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

1sc3339@columbia.edu, Columbia Business School, Columbia University, NY 10027, USA2bruce.kogut@columbia.edu, Columbia Business School, Columbia University, NY 10027, USA

3mathphy@gmail.com, 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.

5

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

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7(0

.217

)(0

.000

)(0

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)(0

.000

)λgd

-0.2

54-0

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(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

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)(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

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)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

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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