The Oxford Handbook of Local Competitiveness 9780199993307, 0199993300

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T h e Ox f o r d H a n d b o o k o f

LOCAL C OM P E T I T I V E­N E S S

CONSULTING EDITORS Michal Szenberg

Lubin School of Business, Pace University Lall Ramrattan

University of California, Berkeley Extension

The Oxford Handbook of

LOCAL COMPETITIVE­NESS Edited by

DAVID B. AUDRETSCH, ALBERT N. LINK, and

MARY LINDENSTEIN WALSHOK

1

3 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. 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 trademark of Oxford University Press in the UK and certain other countries. Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016

© Oxford University Press 2015 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, by license, or under terms agreed with the appropriate reproduction rights organization. Inquiries 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 work in any other form and you must impose this same condition on any acquirer. Cataloging-in-Publication data is on file at the Library of Congress ISBN  978–0–19–999330–7

1 3 5 7 9  8 6 4 2 Printed in the United States of America on acid-free paper

Contents

Preface  Contributors  1. Introduction  David B. Audretsch, Albert N. Link, and Mary Lindenstein Walshok

ix xi 1

PA RT I   T H E C ON C E P T OF L O C A L C OM P E T I T I V E N E S S 2. The Strategic Management of Place  David B. Audretsch

13

3. Talent, Cities, and Competitiveness  Richard Florida and Charlotta Mellander

34

4. Enabling Entrepreneurial Ecosystems  Philip E. Auerswald

54

5. Construction of the Cluster Commons  Örjan Sölvell

84

6. Keeping Up In an Era of Global Specialization: Semi-Public Goods and the Competitiveness of Integrated Manufacturing Districts  Dan Breznitz and Giulio Buciuni 7. Something New: Where Do New Industries Come From?  Maryann P. Feldman and Sam M. Tavassoli

102 125

vi   Contents

PA RT I I   C R I T IC A L DR I V E R S OF L O C A L C OM P E T I T I V E N E S S 8. Local Competitiveness Fostered through Local Institutions for Entrepreneurship  Martin Andersson and Magnus Henrekson

145

9. The National Resource Curse in the Arab Gulf: Rapid Change and Local Culture  Thomas Andersson

191

10. The Role of Universities in Local and Regional Competitiveness  Erik E. Lehmann

211

11. The Grand Challenge Model of R & D  Christopher S. Hayter

237

12. Commercialization or Engagement: Which Is of More Significance for Regional Economies?  Martin Kenney

255

13. Philanthropy, Competition, and Local Competitiveness: A Schumpeterian Conundrum  Zoltan J. Acs

268

14. Local Policies for High-Growth Firms  Erik Stam and Niels Bosma

286

15. Innovation Brokers  Doug Henton and Jessie Oettinger

306

16. Swimming Upstream: Why Regional Economic Development Depends on National Economic Competitiveness  Robert D. Atkinson

320

PA RT I I I   C OM P E T I T I V E N E S S AT T H E L O C A L  L E V E L 17. Competitive Advantages from University Research Parks  Albert N. Link

337

Contents   vii

18. The Co-creation of Locally Useful Knowledge by Business Schools  Simon Mosey, Paul Kirkham, and Martin Binks 19. Entrepreneurship and Sustainable Development: The Relevance of Shaping Intertemporal Local Intangible Conditions  José L. González-Pernía, Maribel Guerrero, and Iñaki Peña-Legazkue

345

355

20. In Search of New Competitive Advantage: Japan’s Local Firms in Sustainable Business  Hideki Yamawaki, Hiro Motoki, and Kayo Hirai

373

21. Assessing State-Level Science and Technology Policies: North Carolina’s Experience with SBIR State Matching Grants  John Hardin, Lauren Lanahan, and Lukas C. Brun

385

22. Clusters, Communities, and Competitiveness: An Emerging Model from America’s Midwest  David Lawther Johnson

401

23. Lessons on Microenterprise Development from a University-Based Microlending Development Program  Paul Miesing, Brad Watts, Donald S. Siegel, and Katharine Briar-Lawson

414

24. A Region in Transition: Bottom-Up Economic Transformation in Postconflict Northern Ireland  Mary Lindenstein Walshok and Steve Orr

429

25. The 2008 Economic Crisis and Its Impact on Universities’ Competitiveness  Shiri M. Breznitz and Paige A. Clayton

445

26. Smart Specialization and European Regional Development Policy  Dominique Foray, Philip McCann, and Raquel Ortega-Argilés

458

Index

481

Preface

In December 2011, Mary and David organized a conference in La Jolla, California, that brought together leading scholars, policymakers, thought leaders, and decision-makers in business from around the world to identify what could be done to enhance the local competitiveness of places. We are grateful to the Dean of the School of Public and Environmental Affairs of Indiana University, John D. Graham, and Robert Strom of the Ewing Marion Kauffman Foundation for providing financial support for that conference. Oxford University Press was so encouraged by this initial conference that editors suggested we put together this Handbook of Local Competitiveness. We would like to thank Scott Parris, Executive Editor of Economics and Finance at Oxford University Press, for his highly valuable support and guidance in preparing several drafts and iterations of this Handbook. We are also grateful to Cathryn Vaulman, Assistant Editor at Oxford University Press, for her very helpful assistance in bringing the Handbook to fruition. We also are very grateful to Ms. Aileen Richardson and Chemain Nanney of the Institute of Development Strategies at Indiana University for their invaluable inputs and assistance.

Contributors

Zoltan J. Acs, London School of Economics Martin Andersson, Lund University Thomas Andersson, Jankoping University Robert D. Atkinson, Information Technology and Innovation Foundation David B. Audretsch, Indiana University Philip E. Auerswald, George Mason University Martin Binks, Nottingham University Niels Bosma, Utrecht University Dan Breznitz, University of Toronto Shiri M. Breznitz, University of Toronto, Munk School of Global Affairs Katharine Briar-Lawson, University at Albany, State University of New York Lukas C. Brun, Duke University Giulio Buciuni, Venice International University Paige A. Clayton, University of North Carolina, Chapel Hill Maryann P. Feldman, University of North Carolina, Chapel Hill Richard Florida, University of Toronto Dominique Foray, École Polytechnique Fédérale de Lausanne José L. González-Pernía, Basque Institute of Competitiveness Maribel Guerrero, Basque Institute of Competitiveness John Hardin, University of North Carolina, Chapel Hill Christopher S. Hayter, Arizona State University Magnus Henrekson, Research Institute of Industrial Economics Doug Henton, Collaborative Economics, Inc.

xii   Contributors Kayo Hirai, Consultant Group Manger, E-Square, Inc. David Lawther Johnson, BioCrossroads Martin Kenney, University of California, Davis Paul Kirkham, Imperial College Lauren Lanahan, University of North Carolina, Chapel Hill Erik E. Lehmann, Augsburg University Albert N. Link, University of North Carolina, Greensboro Philip McCann, Rijksuniversitiet Groningen Charlotta Mellander, Jankoping University Paul Miesing, University at Albany, State University of New York Simon Mosey, Nottingham University Hiro Motoki, President, E-Square, Inc. Jessie Oettinger, Collaborative Economics, Inc. Steve Orr, Northern Ireland Science Park Raquel Ortega-Argilés, Rijksuniversitiet Groningen Iñaki Peña-Legazkue, Basque University of Competitiveness Donald S. Siegel, University at Albany, State University of New York Örjan Sölvell, Center for Strategy and Competitiveness, Stockholm Erik Stam, Utrecht University Sam M. Tavassoli, Institutionen för Industriell Ekonomi, Blekinge Tekniska Högskola Mary Lindenstein Walshok, University of California, San Diego Brad Watts, University at Albany, State University of New York Hideki Yamawaki, Claremont Graduate University

T h e Ox f o r d H a n d b o o k o f

LOCAL C OM P E T I T I V E­N E S S

Chapter 1

I n t rodu c tion David B. Audretsch, Albert N. Link, and Mary Lindenstein Walshok

Introduction Competitiveness is neither a new concept nor a new idea. If we think about its cousin, competition, scholars such as Adam Smith and Charles Darwin immediately jump to mind. Of course, the two terms are linked. Microeconomics teaches that under the assumption of perfect competition, a firm that is competitive will operate where the isoquant is tangent to the isocost curve, enabling it to earn a normal return on invested capital. The field of strategic management in business schools is devoted to providing frameworks for understanding how a firm can be competitive in markets not characterized by perfect competition, with the goal of attaining high and sustainable levels of profitability, or economic rents. A firm enjoys a competitive advantage, or has a high degree of competitiveness, if it is highly profitable for prolonged periods of time. But what about places, such as a city, region, or state? Michael Porter (1990) popularized the idea that the degree to which a place is competitive may vary, and is certainly not a given. Scholars such as Paul Krugman (1996) questioned whether the concept of competitiveness had any valid meaning. In “Making Sense of the Competitiveness Debate,” Krugman (1996, 17) laments that applying the concept of competitiveness to a geographic unit of analysis is the result of “confusion and misconceptions, in which the ‘experts’ you see, hear, and read are usually misinformed about the most basic facts and concepts—and in which even those who are fairly sound on the economics do not understand the nature of the debate. The discussion of competitiveness is a case in point.” The irony is that it is the same Paul Krugman who laid the intellectual foundation and wide attention for his work on economic geography that ultimately led to his being awarded the Nobel Prize in Economics. This work has triggered insight about what

2   Introduction places can do to enhance their economic performance by becoming more . . . competitive. As the chapters in this Handbook make clear, despite Krugman’s admonishment, a generation of scholars as well as policymakers, thought leaders, and decision-makers in business have identified a number of important factors that affect competitiveness across cities, regions, and states and what can be done to enhance competitiveness. Our enthusiasm for developing The Oxford Handbook of Local Competitiveness is based on decades of research on regions and economic growth issues. In fact, over these decades a community of scholarship and practice that didn’t exist when many of us first began our academic careers has developed. Many of the leading voices in that community are included in this volume. This community is not limited to a single academic discipline, scholarly field, or nationality. Thus, the authors contributing to this volume reflect a broad spectrum of scholarly fields and span the globe. We are deeply conscious of the extent to which changes in technology, global markets, and political forces are shaping the world today. They have resulted in new concerns among all nations about how communities can sustain, transform, or totally reinvent their core economies in order to sustain prosperity and community well-being under constantly changing conditions. The increasing impacts of these forces, such as technological change and globalization, felt at the local level, have triggered a surge in scholarship to understand the dynamic forces that enable locales to respond to forces typically beyond their control. For example, the downsizing of traditional employers because of a major loss in market share due to new competitors, acquisition by global firms, or offshoring of production or services was traditionally thought to be beyond the powers of local policymakers, thought leaders, and business decision-makers to respond. In the world of practice, those mandated with or concerned about the economic performance of their place, whether city, region, or state, are increasingly focused on how their area can adapt to these trends and leverage their existing resources to respond to global challenges as an opportunity rather than as a burden. Thus, it seemed a very appropriate time to solicit some of the most interesting scholars, thought leaders, and seasoned practitioners from around the globe to contribute their ideas, research findings, and experiences to a comprehensive overview of knowledge about regional competiveness. This collection—assembled by three social scientists, each of whom lives in a different part of the United States, all of whom have traveled extensively throughout Europe, Asia, and Latin America—reflects an enormous respect for the particularities of place and the importance of understanding regions from the point of view of local history, key economic and human capabilities, and community aspirations. Four key assumptions guided the selection of topics for the Handbook. To properly understand local competitiveness in a postindustrial age it is important to remember these points:

1. 2. 3. 4.

Context matters for all places. Challenges vary by place. Enablers and barriers vary by place. Characteristics of success vary by place.

Introduction   3

Context Matters The paradox of the modern age is that macro forces such as radical changes in technology, the globalization of markets, invention, innovation, production, and consumption, as well as challenging environmental and climate impacts that know no global barriers, are all at play in our lives. As a consequence, place matters more than ever. The local context is significant for how individuals and communities navigate the challenges represented by these forces, especially for how they arrive at the civic and ultimately economic decisions that enable them to integrate and adapt to these forces. Even within the United States, which many from the outside see as one homogeneous culture and economy, there are significant differences between places. The industrial legacies, social dynamics, cultural values, and legal and structural frameworks shaping how a locale can respond to common threats and opportunities vary widely. The Rochester region in upstate New York contrasts vividly with the legacies, capabilities, and social histories of cities such as Atlanta, Memphis, or Phoenix. Large manufacturing centers such as metropolitan Detroit and Pittsburgh have engaged in different forms of production than have equally large manufacturing centers in Seattle and Los Angeles: automobiles versus aerospace, furniture versus clothing. Such industrial legacies over time have shaped how people think about their local economy; who is invited to the table to consider new strategies for assuring regional competitiveness and what resources are available within the region; how to mobilize effective transformation or adaptation. The decision to solicit a wide range of articles representing diverse geographies and contexts for this book reflects the importance we place on this factor.

Challenges to Local Competitiveness Vary by Place Building on the point just made, we recognize that because regions possess different resources or have to address different deficiencies and gaps, the nature of the challenges facing them can be quite different. Much has been made about the 21st century representing the age of biology in the way that physics and engineering dominated the technologies, products, and industries of the 20th century. In the broadest terms the life sciences are relevant to agriculture, to health, to the creation of composite materials that can eventually be used in the construction of cars and housing and alternative energy. The uses and applications of the natural world are unlimited and regenerative. However, the research and development and experimentation capabilities that are required to realize this promise are not equally distributed. Not all communities have research universities or companies like Monsanto or Dow that do research. Not all communities have universities with medical schools or schools of agriculture. Not all communities

4   Introduction have laboratories or vivariums or electron microscopes that enable the kind of product development needed. Thus, the challenges to capturing the promise of the life sciences across a wide range of products and services are going to be quite different in different locales. The challenges a region faces also can be shaped by such things as the sort of transportation infrastructure it has and by historical land use decisions that may inhibit developing the kinds of new facilities that are needed to develop products and incubate businesses for the new economy. The challenges a region can face may have to do with differing histories of labor/management interactions, of race relations, of the impacts of large immigrant populations, which in some cases may be highly skilled because of regional strategies and in other cases underskilled and dependent on more public services. Thus, the challenges any region faces cannot be assessed according to macro principles alone, but must be understood in terms of local context.

Enablers and Barriers to Regional Competitiveness Vary Understanding the local context and identifying the challenges faced in ensuring local competitiveness in light of new global and technological imperatives also means that what enables, and what can represent a barrier to progress is also going to vary from place to place. Enablers are often unrecognized resources that the community has yet to mobilize in support of competitive strategies. Similarly, barriers can be unrecognized patterns of behavior, values, skill gaps, and structural boundaries that inhibit growth and change. Not well understood barriers or gaps can be something as simple as an undersupply of attorneys specializing and well versed in intellectual property in a community that is embarking on a major innovation and commercialization strategy. The sorts of legal, accounting, and business services required by high-risk, technology-based companies can be quite different from those needed by real estate development or global corporate companies in retail or manufacturing. Similarly, the sorts of structural boundaries that potentially inhibit growth and change can be as straightforward as the governance and financing of regional community colleges. In many areas across the United States, community colleges, which can be significant strategic partners in workforce development in advanced manufacturing, clinical research, or computer science and programming, are organized independently, funded by independent tax bases, and have their separate boards of trustees. One of the unintended consequences of this system is often courses that are not transferable between institutions that may only be 10 miles apart, while equipment and facilities that might be useful to education and training on one campus are not accessible to another campus because of the jurisdictional boundaries, and so on. In other places this sort of problem does not exist because of large community college districts with a single governing board and financing base. A

Introduction   5 number of the chapters in this Handbook addresses these sorts of issues and suggest how important it is to have an honest assessment of regional assets and gaps in order to sustain competitiveness in the 21st-century context.

What Success Looks Like Varies Much of the discussion of localization, among both scholars and practitioners, is shaped by the reality of the decline of traditional industrial economies while new information age, knowledge-based economies are not only emerging, but often in new and different places. Clearly, innovation and the ability to adapt to rapid changes in technology and new global markets are imperatives all regions must face. However, how they respond to those imperatives may be quite different. Both Silicon Valley and the Boston area are iconic in the United States. However, they are most certainly not the only models for ensuring local competitiveness. Several of the chapters make this very clear. The breakthrough innovations, the game-changing, industry-changing innovations one associates with Silicon Valley are not the only path forward. Cities across America and across Europe are integrating, into their long histories of industrial strengths and capabilities, new realities, new technologies, and new market opportunities and thus are being innovative and have thereby enhanced their local competitiveness. There is neither one model of success nor a single formula for success. There is, however, the need for communities to be able to articulate in some way what success looks like for them and engage in a process of identifying assets and gaps on the road to that success as well as benchmarking their progress over time. The chapters in this volume introduce a number of examples that underscore this important point. The chapters that follow are organized into three main parts. The first part examines and explains the concept of local competitiveness: what it means for a place to be competitive, what constitutes local competitiveness, and how it can best be characterized, along with why local competitiveness matters. The second part identifies the critical drivers of local competitiveness. In particular, the chapters in this part analyze which underlying forces tend to enhance local competitiveness and which ones tend to impede local competiveness. The final part addresses competitiveness at the local level. The individual chapters in this part provide a detailed analysis of what particular places do to enhance competiveness along with an assessment of what tends to work, what doesn’t, and why.

The Plan of the Book Part I of this Handbook, “The Concept of Local Competitiveness,” consists of six specific chapters. A coherent and compelling framework for analyzing which underlying forces

6   Introduction shape and influence local economic performance is provided in ­chapter 2, “The Strategic Management of Place,” by David Audretsch. The chapter proposes a framework based on four pillars—factors and resources, spatial structure and organization, the human dimension, and policy. By understanding the extent to which these underlying forces are linked to local performance, those mandated with and concerned about the economic performance of any particular city, region, or state can frame strategies and devise policies with specific instruments to enhance local competitiveness. Chapter  3, “Talent, Cities, and Competitiveness,” by Richard Florida and Charlotta Mellander, analyzes why and how talent has emerged as a source of local competitiveness, and what cities do to attract and retain that talent. According to Florida and Mellander, policy instruments supporting tolerance, diversity, and amenities enhancing the quality of life are emerging as the key mechanisms for generating local competitiveness. In ­chapter  4, “Enabling Entrepreneurial Ecosystems,” Philip Auerswald advances the concept of an entrepreneurial ecosystem as driving local economic performance. Auerswald draws on literature from ecology and evolution to suggest that a viable entrepreneurial ecosystem is the key to generating a strong local economic performance. The concept of clusters is well known and serves as one of the key cornerstones shaping local competitiveness. In the fifth chapter, “Construction of the Cluster Commons,” Örjan Sölvell extends the concept of clusters by introducing the concept of the cluster commons. According to Sölvell, a cluster commons exists where firms and organizations exchange information, interact, and collaborate. In this chapter, Sölvell explains how a cluster commons can be created and developed, as well as how the tragedy of the commons can be avoided. The decline of manufacturing has been lamented for some time throughout the leading industrialized developed countries. Just as Mark Twain protested that reports of his death were exaggerated, in ­chapter 6, “Alfred Marshall, Alive and Well? The Rise and Evolution of Innovative Manufacturing Clusters in a Globalizing Economy,” Dan Breznitz and Giulio Buciuni make a compelling case that reports of the demise of manufacturing are also premature. In fact, in their chapter, Breznitz and Buciuni suggest that by leveraging spatial agglomeration activities spanning the complete range of production stages along with interfirm linkages and networks, a city, state, or region can be globally competitive in manufacturing. The authors support their argument by drawing on evidence from the manufacturing clusters of Dongguan, China, and Alto Livenza in northeast Italy. In the final chapter of this part, “Something New: Where Do New Industries Come From?,” Maryann Feldman and Sam Tavassoli point out that there is a paucity of research devoted to understanding the emergence of new industries. To fill this gap in the literature, Feldman and Tavassoli explain why the local context provides a relevant and important organizational platform for spawning the creation of new industries. The authors conclude by emphasizing specific policies that can facilitate and promote the emergence of new industries. The second part of this Handbook, “Critical Drivers of Local Competitiveness,” consists of nine chapters. These chapters all have a focus on identifying and analyzing the

Introduction   7 underlying forces that contribute to and shape local competitiveness. In ­chapter  8, Martin Andersson and Magnus Henrekson provide an analysis titled “Local Competitiveness Fostered through Local Institutions for Entrepreneurship.” In particular, this chapter identifies the role that local institutions play in influencing entrepreneurial activity and ultimately the economic performance of a particular place. The authors provide a compelling case suggesting that both formal institutions, such as taxes, regulations, and stringency of enforcement, and informal institutions, such as attitudes and social legitimacy, influence the extent and type of entrepreneurial activity. The role of institutions is similarly a main focus of ­chapter 9, “The National Resource Curse in the Arab Gulf: Rapid Change and Local Culture,” by Thomas Andersson. In particular, this chapter examines the context of the Arab Spring to analyze how institutions have responded to crucial changes in technology, demographics, and education in ways that have influenced economic performance. Erik Lehmann considers a very different context, Germany, in c­ hapter 10, “The Role of Universities in Local and Regional Competitiveness.” Lehmann identifies the key contributions made by universities in enhancing local competitiveness. Lehmann also identifies a number of specific policy instruments that are used to facilitate the transfer of technology and knowledge spillovers from universities to spur local economic performance. In ­chapter 11, “The Grand Challenge Model of R & D,” Christopher Hayter analyzes the grand challenge model, which suggests that establishing several grand challenge programs is more conducive to local competitiveness than is dissipating research and development efforts across a myriad of projects. Martin Kenney points out in ­chapter 12, “Commercialization or Engagement: Which Is of More Significance for Regional Economies?,” that the bulk of literature addressing the role of universities and their impact on local economies tends to be restricted to the activities of the technology transfer office. In this chapter, Kenney provides a compelling argument and evidence based on the University of California that, in fact, local engagement from the university has a far greater and more meaningful impact than do the more specific transfer activities consisting of patents and licensing activities emanating from the office of technology transfer. Kenney’s chapter has important implications for both scholarship and public policy in thinking about and assessing the role of universities in the economy and society. Few scholars, public policymakers, or business leaders sufficiently consider the role of philanthropy in shaping local competitiveness. However, in ­chapter 13, “Philanthropy, Competition, and Local Competitiveness: A Schumpeterian Conundrum,” Zoltan Acs uses the analytical lens provided by Joseph Schumpeter to explain how philanthropy contributes to local competitiveness by generating opportunities that spur entrepreneurship and innovation. Erik Stam and Niels Bosma, in c­ hapter 14, “Local Policies for High-Growth Firms,” identify specific types of local policies that are conducive to generating high-growth firms. Stam and Bosma provide a compelling analytical framework for distinguishing among different types of transitions in firms’ evolution prior to their exhibiting high growth. They then link particular policies to specific transitional stages

8   Introduction to better understand how and why public policy can enhance high firm growth at the local level. Chapter  15, by Doug Henton and Jessie Oettinger is titled “Innovation Brokers.” The chapter explains how linkages and connections between entrepreneurs and key resources will enhance innovative activity and ultimately economic performance. In ­chapter  16, Robert Atkinson provides an analysis titled “Swimming Upstream:  Why Regional Economic Development Depends on National Economic Competitiveness.” According to Atkinson, local and regional economic development has been undertaken in a policy vacuum in that it has been largely divorced from national economic development policies. This chapter explains that because the US economy has suffered from serious structural decline, it is time to more effectively link national and local economic development efforts if both are to be successful. The third and final part of this Handbook, “Competitiveness at the Local Level,” consists of nine chapters, all of which identify and analyze important policies to spur and enhance local competiveness. Albert Link, in ­chapter 17, “The Competitive Advantage from University Research Parks,” explains how and why university research parks can serve as a key policy instrument enhancing local competitiveness. In particular, Link compares the economic performance of firms located at a university research park with comparable counterparts without the benefit of being part of a university research park. Link provides compelling empirical evidence that the firms located at the university research park exhibit stronger economic performance, suggesting that university research parks can serve as an important instrument of public policy and contribute to local economic performance. In ­chapter 18, “The Co-creation of Locally Useful Knowledge by Business Schools,” Simon Mosey, Paul Kirkham, and Martin Binks provide convincing examples from around the globe of business schools that have successfully contributed to local knowledge and ultimately economic performance. José González-Pernía, Maribel Guerrero, and Iñaki Peña-Legazkue, in ­chapter 19, “Entrepreneurship and Sustainable Development: The Relevance of Shaping Intertemporal Local Intangible Conditions,” identify and explain under which conditions entrepreneurship is more likely to contribute to local competitiveness and strong economic performance and under which conditions entrepreneurship is more likely to have only a tepid impact. Chapter 20, “In Search of New Competitive Advantage: Japan’s Local Firms in Sustainable Business,” Hideki Yamawaki, Hiro Motoki, and Kayo Hirai examine how firms in Japan draw on local capabilities and assets to generate innovative activity and ultimately enhance the competitiveness of their region. Science and technology policies at the local level can play a crucial role in shaping economic performance. In ­chapter 21, “Assessing State-Level Science and Technology Policies: North Carolina’s Experience with SBIR State Matching Grants,” John Hardin, Lauren Lanahan, and Lukas Brun analyze the role of state governments in the US context in supporting R & D activity. In particular, this chapter identifies the growing role of US state governments in supporting R & D activity. The chapter focuses especially on how the state of North Carolina developed and implemented a small business

Introduction   9 innovation program to leverage the federal Small Business Innovation Research (SBIR) program. An important and key finding of this chapter is that the state R & D policies have generated and facilitated important knowledge spillovers, which have contributed to stronger local economic performance. In ­chapter 22, “Clusters, Communities, and Competitiveness: An Emerging Model from America’s Midwest,” David Johnson explains how clusters can enhance local competitiveness. In particular, this chapter highlights central Indiana as a case study for the strategic management of a place that enhances competitiveness and economic performance. Paul Miesing, Brad Watts, Donald Siegel, and Katharine Briar-Lawson, in ­chapter 23, “Lessons on Microenterprise Development from a University-Based Microlending Development Program,” describe the University at Albany’s innovative $2.5 million Small Enterprise Economic Development (SEED) program, a joint initiative sponsored by the School of Social Welfare and the School of Business, the Small Business Development Center (based in the School of Business), a large statewide credit union, New  York’s Empire State Development Corporation, and numerous businesses and nonprofit organizations. The chapter reaches the important conclusion that this unique program, which leverages the resources of the university and its stakeholders, can be adopted by other universities seeking to promote regional economic and social development. In c­ hapter 24, “A Region in Transition: Bottom-Up Economic Transformation in Postconflict Northern Ireland,” Mary Lindenstein Walshok and Steve Orr address the challenges posed by a postconflict region. In trying to answer the question “How does a postconflict region finally realize an economic peace dividend creating opportunities for its population when faced with a relentlessly competitive global economy, an absence of trust among some key parties, and the past often competing with and beating the future for attention?” they look to analogous regions faced with similar challenges. They find that local competitiveness in a postconflict region may be spurred by creating institutions that facilitate interactions between the business community and the university community. The third part also includes a chapter by Shiri Breznitz and Paige Clayton, “The 2008 Economic Crisis and Its Impact on Universities’ Competitiveness.” The chapter examines how changes in university funding impacted local competitiveness by focusing on the experience of Atlanta, Georgia. Creating and sustaining local competitiveness is such a priority that the European Commission recently introduced a new policy approach to enhance the economic performance of regions, the Smart Specialization Policy. In ­ chapter  26, “Smart Specialization and European Regional Development Policy,” the architects designing this new policy approach to generate local competitiveness, Dominique Foray, Philip McCann, and Raquel Ortega-Argilés, explain the thinking and ideas leading to the development of the Smart Specialization Policy. In addition, Foray, McCann, and Ortega-Argilés make it clear how this new policy to enhance local competitiveness is being adapted and implemented in the context of the European Union Cohesion Policy Reforms. In sum, the subsequent twenty-five chapters prepared for this book should provide the reader with ideas, empirical research, and policy implications useful to creating and

10   Introduction sustaining local competitiveness in a manner which draws on the insights garnered from the experiences from other places while incorporating the particularities of any given place.

References Krugman, Paul R. 1996. “Making Sense of the Competitiveness Debate.” Oxford Economic Review 12 (3), 17–25. Porter, Michael. 1990. “The Competitive Advantage of Nations.” Harvard Business Review 68 (2), 73–93.

Pa rt  I

T H E C ON C E P T OF  L O C A L C OM P E T I T I V E N E S S

Chapter 2

The Strat e g i c M anagem ent of Pl ac e David B. Audretsch

1 Introduction There are two compelling observations about the economic performance of places. First, whether place refers to a neighborhood, city, state, or region, economic performance matters. Economic performance is important not just to residents and businesses at the place, but in neighboring and even quite distant places as well. Failure to generate and sustain a strong economic performance will almost invariably lead to a change in political leadership, and in certain extreme situations, even to changes in the political and economic systems. People care, and care deeply, about the economic performance of almost every place. The second observation is that the economic performance varies considerably across places. While some of the variation is no doubt attributable to the economic development context of the country in which the place is located, the variance of places within any single country is sufficiently high that it clearly does not provide any guarantee predetermining or even predisposing the economic performance of any particular place. The phenomenon and importance of what influences the economic performance of a place has not been lost on scholars. In particular, the fields of urban and economics have generated a vast and robust literature focusing on why some places do better while others struggle. As Glaeser and Gottlieb (2009, 986) point out, “Just as macroeconomics explores both differences in growth rates and differences in GDP levels across countries, urban economists wonder why some cities are rich, some cities are growing, and others are doing neither.” However, the fields of urban and regional economics by no means have a monopoly on the subject. Insights that can contribute to understanding the variance in spatial economic performance can also be garnered from a broad spectrum of academic disciplines and fields, ranging from sociology to innovation and technological

14    The Concept of Local Competitiveness change, labor economics, entrepreneurship, growth economics, psychology, regional studies, economic geography, and management. However, when it comes to the formulation and implementation of strategies to enhance and sustain economic performance, no policy field exists providing a framework for informing the decision-making of policy-makers and other interested parties. As Florida (2002, 294)  observes, “I have yet to find an American community whose leaders and citizens have sat down and written out an explicit strategy for building a people climate.” Similarly, Jack Hess, president of the Chamber of Commerce of Columbus, Indiana, complained, “We lack both a play book and board to keep score” (Audretsch 2015). This intellectual vacuum is surprising, since economic performance is of such paramount significance that an entire field of research, strategic management, has emerged to provide a framework for analyzing the performance of firms and organizations. The scholarly field of strategic management has had a focus on strategies deployed by firms to achieve and maintain a competitive advantage. The purpose of this chapter is to suggest the need for a field with the focus of informing not the formulation and implementation of strategies to enhance the performance of firms and other organizations but explicitly for places—the strategic management of place. The second section of this chapter identifies the mandate underlying the strategic management of place. The third section explains the economic rationale for undertaking place-based strategies. The fourth section examines the first element of the framework, resources and factors. The fifth section introduces the second element, spatial structure and organization. The sixth section explains the role of the third element, the human dimension, and the seventh section analyzes the fourth element, public policy. Finally a summary and conclusions are provided in the last section. An important conclusion of this chapter is that many, if not most, places undertake strategies to enhance their economic performance. However, the strategic management of places is generally undertaken without the benefit of a systematic intellectual framework, such as the one introduced by Audretsch (2015).

2  The Mandate The mandate for the strategic management of a place emanates from economic actors—individuals and firms—who have a stake in the economic performance of that place. If the costs, both economic and emotional or psychological, of moving to a different place are zero or trivial, any party, whether an individual or firm, not satisfied with the performance of any particular place, or at least the strategies of that place, would simply move to a different place. This would suggest that the mandate underlying the strategic management of a place comes from people who have invested in some type of sunk cost or place-specific investment that cannot be simply cashed in and redeemed by switching locations.

The Strategic Management of Place    15 If a cost cannot be recovered, it is considered to be sunk. If an individual or organizations has incurred large sunk costs, of both an economic or emotional type, then the compelling decision will be to invest in improving the place rather than move to a different location. In his seminal book Exit, Voice, and Loyalty, Albert O. Hirschman (1970) created a framework of decision-making focusing on the role of loyalty and voice in choice. According to Hirschman (1970), in the absence of an ability to exercise voice, a high degree of loyalty will dissuade a decision-maker from engaging in an exit strategy. It is loyalty that will cause a decision-maker to stick to the status quo. The impact of loyalty, of sticking with the status quo, will be that much greater to the degree that a decision-maker can influence, or at least is optimistic about improving, performance. While Hirschman’s framework was actually designed with consumer choice for commercial products in mind, it also holds for the decision confronting individuals to remain in a place, invest in improving that place, or move to a difference place. Clearly many of the emotional connections that people have to a place contain a sunk dimension to them. For example, people cannot alter the fact of the place where they are born, just as the place hosting the graves of dear relatives cannot normally be moved. The feel or smell of a particular place is often unique and can neither be replaced nor replicated. For organizations, the ability to replace a place that has access to a crucial scarce resource, such as a specialized type of knowledge, or access to a cluster of firms and network of people may also be difficult to replace and replicate. The sunk nature of such connections to a place will tend to generate a demand for the strategic management of that place because there are no other alternatives for the individuals and organizations needing those connections. In some cases, such costs that are sunk are the result of investments in property or plant and equipment, or even housing under certain adverse market conditions. However, the emotional ties and connections to a place that are sunk may be substantially more prevalent and compelling. People have personal and emotional connections and bonds to family, friends, and a place that cannot simply be replicated through market transactions involved in moving to a new place. Perhaps the most important driving force underlying what Al Link (1995) has described as a “generosity of spirit” was the emotional conviction felt by leaders and the population of the Research Triangle region in North Carolina, that if they did not create opportunities within that region for their children, grandchildren, and great-grandchildren, they would move away to pursue opportunities elsewhere. Given the costs incurred in moving multiple generations to keep the family located together, the alternative of investing in new opportunities that could be created within the region seemed to be the better idea. This sense of place for people therefore had a component that was sunk. While their children could certainly follow the American tradition of “heading west,” or at least heading to where the opportunities existed, this also might prove to be disruptive to close family relationships. The sunk component involves not just an economic cost but also an emotional cost.

16    The Concept of Local Competitiveness A different aspect of sunk costs involves networks, linkages, interactions, leadership, identity, and emotional affinity. These are all specific aspects bestowed upon a place by what could be ostensibly characterized as some aspect of social capital. An individual that has a high degree of place-based social capital will also incur a high degree of sunk costs. If she moves away, the investment she made in accessing and procuring that social capital will be lost. Of course, the degree to which social capital is sunk depends upon the extent to which that social capital is exclusive to the place. If it depreciates over geographic space, then investments in creating and accessing that social capital will tend to be sunk. Of course, large and fixed investments in physical capital can also result in sunk costs. For example, Lilly, a major corporation in the pharmaceutical industry, has had its headquarters located in Indianapolis, Indiana, for decades. The company determined in the 1990s that it was spatially isolated, in that its geographic distance from other companies in the biomedical industry prohibited Lilly from interacting and interfacing with those firms. Lilly was spatially isolated in that it was not located in a biomedical or life science cluster. Having access to the ideas, people, and linkages of life science clusters, in places with a viable life science or biomedical cluster, such as San Diego or Research Triangle Park, would have generated a valuable flow of key knowledge and ideas to the company, presumably spurring innovative activity. However, Lilly decided that, rather than move to a different place with a vibrant life science or biomedical cluster, which would have involved disrupting key human relationships and other aspects of social capital, it would instead be economical to champion the strategic management of Indianapolis to develop a life science and biomedical cluster of its own. In the subsequent decade, championed by the Lilly Corporation, the city of Indianapolis, along with the central Indiana region, devoted considerable resources and effort to jump-start a biomedical and life science cluster. This effort involved recruiting the commitment of the Lilly Foundation, which has invested a considerable amount of its funds in order to attract leading scientists and scholars to universities in Indiana, as well as to promoting the life science cluster as a priority in the strategic management of Indiana. The point is that, in the absence of substantial costs that are sunk, perhaps the decision-making of the Lilly Corporation might have been different, and the company would simply have fled its spatial isolation to join an already established and vigorous life science and biomedical cluster at a different location.

2.1  The Rationale The concern and demand for strategic decisions to enhance the economic performance of any particular place provide the basis for the mandate for the strategic management of a place. There is also a compelling economic rationale. This economic rationale addresses why market solutions will not generate an acceptable or desired economic performance for a place. After all, if the market economy results in the optimal

The Strategic Management of Place    17 allocation of resources, there should be no mandate at all for engaging in the strategic management of a place. The doctrine of market failure suggests that in the absence of intervention, market outcomes will be less than desirable and certainly not optimal. The economic rationale justifying policy intervention has its basis in the inability of markets to efficiently allocate scarce resources. The existence of market failure provides such a rationale as to why markets fail to efficiently allocate scarce resources, thereby justifying external intervention. In the case of places, there are four particular types of market failure providing a rationale for devising and implementing the strategic management of places. What has been characterized as network externalities constitutes the first type of market failure. When the value of an activity of either an individual or a firm depends upon the locational proximity of other firms or individuals that are engaged in similar activities, those activities are characterized by network externalities. The existence of such network externalities suggests that a firm or an individual will gain by being located at the same place in order to access the other complementary individuals and organizations. If the externalities of a network are localized, an individual or firm has to be at that location or place in order to access the benefits. The major reason for such network externalities being localized is that they have a strong tacit, rather than codified, component, so that geographic proximity is required to access and absorb the externalities. Thus, when network externalities are localized, the economic value of such an individual or organization will be enhanced, and in any case exceed the economic value attained in the absence of such network externalities. Thus, the lure of accessing network externalities gives both individuals and organizations a compelling incentive to make its locational decision on the basis of locating in the geographic presence of network externalities. Conversely, a paucity of network externalities will tend to deter organizations and individuals from locating at that particular place. An important implication concerning the lure of accessing network externalities for individuals and organizations in making their locational decisions is that if a place suffers from a paucity of network externalities, the strategic management of that place may need to provide compensating differential incentives to induce productive individuals and organizations to locate at that place. Once the network externality has been created, such compensating differential incentives are no longer required to compete for the locational preferences of individuals and organizations. The second source of market failure is the result of knowledge externalities. Arrow (1962) explained how new ideas (and knowledge more generally) that are introduced by an organization or individual can be used by other organizations and individuals, sometimes at low or no cost. According to Arrow, knowledge and ideas are characterized as a public good in that the use by one party does not prevent others from using that same knowledge or idea. However, what makes an idea different from information is its inherently tacit nature. While information can be transmitted at zero marginal cost across geographic space via the Internet, understanding a new idea or knowledge often requires face-to-face and

18    The Concept of Local Competitiveness nonverbal communication. Thus, knowledge and ideas tend to be spatially localized. In order to access such knowledge or ideas, geographic proximity to the source of that knowledge is required. Thus, while knowledge has a high propensity to spill over from the source creating that knowledge to be accessed by third-party individuals, organizations, and firms, spatial proximity to the knowledge source is a prerequisite. Knowledge tends to spill over, but the knowledge spillovers tend to be geographically localized within close proximity to the knowledge source (Audretsch and Feldman 1996; Acs, Audretsch, and Feldman 1994). A place that is rich in knowledge also generates considerable knowledge spillovers. Individuals, organizations, and firms are attracted to such a place that is rich in knowledge in order to access the knowledge spillovers. By contrast, a place with a paucity of knowledge does not generate substantial knowledge spillovers and tends to be less of a magnet for individuals, organizations, and firms seeking to access knowledge (Audretsch, Keilbach, and Lehmann 2006; Audretsch and Keilbach 2007 and 2008). An implication of a place with vigorous knowledge spillovers is that the value created by knowledge investments at that place exceeds the value appropriated by any single individual, organization, or firm. Thus, an important rationale for undertaking the strategic management of a place is to implement appropriate incentives to realign the local value of knowledge with that of the private valuation of knowledge (Audretsch, Keilbach, and Lehmann 2006; Audretsch 2015). A third source of market failure providing an economic rationale for the strategic management of places involves a very different type of externality—the externalities emanating from entrepreneurial failure. In the absence of any externalities, the failure of an enterprise is associated with private losses. However, when an entrepreneurial start-up fails, there are a number of positive externalities that can benefit other firms, individuals, and entrepreneurs. Thus, there can be a positive economic value generated when entrepreneurial start-ups fail (Audretsch and Keilbach 2007). A large body of scholarly research has found consistent and compelling empirical evidence that the likelihood of a new-firm start-up surviving beyond just a few years is not very high (Audretsch 1995). The likelihood of new firm survival is even lower in knowledge-based or high-technology industries (Audretsch 1995). According to Audretsch (1995), the high degree of uncertainty combined with knowledge asymmetries results in particularly low rates of new-firm survival in high-knowledge and high-technology industries. However, even though many of these knowledge-based and high-technology firms fail, there is considerable economic value created in the form of knowledge about what does not work, as well as some aspects of new technologies, ideas, and innovations. Thus, because of the positive externalities generated by entrepreneurial failure, the expected value of a new-firm start-up to a (nascent) entrepreneur will tend to be less than the actual economic value of entrepreneurial activity to a particular place. This would suggest a key role in the strategic management of a place is to find and implement instruments that bring the expected value of an entrepreneurial start-up in line with the economic value to that place.

The Strategic Management of Place    19 In fact, many ideas generated by failed entrepreneurial start-ups have become the basis for subsequent, successful entrepreneurial activity. For example, Intel’s founders used knowledge that they learned from Fairchild electronics, a firm that ran into trouble (Klepper 2009). That is, there is a value to the learning generated by the entrepreneurial failure that can be passed on to other firms. Perhaps the most compelling example of the performance of a place being positively impacted by an entrepreneurial failure involves the founding of the new firm Fairchild, which was one of the first companies to produce semiconductors. Even though Fairchild failed within several years, it is, in fact, generally credited as the catalyst not just for the semiconductor industry, but, perhaps of even greater magnitude, for Silicon Valley, which has become the world’s most innovative and entrepreneurial place (Klepper 2009; Audretsch 2007). Fairchild itself had very little direct impact on either the semiconductor industry or what would become known as Silicon Valley as a place (Klepper 2009). However, it was the indirect impact that triggered not just the takeoff of the semiconductor industry but also what would ultimately become known as Silicon Valley. One of the key engineers at Farichild was Robert Noyce, who had a vision for the product and industry that did not resonate with his employer. Noyce felt that the best strategy for Fairchild to achieve a strong innovative performance would be to link employee compensation to the performance of the company, which at that time was unprecedented. In particular, Noyce argued that employee compensation should include stock options. However, the owners of Fairchild disputed Noyce’s recommendation: “Noyce couldn’t get Fairchild’s eastern owners to accept the idea that stock options should be part of compensation for all employees, not just for management. He wanted to tie everyone, from janitors to bosses, into the overall success of the company” (Cringley 1993, 39). Not only did Noyce’s proposed strategy clash with the corporate policy concerning employee compensation, but it also tried to create a radically different corporate culture. Noyce argued that empowering employees by enabling them to participate in a horizontal decision-making process would spur productivity and innovation. To some degree, Noyce’s vision reflected the emerging entrepreneurial culture of the region. In any case, Noyce’s proposed strategy clashed with the traditional hierarchical corporate structure prevalent at that time, so, not surprisingly, it was rejected by Fairchild. Noyce was so convinced by his own vision and strategy that he decided to leave Fairchild and join Gordon Moore in starting a new semiconductor firm, Intel. Intel, of course, would ultimately become one of the most successful and greatest impact companies in the semiconductor industry. The emergence of the Silicon Valley identity and brand seemed to grow with Intel’s ascendance. Even as Intel ultimately became the flagship company of Silicon Valley, Fairchild’s failure seemingly doomed it to a historical footnote. However, a number of scholars have credited the emergence of Silicon Valley as a high-performing innovative and entrepreneurial place to Fairchild, which ultimately spawned not just Intel, but also a host of other high-powered start-ups, such as Advanced Micro Devices (AMD) and National Semiconductor. These start-ups, in turn, ended up spawning yet another generation of Silicon Valley start-ups, leading Klepper (2009, 80), to classify the spin-offs as “Fairchildren.” Klepper (2009, 80) pointed out that

20    The Concept of Local Competitiveness Fairchild parented and planted the seeds of what emerged as the global brand “Silicon Valley.” According to Klepper (2009, 80), “Nearly all of the spin-offs were descended in one way or another from Fairchild, whose direct descendants are so numerous they have been dubbed the Fairchildren.” As Klepper (2009) makes clear, Fairchild did not seem to generate much significant economic value for its owners. However, the economic value of the entrepreneurial failure to the place, which ultimately would become known as Silicon Valley, is substantial. The example of the entrepreneurial failure of Fairchild in Silicon Valley is not isolated—entrepreneurial failure can often generate positive economic value to other individuals and firms, and even an entire region, even while the firm itself is unable to appropriate the value generated by its entrepreneurial venture. The fourth source of market failure emanates from exactly the opposite situation—not entrepreneurial failure but rather entrepreneurial success. Particularly in places that have had only a paucity of entrepreneurship and which are burdened by an entrepreneurial deficit, the demonstration of successful entrepreneurship can induce individuals and firms both within and beyond that place to alter their behavior. As people learn that entrepreneurship is indeed possible and positive, the decision-making process will induce others to become entrepreneurs. Thus, the demonstration effects are a powerful instrument for the strategic management of a place, in serving as a catalyst to enable a place to become more entrepreneurial (Audretsch 2008). Market failure is not the sole basis for the rationale for undertaking the strategic management of a place. A very different basis lies in the spatial discrepancy between markets and any particular place. In general markets do not map neatly onto and align with the spatial dimension of a specific place. That is, a place is not a market. Just as many goods and services have geographic markets that extend beyond that of any particular place, some markets are global in nature. In any case, in the absence of government-imposed regulations and restrictions, most markets do not correspond with the geographic dimension of the place. Thus, the outcomes of market forces may not coincide with the performance goals of any particular place, giving rise for the strategic management of that place to enhance its performance.

2.2  The Participants While the previous sections explained the mandate and rationale, the next issue is to identify who actually engages in the strategic management of a place. The first response might be that the government undertakes policies for the public good. However, the reality is considerably more nuanced and complicated. Who actually undertakes the strategic management of a place is highly place dependent. In some contexts, it is in fact, largely the government. In other contexts the strategic management of the place is driven largely by private firms and private interests. Most typically, there is a combination of public, private, and nonprofit organizations, interests, and individuals that engage in the strategic management of a place. It is the particular mix of these different actors, roles, and interests that varies considerably from place to place.

The Strategic Management of Place    21 Not surprisingly, given the traditions, along with the political, social, economic, and cultural context, prevalent in Europe, the government tends to play a central role in the strategic management of places there. In particular, the framework of the European Union Cohesion Policy provides for an explicit mandate for the formulation and implementation of the strategic management of places. In order to be eligible for funding under the European development programs, each region must, as a prerequisite develop what is referred to as a smart specialization strategy as a condition for receiving funding in the European development programs (Thissen et al. 2013). Even within the context of Europe, there is considerable variation in the mandated role of the government in ensuring or at least striving to ensure a strong economic performance. For example, a legal and constitutional mandate exists in Germany for local governments to try to ensure a high level of economic performance by engaging in the strategic management of that place, or what is referred to as Standortpolitik. According the former minister of finance in Germany, Wolfgang Schaeuble, Standortpolitik is facilitated by the fundamental Ordnungspolitik, which “is an institution that lays the groundwork for reliable long-term policymaking and that by itself can counteract undesirable fiscal and economic developments” (Hareshan 2012). Practically speaking, what Ordnungspolitik does is provide the legal mandate for the government to ensure an orderly and prosperous economic performance. There is yet an additional legal mandate compelling governments at all levels in Germany to promote economic performance through the strategic management of place. A second feature reinforcing the mandate for the government to be responsible for the strategic management of a place in Germany is Strukturpolitik, which requires the government to ensure that the place has a viable and effective industrial structure that will generate a satisfactory economic performance. The legal and institutional framework for devising and undertaking Standortpolitik provides a mandate for local and state governments to actively engage in the strategic management of their place to ensure a strong economic performance along with prosperity (Audretsch 2015). While local, Länder (state), and the Bund (federal) governments all have a mandate to undertake Standortpolitik, or the strategic management of place, in Germany, there are also other parties and interests involved. Policy in Germany takes place within the framework of the underlying principle of Konsens, or consensus. Forming such a consensus generally includes the views and interests of firms from the private sector (Industrie), and workers in unions (Gewerkschaft), who contribute to policy formulation along with the government. Thus, there is strong input, and support, of both the private sector and workers via the organized unions in formulating Standortpolitik, or the strategic management of places within the German context (Audretsch 2015). Economic performance clearly varies across places within both the European and German contexts. Some places, such as the German Länder of Bavaria and Baden-Württemberg have exhibited very strong economic performances, which seem to reflect highly successful strategies implemented by those places. While unemployment exceeds 20 percent in Spain and 25 percent in Greece, as well as 10 percent across the entire European Union, it was only 3.8 percent in Baden-Württemberg and

22    The Concept of Local Competitiveness 3.5 percent in Bavaria (Hubschmid 2012). This strong and exceptional economic performance clearly reflected the success of the strategic management of those particular places and not the entire country, as evidenced by other places in Germany, such as Bremen, with an unemployment rate of 11.2 percent, and Berlin, with an unemployment rate of 12.2 percent. Not surprisingly, given its political, social, and cultural history and traditions, in the United States, private firms, interests, and individuals play a stronger role in shaping the strategic management of a place. Because of their sunk costs incurred, private companies and individuals have a considerable interest in improving the performance of a place rather than simply moving away to a new location. Nonprofit organizations can also be engaged in the strategic management of a place. Many public universities, and certainly land-grant universities, have an explicit mandate to contribute to the economic performance of their place. For example, Indiana University advises passers-by on a billboard between Bloomington and Indianapolis that it is “Creating an Innovative Indiana.” Especially in the areas of investments in the resources and factors involving knowledge, human capital, and creative capital, universities can play a key role in the strategic management of a place. There are compelling cases where universities join forces with government policy in the strategic management of a place. For example, Indiana University created the Council for Regional Engagement and Economic Development (CREED) and the Innovate Indiana Network to “enhance connectivity and extend the initiative across functional areas of expertise throughout seven Indiana University campuses, each of which an impact on economic development” (Indiana University Newsroom 2013) in the state of Indiana (Audretsch 2015). The above examples highlight the rich interplay of both private and public interests in the strategic management of a place. The most compelling examples of places pursuing successful strategies that positively impact their economy are places where lots of individuals and organizations, in both the private and public sectors, are actively involved.

3  Resources and Factors The role that resources and factors play in shaping the economic performance of a place draws largely on traditional economics, dating back at least to Ricardo. According to models of comparative advantage, the performance of a place is determined by its endowment of resources combined with its productivity in using those resources relative to other locations. While classical economics generally considered natural resources and physical capital, which could be combined with unskilled labor to manufacture goods, neoclassical models focused almost exclusively on two factors—labor and physical capital. Because physical capital was scarce and valuable relative to unskilled labor, those places with an abundant endowment of physical capital were expected to exhibit a superior economic performance.

The Strategic Management of Place    23 Thus, when analyzed through the lens provided by neoclassical economics, the key to enhancing and maintaining the economic performance of a place was to attract and sustain investments in physical capital. Such investments in physical capital could be made by private firms or by public entities, such as governments. Government investments in physical capital could be made through publicly owned enterprises or else in infrastructure. A heated and emotion-laden debate during the Cold War of the 1950s and 1960s suggested that many scholars and policymakers did not have a particular bias as to which type of investment—public or private—in physical capital would be more efficient in enhancing economic performance. In fact, a number of top scholars argued that the public investments made by the governments of the Soviet Union and its Eastern European allies would actually be more productive than the corresponding private investments in physical capital that were more prevalent in the West. The main point, however, was that investments in physical capital were the driving force shaping economic performance in the economy characterized by the Solow model (Solow 1956 and 1957). Those places that were able to accumulate physical capital were also expected to exhibit the strongest economic performance. Thus, the key to economic performance that seemed to span places across the entire spectrum of development contexts was to attract and maintain investments in physical capital, albeit through inward foreign direct investment in the less developed context. This traditional neoclassical framework about the determinants of comparative advantage and economic performance was modified by the observation that places deployed not just unskilled labor, but skilled labor as well. Two-factor labor models incorporated both skilled and unskilled labor. Those places with a higher component of skilled labor generally exhibited a stronger economic performance (Glaeser and Gottlieb 2009; and Glaeser, Ponzetto, and Tobio 2014). The insight that labor is heterogeneous and consists of various skill levels was subsumed in the idea that human capital is a factor that is distinct from physical capital. In fact, over time, human capital has become considered to be a key for economic performance for places in the leading developed context. Certainly the strong economic performance exhibited by places such as Silicon Valley, Austin, and also Copenhagen and Stockholm are more attributable to their endowments of human capital than physical capital. An even broader view of human capabilities that enhance the economic performance of a place was posited by Florida (2002), who suggested that it is human creativity that is most conducive to achieving and maintaining a sustained competitive advantage. In The Rise of Creative Class, Florida (2002) proposed a broader approach to thinking about and measuring what has traditionally been considered to constitute human capital. Instead of using measures of education, such as number of years in school, Florida relies on occupational classifications to categorize workers as members of the creative class, the working class, or the service class. He subsequently classifies the creative class into more specific groups, including the supercreative core, which consists of scientists, academics, and artists. The remaining creative professionals consist of management, finance, law, healthcare, and high-end sales. According to Florida (2002), creativity

24    The Concept of Local Competitiveness needs to measure and reflect more than just years in school; rather than being based on educational attainment, measures need to better reflect the creativity a job involves. Based on his measures of the creative class, Florida (2002) finds a positive relationship between the measures of creativity in cities and their economic performance. A somewhat different but related factor is knowledge. Human capital and skilled labor are unambiguously embedded in people. By contrast, knowledge consists of ideas, which can certainly be embedded in people but also in firms and universities, and even more broadly, the place. Knowledge is often linked to, or considered to result from, research and development (R & D) undertaken in private companies, or research undertaken at universities or public or nonprofit research institutes. Studies consistently find that those places with greater expenditures on R & D and university research tend to exhibit a superior economic performance (Audretsch and Feldman 1996). However, an important caveat is that the contribution of universities in the strategic management of a place is not just the research that is undertaken but also the spillovers of that knowledge for commercialization and ultimately innovative activity (Audretsch, Keilbach, and Lehmann 2006; Audretsch and Keilbach 2008). Universities can be decomposed as consisting of three layers or rings. At the inner core are activities and research that are motivated by basic science—knowledge for its own sake. While research devoted to furthering an academic discipline is a crucial component for knowledge, it is also distant from being commercializable in most cases. This inner ring consists largely of traditional scholarly disciplines, such as philosophy and physics, that represent the view of the universities posited by von Humboldt, which emphasized academic freedom to pursue teaching and research without adhering to the dictates of the church and state (Audretsch 2007). By contrast, the second ring could be characterized as constituting applied research, in that the orientation is not knowledge for its own sake but rather providing a solution to a problem or meeting a demand in society. This ring consists largely of interdisciplinary applied programs, such as informatics and biochemistry. The third ring consists of mechanisms and programs designed to facilitate the spillover of knowledge from the first two rings into society so that they can exert an impact. This ring consists of technology transfer offices, science parks, innovation centers, incubators, and offices of community engagement. Finally, beyond the walls or legal boundaries of the university are institutions and organizations designed with a capacity to absorb the knowledge spillovers from the university and help transform them into innovations. Examples include the Georgia Research Alliance, the Frauenhoffer Institutes, and the Twenty First Century Fund in Indiana, in addition to private firms (Audretsch 2007). While judgments of which factors and resources matter the most have evolved over time, from a focus on natural resources to physical capital, human capital, and subsequently the creative class and knowledge, what they have in common is that the size of the endowment matters—bigger is better.

The Strategic Management of Place    25

4  Spatial Structure and Organization While the first dimension of the strategic management of place framework focuses on the amount or quantity of a particular resource or factor, the second dimension instead focuses on how they, along with the accompanying activity, are structured and organized. There are several important ways in which spatial structure and organization matter—clusters, market power, competition, entrepreneurship, specialization, and diversity. According to Porter (1998a, 1303) in The Competitive Advantage of Nations, a cluster consists of businesses in related industries operating at the same place, “geographic concentrations of interconnected companies, specialized suppliers, service providers, firms in related industries, and associated institutions . . . in particular fields that compete but also cooperate.” As the popularity of the “cluster” concept spread, it has been used to characterize a wide range of economic phenomena. Examples of clusters include software and semiconductors in Silicon Valley, wine in the Boudreaux region of France, banking in London, filmmaking in Hollywood, biotechnology in San Diego, and recreational vehicles in Angola, Indiana. Bresnahan and Gambardella (2004, 351) reviewed the scholarly literature on clusters and found that “Clusters of high-tech industry, such as Silicon Valley, have received a great deal of attention from scholars and in the public policy arena. National economic growth can be fueled by development of such clusters. In the United States the long boom of the 1980s and 1990s was largely driven by growth in the information technology industries in a few regional clusters. Innovation and entrepreneurship can be supported by a number of mechanisms operating within a cluster, such as easy access to capital, knowledge about technology and markets, and collaborators.” Clusters can enhance the economic performance of a place by generating gains accruing from agglomeration economies. Delgado, Porter, and Stern (2011) provide econometric evidence showing that if a firm is located within a cluster, it enjoys greater employment growth, wage growth, and innovative activity. A second aspect of spatial structure and organization involves the degree of market power. Three disparate strands in the academic literature have identified how and why market power can influence economic performance at a place. The first strand comes from the field of industrial organization or industrial economics, which identified how firms with a high share of the market had a positive impact on the economic performance of industries. The second strand of literature is from the field of strategic management within the academic, which analyzed the impact of firm size and power in markets power on performance (Chandler 1977 and 1990). The third strand in the academic literature is what is referred to as the Marshall-Arrow-Romer model in economics (Glaeser et  al. 1992). According to this view, monopoly power generates a superior economic performance for a place than

26    The Concept of Local Competitiveness does competition. The superior economic performance of the place is attributable to the high, sustained performance and rate of the return accruing to market power (Glaeser et al. 1992). However, the higher rate of return for firms with market power has to be transferred or redistributed to the place for the place to share in this greater return. A number of such redistributive or transformative mechanisms exist, which include organized labor, civic engagement, and philanthropy (Acs 2013). The third aspect of spatial organization and structure involves exactly the opposite of market power—competition. Porter (1990) and Jacobs (1969) both suggest a spatial structure characterized by competition is more conducive to a strong economic performance than is market power. Localized competition facilitates knowledge spillovers because if a particular idea is not used by one firm, it is likely to be contested and used by a different enterprise. Ideas that have value are less likely to go unused. It is important to recognize that the competition that Jacobs and Porter argued was particularly important was not necessarily in product markets, which could be global in nature, but rather in input or factor markets, and particularly in the markets for knowledge, ideas, and human capital. Glaeser and coauthors (1992) show that the degree of competition and economic performance are positively related in US cities. Another aspect of spatial organization and structure involves the extent to which a place generates entrepreneurial activity. The knowledge spillover theory of entrepreneurship suggests that the economic performance of a place will be stronger because entrepreneurship facilitates spillovers from organizations producing knowledge to new organizations where those ideas are introduced into the market and transformed into innovations (Feldman and Audretsch 1999). While both private firms and public organizations such as universities and nonprofit research institutions generate knowledge and new ideas, the existence of the knowledge filter can impede the spillover of that knowledge and ultimately the commercialization and innovation of those ideas. By taking those ideas and knowledge that otherwise might not be pursued and commercialized, entrepreneurs serve as a conduit of knowledge spillovers by starting a new firm. Just as the Solow (1956 and 1957) model suggested that knowledge falls like “manna from heaven,” and the Romer (1986) model argued that it would be accessible through knowledge externalities that were assumed to occur, the knowledge spillover theory of entrepreneurship suggests that knowledge spillovers are a byproduct of purposeful entrepreneurship. Entrepreneurs provide a crucial mechanism for facilitating the spillover of knowledge and ideas from the organization or firm where they were created to a new firm where they will be commercialized and transformed into innovative activity. There is a large body of empirical studies validating the knowledge spillover theory of entrepreneurship (Ghlo et al. 2014). These studies provide compelling systematic empirical support of the knowledge spillover theory of entrepreneurship across multiple levels of analysis—the individual, firm, city, state, region, and even country (Braunerhjelm et al. 2010). What is particularly compelling for the strategic management of places is

The Strategic Management of Place    27 that those places with a greater degree of entrepreneurial activity, typically measured in terms of startup rates, also tend to exhibit higher levels of economic performance, typically measured in terms of economic growth. A different aspect of spatial organization and structure is the extent to which economic activity is specialized. Specialization might enhance the economic performance associated with a place by reducing the transactions cost of engaging in business since firms and individuals would be engaged in the same type of activity. By contrast is the view that exactly the opposite, diversity, is more conducive to a strong economic performance. Jacobs (1969) argues that diversity is more conducive than specialization to a strong economic performance on the grounds that interindustry knowledge spillovers are more important than intraindustry knowledge spillovers. According to Jacobs (1969), heterogeneous activity with a complementary basis will be the most conducive to such knowledge spillovers, suggesting that diversity is more important than specialization in promoting economic performance. A solid body of empirical evidence highlights the importance of diversity in promoting economic performance. Glaeser and coauthors (1992) and Audretsch and Feldman (1999) provided the first systematic empirical test linking the extent to which a city is specialized or characterized by diversity to economic performance. Their results provided compelling support for the diversity thesis and led to their rejection of the specialization thesis.

5  The Human Dimension The third main component of the framework for the strategic management of places involves the human dimension, or people. Since human capital, skilled labor, and the creative class are all important aspects of factors and resources, it might at first seem that the role of people, or the human dimension, has already been covered in the framework. However, when analyzed through the lens of factors and resources, the contribution of people to shaping the economic performance of a place is in terms of their endowment of skills, human capital, and creativity—their talents and abilities. The human dimension element of the framework incorporates broader ways in which the people at a place can influence the economic performance of that place. In her book Regional Advantage (1994), Anna Lee Saxenian pointed out that the endowment of human capital between Silicon Valley in California and Route 128 around Boston was roughly equivalent based on standard measures. Her book was devoted to identifying what accounted for the systematically greater economic performance exhibited by Silicon Valley vis-à-vis Route 128. Her answer had a lot to do with the human element. It was not the magnitude of the endowment of human capital, skilled labor, or even what could be termed the creative class. Rather, it was a very different aspect involving people, which are incorporated in the framework presented by Audretsch (2015) as the human dimension.

28    The Concept of Local Competitiveness The human dimension consists of four main aspects—networks and linkages, social capital, identity and image, and leadership. The importance of networks and linkages is the focus of Saxenian’s (1994) poignant example of why the economic performance of Silicon Valley has generally exceeded that of Route 128 around Boston. In her detailed study comparing Silicon Valley and Route 128, Saxenian (1994) emphasizes the key role that networks, linkages, and interactions play in shaping the performance of a place. After documenting that the knowledge factors, in terms of human capital, R & D, and university research, are comparable between the two places, she shows that the economic performance exhibited by Silicon Valley is vastly superior to that of Route 128. She attributes the superior economic performance of Silicon Valley to the rich networks prevalent in Silicon Valley that do not seem to exist around Route 128:  It is not simply the concentration of skilled labor, suppliers and information that distinguish the region. A variety of regional institutions—including Stanford University, several trade associations and local business organizations, and a myriad of specialized consulting, market research, public relations and venture capital firms—provide technical, financial, and networking services which the region’s enterprises often cannot afford individually. These networks defy sectorial barriers; individuals move easily from semiconductor to disk drive firms or from computer to network makers. They move from established firms to startups (or vice versa) and even to market research or consulting firms, and from consulting firms back into startups. And they continue to meet at trade shows, industry conferences, and the scores of seminars, talks, and social activities organized by local business organizations and trade associations. In these forums, relationships are easily formed and maintained, technical and market information is exchanged, business contacts are established, and new enterprises are conceived. . . . This decentralized and fluid environment also promotes the diffusion of intangible capabilities and understandings. (155)

Saxenian (1994) further claims that even the language and vocabulary used by technical specialists can be specific to the region, to the point where a technician from Silicon Valley would not understand one working along Route 128. A different aspect of the human dimension involves social capital. The World Bank (n.d.) defines social capital as “the norms and networks that enable collective action. It encompasses institutions, relationships, and customs that shape the quality and quantity of a society’s social interactions. Increasing evidence shows that social capital is critical for societies to prosper economically and for development to be sustainable. Social capital, when enhanced in a positive manner, can improve project effectiveness and sustainability by building the community’s capacity to work together to address their common needs, fostering greater inclusion and cohesion, and increasing transparency and accountability.” This aspect of the human dimension focuses on social organizations and other groups that facilitate collective action among people (Putnam 2000; Coleman 1988). Putnam defines (2000) social capital thus: “Whereas physical capital refers to physical objects and human capital refers to the properties of individuals, social capital refers

The Strategic Management of Place    29 to connections among individuals—social networks and the norms of reciprocity and trustworthiness that arise from them. In that sense social capital is closely related to what some have called ‘civic virtue.’ The difference is that ‘social capital’ calls attention to the fact that civic virtue is most powerful when embedded in a sense network of reciprocal social relations. A society of many virtues but isolated individuals is not necessarily rich in social capital.” Thus, according to Putnam (2000) and Coleman (1988), social capital reflects the relationships that individuals have in a social context, and in particular it is a concept that reflects how people interact and relate to each other each other. Putnam (2000, 19) makes a direct link between social capital and the economic performance of a place:  “Social capital refers to features of social organization, such as networks, norms, and trust, that facilitate coordination and cooperation for mutual benefits.” A strong and positive statistical relationship has been identified by scholars between various measures of social capital and economic performance (Durlauf 2002; Sobel 2001). A rich body of empirical studies has found that this positive relationship between measures of social capital exists across multiple dimensions of analysis, spanning organizations, firms, cities, and regions (Rupasingha, Goetz, and Freshwater 2002). A different aspect of the human dimension involves the identity and image of a place. The identity that people associate with a place along with its image can reinforce decisions to invest in a place or, alternatively, to disinvest from a place. Scholars have mapped out how distinct identities or personalities vary across geographic space. In “Divided We Stand:  Three Psychological Regions of the United States and their Political, Economic, Social and Health Correlates,” Rentfrow and coauthors (2013) link five major personality traits—openness, conscientiousness, extroversion, agreeableness, and neuroticism—to specific places located in the United States. The Midwest is characterized as being friendly and conventional. The West Coast and Rocky Mountain region are characterized as relaxed and creative. By contrast, the Northeast is characterized as temperamental and uninhibited. As Florida (2013, 101) explains, the underlying personality characteristics reflect that particular place’s identity and is instructive “for understanding the geographic clustering of talent and innovation,” Rentfrow and coauthors (2013, 1001) suggest that the economic performance of a place is shaped by the underlying identity: “Part of the reason why certain regions of the United States are economically vibrant may have to do with the psychological characteristics of residents.” The fourth aspect of the human dimension involves leadership. Leadership can have a strong and positive impact on the economic performance of a place. Link (2002), in his book From Seed to Harvest:  The Growth of Research Triangle Park, attributes the strong performance of the Research Triangle Park region to a combination of leadership, image, and affinity. In particular, Link (2002) shows how leadership enabled the region to shift from being one of the poorest and most impoverished in the United States to one of the wealthiest in the world. Link attributes both the formation of the Research Triangle Park in North Carolina and the growth of the entire Research Triangle region, linking Chapel Hill to Raleigh and Durham, to committed and enlightened leadership in a place where the residents had considerable affinity and loyalty. In A Generosity of

30    The Concept of Local Competitiveness Spirit: The Early History of the Research Triangle Park, Link (1995) singles out the role of leadership as contributing to the economic ascendance of the place.

