organizational foundings in community context: instruments...

Combining insights from organizational ecology andsocial network theory, we examine how the structure ofrelations among organizational populations affects differ-ences in rates of foundings across geographic locales. Wehypothesize that symbiotic and commensalistic interpop-ulation relations function as channels of informationabout entrepreneurial opportunities and that differingaccess to such information influences the founding rate.Empirical analyses of U.S. instruments manufacturerssupport this argument. The founding rate of instrumentsmanufacturers rises with the densities of organizationalpopulations that have symbiotic and commensalistic rela-tionships with instruments manufacturers. These factorsencourage the initial foundings of instruments manufac-turers in areas where such organizations were not previ-ously found. The dominance of organizational popula-tions tied to instruments manufacturing by symbiotic orcommensalistic relations increases the rate of foundingsof instruments manufacturers, whereas the dominance oforganizational populations that lack these relationsdecreases it. Finally, we find that interpopulation relation-ships that hinge on direct contact have less impact on ini-tial foundings as geographic distance increases. Theseresults have implications for research on organizationalecology, entrepreneurship, urban sociology, and econom-ic geography.•Where organizational foundings occur has been a topic ofconsiderable interest to scholars of organizations. Most ofthe work seeking to explain differences in founding rates hasidentified internal attributes of spatial units—be they nations,states, regions, cities, or even zip code areas. Laws (Dobbinand Dowd, 1997), infusions of capital derived from initial pub-lic offerings or acquisitions (Stuart and Sorenson, 2003b), andthe number of preexisting organizations (Carroll and Wade,1991; Lomi, 1995) are examples of internal attributes of spa-tial units that have been shown to influence the foundingrate. The relations that link spatial units to each other andthat determine their structural position have received relative-ly less attention. A few studies have examined the relationsof spatial units in geographical space, focusing in particularon the effect of geographical proximity on founding rates(e.g., Hedstrom, 1994; Wade, Swaminathan, and Saxon,1998). The potential effects on founding rates of the positionthat communities occupy in market space have been largelyoverlooked. Yet previous research has shown that organiza-tions gain access to resources as a function of their structuralposition in networks (Podolny, Stuart, and Hannan, 1996;Dobrev, Kim, and Hannan, 2001; Sørensen, 2004) and thatthe community is most often the habitat in which thesestructured flows of resources have an impact on concreteorganizational populations (Hawley, 1950; McPherson, 1983;Uzzi, 1997; Ingram and Roberts, 2000; Owen-Smith and Pow-ell, 2004).

In terms of foundings, then, where one chooses to start aspecific kind of organization matters greatly, because theresources employed by those who would start such organiza-tions are unevenly distributed in space. Communities are sit-uated in market structures, and the combination of economic

© 2006 by Johnson Graduate School,Cornell University.0001-8392/06/5103-0381/$3.00.

•We are grateful to Howard Aldrich, RonBurt, Henrich Greve, Paul Ingram, JimLincoln, Alessandro Lomi, Anne Miner,Willie Ocasio, Huggy Rao, Martin Ruef,David Strang, Brian Uzzi, and seminar par-ticipants at the Graduate School of Busi-ness, Stanford University, GraduateSchool of Business, University of Chica-go, Kellogg School of Management,Northwestern University, and the Univer-sity of Maryland Conference on Entrepre-neurship for helpful comments and toShahar Maoz, Nydia MacGregor, JasonMills, and Chris Rider for their help inassembling the data. We are also indebt-ed to Don Palmer and the anonymousASQ reviewers for insights and com-ments, which greatly enhanced the paper,and to Linda Johanson for her editorialassistance. Direct correspondence to PinoG. Audia, University of California, Berke-ley, Haas School of Business, 545 Stu-dent Services Building, Berkeley, CA94720-1900; e-mail: [email protected].

OrganizationalFoundings inCommunity Context:InstrumentsManufacturers and TheirInterrelationship withOther Organizations

Pino G. AudiaUniversity of California,BerkeleyJohn H. FreemanUniversity of California,BerkeleyPaul DavidsonReynoldsFlorida InternationalUniversity

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actors in those communities and in nearby communities canprovide differential access to the resources founders need toset up new organizations. Our question here is why found-ings of one kind of organizations occur more often in somecommunities and less often in others. A key input in theorganizational creation process is information about entrepre-neurial opportunities (Aldrich and Zimmer, 1986; Freeman,1986; Romanelli, 1989; Shane, 2000), and the market rela-tions within which local communities are embedded likelyfacilitate or constrain access to such information. Althoughour primary focus in this paper is where communities are sit-uated in the exchange relations among organizational popula-tions, we also consider the importance of a community’sgeographical position, because geographic distance createsfriction in the transfer of information across space (Jaffe, Tra-jtenberg, and Henderson, 1993; Almeida, Dokko, andRosenkopf, 2003).

We examine how the market positions of local communitiesinfluence the spatial distribution of foundings for two relatedreasons. First, ecological research on foundings has focusedmainly on understanding the evolutionary dynamics of singleorganizational populations. The few studies that have exam-ined the impact of multiple organizational populations onentrepreneurial activity have tended to focus on a small sub-set of populations that researchers assume a priori to be par-ticularly influential. Much of this work has focused on therole of venture capital firms and universities (e.g., Zucker,Darby, and Brewer, 1998; Stuart and Sorenson, 2003a).Those studies yielded insights on interpopulation dynamics,but because they focused on a small subset of populationsand did not directly measure relations of interdependence,the research designs did not allow comparisons of relationsof different kinds and strength across a variety of organiza-tional populations tied to the population under study. Focus-ing on the market position of local communities provides anopportunity to address this gap. Second, in studies that focuson the internal dynamics of a single population, members ofthe organizational population must already be present in alocality in order for scholars to examine founding rates there.This restriction has discouraged organizational scholars fromaddressing important questions such as why some unoccu-pied areas experience initial foundings whereas others donot. In contrast, the positional approach suggested hereshifts emphasis from a single population to the relations link-ing organizational populations. The presence in a locality ofthe members of the organizational population under study isnot a prerequisite in our analytical framework because otherpopulations can provide a second-best source for information.

Our empirical research is on the founding rates of instru-ments manufacturers in the United States. We focus oninstruments manufacturing for several reasons. First,although the history of instruments manufacturing extendsover several centuries (Williams, 1994; Baird, 2004), thisindustry is still characterized by high rates of innovation,largely because it lies at the forefront of developments in sci-entific, technical, and industrial research. These high rates ofinnovation create information flows that can define opportuni-

382/ASQ, September 2006

ties for organization founders. These information flows andthe social networks through which they are channeled arecentral to our theoretical interests. Second, creating aninstruments organization often requires modest capital, andscale economies are not large. In general, barriers to entryare low. Consequently, there is a constant flow of new orga-nizations (U.S. Department of Commerce, 1977, 1983, 1990).And third, for most instruments manufacturing companies,the shipping costs of raw materials and finished products areonly a small fraction of the total value of the products (Dar-nay, 1989). As a result, instruments manufacturing does notdepend on proximity to buyers and suppliers. Geographicaldispersion is feasible. This means that the processes of inter-est are unlikely to be overwhelmed by simple transportationeconomies.

FOUNDING PROCESSES IN POPULATIONS OFINSTRUMENTS MANUFACTURERS

The Social Structure of NichesOrganizational ecologists have studied the founding process-es of organizations for more than twenty years (for reviews,see Carroll and Khessina, 2005; Aldrich and Ruef, 2006).Their work revolves around two related problems foundersface. First, they must secure resources for their nascentorganizations. Organizations are started with endowments ofsuch resources as money, technology, commitments ofeffort, and information. Entrepreneurs spend much of theirtime gathering these resources. Second, they attempt to cre-ate legitimacy for the nascent organization, building agree-ment among those whose cooperation they need that thenew organization will operate in acceptable ways. Of course,the legitimacy problem feeds into the resource problem.Organizations lacking legitimacy have a more difficult timedeveloping a resource base.

One of the most important ways in which organizationalfounders solve these two problems is by adopting an existingorganizational form. It is more economical to build a neworganization as a variant on a commonly accepted theme(Hannan and Freeman, 1977; Brittain and Freeman, 1980).Such forms are legitimated through everyday observation.Because the frequency of observation of an organizationalform is partly a function of its density, the more common akind of organization is, the more likely it is to be taken forgranted (Hannan, 1986). This is a central insight in the theoryof density dependence. The legitimacy of a new organizationcan also be enhanced if the founders are personally wellreputed or affiliated with other legitimate organizations(Baum and Oliver, 1992).

Each of these processes works better when distancesbetween organizations are small and the context is local. Theidea of population density itself is conventionally definedwithin a bounded geographical system (Carroll and Wade,1991; Lomi, 1995). Obviously, if legitimation is driven by thefrequency of observation, it matters whether a given popula-tion is spread out over a large expanse or concentrated local-ly. Similarly, the personal reputations of founders and backersare likely to carry more weight in the areas where they are


Organizational Foundings

best known. Only the most famous and highly respectedpeople in a society can establish an organization’s legitimacyfar from home. The same can be said for organizational affili-ation. For a new organization to “borrow” legitimacy fromanother, those who are granting or withholding that legitima-cy have to know the sponsoring organization and accept it aslegitimate.

Evidence supporting the local nature of the founding processcan be found in studies of breweries (Carroll and Wade,1991; Wade, Swaminathan, and Saxon, 1998), banks (Free-man and Lomi, 1994; Lomi, 1995; Greve, 2002), automobileproducers (Bigelow et al., 1997), biotech firms (Stuart andSorenson, 2003a, 2003b), newspapers (Carroll, 1985), andfootwear producers (Sorenson and Audia, 2000), to name afew. A common theme emerging from this literature is that acommunity is not simply a place where a founder happens tobe when the organization creation process begins. Instead,factors in the community produce systematic effects, accel-erating and decelerating the rate of founding. This work,however, suffers from two related limitations. First, althoughorganizational ecology was inspired by Hannan and Free-man’s (1977) question of why there are so many kinds oforganizations, most ecological studies of foundings havegiven only limited attention to the implications of the diversesets of organizational populations envisaged by Hannan andFreeman. Researchers usually compare spatial units byfocusing on the number of organizations of the populationunder study. The few studies that examine relations betweenpopulations focus on failure or exiting processes (e.g., Bar-nett and Carroll, 1987; Barnett, 1990; Ingram and Simons,2000; for a recent review, see Freeman and Audia, 2006).Furthermore, studies of foundings across spatial units havetended to be confined to a small set of organizational popula-tions, usually similar organizational forms performing similareconomic or social functions (e.g., Carroll and Wade, 1991;Lomi, 1995; Zucker, Darby, and Brewer, 1998; Simons andIngram, 2003; Stuart and Sorenson, 2003a). But very dissimi-lar organizational forms can produce interesting and impor-tant effects on the vital rates of any of the populations, asCottrell (1950) showed in his study of the effects of changesin railroads on the survival of towns. Organizational popula-tions ranging from churches to retail stores to schools aroseas the railroads were founded and grew into previouslysparsely settled territories. When these railroad organizationsadopted diesel electric locomotion after World War II, loco-motives required less frequent servicing, and towns alongthe right of way shriveled and died. With them, organizationalpopulations found in those towns went locally extinct. Thisstudy clearly illustrates that other organizational populationsare a key component of the community context and that con-fining the analysis to the demography of single populations,or to the study of a small set of similar forms, may severelyconstrain our ability to explain the trajectories of organization-al populations.

