Cluster Genesis: Technology-Based Industrial Development

Cluster Genesis

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Cluster Genesis Technology-Based Industrial Development

Edited by Pontus Braunerhjelm and Maryann Feldman

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Great Clarendon Street, Oxford ox2 6dp Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide in Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries Published in the United States by Oxford University Press Inc., New York ß Oxford University Press 2006 The moral rights of the authors have been asserted Database right Oxford University Press (maker) First published 2006 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above You must not circulate this book in any other binding or cover and you must impose the same condition on any acquirer British Library Cataloguing in Publication Data Data available Library of Congress Cataloging in Publication Data Data available Typeset by SPI Publisher Services, Pondicherry, India Printed in Great Britain on acid-free paper by Biddles Ltd., King’s Lynn, Norfolk ISBN 0–19–920718–6 1 3 5 7 9 10 8 6 4 2

978–0–19–920718–3

Dedicated To Our Families

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Preface

The common thread in this volume was that these individuals were connected in some way to the coeditors and had serendipitous discussions over a number of years about the need for greater focus on the early origins and the evolutionary trajectories of regional technology based clusters. Funding from Handelsbanken’s Research Foundation allowed this group to come together twice to form the type of temporary spatial agglomeration that proved fruitful and energetic and resulted in an exchange of ideas. It is fair to say that our thinking on the subject evolved as a result of these meetings. Often in a group it is difficult to attribute an idea to one individual or another but certainly our inquiry has been enhanced by the flow of ideas and exchange. The process of working on this book has been an intellectually exciting and rewarding endeavor. We would like to acknowledge support from the Handelsbanken’s Research Foundation and the Rotman School of Management at the University of Toronto. We would like to thank Belinda Lobo, Benny Borgman, and Christian Helgesson for their assistance with the preparation of this manuscript.

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Contents

List of Figures List of Tables List of Contributors

1. The Genesis of Industrial Clusters Maryann Feldman and Pontus Braunerhjelm

xi xiii xv

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Part I. Creation Myths Revisited 2. Origins and Growth of the Hollywood Motion-Picture Industry: The First Three Decades Allen J. Scott

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3. The Coevolution of Technologies and Institutions: Silicon Valley as the Iconic High-Technology Cluster Martin Kenney and Donald Patton

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4. Accounting for Emergence and Novelty in Boston and Bay Area Biotechnology Jason Owen-Smith and Walter W. Powell

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Part II. Considering the Developing Cluster Context 5. Anatomy of Cluster Development: Emergence and Convergence in the US Human Biotherapeutics, 1976–2003 Elaine Romanelli and Maryann Feldman 6. Policy-Induced Clusters: The Genesis of Biotechnology Clustering on the East Coast of China Martha Prevezer and Han Tang

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Contents

7. The Emergence of a European Biotechnology Cluster: The Case of Medicon Valley Pontus Braunerhjelm and Christian Helgesson

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8. The Emergence of Ireland’s ICT Clusters: The Role of Foreign Direct Investment Frank Barry

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9. The Emergence of Israel’s Venture Capital Industry: How Policy Can Influence High-Tech Cluster Dynamics Gil Avnimelech and Morris Teubal

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Part III. Crafting Cluster and Economic Development Policy 10. Clusters and Clustering: Stylized Facts, Issues, and Theories Luigi Orsenigo

195

11. Mors tua, Vita mea? The Rise and Fall of Innovative Industrial Clusters Mario A. Maggioni

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12. Local Antecedents and Trigger Events: Policy Implications of Path Dependence for Cluster Formation David A. Wolfe and Meric S. Gertler

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13. The Role of Public Policy in Emerging Clusters Bo Carlsson

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Bibliography Index

279 315

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List of Figures

2.1: Locations of motion-picture production companies in Los Angeles, 1915

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2.2: Locations of motion-picture production companies in Los Angeles, 1930

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2.3: Average size of establishment in the motion-picture industry in California and the rest of the USA, 1921–37

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3.1: Genealogy of Silicon Valley technologies

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3.2: Employment in four Bay Area counties, 1959–2001

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3.3: Establishments in Four Bay Area counties, 1959–2001

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4.1: Boston main components ties by dyads and year, 1988–99

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4.2: Bay Area main components ties by dyads and year, 1988–99

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4.3: Boston and Bay Area networks: 1988, 1994, and 1999

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6.1: The geographic distribution of biotech research organizations in China

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6.2: The geographic distribution of biotech firms in China

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¨ resund region 7.1: Map defining the O

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7.2: Interlinks between firms and academia in Medicon Valley

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7.3: Number of biotech start-ups in Medicon Valley, 1995–2002

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8.1: Employment levels in computer hardware and software in Ireland

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8.2: Irish GNP per head as percentage of EU-15 average, 1960–2002

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11.1: Agglomeration costs and benefits for incumbents, with critical sizes of a cluster

222

11.2: The development of an industrial cluster (in isolation)

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List of Figures 11.3: 11.4:

The development of the old industrial cluster, stock–flows relation

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The development of the new industrial cluster, stock–flows relation

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11.5:

Learning effects and clusters leapfrogging

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11.6:

The growth and depletion of an industrial cluster

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11.7:

The growth and depletion of an industrial cluster

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11.8:

r-type policy

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11.9:

K-type policy

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11.10: Long-run equilibrium: two clusters coexist

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11.11: Long-run equilibrium: cluster j drives cluster i to extinction

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List of Tables

2.1: Employment in motion-picture production, United States, California, and New York, 1921–37

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4.1: R&D outputs by region, 1988–99

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4.2: Innovation data for Betaseron and Avonex, 1988–99

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5.1: US biotherapeutics firms by types of entry and organizational origins

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5.2: Biotherapeutics firms in MSAs and combined MSAs

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5.3: Entrepreneur relocations across combined MSA for top 12 regions

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5.4: Firm relocations across combined MSA for top 12 regions

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5.5: Distribution of biotherapeutics firms in San Diego

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5.6: Distribution of biotherapeutics firms in Los Angeles

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5.7: Distribution of biotherapeutics firms in New York

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6.1: Summary of science and technology and biotechnology policies

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6.2: Returnees as percentage of top scientists in China, 2003

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6.3: Top 20 country rankings of the most-cited 150 countries in all fields, 2003

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6.4: Type of sector specialization of biotechnology firms: Beijing, Shanghai, and Shenzhen, 2003

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6.5: Ownership type for firms, Beijing, Shanghai, and Shenzhen

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6.6: Biotechnology research projects supported by the National Natural Science Fund by location in 1999

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6.7: Comparison of universities and student numbers: Beijing, Shanghai, and Shenzhen

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6.8: R&D staff in relation to total employees

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Appendix 6.1: Details of interviews used in the analysis

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List of Tables 7.1: Comparison of volume of biotechnology-related articles from three regions

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7.2: Number of citations in biotechnology-related articles

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7.3: Number of firms and employees in Medicon Valley, 2002

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7.4: Medicon Valley labor force with tertiary education, 1999

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8.1: Country shares in world computer hardware exports

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8.2: The relative importance of computer sector employment in EU countries

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8.3: The relative importance of computer software employment in EU countries

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8.4: EU employment in mass market packaged software

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9.1: Israel’s high-tech cluster: selected structural elements, 1970s–1990s

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9.2: R&D support from OSC, million US$

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9.3: Yozma Funds and associated VC companies

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9.4: Venture capital raised and invested

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9.5: Foreign partners of Yozma Fund and privatization

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9.6: Capital raised by private equity organizations in Israel, 1991–2003

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9.7: Start-ups, VC funding, OCS grants, exits, and closures, 1991–2003

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9.8: ICT and software manufacturing: sales, exports, and employees

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11.1: Simulation results

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List of Contributors

Gil Avnimelech is Assistant Professor at the School of Management, Ben Gurion University, Beersheva, Israel. Frank Barry is Professor at the School of Economics, University College Dublin, Ireland. Pontus Braunerhjelm holds the Leif Lundblad Chair in International Business and Entrepreneurship at The Royal Institute of Technology, Stockholm, Sweden. Bo Carlsson is the E. Mandell deWindt Professor of Industrial Economics and the Director of Ph.D. Programs and Research at Weatherhead School of Management, Case Western Reserve University in Ohio, USA. Maryann Feldman holds the Jeffery S. Skoll Chair in Technical Innovation and Entrepreneurship and is Professor of Business Economics at Rotman School of Management University of Toronto, Canada. Meric S. Gertler holds the Goldring Chair in Canadian Studies at the Department of Geography and Program in Planning. He is also CoDirector of the Program on Globalization and Regional Innovation Systems at University of Toronto, Canada. Christian Helgesson has a master degree in political science from Stockholm University. He is a research project leader at the Swedish Center for Business and Policy Studies (SNS) in Stockholm. Martin Kenney is affiliated with the Department of Human and Community Development, University of California, Davis, in the USA. He is also a Senior Project Director at the Berkeley Roundtable on International Economy.

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List of Contributors

Mario A. Maggioni is Professor of Economics at the Department of International Economics, Institutions and Development, Faculty of Political Science, Catholic University, Milan, Italy. Luigi Orsenigo is affiliated to the University of Brescia, and Cespri at the Bocconi University, Milan, Italy. Jason Owen-Smith is Associate Professor of Sociology and Organizational Studies at the University of Michigan in Ann Arbor, USA. Donald Patton is a Research Associate at the Department of Human and Community Development, University of California, Davis, USA. Walter W. Powell is Professor of Education and affiliated Professor of Organizational Behavior, Sociology, and Communications at Stanford University, California. He is also an External faculty member at the Santa Fe Institute, California, USA. Martha Prevezer is Professor at the School of Business and Management, Queen Mary College, University of London, UK. Elaine Romanelli is Associate Professor and Director at the Entrepreneurial Studies Program McDonough School of Business, Georgetown University, USA. Allen J. Scott is Professor at the Department of Policy Studies and Department of Geography, University of California, USA. Han Tang is affiliated with Queen Mary College, positioned in Shanghai, China. Morris Teubal is Professor at Department of Economics, Hebrew University, Jerusalem, Israel. David A. Wolfe is Professor of Political Science at the Centre for International Studies, University of Toronto, Canada.

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1 The Genesis of Industrial Clusters Maryann Feldman and Pontus Braunerhjelm

Every culture and civilization has creation stories or myths about how the society started and took shape. These stories help define identity and order a chaotic, complex universe. Unfortunately, an appreciation of history and context is missing in the contemporary discussion of clusters. While increasingly clusters—regional concentrations of related firms and organizations—are perceived to be the locus of economic growth there is little understanding of how successful clusters come into existence. Moreover, little guidance is provided on the role of policies that are conducive to the formation of clusters—both what policies to promote and, equally important, what policies to avoid. Many places attempt to emulate the world’s most famous industrial cluster Silicon Valley, with its rich institutional landscape of engaged and leveraged research universities, high-flying local venture capitalists, world class supporting business and legal consultants, and rich collaborative networks. Yet these attempts ignore the historic development of these institutions and the way that they have coevolved. Importantly, the ingredients associated with Silicon Valley’s success were not in place initially and Silicon Valley was not the obvious location for the computer industry. Indeed, it was the genesis of Silicon Valley—the dynamic process of creating the industry that created the concomitant location of the institutional ingredients and the social relationships which makes them effective. Our understanding of the origins of industrial clusters needs to move beyond suggestions of a list of ingredients that, once in place, result in economic development. It is as if in the current conceptualization clusters emerge full grown, like Athena from the head of Zeus, without passage through defining developmental stages. Lists of attributes of successful

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clusters tell us little about how these clusters get started and what differentiates successful clusters from places where investments yield no significant benefits for the local economy. While the literature tends to highlight success stories there are plenty of counterfactual examples in which the ingredients appear to be in place yet the local area did not become the site of vibrant innovative activity and competitive industries. Moreover, even Silicon Valley, the archetype, has failed to develop some emerging sectors, suggesting that there is a more complex, underlying process that links location to industrial success. The purpose of this book is to examine the origins and emergence of technology-based industrial clusters in order to understand the forces that promoted economic development. Our view is that while mature clusters may look similar, what really matters is the process by which clusters come into existence. In examining the creation myths of cultures, Joseph Campbell notes that despite the unique idiosyncratic details modern scholarship has found commonalities and universal themes (Campbell 1972: 8–10). It is in the spirit of identifying similarity in the early formation and genesis of industrial clusters that we undertake this edited volume. Borrowing from Nelson and Winter’s notion (1982) of recipes, it appears that unique regional recipes develop for applying knowledge, commercializing discoveries, and creating new industries. Most technology entrepreneurs operate on thin margins and their focus is on short term survival. For the most part, their actions are reactions to immediate problems. Instead of wisely considered, far-sighted solutions, entrepreneurial activity is by necessity messy, complex, and adaptive. When entrepreneurs confront new technological opportunities they fashion solutions that adapt what they have on hand from what is easily accessible. The solutions they adopt are more likely to come from local sources—either through tapping networks of people working on similar things or through serendipitous encounters. Most importantly, these regional recipes use local ingredients in creative and adaptive ways. Solutions that appeared to work are diffused, repeated, and fine-tuned, gradually evolving into accepted routines and operating procedures. These routines are adopted by institutions to define common practices. Over time, a repertoire of actions develops, orchestrated by a common vision of the industry. This encourages further experimentation and adaptation. Knowledge of what does not work, what approaches have previously been tried and led to dead ends are part of this local knowledge. Some histories of clusters stress the brilliance of one or a handful of dedicated individuals. Certainly, these are good stories but the legacy

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of individuals like Fred Terman, Eugene Kleiner, and Robert Noyce lies in the social organization of their model of innovation and its evolution over time. Certainly having a rich supply of entrepreneurs is important, but in Silicon Valley it is the institutions that nurture firms which provides a powerful advantage over other regions. Technology firms located in Silicon Valley are capable of extremely rapid growth because there is an understanding and an appreciation of how things are done and how people may work together. Social scientists call this by various names, an ecosystem, an innovation system, a social structure of innovation, or an incubator region. A critical element is that this set of interrelated institutions formed over time in tandem with the firms that make up the industries in the region. In our view it is the genesis of Silicon Valley or any cluster that defines social relationships and creates a shared vision of the industry’s economic activity. It is this process that ultimately determines the success and sustainability of the cluster. Our conceptualization, which is a common feature throughout this volume, is that cluster formation is a sequential process with an evolutionary logic (Feldman 2001). Rather than provide a mechanical catalog of growth phases or sequences of events, our intention is to provide an appreciation of the endogenous process that leads to local agglomeration, technological change, and economic growth. The conceptualization presented here is a variant and extension of the classic product life cycle model which uses a biological metaphor to describe stages of development (Abernathy and Utterback 1978). Certainly others will come after to dissect the stages and the transition mechanisms. Indeed, we hope that they do. There is simply too much at stake to be uncritical when our focus is long-term economic growth and when significant public funds are used to promote cluster formation activities. In the examples provided here, some triggering events coupled with an entrepreneurial spark seem necessary in order for industry clusters to emerge and enter a sustainable growth trajectory. In our narrative, any agglomeration can, in principle, be accounted for in terms of some initial seeding event—what matters most is what happens next. Our approach recognizes that alternatively there may well be pre-existing social or natural conditions or resources that account for the appearance of the first seedlings of growth in any given place. Developments in the early phase condition the later phase—path dependence sets in and new business models and institutions develop together. The actual shift of a given location into a leading position may have less to do with these pre-existing conditioning influences and more to do with the increasing returns

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and competitive advantages that are, at least in part, derived from the agglomeration’s own internal functional evolution. What distinguishes clusters that grow rapidly from the less successful ones that stagnate or fail to develop is vigorous entrepreneurial activity and the active building of institutions aided by the forces of agglomeration economies. A second cluster development phase begins when one particular location starts to pull ahead of other locations. While there may be multiple nascent agglomerations for an industry, one region may pull ahead and develop some internal logic that provides an advantage to firms located there. This turn of events again can be the result of purely random processes, or it may stem from some unique resources in the location’s developmental logic. What appears to differentiate clusters from agglomerations is the internal social dynamics. A third phase can usually be identified in which a location and industry intensifies its competitive advantages, extends and consolidates its market reach, while other locations enter a period of comparative stagnation or decay—the location simply becomes the place to be. Note that we cannot predict which of the locations identified in the first phase of this process will become the dominant location in the third phase, except to say that it will have achieved this status by reason of its superior command of localized increasing returns effects. In this manner, a historic process begins as an open window of locational opportunity. Our argument is that there is necessarily another and more fundamental set of issues to be settled before we can really understand how industrial agglomerations arise. The history of every industrial agglomeration can be told in terms of a succession of locational events, and of course, prior natural and social conditions predispose regions in some developmental directions rather than others, sometimes decisively so. There may be strong locational factors underlying the initiating events of this process, or there may be none (i.e. the initiating events are random); it may even be that the initiating events are a result of a misperception on the part of relevant decision-makers. The important analytical issue, however, is not so much how the seed of the agglomeration was planted, as to how, once its locational coordinates have somehow or other been established, the emerging local economic system is subject to a structured and selfreinforcing process of growth and development. In conceptual terms, there is a difference between planting of the seed in a cluster and the subsequent achievement of industrial competitiveness. In a policy context, clusters are born and develop on the basis of specific combinations of capabilities, incentives, and opportunities. The three

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elements are inseparable and linked to each other in intricate ways. Competencies obviously contribute to create and define opportunities as well as afford the ability to take advantage of existing opportunities. The latter feeds back on the processes of accumulation and development of new competencies. Competencies without incentives remain unused. But incentives without sufficient capabilities are sterile and might even be destructive. More interestingly, particular sets of capabilities identify sets of appropriate incentives, which in turn and once again influence the speed and directions of the processes of accumulation of competencies. Understanding how different types of opportunities, incentives, and capabilities match with each other would require sophisticated taxonomical exercises and hard theorizing.

Organization of The Book This book is organized into three parts. Part I presents interpretative histories of three well-known specific clusters. Part II focuses on emerging clusters to illustrate the divergent processes, the different stages of development, and the different institutional contexts from which clusters have sprung. Part III considers the implications for economic development policy.

Creation Myths Revisited While standard accounts credit the emergence of Hollywood on the basis of its physical geographic characteristics, Allen J. Scott proposes an alternative view in Chapter 2 which suggests that it was the successful implementation of a new business model that anchored the industry in Southern California. Three decisive factors are presented that allowed Hollywood to emerge as a leading movie production hub, even though the main creative and commercial activities of the motion-picture industry remained in New York. First, even though there were many other conceivable locations, Southern California was a suitable location for film production in the winter months due to the climate and the scenic landscape. Second, an unfavorable institutional set-up—particularly for independent motion producers—prompted a growing accumulation of movie production facilities in Hollywood. That led to various experiments, entry and exit, and created a sufficient agglomeration of activity (‘critical mass’) for take off. The third reason was the invention of a new

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business model. Thomas Ince, who arrived in 1911 in Southern California, established a new studio and reorganized the production process. In Chapter 3, Martin Kenney and Donald Patton consider the iconic cluster: Silicon Valley. The emergence of Silicon Valley shares some features with the motion-picture industry. For instance, both places experienced an inflow of entrepreneurs from other parts of the country and also saw a novel business model emerge. The environment that existed in the late 1950s, later known as Silicon Valley, was not unique. Indeed, similar conditions existed in Boston and New York, for example. Both places were also influenced by policies—institutions in the motion-picture case and governmental spending on defense in the Silicon Valley case—but neither was specifically targeted to support these industries or places. A few individuals also seem to have been instrumental in the evolution of new clusters such as Fred Terman in Silicon Valley and motion picture moguls Cecil B. de Mille and Thomas Ince in Hollywood. The partially random nature of the process is evidenced by the fact that William Shockley, one of the coinventors of the transistor at Bell Labs in New Jersey and the founder of the first semiconductor firm, wanted to be near his elderly and sick mother (Moore and Davis 2001). In Chapter 4, Jason Owen-Smith and Woody Powell contrast the development of the biotech industry in Boston and the San Francisco Bay Area, the two most prominent US clusters. Their analysis rests on three presumptions: high-tech clusters in particular require both the presence of networks and spatial density, interorganizational networks serve the dual purposes of being locus of innovation and the underlying support structure that host the institutional and social context necessary for innovation, and, finally, the form and substance of innovation in successful clusters vary over time and with patterns of emergence. Even though differences in the innovation process in the two clusters can be traced to their origins, a process of convergence has been going on over the last twelve years, where the previous strong dependence on public research organizations (PROs) and venture capital (VC) has been replaced by strong firm alliances. Consequently, even though path dependence and variety in culture and norms determine the trajectory, clusters may still be quite comparable in terms of performance.

Considering the Development Clustering Context To further enhance the insights regarding the forces prompting cluster emergence, Part II provides an empirical comparison of five highly

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innovative clusters in high-technology industries. As evidenced from these analyses, there are many forces that may spark clusters—serendipity does impact how and where clusters emerge. Still, irrespective of the differences in institutions, traditions, and level of development among the clusters analyzed, there are also some apparent similarities that can be identified. One prominent feature of cluster formation seems to be the importance of entrepreneurship as an endogenous process, and the emergence and sustainability of clusters seem to critically interact with entrepreneurial activities. Elaine Romanelli and Maryann Feldman pursue this angle of cluster emergence in Chapter 5 by examining the spatial and temporal dimension of a variety of forms of entrepreneurship across cities in the USA in the human biotherapeutics industry. The first finding is that clusters grow predominantly through the investments of local entrepreneurs, local firms, and local venture capitalists. Second, for three of the regions with the largest clusters—San Diego, Boston, and San Francisco— the critical spur to growth appears to be a tendency of entrepreneurs to leave local, established firms to found their own ventures. Moreover, only those regions that exhibited this secondary, or second-generation, growth, developed grew to substantial sizes relative to other clusters. Cluster locations and growth are, however, not solely a consequence of local investments. More than 32 percent of entrepreneurs relocated from one metropolitan region to another to found new firms. Hence, the attraction of entrepreneurs and firms to a region is a tertiary influence on growth, occurring late in the history of the industry and the clusters. That was also a striking feature of the development of both the Hollywood and the Silicon Valley clusters. This is followed by Chapter 6, in which Martha Prevezer and Han Tang analyze the genesis of three major biotechnology clusters along the east coast of China. Biotechnology is a young but targeted activity for the Chinese government, with the ambition to develop China into a globally leading nation in this area. The evolution of the leading three Chinese clusters are compared and related to the preceding conditions in the respective region to host a biotechnological cluster. Two main phases are distinguished in the evolution of government policy with respect to these clusters. A preceding phase mainly concerned with institutional reform, creating new forms of property rights, setting out strategic programmes for the development of the biotechnology and building up the appropriate knowledge base. Partly as a result of these policies, the entry of companies into the biotech clusters in each region more than doubled during the

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period 1995–2003 as compared to the period before 1995. While most of the entries in the pre-1995 period were government-initiated, many new companies formed in the latter period were founded by scientists returning from abroad. The previous history of each region has influenced the structure and the specialization of the respective cluster. Pontus Braunerhjelm and Christian Helgesson examine the forces that sparked the emergence of a leading European biotechnology cluster— Medicon Valley—in Chapter 7. Medicon Valley displays one characteristic feature which separates it from most other biotechnology clusters: its geographic location stretches over two countries, that is, the southern part of Sweden and the northern part of Denmark. This makes Medicon Valley an especially interesting case since there is a possibility to compare the performance and dynamics of the cluster in a region where institutional differences do appear, simultaneously as the two countries share a number of other characteristics. To some extent that resembles the comparison of the Boston and the Bay areas in the biotech study, and the institutional differences which influenced the location of the film industry. The emergence of the Medicon Valley biotechnology cluster seems to have followed a somewhat different path than the biotech clusters in the USA. Whereas entrepreneurs orchestrated the emergence of clusters in the USA, universities played a crucial role in the Medicon Valley case and entrepreneurs entered—and reinforced—the process in a later stage. The more dynamic and fast growing biotechnology sector in Denmark is argued to be partly policy driven, partly associated with cultural differences. In Chapter 8, Frank Barry presents an analysis of the Irish cluster, proposing that a decisive factor in the emergence of the Irish ICT cluster was the presence of foreign anchor firms, such as Intel. These firms also acted as incubators for domestic entrepreneurs. Half of the Irish manufacturing labor force is now employed in foreign-owned firms, with the bulk in information and communication technology (ICT) firms. The ICT cluster (both hardware and software) are agglomerated around Dublin. Already in the mid-1990s Ireland had become host to the most important ICT cluster in Europe. Equally transformative was institutional reform and fiscal policy that also paved the way for future tax reductions, a newly developed ‘social partnership model’ of wage determination and Ireland’s EU membership in 1973 which guaranteed access to the European markets. Of the factors typically cited as important for development and genesis of innovative clusters is the availability of venture capital. In Chapter 9, Gil Avnimelech and Morris Teubal document the emergence of the

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Israeli VC industry. The process is divided into three phases that spread out over three decades, which also mirrors Israel’s innovation and technology policy. There was a deliberate policy that targeted VC—besides the importance of bringing in foreign expertise at an early stage—once the existence of a sufficient knowledge base had been established. A truly innovative part in Israel’s policy was the inclusion of a mechanism from the very start for the government to withdraw and privatize the VC funds.

