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

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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.

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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.

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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.

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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.

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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.

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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).

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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.”

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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.

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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

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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

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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

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(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.

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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

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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

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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.

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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.

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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.

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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)