Electronic copy available at: http://ssrn.com/abstract=2117104
Industry Expertise on Corporate Boards
Olubunmi Faleye
a, Rani Hoitash
b, and Udi Hoitash
a
July 24, 2012
Abstract
We propose a measure of board industry expertise based on the employment histories of
independent directors and use this measure to study whether, how, and when related industry
experience enhances board effectiveness. We find that board industry expertise is robustly
associated with a significant increase in firm value. We examine potential channels for this effect
by analyzing the impact of industry expertise on internal innovation and acquisitions as
alternative strategies for value maximization. Results suggest that industry experts add value by
facilitating investments in innovation. First, board industry expertise has a positive effect on
innovation but is not associated with acquisition performance. Second, board industry expertise
is significantly associated with CEO termination and compensation incentives that encourage
innovation investments. Finally, the extent to which board industry expertise engenders higher
firm value depends on the importance of corporate innovation in the firm’s value chain.
JEL classification: G34
Keywords: Director qualifications; Industry expertise; Firm value; Corporate innovation.
We thank Divya Anantharaman, Niki Boyson, Ebru Reis, Atul Gupta, Kartik Raman, Jin-Mo Kim, Alexander
Kogan, Karthik Krishnan, Lakshmana Krishna Moorthy, Kristina Minnick, Donald Monk, and seminar participants
at Bentley, Northeastern, and Rutgers Universities for helpful comments. a College of Business Administration, Northeastern University, 360 Huntington Av., Boston, MA 02115.
b McCallum Graduate School of Business, Bentley University, 175 Forest St., Waltham, MA 02452.
Electronic copy available at: http://ssrn.com/abstract=2117104
1
1. Introduction
On December 16, 2009, the U.S. Securities and Exchange Commission (SEC) released
final proxy disclosure enhancement rules. Among other directives, these rules require registrants
to “disclose for each director and any nominee for director the particular experience,
qualifications, attributes or skills that qualified that person to serve as a director … in light of the
company’s business.”1 With the focus on relating directors’ qualifications to the firm’s business,
a prominent feature of these disclosures has been an emphasis on related industry experience. In
its first proxy filing under these rules, Hewlett-Packard stated that director Marc L. Andreessen
“is a recognized industry expert and visionary in the IT industry” who has “extensive leadership,
consumer industry and technical expertise” through his positions at and service on the boards of
public and private technology companies.2 Other major firms making similar claims include
Coca-Cola Co., Wal-Mart Stores, and Bank of America.
These claims are understandable. Industry expertise is perhaps one of the most important
qualifications that directors can bring to the boardroom because it offers a deeper understanding
of industry characteristics, competitive threats, and strategic opportunities. It also provides
potential information advantages through connections with other key players in the industry.
Each of these enables directors to offer better inputs into strategic decision-making. Indeed, these
points are not lost on practitioners, with some suggesting that a significant minority of directors
should be industry-experienced (Carey and Patsalos-Fox, 2006) while others recognize industry
expertise among the top-two most desirable attributes in board members (Spencer Stuart, 2011).
Nevertheless, there is a conspicuous void in the literature concerning the impact of this board
1 U.S. SEC, 17 CFR Parts 229, 239, 240, 249 and 274. Release # 33-9089, p. 29.
2 Hewlett-Packard Company, 2011 Definitive Proxy Statement, p. 26.
2
attribute. We aim to fill this gap by examining whether, how, and in what circumstances
directors’ industry expertise enhances board effectiveness.
Our sample consists of firms in the S&P 1500 indexes over 2000–2009. Using
comprehensive biographical data from BoardEx, we construct a measure of board industry
expertise based on the employment histories of independent directors. We begin our analysis by
relating this measure to firm value as measured by Tobin’s q to examine the overall impact of
industry expertise on board effectiveness. We find that firm value is significantly higher when
industry experts serve on the board. In particular, the presence of an industry expert independent
director is associated with an increase of 4.6% in firm value while an increase of one standard
deviation in the proportion of such directors is associated with an increase of 6.0%. This result is
highly robust to endogeneity concerns, including reverse causality and omitted variable bias. It
also survives several additional robustness tests.
Next, we focus on the question of how industry expertise enhances board effectiveness by
analyzing its impact on alternative channels for value maximization. Prior research (e.g.,
Sougiannis, 1994; Eberhart, Maxwell, and Siddique, 2004) shows that investing in organic
innovation is an important means for creating value. Therefore, we start by proposing and testing
hypotheses relating board industry expertise to corporate innovation investments. We find a
highly robust positive and statistically significant association between board industry expertise
and different measures of corporate innovation. The results are also economically significant: An
increase of one standard deviation in the proportion of board industry experts is associated with
increases of 11.0%, 5.0%, and 1.4% in R&D investments, patents granted by the U.S. Patent &
Trademark Office (USPTO), and patent citations, respectively.
3
DePamphilis (2012) and Xue (2007) suggest that acquisitions are the primary strategic
alternative to in-house innovation in maximizing firm value. Thus, we also propose and test
hypotheses on the effect of board industry expertise on acquisition performance. Following
Faleye, Hoitash, and Hoitash (2011), we measure acquisition performance using short-run
announcement period abnormal returns and long-run operating performance. In each case, we
find that board industry expertise has no effect on acquisition outcomes. Furthermore, we obtain
similar results when we focus on intra-industry deals, even though such deals presumably
provide a greater opportunity for industry expert directors to influence acquisition performance.
These results suggest that industry expertise enhances board effectiveness in value
creation by facilitating investments in corporate innovation rather than through improved
acquisition performance. Manso (2011) argues that facilitating innovation requires the board to
monitor differently than it otherwise would because innovation involves the exploration of novel
ideas that are subject to higher probabilities of failure. He shows that termination decisions must
exhibit a commitment to job security and managerial entrenchment in order to encourage the
CEO to invest in innovative ideas. He also shows that innovation-inducing compensation
contracts involve substantial at-risk pay, especially in the form of long-term stock option awards.
Therefore, we extend our analysis by examining whether industry expertise is associated with
monitoring decisions that are consistent with motivating innovation. We find that this is indeed
the case. Board industry expertise significantly lessens the sensitivity of CEO dismissal to firm
performance, both in terms of operating profitability and stock market returns. Similarly, we find
that board industry expertise is associated with a significant increase in stock option awards and
a significant reduction in cash-based pay.
4
Finally, we examine the circumstances under which board industry expertise enhances
firm value by focusing on the firm’s dependence on corporate innovation for value creation.
Given our earlier results, we expect the impact of industry expertise on firm value to be greater
when corporate innovation is a significant value driver. We employ three proxies for dependence
on corporate innovation: realized innovation, industry competition, and membership in generally
accepted highly innovative industries (i.e., pharmaceuticals, electronics, instruments, and
software industries). Consistent with our expectation, we find that a one-standard-deviation
increase in board industry expertise is associated with an increase of 7.3%, 7.5%, and 7.6% in
firm value among highly innovative firms, firms operating in competitive industries, and those in
highly innovative industries, respectively. In contrast, board industry expertise has no significant
impact on firm value among less innovative companies and those in less competitive and less
innovative industries.
This paper makes several important contributions to the literature. First, we demonstrate
the significance of industry-specific skills in board effectiveness. The extensive literature on
board financial expertise (see, e.g., Xie, Davidson, and DaDalt (2003) and Hoitash, Hoitash, and
Bedard (2009)) suggests that board performance in specific tasks improves when directors
possess expertise in those particular functions. Our results corroborate and extend this literature
by establishing that related industry expertise enhances overall board effectiveness in value
creation. Our findings also present an interesting contrast to studies on generalist skills. Faleye
(2011) and Fahlenbrach, Low, and Stulz (2010) both show that general managerial skills as
measured by directors with CEO-level experience have minimal impact on board effectiveness.
We complement these studies by showing that industry-specific expertise is valuable on the
board of directors, especially when corporate innovation is a significant value driver.
5
We also extend the literature on the role of firm and industry characteristics in
determining the effectiveness of different governance structures. Earlier papers (e.g., Weisbach,
1988; Yermack, 1996) document the impact of several board attributes on corporate governance
effectiveness for the average firm, which essentially assumes that one size fits all. In contrast,
more recent studies (e.g., Linck, Netter, and Yang, 2008; Faleye, 2007; Boone et al., 2007)
suggest that the appropriateness of any given board structure is intricately linked with firm and
industry characteristics. We extend this literature by showing that a well-touted director attribute
contributes to board effectiveness only in very specific circumstances.
The closest studies to our paper are Onal (2011) and Custódio and Metzger (2010). Onal
(2011) studies boards with expertise in customer and supplier industries. He finds that such
boards rely less on stock market information in executive compensation and turnover decisions.
Custódio and Metzger (2010) analyze the effect of CEO industry expertise in diversifying
acquisitions and find that acquisition returns are higher when the CEO is experienced in the
target’s industry. Our study differs from these papers in several important respects. First, we
focus on board expertise in the same industry as the focal firm, which is the definition of industry
expertise commonly referenced in practitioner discussions. We also examine several issues that
are beyond the scope of Onal (2011) and Custódio and Metzger (2010), including corporate
innovation and firm value. Nevertheless, our paper joins theirs in pioneering research into the
impact of industry-specific expertise on corporate governance effectiveness.
The remainder of the paper is organized as follows. We discuss the relevant literature and
develop our hypotheses in the next section. The third section presents the sample, while the
fourth contains our empirical analysis and discussion of results. The fifth section concludes the
paper with a brief summary.
6
2. Background and hypotheses
A perennial issue in corporate governance research and public policy discussions is the
question of what enhances directors’ effectiveness in value maximization. While practitioners
and academics tacitly agree that individual director qualifications are an important aspect of this
debate (The Business Roundtable, 1990; Jensen, 1993), prior research has focused primarily on
broad board attributes such as board size, composition, election methods, leadership structure,
equity ownership, and diversity (see Adams, Hermalin, and Weisbach (2010) for a recent
review). The most significant exception is the literature that examines directors’ financial
expertise, based on the hypothesis that directors with financial and accounting skills are better
able to perform duties pertaining specifically to the firm’s financial reporting process. Xie,
Davidson, and DaDalt (2003) show that financial expert directors are associated with better
earnings quality. Similarly, Hoitash, Hoitash, and Bedard (2009) find a positive relation between
financial expertise and the quality of internal controls while Farber (2006) and Abbot, Parker,
and Peters (2004) show that financial expertise is associated with reduced probabilities of fraud
and accounting restatements, respectively.3 These studies clearly suggest that the professional
background, experience, and knowledge of individual directors can significantly influence the
effectiveness with which the board performs its functions.
Recognizing this and in response to recent calls for better qualified directors, the SEC
adopted new proxy disclosure enhancement rules in December 2009. These rules require covered
companies to discuss the qualifications of each director and nominee in light of the company’s
business and are aimed at encouraging companies to choose well-qualified directors. Inevitably,
3 Other exceptions include studies that focus on directors who are CEOs of other companies (e.g., Faleye, 2011;
Fahlenbrach, Low, and Stulz, 2010).
7
they also raise the question of which specific types of knowledge, backgrounds, and experience
are valuable on the board of directors.
Of all requisite competencies, industry expertise is perhaps the most important attribute
for board members because it equips directors with a deeper understanding of the risks and
opportunities in a specific industry and also enhances directors’ knowledge of the regulatory
environment and key industry players. Day and Lord (1992) show that individuals without
relevant experience need considerably more time to reach effective outcomes, while Carpenter
and Westphal (2001) find that people cope with complex decision-making by relying on
knowledge structures they developed in similar settings. This suggests that industry expertise can
facilitate board effectiveness by flattening or eliminating the learning curve faced by directors
who often must work under strict time constraints (see also Lipton and Lorsch, 1992).
These points are well understood by practitioners. The consulting firm McKinsey & Co.
states in a 2006 report: “…in our work with boards we find that too many simply lack directors
who have industry expertise to participate effectively in shaping strategy… [W]e believe that on
a board of, say, a dozen directors, a litmus test of strategic energy is the presence of at least three
or four members who have deep industry expertise in the core business and market conditions
the company faces” (Carey and Patsalos-Fox, 2006). Similarly, 40% of respondents in a recent
survey of S&P 500 firms identified industry expertise as a desired background for director
candidates, second only to financial expertise at 42% (Spencer Stuart, 2011). Finally, the NYSE
Commission on Corporate Governance (NYSE, 2010, pp. 25-26) argues that “a board which
includes a minority of directors who have some prior familiarity and/or history with the
Company and its industry may allow the board to operate more effectively as a whole.”
8
Prior research in other contexts also illustrates the value of relevant industry experience.
Neal (1995) shows that displaced employees who switch industries suffer a greater wage loss in
comparison to those who find new jobs in their pre-displacement industry, which suggests that
employers value expertise in the industry. Similarly, Eisenhardt and Schoonhoven (1996) find
that industry experience enhances the formation of strategic alliances. More recently, Custódio
and Metzger (2010) show that acquirer returns are higher when the CEO is experienced in the
target’s industry. Based on these studies and the preceding discussion, we hypothesize broadly
that industry expertise enhances directors’ effectiveness in value creation. We summarize this in
our first hypothesis, stated in the alternative form as follows:
H1: Board industry expertise is positively associated with firm value.
