Organizational and Institutional Genesis: Organizational and Institutional Genesis: Why do life science clusters form in some Why do life science clusters form in some locales but not others? locales but not others? Walter W. Powell Walter W. Powell Stanford University Stanford University ~ September 2009 *This paper builds on work done jointly with Kjersten Whittington, Kelley Packalen, and Jason Owen-Smith. To appear in J. Padgett and W. Powell, The Emergence of Organizations and Markets , Ch. 14. Earlier versions were presented at the University of San Andrés, Center for Advanced Study in the Behavioral Sciences, Nobel Symposium on Foundations of Organizations, the Academy of Management distinguished scholar lecture, and Max Planck summer conference on Economy and Society. Sincere thanks to the diverse audiences for very generative feedback.
Organizational and Institutional Emergence: unaddressed questions • What factors make distinctive institutional configurations possible at particular points in time (history, sequence) and space (geography)? How does a collection of diverse organizations emerge and form a field? – The origins of institutions remain largely opaque. Most research works backward from successful cases to fashion an account of why an outcome solved a particular problem or advanced some group’s or entrepreneur’s project. 2
Draw on our several decades of data on the life sciences to analyze institutional emergence and reproduction: The leading sources of knowledge and expertise in the life sciences in the late 1970s and early 1980s were widely distributed across U.S. and globally. In the U.S., public policy and political muscle were flexed to support this field. Many regions had a deep stock of endowments - - Philadelphia, New Jersey, Washington, New York, in particular, but arguably Atlanta, Seattle, Houston, and LA as well. But today, nearly 50% of firms and more than 50% of the outputs (patents, employment, medicines) are from just three regions - - Bay Area, Boston, San Diego. Why such a pronounced pattern of spatial agglomeration? Geographic propinquity: a critical feature of the emergence and institutionalization of the life sciences field. It was not anticipated, given initial founding conditions, nor an obvious outcome but became self-reinforcing and highly resilient. What do I mean by self-reinforcing? An increasing number of participants were attracted, common expectations developed to guide interactions and these were sustained by shared cognitive beliefs. 3
Biotech companies in United States, 1978 (n=30) 4
Biotech companies in United States, 1984 (n=130) 5
Potential candidates for formation of biomedical clusters (early 1980s) Ranking in number of biomedical patents, 1980 1 New York City - - extraordinary array of research hospitals, elite universities and medical schools, venture capital and investment banks Northern New Jersey - - home of major U.S. and foreign pharmaceutical 1 companies, Princeton University 3 Philadelphia - - “the cradle of pharmacy” - - strong pharmaceutical presence, U Penn, Wistar Institute, Fox Chase Cancer Center 4 Bay Area CA - - UCSF, Stanford, venture capital…but crowding from ICT industries? 5 Boston - - MIT and to lesser extent Harvard (commercial involvement by faculty was initially precluded there), numerous research hospitals 6 Washington DC metro area - - home of National Institutes of Health, Johns Hopkins University Medical School Los Angeles CA - - largest early biotech firm – Amgen, Cal Tech, UCLA, City of 7 Hope Hospital 8 Research Triangle NC - - three universities, major state public policy initiative to build a cluster 9 Houston TX - - U Texas Medical Center, Rice University, MD Anderson Hospital 10 Seattle WA - - Fred Hutchinson Cancer Center, U Washington…large investments by Bill Gates and others in biomedicine in 1990s San Diego CA - - sleepy Navy and tourist town, but UCSD, Scripps, Salk, and 10 6 Burnham Institutes
Biotech companies in United States, 2002 (n=368) 7
