global diversification discount and its …...global diversification discount and its discontents:...
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Electronic copy available at: http://ssrn.com/abstract=2507710
Global Diversification Discount and Its Discontents:A Bit of Self-selection Makes a World of Difference∗
Sungyong Chang1, Bruce Kogut2, and Jae-Suk Yang3
[email protected], Columbia Business School, Columbia University, NY 10027, [email protected], Columbia Business School, Columbia University, NY 10027, USA
[email protected], Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea
forthcoming, Strategic Management Journal
Abstract
Research Summary: The documented discount on globally diversified firms isoften cited, but a correlation is not per se evidence that global diversification destroysfirm value. Firms choose to globally diversify based on their firm attributes, some ofwhich may be unobservable. Given these exogenous firm attributes, the decision todiversify globally is endogenous and self-selected. Using the same specifications savefor the Heckman selection instrument, our results contradict past research that didnot address endogeneity. We posit that the global premium should reflect the valueof multinational operating flexibility. We use the 2008-2009 financial crisis as creatingexogenous variation to permit a test for the positive change in firm valuation due toglobal diversification. During the 2008-2009 financial crisis, the premium associatedwith global diversification became larger and more significant than before the 2008-2009 financial crisis. The churn of subsidiaries entering and exiting countries increasedduring the crisis, pointing to the value of an operating flexibility to restructure thegeography of the multinational network. In all, the results contradict past findings andprovide evidence that operating flexibility is more valued during times of high volatil-ity, thus generating the diversification premium.
Managerial Summary: There are thousands of multinational corporations thathave been international for decades and some even longer. They undoubtedly learnedthat there is money to be made in international markets. Yet, the recent academicliterature has found that foreign diversification destroys firm economic value. Our arti-cle uses a statistical technique that corrects for an obvious problem. Some firms invest
∗We thank Nalin Kulatilaka for many discussions on flexibility and options over the years and DonLessard and Evan Rawley for their comments. We are grateful to the Sanford C. Bernstein & Co. Centerfor Leadership and Ethics and Jerome A. Chazen Institute of International Business at Columbia BusinessSchool for funding. All errors are our own.
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Electronic copy available at: http://ssrn.com/abstract=2507710
overseas because they are facing troubles, e.g. slowing growth and are exiting bad homemarkets. Once we correct for this “selection bias”, we find that global diversificationat worse has no negative effect on value. However, during the financial crisis, the valueof multinational companies increased. This finding is consistent with the option theoryof multinational investment whereby operating in multiple countries permits firms toshift their activities among countries. Overall, our results say: there is a reason for whyfirms globalize: it is profitable.
Keywords: Global diversification; Self-selection; Operating flexibility; Financial crisis
JEL Classification: F23, G00, M00
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Electronic copy available at: http://ssrn.com/abstract=2507710
Introduction
The standard economic reasoning for the explanation of the geographic diversification of
the firm over borders has relied upon two well-documented assumptions set out by Stephen
Hymer 50 years ago. The first assumption is that a firm, when it ventures outside its national
market, faces a disadvantage that subsequent literature has christened as the “liability of
foreignness” (Zaheer 1995). The second is that the firm, by consequence of this environmental
disadvantage, must have an offsetting competitive advantage that can be transferred from
one country location to another at a cost that does not wither away the benefits of the
competitive asset. There is considerable empirical research that supports this argument (e.g.
Caves, 1971; Caves, 1996; Morck and Yeung, 1991).
The article by Denis, Denis, and Yost (2002) published in the Journal of Finance claims
the contrary, finding evidence for a global diversification discount of similar magnitude for
the industry discount. They estimated (their Table VI) the diversification discounts to be
0.20 for industrial diversification and 0.18 for global diversification. Their conclusions state
that “commentators today often extol the virtues, if not the competitive necessity, of global
diversification. Consistent with this general view, our findings indicate that an increasing
fraction of U.S. firms have adopted global diversification strategies. However, much like the
situation with conglomerates in the 1960s, we find no evidence that these global diversifica-
tion strategies have created shareholder value, on average” (Denis et al., 2002: 1977).
In the same year of the publication of the Denis et al. paper, Campa and Kedia published
their article on industry diversification also in the Journal of Finance. Both articles use sim-
ilar data, similar variable specifications following Berger and Ofek (1995), but differ in their
econometric specification. Campa and Kedia found that the industry discount disappeared
or reversed once the econometric specification accounted for selection. In other words, in the
October issue 2002, Denis et al. find a global discount without correcting for selection, but
in the August issue of 2002 of the same journal, Campa and Kedia had found no evidence for
an industry diversification discount once controlling for selection. Quite simply, the logical
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question arising from these two articles is to ask what happens to the global diversification
discount once accounting for self-selection.
The answer to this question constitutes the first part of the following article. We show
that upon controlling for self-selection, the incremental value of global diversification turns
from a discount to a premium, for the time period of 2005 to 2011. This reversal echoes
similar reversals found by Shaver (1998) regarding the performance of direct investments
and governance decisions, as well as the Campa and Kedia results.
The second half of the paper explores an operating flexibility perspective to explain
the global premium, drawing on Kogut and Kulatilaka (1994). The interesting twist in the
subsequent analysis of the robustness of the results over years shows that the premium
emerges during the period of the 2008-2009 financial crisis. The period of 2005-2007 shows
no premium. Why would the global diversification effect shift from no premium to premium
in these years?
We argue that this reversal is consistent with the theory of multinational investment
as providing a valuable capability to coordinate a global network of subsidiaries. By the
option characteristics of this flexibility, this value increases in volatility. The crisis increased
volatility within countries depending on exposure. The relation of increased volatility and
a global premium suggests that multinational firms exploited their operational flexibility to
coordinate exit and entry in their country portfolios to reposition their activities. We show
that exit and entry activity increased during the crisis and the pattern of country premia
changed substantially.
Diversification is one of the perennial themes in the history of strategy research. Some
of the giants in the field, such as Edith Penrose (1995) and Alfred Chandler (1990), wrote
foundational books on the subject. The debate over diversification is integrally related to the
debate over corporate versus industry effects that populated the pages of the Strategic Man-
agement Journal two decades ago (e.g. Rumelt, 1991; Brush and Bromiley, 1997; McGahan
and Porter, 1997; Bowman and Helfat, 2001). The field of global strategy is predicated upon
the very definition of the multinational firm as diversified across countries in its activities.
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It is surprising that studies find no value, indeed value destruction, to industry or global
diversification. Thus, the replication of the Denis et al. study returns to a central question
in strategy research: is there value in being diversified and, in this case, in being global.
We turn now to challenging that finding by using a specification and research design that
permits for better causal identification by an exogenous shock.
Methodological and Theoretical Motivations
In this section, we first discuss the self-selection problem in relation to the diversification
choice and second, we develop the theoretical motivation for predicting a global diversification
premium due to operating flexibility. Our replication relies upon using more recent data that
also includes an exogenous shock that was not available to the Denis et al. study.1 Utilizing
this shock, we adapt a model of operating flexibility to explain why the diversification premia
will vary by the states of the world depending on realized volatility. By including a self-
selection correction and exploiting an exogenous shock, we are able to resolve arguably some
of the causes for the mixed results in the global diversification literature.
Self-selection problem in diversification choice
As first argued in Shaver’s (1998) article on endogeneity in foreign investment, the self-
selection design is consistent with a theoretical statement that the decision to invest overseas
is the product of the strategizing by managers to improve the profitability of the firm. Firms
choose the geographical location of their operations and decide whether to operate in a
single country or diversify into multiple countries. The latter decision may be inclusive of
the subsequent value of the operating flexibility of a network of subsidiaries, but need not
be. This value may be recognized and realized ex post, perhaps in response to an exogenous
shock such as the financial crisis. Once a firm has gone overseas, a firm’s choice to diversify1Kuppuswamy and Villalonga (2010) first utilized the crisis as a shock to analyze industry diversification
premia. For other examples, see Lee and Makhija (2009) and also Balachandran, Kogut, and Harnal (2010)study on executive compensation and risk strategy.
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or to manage flexibly its operations, then, is likely to be a response to exogenous changes in
the macro (or sectoral) environment that impacts its value.
Consequently, the observed correlation between diversification and firm value is not
causal: the firm’s diversification decision may be due to negative shocks in its environment,
even if subsequently the option value to respond to later shocks is positive. Adverse shocks
that have depressed the value of the firm can lead to a decision to diversify, but it is the shock
and the impacted firm value that are related, not the diversification and value as predicted
by Denis et al. (2002).2 It is this correction to the effects of the endogenous correlation that
Campa and Kedia (2002) made in their article on sectoral diversification, finding no value
erosion on average for industry diversification. The Shaver 1998 article correcting for the
decision to enter a country (i.e. diversify geographically) is a response to similar endogene-
ity concerns. Shaver notes that firms choose strategies based on their attributes. Therefore,
strategy choice is endogenous and self-selected. Empirical models that do not account for
this selection endogeneity and regress the dependent variable on strategy choice variables
are potentially misspecified and their conclusions incorrect.
The first step in the analysis is therefore replicating the analysis of Denis et al. (2002)
but including a correction for self-selection. The second step, we now describe, is therefore
to explain why the valuation effect should be, sometimes, positive for global diversification.
Operating flexibility
Theoretical arguments suggest that global diversification can have both positive and negative
effects on firm value. The argument predicting a discount to diversification (including global
expansion) relies upon managerial inefficiencies due to agency or influence costs inside of
firms. This latter perspective view is the most frequent explanation for the diversification2We focus here on shocks, as we rely upon the financial crisis as an instrument for unobserved changes in
the macroeconomic environment. Equally plausible is that managers diversify in response to poor profitabilityin their home industry or as a reflection of unobserved variables, such as poor managerial capabilities, thatlead to bad diversification decisions and ex post performance. Here again there is misspecification, since thedecision to diversify and the subsequent performance are related through unobserved managerial ability, notbecause diversification earns a discount sui generis.
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discount; see for example Campa and Kedia (2002) and Denis et al. (2002) for summaries of
this literature. There is no a priori reason to believe that the argument that divisional politics
leading to sub-optimal subsidization across divisions, as analyzed theoretically by Rajan,
Servaes, and Zingales (2000), should fail to apply to the global firm as well. Similarly, the
evidence that despite the advances in communication technology, costs increase in distance
between headquarters and subsidiary due to the difficulty to monitor supports, among other
reasons, the prediction of a global diversification discount in value because of increased
agency activities (Kalnins and Lafontaine, 2013).
Still, there are also very compelling reasons and research that point to the increment in
value due to global investment, and thereby, to global diversification. The value of diver-
sification has its roots in the internalization theory of synergy, proposed by Caves (1971).
According to this view, global diversification increases value in the presence of substantial in-
tangible assets, such as superior production skills, marketing skills, and management quality
(Morck and Yeung, 1991). Caves (1996) reviews the principal early literature. This theoreti-
cal framing is sufficiently broad to include also the transfer of intangible assets, such as tacit
or explicit knowledge, superior practices, and management capabilities that have been the
topic of more recent work.
An unrelated line of research pointed to the incremental value of direct investment and
global operations due to risk diversification. This early work, e.g. Errunza and Senbet (1981,
1984) or Lessard (1973), posited a positive relationship between their measure of excess firm
value and the firm’s degree of internationalization, since global diversification completes the
market for investors who otherwise face barriers to international capital flows. The substan-
tial growth in global direct investment, including to emerging market countries and countries
previously excluded from investment flows due to politics (e.g. the Soviet bloc), has not con-
tradicted as so much lessened the empirical salience of this argument.
