative effect on firm value in these settings when the industry it operates in experiences a
negative shock. Overall, our results are both statistically and economically significant.
For innovative firms with powerful CEOs, a shock to the industry results in, on average, a
0.3 decrease in the firm’s Market-to-Book ratio relative to a less powerful CEO (see Model 2
22
from Panel A of Table 3). Given the mean M/B of 2.85 for firms in this sub-sample, this
corresponds to an 11 percent decrease in firm value (16% of the standard deviation of M/B).
Using patents as a measure of innovation (Model 2 from Panel B of Table 3), the corresponding
impact is a 25 percent decrease in firm value (38% of the standard deviation of M/B). For firms
with powerful CEOs in high-discretion industries, a shock to the industry results in an 11 percent
decrease in firm value (17% of the standard deviation of M/B). Finally, for firms with powerful
CEOs in competitive industries, a shock to the industry results in, on average, a six percent
decrease in firm value (10% of the standard deviation of M/B). The economic significance is
slightly larger when we use the Top 4 Sales Ratio as a measure of competition (Panel B of Table
5).
A potential explanation for our results is greater risk-taking behavior by more powerful
CEOs. Adams, Almeida, and Ferreira (2005) find that more powerful CEOs experience greater
variability in performance. Potentially then, our results could reflect powerful CEOs taking on
greater risks. However, it does not necessarily follow that those risks produce worse performance
on average or worse performance during shock years in particular. Indeed, Adams, Almeida, and
Ferreira (2005) find no evidence that firms with powerful CEOs have on average worse
performance compared with other firms. Moreover, in the regressions we have controlled for
firm fixed-effects and Volatility, the standard deviation of a firm’s daily stock returns over the
previous year. To the extent that firm fixed-effects and Volatility control for a CEO’s risk-taking
behavior, our results are robust to this explanation. Nonetheless, to test this alternative
hypothesis we investigate the impact of CEO power on firm value during positive shock years.
We find powerful CEOs have no effect on firm value or performance in such years suggesting
our results are not the outcome of CEO power mirroring greater CEO risk-taking behavior.
23
To investigate whether our results are robust to various measures of ‘shock’ we use a 10
percent or greater (rather than a five percent or greater) decrease in aggregate industry sales. We
also try returns-based measures and designate a year as a shock year whenever the industry’s
stock-market return decreases by more than five percent (or 10%). In other tests we designate a
year as a shock year whenever the industry’s excess market return is negative. In all cases our
conclusions are unaffected as we obtain similar results to those reported in the paper. We also
test if our results are robust to various measures of innovation. Rather than using R&D or
patents, we use citations as a measure of innovation. Again, we obtain similar results and our
conclusions are unaffected. Econometrically, we use industry fixed-effects rather than firm
fixed-effects and the results are similar to those we report in the paper. We repeat our analysis
with industry-adjusted measures for all variables and find similar results. Excluding the years
2001, 2002 and 2009 (years representing market-wide downturns) does not affect our results.
Finally, we try different measures of CEO power. We use various combinations of the seven
individual components that make-up our CEO Power Index along with each component
separately. Again, the results from doing so are largely unaffected. Thus, our results are robust to
various measures of shocks, innovation, econometric techniques and CEO Power.
An important takeaway from our results is that firms can benefit from having a more
dispersed decision-making structure, especially when the industry is suffering a severe downturn.
However, our results do not necessarily reflect the existence of an agency problem. Even if
CEOs believe they are acting in shareholders’ (and stakeholders’) best interests, their decisions
may be suboptimal due to, for example, a lack of independent advice from the board (or from a
chairman or president). Given the difficulty in changing a CEO’s power once it is obtained, our
results are instructive for regulators that are pushing for firms to have a more dispersed power
24
base. We believe that our findings are generally supportive of efforts over the last several years
to, for instance, encourage board independence and discourage CEOs from also assuming
chairmanship of the board.
Another takeaway is that it is in innovative and high discretion industries, those that may
have the greatest implications for future economic growth, the negative impact of CEO power
during downturns is most evident. Hence, these types of firms may have the most to gain from
organizational structures that enhance decentralized decision-making and improve the channels
of information flow to top management. Finally, CEO power has worse outcomes in competitive
industries: indicating the greater consequences of poor decisions in competitive industries.
