Board Expertise: Do Directors from
Related Industries Help Bridge the
Information Gap?
Nishant Dass
Scheller College of Business, Georgia Institute of Technology
Vikram Nanda
Rutgers Business School
Bunyamin Onal
Aalto University School of Business
Jun Wang
Baruch College
We analyze the role of “directors from related industries” (DRIs) on a firm’s board. DRIs are
officers and/or directors of companies in the upstream/downstream industries of the firm.
DRIs are more likely when the information gap vis-à-vis related industries is more severe or
the firm has greater market power. DRIs have a significant impact on firm value/performance,
especially when information problems are worse. Furthermore, DRIs help firms handle
industry shocks and shorten their cash conversion cycles. Overall, our evidence suggests
that firms choose DRIs when the adverse effects due to conflicts of interest are dominated
by the benefits due to DRIs’ information and expertise. (JEL G34, G39)
Boards of directors play a central role in corporate control and decision
making and are aptly described in Fama and Jensen (1983) as “the apex of
We thank Michael Weisbach (editor) and two anonymous referees for their detailed and thought-provoking
comments that have helped improve the paper. For their helpful comments, we also thank Rob Hansen, Paul Irvine,
Anzhela Knyazeva, Diana Knyazeva, Jim Linck, Ron Masulis, John Matsusaka, Shawn Mobbs, Harold Mulherin,
Bang Dang Nguyen, Jaideep Shenoy, and Lingling Wang, as well as seminar participants at the 23rd Australasian
Finance and Banking Conference, 2011 Summer Research Conference in Finance at the Indian School of
Business, Georgia Institute of Technology, Indian Institute of Management–Bangalore, Tulane University, and
University of Georgia. We thank Husayn Shahrur and Ryan Williams for providing us with a cleaned version of
the Compustat data on the identity of key suppliers and customers, and Stephen Brown for sharing his data on the
quarterly estimates of probability of informed trading. All errors are our own.Send correspondence to Nishant
Dass, Scheller College of Business, Georgia Institute of Technology, 800 West Peachtree Street NW, Atlanta,
GA 30308, USA; telephone: (404) 894-5109. E-mail: [email protected].
© The Author 2013. Published by Oxford University Press on behalf of The Society for Financial Studies.
All rights reserved. For Permissions, please e-mail: [email protected].
doi:10.1093/rfs/hht071
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Omesh Kini
Robinson College of Business, Georgia State University
The Review of Financial Studies / v 27 n 5 2014
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decision control systems.” The governance literature ascribes two primary
functions to boards: monitoring and advising senior management (e.g., Mace
1971; Hermalin and Weisbach 1988, 2003; Adams, Hermalin, and Weisbach
2010). Much of the research has, however, focused on the monitoring role
of boards and has, only lately, devoted more attention to the study of their
advisory function. Recent work underscores the value of such advice and
shows, for instance, that directors are particularly sought for advice when the
firm is complex or when they can provide contacts, financial expertise, and
political influence (e.g., Coles, Daniel, and Naveen 2008; Güner, Malmendier,
and Tate 2008; Goldman, Rocholl, and So 2009, 2013). The salience of the
advisory role is affirmed by survey evidence that indicates that directors
place more emphasis on their role in setting firm strategy rather than on
monitoring firm management (e.g., Demb and Neubauer 1992; Adams 2009).
These two roles for directors are not, however, distinct; they can be performed
simultaneously and are complementary (e.g., Adams and Ferreira 2007;
Brickley and Zimmerman 2010). Thus, the very expertise that makes some
directors valuable as advisors can also strengthen the quality of information
available to the board and therefore can enable more effective monitoring of
firm management.
To better understand the nature and value of these complementary functions,
we highlight an information gap—that is ubiquitous among firms, though
more severe for some than others—which directors with particular types of
information, backgrounds, and connections can help bridge. Specifically, we
focus on “directors from related industries” (henceforth “DRIs”), who are
either executives and/or directors of firms in one of the related upstream
or downstream industries. These directors can bring potentially valuable
knowledge about their own industries as well as facilitate the firm’s access
to contacts in those industries. This knowledge helps the firm overcome
information challenges, such as anticipating industry conditions and trends,
thereby facilitating better management of its factors of production and
protecting it against demand or supply shocks. DRIs’ expertise can be
especially helpful in certain industries, say innovative industries, which face a
highly unpredictable demand and unreliable supply (Lee 2002). By providing
information about related industries, DRIs can improve the board’s ability to
monitor by narrowing the information gap between the board and the firm’s
management. We note that some of the benefits of having a DRI can, in
principle, be obtained by having a director from the firm’s own industry;
however, under Section 8 of the Clayton Act, this is not usually permissible
because directors cannot serve on the boards of competing firms for antitrust
considerations.
Anecdotal evidence supports the notion that DRIs are added to boards
to reduce the information gaps faced by firms vis-à-vis their supplier and
customer industries. For instance, Bruce Claflin, the President and CEO of
3Com Corporation and a director on the board of TW Telecom, is a DRI
Board Expertise
1 Our classification of “related industries,” from which these DRIs are drawn, results in industries that seem
logically related to the given firm’s industry. For example, TW Telecom is from the Telephone Communications
Except Radiotelephone industry (SIC 4813), which as per our definition is a related industry of AMD’s
Semiconductors and Related Devices industry (SIC 3674). Similarly, PRA International is from the Commercial
Physical and Biological Research industry (SIC 8731), which we define to be a related industry of Millipore’s
Laboratory Analytical Instruments industry (SIC 3826). Finally, Covad Communications is from the Telephone
Communications Except Radiotelephone industry (SIC 4813), which according to our definition is a related
industry of Chordiant’s Prepackaged Software industry (SIC 7372).
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of Advanced Micro Devices (AMD) Inc., according to our classification.
Announcing Bruce’s appointment to the board, the Chairman of AMD said,
“Bruce enhances the composition of our board and brings a wealth of
knowledge and complementary industry insights [emphasis added] to our
team” (Business Wire, August 4, 2003). Similarly, Melvin D. Booth is a
DRI of Millipore Corporation because of a board membership at PRA
International. At his appointment to the board of Millipore, a bioscience
company, the CEO and Chairman of its board announced, “Mel brings
exceptional experience and talents to our board. He knows what our
customers need … [emphasis added]” (Business Wire, June 10, 2004). Likewise,
Charles Hoffman, CEO of Covad Communications, is a DRI of Chordiant
Software Inc. Chordiant announced the following at his appointment to its
board: “It has been a priority for us to recruit prominent executives from
our two key vertical markets [emphasis added] of financial services and
telecommunications to the board of directors” (M2 Presswire, January 12,
2005).1
In our study, we address the following related questions. First, what are the
economic factors that determine a firm’s choice to have DRIs on the board?
Second, controlling for endogeneity, what is the impact of these directors on
the firm’s value/performance? Third, what are some specific channels through
which DRIs can benefit the firm? Our empirical analysis relies on an extensive
dataset of officers and directors extracted from Compact Disclosure spanning
the period 1990–2005.
We start our analysis by proposing and then empirically validating the
economic forces that can affect a firm’s decision to have DRIs. We propose
three (not mutually exclusive) hypotheses that we label as the information,
market structure, and conflicts of interest hypotheses. The informationrelated hypothesis is based on the notion that certain firms face a significant
information challenge and can thus benefit more from the industry expertise,
knowledge, and networks of DRIs. In particular, we expect the information
gap between a firm and its related upstream and downstream industries,
and consequently between the board and the management, to be larger
for firms that produce differentiated and innovative goods—which require
specialized inputs and whose level of demand is harder to predict. However,
the availability of other sources of information will diminish the importance
of DRIs as an information channel. Further, if the economic links between
the firm’s industry and related industries are stronger, then the skills and
The Review of Financial Studies / v 27 n 5 2014
2 In principle, DRIs could serve more strategic purposes as well: by being conduits through which information
is conveyed, they can facilitate collusion and/or enable the given firm to foreclose its rival firms from critical
inputs and outlets.
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knowledge of DRIs may be in greater demand. Consistent with the above
arguments, we find that attributes such as R&D intensity, patent grants, patent
citations, and the differentiated nature of an industry’s products increase the
likelihood of having DRIs on the board, whereas attributes such as stock price
informativeness (e.g., Chen, Goldstein, and Jiang 2007) reduce this likelihood.
Additionally, if the average correlation of returns between the firm’s industry
and related industries is higher, then DRIs are more likely to be present on the
board.
Our second hypothesis involves the potential impact of market structure on
the likelihood of having DRIs. For instance, it will be easier for firms with
greater market share to attract such directors. This is because these firms are
likely to be relatively more important as sources of information and network
connections for the firms from which the DRIs are drawn. In addition, common
ownership to reduce coordination and/or contracting costs with potential/actual
suppliers and customers is more likely to be scrutinized by antitrust authorities
in concentrated industries. Hence, firms in concentrated industries may, at least
partially, achieve the same objective by appointing DRIs.2 Consistent with these
arguments, we find that the likelihood that a firm has DRIs is increasing in its
market share and industry concentration. Additionally, we find that the presence
of DRIs is greater in industries with higher degrees of vertical integration,
suggesting that DRIs can alleviate the high coordination costs and contractual
frictions that are likely present in these industries.
Finally, our third hypothesis pertains to the fact that having DRIs on the
board has a potential downside arising from conflicts of interest. For instance,
DRIs from current or potential customers/suppliers may influence the firm’s
management to act in ways that will benefit their affiliated firms rather than
the given firm’s shareholders (e.g., by improving the prices/terms negotiated
or garnering new business for their affiliated firms). We expect that boards
and managers that act in the interest of shareholders will seek to avoid such
conflicts by not having DRIs from current or potential customers/suppliers,
unless the benefits outweigh these influence costs. On the other hand, severe
agency problems can lead to the management developing an implicit quid pro
quo with the DRI—with the managers favoring the DRI’s affiliated firm in
exchange for the DRI’s support. Our evidence is more consistent with firms
seeking to avoid potential conflicts of interest. First, it is relatively uncommon
for DRIs to come from the current customers/suppliers of the firm, and the
DRIs’ affiliated firms rarely become eventual customers/suppliers of the firm.
Specifically, we find that only 1.2% of all DRIs are from the firm’s actual
customers/suppliers and less than 1% of all the firms affiliated with DRIs that
are not current customers/suppliers eventually become so over the next five
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years. Second, the likelihood of DRIs being present is higher when the firm is
less susceptible to DRIs’ attempts to promote their affiliated firms, for instance,
when the CEO is more powerful (e.g., when the CEO is also the Chairman of
the board) or when the firm has a stronger position in its industry (e.g., when
its market share is greater).
We next turn to the second question addressed in this paper: do DRIs impact
firm value/performance? However, in estimating value/performance effects we
need to correct for potential bias due to firms’ endogenous choice of DRIs. Such
a bias can arise if, for instance, there are latent factors that affect the choice
of DRIs on the board as well as influence the firm’s value/performance. In
addition to the standard corrections for endogeneity, we also control for firm
and year fixed effects as well as an array of firm and industry control variables.
In our 2SLS estimations, we choose our instruments based on economic
considerations, for example, measures of the supply of potential directors from
firms in related industries are likely to have a bearing on the use of DRIs by
a firm but should not have a direct effect on firm value. We find that DRIs
appear to have an economically meaningful effect on firm value/performance.
For example, when firms have DRIs on the board, their Tobin’s Q (return on
assets) is estimated to be higher by 0.28 (0.28) standard deviations. We obtain
similar results when we use the proportion of the firm’s board that is made up of
DRIs. These results are robust to using a variety of alternative DRI measures.
We also show that our results predicting the presence of DRIs and their effect on
value/performance are robust to using directors from the firm’s actual suppliers
or customers.
We also investigate the stock-market reaction to the appointment of DRIs
to a firm’s board. Our evidence indicates that the stock price reaction to
announcements of new DRIs is significantly positive. Specifically, over the
[0, +1] days window ([−1,+1] days window) relative to the announcement
date of a DRI appointment, the average firm earns significant abnormal returns
of around 2.0% (2.5%) using different benchmark models. In contrast, we find
that the average abnormal return around the announcement of other (non-DRI)
directors to the board is insignificantly different from zero. These results suggest
that the value benefits are likely due to DRIs’ presence and are not attributable
to firm- and/or industry-specific characteristics.
We conduct a series of tests of our hypotheses about the value impact of
DRIs in different settings. First, indicative of the informational benefits of
having DRIs, we find that their effect on the firm’s value is stronger when the
information gap is likely to be larger. In particular, firms operating in more
innovative environments and those with less informative stock prices seem to
receive greater value benefits from having DRIs. Second, the impact of DRIs
on firm value is found to be stronger for firms with greater market share, which
is consistent with the notion that these firms are less influenced by the DRIs’
conflicts of interest and that the presence of DRIs can enable dominant firms
to foreclose their rivals from critical inputs/outlets. Further, the value impact
The Review of Financial Studies / v 27 n 5 2014
3 In some of our tests described in subsection 6.1, we also consider the directors’ expertise in the firm’s own
four-digit SIC industry (in addition to upstream/downstream industries) when classifying directors as DRIs.
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of DRIs is only significant when the CEO is also the Chairman, which is
consistent with powerful CEOs being less affected by the conflicts of interest
that arise due to the DRIs trying to benefit their affiliated firms. Consistent
with our hypotheses, these results indicate that the value-benefits of DRIs are
concentrated in certain settings.
We address the third question by examining some specific channels through
which DRIs can add value to the firm. To that end, we first analyze whether DRIs
help the firm manage the impact of significant negative and positive industry
sales shocks. We find that firms with DRIs suffer a smaller drop in Tobin’s
Q in the face of negative industry sales shocks. Further, we document some
evidence that firms with DRIs are relatively better positioned to take advantage
of positive industry sales shocks. These results suggest that firms with DRIs
anticipate and manage these shocks better. Next, we analyze the firm’s cash
conversion cycle and find that it is significantly shorter when a DRI is present
on the board. Part of this effect is due to better management of inventories. The
presence of DRIs on the board is also related to a significantly lower sensitivity
of the firm’s cash-to-cash flows (Almeida, Campello, and Weisbach 2004).
Together, these results indicate that DRIs improve the firm’s operations and
help to ease its financial constraints. Overall, the positive impact of DRIs on
firm value/performance indicates that the benefits of DRIs’ industry-specific
information and expertise outweigh the adverse effect arising from potential
conflicts of interest.
Our paper contributes to a few different strands of the corporate finance
literature. First, it is related to the literature on the role of directors’ expertise.
The prior literature has examined the role of financial, political, and legal
expertise of directors (e.g., Güner, Malmendier, and Tate 2008; Goldman,
Rocholl, and So 2009, 2013; Krishnan, Wen, and Zhao 2011). To the best
of our knowledge, our paper is the first to analyze the industry-specific
expertise of directors. More recently, other studies also examine the industry
expertise of directors (e.g., Faleye, Hoitash, and Hoitash 2012; Masulis et al.
2012; Drobetz et al. 2013; Kang, Kim, and Lu 2013; Wang, Xie, and Zhu
2013). However, a key distinction of our paper is that we focus on directors’
expertise in upstream/downstream industries (i.e., related industries), whereas
the above-mentioned papers focus on directors’ experience in the firm’s own
industry.3
Second, our paper is related to an extensive strategy/management literature
that views corporate boards from the perspective of resource dependence
(e.g., Pfeffer 1972; Pfeffer and Salancik 1978). In this literature, corporate
boards are regarded as a means of reducing external dependencies, alleviating
uncertainty in the economic environment, and lowering transactions costs in
dealing with external entities (e.g., Hillman, Cannella, and Paetzold 2000).
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Further, this resource-dependence between firms/industries results in networks
or interlocks of directors across multiple boards (Mizruchi 1996). DRIs, in
our view, reduce a firm’s external dependencies by providing expertise and
links to the firm’s related industries and thereby lessen the uncertainty related
to these industries as well as to the overall economic environment facing
the firm.
Third, our paper adds to the recent literature on the interaction between
product markets and corporate finance. Authors in this strand of literature have
studied trade credit (Petersen and Rajan 1997), acquisitions (Fee and Thomas
2004; Shahrur 2005; Shenoy 2012), and capital structure (Kale and Shahrur
2007). By studying the role of directors from potential or actual suppliers
and customers, we highlight the interplay between product markets and firms’
corporate governance. Fourth, our paper is related to the literature on gray
directors. Although there is an extensive literature that sheds light on the role
of gray directors (e.g., Hermalin and Weisbach 1988), the conflicts of interest
that arise due to the presence of directors affiliated with actual customer/supplier
firms have not been studied before.
Fifth, our paper is related to the recent literature that highlights the two roles
of directors—as monitors and as advisors—and the potential tension between
these roles (e.g., Fama and Jensen 1983; Adams and Ferreira 2007; Harris and
Raviv 2008; Masulis and Mobbs 2011). These papers suggest that insiders
choose to conceal information from outside directors when they are concerned
about being monitored, thus rendering the independent directors ineffective.
Because DRIs bring industry knowledge, expertise, and connections, we expect
them to enhance the information available to the board and make it less
dependent on insiders. Our paper, however, does not explicitly distinguish
between these two roles. In fact, our findings on the benefits of DRIs are
consistent with both advisory and monitoring functions.
Finally, our paper is timely in light of the recent requirement by the SEC that
firms provide investors “the particular experience, qualifications, attributes or
skills that qualified that person to serve as a director of the company, and as a
member of any committee that the person serves on or is chosen to serve on, in
light of the company’s business” (SEC 2009, 29). In particular, our evidence
supports the premise that the expertise of individual directors has consequences
for firm value. DRIs appear to improve firms’ ability to manage industry shocks
and shorten their cash conversion cycle. Hence, the background of individual
directors and the rationale for their selection “will help shareholders make more
informed voting and investment decisions” (SEC 2009, 5).
The rest of the paper is structured as follows. Section 1 presents our
hypotheses, and Section 2 explains the construction of the dataset as well as
the variables used. Sections 3 and 4 document the empirical results for the
determinants and the value-impact of DRIs, respectively. Section 5 proposes
some specific channels through which DRIs add to firm value. Section 6
presents some extensions, and Section 7 concludes the paper.
The Review of Financial Studies / v 27 n 5 2014
1. Development of Hypotheses
In this section we develop nonmutually exclusive hypotheses—based on
information, market-structure, and conflicts-of-interest arguments—on the
potential benefits/costs to firms from having directors that are affiliated with
their supplier or customer industries.
4 Although industry reports can be useful in providing product-market information, this information is likely to be
“hard” (Stein 2002). However, hard information cannot substitute for “soft” information, which can be provided
by directors who have first-hand knowledge of the firm’s product-market landscape.
5 In support of the view that innovative firms/industries face greater information challenges, we find that “analyst
forecast errors” and “dispersion of analyst forecasts” are both higher for such firms/industries.
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1.1 Information-related hypotheses
The information hypothesis that we propose regards DRIs as a possible channel
through which boards of directors and the management receive information
about upstream and downstream industries. They serve as a valuable resource
that can enhance a firm’s ability to anticipate industry conditions and trends
and protect against demand or supply shocks.4 In addition, these directors can
facilitate the firm’s access to valuable contacts in their industries. The improved
flow of information can also augment other board functions, such as evaluating
and monitoring managerial performance.
We contend that the benefits to a firm from having DRIs will likely depend on
the nature of its economic activities and industry. If a firm is not in an innovative
industry and/or produces a relatively homogeneous commodity, say tobacco,
then there may be limited value to bringing in DRIs. On the other hand, a firm in
an innovative industry, producing differentiated products (Rauch 1999), such
as electronic components or specialized chemicals, may need to keep a close
eye on the developments in its supplier and customer industries.5 Consistent
with this view, papers in the supply-chain management literature maintain that
managing the supply chain will depend on the nature of a firm’s products
and can be especially challenging when a firm has innovative products with
unpredictable demand (e.g., Lee 2002).
Coles, Daniel, and Naveen (2008) have argued that “monitoring R&Dintensive firms requires more firm-specific knowledge” (333). This would be
applicable to innovative firms in general. Clearly, inside directors have the
most firm-specific knowledge. However, we believe that the directors that
are next-best suited for monitoring/advising innovative firms are those that
have expertise in related upstream/downstream industries. As such, DRIs with
their knowledge about the firm’s industrial landscape are going to be best
equipped to serve the strategic needs of innovative firms. Furthermore, the
information gap between the management and the board of innovative firms is
likely to be greater, thus making it difficult to infer management ability—unless
the board includes DRIs. In other words, monitoring/advising in innovative
firms is facilitated by the presence of DRIs. Furthermore, relationship-specific
Board Expertise
1.2 Market structure hypotheses
The market share of a firm and the firm’s industry concentration can impact
the firm’s propensity to have DRIs on the board. Officers and directors from
6 Even if the DRI’s firm does not have a direct business relationship with the given firm, they can still help the
firm to overcome contractual problems. For instance, if a DRI on the board of a chip-maker is an executive at a
computer manufacturer, then the industry-specific knowledge of the DRI will allow the chip-maker to contract
against contingencies that it may not have identified in the absence of the DRI. Alternatively, it is likely that the
executive from the computer manufacturer is familiar with contracts written between his own firm and other
chip-makers. This knowledge can then be utilized to write better contracts between the given chip-maker and its
customer firms in the computers industry.