The second limitation of ecological studies of foundings isthat density dependence, the main underlying theory,although one of the most robust organizational theories, does


not fully address issues of location, especially why somecommunities that do not have organizations of a certain kindexperience initial foundings, while others do not. Densitydependence is not well equipped to address this questionbecause it links the probability of foundings to the number ofexisting organizations, presupposing the existence of a givenkind of organization. Because accurate estimation of densitydependence models requires following populations from thestart, this means that populations must be chosen oppor-tunistically. The theory is silent about where one wouldexpect populations to arise.

Some studies implicitly address this limitation but have pro-vided only partial answers. Hannan et al. (1995) argued thatfoundings tend to disperse in space as information about theviability of new organizational forms spreads across geo-graphical boundaries through mechanisms such as printmedia and industry events. This argument, however, appliesonly to the early stage of an organizational population anddoes not explain which geographical areas may be most like-ly to experience initial foundings. Hedstrom (1994) arguedthat foundings disperse in space through localized social net-works that span geographically adjacent communities, but hedid not address whether and when initial foundings emergein isolated areas.

Density dependence alone is also limited in its ability toexplain the question of why communities that have similarnumbers of organizations diverge in their founding rates.Why, for example, did the founding rates of high technologyorganizations in Silicon Valley and Route 128 diverge (Saxen-ian, 1994)? An ecologist might answer this question by delin-eating how relations among organizational populations colo-cated in geographical space facilitate or constrain access toresources. Because density dependence focuses on intrapop-ulation dynamics, this question may be more satisfactorilyanswered by extending density dependence theory to ana-lyze realized niches (Hutchinson, 1957; Hannan and Freeman,1989), niches of organizational populations conceptualized inways that include competitive and mutualistic effects ofother populations. These effects operate through networksand communities.

Networks and Communities

A fundamental point of contact between organizational ecolo-gy and network research is that both draw attention to con-straints imposed on social actors by similarities in resourcedependence. In organizational ecology, the unit of analysis isthe organizational population, which is defined as the set oforganizations manifesting an organizational form. One of themost common defining characteristics of an organizationalform is its niche: the combination of resource abundances inwhich it is found. Because most of the resources organiza-tions utilize come from other organizations, such flows ofresources correspond to relations between organizationalactors.

In social network analysis, actors are said to be structurallyequivalent and to occupy the same position within the socialstructure if they have the same pattern of relations with oth-



ers (Lorrain and White, 1971; White, 1981, 2002; Burt, 1992).The parallel with organizational ecology is especially clear innetwork analyses of markets. White (2002) conceived of mar-kets as assemblages of ego-nets defined around producerorganizations. Each organization acquires resources fromupstream suppliers. Such a producer also pushes productsdownstream to customers. As with the supplier relations,these may be single transactions of standardized goods forprices known in advance or they may be enduring relation-ships based on incomplete contracts. These ego-nets aggre-gate into markets, as structurally equivalent sets of buyersand sellers operate in resource environments that have com-mon properties. Viewed in this way, organizational populationniches reflect structural equivalence among population mem-bers, as both DiMaggio (1986) and Burt (1992: chap. 6)noted. So the link between the ecologist’s concept of nicheand the network analyst’s concept of structural equivalence isbased on the view that organizations provide resources forother organizations and that organizational actors are identi-fied through the resources they provide for and consumefrom other organizations. These resources are exchangedthrough interorganizational networks.

Information is a key resource in the organizational creationprocess, and interorganizational networks are vehicles for thetransfer of information. A rich literature, including insightfulqualitative studies (Larson, 1992; Uzzi, 1997) and quantitativeanalyses of innovations (Bothner, 2003), practices (Davis,1991; Davis and Greve, 1997), investment decisions (Soren-son and Stuart, 2001), and strategic behaviors (Haveman,1993; Greve, 1995; Haveman and Nonnemaker, 2000), pro-vides evidence of information transfer through interorganiza-tional networks. Although much of this work focuses on indi-vidual organizations rather than organizational populations,information transfer should be as relevant to interpopulationrelations as it is to interorganizational relations.

Network research points to two features of the movement ofinformation across organizations that are important to ouranalysis (Strang and Soule, 1998). First, much informationtransfer flowing through interorganizational networks is local-ized in geographical space. A growing body of work indicatesthat the probability that two organizations are connected, likethe probability that two individuals will interact, declines as afunction of the geographic distance between them. Lincoln,Gerlach, and Takahashi (1992) reported that Japanese corpo-rations are more likely to share directors when they are head-quartered in the same prefecture. Kono et al. (1998) and Mar-quis (2003) found the same pattern of local interlocking in theU.S. and also showed that spatially proximate companies aremore likely to share directors when the institutional contextof the community facilitates interactions among the corporateelite. Sorenson and Stuart (2001) found that venture capital-ists are far more likely to invest in entrepreneurial venturesthat are located close by. Romo and Schwartz (1995)observed that the majority of establishments in the State ofNew York do not migrate to lower-wage areas, which theyattributed to organizations’ embeddedness in the local econo-my. It seems clear that the positive effect of geographical


propinquity on network formation occurs partly because thecost of interacting increases with geographic distance. Acomplementary explanation is based on organizational deci-sion makers’ limited ability to collect information about dis-tant places (Pred, 1977).

Local interorganizational linkages are not the only kind of tiesfacilitating the flow of information among colocated organiza-tions. Professional and personal ties often overlap withinterorganizational ties (Saxenian, 1994; Kono et al., 1998;Almeida and Kogut, 1999). An implication of this multiplexityof local networks is that information spills through differentpathways among loosely connected organizations (Owen-Smith and Powell, 2004).

Second, networks linking spatially dispersed organizations arealso vehicles for the transfer of information across geographi-cal boundaries, although geographical distance may decreasethe frequency and quality of the information transfer. Davis(1991) and Davis and Greve (1997) showed that board inter-locks facilitate the diffusion of novel management practicesamong spatially dispersed organizations. Sorenson and Stuart(2001) reported that venture capital firms with many and dis-persed relationships with other venture capital firms over-come the constraints of geographical distance by gainingaccess to information about distant investment opportunitiesthrough these interorganizational linkages. Greve (1995)showed that intra-corporate networks linking spatially dis-persed units facilitate the transfer of information about strate-gic decisions. Similarly, Hansen and Lovas (2004) reportedthat intra-corporate linkages increase the probability of trans-fer of technical information among geographically dispersedunits.

The observation that information travels through interorgani-zational connections both locally and across geographicalboundaries may help us understand the link between thecommunity context and foundings of instruments manufac-turers. A recurring theme in the entrepreneurship literature isthat information about entrepreneurial opportunities is a keyresource for organizational creation (Kirzner, 1973; Freeman,1986; Romanelli, 1989; Venkatraman, 1997; Shane, 2000; fora review, see Audia and Rider, 2005). Researchers suggestthat those who are aware that there are untapped opportuni-ties in a particular business are more likely to take the stepsnecessary to create new organizations of that kind. Untappedopportunities can take many forms, including knowledge ofunmet customer needs, awareness of technological develop-ments that can improve the functionality of existing products,access to inputs under favorable conditions, or detailed infor-mation about market demand. The underlying mechanismlinking organizational creation and access to informationabout entrepreneurial opportunities is motivational. Specificand timely information about entrepreneurial opportunities islikely to increase individuals’ expectations that entrepreneur-ial efforts will lead to entrepreneurial rewards, therebyincreasing entrepreneurial motivation (Vroom, 1964).

Much of this information is not publicly available but, rather,is accessible only by individuals situated in favorable posi-



tions. Ecologists recognize the importance of access to infor-mation about entrepreneurial opportunities in organizationalcreation (Brittain and Freeman, 1980; Freeman, 1986), butthey view these information flows as taking place primarilywithin the boundaries of organizational populations (Sorensonand Audia, 2000; Phillips, 2002; Stuart and Sorenson, 2003a,2003b; for an exception, see Romanelli, 1989). The networkliterature reviewed above complements that argument bysuggesting that information about entrepreneurial opportuni-ties relevant to a particular population also flows from oneorganizational population to another through interpopulationrelations. Therefore, the literature implies that access toentrepreneurial opportunities relevant to instruments manu-facturing in a community is a function not only of the pres-ence of members of the focal population but also of the pres-ence of organizational populations linked to instrumentsmanufacturing. Furthermore, although network studies, likeecological studies of foundings, recognize that much informa-tion flows through interorganizational relationships that arelocalized in space, they add the idea that organizational popu-lations introduce information to the community through rela-tions with organizational populations located elsewhere.

The first step in our study of the foundings of instrumentsorganizations across communities is to identify the organiza-tional populations that are tied to instruments manufacturing.Such organizational populations increase the availability ofinformation about entrepreneurial opportunities relevant toinstruments manufacturing. Network research suggests twomain mechanisms by which a given organizational populationmay be tied to the population of instruments manufacturers:direct contact and competitive monitoring (Burt, 1987; White,2002). These mechanisms parallel two types of interpopula-tion relations studied by ecologists, symbiotic and commen-salistic relationships, respectively (Hawley, 1950; Hannan andFreeman, 1989; Aldrich and Ruef, 2006).

Direct Contact and Symbiotic Relationships

When two organizations engage in a continuing economicexchange, they can be said to be in direct contact. Thereforeboth suppliers and purchasers come into direct contact withinstruments manufacturers. Ecologists would say that suppli-ers and purchasers are tied to instruments manufacturers bya symbiotic relationship because their differences comple-ment each other (Hawley, 1950). A by-product of these eco-nomic relations is the transfer of information across organiza-tional boundaries. Although specific features of the nature ofthe relationship may affect the kinds of information trans-ferred (Uzzi, 1997), undoubtedly some information transferoccurs between the connected parties. Larson (1992) andUzzi (1997), in their qualitative studies of buyer-supplier link-ages, provided excellent examples. In Larson’s study, a retail-er reported how information passed on by a supplier facilitat-ed decision processes and capacity planning. One of Uzzi’sgarment manufacturers reported passing promising marketsignals to a buyer. Information about opportunities is routine-ly communicated in exactly this way.


The social network literature suggests that such informationtransfer may occur both between organizations colocated inspace and between organizations located in different geo-graphical areas. Symbiotic relations with instruments manu-facturing are likely to be both local and remote, becauseinstruments organizations sell to the national market and pur-chase inputs from geographically dispersed suppliers. Meyer(1990) classified instruments manufacturing as a nationalmarket industry, producing high-value goods for which trans-portation costs are a small percentage of the purchase price.Furthermore, although producers locate near suppliers whentransportation costs are a large part of the cost of the input(Weber, 1928), transportation costs are only a small fractionof the value of many of the components and technologiesused to make instruments (Darnay, 1989). Electronic tech-nologies and, in particular, semiconductor devices, whichbecame an integral component of most instruments in thelate 1970s, are an example. In a study of the computer indus-try, a sector that has much in common with instrumentsmanufacturing in terms of technologies and components,Angel and Engstrom (1995) found that computer manufactur-ers usually acquire key technology inputs such as electroniccomponents from suppliers that are geographically dispersed,with the notable exception of computer firms located in Sili-con Valley.

