The Spatial Clustering of Science & Capital, Accounting for Biotech Firm Venture Capital Relationships

7/26/2019 The Spatial Clustering of Science & Capital, Accounting for Biotech Firm Venture Capital Relationships

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Regional Studies, Vol. 36.3, pp. 291305, 2002

The Spatial Clustering of Science and Capital:Accounting for Biotech FirmVenture Capital

Relationships

W A L T E R W . P O W E L L , * K E N N E T H W . K O P U T , J A M E S I . B O W I E a n dL AU R E L S M I T H - D O E R R

*509 CERAS, SCANCOR Building, Stanford University, Stanford, CA 94305, USA. Email: [email protected] McClelland Hall, Department of Management and Policy, University of Arizona, Tucson, AZ 85721, USA.

Email: [email protected] of Sociology, University of Arizona, Tucson AZ 85721, USA.

Department of Sociology, Boston University, Cummington Street, Boston, MA 02215, USA

(Received June 2001; in revised form October 2001)

POWELL W. W., KOPUT K. W., B OWIE J. I. and S MITH-D O E R R L. (2002) The spatial clustering of science and capital:

accounting for biotech rmventure capital relationships, Reg. Studies 36, 291305. This paper focuses on the spatial

concentration of two essential factors of production in the commercial eld of biotechnology: ideas and money. The location

of both research-intensive biotech rms and the venture capital rms that fund biotech is highly clustered in a handful of keyUS regions. The commercialization of a new medicine and the nancing of a high-risk start-up rm are both activities that

have an identiable timeline, and often involve collaboration with multiple participants. The importance of tacit knowledge,

face-to-face contact, and the ability to learn and manage across multiple projects are critical reasons for the continuingimportance of geographic propinquity in biotech. Over the period 198899, more than half of the US biotech rms received

locally-based venture funding. Those rms receiving non-local support were older, larger and had moved research projects

further along the commercialization process. Similarly, as venture capital rms grow older and bigger, they invest in more non-local rms. But these patterns have a strong regional basis, with notable diVerences between Boston, New York and West Coastmoney. Biotechnology is unusual in its dual dependence on basic science and venture nancing; other elds in which product

development is not as dependent on the underlying science may have diVerent spatial patterns.

Biotechnology Venture capital Networks Spatial agglomeration

POWELL W. W., KOPUT K. W., BOWIE J. I. et S MITH- P O W E L L W. W., K O P U T K. W., B O W I E J. I. und S MITH -

D O E R R L. (2002) Le regroupement geog raphique de la D ORRS L. (2002) Raumliche Konzentration von Wissen-

science et du capital: comment expliquer les rapports entre schaft und Kapital: Versuch einer Erklarung der Beziehung

les entreprises du secteur de la biotechnologie et les societes zwischen Biotechnologie und Risikokapital,Reg. Studies 36,

de capital risque,Reg. Studies36, 291305. Cet article porte 291305. Dieser Aufsatz befat sich mit der raumlichen

sur la concentration spatiale de deux facteurs de production Konzentration zweier wesentlicher Faktoren bei der Produk-cles dans le domaine commercial de la biotechnologie: a tion auf dem kommerziellen Gebiet der Biotechnologie:

savoir, les idees et largent. La localisation et des entreprises Ideen und Geldmittel. In den USA treten Standorte

a forte intensite de recherche-developpement du secteur de forschungsintensiver Biotechnologiermen und der Risiko-la biotechnologie, et les societes de capital-risque qui n- kapitalunternehmen, die ihre nanzielle Grundlage bereit-

ancent la biotechnologie, savere tres concentree dans une stellen, stark gehauft in wenigen Schlusselregionen der USA

poignee de regions cle aux E-U. La commercialisation dun auf. Die Kommerzialisierung eines neuen Arzneimittels und

nouveau medicament et le nancement dune creation den- die Finanzierung eines hochriskanten start-ups stellen Akti-

treprise a haut risque sont, tous les deux, des activites qui vitaten in einer klaren zeitlichen Abfolge dar, die oft die

ont une date limite, et necessitent souvent une collaboration Zusammenarbeit mit mehreren Teilnehmern verlangt. Tacit

avec de multiples partenaires. Limportance de la connaissance knowledge, personlicher Kontakt und die Fahigkeit, zu

tacite, du contact direct et de la capacite dapprendre et lernen und mit mehreren Projekten gleichzeitig zurecht zu

dadministrer de multiples projets sont des raisons essentielles kommen, sind dabei entscheidende Grunde fur die Bedeu-pour limportance continuelle de la proximite geographique tung raumlicher Nahe im biotechnologischen Bereich. Im

dans la biotechnologie. Sur la periode de 1988 a 1999, plus Zeitraum 1988 1999 wurden mehr als der Halfte der

de la moitie des entreprises americaines du secteur de la Biotechnikrmen der USA Finanzmittel von lokalen Risiko-biotechnologie ont prote du capital-risque local. Les kapitalunternehmenzur Verfugung gestellt. Firmen, die nicht

entreprises qui benecient du soutien externe etaient plus lokale Unterstutzung genossen, waren alter, groer und

vieilles, plus grandes, et ont fait plus avancer davantage des hatten ihre Forschungsprojekte bereits weitgehend kommer-

0034-3404 print/1360-0591 online/02/030291-15 2002 Regional Studies Association DOI:10.1080/00343400220122089

http://www.regional-studies-assoc.ac.uk
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292 Walter W. Powell, Kenneth W. Koput, James I. Bowie and Laurel Smith-Doerr

projets de recherche le long du processus de commercialis- zialisiert. Auch Risikokapitalunternehmen investieren mitation. De la meme maniere, les societes de capital-risque zunehmendem Alter und zunehmender Groe mehr in

investissent plutot dans des entreprises externes, au fur et a nichtlokale Firmen. Doch diese Muster variieren regional

mesure quelles vieillissent et sagrandissent. Les fondements deutlich u nd zeigen klare Unterschiede zwischen Bosten,

dune telle distribution saverent fortement regionaux, avec New York und den Finanzinstituten der Westkuste. Biotech-

de notables diVerences pour ce qui est de largent provenant nologie ist ungewohnlich in ihrer Doppelabhangigkeit von

de Boston, de New York et de la Cote de louest. La Grundlagenwissenschaften und der Finanzierung durchbiotechnologie est hors du commun etant donne sa double Risikokapital; andere Sektoren, in denen die Produktion-

dependance de la science de base et du capital-risque; il se sentwicklung nicht so stark von Grundlagenforschungpeut que dautres domaines oule developpement des produits abhangt, mogen durchaus andere raumliche Musterne depend pas de la science sous-jacente aient une distribu- aufweisen.

tion geographique diVerente.