Crafting Clusters and Economic Policies To design economic policy prescriptions, a better understanding of the mutual impact and interdependencies underlying cluster formation, entrepreneurship, and the role of government is required. This section contains four chapters, all preoccupied with the development of capabilities to foster the emergence of clusters and the ensuing policy implications. In Chapter 10, Luigi Orsenigo presents a survey of factors previously associated with the crafting of new innovative high-technology clusters, emphasizing the biotechnology industry. There is perhaps no other industry that has been as carefully studied with regard to geographic clustering. The evidence strongly supports the view that there are strong agglomeration forces in high-technology sectors, mainly related to the concentration of scientific knowledge. Adequate incentive structures and entrepreneurial activity are also important, although their nature and their effects may differ substantially across space and time. Still, the evidence seems to support a picture whereby the spatial concentration of innovative activities derives mainly from processes of spin-off from highly capable universities and research centres. A conclusion drawn is that in the case of biotechnology, the emergence of clusters is not a purely random phenomenon. Initial conditions and ‘endowments’ play a crucial role in defining the geography of innovation. However, these do not suffice to account either for the genesis of clusters, neither for the failures. The literature forcefully points to the observation that—as much as agglomeration forces are influenced by ‘structural’ initial conditions— processes are the essence of what clusters are made of. Mario Maggioni develops a formal mathematical model to analyze the rise and fall of clusters in Chapter 11. It is shown how a major technological innovation sets a process of creative destruction into motion, where new clusters appear and replace clusters based on obsolete technologies. In the last stage when the cluster matures, it either achieves

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a national or international leadership in a given sector or technology. Conversely, the cluster may decline both economically and socially, generating migration outflows, mass unemployment and regional stagnation. The decisive element appears to be different institutional frameworks. Technological and regional dynamics go hand in hand and are mutually determined in a complex web of circular cumulative causation with both positive and negative feedbacks, where institutions play a major role. In a simulation exercise the author stresses the type of policy necessary at different stages of cluster development. Basically, to promote endogenous cluster growth after the initial phases, policies that support firm-based micro-level incentives seem to be critical rather than policies aimed at strengthening the ‘carrying capacities’. Most European policymakers overemphasize the latter type of policies as a means to initiate cluster emergence and growth. In Chapter 12, David Wolfe and Meric Gertler examine the degree of path dependency in cluster origin and development. One issue that continues to bedevil the analysis of clusters is the question of their origins and the relative importance of chance events, or serendipity, as opposed to planned policy actions. Particularly controversial is the role of path dependencies created by small, initial—often chance—events as opposed to the role of conscious direct policy initiatives. The presence, or absence, of key institutional elements of the local or regional innovation system may affect both their innovative capacity and their potential to serve as nodes for cluster development. Much of the literature on path dependency, and a certain stream of analysis in the literature on clusters, suggests that they are frequently seeded by chance events or by policy initiatives that frequently had a different intention than the support and development of a cluster. The authors present a more nuanced view where more weight is allocated to the role of policies in generating and developing clusters. In the final chapter, ‘Public Policies in Emerging Clusters’, Bo Carlsson addresses the question of how to design a public policy in a nondeterministic, evolutionary, and highly complex world. That is, a world where the most desirable outcomes are unknown but there may be many possible acceptable outcomes, and where change is characterized by both path dependence and unpredictability. Based on the case studies previously presented, as well as former carefully examined cases in the literature, the cornerstone of a viable policy—defined in terms of functions and requirements of policy instruments—is presented. One conclusion is that policy interventions exaggerate the system characteristics in

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The Genesis of Industrial Clusters

cluster policies at the expense of incentives on the level of individuals, potential entrepreneurs, and firms. Without a proper balance between institutional design and incentive design the probability of a successful emergence and evolution of cluster will diminish. Particularly in Europe, with its tradition of governmentally administrated and targeted policies, there is a risk of downplaying the role of individual initiatives in the creation of clusters. While there are many attempts to develop cluster, these may compete away all the eventual welfare effects that result from clusters.

Synthesizing the Results Cluster formation is a process that relies on the coevolution of technology, business models, and local supporting institutions. Serendipity in cluster emergence is a conspicuous feature in the cases presented here, and has also been confirmed in the previous literature. Seeding events of clusters may either stick and a cluster forms, or slip away and the nascent cluster fails to develop. Louis Pasteur said that fortune favors the prepared mind and cluster formation appears to favor the prepared region. Path dependence and resource accumulation are part, but only part of the story. Within successful clusters there is a degree of self-organization that, while seemingly unique, reflects an underlying complex social process. If policies fail to understand the dynamics of clusters emergence, the risk that a nascent cluster will decline may be evident, particularly since high-technology clusters are involved in a fierce competition for talent, finance, and firms. Some characteristics seem to cut through most of the case studies and also appear in the policy chapters. We like to list and briefly comment upon these. First, a prominent feature of cluster formation is the importance of entrepreneurship. The emergence and sustainability of clusters seem to critically interact with entrepreneurial activities. All the case studies— from the cluster ‘icons’ in Silicon Valley and Hollywood, to the hightechnology cluster cases studies in the USA, Europe, and Israel—point at the importance of entrepreneurs. Entrepreneurs are continuously experimenting with regard to products, markets, and business models. These experiments within regions, as well as nations, augment the knowledge base within the clusters. In China, and to some extent in Ireland and Sweden, the cluster emergence has partly been prompted by other forces and the role of entrepreneurs is not that clear-cut. However, it was also

11

The Genesis of Industrial Clusters

shown that dynamics and growth were lower in less entrepreneurial Sweden, and to some extent in the less entrepreneurial clusters in China, simultaneously as entrepreneurial activity has increased in Ireland. In the case of China, it is, however, too early to speculate about the future viability of their biotechnological clusters. A second observation that seems robust is that entrepreneurs with business experience stand the highest chances of survival and growth. Spin-offs from old firms, or individuals with previous entrepreneurial experience, become crucial assets in the development of clusters. Note also that in all cases, possibly with the exception of the later stages in the Israeli experience, VC firms and support organizations appear subsequent to an influx of entrepreneurs. Third, these observations have immediate policy interpretations. Policies are by no means redundant in the case of cluster emergence, despite a high element of serendipity. Basically, in the early stages policy is decisive in creating and maintaining a knowledge base, which is also illustrated in the case studies. Foremost, the challenge for policymakers is to stimulate entrepreneurship, an area where Europe is lagging behind the USA in particular, but many Asian states as well. That comes out as an unusually robust result and contrasts with the conventional wisdom where focus is on building ‘carrying’ capacities or targeting certain areas, for example the objective within the EU to raise R&D spending to 3 percent of gross domestic product (GDP). In particular, focus has to be turned to the diffusion and exploitation of knowledge through entrepreneurs identifying economic opportunities, rather than static accumulation of knowledge resources. That means looking at incentives and risk–reward ratios to engage the pool of potential entrepreneurs within a region or nation. Finally, and perhaps most important from a policy perspective, is the ability to adopt flexible and coevolving policies designed to foster cluster emergence, as shown in the cases of China, Ireland, Israel, but also in the comparison between Denmark and Sweden. Cluster formation is thus not a deterministic process. The emergence of clusters can be seen as an evolutionary process where the initial seed may be fostered by what look like idiosyncratic forces, but which reflect the development of institutions, technologies, and firms in a dynamic and self-organizing process. An inferior capability to define and adjust policies in a way that reflects the needs of an emerging cluster is probably the most efficient way to halt a process of cluster emergence. By that we do not advocate subsidies in various forms. Rather, the critical ingredient is to find a proper balance between carrying capacities, the institutional set-up, and the individual incentive design.

12

The Genesis of Industrial Clusters

Certainly, there are no guarantees in economic development policy; however, given that economic growth and prosperity are at stake, understanding how clusters begin and transform local economies is an important policy concern that should be informed by analysis and actively debated and evaluated.

13

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Part I Creation Myths Revisited

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2 Origins and Growth of the Hollywood Motion-Picture Industry: The First Three Decades Allen J. Scott

This chapter provides an analytical account of the origins and early development of the motion-picture industry in Hollywood and shows how it then evolved in the period from about 1915 to 1930 into a dense agglomerated complex of production activities. This task entails two broad research thrusts. On the one side, it calls in general for the identification of a conceptual lens through which issues of the genesis and growth of localized industrial systems can be viewed in some theoretically coherent manner. On the other side, it also revolves around effective marshalling of the empirical data on the emergence of the industry in this corner of Southern California so as to achieve an interpretative reconstruction above and beyond the simple descriptive ordering of empirical outcomes in time and space. Needless to say, the task is fraught with much difficulty. For one thing, the conceptual framework offered is at best provisional and speculative, and must be taken as a work in progress rather than a set of finished affirmations. For another, relevant information on the business activities of early film-production companies is hard to find, and most especially, the kind of information that sheds light on the specifics of the industry as a productive agglomeration. Inevitably, then, there are troubling gaps in the empirical record and the explanatory reading that follows falls occasionally onto fragile ground. With these provisos in mind, this chapter attempts to show how Hollywood evolved over the first three decades of the twentieth century from a loose and rather chaotic collection of motion-picture shooting activities

17

Origins of Hollywood Motion-Picture Industry

into a dense interlocking system of production companies, anchored in geographic space by its own virtuous circle of endogenous development. Out of its initial stirrings from about 1907 to 1915, Hollywood rapidly established itself as the preeminent center of motion-picture production not only in the USA but also in the world. By the late 1920s, the classic studio system of production was firmly established, and Hollywood was now poised at the threshold of a golden age that would continue through World War II until the late 1940s (Schatz 1988). This remarkable series of developments has been subject to investigation time and again from the perspective of both the business analyst and the film historian. In spite of a wealth of literature there remains an important set of unresolved questions about just why Hollywood emerged as the core location of the American film industry in the first instance, and how it then established its early supremacy over a number of other equally likely or unlikely production locales. At the heart of these questions, there lies the further issue of how it was that Hollywood came to be constituted as a peculiar kind of industrial district generating streams of localized competitive advantages that have sustained it as the world’s premier center of motionpicture production for close to a century.

Why Hollywood? Observations on the Early Motion-Picture Industry The very earliest stirrings of the motion-picture industry in Europe and North America at the end of the nineteenth century have been documented at length elsewhere (e.g. Bordwell, Staiger, and Thompson 1985; Jacobs 1939; Musser 1994). Most of the details need not detain us here. At the turn of the century production was concentrated in the New York– New Jersey metropolitan area, the two leading firms being Edison Manufacturing Company and the American Mutoscope and Biograph Company. Films at this time comprised diverse short subjects projected in store-front theaters or nickelodeons. In the first decade of the twentieth century, audiences grew rapidly, and the number of production companies expanded greatly in the New York–New Jersey area as well as in other selected parts of northeast. In these early days of the industry, open-air stages lit naturally by the sun were widely utilized for photography, though indoor shooting of films using artificial light was practiced. It was also common for production companies to shoot films in urban or natural settings, and filmmakers were constantly on the search for suitable

18

Origins of Hollywood Motion-Picture Industry

locations. The cold, harsh winters of the northeast presented obvious problems to early filmmakers in this respect, and so every year, teams of camera operators and actors would fan out from the main studios across the southern and western states, and even further afield, in order to enlarge their sphere of filming opportunities. One location that attracted filmmakers from the northeast early on was Southern California; and without any question, the climatic and other physical attributes of the region made it a favored venue at this time. Certainly, in the period from about 1907 to 1915, knowledgeable people in and around the industry were increasingly ready to proclaim the virtues of Southern California as a location for the motion-picture industry, not only because of the region’s warm sunny climate and mild winters but also because of the diversity of landscapes that it offered (cf. Spencer 1911). This reasoning, indeed, has become enshrined in a conventionalized explanation of the development of the motion-picture industry in Hollywood that is now more or less repeated without much further elaboration from one study to another (see e.g. Jacobs 1939; Palmer 1937; Ramsaye 1926; Sklar 1975; Torrence 1982; Zierer 1947). Some analysts have thrown into the ring a further argument related to the formation of the New York– based Motion Picture Patents Company and the strong desire on the part of independent motion-picture producers to put as much distance as possible between themselves and the Company given its aggressive enforcement of the patent rights that it held on critical cinematographic equipment (Hampton 1931; Jacobs 1939). Others propose a much rehearsed but surely improbable gloss to the effect that a further factor in the choice of Los Angeles as a shooting locale was that it was close to the Mexican border, and hence producers who were violating the Company’s patent rights could easily make their escape whenever the authorities appeared on the scene. Sklar (1975) has added that the open-shop labor market arrangements of Los Angeles in the period up to about 1935 were an important attraction to motion-picture producers from the northeast of the country. By contrast, Hampton (1931) claims—not unreasonably— that the puritanical atmosphere of Los Angeles in the early years of the twentieth century should have given San Francisco a clear locational advantage over its southern neighbor as a center of motion-picture production. To what extent, we may ask, do explanatory sketches of these sorts really account for the emergence of the motion-picture industry in Hollywood? Do the factors typically advanced constitute a set of necessary and sufficient conditions, or are they merely contingencies in a much more

19

Origins of Hollywood Motion-Picture Industry

complex set of historic processes that might just as easily have led to altogether different outcomes? Of course, we must always take seriously the reasons that people give for their own decisions. Many representatives of the motion-picture industry at the beginning of the twentieth century were unquestionably persuaded as to the superiority of Southern California as a production locale, and clearly acted on their convictions. Does this truth about the matter provide us with an adequate basis for explanation, as certain idealist historians and geographers might suggest? Alternatively, is the genesis of the motion-picture industry in Hollywood merely a matter of historic and geographic accident? Before any effort to address questions of these sorts directly, this chapter makes a brief excursion into the broad issue of the formation and logic of industrial districts or regional productive agglomerations in general. Armed with whatever insights this exercise might offer us we can then come back to the specific problem of Hollywood and the American motion-picture industry at the beginning of the twentieth century.

The Dynamics of Locational Agglomeration Most statements about the origins of industrial agglomerations rely ultimately on one or other of two approaches that in effect circumvent a genuinely explanatory result. The first is the principle of post hoc propter hoc in which firms are described as following one another in a locational sequence leading formally to an eventual accumulation or cluster of producers in a given place. In this sort of narrative, any agglomeration can in principle be accounted for in terms of an initial seed whose planting constitutes the critical moment out of which all else flows. The second can be characterized as the locational factors approach. In this case, various empirical features of a locale are measured, and a statement then constructed to the effect that in view of these factors there is a certain likelihood for a given kind of industrial agglomeration to occur there (Scott and Storper 1987). Of course, the history of every industrial agglomeration can be told in terms of a succession of locational events; and of course, prior natural and social conditions predispose regions in some developmental directions rather than others, sometimes decisively so. My argument here is that there is necessarily another and more fundamental set of issues to be settled before we can really understand industrial agglomerations in genetic terms. This argument turns on the intertwined problems of (a) when and how a simple accumulation of production units at any given

20

Origins of Hollywood Motion-Picture Industry

place begins to manifest signs of an endogenous development dynamic, and (b) how this place then pulls ahead of actual or latent competitors, and how it then sometimes becomes by far the most advanced production center for its type of output. There may be strong locational factors underlying the initiating events of this process; or there may be none (i.e. the initiating events are random); it may even be that the initiating events are a result of a misperception on the part of relevant decision-makers. The important analytical issue, however, is not so much how the seed of the agglomeration was planted, as to how, once its locational coordinates have somehow or other been established, the emerging local economic system is subject to a structured and self-reinforcing process of growth and development. In conceptual terms, we must distinguish between the planting of the seed and blooming of the organism, that is the first occurrence of a locational signal from the achievement of competitive supremacy. Despite its rather abstract logic, this account of the formation of industrial agglomerations offers us a number of important clues about the early historic geography of the motion-picture industry in the USA. The problem we now face is how to exploit this account in order to identify critical turning points in the industry’s development while at the same time respecting the full complexity of events as they actually occurred on the ground. In order to achieve this difficult synthesis we first of all need to review the main empirical record.

The Early Motion-Picture Industry in the United States: From New York to Los Angeles As we have seen, the most important motion-picture production companies in the USA in the first decade of the twentieth century were located in the New York–New Jersey metropolitan area, with subsidiary centers in Chicago (primarily, Essanay Studios and the Selig Polyscope Company) and Philadelphia (the Lubin Film Company). In 1908, a core group of these firms, under the leadership of the Edison Manufacturing Company and the American Mutoscope and Biograph Company, established a cartel officially designated the Motion Picture Patents Company, but more popularly known as the Trust (Allen 1976; Balio 1976a; Bowser 1990; Brownlow 1979; Cassady 1982; Izod 1988). The other production companies involved in this action were Essanay Studios, Kalem Company, Lubin Film Company, Selig Polyscope Company, and Vitagraph Company, together with three distributors, Kleine Optical

21

Origins of Hollywood Motion-Picture Industry

Company, Me´lie`s, and Pathe´ Fre`res. The Trust functioned as a holding company for the patents owned by its members, and this effectively gave it monopoly control over the then most efficient equipment for both shooting and projecting films. The Trust was accordingly able, through its licensing operations, to exert massive control over the production, distribution, and exhibition of motion pictures in the USA. Its level of control was augmented in 1910 when it established the General Film Company to distribute its members’ products throughout the USA. General Film was notorious for the sanctions it exerted over its licensed exhibitors by threatening to cut off their supply of films should they attempt to deal with independent companies. For a time, the Trust even managed to establish itself as a monopsony for Eastman Kodak film. These arrangements enabled the Motion Picture Patents Company to earn unusually high revenues while at the same time imposing shackles on the business activities of independent competitors. One outcome of this state of affairs was that members of the Trust were encouraged to make the ultimately self-defeating calculation to the effect that their competitive advantage lay more in cultivating their monopoly powers than in improving the quality and appeal of their films. This calculation not only opened the Trust to the scrutiny of regulators in the Department of Justice but also induced its independent competitors to pursue what turned out in the end to be a superior competitive model (see below). In 1915 the Trust ceased to be a major force, and in 1918 it was disbanded entirely. Meanwhile, members of the Trust and independent companies alike were dispatching teams every winter to carry out location shooting in the southern and western parts of the country where the weather was more propitious for filming in this season. For a time, Jacksonville, Florida, was a favored location, and in the early teens Kalem, Lubin, Selig, Thanhouser, and Vim Comedy, among others, were active in the area (Ponti 1992). Southern California was initially chosen as a shooting locale when the Selig Polyscope Company came to Los Angeles in the winter of 1907– 08 to film The Count of Monte Cristo. This was the first of many visits over the next few years by production companies based in the northeast in search of temporary winter havens for filming activities. The first permanent studio in Southern California was constructed by Selig in 1909 in the community of Edendale (now Glendale) just to the east of Hollywood. A number of other companies also built studios in the region; these include the New York Motion Picture Company in 1909, Biograph in 1910, Nestor, Vitagraph, and the Independent Motion Picture Company in 1911, and Mack Sennett’s Keystone Studio and Lubin in 1912 (Bowser 1990; Clarke

22

Origins of Hollywood Motion-Picture Industry

1976; Florey 1923, 1948; Jessen 1915; Slide 1994). In view of the fact that some of these firms were members of the Motion Picture Patents Company, it makes little sense to claim that they came to California in search of refuge from the Company’s agents. By 1912, according to Clarke (1976), there were seventeen production companies at work in Los Angeles. However, most of these companies were headquartered in the northeast, and the biggest studios by far were all still located in the New York area, which remained at this time the main center of motion-picture production in the USA. It is one of the ironies of modern Hollywood, given the recurrent complaints that have been made over the last couple of decades about ‘runaway production’, that its own earliest stirrings came about as a result of a process of locational decentralization.

Hollywood Emergent An alert observer scanning the motion-picture production industry in the USA in the year 1912 or 1913 would have noted the continued dominance of New York, the minor recent clustering of decentralized production units in Jacksonville and Los Angeles, and a scattering of studios in many other parts of the country. In California, apart from Los Angeles, the American Film Company was flourishing in Santa Barbara, an offshoot of Essanay was making Broncho Billy films in Niles in the Bay Area, and the Balboa Studios were just getting off the ground in Long Beach. On the basis of this information, and supposing that premonitions about the theory of locational agglomeration were already in the air, our fictitious observer would no doubt hazard a best guess to the effect that the main creative and commercial center of the motion-picture industry would probably continue to be New York (which, furthermore, contained the country’s largest concentration of writers, actors, producers, scene decorators, stage hands, and so on), and that other parts of the country would at best function as subsidiary satellite locations. Alternatively, even if our observer invested a high degree of faith in the virtues of mild sunny climates as a decisive factor in the success of the motion-picture industry, he or she would surely be as likely to suggest Jacksonville, Palm Beach, Niles, Santa Barbara, San Diego, and a dozen other similar places as being just as attractive, if not more so, as Hollywood. The initial accumulation of motion-picture production companies in Los Angeles up to about 1912, then, can perhaps best be thought of as a rather arbitrary outcome, one that could as easily have occurred at a great many other locations. I would add the speculation that the same outcome

23

Origins of Hollywood Motion-Picture Industry

was no doubt encouraged by self-reinforcing gossip about the merits of the region for camera work as itinerant film crews reported back to their peers in the northeast. There was certainly no lack of promotional propaganda on this matter at the time. Whatever the case may be, the important point to be stressed here is that there is little or no evidence to suggest that the motion-picture industry in the region up to this stage was anything but a distant satellite of New York. Not until the turbulent years from about 1912 to 1915 does the industry in Southern California really begin to show signs that a process of internal transformation and developmental change was occurring. This is a period marked by considerable business effervescence as many new production facilities were established and as others disappeared through bankruptcy or merger. More than anything, Hollywood was now starting to function as an incipient agglomeration with its own distinctive production system and labor market characteristics, and with innovative capacities (in terms of both commercial practices and film content) that seemed to set it strongly apart from the more established firms of the northeast. By 1915, as Figure 2.1 shows, Hollywood and its surrounding area was beginning to assume the geographic form of a prototypical industrial district. This trend was underpinned by the arrival of a number of critical figures from New York, each of whom helped to establish a dynamic Hollywood cinema as such. One of these figures was Cecil B. de Mille who came to Los Angeles in 1913 on behalf of the Lasky Feature Play Company, and proceeded to film The Squaw Man in a barn at the intersection of Selma and Vine Steets. The Squaw Man became the first Hollywood film to enjoy major international success. A year later, Zukor’s Famous Players Company also established production facilities in Hollywood and then in 1916 merged with the Lasky Company. The resulting Famous Players-Lasky Corporation was the forerunner of Paramount, Hollywood’s first major. In addition, in 1915, the Fox Film Corporation set up operations in Hollywood, and Carl Laemmle launched Universal Pictures which he established on a large lot at Universal City in the San Fernando Valley. Lasky, Zukor, Fox, and Laemmle were among the first Hollywood moguls. All four were Jewish immigrant entrepreneurs with modest backgrounds in sales and showmanship, in contradistinction to the more patrician figures (with the exception of Lubin) who were associated with the Motion Picture Patents Company. While the business practices of the members of the Trust were centrally focused on equipment licensing revenues, the new production companies of Hollywood were much more concerned with film content and audience appeal (Jones 2001). One index of the difference between the two groups is

24

Origins of Hollywood Motion-Picture Industry one mile one km

New York Motion Picture Co.

B U R B A N K

MALIBU SANTA MONICA

ay od W ywo Holl

STUDIO CITY Ventura

MAP AREA

PASADENA

LOS AN GELES C OU N T Y LONG BEACH ORANGE COUNTY

Universal Pictures GLENDALE

S H I L L

nd

at

e

Keystone Studio le

Vine Gower

ga en hu Ca

O D W O L Y L H O

G

Mutual Film Corp. Kalem Co. Vitagraph Co. Fine Arts Studio Essanay Studio Fox Lasky Co. Film Lubin Film Co. Masterpiece Corp Majestic and Film Co. Reliance Studios

Christie Film Co. Hollywood

ica

Highland

on

M

La Cienega

a Bre

E & R Jungle Film

Bosworth Studio Su

Wilshire

ns

et

Selig Polyscope Co.

LOS ANGELES CIVIC CTR

Pico

La

Hal Roach Studio CULVER CITY Triangle Studios

Famous Players

Washington Vermont

ta

n Sa

to

Melrose

HILLS

So

t

nse

Su

BEVERLY

Figure 2.1. Locations of motion-picture production companies in Los Angeles, 1915 Source: Addresses were obtained from a great diversity of publications, directories, and web pages. The information shown is probably not complete.

that members of the Trust tended to resist the production of feature films in favor of less demanding short films, whereas the leading independents were much more aggressive in developing feature films—though we should probably not try to press the point of contrast too far.1 Another significant difference is that the Trust was much more opposed than the independents to the emerging star system in the motion-picture industry, and many of its members refused to divulge personal information about or even the names of their main performers. Yet as early as the mid-teens, stars were becoming an important device for branding films and for helping to stabilize markets. At the same time, firms belonging to the Trust rented out their films by the foot irrespective of content, a practice that scarcely gave them much incentive to raise product quality. The Hollywood independents, by contrast, started at an early stage to concentrate

25

Origins of Hollywood Motion-Picture Industry

on feature films and to promote individual stars, while simultaneously building strong narrative dynamics into the films that they made (Allen 1976; Hampton 1931). One of the decisive moments of this trend was the production of Tess of the Storm Country by the Famous Players Film Company in Southern California in 1914, a film that propelled Mary Pickford into superstar status. The vibrancy of the nascent Hollywood cluster was further fortified when Charles Chaplin went to work for Mack Sennett’s Keystone Studio in 1913. To be sure, Chaplin moved on in 1914 to the Essanay studio at Niles in the Bay Area, only to return to Hollywood in 1916 with the Mutual Film Corporation, and then to set up his own studio (at La Brea and Hollywood Boulevards) in 1918. Other major stars who came to prominence at this time were Douglas Fairbanks, William S. Hart, and Roscoe Arbuckle, representing, respectively, the swashbuckling adventure films, westerns, and comedies that were now (among other genre films) pouring out of Hollywood. Above all, the figures of Thomas Ince and D. W. Griffith tower over this historic moment, with an influence on both the business and aesthetic practices of Hollywood production companies that alone was probably sufficient to push them to the leading edge of the industry. Ince can be seen in a sense as the harbinger of the full-blown studio system. He arrived in Southern California in 1911 to produce cowboy films for the New York Motion Picture Company, establishing a studio known familiarly as Inceville in Santa Monica at the point where Sunset Boulevard meets the Pacific Ocean. Ince was the first producer to attempt to industrialize the whole filmmaking process and to push it beyond the rather simple set of craft practices that had largely constituted it up to that point. Instead of the improvization that was widely characteristic of film production at that time, Ince developed much more methodical procedures based on his perfection of the continuity script. Above all, he radically separated conception from production, and broke the shooting process down into disconnected segments that were then reassembled into the final film at the postproduction stage. The continuity script could thus be used much like an industrial blueprint. On these foundations, an advanced division of labor started to make its appearance in the motion-picture industry, and the elements of a modern managerial model of production were installed (Bordwell, Staiger, and Thompson 1985; Staiger 1982). The net result was to endow production companies with a greatly expanded capacity to control the entire filmmaking process, and, above all, to exert discipline over the conduct of talent workers like writers, directors, and actors.