6  The Role of Policy Policy plays an important role in the framework for the strategic management of places in two different ways. The first is through institutions. Some places have institutions that are adept in accessing information and knowledge that facilitate decision-making, and in implementing the strategic management of a place. Similarly, some places invest in institutions with the capacity to scan strategies pursued by other places and to analyze those policies for their appropriateness in different contexts. The second way in which policy plays an important role in the strategic management of places is by intervening in the other three components of the framework to try to positively influence economic performance. Since each of these three components is quite different, policy requires a different approach for each component. In general the policy instruments deployed need to be in concordance with the policy goals. For example, in terms of the first component, resources and factors, policy would typically focus on enhancing the particular resource or factor that has been determined to be important for enhancing the economic performance of a particular place. Policy instruments can beg firm oriented, such as preferential interest rates on loans or subsidies, if the focus is on inducing firm-specific investments in physical capital. By contrast, if the focus is on human capital, instruments such as education may be more relevant for policy. A focus on attracting the creative class shifts the relevant policy instruments to place-specific amenities enhancing the quality of life. Policies with a focus on knowledge may consider instruments that facilitate investment in research and development, along with spillover mechanisms. In terms of the second component, spatial structure and organization, policy would typically implement instruments that modify the structure and organization of economic activity in a manner that yields a more favorable economic performance. For example, if it is determined that infusing or encouraging entrepreneurship would enhance the economic performance of a place, instruments such as providing start-up capital, consulting services, and networking activities among entrepreneurs may have a priority. In terms of the third component, the human dimension, policy would typically implement instruments that either facilitate networks and linkages and social capital more generally, or else brand the place with a particular image or identity. It is important to note that no singular formula or universal prescription exists that can be applied universally to every place at every point in time. Rather, context matters. What makes sense for one place may be less compelling for another. Thus while Wallsten’s (2004, 229) observation that “Policy makers around the world are anxious to find tools that will help their regions emulate the success of Silicon Valley and create

The Strategic Management of Place    31 new centers of innovation and high technology” may be valid, the focus may be more on success, or strong economic performance, than on formulistic tools or simply cloning and copying what Silicon Valley may have done.

7 Conclusions An irony of globalization may be that, as Thomas Friedman (2008) so poignantly explained, while it has resulted in a flat earth, in that every place now has the opportunity to compete for a strong and sustained economic performance, in fact not every place will succeed. To simply say that there will of course be winners and losers is to trivialize the effort and hard work that some places invest in generating a successful performance. While luck may play a role in the economic performance of a place, as it does for all units of economic analysis, so too does strategy (Audretsch, Link, and Walshok 2013). There have been sufficient compelling theoretical reasons confirmed by empirical evidence to be confident that the strategic management of a place can make a difference, both positively and negatively. This chapter has provided a brief overview of Audretsch’s (2015) framework for analyzing the strategic management of a place. It is introduced to move beyond simplistic mantras of public policy that are fixated on a single policy to address what is a complex challenge. While approaches such as cluster policy or creative class policy are certainly bona fide policies in the arsenal for the strategic management of places, they certainly do not represent the end-all of policies and their associated policy instruments. Rather, as this chapter has shown, the framework for the strategic management of places spans a broad spectrum of policy approaches. Just as the field of strategic management has emerged for generating and sustaining a strong competitive advantage for firms and other organizations, so too must an incipient field of strategic management deliver an analogously strong and sustained competitive advantage for places.

References Acs, Z. J. 2013. Why Philanthropy Matters: How the Wealthy Give and What It Means to Our Economic Well-being. Princeton, NJ: Princeton University Press. Acs, Z.  J., D. B.  Audretsch, and M. P.  Feldman. 1992. “Real Effects of Academic Research.” American Economic Review 82, 363–67. Arrow, K. 1962. “Economic Welfare and the Allocation of Resources for Invention.” In R. R. Nelson, ed., The Rate and Direction of Inventive Activity. Princeton, NJ: Princeton University Press. Audretsch, D. B. 1995. “Innovation, Growth and Survival,” International Journal of Industrial Organization 14 (4), 441–457. Audretsch, D. B. 1999. Innovation and Industry Revolution. Cambridge: MIT Press. Audretsch, D. B. 2008. The Entrepreneurial Society. New York: Oxford University Press.

32    The Concept of Local Competitiveness Audretsch, D.  B. 2015. Everything in Its Place:  Creating Entrepreneurial Communities. New York: Oxford University Press. Audretsch, D. B., and M. P. Feldman. 1996. “R&D Spillovers and the Geography of Innovation and Production.” American Economic Review 86, 630–40. Audretsch, D.  B., and M. C.  Keilbach. 2007. “The Theory of Knowledge Spillover Entrepreneurship.” Journal of Management Studies 4, 1242–54. Audretsch, D.  B., and M. C.  Keilbach. 2008. “Resolving the Knowledge Paradox: Knowledge-Spillover Entrepreneurship and Economic Growth.” Research Policy 37 (10), 1697–705. Audretsch, D. B., M. C. Keilbach, and E. E. Lehmann. 2006. Entrepreneurship and Economic Growth. New York: Oxford University Press. Audretsch, D. B., A. Link, and M. Walshok, eds. 2013. Creating Competitiveness: Entrepreneursh ip and Innovation Policies for Growth. Cheltenham Spa: Edward Elgar. Braunerhjelm, P., Z. J.  Acs, D. B.  Audretsch, and B. Carlsson. 2010. “The Missing Link: Knowledge Diffusion and Entrepreneurship in Endogenous Growth.” Small Business Economics 34, 105–25. Bresnahan, T., and A. Gambardella. 2004. “Old-Economy Inputs for New-Economy Outcomes: What Have We Learned?” In T. Bresnahan and A. Gambardella, eds., Building High-Tech Clusters:  Silicon Valley and Beyond. New  York:  Cambridge University Press, 351–58. Chandler, A. 1977. The Visible Hand:  The Managerial Revolution in American Business. Cambridge, MA: Belknap Press Harvard University Press. Chandler, A. 1990. Scale and Scope:  The Dynamics of Industrial Capitalism. Cambridge, MA: Harvard University Press. Coleman, J. 1988. “Social Capital in the Creation of Human Capital.” American Journal of Sociology 94, 95–121. Cringley, R. X. 1993. Accidental Empires: How the Boys of Silicon Valley Make Their Millions, Battle Foreign Competition, and Still Can’t Get a Date. New York: Harper Business. Delgado, M., M. E.  Porter, and S. Stern. 2011. “Clusters, Convergence, and Economic Performance”. Working paper series, Harvard University. Durlauf, S. N. 2002. “On the Empirics of Social Capital.” Economic Journal, November, 459–79. Feldman, M.  P., and D. B.  Audretsch. 1999. “Innovation in Cities:  Science-Based Diversity, Specialization and Localized Competition.” European Economic Review 43 (2), 409–29. Florida, R. L. 2002. The Rise of the Creative Class: And How It’s Transforming Work, Leisure, Community and Everyday Life. New York: Basic Books. Florida, R. L. 2013. “The Myers Briggs States of America.” The Atlantic, October 21. Friedman, Thomas. (2008). Hot, Flat, and Crowded: Why we Need a Green Revolution – And how it can Renew America. New York: Farra, Straus and Giroux. Ghlo, N., M. Guerini, E. E. Lehmann, and C. Rossi-Lamastra. 2014. “The Emergence of the Knowledge Spillover Theory of Entrepreneurship.” Small Business Economics 44, 1–18. Glaeser E. L., and J. D. Gottlieb. 2009. “The Wealth of Cities: Agglomeration Economies and Spatial Equilibrium in the United States.” Journal of Economic Literature 47 (4), 983–1028. Glaeser, E. L., H. D. Kallal, J. A. Scheinkman, and A. Shleifer. 1992. “Growth in Cities.” Journal of Political Economy 100 (6), 1126–52. Glaeser, E. L., G. A. M. Ponzetto, and K. Tobio. 2014. “Cities, Skills and Regional Change.” Regional Studies 48 (1), 7–43.

The Strategic Management of Place    33 Hareshan, M. 2012. “Europe’s Economic Suicide?” Nine O’Clock.ro. Editorial. April 18. http:// www.nineoclock.ro/europe%E2%80%99s-economic-suicide/. Hirschman, A. O. 1970. Exit, Voice, and Loyalty: Response to Decline in Firms, Organizations and States. Cambridge, MA: Harvard University Press. Hubschmid, M. 2012. “Das Ende der Dynamik.” Der Tagesspiegel, June 1, p. 17. http://www. tagesspiegel.de/wirtschaft/arbeitsmarkt-das-ende-der-dynamik/6696496.html. Indiana University Newsroom. 2013. “Network Created to Expand Reach of IU’s Innovative Indiana Initiative.” November 1. Jacobs, J. 1969. The Economy of Cities. New York: Vintage Books. Klepper, S. 2009. “Silicon Valley, a Chip off the Old Detroit Bloc.” In Z. J. Acs, D. B. Audretsch, and R. Strom, eds. Entrepreneurship, Growth, and Public Policy. Cambridge:  Cambridge University Press. Link, A.  N. 1995. A Generosity of Spirit:  The Early History of the Research Triangle Park. Duham: Research Triangle Park: Research Triangle Foundation of North Carolina. Link, A.  N. 2002. From Seed to Harvest:  The Growth of the Research Triangle Park. Durham: Research Triangle Park: Research Triangle Foundation of North Carolina. Porter, M. E. 1990. The Competitive Advantage of Nations. New York City: Free Press. Porter, M. E. 1998a. The Competitive Advantage of Nations. New York: Free Press. Porter, M. E. 1998b. On Competition. Cambridge, MA: Harvard University Press Putnam, R. 2000. Bowling Alone:  The Collapse and Revival of American Community. New York: Simon and Schuster. Rentfrow, P. J., M. Jokela, S. D. Gosling, D. J. Stillwell, M. Kosinki, and J. Potter. 2013. “Divided We Stand: Three Psychological Regions of the United States and Their Political, Economic, Social, and Health Correlates.” Journal of Personality and Social Psychology 105 (6), 996–1012. Romer, P. M. 1986. “Increasing Returns and Long-Run Growth.” Journal of Political Economy 94 (5), 1002–37. Rupasingha, A., S. J.  Goetz, and D. Freshwater. 2002. “Social and Institutional Factors as Determinants of Economic Growth: Evidence from the United States Counties.” Papers in Regional Science 81, 139–151. Saxenian, A. 1994. Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press. Sobel, J. 2001. “Can We Trust Social Capital?” Journal of Economic Literature, March, 139–54. Solow, R. 1956. “A Contribution to Theory of Economic Growth.” Quarterly Journal of Economics 70 (1), 65–94. Solow, R. 1957. “Technical Change and the Aggregate Production Function.” Review of Economics and Statistics 39 (3), 312–20. Thissen, M., F. Van Oort, D. Diodato, and A. Ruijs. 2013. Regional Competitiveness and Smart Specialization in Europe. London: Edward Elgar. Wallsten, S. 2004. “The Role of Government.” In T. Bresnahan and A. Gambardella, eds., Building High-Tech Clusters: Silicon Valley and Beyond. New York: Cambridge University Press. World Bank. n.d. “Overview: Social Capital.” http://go.worldbank.org/C0QTRW4QF0.

Chapter 3

Talent, Ci t i e s , a nd C om petit i v e ne s s Richard Florida and Charlotta Mellander

Introduction Over the past several decades, capitalism has been undergoing a fundamental economic transformation, from an economic and social system that was primarily based on physical production and physical labor to a still emergent one that turns on knowledge and creativity. At the same time, the locus of competitive advantage has shifted from firms to talent, and cities and urban agglomerations have come to replace the firm and the nation-state as the central social and economic organizing units of our time. These transformations have dramatically altered the nexus of local competiveness. In the Industrial Age, the competitiveness of cities, as well as nations, was determined by their ability to attract and grow large firms. Nations invested in physical infrastructure, from ports to roads and airports, and in research and development, and used trade policy to enhance the position of national firms. Globalization has eroded the position of so-called national champions, and industry itself as well as high-paying mid-skill manufacturing jobs have declined alongside the rise of technology and offshoring. The rise of the knowledge economy has shifted the nexus of competitive advantage from firms to talent. Talent is the most important production resource that firms, regions, nations, and especially cities compete for today. Talent is not a fixed stock but a flow. It is mobile. Local competitiveness thus increasingly revolves around the ability of places to cultivate, attract, and retain it. Because of their central role in organizing, harnessing, attracting, and retaining talent, cities have emerged as the central social and economic organizing unit of the knowledge economy. For competitiveness strategy, this entails a shift from firm-oriented strategies that emphasize the business climate to talent-oriented strategies that take shape around quality of place and a people climate.

Talent, Cities, and Competitiveness    35 This chapter outlines the connections between talent, cities, and local competitiveness. The first part examines the rise of talent as the key factor in competitiveness. It begins by exploring the shift from firms to talent in the transition from industrial to postindustrial capitalism. It then turns to the rise of talent, or human capital, as the key attribute of local economic competitiveness. It goes on to discuss alternative ways of conceptualizing and measuring talent based on educational attainment, occupation, and underlying occupational skill. The second part of this chapter examines the ways in which the city has supplanted the firm and the nation-state to become the key economic and social organizing unit of economic growth and development. It examines the concentration of talent in cities as a key feature of globalization and discusses the key attributes of cities and location that enable them to attract and retain talent. The third part discusses the shift in local competiveness’ strategies brought on by the rise of talent and cities. It identifies the shift from an emphasis on the business climate to a focus on the people climate and quality-of-place factors required to attract talent. Finally, we touch on a growing constraint on competitiveness—increasing geographic inequality, spatial sorting, and segregation—which can potentially undermine demand and limit the diversity that drives local innovation and economic development in the first place. We conclude with a recap of major points on the increasingly central role of talent in local competitiveness.

Part 1: From Firms to Talent The Hegemony of the Firm in Industrial Capitalism Since the inception of economic geography as a discipline, and up until only recently, firms, particularly large firms, were the key units of analysis. Whether the context was urban economics, economic geography, or regional science, analyses focused on the locational decisions of firms and later, of their branch plants (Von Thünen 1826; Weber 1929; Christaller 1933; Ohlin 1933; Hoover 1937; 1948). Vernon’s product cycle theory of industrial location (Vernon 1966; 1979)  noted that when production processes become standardized, firms decentralize production through the establishment of branch plants. With that new spatial division of labor, the driving force in economic geography would become the globe straddling multinational firm (Froebel, Heinrichs, and Krey 1979; Massey 1995). Cluster theory also revolved around firms. Marshall’s paradigmatic work argued that firm agglomeration created advantages such as shared labor markets and inputs, and knowledge spillovers (Marshall 1890). Building upon Marshall’s basic insights, later theorists noted that clusters occur in places where economic structures and strategies

36    The Concept of Local Competitiveness are broadly supportive of an industry, though rivalries can be a driving factor as well (Porter 1990). By the 1980s, researchers were paying attention to the ways that industrial districts and flexibly spatialized industrial networks provided alternatives to vertically integrated production by large firms, allowing smaller firms the ability to achieve economies of scale (Christopherson and Storper 1986; Piore and Sabel 1984; Scott 1986). Research also focused on changes in production stemming from the rise of new post-Fordist models. One stream focused on a rising trend in Japanese factories, where the knowledge and intelligence of factory workers was tapped to form new systems of industrial production (Florida and Kenney 1991; 1993; Florida 1995). These new production processes were much less standardized than they had been in the past. Workers were forced to adopt a wider range of expertise, which led to a continuous process of learning (Piore and Sabel 1984; Storper and Christopherson 1987). Social factors, including trust and social capital, became important determinants of the location of talent and hence of geographic industrial clusters (Dei Ottati 1994; Saxenian 1996), as did creativity, communication, culture, and knowledge (Andersson 1985).

From Firms to Talent By the last quarter of the 20th century, scholars had begun to acknowledge the rise of the “knowledge economy” and with it an important class of “knowledge workers” (Machlup 1962; Drucker 1969; 1993; Bell 1973). As Florida (2002) documented, the industrial economy contracted and its workforce declined in parallel with the rise of the creative class. Up until 1960, creative-class workers (people who work with their minds) accounted for just 12 to 16 percent of the US workforce. By 1970 their share was 19 percent; it rose to 24 percent by 1980, and it is about 33 percent today (figure 3.1). The industrial working class accounted for some 40 percent of the workforce in the 1970s. Since then, its share has been halved. With the rise of this post-industrial knowledge or creative economy, a whole new set of factors began to influence the processes of agglomeration. In the old model of Fordist capitalism, production and consumption were spatially delinked. Production could take place virtually anywhere that labor costs and land rents were low. Knowledge production, however, or production that relies on individuals’ creativity or talent, very often produces goods that have a strong service component, and hence a greater sensitivity to distance and a stronger attachment to the region where the economic activity is located (Johansson and Quigley 2003). In the case of high-value goods like new technology, not only is the physical distance between producers and consumers likely to be smaller, but the social and class distances between producers and consumers are collapsed as well. This privileges talent on both the production and the consumption side of the equation. The prosperity of a region increasingly depends upon the skills, education, and creativity of

Talent, Cities, and Competitiveness    37 70% 60% 50% 40% 30% 20% 10%

18 00 18 10 18 20 18 30 18 40 18 50 18 60 18 70 18 80 18 90 19 00 19 10 19 20 19 30 19 40 19 50 19 60 19 70 19 80 19 90 20 00 20 09

0%

% Creative Class

% Agriculture

% Service Class

% Working Class

Figure  3.1  The Growth of the Creative Economy  Share (Source:  Florida 2012,  45.)

the people who live and work in it. This is in stark contrast to earlier models of firm locations, which were dictated in large part by suppliers’ and customers’ geographies (Glaeser 2000). In the next section, we take a closer look at the criteria that we use to define and measure regional talent.

Human Capital Much of the research around talent and skill has been based on the concept of human capital. Human capital is a broad construct that refers to workers’ underlying skills or talents, or as Adam Smith termed them in The Wealth of Nations (1776), where they were identified as a “fourth factor of production” alongside land, labor, and capital, “the acquired and useful abilities of all the inhabitants or members of the society” (Smith 1776, book 1, 7). Marshall claimed that “the most valuable of all capital is that invested in human beings” (1890, 564). Pigou (1928) distinguished investments in human capital that combine consumption and investment from investments in physical or material capital. Becker (1964; 1993) argued that investment in human capital is rational in terms of a simple cost-benefit calculation, taking both returns on investment and cultural factors into account.

38    The Concept of Local Competitiveness

Education Most of the empirical work on human capital and economic growth explicitly defines human capital in terms of educational attainment. Mincer’s classic study (1974) of returns on human capital made educational attainment the standard for measuring human capital in economics. His study used census data from the 1950s and 1960s to show that income increased by 5–10 percent for every additional year of schooling, while skills and age had a smaller but still significant impact on earnings. More recent studies have documented the role of human capital in spearheading growth (Barro 1991; 1997; Mankiw, Romer, and Weil 1992; de la Fuente and Domenech 2006; Cohen and Soto 2007). And there is a huge body of literature documenting the connection between human capital and regional economic growth, which suggests that cities grow faster because of knowledge spillovers and productivity increases (Rauch 1993; Simon and Nardinelli 1996; Glaeser 1998; Glaeser and Saiz 2003). These studies measure and operationalize the construct human capital by virtue of educational attainment, typically either as total years of schooling or percentage of adults that are college grads. They find substantial positive effects of educational attainment on economic outcomes, controlling for other factors.

Occupation But Mincer (1974) also noted that wages are not simply a function of education level; they are also a product of hours worked and specific skill sets. In terms of urban economics, regional wage levels are widely supposed to be in proportion to the prevailing stock of human capital within a region, but this is not always the case. Wage levels at the regional scale also reflect productivity and labor supply and demand. They can also reflect either a homogenous labor force or a labor force consisting of both high- and low-knowledge labor. Occupation provides an alternative measure of skills, as Thompson (Thompson 1986; Thompson and Thompson 1987) was perhaps the first to note. Florida’s (2002) occupational analysis of the workforce used Bureau of Labor Statistics data on occupations to divide workers into three main classes: the creative class, the working class, and the service class. Creative class individuals are knowledge workers; the working class engages in physical work; while the service class performs typically routine services. The creative class is further divided into two broad groups: the “super-creative core,” which includes occupations in computers and math; architecture and engineering; media; design; and the arts and academia; and a second group of “creative professionals” in management, business, finance, law, and healthcare. Florida’s work draws off a large literature in psychology as well as economics. At its most basic level, creativity is the act of bringing something that is useful, that functions, and that is novel into being; it is “the conjunction of novelty, utility and surprise”

Talent, Cities, and Competitiveness    39 (Simonton 2000). Creativity is how humans adapt and react to the constant, ongoing changes that surround us (Csikszentmihalyi 1997). While this is particularly true of the constantly changing world of technological innovation, creativity is beneficial to economic success across the board, being closely related to productivity (Andersson 1985). Creativity informs a large number of occupations. As Sternberg notes, “If one wanted to select the best novelist, artist, entrepreneur, or even chief executive officer, one would most likely want someone who is creative” (Sternberg and Lubart 1999, 3). Broadly speaking, creativity breaks down into three types. The first is technological creativity. This includes new inventions, but also new processes. Second, there is economic creativity or entrepreneurship, the ability to invest in creative and useful ways. Finally, there is artistic or cultural creativity. These types of creativity do not exist in a vacuum; they stimulate and reinforce one another. Moreover, the level of creativity that exists in a place can and often is affected by its social, organizational, and economic contexts. Organizations and environments that value people’s input, challenge them to think innovatively, mobilize resources on their behalf, and are receptive to both small and big ideas as well as changes, experience more creativity. Most importantly, they need to be able to translate their creative assets into actual products or services (Brown and Duguid 2000). Organizations and environments that are too bureaucratic and inflexible can stifle creativity, and with it, their profitability (Whyte 1956). Occupations act as natural units of analysis (Feser 2003), providing a more nuanced measure of skill than education by allowing those who are engaged in knowledge-based work but do not have degrees to be counted. Bill Gates, for example, who dropped out of Harvard, would not be counted under the human capital approach. By focusing on the actual skill content of specific occupations—what people actually do—it is possible to analyze talent clusters in much finer detail than if we simply use education as a broad proxy for talent. While there is some overlap between educational attainment and creative occupations, they are not the same. In the United States for example, nearly three-fourths of adults with college degrees are members of the creative class. But less than 60 percent of the people whose occupations qualify them as members of the creative class have college degrees (Currid-Halkett and Stolarick 2013). In other words, four in 10 members of the creative class would not be counted as high human capital individuals under the educational attainment measure. In Sweden, 37 percent of the population holds a creative class job, but only one-fourth of those have a university degree. However, about 90 percent of the highly educated hold a creative class job (Mellander 2009). A significant body of research shows that the occupation-based creative class measure operates in addition to and through other channels than the standard education-based human capital variable. A large-scale study found that while the creative class has a bigger effect on wages—a key element of regional productivity—education tends to have a greater effect on income (Florida, Mellander, and Stolarick 2008). Independent research by Gabe shows that the creative class continues to have a substantial effect on regional economic growth when controlling for the effects of education and other factors (Gabe 2009). Having a creative-class job brings

40    The Concept of Local Competitiveness economic benefits that extend beyond those of going to college. A college graduate working in the same occupation as a non-college graduate earns approximately 50  percent higher wages. But having a creative-class job adds another 16  percent, about the same as another 1.5  years of additional education, according to Gabe’s research (Gabe 2009). McGranahan and Wojan used sophisticated statistical techniques to gauge the effects of the creative class versus human capital on regional growth (McGranahan and Wojan 2007). To do so, they used systems of simultaneous equations rather than the conventional simple regression models to control for the endogeneity of population and employment growth as well as influences from a range of other local conditions and attributes. Their key findings overwhelmingly confirm the “strong independent influence on employment growth from both the initial share employed in the recast creative-class occupations and its growth over the decade. By contrast, the statistical association with human capital variables is quite weak.” And they add: “the econometric test of the creative class thesis provides strong support for the notion that creativity has an effect on growth independent of the endowment of human capital” (McGranahan and Wojan 2007, 213). A detailed study of regional growth in the Netherlands found that the creative-class measure considerably outperformed the standard education-based human capital measure in accounting for employment growth. This led its authors to conclude that the creative-class measure sets a “new standard” for measuring skill and talent, especially when considering regional labor productivity (Marlet and van Woerkens 2004). “With our Dutch data set we do find evidence that Florida’s creative class is a better predictor of city growth than traditional education standards,” they wrote. “Therefore we conclude that Florida’s major contribution is his successful attempt to create a population category that is a better indicator for levels of human capital than average education levels or amounts of highly educated people. The point is, as Florida stated, not which or how much education people can boast of, but what they really do in working life” (Marlet and Van Woerkens 2004, 2620). A 2012 study used advanced statistical models to compare the effects of the creative class and human capital across the 257 European Union regions. “Our results,” it concluded, “indicate that highly educated people working in creative occupations are the most relevant component in explaining production efficiency” (Marrocu and Paci 2012, 369). Florida, Mellander, and Stolarick (2008) used structural equation models and path analysis to examine the effects of educational versus occupational measures on regional income and wages, and also to isolate the effects of tolerance, consumer service amenities, and universities on their distribution. It found that human capital and the creative class both affect regional development, but through different channels. The creative class outperforms conventional educational attainment measures in accounting for regional labor productivity measured as wages, while conventional human capital better accounts for regional income or wealth. Tolerance is significantly associated with both. Educational human capital may reflect richer places, but it seems that the creative class actually makes a place more productive.

Talent, Cities, and Competitiveness    41

Skills An important wave of recent studies has zeroed in on the specific skills that occupations require. This research uses new data from the US Bureau of Labor Statistics O*NET database, which collects detailed information on the actual skill content of work for more than 800 individual occupations. Bacolod and Blum (2005) examined the association between four key skills—physical, motor, cognitive, and social skills—and found higher returns to cognitive and social skills, which, in turn, can help explain the decreased gender wage gap. A  follow-on study (Bacolod, Blum, and Strange 2009)  observed a geographical connection between skill types, finding social and cognitive skills to be associated with larger cities and metros. Feser (2003) identified the general knowledge requirements across occupations and the economic returns they generate. Gabe (2009) differentiated between skill requirements and the returns they offer in private and public sectors. He also showed how spillover effects enhance earnings in metropolitan regions with higher shares of high-knowledge occupations. Scott (2009) examined the connection between skills and regional employment, finding the largest increases in regional employment to be associated with cognitive-intensive occupations, with substantial employment declines for occupations that depend on physical skills. Florida and co-authors (2011) defined three skill sets—analytical, social, and physical—and noted the increasing returns generated by analytical and social skills over time, and the decreasing returns to physical skill, reinforcing the key findings of Bacolod, Blum, and Strange (2009) regarding the concentration of analytical and social skills in larger metros.

Part 2: The Central Role of the City If industrial production was organized in and around firms, knowledge-based or creative production is organized in and around cities. Because of their central role in organizing, harnessing, attracting, and retaining talent, cities have emerged as the central social and economic organizing unit of global, knowledge-based capitalism. Globalization has been widely associated with the “the end of geography,” captured in Friedman’s famous catchphrase, “The world is flat” (2005). But Friedman’s focus is on globalization’s centrifugal force, its tendency to spread out economic activity. Friedman misses the centripetal force—the clustering in cities—that makes it possible. According to Florida, Gulden and Mellander (2008), just 40 or so mega-regions produce two-thirds of the world’s economic output and nine in 10 of its innovations, while housing just 18 percent of its population. Porter (2006) has dubbed this the “location paradox.” “Location still matters, he said. “The more things are mobile, the more decisive location becomes. This point has tripped up a lot of really smart people.”

42    The Concept of Local Competitiveness Regional jurisdictions tend to be arbitrarily or politically drawn (Duranton 2007). As economically delimited units, cities, metro areas, and even mega-regions operate as the natural economic units that attract and aggregate talent and related resources (Glaeser et al. 2004; Florida, Mellander, and Stolarick 2008; 2010). The importance of talent to cities and vice versa was recognized early on by scholars who noted the importance of human capital and its geographic concentration (Ullman 1958). Jacobs anticipated this geographical shift in 1969, in a critique of Adam Smith’s paradigmatic pin factory. Smith’s story, she argued, emphasized efficiency. While firms deepen and specialize the division of labor, improving productivity and profitability, cities enable the constant combining and recombining of key inputs, including skilled people that are required for innovations, which are the real drivers of economic growth. While firms deepen and specialize the division of labor, cities give rise to new products, new enterprises, and whole new industries. Andersson (1985) initially introduced the concept of creativity into regional analysis. His work conceptualizes creative regions from a historical perspective and finds that major creative city regions have had six fundamental conditions in common: flexible financial capital, deep knowledge and competence, an unbalance between experienced needs and actual resources, environmental diversity, well-functioning transportation and communication, and structural instability or a genuine insecurity. Andersson stressed the importance of structural instability as a necessary condition for regional creativity from both a micro and a macro perspective. Howkins (2001) introduced the construct of the “creative economy” based on industry sectors like software, R & D and design, and industry and film. Landry (2000) introduced the concept of the “creative city.” There are deeper mechanisms that link creativity and talent to regional and urban development. Romer’s theory of endogenous growth formalized the role of human knowledge and talent in overall economic growth (Romer 1990). Lucas (1988), inspired by Jacobs, showed how collocations of skilled, talented, ambitious, and entrepreneurial people lead to so-called human capital externalities and identified talent clustering as the basic underlying mechanism of economic development. Urban density reduces the cost of knowledge transfer and speeds the transmission of ideas. Workers are at their most productive when they are located around other workers at similar levels of education or skill, who can challenge them and push their own work further (Eaton and Eckstein 1997; Black et al. 2000). A huge body of empirical research documents the connection between talent, and national and regional economic growth and development (Barro 1991; 1997; Glaeser et al. 1992; Rauch 1993; Glaeser 1998; 1999; 2000; Glendon 1998; Simon 1998; Florida and Gates 2001; Florida 2002). The clustering of talent is even more important in creative industries. Caves (2000) shows how creative industries are more likely to be organized as geographic clusters of creative individuals as opposed to vertically integrated firms. Creative industries have higher levels of uncertainty and production challenges due to multiplicative production functions, in which every input is non-substitutable and therefore must be present in order to produce. Further, shorter life cycles and a constant need for reinvention

Talent, Cities, and Competitiveness    43 demands a closer interaction between consumers and producers, as well as new skill combinations for the faster generation of ideas. Florida, Mellander, and Stolarick (2010; 2011) document the role of clustering in the music and entertainment industries that have few if any physical constraints. The clustering of talent in urban centers has underpinned an urban shift in start-ups and venture capital from their traditional locations in suburbs to urban centers. Florida (2013) shows that venture capital start-ups and investment are attracted to locations with large shares of talent and higher levels of diversity. The same study identifies the shift in venture-capital-based start-ups and investment from suburban locations like Silicon Valley or the Route 128 suburbs outside Boston to urban centers like downtown San Francisco and Lower Manhattan as well as denser, more walkable suburbs like Cambridge and Palo Alto. Talented, highly skilled individuals exercise wide choices about where they can live (Becker 1993; Edlund 2005; Graves 1979; Graves and Linneman 1979; Mincer 1974; Pandit 1997). Key factors in those choices include artistic and cultural amenities and overall quality of life as well as access to economic opportunity (Ullman 1958, Carlino and Saiz 2008; Florida, Mellander, and Stolarick 2008).

Quality of Place Florida (2002; 2012) advances the construct of quality of place to identify the ways places in attract and harness talent for competitiveness. Quality of place cuts across three key dimensions: what’s there, or the combination of the built environment and the natural environment, the setting it provides for the pursuit of creative lives; who’s there, or the diverse kinds of people that can be found, signaling that anyone can make a life in a community; and what’s going on, the vibrancy of street life, café culture, arts, music, and outdoor activities. The role of amenities in the location choices of talented individuals is the subject of a growing body of empirical research (see Rosen 1979; Roback 1982; Gottlieb 1995; Florida 2002). Glaeser and Maré (2001) also examined the factors that attract skilled labor to cities, and found that higher amenity cities attract more skilled labor and consequently grow faster. Mellander et al (2011) illustrated how place-specific characteristics, such as the ability to meet and make friends, high-quality public schools, and ease of getting from one place to another without too much traffic, are significantly more related to community satisfaction and the likelihood of staying in a place, than economic variables such as the likelihood of getting a job or perceptions of future conditions. Building on research by Rosen (1979) and Roback (1982) on migration across regions, Florida (2002) suggested that in addition to their human capital externalities and productivity and innovation-enhancing functions, places act on the consumption preferences of skilled individuals, a considerable advance over earlier theories that argued that comparative advantage stems merely from business friendly environments, lower taxes, and lower overall firm costs. The presence of cultural amenities like cafes, art galleries, and the like,

44    The Concept of Local Competitiveness are all correlated with economic growth in metro areas (Glaeser 2001; Lloyd and Clark 2001; Lloyd 2002). Clark et al. (2002) argued that successful talent-attracting cities maximize individuals’ overall utilities especially their quality of life preferences, not just their incomes. Albouy (2009) showed that both natural and built amenities are strongly associated with economic productivity in both US and Canadian cities. A detailed study by Shapiro (2006) found that “roughly 60 percent of the employment growth effect of college graduates is due to enhanced productivity growth, the rest being caused by growth in quality of life. This finding contrasts with the common argument that human capital generates employment growth in urban areas solely through changes in productivity,” (Shapiro 2006, 1). Tolerance and openness is another factor that has been shown to affect the distribution and location choices of talent. One of the leading students of creativity, Simonton (2000) identified four key characteristics of places that generate substantial creative activity: domain activity, receptiveness to different intellectual modes, ethnic or cultural diversity, and an openness to political views (Simonton 2000). Florida captures these attributes in his theory of the 3 Ts of economic development—technology, talent, and tolerance. Each alone is a necessary but insufficient condition for talent attraction and creative economic development. Technology and talent are better understood as flows rather than stocks. Tolerance affects the flow of these two critical building blocks of local competiveness. The most competitive regions draw in a broader range of talent by age, ethnicity, marital status and so on. Diversity, particularly in the form of immigration, has been shown to increase regional productivity because having talented people from a number of different places introduces different but complementary skills to an area (Ottaviano and Peri 2005). Furthermore, highly innovative places like Silicon Valley also have higher rates of immigration. A study by Wadhwa et al (2007; 2008) found that immigrants are among the founding teams of between a third and a half of high-tech Silicon Valley start-ups. Tolerant attitudes to the gay and lesbian community have also been found to be associated both with higher levels of innovation and high-tech industry at the metro level (Florida and Gates 2001) and with better economic outcomes across nations (Noland 2005). This is not because gays and lesbians are more likely to be directly involved in innovative activity, but because an open-minded and tolerant social and economic climate reflects an underlying ecosystem that is amenable to attracting and harnessing talent from a wider range of social, racial, ethnic, and demographic groups. Page (2007), for example, shows that cognitive diversity results in greater idea generation, creativity, and innovation and notes that cognitive diversity is in turn associated with demographic diversity. Tolerance is not just an advantage for attracting talent, it increases the probability that new ideas can be turned into economic value. Research by Inglehart and his collaborators found associations between openness and economic growth in studies covering more than 60 countries over four decades (Inglehart 1989 1997; Inglehart and Norris 2003). According to Inglehart, the best indicator of national tolerance is openness to gay and lesbian people. Florida (2002) used gay-friendliness as a proxy for regional openness in the United States (Florida, Mellander, and Stolarick 2008) and found a strong correlation with the distribution of both the highly educated and the creative class across metropolitan areas. Talent and

Talent, Cities, and Competitiveness    45 the people who hold it are more likely to flow to places that have lower barriers of entry (Florida and Gates 2001). Ultimately, quality of place can be understood in terms of Maslow’s famous hierarchy of needs (Maslow 1943), which is often represented as a pyramid with physiological needs at its base, ascending through security, love, self-esteem, and ultimately self-actualization. Quality of place, in turn, encompasses five major needs: physical and economic security (public safety, jobs), basic services (schools, healthcare, housing, transportation), leadership (political and business), openness (tolerance for diversity), and aesthetics (physical beauty, culture, amenities). A large-scale Gallup-Knight survey found that aesthetic values, along with openness and the ability to meet other people were key factors in attracting and retaining talent (Florida 2009; Mellander, Florida, and Stolarick 2011; Florida, Mellander, and Stolarick 2011; Knight Foundation 2010; 2011; Florida, Mellander, and Rentfrow 2013). In sum, the study concluded that: After three years of research, the results have been very consistent, and possibly surprising. First, what attaches residents to their communities doesn’t change much from place to place. While we might expect that the drivers of attachment would be different in Miami, Fla., from those in Macon, Ga., in fact, the main drivers of attachment show little difference across communities. In addition, the same drivers have risen to the top in every year of the study. Second, these main drivers may be surprising. While the economy is obviously the subject of much attention, the study has found that perceptions of the local economy do not have a very strong relationship to resident attachment. Instead, attachment is most closely related to how accepting a community is of diversity, its wealth of social offerings, and its aesthetics. This is not to say that jobs and housing aren’t important. Residents must be able to meet their basic needs in a community in order to stay. However, when it comes to forming an emotional connection with the community, there are other community factors which often are not considered when thinking about economic development. These community factors seem to matter more when it comes to attaching residents to their community. And finally, while we do see differences in attachment among different demographic groups, demographics generally are not the strongest drivers of attachment. In almost every community, we found that a resident’s perceptions of the community are more strongly linked to their level of community attachment than to that person’s age, ethnicity, work status, etc. (Knight Foundation 2011)

Part 3: From Business Climate to People Climate The rise of talent and of cities entails a major shift in local competitiveness strategy. For decades, local competiveness sought to act on the location decisions of firms by

46    The Concept of Local Competitiveness enhancing the so-called business climate of low taxes, minimal regulation, and targeted locational subsidies. But the rise of talent as a key factor in local competiveness conditions a shift to building a people climate (Florida 2002; 2012), a general strategy aimed at attracting and retaining people—especially, but not limited to, creative people. This people climate needs to have something for all people across all age groups, single and married, gay and straight, parents and childless. Places need to provide low barriers to entry for talent across the board, and that means being open and welcoming. They need to focus on quality of place more than quality of life. Pragmatically, this entails a focus on a broader range of smaller, bottom-up projects that are locationally sticky as opposed to business subsidies or top-down mega-projects like giant stadiums, convention centers, or even casinos that seldom generate jobs and the spillover effects that are promised. What makes an enduring difference in a city’s quality of life are small, low-cost, community-initiated, bottom-up improvements like parks, bike paths, neighborhood improvements, and so on.

The Challenge of Spatial Inequality More recent research points to various downsides of clustering. The geographic clustering of talent has given rise to an extreme process of geographic sorting. It is not simply technology per se that underpins growing inequality, but the clustering process itself. This has given rise to both increasing spatial inequality across and within cities and an even more vexing problem of increased geographic segregation. These processes hinder local competitiveness by reducing the incomes and purchasing power of the middle class and by increasing the homogeneity and undermining the diversity of urban areas that has fueled creativity and innovation, potentially limiting the competitiveness that the very clustering of talent makes possible. The knowledge economy has led to a bifurcated labor market. Formerly stable, long-term, higher-paying, mid-skill jobs have been eliminated as the labor market has cleaved into high-paying knowledge and creative jobs and lower-paying, more contingent service sector jobs. Florida (2012) estimates that as much as two-thirds of the United States’ potential workforce is either under- or unemployed. This rising inequality is intrinsically and inexorably spatial. While a large body of research pins the cause of rising inequality on so-called skill-biased technical change, a main factor, if not the main factor itself, is clustering. Clustering not only powers innovation and economic growth, by definition it leads to greater geographic sorting of people and places by skill, talent, and ability. A growing number of studies document the increasing divergence of talent and human capital across cities and metro areas (Berry and Glaeser 2005; Florida 2006). This affects not just economic growth, but also related economic factors like housing values (Shapiro 2006; Gyourko, Mayer, and Sinai 2006). Inequality has been found to be closely associated with city or metro size. Size accounted for roughly 25 to 35 percent of the total increase in economic inequality over

Talent, Cities, and Competitiveness    47 and above the role of effects of skills, human capital, industry composition, and other factors. This effect is more pronounced among lower-wage earners. City size explains 50 percent more of the increase in inequality for the lower half of the wage distribution than for the upper half, the study finds (Baum-Snow and Pavan 2011). The geography of inequality also turns on place-based disadvantage. Florida and Mellander (2014) found that wage inequality explains just 15 percent of the variation of income inequality across regions. Furthermore, while wage inequality is associated with skill-biased technical change, income inequality is more associated with the enduring legacy of race and poverty at the bottom of the socioeconomic order, as well as the unraveling of the postwar social compact between capital and labor. We have already touched on some of the underlying reasons for growing spatial inequalities and geographic segregation. Talent attracts talent. Places with existing concentrations of talent tend to attract more. The very clustering and agglomeration economies of talent that increase productivity and innovation compound the advantages of some place over others. The underlying skills that drive knowledge-based growth—cognitive and especially social skills—tend to be concentrated in larger cities and metros (Bacolod, Blum, and Strange 2009). The result is growing class divisions within as well as between cities and metro areas (Florida et al. 2014). The location choices and clustering of the higher skilled and more affluent also affect and structure the location choices and geography of less advantaged groups. The more affluent and skilled members of creative class tend to cluster in central neighborhoods, around transit and knowledge institutions, and near natural amenities. Rising housing costs exacerbate the ensuing class segregation and division as lower-paid service workers are forced to live further out, in neighborhoods with less amenities and transit. Diamond (2012) shows how this sorting process involves migrations away from as well as to knowledge-based metros. “The combination of desirable wage and amenity growth for all workers causes large amounts of in-migration, as college workers are particularly attracted by desirable amenities, while low skill workers are particularly attracted by desirable wages,” she notes. But this leads directly to higher housing costs that “disproportionately discourage low skill workers from living in these high wage, high amenity cities.” This creates an additional level of inequality—inequality of well-being—where more skilled workers not only take home more money, but benefit from better neighborhoods, superior amenities, and better schools. This well-being inequality, Diamond explains, is an additional 20 percent higher than can be explained by the simple wage gap between college and high school graduates. The effects of neighborhood poverty and disadvantage are long lasting and persistent (Sampson 1995; Sharkey 2013). The result is a divided city and metropolis increasingly defined as areas of concentrated advantage juxtaposed against areas of concentrated disadvantage. As these spatial inequalities intensify, they have a greater and greater potential to undermine development. The erosion of the middle class and of middle-class neighborhoods undermines local demand. Furthermore, increasing socioeconomic

48    The Concept of Local Competitiveness segregation leads to increased homogeneity within neighborhoods, thus limiting the diversity that underpins much of the innovative functions of cities. For these reasons, strategies to cope with geographic inequality and spatial segregation are no longer relevant to just social welfare policy but are increasingly likely to affect local competitiveness strategies. This includes upgrading service jobs by developing strategies to tap the cognitive and social skills of workers, increasing density to provide more housing, and expanding access to transit to further-out neighborhoods.

Conclusion Talent has become the key input in economic growth and development in global, knowledge-based capitalism, and talent spans much more than educational attainment; it also spans occupation and skill. Cities have emerged as the central economic organizing unit of talent and economic development generally. Size and density matter, but so too does the ability to deliver quality of place broadly, made up of services and amenities, openness to diversity, and lower barriers to entry for talent. Success in the knowledge economy thus requires more than just creating an attractive business climate, but also an attractive people climate that can appeal to wide and diverse groups of talent. But the clustering, as an economic force, generates increasing divides as well as powering the process of economic development and competitiveness. The world is increasingly spiky, fractured, and divided. Inequality has grown; economic segmentation and segregation have increased within cities. This threatens to undermine the neighborhood level diversity that functions as the basic mechanism for local innovation, economic development, and competiveness. Developing new strategies for inclusive growth by upgrading service jobs, increasing density, and expanding transit may well be core components of local competitiveness strategies in the future.

References Albouy, David. 2009. “What Are Cities Worth? Land Rents, Local Productivity, and the Capitalization of Amenity Values.” National Bureau of Economic Research Working Paper Series No. 14981, May. http://www.nber.org/papers/w14981. Andersson, Åke E. 1985. “Creativity and Regional Development.” Papers of the Regional Science Association 56 (1), 5–20. Bacolod, Marigee, and Bernardo S. Blum. 2005. “Two Sides of the Same Coin: U.S. “Residual” Inequality and the Gender Gap.” Working paper, University of Toronto. Bacolod, Marigee, Bernardo S. Blum, and William C. Strange. 2009. “Skills in the City.” Journal of Urban Economics 65 (2), 136–53.

Talent, Cities, and Competitiveness    49 Barro, Robert J. 1997. Determinants of Economic Growth:  A  Cross-Country Empirical Study. Cambridge, MA: MIT Press. Barro, Robert J. 1991. “Economic Growth in a Cross Section of Countries.” Quarterly Journal of Economics 106 (2), 407–43. Baum-Snow, Nathaniel, and Ronni Pavan. 2011. Inequality and City Size. Washington, DC: National Bureau of Economic Research. Becker, G. 1993. Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. Chicago: University of Chicago Press. Becker, Gary. 1964. Human Capital. New York: Columbia University Press for the National Bureau of Economic Research. Bell, Daniel. 1973. The Coming of Post-industrial Society:  A  Venture in Social Forecasting. New York: Basic Books. Berry, Christopher R., and Edward L. Glaeser. 2005. “The Divergence of Human Capital Levels across Cities.” Papers in Regional Science 84 (3), 407–44. Black, Dan, Gary Gates, Seth Sanders, and Lowell Taylor. 2000. “Demographics of the Gay and Lesbian Population in the United States: Evidence from Available Systematic Data Sources.” Demography 37 (2), 139–54. Brown, John Seely, and Paul Duguid. 2000. The Social Life of Information. Boston: Harvard Business School Press. Carlino, Gerald, and Albert Saiz. 2008. “City Beautiful.” Working paper, Research Department, Federal Reserve Bank of Philadelphia. Caves, Richard E. 2000. Creative Industries: Contracts between Art and Commerce. Cambridge, MA: Harvard University Press. Christaller, Walter. 1933. Die Zentralen Orte in Süddeutschland. Trans. C. W.  Baskin. Jena, Germany: Gustav Fisher Verlag. Christopherson, Susan, and Michael Storper. 1986. “The City as Studio, the World as Back Lot: The Impact of Vertical Disintegration on the Location of the Motion Picture Industry.” Environment and Planning D: Society and Space 4 (3), 305–20. Clark, Terry N., Richard Lloyd, Kenneth K. Wong, and Pushpam Jain. 2002. “Amenities Drive Urban Growth.” Journal of Urban Affairs 24 (5), 493–515. Cohen, Daniel, and Marcelo Soto. 2007. “Growth and Human Capital:  Good Data, Good Results.” Journal of Economic Growth 12 (1), 51–76. Csikszentmihalyi, Mihaly. 1997. Creativity: Flow and the Psychology of Discovery and Invention. London: Harper & Row. Currid-Halkett, Elizabeth, and Kevin Stolarick. 2013. “Baptism by Fire:  An Occupational Analysis of Creative Sector Impact on Regional Economies.” Cambridge Journal of Regions, Economy and Society 6 (1), 55–69. De la Fuente, Angel, and Rafael Domenech. 2006. “Human Capital in Growth Regressions: How Much Difference Does Data Quality Make?” Journal of the European Economic Association 4 (1), 1–36. Dei Ottati, Gabi. 1994. “Trust, Interlinking Transactions and Credit in the Industrial District.” Cambridge Journal of Economics 18 (6), 529–46. Diamond, Rebecca. 2012. “The Determinants and Welfare Implications of US Workers’ Diverging Location Choices by Skill:  1980–2000.” http://www.stanford.edu/~diamondr/ jmp_final_121813.pdf Drucker, Peter F. 1993. Post-capitalist Society. New York: HarperCollins.

50    The Concept of Local Competitiveness Drucker, Peter F. 1969. The Age of Discontinuity:  Guidelines to Our Changing Society. New York: Harper & Row. Duranton, Gilles. 2007. “Human Capital Externalities in Cities:  Identification and Policy Issues.” In Richard J. Arnott and Daniel P. McMillen, eds., A Companion to Urban Economics. Oxford: Blackwell. Eaton, Jonathan, and Zvi Eckstein. 1997. “Cities and Growth: Theory and Evidence from France and Japan.” Regional Science and Urban Economics 27 (4–5), 443–74. Edlund, Lena. 2005. “Sex and the City.” Scandinavian Journal of Economics 107 (1), 25–44. Feser, Edward. 2003. “What Regions Do Rather Than Make: A Proposed Set of KnowledgeBased Occupation Clusters.” Urban Studies 40 (10), 1937–58. Florida, Richard. 1995. “Toward the Learning Region.” Futures 27 (5), 527–36. Florida, Richard. 2002. The Rise of the Creative Class: And How It’s Transforming Work, Leisure, Community and Everyday Life. New York: Basic Books. Florida, Richard. 2006. “Where the Brains Are.” The Atlantic, October. http://www.theatlantic. com/magazine/archive/2006/10/where-the-brains-are/305202/. Florida, Richard. 2009. Who’s Your City? How the Creative Economy Is Making Where to Live the Most Important Decision of Your Life. Toronto: Random House of Canada. Florida, Richard. 2012. The Rise of the Creative Class, Revisited. New York: Basic Books. Florida, Richard. 2013. “The New Global Start-Up Cities.” Atlantic Cities, June 4. http://www. theatlanticcities.com/jobs-and-economy/2013/06/new-global-start-cities/5144/. Florida, R., and Gates, G. 2001. Technology and Tolerance—The Importance of Diversity to Hightechnology Growth. Washington, DC: Brookings Institute. Florida, Richard, Tim Gulden, and Charlotta Mellander. 2008. “The Rise of the Mega-region.” Cambridge Journal of Regions, Economy and Society 1 (3), 459–76. Florida, Richard, and Martin Kenney. 1991. “Japanese Foreign Direct Investment in the United States:  The Case of the Automotive Transplants.” In Jonathan Morris, ed., Japan and the Global Economy: Issues and Trends in the 1990s. London: Routledge. Florida, Richard, and Martin Kenney. 1993. Beyond Mass Production: The Japanese System and Its Transfer to the U.S. New York: Oxford University Press. Florida, Richard, Zara Matheson, Patrick Adler, and Taylor Brydges. 2014. The Divided City: And the Shape of the New Metropolis. Toronto: Martin Prosperity Institute. Florida, Richard, and Charlotta Mellander. 2014. “The Geography of Inequality: Difference and Determinants of Wage and Income Inequality across US Metros.” Regional Studies Forthcoming. Florida, Richard, Charlotta Mellander, and Peter J. Rentfrow. 2013. “The Happiness of Cities.” Regional Studies 47 (4), 613–27. Florida, Richard, Charlotta Mellander, and Kevin Stolarick. 2011. “Beautiful Places: The Role of Perceived Aesthetic Beauty in Community Satisfaction.” Regional Studies 45 (1), 33. Florida, Richard, Charlotta Mellander, and Kevin Stolarick. 2008. “Inside the Black Box of Regional Development:  Human Capital, the Creative Class and Tolerance.” Journal of Economic Geography 8 (5), 615–49. Florida, Richard, Charlotta Mellander, and Kevin Stolarick. 2010. “Music Scenes to Music Clusters:  The Economic Geography of Music in the US, 1970–2000.” Environment and Planning A 42 (4), 785–804. Friedman, Thomas L. 2005. “It’s a Flat World, after All.” New York Times, April 3. Froebel, Folker, Juerfen Heinrichs, and Otto Krey. 1979. The New International Division of Labor. Cambridge: Cambridge University Press. Gabe, Todd M. 2009. “Knowledge and Earnings.” Journal of Regional Science 49 (3), 439–57.

Talent, Cities, and Competitiveness    51 Glaeser, Edward L. 1998. “Are Cities Dying?” Journal of Economic Perspectives 12 (1), 139–60. Glaeser, Edward L. 2001. “Consumer City.” Journal of Economic Geography 1 (1), 27–50. Glaeser, Edward L. 1999. The Future of Urban Research: Nonmarket Interactions. Washington, DC: Brookings Institution. Glaeser, Edward L. 2000. “The New Economics of Urban and Regional Growth.” In Gordon Clark, Meric Gertler, and Maryann Feldman, eds., The Oxford Handbook of Economic Geography. Oxford: Oxford University Press. Glaeser, Edward L., Hedi D. Kallal, Jose A. Scheinkman, and Andrei Shleifer. 1992. “Growth in Cities.” Journal of Political Economy 100 (6), 1126–52. Glaeser, Edward L., Rafael La Porta, Florencio Lopez-de-Silanes, and Andrei Shleifer. 2004. “Do Institutions Cause Growth?” Journal of Economic Growth 9 (3), 271–303. Glaeser, Edward L., and David C. Maré. 2001. “Cities and Skills.” Journal of Labor Economics 19 (2), 316–42. Glaeser, Edward L., and Albert Saiz. 2003. “The Rise of the Skilled City.” Working paper, National Bureau of Economic Research, December. http://www.nber.org/papers/w10191. Glendon, Spencer. 1998. “Urban Life Cycles.” Working paper, Harvard University. Gottlieb, Paul D. 1995. “Residential Amenities, Firm Location and Economic Development.” Urban Studies 32 (9), 1413–36. Graves, Philip E. 1979. “A Life-Cycle Empirical Analysis of Migration and Climate, by Race.” Journal of Urban Economics 6 (2), 135–47. Graves, Philip E., and Peter D.  Linneman. 1979. “Household Migration:  Theoretical and Empirical Results.” Journal of Urban Economics 6 (3), 383–404. Gyourko, Joseph, Christopher Mayer, and Todd Sinai. 2006. “Superstar Cities.” National Bureau of Economic Research Working Paper Series No. 12355, July. Hoover, Edgar Malone. 1937. Location Theory and the Shoe and Leather Industries. Cambridge, MA: Harvard University Press. Hoover, Edgar Malone. 1948. The Location of Economic Activity. Cambridge, MA:  Harvard University Press. Howkins, John. 2001. The Creative Economy. New York: Allen Lane, Penguin Press. Inglehart, Ronald. 1989. Culture Shifts in Advanced Industrial Society. Princeton, NJ: Princeton University Press. Inglehart, Ronald. 1997. Modernization and Post-modernization. Princeton, NJ:  Princeton University Press. Inglehart, Ronald, and Pippa Norris. 2003. Rising Tide: Gender Equality and Cultural Change around the World. Cambridge: Cambridge University Press. Johansson, Börje, and John M. 2003. Quigley. “Agglomeration and Networks in Spatial Economies.” Papers in Regional Science 83 (1), 165–76. Knight Foundation. 2010. “Soul of the Community 2010: Why People Love Where They Lives and Why It Matters:  A  National Perspective.” http://www.soulofthecommunity.org/sites/ default/files/OVERALL.pdf. Knight Foundation. 2011. “What Attaches People to Places.” Soul of the Community. http:// www.soulofthecommunity.org/. Landry, Charles. 2000. The Creative City: A Toolkit for Urban Innovators. London: Earthscan. Lloyd, Richard. 2002. “Neo-Bohemia:  Art and Neighborhood Redevelopment in Chicago.” Journal of Urban Affairs 24 (5), 517–32. Lloyd, Richard, and Terry Nichols Clark. 2001. “The City as Entertainment Machine.” Research in Urban Sociology 6, 357–78.

52    The Concept of Local Competitiveness Lucas, Robert E. 1988. “On the Mechanics of Economic Development.” Journal of Monetary Economics 22 (1), 3–42. Machlup, Fritz. 1962. The Production and Distribution of Knowledge in the United States. Princeton, NJ: Princeton University Press. Mankiw, N. Gregory, Paul M. Romer, and David N. Weil. 1992. “A Contribution to the Empirics of Economic Growth.” Quarterly Journal of Economics 107 (2), 407–37. Marlet, Gerard, and Clemens van Woerkens. 2004. “Skills and Creativity in a Cross-Section of Dutch Cities.” Discussion Paper Series, Tjalling C. Koopmans Institute, Utrecht. Marrocu, Emanuela, and Raffaele Paci. 2012. “Education or Creativity: What Matters Most for Economic Performance.” Economic Geography 88 (4), 369–401. Marshall, Alfred. 1890. Principles of Economics. London: Macmillan. Maslow, A. H. 1943. “A Theory of Human Motivation.” Psychological Review 50 (4), 370–96. Massey, Doreen B. 1995. Spatial Divisions of Labor:  Social Structures and the Geography of Production. 2nd ed. New York: Routledge. McGranahan, David, and Timothy Wojan. 2007. “Recasting the Creative Class to Examine Growth Processes in Rural and Urban Counties.” Regional Studies 41 (2), 197–216. Mellander, Charlotta. 2009. “Creative and Knowledge Industries:  An Occupational Distribution Approach.” Economic Development Quarterly 23 (4), 294–305. Mellander, Charlotta, Richard Florida, and Kevin Stolarick. 2011. “Here to Stay: The Effects of Community Satisfaction on the Decision to Stay.” Spatial Economic Analysis 6 (1), 5–24. Mincer, Jacob. 1974. Schooling, Experience and Earnings. New York: Columbia University Press for the National Bureau of Economic Research. Noland, Marcus. 2005. “Popular Attitudes, Globalization and Risk.” International Finance 8 (2), 199–229. Ohlin, Bertil. 1933. Interregional and International Trade. Cambridge, MA:  Harvard University Press. Ottaviano, Gianmarco P., and Giovanni Peri. 2005. “Cities and Cultures.” Journal of Urban Economics 58 (2), 304–37. Page, Scott E. 2007. The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton, NJ: Princeton University Press. Pandit, Kavita. 1997. “Cohort and Period Effects in U.S. Migration: How Demographic and Economic Cycles Influence the Migration Schedule.” Annals of the Association of American Geographers 87 (3), 439–550. Pigou, Arthur Cecil. 1928. A Study in Public Finance. London: Macmillan. Piore, Michael J, and Charles F Sabel. 1984. The Second Industrial Divide:  Possibilities for Prosperity. New York: Basic Books. Porter, Michael. 2006. “Q & A with Michael Porter.” Business Week, August 21. Porter, Michael. 1990. The Competitive Advantage of Nations. New York: Free Press. Rauch, James E. 1993. “Productivity Gains from Geographic Concentration of Human Capital: Evidence from Cities.” Journal of Urban Economics 34 (3), 380–400. Roback, Jennifer. 1982. “Wages, Rents, and the Quality of Life.” Journal of Political Economy 90 (6), 1257–78. Romer, Paul M. 1990. “Endogenous Technological Change.” Journal of Political Economy 98 (5), 71–102. Rosen, Sara Thomas. 1979. “Wage-Based Indexes of Urban Quality of Life.” In Peter Mieszkowski and Mahlon Straszheim, eds., Current Issues in Urban Economics. Baltimore: Johns Hopkins University Press.

Talent, Cities, and Competitiveness    53 Sampson, Robert J. 1995. “The Community.” In J. Q. Wilson and J. Petersilia, eds., Crime and Public Policy. San Francisco: Institute of Contemporary Studies Press. Saxenian, Annalee. 1996. Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press. Scott, Allen J. 1986. “High Technology Industry and Territorial Development: The Rise of the Orange County Complex.” Urban Geography 7 (1), 3–45. Scott, Allen J. 2009. “Human Capital Resources and Requirements across the Metropolitan Hierarchy of the USA.” Journal of Economic Geography 9 (2), 207–26. Shapiro, Jesse M. 2006. “Smart Cities: Quality of Life, Productivity, and the Growth Effects of Human Capital.” Review of Economics and Statistics 88 (2), 324–35. Sharkey, Patrick. 2013. Stuck in Place : Urban Neighborhoods and the End of Progress toward Racial Equality. Chicago: University of Chicago Press. Simon, Curtis J. 1998. “Human Capital and Metropolitan Employment Growth.” Journal of Urban Economics 43 (2), 223–43. Simon, Curtis J., and Clark Nardinelli. 1996. “The Talk of the Town:  Human Capital, Information and the Growth of English Cities, 1861–1961.” Explorations in Economic History 33, 384–413. Simonton, Dean Keith. 2000. “Creativity:  Cognitive, Developmental, Personal, and Social Aspects.” American Psychologist 55 (1), 151–58. Smith, Adam. 1776. The Wealth of Nations. London: W. Strahan and T. Cadell. Sternberg, Robert, and Todd Lubart. 1999. “The Concept of Creativity:  Prospects and Paradigms.” In Robert Sternberg, ed., Handbook of Creativity. New  York:  Cambridge University Press. Storper, Michael, and Susan Christopherson. 1987. “Flexible Specialization and Regional Industrial Agglomerations:  The Case of the US Motion Picture Industry.” Annals of the Association of American Geographers 77 (1), 104–17. Thompson, Wilbur R. “Cities in Transition.” 1986. Annals of the American Academy of Political and Social Science 488 (1), 18–34. Thompson, Wilbur, and Philip Thompson. 1987. “National Industries and Local Occupational Strengths: The Cross-Hairs of Targeting.” Urban Studies 24 (6), 547–60. Ullman, Edward L. 1958. “Regional Development and the Geography of Concentration.” Papers and Proceedings of the Regional Science Association 4, 179–98. Vernon, Raymond. 1966. “International Investment and International Trade in the Product Cycle.” Quarterly Journal of Economics 80 (2), 190–207. Vernon, Raymond. 1979. “The Product Cycle Hypothesis in a New International Environment.” Oxford Bulletin of Economics and Statistics 41 (4), 255–67. Von Thünen, Johann Heinrich. 1826. Isolated State: An English Version of “Der Isolierte Staat”. Trans. C. Wartenberg. New York: Pergamon Press. Wadhwa, Vivek, Annalee Saxenian, Ben A. Rissing, and Gary Gereffi. 2007. “America’s New Immigrant Entrepreneurs:  Part I.” Duke Science, Technology & Innovation Paper, Duke University. Wadhwa, Vivek, Annalee Saxenian, Ben A.  Rissing, and Gary Gereffi. 2008. “Skilled Immigration and Economic Growth.” Applied Research in Economic Development 5 (1), 6–14. Weber, Alfred. 1929. Alfred Weber’s Theory of the Location of Industries. Trans. C. J. Friederichs. Chicago: University of Chicago Press. Whyte, William H., Jr. 1956. The Organization Man. New York: Simon and Schuster.

Chapter 4

Enabli ng Entrepre ne u ria l Ec osyst e ms Philip E. Auerswald

1 Introduction It is the systems so formed which, from the point of view of the ecologist, are the basic units of nature on the face of the earth. Our natural human prejudices force us to consider the organisms (in the sense of the biologist) as the most important parts of these systems, but certainly the inorganic “factors” are also parts—there could be no systems without them, and there is constant interchange of the most various kinds within each system, not only between the organisms but between the organic and the inorganic. These ecosystems, as we may call them, are of the most various kinds and sizes. They form one category of the multitudinous physical systems of the universe, which range from the universe as a whole down to the atom. —Arthur George Tansley (1935, 299)

Entrepreneurship is present in all societies but manifests itself differently depending on the context (Baumol 1990).1 Productive entrepreneurship corresponds to the creation and expansion of new firms; unproductive entrepreneurship corresponds to rent-seeking activities; destructive entrepreneurship corresponds to trafficking in illicit 1 

This chapter draws from and integrates prior work with (in chronological order) Stuart Kauffman, José Lobo, Karl Shell, and Lewis Branscomb, as well as conversations over time with Zoltan Acs, Richard Florida, Carl Schramm, Dane Stangler, Mary Lindenstein Walshok, and Richard Zeckhauser. For helpful recent discussions and exchanges I thank Norris Krueger, Sandra Maxey, Valerie Mocker, Rhett Morris, Jonathan Ortmans, and Jenny Stefanotti. All errors are my own.

Enabling Entrepreneurial Ecosystems    55 goods. All these forms of entrepreneurship create economic activity. However, institutions advance and societies progress only when the returns to productive entrepreneurship exceed those to unproductive and destructive entrepreneurship (Baumol, Litan, and Schramm 2009; Baumol 2010; Auerswald 2012). While there is little evidence that government action can affect the overall supply of entrepreneurs in a given economy, there is strong evidence that it can influence the manner in which entrepreneurs—or entrepreneurially inclined individuals—direct their abilities (Baumol 1990, 893). Strategies that support development at a national, regional, or local scale thus must consider not only the quality of the business climate in general, but also, and importantly, the way government actions affect the relative returns to entrepreneurship of different types. Informed by research regarding the importance of entrepreneurship to sustained economic growth and well-being,2 nongovernmental grant-making organizations as well as governments around the world increasingly have sought strategies to encourage entrepreneurial initiative and to support entrepreneurial ventures. In 2009 the Aspen Institute launched the Aspen Network of Development Entrepreneurs, today comprising 190 member institutions operating a variety of programs intended to support small and growing businesses in 150 countries.3 In 2010, the Chilean government launched Startup Chile, a program with the aspiration to convert Chile into the innovation and entrepreneurship hub of Latin America (Melo 2012; Wadhwa 2012); the US government launched Startup America the following year, with more than a dozen other countries launching national start-up initiatives in the intervening four years.4 And in 2011, the World Bank (2010, 2) established an Innovation, Technology, and Entrepreneurship Practice as one of six global practices of the Finance and Private Sector Development Network. In its review of the World Bank Group’s $18 billion portfolio, the Bank’s Independent Evaluation Group articulated a commonly held perspective on the motivation of national and regional programs to support entrepreneurship: “Entrepreneurs serve as agents of change and growth in market based economies, providing a major 2 

Acs and Audretsch (1987; 1990) and Audretsch (1995) set the stage by providing empirical evidence of the significant role of small firms in generating technological innovations in the United States. Acs (1992) went further to sketch multiple pathways by which entrepreneurial activity drives economic growth. Schmitz (1989) offered a formal model of this process in which the entrepreneur is represented as an imitator of incumbents. Acs and Armington (2003) empirically assessed the role of entrepreneurs in promoting knowledge spillovers and growth at the scale of a city, also focused on the United States. Acs and Varga (2004) and Van Stel, Carree, and Thurik (2004) both employed data from the Global Entrepreneurship Monitor (GEM) project to study the relationship between entrepreneurship and growth at the scale of the nation. Michelacci (2003) and Acs et al. (2004) explored the role of entrepreneurs as knowledge “implementers” or “filters,” respectively, and the manner in which those functions drive economic growth. Weitzman (1998) and Michelacci (2003) presented models in which the ultimate limits to growth will lie not in the generation of inventions and new fundamental knowledge that “spills over” from one part of the economy to another, but rather in the availability of Schumpeterian entrepreneurs to guide the conversion of those inventions and new knowledge into practice through innovation. 3  See Aspen Network of Development Entrepreneurs, http://www.aspeninstitute.org/policy-work/ aspen-network-development-entrepreneurs. 4  More information about Startup Nations is accessible at http://www.startupnations.org/.

