A Theory of Preemptive Entrenchment
Dalida Kadyrzhanova
R. H. Smith School of Business
University of Maryland1
January 2007
1
Department of Finance, Robert H. Smith School of Business, University of Maryland, College
Park, MD 20742. Phone: (301) 4053750. Email: dkadyrz@rhsmith.umd.edu. Richard Ericson and
Michael Riordan provided much needed guidance and encouragement from the early stages of the
project. I thank Atila Abdilkadiroglu, Kyle Bagwell, Dirk Bergemann, Patrick Bolton, Anna Bordon,
Yeon-Koo Che, Ulrich Doraszelski, Prajit Dutta, Antonio Falato, Chaim Fershtman, Richard Gilbert,
Laurie Hodrick, Eslyn Jean-Baptiste, Wei Jiang, Kose John, Robert McMillan, Ariel Pakes, Francisco
Perez-Gonzalez, Matthew Rhodes-Kropf, Bernard Salanie, and seminar participants at Columbia University, the 2006 Winter Meeting of the Econometric Society, the IUI/CEPR Conference on Innovation,
Ownership and Competition, Stockholm, the Society for Computational Economics 2005 Meeting, and
the Third "Villa Mondgragone Workshop in Economic Theory and Econometrics, University of Rome
Tor Vergata for helpful comments. All remaining errors are mine.
Abstract
Entrenchment can bene…t shareholders since aggressive managers deter rivals and, thus, make
competition softer in the product market. We formalize this intuition within a simple industry
equilibrium model of optimal entrenchment and test its implications empirically. The key
cross-sectional prediction of the model is that industry leaders bene…t most from preemptive
entrenchment, since they su¤er relatively larger losses in market share from facing tougher
competition. We …nd strong support for this prediction and a number of related crosssectional implications of our model using a large sample of U.S. public …rms between 1990 and
2005 and a wide variety of entrenchment measures, such as external (antitakeover provisions,
state antitakeover laws) and internal (board size and independence, institutional shareholders
and pension funds) governance. In particular, we …nd that (i) industry leaders are more
entrenched than laggards; (ii) the valuation e¤ect of entrenchment is negative for laggards,
but positive for leaders. Moreover, the link between industry leadership and the valuation
e¤ect of entrenchment is more pronounced in industries that are more concentrated, relatively
less heterogeneous, and less subject to foreign competition. These …ndings o¤er a novel
perspective over the debate on whether governance creates value by documenting when that
is actually the case.
1
Introduction
A de…ning characteristic of the modern public corporation is the separation of ownership
and control1 in that, while the ultimate authority rests with shareholders, managers enjoy
substantial discretion. The actual extent of managerial discretion depends on speci…c rules of
governance that shareholders put in place presumably in hopes of maximizing their wealth. A
growing corporate governance literature focuses on the important question of these governance
rules on …rm value.2 However, the important question of why companies di¤er widely in the
amount of discretion they allow their managers has received surprisingly limited attention.
This paper suggests that the balance of power between shareholders and managers is
determined by a fundamental trade-o¤. We develop a theory of optimal corporate governance
in which managerial discretion, or entrenchment, a¤ects …rms’ ability to compete in the
product markets. In the model, greater control over managerial decisions is costly since
it limits managers’ initiative in the product markets. In equilibrium, optimal governance
decisions trade o¤ loss of e¢ ciency under entrenchment and loss of managerial initiative
under tight monitoring.
In particular, we integrate a widely accepted version of the separation of ownership and
control –Jensen’s (1986) ’empire-building’hypothesis –into a dynamic oligopoly model and
study the link between entrenchment and …rm’s position in the industry. We show that
entrenchment is optimal for industry leaders, but not for laggards. Consistent with this
central prediction of the theory, we document empirically that industry leaders tend to be
more entrenched than laggards, even after controlling for …rm characteristics such as size
and age. Moreover, the valuation e¤ect of entrenchment depends on competition position:
while we document a positive valuation e¤ect of entrenchment for leaders, laggards su¤er a
reduction in value from managerial entrenchment.
Finally, by employing a structural model, we can trace the e¤ect of entrenchment back
to market structure. In particular, we show that, due to their greater aggressiveness, leaders
become increasingly dominant and induce smaller rivals to exit. A corollary of this result
is that entrenchment can lead to greater concentration in the product markets. Numerical
simulations reveal that this feedback from entrenchment to market structure is quantitatively
signi…cant. Thus, in a dynamic oligopoly environment in which market structure is endogenous, we show that entrenchment has pronounced real e¤ects.
Model
Our model integrates Jensen’s (1986) ’empire-building’ hypothesis into a dy-
namic industry equilibrium model of homogeneous product, quantity-setting oligopoly (e.g.,
1
Berle and Means (1932) o¤er the …rst systematic treatment of the separation of ownership and control.
La Porta, Lopez-de-Silanes, and Shleifer (1999) emphasize another control problem, namely, that minority
shareholders often have no ability to control dominant shareholders. Stein (2001) surveys the literature.
2
Gompers, Ishii, and Metrick (2003) and Bebchuk and Cohen (2005) …nd that …rm value is negatively
a¤ected by weak governance (see also Bebchuk, Cohen, and Ferrell (2004), Core, Wayne, Rusticus (2004),
and Cremers and Nair (2004)). Other studies focus on the e¤ects of governance on executive compensation
((Bertrand and Mullainathan (1999), and Fahlenbrach (2004)), …rm leverage (Garvey and Hanka (1999)).
1
Ericson and Pakes (1995), Maskin and Tirole (2000)). Firms are heterogeneous and di¤er
in their marginal costs of production. Every period, given current marginal costs, incumbent …rms compete in the product market. Shareholders delegate product market decisions
to empire-building managers who, in the spirit of Jensen (1986), expand …rms beyond the
pro…t-maximizing size. The scope for delegation arises from the fact that the manager has
superior information about product market conditions.
Shareholders cannot observe demand and, following Grossman and Hart (1986) and Hart
and Moore (1990), do not have the ability to contract on managers’actions, i.e. their output
choices, nor to write pro…t-sharing agreements. As a consequence, shareholders cannot use a
standard mechanism to elicit the manager’s private information and, hence, to induce strict
pro…t-maximization. They can, however, monitor managers’ product market decisions by
hiring ”auditors.”
Auditors have a technology to observe output as it is produced, seize the produced goods
so they do not fall under the control of the empire-building manager, and then transfer the
resources back to the shareholders. As our auditing/monitoring technology broadly represents
a variety of internal control mechanisms (e.g., debt, a change of board membership or charter,
board supervision and so on), we refer to it as governance technology and to the optimal choice
of monitoring intensity as governance choice. Thus, we refer to low monitoring intensity as
entrenchment.
We willingly refrain from postulating an exogenous cost of governance and we think of imperfect product market competition as the source of limitations on the ability of shareholders
to control managers. To formalize an endogenous product market cost of corporate control
we model governance and product market decisions as a two-stage game. In the …rst stage,
given the probability distribution of demand, shareholders choose governance to maximize
their expected pro…ts. In the second stage, ’empire-building’ managers observe the realization of demand and then choose output to maximize their objective. Governance decisions
are rational in the sense that shareholders choose monitoring intensity to maximize expected
pro…ts and correctly anticipate the (second-stage) equilibrium of the product market game
between managers. At the end of the period, pro…ts are distributed to shareholders who
then make R&D decisions aimed at lowering marginal costs and, ceteris paribus, increasing
…rm’s market share. Moreover, entry and exit decisions take place. This allows us to focus on
characterizing the two-way link between entrenchment and dynamic interaction between competitors in the product market. Since market structure ultimately results from this dynamic
interaction between competitors, the model allows us to trace the e¤ects of entrenchment on
market structure and its evolution over time.
Results The main empirical prediction of our theory is that industry leaders tend to
be more entrenched than laggards. We …nd strong support for this prediction in a large
sample of publicly-traded companies in the U.S. between 1990 and 2005 using a wide variety
2
of entrenchment measures, such as external (antitakeover provisions, state antitakeover laws)
and internal (board size and independence, institutional shareholders and pension funds)
governance.
Next, we argue that competitive position will have explanatory power for the valuation
e¤ect of entrenchment. In particular, we predict a negative valuation e¤ect for laggards,
but a positive valuation e¤ect for leaders. Again, we …nd strong empirical support for this
cross-sectional prediction of our model.
More formally, our main results stem from the characterization of the Markov Perfect equilibrium of an oligopolistic industry with entrenchment. The intuition behind these results lies
in the nature of the endogenous product market cost of governance. As we show, shareholders
of leading …rms face higher product market cost of governance, and, hence, optimally choose
to entrench their managers. As a consequence, the managers of industry leaders face less
restraint in their ”empire-building”tendencies and pursue more aggressive output strategies,
which in a strategic environment have the added bene…t of eliciting a less aggressive response
from the managers of rival …rms. Thus, through entrenchment, the leader gains a strategic
advantage over its rivals in the form of a more aggressive manager who can marginalize rival
managers, and e¤ectively capture the market. In turn, such a market, naturally, becomes
less attractive to …rms that fall behind since, due to over-production by the leading …rm’s
manager, rivals’market shares decline. Together, these two forces imply that entrenchment
in our model is preemptive in the sense it leads to rival-weakening and exit-inducing behavior.
Finally, our model allows us to derive a number of …ner cross-sectional predictions on the
valuation e¤ect of entrenchment. In particular, we predict a stronger association between
entrenchment and value in industries that are more concentrated, more homogeneous, and
less subject to foreign competition. Again, all of our measures of entrenchment lend robust
empirical support for these predictions.
Ours is a theory of preemption based on imperfections in corporate control. While we
are not the …rst to draw from corporate …nance to understand preemption (see Bolton and
Scharfstein (1990) for a theory of predation based on …nancing problems), our appeal to corporate governance as a source of preemption is novel. We make several distinct contributions
to the entrenchment literature.
First, the idea that managers have empire-building preferences has a fairly long history
(e.g., see Baumol (1959), Marris (1964), Williamson (1964), Jensen and Meckling (1976)).
It has been documented in a number of empirical studies, starting from Donaldson (1984)
and Murphy (1985). Dow, Gorton, and Krishnamurthy (2004) show that it can account
for a number of features of aggregate investment and asset returns, while Philippon (2003)
argues it can help accounting for di¤erences in …rm investment behavior over the business
cycle. Our model is closest to the recent literature which employs managerial ’biases’to derive
endogenously optimal corporate governance arrangements (e.g., Burkart, Gromb and Panunzi
(1997) and Gomes and Novaes (2004) derive the optimal degree of the separation of ownership
3
and control; Dessein (2002) derives the optimal degree of delegation in organizations). The
existing literature assumes perfect competition among …rms, thus ruling out the possibility
of strategic interactions within an imperfectly competitive industry. Our contribution to this
literature is to allow for such strategic interaction and, thus, establish a preemptive rationale
for entrenchment.
Second, our work also contributes to the literature on the relationship between product
markets and corporate governance. Tis literature has mostly focused on executive compensation. Aggarwal and Samwick (1999) and Kedia (2003) build on earlier theoretical contributions of Fershtman and Judd (1987) and Sklivas (1987) and document that some industry level
variables, such as, for example, the Her…ndahl index or whether …rms compete in strategic
complements or substitutes, are determinants of top management compensation. Scharfstein
(1988), Schmidt (1997), and Raith (2003) study the link between product market competition and managerial incentives within models of monopolistic competition. We contribute
to this literature by studying both theoretically and empirically the role of a much broader
set of governance mechanisms. Moreover, and perhaps more importantly, we enrich the set
of determinants of cross-sectional di¤erences in entrenchment and derive implication for the
valuation e¤ect of entrenchment which are new to the literature. Our predictions are particularly important in light of the notorious di¢ culty to …nd empirical proxies for the intensity
of competition.
Third, we contribute to the recent literature in industrial organization (e.g. Ericson and
Pakes (1995), Pakes and McGuire (1994, 2001), Doraszelski and Satterthwaite (2003), and
Besanko and Doraszelski (2004)) that uses dynamic oligopoly models to study the evolution
of industry structure. This literature abstracts from corporate governance issues and assumes
no separation of ownership and control. In contrast, we explicitly model such separation of
ownership and control, which enables us to study the two-way link between entrenchment
and the evolution of industry structure.
Finally, our paper joins a small, but growing literature in corporate …nance that studies
simulated panels based on structural models to recover …rms’decisions (Gomes and Livdan
(2004), Hennessy and Whited (2005, 2006) and Strebulaev (2006)). The structural approach
provides a useful solution to the endogeneity problems embedded within most empirical studies, which, as shown by Coles, Lemmon, and Meschke (2003), are di¢ cult to correct by using
the standard econometric methods.
Outline The remainder of the paper is organized as follows. Section 2 presents our
dynamic oligopoly model of entrenchment and de…nes our notion of equilibrium. In Section
3, we characterize the equilibrium and derive several cross-sectional implications. Section 4
presents our data and tests the cross-sectional predictions of the model empirically. The last
section concludes. All proofs, detailed derivations, and tables are contained in the Appendices.
4
2
A Model of Entrenchment
This section …rst describes the entrenchment model. To ease exposition, we consider an industry without endogenous entry and exit and outline in the Appendix the general model studied
in Section 4. We then de…ne equilibrium entrenchment. Readers who are more interested in
the equilibrium determinants of entrenchment may wish to skim this section and proceed to
the analysis beginning in Section 3.
2.1
Description of the Model
Our entrenchment model is an in…nite-horizon dynamic game in an industry that comprises
N …rms, indexed by i = f1; 2; :::N g : Each …rm consists of a risk-neutral principal, which we
refer to as the shareholder, and a risk-neutral agent, the manager. While the shareholder
can in‡uence the …rm’s marginal costs of production directly by making R&D decisions, his
control over product markets is only indirect and limited by the need of delegating product
market decisions to the manager. Shareholders and managers have a common discount rate
(1 + r)
1
2 (0; 1) :
Costs Firms di¤er in their marginal costs of production. Firm i’s cost is C (! i ) =
c (! i ) + ; where
is a constant, ! i 2 f1; :::; Zg
represents the …rm’s individual state,
and Z < 1. The distribution of …rms’costs, ! = (! 1 ; ! 2 )
2;
summarizes the state of the
industry at each point of time. The model’s primitives as well as …rm’s own state, ! i ; and
the state of the industry, !; are common knowledge. At the beginning of each period, …rms
learn about the current state, !.
R&D and cost evolution Once the state is realized, shareholders make R&D decisions.
Consistent with well documented empirical properties (see, for example, Hall et al. (1986)
and Lach and Schankerman (1988), and Cohen (1995) for a survey), costs are stochastically
decreasing in the R&D level, in the sense that although higher R&D levels increase the
likelihood of success, they do not guarantee cost reduction. Accordingly, state evolution for
…rm i is governed by the following law of motion
! 0i = ! i +
where ! 0i is …rm i’s state in the next period,
reduction, and
i
(1)
i
2 f0; 1g is …rm-speci…c and represents cost
2 f0; 1g is common to all …rms and represents industry-wide depreciation.
As higher states correspond to lower costs, i.e. c(! i + 1) < c(! i ); if
successful at reducing costs through R&D, while if
i
= 1, shareholders are
= 1, the …rm faces higher costs due to
depreciation. An amount x of R&D expenditure increases the probability of higher states,
i.e. P (! 0i j! i ; ! i ; xi ) = P (! 0i jxi ) =
xi
1+xi ;
if
i
= 1. A straightforward implication of our
chosen speci…cation is that the probability of cost reduction is a monotonically increasing
5
concave function of R&D level, a property which, as shown in the next subsection, ensures
uniqueness of the solution to the problem of the optimal R&D choice. Finally, we require
= 0 with probability one if x = 0; i.e. there can be no cost reduction without at least
some R&D, and P (0jx) = 0 for all x. Depreciation is exogenous and iid over time, i.e.
P ( j! i ; ! i ; xi ) = P ( ) = ; if
= 1.
Demand We study the case of linear inverse demand, P = D
bQ; where P is the price
of output and Q is total industry output, and allow for uncertainty in the slope of the demand
curve, b: We do not assume any particular distribution of b, and only require that the support
of the distribution of b is positive and its mean is normalized to one, E (b) = 1: This assumption is convenient as it implies that although managerial decisions and payo¤s are indexed by
demand uncertainty we can safely study them for a given realization of b (and scale for others), thus allowing us to omit indexing by demand uncertainty and ease notation: Moreover,
it is without loss of generality as the alternative assumption of uncertainty over the demand
intercept delivers qualitatively equivalent results. Product market strategies are delegated
to managers since managers can directly observe realized demand while shareholders cannot.
This formalizes the idea that managers have a comparative advantage over shareholders as
they possess more hands-on knowledge of market conditions.
