Concentrating on Governance
DALIDA KADYRZHANOVA and MATTHEW RHODES-KROPF
Abstract
This paper develops a novel trade-o¤ view of corporate governance. Using a simple model
that integrates agency costs and bargaining bene…ts of management-friendly provisions, we
identify the economic determinants of the resulting trade-o¤s for shareholder value. Consistent with the theory, our empirical analysis shows that provisions that allow managers to
delay takeovers have a signi…cant bargaining e¤ect and a positive relation with shareholder
value in concentrated industries. By contrast, non-delay provisions have an unambiguously
negative relation with value, and more so in concentrated industries. Overall, our analysis
suggests that there are governance trade-o¤s for shareholders and industry concentration is
an important determinant of their severity.
Kadyrzhanova is at University of Maryland and Rhodes-Kropf is at Harvard Business School. For
helpful comments and suggestions, we thank Campbell Harvey (the editor), an anonymous referee, Heitor
Almeida, Yakov Amihud, Lucian Bebchuk, Dirk Bergemann, Patrick Bolton, Anna Bordon, James Brickley, Matthew Clayton, Steve Drucker, Richard Ericson, Antonio Falato, Laura Field, Stuart Gillan,
Maria Guadalupe, Laurie Hodrick, Wei Jiang, Kose John, Steve Kaplan, Pete Kyle, Vojislav Maksimovic, Thomas Moeller, Daniel Paravisini, Francisco Perez-Gonzalez, Gordon Phillips, Nagpurnanand
Prabhala, Avri Ravid, Michael Riordan, Bernard Salanie, Tano Santos, Till von Wachter, David Weinstein, Daniel Wolfenzon, Jerold Zimmerman, and seminar participants at Columbia University, Harvard
Business School, INSEAD, London Business School, London School of Economics, McGill University,
NYU, University of Maryland, University of Michigan, University of North Carolina, University of Notre
Dame, University of Pittsburgh, University of Rochester, the 2008 Conference on Corporate Governance
at Drexel University, the 2007 AFA meetings in Chicago, the 2007 Washington University Conference
on Corporate Finance, and 2006 Batten Conference (William and Mary). An earlier draft of this paper
was circulated under the title “Does Governance Pay, or Is Entrenchment the Way? Merger Gains and
Antitakeover Provisions.” All remaining errors are ours. Corresponding author: Dalida Kadyrzhanova,
Department of Finance, Robert H. Smith School of Business, University of Maryland, College Park, MD
20742. Phone: (301) 405-3750. Email: dkadyrz@rhsmith.umd.edu.
1
Electronic copy available at: http://ssrn.com/abstract=891418
The importance of corporate governance has long been recognized among …nancial economists,
institutional investors and practitioners, as well as policy makers. However, as noted by Shleifer
and Vishny (1997), "even in advanced market economies, there is a great deal of disagreement on
how good or bad the existing governance mechanisms are." In order to further our understanding of
existing governance mechanisms, this paper takes a novel approach. We develop a uni…ed account
of the costs and bene…ts of external governance and explore the economic determinants of the
resulting trade-o¤s for shareholder value. Our novel perspective explains shareholder governance
trade-o¤s, why they arise, and how they vary across …rms and industries.
A recent literature starting with Gompers, Ishii, and Metrick (2003) focuses on external governance mechanisms and documents evidence consistent with the classical agency view that it is
costly for shareholders to yield power to managers.1 This view holds that managers pursue private
bene…ts of control whenever their jobs are protected from takeovers or shareholder initiatives such
as proxy …ghts (see Manne (1965), Scharfstein (1988a)). A challenge to the agency view comes
from the alternative bargaining view that it is in the interest of shareholders to yield power to
managers, a popular argument among M&A lawyers and practitioners (Lipton (2002)). Stulz
(1988) argues that takeover defenses lead to higher target premiums by allowing management to
fend o¤ opportunistic o¤ers, and Schwert (2000) …nds a positive although weak relation between
poison pill provisions and target premiums.2 Thus, while governance reforms aimed at repealing
provisions that protect management can do no harm based on the agency view, the bargaining
view holds that repealing these provisions is actually harmful for shareholders.
A middle-ground view would argue that there are both agency costs and bargaining bene…ts
of empowering managers. This third view acknowledges the multifaceted nature of governance.
However, it is seldom formalized or developed since it does not make immediate unambiguous
2
Electronic copy available at: http://ssrn.com/abstract=891418
conclusions about repealing provisions that protect managers. As a consequence, we know little
about whether speci…c governance improvements are justi…ed even after their side e¤ects are
taken into account. For example, does repealing provisions that protect managers from takeovers
have di¤erent side e¤ects for shareholders compared to repealing supermajority requirements that
protect managers from shareholder-proposed bylaw amendments? The existing literature provides
limited guidance on these real-world governance questions, which shareholder advisory groups
routinely face when o¤ering proxy advice.
While the relation between external governance provisions and …rm value is potentially ambiguous based on the middle-ground view, we argue that there are robust conclusions once we explicitly
consider how …rms’economic environment interacts with the costs and bene…ts of corporate governance. Our approach is to develop a novel model of governance trade-o¤s that recognizes that
there is signi…cant heterogeneity across provisions and their e¤ects in a cross-section of industries.
Our empirical results show that, consistent with the model, provisions that enable managers to
impose a delay on potential acquirers (delay provisions) have a signi…cant bargaining e¤ect and
a positive relation with shareholder value in concentrated industries. By contrast, the valuation
e¤ect of non-delay provisions is unambiguously negative, and more so in concentrated industries.
Our trade-o¤ model o¤ers a new and unique perspective over these facts, which are otherwise hard
to explain by appealing exclusively to agency considerations. Overall, by integrating seemingly
opposing views of corporate governance, our analysis o¤ers important new policy implications that
emphasize the joint importance of industry and the nature of governance provisions in deciding
which governance mechanisms should be implemented.
In order to guide our empirical analysis, we develop to the best of our knowledge the …rst
model of corporate governance trade-o¤s. We start with a standard model of competitive takeover
3
bidding (e.g., Fishman (1988)), where external governance provisions that protect managers from
takeovers may have bargaining bene…ts. We add two key assumptions. First, these provisions
have agency costs. Second, takeovers have equilibrium e¤ects in the product market. These
two extensions allow us to develop a systematic analysis of di¤erences in the value of corporate
governance across …rms and the economic forces that cause these di¤erences.
Our model yields a number of unambiguous comparative static results that can be used to assess
corporate governance trade-o¤s. We …rst establish which external governance provisions entail
trade-o¤s: a sub-set of provisions that impose a delay on potential acquirers - "delay provisions"
- should have both bargaining bene…ts and agency costs, while other provisions - "non-delay
provisions" - should only have agency costs. This leads to two main comparative static results.
First, we predict a larger bargaining e¤ect of delay provisions in concentrated industries. The
intuition for this prediction is simple: In concentrated industries, targets are relatively scarce.
Thus, a potential acquirer is more concerned about losing synergy opportunities to industry rivals
and may be willing to bid more in order to not lose the target.3 However, in the absence of a
credible threat such as delay provisions that a¤ord an industry rival an opportunity to jump in the
fray and make a competing bid for the target, the acquirer will not up its bid. Second, we predict
that delay and non-delay provisions should have an interaction e¤ect with industry concentration
on …rm value. Speci…cally, the valuation e¤ect of delay provisions, while overall ambiguous, should
be unambiguously less negative in concentrated industries, and potentially positive if bargaining
bene…ts are su¢ ciently high. By contrast, the valuation e¤ect of non-delay provisions should be
unambiguously negative, and more so in concentrated industries if agency costs are higher.
We assess corporate governance trade-o¤s and their economic determinants using a dataset
that adds comprehensive information on corporate acquisition attempts to a standard panel of
4
S&P 1500 …rms between 1990 and 2006. We start with a panel of 2,123 …rms (15,163 …rm-year
observations) for which we have standard data on external governance provisions from IRRC and
other …rm and industry characteristics. We then collect information on deal characteristics for 872
initial takeover bids for targets in our panel. We construct our key explanatory variables by dividing the 24 IRRC provisions in the original GIM-index (Gompers, Ishii, and Metrick (2003)) into
two sub-indices based on whether they impose a delay on acquirers. We consider two main proxies
for delay provisions. The …rst is an indicator variable for whether the …rm has a classi…ed board,
and the second is the Delay index from Gompers, Ishii, and Metrick (2003) which includes three
more delay provisions.4 The net-GIM index for the remaining 23 and 20 IRRC provisions, respectively, is our proxy for non-delay provisions. Our data provide a new opportunity to understand
both sides of governance trade-o¤s and their link with the market for corporate control.
Our …rst set of results focuses on the market for corporate control and o¤ers new direct evidence
of interactions with …rms’ economic environment. First, we consider the bargaining side of the
trade-o¤. Consistent with our model, we …nd a robust interaction e¤ect of delay provisions and
industry concentration on target acquisition premiums. It is the combination of delay provisions
and industry concentration that enables targets to receive substantial abnormal returns of up
to 30 percent, which is as much as one and a half times greater than the sample mean target
premium. The size of this e¤ect is noteworthy, especially considering that previous research …nds
only a weak, both economically and statistically, relation between governance provisions and target
premiums.5 In contrast to delay provisions, we …nd no signi…cant interaction e¤ect of non-delay
provisions with industry concentration on target premiums. In addition, the result holds up to a
number of robustness checks, including correction for sample selection bias due to di¤erences in
takeover likelihood and pre-bid value, and a variety of speci…cations.
5
Next, we consider the deterrence side of the trade-o¤. We document large sample evidence
consistent with Bebchuk, Coates, and Subramanian (2002) that delay provisions are associated
with much lower likelihood of receiving a takeover bid. However, in our analysis of deterrence we do
not …nd signi…cant interaction e¤ects of delay provisions and industry concentration, suggesting
that shareholders are not relatively more likely to forgo premiums in concentrated industries.
Overall, our …rst set of results suggests that delay provisions may lead to trade-o¤s for shareholder
value and that …rms’economic environment should be an important determinant of these tradeo¤s.
Our second set of results o¤ers an empirical evaluation of governance trade-o¤s using ex-ante
measures of …rm value. First, consistent with our model, we …nd a robust positive interaction
e¤ect of delay provisions and industry concentration on …rm value. The interaction e¤ect is
quantitatively signi…cant. While for the median …rm each provision in the Delay index is associated
with a 2.7 percentage point decrease in …rm value (in line with Gompers, Ishii, and Metrick (2003)
and Bebchuk and Cohen (2005)), one standard deviation increase in industry concentration is
su¢ cient for signi…cant trade-o¤s to emerge. In fact, there is a positive relation between delay
provisions and …rm value in relatively concentrated industries. The interaction e¤ect holds up
to a number of robustness checks and to addressing omitted variable (via GMM estimation)
and endogeneity (using plausibly exogenous UK industry data) issues. Second, we consider the
agency-cost side of the trade-o¤. We document a reliably negative interaction e¤ect of non-delay
provisions and industry concentration on …rm value, in line with contemporaneous work by Giroud
and Mueller (2010, 2011). One standard deviation increase in industry concentration leads to a
valuation e¤ect of the net-GIM index which is about one and a half times more negative than
for the median …rm. Overall, these results o¤er evidence that there are signi…cant trade-o¤s in
6
corporate governance and that industry structure is an important determinant of their severity.