56    The Concept of Local Competitiveness channel through which innovative ideas can be turned into wealth (Dutz 2007; World Bank 2010). Innovation and entrepreneurship can be mutually reinforcing and together can be a powerful source of improved productivity and competitiveness, helping to reduce poverty and stimulate long-term economic growth” (Srinivasan 2004). The increasing interest in launching policies and programs to support entrepreneurship has intersected with an increasing recognition, originating from very different sources, that entrepreneurship is a highly context-dependent activity, with the subnational region, the city, or even the neighborhood being a more natural scale of analysis than the nation. Furthermore, initial experience with programs to support entrepreneurs has called into question the effectiveness of interventions focused solely on financing entrepreneurs or developing their personal abilities without specific attention to operational context (Isenberg 2010). As a consequence, institutional innovators inside and outside of government have come to frame entrepreneurship-related interventions as being aimed at enabling “entrepreneurial ecosystems” at the scale of the city or the subnational region as much as directly supporting entrepreneurs (Feld 2012; Hwang and Horowitt 2012). Academic research has failed to keep up with recent interest on the part of governments and nongovernmental grant-making organizations on entrepreneurship and a central element in local competitiveness. To be sure, as just noted, a scholarly literature over a century old has addressed the importance of entrepreneurship to development and growth. A parallel, comparably robust, literature has explored the link between local characteristics and entrepreneurial vibrancy.5 However, while rigorous studies abound regarding entrepreneurship and the entrepreneurial process and national-scale policy initiatives, very few comparably rigorous studies exist of the effectiveness of interventions of the type on which practitioners are currently focused: those intended to enable local entrepreneurial ecosystems.6 There are various reasons for the relative immaturity of the research on entrepreneurial ecosystems at the scale of the city or the subnational region. In this chapter I propose that one of them is the fact that the default theoretical architecture in economics is not well suited to describing entrepreneurial ecosystems, assessing their vibrancy (or lack thereof), and suggesting policies that may improve their function.7 My objective in this chapter is take seriously the idea of the entrepreneurship “ecosystem,” considering what sort of guidance to research and policymaking is provided by direct analogy to the substantial literature on ecosystems within the fields of evolutionary biology and ecology. There is nothing at all novel about theorizing about economic systems by analogy to biological systems, of course: Nelson and Winter (1982) serves as particularly significant landmark in this literature, but it goes back at least as far as 5 

Auerswald et al. (2007) provides a survey. Relevant context-setting papers include Flora and Flora (1993) and Lichtenstein, Lyons, and Kutzhanova (2004). 7  The epistemological substructure (logical positivism) and default analytic techniques (constrained optimization) of neoclassical economics derive from mathematics, rather than from the life sciences. 6 

Enabling Entrepreneurial Ecosystems    57 Herbert Spencer’s 1857 essay, “Progress: It’s Law and Cause.” In this chapter I will sketch a future path for this line of inquiry, arguing along the way that developments both in economics and in theoretical biology in the last quarter-century have substantially narrowed the representational gap between economics and the life sciences, to the point where the analogy to evolutionary biology and ecology actually starts to provide some tangible conjectures about the functioning of economic systems. The quotation that opens this chapter is from a 1935 paper by Sir Arthur Tansley titled “The Use and Abuse of Vegetational Concepts and Terms” in which the author introduced the term “ecosystem.” As that quote suggests, evolutionary biologists in the 1930s were as naturally inclined to place “the organism” at the center of their inquiry as economists in the 1930s were to place “the firm” at the center production theory. Tansley’s insight was that dynamically stable networks of interconnected organisms and inorganic resources constituted their own distinct domain of analysis. Just as I will argue in this chapter that we cannot consider the entrepreneurial ecosystem simply as a “complex firm,” Tansley rebelled against the application of the term “complex organism” to such networks “because the term [complex organism] is already in common use for an individual higher animal or plant, and because the biome is not an organism except in the sense in which inorganic systems are organisms” (Tansley 1935, 306). In addition to providing us with a foundational definition of the term “ecosystem,” Tansley’s paper offers us a starting point in defining progress in regional development (and, by extension, local competitiveness). Changes in vegetation, termed “succession,” are analogous to the progressive development of practices within an industry or local economy: Succession is a continuous process of change in vegetation which can be separated into a series of phases. When the dominating factors of change depend directly on the activities of the plants themselves (autogenic factors) the succession is autogenic: when the dominating factors are external to the plants (allogenic factors) it is allogenic. The successions (priseres) which lead from bare substrata to the highest types of vegetation actually present in a climatic region (progressive) are primarily autogenic. Those which lead away from these higher forms of vegetation (retrogressive). (Tansley 1935, 306)

Just as succession can be either autogenic or allogenic, the evolution of industries as well as entrepreneurial ecosystems can be either endogeneously driven or exogenously driven. Furthermore, just as Tansley defines successions that lead toward greater biological complexity as progressive change in biological systems, so I am suggesting that the evolution of the capabilities of a city or region toward greater complexity constitutes progressive change in economic systems.8

8 

Auerswald, Kauffman, and Lobo (1994) formalizes this idea.

58    The Concept of Local Competitiveness To jump to the conclusion, here are some of the ways that, according to the argument to follow, a baseline evolutionary/ecological perspective on entrepreneurial ecosystems departs from the neoclassical/default:9 • Default: Entrepreneurship is one factor among many in an economy-wide aggregate production function. Evolutionary/ecological: Entrepreneurship is not a factor in a fixed aggregate production function, but rather the process of creating new firm-level production recipes that can be represented as production functions. • Default: Imperfect appropriability of the returns from entrepreneurial initiative constitutes a primary impediment to entrepreneurial success because the most valuable firm-level production functions (aka recipes) are simple and can easily be copied. Evolutionary/ecological:  Imperfect appropriability of the returns from entrepreneurial initiative constitutes a secondary impediment to entrepreneurial success because the most valuable firm-level production functions (aka recipes) are complex and cannot easily be copied. • Default: Market failures and economic crises undermine entrepreneurs. Evolu­tionary/ecological: Market failures and economic crises create opportunities for entrepreneurs. These differences in theory also imply different practical strategies to encourage entrepreneurship: • Default: Entrepreneurship depends on a favorable business climate. Evolutionary/ecological: A favorable business climate depends on entrepreneurship. • Default: If entrepreneurship generates positive spillovers and thus is an undersupplied input, government policy should subsidize the increased production of entrepreneurs by educational institutions. Evolutionary/ecological:  Entrepreneurship policy potentially can increase economic vibrancy by enabling entrepreneurial ecosystems, but doing so is not as simple as simply subsidizing the production of an undersupplied input. • Default: Conventional, formal program evaluation is essential to enabling entrepreneurial ecosystems. Evolutionary/ecological: Conventional, formal evaluation is unlikely to be of significant value in enabling entrepreneurial ecosystems. I begin in section 2 by defining entrepreneurship (following Schumpeter) as the creation of new combinations—a definition that creates a bridge between biological and economic representations of novelty and exchange in ecosystems. In section 3, I describe the relationship between algorithmic complexity (the consequence of the successive creation of new combinations over time) and economic opportunity. In section 4, 9 

Arthur (2013) presents a compelling analysis. See also Helbing and Kirman (2013).

Enabling Entrepreneurial Ecosystems    59 I describe how market failures are fundamental to the creation of economic opportunity. In section 5, I address issues of political economy as they relate to the evolution of economic opportunity, describing how a favorable business climate depends on entrepreneurship. In section 6, I offer some strategies for enabling entrepreneurial ecosystems that emphasize diversity, dynamism, and deal-flow. In section 7, I offer some cautionary thoughts regarding the use of methodologies of formal assessment in the implementation of strategies to enable entrepreneurship. I conclude in section 8.

2  Entrepreneurship Is the Creation of New Combinations The carrying out of new combinations we call “enterprise”; the individuals whose function it is to carry them out we call “entrepreneurs.” —Joseph Schumpeter ([1912] 1934)

A world of warning is in order at the outset: Throughout this chapter I will be mixing two metaphors, persistently and (mostly) unrepentantly. The first metaphor is that of the “recipe.” This is not really a metaphor, as the culinary recipe is actually a specific example of the general concept to which I am referring. In the first paper to suggest that the recipe might be the basis for a neo-Schumpeterian theory of the firm, Winter (1968) states: If the technology is that of cake baking, the standard economics of the firm describes that technology solely in terms of the list of ingredients. Here the “ingredients” must be understood to include so many oven-hours, so many labor-hours of the cook, as well as eggs and flour. But “knowing how to bake a cake” is clearly not the same thing as “knowing how to bring together all of the ingredients for a cake.” Knowing how to bake a cake is knowing how to execute the sequence of operations that are specified, more or less closely, in a cake recipe. The list of ingredients is understood to be contained in the recipe, but the recipe is not fully revealed by the list of ingredients. (Winter 1968, 9)

Production recipes are implicit in the neoclassical theory of production, but nowhere are they explicitly represented. Sometime ago my coauthors Stuart Kauffman, José Lobo, Karl Shell, and I described how a formal model of production recipes fits into production theory;10 I subsequently have endeavored to connect this model with the Coasean theory of the firm (arguing that the two jointly offer a starting point for understanding entrepreneurship in the theory of the firm) (Auerswald 2008) and to derive some of its first-order implications for industrial organization (Auerswald 2010). But the basic idea 10  Auerswald et al. (2000); this paper built upon Auerswald, Kauffman, and Lobo (1994). Significant precursors are Simon (1967) and Winter (1968).

60    The Concept of Local Competitiveness is very simple: A recipe is just the algorithm employed to transform inputs into outputs (where the outputs may be a service, or another recipe), more or less exactly as in the culinary instance. The second metaphor is that of the ecosystem, as just described. From a modeling standpoint, the connection is a natural one: the model of production recipes introduced in Auerswald and coauthors (2000) is itself based on a formalization of the idea of “fitness landscapes” within evolutionary biology introduced by Kauffman and Levin (1987), based upon Wright (1932). When I appeal to the conceptual structure of the “ecosystem” to describe the environment within which entrepreneurs operate, algorithms of production will correspond to genotypes (also analogous to recipes); specific firms employing those algorithms will correspond to phenotypes; entrepreneurship will correspond to genetic recombination; and the creation of new industries will correspond to speciation.11 I will thus employ “new combinations” and “new production recipes “interchangeably when referring to entrepreneurship: creating new combinations  creating new production recipes  entrepreneurship

Just like culinary recipes, production recipes are comprised of discrete tasks that yield a well-specified output when performed in combination. The individual tasks themselves can be the subject of experimentation. Each task may be refined; different arrangements of tasks (which is to say, different recipes) will result in different outputs. Importantly, some tasks may be performed using tacit knowledge, and therefore will nowhere be encoded. The next section defines complexity in production, on which I have already indicated my definition of progress in regional development—and thus the ultimate source of local competitiveness—is based.

3  Complexity Drives Opportunity There is no such thing as a low-tech industry. There are only low-tech companies. —Michael Porter (1998, 85–86)

Economic complexity can be measured in a number of ways. Most directly, it can be measured in terms of the number of parts in a technological artifact. The John Deere tractor sold today is clearly more complex than McMormick’s reaper, which in turn was 11  I do not address speciation in this chapter. However, I note that the work of Gavrilets (1999; 2003) provides an interesting starting point for developing formal economic theories based on a modeling in evolutionary biology.

Enabling Entrepreneurial Ecosystems    61 more complex than a plow. More complex artifacts, in turn, imply more complex social arrangements required to produce the artifacts; the technical dimensions of complexity (e.g., the number of parts in an artifact, and the intricacy of their assembly) is thus directly linked to the organizational requirements of production. Focusing on the organizational dimensions, rather than the characteristics of artifacts, complexity also may be understood as the extent to which new market innovations require the efforts of teams incorporating multiple distinct fields of inquiry: the average size and/or diversity of teams involved in the creation of new technological innovations (Kash and Rycroft 1999; Adams et al. 2005) or, from a human capital standpoint, as the average investment required by an individual in order to reach the technological frontier (Jones 2005a; 2005b; Jones et al. 2014). Interpreted in this way, increased complexity will be manifest in trends not only toward expanding the scope of the firm via “combination” and “integration” (as described by Coase 1937) that will be represented in single-firm production recipes, but also in the intricacies of buyer-supplier relationships and peer production networks (Appleyard 2003; Auerswald 2008; Agwara et al. 2014). Classic works by Simon (1965, 1967), Marshak and Radner (1972), and Radner (1993) as well as more recent papers12 have explored a third way of thinking about complexity, emphasizing the difficulty of the problem that is solved by a particular (relatively high effectiveness) production recipe. In this view, more complex problems are simply ones for which the search for a good solution is more difficult. Notions of computational complexity related to the difficulty of solving a particular problem are at the heart of algorithmic information theory; as such, they can be can be defined quite formally.13 As applied to production recipes, the greater the complexity of technology, the lower the correlation between the effectiveness of the original recipe (i.e., the leader’s method) and that of the same recipe altered slightly (i.e., an imperfect imitation). In this work the complexity of a production recipe is represented in terms of both the number of tasks and (critically) the extent of the interactions among those tasks units; in the biological model, these tasks in their most irreducible form correspond to alleles in a gene; interactions among tasks correspond to epistatic interactions among alleles. This third definition is the one that most directly drives the implications of complexity for both entrepreneurial ecosystems and industry evolution. The primary causal pathway by which complexity affects market structure and local competitiveness is via the obstacles to imitation that complexity may endogenously create. This part of the story is standard industrial organization economics and strategic management. To the extent that a new method is easily imitable, the quasi-rents accrued by an innovator will be short lived. Imitation of complex production recipes is almost always imperfect—sometimes disastrously so—because modifications in the practices of one unit within the firm will affect the effectiveness of multiple other units. 12  Auerswald et al. (2000); Rivkin (2000); and Auerswald (2008; 2010). The NK framework of Kauffman and Levin (1987) provides a shared inspiration for the models in these four papers. Kogut and Zander (1992) and Teece, Pisano, and Shuen (1997) are in a similar spirit. 13  Specifically, Kolmogorov complexity and NP completeness.

62    The Concept of Local Competitiveness Because a simple production recipe can be easily imitated, it is not likely to yield persistent “above normal” profits to an early adopter. The most enticing opportunities for disruptive entrepreneurship thus typically are ones that require the greatest coordination and have the greatest inherent complexity, as these ventures are the most difficult to imitate when successfully launched.14 As Teece, Pisano, and Shuen (1997, 509) put it: “The competitive advantage of firms is seen as resting on distinctive processes (ways of coordinating and combining), shaped by the firm’s (specific) asset positions (such as the firm’s portfolio of difficult-to-trade knowledge assets and complementary assets), and the evolution path(s) it has adopted or inherited.” The competitive advantage of regions is similarly in capabilities that are not easily copied or replaced by tradable goods (Hidalgo et al. 2009; Hausmann and Hidalgo 2011; Hausmann et al. 2011). The above suggests that the ideas that actually propel growth and development are overwhelming uncodified, context-dependent, and transferable only at significant cost—which is to say, tacit knowledge dominates, information asymmetries are the norm, and transactions costs are significant (Butler et al. 1997). This observation contrasts sharply with the specific new growth formulation famously advanced by Romer (1986, 1990) that ideas are “nonrival” and that “nonexcludable,” economically relevant innovations are characteristically subject to “knowledge spillovers.”15 An information-theoretic/ ecological perspective suggests that, while “knowledge spillovers” of the type emphasized by Romer (and followers) clearly exist, they are of marginal relevance in the practical work of creating the new business entities that drive economic growth and development.16 The reason for this is that entrepreneurship involves the search for ideas that are in fact, rivalrous and excludable (at least temporarily) out of which ventures with proprietary value can be created. The impediments to entrepreneurship that matter most are not lack of appropriability of returns—as the Romer (1986, 1990) version of new growth theory would suggest—but rather the everyday battles involved in communicating ideas, building trust, and making deals.17

14 

Auerswald (2010) provides a proof of this proposition. “Nonrival” means that one person’s use of an idea does not keep another person from using the idea; “nonexcludable” means that it is impossible to keep a person from using an idea once it is “out in the open”; and “knowledge” refers to the costless transmission of ideas that are nonrival and nonexcludable. Romer actually uses the term “recipes” to refer to useful productive knowledge, as Karl Shell, Stuart Kauffman, José Lobo, and I have independently. 16  To emphasize: The focus here is not on web pages and pirated music videos. These digitized products—even including patents—are not the same thing as production recipes. 17  See also Zucker, Darby, and Armstrong (1998). There is no disputing that ideas created by one person, or within one firm, can reach other people or firms through multiple pathways, many of which do not involve direct compensation of the innovator by the beneficiary. If one chooses to refer to such pathways as “knowledge spillovers,” then such spillovers will everywhere be in evidence. Yet when such pathways involve economic benefit derived from entrepreneurship, they also in most cases involve significant costs: recruiting a key employee from a competitor firm or industry leader; undertaking research to invent around a patent; reverse-engineering a product; paying for employee attendance at conferences; hiring consultants; building a trusted relationship with a buyer or supplier. Furthermore, to the extent that the public benefits not captured by the entrepreneur (resulting from “knowledge spillovers” or other mechanisms) are temporally far off or uncertain, it is unlikely that they will be 15 

Enabling Entrepreneurial Ecosystems    63 It is toward easing these struggles that efforts to enable entrepreneurial ecosystems consequently must be directed. The next section is devoted to correcting another conceptual error often made in conceiving strategies to enable entrepreneurial ecosystems: that the goal of interventions is to reduce or eliminate (static) market failures. We shall see that the opposite is true: (static) market failures constitute a vital source of energy to propel entrepreneurial initiative. It is because market failures are pervasive and self-renewing that entrepreneurship—in one form or another—is a globally ubiquitous phenomenon.

4  Market Failures Create Opportunities for Entrepreneurs The creative spark on which serendipity depends, in short, is to see bridges where others see holes. —Ronald Burt (2004, 351 n. 2)

Where conventional wisdom among economists and policymakers holds that the elimination of market failures will encourage entrepreneurial activity, the reality is that the greater the intensity of market failures, the stronger the potential value of an entrepreneurial new combination.18 At one level, this claim can be justified in a very straightforward manner: in an environment of perfect competition where the actions of existing firms fulfill the first welfare theorem (Pareto efficiency achieved via market transactions) and the actions of government fulfill the second welfare theorem (equity achieved via ex post redistribution of wealth), there would be no market space for, much less need for, entrepreneurship. Even allowing for short-term rigidities in entry and exit and exogenously driven fluctuations in demand, the only motivation for a new venture would be to replicate existing economic combinations, not to create new ones. As Coase (1937) famously found, the introduction of transactions costs into an otherwise perfectly competitive market framework is sufficient to motivate the existence of firms: There is a combination when transactions which were previously organized by two or more entrepreneurs become organized by one. This becomes integration when it of greater importance to entrepreneurial decision-making than will be the immediate, first-order challenges of defining a viable business model; building a team to execute; testing an initial product or service; finding customers or otherwise financing the firm’s launch; marketing the product or service and adapting to market response; managing growth and expansion; and otherwise organizing the firm’s operations. 18 

Parts of this section derive from Auerswald et al. (2007).

64    The Concept of Local Competitiveness involves the organization of transactions which were previously carried out between the entrepreneurs on a market. A firm can expand in either or both of these ways. (Coase 1937, 397)

In the Coasean framework, transactions costs are the glue that holds a firm together. When the magnitude of transactions costs increases somewhere in the economy, it follows that new firms may be created and existing firms may either combine or expand (by incorporating transactions previously mediated through the market).19 Burt (2004) takes this further, describing how transactions costs combined with information asymmetries are not only the basis for intellectual arbitrage, but are also the basis for the creation of new conceptual combinations. In prior work, Burt established the particularly important role played within organizations by the relatively few people within an organization possessing the capacity to bridge the “structural holes” that exist as a natural consequence of functional specialization and the separation of the organizations into distinct clusters. In Burt (2004), he extends this to describe how those same individuals are disproportionately responsible for creating new ideas. “Whether in communities or in a geographical region, divisions in a corporation, groups within a profession, or members of a team, people specialize within clusters and integrate via bridges across clusters” (Burt 2004, 351 n. 2; see also Pentland 2014; Freeman and Huang 2014). The structural holes both within and between organizations that Burt has documented in a substantial body of work exist because of market failures of various types. They create opportunities for those who “see bridges where others see holes”—a concise description of the entrepreneurial mindset. Ironically, then, the renewal of structural holes—and the persistence of market failures on which they are based—is a prerequisite for entrepreneurship.20 Until recently, the relationship between complexity, capabilities, and development was a matter of conjecture. However, thanks to a groundbreaking empirical study published in 2009 and further work since by Ricardo Hausmann, César Hidalgo, and coauthors, this conjecture is at last being subject to empirical test. Hausmann and Hidalgo (2011, 13) summarize their results as follows: We have presented a technique that uses available economic data to develop measures of the complexity of products and of countries, and showed that (i) these measures capture information about the complexity of the set of capabilities available in a country; (ii) are strongly correlated with income per capita; (iii) are predictive of future growth; and (iv) are predictive of the complexity of a country’s future exports, making a strong empirical case that the level of development is indeed associated to the complexity of a country’s economy.

19  I elaborate on the relationship between Coasean and Schumpeterian theories of the firm in Auerswald (2008). 20  For formal development of the analogy in evolutionary biology, see Gavrilets (1999).

Enabling Entrepreneurial Ecosystems    65 The approach taken in Hidalgo and Hausmann (2009), Hausmann and Hidalgo (2011), and Hausmann and coauthors (2011) is fundamentally combinatorial, inspired (like this chapter) by Kauffman (1988; 1993) and synchronous with Weitzman (1998). The matrix of products and underlying capabilities that is the core of the analysis in Hidalgo and Hausmann corresponds quite closely to an economic food web, in which lower-complexity production recipes create products that are then “consumed” by higher-complexity production recipes—very much along the lines of the classic notion of succession in biological systems, as described by Tansley (1935) above.21 While Hausmann and Hidalgo have conducted their work on maps of economic complexity at the national level, only data limitations impede the application of their model to a subnational region. At that scale, Jacobs (1961; 1969; 1984) was the first to relate the creation of new combinations to growth and development, advancing the hypothesis that diversity, not specialization, is the key to vitality in cities (see also Bairoch 1988). A pioneering empirical study of growth in a cross-section of US cities by Glaeser and coauthors (1992, 25) found that “at the city-industry level, specialization hurts, competition helps, and city diversity helps employment growth.” Subsequent studies by Feldman and Audretsch (1999) concluded that economic diversity is important in explaining new firm creation and innovative output, respectively.22 Similarly Saxenian (1994, 57) argues that regions in this view are best understood “as networks of relationships rather than as collections of atomistic firms.” The source of regional technological advantage lies not in vague and unmeasurable knowledge spillovers, but in the highly tangible flexibility of economic actors to organize and reorganize flexibly as the need arises.23 In a detailed study of Silicon Valley social networks, Castilla and coauthors (2000) note that “dense networks not only within but between sectors of engineers, educators, venture capitalists, lawyers, and accountants are important channels for the diffusion of technical and market information.” Exchange of ideas within such networks is largely purposive, building “weak ties” that facilitate transactions.24 21 

Hausmann, Hidalgo, and coauthors use the term “capabilities” rather than production recipes. While they do not explicitly model those capabilities, the meaning is essentially analogous. See also McNerney (2013). 22  Quigley (1998) surveys this literature. Working from a broader definition of “diversity,” Florida (2002) argues that the diversity of a region along cultural and other human dimensions suggests a tolerant and creative population, generating the “new combinations” that are, in turn, the actual source of regional competitive advantage. In contrast, Olson (1982) argues that transactions costs are lowered, trust is enhanced, and economic activity is facilitated by cultural homogeneity. 23  As Saxenian stated in a 1998 interview comparing technology development in Silicon Valley with film production in Los Angeles: “You have these very fluid labor markets and these communities of highly skilled people who recombine repeatedly. They come together for one project—in this case a new film, in Silicon Valley it would be a new firm—and then they move on. The system allows a lot of flexibility and adaptiveness. . . . Information about new markets and new technologies flows very quickly. This sustains the importance of geographic proximity, despite the fact that, theoretically, the technology allows you to be anywhere” (Cassidy 1998, 125). 24  Granovetter (1973); see also work by Watts and Strogatz (1998) and others on “small world” networks. The success of each of these institutional types depends on the existence of others, including not only well-recognized entities such as venture capital firms, large corporations, and universities, but also angel networks, university and corporate venture capital funds and incubators, experimental R &

66    The Concept of Local Competitiveness In regional economics, these results are well known. What the work by Hausmann and Hidalgo has done is, for the first time, to indicate the existence of a bridge between formal microeconomic models of production grounded in evolutionary biology with the sort of structure-preserving empirical inquiry that takes the ecological metaphor seriously. In the next section I go in a different direction, discussing the manner in which considerations of political economy bear upon the design and implementation of strategies to enable entrepreneurial ecosystems. Then, in sections 6 and 7, I refer back to complexity to offer some suggestions regarding practical strategies to enable entrepreneurial ecosystems.

5.  A Favorable Business Climate Depends on Entrepreneurship A modern economy is a wondrously complex system that continually converges toward general equilibrium. But it always fails to reach equilibrium because it incessantly faces new opportunities and shocks. There is not even enough information to calculate the present situation of an economy with any detail or accuracy, much less its future position. . . . Because uncertainties are so pervasive and unfathomable, the most dynamic and prosperous societies are those that try many, many different things. They are societies with countless thousands of entrepreneurs. —Mancur Olson (1999, 188–89)25

Entrepreneurship of the type on which I am focused in this chapter (which is to say, Schumpeterian entrepreneurship) is an inherently disequilibrium phenomenon that takes place in a world characterized by uncertainty, asymmetric information, indivisibilities, and nonzero transactions costs. It is for this very reason that entrepreneurship policy has come to the fore in countries around the world. Development is an ongoing process of social change—subject to regular disruption—that involves institutions, culture, and technology. While societies can advance for a short while through incremental adjustments to the status quo, long-term development requires entrepreneurship and

D programs supported by federal and state government, fast-track regulatory clearance services by state and local governments, and specialized services firms (e.g., in law, real estate, or accounting). Auerswald and Branscomb (2002) refer to the efforts to convert new knowledge into commercial innovations in such a context as “collective entrepreneurship,” addressing the balance, typically required for success, of collaboration between different types of people and of individual vision. 25  This single quotation stands in for a deeply considered argument, fully presented in Power and Prosperity, on which the material in this section rests, but which (for obvious reasons) I will not seek to summarize here.

Enabling Entrepreneurial Ecosystems    67 innovation. As Hirschman (1958, 5) noted, “Development depends not so much on finding optimal combinations for given resources and factors of production as on calling forth and enlisting for development purposes resources and abilities that are hidden, scattered, or poorly utilized.” Entrepreneurs and innovators exist in all societies, but not all societies are equally welcoming of the disruptive changes they provoke. Individual entrepreneurs and innovators thus face three options: seek economic rents within the status quo; challenge the status quo through disruptive innovation; leave the society altogether to seek an environment more welcoming of economic creativity. These three options (taken in reverse order) are analogous to the fundamental political options articulated by Hirschman (1970) long ago: exit, voice, and loyalty. When too large a fraction of potential innovators and entrepreneurs choose either to seek rents within the context of the status quo or to leave the society altogether, development slows or comes to a halt. If the above statements are, in fact, true, then creating a place for the future in any country means creating a space for entrepreneurship and innovation—and, in particular, encouraging the subset of potential entrepreneurs and innovators who choose neither to conform nor to depart, but rather to stay and build something new. Conventional wisdom holds that a country can transition to becoming a 21st-century entrepreneurial economy only after its political institutions have fully matured. However, the actual development experience of advanced industrialized economies arguably tells a different story: a country’s political institutions mature only as its economy produces broad-based opportunities on a sustainable basis (Auerswald 2012). If so, then internal security and political stability are not prerequisites for, but the consequence of, broad-based social development that is driven by competition and entrepreneurship and supported by increasing levels of social trust. Actions taken in the name of near-term stability that undermine competition and economic dynamism not only make a country less prosperous—they also make it (dynamically) less secure and less stable. Support for entrepreneurship and innovation similarly is often confused with generic strengthening of the overall “business climate.” What is the nature of the difference? The business climate pertains to all firms—both incumbents and new entrants. Some elements of the business climate (for example, the time required to register a new business or the difficulty of obtaining business licenses) are particularly relevant to entrepreneurship. However, others (for example, the stability of the financial sector) may actually imply the concentration of market power and barriers to entrepreneurial entry.26 If there is more to enabling entrepreneurial ecosystems than “strengthening the business climate,” then what constitutes a viable strategy or set of strategy? I consider this question next.

26 

Competition Commission of Pakistan (2009) provides a case study.

68    The Concept of Local Competitiveness

6  Enable Entrepreneurial Ecosystems by Promoting Diversity, Encouraging Dynamism, and Driving Deal-Flow An entrepreneurial ecosystem implies cooperative and productive relationships among different organizations. In many countries, these relationships are between startups, established companies, universities, and research institutions. In a vibrant ecosystem, people and ideas flow between these organizations, starting new ventures, joining existing ones, and linking innovations together. —Global Entrepreneurship Congress (2014)

As early as 1963, anthropologist Clifford Geertz noted of Javanese traders, “What the entrepreneurial group of . . . small businessmen most lacks is not capital . . . or drive . . . or a sufficient market. What they lack is the power to mobilize their capital and channel their drive in such a way as to exploit the existing market possibilities. They lack the capacity to form efficient economic institutions; they are entrepreneurs without enterprises” (Geertz 1963, 28). A half-century later, a huge disparity still exists between lowand high-income countries in terms of what small and growing businesses contribute to economic growth and employment. The relative dearth of small and growing businesses in low-income countries is sometimes referred to as the missing middle. The motivation for this term is illustrated by figure 4.1 from

SME Contribution to GDP

SME Contribution to Employment

Contribution to GDP (%) 100% 100%

Contribution to Employment (%) 100% 100% Contribution 28% from other sectors 53%

37% The ‘missing middle’

36%

Contribution from other sectors

51%

Contribution from formal SME sector

13%

Contribution from informal sector

16%

47%

Low income countries1

High income countries1

The ‘missing middle’

18% 29% Low income countries1

57%

Contribution from formal SME sector

15%

Contribution from informal sector

High income countries1

Figure  4.1  Small and Medium Enterprise (SME) Contribution to GDP and Employment in Low-Income and High-Income Countries Contribution percentages are medium values for income  group. (Source:  Figure from Aspen Network of Development Entrepreneurs (2009), based on data from Ayyagari, Beck, and Demirguc-Kunt (2003). Reprinted under Creative Commons license.)

Enabling Entrepreneurial Ecosystems    69 Ayyagari, Beck, and Demirguc-Kunt (2003), which displays historical contributions to economic output and employment made by small and medium enterprises in both low- and high-income countries. The contrast is striking and significant. In high-income countries, SMEs are responsible for over half of both gross domestic product (GDP) and employment; in low-income countries, SMEs count for less than a fifth of GDP and employment and the dominant contributors to economic activity are the unregistered microenterprises that constitute the “informal sector” (Ayyagari, Beck, and Demirguc-Kunt 2003). There is more to these figures than simple accounting. What these data represent are the fundamental economic imbalances present in places that lack dynamic entrepreneurial ecosystems. Institutional resources that are taken for granted in the most successful entrepreneurial regions—access to managerial talent, mentoring, and growth capital—are, in many places, absent or insufficient. As a result, in the places that would most benefit from entrepreneur-led development, talent is trapped in two places: in very small, low-productivity microenterprises that have little potential for expansion, capital accumulation, or job creation; and in very large ventures that benefit from economies of scale but often lag behind their SME counterparts in terms of innovation and growth. While all new and rapidly growing firms fall, at first, into the category of SMEs, it is important to note that—data in figure 4.1 notwithstanding—implementing strategies to accelerate entrepreneurship is not the same as building institutions to support SMEs. SMEs are small, but they are not necessarily new or growing. Schumpeterian ventures are new and innovative, but when successful they do not remain small or midsize for long. Indeed, programs to support SMEs, if improperly conceived and implemented, may actually undermine entrepreneurship if they diminish incentives for entrepreneurial innovation and growth-directed strategies—for example, by creating a program of subsidies not available to firms that grow beyond a certain size. A failing entrepreneurial ecosystem is one in which there is no viable bridge linking small and large firms. Small family businesses are essentially precluded from growing into large firms because of managerial oversight limitations; large corporations rarely invest in, or develop, small enterprises. Even buyer-supplier relationships with subcontractors—key to the functioning of large firms in advanced industrialized countries—in most industries are either poorly developed or absent. The economic environment lacks—in addition to trust—an ecosystem that connects the various levels of the private sector: large corporations, innovative high-growth firms, and microenterprises. As in a rainforest (see figure 4.2) the challenge is how to bring all of those elements into an ecosystem where they’re working and reinforcing one another. The argument so far suggests that there is every reason to believe that the underlying problem solved by an entrepreneur will be a complex one, and consequently that any solution found to the problem will not be easily copied. Furthermore, as Olson (1999) describes, there is ample reason to believe that well-intentioned endeavors to create a “business friendly” environment are likely to result in interventions that enhance, or at least reinforce, the advantages of market-leading incumbents. If this is true, then the implied strategy is very different: rather than seek to build product-based clusters through targeted subsidies for incumbent firms, political actors and policymakers

70    The Concept of Local Competitiveness

Overstory

50 m

40 m Canopy

30 m

Understory

20 m

Shrub Layer

10 m

Floor

Figure  4.2  The Forest Ecosystem (Source:  Mongabay.com. Reprinted under Creative Commons license.)

should, whenever feasible, seek opportunities to reduce subsidies for incumbents and broaden pathways for entrepreneurial entry into domestic markets. Repeatedly, entrepreneurs themselves report that conventional tools of “business friendly” policy such as tax incentives, grants, and local regulations have little relevance to their success or to the vitality of local entrepreneurial ecosystems. Instead, entrepreneurs emphasize the importance of access to networks, quality of life, and other intangibles. A recent report by Endeavor (a global organization dedicated to supporting entrepreneurs), based on interviews of 150 founders of high-growth firms in the United States, found the following: • Entrepreneurs at fast-growing firms usually decide where to live based on personal connections and quality-of-life factors many years before they start their firms. • These founders value a pool of talented employees more than any other businessrelated resource that cities can offer. • Access to customers and suppliers is the second most valuable business-related resource that cities can provide, according to these entrepreneurs. • The founders in our study rarely cite low tax rates or business-friendly regulations as reasons for starting a business in a specific city. (Morris 2013)

Enabling Entrepreneurial Ecosystems    71 A World Economic Forum survey of more than 1,000 entrepreneurs in 43 countries similarly found the elements of the ecosystem of greatest concern to entrepreneurs to be funding and finance, human resources, and market opportunity, with government and regulatory issues a comparably significant concern only among entrepreneurs in the Middle East and Africa (World Economic Forum 2013). Given these realities and the foregoing analysis, a few principles for enabling entrepreneurial ecosystems follow. Favor incumbents less. Where governments and multinational institutions such as the World Bank have become increasingly interested in identifying and implementing programs to support entrepreneurs, they also have continued to engage in conventional “development” practices that arguably have the unintended consequence of obstructing the emergence of the very entrepreneurial culture that the former programs seek to develop. Given these realities, wherever particular actors in national governments and INGOs interested in supporting entrepreneurs have the latitude to do so, they should focus first and foremost on adjusting the full portfolio of interventions to favor incumbents less. There may be little point to creating a single program to enable entrepreneurial ecosystems if 10 other programs exist in parallel that undermine the ability of entrepreneurs to succeed because they intensify the power of entrenched economic (and, often, political) incumbents—in the “beneficiary” country as well as in the “donor” countries. Policies that may seem “successful” in terms of near-term metrics, such as job creation, may be dramatically unsuccessful in the longer term when fully considering dynamic impacts on society. Listen to entrepreneurs. As Rodrik (2004) points out, “the conventional approach to industrial policy consists of enumerating technological and other externalities and then targeting policy interventions on these market failures. The discussion then revolves around the administrative and fiscal feasibility of these policy interventions, their informational requirements, their political-economy consequences, and so on” (Rodrik 2004, 2–3). This default process generally leaves the ostensible “beneficiaries” of policy—entrepreneurs and members of the communities within which they reside—on the sidelines until a policy is implemented. Such an approach simply cannot work if the objective is to enable entrepreneurial ecosystems. For one thing, the objective is not to address market failures, but rather to encourage dynamism, promote diversity, and above all increase metabolic activity (ideas explored, products prototyped and sold, services offered, deals closed) in a particular locality. These activities cannot take place at a distance. They require not only positive engagement but also responsive listening. As Motoyama and coauthors (2014) found in their study of a particular effort to enable the entrepreneurial ecosystem in Kansas City: “If the public sector or entrepreneurship support organizations attempt to engage entrepreneurs, they should target local sources and in-person events” (Motoyama et al. 2014, 13). Understand the environment. The creation of open and trusted lines of communication between members of entrepreneurial communities and those who would support and grow their efforts creates a context within which it is possible to map the entrepreneurial ecosystem. At its simplest, an ecosystem map is a simple relational inventory (or

72    The Concept of Local Competitiveness graph): who the participants in the ecosystem are (nodes) and how they are connected (edges). More ambitiously, a map may describe roles and differentiate relationships by type, direction, and magnitude of interaction. The particulars of the map matter less than the use to which it is put: to identify central players, key relational structures, and linked domains of capabilities. Once validated by the entrepreneurs and community members, ecosystem maps may be valuable tools in developing strategies for engagement. Think big, start small, move fast.27 This simple rule that applies for entrepreneurial ventures holds for strategies intended to enable local entrepreneurial ecosystems as well. Strategies to enable entrepreneurial ecosystems can only be effective if they are developed and implemented at a scale and with a level of adaptability that is consistent with the functioning of the ecosystems themselves. Adaptive learning is not just an assessment methodology; it is the practice of implementation itself. Remember that participants in entrepreneurial ecosystems are not potted plants. The ecosystem metaphor helps to remind those planning interventions that entrepreneurs and members of entrepreneurial communities are not potted plants: they do not conform to fixed categories, and they do not remain still. Typically, active participants in entrepreneurial ecosystems will sequentially or simultaneously engage in more than one of the following activities: • Leading or being part of an entrepreneurial team in the creation of a new venture • Acting as an equity investor in, and/or formal advisor to, one or more other ventures • Mentoring other entrepreneurs • Teaching in a formal setting • Working at a large corporation either in research & development or market-facing activities • Providing service (legal, marketing, accounting, etc.) to entrepreneurs and new ventures • Acting as a buyer for goods and services provided by entrepreneurs and new ventures Such multiplicity of roles is the norm for active participants in entrepreneurial ecosystems. Prepare to capitalize on crises. In a rainforest, the rotting trunk of a fallen tree feeds the growth of new trees by releasing nutrients formerly locked within its cell walls. These “spin-off ” saplings are doubly well positioned by being naturally situated above a nutrient source on the forest floor and below a newly created gap in the canopy above—where sunlight for a sapling corresponds to customers for a small and growing firm. Figure 4.3 provides a graphical representation of the dynamics of exploitation, conservation, release, and reorganization in a forest ecosystem, including reference to the ecological concept of “succession” that I introduced at the outset. 27 

I thank Michael Edson for emphasizing to me the importance of this particular formulation for success at scale in complex institutional and social contexts.

Enabling Entrepreneurial Ecosystems    73 2. CONSERVATION Succession Consolidation

1. EXPLOITATION Pioneer Opportunist

3. RELEASE Disturbance: Fire, storm, pest

STORED NUTRIENTS

4. REORGANIZATION Accessible carbon and nutrients

CONNECTEDNESS

Figure 4.3  Succession and Reorganization of Ecosystems as Represented by Bengtsson et al. (2000). Arrows close to each other indicate rapid changes; arrows far from each other indicate slow changes. Following Holling et  al. (1995), Bengtsson et  al. (2000, 43)  note that “diversity in forested landscapes is needed for the transitions between all the four stages, which is why managed forests must be managed so a sufficient number of species is available for the cycle to continue.” (Source:  Image from Bengtsson et  al. (2000).)

A directly analogous phenomenon occurs in economic ecosystems. Simply put: disruption creates entrepreneurial opportunities. This holds at the regional level as well as at the firm level. In the mid-1950s, the economy of Santa Clara Valley in California was disrupted when (following the end of the Korean War) then-secretary of defense Charles Erwin Wilson cut the budget of the US military by $11 billion, eliminating 40,000 civilian jobs along the way.28 These cuts coincided with the beginning of an era in that region when a significant number of radio and electrical engineers joined or created the companies that turned the northern end of the Santa Clara Valley into Silicon Valley. When the Department of Defense undertook cuts of a comparable magnitude in the mid-1980s, entrepreneurial ecosystems in northern Virginia and in San Diego received a similar boost.29 28  From 1951 to 1953, California received $13 billion in prime defense contracts, overtaking New York as the leading recipient of defense funding. That surge abated with cuts initiated by Wilson. See Leslie (2000). 29  For the San Diego story, see Walshok (2013). She observes: “San Diego’s transformative journey began early in the 1980s, when the entire Southwest was dealing with the implosion of the savings and loan industry, which resulted in bankruptcies and an overall decline in the powerful building and real estate financing industries, both of which had been major drivers of regional prosperity since the 1950s. This crisis was paralleled by a significant decline in defense manufacturing; in San Diego this included the aerospace industry and the Atlas missile. In fact, General Dynamics, whose workforce in the San Diego area at one time numbered 60,000, completely closed down over an 18-month period, leaving thousands of engineers, technicians, and other workers without jobs by the late 1980s.”

74    The Concept of Local Competitiveness Providing further evidence of a positive correlation of economic disruption with entrepreneurial opportunity at the national scale in the United States, Stangler (2009) finds that more than half of Fortune 500 companies were started during a recession. The international evidence does not contradict the conjecture that disruption creates entrepreneurial opportunity. In the power vacuum that followed the death of Mao Zedong, farmers in the People’s Republic of China who had formerly been tied to rural collectives—with truly catastrophic results—were left by the state to enjoy new autonomy. They went back to what came naturally—family-based farming on smaller plots of land. In 1979, under the leadership of Deng Xiaoping, the Chinese government enacted reforms to codify the return of family-based farming. The immediate consequence was to increase agricultural productivity. But the implications reached well beyond rural areas and grain silos. The practical impact of these reforms was, as Huang (2008) has documented, to initiate a long-term process in which roughly a fifth of the world’s population would newly have the opportunity to seek, and create, opportunities for themselves. Rapid, entrepreneurship-led renewal has also recently followed devastation in Rwanda (centered on Kigali) and Colombia (centered on Medellín). The takeaway here is not, of course, that leaders in society should engineer large-scale disruptions in order to realize benefits that will follow. Nor is it to suggest that the more dramatic disruptions to which I just alluded are desirable. It is rather that, since disruptions of a less severe nature than those experience in recent decades China, Rwanda, and Colombia are an inevitable part of social life, those involved in the project of enabling entrepreneurial ecosystems should anticipate them, and prepare to make the most of the opportunities for beneficial change that they create. Before concluding, I offer a final comment on the important topic of program assessment.

7  Forget about “Gold Standards” The introduction of rapid response systems in hospitals is a complex, multicomponent intervention—essentially a process of social change. The effectiveness of these systems is sensitive to an array of influences: leadership, changing environments, details of implementation, organizational history, and much more. In such complex terrain, the RCT [randomized controlled trial] is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect. —Donald Berwick (2008, 1153)

The bar for formal assessment of policy interventions is high. We know that, in order to assess rigorously the impact of a policy for regional development as in any other area, it

Enabling Entrepreneurial Ecosystems    75 is insufficient simply to measure the outcomes of interest before and after the program. A correlation between an action and an outcome does not imply a causal relationship from one to the other. In academic environments one frequently hears that the “gold standard” for impact evaluation is the randomized controlled trial (RCT). For example, Chatterji, Glaeser, and Kerr (2013, 26) conclude their review of initiatives to develop and support clusters of entrepreneurship and innovation as follows: The primary need is for experimentation and evaluation, especially with randomization. For example, if a government is creating an entrepreneurial cluster that is oversubscribed, it can randomize at least some of the spaces in the cluster. By comparing the outcomes of winners and losers in the space lottery, the government can evaluate the impact of the policy.

Since RCTs have recently gained favor among economists engaged in program evaluation, and because they do constitute a limiting case in terms of cost and structure, I focus this section on RCTs. At the end I generalize the implications. In an RCT, researchers randomly assign treatment to a subset of the group under study. Randomization may occur at different levels, depending on the analysis (e.g., at the level of the individual, the household, or the local community). Random assignment is critical; randomization assures the researcher that the groups studied are alike in all characteristics except for treatment. If and when controlling for all relevant charactertics is possible, this process ensures that omitted variables and endogeneity do not corrupt the analysis, and permits high degrees of confidence that the changes in the variable of interest in the treatment group relative to the control group are attributable solely to the intervention. To assess the value of RCTs in the adaptive implementation of strategies to enable entrepreneurial ecosystems, it is necessary to distinguish between two types of validity in formal assessment: internal and external. Internal validity is defined as the extent to which analysis is able to credibly identify a causal relationship between two variables. External validity refers to the ability to generalize empirical results to additional contexts of interest. While the internal validity of an RCT in, for an example, a clinical medical setting is fairly clear, results must also be externally valid in order for them to be of use to a regional economic development practitioner. Often overlooked when examining the results of an RCT as an indication of future impact is the distinction between the intervention on paper and its execution, both of which are captured in the results of an RCT. In drug trials, for example, results are contingent upon the compliance of the treatment group. If individuals do not take medication as directed, the trial results will not reveal the true impact of the drug. Implementation in the context of entrepreneurial ecosystems is vastly more complicated than a drug trial. Myriad characteristics of the implementing organization can affect the outcome of an intervention. This is a subtle but critical point: while an intervention in concept can be translated from one context to the next, its execution (and impact) is conditional on the entity

76    The Concept of Local Competitiveness implementing it. Therefore, even if the treatment populations are similar between an RCT and a future intervention, the impact of the future intervention may be different from that of the RCT because a different organization implements it (Haussman 2010; Whittle 2013; Pritchett and Sandefur 2013). If an RCT indicates that a program was a failure, we have no way of knowing if the design of the program was flawed, or if its design was sound but the implementation was poor. The above discussion of complexity is relevant here. I have emphasized that entrepreneurial ecosystems are highly complex, interdependent environments whose function can be reduced only with a high degree of imperfection to a single index or even to an array of indices. To the extent that small changes in experimental conditions lead to large changes in outcomes—that is, to the extent that the policy itself involves the implementation of a complex recipe—then the likelihood that a study conducted in setting X will be relevant to setting Y (or even, to setting X in the future) is correspondingly diminished. Hausmann (2009, 193) succinctly describes the manner in which complexity undermines the notion of “gold standard” research in development practice: A typical program, whether a conditional cash transfer, a micro-finance program or a health intervention can easily have 15 relevant dimensions. Assume that each dimension can only take 2 values. Then the possible combinations are 215 or 32,768 possible combinations. But randomized trials can only distinguish between a control group and 1 to 3 treatment groups. So, many of the design or contextual features are kept constant while just 1 or 3 are being varied. This means that the search over the design space is quite limited, while the external validity of these experiments is reduced by the fact that many of the design or contextual elements are bound to change from place to place. So, for the majority of the design elements, choices must be made in the absence of the support from randomized trials, which will necessarily play a secondary role in the actual practice of policymaking.

In sum, the internal validity of RCTs says nothing about their external validity. As Berwick (2008) states regarding the use of RCTs to inform innovation in public health (see quote above): “In such complex terrain, the RCT is an impoverished way to learn” (Berwick 2008, 1183). The same argument holds with regard to the use of RCTs as a practical tool to assess and develop strategies to enable entrepreneurial ecosystems. While RCTs may be methodologically elegant, they are not necessarily of any greater practical value than structured interviews, surveys, relational maps, (theory-informed) data mining, or any one of an array of methodologies that can increase understanding of a particular ecosystem, at a particular time. When it comes to methodologies to assess strategies intended to enable entrepreneurial ecosystems, there is no “gold standard.” Quality assessments will be ones that are carried out with reference to a clearly defined baseline, connected to the reality of the particular ecosystem (or ecosystems) in questions, and updated on a regular basis. In most cases, such assessments—informed equally by common sense and the desire for

Enabling Entrepreneurial Ecosystems    77 methodological simplicity—will yield information that is more practically useful and at least as generalizable as the ostensible “gold standard.”

8 Conclusion A score of Tatas might do more for India than any government, British or indigenous, can accomplish. —Alfred Marshall (1996, 283)30

The above quotation is from a letter written by Alfred Marshall in 1911. The “Tatas” in question are the sons of Jamsetji Tata, the Indian entrepreneur whose ventures turned into a now-global business empire that has spanned generations. This sentence provides evidence that appreciation among economists of the profound role of entrepreneurship in economic development is neither new nor confined to the work of Joseph Schumpeter. Yet Marshall’s formal contributions to economics—and those of the marginalists who followed him—did little to advance understanding of the complex dynamics alluded to in the simple sentence above. Indeed economists’ efforts to understand the origins of organized commerce and novelty in economic systems find a direct parallel in biologists’ efforts to understand the origins of life and novelty in biological systems.31 Yet, while the discipline of economics has (at its theoretical core) remained tied to the early 20th-century project of deriving theories from axiomatic foundations, biologists long ago aligned themselves with the mid-20th-century project of rethinking all science as information science. Most of the great discoveries in biological sciences in the 20th century—the discovery of the double helix, most notably among them—were inspired by the work of pioneers of information theory, notably including Norbert Weiner and Claude Shannon. Likewise in mathematics and physics. As Sydney Brenner, the 2002 Nobel laureate in medicine, said in 1971: “I feel that this new molecular biology has to go in this direction—to explore the high-level logical computers, the programs, the algorithms of development” (quoted in Gleick 2011, 300). Among biologists the notion that life itself is fundamentally algorithmic earned a central place in theory decades ago. The result was a revolution in the life sciences that continues to unfold today, with dramatic effects. In stark contrast, the discipline of economics has, for the most part, ignored the informational theoretic revolution.32 The two metaphors that provide the structure for this chapter are, in fact, just two different ways of thinking about bringing information theory into economics. 30  Marshall was the author of Principles of Economics, among the foundational works in the field of economics. The original quote, from Marshall’s correspondence, refers directly to the Tata companies. 31  Kauffman (1988; 1993) discusses these parallels explicitly. 32  As I stated above, Marshak and Radner (1972) and Radner (1993) are notable exceptions.

78    The Concept of Local Competitiveness The search for strategies to enable local entrepreneurial ecosystems is a fundamentally practical one, for which current academic research does not provide ready answers, or even compelling conceptual frameworks within which to ask relevant questions. If the argument I have advanced above is valid, then efforts to understand entrepreneurial ecosystems at the scale of the city and subnational region may progress more rapidly if they take the ecosystem more seriously not only as a metaphor, but also as the basis for new theory—one that will cast greater light upon the algorithms of development.

References Acs, Zoltan J. 1992. “Small Business Economics:  A  Global Perspective.” Challenge 35 (November–December), 38–44. Acs, Zoltan J., and Catherine Armington. 2003. “Endogenous Growth and Entrepreneurial Activity in Cities.” Research Paper 03-02, Center for Economic Studies (CES), US Bureau of the Census, January. Acs, Zoltan J., and David B. Audretsch. 1987. “Innovation, Market Structure, and Firm Size.” Review of Economics and Statistics 69, 567–74. Acs, Zoltan J., and David B.  Audretsch. 1990. Innovation and Small Firms. Cambridge, MA: MIT Press. Acs, Zoltan J., David B. Audretsch, Pontus Braunerhjelm, and Bo Carlsson. 2004. “The Missing Link: The Knowledge Filter and Entrepreneurship in Endogenous Growth.” Working paper, Center for Business and Policy Studies. Acs, Zoltan J., and A. Varga. 2004. “Entrepreneurship, Agglomeration, and Technological Change.” Discussion Paper on Entrepreneurship, Growth, and Public Policy 0604, Max Plan Institute for Research into Economic Systems, March. Adams, James D., Grant C. Black, J. Roger Clemmons, and Paula E. Stephan. 2005. “Scientific Teams and Institutional Collaborations:  Evidence from U.S. Universities, 1981–1999.” Research Policy 34 (3), 259–85. Agwara, Hezekiah, Philip Auerswald, and Brian Higginbotham. 2014. “Algorithms and the Changing Frontier.” National Bureau of Economic Research Working Paper No. 20039, April. Appleyard, Melissa M. 2003. “The Influence of Knowledge Accumulation on Buyer-Supplier Co-development Projects.” Journal of Product Innovation Management 20 (5), 356–73. Arthur, W.  Brian. 2013. “Complexity Economics:  A  Different Framework for Economic Thought.” Santa Fe Institute Working Paper 2013-04-012. Aspen Network of Development Entrepreneurs. 2009. 2009 Impact Report. Washington, DC: Aspen Institute. Audretsch, David. 1995. Innovation and Industry Evolution. Cambridge, MA: MIT Press. Auerswald, Philip. 2008. “Entrepreneurship in the Theory of the Firm.” Small Business Economics 30 (2), 111–26. Auerswald, Philip. 2010. “Entry and Schumpeterian Profits: How Technological Complexity Affects Industry Evolution.” Journal of Evolutionary Economics 20 (4), 553–82. Auerswald, Philip. 2012. The Coming Prosperity:  How Entrepreneurs Are Transforming the Global Economy. New York: Oxford University Press.

Enabling Entrepreneurial Ecosystems    79 Auerswald, Philip, Lewis Branscomb, Sean Gorman, Rajendra Kulkarni, and Laurie Schintler. 2007. Placing Innovation: A Geographical Information Systems (GIS) Approach to Identifying Emergent Technological Activity. Report to the US Department of Commerce, Advanced Technology Program/NIST GCR 06-902, May. Auerswald, Philip, Stuart Kauffman, and José Lobo. 1994. “Diversity, Growth, and Economic Webs.” http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1460991. Auerswald, Philip, Stuart Kauffman, José Lobo, and Karl Shell. 2000. “The Production Recipes Approach to Modeling Technological Innovation: An Application to Learning by Doing.” Journal of Economic Dynamics and Control 24, 389–450. Ayyagari, Meghana, Thorsten Beck, and Asli Demirguc-Kunt. 2003. “Small and Medium Enterprises across the Globe:  A  New Database.” World Bank Policy Research Working Paper 3127. Bairoch, P. 1988. Cities and Economic Development. Chicago: University of Chicago Press. Baumol, William J. 1990. “Entrepreneurship:  Productive, Unproductive, and Destructive.” Journal of Political Economy 98 (5), 893–921. Bengtsson, Jan, Sven G.  Nilsson, Alain Franc, and Paolo Menozzidi. 2000. “Biodiversity, Disturbances, Ecosystem Function and Management of European Forests.” Forest Ecology and Management 132 (1), 39–50. Berwick, Donald. 2008. “The Science of Improvement.” Journal of the American Medical Association 299 (10), 1182–84. Burt, Ronald S. 2004. “Structural Holes and Good Ideas.” American Journal of Sociology 110 (2), 349–99. Butler, Patrick, Ted W.  Hall, Alistair M.  Hanna, Lenny Mendonca, Byron Auguste, James Manyika, and Anupam Sahay. 1997. “A Revolution in Interaction.” McKinsey Quarterly 1, 4–23. Cassidy, John. 1998. “Annals of Enterprise: The Comeback.” New Yorker, February 23, 122–27. Castilla, Emilio J., Hokyu Hwang, Ellen Granovetter, and Mark Granovetter. 2000. “Social Networks in Silicon Valley.” In Chong-Moon Lee, William F.  Miller, Marguerite Gong Hancock, and Henry S.  Rowen, eds., The Silicon Valley Edge. Stanford, CA:  Stanford University Press. Chatterji, Aaron, Edward Glaeser, and William Kerr. 2013. “Clusters of Entrepreneurship and Innovation.” National Bureau of Economic Research Working Paper No. 19013, May. Coase, Ronald. 1937. “The Nature of the Firm.” Economica 4, 386–405. Competition Commission of Pakistan. 2009. The State of Competition in Pakistan. Islamabad: Competition Commission of Pakistan. Dutz, Michael. 2007. Unleashing India’s Innovation: Towards Sustainable and Inclusive Growth. Washington, DC: World Bank. Feld, Brad. 2012. Startup Communities: Building an Entrepreneurial Ecosystem in Your City. New York: Wiley. Feldman, Maryann P., and David B.  Audretsch. 1999. “Innovation in Cities:  Science-Based Diversity, Specialization, and Localized Competition.” European Economic Review 43, 409–29. Flora, Cornelia Butler, and Jan L.  Flora. 1993. “Entrepreneurial Social Infrastructure: A Necessary Ingredient.” Annals of the American Academy of Political and Social Science 529, Rural America: Blueprint for Tomorrow (September), 48–58. Freeman, Richard B., and Wei Huang. 2014. “Collaborating with People Like Me:  Ethnic Co-authorship within the U.S.” National Bureau of Economic Research Working Paper No. 19905, February.

80    The Concept of Local Competitiveness Gavrilets, Sergey. 1999. “A Dynamical Theory of Speciation on Holey Adaptive Landscapes.” American Naturalist 154, 1–22. Gavrilets, Sergey. 2003. “Models of Speciation: What Have We Learned in 40 Years?” Evolution 57, 2197–215. Geertz, Clifford. 1963. Peddlers and Princes: Social Development and Economic Change in Two Indonesian Towns. Chicago: University of Chicago Press. Glaeser, Edward H., Hedi Kallal, José Scheinkman, and Andrei Shleifer. 1992. “Growth in Cities.” Journal of Political Economy 100 (6), 1126–52. Gleick, James. 2011. The Information: A History, a Theory, a Flood. New York: Pantheon. Global Entrepreneurship Congress. 2014. “GES Policy Roundtable Summary of Discussions.” Working paper, Ewing Marion Kauffman Foundation. Granovetter, Mark. 1973. “Strength of Weak Ties.” American Journal of Sociology 78, 1360–80. Hausmann, Ricardo. 2009. “The Other Invisible Hand: High Bandwidth Development Policy.” In Jessica Cohen and William Easterly, eds., What Works in Development? Thinking Big and Thinking Small. Washington, DC: Brookings Institution Press. Hausmann, Ricardo, and César A.  Hidalgo. 2011. “The Network Structure of Economic Output.” Journal of Economic Growth 16, 309–42. Hausmann, Ricardo, César A.  Hidalgo, Sebastian Bustos, Michele Coscia, Sarah Chung, Juan Jimenez, Alex Simoes, and Muhammed Yildirim. 2011. The Atlas of Economic Complexity: Mapping Paths to Prosperity. Helbing, Dirk, and Alan Kirman. 2013. “Rethinking Economics Using Complexity Theory.” Unpublished manuscript. Hidalgo, César A., and Ricardo Hausmann. 2009. “The Building Blocks of Economic Complexity.” Proceedings of the National Academy of Sciences of the United States of America 106 (26), 10570–75. Hirschman, Albert O. 1958. The Strategy of Economic Development. New Haven, CT:  Yale University Press. Hirschman, Albert O. 1970. Exit, Voice, and Loyalty:  Responses to Decline in Firms, Organizations, and States. Cambridge, MA: Harvard University Press. Holling, C.  S., D. W.  Schindler, B. Walker, and J. Roughgarden. 1995. “Biodiversity in the Functioning of Ecosystems:  An Ecological Primer and Synthesis.” In Charles Perrings, Karl Göran Mäler, Carl Folke, C. S.  Holling, and Bengt-Owe Jansson, eds., Biodiversity Loss: Ecological and Economic Issues. Cambridge: Cambridge University Press. Huang, Yasheng. 2008. Capitalism with Chinese Characteristics: Entrepreneurship and the State. New York: Cambridge University Press. Hwang, Victor, and Gregory Horowitt. 2012. The Rainforest: The Secret to Building the Next Silicon Valley. San Jose, CA: Regenwald. Isenberg, Daniel J. 2010. “How to Start an Entrepreneurial Revolution.” Harvard Business Review 88 (June), 40–50. Jacobs, Jane. 1961. The Death and Life of Great American Cities. New York: Random House. Jacobs, Jane. 1969. The Economy of Cities. New York: Random House. Jacobs, Jane. 1984. Cities and the Wealth of Nations:  Principles of Economic Life. New  York: Random House. Jones, Benjamin F. 2005a. “Age and Great Invention.” National Bureau of Economic Research Working Paper No. 11359, May.

Enabling Entrepreneurial Ecosystems    81 Jones, Benjamin F. 2005b. “The Burden of Knowledge and the ‘Death of the Renaissance Man’:  Is Innovation Getting Harder?” National Bureau of Economic Research Working Paper No. 11360, May. Jones, Benjamin F., E. J.  Reedy, and Bruce A.  Weinberg. 2014. “Age and Scientific Genius.” National Bureau of Economic Research Working Paper No. 19866, January. Kash, Don E., and Robert Rycroft. 1999. The Complexity Challenge: Technological Innovation for the 21st Century. London: Printer. Kauffman, Stuart. 1988. “The Evolution of Economic Webs.” In P. W. Anderson, K. J. Arrow, and D. Pines, eds., The Economy as an Evolving Complex System. New  York:  Addison Wesley. Kauffman, Stuart. 1993. The Origins of Order:  Self-Organization and Selection in Evolution. New York: Oxford University Press. Kauffman, Stuart, and Simon Levin. 1987. “Towards a General Theory of Adaptive Walks on Rugged Landscapes.” Journal of Theoretical Biology 128, 11–45. Kogut, B., and U. Zander. 1992. “Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology.” Organization Science 3, 383–97. Leslie, Stuart W. 2000. “The Biggest Angel of Them All:  The Military and the Making of Silicon Valley.” In Martin Kenney, ed., Understanding Silicon Valley:  The Anatomy of an Entrepreneurial Region. Stanford CA: Stanford University Press. Lichtenstein, G., T. S. Lyons, and N. Kutzhanova. 2004. “Building Entrepreneurial Communities: The Appropriate Role of Enterprise Development Activities.” Journal of the Community Development Society 35 (1), 5–24. Marshak, Jacob, and Roy Radner. 1972. Economic Theory of Teams. New Haven, CT:  Yale University Press. Marshall, Alfred. 1996. The Correspondence of Alfred Marshall, Economist. Ed. John King Whitaker. Cambridge: Cambridge University Press. McNerney, James. 2013. “The Trophic Theory of Technological Improvement.” Presentation at the Santa Fe Institute workshop “Getting Inside the Black Box: Technological Evolution and Economic Growth.” August 13. Melo, Horatio. 2012. “Prosperity through Connectedness.” Innovations: Technology, Governance, Globalization 7 (2), 19–23. Michelacci, Claudio. 2003. “Low Returns to R&D Due to the Lack of Entrepreneurial Skills.” Economic Journal 113 (January), 207–25. Morris, Rhett. 2013. What Do the Best Entrepreneurs Want in a City? Lessons from the Founders of America’s Fastest-Growing Companies. New York: Endeavor Insight. Motoyama, Yasuyuki, Jared Konczal, Jordan Bell-Masterson, and Arnobio Morelix. 2014. “Think Locally, Act Locally: Building a Robust Entrepreneurial Ecosystem.” Working paper, Ewing Marion Kauffman Foundation, April. Nelson, Richard R., and Sidney G. Winter. 1982. An Evolutionary Theory of Economic Change. Cambridge, MA: Belknap Press of Harvard University Press. Olson, Mancur. 1982. The Rise and Decline of Nations: Economic Growth, Stagflation, and Social Rigidities. New Haven, CT: Yale University Press. Olson, Mancur. 1999. Power and Prosperity:  Outgrowing Communist and Capitalist Dictatorships. New York: Basic Books. Pentland, Alex. 2014. Social Physics: How Good Ideas Spread—the Lessons from a New Science. New York: Penguin Press.

82    The Concept of Local Competitiveness Porter, Michael. 1998. “Clusters and the New Economics of Competition.” Harvard Business Review 76 (6), 77–90. Pritchett, L., and J. Sandefur. 2013. “Context Matters for Size: Why External Validity Claims and Development Practice Don’t Mix.” Working Paper 336, Center for Global Development, Washington, DC. Quigley, John M. 1998. “Urban Diversity and Economic Growth.” Journal of Economic Perspectives 12 (2), 127–38. Radner, Roy. 1993. “The Organization of Decentralized Information Processing.” Econometrica 61 (5), 1109–46. Rivkin, J. 2000. “Imitation of Complex Strategies.” Management Science 46 (6), 824–44. Rodrik, Dani. 2004. “Industrial Policy for the 21st Century.” Center for Economic Policy Research Discussion Paper 4767. Romer, Paul M. 1986. “Increasing Returns and Long-Run Growth.” Journal of Political Economy 94, 1002–37. Romer, Paul M. 1990. “Endogenous Technological Change.” Journal of Political Economy 98 (5), S71–S102. Saxenian, Annalee. 1994. Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press. Schmitz, J.  A., Jr. 1989. “Imitation, Entrepreneurship, and Long-Run Growth.” Journal of Political Economy 97, 721–39. Schumpeter, Joseph A. 1912. Theorie der witschaftlichen Entwicklung. Leipzig:  Duncker & Humblot. Revised English translation by Redvers Opie, The Theory of Economic Development. Oxford: Oxford University Press, 1934. Simon, Herbert A. 1965. The Shape of Automation for Men and Management. New  York Harper & Row. Simon, Herbert A. 1967. “Programs as Factors of Production.” California Management Review 67 (10), 1–12. Srinivasan, T.  N. 2004. Annual Bank Conference on Development Economics:  Accelerating Development. Washington, DC: World Bank. Stangler, Dane. 2009. “The Economic Future Just Happened.” Working paper, Ewing Marion Kauffman Foundation, June. Tansley, Arthur George. 1935. “The Use and Abuse of Vegetational Concepts and Terms.” Ecology 16 (3), 284–307. Teece, David J., Gary Pisano, and Amy Shuen. 1997. “Dynamic Capabilities and Strategic Management.” Strategic Management Journal 18 (7), 509–33. Wadhwa, Vivek. 2012. “The Magic Happens When You Focus on People.” Innovations: Technology, Governance, Globalization 7 (2), 25–27. Watts, Duncan J., and Steven H.  Strogatz. 1998. “Collective Dynamics of ‘Small-World’ Networks.” Nature 393 (4), 440–42. Weitzman, Martin L. 1998. “Recombinant Growth.” Quarterly Journal of Economics 113 (2), 331–60. Winter, Sidney G. 1968. “Toward a Neo-Schumpeterian Theory of the Firm.” Working Paper P-3802, Rand, March. Whittle, Dennis. 2013. “How Feedback Loops Can Improve Aid and Maybe Governance.” Center for Global Development, August. Wright, Sewell. 1932. “The Roles of Mutation, Inbreeding, Crossbreeding and Selection in Evolution.” Proceedings of the Sixth International Congress of Genetics 1, 356–66.