Biotechnologie Risikokapital Netzwerke

Biotechnologie Capital-risque Reseaux Raumliche Konzentration

Agglomeration geographique

I N T R O D U C T I O N on rms that provide venture capital to our sample ofbiotech companies. Venture capital is also spatially

concentrated in the Bay Area, Boston and New York.Our focus is on the relationships between dedicatedbiotechnology companies and the venture capital rms We use descriptive statistics to analyse whether thelinkages between biotech and venture capital arethat nance them. These are, in a sense, unusual

relationships in that they are designed with a termina- exclusively local, have a local component or are non-

local.tion point in mind, at which time the venture capitalist

exits and moves on. Nor are they exclusive relation-ships. A venture capitalist is likely to invest in many

T H E C O - L O C A T I O N O F S C I E N C EdiVerent biotech rms, including some which are likely

A N D C A P I T A L to be competitors in a particular therapeutic area, such

We take as our starting point the spatial concentrationas cardiology, or with a particular technology, such asof two key factors of production in the commercialgenomics. Biotech rms may well have backing fromeld of biotechnology: ideas and money. Casual obser-

multiple venture capitalists, either as part of a collective, vers might wonder why these two endowments, whichsuch as a group or syndicate, or separately as a meansare highly fungible, easily transportable, in short,to nance discrete projects, such as a specialized use ofweightless (L E AD B E ATE R, 2000), are so strongly con-a more general-purpose technology. Biotech rms alsocentrated regionally. Abundant evidence points to thegarner nancial support from multiple sources, throughclustering of both knowledge and capital.government research grants, R & D alliances with major

Ideas, especially knowledge from the frontiers ofcorporations and selling minority equity stakes. For acutting-edge science, have a strong tacit dimensionbiotech rm to become nancially successful, it needs(N E L S O N and W I N T E R, 1982). When knowledge isto develop a promising pipeline with numerous newmore tacit in character, face-to-face communicationmedicines. Each potential product is, in some respects,and interaction are important (V O N H I P P E L, 1994).a separate project that involves diVerent internal staVConsequently, to understand the science, one has toand disparate external collaborators. At a venture rm,participate in its development. Hence new scientica portfolio of investments is developed with divergentadvances have a form of natural excludability (Z U C K E Rlevels of risk, diVerent timelines and varied expectedet al., 1998). In the early years of the biotechnologypayoVs. For both biotechs and venture rms, learningindustry, rms were founded in close proximity toacross partners and projects, and developing experienceresearch institutes and universities where the advancesworking with diverse parties, is critical to successin basic science were being made (KE N N E Y, 1986;(PO W E L L et al., 1996).A U D R E T S C Hand S T E P H A N, 1996; P R E V E Z E R, 1996;We analyse the spatial aspects of these relationships,Z U C K E R et al, 1998). There are two key elements toexamining how the role of location shifts over time asthis clustering process. One aspect is captured byprojects, rms and regions mature. Our data are drawnresearch on knowledge spillovers, where geographicfrom the commercial eld of human biotechnology,proximity facilitates the spread of innovative ideasspecically the wave of founding of new biotech rms

( J A F F E et al., 1993; A U D R E T S C H and FE L D M A N,in the US over the period 198899. This eld is1996). But while intellectual capital is necessary, it mayremarkably clustered spatially, with over 48% of all USnot be suYcient. A supportive institutional infrastruc-rms located in either Northern California, the Bostonture that fosters knowledge transfer and the formationMetropolitan area or San Diego County. We map the

of technology-based companies is also critical (P OW -industrys growth, showing a pattern of cluster-based

proliferation. We match our biotech data to a data set E L L , 1996).


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The Spatial Clustering of Science and Capital 293

Consider the case of Atlanta, Georgia, where there A U D R E T S C H and F E L D M A N, 1996, p. 634, put the

question aptly: even after accounting for the geo-is a major research centre, the Center for DiseaseControl, a technology-based university, Georgia Tech, graphic concentration of the production location, why

does the propensity for innovative activity to clusterand one of the top medical schools in the country at

Emory University. The metropolitan area is reasonably vary across industries? The relevant scientic expertise

in biotech is, by now, broadly distributed throughoutwell-to-do and well-educated, and a number ofFortune500rms are headquartered there. But there is little in the industrial world, with major centres of scientic

excellence in the US, the UK, Sweden, France, Ger-the way of commercial biotechnology, despite abundantintellectual resources. One biomedical entrepreneur at many and Switzerland. But the science is commercial-ized by rms in a signicant manner (by which weGeorgia Tech told us that he has had numerous over-

tures from nanciers and angel investors for his techno- mean the ability to bring novel medicines to a global

marketplace) in only a handful of locations worldwide.logies, but they have all made leaving Atlanta and

moving to California a requirement of obtaining the To understand this phenomenon, we have to explainwhy some regions are hubs for organizational creation,nancing.

Or consider the often-cited list of founders of some that is, populated, by organizations, that are in the

business of creating other organizations (ST I N C H -of the key rms created in the late 1970s and 1980s:Genentech (Herbert Boyer, University of California, C O M B E, 1965). Put diVerently, some regions are incu-

bators and constitute an ecology for organizationalSan Francisco); Biogen (Walter Gilbert, Harvard

University); Hybritech (Ivar Royston, University of formation (BROWN , 2000). These regions have a richmix of diverse kinds of organizations (e.g. universities,California, San Diego); Genetics Institute (MarkPtashne, Harvard University); Systemix (David Balti- law rms specializing in intellectual property, public

research institutes, consultants and venture capitalists)more, Massachusetts Institute of Technology and

Whitehead Institute); and Immulogic (Malcolm Gefter, that contribute in varying ways to founding techno-logy-based companies. The advantages of location,Massachusetts Institute of Technology).1 All of these

eminent scientists retained their university aYliations, then, are very much based on access and information.

Increasing returns are present in the form of over-often full-time. They were able, so to speak, to have

their cake and eat it too, precisely because their univer- lapping networks, recombinant projects, personal andprofessional relationships, and interpersonal trust andsities had created rules and routines that enabled

technology transfer and faculty entrepreneurship. There reputation, all of which are thickened over time. In

such a milieu, access to reliable information about neware many regions where there is scientic excellencebut not the requisite infrastructure to capture the rents opportunities occurs through personal and professional

networks, and these ties are critical in reducing uncer-from knowledge spillovers.

Our emphasis on this infrastructure of university tainty about projects that are not well understood by

non-experts, exceedingly risky in terms of their payoVtechnology transfer, venture capital, law rms, consul-tants and the like is somewhat diVerent from treatments and unclear in terms of their eventual market impact.

Venture capital (VC), dened as independent,of industrial districts, in the tradition of M A R S H A L L,1920.Economists and geographers have long recognized professionally managed, dedicated pools of capital that

focus on equity or equity-linked investments inthe tendency for production to cohere geographically,

whether it is cars in Detroit, steel in the Ruhr, silk in privately held, high growth companies (GO M P E R S

and LE R NE R, 2001, p. 146), is one of the key elementsLyon or lmmaking in Hollywood. Spatial concentra-

tion confers advantages in terms of transportation costs, of the infrastructure of innovation. The private equitymarket has become a major source of nancing foraccess to skilled labour markets, communication net-

works, sophisticated customers and access to technology start-up rms, and has grown at an explosive rate; in

1979 venture rms dispersed US$500 million in funds,(SC O TTand S TO R P E R, 1987; F LORIDA and K E NNE Y,1988; A N G E L, 1991; S AXE NI AN , 1994; ST O R P E Rand that amount climbing to well over $67 billion by

2000 (WR I G H T and R O B B I E, 1998; G O MP E R S andSALAI S , 1997). Once these agglomeration economies

are established, a dynamic process of increasing returns LE R N E R, 2001). Both venture capital rms and ven-ture capital investing are highly concentrated regionally.attracts new entrants, further fuelling the pace of

innovation (AR TH U R, 1990; KR U GMAN , 1991). For example, in the third quarter of 2000, as the global

slowdown in technology companies became moreConsequently, the geographic clustering of production

is a global phenomenon. (PO R T E R, 1998, provides pronounced, VCs still poured $87 billion into newcompanies located in Northern California. This sumnumerous examples.)

Our emphasis is less on the process of economizing represented 337% of the total US venture capital piefor that period for all industries, according to Ventureon the transaction costs of founding a new rm, or the

many attractions that draw entrepreneurs to a region. Economics, a rm that tracks VC investing (SI N T O N,

2000). In 1999, a little more than one-third of allWe are interested in understanding why rms based

on a fast-moving science that is continually creating venture capital disbursements went to California

(G O MP E R S and LE R N E R, 2001).new opportunities are formed in particular locales.