Our paper relates to the existing governance literature in a number of ways. First, we o¤er
new empirical evidence on how …rms’economic environment interacts with the costs and bene…ts
of corporate governance. A vast empirical literature starting from Gompers, Ishii, and Metrick
(2003) studies the relation between external governance provisions and …rm value.6 These studies
do not typically consider interactions with …rms’economic environment. Recent empirical papers
by Cremers, Nair, and Peyer (2008) and Giroud and Mueller (2010, 2011) consider industry
interactions, but focus only on agency costs and do not distinguish between delay and non-delay
provisions. While complementary to these recent papers, our …ndings suggest that there are
important di¤erences in the valuation e¤ect of delay and non-delay provisions in concentrated
industries.
We also contribute to a sizable literature on the relation between takeover defenses and outcomes in the market for corporate control by considering industry interactions. Our …ndings
suggest that the evidence of a weak relation between antitakeover provisions and target premiums
in the previous literature7 (e.g., Comment and Schwert (1995), Bebchuk, Coates, and Subramanian
(2002)) may be due pooling together industries in which the bargaining e¤ect does not matter
with those in which it does. Thus, it is important to take industry interactions into account in
order to fully understand the economic role of antitakeover provisions in the takeover market.
Second, we o¤er a model of corporate governance trade-o¤s that is novel to the literature.8 In
all the existing papers, the approach is partial equilibrium and the costs and bene…ts of governance
are not analyzed together. In contrast, our general-equilibrium approach is important to generate
new empirical results. In addition, we emphasize that our trade-o¤ view o¤ers a unique perspective
over the empirical …ndings, which are hard to explain by appealing exclusively to either agency or
7
bargaining considerations.
The new insights of our paper have important policy implications, which are complementary
to the classical agency view of external governance provisions. We depart from the bargaining
view of these provisions by emphasizing that the existence of potential bene…ts does not constitute
su¢ cient ground to argue against their repeal, and the loss of bargaining power is in some instances
simply an unavoidable side e¤ect. Thus, we share with the agency view the implication that making
managers more responsive to shareholders is generally good for …rm value. However, our …ndings
also highlight the fact that when trade-o¤s are pronounced, it matters for shareholder value which
governance mechanism is implemented. In fact, in concentrated industries shareholders can get
more “bang for the buck”by repealing protection provisions such as severance and director liability
or by strengthening shareholder rights in elections, rather than repealing provisions related to the
takeover market.
The remainder of the paper is organized as follows. Section I outlines a simple model of governance trade-o¤s and their economic determinants. Section II introduces our data and describes
the construction of our variables. Sections III and IV discuss our empirical results. Section V
concludes.
I
A Model of Governance Trade-o¤s
To understand the economic determinants of corporate governance trade-o¤s, we build on the
standard takeover setup of Fishman (1988) and Berkovitch and Khanna (1990) and introduce the
product market e¤ect. The standard setup has two potential acquirers with private information
about their synergy or willingness to pay for a target.9 This value can be either high or low, that is
si 2 fH; Lg, i = 1; 2, with probabilities q and (1
q); respectively. The probabilities are common
8
knowledge. The …rst bidder to make an o¤er is referred to as bidder 1, the potential second bidder
as bidder 2. Either bidder must pay a cost, c, to participate, which can be thought of as the cost
of the due diligence process. If bidder 2 pays this cost, a takeover contest between the bidders
(English auction) ensues.10 A target with antitakeover provisions (ATPs) has the ability to resist
a takeover o¤er and impose a cost, k; on the bidder. For resistance to have an e¤ect, the resistance
cost must be greater than or equal to the participation costs, that is we assume that k
Finally, we denote by
= 1 a target …rm with ATPs and by
c.
11
= 0 a target …rm without ATPs.
We add two key innovations. First, we introduce a product–market stage that takes place after
the takeover contest. In this stage, product market competition is assumed to be di¤erentiatedproduct in prices.12 If no merger occurs, each …rm earns stand alone pro…t, . If a merger occurs,
A
i
the pro…t of the winning bidder, or acquirer, is
or rival, is
R
i
=
i,
where
=
+ si , and the pro…t of the losing bidder,
6= 0 denotes the “product market e¤ect” of the merger on the
rival.13 This e¤ect is supported by a vast theoretical literature in industrial organization and a
sizable empirical literature in …nance (see Kaplan (2006) for a recent survey).14
Second, we allow for an explicit trade-o¤ between the bargaining bene…ts and the agency costs
of ATPs. In particular, we assume that ATPs impose a cost on the target, irrespective of whether
or not the target resists an o¤er. This cost, C, is intended to capture the agency cost of entrenched
management emphasized by the governance literature (see Gompers, Ishii, and Metrick (2003) and
Bebchuk, Cohen, and Ferrell (2009) for recent evidence, and Scharfstein (1988a) for theory) and
is allowed to vary across industries (see Section I.C).
In summary, we develop a tractable model of the economics of ATPs. In contrast to the previous
literature, our model recognizes the general equilibrium implications of takeovers in the product
market. Further, since the model explicitly accounts for agency costs associated with ATPs, it can
9
be used to understand the economic determinants of governance trade-o¤s. In the next subsection,
we solve the model using backward induction and characterize exactly how governance trade-o¤s
depend on the product market e¤ect.
A
ATPs and Takeover Bidding
This subsection illustrates the intuition behind equilibrium bids and expected target and acquirer
payo¤s, and establishes a link between the product market e¤ect and takeover bidding behavior.15
We begin by considering bidding behavior if bidder 2 decides to enter. Were a takeover to have
no consequence in the product market, equilibrium bidding would be standard with both bidders
bidding their types, si : However, since takeovers have a product market e¤ect, bidders generally
adjust their bids to take the this e¤ect into account. As shown in Jehiel and Moldovanu (2001),
the resulting equilibrium bids are given by si +
6= si ; 8si 2 fH; Lg, i = 1; 2:
To illustrate the intuition for this result, consider the following example: there are three …rms in
a di¤erentiated product oligopoly with price (Bertrand) competition. Firms 1 and 2 are relatively
more e¢ cient and make pro…ts of $15.4 each, while …rm 3 is a laggard with pro…ts of $3.4.16 Firm
3 comes up for sale and …rms 1 and 2 face a takeover contest for it. If …rm 1 wins, …rm 2 pro…ts
will decline to $12.1 post-merger, since …rm 1 can produce …rm 3’s product more e¢ ciently by
using its superior technology. Thus, not only does …rm 2 stand to forgo a potential synergy when
giving up the target, but it also loses some of its business to …rm 1. As a consequence, it is willing
to bid the expected pro…t loss ( =$15.4-$12.1=$3.3) in addition to any potential synergy.
Next, we characterize equilibrium bidding and bidder 2’s equilibrium entry decision. We consider pure strategy equilibria and employ Sequential Equilibrium (SE) and the intuitive criterion
of Cho and Kreps (1987) as a re…nement of beliefs, which is standard in the literature.
10
Proposition 1 In the unique separating equilibrium without ATPs, bidder 1’s initial o¤er is 0 if
bidder 1 is low type and q (L + ) if bidder 1 is high type. If bidder 2 is a low type then bidder
2 does not compete. If bidder 2 is a high type then bidder 2 competes only if bidder 1’s o¤er
is below q (L + ). In the unique separating equilibrium with ATPs, bidder 1’s initial o¤er is
n
o
max L 1 k q + ; 0 if bidder 1 is low type and max fq (H k + ) ; L k + g if bidder 1 is
high type. The target never resists initial bids. If bidder 2 is a low type then bidder 2 does not
compete. If bidder 2 is a high type then bidder 2 competes only if bidder 1’s o¤er is below L
k
+
1 q
.
Proof. See Internet Appendix.
There is a unique separating equilibrium.17 In making its initial o¤er, bidder 1 takes into
account the e¤ect of the o¤er on bidder 2’s belief because if bidder 2 believes bidder 1’s type is
high, then bidder 2 chooses not to compete. As a consequence, without ATPs, bidder 1 bids zero
if it is low type and q (L + ) if it is high type, as that is the lowest bid necessary to signal its
high type. This bid, however, is not an equilibrium with ATPs. In fact, the target in this case
…nds it pro…table to reject this bid as it anticipates that bidder 2 will choose to compete against
a high type bidder 1 that must bear the cost of managerial resistance. Since the expected payo¤
to the target from rejecting this initial bid is q(H
k + ); this is exactly what a high type bidder
1 must o¤er in equilibrium to ensure target acceptance.
B
Industry Variation
In this subsection we derive the comparative-static result that the product market e¤ect of mergers
is larger in concentrated industries, and illustrate its intuition. This result constitutes the critical
step to give empirical content to Proposition (1). Notably, the result is in line with M&A analysts’
view that “takeover contests are very intense when the target is a scarce jewel in an industry [...]
11
that is populated by relatively few …rms."18
In the data, concentrated industries consist either of a small number of relatively similar large
…rms, perhaps due to barriers to entry, or of a handful of dominant …rms competing against fringe
…rms with small market shares. In both cases, …rst principles in industrial organization imply
that, since the price-elasticity of …rm pro…ts is high in concentrated industries, mergers have a
potentially large impact on rivals (Chapter 6 in Vives (1999)). Intuitively, in an industry with a few
major players an acquisition decision by any one of them will have a signi…cant e¤ect on rivals. The
existing evidence supports this intuition as acquisition announcements have a signi…cant negative
e¤ect on industry rivals only in relatively concentrated industries (Song and Walkling (2000)).
We continue with our earlier three-…rm example to illustrate the intuition for dominant-…rm
industries. The pre-merger market share distribution is (40%; 40%; 20%), which clearly shows that
…rms 1 and 2 are dominant and corresponds to an industry concentration (HHI) index of 0.36.
Recall that in the example the product market e¤ect
was $3.3, causing …rm 2 to be willing to
pay a premium of at least 97% of the target’s pre-merger value to prevent …rm 1 from acquiring
the target. Now, contrast this industry with a less concentrated one, with pre-merger market
share distribution of (37%; 37%; 26%) and an HHI index of 0.34. In this second industry, …rm 2
is less pro…table and stands to lose less ( =$1.1) from a hypothetical merger of its rival. Thus,
…rm 2 is willing to make a lower bid o¤er - 16% premium over the target’s pre-merger value.
In summary, our discussion suggests that …rms stand to lose more from rivals’ mergers in
industries that are more concentrated. The following proposition formalizes this result:
Proposition 2 The product market e¤ect of mergers,
concentration.
Proof. See Internet Appendix.
12
=
R
; is increasing in industry
An important feature of this result is that it relates the product market e¤ect of mergers directly
to industry concentration, which is observable, rather than to competition, for which only imperfect empirical proxies are available. This is an important issue since in the data industries with
similar concentration ratios might actually di¤er in their degree of competitiveness (see MacKay
and Phillips (2005) for a related discussion of this well-known issue). Moreover, Proposition (2)
does not rely on a comparison with the perfectly competitive benchmark. Rather, it characterizes
how the product market e¤ect varies as industry concentration is varied along a continuum of
degrees of market power. Thus, the result o¤ers a tight link between the cross-industry variation
in concentration observed in the data and the cross-sectional predictions of our theory.
C
Cross-Sectional Implications
The fundamental insight of our model is that shareholders face a trade-o¤ between bargaining
bene…ts and agency costs of ATPs. This subsection establishes a formal link between industry
concentration and shareholders’governance trade-o¤s, and derives the key theoretical results that
constitute the basis for our empirical tests.
The …rst important feature of Proposition (1) is that we can use the model-implied equilibrium
bidding strategies to establish which type of ATPs should have a bargaining e¤ect. In particular,
the equilibrium strategies from Proposition (1) imply that the expected premium earned by the
target, P , is higher with ATPs, i.e. P
=1
>P
=0
. Intuitively, conditional on receiving a takeover
bid, ATPs give target management the ability to credibly threaten to resist the initial bidder.