Though related, theories relying upon the value of operating a network of subsidiaries
is distinct from diversification. This value derives from the options embedded in the net-
work and in the managerial capabilities to respond flexibly to external shocks, e.g. exchange
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rates, productivity, innovations, that affect differentially the attractiveness of operating in
one country versus another. The essential idea relies upon the identification of a stochastic
variate θ that is governed by a stochastic process. The specification of this process depends
upon the content of the model. For many applications in financial economics, there is a
preference for a Wiener random-walk process because of its analytical tractability and fit to
frequent observations (such as daily equity prices or exchange rates). At particular boundary
conditions, the parameter θ strikes a critical value where it is optimal to change the operating
state, such as diversifying to another country, or the subsequent decision to switch production
to another country. Many applications rely upon other processes because θ may be better
treated as mean-reverting. For many applications, the solution is numerical, especially for
models involving many stochastic parameters or complex processes.
To develop the intuition, we suppress the details of the stochastic process but characterize
the essential ideas of the stochastic variate, the state operating variable, and the creation of
incremental profit and value.3 The comparative advantage parameter θ denotes generally an
economic stochastic variable that influences the decision where to operate an activity among
two country locations - a third possibility is to exit both locations. This parameter could be
the real exchange rate, relative productivity, changes in macroeconomic policies such as tax
rates, political risk, etc.. The state variable s is the prevailing operating state: exit, operate
in country X, or operate in country Y . That is,
π(θ, s) = maxs
[0, πx(θ, s), πy(θ, s)] (1)
where the profit π of operating two subsidiaries flexibly depends upon the comparative
advantage parameter θ and the optimal choice of the operating state, s. Figure 1 provides an
illustrative graph of the incremental profit when a firm can flexibly respond to changes in the
state variable θ by choosing globally where to assign its activities between two subsidiaries
located in countries X and Y . If conditions in X country become too averse, the parent will3We draw upon Kogut and Kulatilaka, 1988, 1994, and unpublished notes; see also Huchzermeier and
Cohen, 1996, who analyze a similar problem for multiple exchange rates.
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shut down all operations and exit country X, thereby saving any fixed cost to maintaining an
office. Assuming linear profit functions, the piecewise thick lines give the profits at every given
θ should the firm choose to operate in the most favorable location. The piecewise lines sketch
the maximum boundary specified above by equation 1. Since this upper superior boundary
dominates the profits that would arise by being restricted to one of the two countries, it
is a heuristic visual proof indicating that the incremental profits by the global operating
flexibility of a multinational network is (equal to or) greater than zero.
–Insert Figure 1 about here–
Equation 1 is the static description of the profit from operating flexibility. The dynamic
description relies upon specifying the probability distribution over θ given the optimal choice
of s. The Bellman equation is the workhorse description of this dynamic problem in Figure
1. That is,
V (θt, st) = maxst
[π(θt, st) + ρE(V (θt+1, st+1))] (2)
where s is the current operating state (denoting the assignment of activities between two
subsidiaries) at time t, t+1, . . .. The Bellman equation indicates that in each period the
firm contemplates the optimal operating mode. If a manager chooses to operate in operating
state st, the firm realizes benefits of π(θt, st), and its value at the following period is updated
to V (θt+1, st+1). This value depends on the operating mode chosen, st, as well as on the
value of the state variable next period, θt+1. Because θt+1 is still unknown at time t, we take
expectations; we discount these future expectations to present value at rate ρ.
The solution to the above Bellman equation has the well-known characteristic that mean-
preserving increases in variance lead also to increases in value. That is, increases in the
variance of θ increases the value of a multinational corporation. This result is at the heart
of the research design below that uses the financial crisis and the corresponding increase in
variance among countries (e.g. in liquidity or stock prices) to predict that the value of global
diversification should increase.
Several empirical studies have shown correlational evidence of the relation of foreign
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investment and the value of embedded options. Campa (1994) found that multinationality did
not cause global chemical firms to expand capacity in response to exchange rate movement;
Bell and Campa (1997) found that exchange rate volatility however influences investment
decisions. Kogut and Chang (1996) found that Japanese electronic investments increased in
the US in response to a real appreciation in the yen. In the most extensive study of direct
investment and exchange rates, Rangan (1998) found that US multinational corporations
shift production in response to exchange rates, though the overall magnitude is not large on
average. Studying the behavior of Korean multinational corporations during the Korean crisis
in 1997-1998, Lee and Makhija (2009) found that Korean firms found new export customers
and intra-firm sales increased, providing some evidence of the flexibility to respond to crisis.
Very few studies have looked at value and flexibility of global operations. In their study
of over 20,000 firm years of data from Compustat for the period of 1987-1993, Bodnar,
Tang, and Weintrop (1997) found that global diversification is associated with higher firm
value, whereas industrial diversification shows a discount. A precocious study by Doukas
and Travlos (1988) has the interesting results that firms investing overseas have no positive
excessive gains for a first time investment, or for investing in the same geography, but only
when expanding their multinational network. This supports a real option interpretation
that investments in new geographies offer additional options to respond to macroeconomic
events.4
Data and Methodology
To establish an empirical baseline, we begin by following Denis et al.’s (2002) study in the
following ways: sample selection criteria, variable construction (measure of diversification,
measure of excess value, and control variables), and empirical methodology (OLS). Then, we
change the specification by implementing Heckman’s (1979) self-selection model to control4There is also the real options literature on entry mode choice; see Chi and McGuire (1996) and Tong,
Reuer, and Peng (2008). These are largely within growth options as opposed to across-country options; seeKogut and Kulatilaka (1994) for a discussion and numerical evaluation. Only the latter -the across-countryoptions- is relevant to our paper.
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for potential endogeneity of the diversification decision.
Sample selection
The sample consists of all firms with data reported on the Compustat Industry Segment
database from 2005 to 2011; we use this period to match the Orbis data described below.
Following Denis et al.’s (2002) sample selection criteria, we exclude utility (SIC 4900-4999)
and financial firms (SIC codes 6000-6999), firms incorporated outside of the United States,
firm-years in which any industrial segment has sales less than $20 million and firm-years in
which the total of either business segment sales is not within one percent of total reported
firm sales for that year. The final sample consists of 12,640 firm-years associated with 3,002
firms; the panel is unbalanced as discussed later.
Measure of diversification
Since Compustat does not provide systematically subsidiary data, we use the Orbis database
that was made available to us for the years 2005 to 2011 to determine if a firm is globally
diversified. Although we use a different database, we construct the measure of global diversi-
fication as closely as possible to Denis et al.’s (2002) measure. The Orbis database lists each
firm’s ticker symbol and foreign subsidiary information. The foreign subsidiary information
includes (1) location, (2) operating revenue, and (3) ownership. By using the ticker symbol,
we match our Compustat sample with the foreign subsidiary information in Orbis database.
Then, we exclude foreign subsidiaries in which any operating revenue is smaller than $20,000
and foreign subsidiaries in which the direct ownership of the firm is less than 10 percent.
Finally, if the number of foreign subsidiaries of a firm is more than or equal to one, we classify
it as a globally diversified firm.5
5Prior work used the Compustat Global Segment data to check whether a firm is globally diversified.As Denis et al. (2002) noted that the Compustat Global Segment data has three limitations. First, there isno requirement by either the Financial Accounting Standard Board (FASB) or the Securities and ExchangeCommission (SEC) regarding groupings for geographic areas. Consequently, two companies operating in thesame countries might report their operations very differently. Second, the distinction between export andforeign subsidiary sales is relatively unclear on Compustat. Obviously, export sales by the domestic subsidiary
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To check whether a firm is industrially diversified, we use the same criteria as used by
Denis et al. (2002). If a firm in our sample has more than one business segment, we classify
it as an industrially diversified firm.
Measure of excess firm value
Our analysis is predicated on measuring excess firm value, EVit (i represents firm id and t
represents year). We estimate EVit using the industry multiplier approach described in Denis
et al. (2002). That is,
EVit = ln[AVit
IMVit
](3)
where AVit is actual firm market value which is measured by the sum of the market value
of equity and the book value of debt, and IMVit represents imputed firm value which is
measured by the sum of the imputed stand-alone values for each individual business segment.
We compute the imputed value of each business segment by multiplying the segment
sales by the median market value to sales ratio of comparable single segment firms, V SRjt
(j represents the segment id). Using four-digit, three-digit, and two-digit SIC codes, this
comparable market-to-sales (V SRjt) is computed as median of (AVijt/SALESijt) of compa-
rable single-segment firms in the segment j, where AVijt is the sum of the market value of
equity and the book value of debt and SALESijt is sales. That is,
IMVit =n∑
j=1SALESijt · V SRjt (4)
Control variables
Following Denis et al.’s (2002) choice, we control (1) relative market value of total capital,
(2) relative long-term debt to total capital, (3) relative capital expenditure to sales, (4)
should not be treated as global diversification (Denis et al., 2002). Orbis has an advantage in identifying salesof foreign subsidiaries distinct from export of domestic subsidiaries. Third, Compustat arbitrarily aggregatesthe global data reported by the firm into four global segments, regardless of the number of countries in whicha firm operates. Thus, an individual segment reported on Compustat might represent a single country, or itmight represent a very broad geographic region.
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relative EBIT to sales, (5) relative R&D to sales, and (6) relative advertising to sales in our
regression models. We use Compustat to construct these control variables.
Instrumental variables
The Heckman’s self-selection model without valid instrumental variables can produce unre-
liable results (Bushway et al., 2007, Lennox et al., 2012). Thus, following Campa and Kedia
(2002), in the first stage of the Heckman model, we use two industry instrumental variables
and one firm specific instrumental variable.6
Lang and Stulz (1994) and Maksimovic and Phillips (2002) show that industry charac-
teristics affect the decision to diversify. We use two industry level instrumental variables:
the percentage of industrially diversified firms in the industry and the percentage of sales by
industrially diversified firms in the industry. These industry instrumental variables capture
attractiveness of a given industry, which affects the likelihood of diversification. Consider first
the motivation for the industrial diversification instrument. As an industry becomes more
attractive for reasons we may not observe, a firm will be more likely to diversify into this in-
dustry, thereby becoming an industrially diversified firm. As for global diversification, prior
studies (e.g. Caves, 1996) suggest that industrial diversification and global diversification
may substitute for each other. If that is the case, as an industry becomes more attractive, a
firm will be less likely to diversify globally. The first stage probit regression results in Table
4 confirm these relationships; the table shows a (1) positive association between these vari-
ables and industrial diversification and (2) negative association between these variables and
global diversification. Also, as EVit is the firm’s excess value relative to the median firm in
the industry in any given year, it is, by construction, independent of any characteristics that6We thank the referees and editors for the suggestion to use instrumental variables. Campa and Kedia
(2002) also instrument time effects with four variables: (1) the number of merger/acquisition announcementsin a given year, (2) the annual value of announced merger/acquisitions, (3) the real growth rate of grossdomestic product and its lagged value, and (4) the number of months in the calendar year that the economywas in a recession and its lagged value. As we explore the effect of the 2008-2009 financial crisis by dividingthe sample into subperiod samples, we exclude these time instrumental variables in our regressions. However,even when we include these variables, the results are qualitatively identical. Also, Campa and Kedia use onemore instrument, a dummy that takes the value 1 when the firm is incorporated abroad and 0 otherwise.Since we exclude foreign firms from our sample, this variable is not used in our regression analysis.
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have an influence on the value of all firms in a given industry and year, which appear to sat-
isfy the exclusion restriction condition. Given that our instrumental variables predict firms’
decision to diversify but do not affect relative value directly, we choose them as instrumental
variables.7
The second type is firm specific. We include a dummy that is equal to 1 when the
firm is listed on major exchange markets (NYSE, NASDAQ, or AMEX), and 0 otherwise.
