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Strategic Management JournalStrat. Mgmt. J., 22: 4574 (2001)

ESTIMATING THE PERFORMANCE EFFECTS OF

BUSINESS GROUPS IN EMERGING MARKETS

TARUN KHANNA and JAN W. RIVKIN*Graduate School of Business Administration, Harvard University, Boston,Massachusetts, U.S.A.

Business groupsconfederations of legally independent firmsare ubiquitous in emergingeconomies, yet very little is known about their effects on the performance of affiliated firms.We conceive of business groups as responses to market failures and high transaction costs. Indoing so, we develop hypotheses about the effects of group affiliation on firm profitability:affiliation could either boost or depress firm profitability, and members of a group are likely

to earn rates of return similar to other members of the same group. Using a unique data setcompiled largely from local sources, we test for these effects in 14 emerging markets: Argentina,Brazil, Chile, India, Indonesia, Israel, Mexico, Peru, the Philippines, South Africa, South Korea,Taiwan, Thailand, and Turkey. We find evidence that business groups indeed affect the broad

patterns of economic performance in 12 of the markets we examine. Group affiliation appearsto have as profound an effect on profitability as does industry membership, yet strategy scholarshave a much clearer grasp of industries than of groups. Moreover, membership in a groupraises the profitability of the average group member in several of the markets we examine.This runs contrary to the wisdom, conventional in advanced economies, that unrelated diversifi-cation depresses profitability. Overall, our findings suggest that the roots of sustained differencesin profitability may vary across institutional contexts; conclusions drawn in one context maywell not apply to another. Copyright 2001 John Wiley & Sons, Ltd.

INTRODUCTION

A striking feature of most emerging economies

is the prominent role played by business groups.

While these confederations of firms go by differ-

ent names in different countries (e.g., grupos in

Latin America, business houses in India, andchaebol in South Korea), they share some broad

similarities. Though member firms remain legally

independent, a maze of economic and social ties

typically unites each group. The ties enable mem-

ber firms to coordinate their actions in product

markets or the markets for inputs. Many groups

Key words: business groups; profit differences; corpo-rate strategy; variance components; emerging markets*Correspondence to: Jan W. Rivkin, Harvard Business School,Morgan Hall 239, Boston, MA 02163, U.S.A.

Received 15 December 1998Copyright 2001 John Wiley & Sons, Ltd. Final revision received 31 July 2000

span a diverse set of industries, and most areassociated with a single extended family. Such

groups are ubiquitous in emerging economies,

where they often control a substantial fraction of

a countrys productive assets and account for the

largest and most visible of the countrys firms

(Amsden and Hikino, 1994; Granovetter, 1994;

Khanna and Palepu, 1997).

The sheer ubiquity of business groups sug-

gests that they may affect, in important ways,

the broad patterns of economic performance in

emerging economies. Yet virtually no system-atic empirical evidence exists concerning the

ramifications of group affiliation for firm fi-

nancial performance. We lack answers to even

basic questions. We do not know whether group

affiliates typically earn higher or lower rates of

profit than unaffiliated firms, and theory offers


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46 T. Khanna and J. W. Rivkin

predictions both ways. Likewise, we do not

know how important knowledge of group

affiliation is in improving our understanding of

firm profitability, even after other observable

characteristics of the firm, such as the industry

in which it competes, are known. Answers to

these questions would shed light on the verynature of business groups.

It is these questions that we tackle empirically

in this paper. We first use prior literature to

define what business groups are and to explain

what they do, both concretely and conceptually.

We conceive of groups as a response to market

failures that arise in the particular institutional

contexts of emerging economies. From the con-

ceptual discussion come two sets of hypotheses

about the influence of groups on the performance

of affiliated firms. First, the effect of affiliation

on the level of profitability of the typical groupmember is ambiguous: groups generate benefits

for and impose costs upon members, and only

empirical research will resolve whether the bene-

fits usually outweigh the costs or vice versa.

Second, regardless of which is larger, benefits

and costs will tend to be shared among member

firms. For this reason, we propose that members

of a given group will exhibit profit rates that

move togetherthat is, are more similar to one

another than they are to profit rates of outsiders.

Gathering the data necessary to test these

hypotheses is challenging in emerging economies.Data concerning group affiliation are especially

hard to obtain. Typically, one must piece together

affiliation data from multiple sources, clean the

data sets, confirm the information across the

sources, and merge the data with separate sources

of income statement, balance sheet, and industry

participation data. We do this in 14 emerging

economies: Argentina, Brazil, Chile, India,

Indonesia, Israel, Mexico, Peru, the Philippines,

South Africa, South Korea, Taiwan, Thailand,

and Turkey. Though our primary purpose is to

investigate the within-country performance effects

of business groups, we do standardize the coun-

try-level data sets in ways elaborated below to

facilitate rudimentary cross-country comparisons.

Having gathered, cleaned, and standardized the

data, we employ multiple methods to test our

hypotheses.

Our results regarding the effects of group

affiliation on the level of firm profitability suggest

that affiliates perform better than non-affiliates in

Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)

six countries and worse than non-affiliates in

three, with no difference in profitability levels in

the remaining five countries. We also find that,

in 12 of our 14 countries, the profit rates of

affiliates of a group are closer to one another

than they are to the profit rates of other firms.

We interpret this second result as indicating thatknowledge of a firms group affiliation improves

ones ability to anticipate its profitability, even

after one knows the industry and the time period

in which profitability is observed. In fact, in this

thought experiment, it turns out that knowledge

of group affiliation is more helpful than knowl-

edge of industry affiliation in half of our 14 coun-

tries.

The effects of group membership on prof-

itability differ substantially from one country to

another, and our conceptual discussion gives us

some sense of the institutional conditions thatmight make the effects especially large. With only

14 countries in our sample, we cannot analyze

systematically why membership matters more in

some countries than others. We do present, how-

ever, some simple bivariate correlations between

country conditions and the size of group effects.

We find that the correlations are not consistent

with either of the most prominent views of group

affiliation. Specifically, group affiliates are not

especially profitable where capital markets are

particularly primitive, casting doubt on the idea

that groups are purely responses to capital marketimperfections. Group effects are also not corre-

lated with measures of the possibility of rent-

seeking in our 14 countries, suggesting that this

prominent view of groups does not provide a full

description of the organizational form either.

While admittedly based on a small sample of

countries, these correlations suggest that the nor-

mative debate on the role of business groups

paragons or parasitesis unlikely to be resolved

through a simple one-dimensional categorization

of groups (Ghemawat and Khanna, 1998;

Khanna, 2000).

The findings have implications for researchers

and practicing managers. The positive effects of

business group affiliation on profitability in sev-

eral of our countries are consistent with groups

serving as an organizational response to the

particular institutional context of emerging econo-

mies. Groups can boost the profitability of mem-

ber firms as they fill the voids left by the missing

institutions that normally underpin the efficient


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Performance Effects of Business Groups in Emerging Markets 47

functioning of product, capital, and labor markets.

The neutral and negative effects of group affili-

ation in other countries suggest that group affili-

ation need not always be beneficial. The con-

tinued existence of possibly inefficient groups is

consistent with poorly developed selection

environments, where weak organizational formsare not weeded out. Our results thus suggest the

need for research on the following topics, at least:

Precisely how do groups fill institutional voids?

How do they help affiliates resist selection pres-

sures? In what contexts are business groups likely

to boost private profits and social welfare?

Our results challenge two pieces of conven-

tional wisdom in the strategy field. First, we find

no evidence of a diversification discount in 11

of our 14 countriesif anything, there is often

evidence of a diversification premium. This sug-

gests that results from analyses of U.S. data onthe poor performance of diversified entities do

not generalize to other contexts. There appears

to be considerable room for a diversified organi-

zational form to create value when external mar-

kets are poorly developed, as they surely (almost

tautologically) are in most of our emerging econ-

omies. This raises the general issue of whether

truths from the strategy field apply outside of

the developed-economy realms from which they

emerged. Second, the strategy fields focus on

industry effects in determining firm profitability

has stood it in excellent stead in developed econo-mies. While industry effects undoubtedly remain

important in emerging markets, group affiliation

appears to be at least as important. This suggests

that some reorientation of the strategy fields

research emphasis would be valuable.

Practical implications of our results abound,

especially for the policy experiments related to

business groups currently underway in countries

such as Chile, China, South Africa, and South

Korea. We discuss these implications in the con-

cluding section. There, we also argue that owners

and managers of business groups should be wary

of strategy advice from advisors whose knowl-

edge base originates in advanced economies.

