types of firms generating network ex tern ali ties and mncs' co-location decisions

8/14/2019 Types of Firms Generating Network Ex Tern Ali Ties and MNCs' Co-location Decisions

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Strategic Management JournalStrat. Mgmt. J., 26: 595– 615 (2005)

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/smj.464

TYPES OF FIRMS GENERATING NETWORK EXTER-

NALITIES AND MNCs’ CO-LOCATION DECISIONS

SEA-JIN CHANG1* and SEKEUN PARK2

1 School of Business Administration, Korea University, Seoul, Korea2 The Export–Import Bank of Korea, Seoul, Korea

This study identifies and examines sources of network externalities that influence MNCs toagglomerate their foreign operations in specific regions. Using data for Korean firms that invested in China, this study found that network externalities were sensitive to the types of firmsconstituting a regional network. It also found stronger network externalities within firms thanacross firms, from firms of the same nationality than from those of different nationalities, and

from firms in the same industry than from those of different industries. As we defined the types of firms more precisely, distinctive curvilinear relationships between network externalities and thelikelihood of co-location emerged. Copyright 2005 John Wiley & Sons, Ltd.

Why do multinational corporations decide to locatein one area rather than another? To date, researchon this question has examined firms’ motiva-

tions for these decisions, the modes they usewhen entering an area, and the sequences of their entry decisions in that area. This work has been guided by the theme of foreign directinvestment (Hymer, 1960; Dunning, 1988; Hen-nart and Park, 1994; Chang, 1995; Kogut andChang, 1996). It has also considered such deci-sions at the national level, rather than assessingwhy a firm might enter one region within a nationrather than another. We argue that internationalbusiness scholars should explore regional locationdecisions much more extensively, as these deci-sions shed light on MNCs’ foreign entry strategies.

When MNCs announce they are investing in acountry, they often specify a location they havedecided upon prior to the announcement. Most

Keywords: network externalities; agglomeration;co-location; foreign direct investment∗ Correspondence to: Sea-Jin Chang, School of Business Admin-istration, Korea University, Seoul, Korea 136-701.E-mail: [email protected]

countries consist of many regions, which differgreatly from each other in terms of prevailingwages, populations, technology bases, and infra-

structures. Since MNCs presumably choose loca-tions that seem to fit best with their strategic goals,the location decision within a country (e.g., Shang-hai or Beijing) may be more important than thedecision at the country level is (e.g., opening afactory in China). Furthermore, one firm’s locationdecisions could be influenced by the presence of other firms in a region. Foreign and local incum-bents within a region can pose great threats andchallenges to new entrants. At the same time, theycan be great sources for complementary resourcesand learning. In a country such as China, whichcomprises vastly heterogeneous regions, it is cru-cial to examine location decisions at the regionallevel in order to understand MNCs’ entry strate-gies.

Recently, several studies have focused on loca-tion decisions within a country. Head, Ries, andSwenson (1995), Shaver and Flyer (2000), andChung and Song (2004) studied how Japanesefirms chose manufacturing locations in the United

Copyright 2005 John Wiley & Sons, Ltd. Received 4 June 2003

Final revision received 15 December 2004



596 S.-J. Chang and S. Park

States. They found that Japanese firms locatedtheir manufacturing facilities in states where many

other Japanese firms had been located, althoughthis tendency depended upon these firms’ rela-tive resource strengths and their prior investments.These researchers suggested that positive network externalities might explain this pattern of agglom-eration. A network externality occurs when thebenefit or surplus that an economic agent derivesfrom a good depends in part on changes in thenumber of other agents consuming the same kindof good (Katz and Shapiro, 1985; Arthur, 1990;Liebowitz and Margolis, 1995). It thus denotes sit-uations when a product or service becomes more

valuable as more people or firms use it. For exam-ple, when General Motors entered China in 1997via a joint venture with Shanghai AutomotiveIndustry Corporation (SAIC), it was able to capi-talize on the infrastructure of qualified managers,laborers, and suppliers that Volkswagen, SAIC’sother joint venture partner, had developed since1984. MNCs can also learn from earlier entrants’experiences and avoid making similar mistakes.

This recent work has, however, been limited inthree important ways. First, in focusing exclusivelyon economic reasons for agglomeration, it has notconsidered the possibility that agglomeration mightoccur even without obvious economic reasons.Firms might, for instance, imitate other firms inorder to gain legitimacy or reduce uncertainty(DiMaggio and Powell, 1983; Levitt and March,1988). Foreign investments, especially in countrieswhere the culture and language are distinct fromthat of an MNC’s home country, carry risks thatare captured by the term ‘liabilities of foreignness.’Such risks are greater in transitional economies,such as China. It is possible that foreign firmsinvesting in China flocked to Shanghai or Beijingonly because many other foreign firms had done

so already.Second, most of this work has focused only

on positive forms of network externalities. Whenagglomeration occurs, competition in factor andproduct markets increases costs. For instance, inShanghai, foreign firms now have to pay topsalaries to attract local managers, and housing forexpatriates is extremely expensive. Local firmslocated near an MNC may be able to access tech-nology or know-how by hiring local managers andengineers away. Organizational ecologists havelong argued that increases in population densitydiminish new entrants’ survival rates (Hannan and

Carroll, 1992). Thus, MNCs should evaluate thecosts of both negative and positive externalities.

Third, these studies have ignored variationsamong the types of firms that make up a regionalnetwork. Although some studies considered theheterogeneity of investing firms (Shaver and Flyer,2000; Chung and Song, 2004), most have exam-ined only a subset of all the firms in a region (e.g.,Japanese investors and local firms in the UnitedStates) and often considered only one industry(e.g., electronics). We argue that the degree of externalities is contingent upon the compositionof regional networks. For instance, Korean firmsinvesting in China may derive stronger network

externalities from other Korean firms than theycan from non-Korean firms, and from firms in thesame industries than they can from firms in otherindustries.

This paper considers two empirical questions.First, it uses data for Korean firms that investedin China to examine whether positive or nega-tive network externalities are larger. Although wecannot distinguish empirically between network externalities derived from real economic gains andthose derived from legitimacy, we examine variousarguments for network externalities and develop ahypothesis that argues for a curvilinear relation-

ship. We expect that the likelihood of experiencingnegative network externalities is more substantialwhen firms’ agglomerative behaviors are moti-vated by a desire to gain legitimacy rather thanby real economic gains. Second, this study exam-ines how network externalities vary according tothe types of firms within a regional network. Inother words, this study examines to what degreenetwork externalities are firm specific, nation spe-cific, or industry specific. This study also exploreswhat choices by firms might maximize the net ben-efits of network externalities.

NETWORK EXTERNALITIESAND LOCATION DECISIONS

Network externalities

Economists have long emphasized the impor-tance of network externalities (Marshall, 1920).Porter (1998) summarizes the potential benefitsof agglomeration: (1) it improves accessibility tospecialized factors and workers; (2) it improvesaccess to information about market and tech-nology trends; (3) it promotes complementarities

Copyright 2005 John Wiley & Sons, Ltd. Strat. Mgmt. J., 26: 595– 615 (2005)



Types of Firms Generating Network Externalities 597

among firms and promotes cooperation amongfirms; (4) it improves access to infrastructure and

public goods; and (5) it increases competitive pres-sure among firms. Henderson (1986) empiricallydemonstrated that agglomeration increases fac-tor productivity. Saxenian (1994) documented howmicroelectronics firms clustered in Silicon Val-ley. Krugman (1991) developed a formal model inwhich agglomeration results from manufacturingfirms’ desire to locate in a place of larger demandin order to exploit scale economies and minimizetransportation costs, while the location of demanddepends on the location of manufacturers.

Several studies of MNCs’ regional agglomera-

tion patterns were based upon this economic ratio-nale.1 Smith and Florida (1994) and Head et al.(1995) observed that Japanese firms co-locatedwith other Japanese firms. They pointed to tech-nological spillovers, specialized labor, and otherinputs as the main reasons for agglomeration.Chung and Song (2004) found that Japanese elec-tronics firms in the United States tended to co-locate with other Japanese firms when they hadless prior experience. At the country level, Song(2002) showed how Japanese firms’ prior invest-ment in technological and sourcing capabilities ledto subsequent investment in the same countries.

Chung and Alcacer (2002) also found that firmsin research-intensive industries are more likely tolocate in regions with high R&D intensities.

Although economists generally attribute regionalagglomeration to real economic gains, organiza-tional theorists have argued that agglomerationmight occur for non-economic reasons. Agglom-eration might occur, for instance, when firmswish to improve their legitimacy in order toaccess resources they need for survival and growth(DiMaggio and Powell, 1983; Suchman, 1995). Afirm might locate in a popular place simply because

so many other firms have located there already,thus legitimizing the location. This mimetic behav-ior has been observed in various contexts: adop-tion of the M-form organization structure (Flig-stein, 1985) and the poison pill (Davis, 1991),and acquisition decisions (Haunschild, 1993). Fur-ther, the risk and uncertainty of venturing into aforeign country could increase firms’ imitation of

1 Early work in economic geography studied the impact of income, tax incentives, wage, and unionization on attractingmore foreign direct investment (Coughlin, Terza, and Arromdee,1991; Wheeler and Mody, 1992; Friedman, Gerlowski, andSilberman, 1992).

other firms (Levitt and March, 1988). Empiricalwork has indicated that legitimacy and uncertainty

influence MNCs’ expansions into foreign coun-tries. Guillen (2002) found that emerging multi-nationals that were in the early stages of inter-nationalization imitated other firms. Henisz andDelios (2001) demonstrated that Japanese firmsthat lacked international experience relied moreheavily on the past international expansion deci-sions of other firms in their reference group ascues for their own entry decisions. Knickerbocker(1973) also pointed out that MNCs in oligopolis-tic industries tend to imitate each other when theyexpand into foreign markets.