26

Origins of Hollywood Motion-Picture Industry

If Ince is notable for his managerial innovations, D. W. Griffith is celebrated above all for his influence on the whole conception of cinematic entertainment. He is credited among other things with developing the close-up, the flashback, and fade-out techniques in cinematography (Florey 1923; O’Dell 1970). Griffith’s career is a long and complicated one. After an already active professional life in motion pictures on both the east and west coasts, Griffith achieved his most important directorial success in 1915 with the production of Birth of a Nation for the Epoch Producing Corporation. The film was shot at the Fine Arts Studio located at the intersection of Hollywood and Sunset Boulevards, and is reputed to have cost $85,000, making it five times more expensive than any other film heretofore produced (Clarke 1976; Stern 1945). In addition, the film subsequently earned gross revenues of over $18 million, far more than the earnings of any other motion picture of the silent era so that it stands on record by a very long margin as the first blockbuster film. Birth of a Nation also helped to make cinema acceptable to the respectable middle class in America, which had hitherto largely considered it to be an offensively inferior medium of entertainment (Izod 1988; Slide 1994). This dramatic surge in Hollywood’s business fortunes and reputation was the added spark needed to push the entire complex to the undisputed forefront of motion-picture production. The surge was magnified, moreover, by Griffith’s development of an even more grandiose project in 1916 into which he poured all of his profits from Birth of a Nation. This was the film Intolerance, produced on the enormous budget for the time of $2 million. Although the film failed commercially, the economic energies that it set in motion via its huge cast and lavish sets (built on a lot just across the street from where Birth of a Nation had been produced) no doubt sent ripples through the local community. These two films together stand not only as monuments to early cinematic art but also as major business experiments that contributed significantly to Hollywood’s forward momentum. If there is any breakthrough moment to be discovered in the development of Hollywood as a productive agglomeration, it can surely be best identified with these two films of D. W. Griffith. More generally, Hollywood’s critical takeoff as an industrial complex hinges around the year 1915 when an extraordinarily potent combination of commercial and cultural forces came together, and when Hollywood was finally transformed from its status as a simple branch-plant extension of New York’s motion-picture industry to a composite system with a strong endogenous dynamic of development. After 1915, the number of workers and establishments in the Hollywood motion-picture industry shot up

27

Origins of Hollywood Motion-Picture Industry

both absolutely and relative to the rest of the country. The Motion Picture Studio Directory2 of 1918 records a total of thirty-seven studios in Los Angeles, forty-five in New York, and a scattering in other places, including seven in Jacksonville, and six in Chicago. By 1919, 80 percent of the world’s motion pictures were being made in California (Davis 1993). Two years later, in 1921, according to the first Bienniel Census of Manufactures in the USA, the motion-picture industry in California as a whole employed 5,329 workers in 68 establishments as compared with New York State’s 3,922 workers in 20 establishments. It would certainly be difficult to account for this growth by reference to the physical characteristics of Southern California, except to say that they played a contingent and less than necessary role. Rather, the rise of Hollywood can best be seen as a consequence of the vigorous system of productive organization that evolved out of the disparate collection of branch plants that had drifted into the area in the six or seven years before 1915. The physical geography of Southern California is in fact irrelevant to the question of how the dominating economic and cultural power of the Hollywood motion-picture industry came to be, and it cannot be sufficiently reiterated that a mature motion-picture industrial complex might have sprung up virtually anywhere in the USA in the early years of the twentieth century. However, once the forces of agglomeration are set in motion at any location, increasing returns effects sustain an upward spiral of growth and development while making it increasingly difficult for other locations to compete. In many respects, New York, with its decisive first-mover advantages, might well have been expected to forge ahead as the main production center in the USA, just as other primary metropolitan areas in other countries (e.g. London, Paris, Berlin, and Moscow) maintained a leading role as centers of motion-picture production despite their unfavorable climates. If climatic conditions were so critical to success at this time, we would also need to explain not only why the French film ˆ te d’Azur, but industry did not migrate in any important degree to the Co more significantly, how the industry, given its location in Paris, came to dominate world trade in motion pictures up to World War I (Jarvie 1992; Thompson 1985). New York producers enjoyed the benefits of a head start, but squandered their initial advantages by pursuing a business strategy that undermined their long-term competitive advantages. By contrast, the Hollywood independents, most of whom were aggressively opposed to the Trust, pioneered an alternative and vastly more successful strategy based on the cultivation of mass audiences by means of decisive stylistic and content innovations. The superior dynamic of individual and collective

28

Origins of Hollywood Motion-Picture Industry

development that came to function in Hollywood, allowed it to catch up with and then greatly to surpass New York. These remarks can be taken in a certain sense as a translation into placespecific terms of the developmental model of the motion-picture industry proposed by Jones (2001). In Jones’ model, the origins of the industry as a going concern are analyzed in terms of the coevolution of entrepreneurial careers, institutional rules, and competitive dynamics, that is the main developmental axes of any viable community of producers. As Mezias and Kuperman (2000) have argued, in addition, the multiple overspill effects that run systematically through such communities help to maintain high levels of business vitality. An essential point that lies at the core of the present investigation is that the same effects will also always give rise to a certain propensity for distinctive agglomerations to appear in geographic space.

Consolidation: Toward the Golden Age of Hollywood The final years of World War I saw a slowing down in the pace of motionpicture production in USA, and even after the war was over, the industry remained morose for some time. With the dawning of the 1920s, however, the industry entered a new period of growth, most of which was now predictably concentrated in Hollywood. Among the important events of this period were the formation of the early majors, in addition to Fox, Paramount, and Universal. In 1918, the Warner Brothers Company opened its first west coast studio on Sunset Boulevard. The following year, the United Artists Corporation was founded by Charles Chaplin, Douglas Fairbanks, D. W. Griffith, and Mary Pickford to distribute their independently made films (Balio 1976b). CBC Sales Film Corporation was established in 1920 and then renamed Columbia Pictures in 1924. Metro–Goldwyn–Mayer was born out of a complex merger in the same year, and RKO, which similarly had its origins in a series of mergers, was formed in 1928. By this time, the main outlines of classic Hollywood were in place, and its rise to international preeminence assured. Already, in the mid-1920s, 30 percent of the industry’s total revenues were being generated by exports (North 1926). It was in the mid-1920s, too, according to Koszarski (1990) that the term ‘Hollywood’ started to be used in its generic sense to designate the motion-picture industry at large. The data laid out in Table 2.1 help to summarize the main economic trends in the motion-picture industry in the 1920s and on into the 1930s

29

Origins of Hollywood Motion-Picture Industry Table 2.1. Employment in motion-picture production, USA, California, and New York, 1921–37 Year

USA

California

New York

Employment Establishments Employment Establishments Employment Establishments 1921 1923 1925 1927 1929 1931 1933 1935 1937

10,659 9,904 11,518 16,013 19,602 14,839 19,037 27,592 34,624

127 97 132 142 142 140 92 129 83

5,329 7,137 NA 12,852 NA 11,182 16,417 23,278 30,408

68 48 72 78 NA 71 39 75 35

3,922 1,734 NA 1,907 NA 2,594 1,748 3,240 2,883

20 16 18 21 NA 26 24 24 21

Source : US Department of Commerce, Bureau of the Census, Biennial Census of Manufactures (NA ¼ not available).

in the USA. The data are consistent with the notion that California had by this time shifted into a sort of second phase of development, characterized by rapid absolute growth and increasing shares of both employment and productive capacity. Note in particular that in 1921 California had just under 50 percent of all employment in the US motion-picture industry and that by 1937 its share had grown to 87.8 percent. The industry correspondingly stagnated in New York, with its share of employment falling from 36.8 percent to 8.3 percent over the same period. Not only was Hollywood consolidating its technical, commercial, and cultural domination of the entire motion-picture industry at this time, it was also widening and deepening its roots in the local area. It was in the 1920s that the basic distinction—still in use—between majors and independents came into being, and by 1930 the Hollywood production system could be succinctly described as comprising eight majors together with a surrounding constellation of smaller independents. The majors themselves could be further subdivided into the so-called big five (Fox, MGM, Paramount, RKO, and Warner Brothers), and the little three (Columbia, United Artists, and Universal Pictures), the distinction between them deriving from the fact that the former group owned extensive first-run exhibition facilities while the latter did not. The spatial distribution of both majors and independents in and around Hollywood in 1930 is displayed in Figure 2.2. In comparison with Figure 2.1, the spatial pattern revealed here indicates that a modest westward shift into central Hollywood had occurred in the intervening fifteen years combined with considerable locational intensification. Observe, in

30

Origins of Hollywood Motion-Picture Industry one mile one km

MAP AREA

B U R B A N K o ywo Holl

MALIBU SANTA MONICA

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et

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a nic

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Melrose

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BEVERLY HILLS

Warner Brothers

Washington Vermont

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se un

Figure 2.2. Locations of motion-picture production companies in Los Angeles, 1930 Source: Majors are represented by a square. Addresses were obtained from a great diversity of publications, directories, and web pages. The information shown is probably not complete.

particular, the conspicuous clustering of production companies in the vicinity of Gower Street between Sunset and Santa Monica Boulevards. Many production companies making cheap B movies thronged together in this area, most of them small independents, though Columbia was also distinctly one of their numbers. As a consequence, the area was widely referred to as Poverty Row, or alternatively, Gower Gulch in reference to the large number of cowboy films made locally and the crowds of suitably costumed actors on the street seeking work as extras (Fernett 1973; Mezias and Mezias 2000; Pitts 1997). Figure 2.2 also indicates that some of the majors had by 1930 located in the geographic periphery of the main Hollywood complex where they could find cheap land for their spaceintensive sound stages and back lots.

31

Origins of Hollywood Motion-Picture Industry

As the majors grew by acquisition, merger and internal expansion over the 1920s, they were involved in a trend to vertical integration. In the first place, the big five studios were aggressively engaged in vertical integration of all three main phases of the motion-picture business, namely, production, distribution, and exhibition. By the late 1920s the big five controlled significant segments of the entire industry and exercised something close to monopoly power over first-run theaters. In cases where they did not own the theaters, they were nevertheless able to circumvent open market competition by means of blind-booking and block-booking strategies (Huettig 1944). In the second place, the production phase itself was also subject to vertical integration. Thus, in the major studios all the main tasks of filmmaking—writing, directing, acting, sound-stage operation, musical composition and performance, film editing, and so on—were brought together under one structure of ownership and employment. The most talented workers were signed up to long-term contracts, usually of seven years’ duration. The effect of this second form of vertical integration on plant size is diagnostically revealed in Figure 2.3, which shows that as the 1920s came to an end, the average number of employees per establishment in California’s motion-picture industry began to increase dramatically, rising from 165 employees in 1927 to just over 850 in 1937. These two types of vertical integration in the motion-picture industry in the 1920s and 1930s complemented and reinforced one another. On the one hand, the majors were in control of the entire motion-picture commodity chain and could find outlets for all their films irrespective of quality, hence allowing for stability in the organization of production. On the other hand, their contractual control of the most popular performers ensured that quality was sufficiently high on a sufficient number of occasions that mass defections of audiences did not occur. The Paramount Decree of 1948 undercut this model of productive organization and ushered in a new Hollywood in which vertical disintegration was now the watchword (Scott 2002; Storper and Christopherson 1987). Still, it would be an error to think of the major studios in the 1920s and 1930s as being totally integrated. On the contrary, they retained a degree of flexibility in regard to many types of production tasks that they could not efficiently internalize on a permanent basis. Numerous individuals at this time, even talent workers and skilled craftspeople were employed as temporary or freelance workers by the studios. Koszarski (1990) points specifically to the frequency of this practice in regard to cinematographers. Other types of worker that the studios had subcontract or freelance agreements were writers, dancers, character

32

Origins of Hollywood Motion-Picture Industry California

Employees per establishment

900

700

500

300

Rest of U.S

100

1920

1925

1930

1935

1940

Year

Figure 2.3. Average size of establishment in the motion-picture industry in California and the rest of the USA, 1921–37 Source: US Department of Commerce, Bureau of the Census, Biennial Census of Manufactures.

actors, extras, and so on. The studios were also subject to the normal ebb and flow of labor turnover among their rank-and-file employees, and their location at the heart of a dense and suitably socialized pool of workers certainly helped to keep the costs they had to bear as a result of this activity at a relatively low level. It would seem that a wide array of specialized services and supplies to the motion-picture industry was to be found in Los Angeles at this time, and one source published in 1928, lists multiple addresses under headings like film editing, film laboratories, orchestras, agents, cowboy equipment, costumes and props, animals, and so on, though we have no way of knowing from the information provided what was purchased by the majors and what by independent production companies. The majors also lent out directors, stars, and other elite workers to one another from time to time, and they jointly established the Central Casting Corporation in 1925 so as to bring order into the deployment of temporary labor in the industry. They sought, as well, to improve the

33

Origins of Hollywood Motion-Picture Industry

return on their fixed capital investments by renting out studio space to independents (Fernett 1988). These observations suggest that classic Hollywood represented a much more complicated and diverse production system than is set forth in those accounts that insist on its character as a monolithic structure of vertically integrated majors and mass production. That said, much further historic research is clearly needed on the interplay between organizational forms on the one side and external and internal economies on the other side in the Hollywood of the 1920s and 1930s. This remark applies not only to the majors but also to the much-neglected independents of this period. Over the 1920s and 1930s, then, Hollywood can be described as a distinctive industrial district imbued with multiple spillover effects flowing from its internal transactional order and the dense, many-sided local labor market that had developed in the urban community around it. This local labor market grew greatly in size as migrants from all over the USA and the rest of the world moved into Los Angeles in search of work in the motion-picture industry. Among them, we must count the great number of talented foreigners drawn to Hollywood by its rewards and gratifications, and whose number included, if we consider only directors in the 1920s, Ernst Lubitsch, Friedrich Wilhelm Murnau, Michael Curtiz, Alex¨ stro ¨ m (Robinson 1968, 1977). Like industrial ander Korda, and Victor Sjo districts everywhere, Hollywood also acquired an idiosyncratic superstructure of institutions in response to diverse needs for coordination and collective order. In 1922 the main production companies collaborated to establish the Motion Picture Producers and Distributors of America (MPPDA) as an instrument of joint representation.3 One of the principal early functions of the MPPDA was to regulate what threatened to become an explosive public relations situation as general perceptions of Hollywood’s moral values (hot on the heels of a series of scandals) became evermore negative (cf. Anger 1975; Shindler 1996). In 1930 the MPPDA formally adopted the so-called Hays code, a set of self-imposed ideological and moral injunctions, as a preemptive strike against extra-industry regulation. The MPPDA was also greatly exercised by labor relation issues, and in 1926, it helped to engineer the Studio Basic Agreement, representing one of the first efforts on the part of the industry to ensure smooth management–labor interactions. The Agreement was a simple two-page document signed by nine production companies and five unions in Hollywood that laid out a framework for adjudicating grievances and other disputes4 (Ross 1941, 1947). In the aftermath of the Studio Basic

34

Origins of Hollywood Motion-Picture Industry

Agreement, and in an attempt to head off full-blown labor organizing activities in Hollywood, the studios created the Academy of Motion Picture Arts and Sciences in 1927 to function essentially as an overarching company union (Nielson and Mailes 1995). The Academy, as initially constituted, had five branches representing producers, writers, directors, actors, and technicians. It failed, however, to establish any credibility as a labor organization, and in the wake of the Great Depression, when the studios demanded that workers across the board take deep pay cuts, it steadily gave ground to independent unionization movements5 (Ross 1947; Scott 2005). In spite of this concentrated development of productive activity and institutional order in Hollywood, the executive offices of all the major production companies remained in New York, from which base, they coordinated operations across production, distribution and management, and dealt with vital financial issues. This locational split in the industry’s functions was to last until well into the 1970s. On these foundations, Hollywood entered its golden age, a period of ‘mature oligopoly’ (Balio 1976c) that lasted from the late 1920s to the late 1940s when both the Paramount Decree and the development of television heralded an extended period of crisis and a reversal of earlier trends to vertical integration. The beginning of the golden age can be marked symbolically by Warners’ release of the first sound feature film, The Jazz Singer, in 1927 (Gomery 1976), though this in and of itself was only one expression of a continuing series of technical and cultural innovations (from technicolor to Busby Berkeley’s choreography) that reached into every corner of the motion-picture industry. Hollywood was now manifestly dominated by the major studios, each of which came to acquire distinctive stylistic features, ranging from the lavish mise en sce`ne typical of MGM films to Paramount’s urbane comedies. The motion pictures that flowed copiously out of Hollywood’s production system at this time evinced an unequalled command of narrative economy and visual storytelling that captivated popular audiences all over the world, though they were also roundly condemned by many European intellectuals, such as Adorno, Horkheimer, Duhamel, and Gide (Benjamin by contrast took a more tolerant attitude), for the cultural and political stupefaction that they were alleged to induce. Hollywood’s revenge, so to speak, can be regularly observed every Friday and Saturday evening outside the art house cinemas of Berlin, Paris, and London, as the avatars of the same European intellectuals line up to see the identical films today.

35

Origins of Hollywood Motion-Picture Industry

Conclusions This chapter has tried in both theoretical and empirical terms to understand something of the origins and growth of the Hollywood motionpicture industry. More broadly, the argument can be taken as an attempt to add historic and geographic substance to the kind of location theory that comes out of the work of Arthur (1987) and David (1985) with its synthetic description of agglomeration economies, path dependency, and lock-in. I have tried to outline a brief historic geography of Hollywood showing how it shifted in the mid-teens of the twentieth century from being a branch-plant extension of New York’s motion-picture industry to an economically sustainable agglomeration in its own right. Much of this shift can evidently be explained in terms of the development of a highly successful business model in Hollywood combined with a powerful and complex endogenous dynamic of growth based on its transactional, labor market, and innovative capacities. It is no doubt the case that the early movie pioneers in California really did locate there because they were in search of sunshine and mild winters, and certainly once they arrived there they made ample opportunistic use of the local landscapes. But Southern California does not have a monopoly on sunshine and mild winters and varied landscapes, and numerous other locations in the US south and west might just as easily have acquired (and some did acquire for a time) a reputation among early production companies as the place to be in the winter season. In brief, there might well have been another alternative geography of the motion-picture industry in the USA in the twentieth century in comparison with the one that actually came to pass. Had the early business history of the motion-picture industry played itself out in a different key, there is every reason to suppose that New York might have capitalized on its first mover advantages and retained its preeminence throughout the succeeding century. After about 1915, however, the die was cast in another direction. The Hollywood production complex now surged rapidly ahead, and over the next decade it decisively consolidated its ascendancy. These developments laid the foundations for a golden age that lasted until the late 1940s. But even as the old Hollywood fell into crisis after 1948, a new Hollywood rose up again on its ashes. The new Hollywood gradually and painfully reorganized itself around a highly disintegrated network model of production that actually intensified the play of agglomeration and has been one of the principal foundations of Hollywood’s continued competitive success down to the present day. The interesting question at this point is

36

Origins of Hollywood Motion-Picture Industry

whether the changes now occurring, as globalization begins to run its course, will usher in yet another incarnation of Hollywood, or whether they will open up a new window of locational opportunity, thereby encouraging the rise of new agglomerations of motion-picture producers in other parts of the world.

Acknowledgments This research was supported by the National Science Foundation under grant number BCS-0091921 with supplementary funding from the Haynes Foundation. I wish to thank Yixiu Ye for her able research assistance. This essay is an edited version of Chapter 2 of the author’s book On Hollywood: The Place, The Industry (Princeton: Princeton University Press, 2005). The author and editors are grateful to Princeton University Press for permission to reproduce copyrighted material here.

Notes 1. In 1915, four of the leading independent companies, Famous Players, Fox, Lasky, and Universal produced on average thirty-seven feature films each. The members of the Trust produced on average thirteen feature films each. See Hanson (1988). 2. Published in Los Angeles by the Motion Picture News. 3. The MPPDA is the antecedent of today’s Motion Picture Association of America. 4. Signatories (on the management side) were Universal Pictures, MGM, First National Pictures, Famous Players-Lasky, FBO Studios, Producers’ Distributing Corp., Warner Brothers, Educational Film Exchanges, and Fox Film Corporation, and (on the union side) the International Alliance of Theatrical and Stage Employees, the United Brotherhood of Carpenters and Joiners, the International Brotherhood of Electrical Workers, the International Brotherhood of Painters and Paperhangers, and the American Federation of Musicians. 5. The Academy, which still exists, has long since abandoned its original goals, and is now mainly known for its patronage of the annual Oscar awards.

37

3 The Coevolution of Technologies and Institutions: Silicon Valley as the Iconic High-Technology Cluster Martin Kenney and Donald Patton

In the 1990s Silicon Valley achieved iconic status for economic development planners globally. But how did Silicon Valley come into being? We demonstrate that its rise was a social process of bricolage in which actor’s fashioned solutions for various problems that they confronted with what they found at hand (Garud and Karnoe 2003). Frequently, these solutions were adapted from existing forms and then applied to new purposes. For the most part, they were responses to immediate problems, rather than wisely considered, far-sighted solutions by prescient economic actors maximizing their utility functions. Those solutions that appeared to work were diffused, repeated, and adjusted, gradually evolving into routines and institutions (Nelson and Winter 1982). These routines and institutions enabled further experimentation even as a stable repertoire of actions came into being. Borrowing from Spender’s notion (1989) of industrial recipes, we argue that through an unplanned iterated learning process Silicon Valley actors developed regional recipe for creating and nurturing start-ups. This chapter examines how technology and institutions coevolved to create an ecosystem within which entrepreneurs were able to encapsulate many of the new innovations in separate firms, as opposed to all of the innovations being commercialized by existing firms. The information/computer/electronics (ICE) and, to a far lesser degree, biomedical technologies have formed the core of the venture capitalfinanced start-up economy. For the last five decades, the ICE technologies have experienced exponential rates of improvement in cost and

38

The Coevolution of Technologies and Institutions

functionality combined with extremely large intellectual property content. Also, the ICE technologies have frequently experienced (or, alternatively, entrepreneurs have created) moments when entry barriers have been lowered sufficiently to allow nimble well-placed entrepreneurs to enter new market niches. Thus, on one dimension, understanding Silicon Valley is predicated upon tracing the evolution of technologies and the industries based on them, and, on the other dimension, the evolution of the institutions, practices, and cultural understandings that orient action. The chapter examines the building of the entrepreneurial support infrastructure and its co-evolution with the local high-tech industries. In our discussion we highlight the way Silicon Valley entrepreneurs developed new business models and often combined different technologies to create new business opportunities. We also consider the importance of culture as an explanatory variable arguing that culture particularly is as much a dependent variable as it is an independent variable. In our estimation, it is better to consider culture as having coevolved with the regional business activity—and in the case of Silicon Valley might better be seen as a learned set of guides to action, rather than as something emanating from a Gold Rush mentality or a set of personal attributes. In the conclusion, we reflect upon the implications of our findings for an evolutionary theory of the development of entrepreneurial, high-technology regions.

The Formation of Silicon Valley Entrepreneurship in Silicon Valley involves two separate sets of organizations (Kenney and Burg 1999). The first set of organizations are the ones from which the entrepreneurs emerge and the second are the organizations that specialize in supporting the entrepreneurial process. The primary source of entrepreneurs for Silicon Valley start-ups has been other firms (Gompers, Lerner, and Scharfstein 2003). Though Gordon Moore, a founder of Intel, believes that university research institutions contributed little to the evolution of the semiconductor industry (Moore and Davis 2001), generalizing from the semiconductor industry he underestimates the role of universities and corporate research laboratories in providing the support and intellectual space within which the seeds of new industries could develop (National Research Council 1999a, 1999b). A number of the defining firms in individual industries can be attributed to universities and corporate research laboratories. For example, 3Com,

39

The Coevolution of Technologies and Institutions

Cisco, Yahoo!, Seagate, Google, Sun Microsystems, and Cadence are directly linked to Bay Area corporate research institutes and universities (Kenney and Goe 2004). Having a rich source of entrepreneurs is very important, but it is the institutions that nurture the firms they create which give Silicon Valley a powerful advantage over other regions. Silicon Valley hosts a set of interdependent institutions specialized in supporting firms, particularly technology firms capable of extremely rapid growth. These institutions form what observers termed an ecosystem, a social structure of innovation, or an incubator region (Bahrami and Evans 2000; Eisenhardt and Schoonhoven 1990; Florida and Kenney 1990; Lee et al. 2000; Todtling 1994). However, this ecosystem was not sui generic, but rather formed over time in tandem with the industries that were formed in the region. Establishing when Silicon Valley was formed is not simple. Conventionally, it might be dated to the decision by William Shockley to establish Shockley Semiconductor in Silicon Valley. This was a defining moment and there was an element of serendipity in the choice of Palo Alto for the firm. Shockley also had considered the Boston area where there were already a number of transistor firms using germanium as their substrate, MIT was producing numerous technically capable personnel, and a group of early technology adopters (the minicomputer firms) were on the verge of being established. On reflection, there seems little doubt that a number of other regions, such as Los Angeles, Long Island, or northern New Jersey would have had sufficient technical personnel, lead customers, and other institutional supports to allow an industry to take root. For example, the germanium-based transistor firms on the East Coast particularly Boston might have switched to silicon, which ultimately became the substrate of choice for the most important technology of the late twentieth century as the silicon semiconductor became the ubiquitous enabling technology for digitization. Or, alternatively, Texas Instruments (TI) in Texas might have begun to spin-off firms. Fortunately or unfortunately, TI was not as badly managed as Shockley Semiconductors and it never experienced the mass resignations that led to the creation of Fairchild Semiconductor. It was Fairchild Semiconductor that began leaking people and contributed the start-ups that eventually transformed the region into what the editor of the Electronic News first described in 1973 as the Silicon Valley. If Shockley Semiconductor was the bad seed that soon failed, Fairchild was the most fecund of all. However, in some sense, survival is a function of the environment and the environment into which Fairchild was born was munificent. To illustrate, it is convenient to explore what existed in

40

The Coevolution of Technologies and Institutions

the prehistory of Silicon Valley not as a theological exercise, but as a partial explanation of why the semiconductor industry would find the region such a conducive environment. There was an existing electronics industry that could be traced back as far as Lee De Forest and the invention of the vacuum tube (Sturgeon 2000). Also, other entrepreneur-based, high-tech electronics firms, the most salient of which were HewlettPackard and Varian, were located in the region. Though these firms were not semiconductor firms, they were electronics firms and HP was especially important because it produced the basic equipment that all electronic firms needed. Shockley was not the first person or firm to decide to locate in the South Bay. In 1952 IBM decided to establish a branch of its Yorktown Heights research laboratory in San Jose to tap the skilled personnel in the area.1 Approximately eighteen years later Xerox established its West Coast research facility in Palo Alto to access the skilled labor pool. Already in 1958 there were real business reasons to establish a new high-technology electronics firm in the region. Shockley’s relocation to Palo Alto was serendipitous and, at least, partially motivated by his desire to live close to his mother. However, the history of Silicon Valley also includes the role of Frederick Terman initially Dean of Engineering and then Provost at Stanford (Sturgeon 2000). A professor of electrical engineering, Terman admired the MIT model of university interaction with business. He was also a fervent believer in the economic potential of electronics, championing the establishment of electronics firms in the region and assiduously working to both attract them and support entrepreneurs including those on his faculty wishing to establish firms. Terman urged Shockley to establish his firm in Palo Alto. Serendipity brought Shockley to Silicon Valley, but Fredrick Terman was a centrally located actor actively trying to manipulate fate. The entrepreneurial support system would also evolve with the development of new firms. Yet it would be incorrect to attribute the creation of the VC industry directly to Fairchild. History shows that the capital required to start Fairchild was raised by Arthur Rock, who, at the time, was a manager at the New York investment bank Hayden Stone. And yet there were already a number of informal investors in the region that were willing to invest in new electronics start-ups. Thus, even though the capital to establish Fairchild was raised in New York, there was already a group of proto-venture capitalists in formation at the time of Fairchild’s establishment. In fact, there was a history of Stanford professors and administrators investing in start-ups that can be traced back to the early

41

The Coevolution of Technologies and Institutions

investors in Federal Telegraph (Sturgeon 2000). Though it is difficult to be certain, there is anecdotal evidence that the Bay Area was already one of the national centers for angel investors in electronics.2 The Bay Area VC industry grew with the semiconductor industry, but it is important to note that semiconductors were only one of a number of industries funded. Whether Fairchild should be considered irreplaceable in the formation of the Silicon Valley high-technology cluster is probably unanswerable. Leslie (2000) argues that a microwave technology cluster was established in the region at roughly the same time (1950s) on the basis of defense department research. From 1955 through the early 1960s, there was a high-technology electronics boom and many firms were formed in the region with funding from informal investors. There is reason to believe that a high-technology cluster of some sort was evolving and would have continued to evolve. However, the region is termed Silicon Valley with good reason. To return to our earlier statement, the semiconductor was the most important technology of the twentieth century, and it was a critical input that made new industries such as work stations, personal computers, and computer networking possible. There can be little doubt that the semiconductor is at the heart of the dominance that Silicon Valley has shown for the last three decades.