Firms are more likely to either globally or industrially diversify if they are listed on the
major exchange markets. Listing on major exchange markets fosters a firm’s acquisition by
generating more visibility and decreasing information asymmetries. However, firms listed
on major exchange markets are also likely to have greater liquidity. As firms with higher
liquidity could be valued higher, this could also have an influence on relative firm value. To
control this (i.e., to meet the exclusion restriction condition), following Campa and Kedia
(2002), we include a dummy which is equal to 1 when the firm belongs to the S&P industrial
index or transportation index, and 0 otherwise. As liquidity may impact both relative firm
value and the decision to diversify, we include this dummy in both the first and second stages
of Heckman’s self-selection model.
Estimation methodology
To examine whether diversification increases or decreases firm value, we estimate the effect
of diversification with (1) the OLS regression and (2) the Heckman’s (1979) self-selection
model and compare the results.
At first, following Denis et al.’s (2002) approach, we examine the effect of diversification
on excess firm value by modeling excess firm value as a function of firm characteristics with7Santalo and Bercerra (2008) suggest that (1) industry attractiveness might be related to firm financial
performance, and (2) diversification discount might be heterogeneous across different industries. To handlethese issues, as shown in Table 3, we include industry fixed effects in the second stage of Heckman’s selectionmodel.
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the OLS regression. We use the following specification.
EVit = δ0 + δ1Xit + δ2GDit + δ3IDit + δ4GIDit + eit, (5)
where Xit is a set of observable characteristics of the firm (the aforementioned control vari-
ables), GDit is a dummy variable that has the value of 1 for firms which are globally di-
versified, IDit is a dummy variable that has the value of 1 for firms which are industrially
diversified, and GIDit is a dummy variable that has the value of 1 for firms which are both
globally and industrially diversified, δ = {δ0, δ1, δ2, δ3, δ4} is a vector of parameters to be
estimated, and eit is an error term.
Our intuition is that firms that choose to diversify are not a random sample of firms. If a
firm’s decision to diversify is correlated with the excess value of firm, GDit, IDit, and GIDit
will be correlated with the eit in equation (5). Thus, the OLS estimate of δ2, δ3, and δ4 will
be biased. Specifically, we assume that a firm’s decision to globally diversify is determined
by
GD∗it = βgdZit + µit
GDit = 1 if GD∗it > 0 (6)
GDit = 0 if GD∗it < 0,
where GD∗it is an unobserved variable, Zit is a set of firm characteristics in equation (5) and
instrumental variables that affect decision to diversify, and µit is an error term.
Following Shaver (1998) and Campa and Kedia (2002), we control for the self-selection of
firms that diversify with the Heckman two-step procedure. Since our focus is on replicating
past results for the effects of global diversification (GDit), we explain now at length our es-
timating strategy. For controlling self-selection in industrial diversification (IDit) and global
and industrial diversification (GIDit), we use the same approach used previously by others.
We estimate excess firm value conditional on the globally diversified firms asE(EVit|GDit) =
15
δ0 + δ1Xit + δ2 +E(eit|GDit = 1). Similarly, the expected value conditional on the domestic
firms is E(EVit|GDit = 0) = δ0 + δ1Xit + E(eit|GDit = 0). Following Campa and Kedia
(2002) application of the Heckman selection treatment, we assume that the errors, eit and
µit, have a bivariate normal distribution with means zero, standard deviation σgd and 1, and
with correlation ρgd. There will be no self-selection bias on global diversification, if ρgd is 0.
Then, we have
E(eit|GDit = 1) = ρgdσgdλgd1(βgdZit) = ρgdσgdφ(βgdZit)Φ(βgdZit)
(7)
E(eit|GDit = 0) = ρgdσgdλgd2(βgdZit) = ρgdσgd−φ(βgdZit)
1− Φ(βgdZit), (8)
where φ(.) and Φ(.) are, respectively, the density and cumulative distribution functions of
the standard normal. The difference in the excess value of globally diversified and domestic
firms is given by
E(EVit|GDit = 1)− E(EVit|GDit = 0) = δ2 + ρgdσgdφ(βgdZit)
Φ(βgdZit)(1− Φ(βgdZit)). (9)
The right-hand side of the equation (9) is estimated by the OLS coefficient of GDit in
equation (5). This estimate will be biased downward if ρgd is negative, as hypothesized for
globally diversified firms. Also, the estimate will be biased upward if ρgd is positive.
In the first step, we estimate equation (6) using probit models to get consistent estimates
of βgd, βid, and βgid denoted by β̂gd, β̂id, and ˆβgid. These are then used to get estimates of
λgd, λid, and λgid, the correction for self-selection. That is,
λgd = λgd1(β̂gdZit) ·GDit + λgd2(β̂gdZit) · (1−GDit)
λid = λid1(β̂idZit) · IDit + λid2(β̂idZit) · (1− IDit) (10)
λgid = λgid1( ˆβgidZit) ·GIDit + λgid2( ˆβgidZit) · (1−GIDit).
16
We use three endogenous variables in the second stage, and the third variable, the dummy
for firms that diversify globally and industrially, is an interaction of two other endogenous
variables. There exist some extended two-stage least squares models (2SLS) that incorpo-
rate functional forms of endogenous variables (Bascle, 2008, Wooldridge, 2010). However, as
Angrist and Pischke (2008, 2010) noted, 2SLS models with multiple endogenous variables
are not preferable, because these models are hard to identify and the results can be hard to
interpret. As an extended Heckman’s model that incorporates an interaction of endogenous
variables has yet to be developed, we treat it as a separate endogenous variable and estimate
an inverse mills ratio (λgid) separately as shown in equations (10) and (11). We use three
instrumental variables to identify all these three endogenous variables. In addition, as shown
in Table 3, we also estimate three models, each of which has only one endogenous variable.
The results from these three models are consistent with the results from the model with
multiple endogenous variables. Also, in the sensitivity and robustness tests section, we use
propensity score matching which does not rely on instrumental variables significantly.
In the second step, we estimate δ = {δ0, δ1, δ2, δ3, δ4, δ5, δ6, δ7} by the following:
EVit = δ0 + δ1Xit + δ2GDit + δ3IDit + δ4GIDit + δ5λgd + δ6λid + δ7λgid + ηit, (11)
where δ5 = ρgdσgd, δ6 = ρidσid, and δ7 = ρgidσgid. As explained before, σgd, σid, and σgid are
standard deviations of error terms in the first stage probit models, equations (5). As these
standard deviations cannot have negative values, the signs of δ5, δ6, and δ7 are determined,
respectively, by the sign of ρgd, ρid, and ρgid, the correlations between the error terms in
equations (5) and (6). When we run this second-step OLS regression, for λ, we use robust
standard errors to correct underestimated standard errors.8
Furthermore, we examine the country-level valuation effect of global diversification. We
estimate the country-level effect with (1) the OLS regression and (2) the Heckman’s self-8The variance estimates of the standard least squares model are downward biased except for the case of
no selection bias. Since a data set tends to be censored, it produces smaller standard errors in the secondstage of the Heckman model than the true population estimates (Heckman, 1976, Bushway et al., 2007).
17
selection model. At first, we add a country dummy Cikt (k represents country id) for firms
operate in the focal country to equation (5), the OLS regression model. That is,
EVit = δ0 + δ1Xit + δ2GDit + δ3IDit + δ4GIDit + δ8Cikt + eit, (12)
For each country, we estimate δ8. Next, we estimate the country-level effect with the
Heckman’s self-selection model. To control self-selection for the decision to diversify into a
country, we estimate λk with the probit model in equation (6). Then, we estimate
EVit = δ0 + δ1Xit + δ2GDit + δ3IDit + δ4GIDit + δ5λgd + δ6λid + δ7λgid
+δ8Cikt + δ9λk + ηit. (13)
For each country, we estimate δ8 and δ9 and compare with results from the OLS regression.
Sample statistics
Table 1 reports descriptive statistics on various measures of diversification for the sample of
all the firm-years and for the subsamples which are globally and industrially diversified. The
pattern in the diversification measures in Table 1 is similar to Denis et al.’s (2002) findings.
First, we report two different measures of global diversification: the fraction of firm-years
globally diversified and the average number of foreign countries where firms in our sample
operate. Among all the firm-years, 31.4 percent firm-years are globally diversified. Among the
subsample of the industrially diversified firms, 50.2 percent firm-years are globally diversified.
The average number of foreign countries is 1.182 overall, 3.763 among globally diversified
firm-years, and 2.295 among industrially diversified firm-years.
Also, we report two different measures of industrial diversification: the fraction of firm-
years industrially diversified and the average number of business segments. Among the sample
of all the firm-years, 35.1 percent firm-years are industrially diversified. Among the subsample
of firm-years which are globally diversified, 56.1 percent firm-years are industrially diversified.
18
The average number of industrial segments is 1.622 overall, 2.130 among globally diversified
firm-years, and 2.773 among industrially diversified firm-years. The diversification trend over
time shows an increasing in the percentage of firms diversifying across sectors and countries;
global diversification percentage more than doubles. These trends are reported in Online
Appendix 1.
–Insert Table 1 about here–
Table 2 reports that descriptive statistics on firm characteristics of the partitioned sub-
samples. We partitioned the sample firm-years into four subsamples on the basis of whether
the firm-year is globally or industrially diversified. The statistics in Table 2 are similar to
Denis et al.’s (2002) findings. First, the globally diversified (multinational) firms are larger
than domestic firms, and industrially diversified (multi-segment) firms are larger than single-
segment firms in terms of the market value of total capital. In terms of median values, the
multinational and single-segment firms have a market value of total capital twice as large as
the domestic and multi-segment firms have. Second, multi-segment firms have higher relative
long-term debt to total capital than single-segment firms have, perhaps because their seg-
ment diversification provides them with a natural risk diversification. Third, multinational
firms have higher relative EBIT to sales than domestic firms have, that is, they have higher
operating margins. Fourth, domestic and single-segment firms have the highest relative cap-
ital expenditure and R&D to sales. Finally, domestic firms have higher relative advertising
to sales than multinational firms have. The descriptive statistics on characteristics of all the
firm-years and the correlation matrix are given in Online Appendix 2.
–Insert Table 2 about here–
19
Results
Valuation effect of diversification
We turn now to the first question: is there a global discount after controlling for self-selection.9
Table 3 reports the multivariate regression results for the full sample of 12,551 firm-year
observations. The first two columns are the OLS results without controlling for self-selection.
Column 1 in Tables 3 includes control variables only. The p values for market value, capital
expenditure, and R&D are smaller than 0.00000001, and the p values for long-term debt,
EBIT, advertising are 0.023, 0.001, and 0.081 respectively. The directions and significances
of these coefficients are consistent with Denis et al.’s (2002) findings.
Column 2 reports the results of the OLS regression applying the same variables with
Denis et al.’s model in the first column of Table VI. Overall, the negative valuation effect of
diversification from OLS regression is consistent with the findings of Denis et al.; all the p
values are very small (smaller than 0.00000001), and the effect sizes are also similar. First, the
coefficient of the dummy for firms which are globally diversified is -0.139 (-0.182 in Denis et
al.), suggesting that there exists a discount associated with globally diversified firms. Second,
the coefficient of the dummy for firms which are industrially diversified is -0.198 (-0.204 in
Denis et al.), suggesting that there exists a discount associated with industrially diversified
firms. Finally, the coefficient of the dummy for firms which are both globally and industrially
diversified is -0.342 (-0.322 in Denis et al.), suggesting that there exists a discount associated
with both globally and industrially diversified firms.
Let’s compare the OLS regression results in column 2 and the Heckman self-selection
results given in columns 3-6. In columns 3-5, we control self-selection for each diversification
type. In column 6, we control self-selection for all types of diversification.
