The Spatial Clustering of Science and Capital

The Spatial Clustering of Science and Capital: Accounting for Biotech Firm-Venture Capital Relationships* Walter W Powell Kenneth W Koput 509 CERAS Bldg 405 McClelland Hall Stanford University Department of Management & Policy Stanford, CA 94305 University of Arizona woodyp@stanford.edu Tucson, AZ 85721 fax: 650-725-7395 kkoput@u.arizona.edu fax: 520-621-4171 James I Bowie Laurel Smith-Doerr Department of Sociology Department of Sociology University of Arizona Tucson AZ 85721 Boston University 96 Cummington Street Boston, MA 02215 SEPTEMBER, 2001 Forthcoming, Regional Studies * Research support provided by National Science Foundation (#9710729, W.W Powell and K.W Koput, Co-PIs) We appreciate the helpful comments of Gernot Grabher and Joerg Sydow Abstract This paper focuses on the spatial concentration of two essential factors of production in the commercial field of biotechnology: ideas and money The location of both research-intensive biotech firms and the venture capital firms that fund biotech is highly clustered in a handful of key U.S regions The commercialization of a new medicine and the financing of a high-risk startup firm are both activities that have an identifiable 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 continuing importance of geographic propinquity in biotech Over the period 1988-99, more than half of the U.S biotech firms received locally-based venture funding Those firms receiving non-local support were older, larger, and had moved research projects further along the commercialization process Similarly, as VC firms grow older and bigger, they invest in more non-local firms But these patterns have a strong regional basis, with notable differences between Boston, New York, and West Coast money Biotechnology is unusual in its dual dependence on basic science and venture financing; other fields in which product development is not as dependent on the underlying science may have different spatial patterns KEYWORDS: BIOTECHNOLOGY, VENTURE CAPITAL, NETWORKS, SPATIAL AGGLOMERATION Introduction Our focus is on the relationships between dedicated biotechnology companies and the venture capital firms that finance them These are, in a sense, unusual relationships in that they are designed with a termination point in mind, at which time the venture capitalist exits and moves on Nor are they exclusive relationships A venture capitalist is likely to invest in many different biotech firms, including some who are likely to be competitors in a particular therapeutic area, such as cardiology, or with a particular technology, such as genomics Biotech firms may well have backing from multiple venture capitalists, either as part of a collective, such as a group or syndicate, or separately as a means to finance discrete projects, such as a specialized use of a more general purpose technology Biotech firms also garner financial support from multiple sources, through government research grants, R&D alliances with major corporations, and selling minority equity stakes For a biotech firm to become financially successful, it needs to develop a promising pipeline with numerous new medicines Each potential product is, in some respects, a separate project that involves different internal staff and disparate external collaborators At a venture firm, a portfolio of investments is developed with divergent levels of risk, different timelines, and varied expected payoffs For both biotechs and venture firms, learning across partners and projects, and developing experience working with diverse parties, is critical to success (POWELL, KOPUT, and SMITH-DOERR, 1996) We analyze the spatial aspects of these relationships, examining how the role of location shifts over time as projects, firms and regions mature Our data are drawn from the commercial field of human biotechnology, specifically the wave of founding of new biotech firms in the U.S over the period 1988-1999 This field is remarkably clustered spatially, with over 48% of all U.S firms located in either Northern California, the Boston Metropolitan area, or San Diego County We map the industry’s growth, showing a pattern of cluster-based proliferation We match our