25
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28
Table 1. Descriptive statistics
Panel A: Industries with shocks (total 60 industry shock years)
FF Code
Industry Name
N
FF Code
Industry Name
N
1
Agriculture
3
24
Aircraft
1
5
Tobacco Products
5
25
Shipbuilding, Railroad Equipment
1
6
Recreation
1
28
Non-Metallic and Industrial Metal Mining
4
7
Entertainment
1
29
Coal
4
8
Printing and Publishing
1
30
Petroleum and Natural Gas
2
14
Chemicals
2
33
Personal Service
1
15
Rubber and Plastic Products
1
34
Business Service
1
16
Textiles
3
35
Computers
2
17
Construction Materials
2
37
Electronic Equipment
3
18
Construction
3
38
Measuring and Control Equipment
3
19
Steel Works
2
39
Business Supplies
1
20
Fabricated Products
3
41
Transportation
1
21
Machinery
1
42
Wholesale
2
22
Electrical Equipment
2
44
Restaurants, Hotels, Motels
1
23
Automobiles and Trucks
2
49
Miscellaneous
1
Panel B:. Firm characteristics (total 2,097 firms)
Variable
N
Mean
Median
Std Dev
Minimum
Maximum
Mkt.cap(millions)
17,604
5,953
1,356
14,394
19.169
97,468
M/B
17,604
2.089
1.643
1.394
0.729
9.344
ΔM/B
17,604
-0.074
-0.012
0.864
-3.977
3.162
Leverage
17,604
0.185
0.167
0.162
0.000
0.769
ROA
17,551
0.145
0.143
0.098
-0.320
0.424
ΔROA
17,536
-0.004
0.000
0.058
-0.252
0.217
RD
17,604
0.037
0.003
0.066
0.000
0.382
RD Missing
17,604
0.360
0.000
0.480
0.000
1.000
Capex
17,604
0.062
0.046
0.055
0.003
0.302
Volatility
17,604
0.028
0.025
0.013
0.010
0.079
Shock
17,604
0.070
0.000
0.255
0.000
1.000
Panel C: CEO characteristics (total 3,724 CEOs)
N
Variable
Mean
Median
Std Dev
Minimum
Maximum
Chairman
17,604
0.633
1.000
0.482
0.000
1.000
Chairman+President
17,604
0.254
0.000
0.435
0.000
1.000
Founder
17,604
0.237
0.000
0.425
0.000
1.000
Tenure
17,604
8.018
5.751
7.291
0.501
36.02
CPS
17,604
0.382
0.379
0.133
0.000
1.000
Ownership
17,604
0.023
0.004
0.047
0.000
0.205
Internally-Hired
17,604
0.510
1.000
0.500
0.000
1.000
Dependent Directors
17,604
0.336
0.300
0.178
0.000
1.000
N
Mean
Median
Std Dev
Minimum
Maximum
17,604
3.147
3.000
1.604
0.000
7.000
Panel D: CEO power measure
Variable
CEO Power Index
29
Table 2. Correlation matrix
Panel A: Regression variables
1
2
3
4
5
6
7
8
9
10
11
12
1. M/B
1.000
2. ΔM/B
0.223***
1.000
3. ROA
0.352***
0.036***
1.000
4. ΔROA
0.127***
0.223***
0.325***
1.000
5. RD
0.386***
-0.061***
-0.203***
-0.021***
6. Shock
-0.069***
-0.030***
-0.131***
-0.155***
0.002
1.000
7. CEO Power
0.027***
-0.003
0.010
-0.005
-0.053***
-0.022***
1.000
8. RD Missing
-0.145***
0.020***
0.062***
0.011
-0.425***
-0.007
0.079***
1.000
9. Capex
0.065***
-0.041***
0.250***
-0.018**
-0.108***
-0.052***
0.060***
0.148***
1.000
10. Firm size
0.287***
0.070***
0.274***
0.075***
-0.005