Entrepreneurs, Technologies, Firms, and Industries in Silicon Valley The evolution of Silicon Valley is based on its entrepreneurs, the technologies they commercialize, and the firms they create. Figure 3.1 is a chronology of the most significant technologies that have fueled the region’s growth. In some cases, a technology was developed in Silicon Valley, but eventually shifted out of the region entirely. Of course, the sources are not always local as the semiconductor technology was imported from Bell Laboratories in New Jersey. The level of employment is a good measure of which industries were most important. Figure 3.2 shows that software currently employs the greatest numbers in the region having overtaken components (semiconductors) in 1996. It continued to grow very rapidly in the late 1990s fueled by the dot-com boom. The aerospace sector represented by guided missiles has been largely steady at 20,000 through the entire period with the exception of the 1980s when it doubled due to Reagan’s Star Wars plan. The growth of computer and peripherals’ employment is interesting

42

Stanford, HP, UC Berkeley, Xerox PARC, IBM San Jose, UCSF SEMICONDUCTORS

1960

Memory COMPUTERS 1965 MAGNETIC STORAGE

Minis

1970 SOFTWARE Floppy disk drives

1990

RISC ASIC

COMPUTER NETWORKING

Semiconductor equipment

1985

Relational Hard disk drives PC software databases Games software

Microprocessor Semiconductor design software

1980

Disk drive part suppliers

1975

Local area network

Biotech Game machines

Wide area networks Internet Optics

1995 (c) MartinKenney

Figure 3.1. Genealogy of Silicon Valley technologies

Apple

Micros Workstations

The Coevolution of Technologies and Institutions 200,000 180,000

Computers & peripherals

160,000 140,000

Communications equipment

120,000

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100,000 80,000

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1998

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Figure 3.2. Employment in four Bay Area counties, 1959–2001 Note: Data before 1998 was collected by SIC code. For 1998–2001 data was not available in SIC codes. Therefore data was collected in NAIC codes that approximate SIC codes.

because it peaked in the early 1980s and then decreased to approximately 25,000 in 2001. Benefiting from the dot-com boom, communication equipment employment grew from approximately 20,000 in the 1980s to over 50,000 in the late 1990s. Scientific instruments also have been an important contributor to Bay Area employment since the 1970s. However, the overwhelming growth has been in software, an industry that did not even merit a separate category until the early 1970s; a few years after IBM signed the consent decree unbundling software and hardware. Employment provides one perspective on the structure of the Bay Area high-technology industries. Figure 3.3 indicates the number of establishments in each industry, and this provides a different perspective. The number of establishments in each industry differs so radically that a logarithmic scale was required to present the data. Notice that during the entire period there were no more than six establishments in guided missiles. In the case of components, instruments, communication equipment, and computer and peripherals, the number of firms was in the hundreds, though obviously there was much churn during the entire period. In absolute terms, the number of computer establishments declined since its high in the 1980s. This corresponds with the proliferation and later shakeout of microcomputer and workstation manufacturers. The one industry showing a continuing high rate of entry is software, which, with a few exceptions of which the most notable was the collapse of the

44

The Coevolution of Technologies and Institutions 10,000 Computers & peripherals Communications equipment Components

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Figure 3.3. Establishments in four Bay Area counties, 1959–2001 Note: Data before 1998 was collected by SIC code. For 1998–2001 data was not available in SIC codes. Therefore data was collected in NAIC codes that approximate SIC codes.

dot-com bubble in 2001, has continually grown in the number of establishments and employment since the Census of Manufacturing first began collecting data in 1974. The quantitative indicators and the figure provide an overview of the development of the region. In the following sections, we examine the development of the most salient industries and firms providing a richer description of the coevolution of the region, technologies, and industries.

Semiconductors and Ancillary Industries Over the last four decades, semiconductor technology has been characterized by one overwhelming dynamic, namely Moore’s law, which has correctly predicted that the areal density of transistors would double every eighteen months, and since the cost of a semiconductor device is roughly comparable to the chip’s dimensions, either performance increases for the same price or price drops accordingly. What this means is that each new generation of semiconductor devices is able to process more information than the previous generation, providing the opportunity to increase the speed and capability of any artifact containing semiconductors. As a result, products containing ICs experience constant improvements in functionality, and functionality that formerly was too expensive or even impossible to undertake continually becomes less expensive and enters the realm of the possible.

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The Coevolution of Technologies and Institutions

Semiconductor technology was so fecund in opening new economic spaces that new business opportunities repeatedly emerged, and the cognoscenti had opportunities to create their own firms. This fecundity is illustrated by the fact that Fairchild and its successor firms experienced 134 spin-offs by 1986 (SEMI 1986), and there have been more since then. In tandem with the increase in areal density-making integrated circuits less expensive per transistor, the cost of a fabrication facility doubled every four years (Leachman and Leachman 2004). When Fairchild began producing chips, converted pizza ovens were used for the baking process. By 1975, a fabrication facility cost approximately $50 million (OhUallachain 1997: 220) and in keeping with what product life-cycle theory would predict, entry costs increased to the point at which there were far fewer entrants.3 According to SEMI’s genealogy, from 1974 to 1980 inclusive there were 21 entrants in Silicon Valley (or an average of three per year). In the seven prior years 1967–73 inclusive, there were forty-three start-ups (an average of 6.1 per year). Yet in the following six years from 1981 through 1986 inclusive, forty-six firms were established (an average of 7.7 ¼ 46/6 firms per year). The increased rate of entry after 1981 was the result of a collective action solution to increasing cost of fabrication. Beginning in the early 1980s, a number of start-ups were established to design and market new ICs. However, they contracted for manufacturing from the integrated producers that had spare capacity. This circumvented the entry barrier created by the capital cost of fabrication. The integrated producers benefited, because their expensive fabrication facilities could be fully utilized. The difficulty with this solution was that during semiconductor market booms, the integrated manufacturers reclaimed their capacity, and the fabless firms often lost their access to the fabrication capacity. A market-based solution was the silicon foundry specialized in contract manufacturing. These foundries, which were established in Taiwan, were pure contractors that did not compete with their customers. As specialists they had to be willing to invest, provide excellent service, and rapid turnaround (Leachman and Leachman 2004). Soon, a number of Taiwanese firms were established to produce chips designed by other firms. This developed into a symbiotic relationship eliminating the high cost of manufacturing as an entry barrier and unleashing a plethora of new Silicon Valley semiconductor start-ups that specialized in design and marketing. Semiconductor devices were the foundational industry for the region, and Fairchild was the ideal typical Silicon Valley start-up. However, the key to continuing entrepreneurship in the semiconductor industry has

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been the ability to create new business models. When the cost of a fab became an entry barrier in the early 1970s, a collective action solution was fashioned that reopened the industry to start-ups and the pace of start-up formation, once again, accelerated. Developing this solution was only possible because the actors in the support infrastructure were willing to back start-ups pioneering new business models. Frequently, an industrial cluster will both attract and spawn supplier firms for the core industry (Porter 1998). The roots of the semiconductor equipment industry can also be traced to Fairchild. Fairchild initially built its equipment internally, but soon decided to divest these activities and assisted the spin-off of firms like Electroglass, Kasper, and Micro Tech (Moore and Davis 2001; von Hippel 1988: 173). The most significant surviving Fairchild-linked firms are Applied Materials (established in 1967), which is the largest semiconductor equipment maker in the world; KLA (established in 1976); Tencor (established in 1976); Lam Research (established in 1980); and Novellus (established in 1984); all of which are located in Silicon Valley. Though very few ICs are made in Silicon Valley, it shares with Japan the distinction of being the global center for semiconductor production equipment design and manufacturing. In fact, the headquarters of the Semiconductor Equipment and Materials Industry Association is in San Jose. In the last three decades, a merchant IC design automation software industry emerged. This software was a response to the fact that the increasingly complex IC designs could no longer be done on paper without an unacceptable number of errors. Thus, in the late 1960s the integrated semiconductor firms began developing software tools for design automation. Fairchild was an early leader as its engineers developed Computer-Aided Design (CAD) software (Walker 1998). At the beginning of the 1980s, a number of IC design software start-ups were established. Many of the advances were made at UC Berkeley and certain UCB professors participated in forming start-ups. For example, in 1982 Solomon Design Associates was established by Jim Solomon who was assisted by a number of UCB professors. SDA merged with ECAD, a start-up that was publicly traded, to form Cadence Design Systems (Solomon 1988). Today, Cadence is the world’s largest supplier of electronic design technologies, methodology services, and design services. In 1986, Synopsys, a major competitor, was founded in North Carolina, as a spinout from a General Electric acquisition, Calma. However, at the suggestion of its VC investors moved it to Silicon Valley (de Geus 1988). As the software improved, an ever-greater number of the IC firms abandoned their

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in-house software and purchased software from the design software vendors. The standardization of the design software facilitated the rise of the fabless semiconductor firms as they could purchase their design tools, releasing them from the onerous task of creating their own software. The software also allowed the foundries to stipulate their manufacturing parameters in the software to be used by the designers. In other words, the design software became the interface between the designers and the manufacturers. The development of a supplier industry enriched the semiconductor industry ecosystem. A number of these firms were very successful and thus also enriched the venture capitalists investing in them, thereby contributing to VC industry growth. Each further development of the division of labor reinforced not only the semiconductor industry, but also the institutions supporting the entrepreneurial infrastructure. The semiconductor industry was important for a number of reasons beyond its technological fecundity. First, the large number of spin-offs in the 1960s encouraged the already existent entrepreneurial culture. Second, the semiconductor industry provided significant investment opportunities for venture capital. Third, it attracted attention to the region and many of the region’s entrepreneurs including Robert Noyce, Gordon Moore, and Jerry Sanders, became iconic figures, even as the region became Silicon Valley.

Computers Silicon Valley has been the birthplace of computer firms serving a wide variety of product classes (i.e. IBM-compatible mainframes, minicomputers, work stations, personal computers, etc.), though interestingly enough, Silicon Valley only became dominant in workstations. Rather than discuss the entire history of computer producers in Silicon Valley, the greatest attention is paid to the computers based on microprocessors, a new category of integrated circuits that were pioneered by Silicon Valley semiconductor firms in the early 1970s. These small computers dedicated to individuals were central to the establishment of the networked, distributed computing paradigm that dominates contemporary computing. This period is also interesting, because in personal computing today, Silicon Valley firms produce many of the crucial components, even though Silicon Valley is no longer the center of the PC industry. Initially, of course, the computing industry was dominated by IBM and the various other mainframe producers. It was with the minicomputer,

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which was so important for the building of Route 128, that Silicon Valley firms began to experience success in computing. The greatest success was HP, but many other VC-financed start-ups entered the market; some of them, such as Tandem Computer established in 1975 to offer fail-safe computing, captured unique niches while others were me-too firms. Another important firm was Amdahl, which was founded in 1970 by Eugene Amdahl, a key IBM computer designer, and offered an IBM plugcompatible computer. A number of these computing firms were successful, but they did not spawn waves of new firm creation and entirely new industries, rather they occupied niches and created large capital gains for investors. For Silicon Valley, the great wave of new firm creation in computing would begin in the late 1970s when two technological trajectories combined to create personal computing. The first trajectory was the work at Xerox PARC, which developed an expensive workstation that was a personal computer, that is not a time-shared computer. The Xerox effort, in fact, created a workstation designed by engineers for engineers. Xerox failed to capture the market, but many start-ups entered the market to try where Xerox was failing. Very quickly, a market for workstations developed and an industry emerged led by Sun (Stanford University Network) Microsystems based in Silicon Valley, and Apollo Computers based in Route 128. Sun became the dominant workstation provider, though in the late 1980s and early 1990s, it was challenged by another Stanford spinout, Silicon Graphics, Inc., which specialized in graphics computing. Eventually, the workstation firms would morph into the computer server providers. The other personal computing trajectory was what was then called microcomputers, and it led directly to the PC. Beginning in the mid1970s, many hobbyists and engineers including Apple’s Steve Jobs and Steve Wozniak began building computers using the newly introduced microprocessors from Silicon Valley firms, such as Intel and Zilog, and the non–Silicon Valley firm Motorola. Silicon Valley soon became a hotbed of hobbyist computer start-ups with their locus in the now famous Homebrew Computer Club that met at Stanford University (Freiberger and Swaine 1984; Langlois 1990).4 Of all the start-ups, Apple Computer was the most strategic as Steve Jobs actively tapped the Silicon Valley entrepreneurial support structure (Young 1988: 151). By utilizing this infrastructure and conforming to its requirements, Apple was transformed as investors required the appointment of experienced management and made other changes necessary to establish a real business. This support helped tip the scales for Apple’s survival and growth.

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During the early 1980s microcomputer start-ups proliferated. By all measures, the region was on its way to becoming the industry center. New firms were being established to provide software (e.g. Visicalc was developed by Bay Area entrepreneurs) and components. But then in August 1981, IBM introduced its PC, which rapidly became the dominant design, and nearly all the non-IBM compatible microcomputer firms in Silicon Valley and other places disappeared. Within three short years, most Silicon Valley microcomputer firms, with the notable exception of Apple, left the business (Angel and Engstrom 1995). After the mid-1980s, Silicon Valley would not host any new PC companies with the exception of HP, which entered during this period. The demise of the PC industry did not mean that Silicon Valley would not benefit from the diffusion of PCs. Numerous start-ups found opportunities in supplying components including microprocessors (Intel and AMD), BIOS chips (AMI, Phoenix Technologies, and Chips and Technologies), graphics chips (S3, Nvidia, and Cirrus Logic), hard disk drives (HDDs) (Seagate, Quantum, and Conner Peripherals), and even computer mice (Logitech and Kensington). The loss of the personal computer industry to IBM and then the cloners created new markets for peripherals and components that Silicon Valley firms could supply. With the introduction of the IBM PC, with its simple architecture and the ability of low-cost cloners to enter the market, Silicon Valley’s technological prowess no longer provided any particular advantage for PC assembly. Apple survived, in an ever-narrowing niche, on the basis of marketing and some desirable software features. Despite this, Silicon Valley’s position as a center for computing systems firms deteriorated as the PC turned computing hardware into a commodity, and eroded the workstation market. In historic terms, with each new computing category Silicon Valley firms were early leaders, and yet, in some cases, the industry evolved in ways that prevented them from remaining in that industry.

Peripherals—Magnetic Storage The origins of the magnetic data storage industry can be traced to research conducted in IBM’s San Jose Laboratories. Beginning in the 1970s, after the IBM consent decree, which unbundled IBM’s software and allowed plug-compatibility (see Amdahl above), entrepreneurs began to leave IBM’s San Jose operation to establish firms to exploit the new market opportunity of supplying storage devices for the new entrants. Soon,

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Silicon Valley experienced a wave of spin-offs in HDDs similar to the earlier one in semiconductors. During the 1980s, in tandem with the rapid growth of the PC industry, there were many VC-financed entrants in the HDD industry (McKendrick et al. 2000). Since the integrated HDD manufacturers like IBM and DEC would not sell components to the new entrants, there was enormous demand (Christensen 1992: 95). These small independent HDD manufacturers were eager to buy components, creating opportunities for component supplier start-ups. This prompted a massive wave of start-ups as the venture capitalists initially experienced enormous returns through public stock offerings and acquisitions. However, by the mid-1980s, the HDD industry experienced a powerful shakeout of both HDD manufacturers and component suppliers (Bygrave and Timmons 1992). The collapse of the HDD Bubble did not mean that there were no new opportunities in fields related to magnetic storage. For example, in the 1990s, a new area of VC funding was HDD arrays, which are groups of HDDs using sophisticated software working together so as to provide redundancy and back up (McKendrick, Doner, and Haggard 2000; McKendrick 2001). Still later there was a wave of start-ups commercializing storage area networks, which combined networking technology with storage technology to optimize a firm’s usage of its various data storage systems. In summary, magnetic storage exhibited similar technical and organizational characteristics to semiconductors. The technology was rapidly improving; there was a global class source of ideas and technical personnel located in Silicon Valley, and a similar entrepreneurship and spin-off dynamic. As with semiconductors, as the entry barriers in HDD manufacturing increased, the entrepreneurs found new opportunities in disk arrays and storage area networks. Despite earlier shakeouts, VC proved willing to fund storage-related start-ups with new business models.

Computer Networking5 The first computer networking firms in Silicon Valley were established in the early 1970s (Burg 2001). Time-sharing of minicomputer capacity was one of the earliest forms of computer networking, and a number of startups were established in Silicon Valley and other regions to exploit it. As a greater number of computers were installed on corporate campuses, an opportunity arose to provide technologies that would allow for faster data transfer rates through local area networks (LANs). The initial opportunity was in exchanging data between mainframes.

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The proximate cause for dramatically increased interest in computer networking was an effort that began in the early 1970s to automate the office. This office of the future required a network to share files between computers and expensive peripherals such as printers and data storage devices. A pioneer in this quest was Xerox PARC, which in the mid-1970s created a system of small computers, laser printers, and data storage devices networked by what would be called Ethernet. PARC was not alone in this effort; minicomputer firms such as Wang Computers were also trying to create the future office. At the end of the 1980s, computers were proliferating and entrepreneurs began forming firms to design and produce networking equipment, which interestingly enough was dependent on semiconductors capable of signal processing. At the time, the market was still small and there were no standards to ensure computer interoperability. The critical event that catalyzed the formation of an industry was the 1980 decision by Xerox to offer low-cost licenses for the Ethernet standard. In 1978 Robert Metcalfe left Xerox PARC and in 1979 started 3Com. In rapid succession, Zilog lost three groups of LAN entrepreneurs. As Ethernet became the de facto standard, a positive feedback loop ensued as the increasing number of users created a growing market for yet other innovations (Burg and Kenney 2003), and venture capitalists became more confident in funding firms (Burg and Kenney 2000). The proliferation of LANs, many running different protocols, created an opportunity for an interconnection solution. A number of firms were created to solve this problem. The most successful would be Cisco Systems, a Stanford University spin-off that commercialized a multiprotocol router. In the early 1990s, data communications traffic exploded as LANs proliferated and wide area networks were created. File-sharing and e-mail became standard business applications, and corporations began interconnecting their global operations. The increasing standardization of the datastream meant that a simpler, cheaper, and faster solution, the switch, could be deployed. In typical fashion entrepreneurs began leaving existing firms to establish switching firms with VC financing. To ensure they did not miss this new technology, the established networking firms, such as Cisco, Synoptics, and 3Com, acquired many of these switching start-ups for large premiums, encouraging greater investment and yet more spinouts. In the 1990s, the networking firms, and especially Cisco, developed a strategy of scanning their ecosystem to identify firms developing important new technologies and markets. Start-ups that were experiencing the greatest success were then acquired. In effect firms, especially Cisco, were

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using the VC financing system as an integral component of their R&D strategies (Mayer and Kenney 2004). This encouraged a further proliferation of networking start-ups established in the hope that they too would be acquired. The opportunities in networking were not limited to simply increasing speed and bandwidth. The network also became more complicated, thereby providing entrepreneurial opportunities for network management, security, and other software and hardware such as specialized ICs; many of these opportunities were exploited by start-ups. By the mid-1990s, computer networking had become one of the core Silicon Valley industries. A business model emerged in which venture capitalists-funded start-ups that were established with acquisition as an exit strategy. Cisco pioneered a new corporate strategy of using the Silicon Valley start-up ecosystem to identify the new technologies that would affect its business. As firms competed and grew and yet others were formed, Silicon Valley increasingly became the knowledge center for computer networking. This deep knowledge meant that Silicon Valley firms, entrepreneurs, and venture capitalists would be uniquely positioned to see the next big thing.

The World Wide Web The World Wide Web (WWW) protocols were not a product of Silicon Valley; in 1991–2 they were developed at CERN in Geneva (Abbate 1999; Kenney 2003). At the time, there were few start-ups aiming to exploit the Internet, which was still largely an academic operation funded and controlled by the US federal government. In 1993 entrepreneurs had not yet comprehended the opportunities that the Internet represented. There was also a delay in convincing venture capitalists that the WWW presented an investment opportunity (Ferguson 1999). However, the lag in comprehension did not last long, especially in Silicon Valley, and by early 1994, venture capitalists were receiving business plans from entrepreneurs with ideas for the commercial exploitation of the WWW. The first easy-to-use Web browser Mosaic was developed at the University of Illinois and given away for free. Mosaic formed the basis of one of the earliest Internet startups, Netscape, which was established in April 1994 by Jim Clark, an exStanford professor and founder of Silicon Graphics Inc. He went to the University of Illinois and hired most of the key persons who had designed Mosaic and moved them to Silicon Valley. Less than one and one-half years later, Netscape had an initial stock offering in August 1995 at a

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valuation of nearly $1 billion. Netscape’s remarkable increase in value alerted every venture capitalist and entrepreneur that the WWW was a new opportunity. Given the greater VC resources and large number of entrepreneurs, the Bay Area quickly became the center for WWW startups (Kenney 2003; Zook 2002). As the number of WWW users exploded, new business ideas and opportunities proliferated. This expansion provided opportunities for yet other start-ups to develop new software and Web-based services. Businesses were built around searching and cataloging other sites, providing instant messaging, selling products online, software tools, and Web-hosting services among others. Investors were willing to fund entrepreneurs experimenting with an amazing proliferation of business models. As these firms went public or were acquired at large premiums, and as the user base grew, the high stock market valuations for Internet-related firms unleashed a frenzy of investing encouraging even greater speculation. By mid-1999 there was what might be termed a full-scale investment panic as public investors drove the price of new issues skyward. By the time the Bubble ended in 2000, more than 370 self-identified Internet-related firms had gone public and their total valuation had reached $1.5 trillion, though they had only $40 billion in sales (Perkins 2000). Approximately, 50 percent of all the new Internet firms were headquartered in the Bay Area. In 1999, the average return for early stage VC funds was 91.2 percent, the highest in history (NVCA 2000a).6 The returns for the most successful funds were astronomical—many had annual returns of 100 percent and one even had a 400 percent annualized return. The amount of VC invested in Internet-related firms grew from a nearly negligible $12 million in the first quarter of 1995 to $31 billion in 1999 (NVCA 2000b). In percentage terms, the increase was equally dramatic, growing from a negligible percentage in 1995 to over 60 percent of total investment in the fourth quarter of 1999 (NVCA 2000b: 31). Faster than anywhere else, Silicon Valley entrepreneurs glimpsed the potential of the WWW as a commercial opportunity and then mobilized the resources necessary to try to enact that future.

Software The richness and diversity of software firms in Silicon Valley are remarkable. As mentioned earlier, the highly specialized field of semiconductor design software is almost entirely located in Silicon Valley. In 2003,

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software was the largest employer in Silicon Valley, and despite recent setbacks in the longer term it is expected to grow (see Figure 3.2). In software, as has been the case with other industries, Silicon Valley has pioneered certain software sectors and then lost them. For example, it is no longer a significant producer of PC software, with certain exceptions, such as Intuit for PC financial applications and various PC game software firms. Microsoft’s PC software monopoly resulted in the demise of Silicon Valley firms such as Visicalc and Borland Computer. Even when new PC software such as the Netscape browser is commercialized in Silicon Valley, Microsoft has been able to use its monopoly power to destroy them. The only major survivors have been the tax software producer, Intuit, and the utilities software firm, Symantec—and both of these are threatened by Microsoft. Silicon Valley firms have been far more successful in business productivity software. The most significant of these are relational database software, which was pioneered roughly contemporaneously at IBM’s San Jose Laboratories and UC Berkeley. All of the key independent relational database firms (with the exception of Microsoft, a late entrant) are located in Silicon Valley. The largest of these is Oracle which is the second largest independent software firm in the world. Other important firms include Sybase, Informix (purchased by IBM), and IBM. Oracle, in particular, has spawned other important business software firms including Peoplesoft and Seibel, which pioneered other niches in the business software field. In entertainment software, Silicon Valley also experienced success. Here, the Silicon Valley pioneer was Atari, which later collapsed. Atari’s demise in the 1970s permitted the control over the game boxes to move to Japan, and today Japan is the major competitor for the US game software makers. The largest entertainment software firm in Silicon Valley is Electronic Arts, which is located in Redwood City. Electronic Arts, which used to be a developer, today not only produces games but also distributes them for other producers. They are intimately connected to the cutting-edge PC graphics chipmakers also located in the region, because these graphics capabilities determine software usability. Drawing upon a similar expertise base, the Bay Area is also host to a number of leading computer film animation firms including Pixar and Lucas Arts. Producing special effects, these firms are critical for contemporary cinema and computer games.7 Though Silicon Valley has not proved to be as dominant in software as in some other industries, it is one of the key global software centers. Today’s Silicon Valley start-ups use Linux operating system and programs such as Java as the basis of their products. In fact, the Finnish developer of

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Linux moved to Silicon Valley. Open source offers them a way to outflank Microsoft’s grip on software innovation. Moreover, even as this is written, Silicon Valley start-ups are integrating Indian software production capabilities into their business plans, in the same way, as the semiconductor design firms have integrated Taiwanese fabs into their business plans. In other words, new business models are still being created.