This resistance is costly since it imposes delay and bidders are concerned about being outbid by
potential entrants in the takeover contest. This threat is a bargaining technique that leads to
higher initial bid o¤ers, which is what we call the "bargaining e¤ect" of ATPs. The next result
13
gives a necessary and su¢ cient condition for ATPs to have a bargaining e¤ect.19
Corollary 3 ATPs have a bargaining e¤ect if they can impose a su¢ ciently costly delay on potential acquirers, that is if k
e¤ect, that is P
=1
=P
c; then P
=0
=1
>P
=0
. If k < c; then ATPs do not have a bargaining
= 0:
This result has empirical content since in the data not all ATPs enable a target to impose a delay
on acquirers. For example, provisions such as silver parachutes (that provide change of control
payments to non executive employees) are not e¤ective bargaining tools from the standpoint of
our model compared to charter provisions such as a classi…ed board. Thus, our …rst prediction
identi…es which ATPs have a bargaining e¤ect: not all ATPs increase target premiums, but only
those ATPs that can be used to impose a signi…cant delay on acquirers. In the next section,
we draw from Gompers, Ishii, and Metrick (2003) and legal scholars (Coates (2000), Daines and
Klausner (2001)) to isolate a group of ATPs that satisfy this criterion. We refer to this group of
ATPs as "delay ATPs."
A second important feature of Proposition (1) is that it enables us to derive a key comparative
static result on how the bargaining e¤ect depends on the product market e¤ect, .
Proposition 4 The bargaining e¤ect of delay ATPs is higher when the product market e¤ect, ;
is larger, that is
d(P
=1
d
P
=0
)
0.
Proof. See Internet Appendix.
Intuitively, the threat of delay ATPs is more e¤ective the more concerned acquirers are about
being outbid by potential entrants in the takeover contest. Our model implies that this is the
case when the product market e¤ect is stronger. Since Corollary (2) tells us that the product
market e¤ect is larger in concentrated industries, based on Proposition (4) we expect that targets
14
in concentrated industries to be more likely to reap the bargaining bene…ts of delay ATPs. In
summary, our second prediction allows us to identify when delay ATPs are most useful as a
bargaining tool: ATPs that can impose a delay on bidders are more likely to increase target
premiums in concentrated industries.
Finally, we can use the model-implied equilibrium bidding strategies of Proposition (1) to make
cross-sectional predictions on corporate governance trade-o¤s for shareholder value. Governance
trade-o¤s arise in our model since delay ATPs also have agency costs, as emphasized by a large
literature in corporate governance (see, for example, Gompers, Ishii, and Metrick (2003)). While
our predictions on the bargaining e¤ect hold irrespective of agency costs, these costs are an
important aspect of our predictions on the valuation e¤ect of delay provisions. In particular,
given that delay provisions entail agency costs, C; the relation between ex-ante …rm value and
delay ATPs in our model is given by Q
=1
Q
=0
=1
= (P
P
=0
)
C:20 Thus, a trade-o¤
emerges for shareholder value since delay provisions increase value due to their bargaining e¤ect
(P
=1
> P
=0
), but at the same time also decrease value to their agency costs. We can use
Proposition (4) and Corollary (3) to derive a result about the economic determinants of this
fundamental trade-o¤ in corporate governance:
Corollary 5 (1) The relation between delay ATPs and …rm value is ambiguous, since the valuation e¤ect of delay ATPs re‡ects both bargaining bene…ts and agency costs. However, the valuation
e¤ect of delay ATPs is unambiguously less negative when the product market e¤ect, ; is larger,
that is if k
c;
d(Q
=1
d
Q
=0
)
> 0 if and only if
d(P
=1
d
P
=0
)
> 0:
(2) By contrast, the relation between non-delay ATPs and …rm value is unambiguously negative,
since these ATPs do not have a bargaining e¤ect, that is if k < c; Q
=1
Q
=0
=
C:
In order to make empirical predictions, we must consider the realistic case of agency costs that
15
vary with industry concentration. While theory does not make unambiguous predictions on the
sign of this variation (see Holmstrom and Tirole (1989) for a detailed exposition of this point),21
contemporaneous work by Giroud and Mueller (2010,2011) supports the view that agency costs
are higher in concentrated industries. In the Internet Appendix, we generalize Corollary (5) to
an environment in which agency costs vary with industry concentration and allow for a general
dependence of agency costs on the product market e¤ect, that is
dC
d
S 0: In this general case, the
key qualitative insight of Corollary (5) is unchanged and the comparative-static result continues to
hold, provided that the bargaining e¤ect is su¢ ciently large, which is fundamentally an empirical
question. In summary, our third and …nal prediction identi…es both which ATPs entail a tradeo¤ for shareholder value and when this trade-o¤ is more pronounced: 1) delay ATPs have a
less negative valuation e¤ect in concentrated industries; 2) the valuation e¤ect of other ATPs is
unambiguously negative, and its cross-industry variation depends only on agency costs. We refer
to the latter group of ATPs as "non-delay ATPs."
Finally, there is an important implication of generalizing our model to the case in which
agency costs vary across industries. Empirical estimates of agency costs are more likely to be
contaminated by the bargaining side of the trade-o¤ in concentrated industries. Thus, a less
negative valuation e¤ect of delay provisions does not imply that agency costs are lower or do not
matter in concentrated industries, and is fully consistent with these costs actually being higher.
In fact, since our model makes empirical predictions about which ATPs have bargaining power, it
allows us to distinguish delay ATPs, which have both bargaining bene…ts and agency costs, from
non-delay ATPs, which have only agency costs. Thus, by analyzing the valuation e¤ect of delay
provisions we can address our main question of how governance trade-o¤s vary across industries.
At the same time, by analyzing the valuation e¤ect of non-delay provisions we can also address
16
the important empirical question of cross-industry variation in agency costs.
II
Data
We draw from the Investor Responsibility Research Center (IRRC ) volumes published between
1990 and 2006 and collect information on a set of 24 governance provisions for about 2,500 unique
…rms most of which are in the Standard & Poor’s 1500.22 In order to retrieve information on
accounting variables, we match …rm-year observations from IRRC to Compustat and retain those
with non-missing book value of assets. As is standard in the literature, we exclude dual class
…rms, …nancial …rms (6000-6999 SIC range) and regulated utilities (4900-4999 SIC range), and
…rms in industries classi…ed as "Miscellaneous" (SIC codes ending in 9).23 This selection process
results in a …nal set of 15,163 …rm-year observations for 2,123 …rms from 1990 to 2006.
We also construct a sample of corporate acquisitions of U.S. target …rms ("Acquisition sample").
In particular, we gather information on acquisition attempts for our merged IRRC -
Compustat sample from the Securities Data Corporation’s (SDC ) U.S. Mergers and Acquisitions
database. We include both successful and unsuccessful takeover o¤ers24 subject to the following
standard selection criteria (e.g. Masulis, Wang, and Xie (2007)): the value of the transaction is at
least $1 million and at least 1% of the market value of the acquirer’s assets25 ; the target is a U.S.
…rm traded on NYSE, AMEX, or NASDAQ and the acquirer controls less than 50% of the shares
of the target prior to the acquisition announcement.26 Finally, we include only initial takeover
bids, which ensures that we avoid double-counting in the case of follow-on bids.27 Matching these
initial bids to our merged IRRC-Compustat data by calendar year yields our …nal sample of 872
(623 successful) initial takeover bids between 1990 and 2006.
The remainder of this section describes our key explanatory variables. We divide the 24 IRRC
17
provisions in the original GIM-index (Gompers, Ishii, and Metrick (2003)) into two sub-indexes
based on whether they impose a signi…cant delay on acquirers. Our …rst proxy for delay ATPs is an
indicator variable for whether the …rm has a classi…ed board provision. Classi…ed board provision
is recognized to be the most powerful delay provision - it is di¢ cult to repeal and, once in place, can
impose up to a three-year delay on acquirers28 (see Bebchuk, Coates and Subramanian (2002) and
Gordon (2002)). The net-GIM index of the remaining 23 IRRC provisions proxies for non-delay
ATPs. We also consider a second proxy for delay ATPs which, in addition to classi…ed board,
includes the blank check, special meeting, and written consent provisions. This proxy is the Delay
index from Gompers, Ishii, and Metrick (2003) who emphasize that these provisions, designed to
slow down a hostile bidder, are the most crucial in takeover battles that require a proxy …ght
(see also Coates (2000) and Daines and Klausner (2001)).29 The corresponding net-GIM index
includes the remaining 20 IRRC provisions.
Our primary measure of industry concentration is the Her…ndahl-Hirschman Index (HHI),
de…ned as the sum of the squares of the individual company market shares for all the companies
in an industry and computed for each year using the universe of …rms in Compustat. We use Fama
and French (1997) 48 industries as primary industry classi…cation.30 We use the historical SIC
classi…cation from physical Compustat tapes to correct for the issue that Compustat records only
the most current SIC codes (Kahle and Walkling (1996)). In robustness checks, we consider …ner
industry partitions based on the three-digit SIC industry. While our concentration measure has
the advantage that it can be calculated for each year of our sample, potential selection concerns
arise due to the fact that privately-owned …rms are not covered in Compustat. We verify the
robustness of our results to measures of industry concentration reported by the Census Bureau
that include both public and private …rms. The two Census Bureau measures are the HHI for
18
manufacturing industries, and the four-…rm domestic concentration ratio, de…ned as the ratio of
the sales of the four …rms with the largest market share to total industry sales.31
Table I about here
Table I provides summary statistics for our merged IRRC -Compustat and Acquisition samples.
The Internet Appendix provides sources and detailed de…nitions of the control variables, which
are standard, as well as additional descriptive statistics. Overall, both samples are comparable
to those used in related studies (see Gompers, Ishii, and Metrick (2003), Bebchuk and Cohen
(2005), and Cremers and Nair (2005) for studies that use the IRRC sample, and Schwert (2000)
and Bates, Becher, and Lemmon (2008) for studies that use takeover data). In line with these
studies, takeover targets in our Acquisition sample tend to be relatively smaller, younger, and
somewhat under-performing compared to …rms in the merged IRRC -Compustat sample. Our
empirical analysis in the next section addresses this selection issue directly.
III
A
Assessing the Role of ATPs in the Takeover Market
The Bright Side of the Trade-o¤ : Evidence on Bargaining
The …rst two predictions of our model imply that delay provisions should have a bargaining e¤ect
and this e¤ect should be stronger in concentrated industries. Before presenting the results of our
formal tests, we o¤er some univariate evidence in Figure 1, which shows cumulative abnormal
returns (CARs) around acquisition announcement for portfolios of targets with and without the
classi…ed board provision. Each panel shows CARs for the two portfolios of targets in relatively
unconcentrated (bottom tercile of industry concentration, Panel A) and relatively concentrated
(top tercile, Panel B) industries. Clearly, there is a large di¤erence between the CARs of the two
19
portfolios only in concentrated industries, which o¤ers preliminary evidence in support of our …rst
two predictions.
Figure 1 about here
In order to develop formal multivariate tests of these predictions, we use the following baseline
model:
yit = bt + b1 DelayAT Pit + b2 DelayAT Pit
+b3 N etGIMit + b4 N etGIMit
HHIit
(1)
HHIit + b05 Xit + eit
where yit is the takeover premium for target i at time t. The main explanatory variables are the
target’s index of delay provisions, the Net GIM index of the remaining provisions in the GIM index,
and their interaction with the target industry’s HHI, our proxy for industry concentration. Xit
includes the level of industry concentration and standard target and deal controls (e.g., Schwert
(2000), Bates, Becher, and Lemmon (2008), and Bebchuk, Coates, and Subramanian (2002)).