Why did clusters form in some places and not others? •A diversity of organizational forms and an anchor tenant are critical factors as they increase the possibility and salience of transposition , such that the consequences are linked to, but more consequential than, the initial conditions. • Multiple organizational forms - - a rich soup in which diverse practices, and rules can emerge. There are divergent criteria for evaluating success. Some organizations have a foot in several doors. (This is not unleashed, instrumental action --the MacGyver fetish in org theory-- but cognition in the wild.) • Anchor tenant - - a position that affords access to several domains, but not directly competitive. Having different principles of evaluation enables the anchor to recombine and repurpose diverse activities. The anchor institution protects the values of the local community. •Diversity and Connectivity are not sufficient. The mechanism is cross-network transposition , a form of brokerage that allows ideas to move from one domain to another. •Most cross-network transpositions are selected against because they are likely to fail from at least one perspective. The more multi-purpose an idea or activity is, the more perspectives from which it can be shot down. In those few cases where cross-network transposition is absorbed by the social system, it creates a new channel for activities from one domain to cascade into others, possibly with reorganizing or tipping potential. •Central to my argument is not just statistical reproduction in the sense that something unusual diffused and became accepted, but transposition : the initial participants brought the status and experience garnered in one realm and converted those assets into energy in another domain, for good or bad. 8
‘Cognition in the wild’ or ‘on the hoof category construction’: UC – Berkeley Bancroft Oral History Library Interview in early 1990s, on Recombinant DNA Research at UCSF and its Commercial Application at Genentech Interviewer (Sally Smith Hughes): The next step as I see it is the formation of Genentech in 1976. Had Bob Swanson (the eventual co-founder) approached anybody before he came to your laboratory? Herbert Boyer, UCSF Professor and Co-founder of Genentech: He said he took a list of names associated with the publicity on Asilomar and went through it alphabetically, which means (Nobel Laureate) Paul Berg must have turned him down. I suppose I was next on the list. It was a telephone introduction. He wanted to talk, so I had him come to my lab on a Friday afternoon at quarter to five. He introduced himself, talked about what he wanted to do. Did I think the technology was ready to be commercialized? He said he had access to some money, and I thought it would be a good way to fund some postdocs and some work in my laboratory, because we always needed some money to do that. We spent a good deal of time that evening talking about it. 9
Data sources: • The contemporary life sciences, including dedicated biotech firms, large multinational corporations, research universities, government labs and institutes, research hospitals, nonprofit research centers, and venture capital firms. • Data set covers all the above organizations, as well as their formal inter-organizational collaborations, from 1988-2004. Includes data on earlier years, but is left censored so that we were only able to collect ‘early’ data on firms that were alive in 1988. • Two-mode network: 691 dedicated biotech firms, 3,000 plus collaborators, 11,000 plus collaborations - - both local and global ties • Field work, archival records, interviews with 100s of scientists and managers in DBFs, universities, pharma cos., govt. institutes, technology licensing offices, VC and law firms. 10
Method: Network Visualization, with Pajek • Pajek (Slovenian for ‘Spider’) is a freeware package for the analysis and visualization of large networks created by Vladimir Batagelj and Andrej Mrvar and available online at http://vlado.fmf.uni-lj.si/pub/networks/pajek/ • In Pajek, ‘spring-embedded’ network drawing algorithms enable meaningful representation of social networks in Euclidean space. • ‘Particles’ repel one another, ‘springs’ draw attached particles together • Drawing algorithms seek a ‘solution’ where the energy of the entire system is minimized • In these representations, the positions of nodes are generated by the pattern of ties connecting the entire system • We draw on two such algorithms • Fruchterman-Reingold (FR) (1991) optimizes network configurations without reference to graph-theoretic conceptions of distance • Kamada-Kawai (KK) (1989) positions connected nodes adjacent to one another and makes euclidean distances proportional to geodesic path length in the network 11
Public Science anchors Boston Network, 1988 Figure 1. Boston Network, 1988 MAIN COMPONENT 42.9% Of MGH Boston DBFs DANA FARBER HARVARD Reachable NEMC TUFTS BU MIT PROs REMOVED 0.0% of Boston DBFs Reachable Tie Key: Node Key: R&D Circles = DBFs Finance Triangles = PROs Commercialization Squares = VCs Licensing Diamonds = Pharma Source: Owen-Smith and Powell, Organization Science 2004
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