BUSINESS GROUPS IN EMERGINGMARKETS

Our empirical research draws on a rich heritage

of qualitative studies of business groups, most


focused on individual countries.1 In this section,

we use those studies to define what business

groups are and what they do. This discussion

leads us to hypotheses about the effects of group

affiliation on financial performance. We close the

section by considering a handful of previous stud-

ies that try to pinpoint the performance effectsof business group affiliation.

What is a business group?

The task of defining business group is poten-

tially vexing. On one hand, businesspeople who

operate in most emerging countries have a clear

intuitive notion of what a group is and can

match firms to particular groups with little or

no ambiguity. Reporting from his fieldwork in

Nicaragua, for instance, Strachan (1976: 67)

notes:

There have been 20 to 30 social or semi-socialoccasions at which I was introduced to abusinessman by one of his close friends. At somepoint in the conversation which followed, I havesmiled the smile of an insider and asked, Andwhat group do you belong to? The replies, oftenwith the same smile, have been direct, Oh, Idont belong to any group, or I suppose I ama member of the Banco Nicaraguense Group orin some cases indirect and evasive. Never, how-ever, has that question drawn a blank stare andthe reply, What do you mean by group?

Despite clarity in the minds of businesspeople,

earlier studies have failed to reach a consensus

definition of group. Synthesizing from these

studies, we propose the following, admittedly

broad, definition: A business group is a set of

firms which, though legally independent, are

bound together by a constellation of formal and

informal ties and are accustomed to taking coor-

1 Without any pretense of being comprehensive, for Belgium,

see Daems (1978); for Central America, see Strachan (1976);for China, see Keister (1998a, b) and Walder (1995); forChile, see Khanna and Palepu (1999, 2000b), Lefort andWalker (1999), and Majluf et al. (1995); for France, seeEncaoua and Jacquemin (1982); for Indonesia, see Robison(1986) and Schwartz (1994); for India, see Ghemawat andKhanna (1998), Herdeck and Piramal (1985), Khanna andPalepu (1997, 1999, 2000a), and Piramal (1996); for Japan,see Aoki (1990), Berglof and Perotti (1994), Caves andUekusa (1976), Goto (1982), Hoshi, Kashyap, and Scharfstein(1991), Lincoln, Gerlach, and Ahmadjian (1996), and Nakatani(1984); for Korea, see Amsden (1989) and Chang and Choi(1988); for Mexico, see Camp (1989); for Pakistan, seeWhite (1974).


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dinated action.2 This definition captures the two

features that previous students of groups have

emphasized: the ties that hold group firms

together, and the coordinated actions those ties

enable. We consider each in turn.

What binds groups together?

Two aspects of the ties that bind groups together

stand out from earlier field research. First, the

ties are numerous and overlapping. Second, they

span the economic and the social, the formal and

the informal. Hence Leff (1978: 663) refers to a

business group as a group of companies that does

business in different markets under a common

administrative or financial control and says that

its members are linked by relations of inter-

personal trust, on the basis of a similar personal,

ethnic or commercial background. Strachan(1976) defines a group as a long term association

of a great diversity of firms and the men who

own and manage these firms. He particularly

resists the idea that a groups constituents can be

identified purely on the basis of a single metric,

such as interlocking directorates; thus: even a

ban on interlocks would not destroy nor even

seriously impair the important group relations and

patterns (page 18). Likewise, Encarnation (1989:

45), referring to Indian business houses, empha-

sizes multiple forms of ties among group mem-

bers: [I]n each of these houses, strong social tiesof family, caste, religion, language, ethnicity and

region reinforced financial and organizational

linkages among affiliated enterprises. Granovetter

(1994: 454), synthesizing from single-country

field-based studies, concludes that a business

group is simply a collection of firms bound

together in some formal and/or informal ways.

Statistical efforts building on the field research,

though rare, confirm that numerous, social and

economic ties delineate business groups. Examin-

ing over 100,000 pairs of firms in Chileboth

pairs within a single group and pairs that span

group bordersKhanna and Rivkin (1999) find

that equity cross-holdings, common individual

owners, director interlocks, and family ties dis-

2 Each firm is a distinct legal entity that publishes its ownfinancial statements, has its own board of directors, and isresponsible to its own shareholders. This is in contrast toconglomerates in the United States, for example, where indi-vidual lines of business typically do not have any of theseproperties.


tinguish the within-group dyads from the non-

group. Though Japan is not ordinarily considered

an emerging economy, empirical studies of

Japanese keiretsu deserve mention, both because

they are more numerous than studies of groups

in emerging economies and because business

groups in some emerging markets were explicitlydesigned to mimic Japanese keiretsu.3 Gerlachs

(1992) blockmodel analysis of Japanese networks

shows that keiretsu membership is partially

related to equity ownership, bank relationships,

and director interlocks. Lincoln, Gerlach and Tak-

ahashi (1992) argue that dyad-level cross-

shareholdings and director interlocks among keir-

etsu affiliates are functions of trade and debt

linkages among them, and that cross-

shareholdings are prior to director interlocks (in

the sense that the latter are generally not observed

without the former). Lincoln et al. (1996) con-sider several types of coordination mechanisms

among keiretsu affiliates: direct equity, debt,

directors, trade, and shacho-kai (Presidents

Club) membership.

What do groups do?

The multiple ties among group affiliates enable

them to take coordinated actions. In various coun-

tries, members of particular groups present a uni-

fied front to outside constituencies. Members

may, for example, share a brand name, raisecapital jointly, lobby bureaucrats and politicians

together, recruit managers as a group, and pool

resources to invest in new ventures. Firms within

a group may also exchange resources internally.

Capital may flow to members in distress or to

members whose opportunities outweigh their

ability to generate capital themselves; skilled

managers may be trained jointly and transferred

to where they are needed; information may be

exchanged at formal group meetings, in informal

clubs, or at family gatherings; and members may

buy and sell goods amongst themselves.

To interpret these coordinated actions, we fol-

low a small economic literature that conceives of

business groups as responses to market failures

and associated transactions costs (Caves, 1989;

3 Keister (1998a, 1998b) notes that Chinese groups were thusconceived. Ungson, Steers, and Park (1997) point out thatKorean chaebol bear a strong resemblance to the precursorsof modern keiretsu, the prewar Japanese zaibatsu.


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Leff, 1976, 1978). A market failure arises when

an economically beneficial transactionone that

would benefit both buyer and sellerfails to be

consummated because the indirect costs of the

transaction outweigh the net benefit (Williamson,

1975). Transactions may be particularly costly in

emerging economies because institutions fortrade, contract enforcement, communication, and

information disclosure are weak (Khanna and

Palepu, 1997), exposing partners to a trade to

opportunistic behavior.

For instance, those who provide capital may

hesitate to fund firms in emerging economies

because financial disclosure requirements are

minimal in such settings and the rights of mi-

nority shareholders and creditors are often pro-

tected poorly. As a result, projects may go

unfunded even though their rates of return exceed

the cost of capital. A group can overcome suchobstacles both by transferring capital within the

group and by underwriting security issues with

the entire groups reputation. Internal capital mar-

kets have been shown to operate within keiretsu

(Hoshi et al. 1991; Lincoln et al., 1996), and

Korean chaebol affiliates are known to capitalize

on group reputation in their attempt to access

capital. Groups have also been known to direct

money from existing affiliates towards new ven-

tures (Khanna and Palepu, 2000a). Such actions

may be especially valuable where the institutions

needed to create a vibrant venture capital sectorare missing.