Target controls include sales growth, debt-to-equity ratio, market-to-book ratio, size, and target
stock runup.32 Deal controls include indicator variables for bid hostility, stock payment, and bid
completion. We include year e¤ects, bt ; to control for time variation in takeover activity (see,
for example, Rhodes-Kropf, Robinson, and Viswanathan (2005)). Finally, to allow for potential
serial correlation of deals from the same industry, we evaluate statistical signi…cance using robust
clustered standard errors adjusted for non-independence of observations within industries (see
Wooldridge (2002), p. 275).
The key variables of interest are the two interaction terms with coe¢ cients b2 and b4 . Since the
interaction terms are constructed using HHI, which is a continuous measure of industry concentration, their coe¢ cient estimates provide a tight link between cross-sectional variation in industry
concentration and our model’s comparative static results. In addition, compared to an alterna20
tive approach that would use dummies for high vs low HHI, our estimates of b2 and b4 do not
depend on any arbitrarily chosen concentration cuto¤s and use the full available cross-sectional
information in HHI.33 At the margin, the total bargaining e¤ect of, say, delay provisions can be
calculated by examining the partial derivative of takeover premiums with respect to Delay ATPs:
@yit
@DelayAT Pit
= b1 + b2 HHIit . The null hypothesis is that b2 equals zero.
Table II about here
Table II reports our estimates of baseline speci…cation (1) with target premiums as the dependent variable. Target premiums are calculated using standard event study methodology (see
MacKinlay (1997) for a detailed review) as target cumulative abnormal returns over a ten-day
event window surrounding the announcement of a takeover bid (-5,+5), with the announcement
as reported in SDC’s U.S. M&A database. The …rst two columns in the table correspond to our
two indexes of delay provisions (Classi…ed board and Delay index). The table shows that, consistent with our model, the coe¢ cient on the interaction term is positive and statistically signi…cant
for both these indexes (t-statistic of 2.25 for the Classi…ed board and 3.01 for the Delay index).
Quantitatively, the magnitude of the interaction e¤ect is large. For example, the estimated coe¢ cient of the interaction term in Column 1 implies that in industries with the lowest level of
industry concentration, the classi…ed board provision is associated with a small (2.9%) and not
statistically signi…cant increase in target premiums. By contrast, in industries with the highest
level of industry concentration, the classi…ed board provision is associated with an increase of
31.4% in target premiums, which is highly statistically signi…cant and about 50% larger than
median target premium in our sample.34
An alternative way to gauge the magnitude of the interaction e¤ect is to consider the average
total wealth gain to target shareholders. To capture the change in wealth of target shareholders,
21
we follow Malatesta (1983) and multiply the target premium by the pre-bid market value of target
…rms (as of day -5 relative to the announcement day). Consistent with the results reported in
Table II, in industries with the highest level of industry concentration, shareholders of targets
with classi…ed board provision gain, on average, about $421.7m more than shareholders of targets
without classi…ed board. This is an economically large e¤ect given that the average total wealth
gain to target shareholders at the announcement of a bid is $331.6m.
A second important result in Corollary (3) is that the bargaining e¤ect is not a general property
of any ATP provision. The coe¢ cient estimates for the net-GIM index in Table II o¤er evidence
supporting this result. In particular, when we control for delay provisions, other ATPs in the
GIM index are not statistically signi…cantly related to takeover premiums either in concentrated
or in unconcentrated industries (Columns 1-2). Finally, Column 3 shows that the coe¢ cient of
the interaction of the overall GIM index and industry concentration, while still positive, is only
weakly signi…cant, con…rming that some but not all ATPs are associated with higher premiums.
Table AII (Internet Appendix) shows that the lack of signi…cance for the net-GIM index is
not an artifact of aggregating several provisions into the same index. In particular, we estimate
equation (1) for four sub-indices of the net-GIM index that include provisions related to shareholders’rights in elections (Voting), provisions that protect insiders against job-related liability
(Protection), state laws (State), and a handful of additional ATPs (Other).35 Columns (1)-(4)
report results when we include each of these sub-indices in turn, while Column (5) reports results
when we include all the sub-indices, including Delay, in the same regression. Consistent with our
…rst two predictions, irrespective of the speci…cation chosen, only delay provisions are reliably associated with higher target premiums in concentrated industries. The lack of signi…cance for any
of the other ATP indexes increases our con…dence that the bargaining power of delay provisions
22
is driving our results.
Turning to the control variables in Table II, the coe¢ cient estimates on target and deal characteristics are as expected, suggesting that there are no particular selection issues associated
with sampling takeover attempts from the IRRC volumes. In particular, our estimates con…rm
standard results in the literature that relatively underperforming …rms are more valuable targets, and that all-cash transactions are associated with higher target premiums (see, for example,
Schwert (2000) and Huang and Walkling (1987)). While both …rm and deal characteristics have
explanatory power for target premiums, we emphasize that the interaction of delay provisions and
industry concentration turns out to have a greater quantitative e¤ect than any of these previously
recognized covariates of premiums.
Bargaining and the Method of Payment As an additional test of the bargaining e¤ect of
delay provisions, we examine another bargaining strategy, namely, the likelihood that the target
receives an all-cash o¤er. If delay provisions increase the bargaining power of takeover targets, then
they should also a¤ect important dimensions of takeover bids other than premium. It is generally
held by M&A practitioners (see, for example, Rappaport and Sirower (1999)) that targets consider
all-cash o¤ers more desirable. Therefore, delay provisions should increase the likelihood of all-cash
o¤ers, particularly in concentrated industries.
Table III about here
Table III reports probit estimates of equation (1) with the dependent variable taking the
value of one if the target received an all-cash o¤er. To ease interpretation, we report for all our
explanatory variables the implied marginal e¤ects computed following Ai and Norton (2003) rather
than the underlying probit coe¢ cient estimates. Consistent with our model, the interaction e¤ect
23
of delay provisions and industry concentration is positive and statistically signi…cant for both delay
indexes (t-statistic of 1.75 for the Classi…ed board and 2.09 for the Delay index). The magnitude of
the estimated interaction e¤ects is large. For example, the estimated coe¢ cient of the interaction
term in Column 1 implies that in industries with the highest level of industry concentration, the
classi…ed board provision increases the likelihood of receiving an all-cash o¤er by about 10%, which
is about 40% of the unconditional probability of receiving an all-cash o¤er. Once we control for
delay provisions, other ATPs in the GIM index are not statistically signi…cantly related to the
likelihood of receiving an all-cash o¤er (Columns 1-2). Finally, pooling all ATPs together into
the overall GIM index results in a positive, but not statistically signi…cant, interaction coe¢ cient
with industry concentration (Columns 3).
In summary, the evidence in Tables II and III shows that delay provisions have a signi…cantly
stronger bargaining e¤ect in concentrated industries. By contrast, other external governance provisions are not signi…cantly related to target premiums. These …ndings contribute to the literature
on the relation between takeover defenses and outcomes in the market for corporate control by
o¤ering new direct evidence of industry interactions. The previous literature has found mixed, or
at best weak, evidence of a relation between ATPs and premiums. For example, Comment and
Schwert (1995) …nd that the poison pill provision increases premiums by a weakly statistically signi…cant 3%, and Bebchuk, Coates, and Subramanian (2002) …nd similar results for the classi…ed
board provision. Our …ndings suggest that industry interactions must be taken into account to
fully understand the economic role of ATPs in the takeover market.
24
B
The Dark Side of the Trade-o¤ : Evidence on Deterrence
In our trade-o¤ model, delay provisions have not only bargaining bene…ts for shareholders, but
also agency costs. A vast agency literature emphasizes that an important source of agency costs is
takeover deterrence, that is that ATPs reduce the likelihood of receiving a takeover bid o¤er, thus
insulating managers from the discipline of the market for corporate control. However, existing empirical evidence (see, for example, Bebchuk, Coates, and Subramanian (2002) and Bates, Becher,
and Lemmon (2008)) is limited to relatively small samples and does not address the question of
cross-industry variation. This subsection …lls the gap in the literature and documents large sample
evidence of the deterrence e¤ect. We also verify that our results on the bargaining e¤ect continue
to hold when the deterrence e¤ect is taken into account.
Table IV about here
In the left panel of Table IV (Columns (1)-(3)) we report probit estimates of equation (1) with
the dependent variable taking the value of one if a …rm in our merged IRRC -Compustat sample
(15,613 …rm-year observations) receives a takeover bid in a given year. In addition to our baseline
controls, we also include …rm age at IPO, since there are signi…cant age e¤ects in …rm exit even
after controlling for size (see, for example, Cooley and Quadrini (2001)). To ease interpretation,
we report for all our explanatory variables the implied marginal e¤ects (computed following Ai and
Norton (2003)) rather than the underlying probit coe¢ cient estimates. Classi…ed board (Column
1) and delay provisions (Column 2) signi…cantly reduce the probability of receiving a takeover o¤er
(t-statistic of -3.33 and -1.98, respectively). The magnitude of the deterrence e¤ect is substantial:
having the classi…ed board provision reduces a …rm’s likelihood of receiving a takeover bid by
about 6 percentage points relative to comparable …rms with no classi…ed board provision, which
is about as high as the sample average takeover probability. The coe¢ cient estimates for the net25
GIM index are not statistically signi…cant, supporting our contention that the provisions in the
two delay indexes are the most signi…cant barriers to acquirers. Finally, we do not …nd signi…cant
interaction e¤ects of either delay provisions or the net-GIM index with industry concentration,
which helps to rule out the concern that our results on the bargaining e¤ect are driven by crossindustry di¤erences in investors’anticipation of takeover likelihood.36
Because delay provisions as well as other …rm characteristics such as size and pre-bid value37
are signi…cant determinants of takeover likelihood, our Acquisition sample is clearly not randomly
selected from the population of US public …rms and, thus, our previous estimates of the bargaining
e¤ect may su¤er from sample selection bias (Heckman (1979)). Sample selection is not just
a technical issue in our case, because to have con…dence in the bargaining interpretation of our
results we need to address the alternative story that targets with ATPs have worse agency problems
and are thus worth more to acquirers that can restructure them.
To address this issue, we re-estimate our premium equation (1) jointly in a system of two equations that includes the probit regression for takeover likelihood as a …rst-stage selection equation.
We estimate the model using a standard Heckman’s (1979) two-step procedure:38 based on the
probit estimates in Table IV, we construct estimated inverse Mills ratios and use them to augment
the target premium equation (1) in the second step. The standard errors in the second stage
regression are corrected for the fact that the inverse Mills ratio is estimated. In order to mitigate potential multicollinearity issues (for details, see Wooldridge (2002), p.564) and obtain more
precise estimates, we use age at IPO as an instrument in the probit equation for the likelihood
of receiving a bid. This variable is a valid instrument since it is not related to target premiums,
after controlling for size and age since IPO.39
26
The right panel of Table IV presents a summary of the results. The inverse Mills ratio has a
signi…cant negative coe¢ cient, con…rming that sample selection is a relevant concern in our study
and tends to depress premiums. However, even after controlling for the inverse Mills ratio, there
is a positive and signi…cant interaction between delay provisions and industry concentration. In
addition, the two-step procedure yields estimates of the coe¢ cient on delay provisions that are
similar in magnitude and precision to those reported in Table II. Finally, non-delay ATPs continue
to not have a signi…cant bargaining e¤ect.
In summary, we o¤er large sample evidence in line with Bebchuk, Coates, and Subramanian
(2002) and show that a broader set of delay provisions besides classi…ed boards are powerful
takeover deterrents. Second, we also present new evidence that the deterrence e¤ect of delay
provisions does not display signi…cant variation across industries, suggesting that shareholders are
not relatively more likely to forgo premiums in concentrated industries. Overall, our …rst set of
results suggests that delay provisions may lead to trade-o¤s for shareholder value and that …rms’
economic environment should be an important determinant of these trade-o¤s. We consider this
important question in detail in our valuation analysis.