Enabling Entrepreneurial Ecosystems    83 World Bank. 2010. Competition and Innovation-Driven Inclusive Growth. Washington, DC: World Bank. World Economic Forum. 2013. Entrepreneurial Ecosystems around the Globe and Company Growth Dynamics:  Report Summary for the Annual Meeting of the New Champions 2013. Geneva: World Economic Forum, September. Zucker, Lynn G., Michael R.  Darby, and Jeff Armstrong. 1998. “Geographically Localized Knowledge: Spillovers or Markets?” Economic Inquiry 36 (January), 65–86.

Chapter 5

C onstru cti on of t h e Cluster C ommons Örjan Sölvell

Introduction After Alfred Marshall’s seminal work on industrial agglomerations in the late 19th century (Marshall 1920), and decades of being dormant as a Sleeping Beauty, a century later the economic importance of geographical proximity and clusters found its way back into economics and economic geography through the works of Paul Krugman (Krugman 1991; for a review see Redding 2010) and business strategy through the works of Michael Porter (Porter 1990; Jaffe, Trajtenberg, and Henderson 1993; Enright 1998; Tallman et al. 2004; Bell 2005). Interestingly enough, both these scholars were extremely proximate when this “rediscovery” took place in the late 1980s, as Paul Krugman was spending his sabbatical on HBS campus, next to Michael Porter. After the rediscovery several important academic debates emerged, one in particular regarding the role of localization versus urbanization, with the rhetorical question, Who is right, Marshall or Jacobs? (for an overview see Beaudry and Schiffauerova 2009). The Marshal-Arrow-Romer line of research (MAR externalities; see Glaeser et al. 1992; Chatterji, Glaeser, and Kerr 2013) put focus on knowledge spillovers (Aharonson, Baum, and Feldman 2007), rather than on Marshall’s other cluster effects linked to labor market pooling, lower transport costs, and access to specialized inputs. Recent empirical research has shown ample evidence that clusters offer dynamic externalities leading to innovation (Griliches 1992; Jaffe, Trajtenberg, and Henderson 1993; Audretsch and Feldman 1996; Furman, Porter, and Stern 2002). The question, however, looms: What is needed in order for such innovation dynamics and knowledge spillovers to actually materialize inside clusters? In this chapter I will argue that proximity in itself is not enough to set off massive knowledge spillovers (Lindqvist 2009). There are obvious differences in cluster dynamics across agglomerations in different geographies; across nations; and also in important

Construction of the Cluster Commons    85 ways across regions inside nations (Cooke 2002; Asheim and Gertler 2003). Clusters simply differ in terms of the amount of knowledge spillovers they actually produce, through formal and informal interaction, mobility, and collaboration. Thus, one might suspect that spillovers are not just automatic or natural; and if so, we should look for differences in institutional settings explaining differences in spillovers. The way to capture differences in innovation dynamics, I  propose, is to develop a notion of a “commons,” where individuals, representing different actors within clusters—large and small firms, research organizations, education institutes, capital providers, and various public organizations—can meet, exchange information and ideas, and engage in resource mobility and collaboration. Just as firms in well-developed institutional settings engage in competition in efficient markets, in parallel they engage in interaction and collaboration in clusters. One can certainly imagine knowledge spillovers (e.g., through labor mobility) without any direct contacts between clustered firms and organizations, but I build this chapter on the notion that clusters with a commons offering “paths, bridges, and attractive green grass” will lead to higher levels of spillovers. When arguing my case I do not envision an existing “natural” commons. A cluster commons is constructed, both unconsciously through invisible hands and consciously through visible hands. For visible hands to emerge, some incentives must exist in order for these paths and bridges to be constructed. And in addition, there must be some form of governance to reassure that the interaction and knowledge spillovers will be sustained. Late Nobel laureate Elinor Ostrom wrote several influential works on the theme of collective action and the governance of the commons (see Ostrom 1990). To her it was about governing a commons that was already in place, such as a water reservoir, a fishing ground, or a mountain meadow. She was interested in how individuals make use of such common-pool resources and how they can manage to sustain them. Depending on the institutions, that is, rules and norms, the users of the commons construct, and adherence to those rules, these commons can be sustained for very long periods or be depleted very soon, to the detriment of all users. In line with Ostrom’s argument, a cluster commons must be maintained over time; otherwise it will soon be depleted. This chapter is an attempt to explore how the commons works, who constructs it, and how processes of constructing the commons might take place. In spite of more than two decades of new research on cluster dynamics and knowledge spillovers, based on the mechanisms already identified by Marshall a century ago, we have given relatively little attention to the constructive processes that actually create the foundation for knowledge spillovers to occur. Ostrom (1990) wrote that neither the state nor the market is uniformly successful in enabling individuals to sustain long-term productive use of the commons. Similarly, we can expect cluster construction to be carried out by different private, public, or mixed public/private initiatives. Below we will take a closer look at private/public cluster initiatives, often transformed into formalized cluster organizations, functioning as path openers, bridge builders, and grass growers on the commons.

86    The Concept of Local Competitiveness

The Cluster Commons More than two decades of empirical research on clusters have reported important effects on levels of innovation output (Feldman 1994; Audretsch and Feldman 1996; Baptista and Swann 1998; Aharonson, Baum, and Feldman 2013), and knowledge spillovers have been shown to be accelerated in clusters (Dosi 1988; Lundvall 1988; 1993). Research on the nature of the innovation process has identified three interrelated characteristics that are particularly important for understanding the clustering of related firms (Freeman 1991; Malmberg, Sölvell, and Zander 1996): • The need for incremental reduction of technical and economic uncertainty • The need for continuous interaction between related firms • The need for face-to-face contacts in the exchange and creation of new knowledge The first characteristic derives from the fact that innovative processes are fundamentally uncertain in terms of technical feasibility and market acceptance (Freeman 1982; 1991; Pearson 1991). Although the level of uncertainty varies with the type of invention or innovation, the technical aspects are commonly worked out by means of trial-and-error testing and modification. Incrementalism and trial-and-error problem-solving in turn leads to a need for continuous interaction, both through informal networks and formal collaborative agreements. The second feature of the innovation process is that ideas frequently originate outside the firm that carries out the actual development or manufacturing work (Pavitt 1984; 1991). In fact, only a small proportion of all innovations have been found to be directed toward use within the inventing firm(s) and for improving internal processes (Scherer 1984). The importance of customers as sources of innovation has been testified in a long series of studies (Håkansson 1989; Håkansson et al. 2009), and innovations also develop in interaction with suppliers (Von Hippel 1988). In yet other cases, several firms might be involved in joint development work, where each of the participants supplies a limited component of the resulting innovation. This makes the innovation process highly interactive—between firms and the research infrastructure, between producers and users at the interfirm level, and between firms and their wider organizational environment (Powell, Koput, and Smith-Doerr 1996; Morgan 1997). The importance of long-term relationships for the exchange and creation of knowledge has been noted by innovation scholars (for overviews see Lundvall 1988; 1993). This exchange frequently involves sensitive information, which might cause damage if used opportunistically by the firms involved, and therefore requires a high level of trust between the parties. The third characteristic of innovation involves informal mechanisms for knowledge exchange across actors. In spite of increasingly sophisticated means of communication, the need for personal, face-to-face contacts in the exchange of information has not disappeared (Nohria and Eccles 1992). Also, personal contacts have been identified as

Construction of the Cluster Commons    87 important sources of technological information and improvements in the innovation process (Leonard-Barton 1982; de Meyer 1992). Face-to-face contacts appear to be of particular value for exchanging tacit knowledge (Polanyi 1962), or when the exchange of knowledge involves direct observation of products or production processes in use. This type of knowledge does not typically reside in blueprints and formulas, but is based on personal skills and operational procedures, which do not lend themselves to be presented and defined in either language or writing (Winter 1987). According to seminal work of Utterback (1974), the unanticipated or unplanned personal encounters often turn out to be most valuable. It is in this context that the commons becomes so critical; without a commons there will be no or very few unplanned encounters. If we could be certain that knowledge spillovers will occur, as described above, and the full innovation potential of clusters naturally emerges, Silicon Valley would not be the envy of practitioners and politicians from all around the world. It is obvious to any observer that some geographical clusters constitute the home base for world-leading firms, while other clusters do not. And cluster positions change over time, even if there is a lot of persistence to cluster dynamics (for a discussion of the cluster life cycle see Sölvell 2009). Is it then just a matter of luck, or some natural advantage that explains these persistent differences? Or can we trace constructive efforts that have paid off? I propose that sustained cluster dynamics are the result of more or less conscious acts of continuous construction of the commons, where invisible hands of entrepreneurial action and initiative blend in with visible hands (see discussion below on the construction of Silicon Valley).

The Cluster Commons: A Meeting Place One way to imagine the commons is as the “white space” in between related and supporting firms, research institutions, education organizations, capital providers, and various forms of public and private organizations (figure 5.1). The “quality” of this commons, I believe, is crucial to our understanding of cluster dynamics and the realization of knowledge spillovers. If there is no commons, or the commons is full of ditches and walls, or agents suspect that if they move out onto the Government

Research

Firms

Capital

$

Figure  5.1  Five Main Actors in a Cluster

Education

88    The Concept of Local Competitiveness commons to interact with individuals and organizations from other corners of the cluster, a mine will blow up in their face, we cannot expect any substantial cross-border interaction, and the innovation dynamics will automatically be reduced. In order to understand why a commons is fundamental to the innovation dynamics of a cluster, let us take a look at following chain of arguments. To support high levels of knowledge spillovers a commons offers the following: • A meeting place (“path or bridge”) where cluster identity, trust, and an “insider language” can evolve in new networks, bringing individuals from across cluster firms and organizations closer together, which will allow for • The creation of a foundation from which institutional change (changes to formal and informal rules affecting cross-border interaction, e.g., between private industry and universities) can take place, in order to relax constraints and conventions of what not to do, in order to • Increase formal and informal cross-border interaction, mobility, and collaboration across actor boundaries, which in turn will • Allow for low-cost matching and mobility of resources and competencies. This is of course done through traditional media and in a traded form (e.g., employment websites, market exchanges, etc.), but the commons offers a place for untraded information and learning, a place to get acquainted with others in the cluster, where one can build up knowledge of who controls what resources and capabilities, and resources are exchanged in a low transaction cost setting; and • Allow for knowledge spillovers between firms (in buyer-supplier relationships or through other firm-to-firm relationships), between firms and research organization, and so on, which constitutes the basis for innovation to materialize, where novel ideas are turned into commercial goods, services, and business models Thus, the cluster commons is really a meeting place accessible through paths and bridges, where new institutions emerge that, under the right circumstances, will lead to mobility of resources and individual capabilities (e.g., leading to new firm formation; see Sorenson and Audia 2000; Stuart and Sorenson 2003; Sorenson and Sorenson 2003) and knowledge spillovers.

A Model of Seven Cluster Gaps I have already pointed to the fact that clusters clearly differ in quality of the commons. In order to capture these differences one should study the “gaps” in the cluster commons (Sölvell and Lindqvist 2011), barring interaction and collaboration. I consider five main types of actors inside clusters: large and small firms taking innovations to market; research organizations producing new knowledge; education providers, such as schools and polytechnics producing human capital; capital providers, such as angel networks,

Construction of the Cluster Commons    89 venture capitalists, and banks; and government and public bodies introducing policy and public services. Between each of these five types of actors one can imagine more or less severe gaps. If we first assume that there are no such gaps, we can expect mobility and interaction between the cluster actors. Research groups that produce cutting-edge knowledge in relevant fields will channel those findings to firms in the cluster, or through spin-offs. Colleges will offer specialized education programs and graduate students with skills particularly suited for working in the cluster. Capital providers become experts in technologies and skills related to the cluster, and they will provide “smart money” by being better at assessing risks and opportunities in the cluster. Local government and public agencies learn to understand the needs of the firms and make decisions that promote the cluster and remove obstacles to progress. In all these ways surrounding actors support firms and entrepreneurs and make it easier for them to be innovative. Also, not least important, firms will interact with other related firms, small and medium-sized firms (SMEs) interact with large firms, and so on. They engage with each other as buyers, suppliers, or technology partners. Competing firms also attract staff from each other and imitate each other at a fast rate, and firms in the surrounding cluster simply act as a source of inspiration to aim higher in competition and to set more ambitious goals. Figure 5.2 illustrates all these interactions in a dynamic cluster. In addition to internal cluster dynamics, there are paths and bridges leading to the outside (similar to weak ties; see Granovetter 1973; Harrison 1994), represented by global markets and value chains, and clusters in other sectors. In an ideal cluster these paths and bridges are busy with traffic. People change jobs between actors, network across boundaries, bring news to others in formal and informal gatherings, develop new innovative ideas jointly, and tie the cluster together in a thousand different ways. Knowledge is created, spread, and shared. Through collaboration and rapid reshuffling of resources, the innovation process is secured. Figure 5.2 shows a compelling picture with a cluster commons in an ideal way. It is the way people portray Silicon Valley. Unfortunately, in reality most clusters don’t look like this. Typically, communication between different actors is massively flawed. Small firms who believe they have something new exciting to offer have a hard time meeting with the right people at a large firm. Large firms searching for a new supplier are more likely to look for an established international supplier than go searching among innovative

$$

Figure  5.2  A  Dynamic Cluster with a Commons with Intense Interaction

90    The Concept of Local Competitiveness SMEs located right under their nose. Policymakers often have only vague ideas about what business really needs. Researchers are more interested in academic publishing than commercializing their new findings. Schools formulate their curricula with little knowledge of what skills industry really needs. Entrepreneurs find it difficult to persuade banks to invest in new innovative businesses, and so on. Many business people, particularly in SMEs, would laugh at the idea of approaching the local university to see if it has some skill or new technology they could use. It is not difficult to understand that these connections will not just happen spontaneously. After all, the different types of actors have different roles to play in society. Universities are supposed to do research, not to serve as R & D departments for companies. Policymakers have responsibilities that go far beyond serving companies with whatever they require. Education organizations have many stakeholders to oblige other than firms. And firms are in business to make a profit for themselves, not to provide altruistic support to each other. Even so, with some effort put into the construction of a commons, large innovation benefits can be reaped that often remain neglected. In other words, more often than not, clusters do not live up to the potential of knowledge spillovers that cluster theory grants them. Interaction between actors is not such an easy thing to accomplish. If all it takes were a simple phone call from one individual to another, then clusters would surely be a lot more efficient. But in reality, there are a thousand reasons why that phone call never takes place. The policymaker doesn’t pick up the phone, because she doesn’t expect to hear any deeper insights from the industry of what it really needs. If the college teacher talks to the business world, it is about finding placement positions for students, or arranging recruitment fairs, but certainly not to discuss the curriculum. The businessman has no idea what the researchers at the university are doing; he probably doesn’t know their names, and he certainly doesn’t know within what departments they are organized in. The researcher might want to see her latest discovery turned into a successful commercial innovation, but she knows that her career depends on publishing papers, and it will in no way be furthered by interacting with business people; in fact, it will be hampered. And if, by chance, the businessman and researcher meet and discuss each other’s work, they soon find that they speak different languages and have different mindsets, almost as if they were living in different worlds. It is obstacles like these that prevent the research world from spreading new knowledge to the world of business, and that stop policymakers from seeking advice from business people and vice versa. Obstacles make traffic on the paths and bridges slow and awkward, where it preferably should be rapid and easy. Obstacles isolate systems when they should be connected. In short, obstacles create gaps where there should be paths. The picture of the cluster that I sketched above, with its wide paths and its intense traffic, is not automatic. These gaps, which I believe are quite persistent, have great implications for knowledge spillovers and innovation. The gaps are based on the following: • Poor knowledge of other actors and what resources and capabilities they control • Weak networks with other actors • Misunderstandings based on differences in “language”

Construction of the Cluster Commons    91

7

5

2 1

4 6

$

3

Figure  5.3  A  Static Cluster Lacking a Commons and with Limited Interaction

• Norms and attitudes of how to act and what to do do not match across actors • Lack of trust across actors • Negative incentives to collaborate with other actors and rules/laws hindering interaction The existence of these gaps means that clusters, despite their great potential for dynamic interaction, often only exploit a small share of this potential. People do not make the most of the possibilities found around them, because they lack knowledge about what opportunity is nearby, lack the networks to utilize it, fail to initiate collaboration they would benefit from, and fail to coordinate their actions with others. In short, individuals and organizations lack a commons, and thus the cluster will suffer from knowledge failures, network failures, and cooperation failures, leading to spillover and innovation failures. One can imagine five internal gaps, inside the cluster, shown in figure 5.3:

1. 2. 3. 4. 5.

The firm-to-firm gap barring interaction among firms in the cluster The research gap barring interaction between firms and research organizations The education gap barring interaction between firms and education organizations The capital gap barring interaction between firms and capital providers The government gap barring interaction between firms and public bodies

In addition there are two more gaps, external to the cluster: 6. The cross-cluster gap barring interaction with firms in other clusters representing other technologies 7. The global market gap barring interaction with global markets and value chains

Construction of a Commons In our discussion above I argued that a cluster commons is crucial for spillover processes and interaction to actually take place. In order for these processes to be initiated, there must exist incentives and actors prepared to engage in the construction work.

92    The Concept of Local Competitiveness The construction of a commons is not always intentional, and we should certainly not expect a visible hand governing the whole process (unless we study some of the Asian economies). If we first take a look at the invisible hand, myriads of decisions are taken by entrepreneurs, firms, and organizations “born” inside the cluster, and in addition firms and organizations are attracted to the cluster. We expect that entrepreneurs, firms, and organizations can judge the benefits and drawbacks (high costs, risk of rapid diffusion of resources and capabilities, congestion, etc.) of being located in a cluster. Their strategies and actions have no intention of building a cluster commons, but the very fact that they engage in networking, information sharing, and collaboration will naturally lead to the emergence of some form of paths and meeting places, a commons, which in turn forms the basis for knowledge spillovers. This is the story emphasized by most people when explaining the emergence of Silicon Valley. However, even in the case of Silicon Valley, myriad acts of the invisible hand seem to blend in with visible hands intending to enhance interaction and knowledge spillovers.

Construction of Silicon Valley Most importantly, leaders at Stanford University took early initiatives. A  conscious effort to build world-class research and commercialization capabilities developed decades ago (for an overview, see Lee et al. 2000; Saxenian 1994). These efforts were further promoted by federal research grants and military spending (Mazzucato 2013), that is, public initiatives taken partly to promote the region. The whole notion of a “Silicon Valley” did not exist until media created the icon by writing about it. Beginning in 1971, Don Hoefler, who had worked for Fairchild Semiconductor and RCA, wrote a series of articles—“Silicon Valley USA”—for the weekly tabloid Electronic News, using the phrase Silicon Valley to describe the agglomeration of electronics firms in Santa Clara County. This valley, formerly known for its orchards, became the hotspot for IT hardware and software, and later the Internet boom. Stanford University and colocated research laboratories played a crucial role in offering cluster seeds through educating people and producing advanced research. The most important bridges between research and business were built through the Stanford Research Institute, SRI (1946), and the Stanford Industrial Park (1950s), and later a host of organizations. SRI had been created as a West Coast center for innovation, with the explicit aim of facilitating economic development in the region. SRI carried out contract research in highly varied fields, computing being only one. One of the most important spin-offs was the Augmentation Research Center (ARC) involved in information processing work. In the 1950s Stanford needed new financial means, and decided to lease out land to high-technology firms in the vicinity—the Stanford Industrial Park was established. A range of research centers emerged in the vicinity. An early example was the Stanford Artificial Intelligence Laboratory set up in 1963.

Construction of the Cluster Commons    93 In the valley some firms that had emanated as spin-offs from research grew large. Some became anchor firms, including Shockley Transistor, HP, Fairchild, and Intel, as they constituted platforms for new spin-offs. Varian, Shockley Transistor, and HP have spun off hundreds of new firms. In a traditional Marshallian fashion, the growing electronics and IT industries stimulated specialized upstream and downstream industries, including service suppliers (legal and business services), venture capital, and angel networks. With increasing visibility the valley began to attract more and more resources from the outside. IBM, Lockheed, and NASA moved into the valley in the 1950s. Some of these firms had access to large government grants, and large research grants have been a central component in the construction of many US high-technology clusters. Many individuals were instrumental in setting off this process of bridge building and cluster growth. Professor Frederick Terman, provost of Stanford, was the man behind Stanford Industrial Park and has been labeled “the father of Silicon Valley.” While no government decided that there should be a world-leading center in electronics, later semiconductors, later computers, and even later Internet technologies in Silicon Valley, it is a construction of inventors, entrepreneurs, university leaders, firms, and other organizations. Most of this construction has been unconscious, but some of it, particularly Stanford’s openness to business and innovation, and not just science (quite different from most other leading universities), has been conscious and with a clear constructive purpose. Large investments in scientific discovery, supported by public grants, led to the creation of new firms, and over time the region increased its attractiveness, leading to even more firms, and students turning into entrepreneurs and inventors. Members of the Homebrew Computer Club, established in 1975 to experiment with home computers, led to the creation of some 20 computer companies in the early heyday, Apple being one. Many entrepreneurs and inventors were educated by Stanford, Berkeley, and other universities and colleges in the area. Over time, the valley and these universities would attract students, faculty, and other talent on a global scale, not least from many Asian countries (Saxenian 2006). Several nonprofit organizations for cluster collaboration have emerged in the valley to facilitate interaction and cooperation. The Silicon Manufacturing Valley Group works around issues of quality of life, education, and infrastructure challenges, including transportation, energy, and tax regulation. The Joint Venture: Silicon Valley Network, established in 1993, is a network that provides analysis and action on issues affecting the region’s overall economy and quality of life. They bring together leaders from business, government, academia, labor, and the broader community in order to spotlight issues and work toward a more innovative region; in short they work on enhancing the commons. Founded in 1994, CommerceNet has conducted research and piloting programs that have advanced the commercial use of the Internet. These are conscious and constructive efforts, each having a small but distinct impact on the evolution of the Silicon Valley commons. There has certainly not been one big hand planning it all, but there have been a few hands more important than others in securing the commons of the valley.

94    The Concept of Local Competitiveness

Incentives to Engage in Cluster Construction If the quality and dynamism of clusters hinges upon factors such as norms, information, networks, and collaboration (all of which facilitate knowledge spillovers), it should be obvious to all actors that improvements in all these factors should improve the quality of the cluster, which in turn benefits all incumbents. But who should invest time and money in improving the cluster? With a commons, offering common-pool resources, all incumbents benefit, so how can such incentives be created to avoid free riders? If there are such large advantages from clusters as studies purport, that is, nonpriced externalities offering innovation benefits, should there not be incentives large enough for incumbents to figure this out? From game theory we know that cooperative outcomes will occur if games are repeated and players are highly informed, without any visible public hand (Axelrod 1984). Through evolution (Alchian 1950) one should expect inefficient institutions—in this case, actors failing to interact—to disappear through competitive processes. This view was challenged by Douglass North in several of his works (see discussion in North 1990). Just as the world is full of persistent inefficient institutions, keeping richer and poorer nations apart, we should also expect static clusters, with very limited interaction, to survive over longer periods. Thus, I argue that a “Bob the Builder,” from either the public or private side or jointly, could be instrumental in setting off the innovation potential residing in clusters. Evidence suggests that Bob the Builders cannot create clusters, but they can work on the construction of a commons, increasing the likelihood of knowledge spillovers (Brenner 2003; Depner and Bathelt 2005, Lindqvist, Ketels, and Sölvell 2013).

Organized Cluster Construction There are many individuals and organizations involved in cluster construction. During the 1990s a new term evolved: “cluster initiatives,” that is, organized efforts to increase the growth and competitiveness of clusters within a region, involving cluster firms, government, and/or the research community (Sölvell, Lindqvist, and Ketels 2003). These initiatives range from loose projects to formalized organizations (Lindqvist, Ketels, and Sölvell 2013), and they are initiated either from the outside (e.g., through national or regional economic development programs) or from the inside (e.g., by corporate leaders within the cluster). Cluster organizations typically act as bridge builders, for example connecting business with research and education, or large firms with small firms. They play a central role in cluster construction by providing activities and meeting places where common issues can be discussed and acted on jointly. They help the different actors overcome gaps and

Construction of the Cluster Commons    95 help start interaction. In case of success, they get the traffic moving along the paths and across the bridges. The critical mission for cluster organizations is thus to build meeting places to counteract the seven cluster gaps, and support traffic (e.g., meetings, innovation projects) on those bridges. At first, the idea of a shared commons might be vague to different actors in the cluster, only to emerge more strongly as the number of meetings and activities increases (unless the initiative fails and is closed down). As potential innovation advantages initially are not fully known, we expect a learning curve, where early engaged actors will experience advantages (and drawbacks) of the cluster initiative. Via networks and press, stories and “truths and myths” about cluster activities will spread, possibly leading to the attraction of new individuals and organizations into cluster construction activities. Membership in cluster organizations often includes a range of firms and organizations. It is reasonable to assume that different types of organizations enroll for somewhat different reasons. For example, SMEs might be more interested in commercial cooperation than large or global firms. Public organization might be more interested in attracting more companies to the region (including directly competing firms) than incumbent firms, and so on. Such conflicting interests must be handled by the cluster organization. It might entail involving different types of members in different types of projects, and separating out different streams of revenues with different areas of use, or finding the least common denominator (Sölvell and Williams 2013). Cluster organizations around the world come in many shapes and forms. They differ in the way they are organized and governed (some have explicit members, whereas others work with different sets of firms and organizations depending on the project), the way they are financed (various combinations of public and private funding), and what activities and services they provide. Some activities are more oriented toward building the fundamentals of the cluster commons, whereas other activities and services are geared directly toward firms, for example initiating joint innovation projects. Such collaboration can be divided into two fundamental types: projects with an innovation focus and projects with a business development focus (see figure 5.4). All the three areas interact and overlap. Hence, cluster organizations rest on three pillars of activities. The first pillar is about overall cluster identity, information sharing and networking. Here the cluster organization is deeply involved in building a sense of belonging and identity, general trust, and networking. The second pillar relates more directly to R & D and concrete innovation projects, where the cluster organization helps build bridges and stimulate traffic across the gaps. Bridging to public organizations can lead to improved regulation and redirection of public investments. Bridging to research can involve incubator services and commercialization of research results, and bridging to education can improve supply of specialized staff. The third pillar involves business development among member firms. Typical activities include export promotion and internationalization, joint trade fairs, joint purchasing, and other commercial cooperation, often between SMEs not large enough to carry out these activities on their own.

96    The Concept of Local Competitiveness

I The Commons Cluster identity and brand Meeting places for networking II Innovation Projects

III Business Development Projects

Figure  5.4  Three Types of Activities Performed by Cluster Organizations

The Issue of Free Riders and Sustaining the Commons Cluster organizations develop noncollective goods for its members through projects (pillars II and III in figure 5.4). But in addition, through other activities (particularly pillar I), the cluster organization will most probably develop common-pool resources, available for organized members but also other firms in the cluster. Some firms and organizations will most likely want other firms to invest in the organized cluster activities, in order to reap the benefits free of charge. The problem of free riders will evolve as the construction work becomes more and more visible. If free-riding is sustained and involves a large and increasing number of firms and organizations, the cluster will naturally face the risk of the tragedy of the commons. In order to sustain the commons, more and more actors are needed to invest in it. My data show that the success of cluster construction, carried out by cluster organizations, is tied to the following (Lindqvist, Ketels, and Sölvell 2013): • The presence of an underlying cluster • Political long-term support for clusters • That the cluster organization is considered “neutral,” that is, not purely a public, business, or university body, where neutrality is manifested through an independent form of organization, mixed public-private financing, and a board composition involving different actors

Construction of the Cluster Commons    97 • That the cluster organization is led by a team with legitimacy to build bridges and paths between actors • That the cluster organization can show some results (often through formal evaluation) in building bridges and initiating collaboration in the near term The larger the commons becomes, the larger the risk of attracting free riders. In the initial stages firms and other organizations might see the advantages of becoming paying members, but as the commons grows the incentive to free-ride increases. A counterforce to this is that parts of the commons stay “semipublic” and only accessible to paying members in the cluster organization. Emergence of cluster organizations adds to the institutional density of a cluster, adding a sort of vaccine against the tragedy of the commons.

The Role of Policy So why should we care about cluster construction? On the one hand we have the risk of the tragedy of the commons, and on the other the fact that leading clusters seem to be natural; so why add a constructive component? In fact, one of the most common questions I receive is this: Silicon Valley is the world’s most impressive cluster, and it was not constructed, so why should public or private money be spent on attempts to re-create it? First, as discussed above, the evolution of a dynamic cluster typically combines a mix of invisible and visible hands. Second, public money played a role when attracting talent and investment to the valley. And third, there are numerous static clusters around the world without a commons, where norms and regulation bar interaction, mobility, and collaboration. So why not stimulate knowledge spillovers through constructive hands? Lately, the understanding of cluster dynamics has emerged as a central tool within industrial policy, regional policy (Landabaso 1997; 2000; Tödtling 2005), and innovation policy (Raines 2002; Rodriguez-Clare 2007), sometimes referred to as “cluster policy” (Diez 2001; Swann 2006; Ketels 2006; Ketels and Memedovic 2008; Benner 2012). Inspiration for cluster policy has come from work on regional innovation systems (Cooke 2002; Asheim and Gertler 2003), learning regions (Morgan 1997), knowledge spillovers (Audretsch and Feldman 1996), and most importantly Michael Porter’s work on clusters and competitiveness (Porter 1990; Lundequist and Power 2002). Critics also abound (Martin and Sunley 2003; Benneworth and Henry 2004; Asheim, Cooke, and Martin 2006; Motoyama 2008; Duranton 2011). The literature on cluster policy builds on two highly differing approaches: one that focuses on colocation and attraction of firms to a particular location, and the other on interaction and knowledge spillovers, leveraging existing clusters. Cluster policies tend to focus on the latter (Rosenfeld 1996; 1997), supporting frameworks for networking and collaboration (Morgan and Nauwelaers 1999), in line with the discussion above on constructing the commons.

98    The Concept of Local Competitiveness To sum up: if the cluster commons is not a natural phenomenon and, as I have argued, is critical for mobility and knowledge spillovers to actually take place, then it must be constructed in order to enhance cluster dynamics. Everyone is a potential winner in using these common-pool resources, but the question is how incentives for such investments in time and money can be created. Policy clearly has a role to play in sustaining the commons, but other actors, including private firms, should also see the benefits. One way to open their eyes is to initiate public/private cluster organizations, with the very particular and important role of building paths and bridges and keeping the grass green on the commons.

References Aharonson, B. S., J. A. C. Baum, and M. P. Feldman. 2007. “Desperately Seeking Spillovers? Increasing Returns, Social Cohesion and the Location of New Entrants in Geographic and Technological Space.” Industrial and Corporate Change 16, 89–130. Aharonson, B. S., J. A. C., Baum, and M. P. Feldman. 2013. “Industrial Clustering and Innovative Output.” Paper presented to the Danish Research Unit for Industrial Dynamics (DRUID), Winter. Alchian, A.  A. 1950. “Uncertainty, Evolution and Economic Theory.” Journal of Political Economy 58, 211–21. Asheim, B. T., and M. S. Gertler. 2003. “The Geography of Innovation: Regional Innovation Systems.” In J. Fagerberg, D. C. Mowery, and R. R. Nelson, eds., The Oxford Handbook of Innovation. Oxford: Oxford University Press. Asheim, B. T., P. Cooke, and R. Martin, eds. 2006. Clusters and Regional Development: Critical Reflections and Explorations. Abingdon: Routledge. Audretsch, D., and M. Feldman. 1996. “Knowledge Spillovers and the Geography of Innovation and Production.” American Economic Review 86, 630–40. Axelrod, R. 1984. The Evolution of Cooperation. New York: Basic Books. Beaudry, C., and Schiffauerova, A. 2009. “Who’s Right, Marshall or Jacobs? The Localization versus Urbanization Debate.” Research Policy 38, 318–37. Baptista, R., and P. Swann. 1998. “Do Firms in Clusters Innovate More?” Research Policy 27 (4), 525–40. Bell, G. G. 2005. “Clusters, Networks, and Firm Innovativeness.” Strategic Management Journal 26, 287–95. Benner, M. 2012. “Cluster Policy: Principles and a Tool Box.” MPRA Paper No. 43847. Spaces Online 10 (1). Benneworth, P., and N. Henry. 2004. “Where Is the Value Added in the Cluster Approach? Hermeneutic Theorising, Economic Geography and Clusters as a Multiperspectival Approach.” Urban Studies 41, 1011–23. Brenner, T. 2003. “Policy Measures to Create Localised Industrial Clusters.” In T. Brenner and D. Fornahl, eds., Cooperation, Networks and Institutions on Regional Innovation Systems. Cheltenham: Edward Elgar. Chatterji, A., E. Glaeser, and W. Kerr. 2013. “Cluster of Entrepreneurship and Innovation.” Nation Bureau of Economic Research Working Paper No. 19013. Cooke, P. 2002. Knowledge Economies:  Clusters, Learning and Cooperative Advantage. London: Routledge.

Construction of the Cluster Commons    99 Depner, H., and H. Bathelt. 2005. “Exporting the German Model: The Establishment of a New Automobile Industry Cluster in Shanghai.” Economic Geography 81 (1), 53–81. Diez, M.  A. 2001. “The Evaluation of Regional Innovation and Cluster Policies:  Towards a Participatory Approach.” European Planning Studies 9 (7), 907–23. Dosi, G. 1988. “Sources, Procedures and Microeconomic Effects of Innovation.” Journal of Economic Literature 26, 1120–71. Duranton, G. 2011. “California Dreamin’:  The Feeble Case for Cluster Policies.” Review of Economic Analysis 3, 3–45. Enright, M. 1998. “Regional Clusters and Firm Strategy.” In A. D. Chandler Jr., P. Hagström, and Ö. Sölvell, eds. The Dynamic Firm: The Role of Technology, Strategy, Organizations, and Regions. New York: Oxford University Press. Feldman, M. P. 1994. The Geography of Innovation. Dordrecht: Kluwer Academic Publishers. Freeman, C. 1982. The Economics of Industrial Innovation. 2nd ed. London: Frances Pinter. Freeman, C. 1991. “Networks of Innovators: A Synthesis of Research Issues.” Research Policy 20, 499–514. Furman, J., M. E. Porter, and S. Stern. 2002. “Determinants of National Innovative Capacity.” Research Policy 31 (6), 899–933. Glaeser E., H. Kallal, J. Scheinkman, and A. Shleifer. 1992. “Growth in Cities.” Journal of Political Economy 100, 1126–52. Granovetter, M. S. 1973. “The Strength of Weak Ties.” American Journal of Sociology 78 (6), 1360–80. Griliches, Z. 1992. “The Search for R&D Spillovers.” Scandinavian Journal of Economics 94, 29–47. Harrison, B. 1994. Lean and Mean: The Changing Landscape of Corporate Power in the Age of Flexibility. New York: Basic Books. Håkansson, H. 1989. Corporate Technological Behavior: Co-operation and Networks. London: Routledge. Håkansson, H., D. Ford, L.-E. Gadde, I. Snehota, and A. Waluszewski. 2009. Business in Networks. New York: Wiley. Jaffe, A. B., M. Trajtenberg, and R. Henderson. 1993. “Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations.” Quarterly Journal of Economics 108 (3), 577–98. Ketels, C. 2006. “Michael Porter’s Competitiveness Framework: Recent Learnings and New Research Priorities.” Journal of Industrial Competitiveness and Trade 6. 115–36. Ketels, C., and O. Memedovic. 2008. “From Clusters to Cluster-Based Economic Development.” International Journal of Technological Learning, Innovation, and Development 1, 375–92. Krugman, P. 1991. Geography and Trade. Cambridge, MA: MIT Press. Landabaso, M. 1997. “The Promotion of Innovation in Regional Policy: Proposals for a Regional Innovation Strategy.” Entrepreneurship and Regional Development 9, 1–24. Landabaso, M. 2000. “EU Policy on Innovation and Regional Development.” In F. Boekema et al., eds., Knowledge, Innovation and Economic Growth: The Theory and Practice of Learning Region. Cheltenham: Edward Elgar. Lee, C.-M., W. F.  Miller, M. Gong Hancock, and H. Rowen. 2000. The Silicon Valley Edge. Stanford, CA: Stanford University Press. Leonard-Barton, D. 1982. “Swedish Entrepreneurs in Manufacturing and Their Sources of Information.” Center for Policy Applications, MIT. Lindqvist, G. 2009. Disentangling Clusters: Agglomeration and Proximity Effects. Stockholm: Published Doctoral Dissertation at the Stockholm School of Economics.

100    The Concept of Local Competitiveness Lindqvist, G., C. Ketels, and Ö Sölvell. 2013. The Cluster Initiative Greenbook 2.0. Stockholm: Ivory Tower Publishers. Lundequist, P., and D. Power. 2002. “Putting Porter into Practice? Practices of Regional Cluster Building: Evidence from Sweden.” European Planning Studies 10 (6), 685–704. Lundvall, B.-Å. 1988. “Innovation as an Interactive Process: From User-Producer Interaction to the National System of Innovation.” In G. Dosi et al., eds., Technical Change and Economic Theory. London: Pinter. Lundvall, B.-Å. 1993. “Explaining Interfirm Cooperation and Innovation:  Limits of the Transaction-Cost Approach.” In G. Grabher, ed., The Embedded Firm: On the Socioeconomics of Industrial Networks. New York: Routledge. Malmberg, A., Ö. Sölvell, and I. Zander. 1996. “Spatial Clustering, Local Accumulation of Knowledge and Firm Competitiveness.” Geografiska Annaler 78 (B2), 85–97. Marshall, A. 1920. Principles of Economics. London: Macmillan. Martin, R., and P. Sunley. 2003. “Deconstructing Clusters:  Chaotic Concept or Policy Panacea?” Journal of Economic Geography 3, 5–35. Mazzucato, M. 2013 The Entrepreneurial State:  Debunking Public vs. Private Sector Myths. Bath: Anthem. Meyer, A. de. 1992. “Management of International R&D Operations.” In O. Granstrand, L. Håkansson, and S. Sjölander, eds., Technology Management and International Business: Internationalization of R&D and Technology. Chichester: Wiley. Morgan, K. 1997. “The Learning Region:  Institutions, Innovation and Regional Renewal.” Regional Studies 31 (5), 491–503. Morgan, K., and C. Nauwelaers, eds. 1999. Regional Innovation Strategies: The Challenge for Less-Favoured Regions. London: Regional Studies Association. Motoyama, Y. 2008. “What Was New about the Cluster Theory?” Economic Development Quarterly 22, 353–63. Nohria, N., and R. G. Eccles. 1992. “Face-to-Face: Making Network Organizations Work.” In N. Nohira and R. G. Eccles, eds., Networks and Organizations: Structure, Form, and Action. Boston: Harvard Business School Press. North, D. C. 1990. Institutions, Institutional Change and Economic Performance. New York: Cambridge University Press. Ostrom, E. 1990. Governing the Commons: The Evolution of Institutions for Collective Action. New York: Cambridge University Press. Pavitt, K. 1984. “Sectoral Patterns of Technical Change: Towards a Taxonomy and a Theory.” Research Policy 13, 343–73. Pavitt, K. 1991. “Key Characteristics of the Large Innovating Firm.” British Journal of Management 2, 41–50. Pearson, A. W. 1991. “Managing Innovation: An Uncertainty Reduction Process.” In J. Henry and D. Walker, eds., Managing Innovation. London: Sage. Polanyi, K. 1962. Personal Knowledge: Towards a Post-critical Philosophy. Chicago: University of Chicago Press. Porter, M., E. 1990. The Competitive Advantage of Nations. New York: Free Press. Powell, W. W., K. W. Koput, and L. Smith-Doerr. 1996. “Interorganizational Collaboration and the Locus of Innovation: Networks of learning in Biotechnology.” Administrative Science Quarterly 41, 116–45. Raines, P. 2002. Cluster Development and Policy. Aldershot: Ashgate. Redding, S. 2010. “The Empirics of New Economic Geography.” Journal of Regional Science 50 (1), 297–311.

Construction of the Cluster Commons    101 Rodriguez-Clare, A. 2007. “Clusters and Comparative Advantage: Implications for Industrial Policy.” Journal of Development Economics 82, 43–57. Rosenfeld, S. A. 1996. “Does Cooperation Enhance Competitiveness? Assessing the Impacts of Inter-firm Collaboration.” Research Policy 25, 247–63. Rosenfeld, S.  A. 1997. “Bringing Business Clusters into the Mainstream of Economic Development.” European Planning Studies 5 (1), 3–23. Saxenian, A. 1994. Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press. Saxenian, A. 2006. The New Argonauts: Regional Advantage in a Global Economy. Cambridge, MA:Harvard University Press. Scherer, F.  M. 1984. Innovation and Growth:  Schumpeterian Perspectives. Cambridge, MA: MIT Press. Sölvell, Ö. 2009. Clusters: Balancing Evolutionary and Constructive Forces. Stockholm: Ivory Tower Publishers. Sölvell, Ö., and G. Lindqvist. 2011. Organising Clusters for Innovation: Lessons from City Regions in Europe. Lyon: Grand Lyon CLUSNET Final Report. Sölvell, Ö., G. Lindqvist, and C. Ketels. 2003. The Cluster Initiative Greenbook. Stockholm: Ivory Tower Publishers. Sölvell, Ö., and M. Williams. 2013. Building the Cluster Commons: An Evaluation of 12 Cluster Organizations in Sweden, 2005–2012. Stockholm: Ivory Tower Publishers. Sorenson, J., and O. Sorenson. 2003. “From Conception to Birth:  Opportunity Perception and Resource Mobilization in Entrepreneurship.” In J. Baum, ed., Advances in Strategic Management 20, 71 99. Sorenson, O., and P. G.  Audia. 2000. “The Social Structure of Entrepreneurial Activity: Geographic Concentration of Footwear Production in the United States.” American Journal of Sociology 106 (2), 424–61. Stuart, T. E., and O. Sorenson. 2003. “The Geography of Opportunity: Spatial Heterogeneity in Founding Rates and the Performance of Biotechnology Firms.” Research Policy 32, 229–53. Swann, G. M. P. 2006. “Cluster and hinterland.” In B. Asheim, P. Cooke, and R. Martin, eds., Clusters and Regional Development:  Critical Reflections and Explorations. Abingdon: Routledge. Tallman, S., M. Jenkins, N. Henry, and S. Pinch. 2004. “Knowledge, Clusters and Competitive Advantage.” Academy of Management Review 29 (2), 258–71. Tödtling, T. 2005. “One Size Fits All? Towards a Differentiated Regional Innovation Policy Approach.” Research Policy 34 (8), 1203–19. Utterback, J. 1974. “Innovation in Industry and the Diffusion of Technology.” Science 183, 658–62. Von Hippel, E. 1988. The Sources of Innovation. Oxford: Oxford University Press. Winter, S.  G. 1987. “Knowledge and Competence as Strategic Assets.” In D. Teece, ed., The Competitive Challenge:  Strategies for Industrial Innovation and Renewal. Cambridge, MA: Ballinger.

Chapter 6

Keeping U p In a n E ra of Gl obal Spec ia l i z at i on Semi-Public Goods and the Competitiveness of Integrated Manufacturing Districts Dan Breznitz and Giulio Buciuni

Introduction Industrial districts, or clusters, have long been hailed as a successful model of industrial organization (Piore and Sabel 1984; Pyke et al. 1992; Sabel 1989). Originally conceived as a paradigm alternative to the vertically integrated “Fordist” firm, the core of the industrial district model is a thriving local concentration of highly interconnected, specialized manufacturers, which together consist of most, if not all, of the chain of production stages needed to build specific products, such as eyewear, shoes, or ceramics. Spatial agglomeration of relatively small and medium-sized firms emerged as the hallmark of the archetypal industrial district, in which the few leading firms flourished thanks to the efficiency and flexibility of tightly woven local production networks (Lazerson and Lorenzoni 1999; Marshall 1890; Markusen 1996). The rapid growth of fragmented global production undermined the rationale underlying the industrial district model. Taking advantage of lowering transportation costs and the increasing sophistication of information and communication technologies (ICT), many firms have been increasingly dividing their manufacturing processes into a set of discrete stages, or modules, which they then offshore in distant low-wage locales (Arndt and Kierzkowski 2001; Sturgeon 2000; 2002). These developments led different locales, such as China, Taiwan, and Israel, to specialize not only in specific industries, such as semiconductors, but also in particular stages of production in these industries, such as R & D, fabrication, or assembly. As a result, while the economic rationale of fragmented production by multiple companies is strengthened, the economic rationale of colocating companies specializing in all stages

Keeping Up In an Era of Global Specialization    103 of production within the same locale seems to have lost its allure in industries old and new, from textiles to semiconductors (Breznitz 2007a; 2007b; Gereffi et al. 2005; Gereffi and Korzeniewicz 1994; Gereffi 1996; Sturgeon 2002), numerous multinational corporations (MNCs) delegated production activities to global low-cost suppliers and directed their internal resources toward more profitable functions (Berger 2006; McKendrick et al. 2000). As a result, the idea of industrial clusters, whose strength depends on localized specialized firms engaged in all stages of production from the initial idea stage to final production, has not only become unfashionable, but has been deemed a relic of the past to be displaced by market forces. Indeed, the demise of many of the most famous Italian clusters (such as the Matera’s leather upholstery district, textile production in Prato and Biella, and the gold jewelry industry in Arezzo) and the narrowing focus of clusters such as Silicon Valley and Taiwan’s Hsinchu Park, Israel, or India’s Bangalore on limited stages of production, seem to suggest that the era of the “classic” Marshallian industrial districts has come to an end. This argument is now so pervasive that even the renewed calls for a “new” cluster-based innovation policy or the reinvigoration of manufacturing clusters in the United States adhere to the same logic: production is globally fragmented, and clusters, new or old, needs to specialize in few stages of production of their chosen industry (Cortright 2006; Katz and Muro 2010; Mills et al. 2008). Faced with this overwhelming evidence and the accompanying chorus of scholars, policymakers, and business gurus, we find ourselves with only one option: we beg to differ. Although we acknowledge globalizing patterns of industrial organization and recognize the progressive marginalization of operations in many traditionally successful industrial districts, we claim that full-production-chain manufacturing clusters not only survive, but thrive. More specifically, we argue that spatial agglomeration of activities across all stages of production and local interfirm interdependencies can still enhance firms’ competitive advantage and support both novel product and incremental, production, and process innovation. Furthermore, as we will demonstrate below, while some of the old industrial clusters have declined, not only do many survive, but new clusters in industries both traditional and high tech are achieving global success.1 We elaborate on our argument by analyzing the dynamics underpinning the evolution and recent development of two manufacturing industrial clusters:  the newly formed uninterruptable power supply (UPS) cluster in Dongguan, China, and the long-established Alto Livenza furniture district in northeast Italy. Although the two regions are marked by different characteristics—type of production, year of 1 

The constant churn and destruction of industries and clusters is part of the natural rhyme of innovation-based growth. This is precisely the reason why Schumpeter called the dynamic phenomena of innovation-based growth creative destruction, and not creative we-all-get-rich-together-and-livehappily-ever-after. Indeed, the Lancaster textile clusters that were the basis for Alfred Marshall’s original insights ceased to exist many decades ago.

104    The Concept of Local Competitiveness establishment, recent evolution, and global expansion—we find that both clusters qualify as prime innovation hubs in their respective industries. The UPS cluster has distinguished itself as one of the most dynamic ICT manufacturing clusters in China, even with the central government’s complete disregard (and maybe even disdain) for the UPS industry. The Alto Livenza cluster has emerged over the last few decades as a worldwide reference for furniture production and is the home of several innovative companies. Consistent with our argument, both the UPS and the Alto Livenza regions currently host a plurality of specialized manufacturing firms whose interconnected activities resemble the traditional Marshallian district. To explain how this is possible and offer more generalizable lessons to both theory and policymakers, we develop two sets of arguments. First, we argue that we need to return to a Schumpeterian understanding of innovation—that innovation occurs across the complete chain of actions from generating ideas to the constant delivery of better products and services using the same or lower-cost factors. This is quite different from the formulation of innovation as solely the creation of novel technology and products, which became the focus of academics, policymakers, and the public, and has led to what Breznitz and Murphree termed the techno-fetishism of novelty (2011). Using this Schumpeterian definition of innovation, we can understand why, when product innovation is embedded in production processes (Pisano and Shih 2012), the geographic proximity between R & D and manufacturing is a sine qua non condition for innovation. In spite of the relentless forces of globalization, the integration between tangible and intangible activities and access to distinct context-specific operational capabilities will lead numerous manufacturing firms to keep relying on local interfirm relationships. These, in our view, are the factors that will allow manufacturing clusters to survive and thrive in a globalizing economy. Second, following Breznitz and Cowhey, we contend that there are four semipublic goods that industrial clusters must be able to continuously supply if they are to prosper and remain the loci for production-innovation (Breznitz and Cowhey 2012): 1. Shared production assets: firms need to fund and use assets held in common by a variety of contractual and institutional mechanisms. 2. Effective innovation network structures:  markets, contracts, and firms are not enough to provide adequate “glue” to effectively link pools of innovators. 3. Flexible business models: restructuring the traditional definitions of supply and demand functions in markets is often as important as an innovative product. 4. Specialized financial institutions:  risk assessment capacity and lending/investment models appropriate to different types of innovation are essential. Building on these insights, we will show that in both Dongguan and Alto Livenza these semipublic goods were supplied as part of an informal understanding and collaboration between local officials and industry leaders. Consequently, our empirical analysis allows us to come to important policy suggestions for industrial manufacturing clusters, new or old, in all industrial sectors.

Keeping Up In an Era of Global Specialization    105 The next section provides a literature review; we then proceed to discuss the origin and evolution of the Alto Livenza and the Dongguan clusters and analyze the role of local interdependencies in innovation development. We conclude by suggesting some theoretical and policy suggestions emanating from our analysis.

Theoretical Background Most discussions concerning innovation focus on novel game-changing breakthrough developments. This is “novel product” innovation, where the organization or individual developing it comes up with an entirely new technology or product. However, the emphasis on novel-product innovation confuses the act of invention with innovation. Taking a novel idea or invention from concept to market requires an array of incremental product innovations, such as those seen in the continual improvements in automobile transmissions, together with innovations in production and processes. This concept is known as incremental and process (I & P) innovation. I & P innovation encompasses the improvements in how goods and services are designed, produced, distributed, and serviced. It is here, as Schumpeter observed, that the major impact on economic growth occurs (Schumpeter [1934] 1961). Similarly, looking at the growth miracle of the West, Rosenberg and Birdzell quipped that I & P innovation is the true unsung hero of economic growth (1986). Looking at what might be the greatest innovation in terms of economic growth in the 20th century—air conditioning—it has not been the act of inventing the device that transformed our lives, it is the constant wave of ensuing innovations—improving the original innovation and applying it throughout the economy, from cars, to offices, to manufacturing facilities and data centers, in new products, processes, and technologies—that changed our lives and the world. Some of the world’s most important industries are defined less by rapid product innovation than by continuous process improvements that alter cost and performance capabilities. I & P innovation frequently implies more than small changes in production or products. I & P innovation often requires major shifts in business models that upset expectations about how markets work and at whom a set of products is aimed. Henry Ford’s Model T is an excellent example. The Model T melded process and business model innovation by perfecting the idea of mass production and joining it with the mold-breaking business model of pricing cars within reach of all working households while also paying Ford’s employees enough so they could become leading consumers of their own products. Scholars explaining the German manufacturing clusters’ success emphasize their strength in precisely this kind of innovation (Streeck 1992; Herrigel 1994; Streeck 1997; Culpepper and Finegold 2001). Until very recently, a deep understanding of I & P innovation was the theoretical foundation of the literature on the organization of manufacturing-based industrial districts (Piore and Sabel 1984; Pyke et al. 1992). According to this perspective, industrial districts built their competitive advantage through the large presence of highly interconnected,

106    The Concept of Local Competitiveness specialized firms, in which craft labor was the central activity. Improvements made at a specific node in the chain were easily transferred along the production system, thus enabling a systemic process of innovation. Because the majority of the industrial districts developed around labor-intensive, craft-based industries, innovation typically stemmed from bottom-up improvements tightly connected to the shop floor. The notion of industrial districts has recently evolved toward the concept of innovation systems, emphasizing the need for direct interactions among agents engaged in first critical steps of every innovation process (Asheim and Gerlter 2005; Arthur 2009). In this perspective, a useful contribution has been advanced by the ecosystem construct (Adner 2006; Adner and Kapoor 2010; Iansiti and Levien 2004; Jacobides 2008; Moore 1996). The main idea behind the ecosystem construct is that an ecosystem structure allows the cluster’s leading firms to create a value that no single firm could generate alone (Adner 2006). Accordingly, innovation development does not merely arise from the activity of the lead firm; rather it is contingent upon the contribution of each organization comprised in the value network. However, the way products and services are produced has significantly changed since the 1980s. A new system of production—globally fragmented production—is now apparent. On the one hand, this system seems to augment the classical logic behind industrial clusters: activities along production networks are now routinely done by companies specializing in narrower sets of activities, from high-level R & D to design, manufacturing, and assembly techniques. If IBM and Ford once imagined, designed, coded, and assembled their computers and cars in their own factories, today these activities are the work of a multitude of companies around the world with a rapidly growing percentage in Asia. On the other hand, the detailed codification of each task (in essence standardization) coupled with rapid advancement of ICT, decrease in transportation costs, and political processes creating a much more open trade system, mean that these diverse tasks no longer need to be colocated (Gereffi et al. 2005). As a matter of fact, the logic of global fragmentation suggests that since each stage necessitates different capabilities, each locale should specialize in a narrow set of activities in order to become world class (Breznitz 2005b; 2007a). Since the needs of companies in different stages along this fragmented global production network are significantly varied, a local innovation ecosystem must be able to solve the supply problems of several key resources and semipublic goods in order to survive (Breznitz and Cowhey 2012). First among such semipublic goods are shared production facilities, training, and the codevelopment of nonpatentable innovation. Provision of these semipublic goods has become a critical issue in industries from the most advanced high-tech computer chips to textiles and even agrobusinesses such as wine.2 Further, since by definition the final products or services cannot be produced by one firm, even a leading firm, alone, there is a need to focus our attention on firms’ networks as a unit of 2  A critical problem with the supply of semipublic goods is that these globally fragmented networks can misalign the interests of the state and the taxpayers, who pay for innovation policy with the expectation of higher returns to their locale, and the interests of companies, which are increasingly global. See Breznitz and Zehavi (2010).

Keeping Up In an Era of Global Specialization    107 analysis. For the same reason, the need to fix network failures, and shift our policy analysis from focusing solely on market failures, is growing daily because of the changes in the global production system. Further, in order to survive, these industrial clusters need to continuously allow experimentation in business models, both external, such as what is offered to the final customer, and internal, such as shifting of the firm boundaries and transferring of activities, new and old, in and out of the company. Last but not least is the need for different kinds of financing to allow innovation in very diverse terms of size, capacity, tangible and intangible assets, and stages along production networks. What lead firms need in terms of financing, and what a family firm employing 15 workers in the upholstery industry needs, are strikingly different; the same financial vehicles might not fit both.

An “Italian” Cluster in China: Manufacturing and Innovation in Dongguan’s UPS Industry Dongguan lies between Shenzhen and Guangzhou along the east bank of the Pearl River. The city developed on the basis of an export-oriented industrialization strategy with close reliance on Hong Kong and Taiwanese investment. Today, Dongguan considers ICT hardware and electronics to be its main industries.3 Under the broad ICT industry umbrella, UPS technologies are emergency backup systems and power regulators connected to other hardware systems sensitive to sudden change or loss in voltage from the general electricity supply. UPS technologies are used in home offices, small businesses, data centers, telecommunications applications, power-sensitive manufacturing (like semiconductor foundries), the entertainment industry, medical applications, military, and space industries. These technologies are categorized in three discrete variations in terms of sophistication starting at the simplest, known as the standby or offline UPS, followed by line-interactive UPS, to the most complex, the online UPS.4 3 

Dongguan’s annual economic and social development statistics report argues that IT hardware production is so important that it is termed the “Dragon’s head” of the local economy (DSB 2008). 4  The most basic UPS is the standby or offline type that consists of a battery and a switch. Under normal conditions electricity from the standard power source passes through the UPS directly to the connected system. However, in the event of a major interruption or outage, a switch is tripped, connecting the battery to the system being protected. The battery backup provides typically 15 to 30 minutes of emergency power. This is sufficient, given the momentary loss in power between the signal and the switch closing the battery circuit, for systems not sensitive to the momentary power loss. The second type of UPS is a line-interactive model. A line-interactive UPS routes power through a voltage transformer to guard against partial dips (brownout) or surges in voltage without relying on a battery system. For major interruptions or blackouts, emergency battery backup is provided as well. The most sophisticated models are online UPS. Power is always routed through a battery charger and the equipment runs off the battery under normal conditions rather than directly from the main power

108    The Concept of Local Competitiveness The UPS industry in the greater China region spread from Taiwan to Mainland China in the second half of the 1980s (GlobalSources 2005). China’s first UPS companies and manufacturing facilities emerged in the Pearl River Delta (PRD) cities of Dongguan, Shenzhen, Foshan, and Zhongshan. UPS firms in the early years only produced the simplest standby-type UPS and the necessary batteries, usually low-tech lead acid batteries. Currently the PRD’s UPS firms are the most prominent in China (CA800 2008; NanfangRibao 2007). Altogether Guangdong Province’s UPS firms produce over 50 percent of the total national UPS exports (GlobalSources 2005). The province is home to the most technologically sophisticated brands such as Zhicheng Champion, East Company, and HSK, all three of which are based in Dongguan. All of these companies began as manufacturers of batteries and transformers, but they have since developed into specialized UPS producers. Since their inception, the three companies have steadily upgraded their capabilities and moved into line-interactive and online systems at ever higher volt-ampere (VA) ratings.5 For example, Zhicheng Champion upgraded its capability and designs and produces online UPS rated up to 300,000 VA. It now competes with the leading Taiwanese firms in the higher-value and higher-revenue online UPS market and is fast approaching the global technological frontier for industrial and commercial-scale use. Similarly, East Company produces high-VA models, both for the Chinese space program and for export, which is done mainly through its foreign joint-venture partner, Schneider Electric’s MGE UPS brand. HSK Power became fully independent from an earlier joint venture in 2007 and concentrated on higher-end UPS systems and applications. The company has since focused increasingly on production of LED lighting fixtures, often equipped with their self-developed UPS systems to ensure stable and continuous lighting. During our fieldwork, we found that many UPS companies incorporated in the last decade continued this development pattern: from low-profit-margin, assembly-based operations of foreign-defined technology to having independent brands and innovations. The Dongguan UPS cluster, therefore, has successfully upgraded its technology, and its leading companies are now widely perceived as being as capable as any foreign competitor in current-generation technology with the added benefit of having lower costs. Hence, the UPS industry is thriving on significant internal innovation capabilities, which has been the common goal of business and local government. Nonetheless, supply. A direct line to equipment bypassing the battery serves as backup in the unlikely event of an inverter failure on the battery side. This adds a layer of protection for equipment against surges, drops and blackouts, as well as against failure of the UPS itself. This system is used where it is absolutely critical not to interrupt power or have to deal with fluctuations (like data centers or telecom applications). 5  A volt-ampere is the standard measurement for the capacity of a UPS system. For direct current systems, it is the same as a watt. For alternating current systems, the VA rating is higher than the number of watts the UPS is capable of putting out since almost all systems contain inductors or capacitors that introduce reactance. The difference is usually about 60 percent. Hence, a UPS unit rated at 100,000 VA would be capable of an output of approximately 60,000 watts of power. For a UPS (which uses alternating current), the VA rating must be higher than the wattage the user expects it to output.

Keeping Up In an Era of Global Specialization    109 Dongguan’s UPS companies explicitly dismissed novel-product innovation as a categorical good. In repeated interviews the virtues of utilizing appropriate technology for market needs, incremental improvements, and shortening time-to-market were considered superior to high technology and novelty. For example, when asked to define innovation, the founder of a young UPS company answered: Before I  started this company, I  was in the Chinese Academy of Science. Once I moved to industry I quickly learned that the higher the technology the less likely products would enter the market, at least in a timely fashion. There are three highs: high price, high tech, and high time consumption. These are the three highs people fear. (Authors’ interview)

The feature that allows Dongguan’s UPS companies to continuously excel in such innovation and rise to prominence is the slow development of a classic Marshallian industrial district within the city. The cluster includes specialized suppliers and companies that together encompass most of the stages of UPS development and production, from R & D to final assembly. Networks of local suppliers enable the production of UPS units in a specific area. Because a UPS uses many of the same components as other IT hardware products such as personal computers and home electronics, the strength and demand from other sectors contributes to the development of niche supplier firms. As a result, the environment is highly conducive to the opening of new UPS firms in Dongguan, where both suppliers and market opportunities are abundant. For example, during our interviews an entrepreneur explained why he chose to locate in Dongguan’s township of Qingxi: I chose Qingxi because this industry’s production base is in Qingxi. The providers are all located here. If I had my company in Beijing, then I would have needed to ship all the parts from here to Beijing. Since all the parts are manufactured in Qingxi, it is a great advantage for my company to be here. For example, even if in the morning I realize that I am missing critical parts, I just call my friends and within a few minutes these parts are sent and delivered to me. (Authors’ interview)

Local leaders in Dongguan not only pride themselves on their locale’s “complete” production chain, but see it (and accordingly their role in maintaining it) as crucial to the continuing success of the industry. Local government is actively researching the industry’s production chain, with the aim of filling perceived gaps, either by new start-ups or by attracting companies to invest, in order to have as complete a local production chain as possible. Dongguan’s authorities see a rich and complete local supplier network as the main reason why leading companies are not only established, but continue to stay, in the district. For example, a senior township development officer argued: Zhicheng Champion is located here because of the complete production and supplier networks we have here. It is not that we have only the final UPS companies. We have all the specialized suppliers they need. For example, there is a coordinating supplier

110    The Concept of Local Competitiveness in Tangxia that supplies all three leading UPS manufacturers. So long as we have a complete industry set, there is no reason more UPS manufacturers won’t come, and no reason for these that are here to move away. (Authors’ interview)

The tight and dense network of related suppliers allows individual Dongguan’s companies to focus on a narrow set of activities in which they excel and constantly improve. Most of the UPS manufacturers we interviewed source as much as 90 percent of their components, almost all of them locally, rather than produce components in-house. Even high-end components are typically sourced within Dongguan, although software and integrated circuits frequently come from the Dongguan subsidiaries of foreign firms. Another reason why the UPS industry in Dongguan has managed to grow so rapidly has been the result of tight clustering of companies with long-term relationships. This allows the growth of trust and social capital, as well as effective censoring of renegade firms and entrepreneurs. As a result, the UPS cluster has found a local solution to one of the critical issues faced by the industry—access to finance and growth capital. The formal banking sector is closed to most small and medium-size enterprises in China, even in the UPS sector, where companies have land and capital equipment to be used as collateral, as well as stable and growing revenue streams. Locked out from the central-state-run financial system, suppliers in the UPS sector must rely on alternative sources of capital. This is a critical issue, particularly with regards to financing start-ups and expansion. The rich social capital environment in Dongguan allows companies to structure elaborate credit lines and investment pools, such as intercompany credit (Breznitz and Murphree 2011).6 Such systems, however, are highly susceptible to cheating. In principle, a firm without a legal financing agreement with another entity could renege on payment promises or unilaterally change the terms of an agreement. The fact that the UPS industry is so spatially concentrated and tightly networked acts as a deterrent. Blacklisting of suppliers and individuals who fail to repay in a timely fashion involves the lockout not only from credit lines but from the industry as a whole. Since the components for the UPS industry are all produced in Dongguan, falling from favor would quickly result in an enterprise or individual being forced out of business. The internal financial system also extends to the creation of new enterprises. As the start-up phase of a manufacturing facility involves large capital investment with significant time-lag before revenue generation, without credit such an initial investment would be difficult for even the wealthiest Chinese investor. In Dongguan, trust-based pooled credit among enterprises enables start-ups to commence operations and secure orders in advance. For example, a founder of a UPS company recounted how such arrangements allowed him to open his company: The whole operation is based on trust. If you cannot be trusted, you will be kicked out of the business. When we first set up this company my suppliers helped me since 6 

On the importance of social capital and trust to the creation of community-managed resources, investment, and credit pools, see (Geertz 1962; Hechter 1987; Ostrom 1990).

Keeping Up In an Era of Global Specialization    111 my own capital was insufficient. They provided me with the needed capital goods and first components, assuming I would pay once I sold the final products. In the end it was even better than having a loan from a bank because had I had a loan, I would have to pay interest; now I don’t. The entire business is based on trust to this day. For example, if my company needs $500,000, but I only have $50,000 in cash, all my suppliers support me. And I support my customers too. It’s a trust cycle from beginning to end. (Authors’ interview)

While internal credit mechanisms are a crucial source of capital, the local state has been an important source of supplemental development capital. Realizing that the formal financial sector is closed to its UPS industry, the Dongguan government has subsidized expansion and capital equipment upgrading through subsidies and tax rebates, as well as providing prefabricated manufacturing facilities. The local government believes that savings on capital goods imports encourage UPS companies to buy more advanced equipment and train the local workforce in its use, further enhancing the competitive advantage of the locale. The idea that high-skilled manufacturing labor is the cornerstone for successful innovation is entrenched in the local government policies. Leading UPS companies draw a large part of their R & D financing from local government grants. In recent years, Dongguan’s Municipal Science and Technology Commission earmarked funds annually for projects designed to improve the R & D capabilities and technology quality of Dongguan’s manufacturing SMEs (authors’ interviews). In 2008, the city established a new fund of one billion RMB to help SMEs and newly established enterprises (NanfangRibao 2008; Zhao 2008). These grants go directly to companies; for example, Zhicheng Champion received approximately two million RMB in annual grants from the municipal government in the late 2000s. As other sources of financing are limited, the importance of local government aid to the viability of the cluster should not be underestimated. While all interviewees in Dongguan noted the importance of government support, at no point did any company state it received national level funds. Even those certified as a national leader in the UPS industry by the Ministry of Industry and Information Technology did not mention national financial support. While many specific products have been certified as high-technology products, several UPS firms have been unable to even obtain “High Technology Enterprise” certification for the company as a whole. Many component manufacturers cannot receive any of the benefits afforded to high-technology enterprises despite their own R & D activities. Apart from financial assistance, the Dongguan municipal and Guangdong provincial governments are also crucial in sustaining another critical factor for the success of the cluster—labor for both production and R & D. In terms of production, the UPS industry requires access to a large and steady supply of semiskilled labor. Specifically, UPS companies require a difficult combination of low-cost but relatively skillful and specially trained workforce. Dongguan’s UPS companies employ anywhere from 400 to over 3,000 production workers each. As on the production lines of other local enterprises, the vast majority of Dongguan’s UPS line workers are migrants from China’s interior.