-0.016**
-0.127***
-0.054***
0.014*
1.000
11. Leverage
-0.264***
0.003
-0.127***
-0.013*
-0.238***
-0.007
0.000
0.200***
0.050***
0.013*
1.000
12. Volatility
0.126***
-0.045***
-0.301***
-0.067***
0.327***
0.249***
0.053***
-0.114***
-0.048***
-0.321***
-0.110***
1.000
1.000
Panel B: Individual measures of CEO power
1
2
3
4
5
6
1. Chairman
1.000
2. Chairman+President
0.444***
1.000
3. Founder
0.102***
-0.018**
1.000
4. Dependent Directors
-0.062***
-0.121***
0.270***
1.000
5. Tenure
0.243***
0.042***
0.493***
0.177***
1.000
6. CPS
0.067***
0.141***
-0.148***
-0.148***
-0.059***
1.000
7. Ownership
0.120***
0.009
0.507***
0.291***
0.431***
-0.162***
7
1.000
*** p<0.01, ** p<0.05, * p <0.10
30
Table 3. Effects of CEO power during ‘shock’ years on valuation and performance for innovative firms
Panel A: R&D as a measure of innovation
Dependent Variable
R&D Intensity
Shock
CEO Power
RD
Model (1)
Model (2)
Entire sample
-0.2345***
[0.000]
-0.0120
[0.512]
-0.8027***
[0.001]
High R&D
-0.0868
[0.441]
-0.0282
[0.635]
Shock*CEO Power
Model (3)
ΔM/B
Low R&D
-0.0807**
[0.012]
-0.0071
[0.638]
-0.3001*
[0.058]
-0.0258
[0.576]
-3.6743***
[0.000]
0.3586***
[0.000]
-0.4556**
[0.024]
7.5852***
[0.008]
-2.4405***
[0.000]
4,562
0.168
Yes
Yes
-0.0011
[0.975]
-2.4372***
[0.000]
0.1554***
[0.000]
-0.0753
[0.228]
7.1709***
[0.000]
-0.9883***
[0.000]
13,042
0.111
Yes
Yes
Shock*CEO Power*RD
Shock*RD
CEO Power*RD
RD Missing
Capex
Firm Size
Leverage
Volatility
Constant
Observations
R-squared
Firm Fixed Effect
Year Dummies
0.0066
[0.894]
-2.7233***
[0.000]
0.1966***
[0.000]
-0.2260***
[0.002]
4.7102***
[0.000]
-1.2077***
[0.000]
17,604
0.096
Yes
Yes
Model (4)
Entire sample
-0.1721***
[0.000]
-0.0220
[0.295]
-1.0036***
[0.000]
0.0534
[0.424]
-4.7604***
[0.000]
-0.5420
[0.371]
0.4900*
[0.067]
0.0046
[0.927]
-2.7480***
[0.000]
0.1976***
[0.000]
-0.2243***
[0.002]
4.9173***
[0.000]
-1.2114***
[0.000]
17,604
0.099
Yes
Yes
Model (5)
ΔROA
Entire sample
-0.0310***
[0.000]
-0.0010
[0.497]
-0.0493***
[0.009]
0.0034
[0.461]
-0.1913***
[0.008]
-0.0223
[0.595]
-0.0059
[0.749]
0.0001
[0.982]
-0.1223***
[0.000]
0.0093***
[0.000]
-0.0257***
[0.000]
0.5319***
[0.000]
-0.0561***
[0.000]
17,548
0.058
Yes
Yes
31
Panel B: Patents as a measure of innovation
Dependent Variable
Patents
Shock
CEO Power
RD
Patents
Model (1)
Model (2)
Entire sample
-0.3780***
[0.000]
-0.0162
[0.458]
-0.8015***
[0.005]
-0.0009**
[0.010]
High Patents
-0.2553**
[0.017]
0.0526
[0.314]
0.0163
[0.978]
Shock*CEO Power
Model (3)
ΔM/B
Low Patents
-0.2901***
[0.000]
-0.0250
[0.307]
-0.9008***
[0.008]
-0.6019***
[0.000]
0.0035
[0.972]
-0.0415
[0.861]
-5.2294***
[0.000]
0.2434***
[0.000]
-0.0994
[0.658]
4.9372
[0.117]
-1.7082***