In addition, some research has found that agglo-meration can lead to negative externalities. Forexample, firms can benefit from the spillover of other firms’ knowledge and technologies, but theirown knowledge and technologies can spill over toother firms. Appold (1995) found that agglomer-ation was negatively associated with performancein the U.S. metalworking sector. Shaver and Flyer(2000) argue that benefits and costs firms derivedfrom co-location could differ according to theirown core competences. They contend that firmswith relatively more resources avoid agglomer-ation because, for them, the potential costs of spillovers are greater than the potential benefits.

Agglomeration can also lead to intensified com-petition in both product and factor markets amongadjacently located firms. Baum and Mezias (1992)demonstrated that hotels located in Manhattan thatwere similar in terms of location, price, and sizeposed greater threats to each other and reducedeach other’s chances of survival as the area becamemore crowded. Agglomeration also drives up thecosts of locally sourced inputs, such as wagesof local managers and engineers and housingexpenses for expatriates.

Agglomeration can also reduce innovation viagroupthink (Porter, 1998), thereby creating nega-tive externalities. It can make firms in a regionalcluster look only inward and reject ideas fromother areas. Detroit’s attachment to gas-guzzlingautomobiles in the 1970s amidst oil shortages is aprime example of such rigidity.

We expect that multinational corporations willconsider both positive and negative network exter-nalities. Population ecologists have long arguedthat population density influences startups’ perfor-mance. They have found that an increase in popu-lation density is initially positively correlated with





the founding rate of startups because it provideslegitimacy and acceptance. After a certain level,

however, population density is negatively associ-ated with the founding rate due to increased com-petition in that particular niche (Hannan and Car-roll, 1992). By a similar logic, we expect that firmswill agglomerate up to a point, but that increasesin negative network externalities outweigh anyincrease in positive ones after that level is reached.We expect that marginal benefits from agglomer-ation will decline since knowledge or experiencespillovers and legitimacy gained from an additionalfirm in a region would become redundant and neg-ligible. On the other hand, marginal costs from

agglomeration will increase since the competitionin both product and factor markets will becomemore severe and potential hazards from groupthink would become larger. Above that level, marginalcosts exceed marginal benefits and the likelihoodof co-location will decline (see Figure 1).

The likelihood that marginal costs exceed margi-nal benefits is greater when firms decide to co-locate in order to gain legitimacy rather than foreconomic reasons. When there are no real eco-nomic gains, the marginal benefits of legitimacydrop quickly and will be completely offset by themarginal costs of both intensified competition andthe hazard of groupthink. Since Korean firms wereat an early stage of internationalization and Chinaposed great uncertainty as a transitional economyduring the period we studied, their agglomera-tion was substantially motivated by the desire togain legitimacy and avoid uncertainty. Given thisassumption, we also believe it is more likely that

MB

MC

Net of positive & negativenetwork externalities

Density

Positive externalities

Negative externalities

Figure 1. Rationale for a curvilinear relationship

network externalities were curvilinear as a functionof the number of firms.

There is, however, a possibility that marginalcosts from agglomeration might not increase whenan additional firm locates in a region. Porter(1998) and Porter and Stern (2001) argue thatintensified competition in a regional cluster canpromote innovation, which may lower the marginalcost curve in the long run. Some regional clusters,such as Silicon Valley, have been extremely inno-vative and cost efficient despite intense competi-tion and high levels of agglomeration may neutral-ize any cost increasing pressures. Similarly, somefirms can manage better the hazard of groupthink.

In our study, we test for this relationship byinserting both a monotonic and a quadratic term forthe intertemporal population of firms constituting aregional network. An alternative hypothesis is thatnetwork externalities are monotonically positive ornegative.

Hypothesis 1: The likelihood that a firm chooses

a location increases as the number of firms

already present in the same region increases to

a certain point, and declines after that point.

Types of firms generating network externalities

Most of the agglomeration benefits identified byPorter (1998) originate from flows of experience-based knowledge among firms. By hiring spe-cialized workers and purchasing other inputs, afirm can tap the knowledge embedded in suchresources. Firms can also share knowledge bydetecting market and technological trends and pro-moting cooperation. The international business lit-erature has stressed that it is important for firmsto transfer and seek knowledge or experiencewhen they invest in foreign countries and decide

what entry mode to use. Johanson and Vahlne(1977), Kogut (1983), Barkema and Vermeulen(1998), Chang (1995), Kogut and Chang (1996),and Chang and Rosenzweig (2001) demonstratedhow the knowledge or experience firms gainedfrom their prior entries affected their subsequententries and their choices of entry mode. Shaver,Mitchell, and Yeung (1997) argue that subsequentforeign entrants observe and learn from the successor failure of earlier entrants.

Such benefits are, however, neither automaticnor guaranteed. Kogut and Zander (1992) and Zan-der and Kogut (1995) showed that knowledge that





is less codifiable and teachable and more com-plex is more difficult to transfer even within a

firm. They also demonstrated that, due to this dif-ficulty, firms often hire workers away from thetarget firm to facilitate knowledge diffusion. Szu-lanski (1996) similarly observes that causal ambi-guity and the absorptive capacity of the recipient,among other factors, lead to ‘stickiness’ and inhibitthe transfer of knowledge to other parts of anorganization. Hansen (1999) also shows that weak inter-unit ties among various product developmentteams in a firm help a project team search for use-ful knowledge in other subunits, but strong tiesenable the transfer of complex knowledge. Accord-

ing to these studies, knowledge transfer and shar-ing can be difficult even within a firm or amongaffiliates within the same business group. Thisstudy thus treats a firm’s own prior experience,which has been conventionally taken for granted,as a source of network externalities for itself.

Agglomeration may facilitate flows of knowl-edge among firms or within a firm, as it increasesa firm’s ability to communicate with or hire peopleworking for other firms or other divisions. The pos-sibility of these flows might also be contingent onother factors, such as the identities of the firms tar-geted as sources of knowledge. We expect that it iseasier to transfer knowledge among firms of simi-lar background since there is less causal ambiguityand higher levels of absorptive capacity amongsuch firms. Chung and Kalnins (2001) found thathotels that were similar in size and were affili-ated with a chain derived greater agglomerationbenefits.

The extent to which firms co-locate to gain legit-imacy also depends upon the types of firms thatit can imitate (Haunschild and Miner, 1997). Forexample, trait-based imitation occurs when a firmimitates the behavior of high-status organizations

(Fombrun and Shanley, 1990; Haveman, 1993).Guillen (2002) argues that firms expanding intoforeign markets tend to imitate other firms whoseexperience, history, or location is relevant to theirown situation. As a consequence, emerging multi-nationals tend to imitate other firms with whichthey are familiar because they are in the sameindustry or part of the same business group.

In this study, we test whether network external-ities are firm specific, nation specific, and industryspecific. First, we expect that knowledge sharingand transfer are easiest and imitation is most preva-lent when they occur inside a firm. A firm may

invest more than once in a foreign country. It maylocate its investments in the same region to bene-

fit from the experience it has gained. Since manyfirms are diversified and are organized into prod-uct divisions, co-locating investments for multipledivisions helps firms share plants, equipment, andworkers. Expatriate managers can add more busi-nesses at the same location without hiring moremanagers. Business groups in many countries,where many legally independent firms are underthe same ownership and administrative control,facilitate resource sharing among member firms(Granovetter, 1995). For instance, Samsung Groupuses regional organizations to facilitate knowledge

sharing by its affiliates. Samsung China coordi-nates various activities by the subsidiaries of itsaffiliate firms in China. In turn, these affiliates fre-quently emulate other affiliates when they expandinto a new region. For instance, when SamsungElectronics is located in Tianjin, other affiliatesof the Samsung Group such as Samsung Corpo-ration and Samsung SDI are more likely to locatein Tianjin than they are in other regions since theycan learn from Samsung Electronics’ experience inthe same location and since Samsung Electronics’presence legitimizes their own location choices.For his sample of Korean firms that invested inChina, Guillen (2002) found strong evidence thata firm’s rate of entry increased as its affiliatesset up their own plants. Martin, Swaminathan,and Mitchell (1998) showed that Japanese automo-bile suppliers followed their buyers, competitors,and suppliers into the United States. Bastos andGreve (2003) also found that industry affiliationand board interlocking ties were strong predic-tors for Japanese firms’ mimetic entry behaviorsin Europe. We thus expect that marginal benefitsfrom an additional firm in a region will be greaterif the new firm is in the same boundary than they

are if the new firm is unrelated.On the other hand, the marginal cost increase

associated with agglomeration will be smallerwhen the same firm or affiliated firms are co-locating investments than it is when unaffiliatedfirms co-locate, since a firm or a business groupshould be more able to coordinate its location deci-sions to avoid any competition among its ownforeign operations. A firm or a group’s delib-erate effort to avoid any pitfalls of groupthink can also lower the marginal costs of agglomer-ation. For instance, the LG Group deliberatelyspread the operations of its individual affiliates





over many regions, including several remote areas,while avoiding too much concentration in a few

popular coastal areas. By doing so, it achievedbalanced growth in the Chinese markets and max-imized knowledge sharing at the group level. Wetherefore expect:

Hypothesis 2: Network externalities are stronger

within firms and among firms associated with the

same business groups than they are for unaffili-

ated firms.

We also expect that a firm’s country of originsignificantly affects the degree of network exter-

nalities it can derive from other firms. Since eachcountry has its own culture, national origin mayaffect the types of experiential knowledge a firmcreates (Hofstede, 1980). As a consequence, someknowledge or experience may be nation specific,and firms may be able to learn more from the expe-rience of firms from the same nation than theycan from firms that are from different nations. Forinstance, a Japanese firm that transfers labor rela-tionship practices commonly used in Japan may beuniquely qualified to learn from the past experienceof other Japanese firms that implemented similarpractices in the same foreign location. A Korean

firm may be relatively more able to recruit a localmanager who can speak Korean from a subsidiaryof another Korean firm. Also, when there is a largecommunity of firms from the same country, thesefirms often create country-specific infrastructuressuch as a Swedish school for Swedish expatriates’children.