Partly inspired by the seminal work of Aoki

(1984, 1990) and others on Japans main bank

system, much of the research on business groups

has framed groups as a response to inefficient

capital markets.4 However, labor, product, tech-

nology, and other markets are also prone to fail-

ure in emerging economies. We perceive groups

as responses to all of these failures and, in prior

literature and field research, find support for this

broader view.5

4 Several early studies from Europe also lent support to thisemphasissee Daems (1978) for Belgium, Cable (1985) forWest Germany, and Encaoua and Jacquemin (1982) forFrance.5 In fact, there are reasons to believe that business groups donot remedy capital market problems especially effectively.Groups in many countries do not have group-specific financialinstitutions like the keiretsus main bank and have legalrestrictions in place on internal capital market activity (thoughthese restrictions may be enforced only imperfectly). Khannaand Palepu (2000a) find that, unlike the results for Japanese


Markets for management talent are rife with

imperfection, with managers potentially remaining

for long periods in jobs that poorly match their

skills. Labor immobility in emerging markets

arises in part because reliable information about

a managers abilities is hard to come by in such

settings. The fixed costs incurred to recruit andtrain top managers may also be prohibitive for

some firms in emerging economies. Hence the

leading Korean chaebol, Samsung, pools the

resources of its affiliates in order to afford access

to top-flight international talent. The affiliates of

Indias Tata Group not only recruit top man-

agement talent in a concerted fashion, but also

participate in an internal labor market. This mar-

ket rotates talent to where it is needed within the

group, arguably in a manner more efficient than

the external market could (Khanna and Palepu,

1997).Product and input markets in emerging econo-

mies may suffer from a paucity of participants

and from weak contract enforcement, which may

prevent trade among parties who fear opportu-

nistic behavior. By investing in an umbrella brand

name and a reputation for fair dealing, a group

may credibly commit itself to produce high-

quality goods and not to act opportunistically.

Members of Indias Tata Group, for instance,

benefit from the groups highly respected brand

name, deployed in product markets as diverse as

locomotives and tea. In addition to overcomingmarket failures by developing a favorable repu-

tation, a group can also overcome problems in

product and input markets by trading internally,

where the economic and social punishment for

opportunistic behavior can be severe.

Groups can also resolve failures in the market

for cross-border transfers of capital and tech-

nology. Fearful that their capital or intellectual

property will be expropriated, firms from

advanced economies may hesitate to lend money

or license technology in emerging economies. A

group company may overcome this reluctance by

putting the entire groups reputation at stake. In

line with this reasoning, Amsden and Hikino

(1994) assert that groups play a major role in

assimilating foreign technology in emerging mar-

keiretsu, proxies for firm investment are no more correlatedwith firm cash-flow for Indian group affiliates than for unaf-filiated firms, suggesting that group affiliates do not rely uponinternally generated cash any more than do unaffiliated firms.


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kets. Guillen (1997), focusing especially on

Argentina, Spain, and South Korea, emphasizes

the role of business groups as agents that combine

factors of production within the country in ques-

tion and outside it. Fisman and Khanna (1998)

demonstrate that group affiliates in India are more

likely to issue depositary receipts, financialsecurities to tap international capital markets, than

are unaffiliated firms, but that Chilean group

affiliates and unaffiliated firms are equally likely

to do so. Their interpretation is that groups serve,

in this instance, as capital market intermediaries

where the external markets are underdeveloped

(as in India) but not where they are better

developed (as in Chile). In a related vein,

Belderbos and Sleuwaegen (1996) show that kei-

retsu members help their siblings make foreign

direct investments in Southeast Asia.

Markets for risk-sharing are also prone to fail-ure, creating another need for intermediation.

With capital markets underdeveloped and min-

ority shareholder rights poorly protected, the

owners of large companies in emerging econo-

mies cannot reduce the risk they bear in ways

that are common in advanced economies. They

cannot, for instance, easily sell minority shares

in their ventures; buyers would insist on a dis-

count that reflects agency costs (La Porta et al.

1998). Accordingly, a group structure permits

wealthy individuals and families to diversify their

holdings. Risk reduction comes at a cost, how-ever. The mutual support that group companies

provide their brethren embodies this risk sharing.

In fact, keiretsu affiliates appear to have profit

rates that are more stable, but lower, than unaf-

filiated companies (Caves and Uekusa, 1976; Lin-

coln et al., 1996; Nakatani, 1984).

Coordinated political lobbying, we would

argue, is a special case of group response to

market failure. Access to political power is

especially important where government authorities

play a large and less-than-evenhanded role in the

economy, as they do in many emerging econo-

mies. Open markets for political favors do not

exist. Within a group, however, a favor can be

shifted from firm to firm, to wherever it is of

greatest value. Hence, group firms have partic-

ularly strong incentives to invest in relationships

with political-bureaucratic structures. Studies of

groups in Pakistan (White, 1974), Latin America

(Strachan, 1976), and Indonesia (Schwartz, 1994)

emphasize these ties. The largest Indian business


houses have historically maintained industrial

embassies (Encarnation, 1989) in New Delhi to

coordinate lobbying efforts. Perhaps the most

direct econometric support of the value to groups

of such connections is provided by Fismans

(2000) event study set in Suhartos Indonesia.

He finds that surprise announcements concerningSuhartos health have far greater impact on affili-

ates of groups which are close to the President

or his inner circle than on those more distant

from Suharto.6

In contrast to economists emphasis on market

failure, sociologists have suggested that the raison

detre for groups may have little, if anything, to

do with a quest for economic gain or security.

Rather, goals like institutional legitimacy, political

power, and social fitness (DiMaggio and Powell,

1983) are equally important. The closely related

underlying social process is that people prefer todo business with their friends and family mem-

bers. This is justifiable in part on the grounds of

self-interest that economists emphasize; individ-

uals trust their friends and family because they

know they can punish any improper behavior.

But the preference for friends and family goes

beyond this into the realm of social norms.

Biggart and Hamilton (1992) point out that just

as individualism is institutionalized in Western

societies, other social relationships are insti-

tutionalized in Asia.7

Of course, the economic function of such socialties, regardless of the reasons for their origin, is

consistent with that emphasized by transaction

costs economists. Thus, family and other social

ties can be conceptualized as mechanisms through

which intragroup transaction costs are lowered,

by encouraging information dissemination among

group firms, reducing the possibility of contrac-

tual disputes, and providing low-cost mechanisms

for dispute resolution. Such relationships might

also help in resolving contracting problems, in

much the same way emphasized by Dores (1983)

6 We hasten to add that such connections might have conse-quences for wealth distribution that are undesirable for societyat large. They may pose a direct barrier to the operation ofcompetitive forces in allocating resources efficiently in theeconomy (Bardhan, 1997; Bhagwati, 1982; Krueger, 1974).Finally, they may raise serious ethical concerns.7 Chinese society, for example, assigns overwhelming impor-tance to an individuals reputation for uprightness and forbehavior fitting the particular role which the individual fills(Hamilton, 1996: 288289).


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study of the role of relational contracts in Confu-

cian societies.8

How might group affiliation affect patterns

of performance?

What empirical signatures might business groups,conceptualized as we just described, leave

behind? In discussing this, we note two important

assumptions. First, we assume that group affili-

ation is not a choice variable in the short run;

managers of a firm cannot simply choose to alter

their affiliation if a change would lead to higher

profit. In support of this assumption, we note

that it is extremely rare for firms to switch groups

in most emerging markets. Strong group identity

(Granovetter, 1994; Khanna and Palepu, 1999)

and fierce intergroup rivalry, as among the lead-

ing Korean chaebol, deter switching. Second, theselection pressures on firms in emerging econo-

mies are relatively modest in the medium run,

partly because of the poorly developed markets

for corporate control. The firms we observe in

our data are not exclusively the fittest survivors

of a Darwinian struggle. The wide variation we

observe in firm performance certainly supports

this second premise. Without these two assump-

tions, it is hard to justify a relationship between

group affiliation, a choice variable, and prof-

itability, a selection variable.

Our discussion above suggests that there arebenefits of group membership. Affiliates are able

to consummate favorable transactions that non-

group firms cannot. They can access capital,

labor, and product markets less expensively than

their nongroup peers, and they can exchange

goods and services internally without the hazards

of arms-length exchange. They also have

superior access to the political power structure

and hence draw from a richer pool of opportuni-

ties. This leads us to:

Hypothesis 1: Firms that are affiliated with

groups will be more profitable, ceteris paribus,

than unaffiliated firms.

Indeed, prior work is consistent with the proposi-

8 These are not unfamiliar arguments to students of nonmarket,nonhierarchical forms of organization, such as clans (Ouchi,1980), fiefs (Boisot and Child, 1988), or bazaar economies(Geertz, 1963, 1973).


tion that group membership is associated with

the superior profitability of member firmse.g.,

Khanna and Palepu (2000a, 2000b) in India and

Chile, Chang and Choi (1988) in Korea. Though

we expect to find support for Hypothesis 1, we

acknowledge theoretical arguments that suggest

the converse: group affiliation may be costly. Themost direct costs of group affiliation have to

do with the group undertaking certain central,

expensive actions that may or may not be ben-

eficial. For example, pan-group training programs

may produce more costs than benefits, especially

if the training needs of group affiliates are very

different from one another. In addition to any

direct group burden, affiliated firms are placed

at a disadvantage by other, less obvious factors.