Next, we consider the specific channels through which industry expertise can enhance the
board’s effectiveness in value maximization. Prior research suggests that investing in organic
innovation is an important medium for cultivating higher firm value. Griliches (1986) shows that
R&D investments are associated with significant growth in firm-level productivity while
Sougiannis (1994) estimates that an increase of $1 in R&D investments is associated with
increases of $2 and $5 in profits and market value, respectively. Similarly, Chan, Martin, and
Kensinger (1990) show that share prices increase significantly when firms announce increased
R&D spending while Eberhart, Maxwell, and Siddique (2004) report positive long-term
abnormal operating performance following R&D increases. Trajtenberg (1990) and Harhoff,
Narain, Scherer, and Vopel (1999), among others, report similar value effects for patents and
patent citations. Thus, we first focus on the impact of board industry expertise on corporate
innovation investments.
9
2.1. Industry expertise and corporate innovation
As discussed above, the long-run value of corporate innovation investments is well
understood. Yet executives differ in their willingness to undertake such investments because they
are viewed as highly risky and require nurturing firm-specific human capital (Bhagat and Welch,
1995; Kothari, Laguerre, and Leone, 2002). Furthermore, since accounting rules require most
R&D spending to be expensed as they are incurred, R&D investments typically have a negative
impact on concurrent operating income. Hence, investment in innovation depends on
management’s willingness to engage in risky projects that have a negative short-term impact on
financial performance and possibly its own evaluation.
By enhancing this willingness, board industry expertise can facilitate increased
innovation investments and ultimately higher firm values. First, industry-experienced directors
are better-positioned to understand the nature and characteristics of innovation opportunities in
the industry, for example, gestation periods, cost requirements, and payoff patterns. This permits
a greater tolerance for short-term losses and early failures, which, as Manso (2011) shows, is an
important prerequisite for overcoming managerial myopia in corporate innovation investments.
Second, industry-experienced directors can foster innovation because their industry expertise and
connections can help management to better vet potential innovation opportunities thereby
reducing the likelihood that projects will fail and further alleviating managerial risk aversion.
Thus, we expect a positive relation between directors’ industry expertise and investments in
corporate innovation. We summarize this in the following hypothesis:
H2: Board industry expertise is positively associated with corporate innovation.
Our prediction of a positive effect for board industry expertise on corporate innovation
has important implications for board monitoring decisions. As shown by Manso (2011), the
10
board must monitor differently if its objective is to encourage the CEO to invest in the
exploration of innovative ideas. In the next two sections, we develop hypotheses on the two
major dimensions of board monitoring, that is, CEO termination and compensation decisions.
2.2. Industry expertise and CEO turnover
The decision to replace the CEO is perhaps the most significant board action, with far
reaching consequences for corporate strategy and performance. Ideally, an effective board will
replace the CEO when firm value conditioned on the incumbent is lower than firm value
conditioned on the appointment of an alternative candidate. This requires directors to understand
the marginal contribution of the current CEO relative to those of potential alternatives. Since
evaluating such marginal contributions is difficult, boards typically rely on hard data such as
declining operating and/or market performance in CEO replacement decisions.
Nevertheless, recent work suggests that such an approach, while conducive to preventing
managerial shirking, may be counterproductive in encouraging innovation. Manso (2011) argues
that the threat of termination following poor performance encourages the CEO to pursue routine
investments rather than explore innovative ideas with higher probabilities of failure. He further
demonstrates that the optimal incentive scheme that facilitates innovation exhibits a considerable
tolerance for failure, as well as a commitment to job security and managerial entrenchment.
Thus, in order to encourage investment in innovative ideas, the board may need to commit to not
firing the CEO even if it is ex-post efficient to do so. Consistent with these arguments, Acharya,
Baghai, and Subramanian (2010) find that labor laws with stringent employment protection are
associated with increased corporate innovation. Similarly, Tian and Wang (2011) show that firms
backed by failure-tolerant venture capitalists are significantly more innovative. Based on these
studies, we expect board industry expertise to lessen the sensitivity of CEO turnover to firm
11
performance as a means of encouraging corporate investment in the exploration of new,
innovative ideas. We summarize this in the following hypothesis:
H3: Board industry expertise lowers the sensitivity of CEO turnover to firm performance.
2.3. Industry expertise and CEO compensation
Manso (2011) argues that the optimal compensation scheme that motivates innovation is
fundamentally different from that which motivates managerial effort, proving that an innovation-
inducing compensation contract involves substantial stock option awards. Similarly, Xue (2007)
shows that managers compensated with stock-based pay, especially stock options, are more
likely to innovate internally rather than purchase established technologies via acquisitions. In
addition, Francis, Hasan, and Sharma (2011) find that corporate innovation increases with the
CEO’s stock option compensation but is unrelated with salary and cash bonus. Based on these
studies and our prediction of a positive relation between board industry expertise and corporate
innovation, we hypothesize as follows:
H4: Board industry expertise is associated with higher option-based pay.
DePamphilis (2012) and Xue (2007) argue that external acquisitions are the primary
strategic alternative to organic innovation since acquisitions can enable a firm to access new
ideas, businesses, products, and markets without the in-house innovation that would otherwise be
required. Thus, board industry expertise can facilitate higher firm value via effective acquisition
performance as an alternative (or complement) to its impact on corporate innovation. The next
section develops our hypotheses on the impact of industry expertise on acquisition performance.
2.4. Industry expertise and acquisition performance
Prior work suggests two conditions that are critical for acquisition success: selecting an
appropriate target and effectively integrating the acquired business into the bidding firm
12
(DePamphilis, 2012; Barkema and Schijven, 2008). Industry expertise can enhance board
performance on each task. First, industry-experienced directors are likely to have a better
understanding of the industry’s competitive landscape and the firm’s position therein because of
their intimate knowledge of the industry and connections with executives at other industry firms.
This facilitates the identification of potential targets that can enhance the firm’s strengths and
diminish its weaknesses relative to its competitors. Furthermore, industry-experienced directors
are more likely to understand industry value drivers better. This enables them to facilitate
effective post-merger integration by assisting top management in identifying areas for revenue
enhancement and cost reductions. Industry experts may also be able to assist management in the
early identification of potential areas for legal and regulatory concerns in the integration of
acquired businesses as well as in formulating strategies for dealing with such concerns.
Based on the foregoing, we expect a positive relation between board industry expertise
and acquisition performance. We also expect this relation to be stronger when the acquisition is
intra-industry because industry expertise in this case has the additional benefit of helping to
reduce the information asymmetry between the acquirer’s board and target firms. This facilitates
the preliminary stages of the acquisition process by increasing the efficiency with which the firm
can shift through candidates and also improves the bidder’s bargaining position, thereby
reducing the likelihood of overpaying. Thus, we have the following hypotheses:
H5a: Board industry expertise is positively associated with acquisition performance.
H5b: Board industry expertise is more positively associated with acquisition performance in
intra-industry deals.
13
3. Sample and variables
Our sample consists of all firms in the S&P 1500 indexes, excluding financial firms and
utilities because of differences in regulatory oversight that can limit the board’s role. As
described below, we use BoardEx data to construct measures of board industry expertise for
these firms. We start our sample in 2000 since BoardEx provides very limited information prior
to that year. In addition, we obtain data on other board attributes from the Riskmetrics director
database. We also obtain accounting data from Compustat, stock return data from the Center for
Research in Security Prices (CRSP) database, acquisition data from the Securities Data
Corporation (SDC) database, patent data from the National Bureau of Economic Research
(NBER) patent database, CEO compensation data from Execucomp, and CEO turnover data
from Ertugrul and Krishnan (2011) and Faleye, Hoitash, and Hoitash (2011). Our final sample
includes firms in the intersection of these databases, consisting of 9,078 firm-year observations
for 1,528 unique firms over 2000–2009.
Using data from these sources, we construct several variables that we utilize in all our
empirical tests. These variables include measures of the board’s industry expertise, board
structure, and firm characteristics. We discuss these below. In later sections, we introduce other
variables used in specific tests as we discuss those tests.
3.1. Variable definitions
Our primary variable of interest is the board’s industry expertise. To construct this
variable, we start by creating a comprehensive record of the employment history of each
independent director using data from BoardEx. Next, we identify the primary standard industrial
classification (SIC) code of each public firm at which the director was (or is currently) employed
using data from Compustat North America and Compustat Global. We define a director as an
14
industry expert if the two-digit SIC code of the firm where he serves as a board member equals
the two-digit SIC code of at least one firm in his employment history. We then aggregate this at
the board level to create three measures of board industry expertise. The first is an indicator
variable that equals one if at least one independent director is an industry expert. The second is
the number of industry experts on the board, while the third is the proportion of industry experts
relative to the number of independent directors.
Table 1 presents the trend in board industry expertise during our sample period. In 2000,
44% of boards have at least one industry expert. This increased monotonically to 64% in 2009.
This trend also coincides with an increase in board independence, with the proportion of
independent directors increasing from 55% in 2000 to 80% in 2009. Nevertheless, Table 1 also
shows that the proportion of industry experts increased from 15% to 22% over the same time
period. Thus, the incidence and extent of industry expertise on corporate boards has trended
upward during our sample period.
In addition to our primary variable, we also create variables that allow us to control for
other board attributes that are known to affect directors’ effectiveness. These include board size,
which we measure as the natural log of the number of directors, board independence (the fraction
of independent directors), and director equity ownership (proportion of outstanding shares owned
collectively by all directors). Others are board busyness (an indicator variable that equals one if a
majority of independent directors serve on three or more corporate boards, zero otherwise), CEO
duality (an indicator variable that equals one if the CEO serves as board chair, zero otherwise),
and CEO equity ownership (percentage of outstanding shares owned by the CEO).
We also create variables that measure basic firm characteristics. These are firm size
(natural log of market capitalization), investment opportunities (the ratio of capital expenditures
15
to sales), corporate diversification (combined number of business and geographic segments),
return on assets (ROA, defined as the ratio of operating income before depreciation to total
assets), and leverage (the ratio of total assets to liabilities).
Table 2 presents summary statistics for these variables. The median board has nine
members, seven of whom are independent directors. On average, directors collectively own 8.6%
of outstanding shares, with a median ownership of 3.4%, while CEO ownership averages 3.4%
with a median of 1.2%. The CEO also chairs the board in 61.5% of our sample firms, and 10.6%
of sample boards are busy in the sense that a majority of their independent directors serve on
three or more corporate boards. As expected, sample firms are large, with mean and median
market capitalization of $8.1 billion and $1.7 billion, and mean and median total assets of $5.7
billion and $1.5 billion, respectively. They are also well diversified, operating on average in six
geographical and business segments combined, with a median also of six. On average, they
earned a return on assets of 14.3%, with a median of 13.8%.
4. Empirical results
4.1. Board industry expertise and firm value
As in several previous studies, we measure firm value using Tobin’s q, which we define
as the book value of assets minus the book value of equity plus the market value of equity,
divided by the book value of assets. Mean and median Tobin’s q for the full sample are 2.0 and
1.6, respectively, which are comparable to those reported in the literature. Among firms with
board industry expertise, mean and median Tobin’s q are 2.1 and 1.7, respectively, compared to
1.9 and 1.6 for firms without board industry expertise. The differences are statistically significant
at the 5% level.
16
Next, we estimate regressions of Tobin’s q on our measures of board industry expertise,
controlling for other covariates of firm value identified in prior studies. These control variables
include firm size (Berger and Ofek, 1995), investment opportunities (Yermack, 1996), operating
profitability (Yermack, 1996), and corporate diversification (Berger and Ofek, 1995). Others are
board size (Yermack, 1996), board independence (Rosenstein and Wyatt, 1990), CEO duality
(Rechner and Dalton, 1991), directors’ equity ownership (Morck, Shleifer, and Vishny, 1988),
and board busyness (Fich and Shivdasani, 2006). We also include industry and year fixed effects
and correct standard errors for simultaneous clustering at the firm and year levels (Gow,
Ormazabal, and Taylor, 2010; Thompson, 2011). We present results in Table 3.
Our measure of board industry expertise in the first column is the proportion of industry-
experienced directors relative to the number of independent directors. This variable is positive
and statistically significant at the 1% level. Thus, board industry expertise appears to have a
positive effect on firm value even after controlling for other variables known to explain
differences in long-term firm performance. Its coefficient of 0.530 implies that an increase of one
standard deviation in the proportion of industry-experienced independent directors is associated
with an increase of 12 percentage points in Tobin’s q. Since the sample mean of Tobin’s q is 2.0,
this amounts to an economically significant increase of 6.0% in firm value for the average firm.
Similarly, a movement from the first to the third quartile of board industry expertise is associated
with an increase of 8.8% in firm value, which is again economically significant. We obtain
similar results in the second and third columns of Table 3 when we measure board industry
expertise using the number of industry-experienced independent directors and an indicator
variable for the presence of one such director on the board, respectively.
17
4.1.1. Endogeneity issues
While the above results suggest that industry expertise enhances board effectiveness in
value creation, it is common knowledge that endogeneity problems often plague empirical tests
of governance structure – firm performance relations. For example, it is possible that the positive
relation between board industry expertise and firm value simply reflects the attraction of industry
expert directors to well-performing firms rather than their superior contribution to value creation.
Furthermore, the results could also be driven by some unobservable variables that affect board
industry expertise and firm value. This section reports several tests performed to examine the
sensitivity of our results to these issues.
First, we explore a quasi-natural experiment predicated upon regulatory changes in
2002/2003. During this period, the major stock exchanges approved governance rules that
mandated boards of listed firms to be majority-independent and their compensation, nominating,
and audit committees to be entirely staffed with independent directors.4 Similarly, the Sarbanes–
Oxley Act of 2002 (SOX) requires the audit committee to consist solely of independent directors.
