STRUCTURAL CLOSURE AND EXPOSURE:
MARKET REACTIONS TO ACQUISITIONS AND DIVESTITURES*
Mikołaj Jan Piskorski
Nitin Nohria
Harvard University
Harvard University
Morgan Hall 243
Morgan Hall 339
Harvard Business School
Harvard Business School
Boston, MA 02163
Boston, MA 02163
Tel.
(617) 495 – 6099
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(617) 495 – 6653
Fax.
(617) 496 – 5658
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(617) 496 – 6568
mpiskorski@hbs.edu
nnohria@hbs.edu
November 7, 2006
~ 13,413 words
The Division of Research at Harvard Business School provided generous financial support. Bharat Anand and Peter
Marsden provided very useful comments on the prior draft. Adam Kleinbaum provided comments on this version. All
errors are ours.
*
STRUCTURAL CLOSURE AND EXPOSURE:
MARKET REACTIONS TO ACQUISITIONS AND DIVESTITURES
ABSTRACT
This paper develops an exchange-network perspective on corporate
diversification and proposes two measures of corporate scope:
structural closure and structural exposure. Structural closure focuses on
exchanges of goods and services inside the firm and proxies for the
potential costs of undertaking them through the market instead. By
considering exchange relations inside the firm, this measure
complements the existing indices that focus on asset relatedness.
Structural exposure focuses on exchanges of goods and services across
the firm boundary and proxies for the current market-exchange costs
as compared to undertaking them inside the firm instead. By focusing
on exchanges across the firm boundary, the measure extends the
existing approaches in that it captures what the firm could integrate,
but decided not to. We posit that higher structural closure will increase
firm value, while higher structural exposure will reduce it. We test these
hypotheses using stock market reactions to acquisitions and
divestitures undertaken by Fortune 100 firms between 1979 and
1992. We find that acquisitions that increase firm structural closure
increase firm value, but those that increase structural exposure diminish
it. We find equivalent results for divestitures.
Introduction
Strategy research has long been concerned with establishing the link between firm
diversification and performance (Chatterjee & Wernerfelt, 1991; Montgomery & Wernerfelt,
1988; Palich, Cardinal, & Miller, 2000; Rumelt, 1974). Building on the original theoretical
insights of Coase (1937), Teece (1982) and Barney (1986), most researchers have
hypothesized that firms which diversify into a related set of industries will perform better
than single business firms or those that diversify into unrelated industries. Although
numerous empirical tests of this hypothesis have been undertaken, there is little agreement
on whether related diversification is actually beneficial to firms. The lack of consensus has
been attributed to problems with construct validity of diversification measures (Robins &
Wiersema, 2003) or to the use of SIC codes to determine relatedness (Hoskisson, Hitt,
Johnson, & Moesel, 1993; Robins & Wiersema, 1995). Others have pointed out that it is
unclear to what extent the studies really capture the effect of different diversification
strategies or simply systematic differences in underlying firm and industry characteristics of
firms following different diversification strategies (Campa & Kedia, 2002; Villalonga, 2004a,
2004b).
At the same time, both economists and sociologists were developing a different set of
theories of corporate diversification (Granovetter, 1985; Grossman & Hart, 1986; Pfeffer &
Salancik, 1978; Williamson, 1985). Most scholars in this literature hypothesize that firms
should acquire their downstream or upstream exchange partners under conditions of
substantial mutual dependence and contract incompleteness (Casciaro & Piskorski, 2005;
Grossman & Hart, 1986; Williamson, 1996).1 Although empirical work largely supports this
claim, most of the tests have been constrained to within-industry studies (Hennart, 1988;
1
In transaction cost economics, mutual dependence is called bilateral dependence.
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Klein, 1988; Nickerson & Silverman, 2003; Shelanski & Klein, 1995; Stuckey, 1983).
Substantial data requirements have resulted in only very few large-scale multi-industry tests
of the hypothesis (Caves & Bradburg, 1988; Fan & Goyal, 2006; Levy, 1985; Monteverde &
Teece, 1982).
Although both streams of literature trace their lineage to Coase’s original insight, they have
been developed in relative isolation from each other, with few theoretical integration efforts
ever undertaken. The separation applies not only to theory, but also to the measurement of
firm diversification strategy. Most empirical studies of corporate diversification focus on
relatedness without considering mutual dependence. Similarly, studies that examine
internalization of mutually dependent transactions leave relatedness out of focus. However,
most strategy scholars would agree that a firm’s corporate strategy can be better understood
or described by considering both relatedness and mutual dependence. Part of the reason why
the two types of diversification have not been integrated in empirical studies is that measures
of both have not been equally developed. There exists a family of easy-to-implement
relatedness measures with fairly well understood properties and a well articulated theoretical
link between these measures and firm performance. In contrast, there have been very few
efforts to develop measures for mutual dependence that can be used to capture firm scope
and be deployed across a broad set of industries.
This paper begins to address the situation and proposes two measures of firm scope that
capture mutual dependence and can be easily deployed across a broad set of firms. The first
measure, structural closure, focuses on exchanges of goods and services inside the firm and
proxies for the potential costs of undertaking them through the market instead. By
considering exchange relations inside the firm, the measure is highly complementary to the
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existing indices that focus primarily on relatedness. The second measure, structural exposure,
focuses on exchanges of goods and services across the firm boundary and it proxies for the
current market-exchange costs as compared to undertaking them inside the firm. By
focusing on exchanges across the firm boundary, the measure extends existing measures in
that it focuses on what the firm could have integrated, but chose not to. This measure is
highly complementary to structural closure and the two should always be considered together
to understand conditions under which diversified firms create value.
Having defined these two measures, we propose two hypotheses: (i) higher structural closure
will increase firm value and (ii) higher structural exposure will reduce it. To test them, we follow
recent developments in economic sociology and resource dependence theory and use the
input output representation of the American economy to develop proxies for structural closure
and exposure. With these measures in hand, we study stock market reactions to acquisitions
undertaken by Fortune 100 firms. Consistent with our hypotheses, we find that acquisitions
that increase firm structural closure increase firm value, but those that increase structural exposure
diminish it. To test the robustness of these results, we also examine structural closure and
exposure in the context of divestitures and find exactly analogous results. Consistent with our
expectations, divestitures that reduce firm structural closure reduce firm value, and those that
decrease structural exposure increase it. We conclude the paper with implications for future
research.
Relatedness
Most of the diversification literature in strategy traces its roots to Coase’s (1937) seminal
article which outlined economic conditions for the existence of a firm. Coase argued that
assets should be bound together within a firm if the coordination of the services they render
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yields an outcome superior to that offered in a competitive market. Thus, firms should
internalize transactions for which markets fail until “the cost of organizing an extra
transaction within the firm becomes equal to the costs of carrying out the same transaction
by means of an exchange in the open market or the costs of organizing in another
firm”(Coase, 1937).
Teece (1982) followed this line of reasoning to provide an economic account of a multiproduct firm. He argued that a firm will find it profitable to internalize failing markets when
two conditions are present: the firm has specialized and frequently-used assets and these
assets are in excess supply, which is subject to market failure. A number of assets fulfill these
conditions, such as technological know-how, organizational goodwill, marketing channels
and managerial skills in capital allocation. The greater amount of such non-tradable assets
the firm has, the more beneficial the diversification will be (Teece, 1982). Furthermore, the
benefit of diversification will vary with the relatedness of the market to which these assets
are transferred. The farther the firm departs from its current scope, the larger will be the loss
in efficiency and the lower will be the competitive advantage conferred by the factors
(Montgomery & Hariharan, 1994; Montgomery & Wernerfelt, 1988). Thus, the firm should
transfer its excess factors to the closest market it can enter. If excess capacity remains, the
firm should transfer the resources further afield until the marginal benefit of doing so
reaches zero. At this point, the firm has exhausted the opportunities for profitable
diversification, and should not engage in further expansion.
This reasoning has led to the essential hypothesis of the relatedness view: firms engaged in a
related diversification strategy will outperform both firms that do not diversify and firms that
diversify too broadly (Markides & Williamson, 1994; Montgomery & Hariharan, 1994;
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Porter, 1985; Rumelt, 1982). If firms engage in no diversification, but possess some
resources that are subject to market failure, they are foregoing valuable opportunities to
utilize these resources in related markets and earn positive marginal rents. Hence, they will
perform worse than firms that diversify into related areas. At the same time, firms that
engage in too broad a diversification strategy are likely to suffer, because they will engage in
certain unrelated activities where their marginal rents will be negative. Therefore, these firms
will also perform not as well as firms that are diversified less broadly.
Three different empirical research streams tested this hypothesis. However, none provided
unequivocal evidence to support it. The first stream was initiated by Rumelt (1974) who
found that firms that restricted their scope to a central skill or competence performed better
than others. Some subsequent studies replicated these findings (Bettis, 1981; Hoskisson,
1987; Palepu, 1985), but others have found no differences in performance between related
and unrelated diversifiers (Christensen & Montgomery, 1981; Hall & St. John, 1994; Hill &
Hitt, 1992; Lubatkin & Merchant, 1993). The second stream, prevalent in finance, was
spawned by findings that diversified firms trade at an average discount to specialized firms
(Berger & Ofek, 1995; Lang & Stulz, 1994; Wernerfelt & Montgomery, 1988).2 These
findings were criticized, however, on the basis that poorly performing firms were more likely
to diversify (Hyland, 1997; Lemelin, 1982; MacDonald, 1985; Montgomery & Hariharan,
1994; Servaes, 1996). When such propensity is controlled for, the diversification discount
not only disappears, but sometimes turns into a premium (Campa & Kedia, 2002; Miller,
Relatedness is usually measured by the entropy measure which is a weighted average of firm sales shares
across different industries, with the weight for each segment being the logarithm of the inverse of its share.