They have, for instance, an obligation to bail

out brethren firms that are performing badly; if

unaffiliated, such poor performers would be leftto go bankrupt. Moreover, secure in the embrace

of the group, managers of group firms may have

weak incentives to run their businesses efficiently.

They may also be obliged, or at least prone, to

purchase inputs from sibling firms, efficient or

not (Williamson, 1975). Presumably groups

include the firms whose decisions are most driven

by considerations other than local economic gain.

Social ties keep firms bound to their groups

despite economic costs, and poor performance

can persist because selection pressures are mod-

est. These arguments imply the converse of ourmaintained hypothesis:

Hypothesis 1: Firms that are affiliated with

groups will be less profitable, ceteris paribus,

than unaffiliated firms.

Regardless of whether group membership is

costly or beneficial in total, membership seems

certain to impose costs and benefits that are

shared by members. The windfalls of good central

management or political connections, say, will

improve the performance of many affiliates. Con-

versely, many firms within a group will suffer

when an affiliate has to be helped out or when

family members inadequate for the task are

assigned to run group functions. Equity cross-

holdings will ensure that costs and benefits are

borne together. In line with this:

Hypothesis 2: The levels of profitability of

firms within a particular group will be more


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similar to one another than they are to the

levels of profitability of firms outside the

group.

Note that Hypotheses 1/1 and Hypothesis 2 are

independent; neither necessarily implies the other.

Suppose, for instance, that the profit rates offirms within a group vary together strongly, but

some groups are highly effective and others

equally ineffective. Then it is possible for

Hypothesis 2 to hold true while the average

profit rates of group and nongroup firms are

indistinguishable. Alternately, suppose the aver-

age group firm earns a higher profit rate than its

unaffiliated peer, but profit rates within each

group vary widely. Then Hypothesis 1 may be

confirmed and Hypothesis 2 refuted. Hypotheses

1 and 1 are mutually exclusive, but any other

combination of hypotheses could be confirmed orrefuted by the data. Appendix 1 presents a simu-

lation analysis which demonstrates that Hypoth-

esis 1 does not imply Hypothesis 2, nor vice

versa.

What evidence exists on group affiliation and

performance?

Previous empirical studies have made some prog-

ress in identifying various positive and negative

aspects of business group affiliation. In line with

Hypothesis 1, Keister (1998b) shows that theformation of groups in China improved financial

performance and productivity in the late 1980s,

and that centralized groups did better than others.

Superior profitability has also been associated

with the largest chaebol in Korea (Chang and

Choi, 1988) and the most widely diversified

groups in India and Chile (Khanna and Palepu,

2000a, 2000b).

Consistent with Hypothesis 1, affiliates of

Japanese keiretsu earn a lower return on assets

than do unaffiliated firms (Caves and Uekusa,

1976; Nakatani, 1984). One interpretation that

has been advanced for the lower profitability of

keiretsu affiliates is that they represent a different

point on the riskreturn frontier than do unaffili-

ated firms. They generate lower performance in

exchange for pursuing more conservative strate-

gies, as manifested by a lower variance of prof-

itability (Nakatani, 1984), and thus serve a mutual

insurance function (Aoki, 1984). Also in line with

Hypothesis 1, the members of mid-sized groups


in India and Chile have been found to earn

lower returns than independent firms (Khanna and

Palepu, 2000a, 2000b).

Little prior evidence exists concerning Hypoth-

esis 2, within-group similarity of profit rates in

emerging markets. Chang and Hong (1998)

decompose the variance in the profit rates ofKorean firms and find that a substantial fraction

of the total variance can be attributed to chaebol

effects. Such effects would indeed generate the

pattern in profitability that we predict here, with

profit rates within a group relatively similar to

one another.

Overall, we are unaware of any effort that is

directly comparable to the present paper, that

seeks econometric evidence of the effects of busi-

ness groups on profitability in a broad set of

emerging economies.

DATA

Our efforts to test our hypotheses focus on the

economies of 14 countries: Argentina, Brazil,

Chile, India, Indonesia, Israel, Mexico, Peru, the

Philippines, South Africa, South Korea, Taiwan,

Thailand, and Turkey. For each, we collect data

concerning financial results and business group

affiliation for as many firms as possible in as

many years as possible. It is notoriously difficult

to obtain valid information about business per-formance and interfirm relationships in emerging

economies.9 Because reliable information is elu-

sive in emerging economies, we discuss the

sources, character, veracity, limitations, and biases

of our data in unusual depth.

Country selection

The countries we examine are by no means a

random sample of emerging economies. The

countries selected for analysis share two traits,

and it is safest to generalize the results to other

countries with similar characteristics. First, each

country has a large enough population of firms

with publicly disseminated accounting returns that

9 Indeed, as argued above, it is possible that the dearth ofinformation contributes to the very existence of businessgroups. Groups may serve as substitutes for external capital,product, and labor markets, which cannot function well wheninformation is scarce and suspect.


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statistical research is feasible. Second, in each

country, information concerning clearly delineated

groups is available. Indian firms, for instance,

typically publicize their group affiliation and are

members of only one group (or none at all). In

other countries, in contrast, group affiliation is

kept quiet, and the thick web of connectionsamong firms makes it difficult to delineate one

group from the next. Finally, we acknowledge

that serendipity played a role in selecting the

14 countries examined here. In these particular

countries, we had the personal contacts and sheer

good fortune to obtain reliable data.

We do not believe that these country-level

selection criteria lead us to examine a biased

set of countries in which group membership is

unusually influential. First, there is no indication

that groups are more prominent or influential in

economies with larger firm populations. Groupsare reputed to dominate Central American econo-

mies, for example, which have small populations

of firms (Strachan, 1976). Second, although it is

possible that group identification is easiest where

groups play an especially influential role in the

economy, it actually appears that the conventions

concerning group identification are driven pri-

marily by idiosyncratic, historical circumstances

that have little to do with group importance. In

preliminary investigations, we certainly encoun-

tered countries in which groups are alleged to be

influential, yet it is extremely difficult for out-siders to delineate groups cleanly. As for seren-

dipity, we exhausted our contacts in all emerging

economies, not just those in which we suspected

group affiliation to be vital.

Data on group affiliation

Appendix 2 describes the data sources for each

country in detail. For each company in each

country in our sample, we obtained three critical

pieces of information: the group (if any) with

which the company is affiliated, its financial

results over as many years as possible, and the

industry in which it competes. Only in India and

South Africa did a single primary source provide

all three items. In most cases, we gathered group

affiliation data from one source, collected finan-

cial and industry information from another, and

carefully merged the two.

There exists no common, international data

base of group affiliations. Rather, we relied on


local and, in many cases, multiple sources for

affiliation data. It is difficult to gain access to

such sources, but they tend to be comprehensive

and reliable. In Chile, for instance, the data on

group membership were obtained from the Super-

intendencia de Valores y Seguros in Santiago,

after multiple visits to that regulator revealed theexistence of such data (Khanna and Palepu, 1999,

2000b). Random samples of the group member-

ship lists were shown to knowledgeable observers

in Chile, including academics, regulators, and

managers. We also found independent sources

(typically in Spanish) that described some of the

groups. Finally, several Chilean groups provide

information about affiliated companies in their

annual reports, and this information was always

found to be consistent with that provided by theSuperintendencia. In India, we relied heavily on

a publicly available data base maintained by theCentre for Monitoring the Indian Economy

(CMIE). CMIE, a privately run firm based in

Bombay, identifies a firms group membership by

delving into the firms history, monitoring its

announcements closely, and examining directorate

interlocks. We verified CMIE data against

detailed case studies of three prominent groups,

performed a similar test on a random sample

of smaller groups, and cross-checked additional

prominent groups using local business magazines

and historical accounts (Khanna and Palepu,

2000a, 2000c). More generally, for each country,we gleaned affiliation information from data sets

compiled by regulatory authorities, private infor-

mation providers, stock exchanges, teams of help-

ful local academics, and groups themselves.

The use of multiple, local sources is both a

strength of this paper and a cause for caution.

There is no guarantee that our local sources

use precisely comparable definitions of the term

group. Therefore, our results apply to groups as

delineated within each country, and we make

comparisons across countries warily.