112    The Concept of Local Competitiveness They lack the independent financial resources, especially upon their first arrival in the region, to purchase or rent local property. Many workers, however, stay for long periods of time, and hence require room and board. Additionally, migrant workers must also be able to arrange necessary work permits to reside and receive payment in Dongguan.7 Dongguan’s local government facilitates the availability of production labor for the UPS factories in two ways: first, by supplying necessary factory housing; second, and much more importantly, in opposition to Shanghai and Beijing, Guangdong’s provincial government has long encouraged migrant labor. With regards to housing, municipal-, township- and village-level administrations frequently provide housing for workers as part of park designs. For example, Xie Kang Village built a small industrial zone that includes housing for several hundred workers per factory, and Qingxi Township’s Qing Hu Industrial Park includes a housing district with living space for thousands of workers collectively housed outside of their factories. By providing enterprises with the necessary housing for large numbers of migrant workers, Dongguan’s government helps enterprises lower their operating expenses. From the migrant labor point of view, given that the mandated minimum wage in Dongguan at the end of 2013 was only 1,310 RMB per month, in the context of rapidly rising cost of living, having guaranteed housing is a major attraction (CRN 2008).8 Local governments also take other actions to help recruit migrant labor from all over China. For example, in Dongguan workers arrive daily by train and bus from other areas of China. Once unloaded, they are usually hired rapidly upon arrival and submission of applications at the gates of the various factories. After the fact, authorities quietly arrange for the local work permits. For workers who remain for more than a few years, the city facilitates the transfer of legal residency (Hukou) and, in the meantime, employs a lax attitude toward residency laws (authors’ interviews).9 The effort of labor recruiting is also intimately connected with the efforts of the local government in assisting enterprises with the buildup of their R & D capabilities. Since the PRD has a systemic weakness with regards to academic research and education infrastructure, local authorities are aggressively helping companies recruit and relocate talent from other provinces. For the purpose of educated-labor recruitment efforts, the Hukou regulations have been specifically restructured to facilitate their enticement (Breznitz and Murphree 2011). Hence, UPS and other firms with large R D, quality control, or engineering departments do not find the Hukou system to be an impediment to recruitment. For entry-level engineers and managers, especially from China’s interior provinces, the Hukou remains a major attraction in the PRD. Although 7 

China employs a strict internal house registration system known as Hukou. In effect this system ensures that hundreds of millions of Chinese are illegal immigrants in their own country. Accordingly, our use of migrant workers refers to Chinese who were not born in Dongguan. For a short book that fully discuss this system of exclusion and its social impact, see Wang (2005). 8 See http://www.dongguantoday.com/news/dongguan/201302/t20130207_1755858.shtml. 9  China’s system of residency (Hukou) means that most Chinese internal migrant workers are, in effect, illegal migrants within their own country and suffer discrimination as a consequence. For more on the Hukou system, see Wang (2005).

Keeping Up In an Era of Global Specialization    113 large companies such as Tencent and Huawei have been forced to set up R & D centers in Beijing and Shanghai because graduates of the cities’ top universities are often emphatically unwilling to relocate to the PRD, researchers from strong universities in interior provinces such as Hubei and Sichuan find the offer of a PRD Hukou an important part of their offer. Fortunately, the UPS industry’s needs are much better supported by such universities since they retained large research departments in fields that are no longer considered cutting-edge for universities such as Tsinghua or Peking University. Furthermore, since the growing demand from, and prospect of moving to, the PRD makes these programs more attractive, these universities are thrilled to work with Dongguan UPS companies. Indeed, in a perfect example of utilizing shared assets, companies and universities collaborate to supply shared facilities that alone would not have been profitable to either side. For example, Zhicheng Champion had long-term research partnerships with Huazhong University of Science and Technology and Wuhan University (NanfangRibao 2007). With these universities, the company ran a provincial-level R & D center in its factory campus, working on improved UPS systems and batteries. The researchers from these central China universities need access to laboratory facilities in order to conduct their thesis research, and the company benefits from the low-cost talent while building its in-house research capabilities. In a similar fashion, East Company maintains a postdoctoral scientific research station at its Songshan Lake campus in Dongguan. By providing facilities for PhD and postdoctoral researchers, the company improves its own R & D while keeping it within the company structure and offers the firm a better selection of future employees and managers for its R & D and design teams. Last but not least, the local government assists the industry in achieving other collective action goals, in particular with regards to market research and political lobbying. The municipal and township governments in Dongguan follow two models of research designed to improve the overall industry’s economic growth and industrial strength. One model looks at the need of the overall industry and specifically aims at building the “complete” production chain locally. The second type of research, usually conducted at the municipal level but also by individual townships, involves sending local business and city representatives on fact-finding missions across China and abroad. During these missions, the aim is to both discern best practices and subsidize lobbying by the industry on behalf of the local government. However, many difficulties still remain for the IT clusters of the PRD. The greatest cloud over the future of all of them, and in particular the UPS sector, is gross overcapacity. As early as the late 2000s, capacity had outstripped demand. While the most successful firm, Zhicheng Champion, uses on average 75  percent of its production capacity, two other prominent Dongguan-based UPS companies, East Company and Kewang, used only 15 and 12 percent respectively (GlobalSources 2005). Despite this overcapacity, PRD UPS companies continued to invest in production capital and further expansion of their production lines. It thus remains to be seen whether the newly developed industrial clusters will change the long-term trajectory of the PRD’s IT

114    The Concept of Local Competitiveness industry toward more sophistication and innovation, or whether the cutthroat competition and skewed incentives and price mechanisms lead the industry to overexpansion and self-destruction.

Manufacturing and Innovation in the Alto Livenza Furniture District The Alto Livenza furniture district is situated along the Livenza River and straddles two provinces—Pordenone (Friuli Venezia Giulia) and Treviso (Veneto) in the northeastern part of Italy.10 The local furniture industry is one of the biggest poles for furniture production in Europe. Its overall estimated turnover is approximately 2 billion euros—equaling 25 percent of the entire Italian furniture production. The Alto Livenza district traditionally specialized in home furniture production, and local producers are globally renowned for their flexible manufacturing system and innovation capabilities.11 In a fashion similar to many Italian industrial districts, the Alto Livenza furniture industry started to develop after World War II. Among the factors that enabled the development of the local industry, of particular importance was access to raw materials—above all wood from the neighboring Alps—the large availability of a relatively cheap workforce from the agriculture sector, and the existence of Italy’s oldest, and best developed, professional school for furniture craftsmanship, namely Scuola Professionale del Mobile. Originally the business structure of the Alto Livenza district was anything but a dense network of suppliers. Local companies established in the 1950s were initially characterized by a vertically integrated production system. Only in the 1970s did the move toward division of labor and firm specialization take place, when lead firms started to outsource specific manufacturing activities to small family businesses, born as spin-offs from the parent companies. This phenomenon of spinning off specialized suppliers gave shape both to a dense network of tightly interconnected firms, whose relationships were rarely regulated through equity means, and to an established career and company development pattern. In this pattern, graduates of the Scuola Professionale del Mobile work for several years after graduation in an established firm, and then spin off as a specialized supplier. Today, numerous local companies are still owned by the founding family and only a few have grown above the small and medium-sized category. In line with its most recent evolution, the Alto Livenza’s ecosystem remains characterized by a high level of firm 10  Although officially delimited by the Pordenone and Treviso provinces, over the past decade the Alto Livenza furniture district has expanded its boundaries to include companies located in the bordering province of Venezia. 11  The Alto Livenza territory is highly specialized in furniture production. According to the Distretto del mobile dell’Alto Livenza, the official institution representing the district, 61 percent of the overall local workforce is engaged in the furniture industry.

Keeping Up In an Era of Global Specialization    115 stage-specialization, which allows local manufacturers to focus on discrete stages of production, thus constantly pursuing specialization and product improvement. Interlinked to, and in parallel with, the growing global fragmentation of production, the internationalization of the Alto Livenza district commenced in the 1970s, when local lead firms started to explore the global market in a systematic fashion. Local proximity and a favorable exchange rate initially encouraged local companies to penetrate the German market. In following years, exports were extended to the UK and France, and the US export-orientation very quickly became the main focus of the local production system. Although, similarly to Dongguan, the principal engine leading to the establishment and growth of the Alto Livenza district has been the entrepreneurial activities of local artisans and the growing demand for furniture goods, the role of local institutions should not be neglected. In order to understand the way institutions have created and maintained semipublic goods in the Alto Livenza, we need to address two distinct historical periods separately: namely, the early development of the district and its internationalization. Unlike the Dongguan industrial cluster, where local government’s economic incentives significantly supported the establishment and development of firms engaged in UPS production, public efforts in the Alto Livenza were mainly directed to training programs for the local workforce. Established in the 1930s, the Scuola Professionale del Mobile is the oldest school for furniture craftsmanship in Italy and represents an important source of skilled workers for the territory. The school is a publicly funded institution, which solves the collective action problem inherent to investment in tradable skills, but it is tightly linked to the local furniture industry.12 Similarly to the German system, firms are directly involved in the educational program, primarily through internships and on-site training. Not only does this allow high school students to learn a profession and specialize in specific production tasks, but it also offers manufacturing firms the opportunity to integrate newly trained workers into their production system. Overall, the local furniture school plays the crucial role of preserving the craftsman heritage that has long characterized the way furniture goods are produced in the Alto Livenza. Furthermore, the school acts as a collaborative public space, where owners and managers of all the firms in the region work together on school-related activities and use these same networks and sometimes the same meetings to solve industry-wide issues (Breznitz 2005a). A feature that is extremely hard to replicate, the Scuola Professionale del Mobile is an invaluable source of competitive advantage for local furniture manufacturers and represents a strong incentive for them to continue relying on skilled suppliers located in the district. Together with the local school, a fundamental factor in the development of the Alto Livenza cluster is the local financial system. Consisting of numerous small local banks—called Banche di Credito Cooperativo (BCC)—the local financial system was 12 

On skills as a source for competitive advantage in global manufacturing, see Culpepper and Finegold (2001); Estevez-Abe et al. (2001); Culpepper (2007); Thelen and Busemeyer (2012).

116    The Concept of Local Competitiveness conceived to allow new entrepreneurs, often former skilled workers employed in larger organizations, to establish new specialized ventures. The system solves the same issues as the rotating credit arrangement in Dongguan, namely access to capital by small-scale, highly specialized local entrepreneurs. This sustained the creation of a dense ecosystem of suppliers, around which local brand-name companies strategically organized their production processes. A third public-private set of initiatives that played a critical role in the recent growth and sustainment of the cluster deals with Alto Livenza’s internationalization drive. As the district started to go global and the need for reaching new markets became more acute, the local chamber of commerce and the Pordenone provincial government shifted their attention from tangible to intangible activities. In 2005, this strategy led to the establishment of the Salone Internazionale dei Componenti, Semilavorati ed Accessori per l’industria del Mobile (SICAM).13 Since its establishment, SICAM has rapidly become a key worldwide exhibition of components and semifinished products for the furniture industry. Established thanks to the cooperation between Pordenone Fiere, the chamber of commerce of Pordenone, the municipality of Pordenone, and local furniture entrepreneurs—some of which are part of the SICAM board—the furniture fair was conceived to create a bridge between domestic and global buyers and regional specialized manufacturers.14 Within five years of its establishment, SICAM had become one of the most important industry exhibitions worldwide. In 2012, SICAM hosted 540 exhibitors and 17,000 buyers coming from as many as 93 different countries. Thirty percent of the participants were from outside Italy. In 2012, SICAM was coupled with an additional exhibition exclusively dedicated to the furniture contract industry. Acknowledging the growing importance of the global contract market, this trade show targets a specific niche of buyers—that is, contractors—in the attempt to promote the international development of Alto Livenza furniture firms. Beside traditional exhibitions, the organization of the trade show also includes the “open factory” initiative, an event through which buyers can freely visit furniture plants and interact with local manufacturers beyond the boundaries of the exhibition. As the international trade statistics clearly show, these initiatives have supported the global dynamism of the Alto Livenza furniture district. According to the Centro Studi FederLegnoArredo’s 2012 report, the exports generated by the local industry in 2011 accounted for 24 percent of overall Italian furniture exports. While Western European markets remain the primary destination, non-EU countries are rapidly increasing their importance—specifically Russia, United Arab Emirates, and Morocco. Nonetheless, in spite of the internationalization of the “downstream” activities, the bulk of the Alto Livenza’s furniture production remains local. Unlike the recent dynamics characterizing the evolution of other furniture clusters—for example, the Matera 13  SICAM is also known as the International Exhibition of Components, Semi-finished Products and Accessories for the Furniture Industry. 14  Founded in 2003, Pordenone Fiere is the company in charge of the organization of all of trade shows that take place in Pordenone.

Keeping Up In an Era of Global Specialization    117 upholstery district and case goods production in North Carolina—where global relocation of operations by local firms is threatening the regional industrial structure, Alto Livenza’s companies maintain a high level of control over production activities, and offshoring strategies have rarely been successful. Over the past decade several local manufacturers attempted to offshore production—mainly to the Balkans—however, this strategy did not fit the production logic characterizing their business models. This model, marked by a high level of product customization relying on tight links with local specialized suppliers, means that branded firms rely heavily on flexible, specialized suppliers that were not readily available in low-cost foreign production bases. Consequently, even those companies that tried to offshore production quickly realized that not only did they not enjoy cost advantages, but offshoring reduced their production efficiency and quality. Thus, firms reshored production to Alto Livenza. Continuous access to local manufacturing capabilities is also fundamental in supporting local firms’ product development. It enables them to pursue high-road strategies and remain competitive in the global marketplace (Buciuni et al., 2014). As the following cases illustrate, the manufacturing know-how of local suppliers lays the foundation of local lead firms’ innovation capabilities and recent global expansion.

Magis Established in 1976, Magis produces and sells contemporary plastic and wood case goods. Magis is renowned worldwide for the cutting-edge design of its tables and chairs; several of Magis’ designs are considered to be so exceptional that they are part of the permanent collection of the Museum of Modern Art in New York. Magis’ design strength and its public recognition allowed the company to become a benchmark in the global design industry. Magis currently generates 90 percent of its turnover through exports, particularly to France, the United States, and Japan. In spite of its growing global presence, the company’s production system still relies on a selected network of specialized local suppliers in Alto Livenza. Magis sees local producers’ production flexibility and operational know-how as a strategic source of competitive advantage. In particular, the manufacturing capabilities of Magis’ key suppliers are fundamental in supporting product development. Product development in Magis, from design through creation of a product sample, takes place through repeated transactions between Magis and its key suppliers. Crucial in this phase is the product know-how of local producers, whose abilities enable Magis to translate creative ideas into prototypes and refine them until they match the desired technical and aesthetic criteria. Magis’ owner explaines the critical role of specialized suppliers: Although product development starts here [at Magis] with the sketch of new design, we use external capabilities to give a concrete shape to our designers’ creative ideas. While a first product prototype can be developed internally, we largely rely on our local partners for the refinement and industrialization of any new items. Every

118    The Concept of Local Competitiveness time we come up with a new idea or improvement, we know we must test it with those suppliers that know how to implement it in the production process. (Authors’ interview)

An essential condition to efficiently supporting product innovation is the way the company manages interfirm relations with skilled suppliers. Since many of these relationships are now more than two decades old, Magis’ owner refers to them as “friendships, more than just simple business relationships.”

Uno Contract Tight relationships with local specialized producers are also a prime source of competitive advantage for Uno Contract (UC). UC started as a furniture supplier and evolved to a global leader in the upscale hotel furniture industry. UC currently generates 45 percent of its turnover abroad and is particularly active in those markets where luxury hotels are being built—such as the UAE, Qatar, Morocco, and Russia. The international expansion recently accomplished by the company has been remarkably supported by the local availability of highly specialized suppliers. In fact, furnishing upscale hotels entails high-level manufacturing skills, as many of the products need to not only be of high quality, but are specifically tailor-made and developed as ad hoc solutions to uniquely designed buildings. Besides depending on its internal operational capabilities, UC relies on 20 suppliers. Specialized suppliers provide the company with a broad range of customized components, which allows UC to offer global contractors entirely furnished hotel rooms tailored to specific architectural and style aesthetics.15 Local skilled manufacturers also play a strategic role in the setup of sample rooms that UC uses to show its clients a preview of a future installation. Starting from this sample and following clients’ feedback, UC refines its offer until it matches the expectations of the global contractor. Although this process entails significant up-front investments for both UC and its suppliers, it represents a crucial source of competitive advantage for the firm. As elaborated on by UC’s CEO: We are arguably the only firm competing in this niche that is able to provide a real sample of a hotel room in less than a month. This, for example, allowed us to get an important bid in Qatar this year [2013], where we were competing against cheaper European players. Developing a tailor-made hotel room in four weeks is challenging and requires you to work with skilled, efficient partners. Timing and flexibility are fundamental. This is why we mainly source locally. (Authors’ interview)

15 

Defined by UC as a “turnkey solution,” this service enables global contractors to deal with one single supplier at time, thus decreasing the complexity of managing a large network of independent vendors.

Keeping Up In an Era of Global Specialization    119

Valcucine Founded in 1980, Valcucine is a medium-sized firm that competes in the upscale kitchen industry. Traditionally focused on design, over the past decade the company has distinguished itself as a worldwide leader in environmental sustainability. This was accomplished through a process in which Valcucine launched several models of kitchens, representing progressive radical improvements in terms of materials and production processes used. By combining this strategy with product customization, Valcucine aims to increase the durability of its products as a way to implement a more environmentally friendly business model. Costumers are in fact invited to personalize their kitchen according to their tastes, which should result in a lower product turnover. Valcucine guarantees such customization by involving local artisans in the decoration process. Most of the innovations carried out in Valcucine are developed in close cooperation with specialized suppliers. Because customization and product innovation requires changes in the use of raw materials or components and the redesign of the product, technical integration with skilled partners throughout the production chain is fundamental. In addition, environmentally friendly inputs are typically not readily available on the market, further increasing the need for the firm to engage in close cooperation with skilled suppliers that are able to fulfill Valcucine’s distinct technical requirements. Given the important role played by suppliers in both product development and manufacture, it’s crucial for Valcucine to have constant access to skilled suppliers. As stated by Valcucine’s CEO, the strategic location of the company in Alto Livenza is an indispensable condition for the development of a reliable network of specialized suppliers: We’ve been operating in the district for more than 30 years and have always relied on local suppliers. Except for a few technical components that we import from Germany, all the critical inputs we use are codeveloped together with our suppliers. It’s about capabilities and responsiveness. Just think about what happens when we develop and test new product solutions: you want to make sure that you can meet with them [suppliers] every day, sometime several times each day, talk the same language, share the same way of working. (Authors’ interview)

Conclusions The analysis of the Dongguan UPS and the Alto Livenza furniture districts highlights how local division of labor can still be a critical source of competitive advantage for manufacturing clusters in both “high tech” and “traditional” industries. This local division of labor allows these clusters to excel by maintaining, or even purposefully assembling, the complete chain of production within one locale. This is in direct contradiction

120    The Concept of Local Competitiveness to the view that sees the future of manufacturing as one of fragmented global production with ever more stage specialization of specific locales. The discussion of these two industrial clusters, which resemble the ideal-type Marshallian “Italian” industrial district, reveals that production specialization and tight interfirm interactions foster firms’ competitiveness and support innovation development. In particular, we claim that when manufacturing companies pursue strategies of constant product innovation and differentiation, spatial and cultural proximity with skilled suppliers is a sine qua non condition. Not only does firms agglomeration decrease time to market, but it also enables local lead firms to constantly improve products and processes though repeated transactions with specialized suppliers. This is particularly important in product development, when product prototyping, product testing, and industrialization require manufacturing firms to be closely interconnected. Although they are marked by different dynamics, the study of Dongguan and Alto Livenza shows that local institutions and public-private initiatives play a crucial role in the development and growth of industrial districts. Yet the specific ways in which local governments can act vary significantly according to the cluster’s stage of development. In both Dongguan and Alto Livenza local institutions supplied four essential building blocks: (1) shared production assets; (2) effective innovation network structures that allow the flourishing of collaborative public spaces (Breznitz 2005a); (3) flexible business models that allow constant restructuring of the traditional definitions of supply and demand functions in markets; and (4)  specialized financial institutions that are appropriate to the different types of innovation modes practiced in each locale. Based on our findings, we argue that just as there is an evolution in cluster formation and development, so a parallel coevolution needs to happen in the spheres of public institution and public-private initiatives and policies. This, for example, occurred in the Alto Livenza, where local firms’ internationalization was significantly fostered by the establishment of two global furniture trade shows in the district. As a consequence, we would urge more work that looks at the coevolution of institutions and industry in manufacturing clusters. Only by developing an evolutionary-based theoretical framework can we hope to both expand our understanding and come up with generalizable policy insights. In particular, we encourage further research to address the interactions between public and private initiatives at the local level and shed light on the way these relationships are shaped, regulated, and evolve over time. In an economic scenario still vastly plagued by austerity regimes, the public and private sectors have no option but to synchronize their focuses and efficiently pursue the implementation of regional industrial policies. While this would require policymakers and entrepreneurs to overcome long-standing cultural and political barriers and jointly frame policies for innovation and industrial development, scholars and analysts are asked to devote further attention to this crucial topic and include it in their research agenda. If we look at our framework, it is clear that this is most critical in the spheres of creating shared assets that have long time horizons. These goods are the basis of manufacturing clusters’ resilience, but they suffer from the double curse of both being semipublic and having a long period of investment before

Keeping Up In an Era of Global Specialization    121 their utility becomes clear to private actors. Hence, without public agencies focusing on policy experimentation and on tying together different and sometimes not obviously private and public actors, we should not expect these assets to be produced and sustained (Breznitz and Ornston 2013). In line with Berger (2013) and Schrank and Whitford (2011), we hope that the “revenge” of manufacturing will be accompanied by a serious discussion in the policy and scientific communities leading to reinvigorated debate over the importance of manufacturing industrial clusters in today’s global economy—a debate that has been sidelined by too many since the early 1990s. Sadly, it is here that the structure of social science might inhibit future research. As it is because of silos of interests that public policy specialists hardly interact with business specialists, those who care about finance hardly collaborate with those who care about production, and worse, so-called innovation specialists tend to focus narrowly on a subset of activities and industries that engage mostly in novel product inventiveness and thereby disregard the true engine of innovation-based growth—the routinization of innovation applied to constant improvement of each part and facet of the business, product, or service.

References Adner, R. 2006. “Match Your Innovation Strategy to Your Innovation Ecosystem.” Harvard Business Review, April, 98–107. Adner, R., and R. Kapoor. 2010. “Value Creation in Innovation Ecosystems:  How the Structure of Technological Interdependence Affects Firm Performance in New Technology Generations.” Strategic Management Journal 31 (3), 306–33. Arndt, S. W., and H. Kierzkowski, eds. 2001. Fragmentation: New Production Patterns in the World Economy. Oxford: Oxford University Press. Arthur, W.  B. 2009. The Nature of Technology:  What It Is and How It Evolves. New  York: Free Press. Asheim, B. T., and M. S. Gerlter. 2005. “The Geography of Innovation: Regional Innovation Systems.” In J. Fagerberg, D. C.  Mowery, and R. Nelson, eds., The Oxford Handbook of Innovation. Oxford: Oxford University Press. Berger, S. 2006. How We Compete: What Companies around the World Are Doing to Make It in Today’s Global Economy. New York: Doubleday. Berger, S. 2013. Making in America. From Innovation to Market. Cambridge, MA: MIT Press. Breznitz, D. 2005a. “Collaborative Public Space in a National Innovation System:  A  Case Study of the Israeli Military’s Impact on the Software Industry.” Industry and Innovation 12(1), 31–64. Breznitz, D. 2005b. “Development, Flexibility, and R&D Performance in the Taiwanese IT Industry: Capability Creation and the Effects of State-Industry Co-Evolution.” Industrial and Corporate Change 14(1), 153–87. Breznitz, D. 2007a. Innovation and the State: Political Choice and Strategies for Growth in Israel, Taiwan, and Ireland. New Haven, CT: Yale University Press. Breznitz, D. 2007b. “Industrial R&D as a National Policy: Horizontal Technology Policies and Industry-State Co-evolution in the Growth of the Israeli Software Industry.” Research Policy 36, 1465–82.

122    The Concept of Local Competitiveness Breznitz, D., and P. Cowhey. 2012. “America’s Two Systems of Innovation:  Innovation for Production in Fostering U.S. Growth.” Innovations 7(3), 127–54. Breznitz, D., and M. Murphree. 2011. Run of the Red Queen:  Government, Innovation, Globalization and Economic Growth in China. New Haven, CT: Yale University Press. Breznitz, D., and Ornston, D. 2013. “The Revolutionary Power of Peripheral Agencies: Explaining Radical Policy Innovation in Finland and Israel.” Comparative Political Studies 46, 1219–45. Breznitz, D., and A. Zehavi. 2010. “The Limits of Capital:  Transcending the Public Financer-Private Producer Split in Industrial Policy.” Research Policy 39 (2), 301–12. Buciuni, G., G. Coro’, and S. Micelli. 2014. “Rethinking Manufacturing in Global Value Chains:  An International Comparative Study in the Furniture Industry.” Industrial and Corporate Change 23, 967–96. CA800. 2008. “2007 Nian Dian Yuan Hang Ye Fen Xi Bao Gao—Wo Guo UPS Dian Yuan Hang Ye Xian Zhuang Zong Shu” (2007 Power Supply Industry Analysis Report—China’s UPS Power Supply Industry Summary Situation). (In Chinese.) CA800.com. Cortright, J. 2006. “Making Sense of Clusters:  Regional Competitiveness and Economic Development.” Metropolitan Policy Program, Brookings Institution, Washington, DC. CRN. 2008. “Dong Guan Gong Ren Gong Zi Bao Sheng, Pin Tiao Cao, Gang Qi San Zhao Liu Ren” (Dongguan’s Industrial Worker Wage Increases, Frequent Job Changes: Three Hong Kong Enterprise Perspectives). (In Chinese.) China Review News. Culpepper, P.  D. 2007. “Small States and Skill Specificity:  Austria, Switzerland and Interemployer Cleavages in Coordinated Capitalism.” Comparative Political Studies 40(6): 611–37. Culpepper, D.  P., and D. Finegold, eds. 2001. The German Skills Machine:  Sustaining Comparative Advantage in a Global Economy. Oxford: Berghahn Books. DSB. 2008. 2007 Nian Dong Guan Shi Guo Min Jing Ji He She Hui Fa Zhan Tong Ji Gong Bao (2007 Dongguan City People’s Economic and Social Development Statistics Report). (In Chinese.) S. B. o. Dongguan: Dongguan, China. Estevez-Abe, M., T. Iversen, et  al. 2001. Social Protection and the Formation of Skills:  A  Reinterpretation of the Welfare State. Varieties of Capitalism:  The Institutional Foundations of Comparative Advantage. P. Hall and D. Soskice. Cambridge, Cambridge University Press: 145–83. Geertz, C. 1962. “The Rotating Credit Association: A ‘Middle Rung’ in Development.” Economic Development and Social Change 10 (3): 241–63. Gereffi, G. 1996. “Commodity Chains and Regional Divisions of Labor in East Asia.” Journal of Asian Business 12(1): 75–112. Gereffi, G., J. Humphrey, et  al. 2005. “The governance of global value chains.” Review of International Political Economy 12(1): 78–104. Gereffi, G., and M. Korzeniewicz, eds. 1994. Commodity Chains and Global Capitalism. Westport, CT: Praeger. GlobalSources. 2005. UPS—China Sourcing Report. China Sourcing Reports. Singapore, Global Sources. Hechter, M. 1987. Principles of Group Solidarity. Berkeley: University of California Press. Herrigel, G. 1994. Industry as a Form of Order: A Comparison of the Historical Development of the Machine Tool Industry in the United State and Germany. Governing Capitalist Economies: Performance and Control of Economic Sectors. J. R. Hollingsworth, P. Schmitter, and W. Streeck. New York: Oxford University Press.

Keeping Up In an Era of Global Specialization    123 Iansiti, M., and R. Levien. 2004. The Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, and Sustainability. Boston, Harvard Business School Press. Jacobides, M. G. 2008. “Playing Football in a Soccer Field: Value Chain Structures, Institutional Modularity and Success in Foreign Expansion.” Managerial and Decision Economics 29: 257–76. Katz, B., and M. Muro. 2010. The New “Cluster Moment”: How Regional Innovation Clusters Can Foster Regional Economy. Metropolitan Policy Program. Washington The Brookings Institution. Lazerson, M. H., and G. Lorenzoni. 1999. “The firms that feed industrial districts: A return to the Italian source.” Industrial and Corporate Change 8(2): 235–66. Markusen, A. 1996. “Sticky Places in Slippery Space:  A  Typology of Industrial Districts.” Economic Geography 72(3): 293–313. Marshall, A. 1890. Principles of Economics. London, Macmillan. McKendrick, D., R. F.  Doner, et  al. 2000. From Silicon Valley to Singapore:  Location and Competitive Advantage in the Hard Disk Drive Industry. Stanford, Calif., Stanford University Press. Mills, K. G., E. B. Reynolds, et al. 2008. Clusters and Competitiveness: A New Federal Role For Stimulating Regional Economies. Metropolitan Policy Program. Washington: The Brookings Institution. Moore, J. F. 1996. The Death of Competition: Leadership and Strategy in the Age of Business Ecosystems. New York: Harper Business. NanfangRibao. 2007. “Xiao Qi Lian Yin Ji Huo Chan Ye Chuang Xin Ji Yin” (School-Business Marriage Enlivens Industry Innovation Gene). (In Chinese.) NanfangRibao. 2008. “Dong Guan 10 Yi Yuan Bang Fu Zhong Xiao Qi Ye Rong Zi Qi Ye Xu Wu Bu Liang Xin Yong Ji Lu” (Dongguan’s One Billion RMB to Support SMEs” Enterprises Must Not Have an Unhealthy Credit Record). (In Chinese.) Ostrom, E. 1990. Governing the Commons: The Evolution of Institutions for Collective Action. Cambridge: Cambridge University Press. Piore, M.  J., and C. F.  Sabel. 1984. The Second Industrial Divide:  Possibilities for Prosperity. New York: Basic Books. Pisano, G., and W. C.  Shih. 2012. “Does America Really Need Manufacturing?” Harvard Business Review, March, 94–102. Pyke, F., G. Becattini, et  al. 1992. Industrial Districts and Interfirm Cooperation in Italy. Geneva: International Institute for Labour Studies. Rosenberg, N., and L. E. J. Birdzell. 1986. How the West Grew Rich: The Economic Transformation of the Industrial World. New York: Basic Books. Sabel, C. F. 1989. “Flexible Specialisation and the Re-emergence of Regional Economies.” In P. Hirst and J. Zeitlin, eds., Reversing Industrial Decline? Industrial Structure and Politics in Britain and Her Competitors. Oxford: Berg. Schrank, A., and J. Whitford. 2011. “The Anatomy of Network Failure.” Sociological Theory 29(3), 151–77. Schumpeter, J. A. (1934) 1961. The Theory of Economic Development: An Inquiry into Profits, Capital, Credit, Interest, and the Business Cycle. Cambridge, MA: Harvard University Press. Streeck, W. 1992. “Productive Constraints: On the Institutional Preconditions of Diversified Quality Production.” In W. Streeck, ed., Social Institutions and Economic Performance. London: Sage.

124    The Concept of Local Competitiveness Streeck, W. 1997. “The German Economic Model: Does It Exist? Can It Survive?” In C. Crouch and W. Streeck, eds. Political Economy of Modern Capitalism:  Mapping Convergence and Diversity. London, Sage. Sturgeon, J.  T. 2000. “Turnkey Production Networks:  The Organizational Delinking of Production from Innovation.” In U. Jurgens, ed., New Product Development and Production Networks, 67–84. New York: Springer. Sturgeon, J. T. 2002. “Modular Production Networks: A New American Model of Industrial Organization.” Industrial and Corporate Change 11 (3), 451–96. Thelen, K., and M. R.  Busemeyer. 2012. “Institutional Change in German Vocational Training: From Collectivism toward Segmentalism.” In M. R. Busemeyer and C. Trampusch, eds., 68–100. The Political Economy of Collective Skill Formation. Oxford:  Oxford University Press. Wang, F.-L. 2005. Organizing through Division and Exclusion: China’s Hukou System. Stanford, CA: Stanford University Press. Zhao, D. 2008. “Dong Guan Shi Zhang Li Yu Quan: 10 Yi Yuan She Li Zhuan Xing Sheng Ji Zhuan Xiang Ji Jin” (Dongguan Mayor Li Yuquan:  1 Billion RMB Establishes Reform Promoting Specialized Fund). (In Chinese.) Nanfang Wang.

Chapter 7

Somethin g  New Where Do New Industries Come From? Maryann P. Feldman and Sam M. Tavassoli

1 Introduction The question of why some places grow and prosper while other similar places do not is a fundamental question in both the academic literature and in policy circles. This is seen most notably for radical innovations that involve a wholesale restructuring of economic and social activity. Ever since Schumpeter (1911), academics have explored the origins of new industries and the relationship of technological breakthroughs and entrepreneurial activities to economic growth. Schumpeter identified long cycles of economic growth attributable to basic inventions that create surges of investment as the economic potential of the new discovery becomes known and complementary activities coalesce in close proximity. Specific places or communities are associated with each of the waves of Freeman’s (1974) industrial revolutions:  textile production, steam power and railroads, electricity, mass production, and microelectronics. From Manchester, during the Industrial Revolution to current Silicon Valley, the pronounced clustering of innovation provides great potential for growth at the formative early stages and a means to harness economic growth for a local area that is able to capture that activity. The argument that new industries can transform regions and enhance economic growth is not new. It dates back at least to Schumpeter’s notion of “creative destruction,” where he explored the origins of new industries and the impacts of technological discoveries on economic growth (Schumpeter 1942). Alas, like so many after him, Schumpeter focused on the nation and ignored the more local concentration of new industries. Our concern in this chapter is about how industries take root to transform places. Our focus is on a set of activities that did not exist previously in the form that they currently manifest. Our main aim is on understanding the forces required for an industry to form—what the literature says is needed to go from an idea or single product

126    The Concept of Local Competitiveness to an industry, defined as an aggregation of a set of related firms devoted to a common productive pursuit. Following Schmookler’s scissors, we argue that we know a great deal about push factors and the ways in which scientists or user inventors create knowledge and act toward their commercialization. The literature has explored entrepreneurship as a creative force. We know less about what Schmookler would call pull factors that would need to be in place to create a technological change or the realization of a new industry and subsidiary activities. The focus of this chapter is on the question of how new industries originate in places. There is often confusion between the process of diffusion and the locational factors that give rise to early-stage creative discovery. There is a long and distinguished literature that considers the diffusion of ideas, for example in hybrid corn (Griliches 1957). Diffusion is important, as it influences the general uptake and implementation of ideas across geography but it is a different process than our focus here. We advance the argument that the creation of new industries is a process that has inherently geographic features. Something new is created out of prior knowledge (Neffke, Henning, and Boschma 2011) but a more complex process is required to develop an industry and reap the economic benefits. This chapter is organized as follows. Section 2 argues that new industries are important for economic growth as they have potential to transform old industries and regions. Section 3 elaborates on the main question of this chapter, that is, where new industries come from, by focusing on why some places are able to create new industries while other similar places are not. Section 4 discusses the empirical challenges to study new industries, while section 5 discusses the theoretical challenges for that matter. Finally, section 6 provides some future agenda for research in the topic.

2  The Transformative Potential of New Industries Adam Smith, in The Wealth of Nations, asks the question of why England grew while the rest of Europe stagnated. Smith wrote at the time of the Industrial Revolution focusing on the division of labor. His famous adage that the degree of specialization was limited by the extent of the market is only one of many lessons. Specialization allows for economies of scale to develop and transferred production to firms as a more efficient production unit. Yet firms also aggregated together to form industries and England was creating industries from skilled trades and craft production. The Industrial Revolution transformed the production of textiles and firms located along water arteries as their source of power but with a high degree of localization. The tendency for industry to colocate and become specialized in specific geographic locations persisted, as noted by Alfred Marshall (1890). Most interestingly, the 1900 US Census of Manufacturers reports of the localization of industries in cities (Merriam 1902,table CXXXVIII),

Something New   127 noting that “in some cases the causes are apparent, while in others there is a variety and complexity of causes that makes explanation of this phenomenon a very difficult matter.”1 Geographic investigation fell out of favor with the rise of neoclassical economies, only to be revitalized as the new economic geography in the 1990s. We have a good understanding of the reasons for the geographic clustering of innovation and for factors that promote the clustering of established industries (Audretsch and Feldman 1996). What is still missing is an understanding of reasons that industries develop in some places and why other places are able to generate innovation but not able to capture industrial development. Innovation exists along a continuum that is bracketed by the small incremental improvements weighing heavily on one side and major revolutionary discoveries as a rare event on the other. Most innovations are incremental. Some innovations are arguably so radical that they are termed revolutionary, as in the Industrial Revolution, and involve a wholesale restructuring of economic and social activity. The literature notes that the concept of major revolutionary discoveries driving economic growth has persisted over time in a variety of different theoretical conceptualizations. For example, Nelson and Winter (1982, 257) use the term technological regime to describe “the frontier of achievable capabilities along a complementary set of research trajectories” as the primary drivers of economic growth. Relatedly, Freeman and Perez (1988) define a “techno-economic paradigm” that has widespread consequences for an economy and define platforms that create opportunity for profitable investment in a large set of related innovation, called “carrier branches.” Scholars more recently have expanded on these ideas and focused on the concept of general purpose technology (GPT) (Helpman 1998; Lipsey, Carlaw, and Bekar 2005). According to Lipsey, Carlaw, and Bekar (2005, 96), a GPT is “a single technology, recognizable as such over its whole lifetime that initially has much scope for improvement and eventually comes to be widely used, to have many uses, and to have many spillover effects.” No matter what the term used, the basic idea is that radical breakthroughs have great potential for the economic transformation of place is pervasive and one of the foundations of economic development. GPTs are radical innovations that act as platforms for complementary innovations and hence motivate subsequent incremental innovations, which diffuse in a broad array of established industrial sectors. This eventually leads to transformation of the industry and regions. This way GPTs trigger the emergence of new industry (Hall and Trajtenberg 2004; Lipsey, Carlaw, and Bekar 2005; Bresnahan 2010; Feldman and Yoon 2012). For instance, Feldman and Yoon (2012) showed that the Cohen-Boyer rDNA technique, as a GPT, created substantial new opportunities for systematically searching large protein molecules, triggering the emergence of the biotechnology industry, as a new industry. It is worthy to note that a GPT, in principle, is defined in the same way as a basic innovation or paradigm in the evolutionary tradition (Verspagen 2005). Moreover, three common characteristics of GPTs are identified in the literature: technological complementarity, 1 

The authors thank Andrew Reamer for calling our attention to this source.

128    The Concept of Local Competitiveness applicability, and discontinuity (Hall and Trajtenberg 2004; Lipsey, Carlaw, and Bekar 2005; Feldman and Yoon 2012). New industries may be composed of:  either de alio (large, established firm diversified from related industry) or de novo (new, independent firms) entrants, or both (Khessina and Carroll 2008). Researchers have examined several GPTs in detail. Some of the studies GPTs are in Freeman’s original waves, while more recent studies have gone even beyond such categorization. Starting with the former, some valuable work has been done to examine in detail the GPTs that were seen already in Freeman’s waves. Prominent examples are steam power and electricity (Bresnahan 2010). Other studies examined more recent GPTs, such as biotechnology (Zucker, Darby, and Brewer 1998; Stephan, Audretsch, and Hawkins 2000; Feldman, Kogler, and Rigby 2013), Internet (dot-com firms) (Goldfarb, Kirsch, and Miller 2007), and modern optics (Feldman and Lendel 2010). It is argued that there is a lag between invention of a GPT and its diffusion across application sectors. Moreover, the diffusion rate of GPTs across application sectors has not been the same, because of differences in the nature of GPTs. While some GPTs, such as the Internet, diffuse quickly, others, such as steam, diffused very slowly (Bresnahan 2010). Even further, as Griliches (1957) showed in hybrid corn, a GPT’s diffusion rate can differ across various application sectors in which it diffused. Generally speaking, a major difficulty in studying emerging technology is the limitation of current industrial categories and patent classes. Given the ambiguity in definition, which reflects the evolving nature of the industry, the Standard Industrial Classification (SIC) Codes and North American Classification System (NAICS), which are based on existing industries, are not reliable (Feldman and Lendel 2010; 2011). We will come back to this issue in section 3. Nevertheless, researchers have adopted various methods to study the emergence of new industries. For instance, in the study of modern optics, Sternberg (1992) used directories or membership listings of industry associations (Society of Photographic Instrumentation Engineers, SPIE) to define the industry. Hassink and Wood (1998) interviewed experts at industry associations and universities. Hendry and Brown (2006) relied on the directory of the organization Photonics Spectra to provide a survey frame. Feldman and Lendel (2010; 2011) investigate the geography of optical science by relying on companies that identify themselves as working on optics on the basis of their voluntary membership in the Optics Society of America (OSA). Forbes and Kirch (2011) emphasize the value of historical archives and state that they are a currently underexploited resource for the study of emerging industries. Saying so, they point to an alternative way of studying the industry emergence within a larger historical context that extends backward to include some period of time prior to industry emergence.2 Shearman and Burrell (2007) describe medical lasers in UK, however, in a more descriptive way, without any explicit methodology to define the new industry. Steven Klepper’s works offer invaluable insight in understanding the emergence of new industries. In his earlier works, he pointed out the role of chance in the emergence 2 

An example (in another industry) is the historical analyses of the disk drive industry (Christensen 1993).

Something New   129 of new industries (Klepper and Graddy 1990). In his later works, he specifically emphasizes organizational reproduction and heredity as the primary forces underlying the emergence of clustering in several industries, that is, the automobile industry in Detroit, integrated circuits in Silicon Valley, the tie industry in Akron, and the cotton garment industry in Bangladesh (Klepper 2010; 2011). This is what has been called “neoeconomics,” which aims to trace the intellectual and geographic heritage of the (founders of) new firms that entered the new industries (Klepper 2011). It is found that each of the studied industries had at least one successful diversifier. But why those diversifiers happened to be in those regions remains still an open question.

3  Empirical Challenges to Studying New Industries in Real Time Understanding of new industries is limited by many empirical problems inherent in studying new industries. The most obvious problem is the limitation of existing classification schemes: much of our understanding of industrial activity relies on industrial classification schemes that are inherently backward looking and conservative. The standard schemes, either SIC, NAISC, or patent classification, were designed to describe established activities. Anyone who has ever done time series work appreciates that stability in these categories is desirable, but they mask emerging or early stage activity. Moreover, once firms change their focus, it is highly unlikely that they will change their classification with government agencies, as there is no incentive to make updates. Newer activities emerge at the boundaries of several existing categories. Technological breakthroughs often stem from combining elements of previously unrelated technologies in what Wietzman (1998) calls recombinant growth. While combining previously unrelated domains is more likely to fail, when successful such innovations are also more likely to lead to whole new applications and functionalities, which span new technological trajectories for their further improvement (Dosi 1982). An example is opto-electronics, which at least sounds like the combination of two existing categories. But consider other industries like green technology, which can cover an entire range of activities and may attract firms to self-identify with an emerging field whether this is warranted or not. Research has used industrial directories or membership organizations to capture new activity. The US Patent and Trademark Office also issues change orders to create new categories. Feldman, Kogler, and Rigby (2013) use the creation of a new patent class for recombinant DNA to study its use and diffusion. Strumsky and coauthors (2012) provide a review regarding the use of patent technology codes to study technological change, and point to their usefulness in tasks that relate to the identification of technological capabilities or the definition of technology spaces, or as an indicator of the arrival of technological novelty. For empirical work, when a set of USPTO technology codes is revised, the USPTO reviews all granted patents and

130    The Concept of Local Competitiveness reclassifies those meeting the criteria of the new codes. This provides the researcher with a consistent set of all of the patents that use a specific technology. Another reason emerging industries are difficult to study is the prediction problem: it is often hard to identify emerging industries until after they have taken off and matured (MacMillan and Katz 1992; Forbes and Kirch 2011). While many pundits announce the “next big thing,” they are typically wrong or their time frames are off by decades. Indeed, most of the empirical literature focuses on incremental innovation, because most innovations are small, incremental improvements that are easier to observe. Emerging industries are usually the outcome of a radical innovation rather than incremental ones. By the time a technology become accepted as radical, it is left to historians to carefully construct the narrative (Murmann 2003). There is great opportunity for ex post bias: once an emerging industry is successful, we are left with after-the-fact evaluation and analysis of the historical record that can interpret the results as inevitable or smooth over the difficulties. For example, solar panels have been emerging for 40 years yet even now problems with reliability limit the acceptance of the industry. Many potential emerging industries based on technological breakthroughs failed to grow and become mature, leaving both academic inquiry and the public good worse off. There are many places that were possible venues for the development of a new technology, but there is scant literature on these counterfactuals, except in cases where an industry was expected, for example, Leslie on New Jersey and Orsigeno on northern Italy. Finally, there is the system problem for emerging technology. For example, Kurlansky (2012) writes about Clarence Birdseye, who discovered a method for freezing vegetables. As always, the invention is the easy part to describe and characterize. But consider in this case: what was the innovation? To sell frozen vegetables required changes in the retail and distribution system, culminating with the supermarket concept. The interrelationships are complex. Moreover, the literature tends to focus on consumer products or products that are directly distributed to consumers, but the more profound emerging technologies may be systemic.

4  Theoretical Challenges to Studying New Industries The complexity in the phenomenon of an emerging industry requires rare interdisciplinary cooperation between management, economists, organizational sociologists, and economic historians (Forbes and Kirsch 2011). To this mix we add economic geographers to contribute an investigation of the attributes of place. The academic discussion often centers around product life cycle and industry evolution, positing different locations for different activities in a most deterministic manner. Duranton and Puga (2001) argue that new products are developed in diversified

Something New   131 nursery cities, trying processes borrowed from different activities. On finding their ideal process, firms switch to mass production, and the product becomes mature. The firm relocates to specialized cities where production costs are lower. Jacobian externality (diversity) has its effect on productivity of plants during early phases of industry life cycle, while Marshallian specialization has its effect in later stages (Neffke, Henning, and Boschma 2011). However, our problem is more nuanced: we would like to understand how characteristics of regions contribute to the new industries. In contrast to the life cycle of stages approach, Boschma and Van der Knaap (1999) propose the theory of open windows of location opportunity to explain why it is uncertain and unpredictable where new high-technology industries will emerge in space. First, new high-tech industries reflect a high rate of discontinuity because they place new demands on their local production environment. Second, because of such mismatches, new high-tech industries depend on their creative ability to generate or attract their own favorable production environment. Third, chance events may have a considerable impact on the place where new industries emerge. Braunerhjelm and Feldman (2006) describe five stages of industrial cluster genesis. This process begins with the nascent stage, when the first signs of a new industry form, either through technological discoveries or another source of opportunity to which entrepreneurs respond. The second stage is the emergent stage, when products begin to appear on the market, followed by a takeoff stage, when a dominant design emerges and the rate of growth for the industry starts to attract attention because its potential is well established. For these first three stages, location is critical; firms benefit from location in a cluster. When an industry is new and becoming defined, tacit and sticky knowledge requires geographic proximity and the ability to tap a variety of external sources of technical, market, and financial knowledge. During these early stages there is a high rate of new firm market entry and exit as firms experiment with the new opportunity and learn from their collective mistakes. With the low level of market concentration, the market share of individual firms is also volatile. Ironically, the most successful clusters are associated with lower overall survival rates of new firms. The logic is that the cluster is made more vibrant through this evolving process, which suggests that efforts to save marginal firms are not likely to be associated with higher innovativeness and growth of surviving firms. During the takeoff phase certain places become known as hot or as the place to be for a certain industry. This is a critical juncture, representing the point when the cluster can solidify its lead. This is also the point at which public policy can play the most decisive role in creating conditions conducive to entrepreneurial endeavors and the success of existing small businesses. The last two stages in the life cycle model are maturity and decline. As industries mature, their knowledge based is codified and oriented toward process innovation and incremental product innovation. The opportunities for growth are low unless there is the start of a new industry along a technological trajectory. Some of those clusters that have averted the final stage through diversification include turbine manufacturers in Ohio that were able to literally turn their automobile product on its side to innovate to

132    The Concept of Local Competitiveness meet demand for wind power. Akron redefined itself as a center for polymer research, based on scientific research from corporate R & D labs working with the University of Akron. Also, the computer industry in Silicon Valley has evolved into Internet companies who seek to define new application and service delivery modalities.

5  The Regional Context of the Creation of New Industries New technologies and new industries, while offering potential for economic growth, do not emerge fully developed, but begin rather humbly as scientific discoveries, often made in academic laboratories. At the time of discovery the discovery’s commercial potential is not known and only a few experts may appreciate its significance. Translating the discovery into commercial activity and realizing its economic potential entails a process that has a strong geographic component. Moreover, it requires taking the technology out of the lab and into a community and building companies. What matters more than resources or initial conditions are the social dynamics that occur within a place and define a community of common interest around a nascent technology or emerging industry (Freeman 1979). The important analytical issue is how consensus is achieved, the conditions under which social dialogue takes place, and the appropriate role of governance in creating conditions conducive to the development of industry (Dawley 2014). Also important is who participates in this dialogue and the degree to which consensus-building processes involve outside academic and entrepreneurial circles. Geography provides a platform to organize new industries. This is why we believe that only some regions can host the emergence of new industries, precisely because they have some regional characteristics that trigger the emergence of new industries, while other regions don’t. Yet this conceptualization seems too deterministic. Jacob Schmookler (1966) demonstrated that demand-pull factors were also important:  the more intense the demand, the more creative groups and individuals were drawn to work on an unsolved problem and more patentable inventions they generated. Struggling to reconcile the prevailing knowledge-push hypothesis with the demonstrated importance of demand-pull, Schmookler argued that both could be important, just as it takes two blades of a scissors to cut paper. Schmookler argues that demand might originate in a quite different industry. The importance of considering demand-pull has been reconfirmed in recent survey (Di Stefano, Gambardella, and Verona 2012). Knowledge spillovers have been conceptualized as science-push, yet a flow of knowledge requires a recipient to demand or absorb the new knowledge. The idea of Schmookler’s scissors argues that innovation (leading to emergence of new industries) requires both market- (technology-) pull and science-push. Recently these questions

Something New   133 are raised particularly in case of GPTs (Bresnahan 2010, 764). Callon, Courtial, and Laville (1991) showed that both science-push and technology-pull theories are needed to explain the interaction dynamics of research in the field of polymer chemistry. Peters and coauthors (2012) showed that, in the field of solar photovoltaic modules in 15 OECD countries, while only domestic science-push policies foster innovative output, both domestic and foreign demand-pull policies trigger greater innovative output. Zhao and Guan (2013) used a sample of 20 leading universities active in nanotechnology and showed that nanotechnology is currently a scientific-push rather than the market-pull industry, which has led to limited creation of an industry. This section examines the regional-specific science-push as well as market-pull factors necessary for the emergence of new industries.

5.1  Regional-Specific Push Factors Knowledge spillovers, or the nonpecuniary transfer of knowledge, are a major reason why innovation clusters spatially. Knowledge spillovers are subtle, as individuals observe one another, copying ideas, iterating, and incrementally building up the stock of knowledge with new ideas, components, and design elements. These spillovers are what economists term an externality: they exist because knowledge, once created, is impossible to value and price. Such knowledge spillover usually happens when at least one of the agglomeration economies is in place, such as urbanization, localization, and diversity externalities. The most interesting aspect is that knowledge is subject to increasing returns, meaning that its value increases as more people use it. In addition, knowledge spillovers provide serendipity, which suggests unexpected outcomes. If an innovator knows what information is required, it can search for a source. Knowledge spillovers promote and facilitate new and unexpected ideas. Of course, places are not equal in their ability to benefit from knowledge spillovers. The main reason is that places are not equally endowed when it comes to various specific kinds of agglomeration economies. Urbanization economies are due to the actual size of a place itself, and doubling the size of a city generally creates a productivity increase for firms in the range of 3–8 percent (Sveikauskas 1975; Segal 1976; Tabuchi 1986). Bettencourt and coauthors (2007) find that large metropolitan areas have more inventors proportionally than do smaller cities, and generate more patents. This suggests that an increasing return to patenting exists as a function of city size and therefore that smaller places need to work harder cultivating inventive activity or find strategies to develop ideas within their local proximity. Localization economies are attributed to the concentration of a specific industry in a particular place. Three specific benefits are highlighted in this context: the spatial concentration of input-output linkages between buyer and supplier networks, the character of local labor pools with a high degree of specialization, and embodied knowledge spillovers that facilitate the diffusion of technical knowledge (Marshall 1920). When localization and urbanization economies are investigated together, the results point to

134    The Concept of Local Competitiveness a stronger impact of the localization, indicating that a spatial concentration of a specific industry within a smaller city can be competitive (Henderson 2003; Rosenthal and Strange 2003). However, Marshall (1920, 273–74) recognized the inherent risk that industry localization could make a place vulnerable to external shocks in demand. Jacobs (1969), on the other hand, points to the significance of diversity as a source of external inputs that boost creativity and subsequently spur economic activity. The main argument is that diversity enhances the cross-fertilization of ideas between industrial sectors, through facilitating knowledge spillovers. This leads to the differentiation, diversification, and transformation of the underlying processes of production, which in turn directly influence the creation of new industries. The idea of Jacobian externalities is further refined by showing that existence of “related” sectors (not unrelated sectors) within a region, that is, related variety, leads to growth of regions (Frenken, Van Oort, and Verburg 2007; Boschma and Iammarino 2009; Boschma, Minondo, and Navarro 2012). Jacobian externality also shows its positive effect on regional innovation (Feldman and Audretsch 1999; Ejermo 2005; Antonietti and Cainelli 2011 ; Tavassoli and Carbonara 2014). Diversity and specialization might play very different roles in different industries, especially if one considers the stage of the industry life cycle. Jacobian externality (diversity) has its effect on the productivity of plants during the early phase of the industry life cycle, while Marshallian specialization has its effect in later stages (Neffke, Henning, and Boschma 2011). Diversity and specialization might also play very different roles in terms of the types of innovation that result. The potential to generate radical, disruptive innovative output should be greater when very diverse knowledge bases are combined, while incremental innovation favors the specialized knowledge that accompanies industry localization (Schumpeter 1942; Castaldi, Frenken, and Los 2013). The relationship between the relative importance of localized specialization and diversity to economic growth is best characterized as a continuum from evolutionary to revolutionary innovation. Lest places become too inward looking, the most successful clusters benefit from what Bathelt, Malmberg, and Maskell (2004) call local buzz and global pipelines. Local buzz indicates that there is energy and excitement around the activity in the place. However, much depends on the ability of local firms to export and trade globally and to tap the best expertise and knowledge in the world.

5.2  Technology-Pull Factors Market demand or technology-pull factors certainly play a role in the emergence of new industries in a region. The literature has paid less attention to these factors, but in this section we highlight three demand-side mechanisms: supplier relationships to dominant industries; value chain deepening, especially through outsourcing; and finally government procurement. Clusters burgeon through self-organization as resources are attracted and subsequently accumulate (Tavassoli and Tsagdis 2014). This in turn cultivates a buzz around the potential of opportunities.

Something New   135 Any new industry or new activity requires supplies that provide additional opportunity. The presence of suppliers or their subsequent development may be important to the cost structure of an industry and to its developing advantage. Consider, for example, the California Gold Rush of the 1890s, which provided a certain opportunity. But the discovery of gold, just as with any technological discovery also attracted suppliers. In this case, Levi Strauss invented the denim blue jean as the perfect clothing for prospecting. The demand for equipment created an opportunity—indeed it is said that there is more money to be made selling picks and prospecting supplies than in digging for gold. While the Gold Rush is distant history, Levi is a global brand still headquartered in San Francisco. Indeed, long after the Gold Rush finished, the company and product has endured to become an icon of American style and enigmatic of the city’s sense of style. This example demonstrates the serendipitous nature of opportunity, the importance of entrepreneurs recognizing and building companies within the dynamic environment of a clustered space. Market forces should induce suppliers who recognize the opportunity to flock to that location; however, information about opportunities is often not broadly disseminated. Intellectual property and business development attorneys, certified public accountants, and marketing specialists are often difficult to attract to new locations. Even when this highly skilled human capital exists in an area, the resources to support these specialists are notably scant and take time to develop. The lack of this type of expertise may deter industry growth. Although emerging entrepreneurial firms depend on their expertise and resources, this support network demands considerable compensation. Technological proximity to the existing industrial structure of the region plays an important role in determining the emergence of new industries (e.g., Klepper 2007; Boschma and Wenting 2007; Buerger and Cantner 2011; Bathelt, Munro, and Spigel 2013). The main finding of these studies is that new industries and technologies do not start from scratch but emerge out of regional structures that provide related assets and competences. In addition, there are systematic studies (though still few) that provide empirical evidence for such argument (see Neffke, Henning, and Boschma 2011; Boschma et al. 2013). Yet there is no mechanism specified. It seems likely that a discovery or idea that serves the technological needs of existing industries or adapts new technology to existing uses will potentially result in a new industry. User-based innovation is certainly driven by demand (Von Hipple 1988). Privatesector procurement is likely to produce more incremental innovation, but there are certain historical examples like Eli Whitney’s cotton gin where a user need revolutionized an industry. The cotton gin is a mechanical device that removes the seeds from cotton, a process that had previously been extremely labor-intensive. Whitney invented the cotton (en)gin(e) while visiting the Green plantation, which was on the brink of insolvency due to the difficulty of harvesting cotton.3 Whitney did not make money on the cotton

3 

http://web.mit.edu/invent/iow/whitney.html. The cotton gin not only revolutionized cotton harvesting, but also, unfortunately, made slavery profitable.

136    The Concept of Local Competitiveness gin because the design was easily copied and turned his attention to the invention and manufacture of muskets with more secure and lucrative government contracts. Another way to create a demand in the region is through public procurement. Public procurement is a demand-side innovation policy instrument (Rolfstam 2013). Edquist, Hommen, and Tsipouri (2000) consider procurement as a special case of user-producer interaction. Procurement of innovation becomes a process where the social and collaborative aspects are stressed as users work with suppliers to tailor innovation to meet the users’ specifications. The end result is that a new product has the attributes that customers seek and is ready to match market demand. If the product is not under IP protection by the user but more generally available, we might expect that such products would diffuse more quickly because of the understanding of the attributes that users look for in the products. The role of government procurement has been demonstrated by Mazzucato (2013), who argues that government has also shaped and created markets, paving the way for new technologies and sectors. By reducing risk, the government encourages the private sector to venture in to expand demand. From the Internet to cell phones, Mazzucato demonstrates the role that government played as a lead user. The adaptation of government procurement to more general use is called dual use (Alic et al. 1992), and there are a variety of factors that condition the potential for adoption for dual use (Cowan and Foray 1995). Government is an important lead user because of the amount of resources at its command. Most government procurement is for defense, which has led Ruttan (2006) to ask the provocative question, is war necessary for economic growth? Ruttan (2006) shows that government’s military procurement has been crucial for the emergence of six major GPTs in the United States, that is, nuclear power, airplane technologies, production systems, space technologies, IT, and the Internet (Ruttan 2006). As places around the world attempt to capture new industries, there is little discussion about governance beyond limiting public-sector involvement to create a favorable business climate. Of course, new technologies may pose environmental, health, and safety risks felt immediately at local levels where facilities are located, creating a need for local regulation and oversight. Thus there is a presupposed tension between creating a favorable business climate and protecting local citizens. However, an alternative view is that participatory democracy and open decision-making—that is, widening the social dialogue—may lead to more socially and economically optimal outcomes (Freeman 1979). Regulation may limit firm liability by providing standards and expectations, making a location more attractive for an industry than areas that lack regulation (Lowe and Feldman 2008). Moreover, the process of public discussion and debate about regulating the industry may inform citizens and local officials about the potential of the industry and what it requires to move forward. Thus, process may be more important than simple adoption of a regulation or advocating for a recipe that worked in another location (Lowe and Feldman 2008). Few papers consider the role of community formation around an emerging technology. Lowe and Feldman (2008) researched the origins of the concentration of the biotech industry in Cambridge, Massachusetts, specifically Kendall Square, arguably the

Something New   137 most densely concentrated agglomeration of private firm activity in the world. Their investigation uncovered a contentious early debate that centered on fear of genetic engineering, the early name used for the technology. Indeed, the city of Cambridge passed regulation in 1976 that was more onerous than national standards at the time and engendered great discussion and notoriety. Berkeley, California, another jurisdiction where significant academic research offered commercial opportunity, enacted identical regulation, yet no biotechnology industry took root there. Start-ups from the University of California have instead dispersed to other East Bay communities. Arguably what is different is the process of participation and community building that occurred in Cambridge but was notably lacking elsewhere.

6 Future Agenda New industries certainly originate in place and are defined by local attributes and prior activity while sparked by fruitful serendipitous collisions. In this chapter we offer a series of challenges to study of new industries, not to discourage scholars but to lay out a roadmap for future understanding. Theory is best informed by appreciating empirical examples, and there are enough interesting case studies to provide a corpus of knowledge. Trying to predict the future is a perilous exercise, yet every time government funds are invested in a new technology this is precisely what is required. Policy analysts need social scientists to begin to examine emerging technologies and to understand the factors that promote or inhibit their realization.

References Alic, L. Branscomb, H. Brooks, A. B. Carter, and G. L. Epstein. 1992. “Beyond Spinoff: Military and Commercial Technologies in a Changing World.” Boston: Harvard Business School. Antonietti, R., and G. Cainelli. 2011. “The Role of Spatial Agglomeration in a Structural Model of Innovation, Productivity and Export.” Annals of Regional Science 46, 577–600. Audretsch, D. B., and M. P. Feldman. 1996. “R&D Spillovers and the Geography of Innovation and Production.” American Economic Review 86 (3), 630–40. Bathelt, H., A. Malmberg, and P. Maskell. 2004. “Clusters and Knowledge: Local Buzz, Global Pipelines and the Process of Knowledge Creation.” Progress in Human Geography 28 (1), 31–56. Bathelt, H., A. K.  Munro, and B. Spigel. 2013. “Challenges of Transformation:  Innovation, Re-bundling and Traditional Manufacturing in Canada’s Technology Triangle.” Regional Studies 47 (2), 1111–30. Bettencourt, L. M., J. Lobo, D. Helbing, C. Kühnert, and G. B. West. 2007. “Growth, Innovation, Scaling, and the Pace of Life in Cities.” Proceedings of the National Academy of Sciences 104 (17), 7301–306. Boschma, R., and S. Iammarino. 2009. “Related Variety, Trade Linkages, and Regional Growth in Italy.” Economic Geography 85 (3), 289–311.

138    The Concept of Local Competitiveness Boschma, R., and G.  van der Knaap. 1999. “New High-Tech Industries and Windows of Locational Opportunity: The Role of Labour Markets and Knowledge Institutions during the Industrial Era.” Geografiska Annaler 81 (2), 73–89. Boschma, R., A. Minondo, and M. Navarro. 2012. “Related Variety and Regional Growth in Spain.” Papers in Regional Science 91 (2), 241–56. Boschma, Ron, Asier Minondo, and Mikel Navarro. 2013. “The Emergence of New Industries at the Regional Level in Spain:  A  Proximity Approach Based on Product Relatedness.” Economic Geography 89 (1), 29–51. Boschma, R.  A., and R. Wenting. 2007. “The Spatial Evolution of the British Automobile Industry: Does Location Matter?” Industrial and Corporate Change 16, 213–38. Braunerhjelm, P., and M. P.  Feldman. 2006. “The Genesis of Industrial Clusters.” In P. Braunerhjelm and M. P.  Feldman, eds., Cluster Genesis:  Technology-Based Industrial Development. Oxford: Oxford University Press. Bresnahan, T. 2010. “General Purpose Technologies.” In B. Hall and N. Rosenberge, eds., Handbook of the Economics of Innovation, vol. 2. Amsterdam: North-Holland. Buerger, M., and U. Cantner. 2011. “The Regional Dimension of Sectoral Innovativeness: An Empirical Investigation of Two Specialized Suppliers and Two Science-Based Industries.” Papers in Regional Science 90, 373–93. Callon, M., J. Courtial, and F. Laville. 1991. “Co-word Analysis as a Tool for Describing the Network of Interactions between Basic and Technological Research: The Case of Polymer Chemistry.” Scientometrics 22 (1), 155–205. Castaldi, C., K. Frenken, and B. Los. 2013. “Related Variety, Unrelated Variety and Technological Breakthroughs: An Analysis of US State-Level Patenting.” Eindhoven Centre for Innovation Studies Working Paper 13. Christensen, C.  M. 1993. “The Rigid Disk Drive Industry:  A  History of Commercial and Technological Turbulence.” Business History Review 67 (4), 531–88. Cowan, R., and D. Foray. 1995. “Quandaries in the Economics of Dual Technological and Spillovers from Military to Civilian Research and Development.” Research Policy 24 (6), 851–69. Dawley, S. 2014. “Creating New Paths? Offshore Wind, Policy Activism, and Peripheral Region Development.” Economic Geography 90 (1), 91–112. Di Stefano, G., A. Gambardella, and G. Verona. 2012. “Technology Push and Demand Pull Perspectives in Innovation Studies:  Current Findings and Future Research Directions.” Research Policy 41 (8), 1283–95. Dosi, G. 1982. “Technological Paradigms and Technological Trajectories.” Research Policy 11, 147–62. Duranton, Gilles, and Diego Puga. 2001. “Nursery Cities: Urban Diversity, Process Innovation, and the Life Cycle of Products.” The American Economic Review 9 (5): 1454–77. Edquist, C., L. Hommen, and L. J. Tsipouri, eds. 2000. Public Technology Procurement and Innovation, vol. 16. Springer Science & Business Media. Ejermo, O. 2005. “Technological Diversity and Jacobs’ Externality Hypothesis Revisited.” Growth and Change 36 (2), 167–95. Feldman, M.  P., and D. Audretsch. 1999. “Innovation in Cities:  Science-Based Diversity, Specialization and Localized Competition.” European Economic Review 43, 409–29. Feldman, Maryann P., Dieter F. Kogler, and David L. Rigby. 2013. “rKnowledge: The Spatial Diffusion of rDNA Methods.” Papers in Evolutionary Economic Geography, # 13.11. Feldman, M., and I. Lendel. 2010. “Under the Lens: The Geography of Optical Science as an Emerging Industry.” Economic Geography 86 (2), 147–71.