biotech data to a data set on firms that provide venture capital to our sample of biotech companies Venture capital is also spatially concentrated, in the Bay Area, Boston, and New York We use descriptive statistics to analyze whether the linkages between biotech and venture capital are exclusively local, have a local component, or are non-local The Co-location of Science and Capital We take as our starting point the spatial concentration of two key factors of production in the commercial field of biotechnology: ideas and money Casual observers might wonder why these two endowments, which are highly fungible, easily transportable, in short, weightless (LEADBEATER, 2000), are so strongly concentrated regionally Abundant evidence points to the clustering of both knowledge and capital Ideas, especially knowledge from the frontiers of cutting-edge science, have a strong tacit dimension (NELSON and WINTER, 1982) When knowledge is more tacit in character, face-to-face communication and interaction are important (VON HIPPLE, 1994) Consequently, to understand the science, one has to participate in its development Hence new scientific advances have a form of natural excludability (ZUCKER, DARBY, and BREWER, 1998) In the early years of the biotechnology industry, firms were founded in close proximity to research institutes and universities where the advances in basic science were being made (KENNEY, 1986; AUDRETSCH and STEPHAN, 1996; PREVEZER, 1996; ZUCKER et al, 1998) There are two key elements to this clustering process One aspect is captured by research on knowledge spillovers, where geographic proximity facilitates the spread of innovative ideas (JAFFE, TRAJTENBERG, and HENDERSON, 1993; AUDRETSCH and FELDMAN, 1996) But while intellectual capital is necessary, it may not be not sufficient A supportive institutional infrastructure that fosters knowledge transfer and the formation of technology-based companies is also critical (POWELL, 1996) Consider the case of Atlanta, Georgia, where there is a major research center, the Center for Disease Control, a technology-based university, Georgia Tech, and one of the top medical schools in the country at Emory University The metropolitan area is reasonably wellto-do and well-educated, and a number of Fortune 500 firms are headquartered there But there is little in the way of commercial biotechnology, despite abundant intellectual resources One biomedical entrepreneur at Georgia Tech told us that he has had numerous overtures from financiers and angel investors for his technologies, but they have all made leaving Atlanta and moving to California a requirement of obtaining the financing Or consider the often-cited list of founders of some of the key firms created in the late 1970s and 1980s: Genentech (Herbert Boyer, University of California – San Francisco), Biogen (Walter Gilbert, Harvard), Hybritech (Ivar Royston, University of California – San Diego), Genetics Institute (Mark Ptashne, Harvard), Systemix (David Baltimore, MIT and Whitehead Institute), and Immulogic (Malcolm Gefter, MIT) All of these eminent scientists retained their university affiliations, often full-time They were able, so to speak, to have their cake and eat it too, precisely because their universities had created rules and routines that enabled technology transfer and faculty entrepreneurship There are many regions where there is scientific excellence but not the requisite infrastructure to capture the rents from knowledge spillovers Our emphasis on this infrastructure of university technology transfer, venture capital, law firms, consultants, and the like is somewhat different from treatments of industrial districts, in the tradition of MARSHALL (1920) Economists and geographers have long recognized the tendency for production to cohere geographically, whether it is cars in Detroit, steel in the Ruhr, silk in Lyon, or filmmaking in Hollywood Spatial concentration confers advantages in terms of transportation costs, access to skilled labor markets, communication networks, sophisticated customers, and access to technology (SCOTT and STORPER, 1987; FLORIDA and KENNEY, 1988; ANGEL, 1991; SAXENIAN, 1994; STORPER and SALAIS, 1997) Once these agglomeration