[0.000]
3,096
0.139
Yes
Yes
0.0496
[0.114]
0.0386
[0.532]
-2.4489***
[0.000]
0.2423***
[0.000]
-0.1512
[0.127]
5.8401***
[0.000]
-1.5317***
[0.000]
11,159
0.075
Yes
Yes
Shock*CEO Power*Patents
Shock*Patents
CEO Power*Patents
Patents Missing
RD Missing
Capex
Firm Size
Leverage
Volatility
Constant
Observations
R-squared
Firm Fixed Effect
Year Dummies
*** p<0.01, ** p<0.05, * p <0.10
0.0492
[0.118]
0.0247
[0.680]
-2.7488***
[0.000]
0.2281***
[0.000]
-0.2166**
[0.016]
4.8486***
[0.000]
-1.4418***
[0.000]
14,255
0.081
Yes
Yes
Model (4)
Entire sample
-0.2989***
[0.000]
-0.0217
[0.345]
-0.8141***
[0.004]
-0.0011***
[0.005]
-0.0775
[0.383]
-0.0046***
[0.000]
-0.0003
[0.664]
0.0008**
[0.043]
0.0502
[0.110]
0.0248
[0.679]
-2.7754***
[0.000]
0.2307***
[0.000]
-0.2176**
[0.015]
4.9423***
[0.000]
-1.4621***
[0.000]
14,255
0.083
Yes
Yes
Model (5)
ΔROA
Entire sample
-0.0260***
[0.000]
-0.0010
[0.515]
-0.0509***
[0.007]
-0.0000
[0.172]
-0.0083
[0.160]
0.0000
[0.917]
-0.0001*
[0.060]
0.0000
[0.123]
0.0009
[0.658]
0.0039
[0.322]
-0.1354***
[0.000]
0.0104***
[0.000]
-0.0318***
[0.000]
0.6759***
[0.000]
-0.0679***
[0.000]
14,203
0.056
Yes
Yes
32
Table 4. Effect of CEO power during ‘shock’ years on valuation and performance for firms in high and low managerial discretion industries
Panel A: High versus low managerial discretion industries
Model (1)
Dependent Variable
Discretion
Shock
CEO Power
RD
Industry Discretion
Entire sample
-0.3033***
[0.000]
-0.0121
[0.569]
-0.8897***
[0.001]
-0.0057
[0.941]
Shock*CEO Power
Model (2)
High Disc.
-0.1222
[0.119]
0.0357
[0.330]
-1.2447***
[0.000]
Model (3)
ΔM/B
Low Disc.
-0.4121***
[0.000]
-0.0511**
[0.038]
0.0373
[0.938]
-0.2731**
[0.026]
-0.0054
[0.935]
0.0988
[0.352]
-4.0536***
[0.000]
0.2578***
[0.000]
-0.2945**
[0.030]
4.6780**
[0.012]
-1.7342***
[0.000]
7,183
0.109
Yes
Yes
-0.0740
[0.271]
-2.1134***
[0.000]
0.1763***
[0.000]
-0.1598
[0.109]
5.9218***
[0.000]
-1.0333***
[0.000]
7,548
0.116
Yes
Yes
Shock*CEO Power*Industry Discretion
CEO Power*Industry Discretion
Shock*Industry Discretion
RD Missing
Capex
Firm Size
Leverage
Volatility
Constant
Observations
R-squared
Firm Fixed Effect
Year Dummies
-0.0081
[0.893]
-2.7298***
[0.000]
0.2107***
[0.000]
-0.2193***
[0.009]
4.6826***
[0.000]
-1.3115***
[0.000]
14,731
0.102
Yes
Yes
Model (4)
Entire sample
-0.3363***
[0.000]
-0.0485
[0.102]
-0.8911***
[0.001]
-0.0514
[0.516]
0.0061
[0.939]
-0.2545*
[0.054]
0.0904**
[0.034]
0.1631**
[0.037]
-0.0061
[0.918]
-2.7309***
[0.000]
0.2112***
[0.000]
-0.2192***
[0.009]
4.7550***
[0.000]
-1.2945***
[0.000]
14,731
0.102
Yes
Yes
Model (5)
ΔROA
Entire sample
-0.0379***
[0.000]
-0.0018
[0.387]
-0.0525***
[0.004]
-0.0035
[0.518]
0.0027
[0.617]
-0.0087