Financial and industry data

Wherever they are available, we use local sources

to obtain financial figures and to identify the

industry in which each firm competes. Such

sources cover far more firms than do international

sources, though they typically require translation

and data entry by hand. For example, our source

for Chilean data, a Spanish text entitled Revista


10/30


Valores, gave financial results for 549 firms. The

best international source we could identify

covered only 85 companies.

In several countries, we could obtain no local

source for financial information. In those cases,

we turned to the Company Accounts Database of

Datastream International, one of the most compre-hensive international providers of information on

publicly traded firms. Datastream International, in

turn, relies largely on Worldscope, a division of

Disclosure Corporation which compiles financial

data from public filings of companies around the

globe. In all countries, and especially in emerging

economies, Datastream International and World-

scope provide accounting information for only

the largest, most prominent firms.10 This clearly

raises the specter of selection bias, and accord-

ingly, we have less confidence in our results for

these countries than for the others.11

We use a version of return on assets (ROA)

operating profit divided by total net assetsas

our measure of profitability.12 We opt to examine

operating profit rather than net profit for two

reasons. First, operating profit is not (directly)

distorted by taxation rules, which differ dramati-

cally across countries. Second, a consistent meas-

ure of operating profit is available in more coun-

tries than is net profit. In focusing on operating

return on assets, we follow the vast majority of

variance decomposition studies in the United

States (Furman 1998; McGahan and Porter, 1997,1998; Roquebert, Phillips, and Westfall, 1996;

Rumelt, 1991; Schmalensee, 1985). Accounting

measures of profitability do have limitations

(Benston, 1985), however, and a handful of U.S.

decomposition studies opt to use Tobins q to

address the limitations (McGahan, 1998; Werner-

felt and Montgomery, 1988). Unfortunately, the

data required to calculate Tobins q are available

for far fewer countries and firms than are the

data necessary to compute ROA.

10 Representatives of Datastream International report that thereis no formal process by which it selects firms for coverage.Note that Datastream International collects financial infor-mation for a broad spectrum of firms around the globe, butthey do not provide data on group affiliation.11 We are somewhat comforted by the fact that Furmans(1998) variance decomposition results using only Worldscopedata for the United States were essentially identical to earlierstudies using more comprehensive data sets.12 In India, data constraints force us to use a slight variant:(net income + interest expense (1 tax rate)) / totalnet assets.


Due to data limitations, the country-level data

sets cover different periods of time. The median

starting year among the 14 countries is 199091,

and the median final year is 1997.

Screening of the data

We screened the data in four ways (Table 1).

First, we eliminated observations with no finan-

cial data. Second, following the convention

employed by developed-country variance

decomposition studies, we removed financial ser-

vice and real estate firms from the sample. Firms

in the financial/real estate sector are typically

screened out because the returns in the sector are

calculated in a manner that is inconsistent with

returns in other sectors of the economy (McGahan

and Porter, 1997). Third, we eliminated all firms

identified with a miscellaneous industry or withno industry whatsoever.13

Finally, we tried to eliminate data points con-

taining mistakes and misrepresentations (which

might be especially common in samples from

emerging economies). In a handful of cases, the

assets of a company changed dramatically from

one year to the next. We suspected that such

large swings represented typographical errors in

the original sources or manipulations. Histograms

of year-over-year asset changes revealed that situ-

ations in which assets fell by more than half or

more than doubled in a single year were sta-tistically unusual. Consequently, data points

involving such swings were removed from the

sample.14 Note that none of the screens involved

ROA, the dependent variable.

Despite the last screen, distortions due to

accounting manipulations almost surely remain in

the data. Distortions would be most damaging to

our study if group-affiliated and unaffiliated firms

differ in how often, to what degree, or in what

direction they misrepresent profitability. The

gravest threat in this regard, we believe, is that

publicly traded members of groups may make

quiet payments to privately held affiliates, in

order to hide profits from view. Unaffiliated firms

13 Groups typically diversify by setting up affiliate firms thatspecialize in a particular industry. For this reason, few firmsin our sample wind up in miscellaneous industry categories.14 Data points involving large increases in assets were notremoved in the Brazilian and Turkish data sets. Extremelyhigh inflation made such large increases commonplace inthose economies.


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Table 1. Sample attrition

Argentina Brazil Chile India Indonesia Israel Korea

Initial observations 159 660 2,909 12,670 598 120 2,135No return data 0 0 7 11 1 19 0Financial services/real estate 0 2 838 529 186 13 0

No/misc. industry classification 18 29 152 216 3 0 0Large change in assets 12 0 132 1,383 69 2 28Observations after screening 129 629 1,780 10,531 339 86 2,107Observations with lagged returns 97 503 1,409 7,620 206 65 1,673

Mexico Peru Philippines S. Africa Taiwan Thailand Turkey

Initial observations 442 159 521 2,240 589 2,084 379No return data 36 33 0 367 0 0 89Financial services/real estate 0 0 177 686 5 473 10No/misc. industry classification 51 0 30 0 0 238 7Large change in assets 11 27 33 116 12 44 0

Observations after screening 344 99 281 1,071 572 1,329 273Observations with lagged returns 260 57 182 737 384 1,042 226

could not do likewise. Since our data sets cover

only publicly traded firms, payments to private

affiliates would depress the reported profitability

of group firms, favoring Hypothesis 1 over

Hypothesis 1.

Description of the data

Table 2 gives a profile of the screened data set

for each country. The data sets are as diverse as

the countries themselves. The sample size ranges

from 10,531 observations in India to 99 in Peru

and 86 in Israel. Group firms constitute 64 percent

of the observations in Indonesia, but only 23

percent in the Philippines. The average ROA

varies from a high of 22.2 percent in Turkey to

a low of 4.3 percent in Brazil. Group firms

outperform unaffiliated firms on average by a

statistically significant margin in Brazil, Chile,

and Indonesia, but the opposite is true inArgentina.15 Differences between the average

group firm and the average non-group firm are

not significant at the 5 percent level in the other

10 countries.

15 The statistical significance of these comparisons may beoverstated because the returns of firms within a particulargroup are not fully independent observations.


METHODOLOGY

To test our hypotheses for each country-level data

set, we start with the following model of the

profit-generating process: If firm k competes in

industry i and is a member of group j, then its

profitability in year t, rkt, is modeled as

rkt= + t + i + j + k + kt (1)

In this formulation, t, the year effect, capturesthe macroeconomic factors which influence the

profitability of all firms operating in year t. i isan industry effect felt by all participants in marketi, reflecting conditions of entry and extended

rivalry. j affects all members of group j, whilek is a fixed effect unique to firm k. (j is positedto be zero for firms that do not belong to a

group.) kt captures the remaining idiosyncratic

variation that is specific to a particular firm in aparticular year. This model is essentially identical

to the one adopted by numerous studies of the

structure of profitability among U.S. firms (Brush

and Bromiley, 1997; McGahan, 1998; McGahan

and Porter, 1997, 1998; Roquebert et al., 1996;

Rumelt, 1991, 1998; Schmalensee, 1985; Werner-

felt and Montgomery, 1988). It provides a starting

point for testing both Hypotheses 1 and 1 and

Hypothesis 2.


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Table 2. Descriptive statistics of the country-level data sets


Number of observations 129 629 1,780 10,531 339 86 2,107Group 51% 48% 36% 49% 64% 33% 51%

Number of firms 27 122 445 2,665 133 18 427Group 48% 47% 36% 42% 63% 33% 51%

Number of groups 9 39 45 323 58 3 122Firms per group

Average 1.4 1.5 3.5 3.5 1.4 2.0 1.8Minimum 1.0 1.0 1.0 1.0 1.0 1.0 1.0Maximum 4.0 3.0 19.0 43.0 5.0 3.0 7.0

Return on assets (ROA) (%)Average 6.8 4.3 6.5 10.9 8.9 9.0 6.0Standard deviation 6.9 8.6 12.6 9.7 5.3 7.1 5.3Median 6.1 3.7 6.1 11.1 8.7 8.9 6.0Minimum 9.1 57.7 101.6 69.0 11.7 6.9 27.0Maximum 29.7 43.8 118.4 185.3 32.0 34.2 31.6

ROA among group firms (%)Average 4.9 5.0 8.0 10.8 9.3 9.0 6.0Standard deviation 5.5 7.7 11.5 9.7 5.2 6.2 4.3Median 5.2 4.4 7.3 10.9 9.1 9.4 5.9

ROA among non-group firms (%)Average 8.7 3.7 5.7 10.9 8.1 9.1 6.1Standard deviation 7.6 9.4 13.1 9.7 5.5 7.5 6.3Median 8.5 3.3 5.0 11.3 8.0 8.8 6.0

Industry representation (%)Agriculture/extractive 22 14 16 11 11 5 5Manufacturing 43 45 31 81 74 74 95Transport 12 15 24 1 5 7 0Retail/wholesale 11 6 4 0 6 0 0Services 12 21 25 7 4 14 0

Time span 199097 199097 198896 198995 199395 199297 199195

Statistics are for the sample used to estimate Equation 1. The smaller data sets used to estimate Equation 6 are qualitat-ively similar.