Something New   139 Feldman, M., and I. Lendel. 2011. “The Emerging Industry Puzzle: Optics Unplugged.” In H. Barthlet et al., eds. Beyond Territory: Dynamic Geographies of Innovation and Knowledge Creation, Chapter 6. London: Routledge. Feldman, M. P., and J. W. Yoon. 2012. “An Empirical Test for General Purpose Technology: An Examination of the Cohen-Boyer rDNA Technology.” Industrial and Corporate Change 21 (2), 249–75. Forbes, D. P., and D. A. Kirsch. 2011. “The Study of Emerging Industries: Recognizing and Responding to Some Central Problems.” Journal of Business Venturing 26, 589–602. Freeman, C. 1974. The Economics of Industrial Innovation. Harmondsworth: Penguin. Freeman, C. 1979. “The Determinants of Innovation: Market Demand, Technology, and the Response to Social Problems.” Futures 11 (3), 206–15. Freeman, Christopher, and Carlota Perez. 1988. “Structural Crises of Adjustment, Business Cycles and Investment Behaviour.” In C. Freeman, R. Nelson, G. Silverberg, and L. Soete, eds. Technical Change and Economic Theory. London: Pinter. Frenken, K., F.  van Oort, and T. Verburg. 2007. “Related Variety, Unrelated Variety and Regional Economic Growth.” Regional Studies 41 (5), 685–97. Goldfarb, B., D. Kirsch, and D. A. Miller. 2007. “Was There Too Little Entry during the Dot Com Era?” Journal of Financial Economics 86, 100–144. Griliches, Z. 1957. “Hybrid Corn: An Exploration in the Economics of Technological Change.” Econometrica 25 (4), 501–22. Hall, B. H., and M. Trajtenberg. 2004. “Uncovering GPTs with Patent Data.” National Bureau of Economic Research Working Paper No. 10901. Hassink, R., and M. Wood. 1998. “Geographic ‘Clustering’ in the German Opto-electronics Industry: Its Impact on R&D Collaboration and Innovation.” Entrepreneurship and Regional Development 10, 277–96. Helpman, E. 1998. General Purpose Technologies and Economic Growth. Cambridge, MA: MIT Press. Henderson, V. J. 2003. “Marshall’s scale economics.” Journal of Urban Economies 53, 1–28. Hendry, C., and J. Brown. 2006. “Dynamics of Clustering and Performance in the UK Optoelectronics Industry.” Regional Studies 40, 707–25. Jacobs, J. 1969. The Economy of Cities. New York: Random House, Inc. Khessina, O. M., and G. R. Carroll. 2008. “Product Demography of De Novo and De Alio Firms in the Optical Disk Drive Industry, 1983–1999.” Organization Science 19 (1), 25–38. Klepper, S. 2007. “Disagreements, Spinoffs, and the Evolution of Detroit as the Capital of the U.S. Automobile Industry.” Management Science 53, 616–31. Klepper, S. 2011. “Nano-economics, Spinoffs, and the Wealth of Regions.” Small Business Economics 37, 141–54. Klepper, S., and E. Graddy. 1990. “The Evolution of New Industries and the Determinants of Market Structure.” Rand Journal of Economics 21 (1), 27–44. Kurlansky, M. 2012. Birdseye: The Adventures of a Curious Man. New York: Random House. Lipsey, R. G., K. Carlaw, and C. T. Bekar. 2005. Economic Transformations: General Purpose Technologies and Long-Term Economic Growth. Oxford: Oxford University Press. Lowe, N., and M. Feldman. 2008. “Constructing Entrepreneurial Advantage:  Consensus Building, Technological Uncertainty and Emerging Industries.” Cambridge Journal of Regions, Economy and Society 1 (2), 265–84. MacMillan, I., and J. Katz. 1992. “Idiosyncratic Milieus of Entrepreneurial Research: The Need for Comprehensive Theories.” Journal of Business Venturing 7, 1–9.

140    The Concept of Local Competitiveness Marshall, A. 1920. The Principles of Economics. London: McMillan. Mazzucato, Mariana. 2013. The Entrepreneurial State:  Debunking Public vs. Private Sector Myths. Anthem Press. Merriam, William R. 1902. The Census of Manufactures: Twelfth Census of the United States, Taken in the Year 1900. Washington, D.C: United States Census Office. Murmann, P. 2003. Knowledge And Competitive Advantage:  The Coevolution Of Firms, Technology, And National Institutions. Cambridge University Press. Neffke, F., M. Henning, and R. Boschma. 2011. “How Do Regions Diversify over Time? Industry Relatedness and the Development of New Growth Paths in Regions.” Economic Geography 87 (3), 237–65. Nelson, R.  R., and S. G.  Winter. 1982. An Evolutionary Theory of Economic Change. Cambridge: Harvard University Press. Peters, M., M. Schneider, T. Griesshaber, and V. H.  Hoffmann. 2012. “The Impact of Technology-Push and Demand-Pull Policies on Technical Change—Does the Locus of Policies Matter?” Research Policy 41 (8), 1296–1308. Rolfstam, M. 2013. Public Procurement and Innovation. Edward Elgar Publishing. Rosenthal, Stuart S., and William C. Strange. 2003. “Geography, Industrial Organization, and Agglomeration.” The Review of Economics and Statistics 85 (2), 377–93. Ruttan, V. 2006. Is War Necessary for Economic Growth? Military Procurement and Technology Development. New York: Oxford University Press. Schmookler, J. 1966. Invention and Economic Growth. Cambridge: Harvard University Press. Schumpeter, J. A. 1911. Theorie der wirtschaftlichen Entwicklung. Leipzig: Duncker & Humblot. Schumpeter, J. A. 1942. Capitalism, Socialism and Democracy. New York: Harper and Brothers. Segal, D. 1976. “Are There Returns to Scale in City Size?” The Review of Economics and Statistics 58 (3), 339–50. Shearman, C., and G. Burrell. 2007. “New Technology Based Firms and the Emergence of New Industries: Some Employment Implications.” New Technology, Work and Employment 3 (2), 87–99. Stephan, P., D. Audretsch, and R. Hawkins. 2000. “The Knowledge Production Function: Lessons from Biotechnology.” International Journal of Technology Management 19 (1), 165–78. Sternberg, E. 1992. Photonic Technology and Industrial Policy: U.S. Responses to Technological Change. Albany: State University of New York Press. Strumsky, Deborah, José Lobo, and Sander van der Leeuw. 2012. “Using Patent Technology Codes to Study Technological Change.” Economics of Innovation and New Technology 21 (3), 267–86. Sveikauskas, Leo. 1975. “The Productivity of Cities.” The Quarterly Journal of Economics 393–413. Tabuchi, T. 1986. “Urban Agglomeration, Capital Augmenting Technology, and Labor Market Equilibrium.” Journal of Urban Economics 20 (2), 211–28. Tavassoli, S., & Carbonara, N. (2014). The role of knowledge variety and intensity for regional innovation. Small Business Economics, 43(2), 493-509. Tavassoli, S., and D. Tsagdis. 2014. “Critical Success Factors and Cluster Evolution: A Case Study of the Linköping ICT Cluster Lifecycle.” Environment & Planning A, 46, 1425–44.

Something New   141 Verspagen, B. 2005. “Innovation and Economic Growth.” In J. Fagerberg, D. Mowery, and R. R. Nelson, eds., The Oxford Handbook of Innovation. Oxford: Oxford University Press. Von Hipple, E. 1988. Sources of Innovation. Oxford: Oxford University Press. Weitzman, Martin L. 1998. “Recombinant growth.” The Quarterly Journal of Economics 113 (2), 331–60. Zhao, Q., and J. Guan. 2013. “Love Dynamics between Science and Technology:  Some Evidences in Nanoscience and Nanotechnology.” Scientometrics 94, 113–32. Zucker, L., M. Darby, and M. Brewer. 1998. “Intellectual Human Capital and the Birth of U.S. Biotechnology Enterprises.” American Economic Review 88, 290–306.

Pa rt  I I

C R I T IC A L DR I V E R S OF  L O C A L C OM P E T I T I V E N E S S

Chapter 8

L o cal C om peti t i v e ne s s F ostered th rou g h L o cal Insti t u t i ons f or Entreprene u rsh i p Martin Andersson and Magnus Henrekson

1 Introduction Without implying an analogy with the competitiveness of a firm (cf. Krugman 1994), we can think of local competitiveness as the capacity of a local economy to continuously renew its economic base. New technology, innovations, globalization, and changes in the organization of production imply gradual shifts in the nature of competition and in the geography of production (Dicken 2003). Cities and regions that do not respond and adapt to such changing conditions, for example by diversifying and entering new industries, will decline as the industries or activities upon which the local economy was historically built fall behind in the global competition. A prime example is the US city of Detroit. It went from being one of the most prosperous cities in the country in the 1950s, to losing a significant portion of its population and jobs and to ultimately file for bankruptcy in July 2013. One of the main explanations for this development centers around the city’s historical reliance on a single industry, car manufacturing, and its failure to adapt and diversify into new industries. Detroit accordingly declined when its local automotive industry weakened and faced trouble handling fiercer competition from abroad.1 A key issue then concerns the mechanisms by which a local economy adapts and reinvents itself, and what factors influence those mechanisms. The point of departure of this 1  See for instance the article “Anatomy of Detroit’s Decline” published on December 8, 2013 in the New York Times (http://www.nytimes.com/interactive/2013/08/17/us/detroit-decline.html?_r=0).

146    Critical Drivers of Local Competitiveness chapter is that entrepreneurship is a central driver of renewal. After all, the very definition of entrepreneurship emphasizes that it is about individuals and organizations—be they new, old, large, or small—that actively contribute to renewal and change in an economy (Hébert and Link 2006a). Joseph Schumpeter (1934) indeed attributed to the entrepreneur a critical role in processes of industrial evolution and economic dynamics. The entrepreneur, Schumpeter claimed, is particularly important in inducing structural change because she challenges existing economic structures through innovations that disturb current market equilibria. A large empirical research literature has also shown that small entrepreneurial firms and start-ups are important in bringing in new technology and innovations (Baumol 2002; Audretsch 2002; Schneider and Veugelers 2010), in influencing innovation and productivity of incumbents (Aghion et al. 2004; 2009), and in stimulating overall productivity and employment growth (Fritsch and Mueller 2004; Fritsch and Noseleit 2013; Andersson, Braunerhjelm, and Thulin 2012). It follows that “local competitiveness,” as defined above, hinges to a large extent on the environment for entrepreneurship. The central message in this chapter is that a crucial constituent of the environment for entrepreneurship is the institutional setup, because it influences both the supply and direction of entrepreneurial activity. Following North (1990), institutions are understood as “the rules of the game” that determine the incentives for individual effort and for investments in capital (physical and human) and in technology. Institutions can either be formal (laws, regulations, constitutions) or informal (customs, traditions, norms). The role both types of institutions play for entrepreneurship is also recognized by Schumpeter (1934). In discussing what he referred to as the reaction of the “social environment” to entrepreneurship he stated (86–87): This reaction may manifest itself first of all in the existence of legal or political impediments. But neglecting this, any deviating conduct by a member of a social group is condemned, though in greatly varying degrees according as the social group is used to such conduct or not.

It may seem odd to emphasize the subnational scale in the context of institutions. Taxes, labor market regulations, property rights, and other types of institutions are generally determined at the national level. Such institutions therefore show no or very small variations across subnational regions—at least in nonfederal states—which means that they cannot be an important component of the local environment for entrepreneurship. Their influence on entrepreneurship is accordingly often analyzed in cross-country studies (e.g., Braunerhjelm and Eklund 2014, Djankov et al. 2010 and Nicoletti and Scarpetta 2003). We argue indeed that local institutions for entrepreneurship always develop and emerge against the backdrop of a national institutional context. For instance, when Toyota decided to begin production in Europe, it located its first production plant at Deeside in North Wales. However, before settling on Deeside, Toyota identified the UK as offering the most favorable national framework conditions for their production facilities.

Local Competitiveness Fostered through Local Institutions    147 The local institutional context still matters for at least three reasons. First, there are local variations in the formal regulatory business framework. Even in nonfederal states, the formal regulations concerning, for example, licenses to open or expand a store, warehouse, or manufacturing plant, are to a great extent determined locally. Moreover, local public procurement is to a large extent determined by local authorities in a city or municipality. Not only the regulations as such, but also the complexity, speed, and ultimately the costs imposed on entrepreneurs by local bureaucratic procedures may vary across cities and regions. Second, nationwide regulations may still vary across regions in a country because of local variations in the way in which national-level regulations are interpreted and enforced. For example, many entrepreneurs witness that there are differences across local authorities in the extent to which they interpret and implement the same regulation. Third, a large set of studies emphasize that there are locally embedded values and attitudes toward entrepreneurship, exerting a strong influence on entrepreneurial activities at the local level. The well-known and highly influential study by Saxenian (1994), for example, argued that a prime reason for the success of the Silicon Valley region in California vis-à-vis Route 128 around Boston was to be found in the favorable local or regional “entrepreneurship culture” in Silicon Valley. The concept of regional entrepreneurship culture is often employed to the level of social acceptance and encouragement of entrepreneurs and their activities (see, e.g., Fritsch and Wyrwich 2012 and Beugelsdijk 2007). We may think of such a culture as a type of local informal institution. These three components of the local institutional setup are clearly interrelated in the sense that they may influence each other in complex ways, and thus coevolve over time. The local regulations and bureaucracy surrounding businesses in a city may, for example, be a function of the politicians in charge, which in turn depends on how the local population votes.2 In what follows, we will discuss the various ways in which these components of the local institutional environment impact the supply and direction of local entrepreneurship. We will cover basic theoretical aspects and also discuss a set of recent empirical research papers that explore the effects of the local institutional environment on entrepreneurship. We also discuss the role the local institutional environment plays in local policies aimed to foster productive (high-impact) entrepreneurship. The remainder of the chapter is organized in the following way. In section 2 we present our preferred definition of entrepreneurship, emphasizing the distinction between self-employment and entrepreneurship. In this section we also discuss the key distinction between entrepreneurship policy and SME policy. Section 3 presents an overview of the general literature on institutions and entrepreneurship, focusing on the role institutions play for both the supply and direction of entrepreneurship at the aggregate/ national level. Section 4 focuses on the local level and discusses the various ways in

2 

People living in an environment with a strong entrepreneurship culture are more likely to vote for “business-friendly” local authorities.

148    Critical Drivers of Local Competitiveness which the local institutional setup may influence the supply and direction of entrepreneurship. Section 5 summarizes and concludes.

2  Entrepreneurship and Entrepreneurship Policy 2.1  What Is Entrepreneurship? Entrepreneurship is about individuals and organizations—be they new, old, large, or small—that actively contribute to renewal and change in the economy. This can be either Schumpeterian (Schumpeter 1934) entrepreneurship, which disturbs an existing equilibrium, or it can be Kirznerian (Kirzner 1973) entrepreneurship, which moves the economy toward equilibrium. It does not really matter whether the entrepreneur is the person who provokes change or merely adjusts to it. Entrepreneurial action can mean both creation of opportunity and response to existing circumstances, where entrepreneurs have the wherewithal to embrace risks in the face of uncertainty.3 We analyze entrepreneurship as a function carried out by specific individuals, who, by their own volition, decide whether to supply this function. Given that they choose to do so, these activities may be socially productive, unproductive, or destructive (Baumol 1990; Murphy, Shleifer, and Vishny 1991). Individuals carrying out the entrepreneurial function are self-serving agents. Thus, they can be expected to venture into the type of entrepreneurship that they expect will lead to the highest private return (broadly construed). Following Wennekers and Thurik (1999), a person can be said to engage in an entrepreneurial venture if she either on her own or in teams, and either inside or outside existing organizations, • Perceives and creates new economic opportunities (new products, new production methods, new organizational schemes, and new product market combinations);  and • Introduces her idea in the market, in the face of uncertainty and other obstacles by making decisions on location, form, and the use of resources and institutions.4 Entrepreneurship almost always entails an ambition to grow. This is normally achieved by hiring other factors of production in the market, while the entrepreneur remains the main or

3  Hébert and Link (2006b) provides an extensive overview of the treatment of the entrepreneur in the economic literature from Richard Cantillon (1680–1734) until the present. 4  This is closely related to Casson’s (2003) definition of the entrepreneur as “a specialist in taking judgemental decisions.”

Local Competitiveness Fostered through Local Institutions    149 Table 8.1  Entrepreneurial versus Nonentrepreneurial Self-Employment Entrepreneurial

Nonentrepreneurial

First best

Pursue a business opportunity most suitably pursued in a new firm

1. Seeking independence, a certain lifestyle, etc. 2. Local service production; working in networks in temporary projects

Second best

1. Inferior management by 1. Safety valve to circumvent current employer bars efficient excessive labor market intrapreneurship. regulations 2. Mechanism to escape effect of 2. Means to achieve flexibility discrimination or lack of social capital hindered by other regulations. for marginal groups 3. Mechanism to escape effect of 3.  Necessity entrepreneurship discrimination or lack of social capital for marginal groups 4.  Necessity entrepreneurship

Unproductive/predatory 1. Set up a business to exploit subsidies 1. Transform consumption and tax breaks rather than create expenditure into tax value for customers. deductible business costs. 2. Fraudulence 2. Fraudulence, where revenue is 3.  Looting, warfare, etc. partly unreported, etc. Note: The table lists the major motives for self-employment. There are also intermediate cases. Entrepreneurial self-employment may, for instance, be partly pursued in search of independence.

sole residual claimant to the excess value created through the new combination of resources. However, the distinctions made above underline why self-employment cannot be equated to entrepreneurship, and why it is likely to be a poor proxy in empirical work. Conversely, employees without an ownership role can also be entrepreneurial (intrapreneurship), although this may be difficult to achieve if it is hard to write compensation contracts that provide the right incentives. More often than not, this is the case, which is a major reason why exceptionally talented entrepreneurs tend to found their own business(es) rather than working as employees in established firms with high-powered incentive contracts.5 In table 8.1 we identify different motives to start a business. The top row gives society’s first-best alternatives. Here, an entrepreneurial business is started because it provides the best vehicle for pursuing a business opportunity. Nonentrepreneurial motives are to give the owner the opportunity to pursue a certain lifestyle, to earn her or his living 5 

Assume for a moment Wal-Mart founder Sam Walton had remained an employee at JC Penney, choosing instead to invest the same fraction of his income in public assets with a risk and liquidity profile similar to Wal-Mart’s. It is safe to say that he could not have become the richest man in the world using this strategy. Staunch in his role as employee, Walton could not have retained his billions of dollars’ worth of surplus, which he would have had neither the incentive nor the opportunity to create. Any employee contract attempting to decouple ownership, but retain the incentive structure enjoyed by the owner of an entrepreneurial firm would face insurmountable transaction costs.

150    Critical Drivers of Local Competitiveness independently, or to facilitate the organization of certain projects best pursued as an independent firm. For instance, a franchise could be owned by the manager in order to overcome or mitigate agency problems. In the second-best case the entrepreneurial motives arise as a result of obstacles barring the optimal outcome. Entrepreneurship provides a means to circumvent obstacles that could emanate either from inappropriate institutions or from within the private sector. For instance, inferior management and business organizations may prevent intrapreneurship. Legislation could ban the use of stock option incentives to encourage intrapreneurship, or it could make it prohibitively expensive. Various forms of discrimination often hinder marginal groups from seeking regular employment, leaving self-employment as the remaining opportunity. Becoming self-employed can be a way to evade restrictive employment regulation or pay schedules. Evasive entrepreneurship may also consist of efforts to evade the legal system or to avoid the predatory activities of other agents. Tax evasion and bribes paid to regulators or inspectors used to evade onerous regulations are two examples. Hence, formally illegal evasive activities may in some systems be necessary in order to achieve productive entrepreneurship.6 In the most unfavorable outcome, the incentives are such that entrepreneurs strive to exploit the business opportunities arising from the regulation itself. Entrepreneurial incentives to start a business may then be geared toward exploiting tax breaks and subsidies rather than creating value. Sidestepping or reducing the impact of taxes and other legislation is also a prime motive for nonentrepreneurial businesses under this kind of regime. In cases where the government does not manage to uphold a monopoly on violence and where the rule of rule law does not prevail, entrepreneurship may also take on highly destructive forms such as looting and private warfare. In a dynamic economy, novel ideas continuously challenge old structures, thereby giving rise to structural transformation when new successful innovations, products, firms, and industries rise, while obsolete ones decline and vanish. Empirical studies point out high-growth firms (HGFs) to be main drivers of this process. Stangler (2010) estimates that 1 percent of firms in the United States create 40 percent of all new jobs in a given year and that 5 percent of all firms create almost 70 percent of all new jobs. Henrekson and Johansson (2010) survey the numerous studies of firm growth. They conclude that some general findings emerge: 1. All studies report HGFs to be crucial for net job growth compared to non-HGFs. They generate a large share of all, or more than all (in the case where employment shrinks in non-HGFs), new net jobs. This is particularly pronounced in recessions when HGFs continue to grow, while non-HGFs decline or exit. 2. Small firms are overrepresented among HGFs, but HGFs are of all sizes. In particular, larger firms are important job contributors in absolute terms. A small subgroup of large HGFs are major job creators. 6  van Stel, Storey and Thurik (2007) argue that institutions determine the distribution of business activity between the formal and informal sector of the economy.

Local Competitiveness Fostered through Local Institutions    151 3. Age is of great importance. HGFs are younger on average, and they are overrepresented in young and growing industries with a large inflow of new firms. 4. Young and small HGFs grow organically to a larger extent than large and old HGFs, and therefore make a larger contribution to net employment growth. 5. HGFs are present in all industries. If anything, they are slightly overrepresented in service industries. Haltiwanger, Jarmin, and Miranda (2013) show, based on US data, that although it is true that small firms tend to create most of the new jobs, one cannot infer that policy should target small firms and encourage business start-ups per se. This is due to the fact that, when controlling for firm age, there is no systematic negative relationship between firm size and net job growth. Newly established growth companies tend both to be young and initially small. Most net employment growth takes place in two types of firms: new entrants and young firms that grow from small to large. Small firms that remain small are not important for job growth, and a large share of them will eventually exit. Close to 50 percent of all net jobs emanating from firm entry disappear within five years because of firm exit, but those firms that do survive tend to grow rapidly. The evolution of young firms thus follows an “up or out” dynamic. Henrekson and Sanandaji (2014) identify high-impact entrepreneurs from the Forbes magazine worldwide list of billionaires. Their measure is based on the accumulation of wealth for founders of new business ventures. In this way they identify 996 self-made billionaires who became rich by founding new firms. Using these individuals to construct a per capita rate of high-impact entrepreneurship, they show that this measure is robustly and negatively correlated with self-employment rates, small business ownership rates, and the rate of start-up activity. Table 8.2 summarizes results regarding education and region for the US sample of billionaire entrepreneurs. Billionaire entrepreneurs are highly educated and tend to attend elite universities, indicating ex ante talent. While the 15 highest ranked US colleges account for less than 1 percent of US college enrollment, one-third of the billionaires graduated from these elite universities. Of the largest entrepreneurial firms in the United States founded in the postwar era, one-half were founded by billionaire entrepreneurs on the list, indicating that the billionaire measure manages to capture entrepreneurial activity. California and Massachusetts are strongly overrepresented in billionaire entrepreneurship. Twelve out of 13 Massachusetts-based billionaires live in the Boston Metropolitan region or have founded firms active in Boston. Fifty out of California’s 99 billionaire entrepreneurs live in or founded firms in the Bay Area. Silicon Valley and Boston are often identified as having above-average rates of entrepreneurial activity (Lerner 2009), but compared to the national average these regions had a lower self-employment rate, lower firm density, a lower share of employment in firms with less than 20 employees, and a higher share of employment in firms with more than 500 employees (Small Business Administration 2007; Bureau of Labor Statistics 2008).

152    Critical Drivers of Local Competitiveness Table 8.2  Education and Location of American Billionaire Entrepreneurs Educational attainment (%)

Billionaire entrepreneurs

Self-employed

Salaried workers

High school or less Some college College degree Advanced degree

6.1 10.4 38.5 45.0

31.6 17.6 34.3 16.5

36.8 17.1 33.6 12.5

Geographic region

No.

Relative to population

Northeast (of which New York) (of which Massachusetts) Midwest South (of which Texas) West (of which California)

109 76 13 56 104 36 137 99

1.44 2.85 1.45 0.61 0.71 1.17 1.48 2.02

Note: Educational attainment refers to population aged 25+. Entrepreneurs are assigned to states based on Forbes’ designation. If Forbes did not specify a state, this is based on residence. Relative to population is defined as the share of total entrepreneurs divided by the population share of the state/ region 1996–2009. Source: Henrekson and Sanandaji (2014).

We thus argue that entrepreneurship cannot be equated to self-employment or small business activity more generally. Moreover, the evidence suggests that high-impact entrepreneurship (HIE; Acs 2008)  is particularly important. High-impact entrepreneurial activities commercialize key innovations or create disruptive breakthroughs, extract substantial entrepreneurial rents, spur growth (in both the firm and the economy) and employment, and shift the production possibility frontier outward. Yet a typical start-up is not characterized by HIE, and high-impact entrepreneurship is not necessarily performed within new (or small) firms. How entrepreneurship is conceived and measured is of course crucial for how policies aimed at promoting productive entrepreneurship are designed and implemented. The remainder of this chapter will deal with that issue, first at the national and then at the regional and local levels.

2.2  Entrepreneurship Policy versus SME Policy Entrepreneurship can be encouraged by efforts ranging from specific targeted support, such as technology assistance to small firms, to general macro policies to maintain a stable economic environment. Given the distinctions between entrepreneurial and

Local Competitiveness Fostered through Local Institutions    153 nonentrepreneurial self-employment pointed out in the previous subsection, entrepreneurship policy cannot be equated to policy encouraging self-employment or small business activity, often called SME policy (Lundström and Stevenson 2002). The aim of entrepreneurship policy should not be to stimulate firms but to support an economic system that encourages socially productive entrepreneurial activity irrespective of business form. Moreover, as it is difficult―if not impossible―for policy makers to a priori determine who will be an entrepreneur, measures directed at a specific group or a specific form of business are largely misdirected (Holtz-Eakin 2000). Nor should public policy try to influence the “natural” evolution of firm size, growth, or form through targeted subsidies or tax breaks. Market forces and profit motive alone should govern the evolution of firms. Unless a substantial market failure that can be rectified through public policy exists, targeted programs should be looked upon with skepticism. A system replete with special treats and regulations for select categories results in a complex system with detailed rules, exceptions, and exceptions to the exceptions. In the end, this impairs all activity because of increased administration and information costs. Complex systems also provide opportunities for unproductive and destructive entrepreneurship. Normally, welfare increases if the economy allows and rewards productive entrepreneurial initiatives across the board, independent of firm and individual characteristics. A well-designed entrepreneurship policy facilitates productive entrepreneurial activities and enables the creation and commercialization of valuable knowledge (Acs and Szerb 2007; Braunerhjelm et al. 2010). Whether this implies a high or low rate of self-employment or SMEs is largely irrelevant. Instead of focusing on quantitative aspects of entrepreneurship, entrepreneurship policy should focus more on the qualitative aspects. Empirical evidence suggests that an economy that fosters (a few) high-impact entrepreneurial firms and high-growth firms is superior to an economy that tries to maximize the number of SMEs or the rate of self-employment (Shane 2008; Henrekson and Sanandaji 2014). Figure 8.1 depicts the major distinctions between SME policy and entrepreneurship policy. Nonetheless, the entrepreneur is not the only agent important for economic progress. Successful entrepreneurs who identify and exploit new ideas—thereby creating and expanding businesses—depend on a number of complementary agents, such as skilled labor, industrialists, venture capitalists, and secondary markets. Successful

SME Policy

Entrepreneurship Policy

Quantity

Quality

Firms Self-employment/ SMEs

Individuals High-impact entrepreneurs/ Gazelles

Support

Enable

Figure  8.1  SME Policy versus Entrepreneurship  Policy

154    Critical Drivers of Local Competitiveness entrepreneurs cannot succeed without these complementary competencies and inputs.7 Focusing on just the entrepreneur distracts from important factors necessary for an economy to prosper. Still, entrepreneurship is crucial; a lack of productive entrepreneurs cannot be fully offset by an ample supply of skilled labor or an extensive capital market.

2.3  The Supply and Direction of Entrepreneurship We agree with Baumol’s (1990) point that entrepreneurship can be productive, unproductive, or destructive depending on the “social structure of payoffs.” However, in our view his assumption that the supply of entrepreneurship is constant, while its allocation across activities is the only thing that varies, is untenable. Similar to other economic inputs, entrepreneurship is valuable and scarce (Schultz 1979), has a definable—although hard-to-measure—quantity, and has a shadow market price. The entrepreneur often “creates” the capital of the firm by investing in tangible and nontangible assets that in time create a return, such as developing the product and building firm structures. At any given moment, this capital requires a continued commitment on the part of the entrepreneur, whether or not it is sold externally at value. The growth of the firm is often financed through retained earnings until the point when the firm is sufficiently developed so that it can be sold, or produce cash flow that can be withdrawn by the owner without being detrimental to the firm. Thus, the quantitatively important saving decision does not constitute the initial capital injection, but rather the fact that entrepreneurs refrain from withdrawing the equity value of their firms before they have matured in terms of production efficiency and asset tradability. The entrepreneur is rewarded for both effort and the postponement of consuming firm equity into an uncertain future. But the earnings of owner-managers are likely to be more complicated than a simple additive sum of capital and labor. Successful entrepreneurial firms need several components that are hard or nearly impossible to purchase externally. These include product or business ideas, sufficient managerial skills to implement innovations, and the willingness to exert time and effort to realize an uncertain outcome. Because of well-known agency costs, entrepreneurs must provide a significant share of requisite capital themselves. Lastly, these requirements must be combined with the postponement of consumption (and additional risk taking) in one individual—the entrepreneur. The inability to decouple saving, investment, and effort requires that entrepreneurial talent and opportunity intersect, unlike labor and capital markets. As a result, the supply of entrepreneurship tends to be more constrained than the supply of labor or financial capital. This explains the above-market returns earned by entrepreneurs (controlling for capital and labor output). Moreover, potential entrepreneurs with high-quality ideas and talent are few and far between. High risk, high uncertainty, large 7 

Johansson (2010) and Henrekson and Johansson (2009).

Local Competitiveness Fostered through Local Institutions    155 demands on effort, lack of access to capital markets, and long time-lags before expected returns are materialized and reduce the number even more. This is especially true since the best potential entrepreneurs tend to have the most valuable outside options. In short, the supply of entrepreneurship is likely both to be elastic—good prospects and profit opportunities bring forth more entrepreneurial effort—and its allocation across more or less socially beneficial activities to be governed by the rules of the game. Thus, we find it fair to hypothesize that both the volume of entrepreneurial effort and its allocation are determined by the institutional setup.

3  Entrepreneurship Policy: The National Level The main purpose of this chapter is to discuss the effect of local institutions for entrepreneurship and the local business climate. Still, however astute politicians and other policymakers are in designing favorable local conditions, they are still constrained by the national framework conditions. The local policies and institutions are always developed and maintained within the confines of the national framework. Heeding this fact, we discuss a set of important national institutions before turning to the local level. The discussion is by no means exhaustive. Our aim is simply to provide a context within which local institutions and policies may be evaluated and discussed. Important areas not discussed include the functioning of capital markets, systems for targeted support, and bankruptcy law. For a more comprehensive discussion the reader is referred to Baumol, Litan, and Schramm (2007); Henrekson and Stenkula (2010); and the various contributions in Audretsch, Grilo, and Thurik (2007).

3.1  Pertinent Institutions at the National Level By now there is a fairly large empirical literature aiming to identify the effect of various institutions and policies on the rate of entrepreneurial activity. It is beyond the scope of this chapter to give a complete overview of this literature. However, it is important to make a number of points in order to make clear the extent to which local/regional and national policies and institutions (both formal and informal) interact and reinforce one another. A good starting point is Baumol, Litan, and Schramm’s (2007) four primary tenets underpinning an entrepreneurial economy:

• Ease of starting and growing a business • Generous rewards for productive entrepreneurial activity • Disincentives for unproductive activity, and • Incentives to keep the winners on their toes

156    Critical Drivers of Local Competitiveness Empirical observations have illustrated that entrepreneurs behave differently than comparable wage earners, for instance, by having a higher income elasticity with respect to taxes (e.g., Sillamaa and Veall 2001; Chetty et al. 2011; Saez 2010). Firm growth, investment, hiring of outside labor, and personal effort have all been shown to be significantly affected by taxes (Rosen 2005). In what follows we will highlight what we consider to be the most pertinent institutions and policies determining the extent to which a productive entrepreneurial economy is fostered. However, the discussion will not be exhaustive, since the main contribution of this chapter should be a discussion of local institutions and policies fostering entrepreneurship.

3.2  The Rule of Law and the Protection of Property Rights Based on broad historical studies (e.g., Rosenberg and Birdzell 1986) and more recent econometric work (e.g., Rodrik, Subramanian, and Trebbi 2004 and Acemoglu and Johnson 2005), it is now widely recognized that protection of private property rights is of fundamental importance for economic growth (Besley and Ghatak 2010). With secure exclusive private property rights that can be used in voluntary exchanges based on contracts, productive entrepreneurship is likely to thrive. Then successful entrepreneurs know that they will retain the profits they earn. Specialization and the division of labor are also greatly facilitated, which broadens the range of potential entrepreneurial combinations to be discovered or created. Weaker property rights will spur other types of entrepreneurship, including the production of (increasingly diverse and sophisticated) private security services. The weaker the property rights, the more predatory the entrepreneurial activities are likely to be. In some cases innovativeness and entrepreneurship may also be stifled by overly strong property rights protection. An important contemporary example is the strong intellectual property rights protection of high-tech patents, which allegedly gives large incumbent firms a competitive advantage relative to entrants and potential competitors. Incumbents are said to spend large resources on defensive patenting and purchases of patents from small innovators in order to reduce competition, or to file patents for ideas, business models, software strings, and so on that are obvious and have been around for quite some time without somebody having tried to patent them before (Cohen 2005). As argued by Gans and Persson (2013) strengthened property rights protection does not always go hand in hand with innovative entrepreneurship. Instead competition policy should have a complementary role in promoting innovation in order to keep winners on their toes.

3.3 Tax Policy Income from entrepreneurial activities is not a separate income category in the tax code. Hence, from a tax perspective entrepreneurial income can show up in

Local Competitiveness Fostered through Local Institutions    157 many different forms: labor income, dividends, capital gains, capital gains on stock options, interest income on lending by the entrepreneur to his or her own business, and so on.8 Given the complexity of the tax code in a typical OECD country, the incentive effects of the tax code on entrepreneurial behavior are also highly complex. Some features are likely to be of particular relevance. By the 1970s effective tax rates on business income came to differ tremendously by source of finance and ownership category in rich countries. Debt was the most tax-favored form of financing, and new equity issues the most penalized. Business ownership positions held directly by individuals and families were taxed much more heavily than other ownership categories. The wave of tax reforms that swept the OECD in the 1980s reduced these differences (Jorgenson and Landau 1993). Differences in effective tax rates have potentially powerful effects on the organization of business activity and the industry mix of productive activity, and therefore also on the incentives for entrepreneurship. To the extent that debt financing is less costly and more readily available for larger and firmly established incumbents, high statutory tax rates coupled with tax-deductible interest payments work to the disadvantage of smaller firms and potential entrepreneurs. Debt financing is also more easily available to firms with ready forms of collateral. This favors capital-intensive industries and modes of production relative to labor- and knowledge-intensive ones, which is likely to work to the detriment of entrepreneurial, often equity-constrained, firms. To a large extent the return on entrepreneurial effort is taxed as wage income. First, the tax code may restrict the extent to which income accruing from closely held companies may be taxed as capital income. Second, a great deal of the entrepreneurial function is carried out by employees without an ownership stake in the firm, and for them the labor tax schedule applies. A further mechanism to encourage and reward entrepreneurial behavior among employees is stock options. The efficiency of stock options is highly dependent on the tax code. If the gains on stock options are taxed as wage income, when the stock options are tied to employment in the firm this mechanism will lose much of its incentive effects. The situation would be very different if an employee who accepts stock options can defer the tax liability to the time when the stocks were eventually sold. This effect would be further reinforced if there are no tax consequences to the employee upon the grant or the exercise of the option and if the employee is taxed at a low capital gains rate when the stock acquired from the exercise of the option is sold. In the latter case the tax risk of the options are pushed back to the government. This increases the potential profit from the stock options, and it allows budget-constrained individuals to sell stocks whenever they chose to do so. The United States changed the tax code in the early 1980s along the latter lines, which paved the way for a wave of entrepreneurial ventures in Silicon Valley and elsewhere.

8 

This section largely builds on Henrekson and Sanandaji (2011).

158    Critical Drivers of Local Competitiveness Venture capital firms can also play a crucial role in the development of a small entrepreneurial venture by converting high-risk opportunities to a more acceptable risk level through portfolio diversification, and adding key competencies that the firm may be lacking. This is achieved by arrangements that align the incentives of the three agents—investors, venture capitalists, and entrepreneurial start-ups (Gompers and Lerner 2001). The extent to which this is possible is also largely governed by the tax code for stock options, capital gains, and pension funds, specifically whether the latter are allowed to invest in high-risk securities issued by small or new companies and venture capital funds. A further effect may be that certain actors such as private equity firms can act from offshore tax havens, putting them at an advantage relative to individual entrepreneurs. If this effect is present, it is increasing with respect to the effective tax rates levied on entrepreneurs legally domiciled onshore. To sum up, high taxes may spur self-employment but reduce productive entrepreneurship.9 On the other hand, a high aggregate tax rate is normally associated with a generous welfare system, which reduces the push into self-employment. Moreover, high tax rates encourage entrepreneurship in the black market and evasive entrepreneurship. But most importantly high effective tax rates tend to benefit large incumbent, capital-intensive firms that can have high debt-equity ratios and be owned by institutions, in particular if they are domiciled in tax havens. All in all, there is reason to believe that high taxes stifle productive entrepreneurship, although this effect can be greatly mitigated if the taxation of capital gains and stock options is low.

3.4  Labor Market Institutions The churning of firms and jobs is a ubiquitous feature of modern economies. The extent of this dynamism is illustrated in table 8.3 with data for the US economy averaged over almost three decades. The number of new jobs per year is as high as 18 percent of the total number of jobs. One-third of these new jobs are created in new establishments and two-thirds through the expansion of existing establishments. At the same time, 16 percent of all jobs are lost through the closure and contraction of some establishment, resulting in an annual net job growth rate of 2 percent. Thus, a net gain of 2 percent in the number of jobs is associated with a gross job reallocation rate of 34 (18 + 16) percent. The excess job reallocation rate—the amount of job churning over and above the minimum required to accommodate the net employment change—equals 32 percent. Extensive churning is a pervasive trait of all OECD economies (Martin and Scarpetta 2012). Eighty percent or more of the reallocation of workers takes place within narrowly defined sectors of the economy in developed countries (Caballero 2007). Moreover, excess job reallocation rates are higher for newer plants because of greater uncertainty, more experimentation, and higher variance in quality of the goods produced. 9 

Schuetze and Bruce (2004) and Henrekson and Sanandaji (2014).

Local Competitiveness Fostered through Local Institutions    159 Table 8.3  Job Creation and Destruction, US Annual Averages, 1977–2005 Job creation by entry Job creation by expansion Gross job creation

6% 12% 18%

Job destruction by exit Job destruction by contraction Gross job destruction

Job reallocation rate (gross job creation + gross job destruction) = 18 + 16 Net job growth (gross job creation − gross job destruction) = 18 − 16 Excess job reallocation rate (job reallocation rate − net job growth) = 34 − 2

6% 10% 16% 34% 2% 32%

Source: Davis, Haltiwanger, and Jarmin (2008).

Labor market and wage-setting regulation can influence incentives for entrepreneurship since it restricts the freedom of contracting and therefore curtails possible combinations of factors of production. Labor security regulations fall more heavily on younger, smaller, and less capital-intensive employers. As entrepreneurial firms are overrepresented in these categories, labor security regulation disproportionally burdens entrepreneurial firms. Employment flexibility is likely to be important for entrepreneurial activities. Strong regulation of the employment and dismissal of employees keeps entrepreneurs from adjusting their workforce in correspondence with market fluctuations, thereby increasing the risk of their projects (Audretsch et al. 2002). As an employer determines a worker’s abilities over time, and as those abilities evolve with the accumulation of experience, his optimal work assignment will also likely change. As table 8.3 tells us, this often entails worker mobility between firms; such mobility is more likely to occur when the initial employment relationship was forged in a small, often young, business. A low level of labor market regulations increases the flexibility of high-risk entrepreneurial companies, making it more attractive to be an entrepreneur. Moreover, the relative advantage of being an employee decreases with weak employment protection laws, making it more favorable to undertake entrepreneurial projects as self-employed. Generous, far-reaching labor protection laws also increase an employee’s opportunity cost of changing employers or leaving a secure salaried job to become self-employed. The extent of labor market regulations differs greatly across countries (Skedinger 2010). New research has found that the differences in labor market regulations shape the level of nascent entrepreneurship more than entry regulations. Entrepreneurship tends to be higher in countries where it is relatively easy to hire and dismiss employees (van Stel, Storey, and Thurik 2007). Labor market deregulation can stimulate and has stimulated entrepreneurial activities in many OECD countries. Small firms in the Netherlands, for example, hire fewer employees than needed because of the perceived cost of formal rules and regulation. New firms in the United States on the other hand, expand their employee base more rapidly than firms in Europe (OECD 2003). Europe’s stricter employment protection laws probably induce the relative lack of new, rapidly growing firms in Europe (Baumol, Litan and Schramm 2007, 210ff).

160    Critical Drivers of Local Competitiveness Several European countries have thresholds where labor regulations become more onerous once the size of the firm exceeds a certain limit. This is equivalent to a tax on firm growth. For instance, when French firms reach 50 employees, they must form work councils, give more union representation, and face higher firing costs. As shown by Garicano, Lelarge, and Van Reenen (2013), this creates reluctance among many firms to move beyond that limit. Portugal and Italy have important regulatory limits at 15 employees, which have been shown to have similar growth-impeding effects (Braguinsky, Branstetter, and Regateiro 2011; Schivardi and Torrini 2008). Thus, firms are incentivized to remain small, and many entrepreneurs will not discover that they in fact had the ability to become high-impact entrepreneurs, because they do not even try. If regular employment is highly regulated, there are incentives to circumvent these regulations. Potential entrepreneurs can do so by pursuing entrepreneurial projects as self-employed, using only self-employed labor instead of hiring employees if labor is needed. Compensation and working hours are totally unregulated, and no labor security is mandated for the self-employed. This may boost the level of self-employment, but it is not a sign of exuberant entrepreneurial activity; it is a costly, albeit necessary, measure to evade the effects of stringent labor market regulation. Given the large intrafirm differences in productivity and productivity growth, wages set in negotiations away from the workplace that do not take idiosyncratic factors into account will impair entrepreneurial activities. Intrafirm differences are especially large in young and rapidly expanding industries and firms (Caballero 2007). In developed countries, employees’ general income level is also relatively high, which in turn makes the opportunity cost of leaving salaried employment to start or work in a new venture high as well (Ho and Wong 2007). To summarize, a tightly regulated labor market may create a system in which a large share of economic activity occurs in small firms lacking the ability or the ambition to grow. Onerous regulation makes it difficult and risky to build large companies. Italy is a good case in point, where firms tend to remain small and resort to cooperating with other small firms in clusters (Lazerson and Lorenzoni 1999). By contrast, new firms in the United States tend to expand their businesses more rapidly than their European counterparts.

3.5  The Social Insurance System By providing insurance for unfavorable outcomes, an extensive and generous public social insurance system can in principle encourage individuals to pursue entrepreneurial endeavors (Sinn 1996). At first sight, it seems clear that a generous welfare system should make it less costly to bear uncertainty as an entrepreneur or to move to a risky job in an entrepreneurial firm. In labor markets where job security is closely linked to job tenure, this may no longer hold; what matters is the opportunity cost, or how much an employee has to give up in terms of income security if she transfers to self-employment or a risky job in an entrepreneurial firm. For a tenured employee with a low-risk employer, the opportunity cost rises considerably in many OECD countries.

Local Competitiveness Fostered through Local Institutions    161 In many countries important benefits are tied to employment, such as health insurance in the United States. Many workers and potential entrepreneurs get “trapped” in large companies that provide generous health insurance. Decoupling health insurance from employment would increase labor flexibility and reduce fears of losing adequate health insurance and other important benefits that may be tied to employment. In Denmark, generous welfare systems are combined with weak job security mandates, “flexicurity” (Andersen 2005). This can be contrasted to Sweden, where somebody who voluntarily gives up a tenured position for self-employment may end up having no more security than what is provided by (means-tested) social welfare. Hence, the construction of the public income insurance systems in combination with the employment security legislation tends to penalize individuals who assume entrepreneurial risk. Furthermore, the manner in which savings are channeled to various investment activities influences the type of business organization that can obtain financing. Pension funds are less likely to channel funds to entrepreneurs than business angels or venture capital firms. Hence, the composition of national savings is not neutral in its impact on entrepreneurship and business development. If the government forces individuals to keep a large part of their savings in a national pension fund system, small business financing availability will suffer relative to an alternative policy and institutional arrangements that allow for greater choice by individuals regarding their savings and investments. A final point concerns the design of supplementary pension systems. Supplementary pension plans that are not fully actuarial and individualized contain elements of redistribution and risk-sharing across individuals in a group, like white-collar workers in a certain industry. The pension benefit level may be disproportionately tied to the wage level achieved toward the end of the professional career. Moreover, it may be difficult to transfer the accumulated pension assets when switching employer or industry. To the extent that this is true, the mobility of (especially more senior) workers across firms is hampered, and the hiring of elderly unemployed is discouraged.

3.6  Institutions and Policies at the National Level: Concluding Thoughts It should once more be emphasized that this section on institutional factors determining the climate for productive entrepreneurship at the national level is far from exhaustive. It should just be seen as a backdrop, albeit a necessary one, to the discussion of local determinants in the ensuing section. When reading the next section it is important to keep in mind that the aggregate level of productive entrepreneurship in a nation and its leverage when combined with other complementary factors of production are largely determined by national factors. The fact that one may still find pockets of dynamic entrepreneurship in the impoverished south of Italy and the quickly depopulating north of Sweden should not be taken as evidence against this view. On the other hand, the aggregate level of entrepreneurship in a country is the sum of entrepreneurial activity in the numerous local communities. As we will see, there is

162    Critical Drivers of Local Competitiveness plenty of room for local initiatives and policies to improve the entrepreneurial climate locally. Potentially, this gives rise to healthy institutional competition at the regional level, which improves the climate for entrepreneurship in many locations. At the national level the greater part of the institutional setup is determined through democratic decision making. And if pro-competitive/pro-entrepreneurship policies can be shown to boost job creation, social welfare, and the quality of life, people are more likely to vote for politicians prone to make the institutional setup more favorable for productive entrepreneurship. Obviously, the reverse is also true: If current policies and institutions favor unproductive and predatory entrepreneurship, economic performance will be dismal, and political demands will be raised to curtail predatory entrepreneurship. The ensuing reforms may at least partly succeed in that respect. But if reforms are not designed wisely, if they merely curb the scope for predatory entrepreneurship without providing favorable incentives for productive entrepreneurship, the result could be stagnation.

4  The Local Level: Institutions and Entrepreneurship Policy In the previous section we discussed at some length a number of pertinent institutions and policies that have been identified as important determinants of the rules of the game for entrepreneurship at the national level. Here we discuss the local level and argue that there are a number of institutional framework conditions for entrepreneurship that are determined as well as operate at the local level. We start by providing a general argument for why local institutional conditions are important for entrepreneurship. We then discuss the extent to which formal institutions in the form of taxes and regulations can be influenced by local authorities, and survey the (still fairly few) studies of the effect of institutional characteristics on the local business climate and entrepreneurial activity. This is followed by a discussion of local informal institutions, focusing on the influence of the attitudes and the general level of social acceptance toward entrepreneurship. Finally, in the last subsection (4.4) we synthesize and discuss what aspects of the local institutional environment are critical for the local version of entrepreneurship policy as defined in figure 8.1.

4.1  Why the Local Level Matters and the General Case for the Importance of Local Institutions for Entrepreneurship Spatial variations in entrepreneurship are large even among local jurisdictions embedded in the same national (or federal) institutional environment. The literature on the “geography of entrepreneurship” has for a long time documented substantial interregional differences in the rates of new firm formation (Audretsch and Fritsch 1994;

Local Competitiveness Fostered through Local Institutions    163 300 250 200 150

National average

100 50

Figure 8.2  The Variation in Start-up Rates across Sweden’s 290 Municipalities in 2007 (per 10,000 inhabitants aged 16–64; municipalities ranked in descending order) (Source: Andersson (2012))

Armington and Acs 2002). Notwithstanding the issues associated with proxying entrepreneurship with the raw start-up rate discussed in the previous sections, we can illustrate the main point by looking at the differences in start-up rates across Swedish municipalities (figure 8.2). We first calculate the number of new establishments per 10,000 inhabitants (16–64 years of age) in every municipality in Sweden and then rank them in descending order according to their start-up rate in 2007. The horizontal dashed line represents the national average, which amounts to about 130 new establishments per 10,000 inhabitants. The solid line shows that across municipalities, the start-up rate ranges from just over 50 to nearly 300. This implies that the intermunicipal variation in start-up rates amounts to a factor of almost six.10 These spatial variations do not reflect a transitory structure. Longitudinal analyses reveal a high persistence over time. Figure 8.3 presents the relationship between the start-up rate in 2007 and in 1987 across Swedish municipalities, that is, over a time span of two decades. It is clearly the case that there is persistence in the geography of start-up rates. Municipalities with high start-up rates in 2007 are typically those that had high start-up rates two decades before. Indeed, a simple ordinary least square estimation of the relationship in figure 8.3 shows that the start-up rate 20 years before is capable of accounting for roughly 50 percent of the current variance in start-up rates across municipalities. The large variation in start-up rates across municipalities cannot be explained by national regulations. Potential explanations for the observed pattern must be sought at the subnational level. It may, for instance, reflect persistent spatial variations in the supply of individuals with entrepreneurial skills, differential availability of various types of inputs and supporting structures for entrepreneurs, customer base, local knowledge spillovers, and labor force composition, as well as differences in the local formal and 10 

Startup rates are here measured as the number of new establishments normalized by the regional population in the age interval 16–64.

164    Critical Drivers of Local Competitiveness 350 300

Startups 2007

250 200 150 100 50 0

0

50

100

150

200

250

300

350

400

450

Startups 1987

Figure  8.3 The Relationship between Start-up Rates in 2007 and 1987 across Swedish Municipalities (new establishments per 10,000 inhabitants aged  16–64) (Source: Andersson (2012))

informal institutional setup (cf. Glaeser 2007). Even if the institutional setup is only one of several potential determinants of local entrepreneurship, there are strong arguments in favor of the view that it is a critical factor. Conceptually, the overall role of local institutions for entrepreneurship can be demonstrated by distinguishing between two main effects (Acs et al. 2008): (1) a direct effect understood as the influence of local institutions on the incentives and returns to productive entrepreneurship among local firms and individuals; and (2) an indirect effect reflecting that entrepreneurial firms and individuals may self-select to locations with an institutional environment favoring productive entrepreneurship (see figure 8.4). The indirect effect refers to spatial sorting and implies that institutions influence the local supply of entrepreneurs. While the same type of effects to some extent also apply at the national level, they are particularly important in a local context. The spatial mobility of firms and individuals is generally higher at subnational levels, which implies a stronger potential for indirect effects.11 Therefore, the effects of the institutional environment for entrepreneurship could be argued to be especially high at the local level. A change in local business regulations, for example, will not only impact on entrepreneurial undertakings of firms and individuals already in the locality; it may over time also bring about an inflow of entrepreneurs who are attracted by a more favorable environment for entrepreneurship. Likewise, a deterioration of the institutional setup may reduce the incentives for productive investments among existing businesses while at the 11  Migration rates between regions belonging to the same country are for example an order of magnitude higher than the rates of international migration.

Local Competitiveness Fostered through Local Institutions    165

Local institutional framework

Direct effect

Indirect effect

Incentives for entrepreneurship

Supply of entrepreneurs

Returns to entrepreneurship “Social” encouragement/acceptance

Spatial sorting of entrepreneurial individuals and firms

Figure  8.4 Direct and Indirect effects of the Local Institutional Framework on Entrepreneurship

same time reducing the local supply of entrepreneurs; that is, entrepreneurs may choose to relocate to other places or abstain from entrepreneurship altogether. There are indeed claims that entrepreneurial people are innately mobile and flexible and that being alert to opportunities is a defining trait of entrepreneurship (Kirzner 1973). As a result one may hypothesize that entrepreneurial talent will flow to locations where the business climate is more favorable. The key point is that changes in the institutional framework for entrepreneurship at the local level imply changes in the relative returns to productive entrepreneurship across regions. These spatial variations in returns may induce a response over time where entrepreneurial firms and individuals sort themselves to locations offering better returns. The indirect effect amplifies the influence of local institutions on entrepreneurship.

4.2  Local Variations in Formal Institutions: Taxes and Regulations The extent to which formal local institutions matter is of course linked to whether pertinent institutions, such as taxes and regulations, are determined at the local level. If we define “local” as referring to the subnational level in general, it becomes clear that it is necessary to distinguish between federal and nonfederal states. In general, local authorities in the former regime have greater influence on the local institutional setup. But as we will show, local authorities can play an important role in nonfederal states as well.

Taxes As regards taxation, an area of crucial importance for the returns to entrepreneurship (section 3.3), corporate taxes are under nonfederal regimes most often solely determined at the national level and consequently do not show any spatial variation. Thereby they are by definition not a distinct feature of the local environment. In federal states,

166    Critical Drivers of Local Competitiveness however, they can differ significantly across different parts of the country. US states are, for example, entitled to impose taxes on businesses, and there are also significant interstate differences in business taxation. Given the wealth of evidence on the effect that corporate income taxes have on entry (e.g., Da Rin, Di Giacomo, and Sembenelli 2011), it follows that interstate variations in business taxes may be one reason for differences in entrepreneurship across US states. Papke (1991) indeed finds a significant effect of business taxes on the location of start-ups in US manufacturing, though the responsiveness to tax differentials tends to vary significantly across industries. Kolko, Neumark, and Cuellar Mejia (2013) also show that state-level business climate indexes emphasizing taxes and costs in general do a better job than other indexes in predicting economic growth across states. Decomposing the tax-and-cost indexes, they further identify corporate taxation as one of the components that matters the most. In contrast to corporate taxes, income taxes are often largely determined locally in nonfederal states as well, and vary across local jurisdictions. In Sweden, for instance, local governments play an important role in public finances, and as in many other countries, local income taxes are used to finance local public services such as healthcare, schools, child care, and care of the elderly.12 Income taxes are of course not directly related to businesses in the same way as corporate taxes, but they may still have effects on local entrepreneurship. Successful and wealthy entrepreneurs with high incomes may be repelled by high income taxes, thus reducing the local supply of entrepreneurs (e.g., Haughwout et al. 2004). Even entrepreneurs in low-skilled industries who choose sole proprietorship as the legal form may be more inclined to choose cities with low income taxes as their place of residence. The profits earned by these types of firms are taxed in the context of the income tax bills of the owner. This type of “solo entrepreneurs” are, at least in theory, footloose and may save on taxes by moving themselves (and thereby their firm) to municipalities with lower income taxes. At the same time, high local income taxes could stimulate new firm formation. Self-employment through incorporated businesses, for instance, provides more scope for taxing business profits at the capital gains and/or dividend tax rate rather than as labor income (Edmark and Gordon 2013). The latter tax rate is normally considerably higher. Therefore, in places with high income taxes, the relative gains from selling your services as an entrepreneur rather than as an employee may be higher. Studies on Swedish data generally find negative correlations between local income taxes and broad measures of entrepreneurship. High municipal income tax rates are, for example, negatively associated with the frequency of start-ups and immigration of firms from other regions (Daunfeldt, Elert, and Rudholm 2013), as well as with the growth of the local private sector (Fölster and Peltzman 2010). However, it is difficult to ascertain whether the estimated influence of local taxes reported in these studies reflect an 12  Local income taxes constitute a substantial part of total taxes on labor in Sweden. In 2011, the local income tax of municipalities accounted for about 45 percent of the total income tax (including mandatory social security contributions). Moreover, nearly 60 percent of total tax revenue consisted of tax on labor income including tax on pensions and other taxable transfers (Tax Statistical Yearbook of Sweden 2013).

Local Competitiveness Fostered through Local Institutions    167 influence of taxes per se, or rather an influence of a wider set of local policies that correlate with the level of local income taxes. It should also be stressed that the effect of taxes in a local context is complex, especially when acknowledging both the direct and the indirect effects. High taxes may increase the costs imposed on firms as well as individuals. Yet if those taxes finance high-quality local public services, such as good schools, safe streets, and excellent healthcare, they may also make the region more attractive for both firms and individuals.

Regulations Regulations can be defined as “the diverse set of instruments that governments use to impose requirements on enterprises and citizens” (OECD 1997, 196). They include “laws, formal and informal orders and subordinate rules issued by all levels of government, and rules issued by non-governmental or self-regulatory bodies to which government have delegated regulatory power” (OECD 1997, 196). In general, local authorities have a large impact on regulations. In some areas they have the authority to design regulations locally. In other areas they are responsible for enforcing nationally enacted regulations, but may do so with varying stringency and methods of enforcement. Tannenwald (1997, 83) lists five general areas of regulation that have a direct impact on businesses and where state authorities in the United States have substantial influence:

• Environmental protection and land use • Regulation of labor markets and the workplace • Regulation of financial institutions • Energy production, distribution, and conservation • Transportation

With the exception of regulations concerning labor markets, workplaces and financial institutions, these are areas typically also influenced by local authorities in nonfederal states. Zoning is a prime example. The vast majority of countries allow (or require) local authorities to use zoning as a device for land-use planning (Larsson 2006). Zoning regulations stipulate which types of activities are permitted at various locations in a municipality or county. They also determine the extent of activities in terms of parameters such as space, density, height, and requirements of open space. Zoning regulations also apply to many different types of activities: residential housing, commercial buildings, agriculture, and industrial production. Zoning is also likely to have a broad impact on local entrepreneurship through both direct and indirect effects. It is clear that ill-designed and badly implemented zoning regulations weaken incentives to entrepreneurship by working as a major barrier to entry for new entrepreneurs as well as a barrier to business expansion for incumbents. They may also reduce the supply of productive entrepreneurship in many different ways. Entry barriers caused by zoning could, for instance, repel entrepreneurs such that they instead choose to expand or set up new businesses elsewhere. A second effect may run

168    Critical Drivers of Local Competitiveness indirectly through housing and local consumer service sectors. Residential land-use restrictions may result in a municipality or county not being able to exploit natural advantages like seashores and lakesides to build attractive housing, which lowers the attractiveness of the location for employees and entrepreneurs alike.13 The influence of local governments on regulations is amplified by the fact that how a given set of regulations is implemented in practice is at least as important as how they are codified in writing. On top of local variations in the formal regulatory environment, there may be differences between local authorities also in terms of how regulations (including nationwide regulations) are enforced. In this vein, Tannenwald (1997, 84) rightly states that “controlling for enforcement behavior is more important in evaluating the impact of regulation than the impact of taxation. Yet, measuring regulatory stringency is generally more difficult than measuring the burden of taxation.” It is indeed a daunting challenge for empirical analysts to assess local variations in enforcement of regulations. Available evidence still points to quite large effects. Bertrand and Kramarz (2002) directly estimate the impact of how strictly commercial zoning regulations are enforced. They use the case of France, where nationwide regulations since 1974 stipulate that “the creation or extension of any new large retail establishment has first to be approved by a regional zoning board composed of store owners, consumer representatives, and regionally elected politicians” (p. 1371). By exploiting time- and region-specific variation in the approval decisions of these boards, they are able to identify the effect of these decisions on the development of the local retail sector. The authors find that more stringency in entry deterrence by the regional boards hinders job creation in local retail sectors, and that this effect mainly operates through the positive effect entry deterrence has on retailer concentration. Data availability on aspects of enforcement is still scarce, but one may get a hint of the extent to which local regulations and regulatory stringency vary across local authorities by turning to qualitative data. The Confederation of Swedish Enterprise (CSE) conducts an annual survey of how entrepreneurs in Swedish municipalities perceive the “business climate” of the municipality in which they have their main operations (including headquarters in the case of multiplant firms). The survey asks a large number of questions, spanning from how satisfied entrepreneurs are with the general service local authorities provide to businesses, to their perception of attitudes toward businesses prevailing in local schools. Two questions in the survey are of particular interest in the context of enforcement and design of local regulations. The first asks entrepreneurs about the attitudes of local public officials toward entrepreneurship. In the second question entrepreneurs are asked about their view on how the local authority practices and carries out laws and regulations at large, for instance as reflected in the speed and transparency of the administrative processes associated with various types of permits. While these 13  Moreover, regulations concerning commercial buildings and entry of stores in a city may imply higher retail prices as well as a less vibrant and diversified sector for retailing and other consumer services, which is likely to influence the city’s attractiveness.

Local Competitiveness Fostered through Local Institutions    169 questions are not designed to specifically deal with regulatory stringency, they still provide a reflection of the order of magnitude in the differences that prevail across local authorities. We can certainly expect that regulations are enforced in a more “business friendly” way in municipalities where a large fraction of local entrepreneurs are satisfied with the attitudes of local public officials (such as administrators at various types of local public agencies) and with the way local authorities apply laws and regulations. The CSE survey represents the best available information that we have at our disposal to explore these issues.14 It has also been used in a number of recent studies to derive measures of business-related social capital and to examine their effect on the frequency of start-ups (Westlund, Larsson, and Olsson 2014) and private-sector growth (Fölster and Peltzman 2010) in Swedish regions. So, how large are the variations across municipalities with regard to how local entrepreneurs answer the two questions? Figure 8.5 provides the answer. The vertical axis measures the percentage of the surveyed local entrepreneurs that in 2012 answered “good,” “very good,” or “excellent” on either question. Municipalities are ranked in ascending order according to the questions regarding attitudes toward entrepreneurship amongst local public officials. The figure sends two main messages. First, there is a rather strong correlation between the two questions. When local entrepreneurs are satisfied with the attitudes of local public officials toward entrepreneurship, they also tend to be satisfied with the way in which local authorities apply and enforce laws and regulations. The correlation coefficient between the two series is 0.84, and a simple linear regression shows that perceptions of the attitudes of local public officials toward entrepreneurship can “explain” nearly 70 percent of the variance in the perceptions of how well local authorities apply and enforce laws and regulations. Second, there are substantial variations in the percentage of surveyed local entrepreneurs that are indeed satisfied with the attitudes and practice of local authorities, that is, the percentage that answered “good,” “very good,” or “excellent.” It is evident that across municipalities in Sweden, this percentage ranges from just over 10  percent to about 80 percent. To the extent that these patterns reflect overall differences in the enforcement stringency of regulations, they provide some basis for the claim that there are potentially large overall variations across local authorities in terms of how regulations are designed, applied, and enforced. One reason for these kinds of differences is to be found in the attitudes of the local population at large. It is not a far-fetched conjecture that regions in which the citizens have positive attitudes toward entrepreneurship develop “business friendly”

14  That is not to claim that the CSE-survey is without problems. First, local entrepreneurs may have an incentive not to answer truthfully, either with a negative or a positive bias, depending on whether the local government represents the same political views as the surveyed entrepreneurs. Second, there is a risk that dynamic entrepreneurs that have more contacts with local authorities, for example due to a need to invest in new plants in the municipality, and are therefore more likely to have negative experiences. In short, those who do not need to have contact with local authorities may not experience the “barriers”.

170    Critical Drivers of Local Competitiveness 90 80 70 60 50 40 30 20 10 0

0

50

100

150

200

Implementation of laws and regulations

250

Attitudes of public officials

Figure  8.5 Local Entrepreneurs’ Perceptions of (1)  Attitudes to Entrepreneurship among Local Public Officials and (2)  the Local Authority’s Application of Laws and Regulations across Swedish Municipalities in  2012 (Note:  Municipalities ranked in ascending order according to attitudes toward entrepreneurship amongst local public officials.) (Source: Confederation of Swedish Enterprise (2012))

regulations and enforcement.15 This brings us to the role of local informal institutions for entrepreneurship.

4.3  Informal Institutions: Local Entrepreneurship Cultures New institutional economists often argue that informal institutions have a long-term influence on the design as well as the implementation of formal institutions. Williamson (2000) stresses that “social embeddedness” is the highest level of institutions and that “this is where the norms, customs, mores, traditions, etc., are located” (596). Such informal institutions are claimed to change very slowly over time, and to impose important constraints on the formal institutional environment (the rules of the game) as well as on governance (the play of the game). This type of dependence on slowly changing informal institutions is at least as important for regions as it is for nations. Historical and locally embedded “entrepreneurship cultures” where the collective values and norms are positively oriented toward entrepreneurship could certainly be important in fostering a social environment conducive for entrepreneurs (cf. Aldrich 15 

After all, in democracies voters elect politicians that suggest and implement policies and reforms that voters prefer.

Local Competitiveness Fostered through Local Institutions    171 1990; Aldrich and Fiol 1994).16 The influence of such a culture does not pertain only to issues associated with design and enforcement of local regulations, and attitudes of elected local politicians and bureaucrats. Values and norms also have broad effects that permeate many levels of a (local) economy. Etzioni (1987) argues that the extent of entrepreneurial activities and their effects in a society are ultimately a reflection of the extent to which entrepreneurship is legitimized in that society: The extent to which entrepreneurship is legitimate, the demand for it is higher; the supply of entrepreneurship is higher; and more resources are allocated to the entrepreneurial function. . . . In the terms used by economists, legitimation is a key factor which affects the preferences, the constraints, and the resource allocation simultaneously. (175, 185)

If values are an immediate source of legitimation, as Etzioni (1987) maintains, then it follows that an entrepreneurial culture may have a strong influence on entrepreneurship. There are many different ways in which the effect of a local entrepreneurship culture is materialized and maintained over time. One mechanism runs through role model effects, which may take at least three forms. First, the sheer observation of a high density of entrepreneurs in one’s local environment may stimulate entrepreneurial behavior by inducing motivation and self-confidence, for example, with reference to notions like “If they can do it, I can too” (Sorenson and Audia 2000). Second, in regions with a strong entrepreneurship culture, entrepreneurs have a high social status, and this status effect may trigger entrepreneurial endeavors (cf. Casson 1995). Third, a high local density of role model entrepreneurs automatically translates into a high density of people with experience of running businesses, which means in turn that there is a local abundance of information and knowledge about the practice of entrepreneurship. This increases the odds that local inhabitants acquire entrepreneurial skills and become entrepreneurs (Guiso and Schivardi 2011). Minniti (2005) further maintains that social interactions with established entrepreneurs in a local milieu will reduce ambiguities and uncertainties about the practice of entrepreneurship and the start-up process. The above mechanisms exemplify how a local entrepreneurship culture, whatever its sources, breeds new entrepreneurs, who in turn help sustain the culture over time. In 16  There are of course many potential sources of an entrepreneurship culture. Regions with a history of dependence on one or a few large employers, such as mining and shipyard cities, are often argued to have developed a culture of labor rather than entrepreneurs, and associated high rates of historical unionization rates (cf. Glaeser et al. 2012). In the sociological literature, there are arguments that certain religions have historically been less favorable to entrepreneurship than others. Weber (1930) maintained that Protestant ethics favored entrepreneurship and capitalism. Sombart (1911) asserts that the intellectualism and flexibility of Judaism gave the Jews a catalyst role in the development of capitalism, especially as merchant and financiers. Moreover, the literature on social capital typically claims that persistent differences in economic outcomes have deep historical roots. A prime example is Putnam (1993): who maintains that regional differences in trust and cooperation (which influence current economic outcomes) in Italy are strongly related to the historical circumstances, where certain cities experienced independence in the first few centuries of the second millennium.