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investments can be of particular importance when firms produce specialized
products. Because contractual frictions are symptomatic of these investments,
having DRIs from the firm’s actual customers/suppliers—or, more generally,
from customer and supplier industries—can alleviate the contracting and
information problems that arise in this incomplete-contracts setting (Coase
1937; Williamson 1971, 1979; Klein, Crawford, and Alchian 1978; Grossman
and Hart 1986; Hart and Moore 1990).6
We predict that firms that can obtain information about their opportunities
and prospects from other sources are less likely to value the information that
DRIs bring. To proxy for the availability of other sources of information,
we use measures such as whether the firm is in an industry in which stock
prices are explained largely by industry effects (“industry homogeneity”)
and whether managers/board can learn more about their firm’s prospects
from stock price movements (“stock price informativeness”). In homogeneous
industries, it is easier to benchmark the performance of the firm against other
firms in the industry. Hence, the information gap between the board and the
management of firms in these industries is likely to be small, and thus these
firms may have little to gain from having DRIs. With regard to “stock price
informativeness,” Chen, Goldstein, and Jiang (2007) argue that managers can
garner information regarding the firm’s product markets and other strategic
issues related to competitors from their stock prices. Thus, if the firm’s stock
price is less informative, then managers are more likely to seek DRIs, who can
substitute for this lack of price informativeness by providing insights about the
product-market landscape facing the firm.
The economic links between a firm’s industry and its related industries can
also have a bearing on how valuable DRIs are to the firm. Specifically, if there
is a higher correlation between the stock returns of the firm’s industry and
its related industries (i.e., stronger economic links), then any shocks to the
upstream or downstream industry will be more easily transmitted to the firm.
The firm may, therefore, prepare itself better against such exposures by hiring
DRIs. Further, a higher return correlation also implies that similar economic
forces affect firms in related industries. DRIs will have a better understanding
of these economic forces and can thus provide better advice as well as monitor
managers more effectively.
The Review of Financial Studies / v 27 n 5 2014
1.3 Conflicts of interest hypothesis
A possible downside to having DRIs on the board is that there may be
conflicts of interest if they are affiliated with firms that are actual or potential
customers/suppliers. Specifically, DRIs can seek to influence the management
to take actions that benefit their affiliated firms, possibly at the expense of
the given firm’s shareholders. For example, DRIs can influence the firm to
improve the prices and/or terms negotiated with their affiliated firms and
place alternative customers/suppliers at a disadvantage. Even if DRIs are not
from current customer/supplier firms, they can potentially influence the future
selection of their affiliated firms as customers/suppliers. Boards and managers
that act in the interest of shareholders and expect these conflicts to be dominant
will choose not to have DRIs on the board. By the same token, firms that are
less susceptible to DRI influence—e.g., have powerful CEOs (i.e., who are
also Board Chairmen) or are dominant in their respective industries (i.e., have
larger market share)—will be more willing to have DRIs on their boards.
7 Vertical integration can lead to anti-competitive strategies like foreclosure, in which it is used to raise rivals’ costs
by reducing access to inputs and/or outlets (e.g., Salinger 1988; Hart and Tirole 1990; Ordover, Saloner, and
Salop 1990), or collusion, in which it facilitates coordination between the integrated firm and its non-integrated
rivals (e.g., Chen 2001; Nocke and White 2007).
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related industries will be more willing to serve on a firm’s board if its market
share is larger. This is because ties with prominent firms can benefit the
director’s affiliated firms through enhanced information, influence, and network
connections. In addition, the ability to foreclose rival firms from critical inputs
and outlets can be greater if the firm’s market share is higher. Although vertical
integration is a more effective method for foreclosing rival firms, higher market
share firms may instead use DRIs in the presence of greater regulatory hurdles.
Finally, more concentrated industries can also be subject to greater regulatory
scrutiny, which can preclude vertical integration. In such cases, firms may seek
to reduce coordination costs along their supply chain and/or facilitate collusion
by using DRIs.7
The degree of vertical integration within the firm’s industry can also have
an impact on the likelihood of DRIs’ presence on the board. Ross, Westerfield,
and Jaffe (2008, 817) state that, “The main purpose of vertical acquisitions
is to make coordination of closely related operating activities easier.” An
implication is that coordination costs are likely to be higher in industries in
which a greater proportion of firms are vertically integrated. Furthermore, Jain,
Kini, and Shenoy (2011) argue that firms operating in industries with a higher
proportion of vertically integrated firms tend to rely more on relationshipspecific investments. Contractual and information problems, that are prevalent
in such settings, can be plausibly alleviated by the presence of DRIs. Hence, we
expect to see a greater presence of DRIs in more vertically integrated industries.
Board Expertise
1.5 Impact of DRIs on firm value/performance
As noted under the information and market structure hypotheses, DRIs provide
substantial benefits that can, in principle, exceed the costs due to conflicts of
interest. Thus, we expect DRIs to be present in firms in which the benefits
outweigh the potential costs to the firm and to the managers from having
these directors. Therefore, after controlling for endogenous selection, we expect
DRIs to have a positive impact on firm value and operating performance. This
prediction assumes that the CEO and the board are acting in the interest of
the firm’s shareholders. If, however, there are severe agency problems, the
CEO may favor the selection of DRIs to develop an implicit quid pro quo with
the DRI, with the CEO favoring the DRI’s affiliated firm in exchange for the
DRI’s support for the CEO. Thus, if the DRIs are a way for CEOs to gain
power vis-à-vis their board, then we expect DRIs to be typically from current
or future customers/suppliers and their presence to destroy shareholder value.
We develop and test more specific predictions on the performance impact of
DRIs in Sections 4–6.
2. Data and Variables
2.1 Data description
We draw our data on officers and directors over the period 1990–2005 from
Compact Disclosure, which is a comprehensive database based on SEC filings
of all publicly traded companies in the United States. Appendix A provides
details on the algorithm we use to clean these data and assign unique identifiers
to all the officers and directors in Compact Disclosure. This enables us to
determine the board affiliations for each year and all individuals in the database.
Our initial sample consists of 115,651 firm-year observations and 15,042
firms. After merging with COMPUSTAT and CRSP, we are left with 81,650
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1.4 Other factors
Duchin, Matsusaka, and Ozbas (2010) find that a larger fraction of outside
directors improves the advisory and monitoring functions of the board and
adds value only when these directors have relatively easy access to information
about the firm and its economic environment. Because DRIs are expected to
improve a board’s access to industry information, we anticipate that boards
with a greater fraction of outside directors will be more effective when DRIs
are present. Hence, we predict that DRIs are more likely to be included in
the board when the fraction of outsiders is larger. Whether or not a firm can
have a director on its board from one of its related industries also depends on
the availability of such directors. If the pool of potential directors is larger,
then a firm is mechanically more likely to have a DRI. Furthermore, because
larger firms and diversified firms are likely to be more complex (Coles, Daniel,
and Naveen 2008), information from DRIs can be especially valuable to them.
Thus, larger firms and multisegment firms are more likely to have DRIs.
The Review of Financial Studies / v 27 n 5 2014
8 Unlike databases such as Investor Responsibility Research Center’s (IRRC) Directors data, the sample here is
not restricted to firms that are members of indices. Thus, there is no exit or entry of firms in Compact Disclosure
because of deletion from or addition to an index—they will be included in the database as long as they are
publicly traded.
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firm-year observations and 12,750 firms. These data are more representative
than the limited sample of firms covered in other governance databases, and
consequently, the implications of our study are more general.8 Moreover, the
broader coverage of firms in our sample allows us to more fully capture the
directors’ supply-chain relationships than would be possible with the more
commonly used databases.
Supply-chain relationships between industries are identified using the Use
table from the Benchmark Input-Output (I-O) tables published every five years
by the Bureau of Economic Analysis (BEA). Our approach to identifying related
industries is the following. Consider a pair of distinct I-O industries: industry i
and industry j , where industry i is the industry of the firm whose board we are
examining for the presence of any DRIs. We want to determine the economic
importance of industry j to industry i (and not the other way around) as a
potential supplier or customer industry. Let a% (b%) be the percentage output
(input) of industry i that goes to (comes from) industry j . Industry j is then
regarded as “related” to industry i via the supply chain at the 1%, 5%, or 10%
level if the sum of a% and b% (which we call the vertical-relatedness coefficient,
or simply “VRC”) exceeds 1%, 5%, or 10%, respectively. The average number
of industries that a given firm’s industry is related to at the 1%, 5%, and 10%
VRC thresholds is 105, 37, and 12, respectively. These numbers exceed 100, 20,
and 10, respectively, because the thresholds are based on the sum of percentage
of outputs sold to and percentage of inputs purchased from a related industry. To
provide some perspective on the significance of these cutoffs, we characterize
the distribution of the VRCs across all industry pairs. The mean (median) VRC
for any pair of industries is 1.42% (0.20%). The 10th and 90th percentiles of
the VRCs are 0.01% and 2.80%, respectively. This indicates that the cutoffs
that we have used to identify related industries indeed represent a meaningful
economic relationship between the two industries. More specifically, the 1%,
5%, and 10% cutoffs fall at approximately the 78th, 94th, and 97th percentile
in this distribution.
For the sample period up to 1996, we convert the vertical relationships
based on 1992 I-O tables into those based on four-digit SIC codes.
Similarly, for the sample periods 1997–2001 and 2002–2005, we convert
the vertical relationships based on 1997 and 2002 I-O tables, respectively,
into those based on NAICS codes. We obtain the concordance tables
between I-O and SIC/NAICS industry codes from the BEA Web site:
www.bea.gov/industry/index.htm. We then match these vertical industry
relationships by historical SIC/NAICS codes of COMPUSTAT firms. For
Board Expertise
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consistency throughout the paper, we classify all firms into four-digit SIC
industries.
Next, for each director on the board of a firm in a given year, we identify
all the other firms (and their respective SIC industries) with which this director
is affiliated. The director is designated as a DRI if she is either an officer or
a director in any firm belonging to an industry that is vertically related to the
given firm’s industry. Note that insiders are classified as DRIs only if they also
serve as a director on the board of at least one firm in a related industry. We
find that in firms with DRIs identified using a 1%, 5%, or 10% threshold, the
mean VRC between the firm’s industry and the DRI’s industry is 5.2%, 12.3%,
and 21.7%, respectively. These are at the 94th, 98th, and 99th percentile in the
distribution of VRCs across all industry pairs, respectively.
To further illustrate the figures described above, we use Hewlett-Packard
Company (HP), Honeywell International, and Dow Chemical as examples. HP
belongs to the Computer and Peripheral Equipment Manufacturer industry,
whose mean (median) VRC with its related industries is 1.46% (0.10%).
HP’s top five related industries with the highest VRCs (all greater than
5%) are Software Publishers; Wholesale Trade; Software, Audio, and Video
Media Reproducing; Printed Circuit Assembly Manufacturing; and Electronic
Capacitor, Resistor, Coil, Transformer, and Other Inductor Manufacturing.
HP has also had DRIs over our sample period. For example, Mr. Richard
Hackborn was on HP’s board since 1994; he was also a director at Microsoft
Corp. from 1994–1999 and is therefore classified as HP’s DRI over this period.
Subsequently, Mr. Robert Knowling was on HP’s board from 1999–2005; he
was also a director at Ariba Inc. at the same time and is therefore classified as a
DRI over this six-year period. Note that both Microsoft and Ariba are software
firms, and as indicated above, Software Publishers is the most important related
industry for HP.
Honeywell and Dow Chemical also had DRIs on their board over our sample
period. For instance, Mr. Gordon Bethune was a CEO of Continental Airlines
until 2004 and is classified as a DRI for Honeywell. Continental Airlines
belongs to the Air Transportation, Scheduled industry (SIC 4512) and its VRC
with Honeywell’s Aircraft Parts and Auxiliary Equipment industry (SIC 3728)
is approximately 5%. Similarly, Mr. J. Michael Cook was a director of both
International Flavors and Fragrances and Dow Chemical for the period 2000–
2006. International Flavors and Fragrances belongs to the Industrial Organic
Chemicals industry (SIC 2860), whereas Dow Chemical belongs to the Plastics,
Materials, and Resins industry (SIC 2821). With a VRC of 16% between
these two industries, Mr. Cook is classified as a DRI for Dow Chemical.
Overall, the above discussion suggests that DRIs are typically affiliated with
industries that have more significant economic linkages with the firm’s industry
than is suggested by our VRC cutoffs. Further, the related industries that we
identify for classifying directors as DRIs do seem to be intuitively related as
supplier/customer industries.
The Review of Financial Studies / v 27 n 5 2014
9 Our inferences are qualitatively similar if we assign the same weight to all directors from related industries.
10 Our conclusions remain unchanged if we do not truncate this value.
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2.2 Measures of DRIs on the board
A typical board of directors consists of insiders, who are the firm’s own full-time
employees (i.e., officers), and outsiders, who may be either full-time employees
of other firms or not executives of any firm (i.e., “professional directors”). We
construct several measures for the presence of DRIs (“DRI measures,” for short)
to test our predictions. In doing so, we distinguish between outside directors
who are officers and other outside directors who are professional (nonexecutive)
directors in the related firm. We expect the former to be better informed about
the market conditions facing the firm, and thus, we assign a greater weight to
these directors.
To construct our first set of DRI measures, for each director we add one if the
director is an officer in a related industry and 0.5 if he/she serves as an outside
director in the related industry.9 We then aggregate these values across all the
directorships held by each director. If this sum for a director is greater than one,
then we truncate its value at one.10 Based on these values for all directors, we
construct two broad types of measures for the firm’s board—either a dummy
variable or a proportional measure. The dummy variable equals one to indicate
the presence of at least one DRI on the firm’s board. To compute the proportional
measure, we aggregate individual values across all directors of the firm and
divide this by the firm’s board size. When using the 1% threshold for VRC,
we denote the dummy measure as Dummy_DRI and the proportional measure
as DRI. Correspondingly, for the 5% (10%) threshold, the respective measures
are denoted by Dummy_DRI 5% (Dummy_DRI 10% ) and DRI 5% (DRI 10% ). We
use these alternative thresholds to ensure that our results are not driven by the
choice of this cutoff. Moreover, these larger thresholds will ensure that the DRIs
represent related industries that have a more significant economic relationship
with the firm.
The above measures use only the primary four-digit SIC industry in
determining the supply-chain industries of multi-segment firms. As an
alternative, we identify the related industries (at the 1% VRC threshold) for
all four-digit SIC segments of a firm reported in COMPUSTAT Segment files.
We denote the alternative dummy measure thus defined as Dummy_DRI seg
and denote the corresponding proportional measure as DRI seg . In addition, we
construct measures based on only those DRIs who are “outside” directors for
the firm. In this case, the dummy version of our measure indicates the presence
of outside DRIs, and the proportional measure indicates the ratio of outside
directors from related industries to all outside directors on the board. We follow
the above nomenclature for these measures, except we use the subscript “out”
in their names.
Board Expertise
11 We identify all the four-digit SIC industries in which firms have interlocking directors; we refer to these as
“pseudo-horizontal” links and compare them to industries in which we do not observe such links. We find
that industries with such pseudo-horizontal links are more likely to have differentiated products and to be less
homogeneous. These observations corroborate our belief that these links are likely to be between firms that do
not have competing product lines.
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Even though U.S. antitrust laws restrict competing firms from having
common board members, we find that some directors belong to the same
four-digit SIC industry as the firm itself. Presumably, these are cases in
which vertical relationships are being captured only at the BEA’s I-O industry
classifications, which are finer than four-digit SIC codes, or these firms are
not directly competing in the product market.11 As a result, we construct
additional alternative measures that include directors that are affiliated with
either the supply-chain industries identified above or with firms in the same
four-digit SIC industry. We denote the dummy measure as Dummy_DRI V H
and the proportional measure as DRI V H , where “VH” refers to the vertical or
pseudohorizontal four-digit SIC industry affiliation of the director.
The measures described above account for the presence of directors from
related industries. We define another type of DRI-measure, denoted Breadth,
which captures the extent of directors’ expertise in related industries. It is
calculated as the sum of the VRCs of all the unique industries represented
by the DRIs on the firm’s board. For instance, if the firm has two DRIs, where
the first DRI represents the supply-chain industries i and j , whereas the second
DRI represents the supply-chain industries j and k, then we sum the VRCs of
industries i, j , and k (thereby not double counting industry j ).
Finally, we define another DRI measure, which is based on a new
classification of related industries. For all firms in the given firm’s four-digit
SIC industry, we identify the industries represented by the union of their actual
supplier and customer firms (as obtained from the COMPUSTAT Segments files)
and classify them as “related” industries. Directors affiliated with the abovedefined related industries are identified as DRIs; their presence is indicated by
the variable Dummy_DRI union , and their proportion on the board is measured by
DRI union . Definitions of all the dependent and independent variables, including
the measures for the presence of DRIs, are summarized inAppendix B. Note that
because our sample consists of only publicly listed companies, we understate
the presence of DRIs in our measures because we cannot account for the
possibility that some directors may be affiliated with private firms in related
industries. However, we believe that this omission is likely to bias us against
finding an effect because there may be firms that are in the control group that
have DRIs from private firms.
Descriptive statistics for DRI measures are reported in Table 1. Panel A
provides these statistics for the dummy measures. The mean values on these
measures range from a low of 0.087 for Dummy_DRI 10% to a high of 0.463
for Dummy_DRI seg . The mean value for Dummy_DRI 10% implies that about
8.7% of firm-year observations have at least one director from related industries
The Review of Financial Studies / v 27 n 5 2014
Table 1
Descriptive statistics for measures for directors from related industries (DRIs)
Panel A: Dummy measures
Dummy_DRI
Dummy_DRI seg
Dummy_DRI out
Dummy_DRI V H
Dummy_DRI 5%
Dummy_DRI 10%
Dummy_DRI union
N
Mean
Median
SD
81,650
81,650
81,650
81,650
81,650
81,650
81,650
0.404
0.463
0.365
0.460
0.171
0.087
0.252
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.491
0.499
0.482
0.498
0.376
0.282
0.434
81,650
81,650
81,650
81,650
81,650
81,650
81,650
81,650
33,019
33,019
0.065
0.092
0.074
0.091
0.023
0.012
0.070
0.067
0.160
0.170
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.125
0.066
0.109
0.141
0.130
0.146
0.067
0.050
0.198
0.148
0.119
0.281
Panel B: Proportional measures
This table provides descriptive statistics on various dummy and proportional measures for the presence of
directors from related industries (DRIs). Definitions of all measures are provided in Appendix B. Panel A reports
statistics on the dummy measures that indicate whether the firm has at least one DRI, whereas Panel B provides
statistics on the proportional DRI measures.
identified at the 10% threshold. Similarly, the mean value for Dummy_DRI seg
implies that about 46% of firm-year observations have at least one director from
related industries identified at the 1% threshold using all reported segments
of the firm. Notably, about 40% of the firm-year observations have at least
one director identified at the 1% threshold using only the primary SIC code
of the firm to identify related industries, and although unreported, 60% of
all firms in our sample have such a director at some point over the sample
period.12 Table 1, Panel B, reports the descriptive statistics for the remaining
DRI measures. For the overall sample, the lowest mean value (0.012) is for
DRI 10% , whereas the highest mean value (0.092) is for DRI seg . The mean value
for the proportional measure within the sample of firms with DRIs (i.e., “DRI
Nonzero”) is 0.160. Because the mean board size is about 8 (Table 3), this
mean value of 0.160 suggests that the typical firm with DRIs has either one or
two DRIs.
To briefly characterize the industries in which DRIs are either more or less
prevalent, we present the distribution of Dummy_DRI and DRI by four-digit
SIC industries in Panel A of Table 2. For brevity, we only report the top 5
and bottom 5 industries (each with at least 100 firm-year observations), when
ranked by the DRI measure. A cursory look at the table suggests that industries
12 Although not tabulated, we find that 14% (12%) of unique directors are DRIs at the 1% (5%) vertical-relatedness
threshold as well as CEOs of a firm that belongs to a related industry. A similar proportion of all firm-year
observations with DRIs have a DRI who is also concurrently a CEO in a related industry.
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DRI
DRI seg
DRI out
DRI V H
DRI 5%
DRI 10%
Breadth
DRI union
DRI (Non-zero)
Breadth (Non-zero)
Board Expertise
Table 2
Distribution of measures for directors from related industries (DRIs) by four-digit SIC industries
Panel A: Industries ranked by highest and lowest values of mean DRI
SIC description
Top 5
Biological products, except diagnostic substances
Computer storage devices
Advertising agencies
Instruments for measuring and testing of electricity
and electric signals
In vitro and in vivo diagnostic substances
Mean DRI
Mean Dummy_DRI
1,063
319
108
496
15.60%
14.78%
13.91%
12.98%
78.65%
62.07%
73.15%
61.09%
656
12.95%
66.16%
283
770
112
209
104
1.16%
1.12%
1.04%
1.02%
1.00%
17.67%
10.78%
5.36%
8.61%
10.58%
Panel B: Related industries from which the “Top-5” industries of Panel A draw the highest percentage of their
DRIs
Industry
Related industry from which
the highest percentage of DRIs
are drawn, “Top DRI Industry”
Percentage of all
DRIs that are from
“Top DRI Industry”
VRC of
“Top DRI
Industry”
Biological products, except
diagnostic substances
(SIC 2836)
Computer storage devices
(SIC 3572)
Advertising agencies
(SIC 7311)
Instruments for measuring and
testing of electricity and
electric signals (SIC 3825)
In vitro and in vivo diagnostic
substances (SIC 2835)
Pharmaceutical preparations
(SIC 2834)
56%
30%
Prepackaged software
(SIC 7372)
Television broadcasting stations
(SIC 4833)
Semiconductors and related
devices (SIC 3674)
33%
12%
16%
7%
27%
7%
37%
25%
Pharmaceutical preparations
(SIC 2834)
Panel A of this table reports the top five and bottom five four-digit SIC industries by mean DRI. For reference, we
also report the mean Dummy_DRI of these industries. Both DRI measures are defined at the 1% VRC threshold.