Symbiotic interdependencies between organizational popula-tions produce social relationships through which informationabout entrepreneurial opportunities diffuses. Therefore, thespatial distribution of symbiotic organizational populationsshould influence the spatial distribution of foundings. This isreflected in the history of Baird Associates, a precision instru-ments company founded in the late 1930s in Massachusetts(Baird, 2004: 226–227). Before founding this company, JohnSterner and Walter Baird worked for the Watertown Arsenalin Watertown, Massachusetts. The arsenal used DuPontinstruments to analyze the quality of metals used in guns andordinance. Sterner and Baird recognized an entrepreneurialopportunity when they discovered that the analysis of metalscould be done more effectively by using X-ray diffractiontubes rather than the traditional chemical analysis. By inter-acting with DuPont executives—who were located in Wilm-ington, Delaware—Sterner and Baird realized that their innov-ative idea had value. DuPont, in fact, was the firstorganization that ordered their X-ray diffraction apparatus,which shortly thereafter became the first product of BairdAssociates, a new instruments company based in Cam-bridge, six miles away from Watertown (personal communi-cation, David Baird).

Two clarifications must be noted in our theoretical analysis.First, although our main argument focuses on interorganiza-tional ties as information channels, the linkages arising fromsymbiotic relations also provide individuals with opportunitiesto form social ties with other actors who may support neworganizations in the focal population. These contacts mayprove useful not only for recruiting individuals who have in-depth knowledge of the business but also for securing sup-port from future exchange partners, just as the Baird Associ-



ates founders’ contacts with DuPont helped them securetheir first sales of X-ray diffraction tubes. In addition, suchsocial embeddedness increases the personal credibility andlegitimacy of the new organization. Second, one of the rea-sons we chose to study instruments manufacturing is thatshipping costs of raw materials and finished products areonly a small fraction of the total value of the products in thisindustry (Darnay, 1989). This feature allowed us to alleviatethe concern that the potential positive effect of symbioticorganizational populations on foundings simply reflects entre-preneurs’ preference to create new firms in locales wherethey can economize on transportation costs. In principle theindustry could be geographically dispersed. If a positive rela-tionship between symbiotic organizational populations andfoundings of instruments organizations is found, it is likely tobe due to the tendency of symbiotic organizational popula-tions to facilitate access to information about entrepreneurialopportunities. Defining a community’s symbiosis as theextent to which a local community is characterized by thepresence of organizations that either supply goods to (suppli-er symbiosis) or purchase goods from (purchaser symbiosis)instruments producers, the above argument suggests the fol-lowing prediction:

Hypothesis 1: The greater a community’s symbiosis, the higher therate of instruments manufacturing company foundings.

Competitive Monitoring and CommensalisticRelationships

Competitive monitoring is another mechanism through whichinformation flows within the market structure (Burt, 1987;Haveman and Nonnemaker, 2000; White, 2002; Bothner,2003). Social network researchers suggest that structurallyequivalent actors closely monitor each other because theycompete for prominent positions within the network (Burt,1987). The threat of being replaced prompts structurallyequivalent actors to pay attention to each other. Importantly,direct contact is not a necessary condition for the transfer ofinformation among structurally equivalent actors. As Burtnoted (1987: 1293), structurally equivalent actors are likely tohave “a direct and indirect awareness of each other: directby meeting when interacting with mutual acquaintances andindirect by hearing about each other through mutual acquain-tances.”

This logic can be readily extended to organizational popula-tions. Members of the same organizational population exhibitthe highest degree of structural equivalence because theytarget the same inputs and the same customers. Organiza-tional ecologists refer to this as “diffuse competition.” Thisrelation of structural equivalence prompts them to monitoreach other (Haveman and Nonnemaker, 2000; White, 2002;Bothner, 2003). The resulting information exchange informspotential entrepreneurs about opportunities that emanatefrom changing markets and technologies. The positive effectof information on founding rates closely resembles the legiti-mation effects posited by organizational ecologists in the the-ory of density dependence (Hannan, 1986; Hannan and Free-man, 1989).


If relations of structural equivalence facilitate the flow ofinformation, then information also flows across the bound-aries of organizational populations that have similar patternsof relations within the market structure. Machinery producersand instruments manufacturers, for example, pay attention toeach other because they compete among themselves tosecure electronic components under the best possible condi-tions. These interpopulation relations are commensalistic;commensalism “literally interpreted, means eating from thesame table” (Hawley, 1950: 39). The concept of commensal-ism differentiates relations of structural equivalence amongdistinct organizational populations from relations of structuralequivalence tying members of the same organizational popu-lation.

By monitoring the actions of instruments manufacturers,commensalistic organizational populations such as machineryproducers can anticipate situations that may potentially dam-age their position and, more important, can prepare them-selves to follow suit (White, 2002; Bothner, 2003). And com-mensalistic organizational populations may monitorinstruments manufacturers both locally and nonlocally. Ineither case, information transfer takes place. Obviously, theindividuals doing the monitoring may learn of entrepreneurialopportunities in the instruments business. Some of theseindividuals can then capitalize on the information to whichthey have access by creating new instruments manufactur-ers. This information will not be uniformly distributed inspace. It will be more readily available in communities thathave organizational populations tied to instruments manufac-turing by commensalistic relationships.

The mutualistic effect of commensalism is suggested by astudy of high-technology start-ups in Silicon Valley (Cooper,1970). This research showed that 63 percent of 220 start-upswere founded by individuals who came from organizationsthat served similar markets. Historical accounts of the originsof the automobile and television receiver industries provideadditional evidence. Many of the first automobile producerscame from organizations such as bicycle and horse-drawncarriage makers that served similar markets (Rae, 1959; Car-roll et al., 1996). Similarly, many of the founders of televisionreceiver manufacturers had worked in firms that maderadios—again, an organizational population that served thesame market (Klepper, 2003). Furthermore, detailed historiesof specific companies seem consistent with the idea thatclose monitoring of the actions of commensalistic organiza-tions often precedes the recognition of entrepreneurial oppor-tunities that stimulates organizational creation. Rae (1959:16), for example, reported that Studebaker Brothers, thelargest producer of horse-drawn vehicles, “made a point ofkeeping in touch with every development in the vehiclefield.” Although the company had considered and rejectedthe idea of making bicycles, their constant monitoring oforganizations addressing transportation needs likely led to thedecision to enter automobile production. Similarly, Barber(1917: 72–73) recounted how Charles Dureya, a bicyclemechanic, produced a gasoline-powered automobile by“merely assembling the ideas that had been accumulated.”



Presumably, to assemble these ideas, Charles Dureya musthave paid attention to developments in the vehicle field, aspeople in Studebaker Brothers did.

Just like symbiotic organizational populations, commensalisticorganizational populations are likely to provide individuals notonly with information about entrepreneurial opportunities rel-evant to instruments manufacturing but also with social tiesto resource providers. The fact that commensalistic organiza-tions engage in exchange relations similar to those of thefocal population suggests that aspiring entrepreneurs comingfrom the ranks of these organizations do not have to start anew business from scratch. Compared with individuals locat-ed elsewhere in the social structure, they are in a position toknow exchange partners who might be able and willing tosupport a new organization in the focal population. Their rep-utations may also help them secure favorable exchange con-ditions from suppliers and early commitments from pur-chasers.

We define a community’s commensalism as the extent towhich a community is characterized by the presence of orga-nizational populations that are similar to instruments manu-facturing in terms of the markets they serve or the suppliersfrom which they acquire inputs. Based on the above argu-ment, we make the following prediction:

Hypothesis 2: The greater a community’s commensalism, the high-er the rate of instruments manufacturing company foundings.

Information Redundancy

If much information about entrepreneurial opportunities rele-vant to instruments manufacturing originates within thissame organizational population, then the extent to which theinformation channeled by symbiotic and commensalistic orga-nizational populations is unique and nonredundant will be afunction of the local density of instruments manufacturers. Incommunities that have many instruments manufacturers,information about this business may already be widely avail-able. A large number of instruments organizations meansthat many individuals employed by these organizations knowof entrepreneurial opportunities relevant to this business. Fur-thermore, information that resides in preexisting instrumentsmanufacturers is likely to spill over within the communitythrough professional and personal networks. In contrast, incommunities that have few instruments manufacturers, theinformation channeled by symbiotic and commensalistic orga-nizational populations serves as a “second best” source. Thisreasoning suggests that the effect that symbiotic and com-mensalistic organizational populations have on the foundingrate may be contingent on the local density of instrumentsmanufacturers.

Hypothesis 3: The smaller the number of instruments manufactur-ers, the greater the positive effect of a community’s symbiosis andcommensalism on the rate of instruments manufacturing companyfoundings.


Initial Foundings

Taking this logic one step further, the theoretical argumentdeveloped here provides a possible explanation for the emer-gence of the first instruments manufacturers in unoccupiedcommunities. Symbiotic and commensalistic organizationalpopulations in unoccupied communities by definition are tiedto instruments manufacturers located elsewhere. Theserelated organizational populations introduce information intothe community that would otherwise not be available.Because information about entrepreneurial opportunitiesincreases the probability that some individuals might createnew organizations, the probability of initial foundings shouldbe higher in those communities than in unoccupied commu-nities that do not have organizational populations with com-mensalistic and symbiotic patterns of exchange with instru-ments manufacturers.

Hypothesis 4: The greater a community’s symbiosis and commen-salism, the higher the probability of the first foundings of instru-ments manufacturing companies.

Information Decay

If the effect of symbiotic and commensalistic organizationalpopulations on foundings arises from information transfer, wecan also expect that whether an unoccupied locality is situat-ed in geographical proximity to instruments manufacturers or,instead, is far removed from them will be consequential.Specifically, the flow of information arising from direct con-tact or competitive monitoring should be hindered by geo-graphical distance. As geographic distance increases, theopportunity for face-to-face interaction declines due to timeconstraints and transportation costs. Furthermore, the meansof communication that replace face-to-face interaction likelydecrease the quantity and quality of the informationexchanged. Evidence supporting this reasoning comes fromstudies of intra-organizational information transfer. Hansenand Lovas (2004) found that spatially dispersed teams thathave similar competencies are more likely to transfer infor-mation than spatially dispersed teams that do not have simi-lar competencies, but this effect declines with geographicdistance. Similarly, Audia, Sorenson, and Hage (2001) report-ed that multi-unit organizations benefit from knowledge trans-fer across units but those that are more geographically dis-persed benefit less. The friction created by geographicdistance in the transfer of information leads us to expect thatthe impact of symbiotic and commensalistic organizationalpopulations on initial foundings should be weaker as the geo-graphic distance from instruments manufacturers increases.

Hypothesis 5: The greater the geographic distance between anunoccupied community and instruments manufacturers locatedelsewhere, the smaller the positive effect of a community’s symbio-sis and commensalism on the probability of the first foundings ofinstruments manufacturing companies

Local Dominance

Ecological studies of foundings suggest that founders facetwo problems: finding the necessary resources, one of which



is information, and building legitimacy for the nascent organi-zation. We have argued that information moves among orga-nizational populations tied by symbiotic and commensalisticrelations. This implies that organizational populations that lackthese ties to instruments manufacturing are less likely to bea source of information about instruments manufacturing.But unrelated organizational populations that occupy a posi-tion of local dominance may also influence the legitimacyattributed to the organizational forms present in the commu-nity.