[0.333]
0.0016
[0.592]
0.0102*
[0.056]
0.0006
[0.891]
-0.1244***
[0.000]
0.0090***
[0.000]
-0.0284***
[0.000]
0.5043***
[0.000]
-0.0530***
[0.000]
14,700
0.061
Yes
Yes
33
Panel B: Computer industry versus petroleum and natural gas industry
High Discretion Industry: Computers
Model (1)
Model (2)
Model (3)
Model (4)
Dependent variable
ΔM/B
ΔROA
Shock
-1.1938
-1.0384
-0.0857
-0.0768
[0.156]
[0.219]
[0.161]
[0.210]
CEO Power
0.1069
0.1760
-0.0084
-0.0044
[0.534]
[0.316]
[0.500]
[0.728]
RD
-2.3631*
-2.4728**
-0.2236**
-0.2287**
[0.056]
[0.045]
[0.013]
[0.011]
Shock*CEO Power
-0.6858*
-0.0433
[0.075]
[0.127]
Capex
-6.0210***
-6.2399***
-0.2862**
-0.3019**
[0.002]
[0.002]
[0.046]
[0.035]
Firm Size
0.1931**
0.2103**
0.0164***
0.0176***
[0.021]
[0.012]
[0.007]
[0.004]
Leverage
0.0400
0.0698
-0.0815*
-0.0796*
[0.949]
[0.911]
[0.072]
[0.078]
Volatility
4.5733
4.1610
1.6689***
1.6589***
[0.548]
[0.583]
[0.003]
[0.003]
RD Missing
Constant
Observations
R-squared
Firm Fixed Effect
Year Dummies
-0.6564
[0.531]
514
0.193
Yes
Yes
-0.7660
[0.465]
514
0.199
Yes
Yes
-0.1300*
[0.088]
512
0.141
Yes
Yes
-0.1382*
[0.070]
512
0.146
Yes
Yes
Low Discretion Industry: Petroleum and Natural Gas
Model (5)
Model (6)
Model (7)
Model (8)
ΔM/B
ΔROA
-0.6186***
-0.5790***
-0.0395
-0.0472*
[0.000]
[0.000]
[0.119]
[0.070]
0.0256
0.0395
0.0110
0.0083
[0.492]
[0.312]
[0.102]
[0.239]
-11.8855**
-11.8059**
-1.3425
-1.3581
[0.023]
[0.024]
[0.153]
[0.148]
-0.1040
0.0203
[0.233]
[0.195]
-0.3301
-0.3134
-0.1465***
-0.1498***
[0.255]
[0.281]
[0.005]
[0.004]
0.1674***
0.1662***
-0.0014
-0.0012
[0.000]
[0.000]
[0.812]
[0.842]
0.2095
0.2213
-0.1222***
-0.1245***
[0.214]
[0.190]
[0.000]
[0.000]
-1.8761
-1.8117
-0.7516
-0.7642
[0.547]
[0.560]
[0.179]
[0.172]
0.0418
0.0440
-0.0077
-0.0081
[0.682]
[0.666]
[0.674]
[0.657]
-1.0712***
-1.0779***
0.0770
0.0784
[0.000]
[0.000]
[0.148]
[0.141]
897
897
897
897
0.403
0.404
0.246
0.247
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
*** p<0.01, ** p<0.05, * p <0.10
34
Table 5. Effects of CEO power during ‘shock’ years on valuation and performance for firms in competitive industries
Panel A: Herfindahl index as a measure of competition
Dependent Variable
Industry Competitiveness
Shock
CEO Power
RD
Competitive
Model (1)
Model (2)
Entire sample
-0.2350***
[0.000]
-0.0121
[0.510]
-0.8027***
[0.001]
0.0058
[0.828]
Competitive
-0.2592***
[0.000]
-0.0315
[0.143]
-0.9360***
[0.001]
Shock*CEO Power
Model (3)
ΔM/B
Concentrated
0.0044
[0.962]
0.0522
[0.228]
-0.9861*
[0.080]
-0.1327**
[0.033]
-0.0484
[0.717]
0.0096
[0.869]
-2.7529***
[0.000]
0.2061***
[0.000]
-0.2800***
[0.001]
4.8191***
[0.000]
-1.2639***
[0.000]
13,216
0.101
Yes
Yes
0.0137
[0.904]
-2.9094***