Entrenchment
After R&D and before demand uncertainty is resolved, shareholders
make governance decisions. There is an agency problem in delegation as ’empire-building’
managers, in the spirit of Jensen (1986), expand …rms beyond the pro…t-maximizing size and
shareholders want to make sure managers disgorge the cash rather than wasting it in overproduction. Following Grossman and Hart (1986) and Hart and Moore (1990), we assume
that shareholders do not have the ability to contract on managers’actions in the product market, nor to write pro…t-sharing agreements. As a consequence, they cannot use a standard
mechanism to elicit the manager’s private information and, hence, to induce strict pro…tmaximization. They have, however, one lever to control managers’ output decisions in the
product market. In particular, an auditing/monitoring technology is available to them: every
period at no private cost they can verify the realization of demand by hiring a number ai
of “auditors,” who have a technology to observe demand directly and report truthfully to
shareholders before output is produced. By hiring all available auditors, A; shareholders
can observe demand with certainty. Consequently, a fraction
i
ai
A
of auditors represents
the monitoring intensity, i.e. the probability of the manager being veri…ed. As our auditing/monitoring technology broadly represents a variety of internal control mechanisms (e.g.,
debt, a change of board membership or charter, degree of protection from external takeover
threats, and so on), we refer to it as governance technology and to the optimal choice of
monitoring intensity,
i;
as governance choice. By converse, we refer to (1
i)
as manager-
ial entrenchment. Given the probability distribution of demand, shareholders simultaneously
choose governance to maximize their expected pro…ts net of wage payments to managers,
6
wi . Governance decisions are rational in the sense that shareholders correctly anticipate the
ensuing product market equilibrium.
Product market competition After governance is chosen, managers observe the realization of demand uncertainty and make product market decisions in a homogeneous product,
quantity-setting oligopoly. Consistent with a widely accepted theory of managerial preferences, Jensen’s (1986) ’empire-building’hypothesis, they prefer to run large …rms and, consequently, want to expand production beyond strict pro…t maximization. In particular, in
addition to pro…ts, managers enjoy observable private bene…ts of control, B (qi ; q i ; ! i ) > 0.
To characterize ’empire-building’we assume that
@B(qi ;q i ;! i )
@qi
> 0: The appendix shows that
the second order conditions of the optimal choice of output imply that this assumption is su¢ cient to obtain over-production, i.e. for the manager to expand the …rm beyond its strict-pro…t
maximizing size. As managers expect to be veri…ed and, consequently, lose private bene…ts
with probability
i;
they simultaneously choose output to maximize u (wi )+M (qi ; q i ; ! i ;
where u ( ) is increasing, w
M (qi ; q i ; ! i ;
where
i)
( ) = P (Q) qi
i) ;
0; and
= (1
i) (
( ) + B ( )) +
i(
( ) + 0) =
( ) + (1
c (! i ) qi represents …rm pro…ts. Notice that when
i) B (
i
)
(2)
= 1 (or, equiv-
alently, ai = A) shareholders enforce full pro…t maximization. We assume that the manager
does not respond to monetary incentives, which is a direct implication of our assumption that
pro…ts cannot be contracted upon3 , and normalize manager’s reservation wage to zero
There are several noteworthy features of our model. First, our chosen speci…cation of managerial objectives as having two components, pro…ts and private bene…ts, is entirely standard
in corporate …nance since the seminal contribution of Jensen and Meckling (1976). Further,
our chosen theory of managerial preferences, Jensen’s (1986) ’empire-building’hypothesis, is
widely adopted in the literature to motivate the separation of ownership and control. The
idea that managers are “empire-builders” has a fairly long history (e.g., see Baumol (1959),
Marris (1964), Williamson (1964)). It has been documented in a number of empirical studies,
starting from Donaldson (1984) and Murphy (1985).
Second, to build intuition on the manager’s private bene…ts, B ( ) ; it is useful to observe
that in the context of our model ’empire-building’preferences can arise from two potentially
distinct sources, that have both received attention in the corporate …nance literature: 1) Managers care about revenues more than shareholders do, i.e. B ( ) = P (qi ; q i ) qi ; with
> 0;
in this case their objective ’over-weights’revenues with respect to strict-pro…t maximization,
i.e. M (qi ; q i ; ! i ;
i)
= (1 + (1
i)
) P (qi ; q i ) qi
3
c (! i ) qi : Jensen (1986) observed that
Alternatively, we could assume that the manager is in…nitely risk averse to income risk, and, consequently,
wage is constant every period. See Aghion and Tirole (1997) for the case when pro…ts are contractible and
pro…t-sharing is allowed. In that case, when wages are allowed to vary to provide monetary incentives, a
further complementary motive for delegation arises from the desire to lower wage payments.
7
higher revenues increase manager’s power by increasing the resources under their control.
Murphy (1985) documents that changes in managerial compensation are positively related
to changes in revenues. Donaldson (1984) in his study of 12 large Fortune 500 …rms concludes that the managers of these …rms were not driven by the maximization of the value
of the …rm, but rather by the maximization of corporate wealth, de…ned as the aggregate
purchasing power available to management (p. 3). Finally, higher revenues increase the extent to which managers can extract perks, i.e. non-pecuniary bene…ts like “fancy o¢ ces,
private jets, the easy life, etc... that are attractive to management but are of no interest to
shareholders” (Hart (2001)); 2) Managers care about costs more than shareholders do, i.e.
B ( ) = c (! i ) qi ; with
> 0; in this case their objective ’under-weights’costs with respect
to strict-pro…t maximization, i.e. M (qi ; q i ; ! i ;
i)
= P (qi ; q i ) qi
(1
(1
i)
) c (! i ) qi :
Bertrand and Mullainathan (2003) document that managers appear to care more about workers, especially white-collar workers, than shareholders do. This care for workers and suppliers
in general may result from a desire to avoid con‡ict with unions, ease interactions, or have
higher-quality employees and suppliers. However, managers are likely to care about costs also
if they derive private bene…ts from dealing with suppliers: recent scandals revealed kick back
practices between managers and suppliers were widespread during the 90’s to the point of being characterized in the popular press as a ’kick back culture’(e.g. Business Week, February
2003). For example, Wall Street …rms allocated coveted IPO shares to the private accounts of
CEOs such as Ford Motor Co.’s William Clay Ford and WorldCom Inc.’s Bernard J. Ebbers,
allegedly to win future banking business. On Dec. 20, regulators negotiated a $1.4 billion
settlement with 10 investment banks that, among other requirements, barred such practices.
Third, Appendix B.1 shows that the congruence parameter
> 0 can be conveniently
rescaled so that both formulations imply the same choice of output on the side of the manager.
Consequently, our framework allows us to characterize
as a measure of the ’empire-building’
tendencies of the manager, i.e. of his preference for over-production, without having to
commit to any particular source of such behavior. Moreover, this parametric formulation
of congruence between shareholders’ and managers’ preferences is in line with the recent
optimal delegation literature that employs managerial ’biases’to study the optimal degree of
delegation in organizations (see, for example, Dessein (2002)) and the optimal separation of
ownership and control (see, for example, Burkart, Gromb and Panunzi (1997), Gomes and
Novaes (2004)).
Finally, governance choices,
i;
measure the extent to which shareholders induce strict-
pro…t maximizing behavior on the side of the manager. A straightforward interpretation of
this governance technology is that shareholders, say through the board, can either rubberstamp the production plan proposed by the manager, or they can scrutinize it. Scrutinizing
allows to cut on wasteful over-production and to make sure that the project is implemented
on the right scale. We willingly refrain from postulating an exogenous cost of governance
and we think of imperfect product market competition as the source of limitations on the
8
ability of shareholders to control managers: in our model governance is costly since delegation
fosters managerial initiative by allowing managers to pursue ’tougher’strategies in the product
market. Thus, key to our cross-sectional results is that we do not simply postulate managerial
entrenchment, but rather we derive it as an equilibrium outcome of rational shareholder
decisions.
2.2
Equilibrium
At every point of time, market structure is fully summarized by the current state of the
industry, i.e. the distribution of individual …rm costs, (! i ; ! i ) ; and whenever ! i
…rm i is the current industry leader and …rm
! i;
i is the laggard. The evolution of the state
of the industry is driven by R&D, given the stochastic transition rule for individual states
(1) : We solve for equilibrium in two steps: …rst, for any given market structure, (! i ; ! i ) ;
we use backward induction to solve for the unique symmetric subgame perfect equilibrium
governance and output choices,
pro…ts, P (!) and
(! i ; ! i ) and q (! i ; ! i ) ; and the resulting price and
(! i ; ! i ) ; second, we employ the equilibrium pro…ts obtained in the
…rst step to solve for optimal R&D and the resulting equilibrium market structure, i.e. the
constellation of Markov Perfect equilibrium (MPE) long-run states of the industry.
Optimal governance given market structure We start with a characterization of
the equilibrium governance and output choices. In the second stage subgame, the CournotNash equilibrium in managers’output strategies is characterized by the set of …rst-order conditions
@ (qi ;q i ;! i )
+ @((1
@qi
i )B(qi ;q i ;! i ))
@qi
= 0; 8i = 1; 2. These conditions de…ne managers’out-
put reaction functions in implicit form. The implied solution gives prices, P (! i ; ! i ;
and quantities, q (! i ; ! i ;
i;
i) ;
holders’governance decisions ( i ;
ernance,
it
=
i;
i) ;
as a function of the industry state, (! i ; ! i ) ; given sharei ).
In the …rst stage subgame, shareholders choose gov-
(! i ; ! i ) ; to maximize expected pro…ts
ager output choices q (! i ; ! i ;
i;
i ).
i (! i ; ! i ;
i;
i) ;
given man-
The Nash equilibrium in governance strategies is
characterized by the set of …rst-order conditions
@
i (! i ;!
@
i; i;
i
i)
= 0; 8i = 1; 2: For any
given state of the industry, (! i ; ! i ) ; equilibrium output choices and prices are given by
q (! i ; ! i ) = q ! i ; ! i ;
(! i ; ! i ) =
!i; ! i;
i;
i
; and P (!) = P !;
i;
i
:
i;
i
, and the resulting pro…ts are
Optimal R&D Shareholders choose R&D to maximize the discounted net present value
of pro…ts, V (! i ; ! i ), which is de…ned by the following Bellman equation
V (! i ; ! i ) = max
xi
(! i ; ! i )
xi +
1
E 0 0 V ! 0i ; ! 0
1 + r (!i ;! i )
i
(3)
P
0
0
0
0
where E(!0 ;!0 ) V ! 0i ; ! 0 i =
(!0i ;!0 i )2 2 V ! i ; ! i p ! i ; ! i j! i ; ! i ; xi ; x i is the exi
i
pected value of future pro…ts to the shareholder of …rm i given state ! and Markov strat9
egy fxi (!)g. Denoting the return function of …rm i’s shareholder by Gi (!; x (!) ; Vi ) =
1
1+r E(! 0i ;! 0
0
0
) V ! i ; ! i , we can rewrite the Bellman equation more
compactly as V (! i ; ! i ) = maxxi Gi (!; xi (!) ; x i (!) ; Vi ) : Note that the transition proba-
(! i ; ! i )
xi (!) +
i
bility function P ( ) is continuous, which implies that G ( ) is a continuous function of x (!)
and Vi for all ! and i: An R&D strategy, xi (!) ; that attains the maximum given x
said to be optimal given x
i (!).
i (!)
is
The boundedness and continuity of G ( ) ensures that the
objective is well-de…ned and that optimal R&D strategies exist.
MPE industry structure In equilibrium, market structure is determined by shareholders’ choice of R&D e¤ort, but also by their choice of governance and by managers’
output strategies. Our solution concept for industry structure is Markov perfect equilibrium (MPE). This is subgame perfect equilibrium in Markov strategies, i.e. strategies that
depend only on the ”payo¤-relevant” (Maskin and Tirole (1988, 1995)) state of the game,
! = (! 1 ; ! 2 ). Further, since our governance model implies a symmetric pro…t function, i.e.,
(! i ; ! j ) =
i (! i ; ! j )
and
(! j ; ! i ) =
j
(! i ; ! j ) ; we restrict attention to symmetric MPE.
This implies symmetry in value functions, V (! i ; ! j ) = Vi (! i ; ! j ) and V (! j ; ! i ) = Vj (! i ; ! j ) ;
and in policy functions, x (! i ; ! j ) = xi (! i ; ! j ) and x (! j ; ! i ) = xj (! i ; ! j ) : Formally, we de…ne an MPE as follows
De…nition 1 A vector of strategies, x (!) = xi ; x
i
2 [0; x]2 is an MPE if for any …rm i,
any state !; and any shareholder’s R&D strategy x
~ (!) = (xi ; x
~ i ) 2 [0; x]2 ;
Gi (!; x (!) ; Vi )
Gi (!; x
~ (!) ; Vi )
In words, an MPE is simply a vector of shareholder’s R&D strategies such that each
strategy is optimal given the rival’s strategy, starting from any state. Appendix A shows our
model satis…es the boundedness, continuity, and uniqueness requirements in Proposition 4 in
Doraszelski and Satterthwaite (2003), which allows us to establish the following:
Theorem 1 There exists a symmetric MPE in pure R&D strategies to the governance game
satisfying (3) with the following properties:
V (! i ; ! i ) =
1 =
(! i ; ! i )
xi +
1
E 0 0 V ! 0i ; ! 0
1 + r (!i ;! i )
1
@
E 0 0 V ! 0i ; ! 0
1 + r @xi (!i ;! i )
i
i
(4)
(5)
Proof. See Appendix A.
In the next section we study the impact of imperfect corporate control on product market
outcomes and R&D. Since market structure is ultimately shaped by the product market
rewards to dynamic R&D competition, Section 4 builds on these results and traces the e¤ect
of governance problems on market structure and its evolution over time.
10
3
Empirical Implications
How do shareholders choose governance optimally? What do these choices imply for the valuation e¤ect of managerial entrenchment? Who does managerial entrenchment hurt the most,
leaders or laggards? To address these questions, this section develops an explicit analytic
characterization of the MPE of our governance game. We start by illustrating intuitively
the basic trade-o¤ involved in shareholders’ governance choices. Imperfect product market
competition, we argue, leads industry leaders to entrench managers more than laggards. This
key result allows us to derive a number of cross-sectional implications about the valuation
e¤ect of entrenchment and for which …rms and industries we expect this e¤ect should be more
pronounced.
3.1
Who gains from entrenchment?
Due to imperfect product market competition, optimal governance choices trade-o¤ the marginal bene…t of monitoring managers against its marginal cost. To see how this cost arises
endogenously within our model, consider the …rst stage subgame of our governance game. The
set of …rst-order conditions characterizing shareholders’governance choices can be written as
@Bi @qi
@
=
@qi @ i
@q
@q
i @
i
i
i
;
8i = 1; 2
(6)
The left hand side of equation (6) represents the marginal bene…t of governance for shareholders: stronger governance, i.e. higher monitoring intensity,
over-production as
@qi
@ i
i;
< 0. In particular, by totally di¤erentiating the …rst-order conditions
with respect to governance choices Appendix B.2 shows that sign
Since managers are empire-builders,
tive function implies that
empire-building
allows to cut on wasteful
@2M
i
@q 2 i
i
tendencies ( @B
@qi
@Bi
@qi
@qi
@ i
= sign
@Bi @ 2 M i
@qi @q 2 i
.
> 0; and strict concavity of the managerial objec-
< 0: This bene…t is higher the more pronounced managerial
> 0) are.
E¢ ciency gains, however, are traded-o¤ against the (endogenous) product market cost
@
@q
of governance,
i
i
@q
@
i
i
; as stronger governance weakens …rms in the product market. In
fact, cutting on over-production,
@qi
@ i
< 0; translates into an inward shift of the …rm’s out-
put best-response curve and a consequent ’toughening’ of its rival,
@q i
@qi
< 0; i.e. a move-
ment along its output best-response curve: In fact, Appendix B.2 shows that sign
sign
@2M
@Bi
@qi @q
i
i @qi
ginal revenue of …rm i: This implies that
i
i
=
@q i @qi
@qi @ i
i
i
=
: As the two managers compete in quantities their strategies are strategic
substitutes in the sense that increasing the output of …rm
@q
@
@q
@
@2M i
@q i @qi
i decreases the total and mar-
< 0; and, hence,
@q
@
i
i
> 0: As a result,
> 0: Importantly, this is more costly the larger is the reduction in pro…t
caused by the ’toughening’of the rival,
@
@q
i
i
< 0.