Panel B of this table identifies the related industry from which each of the top-five industries of Panel A draws
the highest percentage of its DRIs.
usually regarded as more innovative (such as Instruments for Measuring and
Testing Electricity and Electric Signals and Computer Storage Devices) or
those producing differentiated goods (such as Biological Products) tend to be
ranked higher than other industries. This is consistent with the notion that, for
such industries, the industry-specific information and expertise of DRIs may be
more valuable. On the other hand, industries producing commodities, such as
Poultry Slaughtering and Processing or Newspapers: Publishing or Publishing
and Printing, rank at the bottom.
In Panel B of Table 2, we illustrate that the highest percentage of DRIs
is drawn from vertically related industries that have a meaningful economic
relationship with the given industry. We report the name of the related industry
for the same top 5 industries as in PanelA. For example, 56% of all DRIs in firms
belonging to the Biological Products Except Diagnostic Substances industry
(SIC 2836) are from Pharmaceutical Preparations industry (SIC 2834).
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Bottom 5
Newspapers: publishing, or publishing and printing
Savings institutions, not federally chartered
Poultry slaughtering and processing
Home health care services
Electric housewares and fans
N
The Review of Financial Studies / v 27 n 5 2014
These two industries clearly seem to be logically related as supplier/customer
industries; this is further evidenced by the 30% VRC between these two
industries. This intuition is also confirmed in the other examples reported
in Panel B. For instance, 16% of all DRIs in firms belonging to Advertising
Agencies industry (SIC 7311) are from the Television Broadcasting Stations
industry (SIC 4833). Intuitively, these two industries are economically related;
this is also confirmed by the 7% VRC between the two industries.
13 In the context of actual customers and suppliers, R&D intensity has also been used as a proxy for relationship-
specific investments (e.g., Allen and Phillips 2000; Kale and Shahrur 2007).
14
We obtain these from NBER’s patent data project: https://sites.google.com/site/patentdataproject/Home/
downloads.
15 We follow Baker and Wurgler (2002), Fama and French (2002), and Huang and Ritter (2009) in constructing
Book_Leverage, and our descriptive statistics are similar to those reported by these authors.
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2.3 Firm and industry characteristics
Descriptive statistics on firm and industry characteristics used in our tests are
provided in Table 3; their definitions are provided in Appendix B. Consistent
with our definitions of related industries, we define industry characteristics
at the four-digit SIC industry. All unbounded variables are winsorized at the
1st and 99th percentiles. All variables denominated in dollars are deflated to
1990 dollars by the CPI. In Panel A of Table 3, firm characteristics are listed
under four headings: measures of innovativeness, other firm characteristics
that are considered in our empirical analysis, governance characteristics, and
performance/value measures.
Firm’s Innovativeness. We employ various firm-specific and industry
measures of innovativeness in our analysis. Research and development (R&D)
intensity is our first measure of firm innovativeness.13 The mean value for
R&D is 4.7%. We also use the number of patents (Patents) and citations
(Citations) as alternative proxies for firm innovativeness.14 The mean number
of patents and citations for our sample firm-years are 5.88 and 108.37,
respectively.
Other Firm Characteristics. We follow Chen, Goldstein, and Jiang (2007) to
estimate stock price informativeness (Price_Info); its mean (median) value is
0.725 (0.749). Market_Share, defined as the firm’s share of industry sales,
has a mean (median) value of 5.4% (0.7%). Other control variables used
in our tests are Firm_Size, Book_Leverage, Tangible_Assets, Multi_Segment,
and Firm_Volatility. The summary statistics of these variables are reported in
Table 3.15
Governance Characteristics. The mean (median) values for the number
of directors on the board (Board_Size) and proportion of outside directors
(Outside_Directors) are 8.064 (7.000) and 0.662 (0.700), respectively. The
CEO also holds the position of Chairman (CEO_Duality) in about 47% of our
firm-year observations. These values are slightly smaller than those reported in
Board Expertise
Table 3
Firm and industry characteristics
Panel A: Firm characteristics
Mean
Median
SD
81,650
81,650
81,650
0.047
5.883
108.370
0.000
0.000
0.000
0.146
56.873
1,191.057
Other firm characteristics
Price_Info
Market_Share
Firm_Size ($m)
Book_Leverage
Tangible_Assets
Multi_Segment
Firm_Volatility
Cash_Holdings
Cash_Flow
71,034
81,650
81,650
81,628
80,936
81,650
80,899
81,610
79,932
0.725
0.054
3,338.946
0.531
0.255
0.265
0.040
0.164
0.005
0.749
0.007
169.330
0.506
0.179
0.000
0.033
0.074
0.058
0.173
0.130
25,657.180
0.296
0.240
0.441
0.029
0.206
0.233
Governance characteristics
Board_Size
CEO_Duality
Outside_Directors
81,650
81,650
81,650
8.064
0.466
0.662
7.000
0.000
0.700
3.492
0.499
0.206
Performance/value
Tobin’s Q
ROA
CCC
ICP
CP
PP
81,631
79,958
69,408
80,108
70,713
70,112
1.895
0.121
82.045
83.249
76.072
101.607
1.270
0.119
66.056
47.900
55.661
41.900
1.713
0.197
161.109
132.966
111.801
497.366
81,650
75,600
68,580
54,893
81,462
81,650
81,650
81,650
81,650
81,650
81,650
81,650
0.349
0.486
0.522
0.543
0.154
0.196
0.084
0.049
0.020
24,972
8,150
2,995
0.000
0.503
0.526
0.550
0.140
0.148
0.040
0.000
0.000
23,720
5,754
861
0.477
0.133
0.161
0.176
0.102
0.164
0.117
0.094
0.057
14,673
7,580
4,161
Panel B: Industry characteristics
Differentiated
Correlation
Correlation5%
Correlation10%
Homogeneity
HHI
Integrated
Integrated 5%
Integrated 10%
DRI_Supply
DRI_Supply5%
DRI_Supply10%
This table provides summary statistics on the firm and industry characteristics used in our empirical analysis.
Definitions of all the variables are provided in Appendix B.
other studies and are likely attributable to the fact that our sample size is much
larger than earlier governance studies using ExecuComp or IRRC data.16
Value/Performance. Lastly, in Panel A of Table 3, we report summary
statistics on firm value (Tobin’s Q) and performance measures, such as return
on assets (ROA) or cash conversion cycle (CCC). Tobin’s Q is defined as the
sum of book debt and market value of equity divided by the book value of
16 In the subsample of ExecuComp firms in our data (26,104 firm-year observations), the mean CEO_Duality is
about 60%, the mean (median) Outside_Directors is 75% (71%), and the mean (median) Board_Size is about 9
(9).
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N
Innovativeness
R&D
Patents
Citations
The Review of Financial Studies / v 27 n 5 2014
17 Because R&D expenditures are treated as period expenses and expensed rather than capitalized, using operating
income before depreciation, interest, and taxes (EBITDA) as a measure of operating cash flow will result in a
downward bias for higher R&D intensity firms. We, therefore, adopt an approach similar to Eberhart, Maxwell,
and Siddique (2004) by adding back R&D expenditures to EBITDA. Our results are qualitatively similar when
we do not make this adjustment or include R&D intensity as a control variable. Also, the mean (median) ROA
without adding the R&D expenses is 7.0% (9.9%).
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assets. ROA is defined as operating profit before depreciation and taxes plus
R&D expenditures divided by lagged total assets.17 The mean (median) Tobin’s
Q and ROA are 1.895 (1.270) and 12.1% (11.9%), respectively. CCC is defined
as the inventory conversion period (ICP) plus collection period (CP) minus
payables period (PP). It has a mean (median) of about 82 (66) days.
Industry Characteristics. Panel B of Table 3 reports descriptive statistics
of industry characteristics. Our industry-specific proxy for innovativeness is
Rauch (1999) classification of industries with differentiated products. Our
sample shrinks if we only consider industries that are classified in Rauch
(1999). Hence, we make the following modification to his classification system.
We assume that among the group of four-digit SIC industries with the same
three-digit SIC code, if there is at least one four-digit SIC industry that
is differentiated, then all other industries in the group are assumed to be
differentiated as well. Because applying this procedure to Rauch’s original
data results in a relatively small proportion of conflicting classifications (less
than 5%), we are comfortable in using the same rule in our entire sample.
Differentiated is a dummy variable that takes the value one if the firm belongs
to the above-defined differentiated industries and is zero otherwise. The mean
value for this variable is 0.349; that is, 34.9% of our firm-year observations
belong to differentiated industries.
Correlation, Correlation5% , and Correlation10% are the average pair-wise
correlation between the stock returns of a given industry and the stock returns
of all its vertically related industries identified at the 1%, 5%, and 10%
threshold, respectively. We find that the mean (median) value for Correlation
is 0.486 (0.503). As expected, the mean and median values of Correlation5%
and Correlation10% are comparatively higher. We follow Parrino (1997) to
estimate industry homogeneity (Homogeneity) at the four-digit SIC level; its
mean (median) value is 0.154 (0.140). Herfindahl-Hirschman Index (HHI) is
defined as the sum of squared market-shares of all firms in a four-digit SIC
code. The mean (median) HHI is 0.196 (0.148). The proportion of vertically
integrated firms in an industry is denoted by Integrated and is defined as the
proportion of firms with at least one segment that is in an industry related (at
the 1% threshold) to the firm’s primary four-digit SIC industry. Integrated 5%
and Integrated 10% are defined similarly using the 5% and 10% thresholds,
respectively. The mean (median) for Integrated is 8.4% (4.0%); not surprisingly,
the mean and median of Integrated 5% and Integrated 10% are comparatively
smaller. Finally, DRI_Supply represents the availability of related-industry
directors and is measured as follows. We first identify all the upstream and
Board Expertise
downstream industries of the given firm (at the 1% threshold), then count the
number of distinct officers and directors in every firm in these industries, and
aggregate these counts across all firms in related industries. DRI_Supply5%
and DRI_Supply10% are defined similarly using the 5% and 10% thresholds,
respectively. DRI_Supply, DRI_Supply5% , and DRI_Supply10% have an average
value of 24,972, 8,150, and 2,995 directorships, respectively.18
3. Which Firms Choose Directors from Related Industries?
DRI_Measurei,t+1 = β0 +β1 Inovativenessi(k),t +β2 Price_Infoi,t
+β3 Correlationk,t +β4 Homogeneityk,t +β5 Market_Sharei.t +β6 HHIk,t
+β7 Integratedk,t +β8 CEO_Dualityi,t +β9 Outside_Directorsi,t
+β10 Ln(Firm_Sizei,t )+β11 Multi_Segmenti,t +β12 Book_Leveragei,t
(1)
+β13 Ln(Board_Size)i,t +β14 Ln(DRI_Supply)k,t +Y earDummies +εi,t+1 .
The regression subscripts refer to firm i, industry k, and year t. Results from the
estimation of various specifications of this model are reported in Table 4. The
dependent variable, DRI_Measurei,t+1 , measures the presence of directors from
related industries on firm i’s board in year t+1. We use probit models to estimate
Equation (1) when the dependent variable is discrete and OLS models when
the dependent variable is a proportional measure. All independent variables are
measured with a one-year lag relative to the dependent variable. We proxy for
the firm’s innovativeness with three different variables (R&D, Differentiated,
and Ln(Patents)) and report the regression results for each of these measures.
Results using Ln(Citations) as an additional proxy for innovativeness yield
similar conclusions to those using the above three measures and are left
unreported for brevity. All specifications include year dummies. The t-statistics
reported in the regressions are based on robust standard errors clustered by firm.
We present the regression results in Table 4. The dependent variables are the
dummy measure Dummy_DRI (Columns (1)–(3)) and the proportional measure
DRI (Columns (4)–(6)). The marginal effects in Columns (1)–(3) are estimated
using probit models, and the coefficients in Columns (4)–(6) are estimated
18 We also define a scaled DRI-supply measure as DRI_Supply divided by the aggregate number of board seats in the
firm’s industry. The average value of this scaled DRI-supply measure at the 1%, 5%, and 10% vertical-relatedness
threshold is 186, 58, and 19, respectively. The use of this scaled measure does not affect our inferences regarding
firms’ use of DRIs. Further, our results are similar when DRI_Supply is defined as the number of potential DRIs,
either from a 100-mile radius or 200-mile radius around the firm’s headquarters.
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In this section, we empirically test our hypotheses about firm and industry
characteristics that make it more likely that a firm has directors from related
upstream and downstream industries on its board. The regression model that
we estimate for analyzing these determinants can be expressed as
The Review of Financial Studies / v 27 n 5 2014
Table 4
Determinants of directors from related industries (DRIs)
Dependent variable:
Measure of
innovativeness:
Innovativeness
Price_Info
Correlation
Market_Share
HHI
Integrated
CEO_Duality
Outside_Directors
Ln(Firm_Size)
Multi_Segment
Book_Leverage
Ln(Board_Size)
Ln(DRI_Supply)
(2)
(3)
(4)
(5)
(6)
R&D
Differentiated
Ln(Patents)
R&D
Differentiated
Ln(Patents)
0.017∗∗∗
[8.78]
−0.034∗∗∗
[−8.26]
0.039∗∗∗
[6.30]
−0.028∗∗∗
[−3.93]
0.032∗∗∗
[4.11]
−0.000
[−0.07]
0.016∗∗
[2.35]
0.005∗∗∗
[3.47]
0.040∗∗∗
[11.69]
0.010∗∗∗
[17.72]
−0.000
[−0.28]
−0.028∗∗∗
[−10.17]
−0.014∗∗∗
[−6.04]
0.025∗∗∗
[15.59]
−0.194∗∗∗
[−11.95]
0.012∗∗∗
[10.40]
−0.033∗∗∗
[−7.91]
0.040∗∗∗
[6.53]
−0.021∗∗∗
[−2.92]
0.018∗∗
[2.33]
0.002
[0.34]
0.017∗∗∗
[2.58]
0.004∗∗∗
[2.70]
0.038∗∗∗
[11.05]
0.008∗∗∗
[14.83]
−0.001
[−0.79]
−0.027∗∗∗
[−9.85]
−0.013∗∗∗
[−5.87]
0.022∗∗∗
[13.97]
−0.159∗∗∗
[−9.85]
yes
61,185
0.073
yes
61,185
0.078
0.512∗∗∗
0.087∗∗∗
[9.69]
[9.57]
−0.149∗∗∗
−0.182∗∗∗
[−7.48]
[−9.31]
0.186∗∗∗
0.172∗∗∗
[6.39]
[5.92]
−0.125∗∗∗
−0.164∗∗∗
[−3.40]
[−4.55]
0.178∗∗∗
0.205∗∗∗
[4.97]
[5.77]
0.077∗∗∗
0.021
[2.99]
[0.79]
∗∗∗
0.143
0.099∗∗∗
[4.69]
[3.24]
0.041∗∗∗
0.041∗∗∗
[6.45]
[6.32]
0.205∗∗∗
0.211∗∗∗
[12.18]
[12.55]
0.063∗∗∗
0.055∗∗∗
[25.16]
[22.74]
0.019∗∗
0.012
[2.28]
[1.44]
∗∗∗
−0.186
−0.159∗∗∗
[−13.83]
[−11.62]
0.035∗∗∗
0.036∗∗∗
[3.95]
[4.02]
0.098∗∗∗
0.117∗∗∗
[13.76]
[15.69]
Constant
Year dummies
Observations
R2
yes
61,185
0.117
yes
61,185
0.115
0.055∗∗∗
0.107∗∗∗
[12.56]
[7.14]
−0.178∗∗∗
−0.027∗∗∗
[−9.12]
[−6.39]
0.184∗∗∗
0.041∗∗∗
[6.38]
[6.42]
−0.134∗∗∗
−0.020∗∗∗
[−3.70]
[−2.65]
0.147∗∗∗
0.027∗∗∗
[4.11]
[3.45]
0.031
0.010∗∗
[1.20]
[2.10]
0.110∗∗∗
0.024∗∗∗
[3.63]
[3.63]
0.036∗∗∗
0.005∗∗∗
[5.54]
[3.55]
0.201∗∗∗
0.038∗∗∗
[12.04]
[11.12]
0.047∗∗∗
0.011∗∗∗
[19.42]
[19.75]
0.008
0.001
[0.98]
[0.51]
∗∗∗
−0.155
−0.033∗∗∗
[−11.43]
[−11.80]
0.038∗∗∗
−0.014∗∗∗
[4.34]
[−6.18]
0.101∗∗∗
0.021∗∗∗
[13.92]
[13.68]
−0.170∗∗∗
[−10.70]
yes
61,185
0.118
yes
61,185
0.083
This table presents evidence on the determinants of DRIs. The dependent variable Dummy_DRI t+1 in Columns
(1)–(3) is a dummy variable that equals one if the firm has at least one director from related industries of the
primary segment of the firm. The dependent variable DRI t+1 in Columns (4)–(6) is the proportion of directors
who are from related industries of the primary segment of the firm. The related industries are identified at
the 1% VRC threshold. Marginal effects from Probit models are reported in Columns (1)–(3), and the OLS
estimates are reported in Columns (4)–(6). Each specification uses one of the three proxies for innovativeness:
R&D, Differentiated, or Ln(Patents). Correlation, Integrated, and DRI_Supply are defined using the 1% VRC
threshold. Definitions of the variables are provided in Appendix B. The independent variables are measured
with a one-year lag relative to the dependent variables. All specifications include year fixed effects. t -statistics
reported in brackets are robust and clustered by firm. ***, **, and * indicate significance at the 1%, 5%, and
10% level, respectively.
using OLS models. We first discuss the results in Column (1). The estimated
marginal effects on information-related variables have the predicted signs. In
this specification, our proxy for the firm’s innovativeness is its R&D intensity.
It has a positive impact (significant at the 1% level) on the likelihood of the
firm having DRIs. Second, the firm’s stock-price informativeness (Price_Info)
has a significant negative impact at the 1% level on the presence of DRIs.
This is consistent with the argument that the potential benefit from such
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Homogeneity
DRI t+1
Dummy_DRI t+1
(1)
Board Expertise
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directors may be lower when a firm’s stock price is more informative. Another
information-related variable is the average pairwise correlation of the firm’s
industry and its related upstream and downstream industries’ stock returns
(Correlation). This correlation is expected to increase the likelihood of the
firm having DRIs because a higher Correlation indicates that the firm might
be more prone to shocks transmitted via the related industries, making the
information from these directors more valuable. Consistent with this notion, the
estimated coefficient is positive and significant at the 1% level. Next, industry
homogeneity (Homogeneity) has a negative impact (significant at the 1% level)
on the likelihood of having DRIs. This result is consistent with the idea that
the input from DRIs is less valuable for monitoring managerial performance
because the rest of the board can evaluate performance relatively easily by
comparing the manager to industry peers.
The coefficient estimates on the set of determinants related to our market
structure hypotheses have the predicted signs as well. The firm’s market
share (Market_Share) has a positive effect (significant at the 1% level) on the
dependent variable. This result suggests that dominant firms within an industry
have an advantage in attracting DRIs. In addition, the benefit to firms with a
higher market share will be greater if these directors can enhance the firm’s
ability to foreclose rivals from critical inputs and/or outlets. This finding is
also consistent with the ability of dominant firms to avert DRIs’ attempts to
influence management in order to favor their affiliated firms. The estimated
coefficient on industry concentration (HHI) is statistically significant at the
1% level. This is consistent with the notion that these directors may enable
the firm to collude with its rivals in more concentrated industries by serving
as conduits of information. These directors can also help alleviate contracting
costs with firms along the supply chain if vertical integration is effectively ruled
out due to regulatory considerations. The estimated coefficient on Integrated is
also positive and significant at the 1% level. This result suggests that a firm is
more likely to employ DRIs in environments in which coordination costs and
contracting frictions are higher, as captured by greater vertical integration in
the industry.
As we have discussed, a powerful CEO is less likely to be influenced by the
conflicts of interest faced by DRIs and thus is more likely to nominate them to
serve on the firm’s board. Consistent with this prediction, we find that when the
CEO is also the board Chairman (CEO_Duality), that is, when the CEO is more
powerful, firms are more likely to have DRIs. We also find that the likelihood of
having DRIs increases with Outside_Directors, Ln(Firm_Size), Multi_Segment,
and Ln(DRI_Supply). The estimated coefficients on these control variables are
in line with our predictions.
In Column (2) in Table 4, we measure innovativeness with the dummy
variable Differentiated that identifies industries producing differentiated goods
and services. The estimated coefficient on Differentiated is significantly
positive at the 1% level. In Column (3), the proxy for innovativeness is the
The Review of Financial Studies / v 27 n 5 2014
4. What is the Benefit of Choosing Directors from Related Industries?
In this section, we examine whether DRIs impact future firm performance. We
study two aspects of firm performance: firm value, as captured by the Tobin’s
Q, and operating performance, as measured by the operating return on assets
(ROA).
19 In Columns (2) and (3), we measure innovativeness with Differentiated and Ln(Patents), respectively. The
economic significance of the coefficients on these innovativeness measures is similar in magnitude to that
obtained for R&D.
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logarithm of one plus the number of patents owned by the firm (Ln(Patents)).
The estimated coefficient on this innovation measure is also positive and highly
statistically significant. The estimated coefficients on all other key determinants
across Columns (2) and (3) are similar to those found in Column (1), except
that HHI loses significance.