More nuanced approaches differentiate between entropy measures for related and unrelated diversification
(Palepu, 1985).
.
2
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2004; Park, 2003; Villalonga, 2004a). The third stream, common to strategy and finance,
examined stock market reactions to acquisitions and divestitures. Some studies have found
that markets react positively to acquisitions in the same industry, but negatively to unrelated
acquisitions (Morck, Shleifer, & Vishny, 1990; Sicherman & Pettway, 1987). Others,
however, found exactly the opposite pattern (Hubbard & Palia, 1999; Hyland, 1997;
Matsusaka, 1993; Schipper & Thompson, 1983).
Yet for divestitures, many studies
confirmed the relatedness hypothesis and found that sale of unrelated assets causes firm
market valuation to rise more than it does for related ones (Alexander, Benson, &
Kampmeyer, 1984; Comment & Jarell, 1995; Jain, 1985; John & Ofek 1995; Kaplan &
Weisbach, 1992; Klein, 1986; Miles & Rosenfeld, 1983; Montgomery & Wilson, 1986;
Rosenfeld, 1984; Zaima & Hearth, 1985). Thus, despite substantial differences in the
methods, none of the three literatures has reached internal consensus linking relatedness of
firm corporate strategy and firm performance.
Mutual Dependence
At the same time as the relatedness literature was being developed in strategy, transaction
cost economists and resource dependence theorists were working on a set of conditions
under which firms should acquire their upstream or downstream exchange partners. Much
like the relatedness literature, this literature was developed on the basis of the Coasian
insight according to which firms should internalize transactions which are subject to market
failures. But even though the two literatures originate from the same theoretical insight, they
developed in relative isolation from each other, with few theoretical integration efforts ever
undertaken. The separation applies not only to theory, but also to the measurement of firm
diversification strategy. Most empirical studies of corporate diversification that focus on
relatedness ignore upstream and downstream dimension of firm scope (with the exception
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of studies that utilize the Rumelt-like classification). Similarly, most studies that examine the
make or buy decision leave relatedness out of focus.
Part of the reason why the two bases for profitable diversification have not been integrated
in empirical studies is that the measures have not been equally well developed. As we
discussed above, there exists a family of easy-to-implement relatedness measures. These
measures have fairly well understood properties and the theoretical link between the measure
and predicted firm performance is well articulated. In contrast, there have been very few
efforts to develop measures for vertical integration that can consistently be deployed across a
broad set of industries and can meaningfully distinguish between value creating and value
destroying vertical integration. In the remainder of this paper, we seek to address this
situation and propose two measures of scope that possess these properties. To do that, we
proceed in three steps. First, we review the theoretical underpinnings of such measures.
Second, we discuss how such measures can be consistently deployed across a broad set of
industries and show how firms can be characterized in terms of the measures. Finally, we
propose a test to establish that the measures we propose are actually related to value
creation.
Theoretical Underpinnings
Both resource dependence and transaction cost economics argue that firms are likely to
create value by integrating two business units when the relationship between them is
characterized by high mutual dependence. A business unit is dependent on another in
proportion to that business unit’s need for resources that its trading partner can provide and
in inverse proportion to the availability of alternative sources capable of providing the same
resources to the focal business unit (Burt, 1983; Jacobs, 1974; Pfeffer & Salancik, 1978;
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Thompson, 1967). A relationship between two business units can be characterized by
simultaneously considering the dependence of a focal business unit on another and vice
versa (Emerson, 1962). Two business units will be mutually dependent on each other if each
one of them depends on the other (Casciaro & Piskorski, 2005). Two theories differ in
whether they talk about ex-ante or ex-post mutual dependence. Resource dependence theory
focuses on ex-ante mutual dependence that arises due to structural restrictions on exchange
(Burt, 1980). Transaction cost economics usually assumes that two parties are initially
independent, but develop ex-post mutual dependence over the course of the relationship
(Williamson, 1985). Despite these differences, both theories suggest that firms should
internalize exchanges subject to high mutual dependence.3
When mutual dependence is high, resources that firms exchange are more critical to their
survival, and few alternative sources exist. Although such high mutual dependence creates
substantial incentives for firms to exchange with each other, it also opens up significant
scope for negotiations. For example, if one firm makes substantial demands on the other,
that other firm cannot easily walk away from exchange, because it cannot easily find an
alternative exchange partner who is capable of providing similar resources. However, that
other firm is also aware that it is difficult for the firm that initiated the demands to locate an
alternative exchange partner. Consequently, it can easily start making counter-demands on
the partner who initiated the bargaining process. Given that there is no clear pattern of
domination, lengthy bargaining process can result and both companies will be unable to
exchange (Emerson, 1962). As a consequence, they will incur significant costs of nonexchange (Piskorski & Casciaro, 2006). Resource dependence theory argues, however, that
Because resource dependence theory focuses on ex-ante mutual dependence, while transaction cost economics
focuses on ex-post mutual dependence, the two theories are not incompatible. However, explicit comparison of
their predictions and mechanisms is beyond the scope of this paper.
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these costs can be avoided if the two firms merge (Burt, 1983; Casciaro & Piskorski, 2005;
Finkelstein, 1997; Pfeffer & Salancik, 1978). For these reasons, resource dependence theory
predicts that when mutual dependence between two businesses is high, they should be
internalized into the same firm and thereby create value.
Transaction costs economics makes a very similar prediction, though it makes a different
assumption about the nature of mutual dependence between firms. In this stream of
literature, firms are presumed ex-ante mutually independent. However, as the relationship
develops, firms engage in relationship-specific investments which make the firms mutually
dependent. Thus, ex-post the firms are better off staying with each other than looking for
other partners, giving rise to the possibility that they will engage in wasteful bargaining over
the allocation of exchange surplus (Williamson, 1985). Such bargaining not only has direct
costs, akin to those described in resource dependence theory, but creates additional
concerns, unique to transaction costs economics. Since each firm’s bargaining power and
resulting share of surplus may bear little relation to its relationship specific investment, both
firms may have wrong ex-ante incentives to invest in the first place (Grossman & Hart, 1986).
It is likely that both of them will underinvest, given that each firm realizes that its partner
could expropriate parts of investment in the bargaining stage. However, by underinvesting
ex-ante, both firms will make the exchange ex-post less valuable than it could have been. In
order to avoid both types of costs, transaction costs economics predicts that firms should
internalize the exchange through a merger.
When choosing between the two measures, there are substantial theoretical benefits to using
the ex-post, rather than the ex-ante, measure of dependence. This is because the ex-ante
dependence will not capture a situation where two ex-ante mutually independent firms invest
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and become ex-post mutually dependent. In contrast, ex-post measures will always capture exante mutual dependence. However, the ex-post measure comes at a cost. Since relationship
specific investments are hard to measure on a consistent basis across a large set of industries,
most scholars have focused on a particular type of transaction. Though such an approach is
necessary in order to provide appropriate tests of the transaction cost theories, it presents
formidable problems when trying to construct a measure of diversification that can be
deployed across a broad spectrum of firms. This is where the ex-ante measure has an
advantage (Burt, 1983; Jacobs, 1974; Pfeffer & Salancik, 1978; Thompson, 1967). For
example, Burt (1980) proposed that dependencies between firms can be derived from inputoutput transactions between economic sectors and the concentration of firms in these
sectors. Numerous tests of this specification have confirmed its usefulness in studies
involving many sectors of the economy (Burt, 1983; Casciaro & Piskorski, 2005; Finkelstein,
1997).4 Given that our goal in this paper is to create a measure that can be easily deployed
across different industries, we adopt an ex-ante measure of dependence to capture the
benefits of integration.5 With this measure in hand, we are now ready to define our
measures.
There have been attempts to develop network based measures of horizontal relatedness whereby two
businesses are considered to be horizontally related if they purchase from similar suppliers or sell to the same
types of buyers. We do not follow this conceptualization as it not clear if it is consistent with the market failure
approach we adopt here. For it to conform with our conceptualization it would have to be true that intangible
assets subject to market failure are more valuable deployed across two businesses units in different industries
which exchange the majority of their inputs or outputs with similar types of businesses. Until this hypothesis is
resolved in the literature, we prefer to rely on the more widely accepted relatedness measure.
5 Despite its shortcomings, the use of the ex-ante measure actually makes it harder to establish any empirical
results relating mutual dependence to the benefits of integration. This is because the ex-ante measure will
classify certain transactions as mutually independent, even though in reality they will be mutually dependent
through ex-post investments. Since integration of ex-post mutual dependence transactions is likely to be positive,
such coding will increase the baseline effect of integrating exchanges coded as ex-ante mutually independent.
This, in turn, will reduce the estimated difference between the effect of integrating exchanges codes as ex-ante
mutually dependent and those coded as ex-ante mutually independent. As a consequence, the bias inherent in
the ex-ante measure will make it more difficult to provide evidence that there exists a statistically significant
difference between integrating ex-ante mutually dependent exchanges and integrating ex-ante mutually
independent exchanges, even if such a difference does really exist.