To sharpen intuition on the basic trade-o¤ shareholders face, consider the case when, for
11
the sake of exposition, we parametrize B ( ) = c (! i ) qi ; set
= 1; and N = 2: Appendix
B.2 contains details of the derivations. The …rst order conditions of optimality of shareholder
governance choices (6) take the following particularly simple and intuitive form
2 (1
i ) c (! i )
= q (! i ; ! i ;
i;
i)
Two features that transpire from this characterization are worth emphasizing. First, the
bene…t of governance is a direct implication of our assumption of ’empire-building’managers:
at the margin, expenditures on an extra unit of output are measured by the unit cost of
production and, hence, c (! i ) measures the marginal bene…t of governance. Nevertheless, the
cost of governance arises fully endogenously from the interaction in product markets, as not
allowing managers to credibly pursue aggressive output strategies at the margin lowers pro…t
by q (! i ; ! i ;
i;
i) :
Second, while the bene…t of governance is a direct function only of
the own state, ! i ; the cost depends also on the state of the rival, ! i : In this sense, the
interaction on imperfect product markets is key to deliver the dependence of equilibrium
governance choices on industry structure. Given market structure, the resulting optimal
governance can be solved for explicitly and is given by
D+2c(! i ) 3c(! i )
:
5c(! i )
(! i ; ! i ) = 1
Hence, given market structure, shareholders optimally entrench managers more the higher is
demand and the larger is their cost advantage with respect to rivals.
The following proposition summarizes some key properties of optimal entrenchment and
its dependence on competitive position:
Proposition 1 (Determinants of Managerial Entrenchment) 8! i ; !
governance choices,
i
( ), are such that:
1. industry leaders are more entrenched than laggards, i.e. if ! i > ! i ; then
2
N;
optimal
(! i ; ! i ) <
(! i ; ! i ) < 1;
2. di¤ erences in entrenchment between leaders and laggards are more pronounced in less
competitive industries, i.e. if ! i > ! i ; then
[
(! i ;!
i)
N
(!
i ;! i )]
> 0:
Proof. See Appendix A.
We discuss these properties in turn. If shareholders adapt governance to the competitive
position of their …rm, i.e. to own and rivals’ states, one is naturally led to ask whether
and how entrenchment varies as own or rivals’state change, i.e. as a …rm advances or falls
behind in the industry. Property 1 states that industry leaders optimally choose to entrench
their managers more than laggards, and more so in less competitive industries. These results
follow from the fact that shareholders of the leading …rm face a lower opportunity cost of
entrenchment: industry leaders with large market shares in our model optimally choose more
entrenchment than laggards simply because their stakes in the product market are higher, i.e.
12
they have more to lose from an aggressive output response of their rivals. Moreover, stakes
are higher the more ahead leaders are with respect to laggards.
Our model has implications also for the valuation e¤ect of entrenchment. The following
proposition summarizes some key properties of equilibrium …rm pro…ts and their dependence
on managerial entrenchment:
Proposition 2 (Valuation E¤ect of Managerial Entrenchment I) 8! i ; !
timal governance choices,
i
( ), are such that:
N;
2
op-
1. for leaders entrenchment has a less negative impact on …rm value than for laggards, i.e.
if ! i > ! i ; then
V (! i ;! i )
(! i ;! i )
>
V (! i ;! i )
(! i ;! i ) ;
2. di¤ erences in the valuation e¤ ect of entrenchment between leaders and laggards are more
pronounced in less competitive industries, i.e. if ! i > ! i ; then
V (! i ;! i )
(! i ;!i )
V (! i ;! i )
(!i ;! i )
N
> 0:
Proof. See Appendix A.
These results are in essence the valuation counter part of Proposition (1) ; and crucially
follow from the de…nition of shareholder value in (4) ; which implies that
V
=
: Again,
the basic intuition is that shareholders of the leading …rm face a lower opportunity cost
of entrenchment since they have more to lose from an aggressive output response of their
rivals. Thus, we can readily test the predictions in proposition (??) by looking at whether
the valuation e¤ect of entrenchment depends on …rms’competitive position.
3.2
Entrenchment and Market Structure
Our …rst set of predictions is derived for a given market structure, i.e. taking …rms’ competitive position as given. However, it is plausible that entrenchment has a feedback on
competitive position, in the sense that …rms can actually pull ahead of their rivals or fall
behind due to their governance choices. In this section we exploit a further advantage of
our structural model and allow asymmetries between competitors to arise endogenously as a
result of the dynamic interaction in the product market. This allows us to derive a second
set of testable implications about the valuation e¤ect of entrenchment.
To this end, we incorporate entry and exit into the basic model of entrenchment presented
in Section 2 so as to allow for an endogenous determination of market structure. Every
period there are n
from state
!e
N heterogeneous …rms active and N
n potential entrants. To enter
shareholders must pay a random sunk cost of xei drawn from a distribution
F e ( ) independently and identically distributed across …rms and periods with E (
Setup costs are private information. We let
e (!;
i
e
i)
e
i)
=
e
.
2 f0; 1g indicate stay out or entry
respectively. If a string of unsuccessful outcomes occurs, shareholders may …nd it optimal to
13
exit and liquidate the …rm, in which case they get a sell-o¤ value of
i
dollars, exit in the next
period and never re-enter again. Following Doraszelski and Satterthwaite (2003), we assume
that scrap values are randomly drawn from a distribution F ( ) with E ( i ) = ; independently
and identically distributed across …rms and periods, and privately observed prior to making
exit and R&D decisions. We let
i (!;
i)
2 f0; 1g indicate exit or continuation respectively.
With respect to our earlier de…nition in Section 2, the symmetric MPE now comprises
R
also an operating probability, which for an incumbent is given by 'i (!) =
i (!; i ) dF ( i )
and represents the probability that incumbent i remains in the industry; while for a potential
R e
e
e
entrant is 'ei (!) =
i (!; i ) dF ( i ) and represents the probability that potential entrant i
enters the industry. We characterize the MPE of this full-‡edged version of the model numer-
ically, simulate it for 10,000 periods, and argue that with realistic discount rates predatory
governance emerges as an economically signi…cant equilibrium strategy. Moreover, it leads
to concentrated product markets both in the short- and in the long-run with sizable adverse
consequences for consumer welfare.
Parameter values The model primitives we need to parametrize are
( ) ; B ( ) ; r; xe ;
; and P, i.e. demand and costs, managerial preferences, technological opportunities, and
the institutional structure of the industry. Table 1 contains a summary of parameter values.
To compute the symmetric MPE, we use a variant of the iterative algorithm of Pakes and
McGuire (1994) which we detail in Appendix C.
Demand and cost patterns determine the pro…t function,
e
!
+
( ) : For marginal costs, c (!) =
2 [ ; + 1] ; we follow standard practice (e.g. Ericson and Pakes (1995), Budd,
Harris and Vickers (1993)) and parametrize them as an exponential function of the state,
while normalizing the minimum unit cost of production, ; to one. We choose the market
size parameter, i.e. the demand intercept, D; so as to have at most three active …rms in the
benchmark model. Consequently, we set the maximum number of active …rms in the industry,
N; to three.
Managerial preferences are parametrized as B (qi; q i ; ! i ) = c (! i ) qi : As we emphasized
in Section 2, the congruence parameter
measures the intensity of the ’empire-building’
preference of the manager, i.e. it controls the overall importance of his preference for overproduction relative to strict-pro…t maximization. Recall from our discussion of the …rst
order conditions of optimal governance choices, that the marginal bene…t of governance is a
direct function of the intensity of ’empire-building’preference of the manager. Consequently,
we choose
to insure that for every possible equilibrium con…guration of the industry, the
governance problem is well de…ned, i.e. it always implies positive (although not necessarily
strictly positive) monitoring, i.e. we set
always high enough to guarantee
so that the marginal bene…t of governance is
(! i ; ! i )
0; 8 (! i ; ! i ) 2
nt :
Setting
= 0 delivers
the benchmark model with perfect corporate control.
Technological opportunities are fully described by the properties of the stochastic process
14
that governs the law of motion between states. Our chosen parameter value for the rate of
depreciation, ; is standard and implies an equal chance of incurring or not depreciation.
Moreover, given this value, normalizing the monopolist’s exit state to one, we calculated the
upper bound on the state space, !; as the state at which it is not optimal for the monopolist
…rm to invest anymore. The implied value of ! is 28 (For further details on this procedure
see Pakes and McGuire (1994)).
Finally, the ”institutional”structure of the industry is described by the common discount
rate, (1 + r)
1
; the scrap value,
; and the sunk entry cost, Xe : We choose r to match a
standard annual interest rate of 4%. Sunk entry cost is chosen so that on average entry costs
are about 1/125th of total production costs within a period. The scrap value is chosen to be
half of the sunk entry cost. This, together with our choice of a relatively high entry state
(! E = 4), ensures that in the benchmark entry is relatively cheap and exit entails a relatively
low value. Consequently, there are relatively few opportunities to monopolize the industry
due to traditional ’barriers to entry’sources.
Market structure Were there no entrenchment, possibly due to an e¤ective public
policy toward governance, the industry would be a relatively symmetric ’natural duopoly’
with …rms operating at roughly similar e¢ ciency levels. Table 2 reports that in this case
there are two …rms active in about 90% of the periods. The industry also displays a relatively
high turnover, with average length of time such that same pair of duopolists is active of
22 periods. There are periods when one …rm falls behind and eventually exits so that its
rival earns monopoly pro…ts, but these periods are negligible in the overall history of the
industry. In contrast, governance issues lead to markedly more asymmetric and concentrated
industry structures as one …rm monopolizes it for 95% of the periods. The turnover rate is 8
times smaller than in the benchmark and the average length of time such that the same …rm
monopolizes the industry is 68 periods. As a result, the Her…ndahl index almost doubles and
is close to one.
Entrenchment have a dramatic impact on industry evolution as well. The right panels
of Figure 7 reveal that, due to entrenchment, the industry converges to highly asymmetric
structures over time. The industry is relatively symmetric in the early stages of competition,
when rivals …ercely battle to get ahead of each other. For example, after T = 5 periods
the industry is characterized by symmetric states such as (4; 4); (5; 5); (6; 6) with probability
of 0.2, 0.2, 0.4 respectively and state (6; 6) is the mode. However, as competition unfolds
over time, one …rm’s R&D fails or the other’s succeeds and a leader soon emerges. For
example, after T = 25 periods, the monopoly state 8 is the mode, with probability 0:18; and
after T = 50 periods, the monopoly state 11 is the mode, with probability 0:12: State (6; 6)
remains the most likely symmetric state, but it becomes less and less likely over time, as it
has probability of 0.16, 0.08 after T = 25; 50 periods, respectively.
An important further implication of entrenchment is that competition is …ercest when
15
…rms are neck-to-neck. In fact it is exactly in these relatively symmetric states that an
industry leader emerges and the outcome of competition is decided. To see this we plot the
sum of the two …rms’R&D, x (! i ; ! j )+x (! j ; ! i ) ; in the bottom panels of Figure 5.1. Clearly,
holding the combined cost level constant, competition is more intense among …rms that are
relatively close in the industry than among …rms that are far from each others. Consider,
for example, state (5; 5) ; where both …rms’R&D is 2.07. On the other hand, once a leader
emerges there is a marked drop in the R&D of the laggard. In fact, if …rm 1 pulls even slightly
ahead and the industry moves to state (6; 5), then …rm 2 scales back its R&D to 1.15 while
…rm 1 increases it to 2.36. This further enhances the initial asymmetry and, as the industry
evolves toward states where rivals are further apart, say (6; 4); …rm 2 continues to scale back
its R&D to 0.56, while …rm 1 keeps it high at 2. Hence, …rm 2 falls further behind. Eventually
in state (6; 3) …rm 2 gives up, hence propelling …rm 1 into a position of dominance.
Table 3 contains detailed information on the characteristics of the resulting product market
outcomes, such as sales-weighted average pro…ts, output, market shares, prices, and markups.
The most striking feature is that if one takes market structure as given and compares just
monopoly periods or just duopoly periods prices are unambiguously lower (and output higher)
with entrenchment. However, due to entrenchment, on average prices are about 20% higher
than in the benchmark. The reason for this apparently paradoxical result is that entrenchment has a signi…cant impact on market structure. In particular, due to governance issues,
monopoly periods are much more frequent. Thus, the question of whether we should pursue
public policy toward corporate governance is ultimately a question of whether the bene…ts
from having a larger number of …rms outweigh the costs from having less e¢ cient and less
intensely competing …rms.
Entrenchment
The central mechanism of our model is that as a …rm advances with
respect to its rivals, shareholders …nd it optimal to entrench managers by choosing weaker
governance. Figure 3 illustrates this point by plotting optimal governance choices as a function
of the state of the industry: relatively small variation in costs (cmax
into substantial variation in governance (j
objective is (P (Q)
i ci ) qi ;
max
min j
cmin = 0:08) translates
= 0:9). In other words, as managers’
an 8% lower physical cost translates into an e¤ective cost to the
manager that is up to 90% lower than the initial cost.
As the resulting period pro…t function in Figure 4 reveals, entrenchment induces a more
skewed distribution of pro…ts across industry con…gurations by changing the product market
rewards to leadership and making pro…ts more sensitive to the rival’s position. In particular,
entrenchment makes pro…ts more sensitive to changes in market structure. A straightforward
measure of the magnitude of this e¤ect is provided by
min!
i
j
i
(! i ; ! i )j, so that with entrenchment,
0:61; 0:61) compared to
x
x
x (!
i)
= max!
i
j
i
(! i ; ! i )j
=(0:96; 0:73; 0:66; 0:62; 0:61; 0:61;
=(0:29; 0:25; 0:24; 0:23; 0:23; 0:23; 0:23; 0:23) in the benchmark.
This e¤ect is inherited by shareholder value, i.e. the maximized expected present value of
16
pro…ts. The right panels of Figure 6 plots each as a function of the state of the industry: the
rival’s position within the industry is a key determinant of the valuation e¤ect of entrenchment. To see this point, consider a symmetric state, say (3; 3) : In that state, by pulling ahead
by only one state a …rm gains a 65% higher probability of advancing further than its rival.
Thus, we expect the valuation e¤ect of entrenchment to be concentrated particularly among
symmetric states. The following proposition states this result:
Proposition 3 (Valuation E¤ect of Managerial Entrenchment II) 8! i ; !
timal governance choices,
i
( ), are such that:
2
N;
op-
1. di¤ erences in the valuation e¤ ect of entrenchment between leaders and laggards are more
pronounced in more symmetric industries, i.e. if ! i > ! i ; then
V (! i ;! i )
(! i ;!i )
V (! i ;! i )
(!i ;! i )
!
< 0;
2. di¤ erences in the valuation e¤ ect of entrenchment between leaders and laggards are less
pronounced in industries that face an adverse external shock, i.e. if ! i > ! i ; then
V (! i ;! i )
(! i ;!i )
V (! i ;! i )
(!i ;! i )
> 0;
Proof. See Appendix A.
We discuss these properties in turn. Property 1 states that industry leaders optimally
choose to entrench their managers more than laggards, and more so in more symmetric
industries. This result follows from the fact that in symmetric states shareholder value is
a¤ected most by changes in market structure, since by pulling only slightly ahead of its rivals
a …rm forces them to catch up and, thus, gains a higher probability of advancing further in
the future. The value of entrenchment inherits this key feature that emerges whenever market
structure is endogenous. Property 2 is a direct consequence of this property as well. In fact,
the main e¤ect of an adverse external shock, i.e. an increase in ; is to force all …rms to
invest less heavily in R&D due to tougher industry conditions. This, in turn, favors laggards
and induces a change in market structure toward relatively more symmetric states and, thus,
higher sensitivity of shareholder value.
4
Data and Empirical Tests
Our industry model predicts an inverse relationship between entrenchment and competitive
position, as we expect industry leaders to have more entrenched managers than laggards.
Moreover, we predict that the bulk of the negative valuation e¤ect of entrenchment should be
concentrated among laggards. In this section, after brie‡y describing our dataset and sources,
we implement an empirical test of these and a number of …ner predictions. In particular,
we divide our empirical analysis in two parts. We start by documenting that entrenchment
17
varies with …rm position in the industry, i.e. that industry leaders tend to have higher levels of
entrenchment than industry laggards. We verify that this …nding is robust to using alternative
entrenchment measures and a variety of controls. Second, we test the cross-sectional valuation
implications of our model stated in Proposition 2. We show that for position within industry
matters for the e¤ect of entrenchment on …rm value, measured by Tobin’s Q. Consistent with
our model, for industry leaders entrenchment increases shareholder value. By contrast, we …nd
that for industry laggards, entrenchment destroys value by more than previous studies had
recognized. , These results continue to hold after we address endogeneity concerns by using
average market-to-book ratios during 1980-1985. Thus, by linking entrenchment to industry
position, we obtain sharp estimates of the e¤ect of corporate governance on shareholder value.