Hawley (1986: 35) defined “dominants” as those units per-forming a key function, noting that “the key function is deter-mined by the comparative importance of production and oftrade as sources of sustenance.” Dominance involves bothpower and resource distribution. At times, dominant actorsare viewed as those having a disproportionate share of theresources. These conceptualizations are especially commonwhen the analyst is studying metropolitan area dominance(Duncan et al., 1960). In other treatments, actors are domi-nant because they have power over others (Friedland andPalmer, 1984). Hawley’s (1986) theory combined the two,arguing that standing higher in the flow of resources of acommunity itself creates hierarchy. Other actors, standingdownstream in the resource flow, depend on those abovethem and are subject to extortionate demands by dominantactors. This would be true whether those actors are citieswithin a metropolitan area, organizations within a region, orfamilies within a city or town.

In this study, we are primarily interested in the dimension ofdominance that pertains to resource distribution. We treat anorganizational population as dominant if it has the largestshare of resources in the area. The presence of a dominantpopulation so defined should influence the type of informa-tion available within the community. Residents likely havegreater access to information relevant to both the dominantpopulation and to the populations to which it is related bycommensalistic and symbiotic relationships. A dominant pop-ulation related to instruments manufacturing by a symbioticor commensalistic relation should have a positive impact onthe flow of information that may propel individuals into creat-ing instruments organizations. A dominant population unrelat-ed to instruments manufacturing should not have an impacton the availability of information relevant to instruments man-ufacturing, but it may modify the founding rate of instru-ments manufacturers by influencing the legitimacy attributedto different organizational forms present in the community.

As Carroll and Hannan noted (2000: 339), an organizationalform becomes legitimate in part “as a greater number of indi-viduals come into contact with it and thereby become awareof its features.” A form acquires legitimacy through repeatedcontact because it is gradually perceived as “a natural way toeffect some kind of collective action” (Carroll and Hannan,2000: 223). Given that the legitimation process is driven bythe frequency of contact, the dominant population often willbe perceived by residents as more legitimate than other orga-nizational forms because it is the organizational form mostfrequently encountered within the community. Furthermore,


the pattern of market relations in which the dominant popula-tion is embedded is likely to influence the extent to whichresidents become aware of the features of other organiza-tional forms. Because the dominant population facilitatesdirect and indirect contact with organizational populations towhich it is related by symbiotic and commensalistic relations,holding other community characteristics constant, theseorganizational populations should be perceived as more legiti-mate than organizational populations that are not tied to thedominant population. This reasoning suggests that in commu-nities that have a dominant population unrelated to instru-ments manufacturing, instruments organizations may sufferfrom a legitimacy gap compared with the dominant popula-tion and with the organizational populations to which thedominant population is related. The frustrations of a NewYork City software entrepreneur named Bernstein help illus-trate this point. According to Stites (1999), investors over-looked Mr. Bernstein’s software venture because New YorkCity, unlike Silicon Valley or Boston’s Route 128, is not recog-nized as a center for software development. New York City,as Stites put it, is known for its content, not for its code.Stites reported that unless Mr. Bernstein can link his ventureto the media industry with which New Yorkers are familiar,he faces a battle in gaining support for his business. Oneventure capitalist approached by Bernstein noted thatinvestors will be persuaded to invest in software in New YorkCity “when they see that the software industry is here andthriving.”

Because New York City has a more diverse local economythan, say, Detroit or Silicon Valley, this example probablyunderstates the legitimacy gap that an organizational formmay incur when its presence in a community is overshad-owed by an unrelated dominant population. Nonetheless, ithelps illustrate our conjecture that foundings of an organiza-tional population may be lower when the organizational popu-lation occupies a peripheral position in a community. Thepresence of a dominant population unrelated to instrumentsmanufacturing is a local condition that renders instrumentsmanufacturing peripheral in the community and, therefore, anorganizational form with dubious standing. The constrainingeffect of an unrelated dominant population should be moder-ated by the density of instruments manufacturers in the com-munity, because the more instruments manufacturers thereare, the more people are likely to take for granted this organi-zational form and the lower the gap between the legitimacyof instruments manufacturing and the legitimacy of the domi-nant population. Furthermore, this constraining effect of anunrelated dominant population on foundings of instrumentsorganizations may also be a reason why some unoccupiedcommunities experience initial foundings whereas others donot.

Hypothesis 6: The larger a community’s unrelated dominant popula-tion, the lower the rate of instruments manufacturing companyfoundings.

Hypothesis 7: The larger the number of instruments manufacturers,the smaller the negative effect of a community’s unrelated domi-



nant population size on the rate of instruments manufacturing com-pany foundings.

Hypothesis 8: In an unoccupied community, the larger a communi-ty’s unrelated dominant population, the lower the probability of thefirst foundings of instruments manufacturing companies.

The Geography of Foundings in InstrumentsManufacturing

Between 1978 and 1988, the period examined in this study,instruments manufacturing experienced sustained growth inthe United States (U.S. Department of Commerce, 1977,1983, 1990). The high costs of energy and raw materials inthe early 1970s were important factors underlying thisgrowth. The surge in energy prices led to demand for instru-ments designed to increase energy efficiency throughimproved measurement and control (e.g., thermostats withtimers to minimize fuel use when buildings are empty). Fur-thermore, federal fuel emissions regulations had a beneficialeffect, as corporations demanded instruments that couldboth measure emissions and limit the release of harmful sub-stances. The introduction of the microprocessor was alsoimportant, as it helped the industry develop new andimproved products. For example, microprocessor technologyenabled smaller devices, which led to the generation of ana-lytical instruments that could be taken outside laboratoriesand into the field.

Under these favorable conditions, the value of shipmentsincreased from $46 billion in 1977 to $105 billion in 1987(constant dollars), and many new organizations were formed.Data from Dun and Bradstreet indicate that the density ofinstruments manufacturers, including both autonomous firmsand branches of existing organizations, went from 10,422 in1978 to 16,295 in 1988. During this period, 11,726 newautonomous firms were added. To determine where thesenew organizations emerged, we used as our unit of observa-tion the Labor Market Area (LMA), a geographical areadefined as the territory where people work and live. CensusBureau analysts used information about commuting patternsto distinguish “bedroom” counties (where people live) from“commuting” counties (where people work) (Tolbert and Kil-lian, 1987). The resulting areas made up 382 LMAs. SomeLMAs, those in rural areas in particular, were aggregated toreach a minimum human population of 100,000.

There are two reasons for preferring LMAs over MetropolitanStatistical Areas (MSA) and/or U.S. states for empirical analy-ses. First, LMAs cover the entire U.S. and, unlike MSAs, arenot required to contain a metropolitan area with at least oneurbanized area of 50,000 people, and they include the ruralU.S. Second, the basis for LMA identification is commutingpatterns and not arbitrary geopolitical boundaries, which isconsistent with human ecologists’ definition of community,the geographical area where people work and live (Hawley,1986).

Examining the distribution of manufacturers in 1978 andthose formed between 1978 and 1988 suggests that mostnew autonomous organizations were formed in areas that


already had organizations of the same type. Fifty-six percentof the establishments were located in 15 LMAs where 24percent of the human population resided in 1978. The largestconcentrations of instruments manufacturers were in LosAngeles, New York City, Chicago, Boston, Philadelphia, Tren-ton, and San Jose. The LMAs with the highest density percapita were San Jose and Boston, with 16 and 13 organiza-tions per 100,000 inhabitants, respectively. Between 1978and 1988, 49 percent of the new organizations were formedin the 15 LMAs that had the highest concentration of organi-zations in 1978. Although this evidence that the spatial distri-bution of founding mirrors the spatial distribution of preexist-ing organizations is consistent with density dependence, acloser look at the data reveals two deviations from this gen-eral pattern. First, local communities with zero density in1978 took different paths. Some did not generate any neworganizations during the entire period, as would be predictedby the theory of density dependence. Others became fertileduring the following decade. For example, Kalispell, Montana,despite having zero density in 1978, generated nine newinstruments manufacturers between 1978 and 1988. Second,areas with similar numbers of instruments organizations alsotook diverging paths. Austin, Fort Collins, Tampa, and SanJose generated many more new organizations during thedecade than were there in 1978, as density dependencewould suggest. Yet other areas that were densely populatedin 1978, such as Evansville, Louisville, Binghamton,Rochester, and Trenton, added new organizations at muchlower rates, showing signs of stagnation. Our study exam-ines how relations among organizational populations mayhelp explain these differences in the location of foundings.

METHOD

DataTo examine how interpopulation relations influence foundingsof instruments manufacturers, we used a dataset assembledby the U.S. Small Business Administration (SBA) containinginformation on U.S. establishments in all two-digit nongovern-mental Standard Industrial Classification (SIC) sectorsbetween 1976 and 1988. The two-digit classification parallelsecological and network definitions of organizational popula-tions as sets of structurally equivalent actors because itgroups establishments by the type of raw materials used andthe skills involved and similarities in the technical organizationof the production process (Miernyk, 1965). It also meets therequirements of an identity-based definition of the boundariesof organizational populations (Carroll and Hannan, 2000). Two-digit SIC categories imply distinct identities, as each definesorganizational properties for inclusion. These identities arealso external in the sense that they are recognized andenforced by outsiders. Government analysts define whetheran organization belongs to SIC 38, which comprises instru-ments manufacturers and related organizations, as opposedto other two-digit-level industries. Empirical evidence sug-gests that securities analysts also view two-digit SIC cate-gories as distinct organizational identities. In a study showingthat firms were more likely to de-diversify when they werenot perceived by analysts to have a clear identity, Zuckerman



(2000: 602) treated cases in which firms replaced a three-digit SIC code in their business segment profiles with anoth-er three-digit SIC code (within the same two-digit category)as a continuation of the firms’ identity. In a related studyshowing that a firm’s “coverage mismatch” has a negativeeffect on the stock value, Zuckerman (1999: 1418) obtainedsimilar results when firms were categorized using two-digitor three-digit SIC codes. Two-digit SIC categories are easilyrecognized by outsiders such as securities analysts becausethey are stable over time and are embedded in societal insti-tutions such as directories published by government organi-zations and business service organizations.

The SBA dataset is based on Dun and Bradstreet’s Dun’sMarket Identifiers files, which assign a numerical identifier toevery U.S. establishment in December of every even-num-bered year from 1976 to 1988 (see Reynolds and Maki,1990). Dun and Bradstreet identifies establishments by com-bining information collected through its credit-reporting func-tion with information gathered from other organizations thatcompile lists of companies. A comparative analysis of thestrengths and weaknesses of alternative sources of data onU.S. establishments showed that Dun and Bradstreet dataprovide better coverage of the entire economy than othersources (e.g., White Pages) and constitute one of the mostuseful sampling frames for studying organizations (Kalleberget al., 1990). An establishment is defined as a single physicallocation where business activity is conducted, and the datadistinguish between autonomous establishments and branch-es (i.e., establishments that belong to a company). A compar-ison of two consecutive years made it possible to identifynew organizations. For each two-year period, organizationalfoundings are defined as those autonomous establishmentspresent only in the second year. As a result, the data incorpo-rate organizational foundings between 1976 and 1978 (whichwe dated 1978) and so on for 1980, 1982, 1984, 1986, and1988. There are six two-year waves of observations for theUnited States. Each wave includes the total density of eachpopulation, which we defined as the count of all establish-ments, both autonomous units and branches.