4
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Structural Closure
Structural closure focuses on exchanges of goods and services inside the firm and proxies for
the potential costs of undertaking them through the market instead. Since the actual costs
are hard to measure, we proxy for them using mutual dependence between business units.
For two-business-unit firms, structural closure is equal to the mutual dependence between the
two business units. However, when there are more than two business units, there will be n(n1)/2 mutual dependencies inside the firm, where n is the number of business units.6 Thus,
we need to aggregate individual mutual dependencies into one score for the firm. Addition
of the mutual dependencies is the most intuitive aggregation. Such a measure will indicate
the extent to which the integrated business units will outperform an equivalent set of stand
alone business units.7 It can also be used to compare diversification strategies of different
firms. Specifically, we can claim that the difference between each firm’s performance and an
equivalent set of stand alone businesses will be higher for the firm with higher structural
closure. However, care needs to be exercised when comparing performance of firms
characterized by different levels of structural closure against each other, without considering the
counterfactual set of equivalent non-integrated units. Since the measure of structural closure is
derived on the basis of such a comparison, direct firm to firm comparisons may generate
spurious results. This issue is not unique to our analysis as it is relevant to all corporate
diversification frameworks. As we discussed above, the substantial confusion in interpreting
The measure of structural closure is undefined for single-business unit firms. We discuss the ways of handling
this issue in the methods section.
7 Adding mutual dependencies ignores potential interactions between mutual dependencies between various
different business units. To the extent that these interactions are positive, the addition of individual mutual
dependencies will give us the lower bound on the likelihood that a set of integrated business units will
outperform an equivalent set of non-integrated business units. Future research should address the issue of
interactions in greater detail.
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results in the relatedness literature can be traced directly to inappropriate selection of the
counterfactual.
To show how firms vary on the measure of structural closure, we choose a few prototypical
firms: Berkshire Hathaway and Sports Arenas – low structural closure firms – and Disney and
Dow Chemical – high structural closure firms – illustrated on the left-hand side of Figure 1.8
The nodes represent business units, with the node shape denoting the industry in which the
business unit operates. Thus, any set of business units denoted with the same shape are in
related industries, while those denoted with different shapes are in unrelated industries. The
width of the ties between the nodes represents mutual dependence. Dashed circles indicate
the scope of a firm. At the top of the Figure 1, we provide a schematic drawing of Berkshire
Hathaway with two business units, one in Candy and one in Insurance. Both business units
are in different 2-digit SIC codes, making Berkshire an unrelated diversifier. Berkshire
Hathaway is also characterized by low structural closure as there are very few mutual
dependencies between its business units. Similar characterization applies to Sports Arenas,
with business units in Real Estate, Bowling Centers and Construction Management
industries. Real Estate and Bowling Centers are in the same 2-digit SIC code, while
Construction management is in a different one. Because none of the transactions within
Sports Arenas are subject to high mutual dependence, Sports Arenas is characterized by low
structural closure.
------------------------------------------------------Insert Figure 1 around here
-------------------------------------------------------
8 We choose the firm examples to illustrate ideal types of diversification. Because firms change their
diversification strategies, their current diversification status may be different from what the firms are now. In
fact, some of our examples no longer exist, largely because their diversification strategies destroyed value.
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Next, we provide a schematic drawing of Disney with two main business units: Amusements
and Radio and TV. Both business units are in different 2-digit SIC codes. However, Disney
has high structural closure, because there are significant mutual dependencies between these
business units.9 Similar characterization applies to Dow Chemical, which has three business
units: Chemicals, Consumer Chemicals and Plastics industries. Chemicals and Consumer
Chemicals are in the same 2-digit SIC code, while the one in the Plastics industry is in a
different one. Dow Chemical also has high structural closure, as there are significant mutual
dependencies between these business units. Each unit sells a significant amount of its output
to and buys a significant amount of its inputs from other units in the firm; there are few
alternatives outside the firm because all the industries in which Dow Chemical operates are
oligopolized.10
Structural closure can also be easily applied to the evaluation of marginal changes in firm scope.
Consider, for example, what happens when Berkshire Hathaway decides to diversify into
Publications, an extension to a new SIC code, as illustrated on the right-hand side of Figure
1. Such an acquisition does not increase Berkshire’s structural closure, and therefore we do not
expect that such an acquisition will increase firm value. We would, however, make a very
different prediction if Disney decided to diversify into the Consumer Goods industry, which
belongs to a different SIC code. Since the acquisition internalizes two exchanges
characterized by high mutual dependence, structural closure of the company increases.
Consequently, if Disney were to acquire a business unit in the Consumer Goods industry, we
It is interesting to note that Disney is one of the most frequently used cases used to teach the principles of
corporate strategy, with animated characters being the basis of value creation. Unfortunately, measures of
relatedness do not pick up the benefits of such a strategy. These benefits are picked up, however, by structural
closure which focuses on flows of goods and services between different parts of Disney.
10 Prior efforts to characterize firm diversification strategies in terms of flows of goods and services have been
undertaken (Caves & Bradburg, 1988; Fan & Goyal, 2006). However, prior papers have usually focused on
dependence rather than mutual dependence. The mutual dependence specification we propose here is much
closer to the theoretical preconditions for value-creating diversification.
9
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expect that the acquisition is likely to increase firm value. Similar logic can be applied to
divestitures. For example, if Sports Arenas were to dispose of its Construction Management
business unit, we predict that such a divestiture is unlikely to reduce firm value, because it
does not reduce the firm’s structural closure.11 In contrast, if Dow Chemical were to divest its
plastics business, we predict that such a divestiture would reduce firm value, because it
would significantly reduce structural closure. Consequently, we hypothesize that:
H1:
Following an acquisition, an increase in firm’s structural closure has a
positive effect on firm market value
H2 :
Following a divestiture, a reduction in firm’s structural closure has a negative
effect on firm market value
Complementarity between Relatedness and Structural Closure
Thus far, we have discussed two ways in which a diversified firm can add value to its
business units over and above what they could generate on their own. We summarize these
in Figure 2, where we utilize the two-business-unit examples from Figure 1. Diversification
can create value when the firm possesses a valuable resource that cannot be traded and
deploys it across related business units, as indicated by the Sports Arenas example. It can
also create value under conditions of mutual dependence, as indicated by Dow Chemical
example. A firm can also generate value both through internalizing related assets and
mutually dependent exchanges, as exemplified by Dow Chemical. Finally, diversification that
does not meet these criteria is considered unrelated and unlikely to create value over and
Other factors may affect changes in firm value following an acquisition or divestiture. For example, in the
Sports Arenas example, markets may reward the sale of the unrelated Construction Management unit. Thus, all
of the value effects we are discussing in the main text pertain to the effect of structural closure on firm value.
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above what the business units can create on their own.12 Berkshire Hathaway is in that
category. For each condition, we discussed measures designed to capture it: the relatedness
measure and the structural closure measure, respectively. Together the measures seek to capture
the two different ways in which corporate diversification creates value.
------------------------------------------------------Insert Figure 2 around here
------------------------------------------------------Separately, Figure 2 also makes it salient that empirical analyses attempting to establish
relationship between different types of diversification and performance effect must
simultaneously use the measures of relatedness and measure of structural closure. Using one
without considering the other will substantially bias the estimated effects of that variable on
performance effect of diversification. To see why this is the case, consider, for example,
what would happen if we are trying to estimate the effect of relatedness on performance.
Relatedness measures are constructed by coding diversification as related, if two or more
business units are in the same SIC code, and unrelated when firms are in separate SIC codes.
By assigning business units in the same SIC code to the related category, firms considered as
related will combine firms pursuing related diversification (Sports Arenas) and those
pursuing related diversification and benefiting from internalizing mutual dependence (Dow
Chemical). Consequently, the average performance effect for firms coded as related will be a
weighted average of the two types of firms, with weights determined by the proportion of
firms in each category. Similarly, by assigning business units in different SIC codes to the
unrelated category, firms considered as unrelated will combine firms pursuing truly unrelated
The validity of this statement depends on the institutional environment in which the firm operates. It is most
applicable in environments with well-functioning capital and labor markets and where contracts can be
enforced with ease. As these markets become less effective, even unrelated diversified firms can create value by
creating internal capital or internal labor markets (Khanna & Palepu, 2000).
12
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diversification (Berkshire Hathaway) and those that are unrelated, but benefiting from
internalizing mutual dependence (Disney). Consequently, the average performance effect for
firms assigned to the unrelated category will be a weighted average of the two types of firms,
with the weights determined by the proportion of firms in each category.
Within this framework, it is easy to see how attempts to establish the effects of relatedness
on performance without considering structural closure will be subject to substantial biases. The
direction of the bias will depend on the proportion of firms that are related and benefit from
internalizing mutual dependence. Consider, for example, an extreme scenario in which there
are no firms such as Dow Chemical that operate businesses in related industries
characterized by substantial mutual dependence. In this case, the average performance of
firms classified as related will reflect the true performance of firms pursuing only the related
diversification strategy. However, the average performance effect for firms classified as
unrelated will be substantially higher than it is in reality. This is because firms assigned to the
unrelated category will comprise unrelated diversifiers and unrelated firms that create value
through integration of mutual dependencies. As a consequence, the difference in
performance effects between firms coded as related and those coded as unrelated will be
smaller than it is in reality. Thus, it is likely that we will reject the hypothesis that relatedness
has effect on firm value, even if that hypothesis is actually true. The reverse will be true if
there are no firms, such as Disney, that own business units characterized by mutual
dependence but in unrelated businesses. In this case, the average performance effect for
firms classified as related will be overestimated. However, the average performance of firms
classified as unrelated will actually reflect the true average performance of unrelated
diversifiers. Consequently, we will be less likely to reject the hypothesis that relatedness has
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effect on firm value, even if that hypothesis is actually false.13 Given these concerns, it
becomes clear that we will obtain much better tests of the relationship between relatedness
(or structural closure) and performance when we consider the two variables together. It is in
this sense that the two measures are complementary.