4.1
Data
In order to test the empirical predictions of our model, we need data on industry position,
ATPs, and …rm valuation (Tobin’s Q). Our dataset has 896 …rms from the IRRC dataset
that collects data on ATPs. For our sample between 1990 and 2005, we combine various
entrenchment data with …rm characteristics, such as Tobin’s Q, size and age, and industry
characteristics, such as concentration and import penetration. This leaves us with a total of
about 8254 observations.4
External Governance We use …ve alternative measures of external governance that
are available for a sample of about 1500 publicly-traded …rms for the years 1990 to 2005. The
…rst is the SB&P index, based on the sum of the staggered board and poison pill provisions,
that ranges from 0 to 2. The second is the SB index, an indicator variable for the presence
of the staggered board provision. The third measure is the G index constructed by Gompers,
Ishii, and Metrick (2003), which indicates the total number of antitakeover provisions in a
…rm’s charter5 and varies between 0 and 24. The data for these indexes are assembled and
reported about every two years (1990, 1992, 1995, 1998, 2000) by the Investor Responsibility
Research Center (IRRC). Higher values of both indexes are associated with more ATPs.
Bebchuk, Cohen, and Ferrell (2004) argue that not all of the 24 provisions in the G index
are e¤ective antitakeover measures and construct a more parsimonious alternative to the Gindex, the E-index, which uses only six provisions: staggered boards, limits to shareholder
bylaw amendments, limits to shareholder charter amendments, supermajority requirements
for mergers, poison pills, and golden parachutes. In line with the argument in Bebchuk et
al. (2004), who show that their index has a stronger association with stock returns and …rm
value than the G index and that an index of the other 18 provisions is not signi…cantly related
4
Note that in using industry controls such as concentration and import penetration, we are limited to the
analysis of manufacturing industries only. This explains the lower (approximately half as many) number of
observations compared to previous studies of the e¤ect of ATPs on …rm value. We also exclude …nancial …rms
(SIC codes 6000-6999) and regulated utilities (SIC codes 4900-4999), which have their own special governance
structure and entrenching devices.
5
A detailed description of takeover defenses included in the G-index can be found in GIM, Appendix A.
18
to …rm value, in our empirical tests we also use the E-index. The G and E indices have a
positive 74% correlation. Following Gompers, Ishii, and Metrick (2003) and Bebchuk, Cohen,
and Ferrell (2004), we assume that all indexes remains unchanged for the years in which IRRC
does not report scores6 .
Finally, we also include an indicator variable for states that have high (greater than 4)
number of state antitakeover statutes. The data on state antitakeover protection are from
Bebchuk and Cohen (2002).
Internal Governance We use four measures of internal governance that are available
for a sample of about 1500 publicly-traded …rms for the years 1990 to 2004. The …rst is
a dummy based on the presence of an institutional blockholder. We de…ne blockholders as
shareholders, external to the …rm, with an ownership greater than 5% of the …rm’s outstanding
shares. The second measure is the percentage of common stock held by the 18 largest public
pension funds as a group. To construct these measures, we use data on institutional share
holdings from Thompson/CDA Spectrum, which collects quarterly information from the SEC
13f …lings. By using institutional blockholding rather than simply institutional ownership, we
mitigate the problem that institutions with minor stakes have few incentives to be involved
in …rm-speci…c decisions and reduce the noise associated with picking up non-monitoring
shareholders.7
Board characteristics are commonly recognized as a valid measure of entrenchment. Previous research suggests that board characteristics that a¤ect the board’s e¤ectiveness include
board size, independence, and composition (John and Senbet (1998)). Thus, our third and
fourth measures of internal governance are the size of the …rm’s board of directors and the
percentage of independent directors. We de…ne an independent director as one that is not
a¢ liated with the …rm as an executive or in any other capacity. Thus, our measure is similar
to those in the prior literature that have classi…ed directors as “inside,”“outside,”or “grey.”
The board data we use is provided annually from 1996 by the Investor Responsibility Research
Center (IRRC).
Concentration Measure
To measure concentration, we draw from the Bureau of Cen-
sus to obtain four-…rm domestic concentration ratios8 , which are reported quinquennially
(1992 and 1997). We use concentration ratios from most current census year as well as concentration ratios at the time of the …rm’s IPO. We use four-digit SIC classi…cations to de…ne
industry membership. Although all of the results in the paper are presented with four-digit
SIC classi…cations, in unreported tables we replicate our …ndings at the three-digit level with
no qualitatively di¤erent results.
6
Although both measures show little within …rm change from point to point, our results do not depend on
the assumption that the value of the antitakeover provision index in-between survey years is unchanged. In
unreported results based solely on data from the survey years, we replicate the reported results.
7
For details on the construction of the internal governance variables see Cremers and Nair (2005).
8
CR4 is the ratio of the sales of the top four …rms in an industry to total industry sales.
19
We also check for robustness of our results to alternative measures of concentration. To this
end, we verify that our results are robust to replacing concentration ratios with the Her…ndahlHirschman Index (HHI) reported by the Census Bureau. Moreover, if there is vigorous import
competition, the e¤ect of a merger on competitors is likely to be less pronounced, making
smaller for any given level of domestic concentration. However, concentration measures
reported by the Census Bureau include only domestic producers. We use import penetration
measures from the NBER Productivity Database from 1990-2001.
Firm value
In order to examine the relation between ATPs and …rm value, we sup-
plement our entrenchment measures with various items from the COMPUSTAT and CRSP
databases. We measure Tobin’s Q as the ratio of market value of assets to book value of assets. Market value of assets is de…ned as total assets (item 6) plus market equity minus book
equity. Market equity is de…ned as common shares outstanding (item 25) times …scal-year
closing price (item 199). Book equity is calculated as stockholders equity (item 216)9 minus
preferred stock liquidating value (item 10)10 plus balance sheet deferred taxes and investment
tax credit (item 35) when available minus post retirement assets (item 336) when available.
Book value of assets is total assets (item 6). For robustness, we use industry-adjusted ROA as
a measure of …rm value, where ROA is the ratio of earnings to average equity for the current
and prior …scal year (item 20/(item 60+ item 60t
1 )).
Other …rm- and manager characteristics Other …rm characteristics are from the
CRSP/Compustat merged industrial annual database (CCM). Outliers are removed by winsorizing the extreme observations in the 1% left or right tail of the distribution. We use
several …rm characteristics, such as age, size, and …nancial position. We use log of the book
value of assets (item 6) to measure …rm size. Firm age is measured as log of the number of
months since …rm was …rst listed. We estimate …rm age based on the …rst date for which
pricing information about a …rm is available from the CRSP database, and supplement remaining information using pre-CRSP data from Jovanovic and Rousseau (2001). To proxy
for the value of growth options in the numerator of Q, we use the ratio of R&D (item 46)
to sales (item 12), the ratio of capital expenditure (item 128) to assets (item 6), and the
ratio of advertising (item 45) to sales (item 12). To proxy for …rms’…nancial position we use
cash holdings (item 162) and dividend payments (item 26), normalized by …rm’s capital stock
(item 8) in previous …scal year.11
We use the ExecuComp database to collect information on managerial ownership. Since
the ExecuComp database does not contain all companies that are part of the IRRC universe,
we retrieve all missing CEO information by looking up the companies’ proxy statements
9
or the …rst available of common equity (item 60) plus preferred stock par value (item 130) or total assets
(item 6) minus total liabilities (item 181)
10
or the …rst available of redemption value (item 56) or par value (item 130)
11
We also check the robustness of our results to normalization by assets (item 6).
20
directly.
Summary statistics Table 1 presents the summary statistics of the key variables in
our data.
Tables 2 and 3 show summary statistics of entrenchment for two subsamples of …rms. In
these tables, we look at the percentage of …rms that have a given level of entrenchment in
industries in the upper and lower quartile of industry position (leaders and laggards, respectively). Note that high entrenchment is markedly more frequent among industry leaders - for
example, there are about twice as many …rms with the highest value of the E index among
leaders as among laggards (4.18% vs 2.85%). We test this formally the next subsection (Tables
4 and 5).
4.2
Test 1: Determinants of Entrenchment
Prediction 1 states that industry leaders are more likely to have higher levels of entrenchment.
To test this prediction we specify a probit model of ATPs that relates the probability that
a …rm has a high level of entrenchment to industry position. Our model implies a positive
coe¢ cient on position. However, if, for example, more entrenched managers compete more
aggressively and drive rivals from the market, …rms with a higher level of entrenchment will
end up industry leaders, biasing the results of our probit analysis. To address potential
endogeneity and omitted variables issues, we also use an instrumental variables approach
using industry position in the …rm’s IPO year as in instrument.
To test the model’s prediction that the industry leadership is associated with the likelihood
of greater entrenchment (Prediction 1), we use the following general regression speci…cation:
Pr EntrenchmentHigh = f ( + P ositioni + Xi )
(7)
where EntrenchmentHighHigh indicates that a …rm has high level of entrenchment. P osition
is our measure of industry leadership, and X is a set of control variables that includes mainly
…rm characteristics, i.e. age, size, and performance, as measured by market value of assets
over book value of assets. X also includes antitakeover provision statutes in the state of
incorporation (overall number of antitakeover provision statutes) and a dummy for whether
…rms are incorporated in Delaware. We also include year- and industry-…xed e¤ects. The null
hypothesis is that ; the coe¢ cient on position, is equal to zero.
One alternative to controlling for industry e¤ects would be to remove all cross-sectional
variation by including …rm-…xed e¤ects in the analysis. Because our measures of entrenchment
do not display a su¢ cient amount of time-series variation, identifying the e¤ect only from
time-series variation within the …rm is typically not feasible. That is, there are an insu¢ cient
number of cases of reversal of …rms from high to low levels of entrenchment in the same
21
…rm to draw a robust inference from any estimations.12 Finally, in order to account for
serial correlation and heteroskedasticity, we compute the standard errors by clustering the
observations within each …rm. This process treats the time series of observations within the
…rm as a single observation, e¤ectively eliminating any serial correlation.
Tables 4 and 5 report our results from estimating (7). We run a set of baseline regressions
to demonstrate how the probability that a …rm has high level of entrenchment is related to
industry leadership. We do using a variety of entrenchment measures: external governance
(Table 4) and internal governance (Table 5). In all speci…cations we …nd a positive and
highly signi…cant coe¢ cient on position (for example, 0.204 for SB&P). Thus, as predicted
by our model, industry leaders have higher levels of entrenchment. The marginal e¤ect of
these estimates is large: moving a …rm from the lowest quartile of position to the highest
increases its likelihood of having a high level of entrenchment by about 0.12, which accounts
for about 50% of the unconditional sample mean. In unreported results, we also estimate a
simple linear probability model (OLS) and use SB&P, SB, E, and G as alternative measures
of ATPs. The results are the same.13
4.3
Test 2: Valuation E¤ect of Entrenchment
In this subsection, we turn to Prediction 2 that implies that the negative impact of entrenchment on …rm value should be smaller for industry leaders. We use Tobin’s Q as a measure
of (unconditional) …rm value. In doing so, we follow earlier work on the association between
corporate arrangements and …rm value (see, e.g., Bebchuk and Cohen (2005), Demsetz and
Lehn (1985), Morck et al. (1988), McConnell and Servaes (1990), Lang and Stulz (1994), Yermack (1996), Daines (2001)). Our variable of interest is the …rm’s position in the industry.
We run a cross-sectional regression of Tobin’s Q on a number of standard controls (size, age,
R&D, advertising expenses, and a dummy for Delaware) and on entrenchment, separately for
industry laggard and for industry leaders. We also include year- and industry-…xed e¤ects.
The results are displayed in Tables 6 and 8. Within the industry laggards subsample,
entrenchment has a pronounced negative e¤ect on …rm value - entrenched …rms have up to 6%
lower valuation. For industry leaders, however, there is a reliable positive association between
entrenchment and …rm value, with a positive (up to 5%) and statistically signi…cant coe¢ cient
on ATPs. This result shows that the positive impact of entrenchment on competitive position
12
The lack of identi…able cases would cause a potentially severe sample selection bias from including …rm …xed
e¤ects in panel regressions and identifying solely out of somewhat anomalous …rms with multiple entrenchment
reversals.
13
Although our choice in this respect is standard, one might argue that our results so far are an artifact of
the choice of threshold in the antitakeover index. To address this issue we estimate a more general ordered
probit model where we do not need to make assumptions about what constitutes a high level of entrenchment
and we can just estimate, for example, the likelihood of adopting one, versus two, versus three antitakeover
measures. In general, the model’s prediction can be represented by an ordered probit model with unknown
thresholds, where position (and a linear combination of position and control variables) determines the estimated
thresholds. Ordered probit results con…rm our …ndings: in all speci…cations we …nd a positive and highly
signi…cant coe¢ cient on position (0.225 in the speci…cation with controls for SB&P).
22
of industry leaders is quantitatively signi…cant. In all speci…cations, we control for managerial
ownership.
In Tables 7 and 9, we also test whether comparative dynamics implications in Prediction
2 are supported in the data. To this end, we continue measuring the e¤ect of entrenchment
on …rm value by position subsamples, but looking at how this e¤ect di¤ers across di¤erent
industries. Consistent with Prediction 2, the mediating e¤ect of position in the relation
between entrenchment and …rm value is present only in concetrated industries, industries
with less import penetration, and relatively symmetric industries.
Thus, the predictions of our simple model of entrenchment and industry dynamics are
con…rmed in the data.
4.4
Robustness
Endogeneity of entrenchment The correlation identi…ed between entrenchment and
…rm value raises the question of simultaneity. Does entrenchment bring about a lower …rm
value, or is the correlation produced by the selection of entrenchment by …rms with lower
value? A similar objection could be raised for our …nding of the mediating e¤ect of industry
position. Our model itself implies that entrenchment is both a cause and an e¤ect. Simple
OLS regressions that relate entrenchment to …rm value are likely to yield seriously biased
results if used to infer the causal impact of right-hand side variables on the left-hand side
variable.
In order to address simultaneity concerns, we include …rms’average Tobin’s Q in the …rst
half of the 1980s to control for the e¤ect of …rm value on adoption of various entrenchment
measures. Lehn and Zhao (2006) show that, controlling for …rms’ average Tobin’s Q in
the …rst half of the 1980s, there is no relationship between contemporaneous measures of
entrenchment and …rm value. The results are in Table 10: even after inclusion of past Tobin’s
Q, we continue to …nd a signi…cant positive relation between …rm value and entrenchment,
but only for industry leaders.
Other measures of value Table 12 shows that our results from Tables 6 and 8 are
robust to using ROA as a measure of …rm value, instead of Tobin’s Q. ROA is a measure
of operating performance, and, as such, less subject to mismeasurement and/or misvaluation
concerns that pertain to using Tobin’s Q as a measure of …rm value. Moreover, since our
model links explicitly entrenchment to operating performance, this alternative measure is
still valid in testing empirical predictions of the model.
Size and hi-tech Using Tobin’s Q as a measure of …rm value over our sample period
(1990-2005), raises the concern that valuation e¤ects might be driven by a few high-tech …rms
during the dotcom bubble of the late 1990s. To alleviate this concern, we include an indicator
for the industry’s high-tech status from Loughran and Ritter (2004) in our tests. Table 11
23
reveals that our results are qualitatively robust to the inclusion of this variable and, thus, are
not likely to be driven by a small number of high-tech …rms.
Finally, in Table 13 we verify that our competitive position variable is not simply picking
up a size e¤ect. In fact, when we run our valuation tests within size classes (small and large),
our results on the di¤erences in valuation e¤ect of entrenchment between leaders and laggards
continue to hold.
5
Conclusion
We have introduced entrenchment into a dynamic oligopoly model and derived and tested
several cross-sectional implications of the model. We have analyzed the dynamic entry, exit
and R&D problem of shareholders faced with the governance problem of monitoring empirebuilding managers in charge of product market decisions. We have characterized the dynamics
of the interplay between market structure and entrenchment by detailing the resulting industry equilibrium.
We have documented empirically that, consistent with the main prediction of our theory, industry leaders tend to be more entrenched than laggards, even after controlling for
…rm characteristics such as size and age. Moreover, the valuation e¤ect of entrenchment is
markedly di¤erent between industry leaders and laggards. Finally, industry characteristics,
such as concentration, homogeneity, and foreign competition, have explanatory power for the
valuation e¤ect of entrenchment. Thus, our theory of preemptive entrenchment o¤ers an
empirically relevant framework to study the link between entrenchment and …rm value.
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Appendix A. Proofs of Theorems and Propositions
Proof of Theorem (1).
We …rst prove existence of a symmetric, pure strategy MPE
by verifying that our governance model (3) satis…es assumptions 1-4 in Proposition 4 in
Doraszelski and Satterthwaite (2005) (DS). Note that for the basic governance model in
Section 2 without entry and exit we only need to provide arguments for existence of R&D
strategies.