A firm may also pay more attention to theactions of other firms from the same nation thanit does to those of different nations, revealinganother type of trait-based imitation. Such imita-tive behavior among firms from the same nation

might be even stronger for firms from countriesthat have strong ethnocentric orientations, such asKorea and Japan. Various researchers have shownthat Korean and Japanese firms frequently emu-late each other (Guillen, 2002; Head et al., 1995;Henisz and Delios, 2001; Chung and Song, 2004;Alcacer, 2004). We thus expect the marginal ben-efits from agglomeration to be higher when firmsof the same nationality co-locate investments thanthey are when firms of different nationalities co-locate.

It is also possible that an agglomeration of firmsfrom the same nation might increase marginal

costs, such as the costs of nation-specific inputsand the hazards of groupthink, than would an

agglomeration of firms from several nations. Weexpect, however, that the proportion of nation-specific inputs to overall costs is low relative to thepotential benefits from spillovers of nation-specificexperience or knowledge, especially for firms inwhich ethnocentrism is strong. Since Korean firmsare as ethnocentric as Japanese firms are, wehypothesize:2


for firms from the same nation than they are for

firms from different nations.

In addition, some network externalities may beindustry specific. Although components of infras-tructure, such as roads, transportation, and housingfor expatriates, are shared by all firms, it may beeasier to share other resources, like specializedsuppliers and workers, within an industry bound-ary. Shanghai General Motors probably benefitedmore from Shanghai Volkswagen than it did fromany other company in Shanghai.

An industry also provides a frame of referenceto all firms in it. It is a social structure that affects

the flow of information and legitimacy from onefirm to another (Guillen, 2002). Firms also measuretheir internal processes and performance againstothers in the same industry (Porac and Rosa, 1996).Therefore, firms tend to imitate other firms inthe same industry to gain legitimacy or to reduceuncertainty, revealing another type of trait-basedimitation. Guillen (2002) found that Korean firmsare more likely to invest in China when more of their domestic competitors have already investedin China. Henisz and Delios (2001) showed sim-ilar results for Japanese firms. We thus expectthat marginal benefits from agglomeration will be

higher when firms in the same industry co-locateinvestments than they are when firms in differentindustries co-locate.

2 Henisz and Delios (2001), Guillen (2002), and Chung and Song(2004) argue that network externalities could be stronger forfirms that had little or no international investment experience.They propose that network externalities from other firms wouldbe weaker or non-existent after firms had such experience. Weexperimented with various interaction effects between a firm orgroup’s prior entry experience and the count of other typesof firms. The interaction terms were generally insignificant,suggesting that our sample firms derived substantial network externalities from other firms even after they had investmentexperience.





On the other hand, negative network externali-ties, such as groupthink and competition in product

and factor markets, may be also industry spe-cific, and thus shift marginal costs upward. Itis not clear whether the marginal benefits andmarginal costs schedules would intersect at ahigher level of agglomeration for firms in the sameindustry or firms in different industries. Porter(1998) and Porter and Stern (2001) argue thatintensified rivalry among firms in the same regionactually promotes innovation, and cite regionalclusters such as Silicon Valley for semiconduc-tors as an example. We argue that the innovation-promoting effects of intensified rivalry among

firms of the same industry within a regional clus-ter are greater than the negative consequences of intensified competition in the factor and productmarkets are. We thus propose:


among firms in the same industry than they are

across industries.

RESEARCH METHODS

SampleKorean firms’ investment activities in China pro-vide an interesting empirical setting to study ourresearch questions. First, Korea ranked as thefifth largest investor in China, after the UnitedStates, Japan, Taiwan, and Singapore.3 Second,compared to U.S., European and Japanese multi-nationals, Korean firms entered China relativelylate. Most Korean investments in China took placeafter China and Korea established a diplomaticrelationship in 1992. As a late entrant, Koreanfirms had many opportunities to evaluate network

externalities when they decided which locationsto enter. Third, Korean firms have a reputationfor being ethnocentric and often emulating eachother (Guillen, 2002). Thus, China provides a goodsetting to test whether network externalities arestronger among Korean firms than they are forother foreign firms. Fourth, many Korean firms

3 According to the official statistics, investments from HongKong and the U.S. Virgin Islands exceeded those from Korea. Alarge portion of these countries’ investments, however, actuallycame from firms in other nations that used Hong Kong and U.S.Virgin Islands as tax havens or as a beachhead for entry intoChina.

are associated with business groups, also knownas chaebols. Within chaebols, individual affiliates

share considerable know-how and expertise. Thus,Korean firms provide an interesting setting togauge how strong network externalities are withinthe boundaries of a firm or a group.

The sample for this study consists of Koreanfirms’ direct investments in China between 1988and September 2002. Korean firms started to investin China through Hong Kong in 1988. Overtime, Korean firms have increased their invest-ments in China. By September 2002, Koreanfirms had invested 6000 times in China, for atotal of $4.1 billion in the manufacturing sec-

tor.4

When Korean firms invest overseas, theyare required by law to report their investmentsto the government-owned Import–Export Bank of Korea, which maintains a database on the names of investors, dates, amounts, and locations of invest-ing firms’ activities. For our sample, we selectedKorean firms’ investments in China in the manu-facturing sector whose declared investment amountexceeded $1 million. We focused only on man-ufacturing investments since manufacturing andnon-manufacturing sectors require rather differ-ent types of experience, knowledge, workers, andother inputs. We also dropped small investments(i.e., those less than $1 million) since we wantedto have investments of comparable size by Koreanfirms and the other foreign firms, as well as localfirms in our database. During 1988– 2002, therewere 661 investments that met our criteria. Amongthese cases, we dropped 121 cases from our samplesince the identities of investors were either individ-uals, rather than firms, or could not be confirmeddue to bankruptcies or closures. We also deletedcases where the intended investment amount wasreported but did not materialize into an actualinvestment by September 2002. The 540 invest-

ments in our sample were worth $3.2 billion, rep-resenting more than 78 percent of Korean firms’total investment in the manufacturing sector inChina, as reported to the Import–Export Bank of Korea.

Several firms in our sample invested in China atleast twice during our time study period. SamsungElectronics and LG Electronics each invested 10times, LG Chemical invested nine times, Hyundai

4 This figure excludes announced but unrealized investments andthose that were liquidated or bankrupt from the Export-ImportBank of Korea statistics, as of September 2002.





Motors invested six times, etc. Many of the firmsin our sample were associated with large business

groups (Chang, 2003). The Export–Import Bank of Korea uses the Korean Fair Trade Commission’sdefinition of affiliation with a business group todetermine whether individual firms belonged tosuch groups.5 If we use this definition, for instance,affiliates of the LG Group, such as LG Electronicsand LG Chemical, made a total of 27 investmentsin China.

China is officially organized into 22 provinces,four special cities (Beijing, Tianjin, Shanghai, andChongqing), and five autonomous regions, such asInner Mongolia and Xinjiang. We adopted China’s

classification of regions since all vital statisticsin China are organized by region. Our sample of firms invested in 18 of these 31 regions. Figure 2shows the geographic distribution of Korean firms’investments in China. Korean firms show someagglomeration in regions such as Beijing, Tian-

jin, Liaoning, Shangdong, Jiangsu, Guangdong,and Shanghai. Compared to other foreign firms,which concentrated heavily in Shanghai, Guang-dong, Beijing, and Tianjin, Korean firms dispersedmore into the northern provinces and other innerregions.

Although Koreans and Chinese are culturally

similar, Korean firms are not free from the liabili-ties of foreignness. These firms reported variousdifficulties, such as their relationships with thegovernment, motivating workers, and protectingtheir intangible resources (Export–Import Bank of Korea, 2002). Therefore, transfers and spilloversof experience-based knowledge were critical toimproving the performance of these firms’ oper-ations.

Measures

In order to reflect the location choices faced byKorean firms at the time of investment, we col-lected descriptive data for each region. We mea-sured the population of each region (in incrementsof 10 million), expecting that firms would pre-fer locations with large markets. We included theaverage wage in the manufacturing sector (in thou-sands RMB) to reflect the relative attractiveness

5 The Korea Fair Trade Commission legally defines a businessgroup as ‘a group of companies, more than 30% of whose sharesare owned by some individuals or by companies controlled bythose individuals, or those that are practically controlled by themdespite lower ownership control.’

of a region as a manufacturing base. Althoughwe acknowledge that highly skilled workers can

be expensive in China and that wages are lessimportant in the capital and technology-intensiveindustries, we assume that wages of skilled laborswill correlate with those of unskilled labors acrossregions. We thus expect that average wages area proxy, albeit an imperfect one, for the relativecost of regional labor in China. Our Highway/area

variable denotes the length of the highways in aregion in km divided by the size of the regionin km2 in order to gauge the quality of physicalinfrastructure. We expect the more well developedthe highway system is, the more likely it is that

a firm prefers the location. The number of patents(in thousands) granted by the Chinese patent officewas included to reflect the technical skills of orga-nizations in a region. As documented by Sun(2000), state and local research institutes as wellas corporations in China have aggressively filedfor local patents. Foreign firms are more likely tofavor a location where there are a large number of local patents. We collected all vital social statisticsfor the above variables from the China Statisti-

cal Yearbook , 1988–2001, and matched these datawith the year that each investment occurred. Since

there are many ethnic Koreans in China who haveworked as translators and middle-level managersfor the Chinese subsidiaries of Korean firms, weincluded the number of ethnic Koreans in a region(in thousands). We expect that the more ethnicKoreans there are in a region, the more attractivethose regions are to Korean firms. We collectedthe distribution of ethnic Koreans from the Popu-lation Survey. Not all statistics were available forevery year. The length of highway was not avail-able until 1995, and the number of ethnic Koreanswas available only for 1990 and 1998. We substi-

tuted data for these variables with data from theclosest year.In order to gauge the degree of network external-

ities, we measured the cumulative counts of priorinvestments by different types of firms at the timeof each investment. Prior studies using count vari-ables noted that the level of locally accumulatedexperience, not just the binary variable noting themere presence of experience, was more critical tosubsequent location decisions (Song, 2002). Count

of a firm’s own prior entry measured the numberof a focal firm’s own prior entries into each regionup to the time of an entry event. Count of entry by





F i g u r e 2 . D i s t r i b u t i o n o f K o r e a n , f o r e i g n , a n d l o c a l fi r m s i n C h i n a





firms within the same group reflected the cumu-lative count of entries by firms associated with

the same business groups in each region up to thetime of each entry event. For firms with no groupaffiliation, this variable is coded as zero. Count of

other Korean firms was measured as the numberof Korean firms not associated with the same busi-ness groups that were operating in each region atthe time of each entry.