Structural Exposure
Having explicitly considered structural closure and its relationship to the relatedness measure,
we now turn to our second measure. Structural exposure focuses on exchanges of goods and
services across the firm boundary and proxies for the current market-exchange costs as
compared to undertaking these exchanges inside the firm instead. By focusing on exchanges
across the firm boundary, the measure differs from existing measures in that it focuses on
what the firm could have integrated, but chose not to. In order to derive a proxy for these
comparative costs, we also use the measure of mutual dependence. To construct this
measure, we do not capture mutual dependence with every business unit with which the firm
is currently exchanging. This is because the firm does not need to acquire every single one of
its suppliers or buyers in order to remove its transactions from the market. For example, in
order to remove market exchange for one of its inputs, the firm only needs to acquire one of
13 Similar argument can be put forward with respect to the structural closure measure. Structural closure cannot
clearly distinguish between related and unrelated diversification. Thus, the observed performance effect for
firms characterized by high structural closure will actually be a weighted average of the performance effect for
related firms with high structural closure, such as Dow Chemical, and the performance effect for firms just with
high structural closure, such as Disney. Similarly, the observed performance effect for firms characterized by low
structural closure will actually be a weighted average of the performance effect of related firms with low structural
closure, such as Sports Arenas, and the performance effect of unrelated firms with low structural closure, such as
Berkshire Hathaway. So, if there are no firms such as Dow Chemical, the effect of structural closure will be
underestimated, but if there are no firms such as Sports Arenas, then the effect of structural closure will be
overestimated. Finally, exactly the same argument can be advanced for marginal changes in relatedness and
structural closure associated with acquisitions or divestitures. Since the argument parallels that for the scope of
the firm, we omit it here for brevity of exposition.
16 As before summing the mutual dependencies ignores interactions between them and thereby likely provides a
lower bound for the measure of structural exposure.
Page 17
its current suppliers of a sufficiently large size so that the firm can source that input
internally. To remove all of its supply exchanges from the market, the firm needs to acquire
at least one such supplier for every type of input that the firm needs to engage in production.
Similarly, in order to remove market exchange for one of its outputs, the firm needs to
acquire just one buyer of sufficiently large size. To remove all such transactions, the firm
needs to acquire at least one such buyer for every type of output that the firm produces.
Consequently, in order to construct the measure of structural exposure we sum mutual
dependencies with representative business unit that the firm would need to acquire to
remove its market transactions across all of its inputs and outputs.16
To show how firms vary on the measure of structural exposure, recall first Berkshire Hathaway
and Dow Chemical illustrated in Figure 1. Though the firms differed on the dimension of
structural closure, they were all similar in that they had no mutual dependencies across its
boundaries. Consequently, they all had low structural exposure. Compare these firms to
International Paper and Stone Products illustrated on the left-hand side of Figure 3.
International Paper also has three business units: Lumber, Paper and Paperboard. All of
these businesses are in different SIC codes. However, all of them are mutually dependent,
suggesting that the firm has high structural closure. The Paper and Paperboard units are also
engaged in mutually dependent relationships that have not been integrated, implying that
International Paper has high structural exposure. Stone Products operates three business units:
stone products and mineral mining and non-ferrous material mining. Mineral mining and
non-ferrous material mining are considered related even at the 3 digit SIC level. There is
some mutual dependence between stone products and mineral mining, but there is very little
of mutual dependence between mineral mining and non-ferrous material mining. This makes
Stone Products a fairly low structural closure firm. There is, however, a substantial mutual
Page 18
dependency across Stone Products’ boundary between that originates from the non-ferrous
material mining business unit. This makes Stone Products a high structural exposure company.
------------------------------------------------------Insert Figure 3 around here
------------------------------------------------------Structural exposure can also be easily applied to the evaluation of marginal changes in firm
scope, as illustrated on the right-hand side of Figure 3. Consider, for example, what happens
if International Paper decided to diversify into the food industry. Before the acquisition
International Paper faced substantial structural exposure. After the acquisition that structural
exposure disappears and becomes structural closure. However, this does not mean that structural
exposure disappears completely. Now, International Paper has to face new structural exposure
between the business unit in the Food industry and other businesses with which the Food
business unit exchanges. In fact, as can be seen in the Figure 3, International Paper now
faces not only more mutual dependencies across its boundaries, but also each of these
dependencies is substantially greater than the original structural exposure. This suggests that by
acquiring the business unit in the Food industry, International paper faces greater structural
exposure than it faced before the acquisition. Such additional structural exposure is likely to
reduce firm value since the firm now needs to engage in more costly exchanges across its
boundaries.
Similar logic can be applied to divestitures. Consider, for example, a divestiture of the nonferrous materials unit by Stone Products. Because the mutual dependence between the
divested unit and the rest of the firm was low, such an acquisition will have a relatively small
impact of firm’s decline in structural closure. However, after the divestiture Stone Products will
face just a small mutual dependence originating form the mineral mining business units,
Page 19
instead of a substantial one that used to originate from the non-ferrous material mining unit.
Consequently, the divestiture will significantly reduce the company’s structural exposure.
Because the company will now face a much smaller mutual dependence across its
boundaries, we hypothesize that such a decrease in structural exposure will increase the firm’s
value. Thus, we hypothesize:
H3:
Following an acquisition, an increase in firm’s structural exposure has a
negative effect on firm market value
H4 :
Following a divestiture, a reduction in firm’s structural exposure has a
positive effect on firm market value
Complementarity between Structural Closure and Structural Exposure
Thus far, we have considered the effects of change of structural closure or structural exposure on
firm value independently of each other. However, as we discussed in the previous section,
every acquisition or divestiture simultaneously affects both structural closure and structural
exposure. Thus, it is impossible to predict the overall effect of changes in scope on firm value
by considering changes of just one of the variables. Instead, we need to consider structural
closure and structural exposure simultaneously. For acquisitions, such simultaneous
consideration can create three possible scenarios. First, an acquisition that simultaneously
increases structural closure and reduces structural exposure will increase firm value. Second, an
acquisition that does not increase structural closure, but increases structural exposure will destroy
value. The most interesting scenarios occur when an acquisition that increases structural closure
but also increases structural exposure. This scenario will apply, for example, to International
Paper’s acquisition discussed above, which increases both closure and exposure. If the negative
wealth effect associated with the increase in structural exposure is smaller than the positive
Page 20
wealth effect associated with the increase in structural closure, the acquisition will create value.
However, if the negative wealth effect associated with increase in structural exposure outweighs
the positive wealth effect associated with the increase in structural closure, the acquisition will
destroy value. This last condition is particularly important, because it indicates that an
analysis of such an acquisition that focused solely on changes in structural closure will predict
that an acquisition will create value. However, in reality the acquisition will actually destroy
value, because of the negative effect of structural exposure. However, a complete analysis that
incorporates both closure and exposure will correctly predict value destruction. Thus, to give
appropriate normative predictions regarding acquisitions or divestitures, changes in structural
closure and structural exposure have to be considered.
Methods
To test the hypotheses we collected data on all acquisitions and divestitures between 1978
and 1992 completed by Fortune 100 firms selected in 1978. Sixteen firms did not remain in
the study for the entire period, as they merged, were reclassified as service companies or
were taken private. These firms are included when they existed and deleted thereafter. The
data were obtained from multiple sources including the Predicasts’ Directory of Corporate Change,
Mergers and Acquisitions, and the Securities Data Corporation for each of the years in the study.
These sources vary in their comprehensiveness depending on the period and type of
transaction. By drawing on multiple sources we believe we have assembled a comprehensive
data set on these transactions. We assigned a 4-digit SIC code to every acquisition or
divestiture. This code was either provided directly in the data source or was assigned from
Standard & Poor’s Directory of Corporations based on the description of the business provided in
the data source. Both the data sources and the Wall Street Journal Index were searched to
Page 21
assign the first announcement date for all transactions. This procedure generated 450
acquisitions and 808 divestitures across all of the firms.
Our dependent variable is the excess stock market return associated with the announcement
of an acquisition or a divestiture. The excess returns method measures the difference
between the stock market’s expected return and a specific firm’s actual market return around
an event period. The logic underlying excess returns analysis is based on the capital asset
pricing model and efficient capital market theory (Fama, 1976). These theories hold that
stock prices accurately reflect investors’ expectations of the long-term performance, and thus
the value, of the firm. Changes in stock prices in excess of those predicted by the capital
asset pricing model are held to derive from the assimilation of new information revealed by a
particular event, such as an acquisition or a divestiture. It follows that the short-term stock
price changes can be used to gauge the long-term performance effects of major corporate
actions. The advantage of using the excess returns methodology lies in its strong controls
and high resolution. Because measurement of excess returns is focused on a very narrow
time period around the event, the expected effect of the action in question is isolated from
that of other events or actions that may have occurred in close temporal proximity. Thus,
excess returns methodology has an advantage over annual financial accounting measures,
which risk confounding the effects of the event in question with either the effects of other
actions the firm may have undertaken during the same year or with the effect of exogenous
shocks.