The results reported above are not only statistically significant but are
also economically meaningful. To illustrate the economic significance, we
use the coefficients estimated in Column (1). The economic significance
of each independent variable should be interpreted in comparison with the
unconditional probability of having DRIs, which is about 40%. The coefficient
estimate on R&D suggests that a one standard deviation increase in R&D
increases the probability of DRIs on the board by 6.6%.19 Similarly, a one
standard deviation increase in Price_Info (Homogeneity) is associated with a
2.6% (1.3%) decrease in DRI probability. Further, a one standard deviation
increase in Correlation, Market_Share, HHI, and Integrated is associated with
a 2.5%, 2.3%, 1.2%, and 1.7% increase in DRI probability, respectively.
The regressions estimated in Columns (4)–(6) of Table 4 are similar to those
reported in Columns (1)–(3), except that we now use the proportional DRI
measure as the dependent variable. The results indicate that the signs and
significance of all the variables are largely similar to those in Columns (1)–(3).
One exception is the sign of the coefficient on Ln(Board_Size), which is now
significantly negative. This may be mechanical because the dependent variable
DRI is standardized by the board size. Overall, the results in Table 4 support
our hypotheses on the determinants of the firm’s decision to select DRIs.
In some specifications, we also include four-digit SIC industry dummies
to control for time-invariant industry heterogeneity. In these models, the
identification of industry-level variables, such as Correlation and HHI, is based
on their time-series variation. Our results are generally robust to including these
industry dummies. We also test for the robustness of the results using a FamaMacBeth-type methodology. Specifically, we estimate a cross-sectional version
of each regression specification every year and then average the time-series of
the coefficients obtained. Our previous inferences remain unchanged. We leave
these results unreported for brevity.
Board Expertise
Perfi,t+1 = β0 +β1 DRI_Measurei,t +β2 Zi(k),t
+Industry (or Firm) and Year Fixed Effects+εi,t+1 .
(2)
The regression subscripts refer to firm i (or four-digit SIC industry k) and year t.
As indicated by the time subscripts, the Perf i,t+1 variables are measured in the
year following the DRI_Measurei,t and various firm- or industry-level control
variables, Zi(k),t . The specifications include industry (or firm) and year fixed
effects to control for any time-invariant heterogeneity within the four-digit SIC
industry (or firm) and year-specific effects, respectively.20 We believe that the
specifications with firm fixed effects can help isolate the causal effect of the
presence of DRIs on firm performance.21 The reported t-statistics are based on
standard errors that are robust and clustered by firm.
We use the following three instruments: Ln(SupplyPerSeat) is the natural
logarithm of the ratio of DRI_Supply to the number of board seats available in
the firm’s four-digit SIC industry; Med_Ind_DRI is the median Dummy_DRI
across all the other firms in the three-digit SIC industry; and Ln(RI_Distance)
is the natural logarithm of the average geographic distance between the given
firm and every firm in its related industries.22 These instruments are likely
20 Our results are robust to including industry-times-year fixed effects, where industry is defined using the
Fama-French 48-industries (Fama and French 1997). We cannot use four-digit SIC industries for these
industry-times-year fixed effects because identification in some of the 2SLS regressions will not be feasible.
21 A potential concern is that DRI measures are sticky at the firm level over time and changes in DRIs on
the board may coincide with other major corporate events. If so, the correlation between DRI measures and
firm value/performance obtained from the estimation of Equation (2) with firm fixed effects may be due to
unobservable events that influence both DRIs and firm value/performance. We find that, in our sample, the DRI
measures are indeed fairly persistent: 82%, 75%, and 70% of the firms that have a DRI in year t also have a DRI
in year t +1, t +2, and t +3, respectively. However, changes in the DRIs do not typically coincide with other major
events; e.g., only 26% of CEO-turnovers and 20% of M&As coincide with changes in the firm’s DRI-status (i.e.,
firms with DRIs continue to have them, and those without DRIs continue not to have them). Therefore, this allays
the concern that our regressions with firm fixed effects may be picking up a correlation between DRIs and firm
value which is driven by some other, unobservable events. Regardless, Angrist and Pischke (2008) have argued
that, in a setting like ours—in which the independent variable of interest is fairly persistent over time—using
instrumental variable regressions is preferred to an OLS with firm fixed effects.
22 Note that the choice of instrumental variables must be different: (1) when we instrument for interactions
of DRI measures with other variables (such as in Tables 6, 7, and 9) or (2) when we use geographicproximity-based instrumental variables in Panel D of Table 5. Further, the instrumental variables used to
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4.1 Impact of DRIs on firm value/performance
The central tenet of the hypotheses we are testing is that DRI presence on firm
boards is the result of endogenous choice. For example, if there are missing
latent variables that affect the choice of DRIs on the board as well as influence
the firm’s value/performance, then the estimates will be biased, possibly
resulting in a spurious relation between the presence of DRIs and the firm’s
value/performance. To deal with this endogeneity, we estimate two-stage least
squares (2SLS) regressions in which our DRI measures are instrumented using
exogenous variables. Using either the firm’s Tobin’s Q or ROA as the dependent
variable (Perf i,t+1 ), we estimate the following second-stage regression:
The Review of Financial Studies / v 27 n 5 2014
instrument Dummy_DRI union (in subsection 6.1) are the same as the three described above, except the “related”
industries are identified as the union of industries represented by actual supplier/customer firms (as obtained
from COMPUSTAT Segments files) of all firms in the four-digit SIC industry instead of the upstream/downstream
industries from I-O tables.
23 We cannot conduct the Durbin-Wu-Hausman test of exogeneity because we use heteroscedasticity-robust standard
errors. We instead use a similar Davidson-MacKinnon test of exogeneity and find that, given the choice of our
instruments, these test statistics reject the null that DRI measures are exogenous. We leave these additional
statistics unreported for brevity.
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to affect the second-stage performance variable only through their effect on
the DRI measure. For the first instrument based on the supply of DRIs in
the industry, there is a clear economic rationale for why the conditions for a
good instrument hold. For example, the greater availability of DRIs is likely to
reduce the difficulty of locating and attracting them to join a firm’s board. At
the same time, there is no particular reason as to why the greater availability of
such directors should directly affect firm value and performance. Further, the
standardization of the supply of DRIs by the aggregate number of board seats
implicitly controls for industry size as the aggregate board size will be larger
for bigger industries.
Similarly, we expect Med_Ind_DRI or Ln(RI_Distance) to be correlated with
the given firm’s use of DRIs. However, there is no clear economic rationale
for a direct relationship between these instruments and the firm’s performance
because these instruments are unlikely to be affected by the individual firm’s
decisions. Moreover, given that we control for industry (or firm) and year fixed
effects, identification is achieved mainly through the time-series variation in
these instruments. For these reasons, we believe that these instruments are likely
to satisfy the exclusion restriction. In all the overidentified 2SLS regressions,
we use the instrumental variable Ln(SupplyPerSeat) in conjunction with one
of the other two instruments mentioned earlier.
The first set of results that we report in Table 5 is based on over-identified
2SLS models; all these regressions use DRIs that are classified at the 1% VRC
threshold. Besides the economic rationale, we require that our instruments
pass the relevance (correlation with the endogenous variable) and validity
(orthogonality to the residual) conditions. First, we ensure that the coefficient
for each instrument is statistically significant in the first-stage regression,
thereby confirming their relevance. Second, we ensure that the F -statistic
associated with all the endogenous variables is statistically significant, thereby
indicating that the chosen instruments are jointly relevant. Next, we make sure
that the Hansen’s J -statistic is unable to reject the null that the instruments
are uncorrelated with the disturbance process, thus providing support for the
validity of the subset of our instruments used in the specific regression. Finally,
we employ the difference in the Sargan C-statistic (χ 2 ) to check for the presence
of omitted variables that may potentially bias our coefficient estimates and
thereby make sure that the use of the 2SLS methodology is appropriate.23 We
also estimate exactly identified versions of the 2SLS regression models with
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Page: 1559
Industry and year fixed effects
Firm and year fixed effects
Constant
ROA(t −1)
ROA
R&D
Outside_Directors
Ln(Board_Size)
CEO_Duality
Firm_Volatility
Book_Leverage
Tangible_Assets
Ln(Firm_Size)
DRI_Measure
DRI_Measure:
yes
no
0.570∗∗
[2.17]
−0.112∗∗∗
[−5.87]
−0.339∗∗∗
[−3.77]
0.291∗∗∗
[4.65]
−2.843∗∗∗
[−4.63]
0.028
[1.55]
−0.042
[−0.93]
−0.216∗∗∗
[−2.63]
2.674∗∗∗
[8.68]
0.736∗∗∗
[5.52]
−0.278∗∗∗
[−4.13]
0.053∗∗∗
[2.92]
−0.080∗∗∗
[−7.96]
−0.402∗∗∗
[−4.59]
0.286∗∗∗
[4.65]
−2.860∗∗∗
[−4.69]
0.042∗∗
[2.56]
0.037
[1.44]
−0.094∗∗
[−2.04]
2.804∗∗∗
[9.25]
0.738∗∗∗
[5.44]
−0.280∗∗∗
[−4.06]
2.185∗∗∗
[22.15]
yes
no
(2)
2SLS
(1)
OLS
(3)
OLS
no
yes
−0.003
[−0.24]
−0.565∗∗∗
[−27.19]
−0.365∗∗∗
[−3.44]
0.504∗∗∗
[9.03]
−1.959∗∗∗
[−5.12]
−0.009
[−0.66]
0.030∗
[1.73]
−0.092∗∗∗
[−3.05]
0.663∗∗∗
[5.43]
0.922∗∗∗
[11.97]
0.096∗
[1.76]
4.726∗∗∗
[36.44]
Dummy_DRI
no
yes
0.479∗∗∗
[3.36]
−0.576∗∗∗
[−27.12]
−0.363∗∗∗
[−3.40]
0.507∗∗∗
[8.92]
−2.005∗∗∗
[−5.17]
−0.011
[−0.74]
−0.061∗
[−1.93]
−0.128∗∗∗
[−3.96]
0.662∗∗∗
[5.38]
0.918∗∗∗
[11.89]
0.099∗
[1.79]
(4)
2SLS
yes
no
0.227∗∗
[2.41]
−0.080∗∗∗
[−7.90]
−0.403∗∗∗
[−4.60]
0.287∗∗∗
[4.66]
−2.865∗∗∗
[−4.70]
0.043∗∗∗
[2.60]
0.046∗
[1.87]
−0.093∗∗
[−1.98]
2.805∗∗∗
[9.24]
0.738∗∗∗
[5.45]
−0.281∗∗∗
[−4.09]
2.171∗∗∗
[22.09]
(5)
OLS
Dependent variable: Tobin’s Qt+1
yes
no
2.296∗∗
[2.07]
−0.107∗∗∗
[−6.04]
−0.353∗∗∗
[−4.01]
0.296∗∗∗
[4.66]
−2.894∗∗∗
[−4.69]
0.037∗∗
[2.07]
0.060∗∗
[2.18]
−0.195∗∗∗
[−2.59]
2.685∗∗∗
[8.74]
0.741∗∗∗
[5.66]
−0.291∗∗∗
[−4.40]
(6)
2SLS
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Panel A: Impact on firm value
Table 5
Impact of directors from related industries (DRIs) on firm value and operating performance
DRI
no
yes
0.004
[0.05]
−0.565∗∗∗
[−27.19]
−0.365∗∗∗
[−3.44]
0.504∗∗∗
[9.03]
−1.960∗∗∗
[−5.12]
−0.009
[−0.66]
0.029∗
[1.70]
−0.092∗∗∗
[−3.07]
0.663∗∗∗
[5.43]
0.922∗∗∗
[11.97]
0.096∗
[1.76]
4.726∗∗∗
[36.41]
(7)
OLS
(continued)
no
yes
1.827∗∗∗
[3.37]
−0.571∗∗∗
[−27.08]
−0.380∗∗∗
[−3.55]
0.504∗∗∗
[8.87]
−1.994∗∗∗
[−5.13]
−0.010
[−0.70]
0.025
[1.44]
−0.132∗∗∗
[−4.01]
0.667∗∗∗
[5.47]
0.922∗∗∗
[11.94]
0.089
[1.61]
(8)
2SLS
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R2
ROA(t −1)
ROA
R&D
Outside_Directors
Ln(Board_Size)
CEO_Duality
Firm_Volatility
Book_Leverage
Tangible_Assets
Ln(Firm_Size)
Med_Ind_DRI
Ln(SupplyPerSeat)
First-stage estimates:
Hansen-J (p-value)
Sargan-C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
Observations
R2
70,239
0.071
(1)
OLS
0.064∗∗∗
[11.12]
0.053∗∗∗
[3.93]
0.063∗∗∗
[19.88]
−0.135∗∗∗
[−4.70]
−0.006
[−0.43]
0.031
[0.24]
0.027∗∗∗
[3.67]
0.155∗∗∗
[20.09]
0.223∗∗∗
[8.07]
0.243∗∗∗
[5.61]
−0.009
[−0.45]
−0.007
[−0.53]
0.132
0.880
0.034
0.067
77.39∗∗∗
69,965
(2)
2SLS
(3)
OLS
70,239
0.105
Dummy_DRI
0.042∗∗∗
[9.96]
0.072∗∗∗
[10.89]
0.021∗∗∗
[3.82]
0.000
[0.01]
−0.010
[−0.65]
0.207∗
[1.91]
0.003
[0.64]
0.188∗∗∗
[27.54]
0.071∗∗∗
[6.19]
0.002
[0.09]
−0.003
[−0.18]
−0.012
[−0.99]
0.044
0.818
< 0.001
0.003
117.3∗∗∗
68,437
(4)
2SLS
70,239
0.071
(5)
OLS
Dependent variable: Tobin’s Qt+1
0.012∗∗∗
[7.76]
0.020∗∗∗
[5.50]
0.013∗∗∗
[17.15]
−0.027∗∗∗
[−4.40]
−0.004
[−1.07]
0.027
[0.84]
0.003
[1.56]
−0.006∗∗
[−2.23]
0.047∗∗∗
[6.81]
0.056∗∗∗
[5.01]
−.004
[−0.84]
0.004
[1.00]
0.072
0.434
0.041
0.067
42.95∗∗∗
69,965
(6)
2SLS
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DRI_Measure:
Panel A: Impact on firm value—continued
Table 5
Continued
DRI
70,239
0.105
(7)
OLS
(continued)
0.008∗∗∗
[8.77]
0.023∗∗∗
[13.81]
0.003∗
[1.96]
0.009
[1.30]
−0.000
[−0.14]
0.045∗∗
[2.05]
0.001
[0.48]
0.002
[0.86]
0.021∗∗∗
[7.05]
−0.002
[−0.43]
−0.003
[−0.77]
0.003
[0.91]
0.022
0.677
< 0.001
0.003
131.2∗∗∗
68,437
(8)
2SLS
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Ln(RI_Distance)
Ln(SupplyPerSeat)
First-stage estimates:
Observations
R2
Hansen−J (p-value)
Sargan-C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
Industry and year fixed effects
Firm and year fixed effects
Constant
R&D
Outside_Directors
Ln(Board_Size)
CEO_Duality
Firm_Volatility
Book_Leverage
Tangible_Assets
Ln(Firm_Size)
DRI_Measure
73,452
0.140
yes
no
0.002
[0.59]
0.015∗∗∗
[7.74]
0.106∗∗∗
[8.53]
−0.046∗∗∗
[−5.27]
−1.579∗∗∗
[−11.46]
−0.005∗∗∗
[−2.67]
−0.016∗∗∗
[−4.75]
0.010∗
[1.75]
−0.067∗∗
[−2.48]
0.108∗∗∗
[7.69]
(1)
OLS
0.051∗∗∗
[8.00]
0.026∗∗∗
[13.62]
0.899
< 0.001
0.001
181.3∗∗∗
73,444
yes
no
0.086∗∗∗
[3.53]
0.009∗∗∗
[4.23]
0.117∗∗∗
[8.85]
−0.046∗∗∗
[−5.14]
−1.584∗∗∗
[−11.44]
−0.007∗∗∗
[−3.60]
−0.029∗∗∗
[−4.79]
−0.009
[−1.27]
−0.086∗∗∗
[−3.22]
(2)
2SLS
(3)
OLS
73,452
0.045
no
yes
0.000
[0.18]
−0.035∗∗∗
[−15.27]
0.016
[1.32]
0.012∗
[1.80]
−0.671∗∗∗
[−11.58]
0.000
[0.09]
−0.003
[−1.59]
−0.003
[−0.90]
−0.014
[−0.54]
0.329∗∗∗
[22.17]
Dummy_DRI
0.042∗∗∗
[10.59]
0.013∗∗∗
[7.31]
0.374
0.003
0.009
90.20∗∗∗
71,880
no
yes
0.055∗∗∗
[2.82]
−0.037∗∗∗
[−15.59]
0.016
[1.30]
0.013∗
[1.87]
−0.682∗∗∗
[−11.72]
−0.000
[−0.04]
−0.013∗∗∗
[−3.22]
−0.007∗
[−1.95]
−0.014
[−0.53]
(4)
2SLS
73,452
0.140
yes
no
0.011
[0.72]
0.015∗∗∗
[7.69]
0.106∗∗∗
[8.53]
−0.046∗∗∗
[−5.26]
−1.579∗∗∗
[−11.45]
−0.005∗∗∗
[−2.67]
−0.016∗∗∗
[−5.13]
0.010∗
[1.70]
−0.067∗∗
[−2.49]
0.107∗∗∗
[7.82]
(5)
OLS
Dependent variable: ROAt+1
0.011∗∗∗
[5.80]
0.005∗∗∗
[11.94]
0.945
< 0.001
0.001
97.87∗∗∗
73,444
yes
no
0.432∗∗∗
[3.55]
0.009∗∗∗
[4.02]
0.117∗∗∗
[8.73]
−0.045∗∗∗
[−4.83]
−1.589∗∗∗
[−11.24]
−0.006∗∗∗
[−3.23]
−0.013∗∗∗
[−4.83]
−0.010
[−1.37]
−0.088∗∗∗
[−3.27]
(6)
2SLS
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DRI_Measure:
Panel B: Impact on firm’s operating performance
Table 5
Continued
DRI
73,452
0.045
no
yes
−0.000
[−0.04]
−0.035∗∗∗
[−15.26]
0.016
[1.32]
0.012∗
[1.80]
−0.670∗∗∗
[−11.58]
0.000
[0.09]
−0.003
[−1.60]
−0.003
[−0.89]
−0.014
[−0.54]
0.329∗∗∗
[22.20]
(7)
OLS
(continued)
0.010∗∗∗
[10.10]
0.002∗∗∗
[4.79]
0.186
0.005
0.009
66.60∗∗∗
71,880
no
yes
0.257∗∗∗
[2.65]
−0.036∗∗∗
[−15.50]
0.014
[1.10]
0.012∗
[1.81]
−0.683∗∗∗
[−11.61]
0.000
[0.00]
−0.003∗
[−1.75]
−0.008∗∗
[−2.11]
−0.014
[−0.51]
(8)
2SLS
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Control variables
Firm and year fixed effects
Observations
Sargan-C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
DRI_Measure
DRI_Measure:
Dependent variable:
Panel C: Exactly identified 2SLS regressions
R2
R&D
Outside_Directors
Ln(Board_Size)
CEO_Duality
Firm_Volatility
Book_Leverage
Tangible_Assets
Ln(Firm_Size)
(1)
OLS
1.996∗∗
[2.43]
yes
yes
68,711
0.014
0.015
114.6∗∗∗
0.429∗∗
[2.42]
yes
yes
68,711
0.013
0.015
134.8∗∗∗
(2)
0.020∗∗∗
[3.80]
0.005
[0.15]
−0.012
[−0.85]
0.258∗∗
[2.48]
0.003
[0.68]
0.186∗∗∗
[28.14]
0.076∗∗∗
[6.84]
0.001
[0.05]
0.041
(4)
2SLS
DRI
Tobin’s Qt+1
(3)
OLS
(5)
OLS
Dependent variable: ROAt+1
Dummy_DRI
(1)
0.067∗∗∗
[19.95]
−0.117∗∗∗
[−4.44]
−0.012
[−0.88]
0.190
[1.47]
0.012∗
[1.80]
0.158∗∗∗
[19.62]
0.212∗∗∗
[9.07]
0.211∗∗∗
[6.16]
0.145
(2)
2SLS
Dummy_DRI
0.047∗∗
[2.17]
yes
yes
71,880
0.028
0.027
138.5∗∗∗
Dummy_DRI
(3)
0.014∗∗∗
[20.06]
−0.023∗∗∗
[−3.93]
−0.005
[−1.56]
0.050∗
[1.74]
−0.000
[−0.21]
−0.005
[−2.05]
0.045∗∗∗
[7.29]
0.048∗∗∗
[5.50]
0.079
(6)
2SLS
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[18:26 1/4/2014 RFS-hht071.tex]
DRI_Measure:
Panel B: Impact on firm’s operating performance—continued
Table 5
Continued
DRI
ROAt+1
(7)
OLS
(continued)
0.220∗∗
[2.18]
yes
yes
71,880
0.027
0.027
117.8∗∗∗
DRI
(4)
0.003∗∗
[2.14]
0.011
[1.53]
−0.001
[−0.29]
0.059∗∗∗
[2.76]
0.000
[0.40]
0.002
[1.02]
0.020∗∗∗
[7.14]
−0.002
[−0.33]
0.014
(8)
2SLS
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68,437
0.985
0.001
0.005
124.4∗∗∗
Observations
Hansen-J (p-value)
Sargan-C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
68,437
0.505
0.003
0.011
131.3∗∗∗
yes
yes
0.420∗∗∗
[2.84]
200-miles
(2)
68,711
0.043
0.047
160.1∗∗∗
0.011
0.012
144.2∗∗∗
yes
yes
0.355∗∗
[1.99]
200-miles
(4)
68,711
yes
yes
0.502∗∗
[2.48]
100-miles
(3)
71,604
0.131
0.087
0.072
181.9∗∗∗
yes
yes
0.025∗
[1.87]
100-miles
(5)
71,604
0.149
0.097
0.087
179.6∗∗∗
yes
yes
0.024∗
[1.77]
200-miles
(6)
0.019
0.018
254.8∗∗∗
71,880
yes
yes
0.038∗∗
[2.35]
100-miles
(7)
(8)
0.025
0.024
247.7∗∗∗
71,880
yes
yes
0.036∗∗
[2.24]
200-miles
Exactly identified
Dependent variable: ROAt+1
Over-identified
In Panel A, we present the effect of DRIs on firm value (Tobin’s Q), using the dummy and proportional DRI measures based on the 1% VRC threshold. The independent variables are
measured with a one-year lag relative to the dependent variables. Columns (1), (3), (5), and (7) use the OLS model for estimation, whereas Columns (2), (4), (6), and (8) use the over-identified
2SLS model to correct for endogeneity. Columns (1), (2), (5), and (6) control for four-digit SIC industry and year fixed effects, whereas Columns (3), (4), (7), and (8) control for firm and
year fixed effects. Coefficients of instruments used in the 2SLS models and their respective t -statistics from the first-stage are reported at the bottom of the panel. The instruments used are
Ln(SupplyPerSeat) and Med_Ind_DRI. t -statistics reported in brackets are robust and clustered by industry in Columns (1), (2), (5), and (6) or by firm in Columns (3), (4), (7), and (8).