The principal result is that the global diversification discount flips to a premium. The
regressions in column 3 and 6 results indicate that excess values are significantly higher
for firms that are globally diversified than they are for domestic firms. The coefficients of9The data and Stata do-files are posted at the authors’ webpages (http://www.sungyongchang.com and
http://www8.gsb.columbia.edu/leadership).
20
the dummy for firms which are globally diversified are 0.326 (p value is 0.053) in column 3
and 0.769 (p value is smaller than 0.00000001) in column 6. When we do not control the
self-selection on global diversification (as in column 2), the coefficients of the dummy for
firms which are globally diversified are negative. Also, the coefficients of λgd are negative in
columns 3 and 6, and the p values are 0.00058 and 0.0000002. These estimates of negative
coefficients to λgd indicate the presence of self-selection effects and that firms with a higher
probability of globally diversifying tend to be discounted for reasons discussed below.
Second, in columns 4 and 6, the excess values are significantly lower for firms that are
industrially diversified than they are for single-segment firms. The coefficient for the dummy
for firms which are industrially diversifying firms is -0.804, and the p value is smaller than
0.00000001. The coefficient for the dummy for firms which are industrially diversified firms
in self-selection model (columns 4 and 6) is even larger than those we do not control self-
selection (column 2). Also, the coefficients of λid are positive, and the p values are 0.00000003
and 0.00006.
Third, in columns 5 and 6, the point estimates of the diversification discount for firms that
are both globally and industrially are negative, and the p values are smaller than 0.00000001.
The size of coefficient for the dummy for firms which are globally and industrially diversified
firms in self-selection model (columns 5 and 6) is even larger than we do not control self-
selection (column 2). The coefficients of λgid are positive, and the p values are smaller than
0.00000001.
In summary, there is significant evidence for self-selection effects in diversification. The
discount from global diversification turns positive. The discount for industrially diversified
firms and both globally and industrially diversified firms is even larger under the self-selection
models. All the p values of the coefficients of λ are smaller than 0.0006, suggesting the
prevalence of self-selection.
–Insert Table 3 about here–
21
Results from probit estimation on diversification choice
In Table 4, we compare the probit estimation results of the probability of diversification
to provide insight into the results. The overall observation is that firm characteristics are
correlated with the decision of firms to diversify but there are insightful differences. First,
as shown in many studies, firms with higher market value and profitability and lower long-
term debt and capital expenditure in their current operations tend to diversify into other
countries. Second, firms with higher market value and lower capital expenditure, R&D, and
advertising tend to industrially diversify. Third, firms with higher market value and lower
long-term debt, capital expenditure, profitability, R&D, and advertising may both globally
and industrially diversified. The results indicate that firms systematically choose to diversify
based on their characteristics.
Following Campa and Kedia (2002), we use the three instrumental variables. First, when
an industry becomes more attractive (when the percentage of industrially diversified firms
or the percentage of sales by industrially diversified firms in the industry are high), firms are
more likely to industrially diversify and both globally and industrially diversify; however,
industry attractiveness is negatively correlated with global diversification. Second, being
listed on the major exchange markets is positively correlated with the probability of all
types of diversification. On top of these instrumental variables, we include a dummy which
takes 1 if a firm belongs to the S&P indexes to control liquidity. The coefficient of this
dummy is negative for the choices to diversify globally or industrially, but positive for the
choice to diversify both globally and industrially.
We test the strength of these instrumental variables with the Wald test. In this Wald test,
we test the null hypothesis that all the coefficients for these variables are zeros. The χ2 statis-
tics are larger than 10 in the three probit models (67.29, 134.89, and 433.11 respectively),
alleviating the concern resulting from weak instrumental variables. The original Heckman’s
self-selection model did not include instrumental variables. Later, instruments were added
to the first-stage choice model (a probit model) when estimating the inverse Mill’s ratio.
Since the probit model is more mathematically complex than the first-stage OLS model of
22
a two-stage least square models, the test statistics for the strength of instrumental variables
are less developed than in the two stage least squares model. Therefore, we further tested
the strength of instrumental variables with the two-stage least squares regressions in the
sensitivity and robustness section. The test results are qualitatively identical.
–Insert Table 4 about here–
Effect of the 2008-2009 financial crisis on valuation effect of diver-
sification
We turn now to the second goal of the paper, that is, to test the theoretical claim that global
diversification reflects incremental value earned through operating flexibility. After all, no
discount is a sufficient finding to support the fundamental theory of direct investment that
firms go abroad to exploit their intangible assets earned at home. Since such an investment
at the margin should bear similar costs to domestic expansion, it is to be expected that there
is no premium. We would like to strengthen the causal argument that operating flexibility,
which is gained through a multinational network, is the source of the premium.
For this reason, we partitioned the data into periods to separate the pre-crisis period from
the crisis itself. The idea is that the crisis provided an unexpected shock by drying up liquidity
and by causing “correlations to go to one” for firms’ stock prices within a country, relatively
shifting the importance of idiosyncratic macroeconomic factors in the determination of value
(Junior and Franca, 2012). This design bears similarity with Kuppuswamy and Villalonga
(2010) who investigated if the crisis put a premium on the access to liquidity, favoring
diversified firms.
In Table 5, we examine the effect of the 2008-2009 financial crisis by estimating the cross-
sectional regressions for each of the three subperiods, 2005 to 2007 (before the financial crisis),
2008-2009 (during the financial crisis), and 2010-2011 (after the financial crisis).
The results indicate that the financial crisis has a positive effect on the premium asso-
ciated with global diversification. Before the financial crisis, the valuation effect of global
23
diversification is positive, and the p value is 0.055. However, during the financial crisis, the
valuation effect becomes larger and more significant (p value becomes 0.003). In ensuing
aftermath to the financial crisis, the valuation effect becomes smaller and less significant (p
value becomes 0.074).
The results indicate that the financial crisis dampens the negative discount associated
with industrial diversification. Although, the discount associated with industrial diversifica-
tion has remained negative over time, the coefficient during and after the financial crisis is
smaller than those of before. This decline in discount during the financial crisis is consis-
tent with Kuppuswamy and Villalonga (2010) and Rudolph et al. (2013). Kuppuswamy and
Villalonga (2010) argue that multi-segment firms’ access to internal capital markets became
more valuable during the financial crisis.
–Insert Table 5 about here–
Finally, the results indicate that the financial crisis has a negative effect on the firm ex-
cess values associated with firms which are both globally and industrially diversified. Before
the crisis, there exists a discount for firms which are both globally and industrially diver-
sified. However, during and after the financial crisis, this discount became larger and more
significant. In Figure 2, we graph the valuation effect for all the types of diversification over
2005-2011 using a box-and-whisker plot. Clearly, the global incremental value is never sig-
nificantly negative, and is very positive in sign and magnitude during the crisis, especially
in 2009. Note that the joint industry and global effect is negative. The post-crisis increase
in the incremental value due to diversification is a pure play on the global network of a
multinational.
–Insert Figure 2 about here–
Churning the Country Portfolio
An explanation for the increase in multinational valuation during the crisis reflects the
exercise of embedded options in a global network. Kogut and Kulatilaka (1994) model the
24
coordination of subsidiaries dispersed throughout the world provides an operating flexibility
that adds value to the firm. Firms are not simply responding to liquidity shocks, but they
are changing the geographic configuration of their assets through exits and entries during
the crisis. Indeed, per Table 5, the value of net profit margins (EBIT to sales) is negative
to flat during the crisis during the time we find a premium to multinational diversification.
As operating flexibility is, by theory, valuable when changes in the state of the world change
the optimal operating mode, these results are not contradictory. The effect of liquidity is a
separate influence on the valuation of global diversification.
The exercise of operating flexibility should lead, we hypothesize, to an increase in the exit
and entry activity among globally-diversified firms. Table 6 Panel A shows the interesting
result that the number of globally-diversified firms that created or withdrew subsidiaries
increased from 21.9 percent in 2006 to 29.3 percent in 2008 and was still at 26.0 percent in
2010. In other words, multinational corporations responded to the shock by restructuring
their global portfolio.
We next consider if firms moved their subsidiaries from countries facing the shock of the
2008-2009 financial crisis. The probit regression analyses in Panel B yield solid evidence on
this restructuring. We created an innovative difference-in-differences analysis to test more
formally if the shock led to restructuring of multinational networks to take advantage of
changes in country exposure. We divide our sample countries into two groups by the change
in GDP during the financial crisis; the dividing line is the median. If the change in GDP
of a country during the crisis is smaller than the median, we classify it as a less affected
country, otherwise a more affected country. Here, the unit of analysis is a subsidiary-year
observation, and the number of total subsidiary-year observations is 22,637. In this difference-
in-differences estimation, the treatment dummy takes 1 if the subsidiary was located in the
less affected countries and the period was during the crisis. The positive coefficient of this
dummy in column 1 means that subsidiaries were more likely to enter to the less affected
countries, and the negative coefficient in column 3 means that subsidiaries were less likely to
exit from the less affected countries. The fixed effect panel models in columns 2 and 4 also
25
offer similar results. In sum, multinational corporations deliberately restructured their global
portfolio during the crisis. These results parallel the findings in Campa and Kedia (2002)
who also found evidence that low performing companies seek to restructure their choice of
industry segments.
–Insert Table 6 about here–
The replication analysis using the correction for self-selection also shows that the esti-
mation of country premia or discounts changes, not surprisingly, as well. Table 7 reports the
country-level valuation effect of global diversification. For each country, we compare our esti-
mates of the coefficients of dummy variables for firms which operate in the focal country for
two models: (1) the OLS regression and (2) the Heckman’s self-selection model. We can esti-
mate coefficients of 88 countries. For the OLS regression (without controlling self-selection),
the coefficients of 32 countries (36.4 percent) are positive, and the coefficients of 56 countries
(63.6 percent) are negative. For the Heckman’s self-selection model, the coefficients of 26
countries (29.5 percent) are positive and significant, the coefficients of 62 countries (70.5
percent) are negative. Overall, controlling self-selection tends to change these coefficients.
–Insert Table 7 about here–
These above results are important to establishing correctly the precise geographic sources
of the value to operating flexibility. It is obvious that to explain the investment behavior of
firms that churn their portfolios, there should be evidence for heterogeneity in premia by
country.10 Among 32 countries which have positive coefficients in the OLS regression, only 16
countries remain positive, and 16 countries flip to negative coefficients per Heckman’s self-
selection model. Also, among 56 countries which have negative coefficients in OLS regression,
46 countries remain negative, and 10 countries turn positive in Heckman’s self-selection
model. The results show large differences in the choice of specification to estimating the sign10The source of this heterogeneity in premia might be due to that some countries may have more globally
undiversified firms than others, or that other country specific effects may also affect the global diversificationpremium or discount. However, examining sources of the country level heterogeneity in premia is beyond thescope of this paper.
26
to the premia and to their magnitude. While this analysis is offered as a guided exploration to
test for the expected patterns, the results echo Santalo and Bercerra’s (2008) similar finding
on the heterogeneous valuation effects across different industries.
Figure 3 provides an insightful visualization of the effects of controlling for selection and
the country pattern. Countries are assigned to a positive or negative contribution to firm
value; some countries were not included in our data as indicated; the US is also excluded
as it is the sole source of the outward investment. France, Germany, and United Kingdom
switch from discount to no discount. China turns from no discount to discount, indicating
that once controlling for the unobserved quality of management and the characteristics of
the firm, diversification is value-losing for that firm compared to other choices.