To capture these excess returns we used beta-adjusted excess returns provided by CRSP. We
also checked the robustness of these results using a market-adjusted model of excess returns
(Brown & Warner, 1985). There were no significant differences in the results using these two
Page 22
different measures. Below we report the average cumulative beta-adjusted excess returns
over a ten-day window around the day of announcement. Shorter window lengths generated
similar results.
In order to obtain measures of our main independent variables: changes in structural closure
and structural exposure associated with each change in corporate scope, we employed the
following procedure. First, we constructed the measures of dependence of business units in
two different industries i and j. on each other. To do that we followed Burt’s (1983)
approach and began with measures of interindustry flows, zij, expressed as the total dollar
value of goods and services sold by industry i to industry j. Subsequently, we derived
dependence of industry i on industry j, which is high to the extent that industry i sells a
significant proportion of its goods and services to industry j, sij, or it buys a significant
proportion of its goods and services from industry j, pij. To convert the measure of
dependence of industry i on industry j to dependence of business units in industry i on
business units in industry j, we multiplied the dependence measure by four-firm
concentration ratios in industry j, Oj.17 Following Burt (1983), we formally define this
measure of dependence of business units in industry i on business units in industry j in year t,
as Cijt:
17 Note that we are using industry-level data to measure the dependence of one business unit on another, and
not firm-to-firm transactions. Although it may seem that the latter approach is preferable, it is important to
remember that observed patterns of exchange cannot be always used to infer dependence. This is where the
industry-level data are superior to firm-to-firm transactions. Consider the following scenario involving industry
i and industry j. Industry i purchases 30 percent of its inputs from industry j, while industry j purchases none of
its inputs from industry i. Only two business units operate in each of the two industries. Business units A and B
operate in industry i, and business units C and D operate in industry j. Business unit A purchases 30 percent of
its inputs from business unit C and 0 percent from D, while B purchases 10 percent of its input from C and 20
percent of its input from D. To infer from this specific allocation of purchases and sales between firm-dyads
that business unit A is more dependent on business unit C than on D, or that B is less dependent on C than A,
is incorrect. All that matters for the definition of A’s and B’s dependence structure is that both business units
need 30 percent of their input from industry j and that only two firms, C and D, can provide those inputs. In
spite of their different patterns of purchases from C and D, therefore, there is no difference in the degree of
dependence of firms A and B on C and D.
Page 23
Cijt = ( sij + pij ) O j
2
where
⎛
⎞
⎛
⎞
⎜ zij ⎟
⎜ z ji ⎟
and sij = ⎜
pij = ⎜
⎟
zqi ⎟⎟
⎜ ∑ ziq ⎟
⎜∑
⎝ q
⎠
⎝ q
⎠
To calculate these measures, we used the Survey of Current Business data published by the U.S.
Department of Commerce. The survey records relations of buying and selling between
markets of the US economy. These data define the network of inter-market transactions as a
square table with 77 rows and columns, in which the elements of the table, zij, defines the
total dollar value of sales from industry i to industry j. We used the data for the 77 broadly
defined product markets distinguished in the 1977, 1982, 1987 and 1992 benchmark inputoutput tables. These market definitions are largely stable over time allowing us to analyze the
data over time. Since the data are made available only every five years, and can change over
time, there was a possibility that our analyses will pick up the results of step-changes every
five years in the values of inputs and outputs to respective markets. In order to avoid this
problem, we constructed annual measures based on linear interpolation between years for
which data were available. Such an expansion may reduce the statistical power of our tests,
since statistical packages will treat the data as if they were collected annually. However, we
do not expect that the loss of power is large, since there is very little between-years variance
(Burt, 1988).
The concentration data, Oj, are not published for input-output table markets and they are not
available from a single source. In manufacturing, four-firm concentration ratios are
published by the Department of Commerce for Standard Industrial Classification industries.
Page 24
A list of SIC industries within each input-output market is published by the Department of
Commerce. A conversion to market four-firm concentration ratios is made using the
formula: O=w1CR1 + w2CR2 + … where the weight wk is the ratio of sales from four-digit
SIC industry k divided by total sales summed across all four-digit SIC industries in the
market. Since concentration ratios are not available for non-manufacturing markets, we used
approximations based on sales data published in News Front compilation of the largest firms
operating in four-digit SIC codes obtained from Professor Ron Burt. With the Survey of
Current Business’ mapping of SIC categories to input-output sectors, the four largest firms in
each sector were identified, their sales summed and the sum divided by the total volume of
sales for the sector reported in the input-output table.
Once the directional dependence measures between business units were defined, our next
step was to convert them into dyadic measures of mutual dependence. We defined mutual
dependence between business units in industry i and industry j in year t as:18
R ijt = C ijt C jit
The third step was to calculate the measures of structural closure and structural exposure both
before and after the event. To construct these measures, we collected data on the business
units owned by the firms in the sample from the Federal Trade Commission Line of
Business Database. Firms are required by the Financial Accounting Standards Board (FASB)
to report disaggregated information for segments that represent 10% or more of their
consolidated sales, assets or profits. Segments represent a higher level of aggregation than
The product of Cijt to Cjit is equal to zero if one of the C’s is zero and all of the constraint is unidirectional.
Alternative measures, such as the sum of Cijt to Cjit yield positive mutual dependence even if it is unidirectional.
Thus, the product measure is most consistent with the definition of mutual dependence and that is why we
adopt it. However, as a robustness check, we also undertook our analyses with the sum measure. The results we
obtain with this measure are similar to the ones that use the product, but the statistical significance of those
results is often much lower. Thus, we report the results using the product measure.
18
Page 25
individual business lines. However, for each segment a number of SIC codes in which the
segment operates are provided. Thus, for each company the dataset provides a list of all
business units together with all SIC codes in which a business unit operates (i.e. we treated
business units with more than one SIC code as a set of separate business units). The use of
these data for diversification studies presents two potential problems for this study. First, the
10% materiality criterion may understate the true extent of corporate diversification. We do
not expect this to be a large problem for the purposes of this study. Business segments that
provide less than 10% of corporate sales, assets or profits are unlikely to be important
sources of market constraints. Second, these data can be easily manipulated by managers,
who can report changes in the operations of a particular company, even though there is no
real change. In order to guard against this possibility, we allowed the firm only to remove or
add an SIC code if we could find a corresponding acquisition or divestiture.19 As before, we
used the Department of Trade translation tables to convert the SIC codes into Department
of Trade markets. Thus, we were able to map each firm’s participation across the various
markets. Using these data, we used the following equations to obtain the values for structural
closure and structural exposure of firm f in year t:20
structural closuref,t = ∑∑ R ijt , i > j
i∈ f j∈ f
structural exposuref,t = ∑∑ Rikt
i∈ f k∉ f
19 Firms can add new SIC codes organically and lose them through plant closures that are not divested. It is
possible, therefore, that some of the SIC changes will not have corresponding acquisitions or divestitures. To
account for this possibility, we re-analyzed our data without any corrections and obtained very similar results.
20 The measure of structural closure is by this definition zero for single business unit firms. This measure will also
be zero for multi-business unit firms if there is no mutual dependence between the units. We estimate our
results using zero for both conditions, but to control for the fact that the two zeros mean very different
conditions, we also eliminate from the analysis all firms in years in which they report operating in a single SIC
code. Since there are very few firm-years in our dataset when the firm reports operating in a single SIC code,
the results are almost identical.
Page 26
The measures of structural closure and structural exposure were subsequently logged to ensure
normality of the distribution of the variables.21 The logging of the two measures did not have
a significant effect on the direction or their statistical significance in the models. However,
the logged measures provide better explanatory power. Hence, all results were reported using
the logged measure. To obtain the measures of changes in structural closure for acquisitions, we
subtracted the value of structural closure before the acquisition (structural closuref1) from the value
of structural closure after the acquisition (structural closuref2). Similarly, to obtain measures of
changes in structural exposure for acquisitions, we subtracted the value of structural exposure
before the acquisition (structural exposuref1) from the value of structural exposure after the
acquisition (structural exposuref2).22 Thus,
∆structural closuref = structural closuref2 − structural closuref1
∆structural exposuref = structural exposuref2 − structural exposuref1
To obtain the measures of changes in structural closure and structural exposure for divestitures,
we followed the same procedure as for acquisitions. To underscore that a divestiture is the
opposite of an acquisition, we inverted the sign of both the structural closure and structural
exposure variables. Thus, in the case of divestitures, changes in structural closure and structural
exposure measure decreases, rather than increases, in structural closure and structural exposure. It is
much more intuitive to interpret the results in this way.
21 There were very few conditions in which structural closure was equal to zero. Since log of zero is not defined,
we have tried two approaches. In the first, we eliminated all firm-year observations where structural closure was
zero – we report these results here. In the other approach, we added .001 to the measure, logged it, and
included it in the analysis. Given how few observations had structural closure of zero, the results were not
affected.
22 If a firm undertook only one acquisition or one divestiture in a year, the structural measures before the
acquisition equaled to the structural measures at the end of the previous year. In all other circumstances, only
the first acquisition or divestiture was treated like that, and subsequent ones took account of all prior
acquisitions and divestitures in that year.
