Do General Managerial Skills Spur Innovation?*
Cláudia Custódio
Arizona State University – W. P. Carey School of Business
claudia.custodio@asu.edu
Miguel A. Ferreira
Nova School of Business and Economics, ECGI
miguel.ferreira@novasbe.pt
Pedro Matos
University of Virginia – Darden School of Business, ECGI
matosp@darden.virginia.edu
This Version: June 2014
Abstract
We show that firms with chief executive officers (CEOs) who gain general managerial
skills over their lifetime work experience invest more in R&D and produce more patents.
We address the potential endogenous CEO-firm matching bias using firm- and CEOfixed effects and variation in the enforceability of non-compete agreements across states
and over time during the CEO’s career. Our findings suggest that generalist CEOs spur
innovation because they have skills that can be applied elsewhere should innovation
projects fail. We conclude that an efficient labor market for executives can promote
corporate innovation by providing a mechanism of tolerance for failure.
JEL classification: G32, J24, O31
Keywords: General human capital, Innovation, Patents, R&D, Risk taking
*
We thank Heitor Almeida, Fred Bereskin, Tony Cookson, Daniel Ferreira, Po-Hsuan Hsu, Dongmei Li, Elena
Loutskina, Angie Low, Alberto Manconi, Amit Seru, Rui Silva, and Tracy Wang; conference participants at the
2014 FIRS Conference, Erasmus University-Workshop on Executive Compensation and Corporate Governance; and
seminar participants at Arizona State University, Queen Mary College, Stanford University, University of Colorado,
University of Hong Kong, University of Utah, and University of Virginia for helpful comments.
1. Introduction
Innovation is a driving force in today’s economy, but investing in new technologies,
products, or services is risky and challenging. Decisions on research and development (R&D)
budgets and the prioritization of research projects fall to top firm managers. In this paper, we ask
whether CEO’s skill set is an important determinant of corporate innovation. And which CEO
skills would be more valuable to produce innovation?1
Managers draw on skills gained throughout a career when they make corporate decisions.
Starting with Becker (1962), researchers have emphasized two types of managerial capital:
general human capital (i.e., skills not specific to any organization and transferable across firms or
industries) and firm-specific human capital (i.e., skills valuable only within an organization). We
test the hypothesis that CEOs with more general skills foster innovation.
Innovation carries a significant risk for top managers, as there are inherent uncertainties in
going from concept to realization of actual profits. We conjecture that generalist CEOs are more
likely to exploit innovative projects because they are less sensitive to the risk of termination,
given their more diverse business experience compared to CEOs with focused professional
experience. A generalist can move across industries more easily, as a failure in one place might
not necessarily give a bad signal of ability in other industries. Thus, the broader set of outside
options available to generalist CEOs and not to specialist CEOs acts as a (labor) market
mechanism of tolerance for failure that could foster innovation. This mechanism can be an
alternative to internal mechanisms such as CEO contracts offering long-term compensation plans
and job security. Manso (2011) shows that the optimal incentive mechanism that motivates
1
Research has shown that managers’ measurable characteristics have significant explanatory power for corporate
innovation (e.g., Barker and Mueller (2002), Hirshleifer, Low, and Teoh (2012)).
1
innovation rewards long-term success but tolerates early failure. Lerner and Wulf (2007) and
Tian and Wang (2014) provide evidence consistent with this idea.
Additionally, a generalist CEO may be better at thinking outside the box, or taking advantage
of knowledge in a field beyond the company’s current technological domain. A CEO who has
worked in multiple positions, firms, and industries may accumulate general human capital that
can be useful when a firm needs to invest in transformative change. For these reasons, we expect
generalists to support innovation with a higher degree of originality and impact.
An alternative hypothesis is that specialist CEOs have more technical expertise that allows
them to identify and promote innovation. Innovation tends to occur in highly specialized areas
such as bio-technology and information technology where managers with an industry
background may have an advantage. Managerial skills in a particular field can encourage
specialists to invest in innovation, and make them better able to identify good projects.2 In fact,
general managerial skills could be simply not unique but available from outside providers such
as consultants. Therefore, it is an empirical question which CEO skills (general or specialist)
matter for the quantity and quality of corporate innovation.
We examine the link between CEOs’ general human capital and corporate innovation using
the panel of Standard & Poor’s (S&P) 1,500 firms over the 1993–2003 period. To measure
general managerial skills, we use the General Ability Index (GAI) developed by Custódio,
Ferreira, and Matos (2013), which captures five aspects of a CEO’s professional career: past
number of (1) positions, (2) firms, and (3) industries in which a CEO worked; (4) whether the
executive held a CEO position at a different company; and (5) whether the CEO worked for a
conglomerate firm. The index of general managerial ability is the first factor of the principal
2
If specialists are better at identifying good innovation projects, generalists may decide to diversify while
innovating. This may be another reason why generalists produce more diverse innovation than specialists.
2
components analysis of the five proxies.3
We start by examining whether firms run by generalist CEOs invest more in innovation, as
measured by R&D expenditures. We find that R&D is positively associated with the GAI of the
CEO. This is a first indication in favor of the hypothesis that generalist CEOs invest more in
innovation. This evidence is preliminary, however, as more R&D spending does not necessarily
lead to more and better innovation.
In the main set of tests, we examine the productivity of a firm’s research activities using
patent-based metrics. We use the NBER patent database to measure the quantity and originality
of a firm’s research output (see Hall, Jaffe, and Trajtenberg (2001)). We measure innovative
activity by the number of patents that each firm files in a given year. We find that firms headed
by generalist CEOs have significantly higher patent counts. A one-standard deviation increase in
GAI is associated with an increase of 9% in patent counts using a firm-fixed effects estimator.
We also measure the impact of a firm’s patents by counting the citations that each patent receives
from subsequent patents. The results suggest that firms with generalist CEOs generate more
citations counts.4 The effect is also important in economic terms: a one standard deviation
increase in GAI is associated with an 11% increase in citation counts using a firm fixed-effects
estimator. The results are robust when we adjust the citation count by truncation bias using the
methods proposed by Hall, Jaffe, and Trajtenberg (2001).
We also study the effect of general managerial skills on the direction of a firm’s innovation
efforts. We find that firms with generalist CEOs hold more diverse portfolios of patents as
proxied by the Herfindahl index of the patent portfolio (patents applied for by the firm over the
3
The growing importance of general skills has contributed to the increase in executive compensation over several
decades (Murphy and Zabojnik (2007) and Frydman (2009)).
4
Hall, Jaffe, and Trajtenberg (2005) show that patent citations are positively correlated with firm market value.
3
previous five years) across technological classes (Hirshleifer, Hsu, and Li (2012)). Moreover,
such firms make use of and produce more diverse patents. We find a positive relation between
GAI and measures of the originality and generality of the portfolio of patents, as indicated by a
wider set of technological classes of patents cited and subsequent citing patents. Manso (2011)
and Almeida, Hsu, and Li (2012) classify innovative strategies into exploitative (i.e., strategies
that refine existing technologies) and exploratory (i.e., strategies that involve a more risky search
for new technologies that can transform a business). We find that generalist CEOs engage more
in exploratory than exploitative strategies than specialist CEOs.
Our findings are robust to the use of alternative econometric specifications (including
negative binomial and Poisson regression models for count-dependent variables) and the
inclusion of many firm-level controls such as firm size, tangibility, stock returns, and
institutional ownership (e.g., see Aghion, Van Reenen, and Zingales (2013)). Conditioning on
R&D expenditures or R&D stock reduces the coefficient of GAI only slightly, suggesting that the
main effect of general managerial skills is to alter the quality and productivity of R&D rather
than simply stimulate more R&D.
The findings are also robust to the inclusion of many CEO-level controls.5 Galasso and
Simcoe (2010) and Hirshleifer, Low, and Teoh (2012) show that psychological biases such as
CEO overconfidence increase a manager’s willingness to take riskier projects. We therefore
control for options-based and media-based CEO confidence measures in our tests (see
Malmendier and Tate (2005)). We also control for the CEO compensation structure and other
5
Researchers have examined whether corporate outcomes are affected by CEO characteristics. Bertrand and Schoar
(2003) show that individual manager fixed-effects matter for investment and financial policies of firms. Fee,
Hadlock and Pierce (2013), however, cast doubt on the methodology for identifying managerial style effects on
policy choices. They argue that CEO turnover events are endogenous and that managerial style changes are
anticipated by corporate boards at the time of a CEO selection decision.
4
observable characteristics such as CEO education, age, and tenure (Barker and Mueller (2002),
Bereskin and Hsu (2012), Coles, Daniel, and Naveen (2006), and Coles, Li, and Wang (2013)).
Our estimates may be biased due to endogenous matching between CEOs and firms. When
we use propensity score matching to compare firms run by generalists to otherwise similar firms
run by specialists, we continue to find a significant difference in innovation output. Unobserved
firm or CEO variation may of course still be driving both innovation and general managerial
ability. Therefore, we account for unobserved factors that are time-invariant using firm and CEO
fixed-effects. The results suggest that the estimates are not driven by a non-random assignment
of generalist CEOs to firms, which is also correlated with innovation. The remaining concern is
that time-variant unobserved factors may drive both innovation and CEO selection. For example,
a firm in distress may decide to change its innovation strategy and switch CEO type at the same
time.
To address omitted variables and reverse causality issues simultaneously, we use
instrumental variables (IV) estimators. We use state-level labor laws on non-compete agreements
as a source of exogenous variation in the generality of human capital of the CEO. Non-compete
agreements are contracts that prevent employees from joining or creating a competing company
after ending an employment contract. The enforceability of such contracts varies across U.S.
states and over time. We use the Garmaise (2009) index on the enforceability of non-compete
agreements during the career of a CEO as an instrument for GAI. The instrument is the average
non-compete agreement enforcement index at the state-year level across all career positions CEO
has had in publicly traded firms (Non-Compete Enforcement Index).
We expect the Non-Compete Enforcement Index to be positively related to GAI, because
enforcement of non-compete agreements limits within-industry manager transfers but enhances
5
between-industry transfers (Garmaise (2009), Marx, Strumsky, and Fleming (2009)). Executives
have an ex-ante incentive to accumulate more general skills if they work in states with stricter
enforcement of non-compete clauses, so that they have more outside options and future mobility.
We find the Non-Compete Enforcement Index to be positively and significantly correlated with
GAI. The IV estimates suggest a causal effect from general managerial skills to innovation.
We provide evidence that the difference in the value of outside options between generalist
and specialist managers can support tolerance for failure, thereby promoting corporate
innovation. In the presence of geographic segmentation, Oyer’s (2004) wage indexation theory
implies that relevant outside opportunities for managers are likely to come from firms in the
same region rather than from firms that are farther away. We use the tightness of the local labor
market as a source of exogenous variation in the value of the outside options of managers (Kedia
and Rajgopal (2009)). As the demand for managers exceeds supply in tight labor markets,
managers are more likely to receive outside job offers from other firms in the region. Moreover,
generalist managers should benefit more than specialists in tight labor markets, because they
have skills that are transferable across firms and industries. Consistent with this idea, we find
that the relation between innovation and GAI is more pronounced in tight labor markets.
A second proxy for the value of outside options is the local beta, that is, the degree of
comovement between a firm’s stock price and stock prices of other firms in the same
Metropolitan Statistical Area (Pirinsky and Wang (2006), Kedia and Rajgopal (2009)). Specialist
executives are less likely than generalists to have outside job opportunities from other firms in
the same region when their employer firm has low local betas. Consistent with this idea, we find
a stronger relation between innovation and the GAI in the sample of firms with low stock beta.
Overall, we conclude that an efficient labor market for executives can promote innovation by
6
serving as mechanism of tolerance for failure. Generalist CEOs are more likely to exploit
innovative growth opportunities because they have skills that can be applied elsewhere, should
risky innovation projects fail. Our findings highlight the importance of general human capital
and managerial skills in a modern knowledge-based economy where innovation is a key
determinant of corporate success.
2. Data and measures
Our sample consists of a panel of CEO-firm-years of Standard & Poor’s (S&P) 1,500 firms
drawn from the EXECUCOMP database over 1993–2003. We manually match the executives in
EXECUCOMP who are identified as CEOs in each year with the BoardEx database to obtain
data on CEO prior professional experience. We then match firms in BoardEx where CEOs
worked in the past to Compustat (US firms) and Datastream (international firms) to obtain the
standard industrial classification (SIC). We use information on all of a CEO’s past positions,
including those in non-S&P 1,500 firms. We use these data to create an index of the generality of
a CEO’s human capital.
We use the NBER patent database to measure innovation for the S&P 1,500 firms (Hall,
Jaffe, and Trajtenberg (2001, 2005)). The patent data are from the 2006 edition of the NBER
patent database, which provides a link to EXECUCOMP by GVKEY. We control for firm
characteristics using accounting data from Compustat, stock returns data from CRSP, and
institutional ownership data from the Thomson CDA/Spectrum 13F Holdings. Variable
definitions are provided in Table A1 in the Appendix.
The sample consists of S&P 1,500 firms in the intersection of EXECUCOMP, BoardEx, and
the NBER patent database. Firms that operate in four-digit SIC industries without any filed
patent in the sample period are excluded. Financial firms and utilities are also excluded. The
7
final sample consists of 2,377 CEOs with GAI from 10,523 firm-year observations between 1993
and 2003. Variables are winsorized at the 1% level at both tails.
2.1. Measuring general managerial ability
Our goal is to examine the effect of general managerial ability on corporate innovation
output. We use the General Ability Index (GAI) of Custódio, Ferreira, and Matos (2013), which
captures the generality of a CEO’s human capital based on lifetime work experience in publicly
traded firms prior to the current CEO position. A CEO who worked in different organizational
areas, for multiple firms, in different industries, in a conglomerate firm, or who has served as
CEO previously has probably acquired more generic skills.
The GAI of CEO i in year t is defined as:
GAIi,t = 0.268 X1i,t + 0.312 X2i,t + 0.309 X3i,t + 0.218 X4i,t + 0.153 X5i,t
(1)
where X1 is the number of different positions that a CEO has had during his or her career; X2 is
the number of firms where a CEO worked; X3 is the number of industries at the four-digit SIC
level where a CEO worked; X4 is the a dummy variable that takes a value of one if a CEO
previously held a CEO position at another firm; and X5 is a dummy variable that takes a value of
one if a CEO worked for a multi-division firm (i.e., reports more than one business segment). A
CEO with a high GAI is likely to have acquired general skills that are transferable across firms
and industries and is also to have more attractive outside options. The weights in equation (1) are
obtained from extracting common components, using principal component analysis, from the
five variables. Higher levels of general human capital are reflected in a higher value of the index.
The index is standardized to have zero mean and a standard deviation of one.
A good example of a generalist executive is Louis Gerstner, who was CEO/Chairman of IBM
8
over 1993–2002. He started his career at McKinsey & Company. He had previously been CEO
of RJR Nabisco and also held senior positions at American Express. Considered an outsider
when he joined IBM, Gerstner was largely credited with turning around IBM’s business, while
John Akers, his predecessor, was an IBM lifer and more immersed in its corporate culture.
Gerstner had a GAI score in the top 1% of the distribution at 3.11 when he joined IBM, with past
experience in 11 positions, 10 firms, and 6 industries, as well as past experience as a top manager
and at a conglomerate.