172    Critical Drivers of Local Competitiveness fact, local social network externalities in entrepreneurship—regardless of whether they have to do with information, knowledge, status effects, or self-confidence—suggest that entrepreneurship is self-reinforcing over time. As Minniti (2005, 3) puts it: “entrepreneurship creates a ‘culture’ of itself that influences individual behavior in its favor.” There are several studies that find robust empirical evidence that regions do have distinct entrepreneurship cultures. One set of evidence comes from analyses of long-term persistence in cross-regional variations in entrepreneurship. Fritsch and Wyrwich (2014) show that differences in self-employment and new firm formation across regions in Germany are not only substantial, but have endured over periods as long as 80 years. They find that the self-employment rate in 1925 in German regions is a highly robust determinant of their entrepreneurial activity in 2005. Since this is a time period covering several disruptive changes in the German economy, Fritsch and Wyrwich interpret this as reflecting a long-lasting influence of slowly changing locally embedded entrepreneurship cultures.17 The main intuition behind this inference is straightforward: culture changes slowly, and so should phenomena dependent on it. While most analyses do not cover such a long period of time, persistent variations in entrepreneurship across regions and a strong influence of lagged start-up rates on current ones have been documented for several different countries and time periods.18 Indirect evidence is also provided by analyses showing that the type of role model effects discussed above are important. Bosma et al. (2012) list three sets of evidence: (1) the decision to become an entrepreneur is positively associated with having entrepreneurs in one’s social network (Kim and Aldrich 2005; Klyver, Hindle, and Schøtt 2007); (2) there is a positive influence of having parents who are (or have been) entrepreneurs (Chlosta et al. 2012; Dunn and Holtz-Eakin 2000); and (3) entrepreneurial behavior of school, workplace, and neighborhood peers, respectively, influence entrepreneurial behavior (Falck, Heblich, and Luedemann 2012; Nanda and Sorensen 2010; Andersson and Larsson 2015; Giannetti and Simonov 2009). These findings point to the empirical relevance of the arguments of positive effects of local role models for entrepreneurship. Case studies offer another set of evidence of entrepreneurial regions. One of the most well-known studies in this vein is Saxenian’s (1994) analysis of Silicon Valley and Route 128. While both regions had a historically strong concentration of knowledge- and technology-intensive sectors and bright prospects for long-term economic resilience, the regions developed along different trajectories after the crisis period in the early 1980s. Silicon Valley continued to flourish, whereas Route 128 declined. Saxenian maintains that one important explanation for the divergent performance of the regions is 17 

As explained by the authors, the disruptive changes are massive and include the Great Depression starting in the late 1920s, World War II, occupation by the allied powers, massive in-migration, a new constitutional base and political system, as well as postwar economic reconstruction. 18  See, e.g., Andersson and Koster (2011) and Andersson (2012) for the case of regions in Sweden and Fotopoulos (2014) for the case of regions in the UK.

Local Competitiveness Fostered through Local Institutions    173 rooted in differences in entrepreneurship culture.19 A Swedish example is the so-called Gnosjö spirit. While this region is far from Silicon Valley in terms of economic vibrancy and technological sophistication, it is an example of a small remote region that manages to retain high employment rates despite its remoteness from leading metropolitan centers and a traditional manufacturing economic base. Its success in this regard is often claimed to be rooted in a strong entrepreneurship culture (Johannisson and Wigren 2006). This spirit is even recognized in the Swedish National Encyclopedia,20 where it is described as follows (translation by the authors): The Gnosjö spirit refers to the enterprising culture that prevails in the municipality of Gnosjö and its neighboring municipalities in the county of Småland. In this region, entrepreneurship is a way of life that dominates the local community, which implies, among other things, that the local authorities, banks, and trade unions adjust their practices to the needs and interests of the [local] enterprises.

It should still be recognized that arguments concerning local cultures for entrepreneurship may be criticized on the grounds that they often draw on experiences from regions not characterized by high-impact entrepreneurship, but rather small-scale family-oriented businesses in traditional (low-tech) sectors. Many examples of “entrepreneurial districts” discussed in the literature, such as the regions of Bologna and Emilia-Romagna known as the “third Italy” (Piore and Sabel 1984; Becattini 1990)  and the Gnosjö region (Johannisson 1984), may indeed be characterized by flexibility, small-scale businesses, and self-employment, but rarely by research- or technology-based new firms. The regions in third Italy are known for a specialization in craft-based industries (Boschma 2005). Similarly, the Gnosjö region is known for a specialization in traditional low-tech manufacturing sectors and has, by Swedish standards, a local labor force with a modest level of formal training. These are indeed characteristics not normally associated with Schumpeterian entrepreneurship in the form discussed in section 2 above.21 In this regard, Silicon Valley is an exception. Still, a strong local entrepreneurship culture is certainly only one of many potential explanations for the region’s success. Klepper (2010; 2011) attributes a large part of the growth of the semiconductor industry in the region to the establishment of a few large initial firms, notably Fairchild Semiconductor, which subsequently spawned new high-quality firms through spin-off processes. 19 

In an often quoted passage Saxenian (1994) quotes an entrepreneur who moved from Boston to Silicon Valley (p. 63): “In Boston, if I said I was starting a company, people would look at me and say: ‘Are you sure you want to take the risk? You are so well established. Why would you give up a good job as vice president at a big company?’ In California, I became a folk hero when I decided to start a company. It wasn’t just my colleagues. My insurance man, my water deliverer—everyone was excited. It’s a different culture out here.” 20  http://www.ne.se/lang/gnosj%C3%B6anda 21  What is more, we know of no empirical paper on peer effects and social network externalities that specifically deal with high-impact entrepreneurs. Instead, many of these papers use self-employment as the main indicator of entrepreneurship.

174    Critical Drivers of Local Competitiveness Related to this is the fact that California has institutional arrangements favoring mobility, including spin-offs. In particular, it proscribes so-called noncompete agreements, that is, agreements stating that an employee leaving a firm is not allowed to enter into or start a similar firm in competition with his or her previous employer. No doubt, the absence of such noncompete clauses may have stimulated spin-off processes and contributed to the region’s vibrant entrepreneurship culture (cf. Gilson 1999). This raises the issue of cultural explanations as proximate or ultimate causes. Klepper’s (2010, 2011) story suggests that a culture may be more an outcome of a first cohort of entrepreneurs that initiate virtuous self-reinforcing processes of spin-offs, labor mobility, and social network externalities. A vivid entrepreneurial culture may not be the cause but rather a byproduct of institutions that foster entrepreneurship (Boettke and Coyne 2009). Culture is then a proximate rather than an ultimate cause. Notwithstanding these critical remarks, there are strong theoretical arguments as well as an empirical research literature suggesting that local entrepreneurship cultures (irrespective of their source) are important in influencing entrepreneurial endeavors in regions, and that they also could influence pertinent formal institutions. The final question is then to what extent local entrepreneurship cultures could be fostered. We argue that, in general, cultural patterns are not immutable. Public policy can alter social attitudes over time. To the extent that norms and attitudes are culturally codified products of the reward structures in society, institutional changes are likely to affect norms and attitudes (Bowles 1998, Baumol, Litan, and Schramm 2007, 203ff; Smith 2003). But such processes may take many years, even decades, to complete. In a recent study, Fritsch et al. (2014) show that after the introduction of a formal institutional framework of a market economy in East Germany, it took about 15 years for the start-up rate to reach West German levels. Their findings suggest that informal institutions, such as entrepreneurship cultures, tend to change very slowly and are more persistent than formal ones.

4.4  A Local Policy for Productive Entrepreneurship: What Role for the Institutional Framework? Having discussed the role played by local formal and informal institutions in fostering local entrepreneurship, we now turn to the role played by the local institutional framework, as defined in sections 4.2 and 4.3. This is the local analog to entrepreneurship policy as defined in figure 8.1. Entrepreneurship policy puts individuals rather than firms at center stage. New firms and new ideas are after all developed by individuals, which means that the level of productive entrepreneurship in a locality is fundamentally linked to the local supply of productive entrepreneurs. Going back to the example of Detroit discussed in the introduction, an argument can certainly be made that the city lacked individual productive entrepreneurs capable of pushing the local economy in new directions. In a post on Economix in the New York Times in 2011 (Glaeser 2011) entitled “Can Detroit Find

Local Competitiveness Fostered through Local Institutions    175 the Road Forward?,” Edward Glaeser indeed claims that the city of Detroit “needed the home-grown talent that could have enabled a new generation of entrepreneurs.”22 This suggests that a key concern for local policy is the local supply of productive entrepreneurs. As discussed in section 4.1, one source of the spatial allocation of entrepreneurs (or people with entrepreneurial skills) is spatial sorting. Individuals are truly heterogeneous and differ in many dimensions, such as socioeconomic backgrounds, education, and work and occupational experience. Thus, they differ in terms of attributes associated with entrepreneurial skills, at least as assessed by the influence those kinds of attributes have on start-up propensities (see, e.g., the transition analyses in Dunn and Holtz-Eakin 2000 and in Andersson and Klepper 2013).23 Individuals are also mobile across space, especially within countries. Therefore, differences in entrepreneurship across places could simply be due to differences in the local supply of entrepreneurial people, which in turn may reflect a process of nonrandom spatial sorting of individuals with respect to entrepreneurial skills. The most recent literature on the sources of spatial disparities in economic outcomes, such as productivity, income, and employment, puts great emphasis on spatial sorting by skills. Analyses of spatial wage equations at the worker level, for instance, more often than not find that spatial sorting is the single most important explanation for differences in earnings across regions (Combes, Duranton, and Gobillon 2008; Larsson 2014; Andersson, Klaesson, and Larsson 2014). In other words, the general message is that who you are is much more important than where you live in explaining individual economic outcomes. People with different characteristics and skills make different choices regarding where to live and work. But what drives sorting of entrepreneurial people to various places? It would certainly be naive to claim that entrepreneurial individuals are forward-looking and completely informed of the institutional environment in various regions. There certainly exist potential entrepreneurs who, knowing that they at some point in the future would like to start a firm, select where to live and work based on the conditions for entrepreneurship. However, they are likely to be atypical. Our interpretation of the evidence is rather that many productive entrepreneurs, such as founders of high-impact start-ups, often did not have a long-term plan to become entrepreneurs. The decision to start a firm is to a great extent driven by contextual factors and serendipity, and often occurs after several years of work experience (Shane 2003). It could, for instance, be a discovery in a research lab, an idea developed while working in an incumbent firm, or an everyday life experience that triggers new firm formation. Howard Schultz, president and CEO of Starbucks, is a good example of the latter. On a trip to Italy he became captivated by Italian coffee bars and the 22 

http://economix.blogs.nytimes.com/2011/02/22/can-detroit-find-the-road-forward. Lucas’ (1978) classic model of entrepreneurship also explains differences in the likelihood of becoming an entrepreneur by the exogenously given “talent for managing”, understood as the ability to extract output from a given combination of inputs. While part of entrepreneurial skills of an individual may indeed be truly exogenous (“inherited”): in reality they are in many cases a side-effect of a sequence of decisions about education, work and place of residence. Yet, at a given moment in time entrepreneurial skills may be considered pre-determined by historical decisions and traits. 23 

176    Critical Drivers of Local Competitiveness coffee experience. This gave him the vision to bring the Italian coffeehouse tradition to the United States. On the other hand, one could argue that entrepreneurs choose a favorable environment once they have decided to start a firm. Still, although entrepreneurs are often characterized as “footloose” in theoretical models (e.g., Pflüger and Südekum 2008), available evidence of the location of start-ups and entrepreneurs suggests that they tend to start their firm in their “home region” where they are embedded and have social networks (Dahl and Sorenson 2009; 2012; Figueiredo, Guimaraes, and Woodward 2002).24 The default location choice of start-ups is typically to remain in the region where the entrepreneur already lives and works. This suggest that spatial sorting of entrepreneurial people may in many cases be due to reasons not directly related to the start-up process or (predetermined) intentions to start a firm. Spatial sorting of individuals with entrepreneurial skills is instead likely to occur in much the same way as it does for individuals in general—that is, as a result of factors such as availability of affordable housing, pleasant living conditions, quality of local public services, and labor market prospects. What may be particularly important for entrepreneurial individuals, however, is the thickness of the market(s) for skills. As the discussion of billionaire entrepreneurs in section 2 suggests, individuals with a potential to become productive entrepreneurs by founding firms that usher in new technologies and radical change are often highly educated with advanced degrees from elite universities. The location choice of talented, ambitious, and entrepreneurial graduates is often governed by career prospects and the long-term expected returns on their human capital. A recent analysis of graduates in Sweden by Ahlin, Andersson, and Thulin (2014) shows that nearly two-thirds of the graduates in natural sciences, engineering, and social sciences in Sweden choose to start their labor market career in one of the country’s three largest cities. They also find that the propensity to move to a large city upon graduation is significantly higher for graduates with greater abilities (or motivation).25 The big cities offer thicker markets for skills and more jobs and occupations where skilled and motivated graduates can exploit their abilities in full.26 In economic geography, the term “escalator region” is sometimes used to describe regions that are attractive to young educated talents (Fielding 1992). These regions offer superior prospects for returns to their abilities and educational investments in terms of progression from education into advanced jobs and churning between professional and managerial occupations. 24 

There are various explanations for this pattern: Attachment to family and friends, local social networks implying access to resources, ties to former colleagues which provide better access to potential employees etc. There is also evidence that firms started “at home” actually perform better than other firms (Dahl and Sorenson 2012; Carias and Klepper 2010). 25  High-school grades and the education-level of the graduates’ parents are used as proxies for latent abilities. 26  Ahlin, Andersson, and Thulin (2014) find that graduates moving to the large cities have higher employment rates, higher initial wages and experience more rapid wage growth in their first eight years on the labor market. Moreover, the faster wage growth is coupled with a higher rate of inter-firm job-switching.

Local Competitiveness Fostered through Local Institutions    177 Fielding (1992) portrays the South East region of England (notably the London region) as an “upward escalator” within the British urban and regional system.27 While it is natural think of big cities, such as London, New York, and Paris, as escalator regions, smaller cities and clusters can also be characterized as escalators within specific areas of expertise. In fact, the local presence of an entrepreneurial and innovation-driven firm may be a decisive factor in attracting talents. At the time when Microsoft moved its headquarters from Albuquerque to Seattle, for example, Seattle was not known as a city with good labor market prospects for skilled programmers and IT professionals (Moretti 2012). After Microsoft entered the city, it became more attractive as a place of residence for entrepreneurial talents in IT and related areas. Similarly, there is no doubt that the high density of entrepreneurial high-tech firms in Silicon Valley, not least Google, is important in attracting (and retaining) computer and programming talents to the region. Moreover, for small cities and regions far from the size and stature of Seattle or Silicon Valley, the local presence of an establishment of a multinational corporation can make a big difference for the ability to retain and attract talents. Despite the small size of the city or region, individuals can make a career through the “internal” labor market within the corporation. That is, the internal career opportunities within a multinational (including facilities abroad) could, from the viewpoint of individuals, “substitute” for a thick regional labor market for skills.28 The above arguments suggest that the spatial allocation of entrepreneurial talent is a function of the spatial distribution of entrepreneurial incumbent firms.29 This view is also consistent with the evidence of the role the existing stock of firms in a region plays for the emergence of new local high-impact firms. Despite the high variance in post-entry survival and growth of new firms (Mata, Portugal, and Guimarães 1995), there is robust evidence in the literature that incumbent firms are important sources for new productive (high-impact) entrepreneurs. In a well-known study, Bhidé (1994) surveyed the “Inc. 500” fastest-growing private firms and found that 71 percent had replicated or modified an idea encountered through previous employment. Several studies confirm that spin-offs in the form of employee start-ups are a particular class of entrants with lower hazards and stronger employment 27  The special role of the London region in England in attracting young talents was already recognized by Alfred Marshall. Referring to London’s attractiveness as place of residence for the “best blood in England” he further wrote: “. . . the most enterprising, the most highly gifted, those with the highest physique and the strongest characters go there to find scope for their abilities” (Marshall 1890). 28  In a survey of 2,000 highly educated people belonging to Swedish career network 4Potentials, it is shown that job opportunities, availability of more qualified positions within firms and the opportunity of working internationally are primary reasons for location decisions (4Potentials 2012). 29  An objection to this line of reasoning may be that there is interdependence between the spatial sorting of entrepreneurial firms and individuals—i.e., “good” firms choose locations where there are many “good” employees and vice versa. While this is certainly true at the aggregate level, the location of incumbent firms is truly exogenous from the viewpoint of an individual’s decision of where to live and work. The location choice of a single individual typically has no effect on the location choice of firms. Yet, the location choice of a single entrepreneurial firm may have an influence on the location choice of several individuals seeking a job.

178    Critical Drivers of Local Competitiveness growth (e.g., Andersson and Klepper 2013 and Eriksson and Kuhn 2006). Spin-offs are also shown to have played an important role in the historical evolution in many industries (US automobile industry, Klepper 2002; laser industry, Klepper and Sleeper 2005; semiconductors, Malone 1985; disk drive industry, Agarwal et al. 2004). There is also evidence that some types of incumbent firms are more likely to spawn spin-offs than others. Klepper (2001) reviews the evidence of spin-offs and concludes that entrepreneurial and innovative incumbents appear to be more inclined to spawn new firms. The Klepper and Sleeper (2005) study of the US laser industry, for example, shows that spin-off rates fall as the industry matures and knowledge becomes more embodied in physical rather than human capital. Moreover, Andersson and Klepper (2013) find that multinationals are somewhat less likely to spawn new firms, but that the firms they spawn have higher postentry survival rates. They interpret this as suggesting that founders of spin-offs from multinationals are of higher “quality”. This may in turn be explained by employees at multinationals having acquired a greater range of valuable experiences and knowledge. Multinationals typically have large R & D budgets and more intangible assets (Markusen 1995), which gives entrepreneurially talented employees good opportunities to acquire largely tacit knowledge, which is more difficult to acquire elsewhere. These patterns are often explained by organizational heredity, that is, spin-offs inherit knowledge and capabilities from their parent firms that boost their performance. But it can also reflect that entrepreneurial incumbents are better at attracting talented and entrepreneurial employees, who are alert to entrepreneurial opportunities and new ideas upon which a spin-off could be based. In summary, we have emphasized that one way in which potentially productive entrepreneurs (with supposedly higher propensity to found new high-impact firms) sort in space is by being attracted to regions offering thick markets for skills or many employment opportunities in entrepreneurial and innovative incumbent firms. When making their location choices these people may not have weighed in any intention of becoming an entrepreneur in the future. Instead, these individuals acquire experience and get ideas as employees in the incumbents, and may at a later stage choose to leave their employer and start a spin-off. There is indeed substantial empirical support for the argument that “incumbent firms are natural training grounds for the next generation of entrepreneurs” (Klepper 2011, 145) and that “the breeding grounds for entrepreneurial firms are more likely to be other entrepreneurial firms” (Gompers, Lerner, and Scharfstein 2005, 612).30 Spin-offs, in turn, typically locate in the same regions as the parent firms.

30  This view seems to also be shared by industry professionals. For example, Gordon Moore, the founder of Intel and known for “Moore’s Law”, has stated that “. . . successful startups almost always begin with an idea that has ripened in the research organization of a large company (or university). Any region without larger companies at the technology frontier or research organizations of large companies will probably have fewer companies starting or spinning off ” (as quoted in Auerswald and Branscomb 2003, p. 236).

Local Competitiveness Fostered through Local Institutions    179 Table 8.4  Percentage of Spin-offs Locating in the Same Municipality or Region as the Parent Firm Same municipality as parent firm Same region but not same municipality Sum

72% 16% 88%

Source: Andersson and Klepper (2013).

To illustrate the empirical relevance of the latter phenomenon, table 8.4 presents data on the fraction (in percent) of spin-offs in Sweden during the period 1993–2005 that locate in the same municipality and region as the parent firm. The original data come from Andersson and Klepper (2013) and are based on 15,103 spin-offs with two or more initial employees. The table shows that 72 percent of the spin-offs during the period located in the same municipality as the parent firm, and another 16 percent remained in the same labor market region although not in the same municipality. Thus, 88 percent of the spin-offs located in the same “home region” as the parent firm. This supports the view that a high rate of spin-offs in a region is strongly linked to the local presence of incumbents that spawn new firms, and illustrates the significant effects the local presence of entrepreneurial incumbents is likely to have on the level of productive entrepreneurship in a region. Given our discussion of underlying drivers of the local supply of entrepreneurs, the natural question concerns our conclusions for policy and, in particular, the role played by the type of local institutions discussed in sections 4.2 and 4.3. A general message from the perspective presented here is that local policy must recognize the interaction between existing businesses and individual location choices in shaping the geography of productive entrepreneurship. Talented people—including latent entrepreneurs—will flow to regions with an innovative and vibrant business sector. Likewise, innovative industries may be drawn to places with good access to a large pool of talented workers. A narrow view focusing either on firms or individuals will miss pertinent policy measures. While our emphasis on entrepreneurial individuals linked back to firms through the argument that they value market thickness and the prospects for returns on their abilities, we also underlined that they are likely to look for what people in general look for: Affordable housing, pleasant living conditions, local public services of high quality, and favorable labor market prospects. Local policy does best in being two-pronged and consider both firms and individuals, that is, the climate both for businesses and for people. Our review and assessment of the various ways in which local formal and informal institutions influence entrepreneurship also make clear that the local institutional framework is important in influencing the local supply of potential productive entrepreneurs as well as spin-off processes. An obvious influence runs through the effect of the local institutional environment on the location of entrepreneurial incumbents. The

180    Critical Drivers of Local Competitiveness extant research literature on the effect of local taxes and regulations (including stringency of enforcement) on local businesses suggests that they influence both the frequency of start-ups and the location decisions of incumbents. Unfavorable taxes and regulations certainly repel not only business activity in general, but also entrepreneurial incumbents that are potential sources of spin-offs and attractors of talented employees. Still, favorable local entrepreneurship policies not only increase the odds that a region develops or manage to attract entrepreneurial incumbents, but also the odds that a region reaps the full potential of having an entrepreneurial incumbent in a region. Consider a decision by an innovative firm to move into a region or the sudden start of a new truly Schumpeterian innovative firm in a region. Such events may or may not be due to the local institutional framework.31 Regardless of what triggered the event, it entails a significant potential for further regional development thanks to considerable multiplier effects. A well-designed entrepreneurship policy may stimulate the development of a local supply of supporting business services, input suppliers, and a variety of consumer services sectors, that is, local factors that are likely to be important in attracting additional productive entrepreneurs and entrepreneurial firms. In short, a process of cumulative growth that often characterizes local development may be initiated (cf. Myrdal 1957). We argue that the local institutional environment is important in enabling and materializing these potential effects. Multiplier effects of the type described above are at least in part materialized in the form of local entrepreneurs acting on the new opportunities. It is reasonable to conclude that the local response to these opportunities depends on local regulations and other types of formal as well as informal barriers to entrepreneurship. For example, an entrepreneurial incumbent firm may need to expand its operations as it attracts employees, and the ease with which such expansion is undertaken will depend at least in part on land-use restrictions. New employees in a region may demand attractive housing, the supply of which is influenced by residential zoning regulations. Furthermore, additional well-paid jobs in a region create demand for local everyday services such as hairdressers and restaurants (Moretti and Thulin 2013). Realization of these kinds of businesses also requires people who act on the new entrepreneurial opportunities (which may be conditioned by the local entrepreneurship culture) as well as enabling zoning regulations for retailing and other sectors. Greater variety in consumer services may in turn make the city more attractive for people valuing “consumer cities” (Glaeser, Kolko, and Saiz 2001). Likewise, the development of ancillary producer services sectors and a local “ecosystem” of supporting competence structures require entrepreneurs acting on those business opportunities.

31 

One event that appears to primarily have been due to serendipity is Microsoft’s decision to move from Albuquerque to Seattle in the late 1970s. Arguably this decision had little to do with returns to entrepreneurship, taxes, local labor pools or any of the standard factors discussed in the literature on business location (Moretti 2012). Seattle just happened to be Bill Gates’ hometown. Nevertheless, this decision turned out to be crucial for the development of Seattle.

Local Competitiveness Fostered through Local Institutions    181 All of the effects discussed above are positive in the sense that they feed back on the local economy. Once realized, they further enhance the region’s attractiveness for entrepreneurial firms as well as for individuals. It is clear that the formal as well as informal institutional framework conditions could facilitate or constrain. Local policymakers have much to gain from actively working on improving the formal as well as informal institutional framework conditions. A  favorable local institutional environment not only increases the odds that strategically important firms develop or move into the region; it also increases the “readiness” to exploit the potential associated with hosting entrepreneurial and knowledge-intensive activities.

5  Summary and Concluding Remarks We have reviewed and assessed the role local institutional framework conditions play in fostering local entrepreneurship. While local institutions always evolve and operate against the backdrop of the national institutional framework, in particular in nonfederal states, we have argued that there is plenty of room for local initiatives and policies to influence the entrepreneurial climate locally. This pertains to both formal (e.g., taxes, regulations, and stringency of enforcement) and informal (e.g., attitudes and social legitimacy) institutions. We also argued that the local institutional environment is essential in any local policy aimed at promoting high-impact entrepreneurship. The reason is that favorable local institutions not only increase the odds that a region develops or manage to attract entrepreneurial firms and individuals, but also the odds that a region reaps the full potential of hosting entrepreneurial and knowledge-intensive activities. There are many ways in which policymakers could improve the local institutional framework conditions. These include measures to reduce the regulatory burden and streamline administrative processes pertaining to businesses. Local regulations governing businesses should be efficient and transparent. They also comprise local taxes, housing regulations, zoning laws, and the overall quality of public services, notably healthcare and schools. It should be recognized that policies addressing the institutional setup are of a horizontal nature, meaning that they pertain to the overall local conditions that affect industry and individuals (cf. Nathan and Overman 2013 and Duranton 2011). Our discussion of policy does not focus on a specific type of high-impact entrepreneurship policy per se. Instead, we argue that the prospects of high-impact entrepreneurship are very much linked to the general institutional framework conditions of a locality, because they determine the overall attractiveness for both incumbent (entrepreneurial) firms and skilled workers, that is, latent entrepreneurs. In view of this we hold that local policy does best in being two-pronged, considering both firms and individuals, that is, the climate both for businesses and for people.

182    Critical Drivers of Local Competitiveness Vertical place-based policies, that is, policies focusing on attracting certain sectors or firms to a region, may be warranted in circumstances when targeted activities have proven to have a potential to generate local spillover effects (Glaeser 2001). For example, if certain establishments offer agglomeration economies, they may motivate location-based tax incentives to such establishments because their local presence could imply a self-reinforcing positive effect that benefits local residents and firms (cf. Greenstone, Hornbeck, and Moretti 2010). However, even if a vertical policy is successful in the sense that, say, a high-tech industry establishes itself in a region, we argue that the magnitude of spillover effects in terms of spin-offs, growth of supporting businesses, and inflow of skilled workers is fundamentally linked to the institutional environment. The odds that such spillover effects are realized depend on the local “readiness,” and this readiness is manifested in general local conditions such as tax codes, regulations, housing policies, zoning laws, and the overall quality of public services. A vertical policy that targets certain entrepreneurial activities with potentially high impact is thus unlikely to be worthwhile unless sound horizontal policies are in place. At the same time, vertical policies are subject to the challenge of targeting the right activities. Our review has also indicated a number of areas for further research. First, most studies focus on the effects of the local milieu on the frequency of entrepreneurship, typically measured by start-up rates of new firms, establishments, or crude measures like the self-employment rate. There is a need to explicitly study the link between the local institutional setup and the nature and direction of entrepreneurship, and consider broader measures of entrepreneurship, such as investments and expansion and diversification of entrepreneurial incumbent firms. Empirical analyses should recognize to a greater extent that entrepreneurship in the Schumpeterian sense cannot be equated with small businesses and self-employment and critically discuss what typical proxies can and cannot capture. Second, there is a need to further analyze the relationship between local institutions and the supply of entrepreneurship. One particular question in this regard concerns the empirical relevance of spatial sorting, that is, the extent to which people with entrepreneurial intentions or abilities (or incumbent firms with intentions to expand) sort themselves to regions with institutional setups favoring productive entrepreneurship. While we conjectured that such a sorting is likely to be important and to occur in interaction between individuals and the existing business structure, there is a dearth of studies that explicitly analyze the extent and nature of sorting of entrepreneurial skills across regions. Third, the literature on the role of local informal institutions is much larger than the literature on the role of local regulatory frameworks. While this may in part reflect that data on regulations at the local level are difficult to find, it is still an important area of research with potentially significant policy implications for local governments. One intriguing aspect concerns the interaction between formal and informal local institutions. Finally, our arguments regarding the role of the local institutional framework for productive entrepreneurship also suggest that the magnitude of local multipliers and growth effects associated with the local presence of entrepreneurial and knowledge-intensive

Local Competitiveness Fostered through Local Institutions    183 activities are a function of the local institutional environment. This is a hypothesis that remains to be rigorously tested in empirical work.

References 4Potentials. 2012. “Att behålla och utveckla talanger:  En studie genomförd hösten 2011 i talangnätverket 4Potentials.” Report at 4Potentials, Stockholm. Acemoglu, Daron, and Simon Johnson. 2005. “Unbundling Institutions.” Journal of Political Economy 113 (5), 947–97. Acs, Zoltan J. 2008. “Foundations of High Impact Entrepreneurship.” Foundations and Trends in Entrepreneurship 4 (6), 535–620. Acs, Zoltan J., Edward L. Glaeser, Robert E. Litan, Lee Fleming, Stephan J. Goetz, William R. Kerr, Steven Klepper, Stuart S. Rosenthal, Olav Sorenson, and William C. Strange. 2008. “Entrepreneurship and Urban Success:  Toward a Policy Consensus.” Economics Faculty Scholarship Paper No. 61, Syracuse University. http://surface.syr.edu/ecn/61 (accessed January 20, 2014). Acs, Zoltan J., and Lásló Szerb. 2007. “Entrepreneurship, Economic Growth and Public Policy.” Small Business Economics 28 (2–3), 109–22. Agarwal, Rajshree, Raj Echambadi, April M. Franco, and M. B. Sarkar. 2004. “Knowledge Transfer through Inheritance: Spinout Generation, Development, and Survival.” Academy of Management Journal 47 (4), 501–22. Aghion, Philippe, Richard Blundell, Rachel Griffith, Peter Howitt, and Susanne Prantl. 2004. “Entry and Productivity Growth:  Evidence from Microlevel Panel Data,” Journal of the European Economic Association 2 (2–3), 265–76. Aghion, Philippe, Richard Blundell, Rachel Griffith, Peter Howitt, and Susanne Prantl. 2009. “The Effects of Entry on Incumbent Innovation and Productivity.” Quarterly Journal of Economics 91 (1), 20–32. Ahlin, Lina, Martin Andersson, and Per Thulin. 2014. “Market Thickness and the Early Labor Market Career of University Graduates: An Urban Advantage?” Spatial Economic Analysis, 9 (4), 396–419. Aldrich, Howard E. 1990. “Using an Ecological Perspective to Study Organization Founding Rates.” Entrepreneurship Theory and Practice 4 (3), 7–24. Aldrich, Howard E., and Marlene C. Fiol. 1994. “Fools Rush in? The Institutional Context of Industry Creation.” Academy of Management Review 19 (4), 645–70. Andersen, Torben M. 2005. “The Danish Labor Market: From Excess to Shortage.” In Martin Werding, ed., Structural Unemployment in Western Europe: Reasons and Remedies. Cambridge, MA: MIT Press. Andersson, Martin. 2012. “Startup Rates, Entrepreneurship Culture and the Business Cycle.” In Pontus Braunerhjelm, ed., Entrepreneurship, Norms and the Business Cycle: The Swedish Economic Forum Report 2012. Stockholm: Swedish Economic Forum. Andersson, Martin, Pontus Braunerhjelm, and Per Thulin. 2012. “Creative Destruction: Entrepreneurship by Type, Sector and Sequence.” Journal of Entrepreneurship and Public Policy 1 (2), 125–46. Andersson, Martin, Johan Klaesson, and Johan P. Larsson. 2014. “The Sources of the Urban Wage Premium by Worker Skills: Spatial Sorting or Agglomeration Economies?” Papers in Regional Science 93 (4), 727–47.

184    Critical Drivers of Local Competitiveness Andersson, Martin, and Steven Klepper. 2013. “Characteristics and Performance of New Firms and Spinoffs in Sweden.” Industrial and Corporate Change 22 (1), 245–80. Andersson, Martin, and Sierdjan Koster. 2011. “Source of Persistence in Regional Startup Rates: Evidence from Sweden.” Journal of Economic Geography 11 (1), 179–201. Andersson, Martin, and Johan P. Larsson. 2015. “Local Entrepreneurship Clusters in Cities.” Journal of Economic Geography, doi:10.1093/jeg/lbu049. Armington, Catherine, and Zoltan J. Acs. 2002. “The Determinants of Regional Variation in New Firm Formation.” Regional Studies 36 (1), 33–45. Audretsch, David B. 2002. “The Dynamic Role of Small Firms: Evidence from the U.S.” Small Business Economics 18 (1), 13–40. Audretsch, David B., and Michael Fritsch. 1994. “The Geography of Firm Births in Germany.” Regional Studies 28 (4), 359–65. Audretsch, David B., Isabel Grilo, and A. Roy Thurik, eds. 2007. Handbook of Entrepreneurship Policy. Northamptom, MA: Edward Elgar. Audretsch, David B., A. Roy Thurik, Ingrid Verheul, and Sander Wennekers, eds. 2002. Entrepreneurship: Determinants and Policy in a European-U.S. Comparison. Boston: Kluwer. Auerswald, Philip E., and Lewis M.  Branscomb. 2003. “Valleys of Death and Darwinian Seas:  Financing the Invention to Innovation in the United States.” Journal of Technology Transfer 28 (2), 227–39. Baumol, William J. 1990. “Entrepreneurship: Productive, Unproductive, Destructive.” Journal of Political Economy 98 (5), 893–921. Baumol, William J. 2002. The Free-Market Innovation Machine. Princeton, NJ:  Princeton University Press. Baumol, William J., Robert E.  Litan, and Carl J.  Schramm. 2007. Good Capitalism, Bad Capitalism, and the Economics of Growth and Prosperity. New Haven, CT: Yale University Press. Becattini, Giacomo. 1990. “Italy.” In Werner Sengenberger, Gary W. Loveman, and Michael J. Piore, eds., The Re-emergence of Small Enterprises: Industrial Restructuring in Industrialised Countries. Geneva: International Labour Organisation. Bertrand, Marianne, and Francis Kramarz. 2002. “Does Entry Regulation Hinder Job Creation? Evidence from the French Retail Industry.” Quarterly Journal of Economics 117 (4), 1369–413. Besley, Timothy, and Maitreesh Ghatak. 2010. “Property Rights and Economic Development.” In Dani Rodrik and Mark R. Rosenzweig, eds., Handbook of Development Economics, vol. 5. Amsterdam: North-Holland. Beugelsdijk, Sjoerd. 2007. “Entrepreneurial Culture, Regional Innovativeness and Economic Growth.” Journal of Evolutionary Economics 17 (1), 187–210. Bhidé, Amar. 1994. “How Entrepreneurs Craft Strategies That Work.” Harvard Business Review 72 (2), 150–61. Boettke, Peter J., and Christopher J.  Coyne. 2009. “Context Matters:  Institutions and Entrepreneurship.” Foundations and Trends in Entrepreneurship 5 (3), 135–209. Boschma, Ron A. 2005. “Social Capital and Regional Development: An Empirical Analysis of the Third Italy.” In Ron A. Boschma and Robert C. Kloosterman, eds., Learning from Clusters: A Critical Assessment from Economic-Geographical Perspectives. Berlin: Springer. Bosma, Niels, Jolanda Hessels, Veronique Schutjens, Mirjam van Praag, and Ingrid Verheul. 2012. “Entrepreneurship and Role Models.” Journal of Economic Psychology 33 (2), 410–24. Bowles, Samuel. 1998. “Endogenous Preferences: The Cultural Consequences of Markets and Other Economic Institutions.” Journal of Economic Literature 36 (1), 75–111.

Local Competitiveness Fostered through Local Institutions    185 Braguinsky, Serguey, Lee G.  Branstetter, and André Regateiro. 2011. “The Incredible Shrinking Portuguese Firm.” National Bureau of Economic Research Working Paper No. 17265. Braunerhjelm, Pontus, Zoltan J. Acs, David B. Audretsch, and Bo Carlsson. 2010. “The Missing Link: Knowledge Diffusion and Entrepreneurship in Endogenous Growth.” Small Business Economics 34 (2), 105–25. Braunerhjelm, Pontus, and Johan E. Eklund. 2014. “Taxes, Tax Administrative Burdens and New Firm Formation.” Kyklos 67 (1), 1–11. Bureau of Labor Statistics. 2008. Current Population Survey 2008. http://www.census.gov/cps/. Caballero, Ricardo. 2007. Specificity and the Macroeconomics of Restructuring. Cambridge, MA: MIT Press. Carias, Cristina, and Steven Klepper. 2010. “Entrepreneurship, the Initial Labor Force and the Location of New Firms.” Paper presented at the International Schumpeter Society Conference 2010, on “Innovation, Organization, Sustainability and Crises,” Aalborg Denmark, June 21–24. Casson¸ Mark C. 1995. Entrepreneurship and Business Culture. Brookfield, MA: Edward Elgar. Casson, Mark C. 2003. The Entrepreneur. An Economic Theory. 2nd ed. Northamptom, MA: Edward Elgar. Chetty, Raj, John N.  Friedman, Tore Olsen, and Luigi Pistaferri. 2011. “Adjustment Costs, Firm Responses, and Micro vs. Macro Labor Supply Elasticities: Evidence from Danish Tax Records.” Quarterly Journal of Economics 126 (2), 749–804. Chlosta, Simone, Holger Patzelt, Sabine B. Klein, and Christian Dormann. 2012. “Parental Role Models and the Decision to Become Self-Employed: The Moderating Effect of Personality.” Small Business Economics 38 (1), 121–38. Cohen, Wesley M. 2005. “Patents and Appropriation:  Concerns and Evidence.” Journal of Technology Transfer 30 (1–2), 57–71. Combes, Pierre-Philipe, Gilles Duranton, and Laurent Gobillon. 2008. “Spatial Wage Disparities: Sorting Matters!” Journal of Urban Economics 63 (2), 723–42. Confederation of Swedish Enterprise. 2012. http://www.foretagsklimat.se. Stockholm. Dahl, Michael S., and Olav Sorenson. 2009. “The Embedded Entrepreneur.” European Management Review 6 (3), 172–81. Dahl, Michael S., and Olav Sorenson. 2012. “Home Sweet Home:  Entrepreneurs’ Location Choices and the Performance of Their Ventures.” Management Science 58 (6), 1059–71. Da Rin, Marco, Marina Di Giacomo, and Alessandro Sembenelli. 2011. “Entrepreneurship, Firm Entry and the Taxation of Corporate Income: Evidence from Europe.” Journal of Public Economics 95 (9–10), 1048–66. Daunfeldt, Sven-Olov, Niklas Elert, and Niklas Rudholm. 2013. “Startups and Firm In-Migration: Evidence from the Swedish Wholesale Industry.” Annals of Regional Science 51 (2), 471–94. Davis, Steven J., John Haltiwanger, and Ron Jarmin. 2008. “Young Businesses, Economic Churning, and Productivity Gains.” Kaufman Foundation Research Series, Turmoil and Growth. Kansas City, MO: Ewing Marion Kauffman Foundation. Dicken, Peter. 2003. Global Shift:  Reshaping the Global Economic Map in the 21st Century. Thousand Oaks, CA: Sage. Djankov, Simeon, Tim Ganser, Caralee McLiesh, Rita Ramalho, and Andrei Shleifer. 2010. “The Effect of Corporate Taxes on Investment and Entrepreneurship.” American Economic Journal: Macroeconomics 2 (1), 31–64.

186    Critical Drivers of Local Competitiveness Dunn, Thomas A., and Douglas J.  Holtz-Eakin. 2000. “Financial Capital, Human Capital, and the Transition to Self-Employment: Evidence from Intergenerational Links.” Journal of Labor Economics 18 (2), 282–305. Duranton, Gilles. 2011. “California Dreamin’: The Feeble Case for Cluster Policies.” Review of Economic Analysis 3 (1), 3–45. Edmark, Karin, and Roger H.  Gordon. 2013. “The Choice of Organizational Form by Closely-Held Firms in Sweden: Tax versus Non-tax Determinants.” Industrial and Corporate Change 22 (1), 219–43. Eriksson, Tor, and Johan Moritz Kuhn. 2006. “Firm Spinoffs in Denmark 1981–2000: Patterns of Entry and Exit.” International Journal of Industrial Organization 24 (5), 1021–40. Etzioni, Amitai. 1987. “Entrepreneurship, Adaption and Legitimation: A Macro-Behavioral Perspective.” Journal of Economic Behavior and Organization 8 (2), 175–89. Falck, Oliver, Stephan Heblich, and Erik Luedemann. 2012. “Identity and Entrepreneurship: Do School Peers Shape Entrepreneurial Intentions?” Small Business Economics 39 (1), 39–59. Fielding, Tony. 1992. “Migration and Social Mobility:  South East England as an Escalator Region.” Regional Studies 26 (1), 1–15. Figueiredo, Octavio, Paulo Guimaraes, and Douglas Woodward. 2002. “Home-Field Advantage: Location Decisions of Portuguese Entrepreneurs.” Journal of Urban Economics 52 (1), 341–61. Fölster, Stefan, and Sam Peltzman. 2010. “Competition, Regulation and the Role of Local Government Policies in Swedish Markets.” In Richard B.  Freeman, Birgitta Swedenborg, and Robert, Topel, eds., Reforming the Welfare State:  Recovery and Beyond in Sweden. Chicago: University of Chicago Press. Fotopoulos, Georgious. 2014. “On the Spatial Stickiness of UK New Firm Formation Rates.” Journal of Economic Geography 14 (3), 651–79. Fritsch, Michael, Elisabeth Bublitz, Alina Sorgner, and Michael Wyrwich. 2014. “How Much of a Socialist Legacy? The Re-emergence of Entrepreneurship in the East German Transformation to a Market Economy.” Small Business Economics 43 (2), 427–46. Fritsch, Michael, and Pamela Mueller. 2004. “Effects of New Business Formation on Regional Development over Time.” Regional Studies 38 (8), 961–75. Fritsch, Michael, and Florian Noseleit. 2013. “Investigating the Anatomy of the Employment Effect of New Business Formation.” Cambridge Journal of Economics 37 (2), 349–77. Fritsch, Michael, and Michael Wyrwich. 2012. “The Role of a Regional Entrepreneurship Culture:  Evidence and Consequences.” In Pontus Braunerhjelm, ed., Entrepreneurship, Norms and the Business Cycle: The Swedish Economic Forum Report 2012. Stockholm: Swedish Economic Forum. Fritsch, Michael, and Michael Wyrwich. 2014. “The Long Persistence of Regional Levels of Entrepreneurship: Germany, 1925–2005.” Regional Studies 48 (6), 939–54. Gans, Joshua S., and Lars Persson. 2013. “Entrepreneurial Commercialization Choices and the Interaction between IPR and Competition Policy.” Industrial and Corporate Change 22 (1), 131–51. Garicano, Luis, Claire Lelarge, and John Van Reenen. 2013. “Firm Size Distortions and the Productivity Distribution: Evidence from France.” CEPR Discussion Paper No. 9495. London Centre for Economic Policy Research, London. Giannetti, Mariassunta, and Andrei Simonov. 2009. “Social Interactions and Entrepreneurial Activity.” Journal of Economics & Management Strategy 18 (3), 665–709. Gilson, Ronald J. 1999. “The Legal Infrastructure of High-Technology Districts: Silicon Valley, Route 128 and Covenants Not to Compete.” New York University Law Review 74 (3), 575–629.

Local Competitiveness Fostered through Local Institutions    187 Glaeser, Edward L. 2001. “The Economics of Location Based Tax Incentives.” Harvard Institute of Economic Research Discussion Paper 1932, Harvard University. Glaeser, Edward L. 2007. “Entrepreneurship and the City.” National Bureau of Economic Research Working Paper No. 13551. Cambridge, MA: NBER. Glaeser, Edward L. 2011. “Can Detroit Find the Road Forward?” New York Times, Economix blog, February 22. Glaeser, Edward L., Sari Pekkala Kerr, and William R. Kerr. 2012. “Entrepreneurship and Urban Growth: An Empirical Assessment with Historical Mines.” National Bureau of Economic Research Working Paper No. 18333. Cambridge, MA: NBER. Glaeser, Edward L., Jed Kolko, and Albert Saiz. 2001. “Consumer City.” Journal of Economic Geography 1 (1), 27–50. Gompers, Paul A., and Josh Lerner. 2001. The Money of Invention: How Venture Capital Creates New Wealth. Cambridge, MA: Harvard University Press. Gompers, Paul A., Josh Lerner, and David Scharfstein. 2005. “Entrepreneurial Spawning: Public Corporations and the Genesis of New Ventures, 1986–1999.” Journal of Finance 60 (2), 557–615. Greenstone, Michael, Richard Hornbeck, and Enrico Moretti. 2010. “Identifying Agglomeration Spillovers: Evidence from Winners and Losers of Large Plant Openings.” Journal of Political Economy 118 (3), 536–98. Guiso, Luigi, and Fabiano Schivardi. 2011. “What Determines Entrepreneurial Clusters?” Journal of the European Economic Association 9 (1), 61–86. Haltiwanger, John, Ron S. Jarmin, and Javier Miranda. 2013. “Who Creates Jobs? Small versus Large versus Young.” Review of Economics and Statistics 95 (2), 347–61. Haughwout, Andrew, Robert Inman, Steven Craig, and Thomas Luce. 2004. “Local Revenue Hills: Evidence from Four U.S. Cities.” Review of Economics and Statistics 86 (2), 570–85. Hébert, Robert F., and Albert N.  Link. 2006a. “The Entrepreneur as Innovator.” Journal of Technology Transfer 31 (5), 589–97. Hébert, Robert F., and Albert N. Link. 2006b. “Historical Perspectives on the Entrepreneur.” Foundations and Trends in Entrepreneurship 2 (4), 261–408. Henrekson, Magnus, and Dan Johansson. 2009. “Competencies and Institutions Fostering High-Growth Firms.” Foundations and Trends in Entrepreneurship 5 (1), 1–80. Henrekson, Magnus, and Dan Johansson. 2010. “Gazelles as Job Creators:  A  Survey and Interpretation of the Evidence.” Small Business Economics 35 (2), 227–44. Henrekson, Magnus, and Tino Sanandaji. 2011. “Entrepreneurship and the Theory of Taxation.” Small Business Economics 37 (2), 167–85. Henrekson, Magnus, and Tino Sanandaji. 2014. “Small Business Activity Does Not Measure Entrepreneurship.” Proceedings of the National Academy of Sciences of the United States of America (PNAS) 111 (5), 1760–65. Henrekson, Magnus, and Mikael Stenkula. 2010. “Entrepreneurship and Public Policy.” In Zoltan J.  Acs and David B.  Audretsch, eds., Handbook of Entrepreneurship Research:  An Interdisciplinary Survey and Introduction. New York: Springer. Ho, Yuen-Ping, and Poh-Kam Wong. 2007. “Financing, Regulatory Costs and Entrepreneurial Propensity.” Small Business Economics 28 (2–3), 187–204. Holtz-Eakin, Douglas. 2000. “Public Policy toward Entrepreneurship.” Small Business Economics 15 (4), 283–91. Johannisson, Bengt. 1984. “A Cultural Perspective on Small Business-Local Business Climate.” International Small Business Journal 2 (2), 32–43.

188    Critical Drivers of Local Competitiveness Johannisson, Bengt, and Caroline Wigren. 2006. “The Dynamics of Community Identity Making in an Industrial District:  The Spirit of Gnosjö Revisited.” In Chris Steyaert and Daniel Hjort, eds., Entrepreneurship as Social Change. Northampton, MA: Edward Elgar. Johansson, Dan. 2010. “The Theory of the Experimentally Organized Economy and Competence Blocs: An Introduction.” Journal of Evolutionary Economics 20 (2), 185–201. Jorgenson, Dale W., and Ralph Landau, eds. 1993. Tax Reform and the Cost of Capital. An International Comparison. Washington, DC: Brookings. Kim, Philip H., and Howard E.  Aldrich. 2005. “Social Capital and Entrepreneurship.” Foundations and Trends in Entrepreneurship 1 (2), 55–104. Kirzner, Israel M. 1973. Competition and Entrepreneurship. Chicago:  University of Chicago Press. Klepper, Steven. 2001. “Employee Startups in High-Tech Industries.” Industrial and Corporate Change 10 (3), 639–74. Klepper, Steven. 2002. “The Capabilities of New Firms and the Evolution of the U.S. Automobile Industry.” Industrial and Corporate Change 11 (4), 645–66. Klepper, Steven. 2010. “The Origin and Growth of Industry Clusters: The Making of Silicon Valley and Detroit.” Journal of Urban Economics 67 (1), 15–32. Klepper, Steven. 2011. “Nano-economics, Spinoffs and the Wealth of Regions.” Small Business Economics 37 (1), 141–54. Klepper, Steven, and Sally Sleeper. 2005. “Entry by Spinoffs.” Management Science 51 (8), 1291–306. Klyver, Kim, Kevin Hindle, and Thomas Schøtt. 2007. “Who Will Be an Entrepreneur? How Cultural Mechanisms and Social Network Structure Together Influence Entrepreneurial Participation.” Frontiers of Entrepreneurship Research 27 (7), 305–20. Kolko, Jed, David Neumark, and Maricol Cuellar Mejia. 2013. “What Do Business Climate Indexes Teach Us about State Policy and Economic Growth?” Journal of Regional Science 53 (2), 220–55. Krugman, Paul R. 1994. “Competitiveness:  A  Dangerous Obsession.” Foreign Affairs 73 (2), 28–44. Larsson, Gerhard. 2006. Spatial Planning Systems in Europe:  An Overview. Amsterdam: IOS Press. Larsson, Johan P. 2014. “The Neighborhood or the Region? Reassessing the Density-Wage Relationship Using Geo-Coded Data.” Annals of Regional Science 52 (2), 367–84. Lazerson, Marco H., and Gianni Lorenzoni. 1999. “The Firms That Feed Industrial Districts: A Return to the Italian Source.” Industrial and Corporate Change 8 (2), 235–66. Lerner, Josh. 2009. Boulevard of Broken Dreams: Why Public Efforts to Boost Entrepreneurship and Venture Capital Have Failed—and What to Do about It. Princeton, NJ:  Princeton University Press. Lucas, Robert E. 1978. “On the Size Distribution of Business Firms.” Bell Journal of Economics 9 (2), 508–23. Lundström, Anders, and Lois Stevenson. 2002. On the Road to Entrepreneurship Policy. Örebro: FSF. Malone, Michael S. 1985. The Big Score: The Billion Dollar Story of Silicon Valley. Garden City, NJ: Doubleday. Markusen, James R. 1995. “The Boundaries of Multinational Enterprises and the Theory of International Trade.” Journal of Economic Perspectives 9 (1), 169–89. Marshall, Alfred. 1890. Principles of Economics. London: Macmillan.

Local Competitiveness Fostered through Local Institutions    189 Martin, John, and Stefano Scarpetta. 2012. “Setting It Right: Employment Protection, Labour Reallocation and Productivity.” De Economist 160 (2), 89–116. Mata, José, Pedro Portugal, and Paulo Guimarães. 1995. “The Survival of New Plants: Start-up Conditions and Post-entry Survival.” International Journal of Industrial Organization 13 (3), 459–81. Minniti, Maria. 2005. “Entrepreneurship and Network Externalities.” Journal of Economic Behavior and Organization 57 (1), 1–27. Moretti, Enrico. 2012. The New Geography of Jobs. New York: Houghton Mifflin Harcourt. Moretti, Enrico, and Per Thulin. 2013. “Local Multipliers and Human Capital in the United States and Sweden.” Industrial and Corporate Change 22 (1), 339–62. Murphy, Kevin M., Andrei Shleifer, and Robert W.  Vishny. 1991. “The Allocation of Talent: Implications for Growth.” Quarterly Journal of Economics 106 (2), 503–30. Myrdal, Gunnar. 1957. Economic Theory and Underdeveloped Regions. London:  University Paperbacks, Methuen. Nanda, Ramana, and Jesper B.  Sorensen. 2010. “Workplace Peers and Entrepreneurship.” Management Science 56 (7), 1116–26. Nathan, Max, and Henry Overman. 2013. “Agglomeration, Clusters, and Industrial Policy.” Oxford Review of Economic Policy 29 (2), 383–404. Nicoletti, Giuseppe, and Stefano Scarpetta. 2003. “Regulation, Productivity and Growth: OECD Evidence.” Economic Policy 18 (36), 9–72. North, Douglass C. 1990. Institutions, Institutional Change and Economic Performance. Cambridge: Cambridge University Press. OECD. 1997. The OECD Report of Regulatory Reform. Paris: OECD. OECD. 2003. The Sources of Economic Growth in the OECD Countries. Paris: OECD. Papke, Leslie E. 1991. “Interstate Tax Differentials and New Firm Location.” Journal of Public Economics 45 (1), 47–68. Pflüger, Michael, and Jens Südekum. 2008. “A Synthesis of Footloose-Entrepreneur New Economic Geography Models:  When Is Agglomeration Smooth and Easily Reversible?” Journal of Economic Geography 8 (1), 39–54. Piore, Michael J., and Charles F.  Sabel. 1984. The Second Industrial Divide:  Possibilities for Prosperity. New York: Basic Books. Putnam, Robert D. 1993. Making Democracy Work: Civic Traditions in Modern Italy. Princeton, NJ: Princeton University Press. Rodrik, Dani, Arvind Subramanian, and Francesco Trebbi. 2004. “Institutions Rule:  The Primacy of Institutions over Geography and Integration in Economic Development.” Journal of Economic Growth 9 (2), 131–65. Rosen, Harvey S. 2005. “Entrepreneurship and Taxation:  Empirical Evidence.” In Vesa Kanniainen and Christian Keuschnigg, eds., Venture Capital, Entrepreneurship and Public Policy. Cambridge, MA: MIT Press. Rosenberg, Nathan, and Luther E.  Birdzell. 1986. How the West Grew Rich:  The Economic Transformation of the Industrial World. New York: Basic Books. Saez, Emmanuel. 2010. “Do Tax Filers Bunch at Kink Points? Evidence, Elasticity Estimation, and Salience Effects.” American Economic Journal: Economic Policy 2 (3), 180–212. Saxenian, AnnaLee. 1994. Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press. Schivardi, Fabiano, and Roberto Torrini. 2008. “Identifying the Effects of Firing Restrictions through Size-Contingent Differences in Regulation.” Labour Economics 15 (3), 482–511.

190    Critical Drivers of Local Competitiveness Schneider, Cédric, and Reinhilde Veugelers. 2010. “On Young Highly Innovative Companies:  Why They Matter and How (Not) to Policy Support Them.” Industrial and Corporate Change 19 (4), 969–1007. Schuetze, Herbert J., and Donald Bruce. 2004. “Tax Policy and Entrepreneurship.” Swedish Economic Policy Review 11 (2), 233–65. Schultz, Theodore W. 1979. Concepts of Entrepreneurship and Agricultural Research. Kaldor Memorial Lecture, Iowa State University, October. Schumpeter, Joseph A. 1934. The Theory of Economic Development. Cambridge, MA: Harvard University Press. Shane, Scott. 2003. A General Theory of Entrepreneurship: The Individual-Opportunity Nexus. Northampton, MA: Edward Elgar. Shane, Scott. 2008. The Illusions of Entrepreneurship:  The Costly Myths That Entrepreneurs, Investors, and Policy Makers Live by. New Haven, CT: Yale University Press. Sillamaa, Mary Anne, and Michael Veall. 2001. “The Effect of Marginal Tax Rates on Taxable Income: A Panel Study of the 1988 Tax Flattening in Canada.” Journal of Public Economics 80 (3), 341–56. Sinn, Hans-Werner. 1996. “Social Insurance, Incentives and Risk Taking.” International Tax and Public Finance 3 (3), 259–80. Skedinger, Per. 2010. Employment Protection Legislation: Evolution, Effects, Winners and Losers. Northampton, MA: Edward Elgar. Small Business Administration. 2007. 2007 Survey of Business Owners. http://www.census.gov/ econ/sbo/. Smith, Vernon. 2003. “Constructivist and Ecological Rationality in Economics.” American Economic Review 93 (3), 465–90. Sombart, Werner. 1911. Die Juden und das Wirtschaftsleben. Leipzig:  Duncker. Trans. M. Epstein as The Jews and Modern Capitalism. Kitchener: Batoche Books, 2001. Sorenson, Olav, and Pino G.  Audia. 2000. “The Social Structure of Entrepreneurial Activity:  Geographic Concentration of Footwear Production in the United States, 1940–1989.” American Journal of Sociology 106 (2), 424–62. Stangler, Dane. 2010. “High-Growth Firms and the Future of the American Economy.” Kaufman Foundation Research Series Firm Formation and Economic Growth. Kansas City, MO: Ewing Marion Kauffman Foundation. Tannenwald, Robert. 1997. “State Regulatory Policy and Economic Development.” New England Economic Review, March–April, 83–107. van Stel, André, David Storey, and A. Roy Thurik. 2007. “The Effect of Business Regulations on Nascent and Young Business Entrepreneurship.” Small Business Economics 28 (2–3), 171–86. Weber, Max. 1930. The Protestant Ethic and the Spirit of Capitalism. Trans. Talcott Parsons. London: Allen and Unwin. Wennekers, Sander, and A. Roy Thurik. 1999. “Linking Entrepreneurship and Economic Growth.” Small Business Economics 13 (1), 27–56. Westlund, Hans, Johan P.  Larsson, and Amy Rader Olsson. 2014. “Startups and Local Entrepreneurial Social Capital in the Municipalities of Sweden.” Regional Studies 48 (6), 974–94. Williamson, Oliver E. 2000. “The New Institutional Economics: Taking Stock and Looking Ahead.” Journal of Economic Literature 38 (3), 595–661.

Chapter 9

T he National Re s ou rc e Cur se in the A ra b G u l f Rapid Change and Local Culture Thomas Andersson

Introduction Following years of obsession with the notion of a “resource curse,” a wealth of natural resources in a country’s possession has recently started to look more like a blessing. At least this appears to be the case when judging the performance of certain emerging economies, including some in the Middle East. In particular, the oil-rich Arab Gulf, the so-called GCC countries,1 have displayed a dramatically strengthened macroeconomic performance, a much increased gross domestic product per capita, and a lift in many aspects of economic competitiveness. The general validity of the curse has been questioned for years. A number of today’s developed and diversified economies were, for instance, clearly helped by rich natural resources at the outset of their industrialization process. The manifestations and alleged causes of the curse have also been changing over time, suggesting that the impact of natural resource abundance on economic performance is heavily influenced by local context. In the Middle East, current social and economic developments are closely associated with what is commonly referred to as the “Arab Spring,” the ultimate outcomes of which are similarly pending. High hopes in some corners at the dawn of bottom-up initiative and constructive engagement by enlightened new generations have increasingly given way to backlash under the influence of conservatives, the military, or another incumbent vested interest, depending on context. 1  The GCC (Gulf Cooperation Council), whose members are the Kingdom of Saudi Arabia, the Kingdom of Bahrain, Kuwait, the State of Qatar, the Sultanate of Oman, and the United Arab Emirates (UAE), promotes cooperation between its members to achieve unity.

192    Critical Drivers of Local Competitiveness Underlying the Arab Spring stand other developments, among them rapid change in technology, demography, and education, all of which interact with institutions and culture that are strongly embedded at the local level. None of these factors can reasonably be viewed as truly independent and capable of dictating the ultimate influence of the others. Reviewing development patterns and trends across the GCC countries, this chapter explores various ways in which the influence of natural resource abundance plays out in this context. It further examines issues and reforms in the Sultanate of Oman, a fairly resource-rich Arab country that has displayed a generally favorable economic performance for several decades, while also apparently being capable of weathering the Arab Spring. Despite Oman’s performance, we observe the presence of costly contradictions in policy and conditions for innovation and entrepreneurship on the ground that are rooted in a combination of resource curse and culture. The chapter is organized as follows. Section 2 provides background on several strands of relevant literature, in part to highlight the notion of the resource curse, its alleged features, and what may lay behind it. Section 3 takes further note of disparate outcomes, including the presence of relatively strong resource-rich performers in the Middle East, and discusses what may explain changes in the role of natural resource abundance. The processes we associate with the Arab Spring are analyzed in section 4. Issues and policies in the Sultanate of Oman are examined in section 5. The last section concludes with recommendations.

Behind the Natural Resource “Curse” In the early days of independence, much attention was paid to the importance for developing countries of gaining control over their natural resource base and local production capacity (Nurkse 1953; Rostow 1960). Confronted with ample evidence of the difficulties that ensued, a dependency theory saw power relations, political and economic, as responsible for leaving developing countries with poor returns for their natural resource wealth (Prebisch 1950). Other strands of literature focused on how to access logistics chains and markets, including through the attraction of foreign direct investment and associated buildup of technical and managerial capabilities. In parallel, others pondered what natural resource wealth actually means for a country and its prospects for development. Anecdotal comparisons such as those presented by World Bank economists between the superb growth trajectories of the resource poor Republic of Korea or Singapore, on the one hand, and the dismal record of Ghana or Kongo, on the other, were claimed to drive home a generally valid point; rather than distracted by how to handle natural resources, countries were better off if they focused on education and how to cultivate a productive workforce. Gradually, studies attempting to explain cross-country variation in growth came up with contradictory findings for the impact of natural resource wealth. The difficulty of nailing down what matters was, however, more pervasive than that. Although some

The National Resource Curse in the Arab Gulf    193 more convincing results lingered in regard to human capital, investment in education, years spent in school, and levels of entrepreneurship were likewise found not to leave much of a mark in growth statistics (Psacharoulos 1994; Barro and Lee 1996; de la Fuente and Dmenech 2000; Audretsch and Thurik 2001; OECD 2001). At the end of the day, the literature has kept scrambling how to unlock the unexplained residual, commonly referred to as total factor productivity (TFP) growth,2 beyond general associations with “technical progress” or “better ways of doing things” (Solow 1957). Examining the mechanisms at play in the economics of natural resource wealth, some studies concluded on negative consequences of dependency on commodity export coupled with price volatility. On the other hand, such outcomes were shown to be contingent on country-specific factors (Van der Ploeg and Poelhekke 2010). Separately, as had first been observed for the Netherlands, a windfall in export earnings may cause an exchange rate appreciation, leading to “crowding out” of other activities in the open and tradable part of the economy, while benefiting public-sector growth and nontradables (Corden 1984). In itself, however, this does not explain a curse resulting in reduced growth (Auty 2001). Because natural resource earnings tend to accrue to governments, there was also the observation of an inflated status for the latter, including reduced accountability to the general public. This has been argued to suppress the proportion of national income that flows to the population at large and bring less use of public resources to support societal objectives (Devarajan, Minh Le, and Raballand 2010). The mechanism would be that of natural resource wealth translating into poor governance, including less incentive to develop democratic institutions and a tendency for natural resource earnings to boost autocratic regimes and investment in “white elephants,” such as monuments or military might to back up those currently in power and control of government coffers. Some studies debated whether the curse operated through institutional linkages or the quality of policies, such as fiscal responsibility. Part of the impact would emanate from less pressure to undertake needed structural reforms, such as those aimed to open up for more competition, sharper frameworks for education, learning and merit-based promotion, and the establishment of new enterprises (Sala-i-Martin and Subramanian 2003; Isham et al. 2005; Bulte, Damania, and Deacon 2005; Arzeki and Bruckner 2009; Amin and Djankov 2009). Both individuals and institutions would have less drive to engage in private-sector development more generally and in risk taking, which is inherent to innovation, entrepreneurship, and start-up activity specifically. Related to this aspect, natural resources serve as a lure for rent-seeking, that is, for gaining privilege and a share of the gains through political clout rather than economic achievement, as well as complacency when it comes to pushing for competition and economic

2  Also referred to as multifactor productivity, TFP reflects the overall efficiency with which capital and labor are put to use. It is boosted by generally improved ways of producing goods and services, and tends to be driven by technical progress and innovation.

194    Critical Drivers of Local Competitiveness efficiency (Sachs and Warner 2001; Torvik 2002). Even where regimes make the effort to distribute the returns widely, there is the danger of picking up the habits of a “cozy life.” To some degree, the notion of a curse was underpinned by empirical evidence derived from cross-country studies (e.g., Sachs and Warner 1995; Gylfason 2001). As was clear from early on, however, that evidence was far from robust. If natural resource earnings, as a share of gross domestic product (GDP), are used as proxy for natural resource assets, the group of natural resource-rich economies will by definition include agriculture-dependent and undiversified economies. These would probably be better defined as “innovation and human capital poor,” and a high share of natural resources in the economy is then as much an outcome of slow growth, and a proven inability to diversify, as the opposite (Smith 2007). Lederman and Maloney (2007) identified various problems with the empirical underpinnings of the curse. Sachs and Warner’s results were found not to hold, for instance, given a different specification of the studied time period or other handling of a few outliers that achieved high growth without natural resources. Herb (2005) and Alexeev and Conrad (2009) concluded against the presence of a natural resource curse, especially in regard to oil and mineral wealth. As for volatility, a well-developed financial system was found to cushion the presence of negative impacts (Van der Ploeg and Poelhekke 2009). The dynamics of education, social capital, and culture in this context require special consideration. Knowledge development represents a social process engaging individuals that exchange tacit and explicit information (Nonaka and Takeuchi 1995). Constructive exchange generally begins with efforts aligning individual cognitive perceptions and deriving a group-level understanding as a basis for collaboration. Learning by doing, face-to-face communication, and personal demonstration relate to geographical proximity and the properties of local environment (Jaffe 1989; Saxenian 1994; Audretsch and Feldman 1995; Almedia and Kogut 1997). Of relevance is the term social capital (Putnam 1993) as a source of trust-based relationships, enabling cooperation between individuals within and across organizations and institutions with consequences for learning and the implementation of new ideas (Storper 1999). Institutions along with culture influence social capital in a particular location (Glaeser, Laibson, and Sacerdote 2004). Established codes of communication meanwhile coevolve with value systems that may work out in different ways. Part of the purpose naturally is about establishing demarcation lines against “others.” Turf may facilitate specialization but also drive inward-mindedness. Also, societal structures built in a past situation no longer prevalent may be embedded in lingering values and attitudes, in effect setting up formidable barriers to relearning and adjustment (Hamel 2004; Kumar and Matsusaka 2004). This matters not least to innovation, which is crucial for turning research and new knowledge into commercial value and, by its nature, is also socially disruptive. Especially in entrenched institutions with established resources and market positions, “open innovation” is commonly promoted as a countermeasure (Chesbrough 2003). Intensive communication through the Internet and digital tools can now replace, or enhance, interfaces that previously had to be local. The arrival of special research and innovation networks enables, for instance, researchers and practitioners in

The National Resource Curse in the Arab Gulf    195 different parts of the world to use e-science3 for joint work on the same data (European Commission 2010). Still, the qualities of a local environment remain critical for its attractiveness and ability to maintain precious but mobile production factors. Higher value added will be supported to the extent that a location stands out as offering unique qualities in particular areas of specialization, including in the development and realization of new knowledge opportunities, requiring a presence of first-rate expertise, technology, financial assets, and supportive services of relevance to those areas. Less mobile factors meanwhile may provide “glue” in this regard (van Hippel 1994), applying to infrastructure as well as societal and environmental amenities that may be critical for shaping a local environment that is favorable to knowledge exchanges. In the present context, the question what richness by way of natural resources means for all this is pivotal.