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A venture capital rm raises money from wealthy unplanned encounters at restaurants or coVee shops,

opportunities to confer in the grandstands during Littleindividuals, pension funds, nancial institutions, insur-ance companies and other sources that are interested League baseball games or at soccer matches, or news

about a seminar or presentation all happen routinely inin investing in technology-based start-ups, but lack the

ability to do so. These investors become limited part- such settings. The combined impact of access to news

and more eVective monitoring help explain the patternners in the VC fund, while the partners in the VCrm manage the money by investing in and advising of VC clustering.

With all these advantages of geographic propinquity,entrepreneurial start-ups. Venture capitalists nancenew rms with the potential for high growth in return it might seem unlikely that more distant relations occurat all. There are, to be sure, several ways that VCsfor partial ownership. When the young company is

suYciently developed, the rm goes public through an overcome some of the liabilities of distance. Both the

creation of branch oYces and involvement in VCinitial public oVering (IPO) or is acquired by another

company. At this point the VC cashes in its ownership syndicates are means to counter the challenges of moredistant relations (SO R E N S E N and STU AR T, 2001).stake, and reaps its rewards. Venture capital obviates the

need to g row slowly via self-nancing, and fuels more Increased size and greater experience could also provide

VC rms with the capability to support more distantrapid growth. As FR E E M A N, 1999, puts it, venturecapitalists buy time. The success of a VC rm in rms. VC rms may follow diVerent approaches when

they are investing their own money versus that ofattracting money is contingent on its past track record

of spotting winners and generating rewards for its limited partners, or when they join another VCs fundas a member of a syndicate. In addition, the pace oflimited partners. The business of identifying opportun-ities is highly uncertain and diYcult. Of course, VCs advancement of new industries and the mix of rms

within them may oVer new opportunities for invest-receive innumerable proposals for new businesses. But

the rejection rate for these proposals is extremely high ment. For example, V Cs may perform a diVerent rolewith an early-stage company than in a rm that has(estimated by S A H L M A N, 1990, to be at 99%). As in

many other walks of life, many call but few are already undergone its rst round of nancing and

shown evidence that its technology can be brought toanswered. More opportunities are identied through

active search by VCs. In part, this is because the market. We turn now to a discussion of the factors thatshape the proclivity of biotechVC relations to occurexpected pay-oVdemanded from VC backing is very

high and the ratio of success to failures about two in on a local or more distant basis.

ten (BYG RAV E and T I M M O N S, 1992; GO M P E R S andL E R NE R, 1999).

E X P L A I N I N G C E NT RE A NDIn the life sciences and other technology-based elds,

P E R I P H E R Yventure rms provide more than money. Because many

of the founders of biotech rms are research scientists, The literature on knowledge spillovers provides usefulleads on both how and when geographic localizationventure capitalists often do much more than monitor

or advise; they may even play a hands-on role in the matters.2 One insight is that the importance of propin-quity can decline over time. J A F F E et al., 1993, reportrunning of the young company. Keeping scientists

focused on key commercial milestones is no small feat. that patent citations to other patents (excluding within-

organization citations) are ve to ten times more likelyA powerful tool for focusing their attention is the

staging of VC nancing, thus the commitment of to occur within the same city. This pattern of localiza-

tion is most pronounced in the rst year following acapital is contingent upon progress (GO MP E R S ,1995). VCs also routinely help in recruiting key staV patents issue, and subsequently declines. In a parallel

vein, they also found that patents in such fast-and important collaborators, and provide referrals to

law and accounting rms, and eventually to investment developing elds as optics and nuclear technology havehigh initial citation rates that fade rapidly. A LME I D Abanks (FLO R I D A and KE N N E Y, 1988). Many VCs

serve on the boards of directors of young rms they and K O GU T , 1997, report similar results for patenting

activity in the semiconductor industry, with high ratesfund. As G I L S O Nand B LACK, 1998, put it, by provid-ing both money and advice, the venture capitalist puts of local citations that subside over time.

The joint eVects of technological evolution and theits money where its mouth is. Obviously, the roles of

monitoring, advising and managing are much more stages in a rms life cycle are not easily disentangled,

however. Two excellent studies of biotechnology pointeasily accomplished when the young rm is locatednearby. Experienced VCs have abundant contacts and out this diYculty. Z U C K E R et al., 1998, show that

the founding of new biotechnology rms in the 1970sdeep knowledge of particular industries; thus, referralsto relevant sources of expertise are another important and 1980s occurred in those regions rich in the

relevant intellectual capital, and that star scientists hadresource they provide. This social network is also

more readily tapped when rms are geographically a direct role in this process as founders and advisors.

A U D R E T S C H and ST E P H A N, 1996, examine a sampleproximate. Finally, there are real advantages that accrue

to rms and venture capitalists to being on the scene of biotech rms at the time of their initial public


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oVerings in the early 1990s and analyse the geographic new and unproven technologies, previous aYliations

can serve as a proxy for quality (PO D O LNY, 1994).location of founders and members of scientic advisoryboards. They nd considerable geographic reach in the Not surprisingly, start-up companies go to considerable

lengths to advertise the backing of elite venture rmscomposition of advisory boards, but somewhat closer

linkages when scientists are involved as founders. This to attract employees and collaborators. In short, social

relationships are essential to the process of garneringcomparison raises two questions: (1) is the contrastbetween the studies a consequence of diVerences in resources to found new organizations.

But can aY

liations compensate for less expertiseroles, i.e. an advisory role involves less direct engage-ment and can be accomplished from a distance, while or capability? Alternatively, can organizations that arepursuing excellent science, but located away from keya founders role entails more hands-on involvement,

requiring the proximity of a scientists rm and labora- centres of activity and lacking access to well-connected

parties, nd much-needed support? Clearly, centralitytory; and (2) do the diVerent ndings reect distinct

stages in the development of a company, with founding in networks and expertise are self-reinforcing (STUART ,1998). But at what point are there diminishing returnsa time when new ideas are being explored among a

select few, and the IP O stage a point when patent to network centrality or local connectivity? We examine

these issues about the dynamics of centre and peripheryrights for these ideas have been secured and the rm isready to reveal to the public a good deal of information by addressing the following empirical questions:

about itself in order to obtain funds? An additional1. To what extent are biotech rms and VC rms

complication is that not only are the rms under study co-located?at diVerent stages in their life cycle, the industry and2. How extensive is the phenomenon of regionalthe nature of technological progress were at diVerent

co-location, such that biotechs receive support frompoints in their development.local VCs and VCs nance local biotechs?To pursue the latter issue, regarding distinctive stages

3. What is the relationship between location of fundingin organizational, industry and technological life cycles,and characteristics of both biotechs and VCs inwe explore whether biotech rms and venture capitalterms of age, size, and centrality in the network?funders are more likely to be co-located when the

4. How do the above patterns and relationships changebiotechs are younger and/or smaller. If biotech rmsover time?are able to wait until they are older and/or larger before

securing venture support, they may well be able to

choose from a broader set of nancial backers. We alsoDA T A SO U R C E Sexplore the other side of this coin, recognizing that

just as biotech rms search for private equity, venture Our starting point in gathering data on biotech com-

panies is BioScan, an independent industry directorycapitalists look for new technologies to bankroll. Thus,we ask, under what circumstances do venture rms founded in 1988 and published six times a year, which

covers a wide range of organizations in the life scienceslook outside their local environments?

There is an unexplored nding in the A U D R E T S C H eld.3 We sample companies that are independentlyoperated, prot-seeking entities involved in humanand S T E P H A N, 1996, study that intrigues us, suggesting

that the relevant actors in diVerent locales have diVerent therapeutic and diagnostic applications of biotechno-

logy. Our focus is on dedicated human biotech rms.propensities to either search locally or at a distance.