These constraints forced some firms to appoint additional independent directors, and while there
are no requirements for such newly appointed independent directors to be industry experts, it is
reasonable to presume that the appointment of an industry expert in these circumstances is
largely a random, exogenous event since the firm would have appointed such directors prior to
these requirements if the appointment is endogenous to firm value or other firm attributes.
Consequently, we identify 104 firms whose boards did not satisfy the imposed regulatory
structures prior to the new regime and who also had no industry expert directors prior to the new
requirements. By 2004, each of these firms has restructured its board to meet the new standards;
4 NYSE and Nasdaq submitted their governance reform proposals to the SEC in August 2002 and October 2002,
respectively. The proposals were approved in November 2003.
18
in the process, some appointed new independent directors with industry expertise while others
did not. We then estimate our firm value regression over these 104 firms for the 2004–2009
period, controlling for each firm’s average Tobin’s q during the pre-regulation years to account
for potential differences in prior performance between firms that appointed industry experts and
those that did not. Results are presented in the first column of Table 4. Consistent with our
earlier findings, board industry expertise is positive and statistically significant at the 5% level,
with a p-value of 0.012. As an additional test, we estimate another regression where we restrict
the sample to the two years immediately following imposition of the governance reforms (i.e.,
2004 and 2005) to address the concern that industry expert appointments in later years are
potentially subject to endogeneity problems even in this subsample. As the second column of
Table 4 shows, results are similar to those for the 2004–2009 period. Thus, board industry
expertise continues to exhibit a positive relation with firm value in this context where its
occurrence is most likely exogenous, which should assuage concerns about endogeneity.
Next, we estimate regressions where the dependent variable is the change in Tobin’s q,
thus eliminating the effects of unobserved firm characteristics by subtracting the observation for
the previous year from the observation for the current year. Our measure of industry expertise in
these regressions is the number of new industry experts appointed to the board in the preceding
year, that is, we regress the one-year change in Tobin’s q from year t-1 to year t on the number of
new industry experts appointed to the board in year t-1. The third column of Table 4 presents
results of a regression estimated over the full sample while the fourth column presents results of
an analogous regression estimated over the sample of firms with no industry experts in the
baseline year, that is, where board industry expertise equals zero in year t-2. In both cases, the
appointment of new industry experts to the board is associated with a statistically significant
19
increase in the change in Tobin’s q subsequent to the appointment. Yet the economic impact is
greater when the firm appoints its first industry experts. Specifically, an increase of one standard
deviation in the number of newly appointed industry experts is associated with an increase of
15.0% in the average change in Tobin’s q for the full sample, compared to an increase of 25.5%
for a similar change when the firm appoints its first industry expert.
We also employ two-stage instrumental variable regression as a further attempt to
assuage endogeneity concerns, using the number of firms in each industry and the average
number of independent directors in the industry as instruments for firm-level board industry
expertise. Our instrument choice is based on the intuition that each of these variables is
positively associated with the supply of industry-experienced directors and not likely to have a
direct impact on firm value.5 Consistent with this argument, both variables are highly significant
in a first stage regression predicting board industry expertise. The F-statistic for this regression is
25.4, well above the recommended value of 10.0, and Hansen’s J test of over-identification does
not reject the null hypothesis that the instruments are valid. The fifth column of Table 4 presents
results of the second stage regression. As the table shows, we continue to find a positive relation
between firm value and (predicted) board industry expertise.
Cheng (2008) addresses potential endogeneity problems by regressing the dependent
variable on lagged values of the endogenous explanatory variable based on the argument that
such historical values are predetermined. In our case, we encounter the difficulty that our
measures of industry expertise are highly correlated over time. For example, the correlation
coefficients between contemporaneous values and the first three lags of the proportion of board
5 We recognize that the number of industry firms can have a direct effect on firm value, especially in the tails of the
distribution of the number of firms, i.e., in cases of monopoly/oligopoly and perfect or near-perfect competition.
However, we believe this is less likely to be the case in the relevant range of the distribution of industry firms in our
sample. Hansen’s J test confirms that our instruments are exogenous in the second stage regressions.
20
industry experts are 0.96, 0.91, and 0.87, respectively. Consequently, we use the longest possible
lag in our data (i.e., the ninth lag) and regress 2009 Tobin’s q on board industry expertise in 2000
in order to allow sufficient time to lessen the time series dependence in board industry expertise.6
The sixth column of Table 4 presents results of the regression using the proportion of board
industry experts in 2000. Similar to the full sample results shown in the first column of Table 3,
the variable is positive and significant at the 1% level. Its coefficient of 0.482 implies that an
increase of one standard deviation in board industry expertise in 2000 is associated with an
increase of 5.9% in firm value in 2009.7
Another potential endogeneity concern is the possibility that industry expert directors are
better qualified directors in general, for example, because of their professional experience or
other personal characteristics. Thus, our tests would simply capture the effects of general director
quality rather than the impact of specific industry expertise. To address this issue, we identify
directors who serve on the boards of at least two firms and who qualify as industry experts on
one board but not on the other. We then create a subsample that includes both sets of firms
provided that the latter firm has no other industry expert director. In effect, we have two types of
firms in this sample: (1) those without industry expert directors that have at least one director
who is an industry expert at a different firm (1,427 observations), and (2) those with at least one
industry expert director who serves on another board where he does not have relevant industry
experience (1,006 observations). Since both sets of firms share the same industry expert
6 The correlation coefficients between 2009 and 2000 values of the proportion, number, and indicator variable for
board industry experts are 0.63, 0.60, and 0.52, respectively. 7 While this addresses concerns about reverse causality, it inevitably raises the question of survivorship bias since
firms must survive from 2000 until 2009 to be included in the regressions. To assuage this, we examine board
industry expertise in 2000 for firms that survived until 2009 (and are included in the regressions) and those that did
not. Mean and median percent board industry experts in 2000 for surviving firms are 14.7% and 0.0%, respectively,
compared to 15.9% and 0.0% for non-surviving firms. Both the t-test and Wilcoxon rank-sum test do not reject the
null hypotheses that the differences in means and medians equal zero, with p-values of 0.46 and 0.72, respectively.
We find similar results for the number and incidence of board industry experts for the two categories of firms.
21
directors, we should find no effect for board industry expertise in regressions estimated over this
sample if our tests only capture general director quality. Conversely, our board industry expertise
variables should be positive and significant if relevant industry experience matters. As the
seventh column of Table 4 shows, this is indeed the case. Thus, our results are more likely
attributable to relevant industry expertise rather than general director quality.
Overall, the various tests discussed in this section suggest that our basic findings are not
mere artifacts of some confounding underlying issues, reverse causality, or other endogeneity
problems. Rather, they indicate that industry expertise significantly enhances the board’s
effectiveness in value creation, which is consistent with our first hypothesis, H1.
4.1.2. Other robustness tests
We also perform several other tests to examine the robustness of our basic results on the
effect of board industry expertise on firm value. First, we examine the robustness of our results
to alternative definitions of board industry expertise to ensure that our findings are not driven by
the specific measures employed in our main tests. As a starting point, we repeat our tests using
industry expertise defined on the basis of directors’ experience in the three-digit and four-digit
SIC code industry rather than the two-digit SIC code industry employed in our main tests. The
first and second columns of Table 5 present results of our basic firm value regression using these
measures. As the table shows, our results remain unchanged.
Next, we define two additional measures based on the number of industry firms in which
directors were/are employed and the total number of years of directors’ experience in the
industry. As the third and fourth columns of Table 5 show, we continue to find a positive and
significant relation between firm value and board industry expertise using these measures. In
addition, we classify industry experts into CEOs and non-CEOs based on whether any portion of
22
a director’s employment history at an industry firm is/was at the CEO level to examine if the
level of industry experience matters. The fifth column of Table 5 shows that both CEO and non-
CEO industry experts have a positive impact on firm value. However, the effect is significantly
stronger for CEO industry experts as one would expect if the results reflect the specific skills of
industry expert directors.
Finally, we recognize that our definition of industry expertise based on each firm’s
primary SIC code is somewhat coarse since many firms operate in more than one SIC code
industries. While this should bias against finding significant results when using our primary
measures of industry expertise, we perform additional robustness checks by focusing on single-
segment firms, defined as those with one operating business segment as disclosed in the
Compustat segment files. This allows us to examine the effect of industry expertise among firms
where such expertise is more cleanly defined. The last column of Table 5 presents results of our
main firm value regression estimated over single-segment firms. As the column shows, industry
expertise remains significantly positively related with firm value.
Overall, our results strongly suggest that industry expertise enhances the board’s
effectiveness in value creation. Next, we turn our attention to the question of the channels
through which industry expertise operates to facilitate higher firm value, focusing first on
corporate innovation investments.
4.2. Board industry expertise and corporate innovation
We employ two measures of corporate innovation. The first is R&D spending, which we
choose because it is commonly used in the literature as a measure of corporate innovation
investments and because the timing of R&D spending is closest to the decision to innovate. We
define R&D spending as the ratio of R&D expenditures to total revenue. As conventional, we set
23
this variable to zero if Compustat reports R&D as missing. The sample average R&D spending is
5.0% of revenue, with a median of 0.4%.
Jensen (1993) suggests that higher R&D spending sometimes reflect managerial
investment in pet projects rather than significant corporate innovation that enhances firm value.
Therefore, we employ patenting activity as an additional measure of corporate innovation to
sidestep this potential limitation of R&D spending. We measure patenting activity using the
number of patents received by each company from the USPTO and the average number of
citations per patent. As stated earlier, we obtain these variables from the NBER patent database
described in Hall, Jaffe, and Trajtenberg (2001). We assume that patenting variables equal zero
for firm-years not represented in the database. We assign each patent to the year the firm applied
for it (rather than the year it was granted) since the timing of innovation is closer to the year the
company filed the patent application.8 Mean and median number of patents are 14.5 and 0.0,
respectively, while mean and median average citations are 1.3 and 0.0, respectively.
Table 6 contains results of regressions of our corporate innovation measures on board
industry expertise and relevant control variables suggested by prior work. These control
variables include firm size, firm age, leverage, growth opportunities, fixed asset intensity, board
size, board independence, CEO duality, CEO equity ownership, and CEO compensation
incentives (Baysinger and Hoskisson, 1990; Hall and Ziedonis, 2001; Faleye, Hoitash, and
Hoitash, 2011). We also control for R&D expenditures in our patent regressions to account for
the effects of differences in R&D spending on patenting activities. We employ the natural log
transformation of (one plus) our patenting activity variables and estimate each regression as a
8 On average, patents are granted 2.1 years after an application is filed. Since our patent data end in 2006 and patents
are not reported until granted, we restrict our patent regressions to 2004 and earlier years to account for this lag. We
also adjust patent citations for the truncation bias stemming from the fact that older patents can garner more citations
simply because of their longer lives.
24
Tobit model because true innovation activity is unobserved for firm-years with zero scores on
our measures, that is, our dependent variables are left-censored at zero. Each regression includes
year and industry fixed effects, with standard errors double clustered at the firm and year levels.
The first column of Table 6 shows that the proportion of industry expert directors is
positive and significant at the 1% level in the R&D regression. Note that, since the regression is
a Tobit model, the marginal effect of each variable on observed R&D does not equal its
regression coefficient. The estimated marginal effect of a one-unit change in the proportion of
industry-experienced directors on R&D spending is 2.4 percentage points when other variables
are evaluated at their sample means. As reported in Table 1, the standard deviation of the
industry expertise variable is 0.23. Thus, an increase of one standard deviation in board industry
expertise is associated with an increase of 55 basis points in the ratio of R&D expenditures to
total revenue, holding all else equal. Relative to the sample average R&D spending of 5.0%, this
represents an economically significant 11% increase in corporate R&D investment. We obtain
comparable results in unreported regressions that use the number of board industry experts and
the board industry expertise indicator variable.
The second column presents results for the (natural log of one plus the) number of
patents. Once again, the proportion of industry expert directors is statistically significant, with a
p-value of 0.027. Its coefficient of 0.554 implies that an increase of one standard deviation in the
proportion of industry experts on the board of directors is associated with an increase of 5.0% in
successful patent applications even after controlling for differences in R&D spending and other
firm and board characteristics. The third column presents similar results for patent citations.
Specifically, the coefficient on the proportion of board industry experts implies that an increase
of one standard deviation in board industry expertise is associated with an increase of 1.4% in
25
citations per patent. In other tests (not tabulated), we find comparable results when we use the
number of board industry experts and the industry expertise indicator variable.
We also perform several additional tests to examine the robustness of these results. These
analyses are analogous to those reported in Tables 4 and 5 for Tobin’s q and include tests
focusing on the regulatory quasi-natural experiment, using changes in R&D and patenting
activity, instrumental variable regressions, using the oldest lagged values of the board expertise
variables, controlling for director quality, and using alternative definitions of industry expertise.
In each case, we obtain results that are quite similar to those reported in Table 6. We do not
tabulate these results because of space considerations but they are available upon request.
Overall, our results suggest that industry expertise enables the board to facilitate top
management’s ability and willingness to invest in corporate innovation. In later sections, we
investigate this further by examining the role of board industry expertise in CEO termination
decisions and the design of executive compensation incentives. In each case, we examine
whether boards with industry expertise are more associated with decisions that cultivate risky
innovation, which would buttress the results in this section. Meanwhile, we turn next to the
impact of board industry expertise on acquisition performance as an alternative (or
complementary) channel for value creation.
4.3. Board industry expertise and acquisition performance
We obtain acquisition data from the SDC database. The data cover 2000–2009 and
include deals valued at $100 million or more and involving a U.S. acquirer. We exclude deals
where the acquirer sought less than 50% of the target and multiple deals announced by the same
bidder on the same date. We also eliminate acquirers with no data in the Compustat, CRSP,
26
Execucomp, and BoardEx databases. The final sample consists of 1,205 deals announced by 558
unique acquirers.