C
Robustness
Table V shows that our main …nding of a positive interaction e¤ect of delay provisions and industry
concentration on target premiums (Table II) holds up under a battery of robustness checks. We
implement four sets of robustness checks. Panel A shows that the result is robust to using either
shorter [-1,+1] or longer [-42,+30] event-windows. Panel B documents robustness to using …ner
levels of industry aggregation (3-SIC) and alternative measures of industry concentration from the
Census Bureau (HHI and top-four concentration ratio). Panel C shows robustness to controlling
27
for an extensive set of other governance mechanisms, including insider ownership, board size and
independence, and CEO status as chairman of the board.
Table V about here
Finally, Panel D shows that the result is robust to limiting the sample to only single-segment
targets (Row 11) or considering only horizontal mergers (Row 12). Since we de…ne industry based
on targets’primary SIC codes, diversi…ed targets will likely have segments in industries that are
not a¤ected by any given takeover, thus potentially making our full-sample estimates more noisy.
In line with this reasoning, the interaction e¤ect is stronger when we include only single-segment
targets. Finally, the interaction e¤ect is also stronger for horizontal mergers, which are more likely
to be synergistic (Bradley, Desai, and Kim (1988)).
IV
Evaluating Governance Trade-o¤s
Based on the evidence we have presented so far, delay provisions have signi…cant bargaining as well
as deterrence e¤ects in the takeover market. But do these e¤ects translate into bene…ts and costs
that matter to shareholders of …rms that are not subject to change of control events? To answer
this question, we turn to our third and last prediction on the valuation e¤ect of ATPs. According
to the third prediction of our model, the valuation e¤ect of delay provisions is ambiguous on
average, since delay provisions have both bargaining bene…ts and agency costs, but it should be
unambiguously less negative in concentrated industries. By contrast, the valuation e¤ect of nondelay provisions is unambiguously negative and should pick up only cross-industry variation in
agency costs. To test this prediction, we use our merged IRRC -Compustat sample of 2,123 IRRC
…rms between 1990 and 2006 (15,613 …rm-year observations) and perform standard valuation
28
regressions.40 Our measure of …rm value is the industry-adjusted Tobin’s Q. Panel B of Table
AI (Internet Appendix) o¤ers univariate evidence. In particular, the negative correlation between
delay provisions and …rm value is reversed in concentrated industries (top concentration tercile).
By contrast, the reliably negative correlation between …rm value and non-delay provisions gets
stronger in concentrated industries.
In order to develop formal multivariate tests of our third prediction, we use a baseline speci…cation for the key explanatory variables that is as in equation (1) ; with the set of controls, Xit ; as in
Gompers, Ishii, and Metrick (2003). In particular, we control for standard characteristics of panel
…rms that determine value, such as …rm size, age, Delaware incorporation, and S&P 500 inclusion.
Finally, we include both year- and industry-…xed e¤ects, and evaluate statistical signi…cance using
robust clustered standard errors adjusted for non-independence of observations within industries
(Petersen (2009), Thompson (2009)).
Table VI about here
Table VI reports the results. The …rst two columns show that, for both our indexes of delay
provisions, there is a positive and statistically signi…cant coe¢ cient on the interaction term with
industry concentration (t-statistics of 2.03 for the Classi…ed board and 2.09 for the Delay index).
The positive interaction e¤ect of delay provisions and industry concentration is quantitatively
signi…cant. To evaluate quantitative signi…cance, we compare the median …rm (HHI of 0.055) with
a …rm in an industry with one standard deviation (0.065) higher concentration. The coe¢ cient
estimates in Column 2 imply that for the median …rm, each provision in the Delay index is
associated with a 2.7 percentage point decrease in …rm value, which is roughly in line with standard
results in the literature (see, for example, Gompers, Ishii, and Metrick (2003) and Bebchuk and
Cohen (2005)). By contrast, for …rms in relatively more concentrated industries, each provision in
29
the Delay index is associated with only a small change (0.4 percentage point) in …rm value. Thus,
a one standard deviation increase in industry concentration is su¢ cient for signi…cant trade-o¤s
involving delay provisions to emerge.
For non-delay provisions in the GIM index (Net-GIM), there is a negative and statistically
signi…cant interaction e¤ect with industry concentration. The coe¢ cient estimates in Column
2 imply that for the median …rm, each provision in the net-GIM index is associated with a
3.1 percentage point decrease in …rm value. For …rms in industries that have a one standard
deviation higher level of concentration, each provision in the net-GIM index is associated with a
4.5 percentage point decrease in …rm value. Thus, there is a reliably negative relation between
non-delay provisions and …rm value, which is stronger in concentrated industries. This result is
consistent with our third prediction that the valuation e¤ect of non-delay provisions should re‡ect
only agency costs.
A complementary way to gauge the magnitude of the interaction e¤ect is to estimate the valuation e¤ect of ATPs within di¤erent subsamples based on the empirical distribution of industry
concentration. Although an important advantage of our baseline speci…cation is that it does not
depend on any pre-speci…ed cut-o¤s for industry concentration and uses the full available crosssectional information in HHI, examining subsamples enables us to determine whether bargaining
bene…ts are large enough to drive the relation between delay provisions and …rm value in concentrated industries, and whether there a signi…cant negative relation between delay provisions and
…rm value at the low end of the distribution of concentration. As is standard in the literature
(see, for example, Maksimovic and Phillips (2008) and Hoberg and Phillips (2010)), we use the
empirical distribution of HHI to de…ne indicator variables for industry terciles. We also report
results using Department of Justice guidelines that denote as “concentrated”industries for which
30
the HHI index is greater than 0.18 and as “competitive”industries for which the index is less than
0.10. We run our baseline Tobin’s Q-regression with the ATP indices interacted with either the
tercile dummies or the DOJ dummies.
Table VII about here
The results of the sample-split speci…cation are reported in Table VII. The top two panels show
that, for both our indexes, there is a positive and statistically signi…cant relation between delay
provisions and …rm value at the top of the distribution of industry concentration. Importantly,
t-tests of the di¤erence of the estimated coe¢ cients of delay provisions across industry quantiles
robustly con…rm that, regardless of whether we use splits based on terciles or DOJ thresholds,
there are always strongly statistically signi…cant di¤erences at the top of the distribution of industry concentration, which is precisely where our model tells us there are bargaining bene…ts.
Moreover, the positive valuation e¤ect of delay provisions in concentrated industries is quantitatively signi…cant. For example, the coe¢ cient estimates in Column 4 imply that for …rms in the
top concentration tercile, each provision in the Delay index is associated with a 6.9 percentage
point higher …rm value. Finally, consistent with bargaining bene…ts being weaker in relatively
less concentrated industries, the relation between delay provisions and …rm value in the bottom
concentration quantiles is always negative, although generally not statistically signi…cant.
By contrast, the valuation e¤ect of non-delay provisions in the GIM index (Net-GIM), which
our model predicts should pick up only cross-industry variation in agency costs, is signi…cantly
more negative in concentrated industries. Moreover, the negative valuation e¤ect of non-delay
provisions in concentrated industries is quantitatively signi…cant. For example, the coe¢ cient
estimates in Column 4 imply that for …rms in the top concentration tercile, each provision in the
net-GIM index is associated with a 6.7 percentage point decrease in …rm value. While qualitatively
31
consistent with the variation in the net-GIM index, we …nd somewhat weaker results for the overall
GIM index, the negative valuation e¤ect of which is very close in magnitude to that of the NetGIM index in relatively unconcentrated industries, but displays notably less variation than the
Net-GIM index as we move toward the high end of the industry distribution, likely owing to the
countervailing variation of delay provisions.
Overall, our results in this section o¤er evidence of signi…cant trade-o¤s in corporate governance and that industry structure is an important determinant of their severity. We …nd important
di¤erences in the valuation e¤ect of delay and non-delay provisions. These di¤erences are particularly pronounced in concentrated industries in which there is a signi…cantly more negative
valuation e¤ect of non-delay provisions, in line with Hart (1983) that agency costs are higher
in concentrated industries, but at the same time, a signi…cantly positive valuation e¤ect of delay
provisions. These results are complementary to recent evidence in Giroud and Mueller (2010,2011)
and Cremers, Nair, and Peyer (2008), who also consider industry interactions, but focus only on
agency costs and do not distinguish between delay and non-delay ATPs. Our trade-o¤ model
o¤ers a new and unique perspective over our new …ndings, which are otherwise hard to explain
by appealing exclusively to agency considerations.
A
Robustness
Table VIII shows that our main …nding of a positive interaction e¤ect of delay provisions and
industry concentration on …rm value (Table VI) holds up under a battery of robustness checks.
We implement …ve sets of robustness checks. Panel A shows that the positive interaction remains
statistically and economically signi…cant when we use median regressions rather than OLS. This
robustness check addresses the concern that our results could be driven by outliers in Tobin’s
32
Q, which has right-skewed distribution. Panel B shows that the …nding is robust to using …ner
levels of industry aggregation (3-SIC) and alternative measures of industry concentration from the
Census Bureau (HHI and top-four concentration ratio). Panel C shows robustness to controlling
for an extensive set of other governance mechanisms, such as insider ownership, board size and
independence, and CEO status as chairman of the board. Panel D shows that our results are
robust to the inclusion of …nancial and utilities …rms (Row 10). In addition, the panel shows
that our valuation results are stronger when we include only single-segment …rms (Row 11). This
robustness check is important as our industry classi…cation based on primary SIC may be noisy
for multi-segment …rms.
Table VIII about here
Finally, an important potential concern with our results is that our OLS estimates may be subject to the endogeneity and omitted variable bias. First, there is a potential endogeneity concern
with industry concentration. In particular, by making acquisitions more di¢ cult, ATPs may lead
to lower industry concentration. As a result, concentration may be the outcome of within-industry
distribution of ATPs. To address this reverse causality issue, we use U.K. industry concentration
as a plausibly exogenous measure (see Ellison, Glaeser, and Kerr (2010) for another paper using
this identi…cation strategy). It is unlikely that ATPs in the U.S. should be systematically related
to industry structure in the U.K. However, to the extent that the same industries in the two
countries share common fundamental factors such as technology and barriers to entry, we would
expect industry rankings based on U.K. concentration to have predictive power for U.S. industries.
Based on this reasoning, we estimate our valuation regressions using U.K. industry concentration.
Panel E shows that the interaction e¤ect is robust to addressing reverse causality concerns (Row
12).
33
Second, a potential endogeneity concern is that both ATPs and …rm value may be a¤ected by
factors that are unobservable to the econometrician. Although our baseline speci…cation includes
an extensive set of controls, one important latent factor is managerial quality. In particular,
managers of low-value …rms may adopt ATPs to entrench themselves. To address this omitted
variable concern, we include a proxy for managerial quality, CEO press coverage, in the baseline
speci…cation. In order to mitigate potential bias that arises when proxies for an omitted variable
are included as controls (see Wooldridge (2002, p.105) for a detailed discussion), we use the socalled "multiple indicator solution" and a second proxy, CEO career quality, as an instrument for
the …rst proxy (see Falato, Li, and Milbourn (2009) for details on these proxies). The intuition for
this approach is that we can exploit the correlation among our multiple proxies that arises from
their common dependence on latent unobservable variables. Panel E shows that the interaction
e¤ect is robust to accounting for managerial quality (Row 13).41
V
Conclusion
There is …rm theory and plenty of evidence that corporate governance can create value for shareholders. But through which channels and how? To make progress on these questions, we have
studied a speci…c channel, the market for corporate control, and developed a competitive takeover
bidding model of the economic determinants of corporate governance trade-o¤s. Using our theory, we have developed empirical tests to identify which governance provisions matter when in a
cross-section of industries, while, at the same time, pointing to a speci…c reason why they matter.