University scientists in Boston, the Bay Area and San Both privately-held and publicly-traded rms are

included in the sample. Companies involved in veterin-Diego that served on biotech advisory boards werevery likely to do so locally, while scientists in New ary and agricultural biotech, both of which draw on

diVerent scientic capabilities and operate in a muchYork, Los Angeles, Maryland and Houston served on

the boards of more distant companies. Such variation diVerent regulatory climate, are omitted. We do notinclude large pharmaceutical corporations, health carein search behaviour may reect diVerences in access to

contacts or diVerent resource endowments. These are companies, hospitals, universities or research institutes

in our primary database; these participants enter theissues at the heart of research on inter-organizationalexchange. One strand of analysis emphasizes that inter- database as partners that collaborate with dedicated

biotech rms. Companies that are wholly-owned subsi-organizational ties are strongly inuenced by social

structure, with previous exchanges shaping subsequent diaries of other rms are excluded. We do, however,

include publicly-held biotech rms that have minorityties (G R ANO VE TTE R, 1985; G U LATI, 1995). Organi-zations privileged by prior access obtain better rates of or majority investments in them by other rms, as long

as the companys stock continues to be independentlynancing (UZ ZI , 1999) and overcome liabilities ofnewness more easily (BAU M and OL I V E R, 1991). traded on the market. Our rationale for excluding

both small subsidiaries and large, diversied chemical,When organizations share a common prior partner,

they nd it easier to engage in exchange (G ULATI and medical or pharmaceutical corporations in the primary

database is that the former do not make decisionsG AR GI U LO, 1999). And, when there is uncertainty

about the merits of an activity, as is often the case with autonomously, while biotechnology may represent only


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a minority of the activities of the latter. Both circum- oYce and $1 million under management, to much

larger rms like Bostons Advent International, withstances generate serious data ambiguities.The sample covers 482 rms over the 12-year period, 16 worldwide oYces managing $4 billion. The sample

of V Cs includes the Silicon Valley household name198899. In 1988, there were 253 rms meeting our

sample criteria. During the next 12 years, 229 rms Kleiner, Perkins, Caueld, and Byers, as well as smaller,

less-known rms such as Hook Partners of Dallas,were founded and entered the database; 91 (of the 482)exited due to failure, departure from the industry or Texas. In addition, we include the venture capital

arms of more traditional nancial institutions, such asmerger. The database, like the industry, is heavilycentred in the US, although in recent years there has NationsBank and J. P. Morgan. The oldest rm in thesample is Scotlands Standard Life Investments, foundedbeen expansion in Europe. In 1999, 80% of the com-

panies in our sample were located in the US and 10% in 1825; in 1999, nine new rms entered the database.

in Europe. For the purposes of this paper, we limit the

sample to US-based companies because of the ease ofM E T H O D S

using US zip codes as a means to determine geographic

location. During the period 198899, 213 US biotech Our objectives are to establish the co-location of

biotech rms and VCs, to explore how geographicalrms received funds from venture capital companies.The reference source BioScan reports information agglomeration inuences whether VC nancing of

biotech rms is done locally or non-locally, and toon a rms ownership, formal contractual linkages to

collaborators, products and current research. In addi- demonstrate the relationship between the locality ofcapital and characteristics of both the biotech rms andtion, detailed information is provided on a companysnancial history, and we drew from this source data on VCs. We use descriptive statistics to accomplish these

objectives, comparing both VCs and the biotechs theyventure capital investments in specic biotech compan-

ies. We also utilize data on the founding date and fund based on their location, stage of development andthe nature of the funding relationships.employment levels of biotech companies. Our database

draws on BioScans April issue, in which new informa- To identify location, we use postal zip codes for USrms and telephone country prexes for those VCstion is added for each calendar year.

For information on venture capital forms, we con- located outside the US. Using these codes, we exam-ined frequencies of rms and V Cs by location, identify-sultedPratts Guide to Venture Capital Sources, a reference

guide to U S and non-US VC rms. The guide was ing nine areas with signicant agglomeration of either

VC or biotech rms. These nine agglomeration clustersrst published in 1970, followed by new editions in1972, 1974 and 1977. Since the fth edition, it has include: (1) Boston; (2) the NY C tri-state region,

including parts of New Jersey and Connecticut; (3)been updated annually, based on information provided

by the VC rms. In addition to information on the Philadelphia; (4) the District of Columbia region,

including part of Maryland proximate to the Nationallocation of home and branch oYces, key staV andfounding dates, the guide covers VC rms preferences Institutes of Health (NI H); (5) Chicago; (6) Houston;

(7) San Diego; (8) the San Francisco Bay Area, includ-in terms of their preferred role in nancing, the typeof nancing they provide, and whether they have ing Berkeley, Oakland and Silicon Valley; and (9)

Seattle. Each biotech rm and VC was then assignedgeographic or industry preferences. The guide also

reports the amount of capital the VC rm manages, a cluster code equal to the agglomeration region it was

in, if any, or 0 if the rm or VC was located elsewhere.and whether the rm primarily invests money raised

from limited partners or its own money. The 1999 For each biotechVC dyad, we dene the funding aslocal if the rm and VC are within a one-hour driveedition reports that the VC rms included have been

selected because they are devoted primarily to venture of one another (by automobile, using Yahoos estimated

driving time between zip codes).nancing, and it goes on to remark on the expansionof VC-type activity by a wide range of diVerent Each biotech rm is then placed into one of three

mutually exclusive categories based on whether it isorganizations: today, venture investment activity covers

a spectrum of interests that encompasses all phases of only involved in dyads with local VCs, only involved

in dyads with non-local VCs, or involved in dyads withbusiness growth. Pratts Guideadopts a more restrictivedenition of venture capital investors than does BioScan, both local and non-local VCs. We do this separately for

when the biotech rm is at two distinct stages ofwhich groups angel investors, pension funds and uni-

versity technology oYces under the category of development, before and after its initial public oVering(IPO). For each biotech rm, we also measure ainvestors. We utilized the Pratts denition because we

want to focus on those companies that are most number of rm attributes, including: its age, experiencein the industrys inter-organizational network (con-oriented towards high-risk, high-involvement, early-

stage investment in entrepreneurial start-up rms. necting biotech with universities, government agencies,

nanciers, nonprot labs, and large pharmaceutical andThere are 208 venture rms that nance the biotechs

in our sample. They vary in size from small rms such chemical corporations); number of employees; time

from founding to IPO; time from its rst network tieas Allergan Capital of Irvine, California, with one


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to I PO; number of V C partners; number of other Diego County as the three largest hubs, and smaller

partners (besides V C); and centrality in four inter- centres in the New York metropolitan area (includingorganizational networks R &D, nance, licensing and the tri-state area of Northern New Jersey, westerncommercialization. Connecticut and the suburbs of New York City) and

Each VC rm is also placed into one of the three the area around the National Institutes of Health inexclusive categories based on whether it only funds Rockville, Maryland.local biotech rms, only funds non-local biotech rms, The map of venture capital rms that invest in

or funds both local and non-local biotech rms. We biotech, presented in Fig. 2, also shows regional con-do this assignment separately for funded biotech rms centration, but with some notable geographic diVer-that are pre- and post-IPO. For each VC, we also have ences. Again the Bay Area and Boston are the twomeasures of age, number of oYces, capitalization, and dominant areas, with Menlo Park, CA, far and awaywhether it is primarily investing its founders own the most active location of all. But New York is thirdmoney or other investors money. and San Diegos position much smaller, a reversal of

their roles in the biotech world, reecting New Yorks

pre-eminence as a nancial centre. Several other areasR E SU L T S are signicant with respect to venture capital Cleve-

land, Los Angeles, Minneapolis and Chicago, but theseWe begin with a graphic presentation of the locationare areas with scant biotech activity. And in 1988, thereof our samples of biotech and VC rms. Our biotech

are areas with some biotech rms such as Seattle,database starts with the year 1988. The oldest rm inPhiladelphia, Madison, WI, Atlanta, Miami, FL withour sample at that point is a Northern Californiano local venture capital presence.company, Alza, founded in 1968. The rst biotechno-