1. Our model has 2 …rms with states ! i 2 f1; :::; M g and M < 1: Firms discount future
payo¤s using (1 + r)
1
2 (0; 1), and we assume that R&D expenditures are bounded
(x < 1). Boundedness of cost function (assumed functional form for costs implies that
c (M + n) = c (M ) 8n) implies that the pro…t function
(! i ; ! j ) is bounded. These
boundedness conditions satisfy assumption 1 in (DS).
2. Vi enters Gi ( ) only through the expected value of …rm i’s future cash ‡ows, ensuring
continuity of Gi ( ) in Vi for all ! and all i. Moreover, current pro…t is additively
separable from investment and the transition probability function P ( ) is continuous,
which implies that G ( ) is a continuous function of x (!) for all ! and i: Continuity of
Gi (!; x (!) ; Vi ) in x (!) and Vi satis…es assumption 2 in (DS).
3. Our transition probability function P (! 0i j! i ; ! i ; xi ) satis…es the unique investment
choice (UIC) admissibility condition in (DS). We assume, in addition, that x >
h
ijSj
with Vi 2 V ; V
; which ensures that assumption 4 in (DS) holds.
V
V
4. Our governance model of product market competition gives rise to symmetric pro…t
functions, i.e.
1 (! i ; ! j )
=
2 (! j ; ! i ) :
Moreover, P1 ! 0i ; ! 0j ; ! i ; ! j ; xi (!) ; xj (!)
=
P2 ! 0j ; ! 0i ; ! j ; ! i ; xj (!) ; xi (!) : This ensures that the local income functions Gi ( ) are
symmetric and exchangeable, and, thus, satisfy assumption 5 in (DS).
Proof of Proposition (1). As shown above,
follows from monotonicity of
(! i ; ! j ) = 1
1
c(! i )
+
1
c(~
!i )
The result
(! i ; ! j ) in !; which is a direct consequence of monotonicity of
the cost function, c (! i ), in !: For part 1, suppose ! i > !
~ i : Then,
D+2c(! j )
5
D
5c(! i )c(! j )
D+2c(! j ) 3c(! i )
:
5c(! i )
(! i ; ! i )
(~
!i; ! i) =
(! i ; ! j )
(! j ; ! i ) =
< 0: For part 2, suppose ! i > ! j : Then,
( c (! j ) + c (! i )) < 0:
Appendix B. Claims and Detailed Derivations
B.1. Equivalence of empire-building formulations
This appendix shows that by rescaling parameter ; we can ensure manager’s output choice
is the same in both revenue and cost maximizing formulations.
29
;
Proof. If B ( ) =
P (qi ; qj ) qi ; then the manager’s …rst order conditions are given by
(1 + ) Ri (qi ; qj ) = c (! i ) : If B ( ) = c (! i ) qi ; then the manager’s …rst order conditions are
given by Ri (qi ; qj ) = (1
) c (! i ) ; which is equivalent to 1 + ~ Ri (qi ; qj ) = c (! i ) ; where
~=
1
:
B.2. Details on subgame-perfect equilibrium in the product
market
This appendix uses backward induction to solve, for any given market structure (! i ; ! i ) ; for
the unique symmetric subgame perfect equilibrium governance and output choices,
and q (! i ; ! i ) ; and the resulting price and pro…ts, P (!) and
Proof. Given (! i ; ! i ) and ( i ;
i) ;
(! i ; ! i )
(! i ; ! i ).
equilibrium in the second-stage managers’game is
characterized by the …rst order conditions
@Ri (qi ; qj )
@Mi
=
@qi
@qi
and a set of second order conditions
@ 2 Mi
@qi2
=
@ 2 Ri
@qi2
i c (! i )
=0
(8)
< 0; where Ri (qi ; qj )
@2M
…rm i’s revenues. Our pro…t function satis…es A
@q12
1
@2M
@q22
P (qi ; qj ) qi denotes
2
1 @ M2
@q1 @q2 @q2 @q1
@2M
2
> 0; which
ensures stability and uniqueness of the symmetric Nash equilibrium in the managers’game.
Solution to (8) gives q1 = q (
1;
2)
and q2 = q (
To characterize the dependence of q on
2;
1) :
’s, we totally di¤erentiate (8) w.r.to q1 ; q2 ; and
to obtain
1
@ 2 M1
@ 2 M1
dq1 +
dq2 = c (! i ) d
2
@q1 @q2
@q1
@ 2 M2
@ 2 M2
dq2 = 0
dq1 +
@q2 @q1
@q22
Using Cramer’s rule, we have that
Similarly,
@q
@
i
i
@q2
@ 1
@2M
1 @Bi
A @qi @q
=
i
i @qi
@q1
@ 1
: Since
1
@ 2 M2
@ 2 M2
1
A c (! i ) @q22 : Since @q22
@2M i
@q i @qi < 0 (competition is
=
< 0 and A > 0;
@q1
@ 1
< 0:
in strategic substitutes),
> 0:
Using our demand function speci…cation, …rm i’s manager best response is given by
Ri (qj ) =
D bqj
i c(! i )
2b
and implied equilibrium price and pro…ts are given by P ( i ;
( i;
Denote gi
@gi
@ i
j)
=
(D+ j c(! j ) 2
j) =
3b
D+ i c(! i )+ j c(! j )
and
3
: Equilibrium output choices are thus given by q ( i ;
1
(D +
9b
( i;
= 0 and is given by
j) :
i c (! i )
+
j c (! j )
3c (! i )) (D
j)
=
2 i c (! i ) +
j c (! j ))
In the …rst stage, shareholders’best-response governance solves
i ( j)
=
1
4c(! i )
(6c (! i )
30
D
j cj ) :
Symmetrically, we can derive
i c(! i ))
j
( i ) ; plug back into this FOC and solve for
i
Notice that
i
to get
i
D + 2c (! j ) 3c (! i )
5c (! i )
=1
does not depend on b: Moreover,
@ 2 gi
@ 2i
< 0 and
@ 2 g1 @ 2 g2
@ 21 @ 22
@
@ 2 g1
@ 2 g2
1@ 2 @ 2@ 1
> 0;
which ensures stability and uniqueness of the symmetric governance equilibrium (Gale-Nikaido
univalence theorem, Nikaido (1968)).
The resulting equilibrium in the product market, given
i;
j
; results from substituting
optimal governance choice into managers’ second stage output, (qi
where (after some algebraic manipulation) each qi
qi (! i ; ! j ) =
implying P (! i ; ! j ) =
D+2c(! i )+2c(! j )
;
5
i;
2 (D + 2c (! j )
5b
j
i;
j
; qj
i;
j
),
is given by
3c (! i ))
and, hence, qi (! i ; ! j ) =
2(P (! i ;! j ) c(! i ))
:
b
The implied
subgame-perfect equilibrium pro…t function is given by
(! i ; ! j ) =
2 (P
c (! i ))2
2
=
b
b
D + 2c (! j )
5
3c (! i )
2
Appendix C. Details of computation
This appendix describes the approach used to solve numerically for the optimal R&D policy
once the parameters of the model are set. The solution to the problem of the …rm is found
using value and policy function iteration method along the lines of Pakes and McGuire (1994).
It exploits the computational simpli…cation entailed by the Markov Perfect assumption combined with the recursivity of the optimization problem. The algorithm iterates on the vector
containing value functions, V , and the vector of R&Ds, X, (one for each state !), until the
maximum of the element-by-element di¤erence between successive iterations in these vectors
is below a pre-speci…ed tolerance level. All computations are carried out in Gauss 3.0.
5.1
Computational algorithm
The algorithm iterates on the V and X matrices until the maximum of the element-by-element
di¤erence between successive iterations in these matrices is below a pre-speci…ed tolerance
level. The calculations in each iteration are performed separately for each row (industry
structure) using only the old values of the matrices V and X: If each element of V and X has
converged, then we are assured of having computed a MPNE of the dynamic game.
We describe the process that provides us with new V and X matrices at every iteration.
31
The computation is done separately for each element of V and X: Thus we describe what
the algorithm does to V [!; n] and X [!; n], where ! is the industry vector, and n stands for
n; N ) :
! i ; for every [!; n] 2 (
Although we illustrate the updating process for the typical
element [!; n] ; this process is done to all possible states [!; n] 2 (
n; N ) :
For a given (!; n), the values of V (!; n) and X (!; n) at each new iteration are calculated
as follows:
V : the value function at the k th iteration is written as
8
1
1
1
P
P
P
>
< ; supx 0 A (!; n) x +
:::
V k 1 (! +
V k (!; n) = max
1 =0
N =0 =0
>
:
p 1 jxk1 1 ; ::p ( h jx; ) ::p N jxkN 1 ; p ( )
; n)
9
>
=
>
;
Denote the …rm’s expected discounted value for each of the two possible realizations of
its R&D process, , as
CV (z; n) =
2
6
4
1
P
:::
h 1 =0
1 =0
p
1
P
k 1
1 jx1 ;
::p
1
P
:::
1
1
P
P
N =0
h+1 =0
k 1
h 1 jxh 1 ;
Vk
1 (z
; n) p ( )
=0
p
k 1
h+1 jxh+1 ;
::p
k 1
N jxN ;
3
7
5
That is, CV ( ) sums over the probability weighted average of the possible states of the
future competitors, but not over the investing …rm’s own future states. Hence, we can
rewrite V k as
V k (!; n) = max
(
; sup
x 0
"
A (!; n)
x+
+
ax
1+ax CV
1
1+ax CV
(! + e (n) ; n)
(!; n)
#)
(9)
where e (j) is a vector of zeros except for the j th element which is one. Then, whenever
V k (!)
V k (!; n) = sup A (!; n)
x+
x 0
ax
1
CV (! + e (n) ; n) +
CV (!; n)
1 + ax
1 + ax
X: denote by xk (!; n) the R&D level that solves (9) ; and by Dx the derivative with
respect to x: Assuming that R&D is non-zero, and the …rm remains active, the optimal
R&D x (!; n) solves
1=
Dx
1=
Dx
ax
1 + ax
ax
1 + ax
CV (! + e (n) ; n) + Dx
v1
Dx
32
ax
1 + ax
v2
1
1 + ax
CV (!; n)
and v1
CV (! + e (n) ; n) and v2
1
1 + ax
Dx
when
= 1 (and, hence, p (x) =
1=
h
[1
=
ax
1+ax ).
a [1
1 = a [1
CV (!; n) : Note that
a
= a [1
(1 + ax)2
p (x)]2
Thus, x (!; n) solves
p (x)]2 v1
p (x)]2 (v1
p (x)]2 =
p (x)]2 v2
a [1
v2)
1
a (v1
s
v2)
1
a (v1
=) p (x) = 1
i
(10)
v2)
Taking the inverse of p (x)
x (!; n) =
p (x)
a ap (x)
where p (x) is as de…ned in (10) :
Finally, we can use the derived formula for the optimal R&D to update the value function
k
V (!; n) = max
(
; sup
x 0
"
A (!; n)
x (!; n) +
+
ax(!;n)
1+ax(!;n) CV
1
1+ax(!;n) CV
(! + e (n) ; n)
(!; n)
#)
Note that if V k (!; n) = , then R&D is 0 with probability one. Hence, the actual R&D
expenditure level is
n
xk (!; n) = V k (!; n)
o
x (!; n)
where f g is the indicator function which takes the value of one when condition inside
is satis…ed, and zero otherwise.
33
Appendix D. Figures and Tables
Table 1: Parameter values
Parameter
Description
Value
D
demand intercept
rate of depreciation
scrap value
sunk entry cost
discount rate
manager preferences
5
0:5
0:1
0:2
0:96
1:2
Xe
34
Table 2: Market Structure
% with 1 …rm active
% with 2 …rms active
% with 3 …rms active
n
No Governance
3.8
90.6
5.6
2.0
Governance
95.2
4.7
0.1
1.0
% with entry and exit
% with entry
% with exit
Total …rms in history
6.0
11.3
11.3
1126
0.8
1.8
1.8
179
HHI
1/N
NVar(ms)
NVar(ms)/HHI
0.51
0.5
0.01
9%
0.98
0.95
0.01
1%
Mean lifespan
Turnover rate
18.78
16.5
42.50
2.8
Average length of runs
1 …rm active
2 …rms active
3 …rms active
1.7
22.0
2.3
68.1
6.8
3.2
R&D
1 …rm active
2 …rms active
3 …rms active
Average
1.33
1.64
2.65
1.7
0.98
2.37
5.57
1.0
Mean price-cost margin
Mean sunk entry inv/output
2.2
1%
2.9
0.0%
Statistics are computed over 10000 periods (years) starting at random draws from the ergodic
distribution of states.
No Governance refers to Markov-Perfect Nash Equilibrium with
= 0: HHI =
PNNotation:
2
i=1 msi is the Her…ndahl index of the industry, where msi is …rm i’s market share and N is the
number of active …rms. Var(ms) is the variance of market shares in the industry. Turnover rate is
computed as {(#periods with entry+#periods with exit-#periods with entry and exit)/total #periods
* 100}
Parameter values: =0.96, =0.1, Xe =0.2, =0.5, D =5
35
Table 3: Prices, Quantities, and Pro…ts
No Governance
Governance
Pro…ts
1 …rm active
2 …rms active
3 …rms active
Average
3.19
1.52
0.93
1.55
3.74
1.07
0.47
3.61
Output
1 …rm active
2 …rms active
3 …rms active
Average
1.83
2.59
2.65
2.57
1.98
3.06
3.00
2.03
Leader’s pro…t share
1 …rm active
2 …rms active
3 …rms active
Average
1
0.56
0.67
0.58
1
0.61
0.60
0.98
Prices
1 …rm active
2 …rms active
3 …rms active
Average
3.17
2.41
2.35
2.43
3.01
1.94
2.00
2.96
Markups
1 …rm active
2 …rms active
3 …rms active
Average
2.49
2.22
1.86
2.21
2.93
1.72
1.31
2.87
Statistics are computed as averages of sales-weighted values over 10000 periods (years) starting at
random draws from the ergodic distribution of states.
Notation: No Governance refers to Markov-Perfect Nash Equilibrium with = 0:
Parameter values: =0.96, =0.1, Xe =0.2, =0.5, D =5
36
Table 4.1: R&D policy x (! i ; ! j ): No Governance
! 1 n! 2
1
2
3
4
5
6
7
8
9
10
1
1.3
1.94
1.63
1.2
1
0.8
0.62
0.49
0.38
0.29
2
0.86
1.74
1.68
1.28
1.02
0.8
0.63
0.49
0.38
0.29
3
0.53
1.43
1.55
1.3
1.02
0.8
0.63
0.49
0.38
0.29
4
0
1.24
1.43
1.24
1
0.79
0.62
0.48
0.38
0.29
5
0
1.13
1.34
1.19
0.97
0.77
0.61
0.48
0.37
0.29
6
0
1.06
1.29
1.15
0.94
0.75
0.59
0.47
0.37
0.28
7
0
1.02
1.26
1.13
0.92
0.73
0.58
0.46
0.36
0.28
8
0
1
1.24
1.11
0.91
0.72
0.57
0.45
0.36
0.28
9
0
0.98
1.23
1.1
0.9
0.72
0.57
0.45
0.35
0.27
10
0
0.97
1.22
1.09
0.89
0.71
0.56
0.44
0.35
0.27
x
x
minj x(i;j)
1.3
0.97
0.46
0.21
0.13
0.09
0.07
0.05
0.03
0.02
1
1
0.38
0.19
0.15
0.13
0.13
0.11
0.09
0.07
Table 4.2: R&D policy x (! i ; ! j ): Governance
! 1 n! 2
1
2
3
4
5
6
7
8
9
10
1
0
1.36
3.08
3.1
1.95
1.33
0.97
0.73
0.56
0.44
2
0
1.55
3.08
3.1
1.95
1.33
0.97
0.73
0.56
0.44
3
0
0
2.28
3.25
1.95
1.33
0.97
0.73
0.56
0.44
4
0
0
0.87
2.33
2.84
2
1.29
0.88
0.63
0.48
5
0
0
0
1.15
2.07
2.36
1.87
1.25
0.84
0.59
6
0
0
0
0.56
1.15
1.78
2.03
1.64
1.12
0.76
7
0
0
0
0.36
0.68
1.03
1.55
1.77
1.43
0.98
8
0
0
0
0.3
0.52
0.63
0.9
1.37
1.57
1.25
9
0
0
0
0.28
0.47
0.5
0.55
0.78
1.22
1.41
10
0
0
0
0.27
0.45
0.46
0.43
0.46
0.68
1.11
Notation: No Governance refers to Markov-Perfect Nash Equilibrium with
Parameter values: =0.96, =0.1, Xe =0.2, =0.5, D =5
37
x
x
minj x(i;j)
0
1.55
3.08
2.98
2.39
1.91
1.63
1.42
1.2
0.97
= 0:
1
1
1
11.0
5.31
4.24
4.08
4.06
3.24
2.21
Figure 1: Timeline of the general model with entry and exit
π it(ωt)
Shareholders
(incumbents)
choose
monitoring,
α it
ωt
Nature
draws
bt
Managers
(incumbents)
choose
output,
q it
φit and φite
Shareholders
collect π t(⋅)
Managers
collect w
ωt+1
Exit
and
entry
realized;
Exit
(incumbents),
entry (entrants),
and R&D (both)
decisions
t
R&D
(remaining
incumbents
and new
entrants)
carried out;
outcomes
realized
t+1
38
Figure 2: Bene…t from cost reduction
No Governance
Q2
R1(Q2)
A
B
R2(Q1)
Q1
Governance
Q2
R1(Q2)
A
B
R2(Q1)
C
Q1
39
Figure 3: Governance
1
0.8
0.6
0.4
0.2
S12
S11
0
S10
S9
S8
Firm 2
S7
S6
S5
S4
S3
S2
S1
1
2
3
4
5
6
7
8
9
10
11
12
Firm 1
The graph plots governance choice of Firm 1, 1 (! 1 ; ! 2 ) ; as a function of the state of the industry,
! = (! 1 ; ! 2 ), assuming that two …rms are active: Firm 1 and Firm 2. Higher states correspond to
lower marginal costs.