Overall, our results o¤er a novel perspective on the current corporate governance debate and can
help to assess the important real-world question of whether speci…c governance improvements are
justi…ed even after their side e¤ects are taken into account.
34
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41
Notes
1
See also Bebchuk, Cohen, and Ferrell (2009), Cremers and Nair (2005), and Masulis, Wang
and Xie (2007).
2
See also Comment and Schwert (1995) and Ryngaert (1988). Bhagat and Romano (2002) is
a survey.
3
A vast literature in industrial organization and …nancial economics supports the notion that
rivals’pro…ts change as a result of a merger (see Kaplan (2006) for a recent survey).
4
These additional provisions are blank check, special meeting, and written consent.
5
For example, Comment and Schwert (1995)) …nd that the poison pill provision increases target
premiums by about 3%, but the relation is only marginally statistically signi…cant. Bebchuk,
Coates, and Subramanian (2002) …nd that staggered boards increase target premiums by about
5% and that the relation is not statistically signi…cant.
6
See, also, Core, Guay, and Rusticus (2006), Bebchuk and Cohen (2005), and Cremers and
Nair (2005).
7
See, also, Schwert (2000) and Bates, Becher, and Lemmon (2008).
8
Classical theoretical contributions include Manne (1965), Scharfstein (1988a), and Stulz (1988).
9
Although we discuss the model in terms of "synergies," its setup does not make any assump-
tions about, and our results do not depend on, the sign of the synergies. Most empirical literature
…nds evidence consistent with positive synergies (see, for example, Jensen and Ruback (1983), Jarrell, Brickley and Netter (1988), and Andrade, Mitchell and Sta¤ord (2001)). However, mergers
42
could be driven by other motives, such as, for example, managerial self-dealing (Morck, Shleifer,
and Vishny (1990), Hartzell, Ofek, and Yermack (2004)), or overvaluation (Shleifer and Vishny
(2003), Rhodes-Kropf and Viswanathan (2004)).
10
If bidder 2 does not enter then the target accepts bidder 1’s initial o¤er if it is above the
reservation value.
11
We relax this assumption in Section 2.3 in order to compare ATPs that do and do not impose
an e¤ective delay cost.
12
In the Internet Appendix, we verify that the comparative-statics of our model also hold un-
der the alternative assumption of di¤erentiated-product competition in quantities. The Internet
Appendix is available at http://www.afajof.org/supplements.asp
13
For simplicity we assume
that
14
1
=
2
= . The model’s predictions are unchanged if we assume
depends on the synergy of the winner.
While theory and empirical evidence are …rm on the existence of the product market e¤ect,
the evidence is mixed on the sign of this e¤ect. Studies of wealth e¤ects of merger announcements
on rivals (Eckbo (1983)) and upstream suppliers and downstream customers (Fee and Thomas
(2004)) suggest that horizontal mergers are driven by a combination of the economic e¢ ciency and
information arguments, i.e.
< 0. Studies of the long-run e¤ect of horizontal mergers on product
prices, on the other hand, generally …nd evidence of price increases consistent with greater market
power (see, for example, Kim and Singal (1993)), i.e.
> 0. Our comparative-statics results hold
irrespective of the sign of the product market e¤ect.
15
For detailed characterization of payo¤s, bids, and target premiums, as well as the formal
43
de…nition of the SE and further technical details on Proposition (1), see the Internet Appendix.
16
A
In particular, we assume that the demand function for each good i is given by
pi +
P
j6=i
pj
with intercept A = 10 and slope
of production is zero, while …rm 3’s is constant at
17
i (P )
=
= 4: Firm 1 and …rm 2’s marginal cost
= 4:
While this is the only equilibrium with ATPs, in the case when (1
q) (H
L) < c there is
also a pooling equilibrium without ATPs: Our result in this case is immediate as there is never
competition without ATPs while the equilibrium with ATPs is the same as in Proposition 1. See
the Internet Appendix for details.
18
The quotation refers to an analyst’s comment on Conrail, one of the most widely-studied
takeover contests in the US. On October 15, 1996, CSX and Conrail, the …rst and third largest
railroads in the eastern United States, were about to close a merger deal worth about $8 billion
that was part of an industry-wide consolidation that promised to change the competitive dynamics
of the eastern rail market. The target’s corporate governance laws mandated a shareholder vote
and so the approval was delayed. This delay allowed Norfolk Southern, a rival carrier, to make
a counter-o¤er, which led to a bidding war that greatly increased the premium to Conrail. As
CSX continued to increase its bid, it made a public estimate of a large loss in revenues that would
result from Norfolk Southern’s synergy from merging with Conrail (see Esty (1998) for details).
19
See the Internet Appendix for proof.
20
For ease exposition, in the text we have implicitly assumed takeover likelihood to be one.
However, it is straightforward to see that our conclusions hold unchanged for the more general
case of a positive probability, pT =0;1 , that a target receives a takeover bid (which depends on its
use of ATPs). In this case, ex-ante …rm value is given by Q
44
=0
=V
=0
+ pT =0 P
=0
without delay
+ pT =1 P
=1
delay ATPs can be written as: Q
=1
ATPs, and Q
21
=1
=V
=1
with delay ATPs. Thus, the relation between …rm value and
Q
=0
C + (pT =1
=
pT =0 ) P
=0
+ pT =1 (P
=1
P
=0
).
In fact, according to Hart (1983) (see also Alchian (1950)) agency costs of ATPs should be
lower in competitive industries, since managers who face sti¤ competitive pressure cannot easily
enjoy the “quiet life” even if protected from takeovers. Scharfstein (1988b), however, shows that
Hart’s (1983) model can generate the opposite prediction that managerial slack and, thus, agency
costs should be higher in competitive industries.
22
IRRC volums are published about every two years (1990, 1993, 1995, 1998, 2000, 2002, 2004,
2006). We follow Gompers, Ishii, and Metrick (2003) in assuming that a provision remains in e¤ect
betwen publication dates. A detailed description of the 24 provisions can be found in Gompers,
Ishii, and Metrick (2003), Appendix A.
23
The reasons for these exclusions are as follows: dual class shares have superior voting rights
that are enough to thwart a takeover attempt and make other defenses unimportant; the underlying
law governing the acquisition of …nancials and utilities di¤ers from that applicable to U.S. public
corporations generally (Daines and Klausner (2001), Gompers, Ishii, and Metrick (2010)); and
concentration is not well de…ned for "Miscellaneous" industries (Clarke (1989)).
24
We do so to avoid an ex-post bias that can arise if the likelihood of completion of announced
takeovers varies systematically with industry concentration.
25
The market value of the acquirer’s assets is measured as market capitalization on the 11th
trading day prior to the announcement date.
26
These deals are further screened to ensure that we include only deal forms coded as “mergers”,
45
“acquisitions”, and “acquisitions of majority interest”and exclude spin-o¤s in which the acquirers
are the …rm’s shareholders.
27
We account for multi-bid auctions and follow-on bidding as in Bates, Becher, and Lemmon
(2008). A bid is considered to be an initial bid if no bid for the target is identi…ed for 365 calendar
days before the announcement.
28
A classi…ed board mandates that only a given proportion - typically 1/3 - of the board can
be elected each year so that it takes three years to turn over the board completely.
29
Gompers, Ishii, and Metrick (2003) do not include the Poison Pill in the set of Delay ATPs
because the pill can be passed with less than one day’s notice so e¤ectively every …rm has a Poison
Pill, whereas the other delay provisions require a shareholder vote.
30
There are clearly trade-o¤s in the choice between coarser and …ner industry partitions, since
too coarse a partition may end up pooling together unrelated industries, while too narrow a
partition may be subject to misclassi…cation. Our choice is a compromise between these two
concerns.
31
Since the Census Bureau data is reported quinquennially (e.g, 1992, 1997, and 2002), we
follow previous studies (see, for example, Aggarwal and Samwick (1999), MacKay and Phillips
(2005), and Campello (2006)) in using the Census data for a given year as a proxy for industry
concentration for that year as well as the two years immediately preceding and following it.
32
All …rm-level variables are measured at the the end of the year prior to the bid o¤er announce-
ment.
33
In Table AIII of the Internet Appendix, we verify that our main results hold when we replace
46
the continuous HHI measure with dummies for HHI terciles.
34
Given the empirical distribution of HHI in the Acquisition sample, the estimated bargaining
e¤ect of delay provisions ranges from b1 + 0:01b2 for targets at the lowest level of industry concentration to b1 + 0:30b2 for …rms in the most concentrated industries. The coe¢ cient for the mean
target is b1 + 0:06b2 .
35
See Gompers, Ishii, and Metrick (2003) for details.
36
In fact, if ATPs were relatively more e¤ective at reducing a …rm’s probability of becoming a
target in concentrated industries, then the positive target premiums we document in concentrated
industries might be due simply to anticipation. That is, target stock prices may rise more for
…rms with ATPs simply because the market is more surprised by the announcement of a takeover
o¤er in concentrated industries.
37
In particular, our estimates for control variables con…rm standard results in the literature that
smaller and relatively underperforming …rms are more likely to become targets (see, for example,
Morck, Shleifer, and Vishny (1988), Comment and Schwert (1995), and Bebchuk, Coates, and
Subramanian (2002)).
38
The Internet Appendix details this procedure and provides results of standard diagnostic tests.
39
After controlling for the number of years since IPO, a regression of target premiums on age at
IPO yields a insigni…cant coe¢ cient. For robustness, in unreported results we re-run the Heckman
procedure including the age at IPO variable in both the probit and the premiums regressions.
Although they lose precision somewhat, our estimates of the model’s coe¢ cients are the same.
40
See, for example, Gompers, Ishii, and Metrick (2003) and Bebchuk and Cohen (2005).
47
41
We estimate the system using optimally weighted GMM rather than 2SLS since GMM es-
timates are more e¢ cient in the presence of heteroskedasticity. Although our GMM approach
mitigates concerns about endogeneity, it does not fully resolve the possibility of spurious correlation arising from other unobservable characteristics. Note, however, that the test design partially
addresses this problem. In fact, our main variable of interest is an interaction variable between
ATPs and concentration and it is di¢ cult to imagine other potential omitted variables that would
have a stronger systematic e¤ect on the valuation e¤ect of ATPs across various industry concentration groups. Thus, our use of interaction term should further limit the risk of spurious
correlation.
48
Figure 1: The Bargaining E¤ect of Delay Provisions Across Industries
This …gure plots cumulative abnormal returns (for trading days (-31, +31) relative to the date of the …rst bid)
to four portfolios of targets of takeover bids. The sample consists of all targets in the Acquisition sample. In both
panels, we separate targets into two portfolios based on whether they have a classi…ed board provision. The top
panel (Panel A) includes only targets in industries with relatively low concentration (bottom tercile of HHI in the
sample). The bottom panel (Panel B) includes only targets in industries with relatively high concentration (top
tercile of HHI in the sample). For details on de…nitions of all variables see the Internet Appendix.