Under Gerstner, IBM stopped development of its own operating system and withdrew from
the retail desktop PC to focus on IT services where the tech industry was headed. Over the
decade of his management IBM produced a record-setting number of patents. IBM is fourth in
the number of patents in our sample, with patent counts increasing from about 1,000 per year to
more than 4,000 in that time. During this period, IBM was also in the top 1% of the distribution
of citations.6
2.2. Measuring innovation
The first measure of innovation we use in our tests is R&D spending, an input-oriented
measure. We use the ratio of R&D expenditures to the book value of assets (R&D). Firm-years
with missing R&D information are assigned a zero value.
Our main tests, though, are based on output-oriented measures of innovation, or patent counts
and citations.7 Therefore, the second measure of innovation is the number of patent applications
filed by a firm in a given year (Patents). One concern with this number is that patents are
6
Bloomberg, “IBM Granted Most U.S. Patents for 20th Straight Year” (January 10, 2013).
Although we focus on direct measures of innovation, we also find a positive relation between stock return
volatility and GAI. This evidence supports the interpretation that innovation is a risky activity.
7
9
included in the database only if they are eventually granted, and there is on average a two-year
lag between application and grant date. As the latest year available in the patent database is 2006,
patents applied for in 2004 and 2005 may not show up. As suggested by Hall, Jaffe, and
Trajtenberg (2001), we end our sample period in 2003 and include year fixed-effects in our
regressions to address time truncation issues.8
The third measure of innovation is the total number of citations to the patents that a firm
applied for in a given year (Citations). Patent counts are an imperfect proxy of innovation
success, as patents vary widely in their technological and economic relevance (Griliches, Hall,
and Pakes (1987)). A common way to measure the relevance of a patent is by its citation count or
the number of citations subsequently received. Hall, Jaffe, and Trajtenberg (2005) show that
citations are positively related to firm value.
Patents created near the ending year of the sample period have less time to accumulate
citations. Therefore, citations suffer from a time truncation bias due to the finite length of the
patent database. We address this concern by adjusting the citation count of each patent in two
different ways. The first adjustment is to multiply each patent’s citation count by a weighting
index in Hall, Jaffe, and Trajtenberg (2001, 2005). The resulting variable, Q Citations, is the sum
of the adjusted patent citations across all patents applied for in each year. The second adjustment
is to scale each patent’s citation count by the average citation count of all patents in the same
two-digit technological class and year. The resulting variable, TT Citations, is the sum of the
adjusted citation count across all patents applied for in each year.
So far, the measures of innovation capture the intensity but not the direction of a firm’s
8
Although we exclude firms operating in four-digit SIC industries with no patents, there are many firm-years with
zero patents and citations. To see if the results are driven by the jump from zero patents to at least one patent, we
rerun the tests deleting firm-years with zero patents or citations. The results in Table IA.I in the Internet Appendix
are similar as to number of patents but less precise for cite-weighted measures of patents.
10
innovation efforts. To proxy for the diversity and complexity of a firm’s activities we use the
Herfindahl index of the number of patents in the firm’s portfolio across two-digit technological
classes (Hirshleifer, Hsu, and Li (2012)). The patent portfolio includes all patents that the firm
applied for over the previous five years. Because we want to capture the diversity of the patent
portfolio we use one minus the Herfindahl index (Innovative Diversity). A high value for the
Innovative Diversity variable indicates a lower concentration of the patent portfolio in terms of
technological fields.
The second measure of the direction of a firm’s innovation efforts is the technological Search
Distance between a firm’s new patents and its patent portfolio proposed by Chao, Lipson, and
Loutskina (2012). We take the current distribution of the number of a firm’s patents across twodigit technological classes and then measure the degree of difference between this distribution
and the analogous distribution calculated for new patents and adjusted for the expected degree of
knowledge spillovers between patent classes (i.e., adjusted for the “closeness” of patent classes).
A higher Search Distance indicates more complex innovative activities.
Another aspect of a firm’s innovative activities is the technological knowledge base
encompassed by the patents. The first measure is one minus the Herfindahl index of the citations
in the patent portfolio across two-digit technological classes as proposed by Hall, Jaffe, and
Trajtenberg (2001). This index looks at backward citations made by the firm in its patent
portfolio. A high Originality Index (lower concentration) indicates that patents cited belong to a
wider set of technological classes. The second measure is one minus the Herfindahl index of the
citations received by the patent portfolio across two-digit technological classes. This index looks
at forward citations of the patent portfolio to measure the impact of the firm’s patent portfolio. A
high Generality Index (lower concentration) indicates that a firm’s patents are cited by
11
subsequent patents across a wide range of fields.
The final set of measures examines a firm’s innovation strategy. We classify firms’ patent
activity into exploratory and exploitative as proposed by Sorensen and Stuart (2000), Katila and
Ahuja (2002), Benner and Tushman (2003) and Almeida, Hsu, and Li (2012). Firms focusing on
their current areas of expertise are expected to produce more exploitative patents, while firms
looking into new areas are expected to produce more exploratory patents. We construct proxies
for exploitative and exploratory patents according to the extent to which a firm’s new patents use
current versus new knowledge. A firm’s existing knowledge consists of its previous patent
portfolio and the set of patents that have been cited by the firm’s patents filed over the past five
years. A patent is categorized as exploitative if at least 80% (60%) of its citations are based on
current knowledge, and a patent is categorized as exploratory if at least 80% (60%) of its
citations are based on new knowledge (i.e., citations not in the firm’s existing knowledge base).
We then calculate the ratio of exploitative patents for a given firm-year as the number of
exploitative patents filed in a given year divided by the number of all patents filed by the firm in
the same year (Exploitative Ratio 80, Exploitative Ratio 60). The ratio of exploratory patents for
a given firm-year is defined as the number of exploratory patents filed in a given year divided by
the number of all patents filed by the firm in the same year (Exploratory Ratio 80, Exploratory
Ratio 60). A higher ratio of exploitative patents suggests a more focused innovative strategy,
while a higher ratio of exploratory patents suggests a more divergent innovative strategy.
2.3. Other explanatory variables
To explain R&D or patents we include several firm characteristics as controls in the base
model, following Hall and Ziedonis (2001) and Aghion, Van Reenen, and Zingales (2013). Firm
size is proxied by sales. Capital intensity is proxied by the ratio of net property, plant, and
12
equipment (PPE) to sales. Innovative activities are affected by institutional holdings, so we
include the percentage of shares held by institutional investors. We also control for past stock
performance in the base model.
Coles, Daniel, and Naveen (2006) show that CEO incentive matters for firm risk taking. In
additional tests, we include controls that take into account CEO incentives: CEO Delta, defined
as the dollar change in a CEO’s stock and option portfolio for a 1% change in stock price,
measures the CEO’s incentives to increases in stock price. CEO Vega, defined as the dollar
change in a CEO’s option holdings for a 1% change in stock return volatility, measures the risktaking incentives generated by the CEO’s option holdings. We calculate CEO Delta and CEO
Vega values using the one-year approximation method of Core and Guay (2002).
Hirshleifer, Low, and Teoh (2012) show that overconfident CEOs invest more in innovation,
so we include measures of CEO overconfidence as explanatory variables. The first
overconfidence measure (CEO Confidence Options) uses data on option compensation following
Malmendier and Tate (2005). This variable takes a value of one if a CEO postpones the exercise
of vested options that are at least 67% in the money, and zero otherwise. The intuition is that it is
optimal for risk-averse and undiversified executives to exercise their own-firm stock options
early if an option is sufficiently in the money (Hall and Murphy (2002)). The second
overconfidence measure (CEO Confidence Press) relies on keyword searches of the text of press
articles in Factiva, following Malmendier and Tate (2008). This variable takes a value of one
when the number of “confident” articles for a CEO in Factiva is greater than the number of
“cautious” articles, and zero otherwise. We also control for the total number of press mentions
over the same period (Total Press Mention).
13
3. General managerial ability and innovation
In this section, we test the hypothesis that CEOs with more general ability spur innovation.
3.1. Univariate tests
Table 1 shows summary statistics for the scale, success, and novelty of corporate innovation,
CEO characteristics, and firm characteristics. The average firm in the sample has an R&D-toassets ratio (R&D) of 3.9%; files 32 patents per year; and subsequently receives 211 citations for
its patents. It also engages more in exploratory than exploitative research: The average
Exploratory Ratio is more than double the average Exploitative Ratio.
Table 2 compares sample means for specialist and generalist CEOs. A generalist CEO is
defined as a top executive who has a GAI above the median in a given year. Firms with
generalist CEOs versus specialist CEOs invest 0.6 percentage points more of their assets in
R&D: and file more than double the patents (43 versus 20); and these patents generate more than
twice as many subsequent citations (285 versus 137). Firms with generalist CEOs also seem to
produce more novel knowledge, as measured by the Innovative Diversity variable of a firm’s
patent portfolio and its Search distance. The Search Distance variable is 25% higher for
generalists, while the Innovative Diversity variable is 51% higher for generalists. The patents
produced by generalists both make use of and produce more general knowledge, as measured by
the Originality Index and the Generality Index, which are 36% and 43% higher for generalists
versus specialists. Finally, firms with generalist CEOs seem to engage more in both exploratory
and exploitative activities (albeit relatively more in exploratory) than firms with specialist CEOs.
The univariate tests suggest an economically meaningful difference in innovation output by
firms with generalist CEOs. At this stage, however, we cannot attribute these differences just to
general managerial ability, as other firm and CEO factors could explain the patterns.
14
3.2. R&D expenditures
Table 3 tests whether generalist CEOs devote more resources to innovation. The dependent
variable in these regressions is R&D expenditure divided by firm total assets, expressed as a
percentage (R&D). The main independent variable of interest is the GAI. We control for
industry-year pair fixed-effects in columns (1) and (3), and for firm and year fixed-effects in
columns (2) and (4). Standard errors are clustered by firm to account for within-firm correlation.
The industry-year fixed-effects control for innovation shocks that are specific to a given industry
and year. Firm fixed-effects control for unobserved time invariant firm heterogeneity.
Following Aghion, Van Reenen, and Zingales (2013), we introduce fixed effects into the
models using the pre-sample mean scaling method first proposed by Blundell, Griffith, and Van
Reenen (1999). Essentially, we exploit the fact that we have a long pre-sample history of R&D
expenditures and patenting behavior (of up to 25 years per firm) to construct the pre-sample
average of cite-weighted patents. This can then be used as an initial condition to proxy for
unobserved heterogeneity under certain conditions (in particular, the first moments of the
variables must be stationary).
We find that R&D is positively and significantly associated with a CEO’s general ability. A
one-standard deviation in GAI is associated with a 0.5 percentage point higher R&D. This
represents 13% of the average R&D of 3.9%. The coefficients of the control variables are in line
with findings in other studies. Smaller firms and less capital-intensive firms tend to spend more
on R&D.
3.3. Patent filing and citations
Table 4 examines the relation between filed patents and the general ability of CEOs. The
dependent variable is the natural logarithm of one plus the number of patents (Patents) in a given
15
year in the ordinary least squares regressions in columns (1)-(4). Hall, Jaffe, and Trajtenberg
(2001) also recommend using count-based models such as negative binomial as alternatives to
the ordinary least squares regression model.9 In negative binomial regressions in columns (5)-(8)
the dependent variable is Patents. We use firm control variables and industry-year pair fixedeffects in columns (1), (3), (5), and (7), and in columns (2), (4), (6), and (8) we use firm and year
fixed-effects using the pre-sample mean scaling method (Blundell, Griffith, and Van Reenen
(1999)).10 Standard errors are clustered by firm to account for within-firm correlation.
We find that firms with generalist CEOs have higher patent counts. The estimates in columns
(1)-(4) indicate that a one-standard deviation increase in GAI is associated with an additional
10% in Patents. When we control for stock returns and institutional ownership in addition to firm
size and tangibility in columns (3) and (4), we find a similar effect of GAI on Patents. The
negative binomial regression estimates confirm the findings using ordinary least squares
regression, and suggest that GAI has an effect on patent counts as great as 32%.
In Table 5 we measure the success of innovation activity using the number of times a firm’s
patents are cited in subsequent patents. The dependent variable in the ordinary least squares
specifications in columns (1)-(4) is the natural logarithm of one plus citations count (Citations),
and Citations in the negative binomial model in columns (5)-(8). Panel A shows the results using
Citations, and Panels B and C show the results using Citations adjusted for truncation bias using
two alternative methods (Q Citations and TT Citations).
The GAI coefficient is positive and significant. The ordinary least squares estimates in Panel
9
Different assumptions with respect to the properties of the error term generate different estimators. A Poisson
model assumes the mean equals the variance, and a negative binomial model relaxes this assumption. In Table IA.II
in the Internet Appendix, a Poisson regression model produces consistent results.
10
Estimates using within-sample firm fixed-effects and CEO fixed-effects are similar as to number of patents, but
less precise for citation counts (cite-weighted measures of patents). Table IA.I in the Internet Appendix reports these
estimates as well regression using changes of dependent and explanatory variables.
16
A, columns (1)-(4), suggest that a one-standard deviation increase in GAI is associated with up to
11%-15% more citations to patents produced by a firm. Using the negative binomial regression
model in Panel A, columns (5)-(8), the GAI coefficient varies between 0.19 and 0.26. These
results suggest that generalist CEOs produce more citation counts, and the effect is both
statistically and economically important. We also obtain consistent estimates using citation
measures adjusted for truncation bias, Q Citation and TT Citations, in Panels B and C of Table 5.
These results overall show a positive and significant relation between GAI and citation counts,
which we take as an indication of the success and effectiveness of innovation activities.
3.4. Relatedness of innovation
We hypothesize that firms with generalist CEOs produce more novel innovation. Generalist
CEOs have more outside options in the executive labor market, which can serve as a mechanism
of tolerance for failure. Thus, generalist CEOs should be willing to take riskier growth
opportunities. We also test whether, while innovating, firms headed by generalist CEOs make
use of a more diverse set of current patents, and whether the patents they produce are also cited
by a more diverse set of technological classes.
To perform these tests we use Innovative Diversity and Search Distance variables for firms’
patent portfolios and new patents, as well as the Originality Index and Generality Index for
knowledge input and output in the firm’s innovation process. We run ordinary least squares
regressions with industry-year fixed-effects and use the same set of controls as in Tables 3-5.11
Table 6 shows the results. We find that firms with generalist CEOs tend to hold a more
diversified portfolio of patents. A one-standard deviation increase in GAI is associated with an
11
We do not use firm fixed-effects for these tests because for the measures calculated with the patent portfolio over
a five-year period we do not have sufficient time series variation for each firm.
17
increase of 0.03 (column (1)) in Innovative Diversity. Column (3) shows that generalist CEOs are
associated with a significant increase in Search Distance of 0.01. This implies that firms headed
by CEOs with general managerial skills are reaching for new technologies that are further from
their current patent portfolios.
The results in columns (5)-(8) using the Originality Index and the Generality Index suggest
that firms with generalist CEOs not only hold more diverse portfolios of patents, but also make
use of and produce a more diverse set of knowledge. The Originality Index (columns (5) and (6))
increases by about 0.03 for a one-standard deviation increase in GAI. The effect is similar for the
Generality Index (columns (7) and (8)).
3.5. Innovation strategy
Manso (2011) among others differentiates exploratory and exploitative activities in the
innovation process. We test the hypothesis that generalist CEOs are more willing to encourage
innovation strategies that pursue both these objectives. Still, we conjecture that for generalist
CEOs the effect should be more pronounced for exploratory activities, which are intrinsically
more uncertain.