Support Infrastructures and Culture The development of a rich and complicated support infrastructure for entrepreneurs provides important advantages to Bay Area entrepreneurs. The goal of the actors in the support infrastructure is to participate in the capital gains that accrue when one of the start-ups is successfully sold, either to the public in an initial public stock offering or through an acquisition. The entrepreneurial support network has become so resource laden that the various actors in the network are willing to fund emerging ideas in new fields as has been the case in biotechnology (see Kenney 1986; Romanelli and Feldman in this volume), superconducting, and, most recently, nanotechnology. If these investments fail, as was the case in superconducting, only a relatively small proportion of the total VC resources and, perhaps, a few venture capitalists will be lost. If the investments succeed, as was the case with biotechnology, a new investment field will be created. Ultimately, the actors are agnostic as to what constitutes a suitable field for investment the market for the firms they support informs them by providing them with capital gains. The Silicon Valley culture benefits from interaction in many venues that contributes to cross-disciplinary information sharing and synthesis. With so many technologists, investors, and others interacting, there are ample opportunities for combining existing technologies to create new products (Hargadon 2003). One often cited example of this is the bioinformatics start-ups that combine the technologies of computing and gene-mapping. Many of these were formed in the Bay Area. The repeated successes in establishing new firms and then being able to garner large capital gains on a significant number of them created a culture of entrepreneurship. Interestingly, this culture differs remarkably from other entrepreneurial cultures that are based on the idea of establishing and then managing and controlling one’s own firm. The Silicon Valley culture is based on establishing and then selling the company to either the public or a corporate acquirer. In either case the entrepreneur loses control

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of the firm. The objective, then, is capital gains, which under normal conditions can only be secured by creating a viable firm (though during the last high-technology bubble many unviable firms were created and foisted on the public). It also bears mentioning that other regions such as Boston and Israel appear to have cultures that are similar. Though we focus on identifiable institutions in this chapter, it is important to note that an entrepreneurial culture developed in Silicon Valley which, though not unique, can be characterized as extreme entrepreneurism. As Saxenian (1994) observes, during the economic boom periods changing jobs is a given part of the labor market in Silicon Valley. Over time, participating in a start-up has become a career path. This acceptance of start-ups as normal has reduced the career risk of becoming an entrepreneur. Moreover, whereas thirty years ago the entrepreneur was expected to use credit card debt and even mortgage their home as part of the process, in the last twenty years such measures are no longer necessary prior to receiving VC. It is not in the venture capitalist’s interest to raise the barriers to entrepreneurship and increase the concerns of the entrepreneur. This lowering of entry barriers has culminated in the mythology that failure will not necessarily prevent an entrepreneur from receiving funding for another start-up. Given that the Silicon Valley economy is based on capital gains, a culture and ideology encouraging entrepreneurship is a prerequisite and a natural outcome. In keeping with the capital gains-driven economy, one of the primary cultural and economic goals is to secure stock options or equity. This has led to an environment within which equity is extended to a large number of persons in the corporate hierarchy. The ownership of options elicits extraordinary effort from the employees and, if the firm is successful, creates many wealthy managers and engineers. A certain number of these experienced and now wealthy individuals will in turn be willing to invest in other entrepreneurs or even launch their own start-up, thereby perpetuating the entrepreneurial cycle. Another aspect of the Silicon Valley culture was memorialized in Michael Lewis’s book (2000) entitled The New New Thing, which described Jim Clark’s involvement in the creation of Netscape. In this case the hero is Jim Clark who ruthlessly capitalizes on the new WWW browser technology and reaps enormous capital gains. The region has developed a corporate environment within which new technologies, a great hack, and huge capital gains are the reigning myths. In this environment a hot new firm or technology attracts attention and floods of resumes. The ability to become involved in the hottest new technologies attracts many of the best

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engineers in that field who desire to be involved in the newest technology. The economic incentives and culture are aligned to encourage high-risk entrepreneurship.

Reflections An evolutionary and systemic perspective provides an appropriate means for understanding Silicon Valley, and, by extension, other entrepreneurial high-technology districts (Avnimelech, Kenney, and Teubal 2003). Often discussions omit or elide the technological trajectories that underpin such industrial districts and overemphasize cultural aspects; we explicitly argue against this. The basis of much of this romanticization of the entrepreneur is a belief that the culture is sui generis. A more appropriate model would treat culture as a constructed and evolving social artifact. The entrepreneurs that have benefited from the system and the actors in the support infrastructure have every reason to support a specific set of cultural beliefs. The environment evolved, though not in a conscious directed manner, as a result of individuals pursuing various goals, one of the most important of which was the capture of capital gains. Viewed from a longer-term historic perspective, what is striking is how a number of the technologies exploited in Silicon Valley, such as semiconductors, magnetic storage, and computer networking, have had trajectories that have unfolded in such a way so as to enable yet further opportunities to establish new firms. In a number of sectors when potential for future start-ups appeared stymied by requirements such as enormous capital investments to create semiconductor fabrication facilities, new business models were developed to circumvent the entry barrier. An evolutionary perspective highlights the region’s remarkable success in repurposing its intellectual assets and attracting new talent from around the world. The constituents of the support infrastructure created their own niches and then were able to draw resources from the environment. They became actors trying to improve their processes, which by definition, included supporting and assisting the entrepreneurs. They also changed the environment by creating demand for entrepreneurs, reinforcing the cultural valuation of the entrepreneur, and routinizing the start-up process. These actions explicitly recognized that the entry barriers for entrepreneurship are not only financial but also social and psychological. The literature has treated the willingness-to-take-a-chance attitude in Silicon Valley as an

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innate characteristic; we see it as a communally created social norm. The support infrastructure assisted in this in a wide variety of ways from developing an understanding that it was not necessary to require that the entrepreneur invest their entire net worth into the firm, to allowing the entrepreneurs to receive the greatest attention. In other words, the agents in the support infrastructure changed the environment to be more favorable to their practice. Institutions and routines developed in the Bay Area ensure that the region can attract the entrepreneurs of the future. For example, its global-class universities and corporate research laboratories continue to attract many of the best and brightest students, researchers, and faculty members. The many very successful high-technology firms, nearly all of which still tout their entrepreneurial origins, attract thousands more engineers and managers some of whom will become entrepreneurs and still more are willing to join start-ups. These individuals are then placed into a munificent environment that values and even glorifies entrepreneurship and, very importantly, places the resources for attempting a start-up within reach. It is a little wonder that new things emerge and attract seed funding in an environment where venture capitalists and a large community of angel investors are willing to invest to explore their business potential. The ability of this process to discover the New Thing is remarkable. Promising technologies receive resources, both managerial and financial, for experimentation. Those technologies that show evidence of yielding significant capital gains encourage other entrepreneurs to launch firms that attract yet more VC. Successful exits can precipitate full-scale investment manias. In contrast, some technologies do not lend themselves to large gains and they are soon dropped as having no promise for this particular methodology of supporting innovation. For example, Thomas Murtha, Lenway, and Hart (2001) show Silicon Valley was the home to a number of flat panel display screen firms funded by venture capitalists, but quickly the venture capitalists came to understand that the industry provided few suitable investment opportunities and abandoned the field. Other industries, such as personal computers, superconductivity, and soon, perhaps, nanotechnology, received VC investment initially, but were later abandoned. An entrepreneur with a new business model need only convince a few venture capitalists to gamble. Moreover, in contrast to personal investors, by the nature of the limited partnership format, the venture capitalists must invest or they cannot continue in the business. This means that they

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are at least willing to listen to high-risk proposals and also willing to invest in high-risk situations. For example, venture capitalists-funded firms established to exploit Linux, such as Redhat and VA Linux, despite the fact that the business models were predicated upon unseating the Microsoft monopoly through the use of a free-operating system. In other words, the infrastructural agents can support very high-risk projects as long as they have a commensurate potential for enormous rewards. Our evolutionary treatment of organizations and technologies presents Silicon Valley as a complex tapestry replete with commensurate coevolution within which both have shaped each other and created routines and a cultural gestalt that is self-reinforcing. The organizations in the support infrastructure function as an initial selection mechanism. Firms without the perceived requisite potential for outside capital gains are not funded, while ideas that appear to be sound—by the standards of the support network—receive funding, thereby perpetuating their survival. In this ecosystem, actor incentives, technological trajectories, and business models have coevolved and become mutually reinforcing.

Notes 1. The San Jose Laboratory would pioneer magnetic data storage media and became IBM’s global center of excellence for magnetic media. Much later many key Silicon Valley disk drive entrepreneurs spun out of IBM to create new disk drive firms (McKendrick et al. 2000a). Later, IBM’s laboratory was an important source of the relational database technology that firms such as Oracle and Sybase commercialized in the mid-1980s. 2. The historic record suggests that in Boston there were very few angels, and this, in fact, is cited as one of the reasons for forming the first formal VC firm American Research and Development (see Hsu and Kenney 2005). 3. The classic citations on industry or product life-cycle theories are Abernathy (1978), Abernathy and Utterback (1978), or, most recently, Klepper (1996). 4. Bill Gates also was far more closely related to this hobbyist stream than he was to PARC. 5. This section is largely drawn from von Burg (2001). 6. The three-year compounded average annual return was a more modest 47.9 percent! 7. This may be changing as they use PC-like computers that are harnessed by sophisticated graphics software.

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4 Accounting for Emergence and Novelty in Boston and Bay Area Biotechnology* Jason Owen-Smith and Walter W. Powell

All happy families resemble one another, each unhappy family is unhappy in its own way. Leo Tolstoy

Existing studies of innovation and clusters all too often begin with Tolstoy’s insight that successful regions resemble one another, while those that falter do so idiosyncratically. This conjecture, however, is rarely an a priori proposition. Instead, we suggest it is a substantive artifact of reliance on methods that emphasize comparative statics over analyses that focus on emergence and dynamics. The San Francisco Bay Area and Cambridge/Boston are the world’s largest and most commercially successful biotechnology regions. The attributes and successes of these regions are widely studied and their efforts broadly emulated (Powell et al. 2007). Despite similarities in scale and outcomes, however, each region emerged through a distinctive process that continues to influence its outputs. These variations, in turn, suggest that there are multiple pathways to similar outcomes and offer a corrective to efforts to transpose a ‘standard’ model of regional innovative success that may never have existed. Drawing upon a data-set that tracks strategic alliance networks in human therapeutic and diagnostic biotechnology over a twelve-year period (1988–99), we examine patterns in the development of two canonically successful biotechnology clusters in the Boston/Cambridge Massachusetts metropolitan region and the San Francisco Bay Area. We emphasize the extent to which interesting variations in the form and substance of

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innovative activity are apparent when viewed with a dynamic lens. Broad similarities in ascendant clusters, we contend, can be outcomes of divergent patterns of development. Moreover, we suggest that these patterned variations can shape the nature of innovations produced by firms.

Regional Advantage and Industrial Development in Biotechnology1 We focus on the commercial field of biotechnology, which developed scientifically in university labs in the 1970s, saw the founding of hundreds of small science-based firms in the 1980s, and matured in the 1990s with the release of dozens of new therapeutics. The field is notable for both its scientific and commercial advances as well as for the diverse cast of organizational players—universities and other Public Research Organizations (PROs), government laboratories, Venture Capital (VC) firms, large multinational pharmaceutical corporations, and smaller dedicated biotechnology firms (DBFs)—involved in its development. In this field, where the sources of scientific and technical leadership are widely dispersed and rapidly developing, and where the relevant skills and resources necessary to produce new medicines are scattered, collaboration among organizations became a necessary component of success. An elaborate system of private governance emerged to orchestrate the interorganizational networks such collaborations constituted (Powell 1990, 1996) and the internal structures and practices of DBFs changed accordingly as firms co-evolved with the networks that characterize the industry. During the very early years of the industry, from the early 1970s to the late 1980s, most biotech firms were very small start-ups that relied, of necessity, on external support. Lacking the skills and resources needed to bring new innovations to market, they became involved in elaborate lattices of relationships with universities and large pharmaceutical firms (Kenney 1986; Powell and Brantley 1992). Lacking a knowledge base in the new scientific field of molecular biology, large companies were drawn to start-ups by the latter’s capabilities in basic and translational science (Galambos and Sturchio 1996; Gambardella 1995). Asymmetries in technological, regulatory, and financial muscle drove early collaborative patterns in the industry (Hagedoorn and Roijakkers 2002; McKelvey 1996; Orsenigo 1989; Orsenigo, Pammolli, and Riccaboni 2001). Despite arguments that the new field would undergo a shakeout as large pharmaceutical firms developed the technical competencies that would

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allow them to assert dominance over weaker small firm partners (Sharpe 1991; Teece 1986), the founding of new firms accelerated. Established firms’ efforts to cherry-pick promising new ventures faced significant obstacles imposed by deeply collaborative R&D efforts and a mobile scientific labor force. Instead of consolidation and shakeout, the industry’s later years witnessed the give-and-take and mutual forbearance characteristic of relational contracting (Macneil 1978), which became the dominant practice in the field. By the late 1980s, several biotech firms (e.g. Biogen, Genzyme, Chiron, Genentech, Amgen, and Immunex) had become large organizations and numerous pharmaceutical firms had created in-house molecular biology research programs. Even when mutual need declined as a spur to collaboration, the pattern of dense interconnection deepened, suggesting that the original motivation of exchanging complementary resources had shifted to a broader focus on mining innovation networks to explore new forms of collaboration and product development (Powell et al. 2005). An analytic story that places networks alone at the heart of biotechnology’s development misses an important component of the analysis, however. Despite the evolution of dense and expansive networks, geography played an essential role in the industry’s evolution and remains an important feature even today. The networks that now characterize this complex commercial field emerged from distinct geographic roots. Beginning in the Bay Area and Boston, then spreading to other areas, such as San Diego, Seattle, and Bethesda, MD, clusters of biotech firms, VC firms, and PROs forged local networks that reached out as they developed, creating a national industry network from regional origins (Owen-Smith et al. 2002). Yet these regions remain important to understanding conditions in the industry. Evidence is mounting that the network effects that drive much of the action in biotechnology vary with the geographic location of partners (Owen-Smith and Powell 2004; Whittington, OwenSmith, and Powell 2006). Networks played an essential role in the development of stable regional clusters, but those clusters seeded the geographically dispersed structures that have come to characterize the field. We thus turn to analyses of network connections in the two largest and most successful US biotechnology regions in order to demonstrate that collaborative arrangements help to underpin successful clusters. Those regional communities vary in their character, evolutionary path, and approach to innovation. We draw upon a data-set of strategic alliance ties involving 482 DBFs and their more than 2,000 partner organizations over the period 1988–99 to

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illuminate patterns in the structures connecting regionally colocated biotechnology firms. Data are drawn from Bioscan, an independent industry directory published quarterly. We focus on independently operated, profit-seeking entities involved in human therapeutic and diagnostic applications of biotechnology—but omit companies involved in agricultural and veterinary applications as those sectors draw on different scientific capabilities and operate in different regulatory environments. Our data-set, like the industry it represents, is dominated by US firms, although recent years have seen considerable expansion in Europe. The sample of firms includes both public and privately held firms, and the former include companies with minority or majority investments by other firms as long as their stock is independently traded. Large pharmaceutical companies, investors, government agencies, and PROs enter the data-set as partners that collaborate with biotech firms. We link these relational data to patent grant and citation information for the period 1976–99 drawn from the National Bureau of Economic Research patent citation database (Jaffe and Trajtenberg 2002). In total, there are 10,067 US utility patents issued to the 482 firms in our sample over this time period. Organizations are identified by type and location, which enables us to isolate ties among colocated organizations in two established biotechnology regions. The San Francisco Bay Area and Boston are well-studied examples of densely connected and intensely innovative regional economies. In our data-set, Boston is home to more than 14 percent of US firms in our sample. Bay Area biotechnology firms account for almost 21 percent of US firms. Together, these regions were issued 51.5 percent of the patents assigned to US biotechnology firms through 1999 and developed 32 percent of all biological therapeutics approved by the US Food and Drug (FDA) administration between 1988 and May 2004.2 Five of the ten best-selling biotechnology drugs in 2001 were developed by firms in these two regions. Boston and the Bay Area thus represent notable success cases for biotechnology regions.

The Bay Area and Boston Networks In order to examine the evolution of the Bay Area and Boston networks, we identify all organizations located in the two regions that have contracted with a local biotechnology firm. Our data-set includes four types of formal interorganizational connections and five types of organizations. In addition to biotech firms, we include VC firms, government agencies, large

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multinational pharmaceutical corporations, and PROs in the partner sample. These diverse organizational forms are connected by four varieties of contractual ties. R&D connections represent agreements for shared research and development efforts. Finance ties reflect investments in one organization by another. Licensing ties are agreements that transfer the rights to intellectual property across organizations. Commercialization ties include downstream product development activities, ranging from clinical trials to manufacturing, sales, and marketing. During the period 1988–99, the Bay Area network is the larger of the two, involving 159 organizations (82 biotech firms, 12 PROs—most notably Stanford University and the Universities of California at Berkeley and San Francisco, one government laboratory—Lawrence Livermore Labs, and some 64 VC firms), connected by 243 local contractual ties. The Boston network is home to 113 organizations (57 biotechs, 19 PROS— including MIT, Harvard University, Massachusetts General Hospital, and the Dana Farber Cancer Center, and 37 VC firms), connected by 201 local contractual ties. Neither region was home to a multinational pharmaceutical corporation during this time period.3 While the regions differ in scale, in the demography of organizational types that occupy them, and in the availability of local VC funds (Powell et al. 2002), both are characterized by organizationally diverse and structurally cohesive networks. How, then, do the regions differ? Figures 4.1 and 4.2 track yearly changes in Boston and the Bay Area in terms of the distribution of dyads that comprise each region’s main network component. The main component of a network is its largest connected subset. In practical terms, the main component represents the largest group of organizations in a structure that can reach one another through network paths of finite length and thus captures the minimal level of connectivity necessary to enable broad information diffusion (Owen-Smith and Powell 2004). Put colloquially, imagine drawing linkages among nodes without ever lifting your pen. These figures paint a very different evolutionary trajectory for the two regions. The most basic unit of a network is the dyad. In this case, a dyad is a pair of organizations connected by a formal R&D, finance, licensing, or commercialization tie. Figures 4.1 and 4.2 characterize dyads in terms of the types of organizations that comprise them, without regard to the class of activity connecting a given pair. Three types of dyads are possible in these main components: Biotech firms can connect with each other (a DBF–DBF dyad), with PROs such as universities or hospitals (a DBF–PRO dyad), or

65

Accounting for Emergence and Novelty 90 DBF VC DBF PRO DBF DBF

80 70 60 50 40 30 20 10 0 1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

Figure 4.1. Boston main component ties by dyads and year, 1988–99

120 DBF VC DBF PRO DBF DBF

100

80

60

40

20

0

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

Figure 4.2. Bay Area main component ties by dyads and year, 1988–99

66

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Accounting for Emergence and Novelty

with VC firms (a DBF–VC dyad).4 In addition to the distribution of dyads, regional networks grow at different paces and in different patterns. Consider Figure 4.1, which tracks the growth of the Boston network. Note first the pattern of growth in ties (each dyad represents a single tie) implied by the height of the histogram bars. Our data-set begins in 1988, when we find a relatively large number of ties in Boston. The number grows slightly into the early nineties and then levels off for several years before climbing again through the latter years of the data-set. The bars are shaded to represent the relative prevalence of different dyads in the network. Note the bar that represents 1988, which shows a remarkable reliance on PROs. Only a very small number of ties link biotech firms to each other or to local VC firms in 1988 or 1989. These ties grow as the network expands and these commercial connections dominate the network by the end of our time frame. The Boston network grew from origins in the public sector (Porter, Whittington, and Powell, 2005). Put differently, public science formed the foundation for commercial application (Nelson 1981, 1986). Industries where commercially viable technical advances emerge from the academic and public sector manifest more open technological trajectories than industries that rely more heavily on industrial R&D (Dosi 1982). The Boston biotechnology community is linked by shared connections to PROs early in its evolution. These connections remain an important part of the network, but increasing patterns of DBF to DBF and DBF to VC ties reflect the development of a commercial network that becomes structurally autonomous, while bearing the imprint of the public sector. Contrast this trajectory with the different pattern illustrated by Figure 4.2. There is no dominant network component in the Bay Area in 1988, though a cohesive network forms in 1989. Unlike Boston’s growth pattern, which saw a plateau in the early 1990s, the Bay Area grew markedly through 1996 before stabilizing in the late 1990s. These differences in volume and velocity are matched by very different dyad distributions. During the first two years when a main component existed, the Bay Area community was composed entirely of ties linking DBFs to local VC firms. Where the stability and technical diversity of Boston PROs anchored that network and fostered a more open technological trajectory (Owen-Smith and Powell 2004), the Bay Area relied heavily on the prospecting and matchmaking efforts of venture investors.5 Later years witnessed the increasing importance of VCs, a smattering of ties involving PROs, and— most importantly—dramatic growth in DBF–DBF connections. By 1999, direct links among Bay Area DBFs outweigh the other two types of dyads.

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Both Boston and the San Francisco Bay Area evolved from dependence on a non-DBF organizational form to a state where significant portions of the network were made coherent by direct connections among sciencebased biotechnology firms. In other words, similar endpoints in the evolution of the networks were reached through different routes. While both relied on the inclusion of organizations different from biotechnology firms, Boston was anchored in the public sector, whereas the Bay Area was dominated by venture capitalists. The endpoints of these trajectories are similar as both regions came to depend heavily on collaborations among ostensible competitors, but their different starting points and the lasting involvement of different partners may have produced distinctive patterns of innovation. Distributions of dyads, however, cannot tell the full story of a network’s evolution; hence, we turn to an assessment of the overall topology of the networks. Figure 4.3 fleshes out differences across the regions with images of the networks in three distinct time periods. These snapshots were generated using Pajek,6 a freeware program designed for the visualization and analysis of large networks. The relative positions of nodes in these images are meaningful and result from two spring-embedded, graph-drawing algorithms. The first treats a network as a physical system where nodes repel each other and ties act as ‘springs’ that pull connected nodes closer together (Fruchterman and Reingold 1991). This algorithm moves unconnected nodes to the periphery of the image, and separates components (groups of two or more nodes) from one another. The second algorithm relocates connected nodes so that the Euclidean distances among them are proportional to their graph theoretic distance (Kamada and Kawai 1989).7 These images, then, are replicable representations where the relative position of organizations is a function of the connectivity of the system and the degrees of separation among nodes.8 The shapes of the nodes in Figure 4.3 represent different types of organizations: circular nodes are DBFs, diamonds are PROs, and triangles are VC firms. Tie patterns likewise represent different types of collaborations. Solid black links are R&D ties, dotted black connections represent financial investments, dotted gray are licenses, and solid gray linkages indicate commercialization deals. The width of a given tie reflects the number of connections linking a pair of partners. When multiple ties are present in a dyad, the pattern of the linkage reflects the most recent type of activity. To gain purchase on the differences between the two networks at a given point in time, read across columns in Figure 4.3. To get a sense of the evolutionary pattern within each region, pick a column and read down.

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Accounting for Emergence and Novelty

San Francisco Bay Area

Boston

1988

1994

1999

Figure 4.3. Boston and Bay Area networks: 1988, 1994, and 1999

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Accounting for Emergence and Novelty

Consider the first image of the Boston region, which bears out Figure 4.1’s emphasis on ties between DBFs and PROs. Note the central role played by diamond nodes and the relatively few connections linking circles to each other. The main component of this network stretches across the center of the image; most of the ties are single connections between different types of organizations. Tie types are rather evenly dispersed, but gray (commercialization) and dotted gray (licensing) ties are in the majority. In the region’s early years, DBF firms are stitched together into a coherent network by their shared connections to PROs. Harvard, the well-connected diamond in the lower right-hand corner of the image, and MIT, the center of the star in the upper left-hand quadrant, are the primary entry points to the network and their relative distance from each other suggests that the early years of the Boston biotechnology community may have been shaped by different kinds of academic involvement. In related work, we show that while both Harvard and MIT faculty have been active in founding Boston-based biotechnology firms, MIT scientists are much more active patentors, while Harvard faculty serve on more scientific advisory boards (Porter et al. 2005). The 1988 Bay Area image paints a very different picture as ties in that year did not aggregate to create a dominant component. Instead, the early years of the region appear to be characterized by small clusters of firms connected either to multiple venture capitalists or, less commonly, to Stanford University (the diamond at the center of the three node ‘chain’ near the bottom of the image) or University of California, San Francisco (UCSF) (the diamond in the dyad—with Genentech, whose founder was a UCSF scientist—near the center of the figure). The patterns suggested by the visualizations are echoed by careful archival research. In an analysis of the career histories of the founders of biotech firms in the Bay Area and Boston, Porter (2004) finds that Boston companies were often started by MIT and Harvard professors, many of whom maintained their university affiliations. In contrast, founders in the Bay Area were much more likely to come from VC or other biotech firms. Another key contrast was that Boston faculty founders were often senior professors with established reputations. When Bay Area faculty were involved in founding, they tended to be younger and much more likely to take a leave from their university positions. Almost all founders in Boston came from the region, while founders in the Bay Area came from diverse locales. Indeed, east coast faculty—from Yale, Columbia, and Duke—came to California to start companies. Turn your attention now to the second row of Figure 4.3, which represents the regional networks near the middle of our time series in 1994. The

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Boston network has grown, but maintains a reliance on PROs and both Harvard (now at the bottom of the image) and MIT (the well-connected diamond node at the top of the image) remain important players in the network. This image also suggests the growing importance of local venture capitalists (notably in the ‘tree’ structures that descend from Harvard’s partners in the lower quadrant of the image) as well as the salience of DBF to DBF ties. Note the cohesive cluster (outlined by a dashed circle) formed by R&D connections among four Boston firms—Genzyme, Genzyme Transgenics (one of several spin-offs from Genzyme), Autoimmune (whose research tie to the Dana Farber Cancer keeps this nascent cluster attached to the larger network), and Creative Biomolecule. These firms, particularly Genzyme—a large and successful ‘first generation’ biotechnology company—seed the development of a dense DBF–DBF region in the Boston network. Compare this view to the image of the Bay Area, whose large main component reflects the dramatic pattern of growth captured in Figure 4.2. This image is dominated by ties linking DBFs (circles) to venture capitalists (triangles). PROs play a minimal role in the network. The single diamond node at the top left is Stanford, which is linked to a young biotechnology firm by a license.9 Note, however, the robust cluster of (often multiple) DBF to DBF ties outlined by a dashed circle at the top of the figure. This group, centered on Genentech—one of the first and most successful biotechnology firms—and Chiron—another large and established player in the industry—is characterized by diverse and repeated ties that directly link biotech firms to one another. Boston’s Genzyme triangle and the Bay Area’s Genentech cluster represent the beginnings of a network centered on collaborations among ostensible competitors. The size of these ties relative to those connecting younger DBFs to venture captital firms suggests a process by which newcomers are identified by investors and then linked into the DBF–DBF segment of the network by forging ties with incumbents or their partners. Where PROs are still the entry way and the gatekeepers of Boston in 1994, Bay Area VCs prospect for new talent, and established firms usher promising newcomers into an increasingly connected segment of the network. These patterns became more robust in 1999, the final year of our data. In both networks the pattern of DBF–DBF linkages expands and deepens (relevant regions are outlined by dashed ellipses) and both sections of the network remain centered on the region’s largest and most successful firms (Biogen and Genzyme in Boston and Genentech and Chiron in the Bay Area). Despite the clear emergence of purely commercial portions of

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both networks, however, the regions still manifest significant differences. While VC firms are important players in the Bay Area network, they only rarely form multiple connections to the same partner and (as one would expect) their ties are overwhelmingly financial. The field of dotted gray lines in the lower left quadrant of the 1999 Bay Area image exemplifies this trend. VCs play an important connective and prospecting role in this network, but their one-dimensional network portfolios suggest that much of the innovative action may emerge from the dense and multiplex cluster of biotech to biotech ties. The linkages formed by PROs shrink in importance in the Boston network in 1999 (recall Figure 4.1). This distributional decline, however, masks the continued importance of these research-oriented public sector organizations. Universities, nonprofit research institutes, and hospitals forge repeated (and multiplex) ties to biotechnology firms (evidenced by the thicker connections linking diamonds to circles), and thus play a very different role in the region than do VC firms (the triangles that dominate a small portion of the Boston graph in the lower left segment of the image). Boston PROs remain important structural components of the network, however. Note the diamond nodes at the center of the image (just to the right of the dotted ellipse) that represent MIT and Massachusetts General Hospital. Both regions developed tightly interconnected DBF–DBF commercial networks, but they did so from different starting points and with very divergent types and levels of involvement from non-DBF partners. Though similar on many dimensions, we suggest that these disparate evolutionary trajectories have enduring effects on the nature of innovation in these regions.