Parameter values:
=0.965, =0.1, Xe =0.2, =0.5, D =5
40
Figure 4: Output and Pro…ts
No Governance
Governance
Output
2.5
2.5
2
2
1.5
1.5
1
1
0.5
0.5
0
0
11
S11
S1
Firm 1
Firm 2
6
S6
11
S11
Firm 1
Firm 2
16
S16
16
S16
6
S6
1
S1
1
Pro…ts
4
3.5
4
3.5
3
3
2.5
2.5
2
2
1.5
1.5
1
1
0.5
0.5
0
0
16
S16
6
S6
6
S6
11
Firm 1
S11
Firm 2
11
Firm 1
S11
Firm 2
16
S16
S1 1
S1 1
The graph plots pro…ts of Firm 1, 1 (! 1 ; ! 2 ) ; as a function of the state of the industry, ! =
(! 1 ; ! 2 ), assuming that two …rms are active: Firm 1 and Firm 2. The two panels correspond to the
benchmark model (”No Governance”, = 0) and the endogenous governance model (”Governance”).
Higher states correspond to lower marginal costs.
Parameter values: =0.965, =0.1, Xe =0.2, =0.5, D =5
41
Figure 5.1: Increasing Dominance
No Governance
Governance
Increasing Dominance
3.5
3.5
0
0
-3.5
-3.5
16
S16
16
S16
11
S11
Firm 2
11
S11
Firm 1
6
S6
6
S6
S1 1
S1 1
Intensity of Competition
5
5
4
4
3
3
2
2
1
1
0
0
11
S11
S1
11
S11
6
S6
6
S6
1
S1
1
The top two panels plot the di¤erence between R&D activity of Firm 1 and Firm 2, x (! i ! j )
x (! j ! i ), as a function of own state and the rival’s state, ! = (! 1 ; ! 2 ). The two panels correspond to the benchmark model (”No Governance”,
= 0) and the endogenous governance model
(”Governance”). Higher states correspond to lower marginal costs.
The bottom two panels plot the sum of the R&D expenditures of Firm 1 and Firm 2, x (! i ! j )+
x (! j ! i ), as a function of own state and the rival’s state, ! = (! 1 ; ! 2 ). The two panels correspond to the benchmark model (”No Governance”,
= 0) and the endogenous governance model
(”Governance”). Higher states correspond to lower marginal costs.
Parameter values: =0.965, =0.1, Xe =0.2, =0.5, D =5
42
Figure 6: Value Function and R&D
No Governance
Governance
Value function
60
60
V (i,j)
100
80
V (i,j)
100
80
40
40
20
20
0
0
16
S16
16
S16
11
S11
Firm 2
6
S6
S1
11
S11
Firm 2
Firm 1
6
S6
1
S1
Firm 1
1
4
3
3
2
X (i,j)
4
2
1
S16
1
S16
0
Firm 2
X (i,j)
R&D
S11
16
0
Firm 2
S11
16
11
S6
6
S1
11
S6
Firm 1
1
6
S1
Firm 1
1
The top two panels plot the value function of Firm 1, V1 (! 1 ; ! 2 ) ; as a function of the state of
the industry, ! = (! 1 ; ! 2 ), assuming that two …rms are active: Firm 1 and Firm 2. The two panels
correspond to the benchmark model (”No Governance”, = 0) and the endogenous governance model
(”Governance”). Higher states correspond to lower marginal costs.
The bottom two panels plot R&D expenditure of Firm 1, x1 (! 1 ; ! 2 ) ; as a function of the state of
the industry, ! = (! 1 ; ! 2 ), assuming that two …rms are active: Firm 1 and Firm 2. The two panels
correspond to the benchmark model (”No Governance”, = 0) and the endogenous governance model
(”Governance”). Higher states correspond to lower marginal costs.
Parameter values: =0.965, =0.1, Xe =0.2, =0.5, D =5
43
Figure 7: Evolution of market structure
No Governance
Governance
T =5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
0
0
16
S16
11
S11
S1
11
S11
6
S6
16
S16
T = 25
1
6
S6
S1
0.2
1
0.2
0.1
0.1
0
0
16
S16
11
S11
S1
11
S11
6
S6
16
S16
T = 50
1
6
S6
S1
0.2
1
0.2
0.1
0.1
0
0
16
S16
11
S11
6
S6
S1
1
16
S16
11
S11
Limiting distribution
6
S6
S1
1
0.2
0.2
0.1
0.1
0
16
S16
0
6
S6
6
S6
11
S11
11
S11
16
S16
S1 1
S1
1
We plot the frequency with which an industry con…guration (! 1 ; ! 2 ) occurs after T = 5; 25; 50
years and the limiting distribution (T = 10000). States ( ; 1) and (1; ) correspond to monopolization
of the industry by Firm 1 (Firm 2). The two panels correspond to the benchmark model (”No
Governance”, = 0) and the endogenous governance model (”Governance”). Higher states correspond
to lower marginal costs.
Parameter values: =0.965, =0.1, Xe =0.2, =0.5, D =5
44
Appendix E. Empirical Results
Table 1: Summary Statistics
The level of antitakeover protection in measured by SB&P, SB, E, and G index. SB&P is a
governance index based on the sum of the staggered board and poison pill indicators that ranges from
0 to 2 and SB is the staggered board dummy. Both indices are from from the IRRC dataset. E
index is from Bebchuk, Cohen, and Ferrell (2004), and G index is from Gompers, Ishii, and Metrick
(2003). For both, higher index value corresponds to more ATPs. Concentration is the market share
of four largest …rms in the industry. Managerial Ownership is the percentage of common equity held
by the CEO through stocks and options. Block is percentage of common stock held by the …rms
largest institutional blockholder. Pension Fund is percentage of common stock held by the 18 largest
public pension funds as a group (Cremers and Nair (2004)). Size is the …rm’s sales at the beginning
of the year, winsorized at 1%. Tobin’s Q is the market value of assets over the book value of assets,
winsorized at 1%. State Laws is a binary variable where 1 signi…es that the state of incorporation
has high (greater than 4) number of state antitakeover statutes. Delaware is binary where 1 signi…es
that the …rm is incorporated in Delaware. Board size is number of directors on the …rm’s board of
directors. Board Independence is number of percentage of independent directors, as de…ned by IRRC.
ROA is the ratio of net income to assets. R&D is the ratio of research and development expenditures
to sales. Advertising is the ratio of advertising and sales expense to sales. CAPEX is the ratio of
capital expenditures to assets. Dividend Ratio is the ratio of dividinds to book equity. Cash‡ow is
the ratio of earnings before extraordinary items plus depreciation to assets. Leverage is the ratio of
long-term debt due in one year to assets. Data is annual for 1990-2004.
Panel A: Summary Statistics
Variable
SB&P
SB
E
G
Concentration
Managerial Ownership
Block
Pension Fund
Board Size
Board Independence
Size
Tobin’s Q
State Laws
Delaware
ROA
R&D
Advertising & Sales Expenses
Capital Expenditures
Dividend Ratio
Cash‡ow
Observations
8969
8969
8969
8969
8819
6484
8969
8969
5321
5321
8969
8251
8969
8969
8969
8969
8969
8880
8969
8969
Mean
1.21
0.59
2.36
9.39
41.58
0.05
0.50
0.41
9.31
0.66
4348.29
0.08
0.33
0.57
0.14
0.08
0.01
0.11
0.08
0.41
Median
1
1
2
9
39
0.01
1
0
9
0.67
1167.07
0.02
0
1
0.14
0.02
0
0.09
0.04
0.35
Standard Deviation
0.78
0.49
1.34
2.80
18.27
0.16
0.50
0.49
2.55
0.19
13236.54
0.47
0.47
0.50
0.10
0.64
0.03
0.07
0.18
1.18
Panel B: Correlations between entrenchment variables and main …rm-level variables
SB&P SB
SB&P
SB
E
G
Concentration
Manag.Own
Block
Pen. Fund
Size
Q
State
Del
Board Size
Board Indep
1
0.80
0.80
0.65
0.068
-0.17
-0.07
0.19
-0.11
-0.07
0.09
-0.03
0.12
0.26
1
0.66
0.53
0.03
-0.04
-0.03
0.10
-0.08
-0.05
0.07
-0.05
0.16
0.15
E
G
1
0.76
0.09
-0.16
-0.04
0.13
-0.13
-0.12
0.21
-0.15
0.15
0.27
1
0.09
-0.17
-0.06
0.16
-0.04
-0.07
0.21
-0.10
0.23
0.29
Conc Man. Block Pen. Size
Own
Fund
Q
State
Del Board Board
Size Indep
1
-0.08
0.001
0.01
0.15
0.02
-0.04
0.04
0.08
0.09
1
-0.02
-0.11
-0.09
0.03
-0.02
0.04
-0.11
-0.31
45
1
-0.20
-0.02
-0.05
-0.04
0.06
-0.09
0.05
1
0.03
0.02
0.02
-0.001
0.07
0.07
1
0.03
0.001
0.03
0.53
0.25
1
0.0001 1
-0.03 -0.81 1
0.09 0.14 -0.08 1
-0.02 0.03 0.05 0.09
1
Table 2: External Governance and Position - Univariate Analysis
The level of entrenchment in measured by SB&P, SB, E, and G indexes. SB&P is a governance
index based on the sum of the staggered board and poison pill indicators that ranges from 0 to 2 and
SB is the staggered board dummy. Both indices are from from the IRRC dataset. E index is from
Bebchuk, Cohen, and Ferrell (2004), and G index is from Gompers, Ishii, and Metrick (2003). For
both, higher index value corresponds to more ATPs. Leaders are …rms in the top quartile by percentile
rank of sales in the industry. Laggards are …rms in the bottom quartile by percentile rank of sales
in the industry. Industry classi…cation is according to Fama and French (1999). Data is annual for
1990-2005 and for manufacturing (SIC 2000-3999) …rms.
Variable
% All …rms
(1)
% Laggards
(2)
% Leaders
(3)
t–test
(4)
SB&P=0
SB&P 1
SB&P=2
22.27
77.73
43.07
27.62
72.38
39.60
20.45
79.55
43.08
1.624
-12.837
-11.187
Observations
8865
2288
2284
SB=0
SB=1
40.78
59.22
42.57
57.43
40.19
59.81
Observations
8865
2288
2284
E=0
E 1
E 2
E 3
E 4
E 5
10.50
89.50
72.56
48.11
21.75
3.54
13.74
86.26
65.76
43.93
18.78
2.85
10.23
89.77
75.27
48.94
22.22
4.18
Observations
9012
2322
2317
G 7
G>7
G 13
26.58
73.42
14.61
38.18
61.82
8.61
19.03
80.97
19.63
Observations
9030
2323
2323
Staggered Board and Pill (SB&P)
Staggered Board (SB)
1.939
-7.456
Entrenchment Index (E)
3.147
-11.884
-12.106
-14.269
-13.705
-5.186
Governance Index (G)
46
4.158
-1.144
-12.274
Table 3: Internal Governance and Position - Univariate Analysis
Entrenchment is measured by block ownership, pension fund ownership, board size and independence. Block is percentage of common stock held by the …rm’s largest institutional blockholder.
Pension Fund is percentage of common stock held by the 18 largest public pension funds as a group
(Cremers and Nair (2004)). Board size is the number of directors on the …rm’s board of directors.
Board Independence is the percentage of independent directors, as de…ned by IRRC. Leaders are …rms
in the top quartile by percentile rank of sales in the industry. Laggards are …rms in the bottom quartile
by percentile rank of sales in the industry. Industry classi…cation is according to Fama and French
(1999). Data is annual for 1990-2005 and for manufacturing (SIC 2000-3999) …rms.
Variable
% All …rms
(1)
% Laggards
(2)
% Leaders
(3)
t–test
(4)
Block 0.35
Block>0.35
49.96
50.04
46.62
53.38
53.18
46.82
-12.837
-11.187
Observations
9152
4404
4622
Pension 0.10
Pension>0.10
58.69
41.31
69.98
30.02
47.32
52.68
Observations
9152
4404
4622
Board Size 9
Board Size>9
56.08
43.92
74.16
25.84
40.07
59.93
Observations
5321
2465
2800
%Independent 0.8
%Independent>0.8
72.54
27.46
78.50
21.50
67.46
32.54
Observations
5321
2465
2800
Institutional Ownp
Pension Fund
1.939
-7.456
Board Size
3.147
-11.884
Board Independence
47
4.158
-1.144
Table 4: External Governance and Position - Multivariate Analysis
This table reports panel-data probit estimates of the likelihood that a …rm has a high level of
entrenchment. The measures of high level of entrenchment are: a binary variable where 1 signi…es
that the …rm has both a staggered board and a poison pill (Column 1), that the …rm has a staggered
board (Column 2), that E 4 (Column 3), and G 13 (Column 4). For all indexes, higher index value
corresponds to more ATPs. Position rank is the percentile rank of the …rm’s sales relative to other
…rms within the industry. Concentration is the market share of four largest …rms in the industry.
Industry classi…cation is according to Fama and French (1999). Size is log of the book value of assets,
winsorized at 1%. Age is the log of the number of months since …rm was …rst listed. R&D is the ratio
of …rm’s R&D expenses to sales. Cash holdings is the ratio of cash and marketable securities to total
assets. Dividend is the ratio of dividend payout to total assets. Pro…tability is the ratio of operating
income before depreciation to book assets. Managerial Ownership is the percentage of common equity
held by the CEO through stocks and options. State Laws is a binary variable where 1 signi…es that
the state of incorporation has high (greater than 4) number of state antitakeover statutes. Delaware is
binary where 1 signi…es that the …rm is incorporated in Delaware. Variation across time is controlled
for by including year …xed e¤ects. Variation across industries is controlled for by including industry
…xed e¤ects. Data is annual for 1990-2005, with only manufacturing (SIC 2000-3999) …rms included.
Coe¢ cients are reported as marginal e¤ects. Standard errors are robust to heteroskedasticity and
arbitrary serial correlation within industry-year cells.
SB&P
(1)
SB
(2)
E
(3)
G
(4)
0.204
(0.029)
0.092
(0.020)
0.081
(0.024)
0.102
(0.022)
0.479
(0.026)
0.344
(0.045)
0.321
(0.015)
0.210
(0.009)
-0.055
-0.017
-0.038
-0.024
(0.007)
-0.008
(0.009)
-0.011
(0.006)
-0.131
(0.093)
-0.023
(0.003)
0.092
(0.069)
-1.685
(0.194)
0.813
(0.728)
0.187
(0.008)
0.119
(0.007)
(0.005)
-0.024
(0.008)
-0.019
(0.006)
-0.025
(0.079)
-0.009
(0.002)
0.003
(0.056)
-0.585
(0.154)
0.672
(0.252)
0.119
(0.015)
0.072
(0.013)
(0.005)
0.011
(0.004)
-0.022
(0.013)
-0.235
(0.049)
-0.017
(0.004)
-0.005
(0.045)
-1.564
(0.146)
1.654
(0.285)
0.197
(0.008)
0.064
(0.011)
(0.003)
0.047
(0.006)
-0.068
(0.037)
-0.055
(0.056)
-0.026
(0.002)
-0.171
(0.041)
-1.019
(0.113)
1.075
(0.240)
0.082
(0.017)
0.066
(0.013)
8296
0.118
8296
0.092
8296
0.161
8296
0.127
Competitiveness
Position Rank
Industry Characteristics
Concentration
Firm Characteristics
Size
Age
R&D
Dividend
Cash holdings
Pro…tability
Managerial Ownership
(Managerial Ownership)2
State Laws
Delaware
Observations
R2
48
Table 5: Internal Governance and Position - Multivariate Analysis
This table reports panel-data probit estimates of the likelihood that a …rm has a high level of
entrenchment. Entrenchment is measured by block ownership, pension fund ownership, board size and
independence. Block is percentage of common stock held by the …rm’s largest institutional blockholder.