It is hard to keep track of how many foreign andlocal Chinese firms existed in each year. Duringthe last decade, the number of foreign firms settingup their operations in China exploded. There wasalso substantial restructuring among local firms in

China. Many formerly state-owned firms and coop-eratives were transformed into joint stock compa-nies and were privatized. During this transforma-tion process, there were many mergers and acqui-sitions among local firms. At the same time, theChinese government encouraged foreign firms toform joint ventures with local firms in order totransfer technology or know-how and to maintainemployment. We collected data for prior entries bynon-Korean foreign firms and local firms from Dun& Bradstreet’s Major Corporations in P.R. China

2001. This directory consists of two volumes. Vol-ume 1 contains a total of 2953 major foreign firms’own subsidiaries and their joint ventures in themanufacturing sector in China. Volume 2 containsinformation on a total of 1341 major Chinese firmsin the manufacturing sector. The directory doesnot supply any other information apart from thenumber of employees, the names of parent firms,the year of entry, and the addresses and contactinformation of operations.6 The average numberof employees of foreign firms in this directory was246, while it was 1580 for local firms. The averagenumber of employees for the 540 Korean firms inour sample was 817, suggesting that the Korean

firms in our database were somewhat larger thanthe foreign firms but smaller than the Chinese firmslisted in Dun & Bradstreet’s directory. Count of

other foreign firms and count of local firms weremeasured as the number of manufacturing oper-ations of non-Korean multinationals and Chineselocal firms, respectively, operating in each regionat the time of each entry.

6 According to our telephone interview with managers at Dun& Bradstreet, this publication used various criteria such as salesand number of employees to select ‘major’ foreign and localfirms to be included in the directory.

We further classified our count measures intotwo parts, reflecting the number of firms in the

same industry and those in different industries.We used the Korean 2-digit Standard IndustryClassification (SIC) code to determine whetherfirms were in the same industry as a focal firm.Dun & Bradstreet classified foreign and local firmsin China according to the 2-digit U.S. SIC, whichwe converted to the 2-digit Korean SIC. In somemodels, we used variables that aggregated thevarious count variables defined above. Count of

all unrelated firms is defined as the sum of countvariables for other Korean firms, other foreignfirms, and local Chinese firms in a region. Count

of all firms in a regional network adds a firm’sown entries and entries by firms affiliated with thesame group on top of count of all unrelated firms,thus comprising all types of firms in a regionalnetwork. In order to measure the joint effects of positive and negative network externalities, wemeasured the monotonic and squared terms of these count variables. If a squared term has anegative coefficient and a monotonic term has apositive one, that discrepancy may indicate aninverted U-shaped relationship.

Methodology

This study uses a conditional logit model to testour hypotheses (McFadden, 1974). Our samplefirm faces a set of location choices, each of whichhas different attributes. Since the conditional logitmodel requires all choices to be selected at leastonce, we included only 18 regions, all of whichwere invested in at least once.7

The conditional logit model has been widelyused to analyze how agents choose from a largeset of alternatives. It is relevant for locationchoices in foreign direct investment (Head et al.,

1995; Shaver and Flyer, 2000). It focuses onthe attributes of each location in the choice set.Attributes for each region, including population,average wage, highway/area, ethnic Koreans, andnumber of patents, were the same for all Koreanfirms investing in China at time t . Count variablesreflecting the number of incumbent firms that weresources of network externalities were measuredaccording to the identities of investing firms. This

7 Refer to our discussion of the robustness tests in which weexamined the independence of irrelevant alternatives in theresults section.





model estimates how each attribute increases ordecreases the chance that a location will be cho-

sen rather than all other potential locations. Letus define an underlying latent variable, V ijt , torepresent the utility a firm i derives from open-ing a manufacturing operation in region j at timet . Since there were m regions where firms couldenter during our time study period, each observa-tion (i.e., a firm choosing a location) has m rowsof data, each corresponding to a specific region.Assuming a linear relationship with the latent vari-able, we can write:

V ijt = βX ijt + eijt (1)

X is a vector of independent variables that affectchoices. If a firm makes choice j in particular, thenwe assume that V ijt is the maximum among the m

utilities. Hence, the statistical model is driven bythe probability that choice j is made, which is:

Prob(V ijt > V ikt ) for all other k = j (2)

Let Y it be a random variable that indicates a choicemade by firm i at time t . Assuming independenceof irrelevant alternatives, the probability that a firm

i chooses j location at time t can be written asfollows:

Prob(Y it = j ) = exp(βX ijt )/

[k=1...mexp(βXikt )] (3)

The maximum likelihood method is used to esti-mate β, which we can use to test whether variousindependent variables significantly affect the prob-ability that one region will be chosen among allthe regions in the choice set. In this conditionallogit model, β cannot be interpreted as marginal

effects as it could be in a linear regression. Themarginal effects can be derived by differentiatingEquation 3 with respect to the independent vari-ables, X. In order to interpret the magnitude of thecoefficient, we can calculate the ‘average probabil-ities elasticity,’ as reported in Head et al. (1995),which referred to an independent variable’s proba-bility elasticity for the average option in the choiceset.8 The elasticity of the probability of a partic-ular firm i choosing region j with respect to an

8 See the appendix of Head et al. (1995) for more details abouthow to derive for the average probability elasticity.

independent variable, Xl , can be calculated by dif-ferentiating Equation 3:

Elasticity1

ijt =∂ Prob (Y it = j )

∂Xl

Xl

Prob (Y it = j )

= βl [1− Prob (Y it = j )] (4)

Summing over all firms and choices, the relation-ship between the average probability elasticity andthe coefficient estimate, βl , is:

Elasticity1=

i

j

t

Elasticity1

ijt = βl

m− 1

m(5)

The above expression discounts the coefficientby (m− 1)/m. Since we have 18 regions in ourdataset, we have to multiply individual coefficientsby 0.94 (=17/18) to calculate the average proba-bilities elasticity.

RESULTS

Tables 1 and 2 show, respectively, descriptivestatistics and results from conditional logit models.The odd-numbered models in Table 2 include onlymonotonic variables, while the even-numbered

models include both monotonic and squared termsto test for curvilinear relationships. Model 1 showsa baseline model, in which the count of all firmsin a regional network was included in additionto regional attributes such as population, averagewage, highway/area, number of ethnic Koreans,and patents. The results showed that Korean firmspreferred regions that were characterized by largepopulations, low wages, a well-developed high-way system, a large population of ethnic Kore-ans, and a large number of patents. The countof all firms in a region was positive and signifi-cant, suggesting the existence of positive network externalities. The chi-square statistic shows themodel to be highly significant, with p < 0.001.The pseudo R2 is 0.1826, suggesting reasonablemodel fit.9

In Model 2, we added a squared term of thecount of all types of firms in a region to test for acurvilinear relationship. When we added this term,both the monotonic and the squared terms of the

9 Pseudo R-squared is defined as one minus the ratio of themaximum likelihood functions of a model without any explana-tory variable divided by the model that includes all explanatoryvariables.





Table 1. Descriptive statistics

Mean S.D. Minimum Maximum

(1) Population (10 million) 4.05 2.32 0.64 9.04(2) Average wage (thousands RMB) 5.68 3.05 1.34 20.41(3) Highway (km)/area (km2

) 40.38 48.35 1.06 277.27(4) Ethnic Koreans (thousands) 105.03 284.45 0.07 1181.96(5) Patents (thousands) 2.13 2.32 0.01 18.26(6) Count of all firms in a regional network 182.96 334.97 0.00 1965.00(7) Count of a firm’s own prior entry 0.002 0.19 0.00 4.00(8) Count of entry by firms within the same group 0.01 0.45 0.00 10.00(9) Count of all unrelated firms 182.85 334.91 0.00 1963.00

(10) Count of other Korean firms 13.28 26.53 0.00 185.00(11) Count of other Korean firms in the same industry 1.06 3.11 0.00 34.00(12) Count of other Korean firms in different industries 12.22 24.54 0.00 184.00(13) Count of other foreign firms 117.25 292.71 0.00 1651.00

(14) Count of other foreign firms in the same industry 6.71 21.44 0.00 206.00(15) Count of other foreign firms in different industries 110.53 277.04 0.00 1651.00(16) Count of local firms 52.31 56.59 0.00 281.00(17) Count of local firms in the same industry 3.24 5.74 0.00 55.00(18) Count of local firms in different industries 49.07 53.43 0.00 281.00

Correlation matrix

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18)

(1) 1.00(2) −0.13 1.00

(3) −0.30 0.65 1.00(4) −0.15 −0.14 −0.20 1.00(5) 0.32 0.54 0.32 −0.11 1.00(6) −0.16 0.63 0.62 −0.17 0.35 1.00(7) 0.05 0.08 0.09 −0.02 0.08 0.06 1.00(8) 0.04 0.14 0.16 −0.03 0.14 0.08 0.61 1.00(9) −0.16 0.63 0.62 −0.17 0.35 0.99 0.06 0.08 1.00