Table 7 shows the results. The dependent variables are the Exploratory Ratio 80 (60) and the
Exploitative Ratio 80 (60). We run ordinary least squares regressions with industry-year pairs
fixed-effects. The GAI coefficient is positive and significant for both the Exploratory Ratio and
Exploitative Ratio dependent variables, but the relation between general skills and innovation is
more pronounced for exploratory than exploitative innovation. The coefficient of GAI in the
Exploitative Ratio regressions is positive and significant, but the coefficients in the Exploratory
Ratio regressions are about three times higher.
Overall, the results are consistent with a conclusion that firms run by generalist CEOs
18
produce more innovation because top management have been exposed to different industries,
firms, and roles in the past. This might help them to promote the R&D teams in the organization
to think outside the box and bring solutions from other contexts to produce more original
innovation.
4. Identification and additional results
There is some concern that our estimates could be biased due to endogeneity of matching
between CEO types and firms. That is, there may be omitted factors correlated with both
innovation and the generality of human capital of a CEO. Despite inclusion of firm-level controls
and industry-year fixed-effects, the GAI coefficient might still be biased. Firms that choose
generalist CEOs may be the same firms that produce more innovation.
We have addressed this concern at least partially by including firm fixed-effects to account
for any unobservable firm characteristic that are time-invariant. Given that our sample period
encompasses only 11 years, the fixed-effect estimator is quite effective in controlling for firmlevel unobservable variables (as opposed, for instance, to including a firm fixed-effect in a panel
of 50 years, where these unobservable variables are likely to change over such a long time).
Nevertheless, firms might decide to change their policies and management team simultaneously
– perhaps to start innovating and choose a generalist CEO as part of a new business strategy – so
the firm fixed-effect would not be enough to identify the effect of GAI on innovation.
4.1. Propensity score match
To address the endogenous matching concerns, we first use propensity score matching to
compare firms run by generalist CEOs (treatment group) with firms run by specialist CEOs
(control group) with virtually no observable differences in firm and CEO characteristics. A
19
generalist (specialist) CEO is defined as a CEO with a GAI above (below) the yearly median. We
construct the control group of specialist CEOs using the nearest-neighbor method with scores
given by a probit regression model of a dummy variable that takes a value of one for generalist
CEOs and zero for specialist CEOs. The explanatory variables are firm characteristics (Sales,
Leverage, Tobin’s Q, ROA, Volatility, Diversification Dummy, Stock Return, Cash, CAPEX,
Firm Age, R&D) and CEO characteristics (CEO Tenure, CEO Age, External Hire Dummy, MBA
Dummy, CEO-Chair Dummy) as well as industry-year dummies.
Panel A of Table 8 reports estimates of the probit regression model. CEOs with more
accumulated general human capital tend to be older, to be hired from outside the firm, to hold a
master of business administration (MBA) degree, and to have a shorter tenure than specialist
CEOs. As expected, we find that firms with generalist CEOs are bigger and more diversified. We
also find that firms with generalist CEOs have more cash holdings and lower capital
expenditures (CAPEX), but invest more in R&D.
Panel B of Table 8 reports the average treatment effect (ATT) estimates, which are consistent
with those obtained using panel regressions in Tables 4-6. Firms with generalist CEOs produce
11% more patents, which subsequently generate 19% more citations than firms with specialist
CEOs. We also find that firms with generalist CEOs have a statistically higher Innovative
Diversity. The propensity score matching results indicate that the non-random assignment of
generalist CEOs to more innovative firms (at least based on observable firm and CEO
characteristics) does not explain our main findings.
4.2. Instrumental variable estimates
Despite the battery of tests run so far, we cannot conclude that causality runs from generalist
managerial skills to corporate innovation. We use instrumental variables methods to address this
20
issue further. We make use of non-compete agreements as an instrument for the generality of
human capital of the CEO. Non-compete agreements are contracts that prevent employees from
joining or creating a competing company in their next job. Garmaise (2009) find that 70% of the
firms in the sample have non-compete agreements with their top executives. Bishara, Martin, and
Thomas (2013) report that non-compete clauses are frequent in CEO contracts (79% of contracts
have this sort of clause in the 1993-2010 period) with some restricting CEO’s post-employment
activities for more than four years. Additionally, there has been a significant trend toward the use
of non-compete clauses in CEO contracts over time. These findings are consistent with other
research on the frequency of noncompete provisions in contracts with entrepreneurs and CEOs
(Kaplan and Stromberg (2003), Gillan, Hartzell, and Parrino (2005)).
The enforceability of these clauses exhibits both cross-sectional variation (varying across
U.S. states) and time series variation (differing in dates of adoption). The cross-sectional and
time series variation of the instrument helps to rule out the concern that other state-level
characteristics explain both GAI and innovation. We use the index on the enforceability of noncompete agreements in Garmaise (2009) during the career of the CEO as an instrument for GAI.
The index takes values between a minimum of zero (e.g., California) and a maximum of nine
(e.g., Florida after 1997).
We follow the career path of the CEO and create a Non-Compete Enforcement Index for each
CEO-year observation, which is the average of the non-compete agreement enforcement index at
the state-year level across all positions the CEO has had in publicly traded firms.12 The index is
based on the location of the firm’s headquarter. This mitigates the concern that the CEO could
12
Non-compete clauses are infrequent in non-executive position contracts. Table IA.III in the Internet Appendix
shows that we obtain similar instrumental variable estimates when we calculate the Non-Compete Enforcement
Index excluding past non-executive positions.
21
strategically choose where to live to avoid non-compete clauses such as living in neighbor states.
A good instrument should be correlated with the endogenous variable (GAI), but not with the
error term on the dependent variables of interest (innovation). We expect the Non-Compete
Enforcement Index to be positively related to GAI since the enforcement of non-compete
agreements limits within-industry transfers and enhances between-industry transfers,
contributing to the accumulation of general managerial skills. Consistent with this idea,
Garmaise (2009) finds that executive job transfers within-industry decline with the level of noncompete enforceability faced by the firm, while transfers between-industry rise.13
There is also a distinction between the ex-ante effects of non-compete agreements (human
capital investment) and the ex-post effects (labor mobility) as suggested by Posner, Triantis, and
Triantis (2004). We expect executives to have an ex-ante incentive to accumulate more general
skills in states with stronger enforcement of non-compete clauses. The idea is that if managers
anticipate moving across industries they might decide to invest more in general human capital
than in firm-specific knowledge to enable more outside options and facilitate ex-post mobility.
Garmaise (2009) offers supporting evidence of this idea. In high-enforcement states, managers
receive lower compensation and more of it in the form of salary, and firms invest less in capitalintensive production.
The second important assumption of the instrumental variables method is that the instrument
should be a variable that can be excluded from the list of variables affecting the variable of
interest (innovation). The exclusion restriction is likely to be satisfied as ex-ante past career
decisions of managers and their positions are not likely to be directly correlated with the
13
Marx, Strumsky, and Fleming (2009) show that non-compete enforcement constrains mobility more for inventors
with firm-specific skills than for those who specialize in narrow technical fields, by exploiting Michigan’s
inadvertent 1985 reversal of its non-compete enforcement policy as a natural experiment.
22
innovation policy of firms where they are currently top managers.
Table 9 shows the results of the instrumental variables estimation for the Patents, Citations,
and Innovative Diversity variables. The regressions include the same control variables as in
previous tables as well as firm and year fixed-effects. The first-stage regression also includes
state fixed-effects. Column (1) reports the first-stage regression estimates. We find that the NonCompete Enforcement Index coefficient is positively and significantly correlated with GAI with a
t-statistic of 5.2.
Columns (2)-(6) present second-stage regression estimates. The effect of GAI on the number
of filed patents is positive and significant at 0.516. The effect of GAI on citations is also positive,
and this is also true when we adjust the citation measure for truncation bias. Finally, we confirm
that firms with generalist CEOs tend to hold more diversified portfolios of patents.
The enforceability of non-compete clauses is harder across states and typically has limited
geographic scope. The idea is that the contract might not have been enforced if the executive
deliberately moved to another state in order to take a job in another firm in the same industry. To
address this problem, we estimate the instrumental variables regression in Table 9 using a sample
that excludes executives who moved to another state but stayed in the same industry at some
point in their professional careers. The results in Panel B of Table IA.III in the Internet Appendix
remain consistent with a positive relation between innovation and GAI.
In alternative, Garmaise (2009) uses the interaction of the Non-Compete Enforcement Index
with the level of in-state competition, because the effect should be more pronounced when a firm
is exposed to more intense in-state competition. In the case of considerable in-state competition,
a high Non-Compete Enforcement Index will substantially reduce the probability that an
executive will leave the firm and join a competitor. The limitation of using the interaction of the
23
Non-Compete Enforcement Index with the level of in-state competition as instrument has to do
with a possible violation of the exclusion restriction, because the level of in-state competition is
likely to be related to innovation through channels other than general human capital.
The effects of GAI on corporate innovation using instrumental variables methods are similar
to those in our main regression tests, suggesting that the positive impact of general managerial
skills on innovation is robust to endogeneity concerns. Overall, the results support a causal effect
of the general ability of CEOs on firm innovation output. We find that making the human capital
of a CEO more general generates an increase in both number of filed patents and citations of
those patents.14
4.3. Value of outside options
So far the results are consistent with the idea that generalist managers innovate more because
their general skills and their potential mobility act as a mechanism of tolerance for failure. Next
we explore exogenous variation in the value of outside options of CEOs to test this hypothesis.
We use measures of local labor market conditions as a source of exogenous variation in the value
of outside options.
In the presence of geographic segmentation, Oyer’s (2004) wage indexation theory implies
that relevant outside opportunities for an employee are likely to come from other firms in the
same region rather than from firms that are farther away. A first proxy for the value of the
outside options of managers is the tightness of a local labor market (Kedia and Rajgopal (2009)).
14
A remaining concern with the instrument is about the validity of the exclusion restriction because of location
decisions of the CEO. There might be an unobserved CEO characteristic that is correlated both with current
innovation and the Non-Compete Enforcement Index, which makes innovation linked to the instrument for other
reasons than GAI. This concern is mitigated by the fact that the Non-Compete Enforcement Index is time varying
within states.
24
As demand for workers exceeds supply in tight labor markets, managers are more likely to
receive outside job offers from other firms in the region. Moreover, generalist managers should
benefit more than specialists in tight labor markets because their skills are transferable across
firms and industries. Thus, we expect the relation between innovation and the GAI to be more
pronounced in tight labor markets.15
A second proxy is the local beta, that is, the degree of comovement between a firm’s stock
price and stock prices of other firms in the Metropolitan Statistical Area (Pirinsky and Wang
(2006), Kedia and Rajgopal (2009)). Specialist managers are less likely to have outside job
opportunities from firms in the same region when their firm has a low local beta. This is not the
case with generalists, as they have skills that can be applied elsewhere. Thus, we expect to find a
stronger relation between innovation and GAI in the sample of firms with low local beta.
Table 10 presents the results of regressions of Patents, Citations, and Innovative Diversity
variables on general managerial effects, taking into account the value of outside options. The
regressions include the same control variables and industry-year dummies as in previous tables.
Panel A presents estimates of regression that include interaction between the GAI and Tight
Labor Market Dummy as an explanatory variable. Tight Labor Market Dummy takes a value of
one if the unemployment rate for a year in the MSA exceeds the median unemployment rate for
the MSA over the full sample period. The unemployment data are from the Bureau of Labor
Statistics.
The interaction term coefficient is positive and significant in columns (1)-(4), indicating a
15
Although there is less geographic segmentation of labor markets for top executives than for other workers, there
is evidence indicating that geography does impact the CEO labor market. Yonker (2009) shows that geography
affects both labor supply and labor demand in the market for CEOs, and Bouwman (2013) shows that geography
affects CEO compensation.
25
stronger relation between corporate innovation (measured by patents or citations) in tight labor
markets. We interpret this result as showing that better outside options of generalist versus
specialist managers in tight labor markets act as a mechanism of tolerance for failure that makes
generalists more willing to exploit innovative growth opportunities.
Panel B of Table 10 presents estimates of regressions that include interaction between the
GAI and Low Local Beta Dummy as explanatory variable. The Low Local Beta Dummy takes a
value of one if the local beta is below the top decile of the distribution, and zero otherwise. The
local beta is estimated using a time series regression of monthly stock return on the return of the
stock’s corresponding MSA index (excluding the particular stock) as well as the return on the
market portfolio and the stock’s 48 Fama-French industry return over two different periods,
1993-1997 and 1998-2003. We require at least 24 non-missing monthly return observations for a
stock, and that there be five stocks in the MSA to enter the regression. Returns in excess of
monthly T-bill rates are taken from CRSP.
The interaction term coefficient is positive and significant in columns (1)-(4), which is
consistent with the idea that the relation between innovation and GAI is attributable to the better
outside options of generalist than specialist managers.
4.4. CEO compensation and overconfidence
A CEO’s decision to support innovation activities is likely to be affected by his or her
incentives such as compensation. Panel A of Table 11 shows the results when we control for
Log(CEO Delta) and Log(CEO Vega). We find very similar GAI coefficients to those in Tables
4-6. The signs of the coefficients of Log(CEO Delta) and Log(CEO Vega) are in line with those
in Coles, Daniel, and Naveen (2006) and Hirshleifer, Low, and Teoh (2012). CEO Delta has a
negative and significant coefficient, which suggests that pay that is more sensitive to stock
26
performance is negatively associated with innovation. CEO Vega has the expected positive
coefficient, as a higher CEO Vega incentivizes risk taking, but it is not statistically significant.
Overall, the results show that differences in CEO pay contracts do not explain the effect of
general human capital on corporate innovation.16
Hirshleifer, Low, and Teoh (2012) show that overconfident CEOs are more willing to invest
in risky projects. A possible explanation for our results may be that generalist CEOs who are
more mobile across firms and industries are also more overconfident, and this may explain why
they are more innovative. Panels B and C of Table 11 shows the results of regressions
controlling for CEO Confidence Options and CEO Confidence Press. The effect of the CEO
Confidence Press on innovation is positive and significant (Panel C), while the coefficient of the
CEO Confidence Options is positive but insignificant in some specifications (Panel B). The
estimated GAI coefficient does not change significantly from our baseline tests in Tables 4-6. We
conclude that overconfidence of CEOs and their general managerial ability are different
mechanisms by which CEOs foster corporate innovation.
4.5. R&D productivity
Another concern we address is that generalist CEOs file more patents and have more
citations simply because they spend more on R&D. Table 12 controls directly for the level of
R&D expenditures using the ratio of R&D-to-assets (Panel A) or R&D Stock (Panel B). When
we control for R&D, the general ability of CEOs has a slightly weaker effect, but we still find an
economically and statistically significant coefficient across all specifications, except for the TT
16
We thank David Hirshleifer, Angie Low, and Siew Hong Teoh for sharing data on proxies of CEO
overconfidence that they use in Hirshleifer, Low, and Teoh (2012).
27
Citations variable in Panel A. That is, results on the relation between innovation and general
managerial ability are not explained by generalist CEOs spending more on R&D. The GAI
coefficients are positive and significant and only slightly reduced than in base specifications in
Tables 4-6. This suggests that the primary effect of general managerial skills is to enhance the
quality and the productivity of R&D rather than simply stimulate added R&D.
An alternative measure of innovation is total factor productivity (TFP). We estimate a firmlevel regression in which the dependent variable is total sales, and the explanatory variables are
the GAI and the logarithms of Labor (as proxied by number of employees) and Capital (as
proxied by net property, plant, and equipment). Table IA.IV in the Internet Appendix shows the
estimates. We find that the GAI is also associated with significant increases in TFP. A onestandard deviation increase in GAI is associated with a 5% to 7% increase in TFP.