economies are established, a dynamic process of increasing returns attracts new entrants, further fueling the pace of innovation (ARTHUR, 1991; KRUGMAN, 1991) Consequently, the geographic clustering of production is a global phenomenon (PORTER, 1998, provides numerous examples.) Our emphasis is less on the process of economizing on the transaction costs of founding a new firm, or the many attractions that draw entrepreneurs to a region We are interested in understanding why firms based on a fast-moving science that is continually creating new opportunities are formed in particular locales AUDRETSCH and FELDMAN (1996:634) put the question aptly: “even after accounting for the geographic concentration of the production location, why does the propensity for innovative activity to cluster vary across industries?” The relevant scientific expertise in biotech is, by now, broadly distributed throughout the industrial world, with major centers of scientific excellence in the U.S., the U.K., Sweden, France, Germany, and Switzerland But the science is commercialized by firms in a significant manner (by which we mean the ability to bring novel medicines to a global marketplace) in only a handful of locations worldwide To understand this phenomenon, we have to explain why some regions are hubs for organizational creation, that is, populated, by organizations, that are in the business of creating other organizations (STINCHCOMBE, 1965) Put differently, some regions are incubators and constitute an ecology for organizational formation (BROWN, 2000) These regions have a rich mix of diverse kinds of organizations (e.g., universities, law firms specializing in intellectual property, public research institutes, consultants, and venture capitalists) that contribute in varying ways to founding technologybased companies The advantages of location, then, are very much based on access and information Increasing returns are present in the form of overlapping networks, recombinant projects, personal and professional relationships, and interpersonal trust and reputation, all of which are thickened over time In such a milieu, access to reliable information about new opportunities occurs through personal and professional networks, and these ties are critical in reducing uncertainty about projects that are not well understood by non-experts, exceedingly risky in terms of their payoff, and unclear in terms of their eventual market impact Venture capital (VC), defined as “independent, professionally managed, dedicated pools of capital that focus on equity or equitylinked investments in privately held, high growth companies” (GOMPERS and LERNER, 2001: 146), is one of the key elements of the infrastructure of innovation The private equity market has become a major source of financing for start-up firms, and has grown at an explosive rate: in 1979 venture firms dispersed $500 million in funds, that amount climbed to well over $67 billion by 2000 (WRIGHT and ROBBIE, 1998; GOMPERS and LERNER, 2001) Both venture capital firms and venture capital investing are highly concentrated regionally For example, in the third quarter of 2000, as the global slowdown in technology companies became more pronounced, VCs still poured $8.7 billion into new companies located in Northern California This sum represented 33.7% of the total U.S venture capital pie for that period for all industries, according to Venture Economics, a firm that tracks VC investing (SINTON, 2000) In 1999, a little more than one third of all venture capital disbursements went to California (GOMPERS and LERNER, 2001) A venture capital firm raises money from wealthy individuals, pension funds, financial institutions, insurance companies, and other sources that are interested in investing in technology-based startups, but lack the ability to so These investors become limited partners in the VC fund, while the partners in the VC firm manage the money by investing in and advising entrepreneurial startups Venture capitalists finance new firms with the potential for high growth in return for partial ownership When the young company is sufficiently developed, the firm goes public through an initial public offering (IPO) or is acquired by