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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
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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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48 T. Khanna and J. W. Rivkin
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.
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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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Performance Effects of Business Groups in Emerging Markets 49
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
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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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50 T. Khanna and J. W. Rivkin
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
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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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Performance Effects of Business Groups in Emerging Markets 51
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).
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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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52 T. Khanna and J. W. Rivkin
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
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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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Performance Effects of Business Groups in Emerging Markets 53
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
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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
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54 T. Khanna and J. W. Rivkin
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.
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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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Performance Effects of Business Groups in Emerging Markets 55
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.
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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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56 T. Khanna and J. W. Rivkin
Table 2. Descriptive statistics of the country-level data sets
Argentina Brazil Chile India Indonesia Israel Korea
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.
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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-
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Performance Effects of Business Groups in Emerging Markets 57
Table 2. (continued)
Mexico Peru Philippines S. Africa Taiwan Thailand Turkey
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
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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( +
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58 T. Khanna and J. W. Rivkin
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.
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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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Performance Effects of Business Groups in Emerging Markets 59
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.
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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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60 T. Khanna and J. W. Rivkin
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
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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-
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Performance Effects of Business Groups in Emerging Markets 61
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
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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.
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62 T. Khanna and J. W. Rivkin
Table 3. Coefficient estimates for group dummy variables
Argentina Brazil Chile India Indonesia Israel Korea
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
Mexico Peru Philippines S. Africa Taiwan Thailand Turkey
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
Copyright 2001 John Wiley & Sons, Ltd. Strat. Mgmt. J., 22: 4574 (2001)
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.
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Performance Effects of Business Groups in Emerging Markets 63
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