In Panel B, we show the effect of DRIs on the firm’s operating performance (ROA), using the dummy and proportional DRI measures based on the 1% VRC threshold. The independent
variables are measured with a one-year lag relative to the dependent variables. Columns (1), (3), (5), and (7) use the OLS model for estimation, whereas Columns (2), (4), (6), and (8) use
the over-identified 2SLS model to correct for endogeneity. Columns (1), (2), (5), and (6) control for four-digit SIC industry and year fixed effects, whereas Columns (3), (4), (7), and (8)
control for firm and year fixed effects. Coefficients of instruments used in the 2SLS models and their respective t -statistics from the first-stage are reported at the bottom of the panel. The
instruments used are Ln(SupplyPerSeat) and Ln(RI_Distance). t -statistics reported in brackets are robust and clustered by industry in Columns (1), (2), (5), and (6) or by firm in Columns
(3), (4), (7), and (8). In Panel C, we present the effect of DRIs on firm value and operating performance using exactly identified 2SLS regressions. We use both dummy and proportional
DRI measures based on the 1% VRC threshold, and the dependent variables are Tobin’s Q and ROA. In all four columns, we control for firm and year fixed effects. The only instrument
used in all four columns is Ln(SupplyPerSeat). All independent variables are measured with a one-year lag relative to the dependent variables. Coefficients of the control variables are not
reported for brevity. t -statistics reported in brackets are robust and clustered by firm. In Panel D, we show the effect of DRIs on firm value and operating performance using instruments that
are based on geographic proximity. For brevity, we only use the Dummy_DRI measure based on the 1% VRC threshold and do not report the results using the proportional DRI measure.
The dependent variables are Tobin’s Q and ROA. In all eight columns, we control for firm and year fixed effects. In all eight columns, we use as an instrument the supply of potential DRIs
from related-industry firms headquartered within the geographic proximity of the given firm (where geographic proximity is defined by a 100- or 200-mile radius around the headquarters of
the given firm). In all over-identified regressions, we use Med_Ind_DRI as the additional instrument. All independent variables are measured with a one-year lag relative to the dependent
variables. Coefficients of the control variables are not reported for brevity. t -statistics reported in brackets are robust and clustered by firm. Definitions of all variables as well as instruments
are provided in Appendix B. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
yes
yes
0.507∗∗∗
[3.18]
100-miles
Control variables
Firm and year fixed effects
Dummy_DRI
Proximity:
(1)
Exactly identified
Dependent variable: Tobin’s Qt+1
Over-identified
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[18:26 1/4/2014 RFS-hht071.tex]
Specification:
Panel D: Instruments based on geographic proximity (defined by 100- or 200-mile radius)
Table 5
Continued
Board Expertise
The Review of Financial Studies / v 27 n 5 2014
industry or firm fixed effects in which Ln(SupplyPerSeat) is used as the sole
instrument. As noted above, we believe that the economic basis for the use
of this supply variable as an instrument for our DRI measures is compelling.
Our results are robust to using the Heckman selection model in which the first
stage is the firm’s decision to have DRIs; these results are left unreported for
brevity. Finally, we also conduct an event study on the announcement effects
in a sample of DRI appointments uncontaminated by other corporate events.
This analysis further helps to identify whether any effect of a DRI on firm value
found in the above tests is attributable to the DRI.
Tobin sQi,t+1 = β0 +β1 DRI_Measurei,t +β2 Ln(Firm_Sizei,t )
+β3 Tangible_Assetsi,t +β4 Book_Leveragei,t +β5 Firm_Volatilityi,t
+β6 CEO_Duality+β7 Ln(Board_Sizei,t )+β8 Outside_Directorsi,t
+β9 R&Di,t +β10 ROAi,t +β11 ROAi,t−1
+Industry (or Firm) and Year Fixed Effects+εi,t.+1 .
(3)
The control variables in these regressions are similar to those employed in
the existing literature (e.g., Yermack 1996; Coles, Daniel, and Naveen 2008;
Masulis and Mobbs 2011). All the variables are as defined earlier. As mentioned
above, we control for industry (or firm) and year fixed effects in all estimated
regressions.
Panel A of Table 5 presents evidence on the relation between the firm’s
Tobin’s Q and DRI measures. In Columns (1)–(4), we use Dummy_DRI,
whereas we use DRI in Columns (5)–(8). Using both Dummy_DRI and DRI,
the odd-numbered columns report estimates using the OLS model, whereas the
even-numbered columns report estimates using the 2SLS regression model: first
using industry and year fixed effects and then using firm and year fixed effects.24
After controlling for industry and year fixed effects, the OLS (Column (1)) and
2SLS (Column (2)) estimates of the coefficient on Dummy_DRI are significant
at the 1% and 5% level, respectively. With firm and year fixed effects, the
OLS estimate of the coefficient for Dummy_DRI is statistically insignificant in
Column (3), whereas in Column (4) the 2SLS estimate is 0.479 (significant at
the 1% level). In terms of economic magnitude, the 2SLS estimates with firm
24 We employ an OLS regression approach in the first stage of the 2SLS regression even when the endogenous
variable in question is a dummy variable (Dummy_DRI). See Angrist and Krueger (2001) for a discussion on
the efficacy of this approach. When using Dummy_DRI, we also estimate Heckman treatment effect models and
find qualitatively similar results. The first-stage Probit regressions in these models are drawn from the selection
models in Table 4.
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4.2 Effect of DRIs on Tobin’s Q
The regression specification we use in examining the relation between Tobin’s Q
and DRI measures is:
Board Expertise
4.3 Effect of DRIs on ROA
In Panel B of Table 5, we present results using the firm’s ROA as the dependent
variable. The regression specification that we use is as follows:
ROAi,t+1 = β0 +β1 DRI_Measurei,t +β2 Ln(Firm_Sizei,t )+β3 Tangible_Assetsi,t
+β4 Book_Leveragei,t +β5 Firm_Volatility,t +β6 CEO_Dualityi,t
+β7 Ln(Board_Sizei,t )+β8 Outside_Directorsi,t +β9 R&Di,t
+Industry (or Firm) and Year Fixed Effects+εi,t+1 .
(4)
The control variables used are similar in spirit to those employed in the prior
literature (e.g., Masulis and Mobbs 2011) and are as defined earlier.26 All our
estimated regression models control for industry (or firm) and year fixed effects.
The setup of Panel B is identical to that of Panel A, except the dependent
variable is the firm’s ROA. The OLS estimates of the coefficient on Dummy_DRI
as well as DRI with industry (or firm) and year fixed effects are statistically
insignificant. The 2SLS estimates using industry (or firm) and year fixed effects
are statistically significant at the 1% level. For example, the 2SLS estimate of
25 We obtain similar results with logarithm of Tobin’s Q as the dependent variable in Equation (3).
26 Coefficient estimates of DRI_Measures in Equations (3) and (4) are similar in magnitude and statistical
significance when we start with only the DRI_Measure (with firm and year fixed effects) and then successively
add control variables.
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fixed effects suggest that the presence of DRIs increases the firm’s Tobin’s Q in
the following year by 0.28 standard deviations. We obtain similar results when
we examine the relation between Tobin’s Q and the proportional DRI measure.
Specifically, after controlling for industry and year fixed effects, the coefficients
on DRI in the OLS (in Column (5)) and 2SLS (in Column (6)) specifications
are significantly positive at the 5% level. With firm and year fixed effects, the
OLS estimate for DRI coefficient in Column (7) is statistically insignificant,
whereas the 2SLS estimate in Column (8) is 1.827. It is significant at the 1%
level, and in terms of economic significance, a standard deviation increase in
DRI leads to a 0.12 standard deviation increase in the firm’s Tobin’s Q.25
We find that the Hansen’s J -statistics in all the 2SLS models of Panel A
are insignificant and are therefore unable to reject the null of exogeneity.
In addition, the F-statistic and the Sargan C-statistic (χ 2 ) are significant,
indicating that the instruments are jointly relevant and that the use of the
2SLS methodology is appropriate, respectively. The first-stage estimates for
our instrumental variables in the 2SLS specifications (reported at the bottom
of Panel A) are significant at the 1% level, thereby indicating that these
instruments are individually relevant. Thus, our instruments are sound based
on both economic and statistical grounds.
The Review of Financial Studies / v 27 n 5 2014
4.4 Exactly identified 2SLS regressions
Roberts and Whited (2013) have argued that (1) good instruments are rare and
hard to find, (2) statistical tests of overidentifying restrictions are unlikely to be
very useful, and (3) the best test for the validity of an instrument is a rigorous
economic argument. Therefore, we examine exactly identified models in which
the instrument used fits all the criteria for a good instrument. The instrument that
we rely on in these tests is Ln(SupplyPerSeat). We present results from these
exactly identified 2SLS regressions with firm and year fixed effects in Panel C
of Table 5. Coefficients on all variables other than the DRI measures are left
unreported for clarity. In Columns (1) and (2), the dependent variable is Tobin’s
Q, whereas that in Columns (3) and (4) is ROA. Not only are the results robust
to this alternative methodology, the coefficients on both DRI measures are also
statistically significant and economically meaningful. In Column (1), we find
that the presence of DRIs leads to an increase of 0.25 standard deviations in
Tobin’s Q. The coefficient in Column (3) suggests that the presence of DRIs
leads to a 0.24 standard deviations increase in ROA. Results using proportional
DRI in Columns (2) and (4) are equally strong. These results help us assuage
concerns about endogeneity because we have chosen an instrument only on
economic grounds and controlled for firm fixed effects.
4.5 Instruments defined using the supply of DRIs within the firm’s
geographical proximity
In Panel D of Table 5, we test for the robustness of the results presented in
Panels A and B by relying on another supply-based instrument: DRI_Supply100
(DRI_Supply200 ) is the supply of DRIs within a 100-mile (200-mile) radius
around the firm’s headquarters. To identify upstream/downstream firms located
within this radius, we calculate the distance between the headquarters of the
given firm and its related firms; this distance is calculated as by Coval and
Moskowitz (2001). These proximity-based instruments also alleviate concerns
about the supply of DRIs being related to industry size and thus will likely
satisfy the exclusion restriction. We rely on these supply-based instruments
because, as argued earlier, there is sound economic rationale for their validity.
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the coefficient for Dummy_DRI with industry and year fixed effects is 0.086 in
Column (2), and it is 0.055 in Column (4) using firm and year fixed effects. These
amount to an increase of 0.44 and 0.28 standard deviations in ROA, respectively,
due to the presence of a DRI in the preceding year. We obtain similar results
when we examine the relation between ROA and the proportional DRI measure.
For example, the coefficient on DRI estimated using 2SLS with firm and year
fixed effects (in Column (8)) is significant at the 1% level and suggests that a
standard deviation increase in DRI leads to a 0.14 standard deviations increase
in the firm’s ROA. The first-stage estimates for our instrumental variables at
the bottom of Panel B and associated 2SLS test statistics suggest that our
instruments are justifiable on statistical grounds.
Board Expertise
4.6 Market reaction to announcements of DRI appointments
We next conduct an event study based on the appointment of DRIs at firms
that do not currently have DRIs. A positive stock market reaction to the
announcement of the appointment of DRIs will reinforce our argument that
the value effects reported earlier are due to the presence of DRIs and are not
an industry- or firm-driven effect.
We start with an initial sample of 7,370 DRI appointments at firms that did not
have DRIs at the time. The announcement dates of director appointments are not
readily available. To automate the process of obtaining these dates, we match
our sample of DRIs with the declaration of director appointments in SEC Form
3 filings (available 1996 and onward through EDGAR).27 The electronic filing
of Form 3 was, however, made mandatory only in June 2003. Our matching
algorithm matches the firm’s CIK numbers, directors’ names, and the year of
appointment and yields a sample of 316 that have an electronically-filed Form
3. We then reconfirm the precise announcement date of director appointments
using news stories from Factiva. This results in a sample of announcement dates
for 285 DRIs with enough returns data available in CRSP to conduct an event
study. We remove observations that have any other simultaneous confounding
events (e.g., multiple director announcements, acquisitions, CEO turnover,
earnings announcements, restructurings, or divestitures) and thus obtain an
uncontaminated sample of 168 observations.
We then conduct event studies around these announcement dates using
a number of different windows and calculate cumulative abnormal returns
(CARs) around the event date. We find evidence of a positive market reaction
following the announcement of DRIs; the abnormal returns (using the FamaFrench three-factor model) are 2.56% and 2.04% over the event days (−1, +1)
27 A description of the SEC Form 3 can be found at www.sec.gov/answers/form345.htm. It states that, “Corporate
insiders – meaning a company’s officers and directors, and any beneficial owners of more than ten percent of a
class of the company’s equity securities registered under Section 12 of the Securities Exchange Act of 1934 –
must file with the SEC a statement of ownership regarding those securities.” However, “[i]f the issuer is already
registered under Section 12, the insider must file a Form 3 within ten days of becoming an officer, director, or
beneficial owner.”
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We estimate 2SLS regressions with firm and year fixed effects; the
dependent variable is Tobin’s Q in Columns (1)–(4) and ROA in Columns
(5)–(8). In Columns (1)–(2) and (5)–(6), we use an additional instrument—
Med_Ind_DRI—to estimate over-identified models. In the remaining columns,
we estimate exactly identified models, where the sole instrument in the odd
(even) numbered columns is DRI_Supply100 (DRI_Supply200 ). We find that
the relation between the presence of DRIs and the firm’s value/performance in
the subsequent year is significantly positive at least at the 5% (10%) level in the
Tobin’s Q (ROA) regressions. These results are also economically meaningful:
as per Column (1) (Column (5)), the presence of DRIs is associated with a 0.29
(0.13) standard deviation increase in Tobin’s Q (ROA).
The Review of Financial Studies / v 27 n 5 2014
4.7 The effect of DRIs on firm value in different economic environments
In developing our hypotheses in Section 1, we argue that the value impact of
DRIs will be larger in certain information, market structure, and governance
environments. In this subsection, we explicitly test these predictions. The results
from this analysis are presented in Table 6. All the specifications in the table
employ 2SLS regression models with firm and year fixed effects. The control
variables are the same as in the equivalent regressions in Table 5, and their
coefficients are not reported for brevity. The dependent variable is Tobin’s Q,
and we conduct these tests by interacting Dummy_DRI with a pair of dummy
variables that denote the settings in which the impact of DRIs is expected to be
higher or lower. Specifically, in Column (1), we interact Dummy_DRI with two
dummy variables, the first denoting firms that invest in R&D (Positive_R&D)
and the second denoting those that do not (Zero_R&D). We similarly interact
Dummy_DRI with dummy variables denoting whether the firm has patents
(Column (2)), whether it has an above-median price-informativeness (Column
(3)), or whether its market share is above the four-digit SIC industry median
(Column (4)). In Column (5), Dummy_DRI is interacted with two dummy
variables denoting whether the fraction of outside directors on the firm’s board
is above or below its four-digit SIC industry median value (Outside_Control
and Inside_Control, respectively). Finally, in Column (6), Dummy_DRI is
interacted with dummy variables, indicating whether or not the CEO is also the
Chairman of the board.
The results in Table 6 show that, indeed, the impact of the presence of DRIs
on firm value is significant in precisely the settings in which we predict it to be
higher. Specifically, we find that the coefficient on Dummy_DRI is positive and
significant at the 1% level for firms that invest in R&D, produce patents, have
lower price informativeness, have higher market share, have a larger fraction of
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and (0, +1), respectively. These results are significant at the 1% level. For
comparison, we examine the announcement effects due to the addition of a
non-DRI director to the boards of comparable firms that share the same fourdigit SIC industry codes and are within 20% of the size of the DRI-adding firms.
This yields a sample of 193 non-DRI announcements. Using the Fama-French
three-factor model, we find statistically insignificant abnormal returns of 0.21%
and 0.33% over the event windows (−1, +1) and (0, +1), respectively, around
the non-DRI announcements. In addition, we also examine the announcement
effects due to the addition of a non-DRI director to the board of a firm that has
a DRI at some point over the sample period but does not add a DRI in the same
year as the non-DRI director. We find that such announcements are associated
with a −0.60% and −0.72% abnormal return over the event windows (−1, +1)
and (0, +1), respectively; these are statistically insignificant. Hence, while the
positive and significant market reaction to the announcement of DRIs suggests
that DRIs add to firm value, there is, on average, no similar positive market
reaction to the announcement of other types of directors.
Board Expertise
Table 6
Effect of directors from related industries (DRIs) on value in different information, market structure,
and governance environments
Dependent variable: Tobin’s Qt+1
(1)
Positive_R&D x Dummy_DRI (β1 )
Zero_R&D x Dummy_DRI (β2 )
Positive_R&D
1.044∗∗∗
[3.12]
0.263
[1.45]
−0.263∗
[−1.74]
Positive_Patent x Dummy_DRI (β1 )
Positive_Patent
1.282∗∗∗
[4.47]
0.209
[0.99]
−0.502∗∗∗
[−3.62]
Low_Info x Dummy_DRI (β1 )
High_Info x Dummy_DRI (β2 )
Low_Info
(3)
0.570∗∗∗
[3.19]
0.081
[0.44]
−0.213∗∗
[−2.15]
High_Mkt_Share x Dummy_DRI (β1 )
Low_Mkt_Share x Dummy_DRI (β2 )
High_Mkt_Share
(4)
1.159∗∗∗
[4.48]
−0.702∗
[−1.66]
−0.808∗∗∗
[−3.25]
Outside_Control x Dummy_DRI (β1 )
Inside_Control x Dummy_DRI (β2 )
Outside_Control
(5)
0.548∗∗∗
[3.07]
0.385∗∗
[2.05]
−0.093
[−0.93]
(6)
Control variables
Firm and year fixed effects
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
0.504∗∗∗
[3.10]
0.305
[1.21]
−0.095
[−1.13]
yes
yes
Observations
Hansen-J (p-value)
Sargan-C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
68,437
0.517
0.001
0.003
30.12∗∗∗
68,437
0.706
< 0.001
< 0.001
46.00∗∗∗
62,396
0.596
0.005
0.012
36.26∗∗∗
68,437
0.229
< 0.001
< 0.001
13.59∗∗∗
68,437
0.791
0.002
0.006
34.40∗∗∗
68,437
0.562
0.002
0.007
44.41∗∗∗
1.073∗∗∗
[12.57]
0.489∗∗
[4.57]
1.861∗∗∗
[9.64]
0.163
[0.49]
0.199
[1.04]
CEO_Duality x Dummy_DRI (β1 )
No_CEO_Duality x Dummy_DRI (β2 )
CEO_Duality
Differences between coefficients (Wald test)
β1 –(β2 )
0.781∗∗
[4.32]
This table shows the effect of DRIs on firm value (Tobin’s Q) in different information, market structure, and governance environments
by interacting Dummy_DRI with the following dummy variables: In Column (1), Positive_R&D is a dummy variable that equals
one if the firm invests in R&D, whereas Zero_R&D is a complementary dummy variable that equals one if the firm has no R&D
investments. In Column (2), Positive_Patent is a dummy variable that equals one if the firm has patents, whereas Zero_Patent
is a complementary dummy variable that equals one if the firm has no patents. In Column (3), Low_Info is a dummy variable
that equals one if the firm’s price informativeness is below the median in its four-digit SIC industry, whereas High_Info is a
complementary dummy variable that equals one if the firm’s price informativeness is equal to or above the industry-median. In
Column (4), High_Mkt_Share is a dummy variable that equals one if the firm’s market share is above the median in its fourdigit SIC industry, whereas Low_Mkt_Share is a complementary dummy variable that equals one if the firm’s market share is
equal to or below that industry-median. In Column (5), Outside_Control is a dummy variable that equals one if the proportion of
outside directors on the board is greater than the median value in the firm’s four-digit SIC industry, whereas Inside_Control is a
complementary dummy variable that equals one if the proportion of outside directors is equal to or below that industry median.
Finally, in Column (6), CEO_Duality is a dummy variable that equals one if the CEO is also the board’s Chairman, whereas
No_CEO_Duality is a complementary dummy variable that equals one if the CEO is not the board’s Chairman. All specifications
use the Dummy_DRI measure, which is defined using industries related to the primary segment of the firm at the 1% VRC threshold.