(10) 0.38 0.26 0.34 −0.07 0.37 0.24 0.13 0.15 0.24 1.00(11) 0.23 0.20 0.28 −0.06 0.28 0.18 0.18 0.26 0.18 0.67 1.00(12) 0.38 0.25 0.33 −0.07 0.36 0.24 0.12 0.13 0.24 0.99 0.60 1.00(13) −0.30 0.59 0.60 −0.14 0.26 0.98 0.03 0.06 0.98 0.07 0.07 0.07 1.00(14) −0.23 0.45 0.46 −0.11 0.20 0.73 0.06 0.08 0.73 0.05 0.13 0.04 0.74 1.00(15) −0.30 0.59 0.60 −0.14 0.26 0.97 0.03 0.06 0.97 0.07 0.06 0.07 0.99 0.71 1.00(16) 0.40 0.51 0.42 −0.22 0.57 0.73 0.11 0.12 0.73 0.57 0.37 0.57 0.59 0.43 0.59 1.00(17) 0.25 0.30 0.22 −0.13 0.38 0.41 0.11 0.14 0.41 0.31 0.34 0.29 0.33 0.38 0.32 0.58 1.00

(18) 0.39 0.51 0.42 −0.22 0.56 0.73 0.10 0.11 0.73 0.58 0.36 0.58 0.59 0.42 0.59 0.99 0.51 1.00

N = 9720 (= 540 × 18).

count variable lost significance. We performed alog-likelihood test by taking the ratio of the maxi-mum probability under the constraint of the nullhypothesis (i.e., the coefficient for the squaredterm equals zero) to the maximum likelihoodwith that constraint relaxed. The −2 log-likelihoodhas an asymptotic distribution of chi-square. Thedifference in chi-square at the bottom of Table 2

was very small, indicating that we could not reject

the null hypothesis. Thus, Hypothesis 1 was not

supported when we lumped all different types of

firms into a single category and measured net-

work externalities. The network externalities from

all types of firms combined into a single measure

seemed monotonic rather than curvilinear.





Models 1 and 2 assumed that the firms consti-tuting a regional network were homogeneous. In

subsequent models, we relaxed this assumption bydisaggregating firms into different types and test-ing hypotheses that corresponded to these types.Models 3 and 4 tested Hypothesis 2 by identifyingwithin a region a focal firm’s own prior entries,entries by affiliated firms, and entries by firms notrelated to a focal firm in any way. In Model 3,which assumes linear relationships, all three countvariables were significantly positive, suggestingpositive network externalities. The coefficients fora firm’s own entry and the count of firms affiliatedwith the same business group were much greater

than was the count of all unrelated firms. Whenwe calculate the average probability elasticity, a100 percent increase in the number of a firm’sprior entries in a region increases the likelihood of choosing that region by 46.6 percent (i.e., 0.496×0.94× 100). Similarly, a 100 percent increase inthe number of prior entries within a region byaffiliate firms increases the likelihood of choosingthat region by 47.7 percent (0.508× 0.94× 100).In contrast, a 100 percent increase in the num-ber of unrelated firms in that region increases thelikelihood that a focal firm chooses this region byonly 0.1 percent (= 0.001× 0.94× 100). We per-formed a log-likelihood test to determine whetherthese differences in the size of coefficients werestatistically significant by positing Model 1 as anull hypothesis, where coefficients for these threetypes of firms were constrained to be equal. Thelog-likelihood test comparing Models 1 and 3rejected the null hypothesis at p < 0.001, suggest-ing that the differences in coefficients were signif-icant. Thus, we found strong support for Hypoth-esis 2.

Model (4) added the squared terms of threecount variables to test for curvilinear relationships.

The results for it showed that the coefficient forcount of entries by affiliate firms was positive andsignificant, while the coefficient for its squaredterm was negative and significant, suggesting acurvilinear relationship. Both the monotonic andthe squared terms for the count of a firm’s ownprior entries and the count of unrelated firms in thatregion were insignificant in Model 4, suggestingmonotonic rather than curvilinear relationships.The log-likelihood test comparing Models 3 and4 rejected the null hypothesis that coefficientsfor all squared terms were zero. Thus, Model 4provided some support for Hypothesis 1. It is

somewhat tricky to test Hypothesis 2 in a non-linear model such as Model 4. Since only the

count of entry by firms affiliated with the samebusiness groups turned significant in a curvilinearmodel, we found some support for Hypothesis 2in Model 4.

It is worth noting that network externalities froma firm’s own prior entries seemed monotonicallypositive, while the impact of entries by firms affil-iated with the same business groups was inverted.The results may suggest that a firm does notexpect much conflict or competition among its owninvestments in the same region. In contrast, affili-ated firms, which transfer their own local managers

to other affiliates or share other resources with eachother, may incur some costs. It is also possible thatbusiness groups discourage too much agglomera-tion from occurring in any one region in order toguard against the harmful effects of agglomerationand achieve a well-balanced approach to the entireChinese market. LG Group’s actions provide someanecdotal evidence for this conjecture.

Models 5 and 6 subdivided the category of unrelated firms by the national origin of firms inorder to test Hypothesis 3. Model 5 showed that thecounts of other Korean firms and local firms in aregion were positively related to the likelihood thata firm located in this region, suggesting positivenetwork externalities. In contrast, the count of other foreign firms was negative and significant,suggesting negative network externalities. Whenwe calculate the average probability elasticities,a 100 percent increase in the number of otherKorean firms in a region increases the likelihoodthat a firm located in this region by 0.8 percent,while the same increase in the number of foreignfirms decreases the likelihood that a firm locatedin this region by 0.1 percent. The log-likelihoodtest comparing Models 3 and 5 rejected the null

hypothesis that coefficients for these three countvariables were equal. Thus, we found support forHypothesis 3.

Model 6 added the squared terms of countvariables. Compared to Model 4, curvilinear pat-terns emerged more strongly when we disag-gregated the category of unrelated firms. Boththe count of other Korean firms and local Chi-nese firms showed inverted U-shaped relation-ships, consistent with our expectation, while thecount of other foreign firms showed a U-shapedrelationship, contrary to our expectation. The log-likelihood test comparing Models 5 and 6 rejected





T a b l e 2 . C o n d i t i o n a l l o g i t m o d e l o f l o c a t i o n c h o i c e s b y K o r e a n fi r m s i n C h i n a

V a r i a b l e s

( 1 )

( 2 )

( 3 )

( 4 )

( 5 )

( 6 )

( 7 )

( 8 )

P o p u l a t i o n ( 1 0 m i l l i o n )

0 . 4 0 6 ( 0 . 0 2 7

) ∗ ∗ ∗

0 . 4 0 4 ( 0 . 0 2 9 ) ∗ ∗ ∗

0 . 3 8 3 ( 0 . 0 2 8 ) ∗ ∗ ∗

0 . 3 8 2 ( 0 . 0 2 9 ) ∗ ∗ ∗

0 . 1 4 9 ( 0 . 0 4 3 ) ∗ ∗ ∗

0 . 0 6

0 ( 0 . 0 4 7 )

0 . 1 3 9 ( 0 . 0 4 3 ) ∗ ∗

0 . 0 5 9 ( 0 . 0 4 7 )

A v e r a g e w a g e ( t h o u s a n d s

R M B )

− 0 . 1 5 6 ( 0 . 0 4 3

) ∗ ∗ ∗

− 0 . 1 5 6 ( 0 . 0 4 3 ) ∗ ∗ ∗

− 0 . 1 9 5 ( 0 . 0 4 5 ) ∗ ∗ ∗

− 0 . 1 9 4 ( 0 . 0 4 5 ) ∗ ∗ ∗

− 0 . 0 2 7 ( 0 . 0 5 5 )

− 0 . 0 8

5 ( 0 . 0 6 3 )

0 . 0 1 9 ( 0 . 0 5 6 )

− 0 . 0 7 9 ( 0 . 0 6 3 )

H i g h w a y ( k m ) / a r e a ( k m 2 )

0 . 0 2 5 ( 0 . 0 0 2

) ∗ ∗ ∗

0 . 0 2 5 ( 0 . 0 0 1 ) ∗ ∗ ∗

0 . 0 2 2 ( 0 . 0 0 2 ) ∗ ∗ ∗

0 . 0 2 3 ( 0 . 0 0 2 ) ∗ ∗ ∗

0 . 0 1 5 ( 0 . 0 0 2 ) ∗ ∗ ∗

0 . 0 1

3 ( 0 . 0 0 2 ) ∗ ∗ ∗

0 . 0 1 4 ( 0 . 0 0 2 ) ∗ ∗ ∗

0 . 0 1 3 ( 0 . 0 0 2 ) ∗ ∗ ∗

N u m b e r o f e t h n i c K o r e a n s

( t h o u s a n d s )

0 . 0 0 1 ( 0 . 0 0 0

) ∗ ∗ ∗

0 . 0 0 1 ( 0 . 0 0 0 ) ∗ ∗ ∗

0 . 0 0 1 ( 0 . 0 0 0 ) ∗ ∗ ∗

0 . 0 0 1 ( 0 . 0 0 0 ) ∗ ∗ ∗

0 . 0 0 1 ( 0 . 0 0 0 ) ∗ ∗

0 . 0 0

1 ( 0 . 0 0 0 ) ∗

0 . 0 0 1 ( 0 . 0 0 0 ) ∗ ∗

0 . 0 0 1 ( 0 . 0 0 0 ) ∗

P a t e n t s ( t h o u s a n d s )

0 . 1 3 6 ( 0 . 0 2 7

) ∗ ∗ ∗

0 . 1 3 2 ( 0 . 0 3 3 ) ∗ ∗ ∗

0 . 1 3 8 ( 0 . 0 2 7 ) ∗ ∗ ∗

0 . 1 3 5 ( 0 . 0 3 3 ) ∗ ∗ ∗

0 . 1 0 1 ( 0 . 0 2 8 ) ∗ ∗ ∗

0 . 1 3

0 ( 0 . 0 3 4 ) ∗ ∗ ∗

0 . 0 9 8 ( 0 . 0 2 8 ) ∗ ∗

0 . 1 3 2 ( 0 . 0 3 4 ) ∗ ∗ ∗

C o u n t o f a l l fi r m s i n a

r e g i o n a l n e t w o r k

0 . 0 0 1 ( 0 . 0 0 0

) ∗

0 . 0 0 1 ( 0 . 0 0 1 )