We also run regressions using Tobin’s Q as the dependent variable and GAI as the main
explanatory variable. As in Custódio, Ferreira, and Matos (2013), the GAI coefficient is positive
but statistically insignificant. This insignificant relation between the GAI and firm performance
may occur because performance is endogenous. However, this result does not mean that
innovation is not affected by general human capital, or that innovation does not increase
shareholder value. If there is an optimal matching based on CEO type (generalist versus
specialist) and firm innovation policy, we will not observe cross sectional differences in firm
performance based on CEO type. In other words, if we replaced a “well matched” generalist with
a specialist CEO, we would observe a reduction in shareholder value.
4.6. Robustness checks
Results of several robustness tests of our primary findings in Tables 4-6 are presented in the
Internet Appendix. We discuss them briefly here.
28
There are two causally distinct interpretations of the results so far. One is that general skills
encourage managers to undertake risky endeavors such as innovation because they have more
outside options should they fail. The other is that firms with promising opportunities for
innovative projects appoint CEOs with general skills. The instrumental variable estimates
address this concern by using exogenous variation in managerial skills.
To gain further insight into whether our findings are driven by a causal effect of managers on
innovation, we restrict the sample to firm-years for which CEO-firm endogenous matching is
likely to be less important, and in which CEOs are more likely to make an impact on the
innovation process. Specifically, we exclude firm-year observations in which the CEO has been
recently appointed, and vary the tenure cutoff from one to five years. Table IA.V summarizes the
results. The GAI coefficient continues to be positively related to patent and citation measures to a
similar degree, regardless of the tenure cutoff. These findings suggest that the relation between
GAI and innovation does not come mainly from innovative firms’ endogenous selection of
managers with general skills.
One potential concern with interpretation of the results is that generalist CEOs might be
matched to firms in more innovative industries. We split our sample into innovative industries
(with average Q Citations per patent for the industry in a given year of above the median) and
non-innovative industries (with average Q Citations per patent for the industry in a given year of
below the median) to further address this concern. Table IA.VI shows that the positive relation
between innovation and general managerial skills holds both for innovative and non-innovative
industries but is substantially stronger for innovative industries. This supports the idea that
innovation by generalist CEOs is not driven purely by the matching of generalist CEOs to firms
in these industries. A related concern is that generalist CEOs are more likely to have worked in
29
innovative industries in the past, which is where they have acquired their ability to innovate.
Because our findings are valid for both innovative and non-innovative industries, however, this
is unlike to be the case.
A possible interpretation of the patent citation results is that firms with generalist CEOs
simply have more citations because they file more patents. We address this concern using
measures of citations per patent, which assess innovation success on a per-patent basis. In
another test, we exclude self-citations at the firm level when calculating citation counts. The
results in Table IA.VII and IA.VIII using per patent measures of innovation and excluding selfcitations, respectively, remain similar.
We have addressed concerns with omitted variable bias including several observable firm
and CEO characteristics and using industry-year fixed-effects or firm fixed-effects in the
regressions. We further control for firm characteristics (Volatility, Firm Age, Diversification
Dummy, Board Size, Board Independence, Herfindahl Index) and several takeover defenses
(Staggered Board Dummy, Supermajority Dummy, Poison Pill Dummy, Cumulative Voting
Dummy).17 Table IA.IX shows that the results remain similar when we include these additional
firm-level control variables. Table IA.X includes additional CEO characteristics as controls
(CEO Tenure, CEO Age, MBA Dummy, Ivy League Dummy, Recession Graduate Dummy, Fast
Track Career CEO). Results are also robust to the inclusion of CEO fixed effects (see Table IA.I
in the Internet Appendix), which control for unobserved time-invariant CEO heterogeneity such
as innate talent, mobility, or risk aversion.
The tests so far use contemporaneous independent variables. In alternative specifications we
17
We obtain similar findings when we include the Gompers, Ishii, and Metrick (2003) governance index of
takeover defenses, rather than individual takeover defenses.
30
lag the independent variables by one, two, or three years. We also estimate regressions using
changes for both the dependent variable and explanatory variables that focus directly on whether
changes in the GAI subsequently affect changes in innovation. The results of these robustness
checks in Table IA.XI confirm our main finding that generalist CEOs promote more and betterquality innovation.
We also perform some robustness checks related to the construction of the GAI. We use a
dummy variable that takes a value of one for generalist CEOs (i.e., top executives with a GAI
above the median in a given year) instead of a continuous variable. Table IA.XII shows the
results. We find that generalist CEOs produce 20% more patents and 26% more citations than
specialist CEOs. Finally, separate regressions run for each individual component of the GAI in
Table IA.XIII show that all individual components are positively associated with innovation
except past experience as CEO.
We also consider several other alternative explanations of a positive relation between
innovation and GAI. Bena and Li (2014) find that firms with large patent portfolios and low
R&D are acquirers, while firms with high R&D and slow growth in patent generation are targets.
Thus, synergies from combining innovation efforts are important drivers of acquisitions. A
possible interpretation of our results is that generalists CEOs promote in-house innovation, while
specialists acquire innovation through mergers and acquisitions. If this is the case, specialists
would not file patents but would still promote innovation. Even though generalists may have
better negotiation skills, it could also be the case that specialists are better at evaluating the
potential synergies of an acquisition, or at identifying good innovation targets.
To address these possibilities, we run regressions to see whether the M&A activity of a firm
is correlated with the GAI. The dependent variable is the Acquisitions Ratio, defined as the value
31
of acquisitions divided by total assets, or the Number of Acquisitions in a given year. We
calculate the Acquisitions Ratio using both the Compustat (data item AQC, which includes cash
acquisitions only) and SDC databases. Number of Acquisitions is given by the number of deals in
the SDC database in which the firm is an acquirer in a given year. The estimates in Table IA.XIV
show that firms with higher patent counts make fewer acquisitions. There is no evidence,
however, that general skills affect the firm’s acquisitions activity in a significant way. The GAI
coefficient is positive and the interaction Patents  GAI coefficient is negative, but they are not
statistically insignificant. If anything, we find that generalists tend more to produce in-house
innovation rather than acquire innovation.
Another possibility is that generalist CEOs are exposed to lower risk of termination
following poor firm performance, which could explain why they promote innovative
opportunities. It is also possible that specialist CEOs might be less sensitive to bad performance
as firms have fewer options available in the executive marketplace to replace them. When
generalist CEOs are poor, firms can quickly replace them because there are more generalists
available in the market. To address these possibilities, we estimate probit regressions in which
the dependent variable is a dummy that takes a value of one if there is a CEO turnover in a given
firm-year. The explanatory variables of interest are interactions between past firm accounting
and stock performance (ROA and Stock Return) and the GAI.
Panel A of Table IA.XV shows the results of the CEO turnover-performance sensitivity
regressions using a sample of all turnovers (CEO Turnover Dummy) in Panel A and a sample of
forced turnovers only (Forced CEO Turnover Dummy) in Panel B. We find a positive relation
between the GAI and CEO turnover, but the relation does not seem to be triggered by poor firm
performance. We find no difference in the sensitivity of CEO turnover to prior firm performance
32
for generalist and specialist CEOs. The interaction term between the GAI and firm performance
is not statistically significant in any of the specifications.18
5. Conclusion
Our examination of whether CEO general managerial skills matter for corporate innovation
finds that CEOs who gain more human capital through their lifetime work experience promote
more and more novel innovation in the organizations that they run. Patent-based metrics indicate
that generalist CEOs promote innovation in the form of both patents with higher impact and
more diversified patent portfolios. Generalist CEOs also incentivize firms to pursue more
exploratory knowledge creation activities. We provide evidence consistent with a causal link
from the generality of CEO human capital to a willingness to take risks using an instrument for
general skills based on the state and time variation in the enforceability of non-compete
agreements.
Our findings support the idea that generalist executives encourage firms to pursue risky
innovation opportunities. While specialist CEOs have skills valuable only within an
organization, generalist CEOs have skills that can be applied elsewhere. Thus, generalist CEOs
have more outside options, which act as a labor market mechanism of tolerance for failure rather
than internal mechanisms such as executive compensation plans. Given the growing importance
of a knowledge-based economy, we provide new insight into why general managerial skills
command a compensation premium in the executive labor market. Future research should
investigate how the growing importance of human capital can impact other corporate outcomes.
18
We thank Dirk Jenter for providing us with the forced CEO turnover data used in Jenter and Lewellen (2010).
33
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38
Table 1
Summary Statistics
This table presents the mean, median, standard deviation, minimum, maximum, and number of observations for each variable.
The sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from BoardEx
and operate in the same four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003 period.
Financial and utility firms are omitted. Variable definitions are provided in Table A1 in the Appendix.
R&D
R&D Stock
Patents
Citations
Q Citations
TT Citations
Q Citations (per patent)
TT Citations (per patent)
Innovative Diversity
Search Distance
Originality Index
Generality Index
Exploitative Ratio 80
Exploitative Ratio 60
Exploratory Ratio 80
Exploratory Ratio 60
General Ability Index
CEO Delta
CEO Vega
CEO Confidence Options
CEO Confidence Press
Total Press Mention
CEO Tenure
CEO Age
External Hire Dummy
MBA Dummy
CEO-Chair Dummy
Non-Compete Enforcement Index
Sales
PPE
Stock Return
Institutional Ownership
Leverage
Tobin's Q
ROA
Volatility
Diversification Dummy
Cash
CAPEX
Firm Age
Standard
Number of
Mean
Median
deviation
Minimum
Maximum observations
Panel A: Innovation Measures
0.039
0.010
0.060
0.000
0.271
10,517
0.106
0.026
0.179
0.000
1.020
10,517
31.7
1.0
152.0
0.0
4,339.0
10,523
210.6
0.0
1,321.5
0.0
45,512.0
10,523
508.1
0.0
2,981.8
0.0
104,792.0
10,523
15.3
0.0
72.9
0.0
2,038.1
10,523
7.5
0.0
15.0
0.0
295.7
10,523
0.338
0.000
0.734
0.000
17.087
10,523
0.345
0.344
0.345
0.000
0.941
10,523
0.109
0.000
0.173
0.000
1.107
10,523
0.423
0.533
0.359
0.000
0.945
10,523
0.392
0.462
0.355
0.000
0.944
10,523
0.097
0.000
0.189
0.000
1.000
10,523
0.140
0.000
0.237
0.000
1.000
10,523
0.278
0.000
0.358
0.000
1.000
10,523
0.335
0.000
0.389
0.000
1.000
10,523
Panel B: CEO Characteristics
-0.020
-0.182
0.958
-1.504
5.854
10,523
0.002
0.001
0.004
0.000
0.179
7,858
0.045
0.025
0.110
0.000
7.727
7,858
0.617
1.000
0.486
0.000
1.000
9,339
0.106
0.000
0.308
0.000
1.000
8,058
3.3
0.0
13.4
0.0
360.0
8,058
8.0
6.0
6.9
1.0
53.0
10,523
55.3
56.0
7.6
29.0
89.0
10,071
0.390
0.000
0.488
0.000
1.000
10,523
0.302
0.000
0.459
0.000
1.000
10,523
0.648
1.000
0.478
0.000
1.000
10,523
4.0
4.7
2.0
0.0
9.0
8,187
Panel C: Firm Characteristics
4,288.5
1,062.0
12,988.1
0.0
257,157.0
10,516
0.388
0.228
0.511
0.012
2.985
10,480
0.167
0.094
0.534
-0.775
2.208
9,134
0.602
0.625
0.197
0.000
1.319
10,319
0.220
0.211
0.176
0.000
0.828
10,478
2.3
1.7
1.6
0.8
8.9
10,495
0.146
0.148
0.101
-0.224
0.417
10,471
0.128
0.109
0.072
0.029
0.374
10,453
0.526
1.000
0.499
0.000
1.000
10,039
0.141
0.060
0.178
0.001
0.751
10,515
0.064
0.050
0.053
0.000
0.286
10,390
22.7
16.0
19.9
0.0
78.0
10,473
39
Table 2
Innovation and General Managerial Ability: Univariate Tests
This table presents the mean of innovation measures for the sample of generalist CEOs (those with General Ability Index above
the yearly median) and specialist CEOs (those with General Ability Index above the yearly median), the associated difference and
its t-statistic. The sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from
BoardEx and operate in the same four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003
period. Financial and utility firms are omitted. Variable definitions are provided in Table A1 in the Appendix.
R&D
Patents
Citations
Q Citations
TT Citations
Innovative Diversity
Search Distance
Originality Index
Generality Index
Exploitative Ratio 80
Exploitative Ratio 60
Exploratory Ratio 80
Exploratory Ratio 60
Generalist CEOs
0.042
43.4
284.6
681.9
20.4
0.415
0.121
0.488
0.461
0.111
0.160
0.319
0.387
Specialist CEOs
0.036
20.0
136.8
334.7
10.2
0.275
0.097
0.359
0.324
0.084
0.119
0.237
0.283
40
Difference
0.006
23.3
147.7
347.1
10.3
0.140
0.024
0.129
0.138
0.027
0.041
0.082
0.103
t-statistic
5.569
7.893
5.743
5.981
7.246
21.290
7.151
18.786
20.247
7.348
8.911
11.882
13.744
Table 3
R&D Expenditures and General Managerial Ability
This table presents estimates of ordinary least squares panel regressions of the ratio of R&D expenditures to total assets (R&D)
on the General Ability Index and firm-level control variables. The sample consists of EXECUCOMP firms for which chief
executive officer (CEO) profile data are available from BoardEx and operate in the same four-digit SIC industries as the firms
included in the NBER patent database in the 1993-2003 period. Financial and utility firms are omitted. Regressions include
industry-year dummies or year dummies in the case of the firm fixed effects model. Firm fixed effects using the Blundell,
Griffith, and Van Reenen (1999) pre-sample mean scaling estimator. Variable definitions are provided in Table A1 in the
Appendix. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the
10%, 5% and 1% levels respectively.
General Ability Index
Log (Sales)
Log (PPE)
(1)
(2)
(3)
(4)
0.464***
[4.646]
-0.684***
[-7.406]
0.007
[0.064]
0.411***
[4.112]
-0.605***
[-6.566]
-0.024
[-0.203]
0.023
[0.223]
-1.586***
[-2.857]
Yes
8,925
0.466
0.453***
[4.412]
-0.892***
[-8.474]
-0.400***
[-4.044]
0.404***
[3.897]
-0.791***
[-7.369]
-0.349***
[-3.374]
0.098
[1.016]
-0.984*
[-1.894]
Yes
8,925
0.422
Stock Return
Institutional Ownership
Firm fixed effects
Number of observations
R-squared
No
10,478
0.465
41
No
10,478
0.430
Table 4
Patent Counts and General Managerial Ability
This table presents estimates of ordinary least squares and negative binomial panel regressions of the number of patents (Patents)
on the General Ability Index and firm-level control variables. The sample consists of EXECUCOMP firms for which chief
executive officer (CEO) profile data are available from BoardEx and operate in the same four-digit SIC industries as the firms
included in the NBER patent database in the 1993-2003 period. Financial and utility firms are omitted. Regressions include
industry-year dummies or year dummies in the case of the firm fixed effects model. Firm fixed effects using the Blundell,
Griffith, and Van Reenen (1999) pre-sample mean scaling estimator. Variable definitions are provided in Table A1 in the
Appendix. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the
10%, 5% and 1% levels respectively.