Panel A: Targets in industries with low HHI (bottom tercile)
30%
25%
Cumulative abnormal return (%)
20%
15%
10%
5%
0%
-32
-24
-16
-8
0
8
16
24
32
24
32
-5%
Days relative to takeover announcement
Target without Classified Board
Target with Classified Board
Panel B: Targets in industries with high HHI (top tercile)
30%
25%
Cumulative abnormal return (%)
20%
15%
10%
5%
0%
-32
-24
-16
-8
0
8
16
-5%
Days relative to takeover announcement
Target without Classified Board
49
Target with Classified Board
Table I: Summary Statistics
The IRRC-Compustat sample is a panel of 2,123 …rms from IRRC in the 1990 to 2006 period. The Acquisition
sample consists of 872 initial acquisition bids for IRRC …rms announced in the 1990 to 2006 period. GIM-index is
the index of 24 provisions from Gompers, Ishii, and Metrick (2003). Classi…ed Board is an indicator variable that
takes the value of one if the …rm has the classi…ed board provision. Delay index is the sum of four delay provisions
(classi…ed board, blank check, special meeting, and written consent) from Gompers, Ishii, and Metrick (2003).
Concentration is the Her…ndahl-Hirschman index (HHI) of sales of all …rms in the same Fama-French industry
(computed using all …rms in Compustat). Size is the logarithm of the book value of assets. Debt-to-assets is the
ratio of long-term debt to book value of assets. Tobin’s Q is the market value of assets divided by the book value
of assets. Sales growth is the log of the ratio of sales in year t to sales in year t-1. Age is number of years since
the …rm was …rst listed. Age at IPO is years from incorporation to when the …rm was …rst listed. S&P500 is an
indicator variable that takes the value of one if the …rm is part of the S&P 500 index in that year. Delaware is an
indicator variable that takes the value of one if the …rm is incorporated in Delaware. Bid frequency is the fraction
of …rms in the IRRC sample that receive an initial bid in a given year. Stock is an indicator variable that takes
the value of one if the method of payment includes bidder’s equity. Cash is an indicator variable that takes the
value of one if the method of payment is 100 percent cash. Success is an indicator variable that takes the value of
one if the deal is completed. Hostility is an indicator variable that takes the value of one if the target management
rejects the o¤er (SDC). Target takeover premium is the cumulative abnormal return to the target …rms’stock for
trading days (-5, +5) relative to the date of the …rst bid. Target stock runup is the cumulative abnormal return to
the target …rms’stock over 40 trading days before the event window. For additional details on variable de…nitions
and sources see the Internet Appendix.
Acquisition Sample
[N=872]
(1)
Variable
IRRC-Compustat Sample
[N=15,613]
(2)
Di¤erence
(1)-(2)
Mean
Std Dev
Mean
Std Dev
Mean
Classi…ed Board
GIM net of Classi…ed Board
Delay
GIM net of Delay
GIM
0.57
8.66
2.25
6.98
9.05
0.50
2.46
1.22
2.17
2.70
0.61
8.60
2.24
6.96
9.21
0.49
2.51
1.22
2.18
2.71
-0.04**
0.06
-0.01
0.02
-0.17*
Size
Tobin’s Q
Debt/Assets
Sales Growth
Age
Concentration
Age at IPO
6.88
1.82
0.25
0.08
21.28
0.06
17.32
1.44
1.10
0.24
0.26
18.96
0.05
20.29
7.06
1.99
0.23
0.10
25.17
0.07
20.32
1.50
1.49
0.22
0.32
20.79
0.07
21.97
-0.18***
-0.17***
0.02
-0.02
-3.89***
-0.01***
-3.00***
5.56%
22.89%
6.38%
73.01%
45.96%
27.53%
22.40%
20.77%
5.36%
24.46%
44.42%
49.69%
44.70%
41.72%
21.37%
20.16%
Bid Frequency
Hostility
Success
Stock
Cash
Tender O¤er
Target Premium (CAR [-5,+5])
Target Runup
50
Table II: Takeover Analysis –Target Premiums
This table reports results for OLS regressions of the target takeover premium on measures of target governance
and its interaction with industry concentration in the Acquisition sample. The table reports results for Classi…ed
Board and the GIM-index net of Classi…ed Board in Column (1), the Delay index and the GIM-index net of the
Delay index in Column (2), and the overall GIM-index in Column (3). Target controls include size, debt-to-assets,
Tobin’s Q, sales growth, target stock runup, industry concentration, and year and industry …xed e¤ects. All controls
are measured at the end of the …scal year before the bid. Deal controls include stock, success, hostility, and tender
o¤er. Variable de…nitions are in Table I and the Internet Appendix. Robust standard errors clustered at industry
level are in parentheses. Levels of signi…cance are indicated by *, **, and *** for 10%, 5%, and 1% respectively.
Variable
Classi…ed Board
Classi…ed Board*Concentration
(1)
0.009
(0.007)
0.469***
(0.156)
Delay*Concentration
Net GIM*Concentration
0.004
(0.004)
0.059
(0.086)
0.002
(0.004)
0.023
(0.056)
GIM
0.005
(0.003)
0.131*
(0.070)
GIM*Concentration
Firm
Size
Debt/Assets
Q
Sales Growth
Runup
Concentration
Deal
Stock
Hostility
Success
Tender O¤er
(3)
0.019
(0.018)
0.970**
(0.432)
Delay
Net GIM
(2)
-0.019***
(0.006)
0.003
(0.046)
-0.006***
(0.002)
0.063*
(0.037)
0.050
(0.042)
0.070
(0.152)
-0.020***
(0.006)
0.004
(0.050)
-0.005**
(0.002)
0.059*
(0.035)
0.051
(0.044)
0.080
(0.168)
-0.019***
(0.006)
0.002
(0.047)
-0.005**
(0.002)
0.062*
(0.036)
0.047
(0.042)
0.082
(0.154)
-0.035*
(0.020)
0.057**
(0.022)
0.106***
(0.018)
0.086***
(0.020)
-0.036*
(0.020)
0.060**
(0.022)
0.110***
(0.017)
0.080***
(0.021)
-0.035*
(0.020)
0.056**
(0.023)
0.107***
(0.017)
0.087***
(0.021)
0.182
872
0.192
872
0.179
872
Adjusted-R2
Observations
51
Table III: Takeover Analysis –Method of Payment
This table reports probit regressions of the likelihood that a target receives all-cash payment in the Acquisition
sample. Column (1) reports results for Classi…ed Board and the GIM-index net of Classi…ed Board, Column (2)
reports results for the Delay index and the GIM-index net of the Delay index, and Column (3) reports results
for the overall GIM-index. Target controls include size, debt-to-assets ratio, Tobin’s Q, sales growth, target stock
runup, industry concentration, and year and industry …xed e¤ects. All controls are measured at the end of the
…scal year before the bid. Deal controls include success and hostility. Variable de…nitions are in Table I and
the Internet Appendix. Coe¢ cients are reported as marginal e¤ects calculated at the means of the independent
variables. Robust standard errors clustered at industry level are in parentheses. Marginal e¤ects and standard
errors of interaction terms are computed as in Ai and Norton (2003). Levels of signi…cance are indicated by *, **,
and *** for 10%, 5%, and 1% respectively.
Variable
Classi…ed Board
Classi…ed Board*Concentration
(1)
-0.002
(0.009)
0.199**
(0.095)
Delay*Concentration
Net GIM*Concentration
0.005
(0.009)
-0.034
(0.032)
0.010
(0.008)
-0.045
(0.051)
GIM
0.006
(0.007)
0.032
(0.081)
GIM*Concentration
Firm
Size
Debt/Assets
Q
Sales Growth
Runup
Concentration
Deal
Hostility
Success
Tender O¤er
(3)
0.010
(0.033)
0.323*
(0.185)
Delay
Net GIM
(2)
-0.046***
(0.015)
-0.482***
(0.096)
-0.010
(0.011)
-0.195**
(0.076)
0.272**
(0.121)
-0.112
(0.432)
-0.045***
(0.015)
-0.445***
(0.092)
-0.011
(0.009)
-0.193**
(0.078)
0.216**
(0.107)
-0.116
(0.403)
-0.043***
(0.015)
-0.480***
(0.089)
-0.012
(0.010)
-0.167**
(0.072)
0.261**
(0.093)
-0.115
(0.485)
0.120
(0.139)
0.069**
(0.032)
0.480***
(0.071)
0.113
(0.135)
0.073**
(0.032)
0.450***
(0.070)
0.103
(0.143)
0.090**
(0.037)
0.448***
(0.064)
0.386
681
0.315
681
0.305
681
Pseudo-R2
Observations
52
Table IV: Takeover Analysis –Likelihood of Receiving a Takeover Bid
This table reports results from joint estimation of takeover likelihood and target premiums. Columns (1)-(3)
report results of probit regressions of the likelihood that a …rm in the IRRC-Compustat sample receives an initial
bid in a given year. Columns (4)-(6) report results for Heckman two-step selection model of the takeover premiums,
where the …rst-stage selection equation is given by the probit estimates from Columns (1)-(3). Columns (1) and
(4) report results for Classi…ed Board and the GIM-index net of Classi…ed Board, Columns (2) and (5) report
results for the Delay index and the GIM-index net of the Delay index, and Columns (3) and (6) report results
for the overall GIM-index. Controls include size, age, debt-to-assets, Tobin’s Q, sales growth, target stock runup,
industry concentration, and year and industry …xed e¤ects. In addition, Columns (1)-(3) include age of the …rm at
the time it was …rst listed. Variable de…nitions are in Table I and the Internet Appendix. Coe¢ cients in Columns
(1)-(3) are reported as marginal e¤ects calculated at the means of the independent variables. Robust standard
errors clustered at industry level are in parentheses. Marginal e¤ects and standard errors of interaction terms in
Columns (1)-(3) are computed as in Ai and Norton (2003). Levels of signi…cance are indicated by *, **, and ***
for 10%, 5%, and 1% respectively.
Takeover Likelihood (Stage I)
Variable
Classi…ed Board
Classi…ed Board*
Concentration
Delay
(1)
Net GIM*
Concentration
(3)
-0.060***
(0.018)
0.113
(0.132)
-0.002
(0.004)
-0.052
(0.049)
Debt/Assets
Q
Sales Growth
Runup
Concentration
Age at IPO
0.006
(0.005)
0.008
(0.072)
-0.008*
(0.005)
-0.039
(0.051)
-0.007*** -0.007*** -0.007***
(0.002)
(0.002)
(0.002)
-0.004*
-0.004*
-0.003
(0.002)
(0.003)
(0.002)
0.032*** 0.036*** 0.034***
(0.010)
(0.010)
(0.009)
-0.007*** -0.007*** -0.007***
(0.003)
(0.003)
(0.003)
-0.004
-0.004
-0.004
(0.005)
(0.005)
(0.005)
-0.009** -0.009** -0.009**
(0.004)
(0.004)
(0.004)
-0.135
-0.156
-0.148
(0.115)
(0.114)
(0.110)
-0.009*** -0.010*** -0.010***
(0.001)
(0.004)
(0.001)
Inverse Mills Ratio
Pseudo-R2
Observations
(6)
0.018
(0.012)
0.452***
(0.156)
-0.003
(0.005)
-0.060
(0.040)
GIM*Concentration
Age
(5)
0.041*
(0.024)
0.951**
(0.482)
GIM
Firm
Size
(4)
-0.019**
(0.010)
0.020
(0.101)
Delay*Concentration
Net GIM
(2)
Target Premiums (Stage II)
0.007
(0.004)
-0.006
(0.058)
0.001
(0.005)
0.109***
(0.040)
-0.031*** -0.032*** -0.031***
(0.005)
(0.005)
(0.005)
-0.043
-0.045
-0.046
(0.052)
(0.051)
(0.052)
-0.026*** -0.026*** -0.025***
(0.006)
(0.006)
(0.006)
0.050
0.049
0.051
(0.033)
(0.033)
(0.034)
0.028***
0.026**
0.025**
(0.011)
(0.011)
(0.011)
0.126
0.026
0.074
(0.185)
(0.161)
(0.173)
-0.093*** -0.091*** -0.096***
(0.027)
(0.029)
(0.029)
0.081
15613
0.081
15613
0.079
15613
53
Table V: Takeover Analysis –Target Premiums, Robustness
This table reports the coe¢ cient estimates of the interaction term between target governance and industry
concentration in OLS regressions of the target takeover premium as in Table II. Column (1) reports results for
Classi…ed Board, Column (2) reports results for the Delay index, and Column (3) reports results for the overall
GIM-index. Controls are as in Table II. Panel A reports results for target premiums de…ned as the CAR over the
(-1, +1) and the (-42,+30) windows. Panel B reports results for concentration measured as the HHI index of all
…rms in the same 3-digit SIC industry as the target (Row 3), the HHI index from the Census Bureau (Row 4), and
the market share of the top four …rms in the industry from the Census Bureau (Row 5). Panel C reports results for
regressions that include additional governance controls: the percentage of common equity held by the CEO through
stocks and options (Row 6), percent of independent directors (Row 7) and the overall number of directors (Row 8)
on target’s board, CEO status as chairman of the board (Row 9), and all of these controls in the same regression
(Row 10). Panel D reports results for the sample restricted to targets that report only one business segment (Row
11) and takeover bids where both the target and the acquirer are in the same Fama-French industry (Row 12).