We employ two measures of acquisition performance. The first is the cumulative
abnormal return (CAR) estimated for two event windows, from three days before to three days
after deal announcement, CAR[-3, +3], and from one day before to one day after deal
announcement, CAR[-1, +1]. We calculate each using parameters from equal-weighted market
model regressions estimated for each acquisition over a period of 255 days (-301, -46) preceding
deal announcement. Mean and median CAR[-3, +3] are -0.40% and -0.20%, respectively, while
mean and median CAR[-1, +1] are -0.20% and 0.00%.
Zollo and Meier (2008, p. 71) argue that short-term abnormal returns may “gauge
something different than actual acquisition performance” and recommend long-run operating
performance as an alternative/complimentary measure. Therefore, we construct a second
measure of acquisition performance that focuses on abnormal operating earnings in the three
post-acquisition years. Following Fee and Thomas (2004), we first match each acquirer to a non-
acquiring firm based on industry, total assets, and operating cash flow margin in the year
preceding the acquisition. 9
We calculate the acquirer’s pre-acquisition assets as the sum of the
target’s and acquirer’s assets, and operating margin as the asset-weighted average of the target’s
and acquirer’s operating margin in the year preceding the deal. Next, we compute one-, two-, and
three-year post-merger change in operating cash flow margin for each acquirer and its match by
subtracting pre-merger operating margin from the operating margin for each post-merger year.
We then define abnormal operating performance as the change in operating margin for the
acquirer minus the change in operating margin for its matching firm. Mean and median abnormal
9 This procedure requires both the acquirer and the target to be publicly traded firms with data available in
Compustat. This results in a significant sample loss that reduces the sample to 377 deals.
27
operating margin are 0.00% and 0.05%, 0.00% and 0.16%, and 0.25% and 0.15% for the first,
second, and third post-acquisition years, respectively.
Next, we estimate regressions of our measures of acquisition performance on board
industry expertise. We employ Heckman selection models in order to mitigate the potential bias
arising from the fact that the sample of acquirers is most likely self-selected rather than
random.10
We also control for several deal, acquirer, and CEO characteristics shown by prior
work as significant determinants of acquisition performance. These include the method of
payment (Travlos, 1987); whether the target is a private company (Chang, 1998), deal size
relative to the acquirer (Asquith, Bruner, and Mullins, 1983); whether both parties operate in the
same or different industries (Morck, Shleifer, and Vishny, 1990); and acquirer’s size (Moeller,
Schlingemann, and Stulz, 2004), leverage (Maloney, McCormick, and Mitchell, 1993), board
size (Yermack, 1996), board independence (Byrd and Hickman, 1992), and CEO duality and
institutional ownership (Faleye, Hoitash, and Hoitash, 2011).
We present results of these regressions in Table 7. In the first and second column, we
show results for CAR[-3, +3] and CAR[-1, +1], respectively, while the third through fifth
columns present results for abnormal operating performance in the first, second, and third post-
acquisition year, respectively. As Table 7 shows, board industry expertise is not statistically
significant in any of the regressions. Rather, it appears that industry expertise does not enhance
the board’s ability to achieve superior acquisition performance, which is contrary to our
expectations under hypothesis H5a.
10
In the first stage, we model the acquisition choice as a function of several variables suggested by prior work,
including cash holdings, prior performance, leverage, internal growth opportunities, and firm size (Harford, 1999).
We supplement these with several governance variables including CEO duality, board size, and directors’ busyness.
Significant variables in this regression are cash holdings (+), firm size (+), performance (+), leverage (+), board size
(-), directors’ busyness (-), and board industry expertise (+). We do not report first stage results to conserve space.
28
Next, we test hypothesis H5b by estimating regressions that include an interaction term
between board industry expertise and an indicator variable that equals one for intra-industry
deals, zero otherwise. We expect this interaction term to be positive and significant under H5b,
since it posits that board industry expertise has a more positive impact on acquisition
performance in intra-industry deals. As Table 8 shows, this is not the case. On the contrary, the
interaction term is not statistically distinguishable from zero in any of the regressions.
These results provide a fascinating contrast to the positive effects of board industry
expertise on corporate innovation activities reported earlier in Section 4.2. The strategy and
finance literatures have long recognized that acquisitions and organic growth fueled by internal
R&D and other innovation activities are the primary alternatives for achieving long-term
corporate success (see, e.g., DePamphilis (2012)). Our results suggest that board industry
expertise is valuable only in the latter case. It appears that the skills and experience of industry
expert directors help facilitate the painstaking and drawn-out process of cultivating internal
growth by encouraging the development of firm-specific human capital and mitigating
managerial risk aversion. Organic innovation also involves tasks that are commonly performed
internally, without the input of external advisors. In contrast, acquisitions are discrete events and
management can and does seek outside advice from appropriate legal and/or investment banking
advisors to remedy any gaps in its knowledge, expertise, or experience as and when needed.
Thus, board industry expertise offers no significant marginal improvements in acquisition
performance. Next, we turn to the impact of board industry expertise on CEO turnover and
compensation decisions to evaluate whether industry expertise influences board monitoring in a
manner consistent with motivating investments in corporate innovation.
29
4.4. Board industry expertise and CEO turnover decisions
We obtain CEO dismissal data from Ertugrul and Krishnan (2011) and Faleye, Hoitash,
and Hoitash (2011). Both studies identify forced turnover using the methodology of Huson,
Malatesta, and Parrino (2004). The sample consists of 242 CEO dismissals over 2000–2008. We
merge these data into the full sample, coding firm-years with forced turnover as one and other
firm-years as zero but excluding firm-years with routine CEO changes. Our primary interest is in
the impact of board industry expertise on the performance-sensitivity of CEO dismissal.
Therefore, we estimate separate logit regressions for firms with no board industry experts and
those with at least one industry expert director.11
We control for other factors known to affect
CEO dismissal, including firm performance (Coughlan and Schmidt, 1985), ownership structure
(Denis, Denis, and Sarin, 1997), board size (Yermack, 1996), CEO duality (Goyal and Park,
2002), and board composition (Weisbach, 1988). Each regression includes year and industry
fixed effects, with standard errors corrected for clustering at the firm and year levels.
In the first two columns of Table 9, we measure performance as the market-adjusted
stock return over the preceding year, where the market is defined as the CRSP equal-weighted
portfolio of NYSE/Nasdaq/AMEX stocks. As the table shows, market-adjusted return is negative
and significant at the 1% level in both regressions. Thus, the probability of CEO dismissal
increases with poor firm performance regardless of whether the board has industry experts or
not. However, the performance-sensitivity is stronger among firms with no industry expert
directors. First, the coefficient of market-adjusted return is larger (in absolute value) among these
firms (-0.799 vs. -0.447) and significantly different from that in the regression for firms with
11
An alternative approach is to estimate a full sample regression with an interaction term between board industry
expertise and measures of firm performance. We do not follow this approach because of the difficulty of interpreting
the interaction of two continuous variables especially in a logit regression. Also, a full sample regression constrains
the other variables to have the same effect on the likelihood of forced turnover among firms with and those without
board industry expertise.
30
board industry expertise. Second, the average marginal effect of a decline of one standard
deviation in adjusted returns is an increase of 2.3 percentage points in the probability of forced
CEO turnover among firms with no industry expert directors. In contrast, the average marginal
effect of a similar decline in adjusted returns among firms whose boards have at least one
industry expert is an increase of only 1.3 percentage points in the likelihood of forced turnover.
We obtain similar results in untabulated regressions where we adjust stock returns using the
value-weighted portfolio of NYSE/Nasdaq/AMEX stocks.
The third and fourth columns of Table 9 show that results are very similar when we
measure performance using operating profitability (ROA) rather than stock market returns.
Specifically, ROA is negative and significant at the 1% level in both regressions but its
coefficient of -3.284 in the regression for firms with no industry expert directors is significantly
larger (in absolute terms) at the 5% level than the corresponding value of -1.742 in the regression
for firms with board industry expertise. Similarly, a decline of one standard deviation in ROA
increases the probability of forced CEO turnover by 1.4 and 2.0 percentage points among firms
with and without board industry expertise, respectively.
These results indicate that industry expertise attenuates the propensity of boards to
dismiss the CEO following poor operating and/or stock market performance, thereby providing
some measure of entrenchment for the CEO. Rather than hurt firm performance, however, our
earlier results suggest that such entrenchment ultimately benefits shareholders by encouraging
investments in corporate innovation. It appears that the deeper understanding of industry
characteristics and innovation pathways by industry expert directors allows these directors to
influence CEO dismissal decisions in a manner that encourages innovation over routine projects.
31
4.5. Board industry expertise and CEO compensation incentives
In this section, we examine whether board industry expertise is associated with
compensation structures that encourage the CEO to invest in corporate innovation. We start by
examining the impact of board industry expertise on the dollar amount of annual salary, stock
option, and restricted stock awards. As a complement, we then analyze the proportions of CEO
pay awarded in the form of cash, stock options, and restricted stocks. In each analysis, we control
for a standard set of variables shown in prior studies as important determinants of executive
compensation. These variables include firm size, firm risk, performance, growth opportunities,
and operational complexity (Core, Holthausen, and Larcker, 1999). Others are board size, board
composition, board busyness, equity ownership, CEO duality, and CEO age (Yermack, 1996;
Faleye, Hoitash, and Hoitash, 2011).
Table 10 presents results of this analysis. In the first column, we regress the natural log of
inflation-adjusted CEO salary on board industry expertise and the control variables enumerated
above as well as industry and year fixed effects. As the table shows, board industry expertise is
negatively associated with the level of CEO salary. Its coefficient implies that an increase of one
standard deviation in the proportion of board industry experts is associated with a reduction of
4.3% in CEO salary. Similarly, we find in untabulated results that CEO salary is reduced by
6.3% when the board has at least one industry expert director, while each additional industry
expert on the board lowers CEO salary by 2.3%.
In contrast, the second column shows that stock option compensation increases
significantly with the proportion of industry expert directors. The coefficients imply that, on
average, the CEO’s stock option compensation increases by 34.1% for an increase of one
standard deviation in the proportion of industry expert directors. Quite interestingly, results in
32
the third column show that industry expert directors do not compensate the CEO with greater
amounts of restricted stock. Recall that prior studies (e.g., Manso, 2011; Xue, 2007) distinguish
between stock options and restricted stock awards in encouraging managerial investment in
corporate innovation and place a greater emphasis on the former. Thus, the fact that industry
expert directors grant the CEO more stock options but not restricted stocks suggests that these
directors are deliberate in structuring the CEO’s compensation to encourage a greater willingness
to innovate.
We explore this further in the last three columns of Table 10 by estimating regressions
where the dependent variables are the proportions of total CEO compensation paid via salary,
stock options, and restricted stocks, respectively.12
Consistent with the earlier results, the fourth
column shows that board industry expertise significantly lowers the fraction of salary as a
proportion of total CEO pay. Specifically, the average marginal effect of an increase of one
standard deviation in board industry expertise is a reduction of 1.6 percentage points in the
proportion of salary to total pay. Relative to the sample average salary ratio of 27.7%, this
represents an economically significant reduction of 5.8% in the fixed component of CEO pay.
Similarly, the fifth column confirms that industry expert directors are associated with a
significant increase in the fraction of CEO remuneration paid using stock options. The
coefficients imply that the average marginal effect of an increase of one standard deviation in
board industry expertise is an increase of 3.1 percentage points in the fraction of option-based
pay. Since the sample average proportion of option compensation is 28.0%, this is an
economically significant increase of 11.1% in the fraction of options-based compensation. In
12
Results reported in Table 10 are from fractional logit models because the dependent variables are proportions
bounded between 0 and 1. We obtain similar results when we estimate Tobit models for the ratio of stock options
and restricted stock awards, where the dependent variables include large numbers of zero-value observations (24.5%
and 51.8% for stock options and restricted stocks, respectively).
33
contrast, the sixth column shows that board industry expertise has no impact on the proportion of
CEO compensation paid via restricted stocks.
Overall, these results suggest that board industry expertise facilitates the design of
compensation incentives that encourage corporate innovation. Industry expert directors shift the
CEO’s remuneration away from fixed, non-contingent pay in the form of salary into long-term,
at-risk pay in the form of stock options, thus permitting an increased tolerance for early failures
while substantially rewarding long-term success. As proven by Manso (2011) and supported by
empirical evidence in Xue (2007) and Francis, Hasan, and Sharma (2011), this is essential in
motivating risk-averse CEOs to engage in the exploration of new innovative ideas.
4.6. Board industry expertise, innovation dependence, and firm value
Our results thus far suggest that industry expertise enhances board effectiveness in value
creation and that a primary channel for this is through the cultivation of corporate investments in
organic innovation. Since firms differ in the extent to which they depend on corporate innovation
for value maximization, this provides an additional opportunity for us to evaluate our results.
Specifically, if our interpretation of the evidence is correct, then the impact of board industry
expertise on firm value should be greater among innovation-dependent firms than among other
firms. In this section, we investigate this by examining different subsamples based on the
importance of corporate innovation as a value driver.