Fast forward to 1998 and you can see the growth oflogy rm to go public was Genentech in 1980. Sothe biotech industry, accompanied by only modestFig. 1, which shows the location of rms by zip code,geographic expansion. The growth is pronounced inis a map of the industry in its adolescent stage. TheBoston, where newspaper accounts now routinelylarger the dots, the more rms located in that zip code.cheer its advance on the Bay Area as the most activeThese maps are simple counts of the number of rmslocale for biotech.4 The Bay Area and San Diego growin an area, and not selected for rm size or marketrapidly as well, but so does the Philadelphia area, thevalue. There is a strong pattern of spatial clustering,

with the Bay Area, the greater Boston area and San Washington-Baltimore corridor, northern New Jersey,

0 300miles

0 500kms

Fig. 1. US dedicated biotechnology rms, 1988


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0 300miles

0 500kms

Fig. 2. US venture capital rms funding biotech, 1988

and the Research Triangle of North Carolina on the both VCs and biorms in such circumstances need to

hunt externally for partners. At the same time, theeast coast, and the Houston area in Texas. Further west,

Boulder, C O, Salt Lake City, Utah, and especially most active areas are likely to be magnets for outside

investors, while rms seek support wherever capital isSeattle emerge as smaller hubs. But the overall patternis one of cluster-based growth. As the number of available. We turn now to an examination of the

biotech-venture capital relationships that result frombiotech rms in our sample climbs by 146, the percent-

age of US companies located outside the main regional the simultaneous searching of biotechs for funds andVCs for opportunities.clusters remains steady at approximately 28%.

Venture capital took oV dramatically in the 1990s. For the entire time period, 213 biotech rms have

relationships with VCs that meet Pratts criteria. TheG O M P E R S and L E R N E R, 2001, report that there were34 funds in 1991 and 228 in 2000. Fig. 4 portrays the number of biotech rms nanced by VCs grows,

almost monotonically, from 27 in 1988 to 118 in 1999,VC rms that funded biotech companies in 1997, and

shows massive growth in the Bay Area, and along the with a dip in 1997. Of these rms, 54% of the biotechrms received local VC support at some point. Thisnorth-east corridor from Washington to Boston. There

still remain several mismatches, however, that is gure varies by location and over time. Among biotechrms located in a cluster, 58% have funding from aregions with VCs but little biotech (Chicago, Cleve-

land, St Louis); areas with very active biotech but local VC at some point, compared to only 48% for

rms outside of any single cluster. The percentage ofnot a great preponderance of venture capital (Seattle,Research Triangle, even San Diego has much more VC-backed biotechs with local funding ranges over

time from 33% in 1988 to over 62% in the mid 1990s,biotech); and areas with no VC but some biotech (Salt

Lake City, Atlanta, Madison). before settling back to 48% in 1999.

On the VC side, 208 VCs provide funds to ourThe maps presented above help frame our presenta-tion of the ndings. There are a handful of locales subsample of US-based biotech rms, with 50% of

those VCs funding biotechs that are local. This percent-abundant in rms and venture capital, and three of theseregions have ourished with this propitious situation for age is slightly higher when VCs are funding post-I PO

(52%), is higher for VCs located in one of the clustersmuch longer than a decade. Other regional centres do

not enjoy a comparably rich co-location of capital (54%), and rises signicantly over our period of obser-

vation, starting at just 30% in 1988.and science. Many parts of the US have only one

endowment money or rms but not both. Clearly We now examine features of biotech rms that


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0 300miles

0 500kms

Fig. 3. US dedicated biotechnology rms, 1998

Fig. 4. US venture capital rms funding biotech, 1997


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Table1. Means and standard deviations (in parentheses) for with outside VC nancing take the longest time togo public 65 years.biotech rms receiving VC funding prior to IPO, by locality

of funding Those rms at the pre-IP O stage with only localVC backing have a diVerent prole. These are the

Non-l ocal Local Bo th l ocalsmallest of the three types in terms of number offunding funding and non-employees, but have the largest percentage of staVwithVariable only only local fundingPh Ds and/or MDs. These biotech rms go public

Firm characteristics

rapidly, on average in 47 years. They also have muchAge 55913 51852 46411 more exclusive relations with venture rms, having(27813) (42678) (26174)Time to IPO from 178 funders, compared to 26 for the non-local

founding date 65000 47273 52188 biotechs and 43 for those with both local and outside(in years) (32027) (16787) (21211) nancing. The latter group apparently are high-prole

Time since rst tie 44754 45185 39625companies. Not only do they attract both sources of(in years) (27131) (43688) (18327)funds, they are the youngest as well, only 46 years onTime to I P O from rst 50588 36364 46250

tie (in years) (28491) (12863) (20439) average. The locally-backed rms have a strong scient-Number of employees 5381 4404 5313 ic prole, suggesting a research orientation and a need

(4317) (3573) (3706) for management assistance and oversight that is bestNumber of P hDs/M Ds 1524 1670 1535

provided by local VCs. The more exclusive ties to one(929) (1603) (1074)

or two VCs also suggests the VCs are more involvedPartner counts in the managing of the rm.Number of Pratts V Cs 26073 17778 42635

Turning to companies at the post-IPO stage, therefunding (19230) (11956) (21281)

are 57 with external VC links, 14 with only localNumber of non-D BF 86208 67451 93368partners (47759) (40578) (48503) support and 62 with both sources. Not surprisingly,

Number of D B F par tners 08 283 051 70 0 5965 these post-IPO rms are considerably larger, as one(13283) (08447) (07559) would expect from companies that are older with more

Number of types of ties 20955 19556 19264nancial security. But again those with only local

(07488) (08233) (07172)funding are notably smaller, and with a higher percent-Number of forms of 32017 24353 28714

partners (13736) (10084) (12699) age of staVwith advanced science degrees. The local-

only rms had much more exclusive relations ties toCentrality measuresVCs, with 12, while those with both sources had

R &D centrality 00035 0.0008 0.0022(00052) (00030) (00044) nearly four V C funders.Finance centrality 00072 00030 00082 Of the 208 VCs that fund biotech rms, 178 of

(00079) (00039) (00080) them nance biotech rms before their IP O, whileLicensing centrality 00022 00014 00015

152 provide funding for subsequent rounds of nancing(00052) (00041) (00036)

to publicly held rms. Obviously, most VCs do bothCommerce centrality 00004 00038 00006(00014) (00002) (00022) kinds of disbursements. The features of the V Cs vary

Number of DBFs 69 27 56 with both locality and the pre- vs. post-IP O distinc-tion. When backing is provided prior to the biotech

rms IP O, the VCs funding locally are about two

years older (14 vs. 12) and larger in terms of oYcesreceive funding from VCs, treating rms that are pre- (19 vs. 17), but have less capital ($229 million vs.and post- IP O separately. Table 1 presents data on $336 million), and are more likely to spend their ownbiotech rms with support from venture capital in money (84% vs. 65%) when compared to V Cs thatadvance of going public. We group the results into fund non-local biotechs. When the support comes afterthree categories: companies with non-local V C support the biotech rms IPO, the story is more complicated.only (of which there are 69); companies with just local Those rms that provide backing exclusively locally orsupport (27 in total); and companies with both local exclusively non-locally are about the same size (15and non-local backing (56). We compare rms with oYces), age (roughly 12 years) and capitalization, butthese three kinds of funding arrangements in terms of those going local only are more likely to be spendingtheir size, age, number of scientic staVand a host of their own money (81% vs. 60%). Those V Cs thatmeasures that capture varying forms of connectivity support publicly held rms both locally and non-locallywithin the industry. Those companies that secure only are much older (173 years), larger (two oYces), more

non-local nance are, on average, larger, older and capitalized ($388 million) and are even more likely tohave a larger number of collaborations with diverse be spending their own money (87%). Thus, older,types of organizations, suggesting that these collabora- more experienced venture capital rms that have thetions may be both a signal to attract VC support and/ benets of being located in technology-rich locationsor a vehicle for obtaining other kinds of resources in are able to be more exible as to where they invest. In

addition, a strong persistent nding is that when theadvance of securing VC backing. Most notably, rms