Variable de…nitions are in Table I and the Internet Appendix. Robust standard errors clustered at industry level
are in parentheses. Levels of signi…cance are indicated by *, **, and *** for 10%, 5%, and 1% respectively.
Speci…cation
[1] CAR[-1,+1]
[2] CAR[-42,+30]
CB*Concentration
Delay*Concentration
GIM*Concentration
(1)
(2)
(3)
Panel A: Alternative Windows
0.622*
0.351***
(0.339)
(0.127)
0.969**
0.565***
(0.473)
(0.205)
0.089**
(0.043)
0.135*
(0.067)
Panel B: Alternative Measures of Industry Concentration
[3] HHI, Compustat-based,
0.682**
0.320***
Industry de…ned at 3SIC level
(0.279)
(0.115)
0.081*
(0.043)
[4] HHI, Census Bureau,
Manufacturing Industries Only
1.339***
(0.372)
0.348**
(0.161)
0.108
(0.078)
[5] Top 4 Market Share, Census Bureau,
Manufacturing Industries Only
0.951**
(0.365)
0.347***
(0.107)
0.108
(0.095)
Panel C: Robustness to Additional Governance Controls
[6] Managerial Ownership Included
0.976**
0.453***
(0.431)
(0.136)
0.129*
(0.069)
[7] Board Independence Included
0.961**
(0.434)
0.457***
(0.154)
0.136*
(0.068)
[8] Board Size Included
1.000**
(0.433)
0.461***
(0.142)
0.119*
(0.068)
[9] CEO is Chairman of the Board
Included
0.977**
(0.434)
0.444***
(0.146)
0.128*
(0.068)
[10] All Governance Controls
Included
1.001**
(0.430)
0.453***
(0.136)
0.127*
(0.066)
0.527***
(0.133)
0.124**
(0.059)
0.521***
(0.179)
0.132*
(0.072)
[11] Single Segment Targets Only
[12] Targets and Acquirer in the Same
Fama-French industry
Panel D: Subsamples
1.315**
(0.538)
1.152*
(0.595)
54
Table VI: Valuation Analysis
This table reports results for OLS regressions of industry-adjusted Tobin’s Q on governance and its interaction
with industry concentration in the IRRC-Compustat sample. Column (1) reports results for Classi…ed Board and
the GIM-index net of Classi…ed Board, Column (2) reports results for the Delay index and the GIM-index net of
the Delay index, and Column (3) reports results for the overall GIM-index. Controls include size, age, S&P 500,
Delaware incorporation, industry concentration, and year and industry …xed e¤ects. Variable de…nitions are in
Table I and the Internet Appendix. Robust standard errors clustered at industry level are in parentheses. Levels
of signi…cance are indicated by *, **, and *** for 10%, 5%, and 1% respectively.
Variable
Classi…ed Board
Classi…ed Board*Concentration
(1)
-0.053**
(0.024)
0.477**
(0.228)
Delay*Concentration
Net GIM*Concentration
-0.014
(0.010)
-0.168**
(0.078)
-0.019
(0.011)
-0.220**
(0.101)
GIM
-0.029***
(0.009)
-0.005
(0.076)
GIM*Concentration
Firm
Size
Age
S&P500
Delaware
Concentration
Adjusted-R2
Observations
(3)
-0.163**
(0.077)
1.395**
(0.688)
Delay
Net GIM
(2)
-0.101***
(0.026)
-0.120***
(0.045)
0.577***
(0.112)
-0.015
(0.057)
1.033
(0.659)
-0.101***
(0.026)
-0.109**
(0.046)
0.570***
(0.113)
-0.028
(0.063)
0.753
(0.701)
-0.101***
(0.027)
-0.111***
(0.041)
0.572***
(0.112)
-0.017
(0.057)
0.372
(0.743)
0.094
15613
0.093
15613
0.092
15613
55
56
-0.034***
(0.001)
-0.030***
(0.002)
GIM
-0.020
(0.369)
-0.026***
(0.006)
-0.063
(0.296)
15613
707
42
(1)
8728
238
14
(2)
Low
5308
234
14
(3)
-0.038***
(0.001)
[-1.68]*
-0.086
(0.255)
[-0.09]
-0.044***
(0.002)
[-1.09]
-0.039
(0.131)
[-0.72]
-0.024**
(0.011)
-0.042***
(0.000)
[-1.78]*
Panel C: GIM
-0.027**
(0.016)
-0.022
(0.284)
Panel B: Delay
-0.019**
(0.043)
-0.088
(0.102)
-0.023**
(0.049)
[0.16]
-0.067***
(0.007)
[-1.78]*
0.069**
(0.045)
[2.58]**
-0.045***
(0.003)
[-1.74]*
0.156**
(0.050)
[2.87]***
1577
235
14
(4)
Industry Terciles
Medium
High
Panel A: Classi…ed Board
All industries
Net GIM
Delay
Net GIM
Classi…ed Board
Firm-year observations
Industry-year observations
Number of industries (per year)
Variable
-0.029***
(0.001)
-0.031***
(0.001)
-0.029
(0.129)
-0.044**
(0.023)
[-0.84]
-0.062***
(0.002)
[-1.60]
-0.003
(0.929)
[0.88]
-0.052***
(0.005)
[-1.54]
0.030
(0.697)
[1.52]
1311
119
7
(6)
-0.026**
(0.044)
[0.37]
-0.082***
(0.001)
[-2.15]**
0.095**
(0.025)
[2.96]***
-0.046***
(0.000)
[-1.72]*
0.139*
(0.089)
[2.39]**
1000
154
10
(7)
DOJ Guidelines
Medium
High
-0.025***
(0.006)
-0.080
(0.106)
13302
434
25
(5)
Low
This table reports results for OLS regressions of industry-adjusted Tobin’s Q. Panel A reports results for Classi…ed Board and the GIM-index net of Classi…ed
Board, Panel B reports results for the Delay index and the GIM-index net of the Delay index, and Panel C reports results for the overall GIM-index. Column
(1) reports results pooled across all industry-years in the sample. In Columns (2)-(4), we rank industry-year observations into three groups based on the
lower, middle, and upper third of the distribution of HHI. In Columns (5)-(7), we rank industry-year observations into three groups using thresholds de…ned
in the Merger Guidelines of the Department of Justice (Low: HHI below 0.10, Medium: HHI between 0.10 and 0.18, High: HHI above 0.18). We then
interact governance indexes with indicator variables for the three groups. Controls include size, age, S&P 500, Delaware incorporation, indicators for industry
concentration groups, the interaction of each of the controls with industry group indicators, and year and industry …xed e¤ects. Coe¢ cients on these variables
are omitted from the table for brevity and are available upon request. Variable de…nitions are in Table I and the Internet Appendix. p-values based on
robust standard errors clustered at industry level are in parentheses. t-statistics for di¤erences with respect to the Low group are in square brackets. Levels
of signi…cance are indicated by *, **, and *** for 10%, 5%, and 1% respectively.
Table VII: Valuation Analysis by Industry Concentration
Table VIII: Valuation Analysis –Robustness
This table reports coe¢ cient estimates of the interaction term between governance and industry concentration
in OLS regressions of industry-adjusted Tobin’s Q as in Table VI. Column (1) reports results for Classi…ed Board,
Column (2) reports results for the Delay index, and Column (3) reports results for the overall GIM-index. Controls
are as in Table VI. Panel A reports results for median regressions (clustered standard errors are estimated via
bootstrapping). Panel B reports results for concentration measured as the HHI index in the 3-digit SIC industry
(Row 2), the HHI index from the Census Bureau (Row 3), and the market share of the top four …rms in the industry
from the Census Bureau (Row 4). Panel C reports results for regressions that include additional governance controls:
the percentage of common equity held by the CEO through stocks and options (Row 5), percent of independent
directors (Row 6) and the overall number of directors (Row 7), CEO status as chairman of the board (Row 8), and
all of these controls in the same regression (Row 9). Panel D reports results for all industries (Row 10) and for the
sample restricted to …rms that report only one business segment (Row 11). Row 12 in Panel E reports results for
concentration measured as the market share of the top …ve …rms in the corresponding industry in the UK. Row
13 reports results for "multiple indicator" GMM approach using CEO press coverage with CEO career quality as
an instrument. Variable de…nitions are in Table I and the Internet Appendix. Robust standard errors clustered
at industry level are in parentheses. Levels of signi…cance are indicated by *, **, and *** for 10%, 5%, and 1%
respectively.
CB*Concentration
Delay*Concentration
GIM*Concentration
(1)
(2)
(3)
0.233***
(0.072)
-0.024
(0.025)
Speci…cation
[1] Median Regression
Panel A: Outliers
0.272**
(0.110)
Panel B: Alternative
[2] HHI, Compustat-based,
Industry de…ned at 3SIC level
[3] HHI, Census Bureau,
Manufacturing Industries Only
[4] Top 4 Market Share, Census Bureau,
Manufacturing Industries Only
Measures of Industry Concentration
1.347*
0.507**
(0.793)
(0.230)
1.357**
0.612**
(0.675)
(0.315)
0.802**
0.334**
(0.402)
(0.151)
0.138
(0.129)
-0.008
(0.137)
0.028
(0.090)
Panel C: Robustness to Additional Governance Controls
[5] Managerial Ownership Included
1.698*
0.592**
(0.873)
(0.293)
[6] Board Independence Included
1.601**
0.517**
(0.776)
(0.255)
[7] Board Size Included
1.594**
0.516**
(0.772)
(0.245)
[8] CEO is Chairman of the Board
1.217**
0.578*
Included
(0.495)
(0.291)
[9] All Governance Controls
1.315**
0.515*
Included
(0.532)
(0.258)
-0.057
(0.107)
-0.069
(0.118)
-0.052
(0.115)
-0.124
(0.118)
-0.134
(0.124)
[10] All industries
(including …nancials and utilities)
[11] Single Segment Firms Only
[12] UK Concentration
[13] GMM
Panel D: Subsamples
0.988*
(0.581)
1.528**
(0.613)
0.387**
(0.194)
0.560**
(0.245)
-0.098
(0.106)
-0.148
(0.192)
Panel E: Endogeneity
1.366**
(0.690)
1.290***
(0.338)
0.637**
(0.275)
0.361**
(0.176)
-0.036
(0.087)
-0.037
(0.076)
57