As a starting point, we focus on highly innovative firms based on the intuition that
innovative firms are more likely to depend on ongoing innovation in order to create and sustain
value. We classify firms as highly innovative if their ultimately approved patent applications are
more than those of the median firm and less innovative otherwise. We then estimate separate
firm value regressions for the two subsamples. As the first column of Panel A of Table 11 shows,
34
board industry expertise is positive and significant at the 1% level in the regression estimated
over highly innovative firms. Its coefficient of 0.655 implies that an increase of one standard
deviation in board industry expertise is associated with an increase of 7.3% in firm value for the
average highly innovative firm. In contrast, board industry expertise is statistically insignificant
in the regression for less innovative firms, as shown in the second column. Furthermore, the
Chow test indicates that the coefficients of board industry expertise in the two regressions are
significantly different from each other at less than the 1% level. Thus, the value effects of board
industry expertise are confined mainly to highly innovative firms. We reach a similar conclusion
when we classify highly and less innovative firms on the basis of third and first quartile scores
on approved patent applications as well as when we use R&D spending rather than patent
applications as the measure of innovation.
Prior research suggests that industry competition increases the need for firms to innovate
in order to maintain and/or improve their competitive advantages through, for example, product
differentiation strategies. For instance, Blundell, Griffith, and van Reenen (1999) and Aghion et
al. (2009), among others, show that firm-level innovation increases with industry competition.
Therefore, we examine whether the effect of board industry expertise on firm value is stronger in
competitive industries to further vet our results.
We create a proxy for industry competitiveness using the assets-based Herfindahl-
Hirschman index (HHI) constructed at the two-digit SIC code level for each year.13
Since a
higher HHI value implies less competition within the industry, we define (less) competitive
industries as those with HHI scores (at or above) below the sample median in each year. We then
estimate separate regressions for firms in competitive and less competitive industries. Results in
Panel B of Table 11 indicate that board industry expertise is positive and significant at the 1%
13
Our results are qualitatively similar when we use three-digit SIC code industries to construct the HHI index.
35
level among firms in competitive industries. Its coefficient implies that an increase of one
standard deviation in industry expertise among these firms is associated with an increase of 7.5%
in Tobin’s q relative to its sample average. In contrast, board industry expertise is not significant
in the regression for firms operating in less competitive industries. The coefficients are
significantly different from each other at the 1% level. Thus, the valuation benefits of board
industry expertise accrue mainly to firms in highly competitive industries where innovation is a
sine qua non for value maximization. For those operating in less competitive environments,
board industry expertise has no impact on firm value.
Finally, we focus on a subset of industries where corporate innovation is generally
accepted as highly germane to firm value, namely, pharmaceuticals, electronics, instruments, and
software industries. Panel C of Table 11 presents results of separate regressions estimated over
firms in these industries and those in other industries. As the first column shows, board industry
expertise is positive and significant at the 1% level in the regression for innovation-dependent
industries. Among firms in such industries, Tobin’s q increases by 7.6% for an increase of one
standard deviation in board industry expertise. In contrast, industry expertise has no significant
effect on Tobin’s q among firms in other industries.
These results clearly suggest that industry expertise enhances board effectiveness in value
creation mainly when firm value is dependent on corporate innovation. This corroborates our
earlier findings that board industry expertise facilitates corporate investments in organic
innovation by enabling directors to design appropriate compensation and dismissal incentives
that attenuate the CEO’s natural inclination toward safer but necessarily less profitable
investments. Thus, firms benefit from board industry expertise when innovation is a significant
value driver. In contrast, industry expert directors have no value effects when innovation is less
36
important in the firm’s value chain. These nuances are interesting in their own right, but they
also provide additional evidence that our main results are most likely not attributable to some
spurious underlying factors. If that were the case, we should observe no differences in the effect
of board industry expertise based on industry and/or firm characteristics.
5. Summary and conclusion
A survey of S&P 500 firms in 2011 identified industry expertise as one of the top-two
most-desired qualifications in board members. Likewise, several major companies recently
supported board nominations with references to nominees’ industry experience. Ultimately, these
claims force a discussion of the role of industry-experienced directors in corporate governance
effectiveness. We seek to facilitate this discussion by examining whether, how, and when
industry expertise enhances the board’s effectiveness in value maximization.
We propose a measure of board industry expertise based on the employment histories of
independent directors. Using this measure, we find that board industry expertise is associated
with a significant increase in firm value. We also examine the channels through which this effect
operates by analyzing the impact of industry expertise on corporate innovation investments and
acquisition performance. Our results suggest that industry experts add value by facilitating
corporate investments in organic innovation through appropriately designed compensation and
termination incentives. We also find that the extent to which industry experts engender higher
firm value depends on the firm’s need for internal innovation.
These results provide some empirical support for the claims of practitioners by
highlighting the important role that related industry experience can play in enhancing board
effectiveness. Recent studies (Faleye, 2011; Fahlenbrach, Low, and Stulz, 2010) suggest that
37
general managerial skills as measured by CEO-level experience have only a minimal impact on
board performance. Our results present an interesting contrast to these findings by showing that
industry-specific skills enhance board effectiveness, especially when corporate innovation is a
significant component of the firm’s value chain. We expect that these results will stimulate
further research into how the professional backgrounds of directors interface with firm and
industry characteristics to determine the board’s effectiveness in performing its functions.
38
References
Abbott, L.J., Parker, S., Peters, G., 2004. Audit committee characteristics and restatements.
Auditing: A Journal of Practice & Theory 23, 69–87.
Acharya, V.V., Baghai, R.P., Subramanian, K.V., 2010. Labor laws and innovation. NBER
working paper #16484.
Adams, R.B., Hermalin, B.E., Weisbach, M. S., 2010. The role of boards of directors in
corporate governance: A conceptual framework and survey. Journal of Economic Literature
48, 58–107.
Aghion, P., Blundell, R., Griffith, R., Howitt, P., Prantl, S., 2009. The effects of entry on
incumbent innovation and productivity. Review of Economics and Statistics 91, 20–32.
Agrawal, A., Nasser, T., 2011. Blockholders on boards and CEO compensation, turnover and
firm valuation. Working paper. University of Alabama and Kansas State University.
Asquith, P., Bruner, R., Mullins, D., 1983. The gains to bidding firms from merger. Journal of
Financial Economics 11, 121–139.
Barkema, H.G., Schijven, M., 2008. Toward unlocking the full potential of acquisitions: The role
of organizational restructuring. Academy of Management Journal 51, 696–722.
Baysinger, B., Hoskisson, R. 1990. The composition of boards of directors and strategic control:
effects on corporate strategy. Academy of Management Review 15, 72–87.
Beck, N., 2001. Time-series–cross-section data: What have we learned in the past few years?
Annual Review of Political Science 4, 271–93.
Berger, P.G., Ofek, E., 1995. Diversification's effect on firm value. Journal of Financial
Economics 37, 39–65.
Bhagat, S. and Welch, I., 1995, Corporate research and development investments: International
comparisons, Journal of Accounting and Economics, 19, 443–470.
Blundell, R., Griffith, R., van Reenen, J., 1999. Market share, market value and innovation in a
panel of British manufacturing firms. Review of Economic Studies 66, 529–554.
Boone, A.L., Casares Field, L., Karpoff, J.M., Raheja, C.G., 2007. The determinants of corporate
board size and composition: An empirical analysis. Journal of Financial Economics 85, 66–
101.
Business Roundtable, 1990. Corporate governance and American competitiveness: A statement
of the Business Roundtable. Business Lawyer 46, 241–252.
Byrd, J.W., Hickman, K.A., 1992. Do outside directors monitor managers? Evidence from tender
offer bids. Journal of Financial Economics 32, 195–221.
Carey, D.C., Patsalos-Fox, M., 2006. Shaping strategy from the boardroom. McKinsey The
Quarterly 3, 90–94.
Carpenter, M.A., Westphal, J.D., 2001. The strategic context of external network ties: examining
the impact of director appointments on board involvement in strategic decision making.
Academy of Management Journal 4, 639–660.
Chan, S.H., Martin, J.D., Kensinger, J.W., 1990. Corporate research and expenditures and share
value. Journal of Financial Economics 26, 255–276.
Chang, S., 1998. Takeovers of privately held targets, methods of payment, and bidder returns.
Journal of Finance 53, 773–784.
39
Cheng, S., 2008. Board size and the variability of corporate performance. Journal of Financial
Economics 87, 157–176.
Core, J.E., Holthausen, R.W., Larcker, D.F., 1999. Corporate governance, chief executive officer
compensation, and firm performance. Journal of Financial Economics 51, 371–406.
Coughlan, A.T., Schmidt, R.M., 1985. Executive compensation, management turnover, and firm
performance: an empirical investigation. Journal of Accounting and Economics 7, 43–66.
Custódio, C., Metzger, D., 2010. The value of CEO’s industry expertise - evidence from mergers
& acquisitions. Working paper, London School of Economics.
Day, D.V., Lord, R.G., 1992. Expertise and problem categorization: the role of expert processing
in organizational sense making. Journal of Management Studies 29, 35–47.
Denis, D., Denis, D., Sarin, A., 1997. Ownership structure and top executive turnover. Journal of
Financial Economics 45, 193–221
DePamphilis, D. M., 2012. Mergers, Acquisitions, and Other Restructuring Activities. Academic
Press, Burlington, MA.
Eberhart, A.C., Maxwell, W.F, Siddique, A.R., 2004. An examination of long–term abnormal
stock returns and operating performance following R&D increases. Journal of Finance 59,
623–650.
Eisenhardt, K.M., Schoonhoven, C.B., 1996. Resource-based view of strategic alliance
formation’ strategic and social effects in entrepreneurial firms. Organization Science 7, 136–
150.
Ertugrul, M., Krishnan, K., 2011. Can CEO dismissals be proactive? Journal of Corporate
Finance 17, 134–151.
Fahlenbrach, R., Low, A., Stulz, R. M., 2010. Why do firms appoint CEOs as outside directors?
Journal of Financial Economics 97, 12–32.
Faleye, O., 2007. Does one hat fit all? The case of corporate leadership structure. Journal of
Management and Governance 11, 239–259.
Faleye, O., 2011. CEO directors, executive incentives, and corporate strategic initiatives. Journal
of Financial Research 34, 241–277.
Faleye, O., Hoitash, R., Hoitash, U., 2011. The costs of intense board monitoring. Journal of
Financial Economics 101, 160–181.
Farber, D., 2006. Restoring trust after fraud: Does corporate governance matter? The Accounting
Review 80, 539–561.
Fee, C.E., Thomas, S., 2004. Sources of gains in horizontal mergers: evidence from customer,
supplier, and rival firms. Journal of Financial Economics 74, 423–460.
Fich, E. M., Shivdasani, A., 2006. Are busy boards effective monitors. Journal of Finance 61,
689–724.
Francis, B., Hasan, I., Sharma, Z., 2011. Incentives and innovation: Evidence from CEO
compensation contracts. Bank of Finland Research Discussion Paper 17-2011.
Gow, I.D., Ormazabal, G., Taylor, D.J., 2010. Correcting for cross-sectional and time-series
dependence in accounting research. The Accounting Review 85, 483–512.
Goyal, V.K., Park, C., 2002. Board leadership structure and CEO turnover. Journal of Corporate
Finance 8, 49–66.
40
Griliches, Z., 1986. Productivity, R&D, and basic research at the firm level in the 1970s.
American Economic Review 76, 141–154.
Hall, B., Jaffe, A., Trajtenberg, M., 2001. The NBER patent citations data file: Lessons, insights
and methodological tools. NBER Working Paper #8498.
Hall, B., Ziedonis, R., 2001. The determinants of patenting in the U.S. semiconductor industry,
1980–1994. RAND Journal of Economics 32, 101–128.
Harhoff, D., Narain, F., Scherer, F.M., Vopel, K., 1999. Citation frequency and the value of
patented inventions. Review of Economics and Statistics 81, 511–515.
Haynes, K.T., Hillman, A., 2010. The effect of board capital and CEO power on strategic
change. Strategic Management Journal 31, 1145–1163.
Hoitash, U., Hoitash. R, Bedard, J.C., 2009. Corporate governance and internal control over
financial reporting: A comparison of regulatory regimes. The Accounting Review 84, 839–
867.
Huson, M.R., Malatesta, P.H., Parrino, R., 2004. Managerial succession and firm performance.
Journal of Financial Economics 74, 237–275.
Jensen, M.C., 1993. The modern industrial revolution, exit, and the failure of internal control
systems. Journal of Finance 48, 831–880.
Jensen, M.C., Murphy, K.J., 1990. Performance pay and top–management incentives. Journal of
Political Economy 98, 225–264.
Jenter, D., Lewellen, K., 2010. Performance–induced CEO turnover. Working paper. Stanford
University and Dartmouth College.
Kothari, S.P., Laguerre, T.E., Leone, A.J., 2002. Capitalization versus expensing: Evidence on
the uncertainty of future earnings from capital expenditure versus R&D outlays. Review of
Accounting Studies 7, 355–382.
Linck, J., Netter, J., Yang, T., 2008. The determinants of board structure. Journal of Financial
Economics 87, 308–328.
Lipton, M., Lorsch, J., 1992. A modest proposal for improved corporate governance. Business
Lawyer 1, 59–77.
Maloney, M.T., McCormick, R.E., Mitchell, M.L., 1993. Managerial decision making and
capital structure. Journal of Business 66, 189–217.
Manso, G., 2011. Motivating innovation. Journal of Finance 66, 1823–1860.
Moeller, S.M., Schlingemann, F., Stulz, R., 2004. Firm size and the gains from acquisitions.
Journal of Financial Economics 73, 201–228.
Morck, R., Shleifer, A., Vishny, R., 1988. Management ownership and market valuation: an
empirical analysis. Journal of Financial Economics 20, 293–315.
Morck R., Shleifer, A., Vishny, R., 1990. Do managerial objectives drive bad acquisitions?