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Table2. Means and standard deviations (in parentheses) for Table3. Means and standard deviations (in parentheses) forVCs funding pre- and post-IPO biotech rms, by locality of biotech rms receiving VC funding after IPO, by locality of

funding funding

Non-local Local Both local Both local

Non-local Local and non-localfunding funding and non-

Variable only only local funding Variable funding only funding only funding

Firm characteristics Funding pre-I PO rms

Age 123553 140408 156180Age 86101 85048 74516(34951) (31038) (26016) (101932) (196373) (81896)

Number of oYces 16942 1915 19018Time to IPO from 47857 52143 43387

founding date (10512) (13619) (12266)

Capital (US$ millions) 3361133 2285154 2626174(in years) (31143) (27506) (23881)

Time since rst tie 66871 72190 65161 (8523067) (4401693) (2103292)

% spending own money 6477 8372 8298(in years) (30893) (33885) (24761)

Time to IPO from rst 28772 39286 34032 Number of VCs 88 43 47

tie (in years) (32518) (30751) (21838)Funding post-IPO rms

Number of employees 16458 12892 17348Age 124262 116874 173147

(20 438 ) (13 530) (161 66)(71 343) (72 631) (90 252)

Number of P hDs/M Ds 2668 2930 3185Number of oYces 15124 14835 19370

(2299) (2722) (2496)(06 832) (07 757) (15 462)

Capital (US$ millions) 1856892 2102204 3889044Partner counts

Number of Pratts V Cs 20161 12500 39734 (3076961) (3822478) (6923246)

% spending own money 5946 8125 8686funding (15298) (08026) (23466)

Number of non-DBF 117545 142679 132397 Number of VCs 74 32 46

partners (62974) (95956) (66534)

Number of DBF

partners 15837 08095 13628

(1 774 6) (0 9582) (1 529 3)is a recursive relationship: as the biotech industry

Number of types of ties 28053 29167 26648matures, the signicance of geographic proximity(0 953 8) (0 6626) (0 658 1)

Number of forms of 43180 48155 42586 declines somewhat as extra-local ties are developed.partners (16488) (16757) (13349) On the other hand, as VC rms mature and become

more experienced, their willingness and ability to workCentrality measuresR &D centrality 00033 00037 00042 with high-risk local start-ups increases.

(0 005 7) (0 0062) (0 005 2) One of the particularities of venture capital is that itFinance centrality 00054 00032 00098 arose and grew in diVerent places at diVerent times.

(0 005 2) (0 0034) (0 006 6)Consequently, there may be distinct patterns of nan-

Licensing centrality 00033 00083 00036cing based on location. To examine this, we collapse(0 005 7) (0 1158) (0 004 8)

Commerce centrality 00027 00027 0001 the regions into three areas the Bay Area, Boston(0 006 4) (0 0054) (0 003 6) and the rest of the country. Between the Bay Area and

Boston, over half of the action occurs, so this tripartiteNumber of DBFs 57 14 62division is sensible. Looking rst across the 12-year

period, there are some discernable patterns. With

respect to companies that only receive local support,venture rms in the Bay Area tend to fund smaller,VCs invest their own money, their disbursements are

very likely to be made locally. younger companies that have collaborations underway

to commercialize new products. In Boston, local onlyWe also checked to see what the relationship wasbetween the age of VCs and the age of biotechs at the funding goes to larger and older biotechs, which are

more involved in R&D collaborations and licensingtime of their IPOs. One speculation is that younger

V Cs bring companies public earlier than older rms in agreements. Outside these two main centres, local VCfunding goes more to medium sized companies. Withorder to build a reputation and raise needed funds

(GO M P E R S, 1996). In our sample, in contrast, there regard to funding that originates outside the home

region, the biotech recipients within the Boston clusterwas a negative relation between VC age and the age

of the biotech rm at IP O. This relationship was are the younger and smaller biotechs, while in the BayArea cluster these rms tend to be older. In the rest ofdriven by experienced, older VCs in the Bay Area and

San Diego that funded local younger rms and East the country, outside support ows to older and largercompanies. Finally, the rms that receive nancingCoast V Cs that manage funds with both local and

non-local younger biotechs. In sum, the gains from both locally and from the outside are older in both

Boston and the Bay Area. But, rms receiving bothexperience for older VCs include both the capacity to

oversee younger rms as well as more geographically types of nancing that are located elsewhere in the US

are among the youngest, smallest and best connecteddistant rms. For the venture capital rms, then, there


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Table4. Means and standard deviations (in parentheses) forbiotech rms in the Boston cluster, San Francisco Bay Areacluster, and outside any regional cluster that received VC

funding prior to IPO, by locality of funding

Both local

Non-local Local and non-local

Variabl e funding only funding only funding

Boston clusterAge 8 883 908

(263) (023) (336)

Number of employees 16044 360 20092

(15 164) (22627) (193 95)

R&D centrality 0049 0085 0052

(0071) (0105) (0056)

Finance centrality 0064 0031 0093

(0069) (0020) (0047)

Licensing centrality 0044 0058 0048

(0086) (0081) (0068)

Commerce centrality 0017 0 0014

(0048) (0) (0029)

Number of DBFs 9 2 12

New York (NY) Boston (B)

Rest of Country (C)

Bay Area (BA) San Diego (SD)

1988

B B

NY

C C

BA BA

SD

1990-95

B B

NY

C C

BA BA

SD1989

B B

NY

C

NY

BA BA

SD

1996-99

C

Pre-IPO

San Francisco Bay AreaAge 73 716 722 Fig. 5. Regional patterns of venture capital

(286) (246) (231)


(49 09) (2797) (18636)

R &D centrality 0059 00066 0025

(0 081) (00121) (0039)


(0 0077) (0048) (0080)

Licensing centrality 0013 00041 0035

(0 018) (00071) (0047)

Commerce centrality 00023 0039 0018

(0 0051) (0067) (0056)Number of DBFs 5 3 22

Not in a clusterAge 855 730 725

(341) (290) (096)


B B

NY

C

NY

BA BA

SD

1996-99

C

B B

NY

C

BA BA

SD

1992-95

C

NY B

BA SD

1988-91

C

Post-IPO

(14142) (10059) (6015)

R &D centrality 0015 00008 0074Fig. 6. Regional patterns of venture capitalbiotech funding

(0 032) (00015) (0073)


(0 043) (00071) (0044)

Licensing centrality 0032 01347 0039IPO, while Fig. 6 covers post-IPO. Essentially, there(0 040) (01693) (0031)

Commerce centrality 0032 0052 00010 are ve clusters the Bay Area, San Diego, Boston,(0066) (0072) (00020) the New York metro area, and the rest of the country.

Number of DBFs 20 5 4 Beginning in 1988 with relationships at the pre-IPOstage, there are only two main regions for venture

capital the Bay Area and New York City. Funds from

the Bay Area owed principally to San Diego and otherinto the world of R& D. Clearly, the threshold forreceiving both types of nancing is higher for compan- parts of the country at this stage (no doubt, due to left

censoring of the data, we miss earlier links betweenies located outside the Bay Area or Boston.