We use the over-identified 2SLS model for estimation throughout the table. We instrument the interaction terms in each column with
interactions between our main instrumental variable Ln(SupplyPerSeat) (used in earlier tables) and the dummy variables defining
the corresponding information, market-structure, and governance environments, as well as the instrumental variable Med_Ind_DRI
or its interactions with the same dummy variables as above. For example, in Column (1), the instruments are the products of
Positive_R&D and Zero_R&D with Ln(SupplyPerSeat) as well as the interaction between Positive_R&D and Med_Ind_DRI, etc.
For every pair of interactions, we denote the first coefficient by β1 and the second coefficient by β2 ; at the bottom of the table,
we report tests of equality between these two estimates from every specification. All independent variables are measured with a
one-year lag relative to the dependent variables. Coefficients of the control variables are not reported for brevity. Definitions of
all the variables and instruments are provided in Appendix B. Firm and year fixed effects are also included. t-statistics reported in
brackets are robust and clustered by firm. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
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Zero_Patent x Dummy_DRI (β2 )
(2)
The Review of Financial Studies / v 27 n 5 2014
5. Channels Through Which DRIs Can Add to Firm Value
5.1 The differential value response of firms with DRIs to negative and
positive industry sales shocks
We argued earlier in the paper that DRIs can enhance the firm’s ability to
overcome information challenges, such as anticipating industry conditions and
trends as well as protecting against demand or supply shocks. In this section,
we test this claim by examining whether firms with DRIs can anticipate and
maneuver around negative industry sales shocks and take better advantage of
positive sales shocks than do firms without DRIs. Because the industry sales
shock is an exogenous event, this setting gives us yet another opportunity to test
whether the relation between firm value and DRIs we documented in Table 5
is attributable to the presence of DRIs. Because the sales shock is exogenous,
we can use OLS estimates instead of relying on the relevance and validity
of specific instruments. However, as the sales shocks are interacted with an
endogenous variable (Dummy_DRI), endogeneity concerns may not be fully
eliminated. Hence, we estimate OLS regressions along with corresponding
2SLS specifications.
In Table 7, we examine whether the impact of a negative (positive) industry
sales shock on value is smaller (larger) for firms that have DRIs. The negative
(positive) sales shock is captured by a dummy variable, Negative_Shock t,t+1
(Positive_Shock t,t+1 ) that indicates whether the firm’s four-digit SIC industry
experienced a decline (rise) in sales of at least 10% between year t and t+1.
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outside directors on the board, or in which the CEO is also the Chairman; it is
not significantly positive in the complementary case (except for the interaction
with Inside_Control in Column (5)). These results suggest that firms operating
in innovative environments face larger information gaps vis-à-vis their supply
chain, which are bridged by DRIs. Firms with greater market share and more
powerful CEOs seem to benefit more from the DRIs’ expertise because they
may be better able to overcome the DRIs’ conflicts of interest. Finally, we find
larger (although statistically insignificant) benefits of DRIs in firms in which
boards have greater outside control: this is in line with our argument that the
information brought by DRIs improves the outside directors’ ability to advise
and monitor management.
Overall, these findings suggest why all firms may not benefit from having
DRIs on their boards; the value impact is concentrated in certain settings,
whereas in other cases the expertise of DRIs is not value enhancing. We also
believe that the findings in Table 6 provide support for attributing the value
effects documented in Table 5 to the presence of DRIs. Specifically, whereas
it is conceivable that an unobserved variable can induce spurious correlation
between the instruments and firm performance, it appears unlikely that the
spurious correlation is selectively present for firms that are predicted to benefit
more from DRIs.
Board Expertise
Table 7
Differential value response of firms with directors from related industries (DRIs) to negative and positive
industry sales shocks
Dependent variable: (FirmQ)t,t+1
Negative_Shock t,t+1 x Dummy_DRI t
Negative_Shock tt+1
Positive_Shock t,t+1 x Dummy_DRI t
Dummy_DRI t
(2)
2SLS
0.076∗∗∗
[2.58]
−0.053∗∗∗
[−2.78]
0.035∗
[1.94]
−0.000
[−0.03]
−0.020
[−1.36]
0.734∗∗
[1.97]
−0.330∗∗
[−2.08]
0.495∗∗∗
[5.17]
−0.189∗∗∗
[−4.85]
−0.088
[−0.79]
−0.129∗∗∗
[−10.92]
0.415∗∗∗
[5.74]
0.257∗∗∗
[5.95]
1.082∗∗∗
[2.74]
−0.004
[−0.42]
0.002
[0.16]
0.062∗∗
[2.53]
−0.128
[−0.98]
−0.936∗∗∗
[−13.71]
−0.099∗
[−1.78]
0.555∗∗∗
[7.12]
−0.132∗∗∗
[−10.95]
0.414∗∗∗
[5.67]
0.255∗∗∗
[5.83]
1.097∗∗∗
[2.74]
−0.008
[−0.70]
−0.045∗
[−1.67]
0.029
[1.09]
−0.134
[−1.04]
−0.931∗∗∗
[−13.59]
−0.097∗
[−1.74]
(IndQ)t,t+1
Ln(Firm_Size)
Tangible_Assets
Book_Leverage
Firm_Volatility
CEO_Duality
Ln(Board_Size)
Outside_Directors
R&D
ROA
ROA(t -1)
Constant
Year fixed effects
Firm fixed effects
Observations
R2
Hansen-J (p-value)
Sargan-C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
(3)
OLS
0.070∗∗
[2.46]
−0.066∗∗∗
[−3.54]
0.018
[1.02]
−0.019∗
[−1.74]
−0.004
[−0.25]
0.739∗∗∗
[35.51]
−0.096∗∗∗
[−8.40]
0.333∗∗∗
[4.77]
0.208∗∗∗
[5.01]
1.295∗∗∗
[3.36]
−0.001
[−0.07]
−0.003
[−0.21]
0.052∗∗
[2.22]
−0.214
[−1.63]
−0.881∗∗∗
[−13.32]
−0.075
[−1.39]
0.403∗∗∗
[5.33]
(4)
2SLS
1.277∗∗∗
[3.20]
−0.581∗∗∗
[−3.42]
0.191∗∗
[2.06]
−0.087∗∗
[−2.33]
0.207∗
[1.70]
0.736∗∗∗
[35.09]
−0.104∗∗∗
[−8.63]
0.329∗∗∗
[4.58]
0.209∗∗∗
[4.92]
1.252∗∗∗
[3.20]
−0.006
[−0.59]
−0.086∗∗∗
[−2.91]
0.015
[0.55]
−0.219∗
[−1.69]
−0.876∗∗∗
[−13.12]
−0.081
[−1.48]
yes
yes
yes
yes
yes
yes
yes
yes
70,239
0.047
68,437
70,239
0.101
68,437
0.266
< 0.001
< 0.001
26.76∗∗∗
0.269
< 0.001
< 0.001
23.15∗∗∗
This table presents evidence on the ability of firms with DRIs to manage negative and positive industry sales
shocks. The dependent variable (FirmQ)t,t+1 is the change in the firm’s Tobin’s Q between years t and t +1. We
conduct the test by interacting Dummy_DRI with the following two dummy variables: Negative_Shock t,t+1 and
Positive_Shock t,t+1 .These are dummy variables that equal one if the firm’s four-digit SIC industry experienced a
decline or rise, respectively, in aggregate industry sales of at least 10% between years t and t +1. All specifications
use the Dummy_DRI measure, which is defined using industries related to the primary segment of the firm at the
1% VRC threshold. In Columns (3) and (4), we also control for oIndQ)t,t+1 , which is the change in the median
Tobin’s Q in the firm’s four-digit SIC industry between years t and t +1. Specifications in Columns (1) and (3)
use OLS for estimation, whereas those in Columns (2) and (4) use the over-identified 2SLS model to control
for endogeneity. We instrument the endogenous variables (Dummy_DRI t and its interactions) in Columns (2)
and (4) with instruments used in earlier tables (i.e., Ln(SupplyPerSeat), Med_Ind_DRI, and Ln(RI_Distance))
and their interactions with dummy variables representing sales shocks. The control variables are measured with
a one-year lag relative to the dependent variables. Definitions of the variables and instruments are provided in
Appendix B. All specifications include year and firm fixed effects. t -statistics reported in brackets are robust and
clustered by firm in all columns. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
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Positive_Shock tt+1
(1)
OLS
The Review of Financial Studies / v 27 n 5 2014
5.2 Effect of DRIs on short-term liquidity management
As argued earlier, the information about the upstream and downstream
industries that the DRIs bring to the board can also help the firm better manage
its factors of production. For instance, by being more attuned to the demand
and supply conditions in their related industries, firms with DRIs can better
manage their inventories. In addition, firms with DRIs may also receive better
terms of trade credit for the following reasons. The ability of firms with DRIs
to maneuver around industry shocks may make these firms less risky and more
reliable for the trading partners in the upstream and downstream industries.
Actual suppliers and customers of the given firm may feel threatened by the
presence of a DRI who is associated with a firm that belongs to a related
industry but does not currently have a trading partnership with the given firm.
Supplier and customer firms may perceive the firms with DRIs to be better
monitored and to have fewer agency problems. Finally, the industry expertise
of the DRIs would suggest that they are also aware of the trade credit terms
offered to other peer firms. A firm with DRIs can utilize that knowledge
and bargain for better trade credit terms. Thus, we predict that firms with
DRIs will have a shorter inventory conversion period (ICP), shorter collection
(receivables) period (CP), and a longer payables period (PP). We synthesize
28 It can be argued that firms with many supply-chain industries somehow perform better—possibly because such
firms can better deal with industry sales shocks—and DRIs are only a proxy for this number of supply-chain
industries. To address this alternative, we directly control for the number of supply-chain industries to which a
firm’s industry is related. We find that the value/performance results reported in Table 5 are robust to the inclusion
of this variable. Moreover, when interacted with Negative_Shock and Positive_Shock in Table 7, we find that
firms with a greater number of related industries are no better at dealing with industry shocks. Importantly, the
effects due to DRIs in Table 7 are largely unaffected.
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The dependent variable in all four columns is the change in the firm’s Tobin’s
Q between years t and t+1 ((FirmQ)t,t+1 ). We present the OLS estimates
in Columns (1) and (3) and 2SLS estimates in Columns (2) and (4). In all
these columns, we interact both Negative_Shock t,t+1 and Positive_Shock t,t+1
with Dummy_DRI t . In Columns (3) and (4), we also include the change in
the industry’s median Tobin’s Q between years t and t+1 ((IndQ)t,t+1 ) as
a control variable. We instrument for the interaction terms by interacting our
instrumental variables for DRIs with the dummy variables for industry shocks.
In all four specifications, we control for firm fixed effects.
Our results indicate that the presence of DRIs on the board moderates the
adverse impact of negative sales shocks; the coefficient on the interaction
of Negative_Shock t,t+1 with Dummy_DRI t is significantly positive at least at
the 5% level in all four specifications. The coefficients on the interaction of
Positive_Shock t,t+1 with Dummy_DRI t are positive and significant at least at
the 10% level in three of the four columns. Taken together, these results indicate
that firms with DRIs perform relatively better in the presence of negative and
positive industry sales shocks, supporting the notion that firms with DRIs can
better anticipate and manage shocks.28
Board Expertise
these predictions into one and argue that the presence of DRIs will be associated
with an overall shorter cash conversion cycle (CCC). We test these predictions
using the following regression model:
ST Liquidityt+1 = α +β1 Dummy_DRIt +β2 Firm_Controlst
+ Firm Fixed Effects+Year Fixed Effects+εt+1 .
(5)
Table 8
Impact of directors from related industries (DRIs) on cash conversion cycle and its constituents
Dependent variable:
Dummy_DRI
Ln(Firm_Size)
Tangible_Assets
Cash_Holdings
R&D
Market_Share
Book_Leverage
ROA
KZ_Index
Year fixed effects
Firm fixed effects
Observations
Hansen-J (p-value)
Sargan-C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
CCC t+1
ICPt+1
CPt+1
(1)
(2)
(3)
PPt+1
(4)
−21.536∗∗
[−2.05]
9.118∗∗∗
[6.37]
−41.227∗∗∗
[−4.13]
−21.897∗∗∗
[−2.99]
−14.436∗∗
[−2.04]
−16.399∗∗∗
[−3.64]
−0.094
[−0.22]
9.245∗∗
[2.19]
0.521
[0.62]
yes
yes
61,437
0.461
0.042
0.082
111.1∗∗∗
−18.184∗
[−1.90]
3.897∗∗∗
[3.62]
−26.280∗∗∗
[−3.71]
1.454
[0.28]
−1.426
[−0.31]
−8.267∗∗
[−2.43]
−0.379
[−1.07]
2.435
[0.78]
0.202
[0.33]
yes
yes
69,761
0.364
0.095
0.128
85.95∗∗∗
−11.636∗
[−1.73]
0.880
[1.05]
−33.308∗∗∗
[−5.74]
−3.413
[−0.83]
0.719
[0.21]
−0.844
[−0.28]
0.327
[1.13]
−9.698∗∗∗
[−4.73]
−0.080
[−0.20]
yes
yes
62,717
0.204
0.095
0.082
79.70∗∗∗
20.984
[0.62]
−14.109∗∗
[−2.57]
−47.973
[−1.07]
49.891∗
[1.82]
34.410
[1.53]
8.477
[0.79]
0.355
[0.45]
−37.511∗∗
[−2.26]
−2.386
[−0.71]
yes
yes
62,072
0.698
0.451
0.727
109.1∗∗∗
This table presents evidence on the ability of firms with DRIs to manage their cash cycle. The dependent variable
is the cash conversion cycle (CCC t+1 ) in Column (1), the inventory conversion period (ICPt+1 ) in Column (2),
the collection period (CPt+1 ) in Column (3), and the payables period (PPt+1 ) in Column (4). We use the overidentified 2SLS model for estimation throughout the table. The instruments are the same as those in earlier tables:
Ln(SupplyPerSeat) and either Med_Ind_DRI or Ln(RI_Distance). Definitions of the variables and instruments
are provided in Appendix B. All independent variables are measured with a one-year lag relative to the dependent
variables. All specifications include year and firm fixed effects. t -statistics reported in brackets are robust and
clustered by firm. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
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Here, the dependent variable ST Liquidityt+1 measures aspects of the firm’s
short-term liquidity position in year t+1. These are CCC, ICP, CP, and PP (as
mentioned above and defined in Appendix B) and are measured in numbers
of days. The control variables are in line with the relevant literature (e.g.,
Dass, Kale, and Nanda 2013; Garcia-Appendini and Montoriol-Garriga 2013;
Shenoy and Williams 2013); these are measured in year t. All the regressions
are estimated using 2SLS with year and firm fixed effects. As in the previous
tables, we use the instrumental variable Ln(SupplyPerSeat) in conjunction with
either Ln(RI_Distance) or Med_Ind_DRI.
We present the results in Table 8. Column (1) shows that the coefficient
on Dummy_DRI is −21.536 (significant at the 5% level), indicating that, on
The Review of Financial Studies / v 27 n 5 2014
average, the cash conversion cycle is shorter by more than 21 days in the
presence of DRIs on the board. As Columns (2)–(4) show, this effect is due to a
significantly shorter inventory conversion period and collection period (shorter
by 18 and 11 days, respectively).Although the coefficient on the payables period
is positive as predicted, it is not statistically significant. Overall, our evidence
shows that the presence of DRIs on the board improves the firm’s short-term
liquidity.
Cash_Holdingst,t+1 = α +β1 (Cash_Flowt+1 ×Dummy_DRIt )
+β2 Cash_Flowt+1 +β3 Dummy_DRIt +β4 Firm_Controlst
+Firm Fixed Effects+Year Fixed Effects+εt+1 .
(6)
The structure of the equation is similar to that estimated in Almeida, Campello,
and Weisbach (2004), with the inclusion of the Dummy_DRI and the interaction
between Cash_Flow and the Dummy_DRI as additional explanatory variables.
Our prediction is that DRIs alleviate financial constraints and, as such, we
expect the coefficient β 1 on this interaction term to be significantly negative.
The estimated OLS and 2SLS coefficients are presented in Table 9. We
instrument for the interaction term by interacting our instrumental variables
for DRIs with Cash_Flow. The estimated β 1 is negative and significant at the
1% level, thus supporting our prediction that the presence of DRIs reduces the
financial constraints faced by firms.
6. Further Extensions
6.1 Robustness of results to alternative methods of identifying DRIs
6.1.1 Which firms choose DRIs: Using alternative definitions of DRI
measures. In this subsection, we check for robustness of the results on
the determinants of DRIs presented in Table 4 by using the alternative DRI
measures defined in Section 2.2 earlier. The dependent variables in Columns
(1)–(3) of Table 10 are Dummy_DRI seg , Dummy_DRI out , and Dummy_DRI V H ,
respectively. Recall that all these DRI measures are defined using related
industries that are identified at the 1% VRC threshold. In Columns (4) and (5),
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5.3 Effect of DRIs on the firm’s financial constraints
Almeida, Campello, and Weisbach (2004) develop a theoretical model in which
more financially constrained firms exhibit a greater tendency to save cash from
cash flows, leading to a higher cash-to-cash flow sensitivity. We argue that DRIs
ease financing constraints faced by the firm because their presence can reduce
the need for precautionary cash holdings due to better anticipation of industry
conditions (Table 7). Further, the improvements in short-term liquidity in the
presence of DRIs (Table 8) will also reduce financing constraints. For these
reasons, we predict that DRIs will reduce the cash flow sensitivity of cash, and
we test it using the following regression:
Board Expertise
Table 9
Impact of directors from related industries (DRIs) on the sensitivity of cash holdings to cash flows
Dependent variable: (Cash_Holdings)t,t+1
(1)
OLS
Cash_Flow x Dummy_DRI
Cash_Flow
Dummy_DRI
Tobin’s Q
Constant
Year fixed effects
Firm fixed effects
Observations
R2
Hansen−J (p-value)
Sargan-C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
73,866
0.011
−0.249∗∗∗
[−2.96]
0.138∗∗∗
[3.92]
0.111∗∗∗
[4.81]
0.005∗∗∗
[6.06]
−0.010∗∗∗
[−6.01]
yes
yes
72,297
0.166
< 0.001
< 0.001
31.31∗∗∗
This table presents evidence on the impact of directors from related industries on the sensitivity of cash holdings
to cash flows. The dependent variable in both columns is the change in the ratio of cash holdings to total assets
between years t and t+1(Cash_Holdingst,t+1 ). Cash_Flowt+1 is measured contemporaneously with the change
in cash holdings. All other independent variables are measured with a one-year lag relative to the dependent
variable. The specification in Column (1) uses OLS for estimation, whereas that in Column (2) uses the overidentified 2SLS model to control for endogeneity. We instrument the endogenous terms (Dummy_DRI and its
interaction) with Ln(RI_Distance) and interactions of Ln(SupplyPerSeat) and Ln(RI_Distance) with Cash_Flow.
Definitions of the variables and instruments are provided in Appendix B. All specifications include year and
firm fixed effects, and t -statistics reported in brackets are robust and clustered by firm. ***, **, and * indicate
significance at the 1%, 5%, and 10% level, respectively.
we use Dummy_DRI measures that are defined at the 5% and 10% threshold,
respectively.29 Because the dependent variable is defined using industries that
are related at the 5% (10%) threshold, the independent variables specific to
related industries (Correlation, Integrated, and DRI_Supply) are also redefined
using the 5% (10%) cutoff to maintain consistency. In Column (6), we use
the dependent variable Breadth, which attempts to capture the related industry
expertise of the board. Finally, in Column (7), we use Dummy_DRI union , which
classifies (four-digit SIC) industries as related on the basis of the industries
represented by the actual customers/suppliers of all firms in the given firm’s
industry.