Page 27
To capture relatedness, we used data on SIC codes in which a firm operates and the SIC
code associated with an acquisition or a divestiture to classify each transaction as related or
unrelated. We used two different classification approaches. First, transactions were classified
as related or not depending on whether they were in the same 4-digit SIC code as the
primary 4-digit SIC code of the firm. The second, less conservative approach involved
coding transactions as related if they were in the same 2-digit code as the primary 2-digit
code of the firm. We report results for both measures of relatedness.
A significant amount of work in finance has established that excess returns associated with
changes in corporate scope may be affected by a host of other drivers. These include firm
performance, average industry performance of the industry of the largest business unit of the
firm, expenditure on R&D as a proportion of sales, firm size and the rate of firm growth. To
control for specificity of firm investment programs, a number of researchers control for the
sum of excess returns associated with previous acquisitions and divestitures. A number of
papers have also found that free cash flow (Swanson, 1995) has a significant effect on the
excess returns. In order to control for these past explanations, we obtained these financial
data from the Compustat. We also use a variable which captures the number of hostile and
friendly takeover bids that the firm has been subject to in the past three years. These data
were collected from the SDC data tapes. Table 1 provides summary data and a correlation
table for all variables used in the event-study of acquisitions. Table 2 provides summary data
and a correlation table for all variables used in the event-study of divestitures.
Results
Table 3 presents results for OLS regressions on excess market return associated with
announcements of acquisitions. In Model 1 we introduce the baseline model in which we
Page 28
include only the financial variables. The results indicate that neither firm performance nor
average industry performance over the past year influence the magnitude of excess returns
associated with announcements of acquisitions. Consistent with the free cash flow
hypothesis, acquisitions undertaken when a firm has substantial amounts of free cash flow
have a negative and significant effect on the market return. Furthermore, the percentage of
sales spent on research and development has a negative and significant (at 10%) effect on
the excess market return associated with an announcement of an acquisition. At the same
time, firm growth over the past three years has no significant effect on the dependent
variable. The number of hostile takeovers that the firm received in the prior three years has
no significant influence on the magnitude of excess return. In contrast, the number of
friendly takeovers that the firm received in the prior three years has a positive and significant
influence (at a 10% level) on the magnitude of excess return.
In Model 2, we keep all of the variables from Model 1 and add a measure of relatedness of
acquisition, which takes the value of one if the acquired business unit is related to the main
business in which the firm is involved at 4-digit SIC code level. Consistent with prior
research (Lewellen, Loderer, Rosenfeld, Mikkelson, & Ruback, 1985; Mitchell & Lehn, 1990;
Morck et al., 1990), the effect is positive and significant at a 10% level. In Model 3, we use a
different measure of relatedness, which takes the value of one if the acquired business unit is
related to the main business in which the firm is involved at 2-digit SIC code level. As
before, the measure is in the expected direction and significant at the 10% level. These
results suggest that markets preferred that managers engage in related diversification and
penalize firms that entered into unrelated fields. As such, this result is consistent with the
relatedness view of the firm.
Page 29
In Model 4 we keep all of the variables from Model 1 and add the measures of changes in
structural closure and structural exposure associated with an acquisition. The estimated effect of
changes in structural closure on the market reaction to announcements of acquisitions is
positive and significant at a 5% level. This result suggests that acquisitions that lead to an
increase in structural closure result in a higher market return than those that do not increase
closure. This result supports Hypothesis 1. Furthermore, the estimate of the effect of an
increase in firm’s structural exposure following an acquisition on the market reaction to the
announcement is negative and significant at a 5% level. This result suggests that acquisitions
that lead to an increase in structural exposure result in a lower market return than those that do
not increase exposure. This finding supports Hypothesis 3.23 In Model 5 we keep all of the
variables from Model 4 and add the more conservative measure of relatedness used in Model
2 to test the robustness of the structural closure and structural exposure measures when standard
relatedness measures are included. Both measures remain significant and in the expected
direction. The measure of relatedness is also significant and in the expected direction. In
Model 6 we keep all of the variables from Model 4 and add the less conservative measure of
relatedness used in Model 3. All variables are significant and in the expected direction. It is
instructive to notice that the coefficient estimates on the relatedness measures in Models 5
and 6 are significant at 5% and about twice the size of the respective estimates in Models 2
and 3. At the same time the estimate on structural closure in Models 5 and 6 is smaller than that
in Model 4. Such pattern of results implies that when relatedness or structural closure measures
The effect sizes of structural closure and structural exposure can be compared directly as they have similar
standard deviations. In almost all other models the positive effect of structural closure is three to four times the
size of the negative effect of structural exposure. This implies that markets seem to pay much more attention to
structural closure than they do to structural exposure.
23
Page 30
are estimated on their own, the resulting estimates are biased.24 This is consistent with our
discussion on complementarity between relatedness and structural closure on pages 14 to 17.
In Table 4 we report additional analyses to check the robustness of the results. The first
three models (7, 8 and 9) are random effect models, whereas the last three models are firm
fixed effect models (10, 11 and 12). For each type of analysis, we first run a model with
changes in the structural closure and structural exposure variables. Subsequently, we introduce the
more conservative, and then the less conservative measure of relatedness. The effects of
structural closure and exposure remain in the expected direction and significantly different from
zero.
We repeat the same sequence of models for divestitures as shown in Table 5. In Model 13
we introduce the baseline model in which we include only the financial variables. The results
indicate that firm performance has a positive and significant (at 10%) effect on the
magnitude of excess returns associated with announcements of divestitures. Average
industry performance in the past year has no significant effect on excess return. Consistent
with earlier findings, divestitures undertaken when a firm has substantial amounts of free
cash flow have a positive and significant effect on the market return. The percentage of sales
spent on research and development has a positive and significant (at 10%) effect on the
excess market return associated with an announcement of a divestiture. At the same time,
firm growth over the past three years has a significant negative effect on the dependent
variable. The number of hostile takeover bids that the firm received in the prior three years
Specifically, the effect of relatedness was underestimated, while the effect of structural closure was slightly
overestimated. As we discuss on page 16, such pattern of biases is most consistent with our sample consisting
of a smaller percentage of acquisitions that were simultaneously related and high structural closure (such as Dow
Chemical in Figure 2) and a large percentage of related acquisitions that were characterized by low structural
closure (such as Sports Arenas in Figure 2).
24
Page 31
has no significant influence on the magnitude of excess return. Similarly, the number of
friendly takeover bids that the firm received in the prior three years has no significant
influence on the magnitude of excess return.
In Model 14, we keep all of the variables from Model 1 and add a measure of relatedness of
divestiture. This measure captures whether the divested business unit is related to the main
business in which the firm is involved. The effect is positive and significant at a 10% level.
This result suggests that markets prefer that managers undertake related divestitures more
than unrelated ones. This is inconsistent with the relatedness view of the firm, which would
predict that markets prefer that managers dispose of unrelated assets. It is possible, however,
that disposal of related assets signals to the markets that managers do not want to engage in
agency behaviors and are prepared to sell even highly related assets. If this explanation is
correct, then related divestitures will obtain greater excess returns than unrelated ones. In
Model 15, we keep all of the variables from Model 1 and add the less conservative measure
of relatedness of divestiture. Its effect is insignificantly different from zero
In Model 16 we keep all of the variables from Model 13 and add the measures of changes in
structural closure and structural exposure associated with a divestiture. The estimate of the effect
of changes in structural closure on the market reaction to announcements of acquisitions is
negative and significant at 1% level. This result suggests that divestitures that lead to a
decrease in structural closure result in a lower market return than those that do not decrease
closure. This result supports Hypothesis 2. Furthermore, the estimate of the effect of an
increase in firm’s structural exposure following a divestiture on the market reaction to the
announcement is positive and significant at a 5% level. This result suggests that divestitures
that lead to a decrease in structural exposure result in a higher market return than those that do
Page 32
not decrease exposure. This finding supports Hypothesis 4. In Model 17 we keep all of the
variables from Model 16 and add the more conservative measure of relatedness used in
Model 14. Both structural measures are significant and in the expected direction. In contrast
to Model 14, the measure of relatedness becomes insignificant. In Model 18 we keep all of
the variables from Model 16 and add the less conservative, measure of relatedness used in
Model 15. Both structural measures are significant and in the expected direction, but the
measure of relatedness is insignificant.
In Table 6 we report additional analyses to check the robustness of these results. The first
three models (19, 20 and 21) are random effect models, whereas the last three models are
firm fixed effect models (22, 23 and 24). For each type of analysis, we first run a model with
changes in the structural closure and structural exposure variables. Subsequently, we introduce the
more conservative, and then the less conservative measure of relatedness. The effects of
structural closure and exposure remain in the expected direction and significantly different from
zero. In the case of fixed-effect models, however, the statistical significance of the key
variables of interest only reaches 10%. All of the relatedness measures remain insignificant.