The Form and Substance of Regional Innovation How do varied starting points and evolutionary trajectories leave lasting imprints on regional innovation patterns? We contend that the networks more clearly dominated by ‘open’ public sector organizations will result in innovations that rely less heavily on internal R&D and that draw more on research conducted in organizations other than biotechnology firms. In short, we expect patents assigned to Bay Area DBFs, a region whose network was always based more on commercial firms, to cite proportionally less non-DBF prior art and to rely more heavily on self-citations than do patents assigned to Boston firms.

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We turn to data on citations made by patents assigned to Boston and Bay Area DBFs to examine how regional effects may shape the process of innovation. We begin by presenting information on the R&D outputs of regional firms in the aggregate from 1988–99. We then turn to consideration of shifting patterns in prior art citations by DBF patents. Next we consider the substance of regional innovation by assessing differences in rates of FDA approvals as well as variation on Orphan Drug Indications10 by region. Finally, we compare the patented innovations underpinning two comparable treatments for multiple sclerosis: Cambridge-based Biogen’s Avonex and Emeryville-based Chiron’s Betaseron. The differences in regional scale that we identify are matched by differences in the volume of innovation. Table 4.1 presents a comparison of R&D outputs by region for the period 1988–99. The 82 Bay Area DBFs in our sample generated some 3,800 US utility patents in this time period, which is an average of slightly more than 46 patents per firm. This contrasts dramatically with Boston DBFs’ average of slightly more than twenty-four patents per firm. In contrast, biotechnology firms located outside these two regions produced only slightly more than fourteen patents on average, suggesting the relative fecundity of both Bay Area and Boston DBFs. These output differences also mask a highly skewed distribution of patents within regions. Bay Area outputs are more stratified than those in Boston. The five most prolific Bay Area patentors account for 63 percent of regional patents, while the top five Boston patentors were issued 42 percent of the region’s patents. Despite these patterns, patents assigned to regional firms had very similar citation impact. Two-tailed t-tests discerned no significant difference between the impact of Bay Area and

Table 4.1. R&D outputs by region, 1988–99

Number of DBFs Number of patents Mean citations received (standardized) Variance in citations received Number of citations made Percentage non-DBF cites Percentage self-cites FDA approved therapeutics Orphan indications and products

Boston

Bay Area

Other locations

57 1,376 1.113 30.270 12,659 71% 12% 18 60

82 3,806 0.979 14.150 41,389 55% 35% 40 51

1,343 4,876 0.944 14.493 43,610 68% 11% 89 109

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Boston DBF patents (t ¼ 0.774, p ¼ 0.439), but did suggest that firms in these regions develop higher impact intellectual property than those located elsewhere (t ¼ 3.837, p < 0.0001).11 The similar impact of Boston and Bay Area innovations masks broad differences in the distribution of highly cited patents within the regions. Patents assigned to Boston firms manifest a much higher variance in forward citations than do Bay Area patents, suggesting that Boston firms may more routinely engage in ‘exploratory’ innovative search, which typically yields a few very high impact patents at the expense of numerous innovations with lower than average future effects (Fleming and Sorenson 2001; Levitt and March 1988). On this view, the Bay Area’s lower citation variance is indicative of a more directed and incremental, exploitative strategy, which is what one might expect of firms that are supported by investor networks that are interested in demonstrable progress. Firms that pursue exploitative strategies generally develop numerous related improvements on established components of their research trajectories. Such incremental innovations are less valuable on average than the riskier outcomes of more broad-ranging innovation efforts, but convey important benefits in terms of overlapping ownership rights. Exploitative patents, then, will have lesser variance in their impacts than will patents that result from more exploratory efforts to develop blockbuster technologies. While impact variations suggest different patterns of search in innovation, prior art citations provide more direct insight into the precursors that firms rely on in developing new intellectual property. Such ‘backward’ citation data allow us to expand upon the relationship between regional networks and innovation in biotechnology. Consider two ideal-typical possibilities: First, firms embedded in networks composed largely of competitors and investors are primarily concerned with speed and with commercial development, hence they pursue a more focused innovation process that relies heavily on internal R&D and attention to the efforts of direct competitors (e.g. other DBFs). As they are situated in structures that lack a significant PRO involvement, such firms may be less likely to rely heavily on innovations developed externally. In contrast, firms that are located in networks anchored by PROs and that lack strong investor involvement may feel somewhat less overt pressure to pursue immediate commercial returns. To the extent that open, public sector research organizations alter the norms that govern information flow within a network, firms in such networks may reach more freely across organizational boundaries in efforts to develop new innovations and their patent citations may evince

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less attention to the research efforts of competitors (Owen-Smith and Powell 2004). Again, we stress that these different patterns may reflect divergent time scales. Were we to have full data on Bay Area firms from the 1970s, we might well find patterns of relationships comparable to Boston in the 1980s and 1990s. We cannot rule out the possibility that these regional differences stem from the earlier start and success of Bay Area firms in bringing new medicines to market. Moreover, in their early years, several notable Boston-based firms opted to license their earliest lead products to large pharmaceutical companies in return for royalty payments (Robbins-Roth 2001). If these two conjectures have validity, we would expect innovations by firms in more overtly commercial networks—such as those in the Bay Area—to rely less heavily on prior art developed by organizations other than DBFs and to rely more strongly on citations to their own prior patents. In contrast, innovations made by firms situated in more open networks dominated by academic and public sector organizations—such as those in Boston—will rely on a broader cross-section of prior art sources and less extensively on internal R&D. Table 4.1 provides descriptive support for these claims. The 1,376 Boston inventions make 12,659 citations to prior US patents, while the 3,806 Bay Area patents acknowledge 41,389 links to prior art. (An average of 9.2 cites per patent in Boston and 10.9 cites per patent in the Bay Area. Firms outside these regions cite just under nine pieces of prior art per patent.) Similar levels of reliance on prior art, though, mask significant variation in terms of the sources from which precursors are drawn. Patents assigned to Boston firms rely more heavily on non-DBF prior art—a full 71 percent of citations—than do either Bay Area patents, which make 55 percent of their citations to non-DBF prior art, or nonregional patents. In contrast, slightly more than one-third (35 percent) of citations made by Bay Area patents are to their own prior art.12 Boston firms do cite their own prior art, but at a much lower (12 percent) rate that more closely accords with the overall trend in the industry. In sum, the R&D portfolios of Bay Area and Boston firms rely on quite different sources of knowledge, and these patterns appear to map onto the structure of the collaborative networks in each region. The form innovation takes, then, is related to the characteristics and trajectories of the networks that support it. While both regions have been quite successful in biotech, and are emulated across the globe, we have shown that their respective origins and paths of development are rather dissimilar. This pattern may continue at the level of market outcomes as

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well as patents. To explore this possibility, we draw on FDA approval records to identify the fifty-eight new drugs developed by Boston and Bay Area DBFs. Fifty-three of those medicines were approved between 1988 and 2004. All five of the drugs that appeared on the market prior to that period were developed by two Bay Area Firms—Alza and Genentech. Again, these early approvals reflect the commercialization strategy pursued in a region with a strong VC community. Eighteen of these products are the work of Boston firms and forty stem from work by Bay Area DBFs. Another 89 therapeutics were developed by the 343 firms located outside these regions, but well-established firms such as Los Angeles’ Amgen, Philadelphia’s Centocor, and Seattle’s Immunex account for much of the action. In terms of market outcomes, the Bay Area appears to be both quicker and more prolific than Boston and both regions represent concentrations of success. This outcome is to be expected given a more commercially focused network and a development-oriented strategy that relies heavily on internal R&D. Indeed, seventeen of the first twenty of these drugs to come to market were produced by Bay Area firms. These differences in market outcomes, though, are much more suggestive of variations in strategy and focus than competency. Consider another source of information about the development of therapeutics, Orphan drug designations. The 1983 Orphan Drug Act was designed to enable the FDA to speed the development of therapies for rare diseases, and orphan designations offer tax breaks and regulatory assistance to organizations that develop such medicines. One hundred and eleven (111) orphan designations have been approved for Bay Area and Boston firms since 1985 (when the first such approval went to Boston’s Genzyme for the drug Ceredase for patients with Gaucher’s disease). Both Bay Area and Boston firms make use of orphan designations, but Boston firms, as one might expect for companies enmeshed in networks dominated by universities and hospitals, rely more heavily on indications for relatively rare diseases. The focus on orphan drugs reflects another difference as well. The Boston-based firms build their product portfolios with an initial focus on smaller markets and medicines that have the added security of orphan drug exclusivity. These medicines, while targeted at relatively small populations, are very much desired by their patient communities. In contrast, Bay Area firms favor medicines for larger markets in which the potential patient population runs in the tens of millions, and for which there is likely to be product competition from other DBFs and major pharmaceutical corporations. This high-risk, high-reward strategy

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demands speed in product development, and shows the obvious imprint of the VC mindset. Descriptive patterns in prior art citations, forward citation impact, and market outcomes are complementary with observed variations in the evolution of the two regional networks. Despite sharing an industry and scientific base, Boston and Bay Area DBFs appear to differ systematically in the substantive focus of their R&D efforts. To further explore these differences in the focus of innovative activity, we turn to a natural experiment and compare the citation patterns for patents underlying two fairly similar biotechnology drugs. Betaseron and Avonex are competing therapies for remitting and relapsing multiple sclerosis and several clinical trials have directly compared their efficacy. Both drugs began life with orphan designations and both are variants of the biological compound interferon-beta, which differ only slightly in chemical makeup.13 The processes by which these compounds are produced are also very similar, and rely on Chinese hamster ovaries, though their differences are manifest enough that an infringement lawsuit between Avonex’s developer and Betaseron’s manufacturer (Biogen vs. Berlex Laboratories) resulted in a judgment of no infringement. Both drugs were approved during the 1990s (Betaseron in 1993 and Avonex in 1996). In short, these two drugs share notable scientific, clinical, and regulatory similarities, but they differ in the physical and organizational location of their development. Betaseron is based on research done by Cetus, an Emeryville, CA biotech firm that was acquired by Chiron, a Berkeley-based DBF. Chiron did the development work on Betaseron and shepherded the drug through the FDA approval process. Betaseron is manufactured and marketed under an arrangement with Berlex Laboratories, an American subsidiary of the pharmaceutical firm Schering-Plough. Avonex, in contrast is based on research done by Boston-based Biogen who also developed, manufactures and markets the drug. We use FDA-labeling information to identify the patents that underpin these drugs. We then turned to the NBER patent citation database to trace prior art citations by those patents and identify the sources of such prior art. In both instances we trace precursor inventions to three generations. Table 4.2 presents summary data for the innovations underlying these two drugs. The patterns suggested by Table 4.2 are in line with the overall results in prior art citations by region, and with our expectations based on the evolution of each cluster’s network. Betaseron relies on a set of four related patents initially assigned to Cetus (three were reassigned to Chiron

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Accounting for Emergence and Novelty Table 4.2. Innovation data for Betaseron and Avonex, 1988–99 FDA approval date (initial indication)

1993-07-23

1996-05-17

Orphan status Developer Initial patent holder Distributor

Y Chiron Cetus Corporation Berlex Laboratories (Schering Plough) Remitting and relapsing Multiple sclerosis 4 4

Y Biogen Biogen Biogen

Initial indication Number core patents Number first generation citations Number second generation citations Number third generation citations Total patents (original þ 3 generations) Number of prior art patents owned by fiduciary firms Number of prior art patents from same region Number of non-US prior art patents Number of shared prior art patents

Remitting and relapsing Multiple sclerosis 1 14

31

32

16

108

55

155

6

0

2

4

26

61

39

39

Notes: Both intra-region cites for Betaseron are to other Silicon Valley DBFs (Genentech, ICN). The within-region cites for Avonex are to PROs (Mass Gen (1) MIT(2)) and a non-DBF firm (Ionics).

following the merger of the two firms; the fourth, a process patent for producing interferon, was reassigned to Berlex Labs). These four patents cite a small group of prior art patents (4). These four ‘first-generation’ precursors make another thirty-one second generation citations, which in turn cite a further sixteen pieces of prior art. All told, Betaseron rests on a history of some 55 interlocking patents. Avonex, which is based on a single compound patent, reaches more broadly into the prior art, relying on 155 separate pieces of intellectual property. None of the prior art on which Avonex depends is owned by Biogen. This last finding is particularly telling, as it suggests that Biogen developed its market leading therapeutic without the benefit of a thicket of intellectual property rights (IPRs), relying instead on a mix of partner’s intellectual property and public domain science. Differences in these two citation networks are instructive. Betaseron’s underlying IP network includes six patents developed by Cetus. Avonex, in contrast, relies on a single Biogen-owned patent that makes no citations to other intellectual property owned by that firm. While internal R&D was

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surely not sufficient to the development of Betaseron, that drug relied much more heavily on a single DBF’s research effort than did its competitor. Both innovation networks reach well beyond the regions in which the two firms are situated. Betaseron cites only two patents held by other Bay Area organizations, but it is notable that both are biotechnology firms (Genentech and ICN). The Avonex citation network, in contrast, cites four patents held by Boston organizations, but none by DBFs. Three belong to PROs with whom Biogen has network ties (MIT holds two patents and the Massachusetts General Hospital a third). The fourth belongs to a non-DBF firm, a purification company called Ionics. While the citation networks are fairly small, comparing these two very similar drugs offers a natural experiment that holds constant important technical, clinical, and regulatory features of biotechnology innovations. Even when such factors are very similar, the patent citation networks underlying these two drugs differ in a fashion that reflects aggregate differences in regional innovation patterns and expectations based on the interorganizational networks that characterize each region. The Bay Area–based drug relies more heavily on internal R&D and on the research efforts of other firms. In contrast, the Boston-based therapy draws on a broader cross-section of prior IP owned by a wider range of organizational types.

Conclusions and Implications The Boston and Bay Area biotech communities became more similar over the twelve-year period under examination, shedding their respective reliance on PROS and VCs, and developing a strong firm-to-firm component. But these divergent roots have a notable impact on the innovation process. Boston-based companies that relied heavily on external sources of knowledge favored more exploratory efforts at discovery. This signature is captured by our measures of patent volume and impact, and by patterns of patent citations. Bay Area biotech firms were more self-reliant in terms of knowledge generation and more persistent in their efforts to further development of in-house intellectual property. Similarly, Bay Area firms were faster and more prolific in terms of new product development, as well as more likely to pursue novel medicines for larger markets where they might face stiff competition. In contrast, Boston firms were more deliberative in their commercial strategies and more likely to focus on medicines for identifiable and active patient populations

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in need of relief from specific illnesses. Most remarkably, these differences persisted even when we held constant market, scientific, and regulatory factors by examining Chiron and Biogen’s approaches to the development of similar treatments for multiple sclerosis. Clearly, the continuing impact of VCs in the Bay Area and PROs in Boston is significant. We lack data on the early scientific roots of technical advance in the life sciences in Boston and the Bay Area. Perhaps the patterns we have observed are the outcroppings of diverse academic approaches to scientific research in the life sciences. Boston is home to the remarkable institutional combination of MIT, a powerful basic science institution that lacks a medical school, Harvard, another powerhouse institution in basic science whose medical school is located across the Charles River at a considerable remove from the main campus, and a number of research-oriented hospitals and institutes. The upshot of this institutional mix appears to us to be a corporate focus on expansive science and patients. In contrast, the biotech community in the Bay Area has its earliest origins in the ‘marriage’ of Herbert Boyer, a UCSF scientist, and Robert Swanson, a prominent venture capitalist, who joined together to create Genentech, one of the very first biotech companies. UCSF is an unusual institution, lacking disciplinary departments and a full panoply of research program and students. The organizational model at UCSF was an interdisciplinary, cross-functional approach to medicine, with an emphasis on translating basic science into clinical application (Varmus and Weinberg 1992). Genentech adopted and refined this interdisciplinary ‘team’ model, adding the impatience and restlessness of VC financiers and the attendant focus on ‘swinging for the fences’ by developing products for such major illnesses as heart disease, cancer, and diabetes. Here, an approach to translational R&D pioneered at an elite PRO is transferred by founding scientists to a region’s leading firm, eventually becoming a dominant arrangement for the region. One of the fundamental features distinguishing between the Boston and Bay Area networks is Boston’s early and continuing reliance on the region’s public sector research organizations. MIT, Harvard, Massachusetts General Hospital, the Whitehead Institute, Dana Farber Cancer Center, and other institutions anchored this network, catalyzed its development, and shaped firm innovation and product development. The imprints of evolutionary patterns, then, are a joint function of institutional roles and particular features that characterize such public sector organizations. In addition to providing stable anchors for networks, universities and hospitals contribute to more open information flows, more expansive

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innovative trajectories, and, possibly, more patient-driven product development strategies. In short, PRO involvement is effective precisely because they operate in different environments and under different rules and constraints than their proprietary partners. In Boston, universities, research institutes, and hospitals—organizations institutionally committed to open information flow, science, and publichealth-based business strategies—altered the efforts of Boston firms by maintaining a formal and deliberate role in their region’s networks. In short, Boston area PROs altered their local networks using formal contractual arrangements that structure collaboration and the transfer of intellectual property rights. In contrast, Stanford and UCSF’s preference for informal, noncontractual ties in their regional network enabled financiers to shape innovative and organizational strategies. The implications are paradoxical: deliberate efforts by hospital and universities to control and shape information and resource flows in networks result in more open and expansive structures, while more informal, ‘hands-off’ approaches help create networks that are more tightly controlled and commercially directed. The role that universities play in regional development, then, appears more complicated than a simple model of technology transfer and technical training would suggest. This coevolutionary dynamic suggests important sources of regional variation and also highlights the potential consequences of organizational action in evolving networks. Firm strategies in Boston and the Bay Area bear the characteristic footprints of their most important partners. At the same time, partners are constrained by the activities of local firms. Consider a brief example. If success in the competitive arena of Bay Area biotechnology depends on early access to venture capital and that access smoothes entry into collaborations with the established firms that are key to innovative and commercial success, then savvy venture capitalists will seek to invest in newcomers whose strategies and arrangements match dominant patterns in the region: the very patterns that VCs’ early efforts helped generate and sustain. Not surprisingly, then, Stuart and Sorenson (2003) find that while VC support became abundant in the Bay Area in the 1990s, the odds for success declined. One possible reason is lock in around a dominant model. The contrast of Boston and the Bay Area, the most prolific biotechnology clusters in the world, should give pause to policymakers who look to successful clusters for models to emulate. Without awareness of underlying institutional variations and distinctive approaches to the development of new medicines, one could easily draw the incorrect inference that

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combining PROs, VC, and small firms provides the ultimate recipe for successful economic development. We emphasize that similar approaches may be very deceiving and mask sharp differences in underlying causes of institutional and technical development.

Notes * We thank Kjersten Bunker Whittington, Maryann Feldman, Martin Kenney, Luigi Orsenigo, David Lane, Elaine Romanelli, and the participants in the 2003 and 2004 Genesis of Clusters workshops for their comments on earlier drafts. We are grateful for research support from the Hewlett Foundation and the Merck Foundation. 1. This section draws upon our earlier work on biotechnology (cf. Powell et al. 2005; Bunker-Whittington, Owen-Smith, and Powell 2005). 2. We base our calculations on approvals for initial indications by the US Food and Drug Administration’s Center for Biological Evaluation and Research. 3. More recently, Pfizer and Novartis have moved R&D activities to Kendall Square in Cambridge, MA. The largest biotech firm, Amgen, has acquired a smaller Bay Area firm, Tularik, and created a beachhead in that region. 4. Our data are structured as a two-mode network (Wasserman and Faust 1994) that tracks connections among DBFs and between DBFs and partner organizations. Linkages between non-DBF partners (e.g. PRO–VC collaborations) are exceptionally sparse and thus are not included. 5. This different trajectory may reflect left censoring in the data. If we had comparable data for the Bay Area for the late 1970s and early 1980s, we might well observe important DBF–PRO ties. In particular, we would expect more linkages connecting UCSF and Stanford to local DBFs. We do know that the Bay Area biotech community developed earlier than Boston (Robbins-Roth 2001), hence the direct comparison for the later 1980s and early 1990s may capture a slower take-off in Boston. 6. A freeware program developed by Vlado Batagelj and Andre Mrvar and available for download online at http://vlado.fmf.uni-lj.si/pub/networks/pajek/ 7. This distance is a function of the number of ‘steps’ it takes to traverse a network path connecting a given pair of nodes. Organizations that are connected by a tie are a distance one. 8. For more detail on network visualization using Pajek, see the appendix of Powell et al. (2005). 9. To be sure, this isolation does not imply that Stanford is not active in technology transfer, but at this point in time the bulk of its formal licensing activities are to firms outside the region. 10. An Orphan Indication is conveyed by the US Food and Drug Administration for products that treat rare diseases and thus have little potential to become

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Accounting for Emergence and Novelty huge commercial successes. Orphan indications are valuable to firms in that they convey tax breaks and reduced regulatory fees as well as short-term market exclusivity. 11. Forward citations—citations from future patents to current innovations—are a commonly used measure of impact. We standardize citation counts by year and technical class to avoid heterogeneity across time and technical areas (Jaffe and Trajtenberg 2002; Trajtenberg 1990). 12. Despite a greater reliance on self-citations, 65 percent of Bay Area prior art citations are to patents developed outside the firm. The majority of innovative work goes on through networks. 13. Interferon-beta-1a (Avonex) is a recombinant compound whose amino acid sequence is identical to natural interferon-beta. Interferon-beta-1b (Betaseron) in contrast is a recombinant compound that differs from natural Interferonbeta by one amino acid.

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Part II Considering the Developing Cluster Context

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5 Anatomy of Cluster Development: Emergence and Convergence in the US Human Biotherapeutics, 1976–2003 Elaine Romanelli and Maryann Feldman

Industry clusters are a persistent and pervasive feature in the spatial arrangements of organizations. Little research, however, has explored either the organizational or geographic origins of entrepreneurs and firms who populate an industry cluster. Although some (e.g. Gompers, Lerner, and Scharfstein 2003; Romanelli 1989; Stinchcombe 1986) have considered the characteristics of organizations that may be more or less prolific generators of new organizations, little empirical evidence has been brought to bear on the question. A much larger literature has considered the influence of regional conditions—typically, metropolitan regions—on rates of entrepreneurial growth; however, there are few comparisons of patterns in cluster development among industries (Klepper 2002; Saxenian 1994; Sorenson and Audia 2000). Fundamental questions regarding the demographic process of cluster development thus remain unanswered. Do clusters emerge primarily through the investments of local entrepreneurs, local investors, and other local agents, with some clusters growing large while others languish due to failures of local resources or investment processes? Alternatively, do clusters develop through a geographic convergence of both local and nonlocal entrepreneurs, firms, and investments based on expectations or information about best places for particular kinds of business activity? Finally, assuming that at least some entrepreneurs and firms relocate and at least some venture capitalists invest outside their home regions, what factors elicit their attention and influence their decisions to relocate or invest in a distant region?

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In this chapter, we seek to shed light on these questions through an intensive examination of the organizational and geographic origins of entrepreneurs and firms in the US biotherapeutics industry over the period 1976 through 2003. Based on detailed information about the entrepreneurs of 688 biotherapeutics firms (both USA and foreign-owned), we explore how variations in the origins of local entrepreneurs and firms and patterns in cross-regional relocations influence cluster growth and convergence. We present detailed data on the organizational origins of entrepreneurs and firms to show, for regions which have developed relatively large clusters, how differences in the organizational origins over time relate to differences in the growth rates of the clusters and influence the relocations of distant entrepreneurs and firms.