Measures

To gather information on relationships of interdependenceamong organizational populations, we focused on patterns ofresource utilization (Pfeffer, 1972; Burt, 1983, 1992). Organi-zational population A is tied to the population of instrumentsmanufacturers by a commensalistic relationship to the extentthat it uses similar inputs in the production process or servessimilar markets. Organizational population B is tied to thepopulation of instruments manufacturers by a symbiotic rela-tionship to the extent that it is either a supplier of inputs or apurchaser of instruments. Because the exchange relations ofinstruments manufacturers often transcend the geographicalboundaries of local communities, we determined these rela-tions by examining the pattern of transactions at the nationallevel. We used information from the U.S. Department ofCommerce’s benchmark input-output tables for 1977 and1987 (U.S. Department of Commerce, 1984, 1994) andobtained input-output information for the missing years


through interpolation. For each sector, the tables report thedollar amount of purchases from other sectors and the dollaramount of sales to other sectors. Direct evidence of transac-tions at the level of the local community—separating transac-tions within the community from transactions among com-munities—would be preferable, but such data do not exist(Dietzenbacher and Lahr, 2001; cf. Romo and Schwartz,1995). Interregional input-output models are usually comput-ed by “regionalizing” national data on the basis of complexestimation procedures (Dietzenbacher and Lahr, 2001), butresearchers have noted that these procedures provide littleimprovement over national averages (Round, 2001; Canningand Wang, 2003). Here we used national exchange patternsto generate weights that are applied to organizational popula-tion densities observed at the LMA level.

To match the input-output dataset to the SBA dataset, wehad to resolve some discrepancies between sectors in theinput-output tables and those in the two-digit SIC classifica-tion. In more recent input-output tables, some of the two-digit SIC sectors have been disaggregated to reflect theexpansion of certain industries, and a few others have beenaggregated to reflect the maturity of certain industries. TheBenchmark Input Output (IO) Account for 1987 (U.S. Depart-ment of Commerce, 1994) reports precise information thatallowed us to closely match IO sectors to two-digit SIC sec-tors. The only major discrepancy between the two datasources that we were unable to address concerned retailorganizations. Although there are ten two-digit SIC retail sec-tors (e.g., food stores, general merchandise, apparel, auto,and gas stations), the benchmark input-output tables differen-tiate only between wholesale and retail trade. Consequently,our data did not allow us to trace patterns of exchangebetween the ten two-digit SIC retail sectors and instrumentsorganizations. For this reason, our community variables donot include information about retail organizations.

We computed two measures of community symbiosis. Com-munity supplier symbiosis is the degree to which a communi-ty is characterized by the presence of organizational popula-tions that supply inputs to the focal population.

CSj = �k

(Zk/Rk) � (dkj/Dj),

where j indexes all communities, k indexes organizationalpopulations excluding the focal population, Zk is the dollarvalue of inputs that the focal population acquires from popu-lation k, Rk is the dollar value of sales made by population kto all organizational populations, dkj is the density of establish-ments of population k in community j, and Dj is the total den-sity in community j. To illustrate how Zk / Rk varies acrossorganizational populations, the value of this ratio is zero orvery close to zero for tobacco, crop production, and metalmining, whereas it has the highest values for electric andelectronic equipment, fabricated metal, and rubber and plas-tic. Community supplier symbiosis equals zero if a communi-ty does not have any organization that supplies inputs toinstruments producers. As the proportion of organizations



that supply inputs to instruments manufacturers increases,and as the proportion of sales made to instruments manufac-turers increases, a community’s supplier symbiosis increases.We used proportions rather than absolute counts of organiza-tions to avoid overwhelming the measure with the effects ofmetropolitan size. The results obtained using this proportionalmeasure of community symbiosis were equivalent to thoseobtained including the total density of the local community asa separate variable and measuring community symbiosis asthe sum of the density of the organizational populations inthe local community weighted by the proportion of trade.

We also used proportion of sales made to the focal popula-tion because the more suppliers depend on transactions withthe focal population to make their sales, the more likely theyare to pay attention to this exchange relationship. Greaterattention implies greater potential recognition of informationabout entrepreneurial opportunities relevant to the focal pop-ulation. Community purchaser symbiosis is the degree towhich a community is characterized by the presence of orga-nizational populations that purchase goods from the focalpopulation and was measured using the same equation. Theonly differences are that Zk is the dollar value of sales fromthe focal population to population k, and Rk is the dollar valueof purchases made by population k from all organizationalpopulations. Health, transportation equipment, and printingand publishing were the organizational populations with thestrongest purchaser symbiosis relationship to instruments,whereas textiles, food and kindred, and tobacco had theweakest, with values of Zk / Rk equal or very close to zero.

To compute community commensalism, which we defined asthe degree to which a community is characterized by thepresence of populations of organizations that have a patternof transactions similar to that of the focal population, we firstcalculated the degree of similarity in transactions with suppli-ers (or transactions with purchasers) between each popula-tion k and the focal population. This is an instance of structur-al equivalence, and, following prior studies (e.g., Burt, 1983),was operationalized as a Euclidean distance converted intosimilarity:

Sk = exp{–[�r

(pkr – pr)2].5}, k ≠ r

where k and r index organizational populations excluding thefocal population, Sk is the degree of similarity between orga-nizational population k and the focal population, pkr is the pro-portion of population k’s input supplied by population r (oroutput sales to population r), pr is the proportion of the focalpopulation’s input supplied by population r (or output sales topopulation r). The value of Sk would be 1 if a population k andthe focal population had identical profiles of transactions. Thedegree of similarity in transactions with suppliers has astrong positive correlation to the degree of similarity in trans-actions with purchasers (r = .95), because sectors similar toinstruments manufacturers in terms of the inputs acquiredtend to serve similar product markets (Burt, 1983). Given thehigh correlation between the two similarity measures, we


computed the community commensalism measure usingonly similarity in transactions with suppliers. Printing and pub-lishing, machinery, and electric and electronic equipment hadthe highest values of Sk, whereas food and kindred, tobacco,and health had the lowest. Community commensalism wascomputed as the weighted sum of the proportion of eachpopulation in a community times its similarity to the focalpopulation.

CCj = �k

Sk � (dkj/Dj),

where j indexes all communities, k indexes organizationalpopulations excluding the focal population, Sk is the degreeof similarity between organizational population k and the focalpopulation, dk is the density of establishments of populationk, and Dj is the total density in community j.

The correlations between our measures of symbiosis andcommensalism are only moderately positive, as reported intable 1. To examine the dampening effects of geographic dis-tance on the effects of symbiosis and commensalism, weconstructed a time-varying, distance-weighted measure ofnonlocal density. We weighted each nonlocal instrumentsorganization’s contribution to the measure according to theinverse of the geographic distance between the communityin which it was located and the focal community. We thensummed these weighted contributions across all nonlocalinstruments organizations. Smaller values on this variableindicate greater geographical distance from instruments man-ufacturers located outside the community.

To calculate the geographic distance between a focal com-munity and nonlocal instruments organizations, we identifiedthe center point of each LMA, which we defined as the cen-ter of the most populous county in the LMA. We thenassigned to each nonlocal instruments organization the lati-tude and longitude of the center of the LMA in which it waslocated and computed the geographic distance from thatpoint to the center of the focal LMA. To take into account thecurvature of the earth, we computed these geographic dis-



Table 1

Descriptive Statistics and Correlations (N = 1910)*

Variable Mean S. D. .01 .02 .03 .04 .05 .06 .07 .08

1. Foundings 5.13 16.802. Community supplier symbiosis � 100 .419 .122 .5243. Community purchaser symbiosis � 100 1.462 .571 .458 .5344. Community commensalism .492 .006 .162 .007 .2455. Community’s dominant population 0. weighted by degree of unrelatedness

.147 .067 –.112 –.122 –.147 –.024

6. Human population per square mile 132.920 328.970 .583 .434 .385 .219 –.0627. Skilled work force –.001 1.055 .423 .599 .632 .250 –.068 .5318. Nonlocal density of instruments 0. manufacturers weighted by geographic 0. distance

24.909 11.094 .148 .126 .465 .329 –.032 .392 .602

9. Local density of instruments manufacturers 33.520 115.100 .956 .497 .472 .192 –.111 .694 .454 .217

*All correlations are significant at the .05 level.

tances using spherical geometry (see Sorenson and Audia,2000). The measure of nonlocal density weighted by geo-graphic distance is given by the following formula:

NLDWj = �u

(Du) � (1/duj), u ≠ h

where j indexes all communities, u indexes communitiesexcluding community j, Du is the local density of instrumentsmanufacturers in community u, and duj is the geographic dis-tance between community u and community j.

We also created a measure of a community’s dominant popu-lation weighted by the degree of interdependence withinstruments manufacturing. The first step was to identify theorganizational population with the largest share of resourcesin each LMA. The best available indicator of resources con-trolled by an organizational population was the total amountof wages paid by each organizational population, which is afunction of both the total number of people employed andthe value of the economic activities performed. The source ofthe wages data was the Bureau of Labor Statistics. For eachpopulation, we then computed the ratio of its aggregatewages over the total wages in the community and identifiedthe local dominant population as the one with the highestratio.

We then created a measure of the degree to which the domi-nant population lacked symbiotic and commensalistic ties toinstruments manufacturing. We first averaged the three indi-cators of supplier symbiosis, purchaser symbiosis, and suppli-er commensalism. We standardized and transformed theseindicators so that they varied between 0 and 1 before theaverage was computed. We then subtracted this averagefrom 1 to obtain a measure of unrelatedness to instrumentsmanufacturing. Producers of tobacco products and food andkindred products had the highest values on this measure,whereas printing and publishing organizations and producersof electronic and electrical equipment had the lowest scores.Finally, we weighted the ratio of the wages of the dominantpopulation over the total wages in the community by thedegree of unrelatedness. The greater the degree of unrelat-edness, the more the dominant population was positivelyweighted. This weighted measure is useful to the extent thatthe effect of a community’s dominant population is contin-gent on the degree of unrelatedness to instruments manu-facturing. We therefore report results of preliminary analysesin which we probed this assumption.

Models

We modeled organizational foundings at the LMA level. Orga-nizational foundings is an event-count variable that takes onlynon-negative values. This type of variable is usually examinedusing the Poisson model unless it displays overdispersion (aviolation of the assumption that the mean and variance areequal). A likelihood ratio test showed significant evidence ofoverdispersion (mixture �2 = 3825.5; p < .05). Consequently,we used negative binomial regression (Cameron and Trivedi,


1998). We clustered observations by LMA and used robustvariance estimates to allow for non-independence of theobservations belonging to the same LMA.

Besides the main independent variables of interest reportedabove, the models included the local density of instrumentsmanufacturers and its squared term. The theory of densitydependence holds that in organizational populations studiedfrom their origin, density has a non-monotonic effect where-by high levels of density increase the number of foundingsbut at a decreasing rate, because the legitimating effect ofdensity fades, and available inputs become scarcer. Ourdataset does not allow a proper test of the theory of densitydependence at the level of the local community becausemany LMAs had organizations that were in existence at thebeginning of the study period. The coefficients of the densitydependence terms therefore must be interpreted with cau-tion. We chose to include them for completion and to con-tribute to comparability.