Pension Fund is percentage of common stock held by the 18 largest public pension funds as a group
(Cremers and Nair (2004)). Board size is the number of directors on the …rm’s board of directors.
Board Independence is the percentage of independent directors, as de…ned by IRRC. Position rank
is the percentile rank of the …rm’s sales relative to other …rms within the industry. Concentration is
the market share of four largest …rms in the industry. Industry classi…cation is according to Fama
and French (1999). Size is log of the book value of assets, winsorized at 1%. Age is the log of
the number of months since …rm was …rst listed. R&D is the ratio of …rm’s R&D expenses to sales.
Cash holdings is the ratio of cash and marketable securities to total assets. Dividend is the ratio of
dividend payout to total assets. Pro…tability is the ratio of operating income before depreciation to
book assets. Managerial Ownership is the percentage of common equity held by the CEO through
stocks and options. State Laws is a binary variable where 1 signi…es that the state of incorporation has
high (greater than 4) number of state antitakeover statutes. Delaware is binary where 1 signi…es that
the …rm is incorporated in Delaware. Variation across time is controlled for by including year …xed
e¤ects. Variation across industries is controlled for by including industry …xed e¤ects. Data is annual
for 1990-2005, with only manufacturing (SIC 2000-3999) …rms included. Coe¢ cients are reported
as marginal e¤ects. Standard errors are robust to heteroskedasticity and arbitrary serial correlation
within industry-year cells.
Block
(1)
Pension Fund Board Size Board Indep
(2)
(3)
(4)
Competitiveness
Position Rank
0.007
(0.059)
0.410
(0.058)
0.115
(0.028)
0.073
(0.040)
0.050
(0.031)
-0.077
(0.041)
0.011
(0.050)
0.151
(0.024)
(0.013)
-0.034
(0.010)
-0.035
(0.007)
-0.186
(0.195)
0.003
(0.002)
-0.107
(0.079)
-0.934
(0.222)
0.850
(0.399)
0.032
(0.016)
0.059
(0.016)
0.021
(0.019)
0.015
(0.017)
-0.046
(0.016)
0.190
(0.125)
0.003
(0.003)
-0.097
(0.134)
0.257
(0.368)
-1.851
(1.154)
-0.041
(0.016)
-0.030
(0.015)
0.151
(0.009)
0.095
(0.008)
0.030
(0.011)
1.049
(0.119)
-0.037
(0.007)
-0.041
(0.092)
-0.221
(0.235)
0.237
(0.632)
0.059
(0.026)
-0.045
(0.024)
-0.006
(0.010)
0.018
(0.010)
0.006
(0.011)
0.376
(0.067)
0.006
(0.003)
-0.124
(0.067)
-4.310
(0.402)
7.249
(1.012)
0.099
(0.021)
0.057
(0.017)
7924
0.131
7924
0.194
5096
0.271
5096
0.165
Industry Characteristics
Concentration
Firm Characteristics
Size
Age
R&D
Dividend
Cash holdings
Pro…tability
Managerial Ownership
(Managerial Ownership)2
State Laws
Delaware
Observations
R2
-0.033
49
Table 6: External Governance and Firm Value: Tobin’s Q by position
This table reports OLS regressions of industry-adjusted Tobin’s Q on measures of entrenchment.
Tobin’s Q is the market value of assets over the book value of assets, winsorized at 1%. Industryadjusted Tobin’s Q is Tobin’s Q minus the median Tobin’s Q in the industry. The measures of high
level of entrenchment are: a binary variable where 1 signi…es that the …rm has both a staggered board
and a poison pill (SB&P), that the …rm has a staggered board (SB), that E 4 (E), that G 13 (G),
and that that the state of incorporation has high (greater than 4) number of state antitakeover statutes
(State Laws). For all indexes, higher index value corresponds to more ATPs. Laggards are …rms in the
bottom half by percentile rank of sales in the industry. Leaders are …rms in the top half by percentile
rank of sales in the industry. Industry classi…cation is according to Fama and French (1999). Size
is log of the book value of assets, winsorized at 1%. Age is the log of the number of months since
…rm was …rst listed. R&D is the ratio of …rm’s R&D expenses to sales. Advertising is the ratio of
…rm’s advertising and selling expenses to sales. Cash holdings is the ratio of cash and marketable
securities to total assets. Dividend is the ratio of dividend payout to total assets. Cash‡ow is the
ratio of earnings after interest and taxes less capital expenditures to book assets.Capital Expenditures
is the ratio of capital expenditures to gross capital in previous year. Managerial Ownership is the
percentage of common equity held by the CEO through stocks and options. Other controls are a
dummy for incorporation in Delaware and inclusion in S&P500. Variation across time is controlled
for by including year …xed e¤ects. Variation across industries is controlled for by including industry
…xed e¤ects. Data is annual for 1990-2005, with only manufacturing (SIC 2000-3999) …rms included.
Standard errors are robust to heteroskedasticity and arbitrary serial correlation within industry-year
cells.
Variable
(1)
(2)
Laggard
(3)
(4)
(5)
(6)
(7)
Leader
(8)
(9)
(10)
ATPs
SB&P
-0.027
(0.009)
SB
0.021
(0.008)
-0.057
(0.014)
E
0.035
(0.012)
-0.020
(0.005)
G
0.005
(0.005)
-0.002
(0.003)
State Laws
0.002
(0.002)
0.003
(0.015)
0.035
(0.012)
Firm Characteristics
Size
0.001
(0.007)
Age
-0.050
(0.010)
R&D
0.037
(0.008)
Advertising
0.622
(0.233)
Dividend
2.516
(0.145)
Cash holdings
0.010
(0.002)
Cash‡ow
0.039
(0.005)
Capital Expenditures
1.593
(0.108)
Managerial Ownership
0.595
(0.219)
(Managerial Ownership)2 -1.968
(0.533)
R2
Observations
0.341
4040
0.001
(0.007)
-0.052
(0.010)
0.037
(0.008)
0.602
(0.233)
2.523
(0.144)
0.010
(0.002)
0.039
(0.005)
1.577
(0.108)
0.626
(0.218)
-1.982
(0.533)
0.001
(0.007)
-0.047
(0.010)
0.037
(0.008)
0.619
(0.233)
2.502
(0.144)
0.010
(0.002)
0.039
(0.005)
1.578
(0.108)
0.564
(0.219)
-1.927
(0.533)
0.0001
(0.007)
-0.050
(0.011)
0.037
(0.008)
0.676
(0.233)
2.502
(0.145)
0.010
(0.002)
0.040
(0.005)
1.601
(0.108)
0.639
(0.219)
-1.969
(0.534)
-0.011
(0.009)
-0.039
(0.011)
0.030
(0.008)
0.778
(0.242)
2.339
(0.157)
0.009
(0.002)
0.046
(0.005)
1.627
(0.108)
0.505
(0.189)
-1.311
(0.430)
(0.006)
0.012
(0.008)
2.200
(0.136)
0.411
(0.192)
2.285
(0.120)
-0.003
(0.005)
0.263
(0.012)
1.282
(0.122)
0.707
(0.193)
-0.300
(0.399)
(0.006)
0.014
(0.008)
2.215
(0.136)
0.407
(0.192)
2.277
(0.120)
-0.003
(0.005)
0.263
(0.012)
1.284
(0.122)
0.673
(0.192)
-0.279
(0.399)
(0.006)
0.015
(0.008)
2.200
(0.136)
0.338
(0.192)
2.285
(0.120)
-0.004
(0.005)
0.262
(0.012)
1.261
(0.122)
0.633
(0.194)
-0.244
(0.399)
(0.006)
0.013
(0.008)
2.207
(0.136)
0.368
(0.191)
2.287
(0.120)
-0.003
(0.005)
0.262
(0.012)
1.277
(0.122)
0.674
(0.193)
-0.286
(0.400)
(0.006)
-0.001
(0.008)
2.133
(0.142)
0.294
(0.176)
2.264
(0.107)
0.024
(0.007)
0.321
(0.014)
1.015
(0.117)
0.465
(0.214)
0.587
(0.551)
0.342
4040
0.342
4040
0.339
4040
0.331
4073
0.509
3909
0.509
3909
0.508
3909
0.508
3909
0.546
4043
50
-0.013 -0.014 -0.015 -0.014 -0.012
Table 7: External Governance and Firm Value: Comparative Statics/Dynamics
This table reports OLS regressions of industry-adjusted Tobin’s Q on measures of entrenchment
by various industry characteristics. Tobin’s Q is the market value of assets over the book value of
assets, winsorized at 1%. The measures of high level of entrenchment are: a binary variable where
1 signi…es that the …rm has both a staggered board and a poison pill (SB&P), that the …rm has a
staggered board (SB), that E 4 (E), that G 13 (G), and that that the state of incorporation has high
(greater than 4) number of state antitakeover statutes (State Laws). For all indexes, higher index value
corresponds to more ATPs. Laggards are …rms in the bottom half by percentile rank of sales in the
industry. Leaders are …rms in the top half by percentile rank of sales in the industry. Concentration
is the market share of four largest …rms in the industry. Import penetration is the ratio of imports to
domestic consumption in the industry. Symmetry is industry-year average proximity to median sales in
the industry. For each industry characteristic, Low/High refers to below/above sample median value.
Industry classi…cation is according to Fama and French (1999). Other controls include: log of the
book value of assets, the log of the number of months since …rm was …rst listed, the ratio of …rm’s R&D
expenses to sales, the ratio of …rm’s advertising expenses to sales, the ratio of cash and marketable
securities to total assets, the ratio of dividend payout to total assets, the ratio of earnings after interest
and taxes less capital expenditures to book assets, the ratio of capital expenditures to gross capital
in previous year, the percentage of common equity held by the CEO through stocks and options, a
dummy for incorporation in Delaware and inclusion in S&P500. Variation across time is controlled
for by including year …xed e¤ects. Variation across industries is controlled for by including industry
…xed e¤ects. Data is annual for 1990-2005, with only manufacturing (SIC 2000-3999) …rms included.
Standard errors are robust to heteroskedasticity and arbitrary serial correlation within industry-year
cells.
Variable
Position
SB&P
(1)
SB
(2)
E
(3)
G
(4)
State Laws N
(5)
Concentration
Low
Laggard
Leader
t-stat
High
Laggard
Leader
t-stat
-0.007 -0.006 -0.012
0.002 0.074
(0.011) (0.018) (0.007) (0.003) (0.019)
-0.016
-0.002 -0.011
-0.001 0.050
(0.009) (0.014) (0.005) (0.003) (0.017)
-1.574 -1.029 -1.111 -1.668
2.554
2034
-0.031 -0.070
-0.014
-0.002 -0.096
(0.012) (0.020) (0.007) (0.003) (0.025)
0.057
0.085
0.020
0.010
0.049
(0.011) (0.016) (0.006) (0.003) (0.018)
3.071
3.492
2.801
2.995
3.419
1949
-0.037 -0.059 -0.031 -0.007 -0.037
(0.011) (0.019) (0.007) (0.003) (0.011)
0.031
0.037
0.012
0.004 0.031
(0.008) (0.018) (0.007) (0.003) (0.008)
3.455
3.316
3.265
3.165
3.655
1894
-0.005 -0.002
-0.012
(0.007) (0.003) (0.013)
-0.001 0.005 0.026
(0.005) (0.003) (0.009)
1.179
1.344
1.351
1772
1837
2021
Import Penetration
Low
Laggard
Leader
t-stat
High
Laggard
Leader
t-stat
-0.012 -0.041
(0.013) (0.020)
0.026
0.044
(0.009) (0.014)
1.349
1.343
1783
1770
Asymmetry
Low
Laggard
Leader
t-stat
High
Laggard
Leader
t-stat
-0.029 -0.069 -0.023
-0.002
-0.030
(0.011) (0.017) (0.007) (0.003) (0.019)
0.030
0.035
0.008
0.003 0.068
(0.010) (0.015) (0.004) (0.003) (0.015)
3.204
3.207
3.071
1.146
2.784
-0.013
(0.014)
-0.004
(0.012)
1.401
0.0001 -0.009 -0.0004 0.071
(0.023) (0.008) (0.004) (0.025)
0.020 -0.036
-0.006 -0.043
(0.020) (0.007) (0.004) (0.018)
1.507
-1.089 -1.311
-3.447
51
2709
2562
1327
1347
Table 8: Internal Governance and Firm Value: Tobin’s Q by position
This table reports OLS regressions of industry-adjusted Tobin’s Q on measures of entrenchment.
Tobin’s Q is the market value of assets over the book value of assets, winsorized at 1%. Industryadjusted Tobin’s Q is Tobin’s Q minus the median Tobin’s Q in the industry. The measures of high level
of entrenchment are: percentage of common stock held by the …rm’s largest institutional blockholder
(Block) and by the 18 largest public pension funds (Pension Fund), the number of directors on the …rm’s
board of directors (Board Size) and the percentage of independent directors (Board Independence).
Laggards are …rms in the bottom half by percentile rank of sales in the industry. Leaders are …rms
in the top half by percentile rank of sales in the industry. Industry classi…cation is according to
Fama and French (1999). Size is log of the book value of assets, winsorized at 1%. Age is the log
of the number of months since …rm was …rst listed. R&D is the ratio of …rm’s R&D expenses to
sales. Advertising is the ratio of …rm’s advertising and selling expenses to sales. Cash holdings is
the ratio of cash and marketable securities to total assets. Dividend is the ratio of dividend payout
to total assets. Cash‡ow is the ratio of earnings after interest and taxes less capital expenditures
to book assets.Capital Expenditures is the ratio of capital expenditures to gross capital in previous
year. Managerial Ownership is the percentage of common equity held by the CEO through stocks and
options. Other controls are a dummy for incorporation in Delaware and inclusion in S&P500. Variation
across time is controlled for by including year …xed e¤ects. Variation across industries is controlled for
by including industry …xed e¤ects. Data is annual for 1990-2005, with only manufacturing (SIC 20003999) …rms included. Standard errors are robust to heteroskedasticity and arbitrary serial correlation
within industry-year cells.
Variable
(1)
Laggard
(2)
(3)
(3)
(4)
Leader
(5)
(6)
(6)
Internal Governance
Block
-0.033
-0.026
(0.014)
Pension Fund
(0.013)
0.048
(0.015)
Board Size
0.034
(0.014)
-0.023
(0.005)
Board Independence
0.005
(0.005)
-0.178
(0.049)
0.112
(0.056)
Firm Characteristics
Size
Age
R&D
Advertising
Dividend ratio
Cash holdings
Cash‡ow
Capital Expenditures
Managerial Ownership
(Managerial Ownership)2
R2
Observations
-0.001
(0.007)
-0.053
(0.010)
0.037
(0.008)
0.693
(0.233)
2.492
(0.145)
0.010
(0.002)
0.040
(0.005)
1.602
(0.108)
0.327
(0.219)
-1.945
(0.534)
-0.007
(0.007)
-0.052
(0.010)
0.038
(0.008)
0.730
(0.233)
2.469
(0.145)
0.010
(0.002)
0.040
(0.005)
1.601
(0.108)
0.652
(0.218)
-1.934
(0.533)
0.020
(0.013)
-0.030
(0.015)
0.049
(0.013)
0.455
(0.350)
2.629
(0.218)
0.013
(0.003)
0.050
(0.008)
1.402
(0.147)
0.177
(0.285)
-0.948
(0.713)
0.005
(0.013)
-0.041
(0.015)
0.048
(0.013)
0.472
(0.352)
2.513
(0.217)
0.015
(0.003)
0.050
(0.008)
1.396
(0.147)
0.225
(0.293)
-0.911
(0.719)
0.340
4040
0.341
4040
0.329
2269
0.324
2269
52
-0.015
-0.016
(0.006)
0.013
(0.008)
2.187
(0.136)
0.391
(0.191)
2.280
(0.120)
-0.003
(0.005)
0.261
(0.012)
1.270
(0.122)
0.635
(0.192)
-0.245
(0.399)
(0.006)
0.013
(0.008)
2.196
(0.136)
0.387
(0.191)
2.282
(0.120)
-0.003
(0.005)
0.261
(0.012)
1.285
(0.122)
0.667
(0.192)
-0.241
(0.399)
-0.012
(0.009)
0.004
(0.008)
2.147
(0.171)
0.482
(0.246)
2.174
(0.143)
0.028
(0.008)
0.308
(0.018)
1.103
(0.167)
-0.540
(0.446)
5.770
(1.659)
-0.008
(0.008)
0.010
(0.011)
2.163
(0.170)
0.473
(0.245)
2.264
(0.144)
0.027
(0.008)
0.306
(0.017)
1.096
(0.168)
-0.928
(0.456)
6.824
(1.675)
0.509
3909
0.509
3909
0.549
2382
0.551
2382
Table 9: Internal Governance and Firm Value: Comparative Statics/Dynamics
This table reports OLS regressions of industry-adjusted Tobin’s Q on measures of entrenchment
by various industry characteristics. Tobin’s Q is the market value of assets over the book value of
assets, winsorized at 1%. The measures of high level of entrenchment are: percentage of common
stock held by the …rm’s largest institutional blockholder (Block) and by the 18 largest public pension
funds (Pension Fund), the number of directors on the …rm’s board of directors (Board Size) and the
percentage of independent directors (Board Independence). Laggards are …rms in the bottom half
by percentile rank of sales in the industry. Leaders are …rms in the top half by percentile rank of
sales in the industry. Concentration is the market share of four largest …rms in the industry. Import
penetration is the ratio of imports to domestic consumption in the industry. Symmetry is industryyear average proximity to median sales in the industry. For each industry characteristic, Low/High
refers to below/above sample median value. Industry classi…cation is according to Fama and French
(1999). Other controls include: log of the book value of assets, the log of the number of months since
…rm was …rst listed, the ratio of …rm’s R&D expenses to sales, the ratio of …rm’s advertising expenses
to sales, the ratio of cash and marketable securities to total assets, the ratio of dividend payout to
total assets, the ratio of earnings after interest and taxes less capital expenditures to book assets,
the ratio of capital expenditures to gross capital in previous year, the percentage of common equity
held by the CEO through stocks and options, a dummy for incorporation in Delaware and inclusion
in S&P500. Variation across time is controlled for by including year …xed e¤ects. Variation across
industries is controlled for by including industry …xed e¤ects. Data is annual for 1990-2005, with only
manufacturing (SIC 2000-3999) …rms included. Standard errors are robust to heteroskedasticity and
arbitrary serial correlation within industry-year cells.