Research Setting Commercial prospects for biotechnology have been widely heralded ever since 1953 when James Watson and Francis Crick ‘correctly theorized the structure and operation of the DNA molecule. This fundamental discovery led to increased research and understanding of the biochemical processes involved in the production and reproduction of life’ (Kenney 1986: 2). For about the next two decades, basic research in the now-called life sciences was restricted to university and government laboratories, along with those of a few large oil and chemical companies, as scientists sought to understand the role of DNA in cellular functioning. These scientists, and their science, formed a critical base of resources for commercial biotechnology in the early 1970s when the US federal government and the National Cancer Institute declared war on cancer, thus providing vast sums of money to support biomedical research. Almost a billion dollars—$989 million to be exact—was allocated to the National Institutes of Health, which was dominated by the National Cancer Institute, in 1981 alone (Robbins-Roth 2001). Perhaps even more important than the money or the science, though both were critical, was a focus on practical research goals—the diagnosis, treatment, and eventually the eradication of cancer. Dreams of extremely lucrative commercial applications—diagnostics and therapeutics—were not far behind. As discussed by Kenney (1986), collaboration between science and industry—what he called the university– industrial complex—was forged on a scale perhaps never before seen. Cetus Corporation, founded in Berkeley, California in 1971 by Ron Cape and Peter Farley, who brought scientific experience from both academic

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and business laboratories is commonly attributed as the first commercial biotechnology firm in the USA. Like most early biotechnology firms, Cetus adopted an exceedingly broad approach to research and product development, aiming for commercial applications of biotechnology in areas as wide ranging as genetically engineered bacteria for alcohol production and oil-spill cleanups, as well as vaccines and therapeutic proteins for the prevention and treatment of human disease. This broad strategy developed in part from the interests of investors, which included Standard Oil, Shell Oil, and National Distillers (Robbins-Roth 2001). In part also, however, it emerged from early optimism about the generalizability of scientific techniques and processes for virtually all forms of life. Even long after the demise of Cetus, which was restructured as Agracetus in 1984 to focus on agricultural biotechnology through a partnership with the W. R. Grace Corporation, and eventually acquired and disbanded by Monsanto in 1996, business researchers distinguished what they called DBFs, encompassing diverse commercial applications, to set them apart from oil, chemical, agricultural, and pharmaceutical organizations who used a mixture of biotechnology and traditional product development techniques. Genentech, perhaps the first commercial biotechnology firm to focus on the development of pharmaceutical products using biotechnology techniques, was founded in 1976 by Bob Swanson, a venture capitalist with Kleiner and Perkins who had been an early investor in Cetus, and Herbert Boyer, a biochemist at the University of California, San Francisco, who had partnered with Stanley Cohen, a Stanford molecular biologist, in the development of a successful ‘gene-splicing’ technique in 1973. The Cohen–Boyer process patent, awarded in 1980, and licensed in 1982 to seventy-one companies across a broad range of industries, set off an explosion of commercial investment. Perhaps more important for the evolution of this industry, business investors and academic scientists, following the lead of Swanson and Boyer at Genentech, continued the practice of teaming in the formation of new biotechnology firms. Although this blurring of these traditionally distinct spheres of interest in basic versus applied research was fraught with controversy (Kenney 1986), academic scientists were drawn to entrepreneurial activity not only by the allure of significant monetary reward but also by the chance to participate in what promised to be one of the great leaps forward in the diagnosis and treatment of disease in history. Thus biotechnology, which might have remained mainly a new technique in the laboratories of big business, became the basis of a wholly new kind of industry.

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From these beginnings, biotechnology has burgeoned into one of the largest and fastest growing new industries, involving more than 2,400 firms in more than 30 countries.1 Along the way, the firms have abandoned their early strategies for developing commercial applications of biotechnology in virtually every arena of life and chemical processes. The mysteries of life have proved more complex and confounding than was ever conceived by the founders of the early firms. Split first into broad categories of human diagnostics and therapeutics, agricultural biotechnology, and industrial and environmental biotechnology, firms today focus on very specific techniques for the production of new, bioengineered drugs, plants, and chemicals. Nonetheless, as traditional pharmaceutical firms decry the dearth of new blockbuster drug discoveries, and merge (and then merge again) to increase the size and scope of their product pipelines, the proliferation of new biotechnology firms, especially in the areas of human diagnostics and therapeutics, has continued. Histories of the biotechnology industry abound, including Kenney’s insightful examination (1986) of the early and often rocky collaborations between commercial investors and university scientists, and we will not attempt to review it further here. Suffice it to say that the business– academic collaboration has persisted. Venture capitalists continue to seek out promising science, and scientists, for the formation of new biotechnology firms especially in the area of human diagnostics and therapeutics. Scientists increasingly view entrepreneurship as a natural stage in their career paths, blurring the distinction even more between academic and commercial research. Just as important, large-established organizations, including now some of the early biotechnology firms (e.g. Amgen and Genentech) invest in the research of young biotechnology firms through licensing agreements and equity stakes that challenge the profitability, if not the science, of smaller firms. And they routinely spin out new firms, either as new independent or subsidiary organizations, to focus their research and investments. For our purposes here, it is important mainly to note two things. First, human biotherapeutics has emerged as a distinct segment of the overall biotechnology field. As we describe below, more than 650 organizations, including start-ups, spin-offs, direct entries by established firms, mergers, and joint ventures, have been located in the USA over the period 1976 through 2003. Second, the origins of entrepreneurs and firms in the industry are diverse, heralding from a variety of different organizations. Thus, the industry holds good promise for an investigation into the demographics of cluster development. Although we cannot offer strong claims

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about the generalizability of our research, since it examines clustering for just one industry, we can suggest that, in its focus on comparing cluster development in more than seventy metropolitan statistical areas (MSAs) spread over thirty-eight states that became home to even one biotherapeutics firm, we greatly extend previous literature which has focused on case histories or formal mathematical modeling.

Research Method We investigated the organizational and geographic origins of entrepreneurs and firms in the US biotherapeutics industry over the period 1976 through 2003. Biotherapeutics refers to the segment of the biotechnology industry that is focused on the discovery and production of drugs for the prevention and treatment of human disease using biotechnological techniques (e.g. genetic engineering, monoclonal antibodies, and gene therapy). Biotherapeutics firms were identified based on evidence, from biotechnology industry research publications (in particular, BioScan, 1987 through 2004), of direct investments in the development of drugs, whether in research, clinical trial, or production and marketing phases. BioScan is a comprehensive industry directory that provides information about the founding dates, management teams, and geographic locations of biotechnology firms as well as their products and strategic alliances. These data were extensively augmented through searches of business and industry press publications available from Lexis–Nexus from the beginning of the industry, firm websites, and financial documents (e.g. prospectuses, annual reports, and 10-K filings) available from the US Securities and Exchange Commission. The result, we believe, is a comprehensive, though likely not exhaustive, longitudinal database on the organizational and geographic origins of firms in the US biotherapeutics industry. We identified 688 firms—including both USA and foreign-owned organizations—that were engaged in human biotherapeutics in the USA over the study period. Although we cannot claim that our sample is exhaustive of all human biotherapeutics firms operating in the USA over the study period, extensive supplemental inquiries into the histories of entrepreneurs and firms in the industry, as well as the evolution of the US biotechnology industry as a whole, revealed only a few, mainly very small and short-lived firms, that were not identified by the above methods. Thus, we believe that our coverage of the industry is comprehensive and accurate through about 2003. Of particular interest to questions explored in

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this paper, we have no reason to believe our coverage is biased toward biotherapeutics activity in particular regions. Below, we describe the data we collected in more specific detail.

Life Demographics To track the organizational origins of entrepreneurs and firms in the industry, we collected data on the types of organizations from which biotherapeutics entrepreneurs emerged. Six categories of organizational origins were used: (a) universities or research institutes, (b) biotherapeutics firms themselves, (c) traditional pharmaceutical organizations, (d ) VC firms, (e) hybrids of the above categories in cases where two or more entrepreneurs emerged from different sources, and ( f ) other. Six categories were used to characterize the firms’ modes of entry into the industry: (a) start-ups, (b) spin-offs, (c) direct entries of established firms via investments in their own laboratories or the acquisition and absorption of an existing biotherapeutics firm, (d ) subsidiaries, whether newly created or acquired, (e) joint ventures, and ( f ) mergers. Four categories of exit mode were specified: (a) failure of the organization, (b) cessation of activities in human biotherapeutics, (c) acquisition, and (d) merger. In some cases, acquired firms were left intact as separate operating subsidiaries of the acquiring organizations, while in other cases their assets were absorbed into the activities of the acquiring organization. In the first scenario, even if the name of the organization did not change, we coded the exit of the original organization and the entry of a new organization as a subsidiary. Similarly, in the case of mergers, even when the merged organizations continued under the name of one of the merging organizations, we coded the exits of the merging organizations and the entry of a new organization. To avoid double counting, we coded the last year of existence for the acquired or merging organizations as the one in which the acquisition or merger occurred and the first year of existence for the new firm as the year following the acquisition or merger event.

Geographic Demographics The geographic origins and destinations of entrepreneurs and organizations were coded using the 2003 US Office of Management and Budget Bulletin No. 03–04 which lists MSAs and combined MSAs, among other groupings, based on information obtained in the 2000 Census. As described in the bulletin, MSAs were designated based on their having ‘at least

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one urbanized area of 50,000 or more population, plus adjacent territory that has a high degree of social and economic integration with the core as measured by commuting ties.’ A total of 362 MSAs (not including Puerto Rico) were listed, encompassing 1,090 counties, approximately 35 percent, and about 83 percent of the US population. Combined MSAs (e.g. the Washington, DC and Baltimore MSAs) were defined as two or more geographically adjacent MSAs with employment interchanges (i.e. commuting ties) of at least 25 percent (or, in a few cases with employment interchanges of only 15 percent, but considered highly integrated based on the opinions of Congressional delegations). The bulletin lists 113 Combined MSAs in the USA, encompassing 310 MSAs. We classified the MSA locations of firms in the study, as well as the organizational origins of their entrepreneurs and antecedent firms, using zip codes, which allowed us to identify the counties and thus the relevant MSAs. The use of MSAs and combined MSAs, rather than city or state geographic boundaries, is attractive for identifying regions of activity in that MSAs are designated on the basis of regional economic integration independent of political boundaries. For example, the Washington, DC MSA— formally designated as the Washington–Arlington–Alexandria, DC–VA– MD–WV Metropolitan Statistical Area—encompasses three states plus the District of Columbia and twenty-one cities or counties which residents would recognize as part of the ‘greater’ Washington, DC area. Residents routinely traverse the political boundaries for living, work, shopping, and entertainment purposes. They read the same newspapers, the Washington Post, the Washington Times, and the Washington Business Journal, and debate important regional issues on a cross-regional basis. Major transportation facilities, including roads and rapid transit systems cross the political boundaries and decisions regarding the development of new transportation facilities involve officials from all political jurisdictions. By virtually any measure, greater Washington, DC, as represented by the MSA, is a single social and economic region. One potential problem arises from our classification procedures that must be discussed. Our study period is long, and the distribution of human populations across regions as well as the patterns of economic integration within regions changed over the study period. Our decision to classify organizations, as well as their origins and destinations, based on the 2003 classification system raises the likelihood that some organizations will be incorrectly located based on classifications at the time of the entry or exit event. For example, an organization founded in Worcester County, Massachusetts in any year up until 2003 would have been

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classified as located within the Boston MSA; under the new classifications, Worcester has been designated as its own MSA, though the Boston and Worcester MSAs are designated as components of a larger, combined MSA. Thus, a firm that never changed its physical address could be classified as residing within different MSAs based on changes in the OMB classifications over the study period. By our procedures, such an organization is classified as being in the Worcester MSA over the entire period. To explore the extent of this difficulty, we tracked the classifications of counties (which are the core units of MSAs) from 1976 through 2003. The classification schemes changed most extensively every ten years following the decennial census; new MSAs were designated and old MSAs were reorganized in terms of their county components, sometimes combining counties that were previously classified as separate MSAs and sometimes reorganizing counties into two or more MSAs. Nonetheless, except for the addition of wholly new MSAs, such as Corvallis, Oregon in the 2003 classification, the MSA classifications over the study period were remarkably consistent. Approximately 85 percent of the MSAs listed in the 2003 bulletin were also listed in the 1976 bulletin; changes in their county compositions reflected mainly the addition of one or more adjacent counties. Indeed, except for the introduction of wholly new MSAs, the 2003 classification looks very similar to the 1976 classification, and they are more similar to one another than either is to the classifications of the intervening decades.2 Thus, assuming that the 2003 classifications represent trends in population growth and regional economic integration which were developing long before the official classifications, we feel comfortable using the single classification scheme to designate regions over the entire study period. To do otherwise would result in counts of regional biotherapeutics populations that varied simply on the basis of changes in classifications; moreover, there would be no way to assess relocation origins and destinations unambiguously.

Limitations These procedures give rise to two limitations to interpretation of the results of our study. First, we do not consider all regions potentially at risk of developing a biotherapeutics cluster. Although, as described below, the firms in our study were located, either originally or via relocation, in more than seventy US metropolitan regions, these represent only about 21 percent of all such regions in the USA. Full analysis of regional conditions that give rise to industry clusters would require consideration of all US

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regions. All but eight of the top 50 largest metropolitan regions, however, are represented in our study, each having been home to at least one biotherapeutics firm. Twenty-five other regions are also represented; however, the majority was home to only one or two biotherapeutics firms. Thus, though not all regions are considered, we think we have sufficient range to draw conclusions about the demographic and geographic processes of cluster development. Second, it will not be possible to say whether our results are generalizable to other industries. Though the study is large, covering most of the firms engaged in human biotherapeutics over more than twenty-five years, it is still a study of only one industry. Our findings suggest that more formal longitudinal research and analysis of entire industries will be necessary to strengthen our understanding of cluster development processes.

The Anatomy of Cluster Development over Time and Space Table 5.1 shows the number of human biotherapeutics organizations established in the USA over the period 1976 through 2003, broken down by type of establishment, organizational origins or firms or entrepreneurs, and by whether at least one member of the founding team or organization (in the case of spin-offs, joint ventures, and mergers) relocated from a different MSA from the one in which the new firm was established. As shown, 688 biotherapeutics organizations, including both USA and foreign-owned organizations, were either established or entered into human biotherapeutics research in the USA over the study period. The majority, 455 (66 percent) were established via the start-up of a wholly new organization. Approximately 45 percent of these, 205 organizations, which accounted for 30 percent of all biotherapeutics organizations, were created by scientists out of universities or private research institutes. Subsidiaries, including both those that were newly created and those that were created via the acquisition of an existing biotherapeutics firm, accounted for 11 percent of the total. No other category accounted for more than 10 percent of the total.

Clustering and Geographic Convergence Table 5.1 also shows information about the relocations of entrepreneurs and firms from one MSA to another. Of the 688 firms, 220 (32 percent)

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Anatomy of Cluster Development Table 5.1. US biotherapeutics firms by types of entry and organizational origins All firms Number Start-ups From university or research institute From an existing biotherapeutics organization From pharmaceutical organization Hybrid, from multiple source types From venture capital firm From other type of firm No information Spin-offs From university or research institute From an existing biotherapeutics organization From a pharmaceutical organization From other type of firm Direct entries by established firms Research in own laboratories Acquired a biotherapeutics organization Subsidiaries Newly created Acquired Mergers Joint ventures Total Migrant firms Grand total

Firms with nonlocal originsa

Percent

Number

0.30 0.10 0.03 0.07 0.09 0.03 0.03

41 20 10 25 22 5 26

0.00 0.05 0.02 0.01

0 7 3 1

455 205 70 24 50 64 21 21

0.66

55 1 34 11 9

7

75

0.03 0.11

0.04 0.07 0.04 0.02

20 39 16 4

688 60

0.00 0.03 0.01 0.01 0.09

0.08 0.01

27 48 30 11

0.19 0.09 0.05 0.11 0.10 0.02 0.11 0.08

62 52 10

Percent

0.09 0.18 0.07 0.02 220

60 748

280

a

These counts include organizations for which at least one of the founding firms (e.g. in the case of mergers) or entrepreneurs migrated from one MSA to another.

were created by entrepreneurs or firms that crossed a regional boundary at the combined MSA level to establish or relocate their organizations.3 Perhaps not surprizingly, since people and capital are more mobile than firms, the majority of these, 184 relocations (84 percent) were accounted for by start-ups and the creation of new subsidiaries. Spin-offs, mergers, joint ventures, and direct entries by established firms occurred predominantly in the same regions as their ‘parent’ organizations. In addition, sixty firms relocated wholesale, at least one year following the year of establishment, from one region to another at the combined MSA level. Table 5.2 presents information about the locations of US biotherapeutics firms. Firms were located in thirty-eight states plus the District of Columbia, encompassing 73 MSAs (19 percent, out of a total of 362 US MSAs) plus three counties that did not meet US Census Bureau criteria for classification as an MSA. Sixteen of the MSAs (22 percent of all

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metropolitan regions with at least one biotherapeutics firm) were home to at least ten biotherapeutics organizations. When combined MSAs were considered, twelve regions (encompassing twenty-four MSAs) were home to at least ten biotherapeutics firms. These twelve regions account for 88 percent of all biotherapeutics firms that were either originally located in one of the regions or that relocated to one of the regions. All of these regions were among the top 50 in the US-based on population size, though three regions that ranked among the ten largest in population, including the Dallas, Detroit, and Atlanta combined MSAs, were not among the top 12 for biotherapeutics firms. All were home to at least one major research university, accounting for approximately 34 percent (51 out of 150) of all ‘research—intensive’ universities as ranked by the Carnegie Foundation (2000). Moreover, all were among the largest recipients of grants from the National Institutes of Health, though many other regions received similar numbers and total amounts of funding.

Table 5.2. Biotherapeutics firms in MSAs and combined MSAs MSA

Combined MSA

Number

Percent

Number

Percent

San Francisco–Oakland–Fremont Napa San Jose–Sunnyvale–Santa Clara Boston–Cambridge–Quincy Manchester–Nashua NH Worcester New York–Newark–Edison Trenton–Ewing New Haven–Milford Bridgeport–Stamford–Norwalk Poughkeepsie–Newburg–Middletown San Diego–Carlsbad–San Marcos

102 2 44 99 1 10 75 11 10 2 1 88

148

0.23

110

0.17

99

0.15

88

0.13

Los Angeles–Long Beach–Santa Ana Oxnard–Thousand Oaks–Ventura Philadelphia–Camden–Wilmington Washington–Arlington–Alexandria Baltimore–Towson Durham Raleigh–Cary Seattle–Tacoma–Bellevue Houston–Bay Town–Sugarland Boulder Denver–Aurora Chicago–Naperville–Joliet Total

36 1 34 28 5 29 3 26 20 10 5 14 656

0.16 0.00 0.07 0.15 0.00 0.02 0.11 0.02 0.02 0.00 0.00 0.13 0.00 0.05 0.00 0.05 0.04 0.01 0.04 0.00 0.04 0.03 0.02 0.01 0.02

37

0.06

34 33

0.05 0.05

32

0.05

26 20 15

0.04 0.03 0.03

14

0.02

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Anatomy of Cluster Development

Thus, regional size and resources appear to be important in the locations of biotherapeutics clusters, but not fully explanatory. As shown in Table 5.2, these regions were home overall to the establishment or relocation of 656 (88 percent) biotherapeutics firms over the study period. Of these, four regions (combined MSAs)—San Francisco (148 establishments), Boston (110 establishments), New York (99 establishments), and San Diego (88 establishments)—accounted for 59 percent (365 MSA entry events) of the locations and relocations of biotherapeutics firms across all regions. The next most clustered regions—Los Angeles, Philadelphia, Washington, DC, and Raleigh–Durham—were home to an average of thirty-four biotherapeutics firms each. Finally, the least clustered of the top 12 regions—Seattle, Houston, Boulder, and Chicago—were home to twenty-six or fewer biotherapeutics firms. Of the remaining fiftytwo regions, most were home to only one or two biotherapeutics firms. Overall, ninety-two firms (12 percent) were located in or relocated to regions outside the top 12. Thus, the US biotherapeutics industry appears substantially clustered. Although, even over almost thirty years of this industry’s existence no single region has emerged as solely dominant, the majority of firms were established or have relocated to a relatively few regions. Moreover, the majority of these, 486 firms (68 percent) were founded by entrepreneurs or created from the activities of organizations that already resided in the region where the new firm was established. Thus, our findings provide moderate support for the argument that clusters arise primarily from the investments of local entrepreneurs, firms, venture capitalists, and other interested parties. Nonetheless, 32 percent of firms (n ¼ 220) were created by entrepreneurs or out of other firms that were located elsewhere. This finding, combined with the finding of sixty firms that relocated wholesale to a new region at the combined MSA level, suggests that a considerable amount of movement of resources is also occurring. To get a better understanding of geographic patterns in entrepreneurial and firm relocations, we next examined whether the relocations of entrepreneurs and firms indicated a convergence of resources to the top 12 metropolitan regions.

Cross-Regional Relocations Tables 5.3 and 5.4 show directional patterns in the relocations of entrepreneurs and firms both between the top 12 regions and from regions, including foreign countries, outside the top 12 regions. For entrepreneurs, sixty-three relocations (61 percent) occurred between regions that

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Anatomy of Cluster Development

comprised the top 12. A striking majority of entrepreneurs who relocated from US regions outside the top 12 (89 percent) or from foreign countries (100 percent) relocated to one of the top 12 regions. Within the top 12, the majority of relocations (n ¼ 20) were done by entrepreneurs who moved from the New York region to other regions, followed by entrepreneurial relocations from the San Francisco region (n ¼ 10). The San Francisco and Boston regions were the principal recipients of entrepreneurs from all other regions, receiving, respectively, twenty and nineteen entrepreneurs from elsewhere. To get a clearer idea about regional gains and losses of entrepreneurs, we calculated the ratio of relocations away from the top 12 regions to relocations into the regions. The last column of Table 5.3 shows these ratios. A number greater than 1 indicates that a region was losing more entrepreneurs than it was gaining. By this measure, New York, Los Angeles, and Chicago all showed a net loss in their numbers of biotherapeutics entrepreneurs. Table 5.4, which shows the relocation patterns of firms that moved from one region to another, reveals a slightly different pattern. A somewhat greater number (45 percent) of firm relocations were from regions outside the top 12 regions. Again, the majority (70 percent) relocated to one of the top 12 regions. Within the top 12 regions, while New York was again the loser of the greatest number of firms (n ¼ 8), it was also the recipient of the largest number of relocating firms (n ¼ 12). Five of the relocations to New York were from regions outside the top 12. The Raleigh–Durham, Houston, and Denver–Boulder regions showed net losses based on the number of firms that relocated in and out of the regions. Thus, we find evidence of geographic convergence toward the top 12 regions. Most of the entrepreneurs and firms from outside the top 12 regions relocated to one of the top 12 regions. Moreover, the large majority of relocating entrepreneurs (90 percent) and firms (76 percent) moved between regions in the top 12. Overall, the San Francisco, Boston, and San Diego regions were the predominant recipients of relocating entrepreneurs and firms. New York, which was also an important recipient of relocating entrepreneurs and firms, was also a considerable source.