To take into account the geographical position of a givenLMA in relation to instrument manufacturers located in otherLMAs, we included as a control nonlocal density weighted bygeographic distance, discussed above. Empirical studiesshow that the density of organizations in nearby communitiesmay have either mutualistic effects (e.g., Hedstrom, 1994) orcompetitive effects (e.g., Sorenson and Audia, 2000) onfoundings. Regardless of the sign of the effect, this form ofspatial interdependence may contribute to correlated errorterms and therefore may be a source of spatial autocorrela-tion (Doreian, 1981). As the distance from instruments manu-facturers located outside the community increases, values onthis variable decrease.

We differentiated between urban and rural communities byincluding human population density, which is the number ofpersons per square mile. The extremes are Alaska (0.7 per-sons per square mile) and the New York City–Long IslandLMA (5,675 persons per square mile). We also controlled forthe availability of a skilled work force, which may rendercommunities particularly attractive to would-be entrepre-neurs, by creating a composite indicator based on U.S. Cen-sus information about persons with postgraduate degreesper 1,000 square miles, professional and technical employeesper 1,000 square miles, patents granted per 1,000 squaremiles, and doctorates granted per 1,000 square miles (reliabil-ity = .99). Finally, we included year dummy variables, using1978 as a base, to rule out the possibility that some of theindependent variables captured the passage of time. We didnot include variables that change over time but are the sameacross LMAs (e.g., national density of instruments manufac-turers, exports) because the coefficients of such variables arenot identified when year-dummies are present. Our specifica-tion emphasizes differences across LMAs. Independent andcontrol variables were lagged and correspond to the previoustwo-year wave beginning with 1978, so five time periodsentered the analyses.



RESULTS

The first set of results are shown in table 2. In model 1, thecoefficient of local density is positive, while the coefficient ofdensity squared is negative. Thus, as the theory of densitydependence predicts, the founding rate rises with increasinglocal density, but the rate of founding tapers off as the carry-ing capacity is reached. Furthermore, the inflection point isapproximately 1,000 instruments manufacturers and fallswithin the range of the data. These effects remain significantwhen community characteristics that capture interpopulationrelations are included. Model 1 also shows several significanteffects for the control variables. Foundings of instrumentsorganizations are less likely in urban areas that have greaterhuman population density. Nonlocal density weighted by geo-graphic distance has a negative and significant coefficient,


Table 2

Negative Binomial Models of the Effect of Community’s Symbiosis and Commensalism on Foundings of

Instruments Manufacturers, Including All Communities (N = 1910)*

ZeroRandom inflated

Variable .1 .2 .3 .4 effects Poisson

Local density of instruments .017• .006• .014• .011• .007• .009•—manufacturers (.002) (.001) (.001) (.001) (.001) (.001)(Local density of instruments –.009• –.003• –.003 –.006• –.009• –.007•—manufacturers)2 / 1000 (.001) (.000) (.004) (.004) (.000) (.003)Community purchaser symbiosis .171• .281• .282• .204• .380•—� 100 (.089) (.083) (.091) (.081) (.089)Community supplier symbiosis � 100 7.901• 7.695• 7.806• 5.926• 5.350•

(.600) (.552) (.553) (.356) (.550)Community commensalism 16.909• 21.792• 17.282• 20.124• 26.230•

(5.874) (5.808) (6.598) (5.142) (6.539)Community purchaser symbiosis � –.003• –.001• –.001• –.001•—Local density of instruments (.000) (.000) (.000) (.000)—organizationsCommunity supplier symbiosis � –.020• –.016• –.009• –.010•—Local density of instruments (.003) (.002) (.002) (.001)—organizationsCommunity commensalism � –.249• –.190• –.093• –.137•—Local density of instruments (.057) (.041) (.024) (.024)—organizationsHuman population per square mile / –.086•† –.032•† .016 .004 .045•† .006—100 (.016) (.009) (.014) (.014) (.022) (.005)Skilled work force .552•† .099 –.031 .067 .095•† –.046

(.094) (.061) (.053) (.072) (.043) (.065)Nonlocal density of instruments –.020•† .006 –.006 –.003 –.009•† –.005—manufacturers weighted by geographic—distance

(.006) (.004) (.003) (.004) (.003) (.003)

Year 1980 .106 .393•† .320•† .358† .297•† .178(.065) (.076) (.074) (.072) (.055) (.111)

Year 1982 .332•† .909•† .799•† .861•† .791•† .569•†

(.081) (.093) (.092) (.097) (.068) (.132)Year 1984 .228•† 1.045•† .885•† .966•† .850•† .567•†

(.088) (.109) (.108) (.117) (.082) (.161)Year 1986 .383•† 1.267•† 1.094•† 1.174•† .993•† .692•†

(.097) (.128) (.126) (.137) (.099) (.168)Constant –.011 –.562•† –.593•† –.002 .329 .162

(.088) (.097) (.094) (.401) (.176) (.151)State fixed-effects .No .No .No .Yes .No .NoLog likelihood –3543.2 –3276.9 –3195.0 –3084.8 –3088.1 –3655.2• p < .05; one-tailed tests unless otherwise marked.* Standard errors are shown in parentheses.† Two-tailed test.

which suggests a competitive effect of instruments manufac-turers in nearby communities. A skilled work force has a pos-itive and significant coefficient, whereas the coefficients ofthe year-dummies indicate that the founding rate is greater inlater years than in 1978. With the exception of the year-dum-mies, however, the effects of these control variables becomeweaker or disappear when independent variables are addedto the models. Throughout the analyses, we used one-tailedtests when the sign of the coefficient was specified theoreti-cally and two-tailed tests for the control variables.

In model 2, we added community variables to discern theeffect of organizational populations related to instrumentsmanufacturing. The coefficients are positive and significantand therefore indicate that community purchaser symbiosis,community supplier symbiosis, and community commensal-ism increase foundings, as predicted in hypotheses 1 and 2,and these variables improve model fit considerably (model 2versus model 1 likelihood ratio test statistic = 532.68; changein d.f. = 3; p < .001). We then entered product terms inmodel 3 to see if there were interaction effects betweeneach of these variables and the local density of instrumentsmanufacturers. Multicollinearity does not appear to be a prob-lem. The correlations among the interaction terms and theindependent variables are not high (the largest is 0.50), prob-ably because we obtained the product terms by using mean-deviated terms. Preliminary analyses show that regressioncoefficients as well as standard errors are stable when prod-uct terms are entered hierarchically. The coefficients of theseinteraction terms are negative and significant, indicating thatthe greater the number of instruments manufacturers, thesmaller the positive effect of related organizational popula-tions’ densities on the founding rate of instruments manufac-turing companies, supporting hypothesis 3. Model 4 showsthat these effects remain unchanged when we include fixedeffects at the state level, which allow us to control for deter-minants of entrepreneurial activity that may vary acrossgeopolitical boundaries (e.g., corporate tax rates, specialincentives). The last two columns show that negative binomi-al regression with random effects and zero-inflated Poissonyielded the same results.

The effect of community supplier symbiosis in these resultsis substantially stronger than the effect of community pur-chaser symbiosis and community commensalism. A possibleexplanation for this is that organizations invest more effortsin downstream transactions than in upstream transactions orcompetitive monitoring, a scenario which, according to someresearchers, seems consistent with the apparent greater con-centration of resources and staff in marketing functions thanin purchasing or competitive intelligence functions (Burt,1983; Romo and Schwartz, 1995). This tendency might havebeen even stronger in the setting of this study becauseinstruments manufacturing experienced a phenomenal periodof growth during our observation period. One would expectsuppliers to be particularly attentive to clients that requestedincreasingly larger volumes of their products. This down-stream orientation may have led to greater information trans-fer between suppliers and instruments manufacturers and



thus to a greater impact on the founding rate of instrumentsorganizations.

Next, we considered the effect of symbiotic and commensal-istic organizational populations on the founding rate of com-munities that did not have instruments manufacturers at thebeginning of the observation period. These analyses helpedus address the question of why some unoccupied communi-ties experience initial foundings whereas others do not. Ofthe 382 LMAs, 52 were unoccupied in 1978, 34 of whichexperienced foundings during the observation period. LMAsare removed from the sample when the first founding takesplace, yielding a sample size of LMA-year observations equalto 190. We dealt with left-censoring by including a variablethat records the local density of instruments manufacturersin 1973. County Business Patterns, a yearly document pub-lished by the Census Bureau, revealed that only seven of the52 LMAs unoccupied in 1978 had one or more instrumentsmanufacturers in 1973. We also checked local densities tenyears prior to the beginning of the observation period (1968)and found that four of these seven LMAs had one or moreestablishments in 1968, whereas the others had zero density.So LMAs that were unoccupied by instruments manufactur-ers at the start of the study had been unoccupied for years.Poisson models are appropriate to examine the probability ofinfrequent events such as initial foundings (Tuma and Han-nan, 1984; Cameron and Trivedi, 1998) and are reported here.In supplemental analyses, we also modeled a community’sprobability of being unoccupied and a community’s probabilityof experiencing initial foundings using bivariate probit regres-sion (Greene, 2000). A bivariate probit model, however, couldnot be estimated due to convergence problems. Failure toobtain estimates is common in models, such as the bivariateprobit model, that try to estimate correlation coefficientsbetween two equations (http://www.limdep.com/support.shtml; accessed 1/10/2006). In this case, the non-converging results may have been caused by too few initialfoundings in the sample. We also report logit models as arobustness check. As in the negative binomial models, weclustered observations by LMA and used robust varianceestimates to allow for non-independence of the observationsbelonging to the same LMA.

Despite the much smaller sample size, the pattern of resultsfor the entire sample is confirmed in our analyses of initialfoundings. Model 1 in table 3 shows that most control vari-ables have negligible effects on entrepreneurial activity inunoccupied LMAs. In model 2, we drop unessential controlvariables to maximize degrees of freedom, and the log likeli-hood remains virtually unchanged. Model 3 shows positiveand significant effects of community purchaser symbiosisand community commensalism, as predicted in hypothesis 4.The coefficient of community supplier symbiosis is not signif-icant, but interpretation of this effect must wait until theinteraction terms between community symbiosis and com-munity commensalism and nonlocal density weighted bygeographic distance are included. Model 4 adds these inter-actions and shows positive and significant interactions forcommunity supplier symbiosis and community purchaser


symbiosis. In addition, the main effect of community suppliersymbiosis turns significant.

As noted above, when the variable nonlocal density of instru-ments manufacturers weighted by geographic distance islarger, the geographical distance between the communityand instruments manufacturers located elsewhere is smaller.These interactions then indicate that the greater the geo-graphic distance between an unoccupied community andinstruments manufacturers, the weaker the positive effect ofcommunity symbiosis on the founding rate. The interactionbetween nonlocal density weighted by geographic distanceand community commensalism is negative and not signifi-cant. This may be because information transfer betweencommensalistic organizational populations depends less onface-to-face interaction than information transfer betweensymbiotic organizational populations. Geographic distanceimpedes information transfer especially by reducing the fre-quency and quality of information transferred through suchforms of direct contact. Thus hypothesis 5 receives only par-tial support.