Journal of Finance 45, 31–48.
Neal, D., 1995. Industry-specific human capital: Evidence from displaced workers. Journal of
Labor Economics 13, 653–677.
New York Stock Exchange, 2010. Report of the New York Stock Exchange Commission on
Corporate Governance. Available on the internet at http://www.nyse.com/pdfs/CCGReport.pdf
41
Onal, B., 2011. To delegate or not to delegate to stock markets? The case of boards with related
industry expertise. Working paper. Aalto University.
Rechner, P. L., Dalton, D.R., 1991. CEO duality and organizational performance: A longitudinal
analysis. Strategic Management Journal 12, 155–160.
Rosen, S., 1982. Authority, control and the distribution of earnings. Bell Journal of Economics
13, 311–323.
Rosenstein, S., Wyatt, J., 1990. Outside directors, board independence, and shareholder wealth.
Journal of Financial Economics 26, 175–192.
Sougiannis, T., 1994. The accounting based valuation of corporate R&D. The Accounting
Review 69, 44-68.
Spencer Stuart, 2011. Spencer Stuart Board Index 2011. Available on the internet at
http://content.spencerstuart.com/sswebsite/pdf/lib/SSBI_2011_final.pdf
Tajfel, H., Turner, J.C., 1979. An integrative theory of intergroup conflict. In: Worchel, S.,
Austin, W.G. (Eds.), The Social Psychology of Intergroup Relations. Brooks/Cole, Monterey.
Thompson, S.B., 2011. Simple formulas for standard errors that cluster by both firm and time.
Journal of Financial Economics 99, 1–10.
Tian, X., Wang, T.Y., 2011. Tolerance for failure and corporate innovation. Review of Financial
Studies, forthcoming, doi: 10.1093/rfs/hhr130.
Trajtenberg, M., 1990. A penny for your quotes: Patent citations and the value of innovations.
RAND Journal of Economics 21, 172–187.
Travlos, N., 1987. Corporate takeover bids, method of payment, and bidding firm’s stock returns.
Journal of Finance 52, 943–963.
Xie, B., Davidson III, W.N., DaDalt, P.J., 2003. Earnings management and corporate
governance: The roles of the board and the audit committee. Journal of Corporate Finance 9,
295–316.
Xue, Y., 2007. Make or buy new technology: The role of CEO compensation contract in a firm’s
route to innovation. Review of Accounting Studies 12, 659–690.
Weisbach, M.S., 1988. Outside directors and CEO turnover. Journal of Financial Economics 20,
431–460.
Yermack, D., 1996. Higher market valuation of companies with a small board of directors.
Journal of Financial Economics 40, 185–213.
Zhou, X., 2001. Understanding the determinants of managerial ownership and the link between
ownership and performance: comment" Journal of Financial Economics 62, 559–571.
Zollo, M., Meier, D., 2008. What is M&A performance? Academy of Management Perspectives
22, 55–77.
42
Table 1. Annual distribution of main board variables
This table presents annual averages for industry expertise and related board variables. Industry
experts are independent directors with prior or current work experience in a firm that operates in
the same two-digit primary SIC code as the focal firm. Board size is the number of directors.
Board independence is the percentage of directors who are unaffiliated with the firm beyond
their directorships. Industry expert board equals one if the board has at least one industry expert,
zero otherwise. Industry expert % is the ratio of industry experts to independent directors.
Year Sample Board Size Board
Independence
Industry
expert boards
Industry
expert %
2000 759 9.522 54.6% 43.9% 15.2%
2001 890 9.328 59.8% 49.2% 17.6%
2002 798 9.365 67.5% 51.8% 16.6%
2003 976 9.230 73.3% 56.1% 17.7%
2004 1,014 9.200 76.2% 58.8% 18.7%
2005 1,019 9.172 77.8% 59.9% 19.5%
2006 982 9.282 79.0% 59.4% 19.2%
2007 801 9.085 79.2% 60.0% 20.4%
2008 907 9.115 80.0% 61.3% 21.3%
2009 932 9.128 80.3% 63.7% 21.9%
All years 9,078 9.237 73.2% 56.8% 18.9%
43
Table 2. Summary statistics for main variables
The sample consists of 9,078 annual observations for 1,528 firms between 2000 and 2009. Board
size is the number of directors. Independent directors are directors with no business or personal
relationship with the firm or any of its employee-directors. Board independence is the percentage
of independent directors. Industry experts are independent directors with prior or current work
experience in a firm that operates in the same two-digit primary SIC code as the focal firm.
Board industry expertise equals one if the board has at least one industry expert, zero otherwise.
%Board industry experts is the ratio of industry experts to independent directors. #Board
industry experts is the number of industry experts. Board ownership is the proportion of
outstanding shares owned by all directors. CEO ownership is the proportion of outstanding
shares owned by the CEO. CEO duality equals one if the CEO also serves as board chair. CEO
tenure is the number of years the CEO as served as such. Total assets and market capitalization
are in millions of dollars. Tobin’s q is the book value of total assets minus the book value of
equity plus the market value of equity, divided by the book value of total assets. Corporate
diversification is the sum of reported geographical and business segments. Investment
opportunities is the ratio of capital expenditures to sales. Return on assets is the ratio of operating
income before depreciation to total assets.
Mean Median
25th
percentile
75th
percentile
Standard
deviation
Board size 9.237 9.000 8.000 11.000 2.278
Independent directors 6.778 7.000 5.000 8.000 2.234
Board independence 0.732 0.750 0.636 0.857 0.152
Board industry expertise 0.568 1.000 0.000 1.000 0.495
%Board industry experts 0.189 0.125 0.000 0.333 0.227
#Board industry experts 1.239 1.000 0.000 2.000 1.508
Board ownership 0.087 0.034 0.014 0.100 0.128
CEO ownership 0.034 0.012 0.005 0.029 0.067
CEO duality 0.615 1.000 0.000 1.000 0.487
CEO tenure 5.136 3.300 1.400 6.700 5.632
Total assets 5,677.091 1,530.443 633.137 4,274.151 12,935.793
Market capitalization 8,102.831 1,693.931 700.613 5,028.845 25,631.637
Tobin’s q 2.027 1.644 1.264 2.342 1.232
Corporate diversification 6.011 6.000 3.000 8.000 3.368
Investment opportunities 0.068 0.036 0.021 0.066 0.109
Return on assets 0.143 0.138 0.093 0.191 0.090
44
Table 3. Board industry expertise and firm value: basic results
The dependent variable in each regression is Tobin’s q, defined as the book value of total assets
minus the book value of equity plus the market value of equity, divided by the book value of
total assets. Industry experts are independent directors with prior or current work experience in a
firm that operates in the same two-digit primary SIC code as the focal firm. %Board industry
experts is the ratio of industry experts to independent directors. #Board industry experts is the
number of industry experts. Board industry expertise equals one if the board has at least one
industry expert, zero otherwise. Firm size is the natural log of the market value of equity.
Investment opportunities is the ratio of capital expenditures to sales. Corporate diversification is
the sum of geographical and business segments. ROA is operating income before depreciation
divided by total assets. ROAt-1 and ROAt-2 are the one- and two-year lagged values of ROA.
Board size is the natural log of the number of directors. Board independence is the percentage of
directors who are unaffiliated with the firm beyond their directorship. CEO duality equals one if
the CEO also serves as board chair. Board ownership is the proportion of outstanding shares
owned by all directors. Busy board equals one when a majority of independent directors serve on
three or more boards, zero otherwise. Each regression includes year and two-digit SIC code fixed
effects. Numbers in parentheses are robust standard errors double clustered at the firm and year
levels. Statistical significance is indicated by ***, **, and * for 1%, 5%, and 10%, respectively.
Each regression is estimated over 2000–2009.
45
Table 3 continued
(1) (2) (3)
Tobin’s q Tobin’s q Tobin’s q
%Board industry experts 0.530***
(4.24)
---- ----
#Board industry experts ---- 0.059***
(3.39)
----
Board industry expertise ---- ---- 0.093*
(1.74)
Firm size 0.335***
(4.62)
0.337***
(4.60)
0.338***
(4.65)
Investment opportunities 0.180
(0.68)
0.221
(0.79)
0.262
(0.95)
Corporate diversification -0.029***
(-4.01)
-0.029***
(-4.00)
-0.030***
(-4.10)
ROA 4.010***
(6.19)
3.973***
(6.08)
3.947***
(6.17)
ROAt-1 0.664**
(2.33)
0.670**
(2.25)
0.647**
(2.15)
ROAt-2 0.702**
(1.98)
0.662*
(1.80)
0.634*
(1.72)
Board size -1.226***
(-5.56)
-1.316***
(-5.71)
-1.294***
(-5.59)
Board independence -0.075
(-0.60)
-0.159
(-1.22)
-0.082
(-0.68)
CEO duality -0.150***
(-4.40)
-0.157***
(-4.41)
-0.165***
(-4.66)
Board ownership 0.432***
(2.86)
0.407***
(2.72)
0.401***
(2.62)
Busy board -0.172***
(-4.32)
-0.163***
(-4.13)
-0.153***
(-3.97)
Constant 2.288***
(9.16)
2.552***
(12.17)
2.551***
(13.00)
Sample size 9,078 9,078 9,078
Adjusted R2 0.449 0.446 0.443
46
Table 4. Board industry expertise and firm value: addressing endogeneity concerns
The dependent variable in columns (1), (2), and (5) – (7) is Tobin’s q, defined as the book value
of total assets minus the book value of equity plus the market value of equity, divided by the
book value of total assets. The dependent variable in columns (3) and (4) is the one-year change
in Tobin’s q. Industry experts are independent directors with prior or current work experience in
a firm that operates in the same two-digit primary SIC code as the focal firm. Board industry
expertise in columns (1), (2), and (5) – (7) is the ratio of industry experts to independent
directors. Board industry expertise in columns (3) and (4) is the number of new industry experts
appointed to the board in the preceding year. The regressions in the first and second columns are
estimated over 2004–2009 and 2004–2005, respectively, for firms forced by regulatory changes
in 2002/2003 to increase the proportion of independent directors. The third column is a changes
regression estimated over the full sample while the fourth column is a similar regression
estimated over firms appointing their first industry expert directors. Control variables in both
columns are annual changes in the respective variables. The fifth column is the second stage of a
2SLS regression in which the number of firms in each industry and the average number of
independent directors in the industry are used as instruments for board industry expertise. The
sixth column is a regression of Tobin’s q in 2009 on board industry expertise in 2000. The
seventh column is estimated over firms that have at least one industry expert who is not an
industry expert at another firm and firms that do not have any industry experts but whose boards
have at least one member who is an industry expert at a different firm. Firm size is the natural
log of the market value of equity. Investment opportunities is the ratio of capital expenditures to
sales. Corporate diversification is the sum of geographical and business segments. ROA is
operating income before depreciation divided by total assets. ROAt-1 and ROAt-2 are the one- and
two-year lagged values of ROA. Pre-regulation Tobin’s q is average Tobin’s q over 2000–2002.
Board size is the natural log of the number of directors. Board independence is the percentage of
directors who are unaffiliated with the firm beyond their directorship. CEO duality equals one if
the CEO also serves as board chair. Board ownership is the proportion of outstanding shares
owned by all directors. Busy board equals one when a majority of independent directors serve on
three or more boards, zero otherwise. Each regression includes year and two-digit SIC code fixed
effects. Numbers in parentheses are robust standard errors double clustered at the firm and year
levels. Levels of significance are indicated by ***, **, and * for 1%, 5%, and 10%, respectively.
47
Table 4 continued
(1) (2) (3) (4) (5) (6) (7)
Tobin’s q Tobin’s q ΔTobin’s q ΔTobin’s q Tobin’s q Tobin’s q Tobin’s q
Board industry expertise 0.782**
(2.52)
1.157**
(2.05)
0.028**
(2.11)
0.058*
(1.79)
2.857***
(9.88)
0.482***
(2.95)
0.475**
(2.55)
Firm size 0.206***
(4.83)
0.117**
(2.56)
0.884***
(31.45)
0.752***
(15.91)
0.300***
(14.31)
0.154***
(5.35)
0.248***
(4.05)
Investment opportunities 0.328
(0.53)
0.865
(1.41)
-0.352***
(-2.97)
0.011
(0.07)
-1.423***
(-6.87)
0.560
(0.93)
0.448
(1.02)
Corporate diversification -0.028***
(-2.75)
-0.036***
(-3.34)
-0.002
(-0.59)
-0.005
(-0.87)
-0.022***
(-3.49)
-0.019**
(-2.31)
-0.012*
(-1.89)
ROA 3.697***
(3.29)
5.801***
(5.82)
0.571***
(3.24)
0.780***
(2.79)
4.461***
(13.76)
3.419***
(5.08)
4.453***
(9.15)
ROAt-1 0.501
(0.43)
-1.326
(-1.20)
---- ---- 0.779***
(2.90)
0.294
(0.50)
1.399***
(4.34)
ROAt-2 -1.690
(-1.31)
-2.334
(-1.18)
---- ---- 1.174***
(3.48)
2.479***
(4.10)
0.521
(0.77)
Pre-regulation Tobin’s q 0.388***
(5.90)
0.551***
(9.49)
---- ---- ---- ---- ----
Board size -0.591***
(-3.41)
-0.296
(-0.96)
-0.148***
(-2.89)
-0.205***
(-2.84)
-0.929***
(-7.63)
-0.767***
(-4.64)
-0.956***
(-4.51)
Board independence -0.253
(-1.19)
-0.221
(-1.31)
0.149*
(1.77)
0.062
(0.49)
-0.176
(-1.02)
-0.128
(-0.42)
0.174
(0.57)
CEO duality 0.046
(0.70)
-0.017
(-0.26)
-0.017
(-1.14)
-0.019
(-0.76)
-0.056
(-1.37)
-0.058
(-1.14)
-0.127***
(-3.28)
Board ownership -0.207
(-0.73)
-0.277
(-1.27)
-0.007
(-0.07)
-0.021
(-0.16)
0.748***
(4.18)
0.457*
(1.68)
0.442
(1.27)
Busy board -0.085
(-1.44)
0.049
(0.84)
-0.030**
(-2.07)
0.006
(0.27)
-0.257***
(-5.42)
-0.127**
(-2.20)
-0.132***
(-3.09)
Constant 1.174***
(3.46)
0.967**
(2.13)
0.043
(1.56)
0.257***
(7.05)
1.123***
(4.14)
1.140***
(2.58)
1.113**
(2.45)
Sample size 479 185 7,126 2,537 9,078 444 2,433
Adjusted R2 0.768 0.838 0.466 0.439 0.273 0.571 0.484
48
Table 5. Board industry expertise and firm value: other robustness checks
The dependent variable in each regression is Tobin’s q, defined as the book value of total assets
minus the book value of equity plus the market value of equity, divided by the book value of
total assets. Each regression is estimated over the full sample, except the one in the last column,
which is estimated over single-segment firms. Industry experts are independent directors with
prior or current work experience in a firm that operates in the same industry as the focal firm.