another company At this point the VC cashes in its ownership stake, and reaps its rewards Venture capital obviates the need to grow slowly via self-financing, and fuels more rapid growth As FREEMAN (1999) puts it, venture capitalists buy time The success of a VC firm in attracting money is contingent on its past track record of spotting winners and generating rewards for its limited partners The business of identifying opportunities is highly uncertain and difficult Of course, VCs receive innumerable proposals for new businesses But the rejection rate for these proposals is extremely high (estimated by SAHLMAN, 1990, to be at 99%) As in many other walks of life, many call but few are answered More opportunities are identified through active search by VCs In part, this is because the expected payoff demanded from VC backing is very high and the ratio of success to failures about in 10 (BYGRAVE and TIMMONS, 1992; GOMPERS and LERNER, 1999) In the life sciences and other technology-based fields, venture firms provide more than money Because many of the founders of biotech firms are research scientists, venture capitalists often much more than monitor or advise; they may even play a hands-on role in the running of the young company Keeping scientists focused on key commercial milestones is no small feat A powerful tool for focusing their attention is the “staging” of VC financing, thus the commitment of capital is contingent upon “progress” (GOMPERS, 1995) VCs also routinely help in recruiting key staff and important collaborators, and provide referrals to law and accounting firms, and eventually to investment banks (FLORIDA and KENNEY, 1988) Many VCs serve on the boards of directors of young firms they fund As GILSON and BLACK (1998) put it, “by providing both money and advice, the venture capitalist puts its money where its mouth is.” Obviously, the roles of monitoring, advising, and managing are much more easily accomplished when the young firm is located nearby Experienced VCs have abundant contacts and deep knowledge of particular industries; thus, referrals to relevant sources of expertise are another important resource they provide This social network is also more readily tapped when firms are geographically proximate Finally, there are real advantages that accrue to firms and venture capitalists to being “on the scene” –unplanned encounters at restaurants or coffee shops, opportunities to confer in the grandstands during Little League baseball games or at soccer matches or news about a seminar or 10 birth of U.S biotechnology enterprises.” American Economic Review 88(1): 290-306 36 Table Means and Standard Deviations (in Parentheses) for Biotech Firms Receiving VC Funding Prior to IPO, by Locality of Funding Non-Local Local Funding Both Local and Variable Funding Only Only Non-Local Funding Firm Characteristics Age Time to IPO from founding date (in years) Time since first tie (in years) Time to IPO from first tie (in years) Number of employees Number of PhDs/MDs Partner Counts Number of Pratt’s VCs funding Number of non-DBF partners Number of DBF partners Number of types of ties Number of forms of partners Centrality Measures R&D centrality Finance centrality Licensing centrality Commerce centrality 5.5913 (2.7813) 6.5000 (3.2027) 5.1852 (4.2678) 4.7273 (1.6787) 4.6411 (2.6174) 5.2188 (2.1211) 4.4754 (2.7131) 4.5185 (4.3688) 3.9625 (1.8327) 5.0588 (2.8491) 3.6364 (1.2863) 4.6250 (2.0439) 53.81 (43.17) 15.24 (9.29) 44.04 (35.73) 16.70 (16.03) 53.13 (37.06) 15.35 (10.74) 2.6073 (1.9230) 1.7778 (1.1956) 4.2635 (2.1281) 8.6208 (4.7759) 8283 (1.3283) 2.0955 (.7488) 3.2017 (1.3736) 6.7451 (4.0578) 5170 (.8447) 1.9556 (.8233) 2.4353 (1.0084) 9.3368 (4.8503) 5965 (.7559) 1.9264 (.7172) 2.8714 (1.2699) 0035 (.0052) 0072 (.0079) 0022 (.0052) 0004 0008 (.0030) 0030 (.0039) 0014 (.0041) 0038 0022 (.0044) 0082 (.0080) 0015 (.0036) 0006 37 Number of DBFs (.0014) (.0002) (.0022) 69 27 56 38 Table Means and Standard Deviations (in Parentheses) for Biotech Firms Receiving VC Funding After IPO, by Locality of Funding Non-Local Local Funding Both Local and Variable Funding Only Only Non-Local Funding Firm Characteristics Age Time to IPO from founding date (in years) Time