Intraregional Organizational Origins As indicated above, the majority of US biotherapeutics firms were established by entrepreneurs or firms in regions where they already resided. To learn more about the growth patterns of the top 12 regions, and perhaps to obtain insights regarding the substantial differences in their growth rates

99

100 Table 5.3. Entrepreneur relocations across combined MSA for top 12 regions Other— Foreign San Boston New San Philadelphia Raleigh– Washington, Los Seattle Houston Denver– Chicago US Francisco York Diego Durham DC Angeles Boulder Other—US San Francisco Boston New York San Diego Los Angeles Philadelphia Washington, DC Raleigh– Durham Seattle Houston Denver– Boulder Chicago Total

3 8 4 2 1 0 2 3

1 2 1 5 2

1

2

2 3 6

3 2

1 1

4 1 1

1 3 1 1

1 1

1 1

1 1 1

1

1 1 1

1 4 3

1

1

2

1

0 1 1

1 1

1 28

12

1

2 1 1

1

10

3

20

8

7

0

1 4

4

0

2

0

5

Table 5.4. Firm relocations across combined MSA for top 12 regions Other— Foreign San Boston New San Philadelphia Raleigh– Washington, Los Seattle Houston Denver– Chicago US Francisco York Diego Durham DC Angeles Boulder

Other—US San Francisco Boston New York San Diego Los Angeles Philadelphia Washington, DC Raleigh– Durham Seattle Houston Denver– Boulder Chicago Total

8 3 1 5 2 1 0 3

2 25

1

1

2

1

3 1 2

Number of relocating firms 1 1 1 1

1

1

1 1

1

2

1 1

2

2

2

1 1

1 1

0 0 0 0

1

1 1

1 1

2

4

4

8

2

1

3

2

3

1

1

101

Anatomy of Cluster Development

over time, we examined the longitudinal organizational and geographic origins of entrepreneurs and firms in each of the regions. Table 5.5 shows patterns for San Diego and Table 5.6 for Los Angeles. They exhibit very different patterns in the organizational and geographic origins of their firms. As we will discuss, with only two exceptions (Raleigh-Durham and New York), these patterns were shown for all other regions and appear to distinguish regions, among the top 12, which became home to large biotherapeutics clusters and those which did not. San Diego, as one of the top four regions, was interesting for study because it was not previously home to other high-technology clusters in contrast to the San Francisco and Boston regions. There are several important things to notice about Table 5.5. First, the San Diego region was home to only six biotherapeutics firms during the first ten years of the industry, from 1976 through 1985. This contrasts with much faster growth rates in New York with thirty-three firms, San Francisco with twenty-five firms and Boston with sixteen firms during the same period. This is a quite striking difference when we consider that San Diego has greatly narrowed the number of firms in the region as compared to these other regions. Second, during the period 1986 through 1995, while the number of scientific start-ups continues and even increases a bit, a large number of new firms are established with entrepreneurial and firm origins in the original six firms. Thus, it appears that San Diego achieved its growth substantially through the founding of new firms by scientists and managers with experience in other local biotherapeutics firms. Although not shown, similar arrays of data for the San Francisco and Boston regions show the same pattern. The significance of this pattern will become clearer when we examine Table 5.6 for the Los Angeles region. Third, beginning during the period 1986–95 but increasing during the period 1996–2003, we see a significant inflow of relocating entrepreneurs and firms as well as capital via the investments of distant firms in San Diego firms.4 Finally, during this final period, we see a slight increase in the number of entrepreneurs and firms who relocated out of San Diego and a substantial increase in the number of San Diego firms that were acquired and their assets absorbed by firms outside the region. Before interpreting the patterns shown for San Diego, which, as noted above, were similar to those for the San Francisco and Boston regions, we should examine the patterns for the Los Angeles region which was home to a considerably smaller number of biotherapeutics firms, although still the fifth largest among the top 12 biotherapeutics regions. From 1986 through 1985, the first ten years of the industry, Los Angeles was home

102

Table 5.5. Distribution of biotherapeutics firms in San Diego

1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Total entries

Science startups

1

1

2 1

2

2

1

2 9 1 8 4 3 6 6 2 8 2 8 5 3 7 2 1 3

Biotherapeutics start-ups

Hybrid start-ups

Biotherapeutics spin-offs

Venture capital start-ups

Pharmaceutical start-ups

Direct entries

Mergers, acquisitions, and joint ventures

In relocations of entrepreneurs

In relocations of firms

In relocations of capital

1

1

Local acquisitions and mergers

2 3 1

2

1 1

1

1

2

1 1

2

1 1 1 1

1

1 1

2 1

1 1 1

Out relocations of assets

1 1

1

1

2

Out relocations of firms

1

1 2

Out relocations of entrepreneurs

1

1

2

3 1 1 2

Failures

1

1

1 1

2 2 2

1 1 2

1

1 1 1

2

1

2

1 2 1

1

1 1

3 1 2

1

1

1

1 2 1 2

1 1

1 1 1

1 1

1 1

1 2 3 1 3 4 3 1 3 4

Table 5.6. Distribution of biotherapeutics firms in Los Angeles

1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Total entries

Science startups

4 4 1

1 2

Biotherapeutics start-ups

Biotherapeutics spin-offs

Venture capital start-ups

Pharmaceutical start-ups

Direct entries

1 1

1

1

Mergers, acquisitions, and joint ventures

In reloca tions of entrepreneurs

In reloca tions of firms

In relocations of capital

Failures

Local acquisitions and mergers

Out relocations of entrepreneurs

1

Out relocations of firms

Out relocations of assets

1

1 1

1

4

Hybrid start-ups

1

1

1

1

1 2

1 2 2 1 1

1

1 1

1

1 1

1

1

2

1

1

1

1

1

2 1 1 1 4 1 3 3 2

1

1 1

1 1

1

1

1 1

1 1

1

1

1

1 1

2 1

1 1

2

Anatomy of Cluster Development

to ten biotherapeutics, though only three founded by local scientists out of universities or research institutes. This is a slightly larger number of early firms than we observed for San Diego. The number of firms founded or relocated to the regions increases a bit during the period 1986 through 1995, but we see no indication of the growth in start-ups out of the earlier biotherapeutics firms that we observed for San Diego. This is perhaps surprising since Los Angeles was and is home to Amgen, one of the largest and most successful biotherapeutics firms in the USA. From 1995 through 2003, Los Angeles continued its slow pace of start-ups by entrepreneurs out of local universities and research institutes, but shows a slight increase in the number of entrepreneurs and firms that relocated to the region. Also, during the third period from 1995 through 2003, Los Angeles shows a noticeable increase in the number of firms it loses due to failure, the relocations of entrepreneurs and firms to other regions, and the acquisition and absorption of its firms by firms in other regions. Again striking in contrast to San Diego is the relatively smaller number of Los Angeles firms that were attractive as acquisitions for firms in other regions. Overall, the pattern shown for Los Angeles was similar to all of the other smaller regions, including Philadelphia and Washington, DC, which were home to about the same number of biotherapeutics firms as Los Angeles, and excluding the Raleigh–Durham region. Thus, out of the twelve top regions, ten exhibit developmental patterns that are highly similar to either San Diego or Los Angeles. San Diego, Boston, and San Francisco, while they continue a paces of biotherapeutics science founding throughout the study period, also exhibit a secondary pattern of biotherapeutics start-ups by entrepreneurs from other local biotherapeutics firms. By contrast, Los Angeles, in a pattern that is similar to all but one of the other smaller regions, exhibits no such secondary pattern. It appears that this secondary growth phenomenon, that is, the start-up of new firms by entrepreneurs from other biotechnology firms, is critical to the overall growth of the cluster. Note, we are not saying simply that higher rates of growth accounts for greater size; rather, we emphasize that it is the development of a particular type of growth that appears to distinguish these regions. Clusters grow when the knowledge and other resources created by the early firms in a cluster are ‘combined and recombined’ (Schumpeter 1934) by entrepreneurs from the early firms for the creation of new organizations. Two regions, Raleigh–Durham and New York, were anomalous with respect to these patterns. Since about 1997, Raleigh–Durham, though we do not show a table, has experienced a substantial increase in the number

105

Anatomy of Cluster Development

of biotherapeutics firms founded out of only a few biotherapeutics firms in the region during the preceding twenty years. Although it is too early to draw strong conclusions about the increase, it appears that Raleigh– Durham may be following the pattern identified for San Diego, San Francisco, and Boston and that the region may thus be poised for substantial growth, though at a much later period in the history of the biotherapeutics industry. If the pattern continues, however, it suggests that cluster growth may not be strongly tied to historic periods. Rather, it may depend more on the characteristics of growth, in particular the tendency of scientists and managers from local, established firms to strike out on their own to found new organizations in the same region. New York exhibits a very different pattern altogether. As shown in Table 5.7, New York shows both a large number of science start-ups and a large number of direct entries during the first ten years of the industry. The large number of direct entries contrasts with other regions, which had few, but is not surprizing given the location in the New York region of a large proportion of US pharmaceutical firms. New York also shows a large number of entrepreneurs who relocate to the region during the early period, in contrast to other regions. Though not indicated in the table, many of these entrepreneurs were from foreign countries or US metropolitan regions that were not one of the top 12 regions. The pattern of relocation to New York is continued in later periods, primarily through the relocations of firms, again many from US regions outside the top 12. Perhaps the most striking feature of the table, however, is that New York, like Los Angeles, exhibits no secondary growth phase through the start-up of new biotherapeutics firms by entrepreneurs from other local biotherapeutics firms, despite the fact that the region was home to more start-ups during the early period than any other region. Moreover, after about 1992, the rate of founding from scientific or any other organizational origins drops to near zero. New York remains attractive, during the later period, to entrepreneurs and firms from other regions, but it has virtually ceased to be generative of any new firms locally.

The Development of Industrial Clusters Industry clusters develop within complex contexts of national and even international scrutiny and influence. Although explanations for the location and growth of industry clusters have emphasized local factors, including the presence of important resources and social processes that

106

Table 5.7. Distribution of biotherapeutics firms in New York Total entries

1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

2 1 6 6 5 4 6 4 5 4 7 4 3 3 6 2 2 3 3 1 1 3 4 4 0 3

Science startups

Biotherapeutics start-ups

Hybrid start-ups

1 1 1 1 3 1 2 1 5 2 1 2

Biotherapeutics spin-offs

Venture capital start-ups

Pharmaceutical start-ups

Direct entries

2 1 2 3 2

1

1

Mergers, acquisitions, and joint ventures

In reloca tions of entrepreneurs

1 1

2 1 1 1

1 1 2

2

In relocations of firms

In relocations of capital

Failures

Out relocations of entrepreneurs

Out relocations of firms

Out relocations of assets

2

1

2 1

1 1 1 1

1 1

1

1

1

1

1

1

1 1

2 1

Local acquisitions and mergers

1 1

1

1 1

2 1 1

2 1 1 3 1

2

2 3

2 1

1 1

2 1 1 1 1 1

1 1

1

2

1

1

2

2

3 1 1

Anatomy of Cluster Development

promote the formation of new firms, few studies have examined the growth of industry clusters in the context of multiple regions that may be at risk for developing a particular kind of industry cluster. It often seems obvious, in retrospect that a cluster would have developed in a particular region. Historic analysis may point to seemingly unique resources that were available in the region; however, we cannot assume this inevitability with evidence, typically, on just one or two prominent clusters. In this chapter, we have explored three important questions relating to the location and growth of industry clusters on a comparative basis. First, we examined whether clusters grow predominantly through the investments of local entrepreneurs, local firms, and local venture capitalists in the formation of new organizations or the entry by established organizations in a new line of business. In support of prevailing theory, our findings reveal a critical importance of local investments in two ways. First, of over seventy-six metropolitan regions in the USA (all that were home to at least one biotherapeutics firm), the majority (68 percent) were founded by entrepreneurs and other investors in the regions in which they already resided. Second, for three of the regions with the largest clusters— San Diego, Boston, and San Francisco—the critical spur to growth appears to be a tendency of entrepreneurs to leave local, established biotherapeutics firms to found additional biotherapeutics firms. Most regions continued to generate new biotherapeutics by entrepreneurs out of local universities and research institutes at a relatively steady pace. Only those regions, however, that exhibited this secondary or second-generation growth from the biotherapeutics firms themselves grew to substantial sizes relative to other clusters. We can only speculate about the conditions and processes that led entrepreneurs and firms in some regions to produce the secondgeneration growth. Our findings are consistent with many case histories of cluster development that emphasize regional cultures and patterns of social interaction (Murtha Lenway, and Hart 2001; Saxenian 1994; Storper and Venables 2002). Second-generation growth, which involves entrepreneurs leaving established firms in a cluster to found competing new organizations, requires that the entrepreneurs believe in their abilities to attract capital and especially human resources to support their new organizations. It is difficult to conclude that such beliefs could develop unless the leadership of the earlier organizations was supportive of new entrepreneurial efforts. Saxenian (1994) explored this question in her examination of eventually profound differences in the growth rates of

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Anatomy of Cluster Development

semiconductor clusters in Boston and Silicon Valley. She argued that the leaders of semiconductor producers in Silicon Valley, most of whom had been the founders of their firms, were far more supportive of employees (managers and engineers) who sought to strike out on their own than the leaders of semiconductor producers, many of whom had not been the founders of their firms. In keeping with this argument, Gompers, Lerner, and Scharfstein (2003) showed that VC-funded organizations were more likely to generate other new entrepreneurial firms than organizations created or funded in other ways. These arguments suggest a potential explanation for our anomalous findings regarding the growth of a biotherapeutics cluster in the New York region. Although the New York biotherapeutics cluster grew faster at an earlier stage in the industry than the clusters in any other region, a large proportion of this growth came about from the entry of large, established pharmaceutical firms in the region. Although local universities produced entrepreneurial scientists in numbers commensurate with other regions, these firms did not generate the second-generation growth of other regions that eventually surpassed New York in the sizes of their clusters. It seems likely, based on Saxenian’s reasoning, that the established pharmaceutical firms, having little experience in memory of entrepreneurial activity—most of the large pharmaceutical firms are close to 100 years old—were not encouraging of any inclinations on the part of their talented employees to leave the firms to pursue an entrepreneurial enterprise. Perhaps these large pharmaceutical firms also exerted a stultifying influence on the pace of start-ups overall. We may speculate, again extending from Saxenian’s findings in semiconductors, that the interconnectedness of firms in the region, and the associated density of information sharing, was inferior, compared to that of other regions. Our findings, however, did not fully support a conclusion that cluster locations and growth are solely the consequence of local investments. More than 200 entrepreneurs (32 percent) relocated from one metropolitan region to another to found new biotherapeutics firms. Moreover, sixty firms relocated, following establishment in one region, to another region. This finding supports the idea that entrepreneurs are scanning regional environments, beyond their home regions, for evidence of more attractive locations and that they are willing to relocate. Our analysis of the directional patterns in entrepreneurial and firm relocations showed that, while the majority of relocations occurred between regions among the top 12, entrepreneurs and firms from regions outside the

109

Anatomy of Cluster Development

top 12 were overwhelming likely to relocate to one of the top 12 regions. San Francisco, Boston, New York, and San Diego, which have become home to the largest biotherapeutics clusters in the USA, were the most frequent recipients of the relocating entrepreneurs and firms. These findings suggest that, while the most clustered regions jockey to attract entrepreneurial talent and the resources of established firms, the industry overall is converging toward a few, larger and larger clusters. Finally, we examined in greater detail the organizational and geographic origins of three of the top five clustered regions. In addition to the importance of second-generation growth, our findings suggest that the attraction of entrepreneurs and firms to a region is a tertiary influence on growth, occurring late in the history of the industry and the clusters. San Diego, which showed a strong increase in second-generation growth, and even Los Angeles, which failed to generate this form of growth, both began to attract entrepreneurs from other regions during the late stage of the industry. New York also attracted many entrepreneurs and firms during this period; however, for this region, the attraction appeared to be a continuation of trends developed very early for this cluster. Overall, most regions did not begin to attract entrepreneurs and firms until later in the development of their clusters, if at all. Although the US biotherapeutics industry may yet be too young to draw strong conclusions about convergent geographic patterns, these findings support theory about the role of expectations in cluster growth, if not necessarily their locations. It is not obvious that small early leads in the growth of industry clusters were influential in the future growth of the clusters. San Diego, which lagged far behind most other regions in its production of early biotherapeutics firms, eventually grew nonetheless to become home to one of the largest biotherapeutics clusters. And New York, which had more early biotherapeutics firms than any other region, including a large complement of science startups, has largely failed to generate growth in its cluster. With the exception of New York, which has been attractive to relocating entrepreneurs and firms throughout the history of its biotechnology clusters, the top regions did not begin to attract entrepreneurs and other firms until they had achieved considerable sizes from local growth and investments. Thus, it is hard to conclude that the locations of industry clusters are accidental. Industry clusters are not only a universal feature in the spatial arrangements of industries, but also an essential component in the economic evolution of industries. Clusters provide not only a near term source of

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Anatomy of Cluster Development

economic wealth, but also a long-term foundation for future economic growth. The knowledge, institutions, and infrastructure of one large cluster, in addition perhaps to a culture of entrepreneurial activity that may be generated by the presence of one large cluster, prepare the local environment for investment in future clusters, both those that may be closely related to the original cluster and those that may be broadly new. Although regional civic and business leaders appear to have recognized these facts, responding with all manner of incentives to attract entrepreneurs and firms to their regions, little research to date has compared demographic patterns in cluster development for evidence of most prolific development. This study presented here, which is the first that we know of to track cluster development processes among regions that may have been similarly at risk of developing a biotherapeutics cluster, suggests that only those regions which generate a community of entrepreneurial activity, represented by what Jane Jacobs (1969) called breakaway firms, that is, firms that are started by entrepreneurs with experience in other entrepreneurial firms in the same industry, may be capable of long-term cluster persistence.

Acknowledgments We are indebted to Martin Doyle, an MBA graduate of the McDonough School of Business at Georgetown University whose earlier master’s degree in microbiology and extensive industry experience aided in the classification of firms engaged in human biotherapeutics research and product development.

Notes 1. Source: BioScan listings, 1987 through 2003. 2. Though the OMB bulletins reporting MSA classifications describe numerical criteria, based on commuting ties, for designating the inclusion or exclusion of counties in particular MSAs, the judgments of the coders, and at times, of regional Congressional delegations also influenced the assignments. 3. We only coded relocations when entrepreneurs or firms crossed geographic boundaries at the combined MSA level. While some combined MSAs are very large, e.g. the San Jose–San Francisco–Oakland combined MSA is spread from

111

Anatomy of Cluster Development Napa to Santa Clara counties, most relocations crossed contiguous counties within the combined MSAs. We focused on this larger geographic level to avoid overcounting the extent of entrepreneurial and firm relocations. 4. These data represent only those investments that resulted in the majority acquisition of San Diego firms. We found much evidence of other, smaller equity investments in the San Diego firms, but were not able to collect complete data on such investments.

112

6 Policy-Induced Clusters: The Genesis of Biotechnology Clustering on the East Coast of China Martha Prevezer and Han Tang

China has recently witnessed the emergence of a number of potentially important biotechnology clusters on the east coast. Notwithstanding that several of these agglomerations are in a very early stage, a pattern can be discerned where the most important clusters are centered around Beijing, Shanghai, and Shenzen/Guangdong. The current, formative stage of the Chinese biotechnology industry implies that the coming decade(s) are likely to be characterized by continuing reorganization, reshuffling of factors of production and a shakeout of less competitive biotechnological sites. The purpose of this chapter is to examine the role of policies in propelling the emergence of biotechnology clusters. To simultaneously build new institutions and reform existing ones, alongside the introduction of policies that encourage greater entrepreneurship and risk-taking, is a highly complex task. The development of policies includes learning from previous institutional reforms (over the last twenty years), and providing a business friendly environment where ex-pats can resettle and find fertile ground for nurturing start-ups. China, as a transitional economy, has been moving from planning to greater market orientation. Still, firms remain predominantly state owned especially for most large- and medium-sized enterprises. Collective and privately owned firms are mostly smaller sized, and entry of new firms has been of these nongovernmental types of enterprise. The next section looks at science and technology policies in China and then focuses on

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The Genesis of Biotechnology

those policies that are targeted specifically at developing biotechnology. Section 3 examines the structure of the biotechnology sector in China, emphasizing the three dominant biotechnology cluster regions on the east coast. Thereafter these three regions are compared in terms of size, knowledge base, and specialization. The final section concludes.

Toward a Market-Oriented Economy The classification of different policy stages outlined in Chapter 1 will serve as the structural framework for the analysis. Emphasis is on the first two phases. The first refers to the creation of background and preemergence conditions, while the subsequent phase features the coevolution of institutions alongside start-ups. These stages characterize the transformation from an—by and large—entirely state-owned and state-run economy to a reorientation toward the creation of start-ups and regional clusters in high-tech sectors. The preemergence phase (1985–95) included a strengthening of property rights, especially for science and technology enterprises, to allow for the creation of spin-offs from research institutes, universities, and large enterprises and to encourage autonomy in decision-making for those enterprises. These are not private property rights as recognized in the West, but they do represent greater autonomy for the enterprise in terms of decision-making. This followed from ‘The Decision on the Reform of the Science and Technology Regime’ in 1985, by which the central government allowed the foundation of Minying Keji Enterprises (MKEs) which are nontraditional, but government-owned technology enterprises. Most of these MKEs were spin-offs where the enterprise was set up by a group of scientists, primarily supported by research institutes and universities (Liu and White 2001; Lu and Lazonick 2001). In 1988 establishment of MKEs was further encouraged (‘Decision on the Further Reform of the Science and Technology Regime’), followed by a speech of Deng Xiaoping in 1992 which eliminated ideological obstacles to developing nonsocialist ownership types and unleashed a boom in the creation of MKEs (Table 6.1). In the subsequent phase these policies were consistently pursued and resulted in a considerable increase in start-ups from the mid-1990s onwards. The numbers of MKEs increased from 26,000 in 1992 to 109,400 in 2002 (Ministry of Science and Technology (MOST) 2003c). The incentives launched to promote increased start-ups included locational stimulus,

114

The Genesis of Biotechnology Table 6.1. Summary of science and technology and biotechnology policies

Science & technology policies

Phase I mid-1980s–95/6

Phase II 1995/6–2003

Creation of MKEs allowed and encouraged Improve infrastructure

R&D expenditures to enterprises increased Enhancing Technological Innovation And Developing High technology, Law of Commercialization of S&T Results 1996

Horizontal support to business sector

Enhance R&D Key Technologies R&D Programme

Attract returnee talent Lower entry barriers for start-ups Direct and indirect financial support for start-ups Establishing Science & Technology Industry Parks Founding Service Centres for Scientific and Technical Entrepreneurs Setting up Innovation Funds for Small Technology Firms Chun Hui Programme 1996 to attract overseas scholars to China

Biotechnology policies

National High-Tech R&D Programme (863 Programme) 1986

Torch Programme 1988 State Key Laboratory Programme

National Basic Research Programme (973 Programme) 1997 National Life Science and Technology Talents Forging Base Programme 2002 Encourage Overseas Scholars to Serve the Country in Different Ways Suggestion on supporting Senior Overseas Scholars Establishing biotechnology parks Incentives to biotechnology firms

lower entry barriers for entrepreneurs, policies to improve financial constraints, and allowing a Chinese VC industry to be established. Government expenditure on R&D also shifted from governmental institutions (from 43 percent in 1997 to 27 percent by 2002) toward R&D expenditure on enterprises receiving more than 60 percent in 2002. Locational incentives and design mainly relied on the imitation of other countries’ science and technology (S&T) industry parks (Lai and Shyu

115

The Genesis of Biotechnology

2005). Since the first S&T industry park was established in 1991, fifty-three national level parks were founded across the country by the end of 2002. The number of enterprises in these sites increased from 2,587 in 1991 to 20,796 in 2000. This was complemented by the establishment of service centers that helped entrepreneurs setting up new businesses and the provision of incubator milieus. In 2002—according to the Ministry of Science and Technology—23,373 enterprises were in and 6,927 firms had ‘graduated’ from these service centers. Less success has been experienced as regards the establishment of an indigenous VC industry, which was high on the agenda already in the mid-1980s; it was not until around 1996 that VC companies began to emerge. Their numbers rose from 23 in 1996 with a capital of RMB3.5 billion to over 246 VC firms in 2001 with capital of RMB40 billion (China Venture Capital Yearbook 2002). The process started with some local governments that set up investment institutes, supplying funds to startups and incubators in high-tech parks.

Biotechnology Policies in China Biotechnology in China was targeted as a key industry in the National High-tech Research & Development Plan (the 863 plan) which was introduced in 1986. The biotech industry had received about a quarter of the funds from the program by 2000. The program is said to be responsible for generating 455 patents, 10,278 papers and 273 biotechnology projects, training over 3,000 postgraduate students and being involved in supporting around 30 biotech firms (MOST 2001). The Torch program, initiated in 1988, was set up to promote the commercialization of key high-technology projects through market mechanisms. It directed development in certain industries, principally the electronics sector, but increasingly also the biotechnology sector. Part of its focus was also the creation of high-technology industry development zones with the aim of assisting the application of R&D to production and commercialization. In the ninth five-year plan (1995–2000), the Chinese government further stressed the importance of biotechnology R&D. At the same time, indigenous companies realized the importance that government was giving to this sector and became interested in biotechnology. Local governments formulated similar policies supporting the development of their local biotechnology industry. A high priority was also put on encouraging the return of Chinese scientists working abroad through, for example providing a favorable environment for returnees to

116

The Genesis of Biotechnology Table 6.2. Returnees as percentage of top scientists in China, 2003 Scientist Academician of Chinese Academy of Engineering Academician of Chinese Academy of Sciences Academician of Chinese Academy of Social Sciences Senior Expert of Chinese Academy of Medical Sciences Academician of Chinese Academy of Agricultural Sciences Scientists in the ‘863’ Programme during the Ninth Five Year Plan (above Director of research project level) Electee of Millions of Talents Programme

Returnees (%) 81 54 4 37 14 72 18

Source: Service Centre for Returnees of Ministry of Personnel, China.

set up their own businesses in specially designed ‘Returnee Parks’. Since the Open Door policy was introduced in 1978, it is estimated that around 700,000 students went abroad to study, over 30 percent of whom were in the field of biotechnology or related areas (Qi C.Y. 2003) and roughly a quarter have returned to work in China. These returnees make a significant contribution to the Chinese Academies of Science (Table 6.2). A key ingredient in the development of biotech clusters has been to secure a skill base in biotechnology, which has been strengthened since the mid-1980s. By a country ranking for all fields of science (Table 6.3), sorted by papers, China was ranked nineteen by number of citations in 2003, and ninth with respect to the number of papers that have been cited. China’s biotech output increased sevenfold between 1991 and 2002, with the number of health-biotech papers published increasing from under 50 in 1991 to over 300 by 2002, roughly on a par with South Korea and well in advance of India and Brazil (The Economist, December 9, 2004). Another indication of scientific progress is the participation by Chinese scientists in the Human Genome Project, where they were responsible for 1 percent of the genomic sequencing. A significant part of this was done at the National Human Genome North Center in Beijing, at the Beijing Genomics Institute (the Beijing Huada Genomics Research Center) and at the National Human Genome South Center in Shanghai. Costs for skilled labor are also significantly lower in China than in the USA or Europe. The average annual salary for 2003 in pharmaceuticals and bioengineering was approximately $5,256.4 (Business Alert—China 2003). This compares with an average US salary in the biotechnology sector of $95,000 in 2003 (American Association for the Advancement of Science). About 21 million scientists, engineers, and other professionals were

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The Genesis of Biotechnology Table 6.3. Top 20 country rankings of the most-cited 150 countries in all fields, 2003 Rank

Sorted by citations Country

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

USA England Germany Japan France Canada Italy Netherlands Switzerland Australia Sweden Spain Scotland Belgium Russia Israel Denmark Finland People’s R China Austria

Citations

Papers

Citations per paper

33,089,756 6,212,840 5,857,244 5,098,499 4,213,581 3,549,116 2,569,970 2,135,032 1,769,220 1,736,998 1,600,307 1,419,447 967,215 935,873 848,345 835,818 827,292 700,902 658,355 574,298

2,705,352 598,470 655,586 713,542 484,291 358,007 310,557 194,710 137,661 211,549 152,632 209,762 93,327 99,226 285,856 95,942 76,889 71,328 236,996 68,610

12.23 10.38 8.93 7.15 8.7 9.91 8.28 10.97 12.85 8.21 10.48 6.77 10.36 9.43 2.97 8.71 10.76 9.83 2.78 8.37

Source: ISI Essential Science Indicators Web-based product from the November 1, 2003 update covering a ten-year and eight-month period, January 1993–August 31, 2003. Notes: Country counts are based on the institutional affiliations given on published chapters. A chapter is attributed to a country if the chapter carries at least one address from that country. All addresses are considered, not only the address listed first. A country is only counted once when it appears more than one time on a chapter. All unique countries on a chapter are credited equally for the chapter. All citations received by a chapter are credited equally to all the countries on the cited chapter. No restrictions are made on the citing items in compiling the citation counts, other than that they are recorded from ISI-indexed journals only.

engaged in China in science and technology institutions and state-owned enterprises in 1999, up from 5 million in 1980. The number of science graduates in China above Masters level has risen by almost 80 percent since 1994 to nearly 10,000 graduates in 2002. In relative terms, (number of scientists and engineers in R&D per million population or R&D spending/GDP), China’s R&D ranks below that of Korea or Taiwan but is higher on average than that of Latin American countries (EIU 2000).

Three Biotechnology Clusters Compared Figures 6.1 and 6.2 show the geographic distribution of biotech research organizations and biotech firms respectively, in China, by province. As can be seen from the figures, three specifically densely populated biotechnology areas appear: Beijing, Shanghai, and Shenzhen (Guangdong).

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The Genesis of Biotechnology

Heilongjiang

Beijing

Jilin Liaoning

Xinjiang Gangsu

Inner Mongolia

ei

.

Heb xi Sh a

Qinghai

Tianjin

ng

Ningxia

Shangdong

Shanggxi Henan Anhui

Tibet

J