Same industry is defined at the three- and four-digit SIC code levels in the first and second
columns, respectively. %Board industry experts is the ratio of industry experts to independent
directors. Firm size is the natural log of the market value of equity. Investment opportunities is
the ratio of capital expenditures to sales. Corporate diversification is the sum of geographical and
business segments. ROA is operating income before depreciation divided by total assets. ROAt-1
and ROAt-2 are the one- and two-year lagged values of ROA. Board size is the natural log of the
number of directors. Board independence is the percentage of directors who are unaffiliated with
the firm beyond their directorship. CEO duality equals one if the CEO also serves as board chair.
Board ownership is the proportion of outstanding shares owned by all directors. Busy board
equals one when a majority of independent directors serve on three or more boards, zero
otherwise. Each regression includes year and two-digit SIC code fixed effects. Numbers in
parentheses are robust standard errors double clustered at the firm and year levels. Levels of
significance are indicated by ***, **, and * for 1%, 5%, and 10%, respectively. Each regression
is estimated over 2000–2009.
49
Table 5 continued
(1) (2) (3) (4) (5) (6)
Tobin’s q Tobin’s q Tobin’s q Tobin’s q Tobin’s q Tobin’s q
%Board industry experts 0.780***
(6.01)
0.659***
(4.44)
---- ---- ---- 0.545***
(2.99)
#Industry expert firms ---- ---- 0.330***
(4.86)
---- ---- ----
Years industry expert ---- ---- ---- 0.034***
(4.60)
---- ----
CEO industry experts ---- ---- ---- ---- 0.695***
(2.70)
----
Other industry experts ---- ---- ---- ---- 0.498***
(3.93)
----
Firm size 0.326***
(4.55)
0.332***
(4.60)
0.332***
(4.61)
0.334***
(4.66)
0.336***
(4.62)
0.365***
(6.41)
Investment opportunities 0.060
(0.22)
0.108
(0.39)
0.138
(0.53)
0.150
(0.57)
0.184
(0.69)
0.170
(0.67)
Corporate diversification -0.027***
(-3.82)
-0.029***
(-4.03)
-0.028***
(-3.98)
-0.029***
(-4.04)
-0.029***
(-4.01)
-0.018
(-0.89)
ROA 4.065***
(6.21)
4.017***
(6.25)
4.030***
(6.16)
4.023***
(6.29)
3.999***
(6.17)
4.064***
(10.10)
ROAt-1 0.679**
(2.32)
0.677**
(2.27)
0.688**
(2.34)
0.668**
(2.33)
0.668**
(2.37)
-0.024
(-0.12)
ROAt-2 0.751**
(2.08)
0.668*
(1.85)
0.749**
(2.10)
0.711**
(2.00)
0.696**
(1.96)
1.347***
(3.61)
Board size -1.171***
(-5.33)
-1.216***
(-5.51)
-1.197***
(-5.51)
-1.232***
(-5.71)
-1.227***
(-5.54)
-1.265***
(-6.62)
Board independence -0.082
(-0.67)
-0.069
(-0.56)
-0.070
(-0.57)
-0.066
(-0.53)
-0.078
(-0.63)
-0.174
(-1.35)
CEO duality -0.140***
(-4.15)
-0.156***
(-4.40)
-0.143***
(-4.17)
-0.151***
(-4.27)
-0.151***
(-4.39)
-0.151***
(-4.57)
Board ownership 0.427***
(2.90)
0.392***
(2.62)
0.442***
(2.84)
0.416***
(2.62)
0.434***
(2.86)
0.387***
(2.76)
Busy board -0.159***
(-4.13)
-0.151***
(-3.85)
-0.177***
(-4.46)
-0.175***
(-4.39)
-0.172***
(-4.30)
-0.104***
(-2.99)
Constant 2.372***
(10.68)
2.449***
(11.20)
2.280***
(9.36)
2.389***
(10.38)
2.302***
(9.39)
3.706***
(11.73)
Sample size 9,078 9,078 9,078 9,078 9,078 2,272
Adjusted R2 0.454 0.448 0.453 0.452 0.449 0.520
50
Table 6. Board industry expertise and corporate innovation
The dependent variables are the ratio of R&D expenditures to revenue, patents granted by the USPTO,
and average cites per patent in the first, second, and third columns, respectively. The regression in the
first column is estimated over 2000–2009 while those in the second and third columns are estimated over
2000–2004 to reduce potential truncation bias. %Board industry experts is the ratio of independent
directors with prior or current work experience in a firm that operates in the same two-digit primary SIC
code as the focal firm to all independent directors. Firm size is the natural log of market capitalization.
Leverage is the ratio of long-term debt to total assets. Investment opportunities is the ratio of capital
expenditures to sales. Asset tangibility is the ratio of fixed assets to total assets. Firm age is the natural log
of the number of years since incorporation. Board size is the natural log of the number of directors. Board
independence is the percentage of directors who are unaffiliated with the firm beyond their directorship.
CEO duality equals one if the CEO also serves as board chair. CEO ownership is the percentage of the
firm’s shares owned by the CEO. CEO equity-based pay is the ratio of the value of stock options and
restricted stock awarded the CEO to the CEO’s total compensation. CEO tenure is the number of years
the CEO has served as such. Each regression includes year and two-digit SIC code fixed effects. Numbers
in parentheses are robust standard errors double clustered at the firm and year levels. Levels of
significance are indicated by ***, **, and * for 1%, 5%, and 10%, respectively.
(1) (2) (3)
R&D Patents Patent cites
%Board industry experts 0.113***
(8.80)
0.554**
(2.21)
0.449**
(2.37)
Firm size 0.006***
(2.61)
0.711***
(16.76)
0.354***
(13.56)
Leverage -0.045
(-1.63)
-0.059
(-0.21)
-0.173
(-0.53)
Investment opportunities 0.488***
(3.49)
-2.341**
(-2.01)
-1.055
(-1.26)
Asset tangibility -0.362***
(-9.43)
0.971**
(2.15)
-0.077
(-0.24)
Firm age -0.001***
(-3.32)
0.021***
(5.23)
0.013***
(4.59)
Board size -0.031**
(-2.33)
0.807***
(2.94)
0.219
(1.27)
Board independence 0.028
(1.44)
1.171***
(3.50)
0.556**
(2.41)
CEO duality -0.007
(-1.50)
0.024
(0.28)
0.068
(1.05)
CEO ownership -0.061
(-1.26)
-1.256
(-1.60)
-1.821**
(-2.07)
CEO equity pay 0.002***
(3.90)
0.013
(1.19)
0.006
(1.03)
R&D ---- 3.410***
(10.09)
1.599***
(2.81)
Constant 0.134***
(4.37)
-16.688***
(6.75)
-10.572***
(-3.68)
Sample size 8,440 3,956 3,956
F-statistic 10.17*** 44.19*** 60.73***
51
Table 7. Board industry expertise and acquisition performance
The dependent variables are the seven-day and three-day cumulative abnormal returns in the first and second
columns, respectively. The dependent variables in the third through fifth columns are the change in operating margin
for the acquirer minus the change in operating margin for an industry- and size-matched firm during the first,
second, and third post-acquisition year, respectively. Reported coefficients are from the second stages of Heckman
selection models where the selection stage predicts acquisition choice. %Board industry experts is the ratio of
independent directors with prior or current work experience in a firm that operates in the same two-digit primary
SIC code as the focal firm to all independent directors. Percent cash is the percentage of the deal value paid in cash
by the acquirer. Private target equals one when the target is privately held, zero otherwise. Relative size is the ratio
of the deal value to the acquirer's market capitalization at the end of the year prior to the deal. Intra-industry equals
one when the target and acquirer operate in the same two-digit primary SIC code industry, zero otherwise. Firm size
is the natural log of total assets. Leverage is the ratio of long-term debt to total assets. Board size is the natural log of
the number of directors. Board independence is the percentage of directors that are unaffiliated with the firm beyond
their directorship. CEO duality equals one if the CEO also serves as board chair. Busy board equals one when a
majority of independent directors serve on three or more boards, zero otherwise. Institutional ownership is the
percentage of outstanding shares owned by institutional investors. Each regression includes year and two-digit SIC
code fixed effects. Numbers in parentheses are robust standard errors clustered simultaneously at both the firm and
year levels. Levels of significance are indicated by ***, **, and * for 1%, 5%, and 10%, respectively.
(1) (2) (3) (4) (5)
CAR [-3, +3] CAR [-1, +1] Abnormal
ROA - Yr. 1
Abnormal
ROA - Yr. 2
Abnormal
ROA - Yr. 3
%Board industry experts -0.013
(-0.66)
-0.007
(-0.96)
-0.015
(-1.25)
-0.016
(-1.37)
-0.016
(-1.34)
Percent cash 0.000***
(2.98)
0.000***
(5.01)
-0.000
(-0.38)
-0.000
(-0.38)
-0.000
(-0.14)
Private target 0.012**
(2.36)
0.008**
(2.41)
-0.008
(-0.31)
-0.036*
(-1.74)
-0.053***
(-2.91)
Relative size -0.020*
(-1.79)
-0.035***
(-4.44)
-0.017
(-0.63)
-0.022
(-1.00)
-0.016
(-0.74)
Intra-industry 0.015***
(3.20)
0.011***
(3.39)
-0.002
(-0.19)
0.010
(1.04)
0.011
(1.11)
Firm size 0.000
(0.03)
-0.005*
(-1.66)
0.008
(0.72)
0.002
(0.24)
0.000
(0.01)
Leverage 0.015
(0.89)
0.017
(1.57)
0.057
(1.53)
0.032
(0.86)
0.038
(1.05)
Board size -0.001
(-0.10)
-0.002
(-0.24)
0.081**
(2.27)
0.073**
(2.26)
0.081**
(2.35)
Board independence -0.032
(-1.52)
-0.024*
(-1.72)
0.110*
(1.86)
0.106*
(1.89)
0.109*
(1.95)
CEO duality 0.009*
(1.88)
0.006*
(1.77)
-0.009
(-0.73)
-0.002
(-0.17)
0.008
(0.66)
Busy board -0.002
(-0.29)
-0.003
(-0.76)
-0.001
(-0.06)
-0.003
(-0.20)
-0.007
(-0.49)
Institutional ownership 0.004
(0.33)
-0.001
(-0.06)
-0.002
(-0.06)
0.026
(0.82)
0.029
(0.92)
Constant -0.095
(-0.85)
-0.026
(-0.78)
-0.241
(-1.62)
-0.260**
(-2.03)
-0.320**
(-2.19)
Sample size 7,513 7,513 6,685 6,685 6,685
Acquirers 1,205 1,205 377 377 377
Log Likelihood -1,035.46 -625.28 -715.01 -681.39 -675.42
52
Table 8. Board industry expertise and acquisition performance in intra-industry deals
The dependent variables are the seven-day and three-day cumulative abnormal returns in the first and second
columns, respectively. The dependent variables in the third through fifth columns are the change in operating margin
for the acquirer minus the change in operating margin for an industry- and size-matched firm during the first,
second, and third post-acquisition year, respectively. Reported coefficients are from the second stages of Heckman
selection models where the selection stage predicts acquisition choice. %Board industry experts is the ratio of
independent directors with prior or current work experience in a firm that operates in the same two-digit primary
SIC code as the focal firm to all independent directors. Percent cash is the percentage of the deal value paid in cash
by the acquirer. Private target equals one when the target is privately held, zero otherwise. Relative size is the ratio
of the deal value to the acquirer's market capitalization at the end of the year prior to the deal. Intra-industry equals
one when the target and acquirer operate in the same two-digit primary SIC code industry, zero otherwise. Firm size
is the natural log of total assets. Leverage is the ratio of long-term debt to total assets. Board size is the natural log of
the number of directors. Board independence is the percentage of directors that are unaffiliated with the firm beyond
their directorship. CEO duality equals one if the CEO also serves as board chair. Busy board equals one when a
majority of independent directors serve on three or more boards, zero otherwise. Institutional ownership is the
percentage of outstanding shares owned by institutional investors. Each regression includes year and two-digit SIC
code fixed effects. Numbers in parentheses are robust standard errors clustered simultaneously at both the firm and
year levels. Levels of significance are indicated by ***, **, and * for 1%, 5%, and 10%, respectively.