C o u n t o f a fi r m ’ s o w n p r i o r

e n t r i e s

0 . 4 9 6 ( 0 . 1 9 5 ) ∗

0 . 5 7 1 ( 0 . 3 6 9 )

0 . 3 8 7 ( 0 . 1 9 7 ) ∗

0 . 3 2

0 ( 0 . 3 7 1 )

0 . 3 9 3 ( 0 . 1 9 8 ) ∗

0 . 2 3 9 ( 0 . 3 6 8 )

C o u n t o f e n t r y b y fi r m s i n

t h e s a m e g r o u p

0 . 5 0 8 ( 0 . 1 0 8 ) ∗ ∗ ∗

0 . 8 9 9 ( 0 . 1 9 2 ) ∗ ∗ ∗

0 . 5 4 2 ( 0 . 1 0 8 ) ∗ ∗ ∗

0 . 9 4

1 ( 0 . 1 9 3 ) ∗ ∗ ∗

0 . 4 9 5 ( 0 . 1 0 6 ) ∗ ∗ ∗

0 . 9 2 0 ( 0 . 1 9 3 ) ∗ ∗ ∗

C o u n t o f a l l u n r e l a t e d fi r m s

( o t h e r K o r e a n , f o r e i g n ,

l o c a l s )

0 . 0 0 1 ( 0 . 0 0 0 ) ∗ ∗ ∗

0 . 0 0 1 ( 0 . 0 0 0 )

C o u n t o f o t h e r K o r e a n fi r m s

0 . 0 0 9 ( 0 . 0 0 2 ) ∗ ∗ ∗

0 . 0 3

3 ( 0 . 0 0 4 ) ∗ ∗ ∗

— i n t h e s a m e i n d u s t r y

0 . 0 6 2 ( 0 . 0 1 4 ) ∗ ∗ ∗

0 . 1 2 7 ( 0 . 0 3 0 ) ∗ ∗ ∗

— i n d i f f e r e n t i n d u s t r i e s

0 . 0 0 5 ( 0 . 0 0 2 ) ∗

0 . 0 2 5 ( 0 . 0 0 4 ) ∗ ∗ ∗

C o u n t o f o t h e r f o r e i g n fi r m s

− 0 . 0 0 1 ( 0 . 0 0 0 ) ∗

− 0 . 0 0

2 ( 0 . 0 0 1 ) ∗


0 . 0 0 6 ( 0 . 0 0 3 ) ∗

0 . 0 1 2 ( 0 . 0 0 7 ) †


− 0 . 0 0 1 ( 0 . 0 0 0 ) ∗ ∗

− 0 . 0 0 3 ( 0 . 0 0 1 ) ∗ ∗

C o u n t o f l o c a l fi r m s

0 . 0 0 8 ( 0 . 0 0 2 ) ∗ ∗ ∗

0 . 0 1

5 ( 0 . 0 0 4 ) ∗ ∗ ∗


0 . 0 1 0 ( 0 . 0 0 9 )

0 . 0 2 4 ( 0 . 0 2 6 )


0 . 0 0 8 ( 0 . 0 0 2 ) ∗ ∗ ∗

0 . 0 1 5 ( 0 . 0 0 5 ) ∗ ∗

C o u n t o f a l l fi r m s i n a

r e g i o n a l n e t w o r k 2

0 . 0 0 0 ( 0 . 0 0 0 )

C o u n t o f a fi r m ’ s o w n p r i o r

e n t r i e s 2

− 0 . 0 8 5 ( 0 . 1 3 1 )

− 0 . 0 3

5 ( 0 . 1 3 0 )

− 0 . 0 0 8 ( 0 . 1 2 6 )

C o u n t o f e n t r y b y a f fi l i a t e

fi r m s 2

− 0 . 0 6 4 ( 0 . 0 2 6 ) ∗

− 0 . 0 6

9 ( 0 . 0 2 6 ) ∗ ∗

− 0 . 0 7 5 ( 0 . 0 2 6 ) ∗ ∗

C o u n t o f a l l u n r e l a t e d fi r m s 2

0 . 0 0 0 ( 0 . 0 0 0 )





C o u n t o f o t h e r K o r e a n

fi r m s 2 / 1 0 0 0

− 0 . 1 3 3

( 0 . 0 2 5 ) ∗ ∗ ∗

— i n t h e s a m e

i n d u s t r y 2 / 1 0 0 0

− 3 . 0 8 5 ( 1 . 1 0

3 ) ∗ ∗ ∗

— i n d i f f e r e n t

i n d u s t r i e s 2 / 1 0 0 0

− 0 . 1 2 3 ( 0 . 0 2

9 ) ∗ ∗ ∗

C o u n t o f o t h e r f o r e i g n

fi r m s 2 / 1 0 0 0

0 . 0 0 1

( 0 . 0 0 1 ) ∗


i n d u s t r y 2 / 1 0 0 0

− 0 . 0 3 1 ( 0 . 0 3

8 )


i n d u s t r i e s 2 / 1 0 0 0

0 . 0 0 2 ( 0 . 0 0

1 ) ∗ ∗

C o u n t o f l o c a l fi r m s 2 / 1 0 0 0

− 0 . 0 4 6

( 0 . 0 2 0 ) ∗


i n d u s t r y 2 / 1 0 0 0

− 0 . 8 7 2 ( 0 . 7 6

5 )


i n d u s t r i e s 2 / 1 0 0 0

− 0 . 0 4 8 ( 0 . 0 2

2 ) ∗

L o g - l i k e l i h o o d

− 1 2 7 5 . 8

− 1 1 2 5 . 8

− 1 2 4 5 . 2

− 1 2 4 0 . 2

− 1 2 1 9 . 0

− 1 1 8 8 . 1

− 1 2 1 0 . 0

− 1 1 7 5 . 7

C h i - s q u a r e ( d . f . )

5 6 9 . 9 ( 6 ) ∗ ∗

∗

5 7 0 . 0 ( 7 ) ∗ ∗ ∗

6 3 1 . 2 ( 8 ) ∗ ∗ ∗

6 4 1 . 3 ( 1 1 ) ∗ ∗ ∗

6 8 3 . 5 ( 1 0 ) ∗ ∗ ∗

7 4 5 . 3

( 1 5 ) ∗ ∗ ∗

7 0 1 . 5 ( 1 3 ) ∗ ∗ ∗

7 7 0 . 1 ( 2 1 ) ∗ ∗

∗

P s e u d o R 2

0 . 1 8 2 6

0 . 1 8 2 6

0 . 2 0 2 2

0 . 2 0 5 4

0 . 2 1 9 0

0 . 2 3 8

8

0 . 2 2 4 7

0 . 2 4 6 7

L o g - l i k e l i h o o d t e s t :

( 2 ) v s . ( 1 )

( 3 ) v s . ( 1 )

( 4 ) v s . ( 3 )

( 5 ) v s . ( 3 )

( 6 ) v s .

( 5 )

( 7 ) v s . ( 5 )

( 8 ) v s . ( 7 )

C o m p a r i n g m o d e l s

D i f f e r e n c e s i n c h i - s q u a r e s

( d . f . )

0 . 1 ( 1 )

6 1 . 3 ( 2 ) ∗ ∗ ∗

1 0 . 1 ( 3 ) ∗

7 . 3 ( 2 ) ∗

6 1 . 8

( 5 ) ∗ ∗ ∗

1 8 . 0 ( 3 ) ∗ ∗ ∗

6 4 . 6 ( 8 ) ∗ ∗ ∗

∗ ∗ ∗ p <

0 . 0 0 1 ; ∗ ∗ p <

0 . 0 1 ; ∗ p <

0 . 0 5 ; † p <

0 . 1 0 . S t a n d a r d d e v i a t i o n s a r e i n p a r e n t h e s e s .

N

=

9 7 2 0





T a b l e 3 . R o b u s t n e s s t e s t s o f t h e i n d e p

e n d e n c e f r o m i r r e l e v a n t a l t e r n a t i v e s

E x c l u d i n g S h a n g h a i

E x c l u d i n g r e g i o n s w i t h

l e s s t h a n 3 e n t r i e s

V a r i a b l e

( 1 )

( 2 )

( 3 )

( 4 )

P o p u l a t i o n ( 1 0 m i l l i o n )

0 . 1 2 3 ( 0 . 0 4 7 ) ∗ ∗

− 0 . 0 4 2 ( 0 . 0 5 5 )

0 . 1 3 0 ( 0 . 0 4 4 ) ∗ ∗

0 . 0 6 6 ( 0 . 0

4 8 )

A v e r a g e w a g e ( t h o u s a n d s R M B )

− 0 . 0 4 4 ( 0 . 0 8 4 )

− 0 . 3 2 7 ( 0 . 0 9 2 ) ∗ ∗ ∗

−

0 . 0 1 5 ( 0 . 0 5 6 )

− 0 . 1 0 1 ( 0 . 0

6 3 )

H i g h w a y ( k m ) / a r e a ( k m 2 )

0 . 0 1 8 ( 0 . 0 0 3 ) ∗ ∗ ∗

0 . 0 1 1 ( 0 . 0 0 3 ) ∗ ∗ ∗

0 . 0 1 2 ( 0 . 0 0 2 ) ∗ ∗ ∗

0 . 0 1 1 ( 0 . 0

0 3 ) ∗ ∗ ∗

N u m b e r o f e t h n i c K o r e a n s ( t h o u s a n d s )

0 . 0 0 1 ( 0 . 0 0 0 ) ∗ ∗

0 . 0 0 1 ( 0 . 0 0 0 ) ∗

0 . 0 0 0 ( 0 . 0 0 0 ) †

0 . 0 0 0 ( 0 . 0

0 0 )