–Insert Figure 3 about here–
Sensitivity and robustness tests
First of all, we test whether the global diversification premium is robust to (1) the use of
asset multipliers and (2) the use of alternative industry fixed effects. First, we use sales
multipliers in our main analysis. As shown in Online Appendices 3 and 4, even when we
use asset multipliers in calculating excess values, the valuation effects of diversification and
probit estimates on diversification choice are qualitatively identical. Second, we use four-digit
SIC codes to control industry fixed effects in our main analysis. For alternative industry fixed
effects, we use (1) first two-digits and (2) first three-digits of SIC codes. Online Appendix
5 shows that the regression results are qualitatively identical. The primary quantitative
difference is that the coefficients for the dummy for globally and industrially diversified are
more consistently significant for the broader definitions of industry at the 2 or 3 digit SIC
levels than at the 4 digit SIC codes.
In addition, we test whether the global diversification premium is robust to the two alter-
native regression models: (1) the two-stage least square regression (2SLS) and (2) propensity
score matching.
27
First, in the first stage of the 2SLS model for global diversification, we use the equation (6)
in the estimation methodology section and estimate ˆGDit(= β̂gdZit). Then, we estimate the
coefficients of the second stage OLS model: EVit = δ0+δ1Xit+δ2 ˆGDit+[ηit+δ2(GDit− ˆGDit)].
Also for the other types of diversification (IDit and GIDit), we use the same model. To
run these regressions, we use the ivregress 2sls routine in Stata. The regression results in
Online Appendix 6 show that the global diversification premium is robust to the use of 2SLS
model.11 As discussed in footnote 8, a benefit of using 2SLS is that we can utilize developed
test statistics for instrumental variables, such as (1) the test for the strength of instrumental
variables and (2) the test for overidentification. We test the strength of instrumental variables
for the model in column 1, global diversification. The partial R2 is 0.005 and the F test
statistics (30.44) is larger than 10. This result alleviates the concern resulting from weak
instrumental variables. Finally, we test whether there exists an overidentification problem in
the same model. The Hansen’s J statistic (χ2(2)) is 3.86 (p = 0.145), alleviating concerns
about an overidentification problem.
Second, as several studies have used propensity score matching to control the endogene-
ity of global diversification (e.g. Villalonga, 2004), we use this method for a robustness
check. We calculate propensity scores (p(Xit)) of firms in our sample by using the equa-
tion (6) in the estimation methodology section, and match firms whose propensity scores
are within the radius caliper 0.05. The results are robust to the use of different values for
radius caliper. The propensity score matching estimator for the average treatment effect
on the treated is the mean difference in excess firm values: δAT T = E[E[EVit(1)|GDit =
1, p(Xit)]−E[EVit(0)|GDit = 0, p(Xit)]]. We estimate the average treatment effects of global
diversification and industrial diversification. For this analysis, we use the psmatch2 routine
in Stata. As shown in Online Appendices 7 and 8, even when we control the endogeneity
with propensity score matching, the global diversification premium is robust.11As discussed earlier in footnote 8, 2SLS models with multiple endogenous variables are hard to identify
and the results can also be hard to interpret. From this perspective, the estimated valuation effects ofdiversification in the first three columns might be more valuable than column 4.
28
Conclusions
Research on the diversification discount has a relatively long history but nevertheless plateaued
in the early part of the last decade due to the findings showing discounts for both industry
and global diversification. By introducing an econometric correction, the article by Campa
and Kedia (2002) found no discount and a mild premium for industry diversification. De-
spite this reversal of a well-held finding of a diversification discount, the finding of a global
diversification discount was published in the same year by Denis et al. (2002), without con-
sidering the implications of a self-selection endogeneity effect. However, their results hold for
certain cross-sections and time periods, but not for periods of high volatility when operating
flexibility should be most valuable.
Our results show that selection effects matter for understanding global diversification in
three ways: (1) reversing the finding of a global discount, (2) proposing and supporting a
theory of operating flexibility as the explanation, and (3) integrating the choice of diversi-
fication within a broader theory of strategy and capability. The reversal is simply that the
Heckman specification flips the global diversification discount to a premium for the entire
observation period. That diversification changes from the years prior to, during, and after
the financial crisis is consistent with the theory of the incremental value accruing to a multi-
national through the potential to operate flexibly its network of subsidiaries. The increased
volatility of markets during the crisis rendered multinational networks more valuable, as
the theory predicts. Moreover, to realize the value of higher volatility, firms must restructure
their country portfolios through exiting and entering countries; we provided evidence to show
that the churn percentage increased substantially during the crisis, and firms moved their
subsidiaries from the countries that were affected more by the financial crisis to the countries
that were affected less.
The question that naturally arises from this study is why global diversification shows,
on average, a premium, whereas industry diversification is marked by a discount or no effect
on value at best. We offer the speculation that while transferring knowledge overseas can be
29
expensive, the decision to diversify is nevertheless conditioned on the expectation that the
key assets permit, per the Hymer hypothesis, a firm to compete successfully against local
competition. But this argument of entry needs also to be matched by the expectation that
errors are made and/or states of the world change leading to a firm to withdraw from a
country. This type of analysis leads to the relevance of hysteresis, that is dynamic switching
costs, as a factor that might explain persistence in a country (or industry) despite losses
(Kogut and Kulatilaka, 1994). Hysteresis as an explanation has the virtue of expecting firms,
or subsidiaries, to persist in being loss making until critical bounds are crossed that justify
exit or further investment. By implication of this perspective, lower hysteresis costs might
exist more for global expansion than for industry diversification because of the differences
in the entry and exit costs; firms may find it less expensive to transfer knowledge between
locations than enter and exit new industries. This speculation is a complicated analysis and
likely to vary by country and industry specific facts.
The broader implication is to have a more seasoned view and nuanced theoretical de-
scription of the motivations of firms to make big decisions, such as diversifying into new
industries and investing in new countries. It is not surprising that firms often make these
decisions when they are located in declining industries facing negative shocks, or in countries
facing the shock of a financial crisis. Having the capability, such as the managerial capacity
to operate a multinational network flexibly, is valuable in such situations.12 Once correcting
for the value loss due to unobserved negative shocks, the decision to diversify globally is on
average positively incremental to firm value.
It appears logical that firms will often want to respond to a poorly-dealt hand of cards by
diversifying, or innovating, or acquiring. It is not surprising that these efforts often do not
succeed for those ill-prepared. This is the seasoned view. The nuanced theoretical framing
is to identify the heterogeneous capabilities inside of firms that permit some to respond
positively to adverse situations. The results of our analysis suggest that multinational firms
benefited positively from a strategy to diversify globally during a period of increased volatility12This observation is consistent with Helfat and Lieberman’s (2002) argument that in general, prior history
of investing in capabilities is valuable to the subsequent deployment of related resources in new domains.
30
and macroeconomic uncertainty. This is not luck; it’s the advantage gained through a strategy
to invest in a global network.
31
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35
Table 1: Sample statistics on global and industrial diversification
Globally IndustriallyAll Firm-Years Diversified Diversified(n = 12, 640) (n = 3, 969) (n = 4, 437)
Mean Median Mean Median Mean Median
Global diversificationFraction of firm-years 0.314 N.A 1.000 N.A. 0.502 N.A.
globally diversified
Number of foreign countries 1.182 0.000 3.763 2.000 2.295 1.000
Industrial diversificationFraction of firm-years 0.351 N.A. 0.561 N.A 1.000 N.A.
industrially diversified
Number of segments 1.622 1.000 2.130 2.000 2.773 2.000
Note: Mean and median measures of industrial and global diversification for 12,640 firm-yearsassociated with 3,002 firms over the period 2005–2011.
36
Table 2: Firm characteristics
Single-segment Multi-segment Single-segment Multi-segmentFirm Characteristic Domestic Domestic Multinational Multinational
Market value of total capital 905.846 1724.834 4688.141 9558.768($million) 207.805 497.812 1053.358 2132.833
Relative long-term debt 0.162 0.194 0.166 0.188to total capital 0.038 0.140 0.100 0.172
Relative EBIT to sales -0.021 0.077 0.100 0.1050.047 0.071 0.095 0.102
Relative capital expenditures 0.109 0.067 0.079 0.049to sales 0.030 0.028 0.031 0.028
Relative R&D to sales 0.114 0.028 0.069 0.0420.000 0.000 0.010 0.016
Relative advertising to sales 0.014 0.010 0.013 0.0090.000 0.000 0.000 0.000
Note: Means are reported with median values below. 12,640 firm-year observations over the period2005–2011. The sample is partitioned into four groups on the basis of whether the firm is industriallyor globally diversified in the given firm-year.
37
Tab
le3:
Mul
tiva
riat
ere
gres
sion
test
son
valu
atio
neff
ect
ofdi
vers
ifica
tion
(1)
(2)
(3)
(4)
(5)
(6)
Exce
ssVa
lue
Exce
ssVa
lue
Exce
ssVa
lue
Exce
ssVa
lue
Exce
ssVa
lue
Exce
ssVa
lue
Dum
my
equa
lto
one
ifon
lygl
obal
lydi
vers
ified
-0.1
390.
326
-0.1
36-0
.142
0.76
9(0
.000
)(0
.053
)(0
.000
)(0
.000
)(0
.000
)D
umm
yeq
ualt
oon
eif
only
indu
stria
llydi
vers
ified
-0.1
98-0
.197
-0.9
28-0
.210
-0.8
04(0
.000
)(0
.000
)(0
.000
)(0
.000
)(0
.000
)D
umm
yeq
ualt
oon
eif
both
indu
stria
llyan
dgl
obal
lydi
vers
ified
-0.3
42-0
.333
-0.3
33-1
.005
-1.0
00(0
.000
)(0
.000
)(0
.000
)(0
.000
)(0
.000
)R
elat
ive
mar
ket
valu
eof
tota
lcap
ital
0.11
70.
155
0.14
30.
163
0.21
40.
190
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
Rel
ativ
elo
ng-t
erm
debt
toto
talc
apita
l-0
.052
-0.0
60-0
.037
-0.0
45-0
.106
-0.0
43(0
.023
)(0
.006
)(0
.114
)(0
.044
)(0
.000
)(0
.084
)R
elat
ive
capi
tale
xpen
ditu
res
tosa
les
0.50
60.
478
0.49
60.
443
0.40
20.
410
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
Rel
ativ
eEB
ITto
sale
s0.
141
0.11
60.
090
0.10
60.
065
0.00
8(0
.001
)(0
.004
)(0
.027
)(0
.007
)(0
.099
)(0
.819
)R
elat
ive
R&
Dto
sale
s0.
762
0.70
40.
698
0.64
30.
602
0.54
1(0
.000
)(0
.000
)(0
.000
)(0
.000
)(0
.000
)(0
.000
)R
elat
ive
adve
rtisi
ngto
sale
s0.
347
0.25
00.
246
0.04
9-0
.005
-0.1
63(0
.081
)(0
.188
)(0
.196
)(0
.800
)(0
.977
)(0
.382
)D
umm
yeq
ualt
oon
eif
belo
ngs
toS&
Pin
dexe
s-0
.031
-0.0
840.
043
0.10
7(0
.217
)(0
.000
)(0
.067
)(0
.000
)λgd
-0.2
54-0
.496
(0.0
06)
(0.0
00)
λid
0.41
40.
339
(0.0
00)
(0.0
00)
λgid
0.40
00.
403
(0.0
00)
(0.0
00)
Con
stan
t-1
.233
-1.4
14-1
.417
-1.3
52-1
.613
-1.5
47(0
.000
)(0
.000
)(0
.000
)(0
.000
)(0
.000
)(0
.000
)In
dust
ryfix
edeff
ect
Yes
Yes
Yes
Yes
Yes
Yes
Hec
kman
self-
sele
ctio
nN
oN
oYe
sYe
sYe
sYe
sR
20.
338
0.36
40.
365
0.36
70.