Conclusions
Our paper was motivated by the observation that there exist at least two different bases for
profitable firm diversification – relatedness and mutual dependence – both of which trace
their lineage to Coase’s discussion of market failures. The two bases differ, however, in the
nature of market failures. The first one captures failures in markets for inputs that can be
shared across related activities. The second focuses on market failures in the exchange of
goods and services across subsequent stages in production. Even though most strategy
scholars would agree that we should simultaneously analyze both types of market failures to
Page 33
understand a firm’s diversification strategy, few such theoretical integration attempts have
ever been undertaken. Similar concerns are present in the measurement of firms’ corporate
strategies. However, with rare exceptions, studies of corporate diversification focus on either
relatedness or mutual dependence. We argued that this state of affairs can be partly
attributed to the lack of theoretically informed measures of mutual dependence that can be
deployed across many industries. We sought to rectify the situation by proposing two
measures: structural closure and structural exposure, and proposed that structural closure should
increase firm value, while structural exposure should reduce it. Our analyses of market reactions
to announcements of acquisitions and divestitures have provided unequivocal support for
the hypotheses across a large number of different specifications and estimation methods.
An important part of our argument was that the two structural measures should not be seen
as substitutes to the existing relatedness measures, but as direct complements. This
complementarity arises as each measure incorrectly assigns certain types of value creating
diversification to the value destroying category, but the counterpart measure corrects that
problem. Specifically, the measures of relatedness assign both unrelated diversification and
value creating diversification based on internalizing mutual dependence to the category of
unrelated diversification. However, the measure of structural closure can clearly separate mutual
dependence from unrelated diversification and thus correct for the misclassification in the
relatedness measure. Similarly, the measure of structural closure confounds unrelated
diversification with related diversification and codes both as creating no value. However, the
measure of relatedness can clearly separate relatedness from unrelated diversification and
thus correct the misclassification in the structural closure measure. As we discussed above, such
complementarity not only gives us a more complete picture of firm diversification, but it also
reduces biases in estimating the effects of relatedness or structural closure on firm
Page 34
performance. In fact, our analyses, particularly those for acquisitions, provide evidence of
biased estimates when relatedness or structural closure are estimated on their own.
The second important part of our argument was that every change in firm scope entails
changes both in structural closure and structural exposure. Consequently, the two measures have
to be considered together to evaluate the wealth effects of changes in firm scope. The
concern becomes particularly salient for an acquisition which simultaneously increases
structural closure, which increases firm value, but also increases structural exposure, which reduces
firm value. If the negative effect of structural exposure outweighs the positive effect of structural
closure, such acquisitions destroy value and therefore should not be undertaken. However,
analyses that only consider structural closure will only pay attention to the positive wealth
effects, and therefore will incorrectly recommend that the firm undertake the acquisitions.
Similar concerns are present for divestitures that reduce structural exposure, but also
substantially reduce structural closure. Consequently, the joint use of structural closure and
structural exposure to evaluate firm’s scope is necessary.
Despite the paper’s contributions, it is important to recognize that it suffers from a number
of potential shortcomings. First, it is possible that the results we present here may arise out
of certain measurement biases. These may arise as we do not directly observe mutual
dependence using firm level data, but infer it from the set of industries in which the firm
operates. This problem is endemic to most measures of corporate diversification. For
example, measures of relatedness do not explicitly measure whether firms utilize common
inputs in production of different goods. Instead, they implicitly assume that if a firm
operates in two related SIC codes, then it shares common inputs. Similarly, our measures of
mutual dependence assign the average mutual dependence for business units in two
Page 35
industries to all dyads of business units in these two industries. Although it is exactly this
procedure that allows us to derive measures of diversification across a broad spectrum of
industries, it could present a problem if it made it more likely that we find effects of structural
closure and structural exposure when there are none in reality. However, we do not expect this to
be a substantial problem for a number of reasons. First, most firms in the same industry
have similar business models and therefore require similar inputs and are capable of
producing similar outputs. Therefore, the average mutual dependence is actually likely to
reflect the actual underlying mutual dependencies. Second, even if firms in industries varied
significantly, the bias is likely to work in the opposite direction. Consider, for example, an
industry which comprises generalist producers who sell to generalist consumers and a set of
niche producers whose products are attractive only to a set of niche customers. By using the
average of mutual dependence, we overestimate mutual dependence for generalist pairs and
underestimate mutual dependence for niche pairs. If niche players are smaller and thus less
likely to show up in our dataset, then we would only observe firms for which we have
overestimated mutual dependence. Thus, our estimate that one unit of mutual dependence
causes valuations to increase or decrease by a certain amount means that in reality the effect
of less than one unit of mutual dependence has the estimated effect on performance. This
implies that in reality the effect of one unit of mutual dependence is greater than we
estimated here.25
The only condition in which the reported effect would overestimate the real effects of mutual dependence
effects (and therefore structural closure and structural exposure) is if the two industries were occupied only by niche
producers, each of which can only sell to or buy from a restricted type of very different set of firms. Because
we are taking an average across all these firms, it seems that firms can sell to or buy from a broad set of
different firms, whereas each of them is actually fairly constrained. In this case, the average of mutual
dependence for firms in the two industries underestimates the true mutual dependence between them. Since
the real mutual dependence between firms is higher than we capture here, the estimated effects of mutual
dependence on performance will actually overestimate the true effect of mutual dependence (and thus structural
closure and structural exposure) on firm performance. However, since there are probably very few such industry
dyads with niche players only, we do not expect that this bias will be prevalent.
25
Page 36
We also cannot completely rule out the hypothesis that unobserved heterogeneity is
responsible for the effects we report here. Indeed, the prevailing issue in the literature
linking diversification to performance is that firms that are likely to diversify are also
characterized by certain unobserved qualities that are related to firm performance (Campa &
Kedia, 2002; Villalonga, 2004b). Empirical studies that do not control for these qualities are
likely to attribute performance differences to diversification strategies, whereas in reality
these effects are artifacts of different types of firms following different diversification
strategies. We undertook three steps to mitigate these problems and minimize the possibility
that our results can be attributed to unobserved heterogeneity. First, we explicitly controlled
for different indicators of firm performance. Second, we focused on marginal changes in
scope and attempted to differentiate between value creating and value destroying
diversification. Third, we tested the stability of our results by including firm fixed effects.
The inclusion of firm fixed effects eliminates all time-invariant firm effects, leaving only
unobserved characteristics that change with time to potentially explain our results. The three
steps taken together imply that for unobserved heterogeneity to account for our results,
there would have to be time-varying firm characteristics that are correlated with firm value,
but we did not control for, which make it more likely that the firm undertakes high structural
closure acquisition and simultaneously make it less likely that the firm undertakes high
structural closure divestiture. An equivalent statement could be put forward with respect to
structural exposure. Thus, unlike the traditional concerns of unobserved heterogeneity that
merely require that poorly performing firms diversify more frequently, the conditions that
would explain our results with unobserved heterogeneity are much more complex. Although
we cannot completely reject the possibility that such unobserved factors exist, we believe
Page 37
that the complexity of criteria they would have to fulfill make them unlikely to be present in
the context of our study.
Overall, despite these potential shortcomings, we believe that the results we present here
point to the importance of considering both sources of value creation in firm diversification.
We hope that future research will use the measures we developed here to provide even
greater insights into corporate diversification.
Page 38
Figure 1
Structural Closure: Acquisitions and Divestitures
Insurance
Insurance
Candy
Candy
Publications
Publications
Bowling
Centers
Bowling
Centers
Berkshire
Real Estate
Real Estate
Construction
Management
Construction
Management
Sports Arenas
Radio/TV
Radio/TV
Amusements
Amusements
Consumer
Goods
Disney
Consumer
Goods
Chemicals
Chemicals
Consumer
Chemicals
Consumer
Chemicals
Plastics
Plastics
Dow Chemical
Page 39
Figure 2
Relatedness, Structural Closure and Value Creation
Value Created
Relatedness
Closure
Yes
High
High
Yes
High
Low
Yes
Low
High
No
Low
Low
Chemicals
Consumer
Chemicals
Dow Chemical
Bowling
Centers
Real Estate
Sports Arenas
Radio/TV
Amusements
Disney
Insurance
Candy
Berkshire
For clarity of exposition, we only used the two business unit examples from Figure 1.
Page 40
Figure 3
Structural Exposure
Furniture
Furniture
Paper
Paper
Lumber
Lumber
Publishing
Publishing
Paperboard
Paperboard
Food
Food
International Paper
Acquisition
Mineral
Mining
Non-ferrous
metal mining
Mineral
Mining
Stone
Products
Non-ferrous
metal mining
Stone Products
Divestiture
Page 41
Stone
Products
Table 1: Correlation Table for Variables Used in Acquisitions Study
Mean St. Dev.
1
1. Ten Day Excess Return
.00
.05
2. Returns on Past Acquisitions
.01
.20
.06
3. Return on Assets
.16
.06
.02
4. Ind. Average Return on Assets
.12
.04
-.09
5. Log (Market Value)
9.18
1.07
-.02
6. Average Free Cash Flow
.18
.12
-.13
7. Average R & D/Sales
.03
.02
-.02
8. Increase in Sales
.15
.22
-.10
9. Hostile Takeover Bids
.02
.18
.13
10. Friendly Takeover Bids
.05
.23
.06
11. Related: Main Business Line
.58
.42
.10
12. Related: Any Business Line
.66
.48
.10
13. ∆ Log (Structural Closure)
-1.54
1.03
.05
14. ∆ Log (Structural Exposure)
.80
1.18
-.01
Table 2: Correlation Table for Variables Used in Divestitures Study
Mean St. Dev.
