1. Networks, Regions, and Knowledge Communities Jason Owen-SmithWalter W. Powell University of MichiganStanford University/SFI For presentation at conference on Advancing Knowledge and the Knowledge Economy at the National Academies, 10-11 January, 2005

2. Regions and Networks  Why does agglomeration spur innovation? Economic Geography Increasing returns – concentrated supply of skilled labor, support services etc. Information spillovers – “the secrets of industry are in the air” Economic Sociology Relational Density – networks channel information and resources, contributing to the generation of novel ideas Institutional Diversity – for profit, non-profit, public and private organizations create a robust community ecology

3. Biotechnology firms Biotechnology Firms & Universities Biotechnology Firms, Universities, Research Institutes & Hospitals The Boston Biotechnology Knowledge Community In Boston, organizational diversity drives innovation networks.

4. Knowledge Communities  Proximity, diversity, and long term relationships forge communities with unique characteristics Forbearance and relational contracting Local norms for collaboration and knowledge sharing Information rich markets for labor, technology, services  Knowledge communities are the synergy between proximity and relationships  But the institutional anchors and growth trajectories of networks stamp the character of innovative communities and the knowledge they produce.

5. Consider two key biotechnology regions  Boston and the San Francisco Bay Area are the most successful and widely emulated biotechnology regions in the world  Both are home to regionally bounded but relationally defined communities  Webs of collaboration connecting firms grew from a substrate of ties to other types of organizations  In Boston Public Research Organizations (PROs) including Universities, Research Hospitals & Non-Profit Research institutes anchor the community  In the Bay Area early Venture Capitalists link the community

6. Boston and Bay Area Networks, 1988, 1994, 1999 1988 Harvard MIT 1994 Harvard Genzyme Autoimmune 1999 MIT Harvard Bay Area Stanford UCSF Genentech Stanford Genentech Chiron Stanford Chiron Genentech 1988 1994 1999 Boston

7.  Boston and Bay Area knowledge communities evolved along different paths from disparate starting points  Their trajectories stamp the character of the regions and of the knowledge they produce  Mix of technologies and impact of discoveries is the same but approach to innovation varies dramatically  Differences hold in the aggregate and for a matched pair of drugs for the same indication BostonBay Area Exploration – high variance discovery strategy increases diversity among research programs Exploitation – low variance discovery strategy increases convergence among research programs Research trajectories tied to academe Research trajectories tied to other firms Lesser reliance on internal R&D Greater reliance on internal R&D More patient centered, ‘clinical’ approach More market centered, ‘home run’ approach

8. BostonBay Area # DBFs 57 82 # Patents 1,376 3,806 # PROs 19 3 # VCs 29 64 Mean Impact (standardized) 1.113 0.979 Impact Variance30.27014.150 # Citations made12,65941,389 % Non-DBF cites 71% 55% % Self Cites 12% 35% FDA Approved Drugs 18 40 Orphan Products 60 51 Boston & Bay Area R&D Outputs Differ, 1988-99 A Quick Look at the Numbers

9. Implications for Discussion  The paradox of university engagement formal connections to academe generate a more ‘open’ research trajectory in Boston, informal involvement in the Bay Area allows VC model to emerge but overly tight connections to firms limits the distinctiveness of university innovation an unintended danger of corporate capture for academe?  The dangers of late stage emulation innovation intensive regions require more than an ‘add institutions and stir’ policy similar end states can be reached from diverse starting points and the paths regions take matter imitating end states may fail to produce desired results

10.  Matching formal and informal networks and institutions Do inter-organizational ties grow from or catalyze social connections? How can more ‘diffuse’ organizations (e.g. patient groups, social movements, professional associations) be included in knowledge communities?  National and International Policy Will seeding disparate types of knowledge communities strengthen national innovation systems, or increase regional disparities? How do diverse regional approaches diffuse? Can distant organizations benefit from ties to knowledge communities? Implications for Discussion

11. Related papers Owen-Smith et. al. (2002) “A Comparison of U.S. and European University- Industry Relations in the Life Sciences.” Management Science. 48(1): 24- 43. Owen-Smith & Powell (2003) “The Expanding Role of University Patenting in the Life Sciences: Assessing the Importance of Experience and Connectivity.” Research Policy 32(9): 1695-1711. Owen-Smith & Powell (2004) “Knowledge Networks as Channels and Conduits: The Effect of Formal Structure in the Boston Biotechnology Community.” Organization Science. 15(1): 5-21. Powell et al. (2005) “Network Dynamics and Field Evolution: The Growth of Interorganizational Collaboration in the Life Sciences.” American Journal of Sociology 110,4 (Jan.) Owen-Smith & Powell (Forthcoming) “Accounting for Emergence and Novelty in Boston and Bay Area Biotechnology.” in P. Braunerhjelm & M. Feldman (eds.) Cluster Genesis: The Emergence of Technology Clusters and Their Implications for Government Policy