Table 3

Poisson Models of Initial Foundings of Instruments Manufacturers in Unoccupied LMAs (N = 190)*

Variable .01 .02 .03 .04 .05 .06 Logit

Community purchaser symbiosis � 100 .937• 1.188• 1.161• 1.008• 2.003(.463) (.560) (.551) (.604) (1.893)

Community supplier symbiosis � 100 2.605 5.837• 5.693• 7.007• 7.832(3.207) (2.718) (2.764) (2.964) (5.469)

Community commensalism 52.030• 22.053 30.377• 31.882• 41.308•(19.964) (17.218) (15.604) (15.247) (20.453)

Community purchaser symbiosis � Nonlocal .153• .149• .160• .305•—density weighted by geographic distance (.062) (.060) (.068) (.129)Community supplier symbiosis � Nonlocal .513• .505• .556• .665•—density weighted by geographic distance (.241) (.225) (.225) (.363)Community commensalism symbiosis � –.871—Nonlocal density weighted by geographic —distance

(2.665)

Community’s dominant population weighted –3.604 –4.759•—by degree of unrelatedness (2.307) (2.567)Human population per square mile / 100 .078 .043 –.380 –.510 –.531 –.574 –.366

(.716) (.516) (.490) (.484) (.463) (.401) (.618)Skilled work force –.158

(.310)Nonlocal density of instruments manufacturers –.036 –.059 –.071 –.038 –.037 .047 .080—weighted by geographic distance (.037) (.032) (.053) (.047) (.045) (.053) (.093)Local density of instruments manufacturers –.069—in 1973 (.376)Year 1980 –.170

(.350)Year 1982 .613•† .749•† .791•† .785•† .783•† .820 1.079•†

(.307) (.307) (.251) (.246) (.248) (.255) (.357)Year 1984 –.214

(.393)Year 1986 1.212•† 1.439•† 1.393•† 1.327•† 1.298•† 1.319 1.906•†

(.502) (.433) (.305) (.458) (.475) (.439) (.801)Constant –2.35•† –2.47•† –1.59•† –1.36•† –1.35•† –.774•† –.393

(.619) (.313) (.441) (.381) (.384) (.431) (.756)Log likelihood –86.5 –86.7 –82.4 –79.7 –79.7 –78.9 –72.3• p < .05; one-tailed tests unless otherwise marked.* Standard errors are shown in parentheses.† Two-tailed test.

Model 5 does not include the non-significant interactionbetween community commensalism and nonlocal densityand provides the best fit over the reduced baseline model(model 5 versus model 2 likelihood ratio test statistic =13.96; change in d.f. = 5; p < .05). Model 7 shows that logitestimates yielded the same results. Additional analyses (notshown) indicate that the interactions between communitysymbiosis and community commensalism and nonlocal den-sity weighted by geographic distance are non-significant inthe models that include all LMAs. Those interaction terms,however, do not provide a meaningful test of geographicdecay in information transfer because, in communities thathave instruments organizations, symbiotic and commensalorganizational populations can channel information aboutinstruments manufacturing through both local and nonlocalrelations.

The last set of analyses, reported in table 4, examinedwhether unrelated organizational populations that occupy aposition of local dominance within the community have anegative effect on foundings. We started by exploringwhether the effect of a dominant population is contingent onits degree of relatedness to instruments manufacturing. Ifinformation about instruments manufacturing flows throughinterpopulation linkages and greater access to such informa-tion increases the founding rate, as implied in hypotheses 1and 2, then a dominant population related to instrumentsmanufacturing by a symbiotic or a commensalistic relation-ship should increase the founding rate. If a dominant popula-tion unrelated to instruments manufacturing decreases theperceived legitimacy of unrelated organizational forms, as wesuggest, then it should have a negative effect on foundings.

Model 1 reports the effect of the unweighted measure of acommunity’s dominant population, which equally weightsorganizational populations occupying a position of local domi-nance regardless of their relation of interdependence toinstruments manufacturing. Models 2 and 3 separately exam-ine the effect on the founding rate of dominant populationsstrongly related to instruments organizations (i.e., with valuesbelow the median of the degree of unrelatedness) and theeffect on the founding rate of dominant populations weaklyrelated to instruments organizations (i.e., with values abovethe median of the degree of unrelatedness). The results areconsistent with our expectations. Dominant populations relat-ed to instruments organizations increase the founding rate,whereas dominant populations unrelated to instruments orga-nizations decrease it. The overall effect of the unweightedmeasure is negative but non-significant. Because the effectof dominant populations unrelated to foundings is alreadyreflected in the measures of community symbiosis and com-munity commensalism, our interest lies in examining themeasure that gives greater weight to dominant populationsunrelated to instruments manufacturing.

In model 4, the coefficient for this variable is negative andsignificant, providing support for hypothesis 6. Model 5 thenincludes the interaction term with the local density of instru-ments manufacturers. The coefficient is positive and signifi-cant, which means that the negative effect of unrelated dom-




Table 4

Negative Binomial Models of the Effect of Community’s Dominant Population on Foundings of Instruments

Manufacturers, Including All Communities (N = 1910)*

Variable .01 .02 .03 .04 .05 .06 .07

Local density of instruments .017• .017• .017• .017• .018• .007• .014•

—manufacturers (.002) (.002) (.002) (.002) (.003) (.001) (.001)(Local density of instruments –.009• –.009• –.009• –.008• –.008• –.003• –.002•

—manufacturers)2 /1000 (.001) (.001) (.001) (.001) (.001) (.000) (.000)Community’s dominant –.876—population (.613)Community’s dominant –.971•

—population with high degree (.360)—of unrelatednessCommunity’s dominant .804•

—population with low degree (.421)—of unrelatednessCommunity’s dominant –1.381• –2.118• –1.145• –.721—population weighted by (.670) (.703) (.643) (.631)—degree of unrelatednessCommunity’s dominant .058• .038• .022•

—population weighted � Local (.031) (.015) (.010)—density of instruments —manufacturersCommunity purchaser .170• .279•

—symbiosis � 100 (.088) (.083)Community supplier 7.899• 7.629•

—symbiosis � 100 (.608) (.537)Community commensalism 17.148• 22.491•

(5.899) (5.857)Community purchaser –.003•

—symbiosis � Local density (.001)—of instruments organizationsCommunity supplier symbiosis –.021•

—� Local density of (.004)—instruments organizationsCommunity commensalism � –.266•

—Local density of (.059)—instruments organizationsHuman population per square –.084•† –.085•† –.087•† –.083•† –.085•† –.035•† –.019—mile / 100 (.016) (.016) (.016) (.016) (.015) (.009) (.014)Skilled work force .557•† .536•† .534•† .552•† .547•† .094 –.034

(.092) (.092) (.093) (.092) (.091) (.059) (.053)Nonlocal density of –.021•† –.019•† –.019•† –.020•† –.021•† –.007 –.005—instruments manufacturers (.006) (.006) (.005) (.006) (.005) (.004) (.003)—weighted by geographic —distanceYear 1980 .120 .147•† .126•† .130•† .091•† .369•† .324•†

(.065) (.066) (.066) (.066) (.070) (.078) (.074)Year 1982 .346•† .347•† .331•† .350•† .321•† .892•† .795•†

(.082) (.080) (.080) (.081) (.084) (.095) (.091)Year 1984 .253•† .218•† .196•† .249•† .212•† 1.021•† .881•†

(.090) (.086) (.087) (.088) (.093) (.111) (.107)Year 1986 .407•† .346•† .331•† .392•† .357•† 1.240•† 1.083•†

(.098) (.096) (.097) (.097) (.099) (.129) (.124)Constant .139 .085 –.068 –.181 .294•† .383•† –.575•†

(.145) (.095) (.094) (.135) (.141) (.139) (.136)Log likelihood –3541.3 –3537.5 –3540.2 –3539.6 –3531.7 –3266.9 –3193.12• p < .05; one-tailed tests unless otherwise marked.

* Standard errors are shown in parentheses.† Two-tailed test.

inant populations is weaker in communities that have greaternumbers of instruments manufacturers, as we predicted inhypothesis 7. The pattern of results remains unchangedwhen we include community symbiosis and community com-mensalism in model 6 and model 7 and when we includestate-fixed effects (not shown). The main effect of theweighted measure of a community’s dominant population isnegative but becomes non-significant in model 7 when theother interaction terms are included. The interaction term,however, remains positive and significant. We also examinedwhether a dominant population decreases the probability ofinitial foundings, as predicted in hypothesis 8 (table 3,model 6). The sign of the coefficient is negative as predicted,but the one-tailed test is only significant at the .10 level.

A final issue concerns the level of aggregation implied bytreating two-digit SIC categories as distinct organizationalpopulations. As we noted above, the best available evidencesupporting the idea that outsiders such as analysts may viewtwo-digit SIC categories as distinct organizational identitiescomes from Zuckerman’s (1999, 2000) research. A skeptic,however, might argue that the results are driven by hetero-geneity within SIC 38. Four considerations discount this con-cern. First, using information contained in County BusinessPatterns, we were able to identify the three-digit level of theinitial foundings in previously unoccupied LMAs. We foundthat these foundings belonged to five different three-digit-level industries within SIC 38: 382, 383, 384, 385, and 386.This alleviates the concern that the results of the initialfounding analysis might have been driven by a particular sub-population.

Second, supplemental analyses indicate that the negativeeffect of a community’s dominant population weighted by thedegree of relatedness to instruments organizations holdswhen restricting the analysis to states with a prevalence in1978 of establishments in SIC 382 (measuring and controllinginstruments) or in SIC 384 (medical instruments), the twolargest three-digit industries by value of shipments within SIC38. These states were identified using 1978 data aboutestablishment counts at the three-digit level made availableby the Bureau of Labor Statistics (BLS) Covered Employmentand Wages program.

Third, heterogeneity within SIC 38 might imply very differentpatterns of spatial location of establishments at the three-digit level within SIC 38. But, using the BLS data, we foundthat three-digit-level industries within SIC 38 tended to co-locate in space. Because of this tendency of three-digit indus-tries within SIC 38 to co-locate in space, it was difficult toidentify states that were dominated by a certain type ofinstruments manufacturer. This was evidenced by a clusteranalysis, which showed that three-digit industries within SIC38 emerged as an independent cluster, and by a multivariateanalysis of variance, which showed greater variance in spatiallocation between two-digit industries than within them(details of these analyses are available upon request).

Fourth, the indications that we derive from our supplementalanalyses are consistent with the only study (Moomaw, 1998)


that explicitly examined what is lost when two-digit data areused instead of three-digit data in analyses of localizedeconomies. That study concluded that estimates obtainedusing two-digit data are similar to estimates obtained usingthree-digit data.

DISCUSSION AND CONCLUSION

Suppose there is a general awareness in society that a newtechnology has been developed that will create opportunitiesto start businesses. Suppose, further, that potential entrepre-neurs are randomly distributed through the human popula-tion. They are to be found in every town and city. The ques-tion is, which of them will actually start a company? Thepremise of this paper has been that the social structure chan-nels the resources that will impel some to engage in entre-preneurial activity, while others fail to take action. Specifically,we have argued that those who reside in communities thatprovide access to information about entrepreneurial opportu-nities are more likely to start a new company because suchinformation propels them into action. Furthermore, we haveproposed that organizations play a key role in this naturalselection process because they are an important vehicle bywhich this information is made available in geographicalspace.