Turning from the cross-sectional portrait to a more Bay Area VCs and biotechs), while New York money

went to Boston and the rest of the nation. In 1989,dynamic account, Figs. 5 and 6 present the sequenceof funding patterns during key periods in the industrys Boston-based VCs enter the picture and fund local

companies, a pattern that holds for all subsequent timeevolution. These patterns were generated by examiningcross tabulations of the locations of each partner for periods. New York money continues to head north to

Boston and throughout the country, and Bay Areaall funderfundee dyads separately for each year. We

highlight the predominant ow of VC funds in each funding picks up locally and continues in San Diego

and elsewhere. Over the years 199095 the only changetime period with a thick line. A dashed line indicates

a less active pattern. Fig. 5 captures relationships before is New York money heads west to San Diego. But in


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the most recent period, 199699, the picture changes rms that received local support with those that

attracted non-local nancing. The locally-funded rmsand Bay Area money moves to Boston and other partsof the country, while New York money enters the Bay were smaller, younger, more science focused (measured

by the percentage of PhDs and MDs on their payrollArea and begins seeding rms in the New York metro

region. Over all the years, money from outside New and their number of R &D collaborations) and likely

to have more exclusive relations with only one or twoYork or the Bay Area goes to other parts of the countryand never invades the home turf of the most active VCs. The biotechs that garnered external support were

larger in size, older, and had advanced to a stage wherebiotech clusters.Turning to post-IP O nancing, again New York their work had moved further down the product lifecycle (measured by their ties to other organizations toand the Bay Area are the primary locales for venture

funds for the years 198891. In 1992, both Boston assist in commercializing products). Thus, local V C

support is directed to much earlier stage companies,money and funds located elsewhere become active.

Once again, Boston money generally stays home. while external support ows to companies that have toshow more in order to attract nancing.New York and Bay Area money moves more, especially

in this last period, 199699. In this later stage, Bay For venture capital rms, there is evidence that, as

the VCs grow older and larger, they invest more inArea money ows locally to San Diego and to therest of the country and, less signicantly, to Boston. both younger and more distant biotech companies.

These gains from experience are tempered somewhatNew York money begins to go to New York rms,

and continues to Boston and the rest of the country. by location. Boston VC money evinces a strong tend-ency to stay home. New York money is restless, movingIn sum, VCs located in the Bay Area hunt in theirown backyards, in San Diego and all over the nation, around to Boston, San Diego, and the rest of the

country. Bay Area VCs start out in California, wherepoaching in Boston as well. New York money moves

widely, and in later stages, as a biotech presence biotech activity is very expansive, but by the latter partof the 1990s, California money goes to Boston anddevelops in the New York area, local rms are sup-

ported too. The rest of the nation stays out of the other parts of the country. The reciprocal move neverhappens, as outside money rarely encroaches on theirestablished clusters, and Boston money remains local.

home turf in the Bay Area. We report a rather similarpattern in an examination of the portfolio of collabora-

S U M M A R Y A N D D I S C U S S I O Ntions that US biotech rms are involved in over the

period 198899 (O W E N -S M I T H et al., 2002). Initially,Venture capital rms have become a key componentof the innovation process, and play an important role nearly half of all inter-rm alliances were locally based

and clustered in a few dense regions. By the end of thein high technology regions in the US. VC-backed

R& D is three times as likely to generate patents as 1990s, most alliances were extra-local. But this process

was driven by a reaching out from established clusterscorporate-sponsored R&D (K O R TU M and L E R N E R,2000). In large part, this eVect is due to the direct stake to other new areas.

These patterns suggest the diYculty of trying toentrepreneurs have in start-up rms and the fact thatentrepreneurs in large organizations receive only a small intentionally create high-tech regions. Despite abun-

dant attempts by policy makers and entrepreneurs inshare of the rewards from corporate innovation. But

venture capital support also has a catalytic eVect. Many many parts of the world, the relationships between

nance and R&D are, in many respects, based on per-companies report that VC funding is a key milestone,

and symbolically more important than other kinds of sonal ties, fostered in regions with extensive two-waycommunication among the relevant parties. Such rela-nancing (HE L L M A N and P UR I, 2000). Our results

show that V C backing is a strong signal, attracting tions are not easily created by formal policies. More-

over, in the case of biotech, there has been a strong co-other VCs from outside the local area and sustaininga process where subsequent rounds of support are evolution of the worlds of science and nance. The

presence in the most active regions of key publicgarnered at the post-IPO stage.

We nd a strong pattern of spatial concentration in research organizations, such as research universities andnon-prot institutes, that are buVered from marketbiotech and venture capital. Given that VCs and

biotech are both found in considerable number in the forces means that the science plays a critical and auto-

nomous role in industry evolution. This dual contribu-Bay Area and Boston, it is not surprising that much

VC support is locally-based. A little more than half tion of money and ideas makes biotech rather diVerentfrom other high-tech elds that are less steeped in basicthe biotech rms in our sample received local VC

disbursements, and that percentage rose to 58% within research. Other elds in which product development ismore rapid and more in the hands of commercialour key geographic clusters. But the tendency of VCs

to nance local companies increased over the decade inventors may not have the same co-location patterns

of biotech.of the 1990s, indicating the continuing strong role of

VC in sponsoring R&D within a region. We see this The recurrent collaboration and mutual inter-

dependence of money and ideas raise a number ofpattern most clearly when comparing the proles of


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Acknowledgements Research support provided byinteresting questions for further research. What do theNational Science Foundation (No. 9710729, W. W. Powellperformance proles of biotech rms and VCs lookand K. W. Koput, Co-PIs). We appreciate the helpfullike in the dense regions compared to areas that are lesscomments of Gernot Grabher and Joerg Sydow.active? Clearly, a certain level of activity is necessary

for mobilization, but is there a point where a crowding

out eVect sets in? Understanding the point at whichdensity might become a deterrent would provide lever-

N O T E S

age in explaining when and where new concentrationsmight emerge. In other developed countries, such as 1. For accounts of these foundings, see H AL L , 1987;Germany and Sweden, the state has played a very active T EITELMAN , 1989; WERTH , 1994; R O B B I N S -R O T H,

2000.role in trying to stimulate venture capital disbursements.2. See FELDMAN , 1999, for an excellent survey of empiricalSignicant sums of money have been made available in

studies of spillovers.the form of matching grants. We do not as yet know3. To supplement information about biotech companies orwhether this public policy-driven process of nancing

their various partners, we consulted other courses, includ-innovation operates in a similar manner as the privateing various editions ofGenetic Engineering and Biotechnologyequity market. One might speculate that policy makersRelated Forms Worldwide, Dun and Bradstreets Who Owns

would be less content with strong patterns of regionalWhom? and Standard and Poors. In addition, we utilized

concentration on distributional grounds. In contrast,annual reports, Securities and Exchange Commission

however, if the criteria for evaluation are rates oflings and, when necessary, made phone calls.founding of new organizations, then the US model 4. See, for example, the story in the Boston Globe about

of spatial co-location of capital and science has been biotech bragging rights, contending that by includingan expansive and robust one. In the case of biotechno- small private rms, Boston has a greater number of rmslogy, it is safe to say that without venture capital and than the Bay Area, but recognizing that the market valueregional agglomeration, the industry would not exist of the public companies in the Bay Area was nearly

double that of Boston (A O KI , 2000).in the form that it does today.

R E F E R E N C E S

A LMEIDA P. and K OGUT B. (1997) The exploration of technological diversity and the geographic localization of innovation,

Small Bus. Econ. 9, 2131.A NGEL D. P. (1991) High-technology agglomeration and the labor market: the case of Silicon Valley, Environ. Plann. A 23,

1,50116.

A O KI N. (2000) N.E. see gaining as top US biotech area, Boston Globe, 31 October, p. E1, p. E6.A RTHUR B. (1990) Positive feedback in the economy, Scientic American, February, pp. 9299.

A UDRETSCH D. and F ELDMAN M. (1996) R&D spillovers and the geography of innovation and production, Am. Econ. Rev.

86(3), 63040.A UDRETSCH D. B. and S TEPHAN P. E. (1996) Company-scientist locational links: the case of biotechnology, Am. Econ. Rev.

86(2), 64152.