P a t e n t s

0 . 1 3 4 ( 0 . 0 4 2 ) ∗ ∗

0 . 1 0 7 ( 0 . 0 3 8 ) ∗ ∗

0 . 0 8 1 ( 0 . 0 2 9 ) ∗ ∗

0 . 1 2 2 ( 0 . 0

3 4 ) ∗ ∗ ∗

C o u n t o f a fi r m ’ s o w n p r i o r e n t r y

0 . 4 3 2 ( 0 . 2 0 3 ) ∗

0 . 2 6 4 ( 0 . 3 7 5 )

0 . 3 6 9 ( 0 . 1 9 7 ) †

0 . 2 1 8 ( 0 . 3

6 8 )

C o u n t o f e n t r y b y fi r m s i n t h e s a m e g r o u p

0 . 5 0 2 ( 0 . 1 1 2 ) ∗ ∗ ∗

0 . 8 7 6 ( 0 . 1 9 7 ) ∗ ∗ ∗

0 . 4 9 1 ( 0 . 1 0 6 ) ∗ ∗ ∗

0 . 9 1 3 ( 0 . 1

9 3 ) ∗ ∗ ∗

C o u n t o f o t h e r K o r e a n fi r m s i n t h e s a m

e

i n d u s t r y

0 . 0 5 8 ( 0 . 0 1 5 ) ∗ ∗ ∗

0 . 1 3 0 ( 0 . 0 3 1 ) ∗ ∗ ∗

0 . 0 6 7 ( 0 . 0 1 4 ) ∗ ∗ ∗

0 . 1 2 5 ( 0 . 0

3 0 ) ∗ ∗ ∗

C o u n t o f o t h e r K o r e a n fi r m s i n d i f f e r e n

t

i n d u s t r i e s

0 . 0 0 3 ( 0 . 0 0 3 )

0 . 0 2 0 ( 0 . 0 0 5 ) ∗ ∗ ∗

0 . 0 0 4 ( 0 . 0 0 2 ) †

0 . 0 2 4 ( 0 . 0

0 5 ) ∗ ∗ ∗

C o u n t o f o t h e r f o r e i g n fi r m s i n t h e s a m

e

i n d u s t r y

0 . 0 1 3 ( 0 . 0 0 6 ) ∗

0 . 0 0 2 ( 0 . 0 1 9 )

0 . 0 0 7 ( 0 . 0 0 3 ) ∗

0 . 0 1 2 ( 0 . 0

0 7 ) †

C o u n t o f o t h e r f o r e i g n fi r m s i n d i f f e r e n

t

i n d u s t r i e s

− 0 . 0 0 2 ( 0 . 0 0 1 ) ∗

0 . 0 0 3 ( 0 . 0 0 3 )

−

0 . 0 0 1 ( 0 . 0 0 0 ) ∗

− 0 . 0 0 3 ( 0 . 0

0 1 ) ∗

C o u n t o f l o c a l fi r m s i n t h e s a m e i n d u s t r y

0 . 0 0 6 ( 0 . 0 1 0 )

0 . 0 3 6 ( 0 . 0 3 1 )

0 . 0 0 9 ( 0 . 0 0 9 )

0 . 0 1 8 ( 0 . 0

2 6 )

C o u n t o f l o c a l fi r m s i n d i f f e r e n t i n d u s t r i e s

0 . 0 0 9 ( 0 . 0 0 3 ) ∗ ∗ ∗

0 . 0 3 5 ( 0 . 0 0 7 ) ∗ ∗ ∗

0 . 0 0 7 ( 0 . 0 0 2 ) ∗ ∗ ∗

0 . 0 1 1 ( 0 . 0

0 4 ) ∗

C o u n t o f a fi r m ’ s o w n p r i o r e n t r y 2

− 0 . 0 0 1 ( 0 . 1 2 6 )

0 . 0 0 1 ( 0 . 1

2 6 )

C o u n t o f e n t r y b y t h e s a m e g r o u p fi r m s 2

− 0 . 0 7 1 ( 0 . 0 2 7 ) ∗ ∗

− 0 . 0 7 5 ( 0 . 0

2 6 ) ∗ ∗

C o u n t o f o t h e r K o r e a n fi r m s i n t h e s a m

e

i n d u s t r y 2 / 1 0 0 0

− 3 . 0 8 5 ( 1 . 1 0 3 ) ∗ ∗ ∗

− 2 . 8 4 5 ( 1 . 1

1 2 ) ∗ ∗

C o u n t o f o t h e r K o r e a n fi r m s i n d i f f e r e n

t

i n d u s t r i e s 2 / 1 0 0 0

− 0 . 1 2 3 ( 0 . 0 2 9 ) ∗ ∗ ∗

− 0 . 1 2 1 ( 0 . 0

2 9 ) ∗ ∗ ∗

C o u n t o f o t h e r f o r e i g n fi r m s i n t h e s a m

e

i n d u s t r y 2 / 1 0 0 0

0 . 2 7 0 ( 0 . 3 4 4 )

− 0 . 0 2 8 ( 0 . 0

3 8 )

C o u n t o f o t h e r f o r e i g n fi r m s i n d i f f e r e n

t

i n d u s t r i e s 2 / 1 0 0 0

− 0 . 0 0 9 ( 0 . 0 0 6 )

0 . 0 0 2 ( 0 . 0

0 1 ) ∗

C o u n t o f l o c a l fi r m s i n t h e s a m e i n d u s t r y 2 / 1 0 0 0

− 1 . 5 1 0 ( 0 . 9 5 7 )

− 0 . 6 6 9 ( 0 . 7

5 6 )

C o u n t o f l o c a l fi r m s i n d i f f e r e n t

i n d u s t r i e s 2 / 1 0 0 0

− 0 . 1 2 1 ( 0 . 0 2 9 ) ∗ ∗ ∗

− 0 . 0 3 5 ( 0 . 0

2 2 )

L o g - l i k e l i h o o d

− 1 0 8 1 . 8

− 1 0 3 8 . 8

− 1 1 6 2 . 0

− 1 1 3 1 . 8

C h i - s q u a r e ( d . f . )

7 2 0 . 6 ( 1 3 ) ∗ ∗ ∗

8 0 6 . 6 ( 2 1 ) ∗ ∗ ∗

5 7 9 . 1 ( 1 3 ) ∗ ∗ ∗

6 3 9 . 4 ( 2 1 ) ∗ ∗ ∗

P s e u d o R 2

0 . 2 4 9 8

0 . 2 7 9 7

0 . 1 9 9 5

0 . 2 4 6 7

N

8 6 5 3

8 6 5 3

8 0 4 0

8 0 4 0

∗ ∗ ∗ p <

0 . 0 0 1 ; ∗ ∗ p <

0 . 0 1 ; ∗ p <

0 . 0 5 ; † p <

0 . 1 0 . S t a n d a r d d e v i a t i o n s a r e i n p a r e n t h e s e s .





the null hypothesis that all the coefficients forsquared terms equaled zero. Model 6 provided sup-

port for Hypothesis 1.The results for Model 6 showed that the number

of unrelated Korean firms initially increased thelikelihood of co-location up to a certain level (theestimated inflection point seemed to be around 124entries) but depressed it after that level. Koreanfirms may compete with each other for someKorean-specific inputs. For instance, Korean firmsmay compete to hire managers who understandthe local culture and speak Korean. The intensifiedcompetition in factor markets associated with in-creased co-location could outweigh any additional

positive externalities derived from agglomeration.On the other hand, the results for foreign firmswere initially negative, but turned positive after acertain level (the estimated inflection point seemedto be around 1000 entries). Again, it is hard totest Hypothesis 3 in a non-linear model. Yet thefact that network externalities from other Koreanfirms were positive up to 124 entries but werenegative for other foreign firms up until 1000entries provides some support for Hypothesis 3.This result seemed consistent with the distribu-tion of Korean and foreign firms. Korean firmsentered both regions where many foreign firmswere present and regions where very few foreignfirms entered.

Models 7 and 8 used industry classification todistinguish firms. For Model 7, where monotonicrelationships are assumed, the coefficient for thecount of Korean firms in the same industry wasmuch larger than that of Korean firms in differ-ent industries was. If we calculate the averageprobability elasticities, a 100 percent increase of other Korean firms in the same industry in a regionincreases the likelihood that a firm locates in thisregion by 5.8 percent. On the other hand, the same

increase in other Korean firms in different indus-tries increases the likelihood a firm locates in thatregion by only 0.5 percent. This result suggeststhat Korean firms in the same industries createdlarge network externalities among themselves bysharing technology or know-how and providinglegitimacy.

The results for Model 7 also demonstrated thatthe negative coefficient for the count of otherforeign firms in Model 5 was driven by foreignfirms in different industries. Korean firms derivedpositive network externalities from foreign firms inthe same industries. These firms derived negative

network externalities by co-locating with foreignfirms from different industries, perhaps because

of increased competition in factor markets. Incontrast, the presence of local firms in the sameindustry did not seem to attract Korean firms inChina. Since many local Chinese firms trailedKorean firms in terms of technological know-how,the net flow of knowledge was from Korean firmsto local firms. A Korean firm might derive morebenefits from locating in a region where localfirms in industries different from the Korean firm’shad already created an industrial infrastructure andan ample supply of workers. The log-likelihoodtest comparing Models 5 and 7 rejected the null

hypothesis that the coefficients for count variableswere the same regardless of industry. Thus, wefound support for Hypothesis 4.

Model 8 tested for curvilinear relationships byadding the squared terms of the count variablesin Model 7. All these variables showed invertedU-shaped relationships in Model 6 except for thecount of foreign firms in different industries, whichshowed a U-shaped relationship, the count of for-eign firms in the same industry, which was mono-tonically positive, and the count of local firms inthe same industry, which was insignificant. Thelog-likelihood test comparing Models 7 and 8rejected the null hypothesis that the coefficients forall squared terms were zero, providing additionalsupport for Hypothesis 1.