[0.000]
0.2676***
[0.000]
0.0984
[0.588]
6.3646***
[0.004]
-1.8428***
[0.000]
4,388
0.107
Yes
Yes
Shock*CEO Power*Competitive
CEO Power*Competitive
Shock*Competitive
RD Missing
Capex
Firm Size
Leverage
Volatility
Constant
Observations
R-squared
Firm Fixed Effect
Year Dummies
0.0067
[0.893]
-2.7228***
[0.000]
0.1967***
[0.000]
-0.2260***
[0.002]
4.7174***
[0.000]
-1.2133***
[0.000]
17,604
0.096
Yes
Yes
Model (4)
Entire sample
-0.0105
[0.893]
0.0392
[0.271]
-0.8301***
[0.001]
0.0435
[0.163]
-0.0599
[0.616]
-0.0693
[0.607]
-0.0582
[0.143]
-0.2328***
[0.006]
0.0015
[0.975]
-2.7101***
[0.000]
0.1962***
[0.000]
-0.2273***
[0.002]
4.7232***
[0.000]
-1.2402***
[0.000]
17,604
0.097
Yes
Yes
Model (5)
ΔROA
Entire sample
-0.0312***
[0.000]
-0.0021
[0.399]
-0.0530***
[0.002]
0.0016
[0.473]
0.0182**
[0.027]
-0.0273***
[0.003]
0.0011
[0.695]
-0.0010
[0.859]
-0.0002
[0.962]
-0.1208***
[0.000]
0.0093***
[0.000]
-0.0259***
[0.000]
0.5239***
[0.000]
-0.0568***
[0.000]
17,548
0.058
Yes
Yes
35
Panel B: Top 4 sales ratio as a measure of competition
Dependent Variable
Industry Competitiveness
Shock
CEO Power
RD
Competitive
Model (1)
Model (2)
Entire sample
-0.2394***
[0.000]
-0.0127
[0.489]
-0.7976***
[0.001]
0.0821***
[0.002]
Competitive
-0.2538***
[0.000]
-0.0329
[0.124]
-0.7956***
[0.004]
Shock*CEO Power
Model (3)
ΔM/B
Concentrated
0.0829
[0.353]
0.0409
[0.344]
-1.1336*
[0.060]
-0.1402**
[0.025]
-0.0433
[0.728]
0.0245
[0.663]
-2.6134***
[0.000]
0.2115***
[0.000]
-0.2668***
[0.001]
6.1234***
[0.000]
-1.3399***
[0.000]
13,524
0.103
Yes
Yes
0.0107
[0.932]
-2.6595***
[0.000]
0.2349***
[0.000]
0.0906
[0.617]
5.1737**
[0.023]
-1.6557***
[0.000]
4,080
0.095
Yes
Yes
Shock*CEO Power*Competitive
CEO Power*Competitive
Shock*Competitive
RD Missing
Capex
Firm Size
Leverage
Volatility
Constant
Observations
R-squared
Firm Fixed Effect
Year Dummies
*** p<0.01, ** p<0.05, * p <0.10
0.0059
[0.906]
-2.7119***
[0.000]
0.1974***
[0.000]
-0.2297***
[0.002]
4.8698***
[0.000]
-1.2869***
[0.000]
17,604
0.097
Yes
Yes
Model (4)
Entire sample
0.0004
[0.996]
0.0229
[0.529]
-0.8270***
[0.001]
0.1113***
[0.000]
-0.0456
[0.700]
-0.0856
[0.523]
-0.0370
[0.358]
-0.2523***
[0.003]
0.0019
[0.969]
-2.6885***
[0.000]
0.1970***
[0.000]
-0.2345***
[0.001]
4.8751***
[0.000]
-1.3106***
[0.000]
17,604
0.098
Yes
Yes
Model (5)
ΔROA
Entire sample
-0.0305***
[0.000]
-0.0033
[0.197]
-0.0532***
[0.002]
-0.0002
[0.914]
0.0184**
[0.024]
-0.0276***
[0.003]
0.0026
[0.352]
-0.0017
[0.762]
-0.0001
[0.986]
-0.1198***
[0.000]
0.0093***
[0.000]
-0.0261***
[0.000]
0.5219***
[0.000]
-0.0552***
[0.000]
17,548
0.058
Yes
Yes
36
Figure 1. Average changes in M/B (demeaned) and average changes in ROA (demeaned)
during ‘shock’ years
37