B AU M J. and O L I V E R C. (1991) Institutional linkages and organizational mortality, Admin. Sci. Quart. 42, 30433.B ROWN J. S. (2000) Foreword, in KENNEY M. (Ed) Understanding Silicon Valley, pp. ixxvi. Stanford University Press,

Stanford, CA.

BYG RAV E W. D. and T IMMONS J. A. (1992) Venture Capital at the Crossroads. Harvard Business School Press, Boston, MA.FELDMAN M. P. (1999) The new economics of innovation, spillovers, and agglomeration,Econ. Innovation & New Technol. 8,

525.

FLORIDA R. L. and K ENNEY M. (1988) Venture capital, high technology and regional development, Reg. Studies 22, 3348.FREEMAN J. H. (1999) Venture capital as an economy of time, in L ENDERS R. and G ALBAY S. (Eds) Corporate Social Capital,

pp. 46079. Kluwer, Norwell, MA.

G I L S O N R. J. and B LACK B. S. (1998) Venture capital and the structure of capital markets: banks versus stock markets,

J. Financial Econ. 47, 24377.

GO M P E R S P. A. (1995) Optimal investment, monitoring, and the staging of venture capital,J. Finance50, 1,46189.

GO M P E R S P. A. (1996) Grandstanding in venture capital industry, J. Financial Econ. 42, 13356.GO M P E R S P. and LE R N E R J. (1999) The Venture Capital Cycle. MIT Press, Cambridge, MA.

GO M P E R S P. and LE R N E R J. (2001) The venture capital revolution, J. Econ. Perspectives 15(2), 14568.

GRANOVETTER M. (1985) Economic action and social structure: a theory of embeddedness, Am. J. Sociol. 91, 481510.GULATI R. (1995) Does familiarity breed trust the implications of repeated ties for contractual choice in alliances, Acad.

Mgt. J. 38, 85112.

GULATI R. and GA RG IU LO M. (1999) Where do interorganizational networks come from?, Am. J. Sociol. 104, 1,43993.HA LL S. (1987) Invisible Frontiers: The Race to Synthesize a Human Gene. Atlantic Monthly Press, New York.

HELLMAN T. and PUR I M. (2000) The interaction between product market and nancing strategy: the role of venture capital,

Rev. Financial Studies 13, 95984.
http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0308-518X^28^2923L.1[aid=41846]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0002-8282^28^2986:3L.630[aid=41603]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0001-8392^28^2942L.304[aid=2315591]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0001-8392^28^2942L.304[aid=2315591]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/1043-8599^28^298L.5[aid=42796]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/1043-8599^28^298L.5[aid=42796]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/1043-8599^28^298L.5[aid=42796]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0002-8282^28^2986:3L.630[aid=41603]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0308-518X^28^2923L.1[aid=41846]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0893-9454^28^2913L.959[aid=2315595]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0002-9602^28^29104L.1[aid=2315594]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0002-9602^28^2991L.481[aid=42066]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0304-405X^28^2942L.133[aid=361655]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0304-405X^28^2947L.243[aid=789297]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0034-3404^28^2922L.33[aid=42961]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/1043-8599^28^298L.5[aid=42796]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0001-8392^28^2942L.304[aid=2315591]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0002-8282^28^2986:3L.630[aid=41603]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0308-518X^28^2923L.1[aid=41846]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0921-898X^28^299L.21[aid=42203]


15/15


JA F F E A., TRAJTENBERG M. and H E N D E R S O N R. (1993) Geographic localization of knowledge spillovers, as evidence bypatent citations,Quart. J. Econ. 108, 863911.

K ENNEY M. (1986)Biotechnology: The UniversityIndustry Complex.Yale University Press, New Haven, CT.

K ORTUM S. and L ERNER J. (2000) Does venture capital spur innovation?, Rand J. Econ. 31, 67492.K RUGMAN P. (1991) Increasing returns and economic geography, J. Pol. Econ. 99(3), 48399.

LEADBEATER C. (2000) The Weightless Society. Texere, New York.

MA R S H A L L A. (1920) Principles of Economics, 7th edition. Macmillan, London.NE L S O N R. and W INTER S. (1982)An Evolutionary Theory of Economic Change. Harvard University Press, Cambridge, MA.

O WEN-S M I T H J., R ICCABONI M., P A M M O L L I F. and P OWELL W. W. (2002) A comparison of US and Europe university-industry relations in the life sciences, Mgt. Sci. 48(1), 2143.

PO D O L N Y J. M. (1994) Market uncertainty and the social character of economic exchange,Admin. Sci. Quart. 39, 45883.

PO R T E R M. (1998) Clusters and the new economics of competition, Harv. Bus. Rev. November /December, 7790.

POWELL W. W. (1996) Interorganizational collaboration in the biotechnology industry,J. Inst. & Theoretical Econ.120, 197215.POWELL W. W., KO P U T K. W. and S MITH- D E RR L. (1996) Interorganizational collaboration and the locus of innovation:

networks of learning in biotechnology,Admin. Sci. Quart. 41, 11645.

PR E V E Z E R M. (1996) The dynamics of industrial clustering in biotechnology, Small Bus. Econ. 8, 117.R O B B I N S -R O T H C. (2000) From Alchemy to IPO. Persens, Cambridge, MA.

S AHLMAN W. A. (1990) The structure and governance of venture capital organizations,J. Financial Econ. 27, 473521.

S AXENIAN A. (1994) Regional Advantage. Harvard University Press, Cambridge, MA.S COTT A. J. and S TORPER M. (1987) High technology industry and regional development: a theoretical critique and

reconstruction,Int. Soc. Sci. J. 34, 21532.S INTON P. (2000) Venture capital epicenter, San Francisco Chronicle, 3 November, p. B1, p. B4.S O R E N S E N O. and S TUART T. E. (2001) Syndication networks and spatial distribution of venture capital investments, Am. J.

Sociol. 106(6), 1,54688.

S TINCHCOMBE A. (1965) Social structure and organizations, in M ARCH J. G. (Ed) Handbook of Organizations, pp. 14293.Rand McNally, Chicago, IL.

S TORPER M. and S ALAIS R. (1997)Worlds of Production. Harvard University Press, Cambridge, M A.

S TUART T. E. (1998) Network positions and propensities to collaborate: an investigation of strategic alliance formation in ahigh-technology industry, Admin. Sci. Quart. 43, 66898.

T EITELMAN R. (1989) Gene Dreams: Wall Street, Academics, and the Rise of Biotechnology. Basic Books, New York.

UZ ZI B. (1999) Embeddedness in the making of nancial capital, Am. Sociol. Rev. 64 , 481505.VO N HI P P E L E. (1994) Sticky information and the locus of problem solving,Mgt. Sci. 40(4), 42939.

W ERTH B. (1994) The Billion-dollar Molecule. Touchstone, New York.W RIGHT M. and R O B B I E K. (1998) Venture capital and private equity: a review and synthesis, J. Bus. Finance & Accounting

24, 52170.

Z UCKER L. G., D A R B Y M. R. and B R E W E R M. B. (1998) Intellectual human capital and the birth of US biotechnology

enterprises,Am. Econ. Rev. 88(1), 290306.
http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0306-686X^28^2924L.521[aid=2315602]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0002-9602^28^29106:6L.1[aid=2315600]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0002-8282^28^2988:1L.290[aid=718740]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0306-686X^28^2924L.521[aid=2315602]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0003-1224^28^2964L.481[aid=1287729]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0001-8392^28^2943L.668[aid=2315601]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0002-9602^28^29106:6L.1[aid=2315600]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0304-405X^28^2927L.473[aid=223346]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0921-898X^28^298L.1[aid=2278695]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0001-8392^28^2941L.116[aid=56557]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0001-8392^28^2939L.458[aid=348828]http://ernesto.ingentaselect.com/nw=1/rpsv/cgi-bin/linker?ext=a&reqidx=/0022-3808^28^2999:3L.483[aid=41787]

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