since first tie (in years) Time to IPO from first tie (in years) Number of employees Number of PhDs/MDs Partner Counts Number of Pratt’s VCs funding Number of non-DBF partners Number of DBF partners Number of types of ties Number of forms of partners Centrality Measures R&D centrality Finance centrality Licensing centrality Commerce centrality 8.6101 (3.4951) 4.7857 (3.1143) 8.5048 (3.1038) 5.2143 (2.7506) 7.4516 (2.6016) 4.3387 (2.3881) 6.6871 (3.0893) 7.2190 (3.3885) 6.5161 (2.4761) 2.8772 (3.2518) 3.9286 (3.0751) 3.4032 (2.1838) 164.58 (204.38) 26.68 (22.99) 128.92 (135.30) 29.30 (27.22) 173.48 (161.66) 31.85 (24.96) 2.0161 (1.5298) 1.2500 (.8026) 3.9734 (2.3466) 11.7545 (6.2974) 1.5837 (1.7746) 2.8053 (.9538) 4.3180 (1.6488) 14.2679 (9.5956) 8095 (.9582) 2.9167 (.6626) 4.8155 (1.6757) 13.2397 (6.6534) 1.3628 (1.5293) 2.6648 (.6581) 4.2586 (1.3349) 0033 (.0057) 0054 (.0052) 0033 (.0057) 0027 0037 (.0062) 0032 (.0034) 0083 (.1158) 0027 0042 (.0052) 0098 (.0066) 0036 (.0048) 001 39 Number of DBFs (.0064) (.0054) (.0036) 57 14 62 40 Table Means and Standard Deviations (in Parentheses) for VCs Funding Pre- and Post-IPO Biotech Firms, by Locality of Funding Non-Local Local Both Local and Variable Funding Funding Only Non-Local Only Funding Funding Pre-IPO Firms Age Number of offices Capital (in millions of US dollars) Percent spending own money Number of VCs Funding Post-IPO Firms Age Number of offices Capital (in millions of US dollars) Percent spending own money Number of VCs 12.3553 14.0408 (10.1932) (19.6373) 1.6942 1.915 (1.0512) (1.3619) 336.1133 228.5154 (852.3067) (440.1693) 15.6180 (8.1896) 1.9018 (1.2266) 262.6174 (210.3292) 64.77 83.72 82.98 88 43 47 12.4262 11.6874 (7.1343) (7.2631) 1.5124 1.4835 (.6832) (.7757) 185.6892 210.2204 (307.6961) (382.2478) 17.3147 (9.0252) 1.9370 (1.5462) 388.9044 (692.3246) 59.46 81.25 86.86 74 32 46 41 Table Means and Standard Deviations (in Parentheses) for Biotech Firms in the Boston Cluster, San Francisco Bay Area Cluster, and Outside Any Regional Cluster That Received VC Funding Prior to IPO, by Locality of Funding Variable Boston Cluster Age Number of employees R&D centrality Finance centrality Licensing centrality Commerce centrality Number of DBFs Non-Local Funding Only Local Funding Only Both Local and Non-Local Funding (2.63) 160.44 (151.64) 049 (.071) 064 (.069) 044 (.086) 017 (.048) 8.83 (.23) 360 (226.27) 085 (.105) 031 (.020) 058 (.081) (0) 9.08 (3.36) 200.92 (193.95) 052 (.056) 093 (.047) 048 (.068) 014 (.029) 12 7.3 (2.86) 98.75 (49.09) 059 (.081) 016 (.0077) 013 (.018) 0023 (.0051) 7.16 (2.46) 63.67 (27.97) 0066 (.0121) 041 (.048) 0041 (.0071) 039 (.067) 7.22 (2.31) 184.45 (186.36) 025 (.039) 120 (.080) 035 (.047) 018 (.056) 22 8.55 (3.41) 170.89 (141.42) 015 (.032) 049 (.043) 032 (.040) 7.30 (2.90) 103.75 (100.59) 0008 (.0015) 0072 (.0071) 1347 (.1693) 7.25 (.96) 88.25 (60.15) 074 (.073) 072 (.044) 039 (.031) San Francisco Bay Area Age Number of employees R&D centrality Finance centrality Licensing centrality Commerce centrality Number of DBFs Not In A Cluster Age Number of employees R&D centrality Finance centrality Licensing centrality 42 Commerce centrality Number of DBFs 032 (.066) 20 052 (.072) 0010 (.0020) 43 44 45 46 47 Figure 5: Regional Patterns of Venture Capital Pre-IPO New York (NY) Boston (B) Rest of Country (C) Bay Area (BA) B NY C San Diego (SD) 1988 B BA C BA SD 1989 B B NY C BA B B NY NY C C BA BA SD 1990-1995 C BA SD 1996-1999 48 Figure 6: Regional Patterns of Venture Capital-Biotech Funding Post-IPO NY B C BA B B NY NY SD 1988-1991 B C C BA BA B NY C C BA BA SD SD 1992-1995 1996-1999 49 ... financing, the type of financing they provide, and whether they have geographic or industry preferences The guide also reports the amount of capital the VC firm manages, and whether the firm primarily... a sample of biotech firms at the time of their initial public offerings in the early 1990s and analyze the geographic location of founders and members of scientific advisory boards They find... because the expected payoff demanded from VC backing is very high and the ratio of success to failures about in 10 (BYGRAVE and TIMMONS, 1992; GOMPERS and LERNER, 1999) In the life sciences and other