Hypotheses 1/1: profitability of group

affiliation

Consistent with prior studies, we assume that t,i, k, and kt are drawn from distributions withmeans of 0 and variances

2, 2,

2, and

2 respectively. j is drawn from a symmetric

distribution with variance 2, but the mean ofthe distribution, , need not be 0. Indeed, theheart of testing Hypotheses 1 and 1 is to deter-

mine whether is positive (Hypothesis 1) ornegative (Hypothesis 1). If the mean of jsdistribution is positive, then a group-affiliated

firm will typically be more profitable than an

independent firm operating in the same industry

and time period. The converse is true if isnegative.


We estimate Equation 1 using ordinary least

squares (OLS) to examine the mean of the distri-

bution of j. Under this approach, one regressesprofitability on sets of dummy variables rep-

resenting years, industries, groups, and firms. The

item of interest, then, is the average coefficient

estimate bj of the group dummies. Conventional

F-tests enable one to judge whether this average

is significantly different from zero.

It is tricky, however, to perform OLS on nested

models like the one in Equation 1. (By nested,

we mean that one set of effects resides entirely

inside another; i.e., groups consist precisely of

subsets of firms.) Specifically, one cannot pin-

point an unbiased estimate of the higher-level

coefficients, the js, via OLS. For a formaldemonstration of this, see Rumelt (1998). A prac-


13/30


Table 2. (continued)


Number of observations 344 99 281 1,071 572 1,329 273Group 32% 26% 23% 49% 48% 61% 45%

Number of firms 70 29 95 321 186 278 45Group 27% 24% 19% 45% 44% 59% 56%

Number of groups 12 5 10 38 57 79 9Firms per group

Average 1.6 1.4 1.8 3.8 1.4 2.1 2.8Minimum 1.0 1.0 1.0 1.0 1.0 1.0 1.0Maximum 5.0 3.0 4.0 27.0 5.0 18.0 12.0

Return on assets (ROA) (%)Average 7.5 6.2 6.4 13.6 5.6 6.4 22.2Standard deviation 6.5 12.5 11.5 18.8 6.3 8.9 13.5Median 7.2 6.0 5.5 11.4 4.9 6.1 21.9Minimum 47.1 31.2 46.8 40.9 18.4 52.3 20.5Maximum 25.8 39.7 50.6 218.4 33.0 58.6 60.9

ROA among group firms (%)Average 8.1 3.1 6.7 13.2 5.3 6.1 23.2Standard deviation 4.8 12.2 12.4 17.4 4.9 8.8 12.6Median 8.0 5.1 7.4 11.6 4.3 5.4 22.4

ROA among non-group firms (%)Average 7.3 7.3 6.3 14.0 5.9 6.8 21.3Standard deviation 7.2 12.4 11.2 20.1 7.3 9.1 14.1Median 6.7 6.0 4.0 11.0 5.3 7.3 21.2

Industry representation (%)Agriculture/extractive 18 30 44 25 15 19 22Manufacturing 36 44 29 36 64 51 33Transport 11 3 17 6 6 7 11Retail/wholesale 18 0 0 20 5 8 9Services 17 22 10 13 10 15 25

Time span 198897 199197 199297 199396 199097 199297 198897

tical demonstration is afforded by considering

how one might try to estimate the js. Directestimation of Equation 1 with dummy variables

fails; each group consists of a set of firms, so

an effort to estimate j for group j and k for allfirms in j leads to perfect collinearity among the

dummy variables. One could respond by dropping

one firm-level dummy per group, thereby permit-

ting an estimate of j. The estimate would, inpart, proxy for the missing k, however, andaccordingly, would vary depending on which

firm-level effect were dropped.16

16 To see this more clearly, consider a simplified version ofEquation 1 that excludes year and industry effects:

rkt = + j + k + kt.

Refer to the OLS estimates of , j, and k as m, bj, and dkrespectively. To perform OLS as proposed here, one must


Despite this problem, we proceed with OLS,

in two ways. First, we drop one firm effect per

group, with the omitted firm selected at random,

and examine the average coefficient estimate bj.

Our supposition is that, though each bj is affected

omit one firm effect per group and one firm effect related toa nongroup firm, to avoid perfect collinearity. OLS will

minimize the sum of squared residuals by ensuring that (1)m + bj + dk is equal to the average over time of theprofitability of each group-affiliated firm k, a member ofgroup j, whose firm effect is included in the regression; (2)m + bj is equal to the average of the profitability of themember of group j whose firm effect is omitted; (3) m + dkis equal to the average of the profitability of each unaffiliatedfirm k whose firm effect is included; and (4) m is equal tothe average of the profitability of the non-group firm whosefirm effect is omitted. From facts (2) and (4), one can seethat bj is equal to the difference between the average prof-itability of the excluded member of group j and the excludednon-group firm. Label the excluded member of group j firmk and the excluded nongroup firm k. Hence E(bj) = E( +


14/30


by the missing firm effect and hence E(bj) jfor any particular group j, the biases created by

the missing firm effects average out over the set

of groups so that E(bj) . Second, we omitevery possible group member one at a time and

repeat the OLS estimation. We examine, for

instance, the coefficient on Koreas SamsungGroup as each of its seven publicly traded mem-

bers is omitted from the regression and then

average the groups coefficient estimates over

the full set of possible omitted firm dummies.

Averaging over a larger set of estimates should

further reduce the bias. Overall, in interpreting

our results, we look for results that are robust to

the multiple methods we employ.

Hypothesis 2: within-group similarity of

profitability

Our second hypothesis is that the profit rates of

firms within a particular group will be more

similar to one another than they are to the profit

rates of firms outside the group. We test this

hypothesis in two ways: one simple and direct,

and one involving OLS.

Our first approach is to compare within-group

profit differences to beyond-group differences.

We first compute the average profitability over

time of each firm. We then look at every within-

group pair of firms, calculate the absolute differ-

ence in the profit rates of the two firms, andaverage over all within-group dyads. This gives

a measure of how similar profit rates typically

are for two firms in the same group. Next we

examine every pair of firms that spans a group

boundary,17 calculate the absolute difference in

the profit rates of the two firms, and average

over all dyads. This gives a measure of how

different profit rates usually are when two firms

are not members of the same group. Finally, we

compare the two measures to see if the profit

rates of firm-pairs within a group are closer

together than the profit rates of firm-pairs that

span a group border.

An examination of Equation 1 makes clear the

strengths and weaknesses of this simple approach.

j + k + kt k kt) = j + k k, notsimply j.17 That is, every dyad consists of a group member and asecond firm which is either unaffiliated or a member of adifferent group.


Averaging profitability across time for each

firm gives

rk = + + i + j + k + k

where rk, , and k are averages over time of rkt,

t, and kt respectively. Within a group,

rk rk = i i + k k + k k (2)

where k and k are different members of the

same group. When k is a member of no group,

rk rk = i i + j + k (3) k+ k k

And when k is a member of a different group j,

rk rk = i i + j j + k (4) k + k k

Equation 3 has a lower expected absolute value

than Equation 4 or 5; the method accurately

captures the notion that returns of firms within a

group co-vary because of j and are thereforemore alike than those of firms in different groups.

The method fails, however, to control for

industry effects in any way, and it operates purely

at the level of firm-pairs. A more sophisticated

approach to Hypothesis 2 begins with a reexami-

nation of Equation 1. Consider a set of residualreturns which controls for year and industry

influences:

rkt = rkt t i = j + k + kt

In order for residual returns to be more similar

within a group than beyond group borders, the

within-group means, reflected in the js, mustbe distinct from one another (and distinct from

the mean of non-group firms). Without such dis-

tinctions, residual returns would simply be simi-

larly distributed around identical means, and the

residual return of a firm would be no closer to

the residual returns of its group affiliates than to

the residual returns of all other firms.