Past studies have emphasized that much of this informationoriginates and resides within the same organizational popula-tion. The results of this paper support the view that this infor-mation also flows from one organizational population toanother through direct contact and through competitive moni-toring. These are two network mechanisms of informationtransfer that we think underlie ecological relations of symbio-sis and commensalism, respectively. As a result, at the com-munity level, the founding rate is affected not only by thenumber of organizations in the same population but also bythe market relations in which the community is embedded.

Concretely, if the community already has a population oforganizations such as that being contemplated, residents inthat community are more likely to start new companiesbecause they have specific knowledge about entrepreneurialopportunities and about how to create products and operateorganizational routines that have been developed previously.Populations of organizations doing business with the kind oforganization in question (symbiotic organizational populations)and populations of organizations doing business similar to thekind of organization in question (commensalistic organization-al populations) can also provide such information. So it is eas-ier to start an instruments company if there are many com-puter hardware and software firms and microelectronicsfirms nearby, which explains why some communities that donot have organizations of a certain kind experience initialfoundings, whereas others do not. In contrast, an interestedpotential entrepreneur is more likely to remain passive if veryfew people in the area know about the business in questionor similar kinds of businesses. If the local environment isbuilt around some very different kind of organization, howev-er, service suppliers such as local bankers and accountantsare likely to know about that business and will probably dis-



play less interest in supporting a kind of organization far fromtheir experience. So a dominant, unrelated kind of organiza-tion can discourage foundings of an incompatible organiza-tional form. This is the reverse of the legitimation processthat has most interested organizational ecologists. Finally, ofcourse, the information and knowledge that existing organiza-tional populations generate is less useful when nascentfounders are far away. This is especially important in theprocesses that convert general interest into action. Suchinformation is likely to be communicated by accidental, infor-mal social interaction.

Although a strength of our analysis is that it directly exam-ined interpopulation relations focusing on patterns ofresource utilization evidenced by input-output flows, we didnot directly measure the information flows that take place asa consequence of the position that organizational populationsoccupy in the market structure. To alleviate the concern thatinformation diffusion might be the primary mechanism under-lying the results, we developed corollary hypotheses that fol-lowed from our core argument. The findings on informationredundancy (hypothesis 3) and information decay (hypothesis5) are consistent with the information diffusion argument butcannot be easily explained by prevalent accounts of theforces underlying the geographic distribution of industries,such as, for example, proximity to other resources. It is pos-sible, however, that other processes complement rather thanreplace our information diffusion argument. For example,research on embeddedness suggests that exchange relationsoften foster the development of trust among actors (Gra-novetter, 1985; Uzzi, 1997). This in turn may facilitate entre-preneurial activity by encouraging the observance of normsof mutual support (Aldrich and Zimmer, 1986).

This study makes a number of contributions to organizationalecology. First, we add to the few ecological studies thatemphasize the importance of information diffusion in thefounding process. Hannan et al. (1995) suggested that, in theearly stages of an organizational population, foundings dis-perse in space as processes of cultural diffusion make a neworganizational form legitimate beyond its home ground. Theyemphasize print media and industry events as mechanismsby which information about the new organizational form dif-fuses. Hedstrom (1994) suggested that information diffusesthrough localized social networks that span adjacent geo-graphical areas. Our analysis complements that work byadding that information also flows from one organizationalpopulation to another through direct contact and throughcompetitive monitoring. Although our results show how sym-biotic and commensalistic relations influence where found-ings occur in an already established organizational population,a potentially important extension would be to examinewhether these interpopulation relations also influence the dis-persal of foundings in the early stage of a new organizationalpopulation. If a new organizational form becomes legitimatein part “as a greater number of individuals come into contactwith it and thereby become aware of its features” (Carrolland Hannan, 2000: 339) and if symbiotic and competitiveorganizational populations facilitate such contact, then the


presence of symbiotic and commensalistic organizationalpopulations in an area may accelerate the accretion of legiti-macy of new organizational forms.

Viewing symbiotic and commensalistic relations as vehiclesof information diffusion has broader implications for the studyof interpopulation relations. In particular, our information-based perspective leads us to predict that commensalisticorganizational populations may increase each other’s found-ing rates. This prediction departs from the often-held viewthat organizational populations that use similar resources andserve similar markets constrain each other’s growth (McPher-son, 1983; Hannan and Freeman, 1989). The premise of thisalternative view is that commensalistic organizational popula-tions inevitably decrease the stock of resources available forwould-be entrepreneurs. Based on this premise, researchershave argued that greater competition for resources may dis-suade entrepreneurs from starting an organization in analready crowded niche (Baum and Singh, 1994; Baum andOliver, 1996).

Our argument differs from this work because we highlightedthe importance of information as a critical resource in theorganizational creation process. Information differs fromother resources, such as specialized labor or raw materials, inthat it can be used by those who have access to it concur-rently as well as sequentially without being diminished(Arrow, 1962). Our focus on information leads us to viewcommensalistic organizational populations not as depletingthe stock of a finite resource but rather as vehicles of diffu-sion of this resource. It is also true that commensalistic orga-nizational populations consume resources that are depletedwith use. But unlike the competitive relation tying membersof the same population, commensalism entails only a partialoverlap in the kinds of depletable resources used. These par-tial overlaps are likely to have only a modest negative impacton the carrying capacities of commensalistic organizationalpopulations in comparison to the negative impact that anincreasing number of members of the same population hason the common pool of resources from which a populationdepends.

The second contribution to ecological research is the opera-tionalization of interpopulation relations, focusing on patternsof resource utilization evidenced by input-output flows. Thismeasurement method is a viable alternative to the morecommon approach, which consists of hypothesizing relationsof interdependence on the basis of qualitative considerationsspecific to the organizational populations under study andthen estimating the effects of these relations (for reviews,see Rao, 2002; Aldrich and Ruef, 2006). The advantage of themethod used here is that it sets criteria for determining thepresence of interpopulation relations that can be appliedacross studies and facilitates the accumulation of comparablefindings. The disadvantage is that it produces plausible coeffi-cients of interdependence only when all populations are con-sidered. This disadvantage suggests that when researchersare interested in examining only a small set of populations, aqualitative assessment of interpopulation relations may stillbe the most practical measurement approach.



This study’s third contribution is to research that emphasizesthe social structural conditions that promote entrepreneur-ship (e.g., Freeman, 1986; Burton, Sørensen, and Beckman,2002; Audia and Rider, 2005). A key insight of this literatureis that members of established organizations are in a favor-able position to create organizations of that same typebecause work experiences help would-be entrepreneurs tobuild confidence, form social ties to resource providers, andgain access to information about entrepreneurial opportuni-ties. Our findings extend that perspective by suggesting thatmarket relations may also be important points of access toresources critical to the founding process. Our results sup-port the view that would-be entrepreneurs are affected bythe market relations characterizing the community in whichthey reside. The market relations present in a communitychannel information that propels individuals into creating cer-tain kinds of organizations. In other words, our study sug-gests that, like other forms of economic action (Granovetter,1985), entrepreneurial activity is embedded in social and mar-ket relations.

A fourth contribution is to research in urban sociology thatconceptualizes the urban structure on the basis of the distrib-ution of economic activities in space (Wilson, 1984; Sassen,1990). A classic distinction in that body of work is thatbetween higher-order places that perform the coordinatingfunction and lower-order places that specialize in the produc-tion of goods (e.g., Lincoln, 1978; Meyer, 1990; Palmer et al.,1990). In recent work, attention has shifted to the positionthat spatial units occupy within both national and global net-works. The different functions that spatial units perform with-in these networks underlie the newer concepts of globalcities (Sassen, 1991) and edge cities (Knox, 1997). Althoughtheorists increasingly refer to spatial units as nodes in net-works (Perry and Harding, 2002), this insight is rarely carriedforward in empirical work. The positions of spatial units areusually inferred from internal attributes such as size, occupa-tional specializations, and local industry mix, rather than frommarket relations linking spatial units to each other and to thebroader system (for an exception, see Goe, 1994). This studyadds to that literature by suggesting one way in which theposition of spatial units can be defined by explicitly takinginto account market relations. By examining the pattern ofmarket relations linking organizational populations dispersedin geographical space, we have shown that it is possible toarray communities within the market space on the basis oftheir symbiotic and commensalistic relations to a focal popu-lation. We have also shown that a community’s market posi-tion influences where foundings of a particular kind of organi-zation are more likely to occur. This approach can be readilyextended to studying the position that communities occupyin relation to each other. For example, future studies couldexamine how communities are affected by the extent towhich they occupy a crowded niche.

This study’s explicit consideration of how market relationscharacterize the position of communities within the urbansystem also highlights how this study differs from the bur-geoning literature in economic geography that focuses on


information spillovers. For example, an important and stillunresolved debate concerns whether cities benefit morefrom intra-industry information spillovers or from inter-indus-try information spillovers (Glaeser et al., 1992; for reviews,see Quigley, 1998; Audretsch and Feldman, 2003). As in thatwork, we acknowledge the localized nature of informationflows arising from both market and nonmarket relations thattake place within the geographical boundaries of communi-ties. The difference is that this study gives greater emphasisto market relations as a key mechanism of information diffu-sion and explicitly recognizes that these relations take placenot only within the community but also among communities.Our analysis of initial foundings illustrates this difference wellbecause it indicates that the nonlocal market relations inwhich communities are embedded influence the kinds ofnew economic activities that they may undertake. In addition,while economic geographers debate whether the stock ofinformation that stimulates cities’ growth is more a functionof the presence of agglomerations than of industrial diversity,our information-based theoretical framework is more fine-grained in that it seeks to explain the specific types of infor-mation present in a locality as a function of the organizationalpopulations that it comprises and the pattern of market rela-tions in which they are involved.

The effects of the structure of interpopulation relations onthe rate of organizational foundings highlighted by this studymay be contingent on at least two conditions that set bound-aries on the generalizability of our results. First, our argumentassumes that information about entrepreneurial opportunitiesrelevant to a focal population is unevenly distributed in bothmarket and geographic space. Interpopulation relations mayhave little or no effect on the foundings of organizational pop-ulations for which the information that propels individuals intoentrepreneurial action is widely available. Second, our coreargument implicitly assumes that the information flowingthrough interpopulation relations points to attractive entrepre-neurial opportunities. This assumption fits instruments manu-facturing well. Due to a confluence of technological and mar-ket developments, this industry was growing rapidly duringthe period of this study. But when a focal organizational pop-ulation is experiencing a sustained period of decline, it is pos-sible that symbiotic and commensalistic organizational popu-lations may not have the positive effect on foundings shownin this research.

This study has reported research in which concepts and ana-lytic techniques from social network analysis were applied incombination with those of the population ecology of organiza-tions. One of the important points of intersection of thesesocial research traditions is the local community. Organiza-tions conduct their activities in a local context and in waysthat show the commonalities of structure and process thatare reflected in organizational forms. These commonalitiesrepresent structural equivalence in markets, and they alsorepresent patterns of competition and mutualism amongorganizational populations. The structure of the communitiescan be seen as mechanisms that provide easy or difficultaccess to both resources and information. Opportunity is



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