In order to check the robustness of our results,we implemented several alternative formulations.The conditional logit model relies on the assump-tion of independence of irrelevant alternatives,which means that the relative probability of choos-ing two alternatives does not depend on the avail-ability of other alternatives (McFadden, 1974;Hausman and McFadden, 1984). To demonstraterobustness regarding the assumption of indepen-

dence from irrelevant alternatives, we experimen-ted with different subsamples. Models 1 and 2 of Table 3 showed the same specifications as Models7 and 8 of Table 2, while we dropped Shanghaifrom our choice set. Shanghai received a totalof 1651 foreign entries, which represented morethan 55 percent of all foreign entries. We droppedShanghai from the choice set in order to seewhether negative network externalities from for-eign firms in different industries might disappear.As in Model 1 of Table 3, the negative impactof foreign firms in different industries was sus-tained even after we dropped Shanghai from the





choice set. The relationship between the count of foreign firms in different industries and the likeli-

hood of co-location seemed to be monotonic ratherthan curvilinear after we dropped Shanghai. Wealso experimented with different cut-off points indetermining the number of regions to be includedin the choice set. For instance, when we droppedregions that received fewer than three entries, Xin-

jian, Anhui, and Hubei were excluded from thischoice set. Models 3 and 4 showed results withonly 15 choice sets. These results seemed consis-tent with the ones in Table 2.

We also tested whether multiple entries by thesame firms could affect our estimations. In a linear

regression, it is relatively easy to include eitherfixed effects or random effects to account forsuch panel data. Including such effects is trickier,however, for the conditional logit model, as thismodel calculates the conditional likelihood foreach choice set. We therefore experimented bydropping firms that invested more than once inChina. The results of this test were consistent withthose reported in this study. We also experimentedwith a model where we grouped multiple entriesand their other alternatives in the choice sets by thesame firm (or group) into a single choice set andcalculated the conditional likelihood. For example,

when a firm (or group) invested twice during thetime study period, there were two regions that thefirm (group) entered and 34 (i.e., 2× 17) regionsthat it did not enter in the new choice set. Suchspecification generated results very similar to thosereported in this study.10

DISCUSSION

Multinational corporations’ location choices withina foreign country have only recently received

10 We also experimented with alternative specifications of attri-butes. Several prior works that studied agglomeration patterns,such as Head et al. (1995) and Chung and Song (2004), included‘alternative specific constants’ instead of various attributes foreach region, which we used in this study. Alternative specificconstants are equivalent to region-specific dummy variables.Although such regional dummy variables may be able to cap-ture unspecified regional attributes other than the five attributevariables specified in this study, they have the great limita-tion of being time-invariant. In a dynamic country such asChina, such variables seemed inappropriate. When we addedthese region-specific dummy variables to the regional attributesin our study, however, most of these regional attribute variablesturned insignificant. Since we were interested in observing whichattributes explained location choices, we preferred using attributevariables rather than regional dummy variables.

attention. Our study confirmed a regional agglom-eration pattern for our sample of Korean firms

in China. It is consistent with prior research thatobserves the agglomeration of Japanese firms inthe United States (Head et al., 1995; Shaver andFlyer, 2000; Chung and Song, 2004), and theinnovation-promoting aspects of regional clusters(Porter, 1998; Porter and Stern, 2001). Thoseworks treated network externalities as location spe-cific but not as firm specific. By doing so, theymight have erred in treating all firms within thenetwork as homogeneous. This study enhances ourtheoretical understanding by defining and measur-ing network externalities as specific to a focal firm

in question. It highlights the fact that investingfirms derive levels of network externalities thatvary according to firm boundaries, nationality, andindustry affiliation. This new perspective on net-work externalities as firm specific generates severalimplications for further research.

First, we empirically confirmed that the compo-sition of a network influences what network exter-nalities exist. The types of firms that constituted aregional network were heterogeneous and createdvarying degrees of network externalities. We foundthat network externalities were stronger amongfirms in the same business group, among firmsof the same nationalities, and among firms in thesame industries.

Second, while prior work portrayed network externalities as monotonic, we found they werecurvilinear, especially when we defined network externalities as firm specific and classified thetypes of firms constituting a regional network moreprecisely. We argued that negative network exter-nalities occur when firms have spillovers of theirown technology or knowledge, when there is inten-sified competition in factor markets, and when thepotential for groupthink exists, and that these costs

outweighed any positive network externalities aftera certain level. It is interesting to note that agglom-eration of firms of the same type is subject to acurvilinear relationship, suggesting that agglom-eration of the same type of firms is not benefi-cial beyond a certain point. On the other hand,agglomeration of other foreign firms in the sameindustry shows a monotonically increasing pattern.This finding is consistent with Porter and Stern’s(2001) argument that agglomeration of firms withdiverse backgrounds facilitates innovation.

Third, this study found that a firm’s own priorentry, as well as entries by firms associated with





the same business groups, created the greatest net-work externalities. The impact of a firm’s own

prior entry or entries by firms in the same busi-ness groups, gauged by average elasticity, was 470times higher than that of an unrelated firm’s entry(i.e., 46.7–47.7% vs. 0.1%). This result suggeststhat experience-based knowledge is best trans-ferred or spilled over within the boundaries of afirm, confirming that it is difficult to transfer tacitknowledge across firm boundaries (Kogut and Zan-der, 1992; Szulanski, 1996). The results may alsoindicate that firms imitate other firms in the samebusiness group to gain legitimacy or reduce uncer-tainty. This study therefore provides a rationale

for the regional patterns of agglomeration by busi-ness groups that have been observed for Japanesevertical keiretsu in the United States (Head et al.,1995). This study also reconfirms the findings of Chung and Song (2004) that agglomeration occursmore within firms than between firms.

Fourth, it advances our understanding of network externalities by integrating literature from orga-nizational theory and economics. Our hypothesesreflected economists’ arguments that agglomera-tion occurs because of phenomena like knowledgespillovers and the sharing of various infrastructures

(Marshall, 1920; Krugman, 1991). Our hypothe-ses were also consistent with organizational the-ory, namely in arguing that network externali-ties occurred because firms wished to gain legit-imacy and reduce uncertainty through imitation(DiMaggio and Powell, 1983; Levitt and March,1988; Guillen, 2002; Henisz and Delios, 2001). Weargued that the curvilinear relationship between thenumber of firms and the likelihood of co-locationwould be more apparent when agglomeration wasmotivated by a desire to gain legitimacy ratherthan by real economic gains. In demonstrating this

relationship, our study improves understanding of co-location.This study has several limitations. First, we

could not empirically distinguish economic rea-sons for agglomeration from reasons proposed byorganizational theorists, such as uncertainty avoid-ance and legitimacy. Researchers should developmeasures that can empirically distinguish these twosets of reasons other than simple count-based mea-sures. Second, it is possible that the results of thisstudy may reflect idiosyncrasies of Korean firms.Korea is closely located to China, and Koreansuse Chinese characters, so it is easier for them to

learn Chinese and absorb Chinese culture. Kore-ans also share some cultural heritage with China.

Korean firms might thus feel more confident aboutoperating in China relative to firms from othercountries, which may explain why Korean firmsspread into many geographic regions while foreignfirms tended to concentrate in a few locations.

Third, there might be strong network externali-ties among Korean firms because of ethnocentrism.Future research should examine whether MNCsoriginating in other countries show similar patternsof strong network externalities with firms of thesame nationalities. Fourth, this study lumped allother foreign companies into one category. MNCs

from other nations may derive stronger network externalities from firms of similar culture than theydo from those of dissimilar ones. Disaggregatingthe ‘foreign firms’ category into subgroups accord-ing to cultural similarities may show much strongerrelationships from foreign firms than the ones wedocumented. Similarly, this study did not explicitlyconsider relatedness/complementarities of indus-tries. Considering industries at a more disaggre-gated level and examining relatedness among themwould help capture network externalities moreprecisely.

Fifth, this study focused only on the hetero-geneity of firms that were already in China andhad created network externalities, but it did notaddress the heterogeneity of investing firms them-selves. Shaver and Flyer (2000) and Chung andSong (2004) argued that investing firms’ hetero-geneity also affects co-location decisions. Furtherstudies to assess the effects of such heterogeneityare warranted.

This study demonstrated that firms tended to co-locate with others to benefit from network external-ities. Our results suggest, however, that this gen-eralization is misleading. Because network exter-

nalities showed curvilinear patterns as we definedtypes of firms within the network more narrowly,above a certain threshold, negative network exter-nalities could outweigh positive ones. Since aregional network comprises many types of firms,managers who make location decisions should bal-ance between positive and negative network exter-nalities.

Furthermore, this study showed that some firmscoordinated location decisions to hedge against thepossible negative impacts of agglomeration in afew locations. LG Group’s strategy to spread theoperations of its individual affiliates over many





regions is a good example. Business divisions inan affiliate may be attracted to the immediate ben-

efits from locating near its own previous invest-ment. Since most diversified MNCs are organizedinto semi-autonomous business divisions that maketheir own foreign entry decisions, the hazards of negative network externalities that come from toomuch agglomeration can be substantial. Thus, ourresults suggest that managers of MNCs shoulddevelop an overall foreign entry strategy to guardagainst the hazard of too much agglomeration in afew popular locations.

ACKNOWLEDGEMENTS

We thank Jemo Chung, John Lafkas, Panseop Lee,Chunkyu Park, Hweonjung Park, Jaehyuk Rhee,seminar participants at National University of Sin-gapore, New York University, London BusinessSchool, Copenhagen Business School and Norwe-gian School of Management, and two anonymousreviewers for helpful comments and suggestions.Financial support from the Korea Research Foun-dation (2004-041-B00246) is gratefully acknowl-edged. Additional support from Korea Univer-sity Business School through an SK Distinguished

Research Award is also gratefully acknowledged.

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types of firms generating network ex tern ali ties and mncs' co-location decisions

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