We test for distinct within-group means using

a method that is commonplace among efforts to

decompose the variance of profitability in the

U.S. (Schmalensee, 1985; Wernerfelt and

Montgomery, 1988; Rumelt, 1991; Roquebert et

al., 1996; McGahan and Porter, 1997, 1998;


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McGahan, 1998; Furman, 1998). We estimate

Equation 1 by OLS, sequentially adding in classes

of dummy variables: year effects, then industry

effects, then group effects, and finally firm

effects.18 At each stage, we examine (a) the

incremental contribution to R2 of the newly

included class of effects and (b) the joint signifi-cance of the newly included effects. Inclusion of

the first two sets of dummy variables controls

for year and industry influences. With the addition

of the group dummies, the fitted return of each

group-affiliated observation is set equal to the

group mean return. A finding that the group

dummy variables are jointly statistically signifi-

cant, then, implies that the group averages are

meaningfully different from one another (after

controlling for year and industry effects, though

not firm effects).

We interpret this method in terms of the fol-lowing thought experiment. Suppose that we have

analyzed, say, all 10,531 observations from India

in the manner described here. We are now told

that a 10,532nd observation exists. If we know

nothing about the observationnot the year,

industry, group, or firm to which it belongsour

best guess of the observations profitability is

simply the mean over the 10,531 observations we

have seen, but we have little confidence in the

guess. Suppose we are now told the year of the

new observation. We can now make a better

guess based on the model with year effects only,and the increase in R2 associated with that model

reflects how much more confident we are in the

guess. Suppose next we are told the industry as

well as the year of the observation. We refine

our guess based on the model with year and

industry effects, and our confidence grows in line

with the incremental R2. Next the group with

which the observation is affiliated is revealed to

us, and we refine our guess further. This method

examines whether the information about group

affiliation allows us to anticipate the new obser-

18 Note that most of the prior studies (Schmalensee, 1985;Wernerfelt and Montgomery, 1988; Rumelt, 1991; McGahanand Porter, 1997) actually could not employ OLS, or standardANOVA, techniques; the computers available to theseresearchers could not handle the massive number of dummyvariables involved. Instead earlier researchers resorted tonested ANOVA, a second-best but feasible technique. SeeMcGahan and Porter (1998) for further discussion. Only themost recent studies of developed economies (McGahan andPorter, 1998; McGahan, 1998; Furman, 1998) exploit improve-ments in computing power to perform OLS, as we do here.


vations profitability with significantly greater

confidence. It does so to the extent that returns

within groups are similar to one another.

We want to be clear that this method does not

isolate 2. An alternative, and more direct, test

of Hypothesis 2 is to examine whether 2 is

significantly different from 0; under the model inEquation 1, this would generate differences in

within-group means and hence intra-group returns

that are more similar to one another than are

extra-group returns. Ideally, we would like to

estimate 2 and test its statistical significance.

Unfortunately, we know of no technique to do

so adequately. The technique that comes closest

is the components of variance (COV) approach,

pioneered by Schmalensee (1985). We feel that

the COV approach is inappropriate here for two

reasons. First, the COV approach assumes strong

independence among the effects in Equation 1:each t, i, j, k, and kt is posited to be drawnindependently from all others. Covariation across

the effects is suppressed almost entirely; industry

and firm effects are assumed to be drawn inde-

pendently, for instance.19 The assumption of inde-

pendence across effects, especially between indus-

try and corporate effects, has been questioned in

studies using developed country data (McGahan

and Porter, 1997). Conceptually, we suspect that

such covariation will be, if anything, stronger in

emerging economies. Business groups with strong

political connections, for instance, may benefitacross their range of businesses (high j) andalso be granted access to the most attractive

industries (high i). Well-managed groups (highj) may be poised to acquire well-managed firms(high k).

Second, the COV approach does not force the

estimates of the variances to be positive. Negative

estimates are generally taken as an indication that

the underlying model is poorly specified (Rumelt,

1991: 176). When we attempt the COV approach

on our data, we find that at least one variance is

estimated to be negative for 13 of 14 countries.

We conclude that the COV specification is poor

and suspect that the problem lies in the indepen-

dence assumption. (Indeed, in our OLS esti-

19 Some studies permit covariation between industry andcorporate effects. This allows patterns in which, say, well-managed corporations have the greatest impact in structurallyunattractive industries (Rumelt, 1991; McGahan and Porter,1997).


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mation, we observe substantial covariation among

the estimated coefficients of the various dummy

variables.)

Echoing the methodological concerns of

Rumelt (1998), we also want to emphasize that

the increments to R2 from our OLS approach are

not reliable indicators of the various 2

s. Groupeffects added before firm effects will pick up, in

part, some of the variance due to the omitted

firm-level variables. Accordingly, in adding group

effects and firm effects in order, one is not

isolating 2 from

2. Rather, in Rumelts (1998:

8) words, one is simply discovering how much

of the variance is explained by placing the raw

observations in [group-level] categories without

controls for [firm-level] effects (italics in the

original). That is, one is finding out how well

one can explain variation in profitability if one

knows the group with which each observation isaffiliated but not the firm. Fortunately, it is not

necessary to isolate 2 to test our particular

Hypothesis 2.

Details of the OLS approach

Two issues arise in the OLS estimation of Equa-

tion 1. First, though business groups consist of

multiple firms, in some cases financial results are

reported for only one firm within a particular

group. For example, the Sahid Group in Indonesia

is a highly diversified entity composed of manyfirms, but financial results are available only for

its hotel and travel service operation, PT Hotel

Sahid Jaya International. Consequently, when we

introduce a dummy variable for the Sahid Group,

the variable captures both the group effect for

Sahid as a whole and the firm effect for PT

Hotel Sahid Jaya International. In countries where

groups with single data points are prominent, this

problem inflates the incremental contribution of

group effects to R2 and adjusted R2. As Table 2

shows, the problem is most acute in Argentina,

Indonesia, Peru, and Taiwan. More generally,

among researchers who decompose variance using

U.S. data, there is an active debate about how

single-business firms should be treated. One

school of thought contends that, to see the full

effects of diversified entities, one must remove

non-diversified corporations from any sample

(Bowman and Helfat, 1998). Accordingly, we

examine results not only on the full sample for

each country, but also on a subsample: members


of groups with multiple firms present in the data

base. This robustness check is reported among

the results below.

A second issue concerns serial correlation in

the error term of Equation 1. The term kt cap-tures transient shocks to profitability. There is no

guarantee, however, that such shocks will affecta firms profitability for only one year and then

disappear; their effects may reverberate for sev-

eral years. Suppose that shocks do persist, but

one ignores the persistence and falsely assumes

that transient shocks fade entirely within a year.

In the estimation of Equation 1, this mistake may

inflate the incremental R2 of industry, group, and

firm effects as those effects capture the impact

of a slowly fading shock.

To avoid such a mistake, we follow McGahan

and Porter (1997) in modeling kt as a first-order

autoregressive process:

kt = kt 1 + kt

where kt is the truly new, independent shockthat arises each year. Hence the modeled equ-

ation becomes

rkt = (1 ) + rkt 1 + t t 1+ (1 ) i + (1 ) j (5)+ (1 ) k + kt

To examine this model in the sequential mannerdescribed above, we first perform OLS on the

full model in order to obtain an estimate of the

persistence parameter . Then, fixing at theestimated value, we introduce year, industry,

group, and firm effects in order and examine the

contribution of each set of effects.

Conceptually, Equation 5 is superior to Equ-

ation 1. Because Equation 5 requires a lagged

return, however, we lose data points when we

adopt that formulation. Since data points are so

hard to come by in some of the emerging econo-

mies we examine, we report results for both

equations and look for robust findings.

Comparisons across countries

Our primary objective is to examine the effects

of business group affiliation on the pattern of

profitability within individual emerging econo-

mies, not to make comparisons across countries.

Nonetheless, we take two steps to facilitate com-


17/30


parisons. First, we make extensive efforts to use

the same industry definitions in each country.

Inconsistent classifications confound cross-country

comparisons since many of the interesting cross-

country questions involve industry effects at least

indirectly. (E.g., are returns within groups similar

after controlling for industry effects?) Our effortsto harmonize industry definitions do lead to a

least-common-denominator problem: industry

boundaries are dictated by the country with the

roughest-grained classification system. The har-

monized definitions correspond roughly to 2-digit

SIC codes in the manufacturing sector and 1-

digit SIC codes in other sectors.