General Ability Index
Log (Sales)
Log (PPE)
(1)
0.107***
[3.233]
0.506***
[16.536]
0.194***
[5.481]
Stock Return
Institutional Ownership
Firm fixed effects
Number of observations
R-squared
No
10,479
0.412
Ordinary least squares
Log (Patents)
(2)
(3)
0.098*** 0.101***
[3.623]
[2.909]
0.012
0.534***
[0.659]
[16.404]
-0.033*
0.210***
[-1.776]
[5.576]
0.104***
[3.930]
-0.376**
[-2.170]
Yes
No
10,479
8,925
0.615
0.435
42
(4)
0.090***
[3.228]
0.016
[0.741]
-0.028
[-1.328]
0.100***
[4.341]
-0.019
[-0.143]
Yes
8,925
0.621
(5)
0.187***
[3.269]
0.774***
[18.354]
0.381***
[5.458]
No
10,479
Negative binomial
Patents
(6)
(7)
0.317*** 0.158***
[4.258]
[2.827]
0.264*** 0.806***
[5.185]
[18.213]
-0.025
0.354***
[-0.350]
[4.865]
0.148***
[2.924]
-0.650**
[-2.192]
Yes
No
10,479
8,925
(8)
0.267***
[3.808]
0.284***
[5.521]
-0.030
[-0.384]
0.274***
[4.712]
-0.925**
[-2.328]
Yes
8,925
Table 5
Patent Citations and General Managerial Ability
This table presents estimates of ordinary least squares and negative binomial panel regressions of the number of citations
(Citations) on the General Ability Index and firm-level control variables. The sample consists of EXECUCOMP firms for which
chief executive officer (CEO) profile data are available from BoardEx and operate in the same four-digit SIC industries as the
firms included in the NBER patent database in the 1993-2003 period. Financial and utility firms are omitted. Regressions include
industry-year dummies or year dummies in the case of the firm fixed effects model. Firm fixed effects using the Blundell,
Griffith, and Van Reenen (1999) pre-sample mean scaling estimator. Variable definitions are provided in Table A1 in the
Appendix. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the
10%, 5% and 1% levels respectively.
(1)
General Ability Index
Log (Sales)
Log (PPE)
0.149***
[3.447]
0.577***
[15.492]
0.186***
[4.045]
Stock Return
Institutional Ownership
Firm fixed effects
Number of observations
R-squared
General Ability Index
Log (Sales)
Log (PPE)
No
10,479
0.425
0.192***
[3.713]
0.669***
[15.592]
0.225***
[4.085]
Stock Return
Institutional Ownership
Firm fixed effects
Number of observations
R-squared
No
10,479
0.392
Ordinary least squares
Log(Citations)
(2)
(3)
(4)
Panel A: Citations
0.126*** 0.134*** 0.109***
[3.628]
[3.029]
[3.154]
0.036
0.600***
0.046*
[1.552]
[15.408]
[1.787]
-0.069*** 0.200*** -0.057**
[-2.821]
[4.178]
[-2.134]
0.157*** 0.147***
[4.101]
[4.403]
-0.276
0.107
[-1.268]
[0.634]
Yes
No
Yes
10,479
8,925
8,925
0.606
0.457
0.623
Panel B: Q Citations
0.169*** 0.177*** 0.154***
[3.958]
[3.339]
[3.574]
0.033
0.701***
0.044
[1.143]
[15.532]
[1.407]
-0.077** 0.246***
-0.061*
[-2.500]
[4.229]
[-1.804]
0.171*** 0.142***
[3.641]
[3.483]
-0.326
0.089
[-1.217]
[0.418]
Yes
No
Yes
10,479
8,925
8,925
0.571
0.419
0.587
43
(5)
Negative binomial
Citations
(6)
(7)
0.246***
[3.662]
0.704***
[15.641]
0.281***
[3.313]
0.262***
[3.317]
0.223***
[4.051]
-0.071
[-0.746]
No
10,479
Yes
10,479
0.278***
[4.032]
0.695***
[15.476]
0.274***
[3.201]
0.263***
[3.340]
0.219***
[4.024]
-0.096
[-0.979]
No
10,479
Yes
10,479
(8)
0.188***
[2.969]
0.747***
[14.923]
0.258***
[2.773]
0.259***
[3.927]
-0.737**
[-1.972]
No
8,925
0.187***
[2.588]
0.268***
[4.801]
-0.120
[-1.126]
0.272***
[4.344]
-0.636
[-1.555]
Yes
8,925
0.219***
[3.327]
0.740***
[14.789]
0.237**
[2.528]
0.227***
[3.272]
-1.059***
[-2.834]
No
8,925
0.182**
[2.505]
0.267***
[4.905]
-0.158
[-1.434]
0.292***
[4.176]
-0.709*
[-1.736]
Yes
8,925
Table 5: Continued
General Ability Index
Log (Sales)
Log (PPE)
(1)
0.077***
[2.610]
0.395***
[14.589]
0.146***
[4.747]
(2)
0.086***
[3.555]
0.023
[1.415]
-0.037**
[-2.249]
No
10,479
0.364
Yes
10,479
0.559
Stock Return
Institutional Ownership
Firm fixed effects
Number of observations
R-squared
Panel C: TT Citations
(3)
(4)
0.067**
0.073***
[2.182]
[2.963]
0.422***
0.033*
[14.520]
[1.722]
0.157***
-0.030*
[4.729]
[-1.661]
0.095*** 0.087***
[3.689]
[3.789]
-0.346**
-0.031
[-2.321]
[-0.266]
No
Yes
8,925
8,925
0.389
0.570
44
(5)
0.185***
[2.988]
0.609***
[14.710]
0.224***
[2.796]
(6)
0.245***
[3.473]
0.173***
[3.391]
-0.133
[-1.472]
No
10,479
Yes
10,479
(7)
0.135**
[2.343]
0.650***
[14.419]
0.211**
[2.463]
0.234***
[3.692]
-0.852**
[-2.298]
No
8,925
(8)
0.193***
[2.873]
0.205***
[3.914]
-0.146
[-1.467]
0.271***
[4.305]
-0.873**
[-2.115]
Yes
8,925
Table 6
Innovation Diversity, Originality and Generality and General Managerial Ability
This table presents estimates of ordinary least squares panel regressions of the patent portfolio diversity (Innovative Diversity and
Search Distance) and originality and generality (Originality Index and Generality Index) on the General Ability Index and firmlevel control variables. The sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are
available from BoardEx and operate in the same four-digit SIC industries as the firms included in the NBER patent database in
the 1993-2003 period. Financial and utility firms are omitted. Regressions include industry-year dummies. Variable definitions
are provided in Table A1 in the Appendix. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and
***
indicates significance at the 10%, 5% and 1% levels respectively.
General Ability Index
Log (Sales)
Log (PPE)
Stock Return
Institutional Ownership
Number of observations
R-squared
Innovative Diversity
(1)
(2)
0.032*** 0.034***
[4.603]
[4.607]
0.067*** 0.068***
[14.686]
[13.904]
0.025*** 0.029***
[3.394]
[3.780]
0.002
[0.301]
0.007
[0.191]
10,479
8,925
0.355
0.369
Search Distance
(3)
(4)
0.010*** 0.010***
[2.962]
[2.961]
-0.002
-0.002
[-0.784]
[-0.878]
-0.001
0.001
[-0.358]
[0.314]
-0.002
[-0.588]
0.009
[0.618]
10,479
8,925
0.072
0.072
45
Originality Index
(5)
(6)
0.029*** 0.029***
[4.005]
[3.780]
0.060*** 0.061***
[12.836]
[12.295]
0.020**
0.025***
[2.514]
[2.988]
-0.001
[-0.247]
-0.010
[-0.264]
10,479
8,925
0.344
0.357
Generality Index
(7)
(8)
0.033*** 0.033***
[4.640]
[4.460]
0.061*** 0.063***
[12.812]
[12.482]
0.018**
0.022***
[2.245]
[2.627]
-0.000
[-0.031]
-0.044
[-1.206]
10,479
8,925
0.341
0.356
Table 7
Innovation Strategy and General Managerial Ability
This table presents estimates of ordinary least squares panel regressions of innovation strategies (Exploratory Ratio and
Exploitative Ratio) on the General Ability Index and firm-level control variables. The sample consists of EXECUCOMP firms for
which chief executive officer (CEO) profile data are available from BoardEx and operate in the same four-digit SIC industries as
the firms included in the NBER patent database in the 1993-2003 period. Financial and utility firms are omitted. Regressions
include industry-year dummies. Variable definitions are provided in Table A1 in the Appendix. Robust t-statistics adjusted for
firm-level clustering are reported in brackets. *, **, and *** indicates significance at the 10%, 5% and 1% levels respectively.
General Ability Index
Log (Sales)
Log (PPE)
Stock Return
Institutional Ownership
Number of observations
R-squared
Exploratory Ratio 80
(1)
(2)
0.024*** 0.023***
[3.809]
[3.574]
0.024*** 0.025***
[6.622]
[6.389]
0.001
0.004
[0.224]
[0.702]
0.012
[1.600]
0.019
[0.650]
10,479
8,925
0.147
0.148
Exploratory Ratio 60
(3)
(4)
0.028*** 0.028***
[4.028]
[3.801]
0.035*** 0.036***
[8.688]
[8.392]
0.003
0.006
[0.465]
[0.838]
0.011
[1.453]
0.022
[0.662]
10,479
8,925
0.191
0.195
46
Exploitative Ratio 80
(5)
(6)
0.007**
0.007**
[2.205]
[2.112]
0.013*** 0.015***
[4.909]
[5.285]
0.011*** 0.012***
[3.254]
[3.268]
0.004
[0.982]
-0.029
[-1.613]
10,479
8,925
0.112
0.120
Exploitative Ratio 60
(7)
(8)
0.009**
0.009**
[2.364]
[2.155]
0.022*** 0.024***
[6.407]
[6.780]
0.014*** 0.016***
[3.387]
[3.332]
0.004
[0.788]
-0.039*
[-1.721]
10,479
8,925
0.135
0.145
Table 8
Innovation and General Managerial Ability: Propensity Score Matching
This table presents estimates of difference in the number of patents (Patent), number of citations (Citation), and innovation
diversity (Innovative Diversity) between the treatment group (generalist CEOs) and the control group (specialist CEOs). The
matched sample is constructed using a nearest-neighbor propensity score match with scores given by a probit model in which the
dependent variable (General Ability Dummy) is a dummy variable that takes a value of one if a CEO has a General Ability Index
above the median in a given year. The sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile
data are available from BoardEx and operate in the same four-digit SIC industries as the firms included in the NBER patent
database in the 1993-2003 period. Financial and utility firms are omitted. Variable definitions are provided in Table A1 in the
Appendix. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the
10%, 5% and 1% levels respectively.
Panel A: Probit (General Ability Dummy)
CEO Tenure
-0.037***
[-14.726]
CEO Age
0.031***
[12.621]
External Hire Dummy
0.350***
[10.031]
MBA Dummy
0.382***
[11.164]
CEO-Chair Dummy
0.420***
[11.844]
Log (Sales)
0.202***
[14.509]
Leverage
0.137
[1.300]
Tobin’s Q
-0.003
[-0.207]
ROA
-0.802***
[-3.691]
Volatility
0.126
[0.393]
Diversification Dummy
0.214***
[5.809]
Stock Return
-0.018
[-0.524]
Cash
0.355***
[2.654]
CAPEX
-1.286***
[-3.334]
Firm Age
-0.000
[-0.034]
R&D
1.922***
[4.695]
Observations
7,492
Panel B: Average Treatment Effect on the Treated (ATT)
Log (Patents)
(1)
0.110*
[1.890]
Log (Citations)
(2)
0.188**
[2.370]
Log (Q Citations)
(3)
0.226**
[2.410]
47
Log (TT Citations)
(4)
0.070
[1.400]
Innovative Diversity
(5)
0.045***
[3.990]
Table 9
Innovation and General Managerial Ability: Instrumental Variables
This table presents estimates of instrumental variables methods using two-stage least squares (2SLS) panel regressions of the number of patents (Patents), number of citations
(Citations), and innovation diversity (Innovative Diversity) on the General Ability Index and firm-level control variables. Non-Compete Enforcement Index is the average Garmaise
(2006) non-compete agreement enforcement index at the state-year level across all positions the CEO has had on past work experience in publicly traded firms. The sample
consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from BoardEx and operate in the same four-digit SIC industries as the firms
included in the NBER patent database in the 1993-2003 period. Financial and utility firms are omitted. First-stage regressions include state and year dummies. Second stage
regressions include year dummies. Firm fixed effects using the Blundell, Griffith, and Van Reenen (1999) pre-sample mean scaling estimator. Variable definitions are provided in
Table A1 in the Appendix. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the 10%, 5% and 1% levels
respectively.
(1)
First Stage:
General
Ability Index
General Ability Index
Log (Sales)
Log (PPE)
Stock Return
Institutional Ownership
Non-Compete Enforcement Index
Firm fixed effects
R-squared
Observations
0.136***
[15.970]
0.047***
[3.810]
-0.073***
[-3.210]
0.021***
[0.330]
0.055***
[5.200]
Yes
0.112
7,059
(2)
(3)
Log
(Patents)
0.516***
[3.107]
-0.033
[-1.001]
-0.028
[-0.964]
-0.125
[-0.700]
0.146***
[4.297]
Log
(Citations)
0.417**
[2.163]
0.005
[0.137]
-0.059*
[-1.706]
0.029
[0.133]
0.198***
[4.472]
Yes
Yes
Yes
Yes
Yes
7,059
7,059
7,059
7,059
7,059
48
(4)
(5)
Second Stage:
Log
Log
(Q Citations)
(TT Citations)
0.569**
0.312**
[2.392]
[2.160]
-0.017
-0.006
[-0.356]
[-0.206]
-0.067
-0.044*
[-1.537]
[-1.875]
-0.030
-0.101
[-0.112]
[-0.671]
0.196***
0.120***
[3.592]
[3.779]
(6)
Innovative
Diversity
0.253***
[5.002]
0.020*
[1.911]
0.019*
[1.674]
-0.006
[-0.103]
0.021**
[2.220]
Table 10
Innovation and General Managerial Ability: Effect of Outside Options
This table presents estimates of ordinary least squares panel regressions of the number of patents (Patents), number of citations
(Citations), and innovation diversity (Innovative Diversity) on the General Ability Index and firm-level control variables. Tight
Labor Market Dummy is a dummy variable that takes a value of one if the unemployment rate for a year in the MSA exceeds the
median unemployment rate for the MSA over the full sample period. Low Local Beta Dummy is a dummy that takes a value of
one if the beta of a stock return on the return of the stock’s corresponding MSA index is below the top decile of the distribution.
The sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from BoardEx
and operate in the same four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003 period.
Financial and utility firms are omitted. The regressions include the same firm-level control variables as in Table 4 (coefficients
not shown). Regressions also include industry-year dummies. Variable definitions are provided in Table A1 in the Appendix.
Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the 10%, 5%
and 1% levels respectively.