When the DRI measures are dummy variables, the reported coefficients are
estimated using probit regression models. As Breadth is a continuous variable,
we use the OLS regression model to estimate the corresponding coefficients. In
all seven columns, we proxy for innovativeness with R&D. Results are similar
29 Our inferences using proportional DRI measures are similar, and thus these results are left unreported for brevity.
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Ln(Firm_Size)
−0.032∗∗∗
[−3.74]
0.047∗∗∗
[7.70]
0.001
[1.19]
0.004∗∗∗
[5.99]
−0.007∗∗∗
[−5.14]
0.028∗∗∗
[3.53]
yes
yes
(2)
2SLS
The Review of Financial Studies / v 27 n 5 2014
Table 10
Determinants of directors from related industries (DRIs): Alternative DRI definitions
Dependent variable
based on:
Innovativeness
(proxied by R&D)
Price_Info
Correlation
Homogeneity
HHI
Integrated
CEO_Duality
Outside_Directors
Ln(Firm_Size)
Multi_Segment
Book_Leverage
Ln(Board_Size)
Ln(DRI_Supply)
OUT
(2)
VH
(3)
0.528∗∗∗
0.466∗∗∗
0.671∗∗∗
[9.53]
[9.82]
[15.92]
−0.160∗∗∗ −0.148∗∗∗ −0.192∗∗∗
[−7.74]
[−7.79]
[−9.63]
0.186∗∗∗
0.180∗∗∗
0.141∗∗∗
[6.19]
[6.41]
[4.55]
−0.144∗∗∗ −0.139∗∗∗ −0.147∗∗∗
[−3.76]
[−3.99]
[−3.94]
0.240∗∗∗
0.157∗∗∗
0.130∗∗∗
[6.12]
[4.68]
[3.70]
0.088∗∗∗ −0.043
0.080∗∗∗
[3.02]
[3.53]
[−1.32]
0.209∗∗∗
0.132∗∗∗
0.113∗∗∗
[6.45]
[4.49]
[3.77]
0.049∗∗∗
0.033∗∗∗
0.040∗∗∗
[5.99]
[8.01]
[5.10]
0.247∗∗∗
0.231∗∗∗
0.241∗∗∗
[13.70]
[15.28]
[13.38]
0.058∗∗∗
0.069∗∗∗
0.071∗∗∗
[26.87]
[24.15]
[26.62]
0.011
0.017∗∗
0.105∗∗∗
[12.26]
[1.45]
[2.07]
−0.193∗∗∗ −0.182∗∗∗ −0.246∗∗∗
[−14.02]
[−14.11]
[−17.65]
0.039∗∗∗
0.032∗∗∗
0.019∗∗
[4.32]
[3.75]
[1.98]
0.078∗∗∗
0.092∗∗∗
0.097∗∗∗
[10.77]
[13.43]
[12.57]
5%
(4)
10%
(5)
0.295∗∗∗
[8.74]
−0.073∗∗∗
[−5.01]
0.179∗∗∗
[9.86]
0.031
[1.16]
0.086∗∗∗
[3.55]
0.060∗∗∗
[3.13]
0.128∗∗∗
[5.05]
0.014∗∗∗
[2.96]
0.095∗∗∗
[7.55]
0.027∗∗∗
[15.04]
0.016∗∗∗
[2.66]
−0.101∗∗∗
[−9.95]
0.018∗∗∗
[2.64]
0.053∗∗∗
[19.04]
0.166∗∗∗
[7.45]
−0.025∗∗
[−2.29]
0.134∗∗∗
[10.76]
0.049∗∗
[2.58]
0.004
[0.20]
0.049∗∗∗
[3.34]
0.153∗∗∗
[5.05]
0.006
[1.61]
0.036∗∗∗
[3.98]
0.014∗∗∗
[11.21]
0.001
[0.13]
−0.048∗∗∗
[−6.65]
0.017∗∗∗
[3.54]
0.026∗∗∗
[20.37]
yes
55,318
0.089
yes
44,153
0.128
Constant
Year dummies
Observations
R2
yes
61,185
0.118
yes
61,185
0.095
yes
61,185
0.101
Breadtht+1
(6)
Union
(7)
0.199∗∗∗
0.710∗∗∗
[6.72]
[17.61]
−0.045∗∗∗ −0.210∗∗∗
[−5.24]
[−13.09]
0.070∗∗∗
0.242∗∗∗
[5.19]
[9.75]
0.022
−0.445∗∗∗
[1.26]
[−13.67]
0.040∗∗
−0.205∗∗∗
[2.37]
[−5.94]
0.009
−0.150∗∗∗
[1.03]
[−6.09]
0.098∗∗∗ −0.111∗∗∗
[5.79]
[−4.27]
0.009∗∗∗
0.010∗∗
[3.75]
[1.98]
∗∗∗
0.041
0.192∗∗∗
[7.05]
[14.14]
0.015∗∗∗
0.050∗∗∗
[15.93]
[23.42]
0.039∗∗∗
0.008
[10.68]
[1.20]
−0.032∗∗∗ −0.285∗∗∗
[−6.89]
[−23.29]
0.030∗∗∗ −0.021∗∗∗
[8.72]
[−3.04]
0.005
0.028∗∗∗
[1.53]
[4.74]
−0.147∗∗∗
[−4.83]
yes
yes
61,185
61,185
0.085
0.157
This table provides evidence on the determinants of DRIs using alternative DRI definitions. Dummy_DRI seg,t+1 in
Column (1) is a dummy variable that equals one if the firm has a director from a related industry of any major segment
of the firm. Dummy_DRI out,t+1 in Column (2) is a dummy variable that equals one if the firm has an outside director
from a related industry of the primary segment of the firm. Dummy_DRI V H,t+1 in Column (3) is a dummy variable
that equals one if the firm has a director either from the same four-digit SIC industry as the primary segment of the
firm or from an industry related at the 1% VRC threshold. Dummy_DRI 5%,t+1 and Dummy_DRI 10%,t+1 in Columns
(4) and (5) are dummy variables that equal one if the firm has a director from an industry related to the primary
segment of the firm at the 5% or 10% VRC threshold, respectively. Breadtht+1 in Column (6) is the sum of VRCs of
all the unique industries represented by the DRIs on the board. Dummy_DRI union in Column (7) is a dummy variable
that equals one if the firm has a director who is an officer or director in the union of industries represented by actual
suppliers and customers of all firms within the same four-digit SIC industry. Marginal effects from Probit models are
reported in Columns (1)–(5) and (7), whereas Column (6) reports OLS estimates. All models use R&D as the proxy
for innovativeness. Definitions of the variables are provided in Appendix B. The independent variables are measured
with a one-year lag relative to the dependent variables. All specifications include year fixed effects. t-statistics reported
in brackets are robust and clustered by firm. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
when we use either Differentiated or Ln(Patents) to measure innovativeness.
These additional tests are left unreported for brevity. Overall, our results in
Table 10 indicate that using the alternative Dummy_DRI measures does not
alter any of our earlier inferences. In Column (7), where the dependent variable
is Dummy_DRI union , the coefficients on all the variables, other than those
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Market_Share
SEG
(1)
Board Expertise
motivated by the market structure hypothesis, have the same signs as in Table 4
and are generally significant at the 1% level.30
30 The negative coefficients on Market_Share and HHI in Column (7) are inconsistent with the market structure
hypothesis. Recall, in order to define Dummy_DRI union , we identify related industries using industries of the
actual customers and suppliers. When an industry is concentrated (i.e., there are fewer firms in the industry),
the number of actual customers/suppliers obtained from COMPUSTAT Segments will be mechanically smaller,
leading to a downward bias in the number of related industries and, consequently, directors classified as DRIs.
Thus, the manner in which DRIs are defined can mechanically lead to a negative relation between DRIs and
Market_Share or HHI.
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6.1.2 Impact of DRIs on firm value/performance: Using alternative
definitions of DRI measures. So far, we have examined the effect of DRIs
classified using a 1% VRC threshold on firm value/performance. We test
for the robustness of these results by instead using 5% and 10% thresholds
to classify DRIs. The DRIs thus classified will represent industries that
have stronger economic linkages with the given firm’s industry. Using these
DRI measures, we estimate Equations (3) and (4) with firm and year fixed
effects and report the results in Table 11. The dependent variable in Panel
A is Tobin’s Q, and that in Panel B is ROA. In Columns (1)–(3) of both
Panels, the definition of DRI is based on the 5% VRC threshold, whereas
that in Columns (4)–(6) is based on the 10% VRC threshold. We report
the OLS estimates in Columns (1) and (4), over-identified 2SLS estimates
in Columns (2) and (5), and exactly identified 2SLS estimates in Columns
(3) and (6). As in Table 5, the instruments used in the over-identified 2SLS
specifications in Panel A are Ln(SupplyPerSeat) and Med_Ind_DRI, whereas
in Panel B, we use Ln(SupplyPerSeat) and Ln(RI_Distance) as instruments.
We use Ln(SupplyPerSeat) as the only instrument for all the exactly identified
2SLS models in both panels.
We find that using these higher thresholds, the OLS coefficients (with firm
and year fixed effects) on the DRI measures are always positive and mostly
statistically significant. Using both Tobin’s Q and ROA, the over-identified
2SLS coefficients for the DRI measures are also positive and significant at
least at the 5% level; our instruments pass all the statistical tests for relevance
and validity. These over-identified 2SLS results are also economically large;
we find that the presence of DRIs from industries related at the 10% threshold is
associated with 0.71 (0.92) standard deviations increase in next year’s Tobin’s
Q (ROA). Estimates using exactly identified 2SLS models are similar and
significant at the 5% level. Results using the proportional DRI measures lead
to similar inferences; we leave these unreported. As such, we find consistent
support for our hypothesis in this table; the presence of DRIs from strongly
linked industries appears to have a significant impact on the firm’s value and
performance.
In Table 12, we examine whether the positive relation between firm
value/performance and DRIs documented in Table 5 is also robust to alternative
The Review of Financial Studies / v 27 n 5 2014
Table 11
Impact of directors from related industries (DRIs) on firm value and operating performance: Alternative
VRC thresholds
Panel A: Dependent variable is Tobin’s Qt+1
DRI_Measure:
Dummy_DRI 5%
Dummy_DRI 10%
(2)
(3)
(4)
(5)
(6)
2SLS
overidentified
2SLS
exactlyidentified
OLS
2SLS
overidentified
2SLS
exactlyidentified
0.023
[1.24]
1.041∗∗∗
[3.18]
0.990∗∗
[2.28]
0.058∗∗
[2.30]
1.208∗∗∗
[3.20]
1.400∗∗
[2.21]
Control variables
Firm and year fixed effects
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Observations
R2
Hansen J (p-value)
Sargan C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
70,239
0.105
68,437
68,711
70,239
0.105
68,437
68,711
0.986
0.001
0.003
31.1∗∗∗
0.016
0.014
31.6∗∗∗
0.624
0.001
0.003
33.8∗∗∗
0.018
0.014
23.1∗∗∗
0.003∗
[1.67]
0.127∗∗
[2.49]
0.109∗∗
[2.07]
0.181∗∗
[2.47]
0.154∗∗
[2.03]
Control variables
Firm and year fixed effects
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
Observations
R2
Hansen J (p-value)
Sargan C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
73,452
0.045
71,880
71,880
73,452
0.046
71,880
71,880
0.215
0.006
0.009
18.3∗∗∗
0.032
0.027
32.7∗∗∗
0.269
0.006
0.009
13.7∗∗∗
0.036
0.027
24.0∗∗∗
DRI_Measure
Panel B: Dependent variable is ROAt+1
DRI_Measure
0.008∗∗∗
[2.59]
This table shows the effect of DRIs on firm value and operating performance using alternative VRC thresholds.
The dependent variable in Panel A is Tobin’s Q, and that in Panel B is ROA. In Columns (1)-(3) of both panels,
the definition of DRI is based on the 5% VRC threshold, while that in Columns (4)-(6) is based on the 10% VRC
threshold. We report the OLS estimates in Columns (1) and (4), over-identified 2SLS estimates in Columns (2)
and (5), and exactly-identified 2SLS estimates in Columns (3) and (6). As in Table 5, the instruments used in the
over-identified 2SLS specifications in Panel A are Ln(SupplyPerSeat) and Med_Ind_DRI; while in Panel B, we
use Ln(SupplyPerSeat) and Ln(RI_Distance) as instruments. We use Ln(SupplyPerSeat) as the only instrument
for all the exactly identified 2SLS models in both Panels. Definitions of all variables as well as instruments are
provided in Appendix B. All independent variables are measured with a one-year lag relative to the dependent
variables. Coefficients of the control variables are not reported for brevity. Firm and year fixed effects are also
included. t -statistics reported in brackets are robust and clustered by firm. ***, **, and * indicate significance
at the 1%, 5%, and 10% level, respectively.
definitions of DRIs. We use the same alternative definitions of DRI measures
as those used in Table 10: Dummy_DRI seg , Dummy_DRI out , Dummy_DRI V H ,
Breadth, and Dummy_DRI union . Although we only report results using the
dummy DRI measures, they are robust to using the proportional measures
DRI seg , DRI out , DRI V H , and DRI union as well.
The dependent variable in Panel A is Tobin’s Q, and that in Panel B is ROA. In
both panels, we report the over-identified 2SLS estimates in Columns (1)–(5)
and exactly identified 2SLS estimates in Columns (6)–(10). The DRI measure
is Dummy_DRI seg in Columns (1) and (6), Dummy_DRI out in Columns (2) and
(7), Dummy_DRI V H in Columns (3) and (8), Breadth in Columns (4) and (9),
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(1)
OLS
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68,437
0.781
0.001
0.003
89.2∗∗∗
Observations
Hansen-J (p-value)
Sargan-C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
71,880
0.452
0.003
0.009
70.4∗∗∗
Observations
Hansen−J (p-value)
Sargan-C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
71,880
0.359
0.004
0.009
73.8∗∗∗
yes
yes
0.063∗∗∗
[2.78]
68,437
0.947
< 0.001
0.003
104.8∗∗∗
yes
yes
71,880
0.678
0.002
0.009
56.4∗∗∗
yes
yes
0.072∗∗∗
[2.92]
68,437
0.894
0.001
0.003
64.1∗∗∗
yes
yes
0.653∗∗∗
[3.29]
VH
(3)
71,880
0.292
0.006
0.009
9.7∗∗∗
yes
yes
0.293∗∗
[2.32]
68,437
0.449
0.001
0.003
12.1∗∗∗
71,880
0.540
0.006
0.021
103.8∗∗∗
yes
yes
0.069∗∗
[2.50]
68,711
0.178
0.001
0.002
453.2∗∗∗
yes
yes
0.284∗∗∗
[3.29]
2.691∗∗∗
[2.71]
yes
yes
Union
(5)
Breadth
(4)
71,880
0.029
0.027
114.3∗∗∗
0.028
0.027
103.8∗∗∗
yes
yes
71,880
yes
yes
0.054∗∗
[2.16]
0.013
0.014
112.0∗∗∗
0.012
0.014
101.6∗∗∗
0.053∗∗
[2.15]
68,711
yes
yes
0.488∗∗
[2.41]
OUT
(7)
68,711
yes
yes
0.485∗∗
[2.40]
SEG
(6)
0.027
0.027
68.3∗∗∗
71,880
yes
yes
0.066∗∗
[2.15]
0.013
0.014
68.3∗∗∗
68,711
yes
yes
0.589∗∗
[2.37]
VH
(8)
0.034
0.027
16.7∗∗∗
71,880
yes
yes
0.250∗
[1.93]
0.016
0.014
16.9∗∗∗
68,711
yes
yes
2.227∗∗
[2.10]
Breadth
(9)
Exactly identified regressions
0.173
0.175
52.8∗∗∗
71,880
yes
yes
0.054
[1.33]
0.001
0.002
894.0∗∗∗
68,711
yes
yes
0.265∗∗∗
[3.09]
Union
(10)
This table shows the effect of DRIs on firm value and operating performance using alternative definitions of Dummy_DRIs. The dependent variable in Panel A is Tobin’s Q, and that in Panel B
is ROA. In both Panels, we report the over-identified 2SLS estimates in Columns (1)–(5) and exactly identified 2SLS estimates in Columns (6)–(10). In Columns (1) and (6), Dummy_DRI seg
indicates the presence of at least one DRI from an industry related to any segment of the firm at the 1% VRC threshold. In Columns (2) and (7), Dummy_DRI out indicates the presence of at
least one outside director from an industry related at the 1% VRC threshold. In Columns (3) and (8), Dummy_DRI V H indicates the presence of at least one director who is either from the
same four-digit SIC industry as the firm’s primary segment or from an industry related at the 1% VRC threshold. In Columns (4) and (9), we use Breadth, which is the sum of VRCs of all the
unique industries represented by the DRIs on the board. In Columns (5) and (10), Dummy_DRI union indicates the presence of at least one director from the union of industries represented by
actual suppliers and customers of all firms within the same four-digit SIC industry. We always use the instrument Ln(SupplyPerSeat). Further, as in the over-identified regressions reported
in Table 5, we additionally use Med_Ind_DRI when the dependent variable is Tobin’s Q and Ln(RI_Distance) when the dependent variable is ROA. When using the Dummy_DRI union , the
instrumental variables are the same, except the related industries are defined as per the “union of industries” described above. All independent variables are measured with a one-year lag
relative to the dependent variables. Coefficients of the control variables are not reported for brevity. Definitions of all variables and instruments are provided in Appendix B. Firm and year
fixed effects are also included. t -statistics reported in brackets are robust and clustered by firm. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
yes
yes
0.061∗∗∗
[2.82]
Control variables
Firm and year fixed effects
DRI_Measure
Panel B: Dependent variable is ROAt+1
yes
yes
0.526∗∗∗
[3.35]
0.553∗∗∗
[3.33]
Control variables
Firm and year fixed effects
DRI_Measure
OUT
(2)
SEG
(1)
Over-identified regressions
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[18:26 1/4/2014 RFS-hht071.tex]
DRI_Measure based on:
Panel A: Dependent variable is Tobin’s Qt+1
Table 12
Impact of directors from related industries (DRIs) on firm value and operating performance: Alternative DRI measures
Board Expertise
The Review of Financial Studies / v 27 n 5 2014
6.1.3 Impact of CEO-DRIs and non-CEO DRIs on firm value and
performance. We further test for the robustness of our results by separately
analyzing DRIs (identified at the 1% VRC threshold) who are CEOs of a firm in
a related industry and DRIs who are not CEOs. We do this because Fich (2005)
has shown that directors who are CEOs of other firms bring value to the board.
We find that not only the CEO DRIs but, importantly, the non-CEO DRIs also
have a significantly positive impact on firm value/performance, although the
impact due to CEO DRIs is significantly larger. Further, we find that the ratio
of DRIs who are CEOs to all other directors who are CEOs is also strongly
positively related to firm value and performance. Note that this ratio can only
be defined for those firm-year observations that have at least one CEO on the
board (other than the given firm’s own CEO). This latter result suggests that
CEOs who are DRIs tend to benefit the firm more than do other directors who
are also CEOs. We leave these results unreported for brevity.
6.2 DRIs from actual customer or supplier firms
A natural question that arises is whether the DRIs identified above are from the
actual customers and suppliers of a given firm or whether they are only affiliated
with related industries. These directors from actual customer/supplier firms fall
into the broader category of “gray” directors, that is, directors with a family
or business relationship with the firm. Whereas the role of “gray” directors
(Hermalin and Weisbach 1988) has been substantially studied, the agency
conflicts arising due to directors from actual customer/supplier firms have not
been studied before.32 One can argue that DRIs from actual customers and
31 We find similar results (left unreported) when DRIs are based only on the industries represented by the actual
customers or suppliers of the given firm instead of the union of such industries across all firms in the same
four-digit SIC industry.
32 As described earlier, DRIs from firms that are not actual suppliers or customers can also have conflicts of
interest—they may be more focused on developing an actual buying/selling relationship with the given firm
instead of providing it with sound advice. However, we find that, across the years in our sample, on average only
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and Dummy_DRI union in Columns (5) and (10). The control variables are the
same as those employed in equivalent regressions in Table 5; their coefficients
are left unreported for brevity. The results are estimated using a 2SLS model
that includes firm and year fixed effects. The instruments used are the same
as those described earlier, and they pass our statistical tests for relevance (all
models) and validity (over-identified models). For Dummy_DRI union , however,
we define these instruments using the union of industries represented by actual
customers/suppliers of all the firms in the same four-digit SIC industry. Overall,
we find that the coefficient on the various DRI measures is positive in all
specifications for both Tobin’s Q and ROA and is generally significant at least
at the 5% level. Thus, the evidence of a positive impact of DRIs on firm
value/performance is robust to alternative DRI measures.31
Board Expertise
11 (10) out of 3,886 firms that are represented by DRIs eventually become actual customers (suppliers) over
the next five years. This suggests that DRIs representing actual customer/supplier firms face greater conflicts of
interest than DRIs from supply-chain firms that are not currently actual customers/suppliers.
33 Because public firms are required (as per Statement of Financial Accounting Standards no. 14 and 131) to
disclose all customers contributing at least 10% of their revenue, these data do not capture all supplier-customer
relationships.
34 Fee, Hadlock, and Thomas (2006) demonstrate that contractual frictions can be alleviated by equity ownership
of a customer firm in its supplier. They argue that one channel through which equity ownership can reduce
contractual frictions is the actual customer’s representation on the supplier’s board. However, directors from
significant customer firms are not very prevalent in our sample, and therefore, equity ownership by actual
customers cannot account for our findings on the presence and the value impact of DRIs. Moreover, our results
are unchanged if we exclude these 974 observations.
35 We find that the average VRC between a firm’s industry and its actual supplier’s/customer’s industry is 3%,
whereas the VRC between the firm’s industry and the industry represented by DRIs who are from an actual
customer/supplier firm is 7.7%. This statistic is comparable to the mean VRC of 5.2% with the DRIs’ industries
when the DRI is identified using a 1% cutoff. It also indicates that DRIs identified at the 1% VRC cutoff are
about as economically important as DRIs from industries represented by actual suppliers/customers.
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suppliers are better positioned to reduce the information gap. These directors,
however, will have conflicts of interest that can adversely impact their advising
and monitoring roles. Given that the board and the management choose DRIs,
barring any agency problems, they will do so only if the benefits are greater
than the costs. Therefore, we expect the overall impact of DRIs from actual
customer/supplier firms to be positive for firm value/performance.
We follow Kale and Shahrur (2007) to identify the customer and supplier
firms from the Compustat Segment files.33 Using these data, we find only
974 firm-year observations that have DRIs from actual customer and supplier
firms, and these are split roughly equally between them. These observations
comprise 1.2% of the full sample of 81,650 firm-year observations.34 We
identify DRIs based on these firm-level supplier-customer relationships and
repeat our analysis of both the choice of these directors and their impact on
the firm’s value and performance.35 In modeling the choice of having these
directors on the board, the dependent variable, Dummy_CSD, is a dummy
variable that indicates the presence of “customer/supplier directors”, that is,
directors from actual customer and/or supplier firms. Here, we exclude all firmyear observations that contain DRIs that are identified at the 1% VRC threshold
but are not from actual customer or supplier firms. Marginal effects obtained
from Probit regressions are reported in Panel A of Table 13. The regression
specification is the same as that used in Table 4 with year dummies. As with
DRIs in general, we find that the probability of having directors from an actual
customer and/or supplier firm is significantly higher when the firm invests
more in R&D (in Column (1)), belongs to a differentiated industry (in Column
(2)), or produces more patents (in Column (3)). Additionally, the probability
of having these directors is significantly lower when the firm’s stock price is
more informative.