376
0.37
9N
12,5
5112
,551
12,5
5112
,551
12,5
5112
,551
Not
e:p
valu
esin
pare
nthe
ses
38
Tab
le4:
Pro
bit
esti
mat
esof
dive
rsifi
cati
onch
oice
(1)
(2)
(3)
Glo
bal&
Glo
bal
Indu
stria
lIn
dust
rial
Div
ersifi
catio
nD
iver
sifica
tion
Div
ersifi
catio
nR
elat
ive
mar
ket
valu
eof
tota
lcap
ital
0.11
00.
025
0.41
7(0
.000
)(0
.003
)(0
.000
)R
elat
ive
long
-ter
mde
btto
tota
lcap
ital
-0.2
320.
084
-0.2
50(0
.001
)(0
.160
)(0
.001
)R
elat
ive
capi
tale
xpen
ditu
res
tosa
les
-0.2
45-0
.277
-1.3
22(0
.001
)(0
.000
)(0
.000
)R
elat
ive
EBIT
tosa
les
0.46
1-0
.011
-0.4
17(0
.000
)(0
.886
)(0
.000
)R
elat
ive
R&
Dto
sale
s0.
046
-2.3
67-2
.229
(0.7
04)
(0.0
00)
(0.0
00)
Rel
ativ
ead
vert
ising
tosa
les
0.04
1-1
.216
-3.2
26(0
.928
)(0
.010
)(0
.000
)D
umm
yeq
ualt
oon
eif
belo
ngs
toS&
Pin
dexe
s-0
.391
-0.0
980.
423
(0.0
00)
(0.0
46)
(0.0
00)
Perc
enta
geof
indu
stria
llydi
vers
ified
firm
sin
the
indu
stry
-0.3
721.
345
0.09
0(0
.012
)(0
.000
)(0
.560
)Pe
rcen
tage
ofsa
les
byin
dust
rially
dive
rsifi
edfir
ms
inth
ein
dust
ry0.
175
-0.0
371.
993
(0.0
51)
(0.6
52)
(0.0
00)
Dum
my
equa
lto
one
iflis
ted
onth
em
ajor
exch
ange
mar
kets
0.52
10.
231
1.11
7(0
.000
)(0
.000
)(0
.000
)C
onst
ant
-2.2
62-1
.682
-6.2
83(0
.000
)(0
.000
)(0
.000
)χ
248
5.9
539.
134
66.4
N12
,573
12,5
7312
,573
Not
e:p
valu
esin
pare
nthe
ses
39
Tab
le5:
Effe
ctof
the
2008
-200
9fin
anci
alcr
isis
onva
luat
ion
effec
tof
dive
rsifi
cati
on
(1)
(2)
(3)
(4)
(5)
(6)
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
2005
–200
720
08-2
009
2010
–201
120
05–2
007
2008
-200
920
10–2
011
Dum
my
equa
lto
one
ifon
lygl
obal
lydi
vers
ified
-0.1
42-0
.133
-0.1
450.
432
1.04
60.
694
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
55)
(0.0
03)
(0.0
74)
Dum
my
equa
lto
one
ifon
lyin
dust
rially
dive
rsifi
ed-0
.215
-0.1
56-0
.184
-1.2
24-0
.896
-0.3
70(0
.000
)(0
.000
)(0
.000
)(0
.000
)(0
.001
)(0
.054
)D
umm
yeq
ualt
oon
eif
both
indu
stria
llyan
dgl
obal
lydi
vers
ified
-0.3
39-0
.353
-0.3
72-0
.954
-1.2
59-1
.202
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
Rel
ativ
em
arke
tva
lue
ofto
talc
apita
l0.
146
0.17
50.
159
0.19
60.
223
0.21
5(0
.000
)(0
.000
)(0
.000
)(0
.000
)(0
.000
)(0
.000
)R
elat
ive
long
-ter
mde
btto
tota
lcap
ital
-0.0
68-0
.044
-0.0
68-0
.063
-0.0
29-0
.043
(0.0
20)
(0.3
52)
(0.2
18)
(0.0
53)
(0.5
84)
(0.4
75)
Rel
ativ
eca
pita
lexp
endi
ture
sto
sale
s0.
561
0.37
70.
419
0.46
70.
280
0.34
6(0
.000
)(0
.000
)(0
.000
)(0
.000
)(0
.000
)(0
.000
)R
elat
ive
EBIT
tosa
les
0.22
90.
018
0.08
80.
125
-0.0
82-0
.107
(0.0
00)
(0.7
08)
(0.3
54)
(0.0
33)
(0.0
39)
(0.2
86)
Rel
ativ
eR
&D
tosa
les
0.81
30.
768
0.62
20.
619
0.56
80.
374
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
05)
Rel
ativ
ead
vert
ising
tosa
les
0.34
9-0
.170
0.94
7-0
.026
-0.7
560.
161
(0.2
04)
(0.6
57)
(0.0
13)
(0.9
24)
(0.0
67)
(0.6
69)
Dum
my
equa
lto
one
ifbe
long
sto
S&P
inde
xes
0.08
40.
169
0.15
9(0
.044
)(0
.004
)(0
.002
)λgd
-0.3
08-0
.651
-0.4
74(0
.011
)(0
.001
)(0
.029
)λid
0.57
10.
407
0.09
7(0
.000
)(0
.008
)(0
.366
)λgid
0.36
80.
556
0.51
0(0
.000
)(0
.000
)(0
.000
)C
onst
ant
-1.1
28-1
.520
-1.6
87-1
.270
-1.6
95-1
.966
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
Indu
stry
fixed
effec
tYe
sYe
sYe
sYe
sYe
sYe
sH
eckm
anse
lf-se
lect
ion
No
No
No
Yes
Yes
Yes
R2
0.39
00.
425
0.43
50.
405
0.44
70.
453
N5,
775
3,44
43,
332
5,77
53,
444
3,33
2N
ote:
pva
lues
inpa
rent
hese
s
40
Table 6: Restructuring global subsidiary portfolio during the financial crisis
Panel A. Number of firms that created or withdrew subsidiaries over timeNo. of firms that No. of firms that No. of firms that
Year created subsidiaries withdrew subsidiaries created or withdrew subsidiaries2006 309 (20.8%) 145 (9.9%) 371 (21.9%)2007 265 (17.3%) 197 (13.3%) 359 (21.0%)2008 405 (25.3%) 267 (17.4%) 521 (29.3%)2009 347 (23.2%) 292 (18.3%) 452 (25.8%)2010 286 (19.3%) 308 (20.6%) 435 (26.0%)
Panel B. Difference-in-differences estimation on entry and exit of subsidiaries††
(1) (2) (3) (4)Dummy equal to one Dummy equal to oneif the subsidiary was if the subsidiary was
created at year t withdrawn at year tTreatment Dummy††† 0.290 0.581 -0.214 -0.163
(0.000) (0.000) (0.000) (0.002)Dummy equals to one if located 0.213 0.069in the less affected countries (0.000) (0.028)Constant 0.092 0.604 -0.659 -0.535
(0.000) (0.000) (0.000) (0.001)Time period dummies Yes Yes Yes YesParent firm fixed effect No Yes No Yesχ2 3,109.8 4,851.0 1,405.7 2,895.1N 22,637 22,086 22,637 22,134Note: p values in parentheses†† Each data point is a subsidiary-year observation. We run probit regressions.††† The treatment dummy takes 1 if the subsidiary was located in the less affected countries andwhen the period was during the crisis.
41
Table 7: Country-level valuation effect of global diversification
without Controlling with Controlling without Controlling with ControllingSelf-selection Self-selection Self-selection Self-selection
Country Coefficient (p value) Coefficient (p value) Country Coefficient (p value) Coefficient(p value)Oman 0.772 (0.000) 2.008 (0.000) Portugal -0.020 (0.027) -0.100 (0.211)Pakistan 0.239 (0.006) 2.085 (0.000) Argentina -0.089 (0.002) -0.383 (0.153)France -0.002 (0.206) 0.065 (0.016) Sweden -0.039 (0.002) -0.263 (0.154)Malawi -0.073 (0.357) 5.746 (0.017) Finland -0.092 (0.000) -0.456 (0.133)Bangladesh 0.169 (0.149) 2.204 (0.026) Equatorial Guinea 0.295 (0.000) -7.073 (0.130)Estonia 0.203 (0.077) 1.848 (0.037) Poland -0.048 (0.002) -0.312 (0.126)Monaco 0.510 (0.013) 3.075 (0.064) United Arab Emirates -0.281 (0.005) -5.277 (0.105)Zambia 0.268 (0.030) 10.212 (0.080) Greece -0.010 (0.728) -0.260 (0.096)United Kingdom -0.009 (0.003) 0.169 (0.118) Lithuania 0.070 (0.722) -3.446 (0.080)Nigeria 0.048 (0.320) 2.337 (0.120) Turkey 0.005 (0.933) -1.153 (0.057)Ukraine 0.245 (0.028) 1.653 (0.164) Netherlands -0.057 (0.000) -0.364 (0.027)Ecuador 0.047 (0.695) 0.486 (0.178) Romania -0.001 (0.984) -0.900 (0.021)Slovenia 0.129 (0.424) 0.324 (0.210) Hungary -0.081 (0.005) -0.499 (0.016)Tanzania 0.529 (0.000) 0.161 (0.243) Croatia 0.282 (0.004) -1.503 (0.008)Kenya -0.045 (0.762) 2.607 (0.285) Philippines 0.006 (0.781) -1.120 (0.005)Latvia 0.150 (0.362) 2.935 (0.352) Hong Kong, China -0.051 (0.122) -0.715 (0.004)Honduras 0.350 (0.007) 0.572 (0.423) Venezuela -0.114 (0.193) -1.254 (0.003)Guatemala 0.363 (0.002) 3.144 (0.477) Israel -0.070 (0.458) -4.000 (0.002)Serbia 0.518 (0.000) 1.507 (0.524) Republic of Korea -0.054 (0.001) -0.319 (0.003)Kuwait -0.072 (0.521) 0.473 (0.579) Barbados -0.015 (0.852) -1.413 (0.002)Spain -0.005 (0.172) 0.023 (0.590) Thailand 0.008 (0.760) -0.656 (0.001)Germany -0.010 (0.000) 0.033 (0.845) Peru -0.061 (0.496) -3.268 (0.001)Algeria -0.053 (0.788) 0.213 (0.892) Slovak Republic -0.032 (0.370) -1.694 (0.001)Ireland -0.040 (0.000) 0.016 (0.892) China 0.005 (0.399) -0.203 (0.000)Mozambique 0.041 (0.817) 0.593 (0.905) Austria -0.015 (0.490) -0.665 (0.000)Jamaica -0.200 (0.107) 0.118 (0.918) Japan -0.018 (0.233) -0.496 (0.000)Albania 0.373 (0.002) -0.114 (0.948) Cayman Islands -0.436 (0.000) -1.624 (0.000)Mauritania 0.373 (0.002) -0.114 (0.948) India -0.065 (0.008) -0.612 (0.000)Bulgaria 0.057 (0.483) -0.079 (0.928) Denmark -0.070 (0.000) -0.574 (0.000)South Africa -0.005 (0.903) -0.055 (0.925) Norway -0.039 (0.032) -0.632 (0.000)Lebanon -0.522 (0.000) -0.991 (0.838) Czech Republic -0.070 (0.000) -0.740 (0.000)Malaysia -0.032 (0.176) -0.035 (0.820) Chile -0.044 (0.716) -1.723 (0.000)Malta 0.061 (0.422) -0.220 (0.798) Colombia -0.061 (0.032) -0.528 (0.000)Viet Nam -0.144 (0.034) -0.047 (0.798) Panama -0.656 (0.004) -3.330 (0.000)Belgium -0.011 (0.224) -0.048 (0.765) Bahrain 0.058 (0.296) -3.607 (0.000)Switzerland -0.008 (0.527) -0.040 (0.698) Egypt -0.006 (0.907) -2.379 (0.000)Russian Federation -0.004 (0.910) -0.230 (0.644) Singapore -0.062 (0.001) -0.620 (0.000)Indonesia 0.041 (0.412) -0.146 (0.590) Luxembourg -0.112 (0.005) -1.303 (0.000)Canada -0.058 (0.000) -0.081 (0.552) Brazil -0.087 (0.000) -0.697 (0.000)Mauritius 0.409 (0.005) -5.451 (0.594) New Zealand 0.002 (0.950) -1.820 (0.000)Italy -0.019 (0.011) -0.132 (0.260) Mexico -0.134 (0.000) -0.867 (0.000)Bermuda 0.028 (0.830) -3.396 (0.251) Saudi Arabia -0.493 (0.075) -4.353 (0.000)Qatar -0.663 (0.000) -0.201 (0.244) Australia -0.060 (0.002) -0.716 (0.000)Taipei, Chinese -0.058 (0.396) -0.772 (0.237) Tunisia -0.382 (0.203) -36.678 (0.000)
42
Pro
fit
Comparative Advantage (θ)
Produce in Country X
πx(θ, s)
Produce in Country Y
πy(θ, s)
(Represented by the thick red line)
Produce in Country X and Y
π(θ, s) = maxs[0, πx(θ, s), πy(θ, s)]
Note: The profit function of a firm which has foreign subsidiaries is the envelope (the upper superiorboundary) of profit functions of the foreign subsidiaries. The envelope of profit functions is represented bythe thick red line.