Panel A: Tight Labor Markets
General Ability Index
Tight Labor Market Dummy
General Ability Index  Tight Labor Market Dummy
Log (Sales)
Log (PPE)
Number of observations
R-squared
General Ability Index
Local Beta Dummy
General Ability Index  Low Local Beta Dummy
Log (Sales)
Log (PPE)
Number of observations
R-squared
Log
Log
Log (Patents) Log (Citations) (Q Citations) (TT Citations)
(1)
(2)
(3)
(4)
0.056
0.072
0.113*
0.025
[1.485]
[1.545]
[1.947]
[0.765]
0.013
0.011
0.001
-0.004
[0.236]
[0.160]
[0.007]
[-0.087]
0.077**
0.116**
0.119**
0.078**
[2.098]
[2.405]
[2.016]
[2.443]
0.506***
0.577***
0.669***
0.396***
[16.553]
[15.512]
[15.609]
[14.606]
0.193***
0.184***
0.224***
0.145***
[5.456]
[4.016]
[4.059]
[4.719]
10,479
10,479
10,479
10,479
0.413
0.425
0.393
0.364
Panel B: Local Beta
Log
Log
Log (Patents) Log (Citations) (Q Citations) (TT Citations)
(1)
(2)
(3)
(4)
-0.062
-0.056
-0.063
-0.038
[-0.927]
[-0.633]
[-0.552]
[-0.597]
-0.012
-0.116
-0.089
-0.083
[-0.136]
[-0.990]
[-0.602]
[-1.037]
0.190***
0.229**
0.286**
0.128*
[2.601]
[2.399]
[2.325]
[1.875]
0.506***
0.581***
0.671***
0.398***
[16.451]
[15.445]
[15.492]
[14.584]
0.193***
0.188***
0.226***
0.147***
[5.471]
[4.101]
[4.115]
[4.821]
10,479
10,479
10,479
10,479
0.413
0.426
0.393
0.365
49
Innovative
Diversity
(5)
0.034***
[4.131]
-0.004
[-0.343]
-0.003
[-0.448]
0.067***
[14.686]
0.025***
[3.393]
10,479
0.355
Innovative
Diversity
(5)
0.007
[0.392]
-0.009
[-0.461]
0.028
[1.492]
0.068***
[14.587]
0.025***
[3.408]
10,479
0.356
Table 11
Innovation and General Managerial Ability:
Controlling for CEO Delta, Vega and Overconfidence
This table presents estimates of ordinary least squares panel regressions of the number of patents (Patents), number of citations
(Citations), and innovation diversity (Innovative Diversity) on the General Ability Index and other CEO- and firm-level control
variables. The sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from
BoardEx and operate in the same four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003
period. Financial and utility firms are omitted. The regressions include the same firm-level control variables as in Table 4
(coefficients not shown). Regressions also include industry-year dummies. Variable definitions are provided in Table A1 in the
Appendix. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the
10%, 5% and 1% levels respectively.
General Ability Index
Log (CEO Delta)
Log (CEO Vega)
Number of observations
R-squared
General Ability Index
CEO Confidence Options
Number of observations
R-squared
Log
Log (Patents) Log (Citations) (Q Citations)
(1)
(2)
(3)
Panel A: CEO Delta and Vega
0.122***
0.168***
0.215***
[3.392]
[3.618]
[3.876]
-0.112***
-0.139***
-0.157***
[-5.002]
[-4.741]
[-4.490]
0.012
0.020*
0.024*
[1.319]
[1.699]
[1.729]
7,828
7,828
7,828
0.432
0.447
0.409
Panel B: Option Based Confidence Measure
0.107***
0.149***
0.192***
[3.056]
[3.306]
[3.569]
0.106*
0.165**
0.180**
[1.883]
[2.224]
[1.985]
9,308
9,308
9,308
0.422
0.437
0.402
Log
(TT Citations)
(4)
Innovative
Diversity
(5)
0.087***
[2.711]
-0.099***
[-5.059]
0.009
[1.324]
7,828
0.383
0.036***
[4.837]
-0.006
[-1.402]
0.002
[1.055]
7,828
0.363
0.075**
[2.427]
0.111**
[2.347]
9,308
0.375
0.035***
[4.771]
-0.003
[-0.258]
9,308
0.356
0.065**
[2.026]
0.262***
[2.641]
0.016***
[7.401]
8,029
0.400
0.031***
[3.798]
0.036*
[1.841]
0.001**
[2.045]
8,029
0.379
Panel C: Press Based Confidence Measure
General Ability Index
CEO Confidence Press
Total Press Mention
Number of observations
R-squared
0.099***
[2.664]
0.312***
[2.812]
0.016***
[5.828]
8,029
0.448
0.137***
[2.823]
0.341**
[2.453]
0.015***
[4.808]
8,029
0.458
50
0.177***
[3.017]
0.455***
[2.710]
0.021***
[5.679]
8,029
0.425
Table 12
Innovation and General Managerial Ability: Controlling for R&D Levels
This table presents estimates of ordinary least squares panel regressions of the number of patents (Patents), number of citations
(Citations), and innovation diversity (Innovative Diversity) on the General Ability Index and firm-level control variables. The
sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from BoardEx and
operate in the same four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003 period.
Financial and utility firms are omitted. The regressions include the same firm-level control variables as in Table 4 (coefficients
not shown). Regressions also include industry-year dummies. Variable definitions are provided in Table A1 in the Appendix.
Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the 10%, 5%
and 1% levels respectively.
Log (Patents)
(1)
General Ability Index
Log (R&D)
Number of observations
R-squared
0.062**
[2.004]
10.611***
[12.925]
10,478
0.471
Log
Log (Citations) (Q Citations)
(2)
(3)
Panel A: R&D
0.091**
0.121**
[2.225]
[2.491]
13.809***
16.780***
[12.501]
[12.759]
10,478
10,478
0.476
0.447
Log
(TT Citations)
(4)
Innovative
Diversity
(5)
0.039
[1.399]
8.886***
[11.913]
10,478
0.420
0.025***
[3.772]
1.651***
[10.740]
10,478
0.391
0.042
[1.480]
2.714***
[8.998]
10,478
0.415
0.025***
[3.756]
0.532***
[8.679]
10,478
0.391
Panel B: R&D Stock
General Ability Index
Log (R&D Stock)
Number of observations
R-squared
0.065**
[2.074]
3.269***
[9.688]
10,478
0.466
0.097**
[2.361]
3.995***
[9.537]
10,478
0.466
51
0.128***
[2.611]
4.905***
[9.144]
10,478
0.438
Appendix
Table A1
Variable Definitions
Variable
Description
Panel A: Innovation Measures
R&D
Research and development expenses divided by total assets (Compustat XRD / AT).
R&D Stock
Cumulative R&D expenses assuming an annual depreciation rate of 15% divided by total assets (Compustat).
Patents
Number of patent applications by a firm in a given year (NBER patent database).
Citations
Total number of citations subsequently received by the patents that a firm applied for in a given year (NBER patent database).
Q Citations
Sum of the adjusted patent citations across all patents that a firm applied for in a given year; the weighting index is from Hall, Jaffe, and Trajtenberg
(2001, 2005) (NBER patent database).
TT Citations
Sum of the adjusted citation count across all patents that a firm applied for in a given year; the scaling is based on the average citation count of all
patents in the same two-digit technological class and year (NBER patent database).
Innovative Diversity
One minus the Herfindahl index of the patent portfolio (patents filed by the firm in the previous five years) using two-digit technological classes
(NBER patent database).
Search Distance
Degree of difference between the current distribution of a firm’s patent portfolio across two-digit technological classes and the analogous distribution
calculated for new patents and adjusted for the expected degree of knowledge spillovers expected between patent classes (NBER patent database).
Originality Index
One minus the Herfindahl index of the citations made by the patent portfolio (patents filed by the firm in the previous five years) based on two-digit
technological classes (NBER patent database).
Generality Index
One minus the Herfindahl index of the citations received by the patent portfolio (patents filed by the firm in the previous five years) based on twodigit technological classes (NBER patent database).
Exploitative Ratio 80
Number of exploitative patents filed in a given year divided by the number of all patents filed by the firm in the same year; a patent is classified as
exploitative if at least 80% of its citations are based on current knowledge (NBER patent database).
Exploitative Ratio 60
Number of exploitative patents filed in a given year divided by the number of all patents filed by the firm in the same year; a patent is classified as
exploitative if at least 60% of its citations are based on current knowledge (NBER patent database).
Exploratory Ratio 80
Number of exploratory patents filed in a given year divided by the number of all patents filed by the firm in the same year; a patent is classified as
exploratory if at least 80% of its citations are based on new knowledge (NBER patent database).
Exploratory Ratio 60
Number of exploratory patents filed in a given year divided by the number of all patents filed by the firm in the same year; a patent is classified as
exploratory if at least 60% of its citations are based on new knowledge (NBER patent database).
52
Panel B: CEO Characteristics
General Ability Index
First factor of applying principal components analysis to five proxies of general managerial ability: past Number of Positions, Number of Firms,
Number of Industries, CEO Experience Dummy, and Conglomerate Experience Dummy (BoardEx).
General Ability Index Dummy
Dummy variable that takes a value of one if the CEO’s general ability index is above the yearly median, and zero otherwise (BoardEx).
Number of Positions
Number of positions CEO has held in publicly traded firms (BoardEx).
Number of Firms
Number of firms CEO has worked in publicly traded firms (BoardEx).
Number of Industries
Number of industries [four-digit standard industrial classification (SIC)] in which CEO has worked (in publicly traded firms) (BoardEx).
Multi-Industry Dummy
Dummy variable that takes a value of one if the number of industries (four-digit SIC) in which CEO has worked (in publicly traded firms) is
greater than one and zero otherwise (BoardEx).
CEO Experience Dummy
Dummy variable that takes a value of one if CEO held a CEO position at another publicly traded firm, and zero otherwise (BoardEx).
Conglomerate Experience
Dummy
Dummy variable that takes a value of one if CEO worked at a multi-segment publicly traded firm and zero otherwise (BoardEx).
CEO Delta
Dollar change in a CEO’s stock and option portfolio for a 1% change in stock price using the one-year approximation method of Core and Guay
(2002).
CEO Vega
Dollar change in a CEO’s option holdings for a 1% change in stock return volatility using the one-year approximation method of Core and Guay
(2002).
CEO Confidence Options
Dummy variable that takes a value of one if a CEO postpones the exercise of vested options that are at least 67% in the money, and zero otherwise.
CEO Confidence Press
Dummy variable takes a value of one when the number of “confident” articles for a CEO is greater than the number of “cautious” articles, and zero
otherwise (Factiva).
Total Press Mentions
Number of press articles mentioning the CEO (Factiva).
CEO Tenure
Number of years as CEO in the current position (BoardEx).
CEO Age
Age of CEO in years (BoardEx).
External Hire Dummy
Dummy variable that takes a value of one if CEO was hired from outside the firm, and zero otherwise (BoardEx).
MBA Dummy
Dummy variable that takes a value of one if CEO has a MBA degree, and zero otherwise (BoardEx).
CEO-Chair Dummy
Dummy variable that takes a value of one if CEO is also chair of the board, and zero otherwise (BoardEx).
Non-Compete Enforcement
Index
Average Garmaise (2006) non-compete agreement enforcement index at the state-year level across all positions the CEO has had in publicly traded
firms.
Ivy League Dummy
Dummy variable that takes a value of one if CEO attended an Ivy League school (Brown University, Columbia University, Cornell University,
Dartmouth College, Harvard University, University of Pennsylvania, Princeton University, and Yale University) at any academic level and zero
otherwise (BoardEx).
Recession Graduate Dummy
Fast Track Career CEO
Dummy variable that takes a value of one if the CEO graduated (first academic degree) in a National Bureau of Economics Research recession
year and zero otherwise (BoardEx).
Age at which CEO became CEO for the first time (BoardEx).
CEO Turnover Dummy
Dummy variable that takes a value of one if there is a CEO turnover (Execucomp).
Forced CEO Turnover Dummy
Dummy variable that takes a value of one if there is a forced CEO turnover.
53
Panel C: Firm Characteristics
Sales
Sales in millions of dollars (Compustat SALE).
PPE
Net property, plant, and equipment divided by sales (Compustat (PPENT / SALE)).
Stock Return
Annual stock return (Compustat (PRCC_F(t) / AJEX(t) + DVPSX_F(t) / AJEX(t)) / (PRCC_F(t-1) / AJEX_F(t-1))).
Institutional Ownership
Shares held by institutional investors as a fraction of shares outstanding (Thomson CDA/Spectrum 13F Holdings).
Leverage
Total debt, defined as long-term debt plus debt in current liabilities, divided by total assets (Compustat (DLC + DLTT) / AT).
Tobin’s Q
Assets plus market value of equity minus book value of equity divided by assets (Compustat (AT + CSHO  PRCC_F - CEQ) / AT)).
ROA
Earnings before interest and taxes divided by total assets (Compustat EBIT / AT).
Volatility
Standard deviation of monthly stock returns (CRSP).
Diversification Dummy
Dummy variable that takes a value of one if a firm has more than one business segment, and zero otherwise (Compustat).
Cash
Cash and short-term investments divided by total assets (Compustat CHE / AT).
CAPEX
Capital expenditures divided by total assets (CAPX / AT).
Firm Age
Number of years since a firm listed its shares (CRSP).
Tight Labor Market Dummy
Board Size
Dummy variable that takes a value of one if the unemployment rate for a year in the MSA exceeds the median unemployment rate for the MSA over
the full sample period (Bureau of Labor Statistics).
Dummy variable that takes a value of one if the beta of a stock return on the return of the stock’s corresponding MSA index is below the top decile of
the distribution; local beta is estimated using a time series regression of monthly stock return on the return of the stock’s corresponding MSA index
(excluding the particular stock) as well as the return on the market portfolio and the stock’s 48 Fama-French industry return over two different
periods, 1993-1997 and 1998-2003, such that at least 24 non-missing monthly return observations for a stock and five stocks in the MSA enter the
regression; returns are in excess of monthly T-bill rates (CRSP).
Number of directors (IRRC).
Board Independence
Ratio of number of independent directors to number of directors (IRRC).
Industry Sales Herfindahl
Herfindahl index calculated as the sum of squared market shares of firms’ sales (Compustat SALE) at the two-digit SIC industry level.
Staggered Board Dummy
Dummy variable that takes a value of one if a firm has a staggered board, and zero otherwise (IRRC).
Supermajority Dummy
Dummy variable that takes a value of one if a firm has a poison pill provision, and zero otherwise (IRRC).
Poison Pill Dummy
Dummy variable that takes a value of one if a firm has a cumulative vote provision, and zero otherwise (IRRC).
Low Local Beta Dummy
Cumulative Voting Dummy
Dummy variable that takes a value of one if a firm has a supermajority vote requirement, and zero otherwise (IRRC).
Governance Index
Governance index of Gompers, Ishii, and Metrick (2003), which is based on 24 antitakeover provisions (IRRC).
Acquisition Ratio
Value of acquisitions divided by total assets (Computstat AQC/AT or SDC).
Number of Acquisitions
Number of acquisitions (SDC).
Labor
Number of employees in thousands (Compustat EMP).
Capital
Net property, plant, and equipment (Compustat PPENT).
54
Internet Appendix for
“Do General Managerial Skills Spur Innovation?”
Cláudia Custódio
Arizona State University – W. P. Carey School of Business
claudia.custodio@asu.edu
Miguel A. Ferreira
Nova School of Business and Economics, ECGI
miguel.ferreira@novasbe.pt
Pedro Matos
University of Virginia – Darden School of Business, ECGI
matosp@darden.virginia.edu
This Version: May 2014
Table IA.I
Zero Patents and Firm and CEO Fixed Effects
This table presents estimates of ordinary least squares panel regressions of the number of patents (Patents) on the General Ability
Index and firm-level control variables. Column (1) presents estimates of a sample that excludes observations with zero patents.
Column (2) presents estimates of a regression using annual changes of dependent and explanatory variables. Column (3) presents
estimates of a firm fixed effects regression. Column (4) presents estimates of a CEO fixed effects regression. The sample consists
of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from BoardEx and operate in the same
four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003 period. Financial and utility firms
are omitted. Regressions include industry-year dummies or year dummies in the case of the firm fixed effects model. Variable
definitions are provided in Table A1 in the Appendix of the paper. Robust t-statistics adjusted for firm-level clustering are
reported in brackets. *, **, and *** indicates significance at the 10%, 5% and 1% levels respectively.