Variable
Position
Block Pension FundBoard SizeBoard Indep
(1)
(2)
(3)
(4)
N
N
Cols 1-2Cols 3-4
Concentration
Low
Laggard
Leader
t-stat
High
Laggard
Leader
t-stat
-0.016
(0.021)
-0.046
(0.018)
-1.664
0.059
(0.023)
0.033
(0.019)
-1.271
-0.011
(0.006)
0.002
(0.006)
1.062
0.210
(0.078)
-0.212
(0.073)
-2.485
2036
1173
1835
1116
-0.019
(0.023)
-0.063
(0.018)
-3.219
0.003
(0.026)
0.047
(0.020)
3.113
-0.034
(0.009)
0.008
(0.006)
2.004
-0.146
(0.095)
1919
1066
-0.204
(0.078)
-3.865
2047
1192
-0.023
(0.023)
-0.061
(0.021)
-3.114
0.029
(0.024)
0.067
(0.023)
3.198
-0.038
(0.007)
0.012
(0.007)
3.320
-0.006
(0.085)
2034
1239
-0.319
(0.083)
-3.562
1643
1044
-0.012
(0.024)
-0.056
(0.017)
-2.216
-0.006
(0.026)
0.027
(0.017)
1.024
-0.001
(0.009)
0.010
(0.005)
1.589
0.436
(0.100)
0.143
(0.075)
-2.386
1586
779
1952
1052
-0.006
(0.018)
-0.069
(0.015)
-3.406
0.015
(0.020)
0.033
(0.016)
3.704
-0.029
(0.006)
0.005
(0.005)
3.002
0.085
2699
1546
-0.200
(0.062)
-3.986
2572
1563
-0.009
(0.026)
-0.032
(0.020)
-1.111
0.031
(0.030)
0.024
(0.020)
-1.273
0.002
(0.008)
-0.0003
(0.006)
-1.626
(0.088)
-0.192
(0.079)
-1.211
0.083
1290
723
1384
734
Import Penetration
Low
Laggard
Leader
t-stat
High
Laggard
Leader
t-stat
Asymmetry
Low
Laggard
Leader
t-stat
High
Laggard
Leader
t-stat
53
(0.072)
Table 10: Entrenchment and Firm Value: Endogeneity
This table reports OLS regressions of industry-adjusted Tobin’s Q on measures of entrenchment.
Tobin’s Q is the market value of assets over the book value of assets, winsorized at 1%. Industryadjusted Tobin’s Q is Tobin’s Q minus the median Tobin’s Q in the industry. In Panel A, the measures
of high level of entrenchment are: a binary variable where 1 signi…es that the …rm has both a staggered
board and a poison pill (SB&P), that the …rm has a staggered board (SB), that E 4 (E), that G 13
(G), and that that the state of incorporation has high (greater than 4) number of state antitakeover
statutes (State Laws). In Panel B, the measures of high level of entrenchment are: percentage of
common stock held by the …rm’s largest institutional blockholder (Block) and by the 18 largest public
pension funds (Pension Fund), the number of directors on the …rm’s board of directors (Board Size)
and the percentage of independent directors (Board Independence). Laggards are …rms in the bottom
half by percentile rank of sales in the industry. Leaders are …rms in the top half by percentile rank
of sales in the industry. Industry classi…cation is according to Fama and French (1999). To address
endogeneity of entrenchment variables, we control for …rms’average Tobin’s Q in the …rst half of the
1980s. Other controls include: log of the book value of assets, the log of the number of months since
…rm was …rst listed, the ratio of …rm’s R&D expenses to sales, the ratio of …rm’s advertising expenses
to sales, the ratio of cash and marketable securities to total assets, the ratio of dividend payout to
total assets, the ratio of earnings after interest and taxes less capital expenditures to book assets,
the ratio of capital expenditures to gross capital in previous year, the percentage of common equity
held by the CEO through stocks and options, a dummy for incorporation in Delaware and inclusion
in S&P500. Variation across time is controlled for by including year …xed e¤ects. Variation across
industries is controlled for by including industry …xed e¤ects. Data is annual for 1990-2005, with only
manufacturing (SIC 2000-3999) …rms included. Standard errors are robust to heteroskedasticity and
arbitrary serial correlation within industry-year cells.
Panel A: External Governance
Variable
SB&P
SB
(1)
(2)
0.003
(0.008)
-0.014
(0.012)
E
Laggard
(3)
(4)
(6)
0.025
(0.007)
Leader
(8)
(7)
(9)
-0.041
(0.015)
0.008
(0.012)
-0.006
(0.019)
State Laws
-0.004
(0.013)
0.015
(0.013)
0.541 0.542
3923 3923
(10)
0.038
(0.010)
G
R2
Observations
(5)
0.542
3923
0.541 0.541
3923 3923
0.018
(0.010)
0.664
3858
0.664
3858
0.663 0.663 0.663
3858 3858 3858
Panel B: Internal Governance
Variable
BLock
Pension Funds
Board Size
Laggard
(1)
(2)
(3)
0.003
(0.013)
-0.020
(0.014)
-0.005
(0.005)
Board Independence
R2
Observations
(4)
(5)
-0.026
(0.011)
Leader
(6)
(7)
-0.004
(0.011)
0.010
(0.003)
-0.111
(0.046)
0.130
(0.051)
0.543 0.544 0.476
4002 4002 2234
54
(8)
0.478
2234
0.663
3945
0.663
3945
0.640
2313
0.639
2313
Table 11: Entrenchment and Firm Value: Robustness of valuation e¤ects
This table reports OLS regressions of industry-adjusted Tobin’s Q on measures of entrenchment.
Tobin’s Q is the market value of assets over the book value of assets, winsorized at 1%. Industryadjusted Tobin’s Q is Tobin’s Q minus the median Tobin’s Q in the industry. In Panel A, the measures
of high level of entrenchment are: a binary variable where 1 signi…es that the …rm has both a staggered
board and a poison pill (SB&P), that the …rm has a staggered board (SB), that E 4 (E), that G 13
(G), and that that the state of incorporation has high (greater than 4) number of state antitakeover
statutes (State Laws). In Panel B, the measures of high level of entrenchment are: percentage of
common stock held by the …rm’s largest institutional blockholder (Block) and by the 18 largest public
pension funds (Pension Fund), the number of directors on the …rm’s board of directors (Board Size)
and the percentage of independent directors (Board Independence). Laggards are …rms in the bottom
half by percentile rank of sales in the industry. Leaders are …rms in the top half by percentile rank
of sales in the industry. Industry classi…cation is according to Fama and French (1999). To address
the concern that the valuation e¤ects are driven by high-tech …rms, we control for the industry’s high
tech status (Loughran and Ritter (2004)). Other controls include: log of the book value of assets, the
log of the number of months since …rm was …rst listed, the ratio of …rm’s R&D expenses to sales, the
ratio of …rm’s advertising expenses to sales, the ratio of cash and marketable securities to total assets,
the ratio of dividend payout to total assets, the ratio of earnings after interest and taxes less capital
expenditures to book assets, the ratio of capital expenditures to gross capital in previous year, the
percentage of common equity held by the CEO through stocks and options, a dummy for incorporation
in Delaware and inclusion in S&P500. Variation across time is controlled for by including year …xed
e¤ects. Variation across industries is controlled for by including industry …xed e¤ects. Data is annual
for 1990-2005, with only manufacturing (SIC 2000-3999) …rms included. Standard errors are robust
to heteroskedasticity and arbitrary serial correlation within industry-year cells.
Panel A: External Governance
Variable
SB&P
(1)
-0.016
(0.009)
SB
(2)
Laggard
(3)
(4)
(6)
0.018
(0.008)
-0.033
(0.015)
E
Leader
(8)
(9)
(7)
(10)
0.023
(0.011)
-0.052
(0.018)
G
0.001
(0.013)
-0.004
(0.023)
State Laws
R2
Observations
(5)
-0.016
(0.015)
0.002
(0.015)
0.334
3989
0.334
3989
0.335
3989
0.333 0.333
3989 3989
0.034
(0.012)
0.547 0.547 0.546 0.546
3956 3956 3956 3956
0.547
3956
Panel B: Internal Governance
Variable
Institutional Ownp
Pension Funds
Board Size
Laggard
(1)
(2)
(3)
-0.013
(0.015)
0.030
(0.016)
-0.023
(0.005)
Board Independence
(4)
(5)
-0.058
(0.012)
Leader
(6)
(7)
0.031
(0.013)
0.004
(0.004)
-0.175
(0.050)
0.105
(0.058)
R2
Observations
0.331 0.332
4073 4073
0.331
2276
55
(8)
0.325
2276
0.548
4043
0.546 0.549
4043 2386
0.551
2386
Table 12: Entrenchment and Firm Value: Robustness to other measures of value
This table reports OLS regressions of industry-adjusted ROA on measures of entrenchment. ROA
is the ratio of net income to total assets. Industry-adjusted ROA is ROA minus the median ROA in
the industry. In Panel A, the measures of high level of entrenchment are: a binary variable where
1 signi…es that the …rm has both a staggered board and a poison pill (SB&P), that the …rm has a
staggered board (SB), that E 4 (E), that G 13 (G), and that that the state of incorporation has
high (greater than 4) number of state antitakeover statutes (State Laws). In Panel B, the measures
of high level of entrenchment are: percentage of common stock held by the …rm’s largest institutional
blockholder (Block) and by the 18 largest public pension funds (Pension Fund), the number of directors
on the …rm’s board of directors (Board Size) and the percentage of independent directors (Board
Independence). Laggards are …rms in the bottom half by percentile rank of sales in the industry.
Leaders are …rms in the top half by percentile rank of sales in the industry. Industry classi…cation is
according to Fama and French (1999). Other controls include: log of the book value of assets, the
log of the number of months since …rm was …rst listed, the ratio of …rm’s R&D expenses to sales, the
ratio of …rm’s advertising expenses to sales, the ratio of cash and marketable securities to total assets,
the ratio of dividend payout to total assets, the ratio of earnings after interest and taxes less capital
expenditures to book assets, the ratio of capital expenditures to gross capital in previous year, the
percentage of common equity held by the CEO through stocks and options, a dummy for incorporation
in Delaware and inclusion in S&P500. Variation across time is controlled for by including year …xed
e¤ects. Variation across industries is controlled for by including industry …xed e¤ects. Data is annual
for 1990-2005, with only manufacturing (SIC 2000-3999) …rms included. Standard errors are robust
to heteroskedasticity and arbitrary serial correlation within industry-year cells.
Panel A: External Governance
Variable
SB&P
SB
E
G
State Laws
R2
Observations
Laggard
(1)
(2)
(3)
(4)
(5)
-0.002
(0.002)
0.001
(0.003)
0.0003
(0.001)
0.0008
(0.005)
0.002
(0.003)
0.622 0.622 0.622 0.622 0.655
3989 3989 3989 3989 3989
(6)
0.003
(0.001)
Leader
(8)
(9)
(7)
(10)
0.004
(0.002)
0.002
(0.007)
0.003
(0.003)
0.005
(0.002)
0.517 0.517 0.517 0.516
3956 3956 3956 3956
0.561
3956
Leader
(6)
(7)
(8)
Panel B: Internal Governance
Variable
Institutional Ownp
(1)
0.006
(0.003)
Laggard
(2)
(3)
(4)
(5)
-0.007
(0.002)
-0.008
Pension Funds
0.007
(0.002)
(0.003)
Board Size
-0.002
(0.001)
Board Independence
R2
Observations
0.656
4073
0.656
4073
0.002
(0.001)
0.003
0.004
(0.010)
(0.007)
0.654 0.653
2420 2420
56
0.562
4043
0.562
4043
0.547
2749
0.545
2386
Table 13: Entrenchment and Firm Value: Robustness to Size
This table reports OLS regressions of industry-adjusted Tobin’s Q on measures of entrenchment by
various industry characteristics. Tobin’s Q is the market value of assets over the book value of assets,
winsorized at 1%. Tthe measures of high level of entrenchment are: a binary variable where 1 signi…es
that the …rm has both a staggered board and a poison pill (SB&P), that the …rm has a staggered
board (SB), that E 4 (E), that G 13 (G), that that the state of incorporation has high (greater
than 4) number of state antitakeover statutes (State Laws); percentage of common stock held by the
…rm’s largest institutional blockholder (Block) and by the 18 largest public pension funds (Pension
Fund), the number of directors on the …rm’s board of directors (Board Size) and the percentage of
independent directors (Board Independence). Size is log of the book value of assets, winsorized at
1%. Small …rms are …rms in the bottom half by percentile rank of size in the industry. Large ¢ rms
are …rms in the top half by percentile rank of size in the industry. Laggards are …rms in the bottom
half by percentile rank of sales in the industry. Leaders are …rms in the top half by percentile rank of
sales in the industry. Industry classi…cation is according to Fama and French (1999). Other controls
include: the log of the number of months since …rm was …rst listed, the ratio of …rm’s R&D expenses
to sales, the ratio of …rm’s advertising expenses to sales, the ratio of cash and marketable securities to
total assets, the ratio of dividend payout to total assets, the ratio of earnings after interest and taxes
less capital expenditures to book assets, the ratio of capital expenditures to gross capital in previous
year, the percentage of common equity held by the CEO through stocks and options, a dummy for
incorporation in Delaware and inclusion in S&P500. Variation across time is controlled for by including
year …xed e¤ects. Variation across industries is controlled for by including industry …xed e¤ects. Data
is annual for 1990-2005, with only manufacturing (SIC 2000-3999) …rms included. Standard errors are
robust to heteroskedasticity and arbitrary serial correlation within industry-year cells.
Size
Variable
Small
Laggard Leader
(1)
(2)
Large
Laggard Leader
(3)
(4)
-0.013
(0.010)
-0.027
(0.016)
-0.049
(0.019)
0.001
(0.003)
0.013
(0.016)
3551
-0.015
(0.027)
-0.052
(0.043)
0.032
(0.055)
-0.001
(0.008)
0.163
(0.047)
543
-0.041 0.024
(0.035) (0.008)
-0.011
(0.016)
0.032
(0.018)
-0.024
(0.006)
0.144
(0.064)
3551
1894
-0.025
(0.047)
0.016
(0.043)
-0.007
(0.029)
-0.179
(0.315)
543
188
External Governance
SB&P
SP
E
G
State Laws
N
-0.148
(0.055)
-0.183
(0.068)
-0.022
(0.010)
-0.180
(0.076)
484
0.029
(0.013)
0.002
(0.014)
0.003
(0.002)
0.016
(0.013)
3413
Internal Governance
Institutional
Pension Fund
Board Size
Board Indep
N
N (Board variables)
57
0.068 -0.058
(0.071) (0.013)
0.006
0.045
(0.082) (0.014)
-0.028
0.005
(0.015) (0.004)
0.121 -0.161
(0.192) (0.052)
438
375
3413
2194