We then examine the impact of these directors on firm value and performance.
The results from this analysis are reported in Panel B of Table 13. Column (1)
The Review of Financial Studies / v 27 n 5 2014
reports 2SLS results using firm and year fixed effects for Tobin’s Q as the
dependent variable, and Column (2) reports the same for ROA as the dependent
variable. The control variables are the same as those used in Table 5; we do
not report their coefficients for brevity. We find that the presence of these
directors has a significantly positive impact on both Tobin’s Q and ROA. These
results are similar to our earlier findings for DRIs from the related industries
and suggest that firms choose DRIs from actual customers/suppliers when the
benefits outweigh the costs that arise because of conflicts of interest.
Table 13
Directors from actual customer or supplier firms
Panel A: Determinants of directors from actual customer or supplier firms (CSDs)
Dependent variable: Dummy_CSDt+1
Measure of innovativeness:
Innovativeness
Price_Info
Correlation
Homogeneity
Market_Share
HHI
Integrated
CEO_Duality
Outside_Directors
Ln(Firm_Size)
Multi_Segment
Book_Leverage
Ln(Board_Size)
Ln(DRI_Supply)
Year fixed effects
Observations
Pseudo R 2
(1)
R&D
0.022∗∗∗
[3.49]
−0.014∗∗∗
[−4.69]
0.011∗∗
[2.14]
−0.016∗∗
[−2.63]
0.010∗∗
[2.08]
0.009∗∗
[1.98]
0.004
[0.75]
0.002∗∗
[1.99]
0.014∗∗∗
[4.82]
0.004∗∗∗
[10.77]
0.001
[0.50]
−0.013∗∗∗
[−5.38]
0.004∗∗∗
[2.84]
0.006∗∗∗
[4.75]
(2)
Differentiated
0.009∗∗∗
[5.76]
−0.014∗∗∗
[−4.87]
0.007
[1.41]
−0.015∗∗
[−2.52]
0.011∗∗
[2.33]
0.005
[1.18]
0.001
[0.15]
0.002∗∗
[2.07]
0.013∗∗∗
[4.68]
0.004∗∗∗
[10.18]
0.000
[0.29]
−0.010∗∗∗
[−4.16]
0.004∗∗∗
[2.86]
0.008∗∗∗
[6.27]
(3)
Ln(Patents)
0.005∗∗∗
[8.15]
−0.014∗∗∗
[−4.50]
0.007
[1.33]
−0.011∗
[−1.75]
0.006
[1.14]
0.006
[1.35]
0.001
[0.25]
0.001
[0.84]
0.012∗∗∗
[4.18]
0.003∗∗∗
[7.36]
0.000
[0.16]
−0.010∗∗∗
[−4.04]
0.005∗∗∗
[3.15]
0.007∗∗∗
[5.14]
yes
yes
yes
38,843
0.189
38,843
0.189
38,843
0.205
(continued)
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6.3 Board changes around DRI appointments
We examine the board’s characteristics around the appointment of DRIs to
check whether the positive value impact documented in the paper is attributable
to the DRIs or the removal of particular types of directors from the board. To
address this issue, we analyze the number of unique industries represented on
the board (excluding the firm’s own industry). We find that the mean (median)
Board Expertise
Table 13
Continued
Panel B: Value/Performance effects of directors from actual customer or supplier firms (CSDs)
Dependent variable:
Dummy_CSDt+1
Control variables
Firm and year fixed effects
0.866∗
[1.73]
yes
yes
39,685
0.455
0.082
0.190
34.00∗∗∗
ROAt+1
(2)
0.126∗∗
[2.13]
yes
yes
42,291
0.857
0.040
0.089
61.02∗∗∗
In Panel A, we provide evidence on the determinants of directors from an actual customer and/or supplier of
the given firm. The dependent variable Dummy_CSDt+1 indicates the presence of at least one director from an
actual (current) customer or supplier of the given firm. The independent variables are the same as those used
in Table 4. We use either R&D, Differentiated, or Ln(Patents) as the measure of firm’s innovativeness. The
marginal effects in all three columns are estimated using the Probit model with year fixed effects. t -statistics
reported in brackets are robust and clustered by firm. In Panel B, we present evidence on the impact of directors
from an actual (current) customer and/or supplier on the firm’s value and performance. We exclude all firm-year
observations that have DRIs from industries related at 1% VRC threshold, but these directors are not affiliated
with an actual (current) customer and/or supplier of the given firm. All specifications use the over-identified
2SLS model to control for endogeneity and include the same control variables as in Table 5. The instruments are
defined in the same manner as in the earlier tables, except the related industries are defined using the industries
represented by actual suppliers and customers of the given firm. Firm and year fixed effects are also included.
All independent variables are measured with a one-year lag relative to the dependent variables. Coefficients of
the control variables are not reported for brevity. t -statistics reported in brackets are robust and clustered by firm.
Definitions of all variables are provided in Appendix B. ***, **, and * indicate significance at the 1%, 5%, and
10% level, respectively.
number of unique industries before the addition of a DRI is 3.56 (2.00). After
the addition of the DRI to the board, the mean (median) number of unique
industries represented by the other board members is 3.28 (2.00). Thus, the
firm keeps the number of industries represented by the other directors on the
board largely unchanged. Perhaps, this is due to the removal of a director from
an industry that is already represented on the board by another director; we
denote these as “duplicated” industries. Moreover, if we include the industries
represented by the DRI, then the same mean (median) is 4.38 (3.00). These
figures suggest that the firms are enhancing the industry-specific expertise on
the board by adding the related industry expertise of the DRI. To corroborate
this further, we check the number of “duplicated” industries on the board of
each firm that adds a DRI; these are calculated for directors other than the
DRI and exclude the firm’s own industry. We find that the mean (median)
number of “duplicated” industries goes from 0.50 (0.00) to 0.44 (0.00). This
decline suggests that one way by which firms maintain the number of industries
represented by the other directors on the board is by replacing directors from a
“duplicated” industry.
Finally, we analyze the number of unique industries (excluding the firm’s own
industry) using a concentration measure. Note that most directors have only
one directorship overall, and therefore such directors will not be counted in this
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Observations
Hansen-J (p-value)
Sargan-C (p-value)
Anderson-Rubin (p-value)
Kleibergen-Paap F -statistic
Tobin’s Qt+1
(1)
The Review of Financial Studies / v 27 n 5 2014
7. Conclusion
Directors have two complementary functions in a firm: that of monitoring
and offering strategic advice. Directors with current expertise in the firm’s
own industry have the requisite information and therefore are clearly suited to
perform these functions effectively. However, antitrust laws prohibit firms from
having directors from other firms that compete in the same product market.
Given these constraints, “directors from related industries” (DRIs) are wellpositioned to perform these critical functions, particularly when firms face
a severe information gap vis-à-vis their related upstream and downstream
industries. For instance, DRIs can improve a firm’s ability to respond to
demand/supply shocks or forecast trends in related upstream/downstream
industries. They can also help shrink the information gap between the firm’s
board and its managers regarding conditions in related industries, thereby
enhancing the board’s ability to monitor managerial performance.
In this paper, we study why firms choose directors from related industries,
whether there is value in having these directors, and some specific channels
through which these directors affect firm value. We develop hypotheses in
which the rationale for having DRIs on the board depends on information,
market structure, and conflicts of interest considerations. Overall, our empirical
results support these hypotheses for the use of DRIs. We find that attributes
such as firm and industry innovativeness, average correlation between stock
returns of the firm’s industry and its related industries, firm’s market share,
industry concentration, degree of vertical integration in the firm’s industry, and
CEO duality increase the likelihood of DRIs on the board, whereas stock price
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concentration calculation because we are excluding the firm’s own industry. For
example, let there be three directors with directorships other than the one on
the given firm’s board. Say, two of these three directors are affiliated with the
same industry (i.e., this industry is “duplicated”) and the third director is from
another unique industry. Then the concentration would be (2/3)2 + (1/3)2 = 0.56.
If the firm adds a DRI and replaces a director from a “duplicated” industry (as
suggested by the statistics reported above), then the new concentration measure
(including the DRI’s related industry) will be (1/3)2 + (1/3)2 + (1/3)2 = 0.33.
We calculate these two statistics in our sample and find that the mean (median)
industry concentration is 0.51 (0.44) before the firm adds a DRI and 0.43
(0.33) after the DRI is added. This decline in industry concentration suggests
that the firm is enhancing the industry expertise of the board with the addition
of a DRI.
We conclude from this analysis that the firms add a DRI to obtain expertise
in a related upstream/downstream industry, but doing so is not at the cost of
losing expertise in some other industry. Thus, we believe that the effects that
we document in the paper and attribute to DRIs are likely due to the related
industry expertise that they bring to the board.
Board Expertise
Appendix A: Algorithm to Assign a Unique Identifier to Each Director
in Compact Disclosure
To identify whether a director is from a related industry, we need to be able to accurately determine
all the other affiliations of the director for a given year, that is, all the other firms in which the director
serves as an officer and/or director (henceforth director). We begin this process by extracting
personal information about officers and directors, such as their first, last, and middle names,
generation (Jr./Sr. or I, II, etc., notation), and age for each firm-year from Compact Disclosure.
Assigning a unique identifier to each director is not, however, straightforward because (1) there is
no uniform method used to list the names of directors associated with each firm in the Compact
Disclosure files and (2) typographical errors are commonplace.36 To deal with these problems and
assign unique identifiers to each director in the database, we follow the procedure sketched below.
36 Compact Disclosure attempts to record data that is available from proxy statements. Because firms often report
director names differently or the same firm reports director names inconsistently across years, these differences
will be reflected in the Compact Disclosure files. For example, the first name may be reported as the actual first
name or as a nickname; the middle name may be fully spelled out or just its initial may be listed; the first and
middle name may be switched, etc. Furthermore, some firms consistently report the age of a director, whereas
others do not. The age of the same director across firms for the same year can also be different based on when
the information was collected and input into the proxy statement.
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informativeness and, to a lesser extent, industry homogeneity decrease this
likelihood.
After endogeneity corrections, we find that directors from related industries
have an economically significant positive impact on firm value (Tobin’s Q) and
performance (ROA). The benefits are substantially larger in firms that face more
severe information problems. Also, DRIs appear to enhance the ability of firms
to handle industry shocks, help shorten the firm’s cash conversion cycle, and
alleviate financial constraints. Finally, announcements of DRI appointments
are associated with significant positive abnormal returns. Overall, our results
suggest that the documented positive relation between firm value/performance
and DRIs is likely to be a director effect rather than an industry or firm
effect.
In light of the above findings, a natural question that arises is why does
every firm not have DRIs on the board? We argue that there may be more
than one explanation. First, when DRIs are from actual or potential suppliers
and customers, the firm has reason to be concerned about conflicts of interest.
DRIs can also be a potential source of proprietary information leakage to rival
firms. Additionally, bringing in DRIs has opportunity costs. A firm, presumably,
arrives at its board size by trading off the benefits of an additional director with
the difficulty of decision making in a larger board. Hence, bringing in a DRI
may well imply not having another director who can add value—say, one with
valuable political connections or financial expertise. Thus, consistent with the
view in Adams, Hermalin, and Weisbach (2010), we believe that DRIs are
endogenously chosen as the “solution to the constrained optimization problem
the organization faces” (59); that is, DRIs will be present when the benefits of
having them outweigh the costs to both the firm and the managers.
The Review of Financial Studies / v 27 n 5 2014
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Our first step is to match directors across years for each company. We begin by normalizing the
age of each director to what his/her age would have been in 2000. By doing so, we can compare
all the reported identifying information for directors across years for a given firm. If two directoryear observations have exactly the same director identifier information (same surname, first name,
middle name, generation identifier, and age), we deem them to be a match and give them the same
director identifier. We then attempt to determine whether or not two directors with “similar”, but
not the “same”, identifier information are the same person. We use the Levenshtein (1965) and
Jaro-Winkler (Jaro 1989; Winkler 1999) distance measures to compare text strings. Specifically,
we use the Levenshtein measure to identify cases in which there is a typographical error of just
one character, and we use the Jaro-Winkler distance to assess the similarity between two entire
text strings. With the help of these measures, we take the following cases to be matches and give
all these director matches the same identifier.
Case 1: Ages match exactly; first names match exactly or both first names are missing; middle
names match exactly or both middle names are missing; surname differs by one character only
(using Levenshtein distance of one).
Case 2: The first part of a hyphenated name matches the other surname; first names match
exactly or by nickname; middle name matches exactly or by nickname; age does not differ by more
than one year.
Case 3: The combinations of surname, first name, and middle name are the same; ages are not
different by more than one year; and generation fields are the same.
Case 4: The combinations in Case 3 are not the same but have a Levenshtein distance less than
10% of the combined length, both surnames are uncommon surnames, ages are not different by
more than one year, and generation fields match.
Case 5: The surnames are close with Jaro-Winkler distance greater than 0.97; ages are not
different by more than one year; one of the following conditions is satisfied: (1) if the first names
match exactly or both are missing and the middle name has no conflict, (2) if the middle names
match exactly or both are missing and the first name has no conflict, (3) if one of the middle names
is blank, and the first name has two parts separated by space or hyphen, if the two parts match the
first name and middle name of the other, or (4) if one first name matches the other middle name
and vice versa.
Case 6: The surnames match exactly; there is no obvious conflict in ages or generation field;
and one of the following conditions is satisfied: (1) both first names match closely enough (the
two first names have Jaro-Winkler distance greater than 0.955) or both are missing, and both
middle names match closely enough (the two first names have Jaro-Winkler distance greater
than 0.955) or both are missing, or (2) one first names matches the other middle name and vice
versa.
Case 7: The surnames match exactly; the surname is an uncommon surname; there is no obvious
conflict in ages or generation field; and one of the following conditions is satisfied: (1) first names
match closely (Jaro-Winkler distance greater than 0.955) or match by nickname; (2) ages do not
differ by more than two years, and first names match closely, or one is an initial and matches the
other; or (3) generation matches exactly, ages do not differ by more than two years, and middle
names match closely (Jaro-Winkler distance greater than 0.955).
After matching all the directors across years for each company, we retain the most detailed
information for a director. Specifically, we keep the full surname, first name, middle name, age,
and generation extension wherever possible. When there are multiple names or ages, we choose
the one with the highest appearing frequency.
Our next step is to match directors across companies, using the detailed information we extracted
from the first step. As before, when two directors have the same first name, middle name, surname,
age, and generation extension, we treat the two as the same person. We use the following cases to
identify matches when the director information is not exactly the same. All matches are given a
unique director identification number.
Case 1: Both surnames match (if both surnames are common surnames, then an exact match
is required; if one of surnames is not common, then the Jaro-Winkler distance between the two
Board Expertise
Appendix B. Definitions of variables
Variable
Source
Definition
Firm characteristics
Proportional measures for directors from related industries (DRIs)*
DRI, DRI 5% , and DRI 10%
Compact disclosure
DRI seg
Compact disclosure
DRI out
Compact disclosure
DRI V H
Compact disclosure
Breadth
Compact disclosure
DRI union
Compact disclosure
Proportion of the board that consists of officers or
directors from industries that are vertically related at
1%, 5%, and 10% thresholds (respectively) to the
primary industry of the firm.
Proportion of the board that consists of officers or
directors from industries that are vertically related at
1% threshold to any segment of the firm.
Proportion of all outside directors who are either
officers or directors from industries that are
vertically related at 1% threshold to the primary
industry of the firm.
Proportion of all directors who are either officers or
directors from industries that are either vertically
related at 1% threshold or pseudo-horizontally
related to the primary industry of the firm.
Sum of the “vertical-relatedness coefficients
(VRCs)” of all the unique industries represented by
the DRIs (identified at the 1% threshold) on the
firm’s board.
Proportion of all directors who are officers/directors in
the union of industries represented by actual
suppliers and customers (from Compustat Segments)
of all firms within the same four-digit SIC industry.
* For brevity, only definitions of the proportional measures are given. The corresponding dummy measures equal
one if the proportional measure is greater than zero. The notation for dummy measures starts with a “Dummy”
prefix.
(continued)
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should be greater than 0.955) and one of the following conditions is satisfied: (1) first names match
closely (including nickname match) or by initial and both surnames are common: (a) ages match
exactly and middle names match closely or by initial, generation fields match; (b) ages are not
off by more than two years, middle names match closely, and generation fields are the same; (c)
first names match very closely (excluding nickname match), middle names match very closely
(excluding nickname match), and generation fields are the same; (2) First names match exactly:
(a) one middle name is missing, both surnames are uncommon, and there is no conflict in age
or generation fields; (b) both surnames are uncommon, and middle initials do not match, ages
and generation fields are the same; (3) both surnames are uncommon, first names differ by one
character, ages match, and middle names match closely or by initial.
Case 2: Surnames match by half (separated by space or hyphen) and one of the following
conditions is satisfied: (1) first names match closely or by nickname, middle names match closely
or by initial, no conflict in age or generation fields; (2) if, after combining the surname, first name
and middle name together, the combined names match.
Case 3: Everything else (first name, middle name, age, generation field) matches, and the
surnames differ by one character only.
Case 4: One first name matches the middle name of the other and vice versa, surnames match
and are not common, age and generation fields agree.
Finally, to verify that the procedure is producing the desired matches, we hand-check a random
sample of our matches and confirm that our automated matching works well. We then use these
unique director identification numbers to determine whether a director on the board of a firm is
also an officer and/or director with firms operating in its customer or supplier industries.
The Review of Financial Studies / v 27 n 5 2014
Appendix B
Continued
Variable
Source
Proxies for firm innovativeness
R&D
Compustat
Definition
Ratio of research and development expenditures to
total assets. Following the literature, we replace
missing values of R&D with zero.
Total number of patents.
Total number of all citations.
NBER
NBER
Other firm characteristics
Firm_Size
Market_Share
Book Debt
Compustat
Compustat
Compustat
Book_Leverage
Tangible_Assets
Compustat
Compustat
Multi_Segment
Compustat
Price_Info
CRSP
Firm_Volatility
CCC
CRSP
Compustat
ICP
CP
PP
Cash_Holdings
Cash_Flow
Compustat
Compustat
Compustat
Compustat
Compustat
KZ_Index
Compustat
Dividend_Ratio
Compustat
Governance variables
Board_Size
Outside_Directors
CEO_Duality
Compact disclosure
Compact disclosure
Compact disclosure
Number of directors on the board.
Proportion of outside directors on the board.
Indicator variable that equals one if the CEO is also the
Chairman.
Performance/value
ROA
Compustat
Tobin’s Q
Compustat
Sum of operating profit before depreciation and R&D
expenditures, divided by lagged total assets.
Sum of book debt and market value of equity divided
by total of assets.
Total assets.
Ratio of firm sales to industry sales.
Total liabilities plus preferred stock minus deferred
taxes minus convertible debt.
Ratio of book debt to total assets.
Ratio of net plant, property and equipment to total
assets.
Indicator variable that equals one if the firm has two or
more segments.
(1-R 2 ) from the regression of the firm’s monthly
returns on monthly returns of the market and the
firm’s industry, estimated with at least two years of
data over a three-year rolling window.
Standard deviation of daily stock returns.
Days in inventory plus days in sales outstanding minus
days in accounts payable.
Days in inventory.
Days in receivables.
Days in accounts payable.
Ratio of cash and short-term investments to total assets.
Sum of income before extraordinary items and
depreciation & amortization divided by total assets.
(–1.002*Cash_Flow) – (39.368*Dividend_Ratio) –
(1.315*Cash_Holdings) + (3.139*Book_Leverage)
+ (0.283*Tobin’s Q) (Baker, Stein, and Wurgler
2003)
Sum of common dividends and preferred dividends
divided by lagged total assets.
Industry characteristics
Differentiated
Rauch (1999)
DRI_Supply
(DRI_Supply5% or
DRI_Supply10% )
Compact disclosure
Correlation(Correlation5%
or Correlation10% )
CRSP
Indicator variable that equals one if at least one 4-digit
SIC industry under a given 3-digit SIC group is
classified as “differentiated” by Rauch (1999) and is
zero otherwise.
Total number of potential directors from related
industries based on a vertical-relatedness threshold
of 1% (5% or 10%) for a given year and 4-digit SIC
industry.
Correlation between firm’s industry monthly returns
and monthly returns of all industries that are related
to it at the 1% (5% or 10%) vertical-relatedness
threshold. We calculate these over rolling three-year
windows and require that every industry pair have at
least two years of concurrent monthly returns.
(continued)
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Patents
Citations
Board Expertise
Source
Definition
Homogeneity
CRSP
HHI
Compustat
Integrated (Integrated 5%
or Integrated 10% )
Compustat
Following Parrino (1997), it is the average partial
correlation coefficient between monthly stock
returns of all firms in the same 4-digit SIC code and
monthly industry returns calculated at the 4-digit SIC
level. We use rolling three-year windows and require
that firms have at least two years of monthly returns.
Sum of square of the market shares of firms in a given
4-digit SIC industry.
Proportion of firms in the 4-digit SIC industry that have
at least one secondary segment that is vertically
related to its primary segment in a given year at the
1% (5% or 10%) vertical-relatedness threshold.
Instrumental variables
Ln(SupplyPerSeat)
Med_Ind_DRI
Ln(RI_Distance)
Natural logarithm of the ratio of DRI_Supply to the
number of board seats available in the firm’s
four-digit SIC industry.
The median Dummy_DRI across all other firms within
the same three-digit SIC industry.
Natural logarithm of the average distance of the firm
from all other firms in its related industries.
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