General Ability Index
Log (Sales)
Log (PPE)
Number of observations
R-squared
Exclude Zero
Patents
(1)
0.055***
[3.060]
0.577***
[49.987]
0.269***
[11.652]
5,530
0.452
Changes
(2)
0.031**
[2.378]
0.202***
[6.946]
0.109***
[4.287]
8,841
0.010
1
Firm Fixed
Effects
(3)
0.045**
[2.385]
0.279***
[8.085]
0.182***
[6.145]
10,479
0.062
CEO Fixed
Effects
(4)
0.128**
[2.062]
0.315***
[8.528]
0.135***
[3.775]
10,479
0.070
Table IA.II
Poisson Model
This table presents estimates of Poisson panel regressions of the number of patents (Patents) and number of citations (Citations)
on the General Ability Index and firm-level control variables. The sample consists of EXECUCOMP firms for which chief
executive officer (CEO) profile data are available from BoardEx and operate in the same four-digit SIC industries as the firms
included in the NBER patent database in the 1993-2003 period. Financial and utility firms are omitted. Regressions include
industry-year dummies. Variable definitions are provided in Table A1 in the Appendix of the paper. Robust t-statistics adjusted
for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the 10%, 5% and 1% levels respectively.
General Ability Index
Log (Sales)
Log (PPE)
Number of observations
Patents
(1)
0.059**
[2.269]
0.961***
[66.043]
0.601***
[10.998]
10,479
Citations
(2)
0.056**
[1.963]
0.929***
[57.640]
0.563***
[8.166]
10,479
2
Q Citations
(3)
0.049*
[1.734]
0.930***
[63.440]
0.612***
[8.800]
10,479
TT Citations
(4)
0.076**
[2.435]
0.802***
[53.556]
0.525***
[7.884]
10,479
Table IA.III
Instrumental Variables: Excluding Non-Executive Past Positions
This table presents estimates of instrumental variables methods using two-stage least squares (2SLS) panel regressions of the
number of patents (Patents), number of citations (Citations), and innovation diversity (Innovative Diversity) on the General
Ability Index and firm-level control variables. Non-Compete Enforcement Index is the average Garmaise (2006) non-compete
agreement enforcement index at the state-year level across positions the CEO has had on past work experience in publicly traded
firms excluding non-executive positions. The sample consists of EXECUCOMP firms for which chief executive officer (CEO)
profile data are available from BoardEx and operate in the same four-digit SIC industries as the firms included in the NBER
patent database in the 1993-2003 period. Financial and utility firms are omitted. First-stage regressions include state and year
dummies. Second stage regressions include year dummies. Firm fixed effects using the Blundell, Griffith, and Van Reenen
(1999) pre-sample mean scaling estimator. Variable definitions are provided in Table A1 in the Appendix. Robust t-statistics
adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the 10%, 5% and 1% levels
respectively.
Panel A: Sample Excluding Non-Executive Positions
First Stage:
Second Stage:
General Ability
Log
Log
Index
Log (Patents) Log (Citations) (Q Citations) (TT Citations)
(1)
(2)
(3)
(4)
(5)
General Ability Index
0.590***
0.487**
0.666**
0.398**
[3.059]
[2.272]
[2.525]
[2.530]
Log (Sales)
0.134***
-0.050
-0.010
-0.036
-0.023
[15.330]
[-1.469]
[-0.244]
[-0.741]
[-0.796]
Log (PPE)
0.048***
-0.025
-0.052
-0.063
-0.043*
[3.730]
[-0.810]
[-1.439]
[-1.364]
[-1.693]
Stock Return
-0.065***
-0.114
0.013
-0.065
-0.098
[-2.780]
[-0.650]
[0.062]
[-0.245]
[-0.650]
Institutional Ownership
0.013
0.138***
0.182***
0.179***
0.111***
[0.190]
[3.971]
[4.131]
[3.285]
[3.482]
Non-Compete Enforcement Index
0.045***
[3.870]
Firm fixed effects
Yes
Yes
Yes
Yes
Yes
R-squared
0.113
Number of observations
6,573
6,573
6,573
6,573
6,573
Panel B: Sample Excluding Executives that Move State and Stay in Same Industry
First Stage:
Second Stage:
General Ability
Log (Q
Log (TT
Index
Log (Patents) Log (Citations) Citations)
Citations)
(1)
(2)
(3)
(4)
(5)
General Ability Index
0.596***
0.493**
0.676**
0.401**
[3.089]
[2.295]
[2.556]
[2.557]
Log (Sales)
0.135***
-0.050
-0.009
-0.036
-0.022
[15.320]
[-1.465]
[-0.226]
[-0.732]
[-0.781]
Log (PPE)
0.048***
-0.026
-0.053
-0.064
-0.044*
[3.740]
[-0.830]
[-1.447]
[-1.374]
[-1.702]
Stock Return
-0.064***
-0.112
0.016
-0.062
-0.096
[-2.730]
[-0.635]
[0.078]
[-0.232]
[-0.634]
Institutional Ownership
0.010
0.139***
0.184***
0.181***
0.112***
[0.150]
[4.019]
[4.158]
[3.318]
[3.510]
Non-Compete Enforcement Index
0.046***
[3.890]
Firm fixed effects
Yes
Yes
Yes
Yes
Yes
R-squared
0.561
Number of observations
6,540
6,540
6,540
6,540
6,540
3
Innovative
Diversity
(6)
0.253***
[4.728]
0.018
[1.630]
0.021*
[1.703]
-0.027
[-0.469]
0.018*
[1.920]
Yes
6,573
Innovative
Diversity
(6)
0.256***
[4.754]
0.018
[1.605]
0.020*
[1.669]
-0.026
[-0.453]
0.019*
[1.956]
Yes
6,540
Table IA.IV
Total Factor Productivity
This table presents estimates of ordinary least squares panel regressions of the logarithm of sales. The sample consists of
EXECUCOMP firms for which chief executive officer (CEO) profile data are available from BoardEx and operate in the same
four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003 period. Financial and utility firms
are omitted. Regressions include industry-year dummies. Variable definitions are provided in Table A1 in the Appendix of the
paper. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the 10%,
5% and 1% levels respectively.
GAI
Log (Labor)
(1)
0.067***
[3.427]
0.884***
[55.642]
(2)
0.046***
[2.819]
0.603***
[27.993]
0.306***
[16.028]
10,417
0.821
10,382
0.855
Log (Capital)
Log (R&D Stock)
Number of observations
R-squared
4
(3)
0.051***
[3.130]
0.597***
[28.293]
0.307***
[16.098]
-0.358**
[-2.014]
10,382
0.856
Table IA.V
Sample of CEOs
This table presents estimates of ordinary least squares panel regressions of the number of patents (Patents), number of citations
(Citations), and innovation diversity (Innovative Diversity) on the General Ability Index and firm-level control variables. The
sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from BoardEx and
operate in the same four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003 period. CEOs
with tenure below two years (Panel A) or tenure below three years (Panel B) are omitted. Financial and utility firms are omitted.
Regressions include industry-year dummies. Variable definitions are provided in Table A1 in the Appendix of the paper. Robust
t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the 10%, 5% and 1%
levels respectively.
General Ability Index
Log (Sales)
Log (PPE)
Number of observations
R-squared
General Ability Index
Log (Sales)
Log (PPE)
Number of observations
R-squared
General Ability Index
Log (Sales)
Log (PPE)
Number of observations
R-squared
Log
Log
Log (Patents) Log (Citations) (Q Citations) (TT Citations)
(1)
(2)
(3)
(4)
Panel A: Sample of CEOs with Tenure above 1 Year
0.107***
0.147***
0.193***
0.074**
[3.111]
[3.246]
[3.579]
[2.411]
0.509***
0.585***
0.676***
0.400***
[16.328]
[15.336]
[15.374]
[14.493]
0.195***
0.185***
0.223***
0.145***
[5.379]
[3.918]
[3.925]
[4.614]
9,684
9,684
9,684
9,684
0.411
0.426
0.392
0.364
Panel B: Sample of CEOs with Tenure above 3 Years
0.115***
0.148***
0.198***
0.072**
[2.949]
[2.868]
[3.232]
[2.086]
0.501***
0.584***
0.671***
0.396***
[15.180]
[14.287]
[14.248]
[13.798]
0.186***
0.175***
0.205***
0.134***
[4.962]
[3.562]
[3.502]
[4.148]
7,453
7,453
7,453
7,453
0.412
0.432
0.400
0.369
Panel C: Sample of CEOs with Tenure above 5 Years
0.115***
0.148***
0.198***
0.072**
[2.949]
[2.868]
[3.232]
[2.086]
0.501***
0.584***
0.671***
0.396***
[15.180]
[14.287]
[14.248]
[13.798]
0.186***
0.175***
0.205***
0.134***
[4.962]
[3.562]
[3.502]
[4.148]
5,778
5,778
5,778
5,778
0.410
0.437
0.409
0.374
5
Innovative
Diversity
(5)
0.033***
[4.585]
0.068***
[14.377]
0.025***
[3.309]
9,684
0.355
0.037***
[4.531]
0.068***
[13.195]
0.024***
[3.052]
7,453
0.360
0.037***
[4.531]
0.068***
[13.195]
0.024***
[3.052]
5,778
0.359
Table IA.VI
Innovative versus Non-innovative Industries
This table presents estimates of ordinary least squares panel regressions of the number of patents (Patents), number of citations (Citations), and innovation diversity (Innovative
Diversity) on the General Ability Index and firm-level control variables. An industry is classified as innovative if the average Q Citation for the industry is above the median Q
Citations across all industries in a given year. The sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from BoardEx and
operate in the same four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003 period. Financial and utility firms are omitted. The regressions
include the same firm-level control variables as in Table 4 (coefficients not shown). Regressions also include industry-year dummies. Variable definitions are provided in Table A1
in the Appendix of the paper. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the 10%, 5% and 1% levels
respectively.
General Ability Index
Number of observations
R-squared
Log (Patents)
Innovative Non-innov.
Industries
Industries
(1)
(2)
0.159***
0.063*
[3.374]
[1.835]
5,738
4,741
0.362
0.296
Log (Citations)
Innovative Non-innov.
Industries
Industries
(3)
(4)
0.211***
0.100**
[3.240]
[2.257]
5,738
4,741
0.345
0.190
Log (Q Citations)
Innovative Non-innov.
Industries
Industries
(5)
(6)
0.255***
0.139**
[3.364]
[2.543]
5,738
4,741
0.284
0.201
6
Log (TT Citations)
Innovative Non-innov.
Industries
Industries
(7)
(8)
0.115***
0.047*
[2.658]
[1.741]
5,738
4,741
0.322
0.222
Innovative Diversity
Innovative Non-innov.
Industries
Industries
(9)
(10)
0.026***
0.041***
[2.855]
[4.254]
5,738
4,741
0.204
0.276
Table IA.VII
Patent Citations per Patent
This table presents estimates of ordinary least squares and negative binomial panel regressions of the number of citations per
patent (Citations) on the General Ability Index and firm-level control variables. The sample consists of EXECUCOMP firms for
which chief executive officer (CEO) profile data are available from BoardEx and operate in the same four-digit SIC industries as
the firms included in the NBER patent database in the 1993-2003 period. Financial and utility firms are omitted. Regressions
include industry-year dummies. Variable definitions are provided in Table A1 in the Appendix of the paper. Robust t-statistics
adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the 10%, 5% and 1% levels
respectively.
Ordinary least squares
General Ability Index
Log (Sales)
Log (PPE)
Number of observations
R-squared
Log (Citations)
(1)
0.149***
[3.447]
0.577***
[15.492]
0.186***
[4.045]
10,479
0.425
Log
(Q Citations)
(3)
0.192***
[3.713]
0.669***
[15.592]
0.225***
[4.085]
10,479
0.392
Negative binomial
Log
(TT Citations)
(5)
0.077***
[2.610]
0.395***
[14.589]
0.146***
[4.747]
10,479
0.364
7
Citations
(2)
0.246***
[3.662]
0.704***
[15.641]
0.281***
[3.313]
10,479
Q Citations
(4)
0.278***
[4.032]
0.695***
[15.476]
0.274***
[3.201]
10,479
TT Citations
(6)
0.185***
[2.988]
0.609***
[14.710]
0.224***
[2.796]
10,479
Table IA.VIII
Patent Citations Excluding Self-Citations
This table presents estimates of ordinary least squares and negative binomial panel regressions of the number of citations
excluding self-citations (Citations) on the General Ability Index and firm-level control variables. The sample consists of
EXECUCOMP firms for which chief executive officer (CEO) profile data are available from BoardEx and operate in the same
four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003 period. Financial and utility firms
are omitted. Regressions include industry-year dummies or year dummies in the case of the firm fixed effects model. Firm fixed
effects using the Blundell, Griffith, and Van Reenen (1999) pre-sample mean scaling estimator. Variable definitions are provided
in Table A1 in the Appendix of the paper. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and
***
indicates significance at the 10%, 5% and 1% levels respectively.
General Ability Index
Log (Sales)
Log (PPE)
(1)
0.149***
[3.447]
0.577***
[15.492]
0.186***
[4.045]
Stock Return
Institutional Ownership
Firm fixed effects
Number of observations
R-squared
No
10,479
0.425
Ordinary least squares
Log (Citations)
(2)
(3)
0.126***
0.134***
[3.628]
[3.029]
0.036
0.600***
[1.552]
[15.408]
-0.069***
0.200***
[-2.821]
[4.178]
0.157***
[4.101]
-0.276
[-1.268]
Yes
No
10,479
8,925
0.606
0.457
(4)
0.109***
[3.154]
0.046*
[1.787]
-0.057**
[-2.134]
0.147***
[4.403]
0.107
[0.634]
Yes
8,925
0.623
8
(5)
0.246***
[3.662]
0.704***
[15.641]
0.281***
[3.313]
No
10,479
Negative binomial
Citations
(6)
(7)
0.262***
0.188***
[3.317]
[2.969]
0.223***
0.747***
[4.051]
[14.923]
-0.071
0.258***
[-0.746]
[2.773]
0.259***
[3.927]
-0.737**
[-1.972]
Yes
No
10,479
8,925
(8)
0.187***
[2.588]
0.268***
[4.801]
-0.120
[-1.126]
0.272***
[4.344]
-0.636
[-1.555]
Yes
8,925
Table IA.IX
Firm-Level Controls
This table presents estimates of ordinary least squares panel regressions of the number of patents (Patents), number of citations
(Citations), and innovation diversity (Innovative Diversity) on the General Ability Index and firm-level control variables. The
sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from BoardEx and
operate in the same four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003 period.
Financial and utility firms are omitted. Regressions include industry-year dummies. Variable definitions are provided in Table A1
in the Appendix of the paper. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates
significance at the 10%, 5% and 1% levels respectively.