Figure 1: (Color Online) Benefits from operating flexibility
43
-3
-2
-1
0
1
2
2005 2006 2007 2008 2009 2010 2011
Coe
ffici
ent
Year
GlobalIndustrial
Global & Industrial
Note: Error bars = 95% Confidence intervals.
Figure 2: (Color Online) Valuation effect of diversification over time
44
(a) Without controlling self-selection
(b) With controlling self-selection
Figure 3: (Color Online) Effect of controlling self-selection on country-level val-uation effect of global diversification
45
Appendix 1: Global and industrial diversification trend 2005-2011
Globally IndustriallyDiversified Diversified
Firm-Years Only Firm-Years OnlyFraction Number of Fraction Number ofGlobally Foreign Industrially Industrial
Year N Diversified Countries Diversified Segment
Panel A: Full Sample2005 1951 0.215 3.271 0.332 2.7592006 1948 0.261 3.210 0.325 2.7782007 1907 0.280 3.315 0.335 2.7452008 1789 0.338 3.643 0.344 2.7822009 1684 0.353 3.788 0.354 2.7202010 1712 0.385 4.363 0.386 2.8052011 1649 0.393 4.366 0.392 2.816
Panel B: 2005 Sample2005 1951 0.215 3.271 0.332 2.7592006 1653 0.276 3.282 0.330 2.7802007 1451 0.312 3.365 0.351 2.7762008 1282 0.386 3.826 0.366 2.8322009 1139 0.405 4.059 0.378 2.7522010 1095 0.437 4.573 0.415 2.8432011 1010 0.454 4.688 0.433 2.849
Note: The full sample includes 12,640 firm-years over the period 2005–2011. The 2005 sampleincludes those firms for which there is Compustat and Orbis data available in 2005. We thenfollow this set of firms between 2005 and 2011.
46
App
endi
x2:
Cor
rela
tion
tabl
ean
dde
scri
ptiv
est
atis
tics
ofva
riab
les
12
34
56
78
910
1Ex
cess
valu
e1.
000
2D
umm
yeq
ualt
oon
eif
only
glob
ally
dive
rsifi
ed0.
039
1.00
0
3D
umm
yeq
ualt
oon
eif
only
indu
stria
llydi
vers
ified
-0.0
57-0
.184
1.00
0
4D
umm
yeq
ualt
oon
eif
both
indu
stria
llyan
dgl
obal
lydi
vers
ified
-0.0
03-0
.184
-0.2
141.
000
5R
elat
ive
mar
ket
valu
eof
tota
lcap
ital
0.29
00.
148
0.04
00.
414
1.00
0
6R
elat
ive
long
-ter
mde
btto
tota
lcap
ital
0.04
9-0
.012
0.04
10.
030
0.21
11.
000
7R
elat
ive
capi
tale
xpen
ditu
res
tosa
les
0.12
9-0
.013
-0.0
38-0
.071
0.08
80.
134
1.00
0
8R
elat
ive
EBIT
tosa
les
-0.0
320.
072
0.05
40.
091
0.19
8-0
.003
-0.0
461.
000
9R
elat
ive
R&
Dto
sale
s0.
143
-0.0
18-0
.094
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69-0
.054
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000
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elat
ive
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rtisi
ngto
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s0.
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0
Obs
erva
tion
12,6
4012
,640
12,6
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,640
12,6
4012
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12,6
2912
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4012
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n0.
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80.
175
0.17
66.
645
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30.
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0.03
50.
080
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anda
rdD
evia
tion
0.59
70.
345
0.38
00.
381
1.85
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235
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356
0.25
20.
034
Min
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00
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00
Max
1.38
61
11
12.9
276.
879
5.24
70.
983
6.52
80.
760
47
App
endi
x3:
Val
uati
oneff
ect
ofdi
vers
ifica
tion
-A
sset
mul
tipl
iers
(1)
(2)
(3)
(4)
(5)
(6)
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Dum
my
equa
lto
one
ifon
lygl
obal
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vers
ified
-0.1
150.
205
-0.1
08-0
.110
0.66
5(0
.000
)(0
.037
)(0
.000
)(0
.000
)(0
.000
)D
umm
yeq
ualt
oon
eif
only
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stria
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vers
ified
-0.2
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.000
)(0
.000
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.000
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.000
)(0
.000
)D
umm
yeq
ualt
oon
eif
both
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stria
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obal
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vers
ified
-0.2
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094
(0.0
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(0.0
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00)
Rel
ativ
elo
ng-t
erm
debt
toto
talc
apita
l-0
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-0.1
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elat
ive
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res
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les
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01)
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ativ
eEB
ITto
sale
s0.
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033
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043
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.000
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elat
ive
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ngto
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300
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.001
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)D
umm
yeq
ualt
oon
eif
belo
ngs
toS&
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dexe
s0.
055
0.02
10.
115
0.20
4(0
.005
)(0
.202
)(0
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)(0
.000
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-0.1
78-0
.428
(0.0
01)
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00)
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0.24
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187
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00)
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05)
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00)
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stan
t-0
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-0.9
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.000
)(0
.000
)(0
.000
)(0
.000
)(0
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.000
)In
dust
ryfix
edeff
ect
Yes
Yes
Yes
Yes
Yes
Yes
Hec
kman
self-
sele
ctio
nN
oN
oYe
sYe
sYe
sYe
sR
20.
191
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229
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12,4
9212
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12,4
8212
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Not
e:p
valu
esin
pare
nthe
ses
48
App
endi
x4:
Pro
bit
esti
mat
esof
dive
rsifi
cati
onch
oice
-A
sset
mul
tipl
iers
(1)
(2)
(3)
Glo
bal&
Glo
bal
Indu
stria
lIn
dust
rial
Div
ersifi
catio
nD
iver
sifica
tion
Div
ersifi
catio
nR
elat
ive
mar
ket
valu
eof
tota
lcap
ital
0.14
40.
039
0.35
8(0
.000
)(0
.000
)(0
.000
)R
elat
ive
long
-ter
mde
btto
tota
lcap
ital
-0.3
220.
047
-0.1
84(0
.000
)(0
.490
)(0
.034
)R
elat
ive
capi
tale
xpen
ditu
res
tosa
les
0.02
0-0
.002
-0.0
17(0
.005
)(0
.735
)(0
.001
)R
elat
ive
EBIT
tosa
les
0.21
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.015
-0.1
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.001
)(0
.734
)(0
.000
)R
elat
ive
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sale
s-0
.088
-4.2
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.332
(0.3
49)
(0.0
00)
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00)
Rel
ativ
ead
vert
ising
tosa
les
0.46
1-1
.770
-5.0
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.282
)(0
.001
)(0
.000
)D
umm
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oon
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belo
ngs
toS&
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dexe
s-0
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637
(0.0
00)
(0.5
18)
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00)
Perc
enta
geof
indu
stria
llydi
vers
ified
firm
sin
the
indu
stry
-0.3
811.
260
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1(0
.004
)(0
.000
)(0
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)Pe
rcen
tage
ofsa
les
byin
dust
rially
dive
rsifi
edfir
ms
inth
ein
dust
ry0.
305
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41.
906
(0.0
01)
(0.1
20)
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00)
Dum
my
equa
lto
one
iflis
ted
onth
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ajor
exch
ange
mar
kets
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.000
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onst
ant
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028
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,527
Not
e:p
valu
esin
pare
nthe
ses
49
App
endi
x5:
Rob
ustn
ess
chec
kto
alte
rnat
ive
indu
stry
fixed
effec
ts
(1)
(2)
(3)
(4)
(5)
(6)
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Dum
my
equa
lto
one
ifon
lygl
obal
lydi
vers
ified
-0.1
17-0
.133
-0.1
430.
158
0.65
30.
848
(0.0
00)
(0.0
00)
(0.0
00)
(0.3
39)
(0.0
00)
(0.0
00)
Dum
my
equa
lto
one
ifon
lyin
dust
rially
dive
rsifi
ed-0
.161
-0.1
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3-0
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.000
)(0
.000
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)(0
.000
)D
umm
yeq
ualt
oon
eif
both
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stria
llyan
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obal
lydi
vers
ified
-0.2
74-0
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-0.3
28-0
.885
-0.9
62-0
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(0.0
00)
(0.0
00)
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00)
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00)
(0.0
00)
(0.0
00)
Rel
ativ
em
arke
tva
lue
ofto
talc
apita
l0.
121
0.14
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142
0.15
60.
185
0.17
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.000
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.000
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elat
ive
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mde
btto
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ital
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005
(0.0
04)
(0.0
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48)
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00)
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96)
(0.8
49)
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ativ
eca
pita
lexp
endi
ture
sto
sale
s0.
221
0.50
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510
0.20
40.
436
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.000
)(0
.000
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)R
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ive
EBIT
tosa
les
0.09
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135
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070
0.03
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031
(0.0
02)
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01)
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ativ
eR
&D
tosa
les
0.42
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659
0.70
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492
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503
(0.0
00)
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00)
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00)
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00)
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ativ
ead
vert
ising
tosa
les
0.79
00.
503
0.90
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774
0.11
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490
(0.0
00)
(0.0
05)
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00)
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00)
(0.5
07)
(0.0
04)
Dum
my
equa
lto
one
ifbe
long
sto
S&P
inde
xes
0.18
00.
095
0.20
0(0
.000
)(0
.001
)(0
.000
)λgd
-0.1
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.000
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)λid
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288
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.000
)C
onst
ant
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02-1
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-1.5
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(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
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00)
(0.0
00)
Indu
stry
fixed
effec
tN
o3-
digi
tsSI
C2-
digi
tsSI
CN
o3-
digi
tsSI
C2-
digi
tsSI
CH
eckm
anse
lf-se
lect
ion
No
No
No
Yes
Yes
Yes
R2
0.14
90.
325
0.25
70.
167
0.33
90.