1
1. Ten Day Excess Return
.00
.06
2. Returns on Past Acquisitions
-.03
.30
.06
3. Return on Assets
.15
.06
.12
4. Ind. Average Return on Assets
.12
.04
.02
5. Log (Market Value)
8.99
1.00
.00
6. Average Free Cash Flow
.18
.25
.09
7. Average R & D/Sales
.03
.02
.11
8. Increase in Sales
.11
.24
-.10
9. Hostile Takeover Bids
.05
.27
.02
10. Friendly Takeover Bids
.07
.29
.04
11. Related: Main Business Line
.48
.50
.03
12. Related: Any Business Line
.56
.50
.02
13. ∆ Log (Structural Closure)
-1.83
1.17
-.08
14. ∆ Log (Structural Exposure)
.80
1.05
.02
2
3
4
5
6
7
8
9
10
11
12
13
-.04
-.14
.04
-.03
.05
-.03
.09
.08
.12
-.03
.30
-.13
.13
.38
.01
.06
.03
.01
-.12
-.02
-.06
-.07
.04
.04
.10
-.39
.12
-.03
-.09
-.01
-.01
.24
.29
.08
.09
-.08
.02
-.05
-.10
-.16
.09
.11
-.07
.04
.06
.03
-.06
-.06
.11
.02
.19
-.06
-.02
-.07
.04
-.37
-.52
-.18
.00
-.12
-.14
-.06
-.11
.12
-.08
-.06
.03
.08
-.03
.03
.02
-.05
.48
.16
.06
.08
.10
.34
2
3
4
5
6
7
8
9
10
11
12
13
.18
.11
.13
.22
.19
.18
-.27
.06
-.14
-.08
-.13
.09
.25
.36
.20
.01
.19
.09
-.09
.08
.05
.04
.08
.02
-.06
-.26
.03
.01
-.11
.03
-.02
.05
.14
.19
.03
.11
.04
-.08
.17
.11
.14
.20
-.09
.22
.06
.04
-.01
.11
.09
.07
.14
-.08
.04
.01
-.06
-.17
-.30
-.11
.01
-.02
.01
.00
-.10
.17
.00
.05
.08
.06
-.06
-.06
-.03
-.07
.49
.30
.16
.38
.27
.44
Page 42
Table 3: Ten Day Excess Return Associated with an Acquisition x by Firm f in Year t
1
2
3
4
5
.0134
.0051
.0173
.0341
.0208
Intercept
Return on Assetsf,t−1
Average Return on Assets in Industryf,t−1
Log (Market Valuef,t−1)
Average Free Cash Flow(f,t−3→t)
Average R&D/Sales(f,t−3→t)
Increase in sales over the past 3 yearsf,t
Hostile Takeover Bids(f,t−3→t)
Friendly Takeover Bids(f,t−3→t)
Acquisition x related to main business unitf,t
Acquisition x related to any business unitf,t
∆ log (structural closuref,x)
.0407
-.0574
-.0003
-.027**
-.1614*
-.0061
.0122
.0122*
.0379
-.0546
-.0002
-.0264**
-.1495*
-.0047
.0135
.0125**
.0041*
.0686
-.1726**
-.0000
-.0392***
-.2099
-.0039
.0175
.0192
.0833
-.1625**
-.0017
.0415***
-.2079
-.013
.0328***
.0143
.0798
-.1572*
-.0011
.0401***
-.2193
-.0109
.0349***
.0148
.0109**
.0044*
∆ log (structural exposuref,x)
6
.0187
.0802
-.1671**
-.0007
.0399***
-.2612*
-.0093
.0350***
.0129
.027**
.0243**
.0121**
.0253**
-.0062**
-.0065**
-.0069**
Firm Random Effect
Firm Fixed Effect
No
No
No
No
No
No
No
No
No
No
No
No
R2
.0190
.0253
.0462
.0674
.0736
.0758
.0106
.0160
.0288
.0463
.0505
.0525
Adjusted
R2
Table 4: Ten Day Excess Return Associated with Acquisition x by Firm f in Year t
7
8
9
10
11
Intercept
.0302
.0166
.0140
-.1204
-.1689
Returns Associated with Past Acquisitionsf,t−1
-.2132*** -.2175***
Return on Assetsf,t−1
.0907*
.0889*
.0905*
.3023***
.2933***
Average Return on Assets in Industryf,t−1
-.1751*
-.1697*
-.1799*
-.6881*** -.6839***
Log (Market Valuef,t−1)
-.0014
-.0008
-.0004
.0149
.0213*
Average Free Cash Flow(f,t−3→t)
-.0424*** -.0409*** -.0407*** -.0399**
-.0384**
-.2031
-.2131
-.2583
.0893
.0697
Average R&D/Sales(f,t−3→t)
Increase in sales over the past 3 yearsf,t
-.0114
-.0094
-.0076
-.0025
-.0013
Hostile Takeover Bids(f,t−3→t)
.0333*** .0353*** .0361*** .0636***
.0648***
Friendly Takeover Bids(f,t−3→t)
.0150
.0156
.0133
.0194
.0207
Acquisition x related to main business unitf,t
.0104
.0128*
Acquisition x related to any business unitf,t
.0125**
∆ log (structural closuref,x)
.0302*** .0278**
.0302**
.0454***
.0394**
∆ log (structural exposuref,x)
-.0066** -.0068**
-.0066** -.0127*** -.0122***
12
-.1605
-.2167***
.2984***
-.7182***
.0200*
-.0369**
.1218
.0024
.0704**
.0154
.0195***
.0446***
-.0148***
Firm Random Effect
Firm Fixed Effect
Yes
No
Yes
No
Yes
No
No
Yes
No
Yes
No
Yes
R2
.0712
.0760
.0754
.0051
.0058
.0073
R2 within
.0672
.0650
.0842
.1818
.1893
.1981
.0563
.0639
.0506
R2 between
.0672
.0732
.0560
*** - significant at less than 1%, ** - significant at less than 5%, * - significant at less than 10%
Page 43
Table 5: Ten Day Excess Return Associated with Divestiture x by Firm f in Year t
13
14
15
16
17
Intercept
-.0043
-.0043
-.0098
-.0114
-.0117
Return on Assetsf,t−1
.0517*
.0482
.1438*** .1524***
.1507***
Average Return on Assets in Industryf,t−1
.0529
.0552
.0555
.0470
.0474
Log (Market Valuef,t−1)
-.0140
-.0017
-.0034*
-.0043*
-.0047**
Average Free Cash Flow(f,t−3→t)
.0193*** .0195*** .0241*** .0268***
.0272***
Average R&D/Sales(f,t−3→t)
.2625*** .2666*** .4028*** .4529***
.4449***
Increase in sales over the past 3 yearsf,t
-.0221*** -.0221*** -.0253*** -.0320*** -.0317***
Hostile Takeover Bids(f,t−3→t)
.0032
.0031
.0022
-.0003
-.0004
Friendly Takeover Bids(f,t−3→t)
-.0018
-.0016
.0121
.0139
.0144
Divestiture x related to main business unitf,t
.0029*
.0058
Divestiture x related to any business unitf,t
.0035
-∆ log (structural closuref,x)
-.0063*** -.0050***
-∆ log (structural exposuref,x)
18
-.0153
.1520***
.0521
-.0044**
.0263***
.4479***
-.0320***
-.0005
.0146
.0061
-.0052***
.0031**
.0031**
.0028**
Firm Random Effect
Firm Fixed Effect
No
No
No
No
No
No
No
No
No
No
No
No
R2
.0239
.0320
.0652
.0852
.0873
.0876
.0186
.0260
.0559
.0737
.0747
.0750
Adjusted
R2
Table 6: Ten Day Excess Return Associated with Divestiture x by Firm f in Year t
19
20
21
22
23
Intercept
-.0114
-.0117
-.0153
-.1886*** -.1873
Returns Associated with Past Acquisitionsf,t−1
-.1096*** -.1804
Return on Assetsf,t−1
.1524*** .1507*** .1520*** .0930
.0953
Average Return on Assets in Industryf,t−1
.0470
.0474
.0521
.1677
.1677
Log (Market Valuef,t−1)
-.0043*
-.0047**
-.0044** .0109
.0104
Average Free Cash Flow(f,t−3→t)
.0268
.0272*** .0263*** .0257***
.0258***
Average R&D/Sales(f,t−3→t)
.4529*** .4449*** .4479*** 1.7431*** 1.7306***
Increase in sales over the past 3 yearsf,t
-.0320*** -.0317*** -.0320*** -.0348*** -.0347***
Hostile Takeover Bids(f,t−3→t)
-.0003
-.0004
-.0005
.0028
.0030
Friendly Takeover Bids(f,t−3→t)
.0139
.0144
.0146
.0194*
.0194*
Divestiture x related to main business unitf,t
.0058
.0042
Divestiture x related to any business unitf,t
.0061
-∆ log (structural closuref,x)
-.0043*** -.0050*** -.0052*** -.0038*
-.0043**
24
-.1888**
-.1091***
.0944
.1701
.0107
.0255***
1.7465***
-.0350***
.0028
.0193*
.0019
-.0040*
-∆ log (structural exposuref,x)
.0021**
.0021**
.0028*
.0022*
.0021*
.0021*
Firm Random Effect
Firm Fixed Effect
Yes
No
Yes
No
Yes
No
No
Yes
No
Yes
No
Yes
R2
.0852
.0873
.0876
.0093
.0100
.0096
R2 within
.0361
.0388
.0384
.1262
.1271
.1263
.0009
.0011
.0011
R2 between
.2597
.2549
.2588
*** - significant at less than 1%, ** - significant at less than 5%, * - significant at less than 10%
Page 44
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