General Ability Index
Log (Sales)
Log (PPE)
Stock Return
Institutional Ownership
Volatility
Firm Age
Diversification Dummy
Board Size
Board Independence
Herfindahl Index
Staggered Board Dummy
Supermajority Dummy
Poison Pill Dummy
Cumulative Voting Dummy
Observations
R-squared
Log (Patents)
(1)
0.086**
[2.174]
0.509***
[12.559]
0.200***
[4.666]
0.144***
[4.225]
-0.630***
[-2.769]
2.023***
[3.525]
0.012***
[4.205]
-0.037
[-0.472]
0.011
[0.601]
0.786***
[3.560]
-1.633*
[-1.863]
-0.076
[-0.852]
-0.331***
[-3.217]
0.003
[0.039]
0.314**
[2.053]
5,464
0.484
Log (Citations)
(2)
0.100**
[2.059]
0.563***
[11.764]
0.195***
[3.741]
0.205***
[4.302]
-0.591**
[-2.162]
2.805***
[3.989]
0.013***
[3.862]
-0.048
[-0.493]
0.013
[0.612]
0.910***
[3.386]
-1.872*
[-1.897]
-0.124
[-1.147]
-0.381***
[-3.009]
0.050
[0.486]
0.368*
[1.955]
5,464
0.504
9
Log
(Q Citations)
(3)
0.138**
[2.301]
0.667***
[11.445]
0.237***
[3.658]
0.239***
[4.042]
-0.734**
[-2.139]
3.315***
[3.724]
0.016***
[3.950]
-0.058
[-0.478]
0.014
[0.530]
1.183***
[3.488]
-2.321*
[-1.956]
-0.127
[-0.941]
-0.466***
[-2.921]
0.101
[0.793]
0.498**
[2.168]
5,464
0.469
Log
(TT Citations)
(4)
0.061*
[1.703]
0.439***
[11.598]
0.159***
[4.164]
0.134***
[3.949]
-0.665***
[-3.337]
1.906***
[3.632]
0.007***
[2.711]
-0.070
[-0.987]
0.005
[0.330]
0.634***
[3.321]
-1.320**
[-1.991]
-0.107
[-1.369]
-0.256***
[-2.851]
0.010
[0.131]
0.288**
[2.142]
5,464
0.439
Innovative
Diversity
(5)
0.024***
[2.877]
0.047***
[6.680]
0.028***
[3.095]
0.009
[1.302]
-0.014
[-0.329]
0.215*
[1.953]
0.003***
[5.751]
0.029*
[1.844]
-0.000
[-0.065]
0.192***
[4.186]
-0.093
[-0.649]
0.002
[0.095]
-0.049**
[-2.212]
0.007
[0.468]
0.037
[1.311]
5,464
0.419
Table IA.X
CEO Controls
This table presents estimates of ordinary least squares panel regressions of the number of patents (Patents), number of citations
(Citations), and innovation diversity (Innovative Diversity) on the General Ability Index and firm-level control variables. The
sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from BoardEx and
operate in the same four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003 period.
Financial and utility firms are omitted. Regressions include industry-year dummies. Variable definitions are provided in Table A1
in the Appendix of the paper. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates
significance at the 10%, 5% and 1% levels respectively.
General Ability Index
Log (Sales)
Log (PPE)
Stock Return
Institutional Ownership
CEO Tenure
CEO Age
External Hire Dummy
MBA Dummy
Ivy League Dummy
Recession Graduate Dummy
Fast Track Career CEO
Number of observations
R-squared
Log (Patents)
(1)
0.091**
[2.479]
0.531***
[16.147]
0.217***
[5.717]
0.099***
[3.637]
-0.385**
[-2.217]
0.001
[0.182]
-0.007
[-1.106]
0.001
[0.012]
0.133*
[1.761]
0.145
[1.635]
0.113
[1.551]
-0.001
[-0.182]
8,527
0.440
Log (Citations)
(2)
0.117**
[2.497]
0.598***
[15.056]
0.210***
[4.372]
0.154***
[3.933]
-0.278
[-1.271]
-0.001
[-0.098]
-0.007
[-0.788]
-0.023
[-0.254]
0.170*
[1.726]
0.145
[1.283]
0.146
[1.562]
-0.004
[-0.603]
8,527
0.460
10
Log
(Q Citations)
(3)
0.162***
[2.864]
0.702***
[15.242]
0.258***
[4.450]
0.168***
[3.485]
-0.331
[-1.226]
0.001
[0.062]
-0.011
[-1.022]
-0.016
[-0.140]
0.223*
[1.862]
0.154
[1.125]
0.200*
[1.767]
-0.004
[-0.458]
8,527
0.424
Log
(TT Citations)
(4)
0.061*
[1.873]
0.424***
[14.336]
0.163***
[4.901]
0.089***
[3.399]
-0.345**
[-2.307]
-0.000
[-0.075]
-0.007
[-1.349]
-0.001
[-0.011]
0.117*
[1.737]
0.081
[1.064]
0.062
[0.996]
-0.003
[-0.676]
8,527
0.393
Innovative
Diversity
(5)
0.030***
[3.768]
0.067***
[13.297]
0.029***
[3.802]
0.002
[0.372]
-0.001
[-0.025]
-0.000
[-0.261]
-0.000
[-0.073]
0.004
[0.251]
0.024
[1.531]
0.032*
[1.741]
0.023
[1.550]
0.000
[0.309]
8,527
0.374
Table IA.XI
Lagged Explanatory Variables
This table presents estimates of ordinary least squares panel regressions of the number of patents (Patents) and number of
citations (Citations) on the General Ability Index and firm-level control variables. The sample consists of EXECUCOMP firms
for which chief executive officer (CEO) profile data are available from BoardEx and operate in the same four-digit SIC industries
as the firms included in the NBER patent database in the 1993-2003 period. Financial and utility firms are omitted. Regressions
include industry-year dummies. Variable definitions are provided in Table A1 in the Appendix of the paper. Robust t-statistics
adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates significance at the 10%, 5% and 1% levels
respectively.
Log (Patents)
(1)
General Ability Index
Log (Sales)
Log (PPE)
Number of observations
R-squared
General Ability Index
Log (Sales)
Log (PPE)
Number of observations
R-squared
0.117***
[3.158]
0.512***
[15.983]
0.187***
[4.961]
8,845
0.420
0.126***
[2.783]
0.519***
[15.081]
0.166***
[3.961]
6,146
0.439
Log
Log (Citations) (Q Citations)
(2)
(3)
Panel A: One-year Lag
0.152***
0.206***
[3.238]
[3.633]
0.577***
0.675***
[15.050]
[15.130]
0.175***
0.214***
[3.663]
[3.682]
8,845
8,845
0.440
0.405
Panel A: Three-year Lag
0.152***
0.219***
[2.810]
[3.303]
0.558***
0.675***
[14.192]
[14.349]
0.136***
0.170***
[2.679]
[2.698]
6,146
6,146
0.469
0.432
11
Log
(TT Citations)
(4)
Innovative
Diversity
(5)
0.082**
[2.477]
0.403***
[14.227]
0.143***
[4.363]
8,845
0.374
0.034***
[4.554]
0.068***
[14.298]
0.022***
[2.899]
8,845
0.364
0.088**
[2.153]
0.411***
[13.524]
0.121***
[3.330]
6,146
0.394
0.035***
[4.037]
0.069***
[13.519]
0.019**
[2.164]
6,146
0.382
Table IA.XII
General Managerial Ability Dummy Variable
This table presents estimates of ordinary least squares panel regressions of the number of patents (Patents), number of citations
(Citations), and innovation diversity (Innovative Diversity) on the General Ability Index Dummy and firm-level control variables.
The General Ability Index Dummy takes a value of one when a top executive has a General Ability Index above the median in a
given year. The sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from
BoardEx and operate in the same four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003
period. Financial and utility firms are omitted. Regressions include industry-year dummies. Variable definitions are provided in
Table A1 in the Appendix of the paper. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and ***
indicates significance at the 10%, 5% and 1% levels respectively.
General Ability Dummy
Log (Sales)
Log (PPE)
Number of observations
R-squared
Log (Patents)
(1)
0.175***
[2.845]
0.510***
[16.617]
0.196***
[5.570]
10,479
0.411
Log (Citations)
(2)
0.245***
[2.998]
0.583***
[15.667]
0.189***
[4.132]
10,479
0.424
12
Log
(Q Citations)
(3)
0.317***
[3.212]
0.676***
[15.833]
0.229***
[4.177]
10,479
0.391
Log
(TT Citations)
(4)
0.108**
[2.007]
0.400***
[14.663]
0.148***
[4.825]
10,479
0.363
Innovative
Diversity
(5)
0.060***
[4.773]
0.068***
[14.980]
0.025***
[3.507]
10,479
0.355
Table IA.XIII
General Managerial Ability Components
This table presents estimates of ordinary least squares panel regressions of the number of patents (Patents), number of citations
(Citations), and innovation diversity (Innovative Diversity) on the General Ability Index components and firm-level control
variables. The sample consists of EXECUCOMP firms for which chief executive officer (CEO) profile data are available from
BoardEx and operate in the same four-digit SIC industries as the firms included in the NBER patent database in the 1993-2003
period. Financial and utility firms are omitted. Regressions include industry-year dummies. Variable definitions are provided in
Table A1 in the Appendix of the paper. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and ***
indicates significance at the 10%, 5% and 1% levels respectively.
Number of Positions
(1)
0.050***
[3.642]
Number of Firms
(2)
(3)
(4)
0.050***
[2.791]
Number of Industries
0.049**
[2.463]
Conglomerate Experience Dummy
0.139**
[2.088]
CEO Experience Dummy
Log (Sales)
Log (PPE)
Number of observations
R-squared
(5)
0.483***
[15.682]
0.182***
[5.147]
10,479
0.416
0.513***
[16.876]
0.194***
[5.493]
10,479
0.411
13
0.514***
[16.893]
0.195***
[5.521]
10,479
0.411
0.518***
[17.277]
0.199***
[5.658]
10,479
0.410
-0.007
[-0.112]
0.525***
[17.607]
0.198***
[5.623]
10,479
0.409
Table IA.XIV
Acquisitions
This table presents estimates of ordinary least squares panel regressions of the Acquisition Ratio and Number of Acquisitions. The
Acquisitions Ratio is the ratio of the annual value of acquisitions (from Compustat or SDC) to total assets. The Number of
Acquisitions is the annual of number of acquisitions in SDC. The sample consists of EXECUCOMP firms for which chief
executive officer (CEO) profile data are available from BoardEx and operate in the same four-digit SIC industries as the firms
included in the NBER patent database in the 1993-2003 period. Financial and utility firms are omitted. Regressions include
industry-year dummies or year dummies in the case of the firm fixed effects model. Variable definitions are provided in Table A1
in the Appendix of the paper. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and *** indicates
significance at the 10%, 5% and 1% levels respectively.
Log(Patents)
General Ability Index
Log(Patents)  General Ability Index
Log (Sales)
Log (PPE)
Institutional Ownership
Volatility
Firm Age
Diversification Dummy
CEO Tenure
CEO Age
Firm fixed effects
Number of observations
R-squared
Compustat:
Acquisitions Ratio
(1)
(2)
-0.003***
-0.002
[-5.223]
[-1.250]
0.002
0.002
[1.325]
[1.454]
-0.000
-0.000
[-0.129]
[-0.912]
0.000
0.010***
[0.091]
[4.603]
-0.002**
0.017***
[-2.559]
[6.593]
0.022***
0.027***
[5.494]
[4.404]
-0.090*** -0.048***
[-6.724]
[-3.358]
-0.000***
-0.000
[-3.274]
[-0.575]
0.009***
-0.000
[4.933]
[-0.213]
0.000
0.000**
[0.133]
[2.086]
-0.000
-0.000***
[-1.165]
[-2.705]
No
Yes
9,351
9,351
0.055
0.026
14
SDC:
Acquisitions Ratio
(3)
(4)
-0.001**
-0.002
[-2.182]
[-1.116]
0.001
0.001
[1.425]
[0.375]
-0.000
-0.001*
[-0.935]
[-1.767]
0.002***
0.006**
[2.663]
[2.396]
0.001
0.006***
[0.800]
[2.693]
0.014***
0.014*
[4.079]
[1.905]
-0.002
-0.011
[-0.201]
[-0.670]
-0.000**
-0.000
[-2.560]
[-1.175]
-0.022*** -0.023***
[-14.779]
[-8.487]
0.000
0.000
[0.916]
[1.485]
-0.000
-0.000
[-1.368]
[-0.525]
No
Yes
9,351
9,351
0.057
0.022
SDC:
Number of Acquisitions
(5)
(6)
-0.030***
-0.008
[-2.689]
[-0.515]
-0.011
-0.008
[-0.446]
[-0.385]
0.001
-0.011
[0.114]
[-1.081]
0.083***
0.151***
[6.038]
[6.635]
-0.009
0.072***
[-0.510]
[3.154]
0.190**
0.100
[2.401]
[1.550]
-0.793***
-0.162
[-3.702]
[-1.236]
-0.000
-0.002
[-0.171]
[-0.772]
-0.850***
-0.775***
[-22.276]
[-18.221]
0.004
0.003
[1.581]
[1.170]
0.004
-0.001
[1.611]
[-0.495]
No
Yes
9,351
9,351
0.265
0.231
Table IA.XV
CEO Turnover
This table presents estimates of probit regressions of CEO turnover on the General Ability Index and firm performance. Panel A
includes all CEO turnovers and Panel B includes forced CEO turnovers only. The measures of performance are return on assets
(ROA), industry-adjusted return on assets (Industry Adjusted ROA), Stock Return, and stock return minus the value-weighted
stock market return (Abnormal Stock Return). The regressions also include year and industry dummies. The sample consists of
EXECUCOMP firms for which CEO profile data are available from BoardEx in the 1993-2007 period. Variable definitions are
provided in Table A1 in the Appendix. Robust t-statistics adjusted for firm-level clustering are reported in brackets. *, **, and ***
indicates significance at the 10%, 5% and 1% level respectively.
Panel A: CEO Turnover Dummy
(1)
(2)
General Ability Index
0.118*** 0.131***
[3.657]
[6.754]
General Ability Index  ROAt-1
0.084
[0.482]
General Ability Index  Industry Adjusted ROAt-1
-0.119
[-0.629]
General Ability Index  Stock Returnt-1
(3)
0.147***
[7.075]
(4)
0.144***
[6.874]
-0.029
[-0.692]
General Ability Index  Abnormal Stock Returnt-1
-0.046
[-0.983]
ROAt-1
-0.682***
[-3.728]
Abnormal ROAt-1
-0.618***
[-3.163]
Stock Returnt-1
-0.184***
[-4.214]
Abnormal Stock Returnt-1
Log(Sales)
0.052***
[4.546]
8,835
0.033
Observations
Pseudo R-square
15
0.051***
[4.468]
8,835
0.028
0.030**
[2.539]
7,649
0.027
-0.218***
[-4.636]
0.030**
[2.524]
7,649
0.027
Table IA.XV: Continued
Panel B: Forced CEO Turnover Dummy
(1)
(2)
General Ability Index
0.043
0.065*
[0.796]
[1.759]
General Ability Index  ROAt-1
0.145
[0.481]
General Ability Index  Industry Adjusted ROAt-1
0.050
[0.153]
General Ability Index  Stock Returnt-1
(3)
0.064*
[1.669]
(4)
0.068*
[1.657]
-0.080
[-0.829]
General Ability Index  Abnormal Stock Returnt-1
-0.029
[-0.295]
ROAt-1
-1.104***
[-3.008]
Abnormal ROAt-1
-1.072***
[-2.642]
Stock Returnt-1
-0.561***
[-4.175]
Abnormal Stock Returnt-1
Log(Sales)
0.041*
[1.699]
8,835
0.075
Observations
Pseudo R-square
16
0.039
[1.606]
8,835
0.073
0.026
[0.981]
7,649
0.091
-0.549***
[-4.303]
0.022
[0.857]
7,649
0.091