274
N12
,573
12,5
5112
,573
12,5
7312
,551
12,5
73N
ote:
pva
lues
inpa
rent
hese
s
50
App
endi
x6:
Rob
ustn
ess
chec
k-
two-
stag
ele
ast
squa
res
mod
el
(1)
(2)
(3)
(4)
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Exce
ssva
lue
Endo
geno
usva
riabl
e(s)
Glo
bal
Indu
stria
lG
loba
l&In
dust
rial
Thr
eety
pes
ofD
iver
sifica
tion
Div
ersifi
catio
nD
iver
sifica
tion
Div
ersifi
catio
nD
umm
yeq
ualt
oon
eif
only
glob
ally
dive
rsifi
ed1.
359
0.18
70.
312
1.52
5(0
.000
)(0
.005
)(0
.000
)(0
.019
)D
umm
yeq
ualt
oon
eif
only
indu
stria
llydi
vers
ified
0.19
70.
872
0.24
2-0
.687
(0.0
06)
(0.0
00)
(0.0
03)
(0.3
74)
Dum
my
equa
lto
one
ifbo
thin
dust
rially
and
glob
ally
dive
rsifi
ed0.
253
0.13
61.
048
1.57
8(0
.019
)(0
.161
)(0
.000
)(0
.082
)R
elat
ive
mar
ket
valu
eof
tota
lcap
ital
0.05
20.
091
0.01
5-0
.058
(0.0
07)
(0.0
00)
(0.5
74)
(0.4
74)
Rel
ativ
elo
ng-t
erm
debt
toto
talc
apita
l-0
.019
-0.0
86-0
.034
0.05
4(0
.533
)(0
.003
)(0
.225
)(0
.464
)R
elat
ive
capi
tale
xpen
ditu
res
tosa
les
0.54
30.
501
0.52
00.
571
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
Rel
ativ
eEB
ITto
sale
s0.
098
0.14
50.
210
0.19
3(0
.013
)(0
.001
)(0
.000
)(0
.022
)R
elat
ive
R&
Dto
sale
s0.
791
0.81
00.
905
0.93
1(0
.000
)(0
.000
)(0
.000
)(0
.000
)R
elat
ive
adve
rtisi
ngto
sale
s0.
502
0.61
30.
523
0.37
9(0
.034
)(0
.008
)(0
.027
)(0
.234
)D
umm
yeq
ualt
oon
eif
belo
ngs
toS&
Pin
dexe
s-0
.056
-0.0
84-0
.242
-0.2
86(0
.037
)(0
.001
)(0
.000
)(0
.069
)C
onst
ant
-0.9
18-1
.103
-0.7
41-0
.393
(0.0
00)
(0.0
00)
(0.0
01)
(0.3
76)
F-t
est
stat
istic
s:fir
stst
age
30.4
3624
.445
33.1
89Pa
rtia
lR2 :
first
stag
e0.
005
0.00
70.
007
Indu
stry
fixed
effec
tYe
sYe
sYe
sYe
sN
12,4
5512
,455
12,4
5512
,455
Not
e:p
valu
esin
pare
nthe
ses
51
App
endi
x7:
Rob
ustn
ess
chec
k-
prop
ensi
tysc
ore
mat
chin
g(w
ith
lagg
edin
depe
nden
tva
riab
les)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Sale
sm
ultip
lier
Ass
ets
mul
tiplie
rR
educ
edm
odel
Exte
nded
mod
elR
educ
edm
odel
Exte
nded
mod
elG
loba
lIn
dust
rial
Glo
bal
Indu
stria
lG
loba
lIn
dust
rial
Glo
bal
Indu
stria
lAv
erag
etr
eatm
ent
effec
tof
glob
aldi
vers
ifica
tion
0.03
00.
028
0.03
40.
035
(0.0
67)
(0.1
10)
(0.0
07)
(0.0
13)
Aver
age
trea
tmen
teff
ect
ofin
dust
riald
iver
sifica
tion
-0.0
87-0
.066
-0.0
98-0
.092
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
Prob
itre
gres
sions
for
estim
atin
gpr
open
sity:
Rel
ativ
em
arke
tva
lue
ofto
talc
apita
l(1-
year
lag)
0.12
30.
027
0.10
80.
004
0.17
80.
041
0.15
30.
016
(0.0
00)
(0.0
02)
(0.0
00)
(0.6
82)
(0.0
00)
(0.0
00)
(0.0
00)
(0.1
52)
Rel
ativ
elo
ng-t
erm
debt
toto
talc
apita
l(1-
year
lag)
-0.1
25-0
.060
-0.0
900.
010
-0.2
21-0
.075
-0.1
530.
008
(0.0
92)
(0.3
80)
(0.2
58)
(0.8
92)
(0.0
04)
(0.3
32)
(0.0
74)
(0.9
28)
Rel
ativ
eca
pita
lexp
endi
ture
sto
sale
s0.
035
0.00
7-0
.264
-0.2
310.
000
0.00
10.
026
0.00
0(0
.000
)(0
.379
)(0
.023
)(0
.042
)(0
.938
)(0
.885
)(0
.004
)(0
.984
)R
elat
ive
EBIT
tosa
les
(1-y
ear
lag)
0.37
00.
070
0.41
30.
023
-0.0
040.
027
0.26
40.
015
(0.0
00)
(0.3
16)
(0.0
00)
(0.8
08)
(0.8
56)
(0.4
66)
(0.0
01)
(0.7
98)
Rel
ativ
eR
&D
tosa
les
(1-y
ear
lag)
0.09
9-2
.170
0.13
0-2
.193
-0.1
65-3
.785
-0.0
18-4
.044
(0.1
03)
(0.0
00)
(0.3
34)
(0.0
00)
(0.0
07)
(0.0
00)
(0.8
79)
(0.0
00)
Rel
ativ
ead
vert
ising
tosa
les
(1-y
ear
lag)
0.55
0-0
.540
0.76
8-0
.592
0.49
8-1
.953
1.05
0-1
.819
(0.2
58)
(0.2
79)
(0.1
45)
(0.3
05)
(0.2
51)
(0.0
01)
(0.0
48)
(0.0
09)
Dum
my
equa
lto
one
ifbe
long
sto
S&P
inde
xes
(1-y
ear
lag)
-0.4
60-0
.046
-0.5
710.
067
(0.0
00)
(0.4
47)
(0.0
00)
(0.2
92)
Perc
enta
geof
indu
stria
llydi
vers
ified
firm
sin
the
indu
stry
(1-y
ear
lag)
0.69
50.
733
0.58
30.
417
(0.3
49)
(0.2
73)
(0.3
62)
(0.5
06)
Perc
enta
geof
sale
sby
indu
stria
llydi
vers
ified
firm
sin
the
indu
stry
(1-y
ear
lag)
0.18
3-0
.741
0.29
7-0
.705
(0.6
74)
(0.0
63)
(0.4
84)
(0.0
73)
Dum
my
equa
lto
one
iflis
ted
onth
em
ajor
exch
ange
mar
kets
(1-y
ear
lag)
0.60
50.
291
0.68
60.
188
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
02)
Con
stan
t-2
.257
-0.8
03-3
.146
-0.5
13-2
.671
-1.1
31-3
.259
0.35
6In
dust
ryfix
edeff
ect
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
χ2
447.
067
4.7
465.
153
9.4
661.
382
4.6
697.
463
7.4
N11
,808
11,9
339,
390
9,37
811
,766
11,8
299,
387
9,33
1N
ote:
pva
lues
inpa
rent
hese
s
52
App
endi
x8:
Rob
ustn
ess
chec
k-
prop
ensi
tysc
ore
mat
chin
g(n
on-l
agge
din
depe
nden
tva
riab
les)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Sale
sm
ultip
lier
Ass
ets
mul
tiplie
rR
educ
edm
odel
Exte
nded
mod
elR
educ
edm
odel
Exte
nded
mod
elG
loba
lIn
dust
rial
Glo
bal
Indu
stria
lG
loba
lIn
dust
rial
Glo
bal
Indu
stria
lAv
erag
etr
eatm
ent
effec
tof
glob
aldi
vers
ifica
tion
0.01
40.
005
0.01
00.
009
(0.3
95)
(0.7
41)
(0.4
35)
(0.4
65)
Aver
age
trea
tmen
teff
ect
ofin
dust
riald
iver
sifica
tion
-0.0
92-0
.091
-0.1
09-0
.110
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
00)
Prob
itre
gres
sions
for
estim
atin
gpr
open
sity:
Rel
ativ
em
arke
tva
lue
ofto
talc
apita
l0.
119
0.04
10.
113
0.02
70.
163
0.05
30.
157
0.04
0(0
.000
)(0
.000
)(0
.000
)(0
.003
)(0
.000
)(0
.000
)(0
.000
)(0
.000
)R
elat
ive
long
-ter
mde
btto
tota
lcap
ital
-0.1
760.
031
-0.1
430.
064
-0.2
290.
004
-0.1
770.
036
(0.0
18)
(0.6
31)
(0.0
57)
(0.3
26)
(0.0
03)
(0.9
52)
(0.0
23)
(0.6
26)
Rel
ativ
eca
pita
lexp
endi
ture
sto
sale
s-0
.290
-0.2
93-0
.289
-0.3
000.
026
-0.0
040.
019
-0.0
04(0
.005
)(0
.002
)(0
.005
)(0
.002
)(0
.001
)(0
.482
)(0
.018
)(0
.462
)R
elat
ive
EBIT
tosa
les
0.51
5-0
.005
0.43
5-0
.023
0.27
1-0
.024
0.19
2-0
.027
(0.0
00)
(0.9
46)
(0.0
00)
(0.7
77)
(0.0
00)
(0.5
94)
(0.0
05)
(0.5
48)
Rel
ativ
eR
&D
tosa
les
0.22
2-2
.264
0.05
9-2
.366
0.04
1-3
.963
-0.1
10-3
.969
(0.0
74)
(0.0
00)
(0.6
46)
(0.0
00)
(0.6
80)
(0.0
00)
(0.2
91)
(0.0
00)
Rel
ativ
ead
vert
ising
tosa
les
0.40
7-1
.167
0.28
2-1
.183
0.88
0-2
.383
0.74
9-2
.359
(0.3
86)
(0.0
21)
(0.5
52)
(0.0
19)
(0.0
51)
(0.0
00)
(0.1
00)
(0.0
00)
Dum
my
equa
lto
one
ifbe
long
sto
S&P
inde
xes
-0.4
65-0
.038
-0.5
640.
060
(0.0
00)
(0.4
73)
(0.0
00)
(0.2
74)
Perc
enta
geof
indu
stria
llydi
vers
ified
firm
sin
the
indu
stry
0.77
21.
398
-0.1
741.
246
(0.2
11)
(0.0
08)
(0.7
39)
(0.0
11)
Perc
enta
geof
sale
sby
indu
stria
llydi
vers
ified
firm
sin
the
indu
stry
0.38
5-0
.862
0.70
2-0
.591
(0.3
03)
(0.0
08)
(0.0
46)
(0.0
63)
Dum
my
equa
lto
one
iflis
ted
onth
em
ajor
exch
ange
mar
kets
0.57
50.
243
0.64
90.
156
(0.0
00)
(0.0
00)
(0.0
00)
(0.0
02)
Con
stan
t-2
.238
-1.0
44-3
.350
-0.5
76-2
.615
-1.2
12-3
.247
-0.7
28(0
.000
)(0
.022
)(0
.000
)(0
.288
)(0
.000
)(0
.000
)(0
.000
)(0
.093
)In
dust
ryfix
edeff
ect
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
χ2
461.
471
1.0
594.
574
6.0
679.
786
3.3
871.
788
3.0
N12
,299
12,4
3712
,299
12,4
3712
,335
12,4
0912
,335
12,4
09N
ote:
pva
lues
inpa
rent
hese
s
53