(1) (2) (3) (4) (5)
CAR [-3, +3] CAR [-1, +1] Abnormal
ROA - Yr. 1
Abnormal
ROA - Yr. 2
Abnormal
ROA - Yr. 3
%Board industry experts 0.004
(0.16)
-0.005
(-0.42)
-0.006
(-0.14)
-0.031
(-0.76)
-0.042
(-1.00)
Intra-Industry 0.019***
(3.10)
0.012***
(2.79)
-0.004
(-0.28)
0.004
(0.31)
0.002
(0.17)
%Board industry experts ×
Intra-industry
-0.022
(-1.18)
-0.003
(-0.19)
0.008
(0.17)
0.023
(0.49)
0.035
(0.81)
Percent cash 0.000***
(3.00)
0.000***
(5.02)
-0.000
(-0.43)
-0.000
(-0.45)
-0.000
(-0.22)
Private target 0.012**
(2.39)
0.008**
(2.41)
-0.006
(-0.23)
-0.037*
(-1.83)
-0.054***
(-3.00)
Relative size -0.020*
(-1.84)
-0.035***
(-4.44)
-0.017
(-0.63)
-0.022
(-0.98)
-0.016
(-0.72)
Firm size 0.001
(0.07)
-0.005*
(-1.65)
0.010
(0.83)
0.002
(0.19)
-0.001
(-0.07)
Leverage 0.015
(0.91)
0.017
(1.57)
0.060
(1.55)
0.037
(0.97)
0.044
(1.19)
Board size -0.003
(-0.18)
-0.002
(-0.26)
0.080**
(2.24)
0.072**
(2.21)
0.080**
(2.31)
Board independence -0.032
(-1.48)
-0.024*
(-1.72)
0.104*
(1.79)
0.100*
(1.82)
0.102*
(1.87)
CEO duality 0.009*
(1.84)
0.006*
(1.75)
-0.009
(-0.63)
-0.002
(-0.13)
0.008
(0.71)
Busy board -0.003
(-0.35)
-0.003
(-0.77)
-0.002
(-0.13)
-0.003
(-0.19)
-0.007
(-0.44)
Institutional ownership 0.003
(0.25)
-0.001
(-0.08)
-0.002
(-0.06)
0.026
(0.80)
0.028
(0.91)
Constant -0.098
(-0.86)
-0.026
(-0.79)
-0.270
(-1.58)
-0.255*
(-1.72)
-0.305*
(-1.74)
Sample size 7,513 7,513 6,685 6,685 6,685
Acquirers 1,205 1,205 377 377 377
Log Likelihood -1,034.75 -625.26 -714.99 -681.27 -675.13
53
Table 9. Board industry expertise and forced CEO turnover
The dependent variable equals one for firm years with forced CEO turnovers and zero for firm-
years with no turnovers. Columns labeled “Experts” (“No experts”) are estimated over firms with
at least one (no) independent director with prior or current work experience in a firm that
operates in the same two-digit primary SIC code as the focal firm. Excess return is prior year
annual stock return less same-period return on the CRSP equal-weighted portfolio of
NYSE/Amex/Nasdaq stocks. Return on assets is the ratio of net income before extraordinary
items to total assets. Firm size is the natural log of market capitalization. Board size is the natural
log of the number of directors. Board independence is the percentage of directors who are
unaffiliated with the firm beyond their directorship. CEO duality equals one if the CEO also
serves as board chair. CEO ownership is the proportion of outstanding shares owned by the
CEO. CEO age is the natural log of CEO age in years. Each regression includes year and two-
digit SIC code fixed effects. Numbers in parentheses are robust standard errors clustered
simultaneously at both the firm and year levels. Levels of significance are indicated by ***, **,
and * for 1%, 5%, and 10%, respectively. The Chow test is for the null hypothesis that the
coefficients of Excess return are the same across the two regressions in each panel.
(1) (2) (3) (4)
No experts Experts No experts Experts
Excess return -0.799***
(-4.63)
-0.447***
(-2.68)
----
----
ROA ----
----
-3.284***
(-5.78)
-1.742***
(-3.25)
Firm size -0.099**
(-2.25)
-0.017
(-0.52)
-0.047
(-1.16)
0.007
(0.21)
CEO duality -0.306*
(-1.90)
-0.357***
(-4.49)
-0.288**
(-2.12)
-0.361***
(-4.34)
CEO ownership -8.401
(-1.64)
-1.725
(-1.61)
-7.968*
(-1.65)
-1.186
(-1.01)
Board size 0.305
(1.07)
-0.041
(-0.18)
0.214
(0.76)
0.007
(0.03)
Board independence 0.524
(1.19)
0.561**
(2.30)
0.349
(0.83)
0.616**
(2.41)
CEO age 0.015
(1.49)
0.012
(1.30)
0.015
(1.50)
0.013
(1.39)
Constant -5.577***
(-6.62)
-4.898***
(-6.35)
-5.409***
(-6.67)
-5.397***
(-6.76)
Observations 2,311 3,693 2,311 3,693
Pseudo R2 0.149 0.060 0.142 0.069
Chow test
(p-value)
3.300*
(0.069)
4.750**
(0.029)
54
Table 10. Board industry expertise and CEO compensation incentives
The dependent variable in the first three columns are the natural logs of inflation-adjusted CEO salary,
stock option awards (Black-Scholes value), and restricted stock awards, respectively. The dependent
variable in the last three columns are the ratios of each pay component to total CEO compensation.
%Board industry experts is the ratio of independent directors with prior or current work experience in a
firm that operates in the same two-digit primary SIC code as the focal firm to all independent directors.
Firm size is the natural log of total assets. ROA is operating income before depreciation divided by total
assets. Operating risk is the standard deviation of ROA over the preceding five years. Market risk is the
standard deviation of percentage stock return over the preceding five years. Leverage is the ratio of long-
term debt to total assets. Board size is the natural log of the total number of directors. Busy board equals
one when a majority of independent directors serve on three or more corporate boards, zero otherwise.
CEO duality equals one when the CEO also serves as board chairman, zero otherwise. CEO age is the
natural log of CEO age in years. CEO ownership is the proportion of outstanding shares owned by the
CEO. Board ownership is the proportion of outstanding shares owned collectively by all directors. Each
regression includes year and two-digit SIC code fixed effects. The regression in the first column is an
OLS regression with standard errors double clustered at the firm and year levels. The regressions in the
second and third columns are Tobit models with double clustered standard errors. The last three columns
are fractional logit models with standard errors clustered at the firm level. Levels of significance are
indicated by ***, **, and * for 1%, 5%, and 10%, respectively.
(1) (2) (3) (4) (5) (6)
Salary Options
Restricted
stock
Salary
ratio
Options
ratio
Stock
ratio
%Board industry experts -0.198**
(-2.45)
1.486***
(5.90)
-0.695
(-1.55)
-0.375***
(-4.13)
0.679***
(6.00)
-0.105
(-0.76)
Firm size 0.101**
(2.21)
0.618***
(9.79)
0.426***
(4.90)
-0.319***
(-13.58)
0.168***
(6.97)
0.090***
(3.39)
ROA 0.225
(1.60)
2.156***
(3.79)
-0.893
(-0.88)
-1.825***
(-7.79)
0.453*
(1.74)
-0.550*
(-1.72)
Operating risk -0.014
(-0.07)
2.904***
(4.43)
-2.800**
(-2.10)
-1.024***
(-3.45)
1.302***
(3.37)
-0.492
(-1.00)
Stock market risk -0.313***
(-2.62)
0.201
(0.91)
-0.863**
(-2.13)
-0.220**
(-2.57)
0.425***
(4.28)
-0.266*
(-1.86)
Leverage 0.371***
(2.60)
-0.599*
(-1.89)
3.471***
(6.30)
0.146
(1.24)
-0.603***
(-4.24)
0.823***
(4.78)
Board size 0.265*
(1.79)
0.764**
(2.51)
2.196***
(4.92)
-0.061
(-0.62)
-0.181
(-1.50)
0.292*
(1.85)
Busy board 0.105*
(1.90)
0.197**
(2.28)
0.753***
(5.03)
-0.084**
(-2.23)
-0.033
(-0.74)
0.104*
(1.83)
CEO duality 0.175***
(3.07)
0.241**
(2.32)
0.280*
(1.71)
-0.080**
(-2.31)
-0.021
(-0.46)
0.069
(1.25)
CEO age 0.008***
(3.63)
-0.032***
(-4.14)
-0.044***
(-3.82)
0.005**
(2.10)
-0.010***
(-3.24)
-0.012***
(-2.98)
CEO ownership -0.847**
(-1.99)
-8.698***
(-5.49)
-11.771***
(-3.77)
2.177***
(4.79)
-1.876***
(-3.22)
-3.252***
(-3.51)
Board ownership -0.071
(-0.41)
-3.461***
(-5.46)
-1.827
(-0.96)
0.639***
(3.15)
-0.952***
(-3.67)
-0.504
(-1.43)
Sample size 8,430 8,453 8,471 8,429 8,428 8,341
Adjusted R2 0.162 n.a. n.a. n.a. n.a. n.a.
55
Table 11. Board industry expertise, innovation dependence, and firm value
The dependent variable in each regression is Tobin’s q, defined as the book value of total assets minus the book
value of equity plus the market value of equity, divided by the book value of total assets. In Panel A, firms are
classified into high (low) category if their ultimately approved patent applications are more (less) than those of the
median firm. In Panel B, high (low) competition industries are those where the assets-based Herfindahl-Hirschman
index is less (equal or greater) than the sample median. In Panel C, innovative industries are pharmaceuticals,
electronics, instruments, and software industries. %Board industry experts is the ratio of independent directors with
prior or current work experience in a firm that operates in the same two-digit primary SIC code as the focal firm to
all independent directors. Firm size is the natural log of the market value of equity. Investment opportunities is the
ratio of capital expenditures to sales. Corporate diversification is the sum of geographical and business segments.
ROA is operating income before depreciation divided by total assets. ROAt-1 and ROAt-2 are the one- and two-year
lagged values of ROA. Board size is the natural log of the number of directors. Board independence is the
percentage of directors who are unaffiliated with the firm beyond their directorship. CEO duality equals one if the
CEO also serves as board chair. Board ownership is the proportion of outstanding shares owned by all directors.
Busy board equals one when a majority of independent directors serve on three or more boards, zero otherwise.
Each regression includes year and two-digit SIC code fixed effects. Numbers in parentheses are robust standard
errors clustered simultaneously at both the firm and year levels. Levels of significance are indicated by ***, **, and
* for 1%, 5%, and 10%, respectively. The Chow test is for the null hypothesis that the coefficients of %Board
industry experts are the same across the two regressions in each panel.
A: Firm innovation B: Industry competition C: Innovative industry
High Low High Low Yes No
%Board industry experts 0.655***
(4.95)
-0.016
(-0.12)
0.683***
(5.09)
0.042
(0.36)
0.701***
(4.72)
0.133
(1.15)
Firm size 0.407***
(4.37)
0.215***
(5.85)
0.420***
(4.51)
0.201***
(6.59)
0.453***
(4.64)
0.238***
(4.92)
Investment opportunities 0.671*
(1.75)
-0.247
(-0.98)
0.143
(0.65)
-0.142
(-0.31)
0.471
(1.18)
-0.221
(-0.92)
Corporate diversification -0.046***
(-4.28)
-0.004
(-0.61)
-0.048***
(-4.58)
-0.004
(-0.57)
-0.061***
(-4.08)
-0.010*
(-1.81)
ROA 4.249***
(5.47)
4.096***
(5.72)
4.299***
(6.01)
4.201***
(6.17)
4.753***
(6.81)
3.827***
(5.43)
ROAt-1 0.560**
(1.98)
0.629
(1.25)
0.542*
(1.82)
0.550
(1.47)
0.111
(0.98)
0.825*
(1.66)
ROAt-2 0.144
(0.36)
1.731***
(4.00)
0.158
(0.38)
1.815***
(5.57)
-0.149
(-0.35)
1.570***
(3.71)
Board size -1.606***
(-5.27)
-0.664***
(-5.75)
-1.556***
(-5.62)
-0.669***
(-6.22)
-1.771***
(-5.78)
-0.789***
(-5.47)
Board independence 0.036
(0.18)
-0.180
(-1.22)
0.010
(0.06)
-0.161
(-1.17)
0.151
(0.61)
-0.186
(-1.43)
CEO duality -0.170***
(-3.23)
-0.088***
(-2.70)
-0.173***
(-3.54)
-0.088***
(-2.92)
-0.169***
(-2.81)
-0.116***
(-3.57)
Board ownership 0.579**
(2.00)
0.379**
(2.41)
0.714***
(2.85)
0.247*
(1.92)
0.728**
(2.26)
0.329**
(2.43)
Busy board -0.193***
(-3.11)
-0.145***
(-4.45)
-0.178***
(-2.62)
-0.148***
(-4.73)
-0.218***
(-2.79)
-0.128***
(-4.57)
Constant 2.430***
(8.06)
1.721***
(6.49)
2.628***
(9.86)
1.705***
(5.89)
3.147***
(8.47)
1.877***
(8.08)
Sample size 4,703 4,375 4,556 4,522 3,351 5,727
Adjusted R2 0.426 0.529 0.420 0.545 0.418 0.505
Chow test
(p-value)
12.670***
(0.000)
14.320***
(0.000)
9.240***
(0.002)