Second, we examine a version of our results

for the manufacturing sector only. Studies of

profitability in developed countries have found

that different sectors of the economy exhibit sub-

stantially different patterns of year, industry,corporate, and firm effects (McGahan and Porter,

1997; Rivkin, 1997; Furman, 1998). Our emerg-

ing-economy samples span different parts of the

economy (Table 2). The Indian data set, for

instance, covers a broad range of economic

activity, while the South Korean sample focuses

heavily on the manufacturing sector. By examin-

ing the manufacturing sector only, we ensure that

the differences we observe across countries are

not simply sectoral contrasts in disguise.

RESULTS

Hypotheses 1/1: Profitability of group

affiliation

Table 3 shows the results of our OLS estimation.

First we estimate Equation 5 by dropping one

firm effect per group, chosen at random, and

examine the average coefficient estimate for the

group dummies. (The sole exception is Thailand.)

Group effects are positive and significant

(Hypothesis 1) in India, Indonesia, and Taiwan,

and significantly negative (Hypothesis 1) in

Argentina. Three countries present some limited

evidence of positive group effects. The signs of

the Israeli and Peruvian results in Table 3 are

consistent with Hypothesis 1, but the samples in

the two countries are too small to give significant

results. In South Africa, the large, positive aver-

age group effect and the negative median effect

suggest the presence of a small number of

extremely profitable South African groups. On the


other hand, the Chilean and Philippine evidence is

largely consistent with Hypothesis 1, but the

results in Table 3 are not statistically significant.

To check that the OLS results are not sensitive

to the particular firm effect which happens to be

omitted for each group, we omit every possible

group member one at a time. Averaging thegroups coefficient estimates over the full set of

possible omitted firm dummies, we obtain results

that are reported in the lower portion of Table

3. The findings for each country are qualitatively

very similar to those shown in the upper portion

of Table 3. This method does not, however,

permit tests of statistical significance. We also

conduct an additional matched-pair test. In each

country, we compare each group observation with

a randomly chosen unaffiliated counterpart in the

same industry and time period. Our results are

largely consistent with the OLS results reportedabove.20

To summarize, our various efforts to assess the

profitability of group affiliation, taken together,

give robust evidence that group affiliates enjoy

higher profitability than unaffiliated firms

(Hypothesis 1) in three countries: India, Indone-

sia, and Taiwan. There is weaker evidence for

this conclusion in Israel, South Africa, and Peru.

Group firms appear to perform worse than inde-

pendents (Hypothesis 1) in Argentina. We see

some weaker support for this conclusion in Chile

and the Philippines. The results for Brazil, Korea,Mexico, Thailand, and Turkey suggest a balance

between the costs and benefits of group affiliation

in those locales.

Hypothesis 2: Within-group similarity of

profitability

Table 4 reports the results of our first and most

direct test of this hypothesis. We consider firm-

20

Specifically, we examine

kkt

= rkt

rkt

= j

+ k

k + kt kt, where firm k is a member of group jand k is unaffiliated, and rkt and rkt are the returns ofthese firms. Note that E(kkt) = E(j) = . A positivemean kkt supports Hypothesis 1, while a negative meankkt supports Hypothesis 1. If = 0, then kkt has anequal chance of being positive and negative. We compare theportion of the time that kkt is positive or negative to 50percent andto provide a benchmarkwe use the binomialdistribution to calculate the odds that the observed deviationfrom a 50/50 split arose purely by chance. We find that theseodds are typically less than 5 percent, though we note thatthe statistical significance of this result is overstated since weare effectively assuming independence among coin tosses.


18/30


Table 3. Coefficient estimates for group dummy variables


OLS, deleting one firm per group selected at randomNumber of coefficient estimates 9 37 47 318 48 3 122(i.e., number of groups)

Mean coefficient estimate 2.8 0.1 0.4 4.0 2.2 2.4 0.6Standard deviation 3.8 5.9 5.8 10.8 6.1 3.5 5.2Median coefficient estimate 2.5 0.0 0.7 3.8 0.9 1.6 0.7t-Statistic for H0: Average of 2.4 0.1 0.5 2.1 3.4 1.1 0.6coefficients is zero

OLS, deleting each group firm one at a time, and averaging over all coefficient estimatesNumber of coefficient estimates 12 54 80 1,067 62 6 214(i.e., number of group firms)Mean coefficient estimate 1.4 0.3 2.0 3.5 2.4 3.3 0.9Standard deviation 4.3 6.2 7.2 9.9 6.6 3.2 5.2Median coefficient estimate 0.4 0.3 2.0 3.7 1.1 2.6 0.8


OLS, deleting one firm per group selected at randomNumber of coefficient estimates 12 5 10 38 53 77 8(i.e., number of groups)Mean coefficient estimate 0.6 9.5 1.9 1.6 1.9 1.1 2.5Standard deviation 1.8 10.9 14.0 17.5 4.2 10.9 8.8Median coefficient estimate 0.2 4.1 3.4 0.7 2.1 0.8 4.0t-statistic for H0: Average of 0.7 1.8 0.8 0.7 2.3 0.7 0.5coefficients is zero

OLS, deleting each group firm one at a time, and averaging over all coefficient estimatesNumber of coefficient estimates 17 6 15 130 75 155 18(i.e., number of group firms)

Mean coefficient estimate 0.3 6.6 3.1 1.9 2.4 2.1 1.1Standard deviation 2.6 12.1 11.5 21.4 4.3 10.7 10.6Median coefficient estimate 0.1 3.7 4.3 1.2 2.1 0.5 0.1

Based on model with serial correlation (Equation 5). Because Equation 5 requires a lagged return, the sample used for theestimation of Equation 5 is smaller than the sample described in Table 2. For this reason, the number of groups reportedhere is smaller than the number in Table 2.

average returns over time, calculate the absolute

difference in returns between each pair of firms,

and compare the difference that arises between

firms in the same group to the difference that

arises when a firm-pair spans a group border. In

10 of 14 countries, within-group return differ-

ences are smaller than beyond-group return differ-

ences, as predicted by Hypothesis 2. The bottom

line of Table 4 tests the statistical significance of

this comparison; nine of the 10 appear significant

at conventional levels. Note, however, that these

significance levels are inflated; the tests assump-

tion that observations are independent is not


valid.21 The results for Argentina, Mexico, Peru,

and Turkey in Table 4 run contrary to Hypothesis

2: within-group differences are larger than

beyond-group distinctions. Only the Argentine

distinction is significant, however, even with

inflated significance levels.

The analysis in Table 4 does not control for

industry effects, nor does it provide valid signifi-

21 Specifically, if the return of firm 1 in group j is similar tothe return of firm 2 in group j, and the return of firm 1 isalso close to the return of firm 3 in group j, then the returnsof firms 2 and 3 are similar.


19/30


Table4.

Within-groupandb

eyond-groupdifferencesinprofitability

Argentina

Brazil

Chile

India

Indonesia

Israel

Korea

Numberoffirm

pairsobserved

Within-groupfirm

pairs

7

23

133

3,7

52

40

4

199

Beyond-groupfirm

pairs

324

6,851

19,4

54

3,0

05,4

80

11,0

08

94

92,4

70

Averageabsolutedifferenceinreturnsofthetwofirms

Within-groupfirm

pairs

6.4

4.4

8.1

8.1

3.2

5.5

3.0

Beyond-groupfirm

pairs

5.6

5.9

9.1

8.3

4.8

6.3

4.1

Standarddeviationacrossabsolutedifferences

Within-groupfirm

pairs

2.6

1.2

4.4

5.2

0.8

0.5

1.7

Beyond-groupfirm

pairs

3.9

4.1

8.3

6.2

3.5

4.3

3.3

p-valueonH

0:within-group

and

46.6

%

0.0

%

1.0

%

0.5

%

0.0

%

12.8%

0.0

%

beyond-groupaverageabsolute

differencesareequal

Mexico

Peru

Philippines

S.

Africa

Taiwan

Thailand

Turkey

Numberoffirm

pairsobserved

Within-groupfirm

pairs

14

9

11

556

36

296

74

Beyond-groupfirm

pairs

1,2

83

190

1,6

70

44,8

53

14,9

13

44,8

36

952

Averageabsolutedifferenceinreturnsofthetwofirms

Within-groupfirm

pairs

7.9

22.6

7.5

performance effects of business groups

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