Disparate Outcomes The growing literature examining the natural resource curse in essence has failed to conclude on any easily generalized uniform relationships. Naturally this may reflect the diversity of influences caused by natural resource earnings and variation in the way these play out in different kinds of context. Some of the contradictory empirical results have to do with developed countries that once were resource based but able to gradually diversify their economies. Finland, Norway, and Sweden in northern Europe, as well as Australia and Canada, belong in that category.4 While these resource-rich countries managed to diversify and develop endogenous knowledge capabilities, they have mostly had a history of strong state intervention, large state-run corporations, and well-organized vested interests. Influenced to this day by their availability of natural resources and associated traditions, they in effect set a precedent in regard to the potential value of natural resources as a basis for productive investment, applying to physical infrastructure as well as to education, health, environmental protection, and so forth. Several emerging or developing natural resource-rich countries across the Middle East, Africa, Latin America, and East Asia have likewise demonstrated that such success is possible (Fasano 2002; Sturm, Adolf, and Peschel 2008; Frankel 2010). Some of the starkest examples are found in the category of oil and gas producers, notably among the GCC countries.5 Despite exceptional dependence on their natural resource base, this 3 

E-science is generally defined as the combination of three factors: the sharing of computational resources, distributed access to massive data sets, and the use of digital platforms for collaboration and communication. The concept covers the spectrum of modern research and education from the arts to the traditional physical sciences, from the theoretical to the experimental, from the commercial to the academic, among numerous other dimensions (European Commission 2010). 4  Finland and Sweden have been shown to benefit from spillovers emanating from the pulp and paper industry (Blomström and Kokko 2007). 5  Meanwhile, the fact that such competitiveness rankings are based on opinion surveys, in this case interviews among business executives, rather than official data, does not refute their relevance. The views held by such agents are of tangible importance for the direction and outcomes of economic processes.

196    Critical Drivers of Local Competitiveness country grouping has displayed very high economic growth over several decades, with all the GCC countries now beyond the middle-income economy status. Saudi Arabia, Oman, Qatar, and the United Arab Emirates occupy, for instance, places 4–7 respectively in the most recent World Competitiveness Report’s ranking of macroeconomic stability (World Economic Forum 2013). On a policy variable such as “Government Procurement of Advanced Technology,” they rank 6, 12, 1, and 3 worldwide.6 From having been basically medieval societies just a few decades ago, these countries thus now rank among the most stable, richest, and also, in some respects, most sophisticated economies in the world. Although their development displays significant downsides as well, which will be returned to, their rise would not have been possible had it not been for oil and gas riches. What has been the cause of such change in perspective? For one, the economics of commodities is evolving. The past century of secularly falling commodity prices was overturned in the past decade. The price increases in commodities that took place between 2000 and 2008 basically erased all the declines of the 20th century. Following a temporary decline with the subsequent financial crisis, commodity prices took off again. On the other hand, volatility has increased as well across a range of products and markets, with hydrocarbon prices contracting dramatically in 2014. Widespread effort by individual actors now aims to secure more reliable terms for trading critical natural resources (Bleischwitz 2014).7 An increasing ability of resource-rich developing countries to widen their control of stages in the value-added chain is also at work. In this, increased transparency and competition following from a combination of technical progress, liberalization, and regulatory reforms, inevitably giving rise to more seamless and accessible transport and logistics chains, have boosted their capacity. Reviewing the prevalence of natural resource-rich economies within the category of “rising stars” with respect to knowledge generation and development suggests they closed the gap between themselves and previously more developed economies in strategically important respects. The diffusion of information and communication technology (ICT), displayed in figure 9.1, provides an illustration. As can be seen, the GCC countries continue to lag in, for example, fixed networks. In regard to mobile telephony, by contrast, the rate of diffusion in the “new stars” runs ahead even leading OECD countries. Developing countries that lack any corresponding natural resource wealth are much behind. Such progress is visible across several categories of technology and human capital indicators. In the Middle East, natural resource wealth has generally led to more 6 

This is despite the fact that a prime initial outlier in terms of economic expansion in the region, the Emirate of Dubai, is basically lacking a natural resource base. As the financial bubble of 2008 burst, on the other hand, Dubai was saved from economic collapse by the helping hand of the oil-rich Emirate of Abu Dhabi. 7  These patterns have to do with the fundamental impact of increased human disturbances to the global environment, coupled with increasing connectivity causing local turbulence to spread rapidly, with unintended side-effects on a range of resources and regions. Markets everywhere are upset along the dimensions of prices, actors, and environmental dynamics.

The National Resource Curse in the Arab Gulf    197 200 180 160 140 120 100 80 60 40 20 0 Saudi Arabia

Bahrain UAE

Qatar

Kuwait

Fixed telephone lines Internet users

Egypt

Oman

Jordan Yemen EU12+3

Mobile cellular subscriptions Fixed broadband subscriptions

Figure 9.1  Penetration of Selected ICTs, 2013 or Latest (Per 100 People) (Source: “International Telecom Union” (2015).)

resources being devoted to a range of investments that matter for growth and social progress (Andersson 2011). From these observations it does not follow, however, that natural resources are solely a blessing, and not a curse. For one, increased levels of investment do not guarantee desired outcomes. In fact, all available information points to the presence of lingering quality problems across the Arab Gulf with educational outcomes, workforce skills, and work ethics (Sala-i-Martin and Artadi 2002; World Economic Forum 2013). A limited supply of skilled professionals blends with traditionalist trading culture that is prone to a cautious, marginalist, and short-term approach to commercial deals with outsiders, tight control of financial resources, and high reliance on tangible investment (Bizri 2012). What progress will occur hence cannot be understood without taking into consideration the influence of culture. Despite much heterogeneity, countries across the Arab world display distinct commonalities that emanate from history, language, and shared core values. While human relations are critically important, the focus is on one's own tribe, organization, or network, with a strong sense of loyalty and adherence to hierarchy within that sphere. This is also manifested in the concept of “wasta,” important connections or favoritism (Bellow 2003; Al-Ramahi 2008). As for external relations, Arabs are known for profound hospitality towards foreigners as well as a non-confrontational approach in negotiation (Hutchings and Weir 2006). These features matter for cross-border and inter-sectoral collaboration as well. There is a tendency for human resources to be scattered across fragmented organizations that display limited exchanges between them, instead forming isolated islands. Separately, the overriding approach to natural resource exploitation and use has entered a new phase, marked by strong links to environmental concerns and consideration for their

198    Critical Drivers of Local Competitiveness entire life cycles. While minerals and energy fuels have long been viewed as commodities, water resources, land, and food production have usually been seen as subject to more standard property rules. With increasing numbers of people and growing strains on the environment, both locally and globally, the conception of commodities has changed. Each phase in exploitation of raw materials, production of food or water, and the associated product cycle matter for replenishment, waste, health, and security of everyday life. Hence commodities increasingly stand at the core of political economy analysis, extending beyond traditional consumer behavior to rise the deep dependency on local attitudes and actions. At the same time, crafting sustainable solutions requires consideration to cross-border developments and global trends. There is also a distinct need, to move away from focus on single resources or products to the understanding of entire systems, including both social and environmental factors.

The Pending Outcome of the “Arab Spring” The last few years have seen a radical change in the economic and political dynamics of the Middle East. An important element of this is what has been labeled the “Arab Spring,” gradually seen as the start of much stormier season. To some the Arab Spring “fell from the sky,” an absurd consequence of the insult at the hands of a female police officer experienced by a greengrocer who set himself ablaze in downtown Tunis in December 2010. In reality, there had been signs, and examples of similar rage, for years, even decades (Andersson and Abdelkader 2012). The difference this time had to do with the momentum, the remarkably forceful, apparently unstoppable, human energy that was unleashed by it. Within less than a year, the Arab Spring claimed the downfall of four heads of state (Tunisia, Egypt, Libya, and Yemen), all of whom had held power for many years and appeared firmly in control when it all began. Following far-reaching unrest, it toppled innumerable cabinet ministers and high-ranking public officials in a range of countries and impacted on policy agendas across basically all the 22 countries of the Arab League of Nations.8 While the movement had no clear leadership but multiple inspirational figures, at the core of its momentum in the early days of 2011 stood a “promise” to change relations 8 

One may dispute to what extent the uprisings in the Middle East were just one set of activism among others. The courage displayed by the individuals who took action in the Arab Spring attained extreme visibility, however, creating an unprecedented demonstration of what coordinated action among the grass roots can achieve even in the face of determined, systematic suppression. Their successes paved the way for uprisings elsewhere in situations where resistance previously had been viewed as futile. The Occupy Wall Street movement and the Moscow protests of December 2011 clearly carried inspiration from Tunis and Cairo.

The National Resource Curse in the Arab Gulf    199 between government and their constituencies, through which there would be a new era of openness to diversity and new initiative.9 This combined with the known ability of visions, values, and behaviors to propagate at high speed throughout the entire umma of the Arab world, raised the prospect of a new economic, political, and societal model taking hold following only a brief transition period, once the dust had settled after the struggle against change-resistant regimes, and when the new model would have been able to prove itself. The ensuing development has evolved differently. In societies where alternative political and social movements had been suppressed for decades, with strong tribal sentiments, religious and ethnical division, and low levels of education for most of the population, a combination of internal conflict, conservatism, military interference, and vested interests have come to dominate the scene. Tourism is still down as of 2015, capital flight is abundant, the macroeconomy has weakened, and unemployment has soared. Violent crimes have kept promulgating. All of this has left us in the dark on the overall direction and eventual outcome of it all. It is important, however, to be more precise on what was behind the Arab Spring and also decisive for shaping the directions it subsequently followed across different countries. I argue that four coinciding factors were critical: •  The arrival and diffusion of ICT, especially among the young, in a situation of generally underperforming media coverage and education systems •  Demographic change in the shape of rapidly growing young generations and an average age between only 21 and 24 across the region •  Weak economic opportunity for the vast numbers of young and increasingly networked and educated citizens even though overall resources appear abundant, with enormous and visible wealth accumulated among a small number of privileged individuals •  Until the present, a deficit of other channels than outright unrest for making your voice heard, that is, an undeveloped civil society, a relatively inactive press, and so on10 Reflecting the prevalence of these factors in different economies, including those rich in natural resources versus those that are not, within the Middle East and also other parts of the world, a certain pattern appears, as indicated in table 9.1. Although ICT has arrived everywhere, it brings less radical change in terms of access to information in established market economies. While dramatic demographic change is underway in emerging and developing countries throughout, the industrialized countries and previously planned economies have hit a period of the ageing society. The lack of   9  These sentiments are not new or, as sometimes claimed, a making of foreign—or Western—values. The Middle East has its distinct history and strong base in Bedouin culture, a very particular manifestation of nomad life, with people living near to nature and able to move beyond the reach of any delimiting civilization thanks to their knowledge of the desert and the stamina of the camel. 10  This situation has also been exploited by those trying to recruit for violent resistance, or jihad, against what they have characterized as Western imperialism, notably al-Qaeda. Only a slight fraction of Muslim youth have, however, demonstrated any interest in such following (Cole 2009).

200    Critical Drivers of Local Competitiveness Table 9.1  Presence of Factors Pushing the Arab Spring, for Different Kinds of Economies Types of countries Developed  market economies Types of conditions

Diffusion of ICT Diffusion of InformationICT, yes, but starved less dramatic societies contrast Demography No, ageing With sharp increase societies in young population Lack of economic Yes, opportunity for increasingly the young Resource High but availability diminishing Lack of In principle it is opportunity to possible (so make your voice no) heard

Previously stateplanned economies

Developing/emerging economies Resource poor

Resource rich

Yes

Yes, certain contrast

Yes, dramatic contrast

No, ageing Societies

Yes

Yes

Yes, but part of Yes, stark context all for same Rising from No, at low level Yes, but modest level concentrated Has improved So far, has only So far, has only now arrived, now arrived, and still and still stifled stifled Yes, but on the rise

economic opportunity is even worse in resource-poor developing countries, but the resource-rich economies have more visible wealth concentrated in fewer hands. Lack of voice, meanwhile, is similar for resource-poor and resource-rich developed/emerging economies. The Arab Spring took off in northern Africa among both resource-poor and resource-rich economies, spreading from Tunisia to Egypt and Libya, where it pushed the old regimes aside. Others, such as Morocco and Algeria, were somewhat affected, responded with light reforms, and remained largely unchanged and yet stable. Elsewhere, dramatic uprisings unfolded in relatively resource-poor Yemen and Syria, where they were transformed into tribal battles for power, instigating deep-seated conflict to which there appears to be no end in sight. In the oil-rich Gulf, the Arab Spring has been less visible. Only Bahrain had its share of extensive violent uprisings, obviously related to its delicate Sunni-Shia division. Yet it has been strongly present behind the scenes. In Saudi Arabia and Oman, which have the largest domestic populations, the Arab Spring fueled extensive reforms. Following a swirl of subsidies, extra salaries, increased minimum wages, and expansion of public-sector employment, the focus has shifted toward trying to create jobs through training, support for entrepreneurship, and SME development.

The National Resource Curse in the Arab Gulf    201 Today, many questions may be asked about whether the Arab Spring was necessary or desirable. Stable regimes were overthrown and millions of vendors, traders, providers of cultural and tourism services, and so on, were smashed by the downturn. Yes, for many the present is worse than the past. The fact is that, in most countries to date, the uprisings failed to achieve their apparent objectives. The quest to generate new opportunities and more quality information has had patchy results. Measured by the number of job opportunities, the situation has become worse for most people, not only because of deterioration of the general economy but also because of many regulations instituting new burdens. In Tunisia, an orthodox Islamist regime took hold and pursued other objectives, limiting the rights of women and their role in the workplace, until the regime’s downfall. In Egypt the situation was similar with the Brotherhood’s time in power, which has been followed by suppression enacted by the military. In Libya, Syria, and Yemen, violent conflict has presided. In Morocco and Algeria reforms have been bleak. The fact is, only the resource-rich GCC countries went on a spending spree that gradually encompassed supporting economic diversification and new jobs through entrepreneurship and SMEs. Their resource base allowed these countries to “buy” calm, and they are looking for policies to retain stability. In Oman, more economic favors were accompanied by greater freedom for the press and a much stronger mandate for the democratically elected Majlis Shura, an advisory council in effect awarded real powers. The same does not quite apply to Saudi Arabia, although some movement granting greater rights to women has been observed. The stability and overall development of these countries is, however, due to their natural resource wealth, which has allowed them to perform better than their less natural resource-rich neighbors. But is this to say that natural resources have turned into a true blessing? In order to examine this issue more closely, the next section takes a closer look at the Sultanate of Oman and where its intensive reform process appears to be leading.

A Natural Resource-Rich Economy Coping with the Arab Spring: The Case of Oman The Sultanate of Oman has developed strongly over the last decades, starting out from a weak economic base. The country’s ranking on selected competitiveness indicators vis-à-vis suitable countries of comparison, shown in table 9.2, points to a favorable position in several important areas. This includes those that have to do with macroeconomic stability, but also the quality of institutions and specific policy areas, such as government procurement (commented on above). Oman’s economic achievement would have been impossible without the hydrocarbon sector, which accounts for some 70 percent of the country’s export revenue and just

202    Critical Drivers of Local Competitiveness Table 9.2  Competitiveness Ranking, Oman and Selected Countries, 2013–2014 Index rank Growth Index rank-GCI Macroeconomic environment Institutions Technology readiness Quality of sci. research institutions Uni-industry collaboration Govt. procurement of advanced tech. Financial Market Development Higher educ. and training Business Sophistication Innovation Labor market efficiency Intellectual property pro.

United Sweden Germany States

Japan

Oman

Qatar

Saudi Arabia

UAE

Egypt

6

4

5

9

33

13

20

19

118

14

27

117

127

5

6

4

7

140

5 1

15 14

35 15

17 19

13 56

4 31

20 41

11 28

117 100

15

6

5

9

71

12

39

34

127

10

9

3

17

45

7

31

24

133

22

17

15

37

12

1

6

3

116

8

29

10

23

21

13

27

24

119

8

3

7

21

57

29

48

35

118

7

3

6

1

32

10

28

16

84

6 18

4 41

7 4

5 23

45 28

16 6

30 70

28 9

120 146

16

14

25

11

24

4

27

20

94

Source: World Economic Forum (2013).

above half of GDP (UNCTAD 2014). Relatively good governance and competent policymaking have added critically by providing substantive infrastructure investment as well as public services in health, education, and so forth throughout the country. Oman has pursued long-term and mutually beneficial collaboration with multinational oil giants such as Shell and BP along with the buildup of strong local development capacity and a skilled workforce in the energy sector. This, coupled with natural conditions, has enabled Oman to place itself as an international center for deployment of new technology and training in and around enhanced oil recovery (EOR; see further below). Oman is known for other assets, too, including pristine waters and ocean life, a precious cultural heritage, safety and security, a preference for diplomacy over conflict, a tolerant society, and so on.11 While highly dependent on oil and gas revenue, meaning 11  Omani culture is traditionally open and tolerant, shaped through thousands of years of exploration and a dominant role in commercial trade around the Indian Ocean, which contrasts with the present era of high wealth generated by the hydrocarbon sector.

The National Resource Curse in the Arab Gulf    203 that other sectors are relatively underdeveloped, Oman faces a stark challenge to educate and train its large young generation and channel it into orderly jobs. Following the onset of the Arab Spring, the Eighth Five-Year Plan (2011–15) of the government of Oman listed its main objectives, as shown in table 9.3. With that said, international benchmarking consistently points to weaknesses in regard to labor market regulation, a lack of relevant skills, and a poor work ethic as major problems. Interviews with private companies, international as well as Omani, point to these issues as major obstacles to business development. Meanwhile, the educational system is underperforming, and many young Omanis seem to enter the labor market with a lax attitude, a preference for stable but stagnant public jobs, and little genuine interest in substance. This problematic situation is reflective of conditions both in the school system and in the workplace. Despite all the investment in education, most teachers have poor qualifications and schoolchildren not only perform badly in mathematics and “hard sciences” but have little opportunity for teamwork and developing soft skills. Most workplaces lack a policy for receiving, motivating, and training new recruits. Female students outperform male students, as is also the case in Saudi Arabia and other countries in the region (and mostly elsewhere). While this may not be seen as a problem, the better-educated young (female) half of the Omani population runs into serious mismatch with the labor market and is unable to fulfill its potential contribution to the national economy. The disinterest many young men show in educating themselves may be quite rational, given the situation in which they find themselves. Titles are appreciated but competencies and contributions to productivity weigh lightly in the workplace and for career prospects.

Table 9.3  Eighth Five-Year Development Plan, 2011–2015 •  Realizing a growth rate of not less than 3% and low inflation rates •  Boosting social development, to be granted priority in allocating government expenditure •  Expanding new work opportunities for national workforce •  Achieving full enrolment rates in general education, raising intake capacity of higher education and upgrading education quality •  Improving coordination between fiscal and monetary policies •  Giving more attention to the regional and environmental dimensions •  Increasing production rates of oil and gas and better managing reserves and power resources •  Developing the sectors of tourism, industry, agriculture, fisheries and water resources •  Stimulating investment of domestic and foreign private sectors and development of small and medium enterprises (SME) •  Implementing strategies of scientific research and Oman Digital Society •  Developing and raising the efficiency of the Governments administrative apparatus, upgrading the statistical work and directing media activity to serve the development process Source: UNCTAD (2014).

204    Critical Drivers of Local Competitiveness The causes of all this are rooted in a combination of institutions and culture under the influence of the resource curse, which has further evolved with the Arab Spring. An important aspect is a deeply rooted “commercial” or “rentier” culture, marked by focus on tangible assets, notably real estate and physical infrastructure, at the expense of “intangible” assets, such as soft skills, which feeds “risk aversion” (Beblawi 1990; Bizri 2012). The impact is observed across the wider region and extends to many aspects of society. Nationals take advantage of positions they have come to occupy, sometimes merely as a consequence of citizenship, to collect rent in their own right. Production and service activities are thus commonly undertaken by expatriate individuals or firms paying rent to nationals for rights of access to the local market. This applies even to larger international corporations, which must operate through domestic partners to navigate hierarchies and bureaucratic hurdles often put in place solely to preserve the rentier economy. The rentier mentality puts the focus on furthering existing assets and has significant negative implications for the work ethic along with technological, innovative, and entrepreneurial ventures, since efforts to embrace alternative productive models are systematically hampered (Bizri 2012). Analysis of Oman’s innovation system reveals high inefficiency in taking advantage of available development opportunities.12 For instance, rankings of inputs by the Global Innovation Index places Oman in 53rd place in innovation inputs, but only 111th for innovation output (UNCTAD 2014). As for prospective entrepreneurs, why bother if they qualify for a six-hour-a-day public-sector job with ample privileges, job security for life, and few demands along the way? How are policy frameworks responding? As a key feature of today’s reform process, new barriers have been raised for immigrants to obtain visas, and companies are forced to recruit Omanis for higher salaries. Meanwhile, a standardized salary scale is being introduced for all public-sector jobs, entailing a combination of increased pay and reduced flexibility, reportedly with strict respect for seniority. In regard to entrepreneurship, the government grants financial support to start-ups by Omanis and pushes for a share of public procurement orders to be directed to Omani-owned SMEs. It has further been considering a scheme through which basically all Omani public-sector officials can apply for and be granted the right to take a leave of absence, while receiving a salary for a full year, to engage in entrepreneurship “without taking risk.” Increasing the share of Omanis in the workforce is clearly a legitimate and desirable goal from the national perspective, as is the increase in Omani entrepreneurship. Policies enacted for this purpose should not, however, inflict excessive damage on productivity. As is, Omanization is pushed on terms that hinder merit-based promotion and twist incentives for all parties, including competent expatriate workers who meet 12 

For instance, rigid salary scales in the public sector, regulations preventing the commercialization of new products, and, conversely, a lack of regulations securing orderly dispute resolution mechanisms or preventing excesses in pollution, along with public subsidies boosting unsustainable water and energy consumption, exemplify the myriad of elements in today’s Oman that are deterrents for establishing an innovation climate (UNCTAD 2014).

The National Resource Curse in the Arab Gulf    205 with reduced career prospects. Since training and competence-enhancing programs are weakly present, the consequences can be expected to be dire. While the effects may not be directly observable for the public sector, which can keep raising salaries unrelated to performance, increased public-sector costs accompanied with worsening public service will eventually hurt everyone. For the private sector the impact is worse since key competences are lost while new Omani workers have to be added at higher costs, irrespective of their skills levels. A number of contradictory but salient features present themselves. Oman has adopted an ambitious research and innovation policy, with commitments to raise R & D to 1 percent of GDP in a situation where very little research is performed apart from one public university and the energy sector. The rules of research grants prevent researchers who apply from receiving any compensation themselves, however. Yet their salary from universities is almost totally tied to education, with little or no room for research. While universities are challenged to raise their academic standard and move into research, they enjoy limited autonomy. The prime public university’s rules for procurement, employment, salary scales, and payments are subject to the full weight of the civil service and public bureaucracy. Private companies cannot obtain any government support for research, and a proper innovation policy is essentially lacking. E-government is designed to support citizens, but the responsible authority pays more attention to its own involvement than to enabling private-sector development. The country’s precious environmental resources, ranging from the desert to mountain areas, wadis and aquifers, and the ocean bed, managed with skill and care over thousands of years, are now being degraded over the period of a few decades. With rapidly changing lifestyles, noncommunicable disease is exploding, with obesity and diabetes rates approaching the highest in the world, while the general public lacks information and takes hardly any interest in improved nutrition, eco-food, or physical exercise. Where there is also a lack of action and preparedness to meet with rapidly increasing rates of cancer, the Ministry of Health is hesitant about research or making use of modern information technology tools to identity and address the issues. General attitudes to misfortune, meanwhile, are influenced by social stigma, with serious disease and road accidents widely seen as inevitable manifestation of higher power. All in all, Oman has been able to withstand the tide of turmoil that followed from the Arab Spring across much of the Arab world. Its prime responses included expansion of public-sector jobs, replacement of non-Omanis by an Omani workforce, increased benefits and a reformation of public-sector salary scales that brings more rigidity and less encouragement for demonstrating competency in the workplace. The educational system is suffering from poor teaching and a lack of inspiration. Innovation, entrepreneurship, and SME growth are pushed by a range of vigorous policies, but risk taking is rejected and the difficulties of balancing cash flow not well understood. In the aggregate, policy outcomes are hence marked by serious inconsistencies, reflective of a culture in which horizontal inter-organizational collaboration meets with particular challenges. A strong sense of loyalty and responsibility for the individual area promotes "own performance," here coupled with the mindset and rent-seeking culture of a resource-rich

206    Critical Drivers of Local Competitiveness economy. The policy mix in place is unsustainable and would have been impossible even in the short term in the absence of the resource rents flowing in the present era.

Concluding Remarks The Arab Spring signaled a stronger bottom-up engagement by new generations, challenging authoritarian regimes and calling for greater accountability. Riches in natural resources helped the GCC countries wither the crisis and led to more investment in education and are now used to promote jobs, notably taking the form of Omanization in the Sultanate of Oman and the Saudization policy in Saudi Arabia. At the same time, the availability of natural resource rents coupled with local culture presents severe challenges. In effect, these translate into a bloated public sector, poor education, and weak labor market performance, along with a resistance to risk taking and innovation, which runs counter to the ambitious attempts to support start-ups and SME growth. This situation in the GCC countries demonstrates that the observations made over the years of a resource “curse” are indeed real, even for those countries that now seem to benefit from their natural resource wealth. The issue at hand is strongly influenced by local context, however, with a blend of formal and informal distortions away from productive effort and investment. Part of the challenge, as is generally concluded in mainstream policy reviews, has to do with policy fragmentation, that is, the lack of a coordinated response capable of reducing the overall burden of government bureaucracy, and of enacting reforms that are consistent in supporting well-functioning labor markets, innovation, entrepreneurship, and so forth. In contrast to mainstream recommendations, however, analysts as well as policymakers should pay attention to the way that major structural challenges are perpetuated by resource abundance or the legacy of the Arab Spring, while also embedded in institutions and culture at the local level. Unless lock-in of that sort can be disentangled, recommendations are likely to fall flat to the ground, as the risk is one will miss out on what fundamentally needs to be overcome in order to implement those reforms that matter. In the specific context of the resource-rich GCC countries today, the analysis in this chapter further extends to recommendations along the following lines: • A comprehensive effort is needed to come to grips with the nature of innovation and entrepreneurship and put in place conditions that allow for and reward experimentation and risk taking, as well as accept failure, not merely invoke more planning in an attempt to reduce risk for “selected winners.” • Introduce initiatives and incentives to realize cross-departmental collaboration in enabling private-sector development, cutting back on excessive government regulation and reducing the privilege of public-sector jobs. • Put soft skills at the core of school reform to raise the quality of education, including by introducing team work and creativity-enhancing,

The National Resource Curse in the Arab Gulf    207 boundary-spanning elements to the curriculum, enabling stronger linking and synergy between arts and culture studies on the one hand and science and technology on the other. • Take action to counter rentier culture, rolling back a focus on existing assets, highlighting “people” as the main resource, and embracing creation of new unique values in a creative environment. • Take advantage of new trends in natural resource use and commodities, for instance:   o Increase and deepen value added in areas of strength (such as EOR in the case of Oman), further leveraging the country’s superb industrial skill in this area with R & D and innovation, generating linkages and spillovers to other parts of the economy.   o Respond to the needs of addressing environmental, social, and health concerns, turning serious challenges into a source of opportunity. By contrast, many of the reforms recently initiated, as illustrated by the case of Oman, will work only as long as the natural resource rents continue to flow. If this pattern prevails, productivity will remain low and growth will not be sustainable. These countries may then in the end be back to where they started, but with their natural resource base depleted. Since culture does not change quickly, they may still be marked by the problems of resource-rich economies. If so, their eventual destiny will be bleak. I argue that the trick is for natural resource wealth to back local “culture” that is constructive, conducive to more effective policy coordination, and serve to support value-enhancing processes instead of rent-seeking. The analyst as well as the policymaker should pay attention to this nexus of issues so as to get to the key to the implementation of reforms that are needed to ensure better outcomes in the long term.

References Alexeev, M., and R. Conrad. 2009. “The Elusive Curse of Oil.” Review of Economics and Statistics 91 (3), 586–98. Almedia, P., and B. Kogut. 1997. “The Exploration of Technological Diversity and the Geographic Localization of Innovation.” Small Business Economics 9, 21–31. Al-Ramahi, A. 2008. “Wasta in Jordan: A Distinct Feature of (and Benefits for) Middle Eastern Society.” Arab Law Quarterly 22, 35–62. Amin, M., and S. Djankov. 2009. “Natural Resources and Reforms.” Financial and Private Sector Vice Presidency, World Bank, Washington, DC. Andersson, T. 2011. “Natural Resource Dependency and Innovation in the GCC Countries.” In F. S.  Nobre, D. S.  Walker, and R. J.  Harris, eds., Technological, Managerial and Organizational Core Competencies:  Dynamic Innovation and Sustainable Advantage. Hersey, PA: IGI Global. Andersson, T., and D. Abdelkader. 2012. The Real Issues of the Middle East and the Arab Spring: Research, Innovation, and Entrepreneurship. New York: Springer.

208    Critical Drivers of Local Competitiveness Arzeki, R., and M. Bruckner. 2009. “Oil Rents, Corruption, and State Stability:  Evidence from Panel Data Regressions.” IMF Working Papers 09/267, International Monetary Fund, Washington, DC. Audretsch, D., and M. P. Feldman. 1995. “R&D Spillovers and the Geography of Innovation and Production.” American Economic Review 86 (3), 630–40. Audretsch, D., and R. Thurik. 2001. “Linking Entrepreneurship to Growth.” OECD Growth Working Paper 27, Paris. Auty, R. 2001. Resource Abundance and Economic Development. Oxford: Oxford University Press. Barro, R., and J. W.  Lee. 1996. “International Measures, of Schooling Years and Schooling Quality.” American Economic Review Papers and Proceedings (86), 218–23. Beblawi, H. 1990. “The Rentier State in the Arab World.” In Giacomo Luciani, ed., The Arab State. London: Routledge. Bellow, A. 2003. In Praise of Nepotism:  A  Natural History. Westminster, MD:  Doubleday Publishing. Bizri, O. 2012. “Research, Innovation, Entrepreneurship and the Rentier Culture in the Arab Countries.” In T. Andersson and D. Abdelkader, eds., The Real Issues of the Middle East and the Arab Spring: Research, Innovation, and Entrepreneurship. New York: Springer. Bleischwitz, R. 2014. “Beauty and the Beast: Opportunities and Threats Arising from Distorted Global Material Flows.” Global Flow Security Papers, Chapter  5. Washington:  The Paul H. Nitze School of Advanced International Studies, The John Hopkins University. Blomström, M., and A. Kokko. 2007. “From Natural Resources to High-Tech Production: The Evolution of Industrial Competitiveness in Sweden and Finland.” in Lederman, D. and Maloney, W. eds., Natural Resources:  Neither Curse nor Destiny. Washington, DC: World Bank. Bulte, E., R. Damania, and R. Deacon. 2005. “Resource Intensity, Institutions and Development.” World Development 33 (7), 1029–44. Chesbrough, H. 2003. Open Innovation:  The new imperative for creating and profiting from technology, Harvard Business Press, Cambridge. Cole, J. 2009. Engaging the Muslim World. New York: Macmillan. Corden, M.  W. 1984. “Booming Sector and Dutch Disease Economics, Survey and Consolidation.” Oxford Economic Papers 36, 359–80. de la Fuente, A., and R. Dmenech. 2000. “Human Capital in Growth Regressions: How much Difference Does Data Quality Make?.” OECD Growth Project Background Paper 23, OECD, Paris. Devarajan, S., T. Minh Le, and G. Raballand. 2010. Increasing Public Expenditure Efficiency in Oil-rich Economies, Africa Region, Chief Economist Office, The World Bank, Washington. European Commission. 2010. Riding the Wave. Brussels. Fasano, U. 2002. “With open economy and sound policies, U.A.E. has turned oil curse into a blessing.” IMF Survey 21, October, 330–32. Frankel, J. 2010. The Natural Resource Curse: A Survey. Cambridge, Harvard Kennedy School, Faculty Research Working Paper Series, February. Glaeser, E. L., D. Laibson, and B. Sacerdote. 2004. “An Economic Approach to Social Capital.” Economic Journal 112, November, F437–58. Gylfason, T. 2001. “Natural Resources, Education and Economic Development.” European Economic Review 45 (4/6), 847–59.

The National Resource Curse in the Arab Gulf    209 Hamel, J.  L. 2004. Knowledge Policies for Sustainable Development in Africa, A  Strategic Framework for Good Governance, ECA/SDD, Addis Ababa. Herb, M. 2005. “No Representation without Taxation? Rents, Development and Democracy.” Comparative Politics 37 3), 297–317. von Hippel, E. 1994. “ ‘Sticky Information’ and the Locus of Problem Solving: Implications for Innovation.” Management Science 40 (4), 429–39. Hutchings, K., and D. Weir. 2006. “Understanding networking in China and the Arab World: Lessons for international managers.” Journal of European Industrial Training 30, 272–90. International Telecom Union. 2015. World Telecommunication/ICT Indicators Database. Geneva. Isham, J., M. Woolcock, L. Pritchett, and G. Busby. 2005. “The Varieties of Resource Experience: Natural Resource Export Structures and the Political Economy of Economic Growth.” World Bank Economic Review 19 (2), 141–74 Jaffe, A. 1989. “Real Effect of Academic Research.” American Economic Review 79, 957–70. Kumar, K., and J. Matsusaka. 2004. “Village versus Market Social Capital: An Approach to Development.” Marshall School of Business, University of Southern California, Los Angeles. Lederman, D., and W. Maloney. 2007. “Neither Curse nor Destiny:  Introduction to Natural Resources and Development.” In D. Lederman and W. Maloney, eds., Natural Resources: Neither Curse nor Destiny. Palo Alto, CA: Stanford University Press; Washington, DC: Latin American Development Forum Series, World Bank. Nonaka, I., and H. Takeuchi. 1995. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation. Oxford: Oxford University Press. Nurkse, R. 1953. Problems of Capital Formation in Underdeveloped Countries. Cambridge: Cambridge University Press. OECD. 2001. The New Economy: Beyond the Hype. Paris: OECD. Prebisch, R. 1950. The Economic Development of Latin America and Its Principal Problems. New York: United Nations. Psacharoulos, G. 1994. “Returns to Investment in Education:  A  Global Update.” World Development 22 (9), 1325–43. Putnam, R. 1993. Making Democracy Work:  Civic Traditions in Modern Italy. Princeton, NJ: Princeton University Press. Rostow, W.  W. 1960. The Stages of Economic Growth:  A  Non-communist Manifesto. Cambridge: Cambridge University Press. Sachs, J., and A. M. Warner. 1995. “Natural Resource Abundance and Economic Growth.” In G. Meier and J. Rauch, eds., Leading Issues in Economic Development. New York: Oxford University Press. Sachs, J., and A. M. Warner. 2001. “Natural Resources and Economic Development: The Curse of Natural Resources.” European Economic Review 45, 827–38. Sala-i-Martin, X., and E. V.  Artadi. 2002. “Economic Growth and Investment in the Arab World.” Columbia University Department of Economics Discussion Paper 020308, New York. Sala-i-Martin, X., and A. Subramanian. 2003. “Addressing the Natural Resource Curse: An Illustration from Nigeria.” IMF Working Paper 139, 3. Saxenian, A. 1994. Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press. Smith, K. 2007. “Innovation and Growth in Resource-Based Economies.” Australian Innovation Research Centre, University of Tasmania, Hobart.

210    Critical Drivers of Local Competitiveness Solow, R. 1957. “Technical Progress and the Aggregate Production Function.” Review of Economics and Statistics 99, 312–21. Storper, M. 1999. “Globalization and Knowledge Flows.” In J. H. Dunning, ed., Globalization, Regions and the Knowledge-Based Economy. New York: Oxford University Press. Sturm, M., P. Adolf, and J. Peschel. 2008. “The Gulf Cooperation Council Countries: Economic Structures, Recent Developments and Role in the Global Economy.” European Central Bank Occasional Paper Series 92, July. Torvik, R. 2002. “Natural Resource Rent Seeking and Welfare.” Journal of Development Economics 67 (2), 107–30. UNCTAD. 2014. Science, Technology and Innovation Policy Review. Geneva: UNCTAD. Van der Ploeg, F., and S. Poelhekke. 2009. “Volatility and the Natural Resource Curse.” Oxford Economic Papers 61 (4), 727–60. Van der Ploeg, F., and S. Poelhekke. 2010. “The Pungent Smell of Red Herrings:  Subsoil Assets, Rents, Volatility and the Resource Curse.” Journal of Environmental Economics and Management 60 (1), 44–55. World Economic Forum. 2013. World Competitiveness Report 2013/14. Geneva:  World Economic Forum.

Chapter 10

T he Role of Uni v e rsi t i e s in L o cal and Re g i ona l C om petiti v e ne s s Erik E. Lehmann

Introduction The small band of British scientific men have made revolutionary discoveries in science; but yet the chief fruits of their work have been reaped by businesses in Germany … where industry and science have been in close touch with one another. —Alfred Marshall (cited in Cosh and Hughes 2010, 71)

In the mid-19th century, British industry suffered from German exports. The low costs and low-quality products led to crowding-out effects on British domestic production. Although it was the motherland of the Industrial Revolution, other countries, particularly Germany, successfully imitated British domestic products, but at lower costs and lower quality. Britain reacted by labeling these products “Made in Germany,” to protect consumers from buying low-quality, me-too products. At the same time, policymakers in the different German countries shifted the focus of their economic policy toward universities as the center of knowledge and human capital, leading to strong university-industry relationships. Germany created a new type of university, called a technical university, which has a strong focus on natural sciences and engineering. As a consequence, product quality, product innovations, and production costs improved, leading to a competitive advantage of German products, and the “Made in Germany” label became a signal for high-quality products. These universities, technical universities (TU), are the backbone of outstanding and successful regions in Germany (Audretsch and Lehmann 2005a).

212    Critical Drivers of Local Competitiveness Conventional wisdom predicts that globalization renders the demise of the region as a meaningful unit of economic analysis. With the recent advances in communication technology, the access to knowledge is greater than ever, leading management gurus and consultants to proclaim the “death of distance.” The cost determinants of communication are seen as the single greatest economic force shaping society in the first half of the 21st century. Without a doubt, the costs of communication have significantly decreased in the past decades in an unexpected way, making the world a flat one (Friedman 2005). Costs of communication are only one side of the coin. As markets become more transparent and competitive, the necessity and pressure to invest in new products and future technologies increase. This shifts the competitive advantage of companies based on the accumulation and access of tangible factors, such as capital and labor, toward intangible assets like knowledge and human capital (Audretsch and Thurik 2001; Breznitz and Feldman 2012; Audretsch, Lehmann, and Wright 2014). While the costs of communication are decreasing, the costs of transferring knowledge are rising. Industrial competitiveness shows a progressive rate that is shaped by these costs, leading to a “gravity of knowledge” (Keller and Yeaple 2013) instead a “flat world” effect. The location of firms in an imperfectly competitive industry has always been based on the cost and quality of inputs at each location, the cost of market access, the mobility of factors, and growth opportunities. These costs were primarily shaped exogenously, given geographical and biological conditions in the past, and by regional competitiveness shaped by the presence or absence of such conditions. Thus modern theories of growth shifted their emphasis away from accumulation and access to tangible factors, such as capital and labor, toward the access of knowledge. With growing competition in a globalized world, universities are seen as the key organization and supporters in the national innovation system, in particular by providing knowledge (Audretsch, Keilbach, and Lehmann 2006). Globalization today, with its increasing dynamic of interrelations of markets, cultural and social life, decreasing costs of communication, and mobility drastically reshapes the landscape and importance of universities for societies. The war of talents is renamed the war of universities. This chapter summarizes recent thoughts and evidence on the role of universities and scientific institutions on regional competitiveness. As academic research has moved from studying the proximate determinants of growth and development, to countries and nations (Romer 1986), to regions and geographical places (Krugman 1991; Glaeser et al. 1992; Audretsch 1995; Florida 2002a; 2002b), important questions have arisen: through what specific mechanisms do long- and short-term factors affect regional performance today? How much time persistence is there in development outcomes, and why do institutions like universities matter for regional competitiveness? What is the scope of effects that academic policy can have on the wealth and development of regions? This chapter aims to provide some answers to these issues. It differs from related surveys on this topic (see Rothaermel, Agung, and Jiang 2007), as it shifts the focus toward path-dependent and long-term effects on regional competitiveness and performance today and the co-evolutionary effect of institutions, in particular, universities. Referring

The Role of Universities in Local and Regional Competitiveness    213 to recent research on the long-term effects of regions and countries (Spolaore and Wacziarg 2013), the path-dependent effect explains variations in income today that are not entirely based on “guns, germs, and steel,” as prominently proposed by Diamond (1997), but by populations and human capital. Ever since the first humans left Africa, location has mattered—not solely geographical and biological conditions, but also the traits of their populations and their human capital.

2  Regional Geography, Knowledge Acceleration, and Development 2.1  Paradigm Shift from Guns and Steel toward Spillovers If you build it, he will come. —Voice whispering to Ray Kinsella in Field of Dreams

Geographical regions differ worldwide in economic wealth and prosperity. Income per capita, quality of life, and other measures of welfare are higher in some societies and regions and lower in others. For some decades, academic research emphasized the accumulation of capital, technological progress, wars, diseases, or climate change to explain variations in income and wealth, but the attention has moved toward two main explanations: geographical and biological conditions on the one hand, and populations and their ability and willingness to create institutions on the other (Spolaore and Wacziarg 2013). This literature is concerned with how and why the proximate determinants of income and wealth vary across countries, arguing that long-term geographic and historical factors and path dependence lead to path dependencies affecting regional competitiveness. Recent developments induced and affected by globalization shift the lenses of interest away from nations and countries toward the periphery, the geographical region (Audretsch 1995; Acs et al. 2009). The paradigm shifts away from “guns, germs, and steel” (Diamond 1997) as the proximate determinants of economic success toward spillovers of knowledge and entrepreneurial and human capital (Audretsch, Keilbach, and Lehmann 2006; Acs, Audretsch, and Lehmann 2013). The production, acquisition, absorption, reproduction, and dissemination of knowledge are thus seen as the main and fundamental characteristic of contemporary competitive dynamics of regions today. Within this context, academic research and policymakers have identified universities and scientific institutions as the main drivers of knowledge production and knowledge spillovers to shape regional competitiveness (Jaffe 1989). One striking result of research is that some locations experience stronger economic performance than others, especially in fostering knowledge-based entrepreneurial activity (Audretsch, Keilbach, and Lehmann 2006; Porter 2003; Link and Scott

214    Critical Drivers of Local Competitiveness 2003). The obsession of policymakers around the globe to create the next Silicon Valley reveals the increased importance of geographic proximity and regional agglomerations. Research on the importance of regions and regional development is based on long historical roots. While geographical and biological conditions count for a large part of the variation in today’s income, recent studies highlight that long-term historical factors predict income per capita, and that these factors become much more important than pure geographical and biological conditions when considering the history of populations rather than the locations. Populations differ in their willingness and ability to create and implement institutions for wealth-increasing investments and innovation. Movements of populations, either voluntary or not, have brought with them, not the formal institutions, but rather themselves, their human capital, and in particular, their ability, willingness, and motivation to create institutions. The importance of human capital for the acceleration of technological innovation and its positive effects on profits is beyond controversy. Getting access to these resources, and the ability to exploit and appropriate them, increases income and wealth. Regional competition and competitiveness are about attracting, exploiting, and appropriating resources, in particular human capital. While regional competition in the past has usually occurred by involuntary appropriation, through annexation, enslavement, and exploitation, that is “germs, guns, and steel,” the paradigm also shifts from aggressive competition toward competiveness: increasing the attractiveness of places to allure the scarce resource, human capital: if you build it, they will come. Since the first civilizations, the Egyptian pharaohs, the Roman emperors, the Arabian caliphs, and the Chinese and German emperors have all tried to attract the best experts from the known world. Because specific human capital cannot creatively work under hierarchical pressure, experts were almost always hired away from other places as free men and not as slaves (and were highly paid)—an early phenomenon called the brain drain today. However, as for other fields of economics, the impact of geography has not escaped the attention of scholars, who conclude that the impact of geographic characteristics is anything but neutral. Although geographical, biological, and historical factors are essential in explaining variations in economic development today, they all work as complementary mechanisms and channels for the creation and dissemination of knowledge and technologies. The institutions to build and foster the acceleration of technological innovation are universities. Starting with the building of the first university, Bologna University, in 1088, universities have since been the spearhead in regional policy and competitiveness. However, without taking into account the role of long- and short-term variables, conclusions about the effects of specific regional policies and the role of universities are misleading.

2.2  Long-Term Effects on Regional Competitiveness Spolaore and Wacziarg (2013) survey the literature on long-term effects of geographic factors affecting productivity and economic development. About 44 percent of the

The Role of Universities in Local and Regional Competitiveness    215 variance in recent per capita income can be explained by three major variables: absolute latitude, being landlocked, and an island dummy. Including additional geographic and biological condition variables, the percentage of explained variation increases to about 51 percent, excluding the neo-European countries (Australia, Canada, New Zealand, and the United States) and about 65 percent of countries from the Old World are included. They conclude that the deep roots of economic development, in particular in the Old World, are identified in Diamond’s (1997) findings and lie in a series of environmental advantages enjoyed by the inhabitants of Eurasia, starting in 10,000 bc with the Neolithic Revolution. These advantages include not only the pure size of the countries, but also the diversity of animals and plants available for domestication and the east-west orientation of Eurasia (instead of north-south), which facilitates the spread of agricultural innovations (Spolaore and Wacziarg 2013, 328). These advantages lead to a population explosion, an early acceleration of technological innovations, and thus long-term comparative development unbroken until the present age. While geographic and biological conditions and (pre)historical events could explain large parts of the variation of income today (Olsson and Hibb 2005), there are still some questions that are unanswered: How and why does a technological innovation spread over geographical distance, and over how much time? And why do those conditions seem to persist in explaining variations in income today, after thousands of years, although most of these conditions are less important today (such as the domestication of animals and the climate for plants and crops). This shifts the interest of academic research toward the role of population and the importance of institutions (Nunn 2009; Acemoglu, Johnson, and Robinson 2002; Putterman and Weil 2010). Based on recent empirical findings, Spolaore and Wacziarg (2013, 342) consider the effect of ancestry and genetic distance from populations. They postulate that populations that share more recent common ancestors have had less time to diverge in a wide range of traits and that those traits facilitate communication and learning, and hence lead to the diffusion and adoption of complex technological and institutional knowledge. Genetic distance as a summary measure of differences in allele frequencies between populations across a range of neutral genes shapes human traits. But human traits also act to hinder development through a barrier effect in that closely related societies are more likely to learn from each other. Genetic distance per se isn’t what matters, but genetic distance captures genealogical relations between populations. Most traits examined by Spolaore and Wacziarg, in particular those affecting family structure and kinship, showed great perseverance over generations, most probably transmitted by family members. Thus, some populations are more likely than others to introduce institutions to encourage investment in regions (Acemoglu, Johnson, and Robinson 2002). More closely related societies are more likely to learn from each other and adopt each other’s innovations. Based on the genetic distance, Spolaore and Wacziarg argue “that it is easier for someone to learn from a sibling than from a cousin, and easier to learn from a cousin than from a stranger” (p. 343). This leads Spolaore and Wacziarg (2013, 248) to conclude that “differences in traits … are transmitted vertically from one generation to the next through a variety of mechanisms, biologically

216    Critical Drivers of Local Competitiveness and culturally, as well as through the interactions of the two inheritance systems (gene-culture-evolution).”

2.3  Short-Term Performance of Geography and Universities The persistency of technological advantages, dating back to technological adoptions beginning in 1000 bc, is still a significant predictor of income per capita and technology adoptions today. This dates back to the establishment of the first formal institutions more than 10,000 years ago, when the first towns where established. These were based on the advantages of specialization and division of labor instead of autarky, at the cost of interdependencies. Formal hierarchical government and policy rules are implemented to reduce the costs of interdependencies and to coordinate and motivate the inhabitants to increase overall benefits. This leads to two major developments with path-depending effects for regional competitiveness. First, specialization and division of labor lead to economies of scale and scope but are bound to specific human capital, and that knowledge spills over. Close proximity to the initial source of knowledge, often within the family, and knowledge exchange between craftsmen, farmers, and others, leads to cost advantages, and thus to agglomeration effects. The second development is in administration. While knowledge in the production sector is specific and tacit and bound to human capital, knowledge in the administration sector is more general and codified. This leads to the one of the most important innovations of mankind: letters and numbers. While specific knowledge is shared vertically within families, general knowledge and the use of numbers and letters is shared horizontally within networks and vertically within specific organizations. These organizations were established and financed by the rulers and lawyers, but also by religious groups. Since the settlement of the New World, history has shown that the most important asset brought with settlers and migrants was their human capital, both general and specific, allowing them to share the benefits of specialization and labor division by introducing formal and informal institutions to encourage local investments. Investments in higher education, first informal, as seen by the invention of writing, have been known since 3500 bc. Philosophers and writers in ancient Greece, like Hesiod (about 700 bc), Xenophon, or later on Socrates, Plato, and Aristotle, were engaged in building up father-and-son-relationships—sharing and transmitting existing knowledge and generating new knowledge and insights through academic discussions. One and a half millennia later, such informal relationships or schools of thought led to new types of institutional arrangements: abbeys and universities as focal points of knowledge production and knowledge transfer. Although the first university (Bologna) was founded in 1088, abbeys and monasteries were the dominant institutions until the middle of the 16th century as the main source of knowledge production. While universities during this epoch were mainly focused on the artes liberals, the trivium (grammar, dialectic, rhetoric) and the quadrivium (arithmetic, geometrics, astronomy, music),

The Role of Universities in Local and Regional Competitiveness    217 abbeys and monasteries were the first institutions to focus on the natural sciences and engineering (initially derived from the Latin ingenium, spirit and intellect, but since the Middle Ages ingeniator, engignor, for somebody with technical skills and creativity). Examples of early knowledge spillovers are found in the construction and building of canal systems, bridges, ships, cranes, and also weapons, which increased local and regional competitiveness. In the middle of the 15th century, the first patent protections occurred, promising protections to inventors if they were able to construct a functional exemplar of their inventions. This kind of patent protection was enforced and guaranteed by the local and regional government, often the nobility, and in consequence led to strategic advantages in attracting inventors and experts. The increased importance of universities (universitas magistrorum et scholarium— society of teachers and learners) as a backbone of local and regional competitiveness started with their focus on the natural sciences, and in particular was shaped by the discovery of the New World and the importance of firearms and technical instruments to coordinate warfare and trade. The first academies that had a strong focus on engineering were thus founded in the Netherlands, Spain, and Italy about 1600 bc, and in the Écoles in France in the 18th century. These universities or academies put a strong focus on applied sciences in both research and teaching. Starting with the Industrial Revolution, accumulation and access to technical knowledge was seen as a major competitive advantage for both countries and firms. The derived demand for knowledge and skilled human capital shifted the focus of universities away from “liberal arts” toward the natural sciences and engineering, at the beginning of the 19th century (see Bruland and Movery 2005). A prominent example of this new academic policy is the establishment of the technical universities in Germany. Since then universities have emerged as the main institution in the creation and dissemination of knowledge and the acceleration of technological innovation. They create value—not just in an economic sense, but also as a cultural focal point within regions, stimulating growth and wealth. Most of the earliest foundations of universities are based on an economic calculus by the national government, emperors, kings, or the regional upper nobility. The long-lasting impact of universities can be identified in those geographical regions where old universities still have an outstanding economic, social, and cultural impact—local hot spots, like Oxford and Cambridge, the northern part of Italy (Bologna), Paris in France, or the southwest region in Germany (Heidelberg). For a long time it remained rather unclear how and why location matters for regional growth and development. Alfred Marshall (1920) named the relevant context the “atmosphere,” indicating that the mystery of industry is in the surrounding air, and also that children unconsciously learn many of the significant cultural traits. Economic growth in the 20th century was accompanied by two developments that were largely unanticipated. The first was that regions and geographic proximity re-emerged as important units of economic activity. The second was that much of the innovative activity was associated less with footloose multinational corporations than with high-tech innovative regional clusters with small and entrepreneurial firms located in geographical proximity to research-intensive universities and scientific institutions.

218    Critical Drivers of Local Competitiveness The rediscovery of the importance of geographic proximity in shaping economic performance has not escaped the attention of scholars. A careful and systematic series of studies helped trigger a new literature with the goal of understanding the spatial dimension of innovative activity around research-intensive universities and research institutions, specifically the determinants and mechanisms that underlie the propensity of innovative activity to cluster spatially. The view that knowledge spills over from local firms to others in geographic proximity (Krugman 1991) and that knowledge spills over from small and entrepreneurial firms (Acs et al. 2009) is supported by theoretical models and has been tested by numerous empirical studies (Jaffe 1989; Jaffe, Trajtenberg, and Henderson 1993; Acs, Audretsch, and Feldman 1992; 1994; Audretsch and Feldman 1996; Audretsch and Stephan 1996; Audretsch and Lehmann 2005b; Audretsch, Lehmann, and Warning 2005). There exists compelling empirical evidence that knowledge spillovers augment regional competitiveness and regional growth (Audretsch, Keilbach, and Lehmann 2005, 2006; Acs, Audretsch, and Lehmann 2013). The regionality of this influence is shown by Jaffe (1989). By employing a regional knowledge production function on the state level, he shows a significant positive influence of university R & D spending on firm R & D and firm patents. Similar effects have been shown for product innovations (Acs, Audretsch, and Feldman 1992; 1994; Audretsch and Feldman 1996). These effects have been reproduced in several studies, on a more detailed level, of metropolitan areas, where an even stronger regionalization effect of university spillovers is established for the United States (Acs et al. 2012; Anselin, Varga, and Acs 1997) but also other countries (Audretsch and Lehmann 2005a; 2005b; Audretsch, Lehmann, and Warning 2005).

3  Type of Knowledge, Knowledge Spillovers, and Regional Competitiveness Knowledge spillovers are externalities accessed by firms, for which the source of the spillover is not fully compensated. Because firms access external knowledge at a cost that is lower than the cost of producing this value internally, or of acquiring it externally from a larger geographic distance, they exhibit ceteris paribus, or higher profits. Since the cost of transferring and absorbing knowledge spillovers increases with distance, the incentive to locate in close geographic proximity to the knowledge source is endogenously given by the costs and benefits of appropriating its knowledge spillovers. Arrow (1962) identified externalities associated with knowledge due to its nonexclusive and nonrivalrous use. However, what has been contested is the geographic range of knowledge spillovers: knowledge externalities are so important and forceful that there is no reason that knowledge should stop spilling over just because of borders, such as a

The Role of Universities in Local and Regional Competitiveness    219 city limit, state line, or national boundary. Thus, academic research has tried to identify different types of knowledge and its impact on the costs of transferring and appropriation. Within this field of research, Kogut and Zander (1992) identified tacit and codified knowledge. An implication of the distinction between codified and tacit knowledge is that the marginal cost of transmitting codified knowledge across geographic space has been rendered invariant by the revolution in telecommunications, while the marginal cost of transmitting tacit knowledge is lowest with frequent social interaction, observation, and communication. While codified or explicit knowledge could be effectively expressed using symbolic forms or representation, this is not the case for tacit knowledge, which defies such representation. Since tacit knowledge is important for new and highly innovative firms and it cannot be easily transferred over large distances or bought via the market, it has become a major determinant in the competitiveness of regions and locations of firms (see Porter 2003; Florida 2002a; 2002b; 2004). Another distinction is made in trade theory, differentiating between embodied and disembodied knowledge (Keller and Yeaple 2013). Inputs with high costs of transferring knowledge across the affiliates of firms are referred to as “embodied knowledge” (embodied in the input) and are produced at home, that is, in close proximity to the source of the knowledge. Otherwise, when costs of transferring knowledge are rather low (disembodied knowledge), the inputs could be produced abroad (leading to negative effects for the local economy). The kind of science—codified or tacit knowledge—matters for spillover effects. Most relevant elements of know-how and operations are tacit and cannot be codified easily in a blueprint, a contractual document, or a published article. Tacit knowledge needs oral communication and reciprocity, which may be ineffective or infeasible over longer distances. While locating spatially close to universities is not necessary to transmit and disseminate findings where knowledge is codified, it is important where knowledge is tacit (Audretsch and Stephan 1996; 1999; Audretsch, Keilbach, and Lehmann 2006). Knowledge in the natural sciences is characterized by a greater degree of codification. Strict adherence to the scientific method ensures that academic research embodies a high component of codified and specific knowledge in the natural sciences. By contrast, the more limited applicability of the scientific method implies that research in the social sciences will embody less codified knowledge and is therefore more tacit in nature (Stephan 1996). As it became more apparent that, rather than the firm acting as the main source of knowledge production, with knowledge spilling over only within the firm in future periods, academic research shifted the unit of analysis for estimating the model of the knowledge production function away from the firm toward other units of externalities (Audretsch, Keilbach, and Lehmann 2006). In refocusing the model of knowledge production to a spatial unit of observation, scholars confronted two challenges. The first one was theoretical. What was the theoretical basis for knowledge to spill over while remaining spatially within some geographic unit of observation? These frictions are not well understood. The second challenge involved measurement. How could knowledge spillovers be measured and identified? More than a few scholars heeded Krugman’s

220    Critical Drivers of Local Competitiveness warning (1991, 53) that empirical measurement of knowledge spillovers would prove to be impossible because “knowledge flows are invisible, [and] they leave no paper trail by which they may be measured and tracked.” In confronting the first challenge, which involved developing a theoretical basis for geographically bounded knowledge spillovers, scholars turned to the emerging literature of new growth theory. In explaining the increased divergence in the distribution of economic activity between countries and regions, Krugman (1991) and Romer (1986) relied on models based on increasing returns to scale in production. By increasing returns, however, Krugman and Romer did not mean at the level of observation most familiar in the industrial organization literature—the plant, or at least the firm—but rather at the level of a spatially distinguishable unit. In fact, it was assumed that the externalities across firms and even industries would generate increasing returns in production. In particular, Krugman (1991) focused on convexities arising from spillovers from (1) a pooled labor market; (2) pecuniary externalities enabling the provision of nontraded inputs to an industry in greater variety and at lower cost; and (3) information or technological spillovers. Krugman (1991), and others, did not question the existence or importance of such knowledge spillovers. In fact, they argue that such knowledge externalities are so important and forceful that there is no reason for a political boundary to limit the spatial extent of the spillover. In applying the model of the knowledge production function to spatial units of observation, theories of why knowledge externalities are spatially bounded and why knowledge does not spill over automatically were needed. Audretsch, Keilbach, and Lehmann (2006), Braunerhjelm and coauthors (2010), and Acs and coauthors (2012) suggest that the automatic spillover of knowledge from its source is impeded by what they term the knowledge filter. The knowledge filter prevents or at least impedes knowledge from automatically spilling over for innovation and commercialization. Regulations and legal restrictions may account for some of the knowledge filter. However, the broadest and most prevalent source contributing to the knowledge filter is the conditions inherent in knowledge—uncertainty, asymmetries, and high costs of transaction. Knowledge spillover entrepreneurship is important and significant because it provides a conduit penetrating the knowledge filter and serves as a catalyst for the commercialization of knowledge and ideas created in one organizational context while also generating innovative activity in the context of a new firm, which ultimately contributes to economic growth, employment creation, and global competitiveness. Estimation of the knowledge production function has typically varied the spatial unit from relatively broad geographic units of observations, such as states, to much more focused geographic units of observations such as cities, counties, or even postal codes. Most scholars concur that states are probably too large to represent an appropriate geographic unit of observation. Besides the theoretical aspects, scholars have also continued to work in this tradition, adding new measures of innovative inputs and outputs. Jaffe (1989) dealt with the measurement problem raised by Krugman (1991) by linking the patent activity within technologies located within states to knowledge inputs located within the same

The Role of Universities in Local and Regional Competitiveness    221 spatial jurisdiction. Knowledge, or what is sometimes referred to as tacit knowledge, is vague, difficult to codify, and often only serendipitously recognized. While information is codified and can be formalized and written down, tacit knowledge, by definition, is noncodifiable and cannot be formalized and written down. Academic and scientific research, identified as the main source of knowledge spillovers, is often measured by the amount of money spent on R & D, the number of articles published and cited in academic and scientific journals, the number of employees employed in R & D, or the number of patents and patent applications (see Varga 2000; Henderson, Jaffe, and Trajtenberg 1998; Hall, Link, and Scott 2003; Hülsbeck, Lehmann, and Starnecker 2013). All these measures exhibit their own specific advantages and disadvantages. While the number of patents, academic articles, and spending on R & D may be easy to measure, they often lack of causality and quality effects. This creates an interesting dilemma since the production of knowledge as measured by patents, patent inventions, and academic and scientific research articles has increased drastically since the 1990s (see Kortum and Lerner 1997, 1). A large part of this observed patent inflation among universities and research laboratories is driven by changes in incentive structures by universities and inventors. The Bayh-Dole Act in the United States and the Employment Invention Act 2002 in Germany increased the incentives for an organization to increase the number of patent applications and patent licensing (Hülsbeck, Lehmann, and Starnecker 2013). Another way to measure performance is whether university spillovers reduce the cost of R & D for firms (Harhoff 2000). Griliches (1979) suggested using hedonic price functions to analyze whether the quality of new products increases because of spillovers relative to the old product. One branch of research analyzes the productivity effects of spillovers (see Nadiri 1997 for a survey). Perhaps the most prevalent and established finding in the spillover literature is that innovative output and growth are higher in the presence of knowledge inputs (Feldman 2000). This literature has been established for the unit of observation of the region or city. Geographic proximity matters in transmitting knowledge, because tacit knowledge is inherently nonrivalrous in nature, and knowledge developed for any particular application can easily spill over and have economic value in very different applications (see Audretsch et al. 2005a; 2005b). A rich empirical literature has emerged analyzing when and how distance matters (see Audretsch, Keilbach, and Lehmann 2006). For instance, Audretsch and Lehmann (2005b; 2005c) found that young high-tech start-ups locate close to highly productive universities with a large number of students in both natural and social sciences. De Silva and McComb (2012) demonstrate that proximity to universities positively affects entrepreneurship rates in Texas, but the new firms created in proximity with universities do not benefit from university knowledge spillovers after the start-up phase. While the overwhelming majority of the empirical studies confirm the importance of close proximity with the next source of knowledge, the results differ across countries. As to incumbent firms, knowledge spillovers on new firm creation tend to be geographically bounded as well and decay rapidly across geographic space (Lee, Hong, and Sun 2013).

222    Critical Drivers of Local Competitiveness However, the measurement problem relies on what is meant by close distance, regionality, and geographic proximity (Audretsch, Lehmann, and Warning 2004; Audretsch, Keilbach, and Lehmann 2005). As Jaffe (1989) points out, geographical location is important in capturing the benefits of spillovers when the mechanism of knowledge is informal conversation, as is the case for tacit knowledge: “geographic proximity to the spillover source may be helpful or even necessary in capturing the spillover benefits” (Jaffe 1989, 957). Thus, the limited geographic reach of such channels for the exchange of information and know-how is assumed to be one of the main reasons why geographic proximity promotes firm performance, since it leads to a competitive advantage over similar firms that are not located close to universities. During the innovation process firms are confronted with a wide range of possible problems and difficulties that may be beyond the firm’s own problem-solving capacity. Close location gives support to both management capacity and technological input. After all, geographic proximity matters in transmitting knowledge, because as Glaeser et al. (1992, 1126) observe, “intellectual breakthroughs . . . cross hallways and streets more easily than oceans and continents.”

4  Sources, Channels, and Mechanisms for Knowledge Spillovers The ability of research universities to create benefits for their local economies has created a new mission for research universities, and a developing literature is trying to identify and examine the channels, mechanisms, and processes of technology transfer. Knowledge spillovers may arise from personal networks of academic and industrial researchers (Liebeskind et al. 1996; MacPherson 1998; Feldman and Desroche 2003), participation in conferences and presentations, pre-employment possibilities with students (Varga 2000), patents (Jaffe 1989), the number and quality of scientific publications (Audretsch and Feldman 1996), human capital and its mobility (Audretsch, Lehmann, and Warning 2004), academic spin-offs, and also intermediate products (Keller and Yeaple 2013). While an overwhelming part of the empirical literature confirms the general positive effects of university spillovers (Acs, Audretsch, and Feldman 1992; 1994; Jaffe, Trajtenberg, and Henderson 1993; Audretsch and Feldman 1996; Audretsch, Lehmann, and Warning 2004; Anselin, Varga, and Acs 1997; Varga 2000; Mowery and Ziedonis 2001), there is mixed evidence about the impact of the different kinds of spillovers (Carlsson et al. 2009; Rothaermel, Agung, and Jiang 2007). The most prominent and discussed spillover mechanisms are patents. According to Griliches’s model of the knowledge production function, the crucial innovative input is new technological knowledge generated by R & D, and the relevant innovative output is technological knowledge resulting in patent innovations (Griliches 1979; 1984).

The Role of Universities in Local and Regional Competitiveness    223 A closely related line of research is concerned with different levels of innovativeness across regions. The most influential studies in this field have all confirmed a joint positive influence of industry and university innovation on regional innovation as measured by patent or innovation counts (Anselin, Varga, and Acs 1997; Jaffe 1989). Jaffe, Trajtenberg, and Henderson (1993) analyze patent families—patents that reference or cite each other and indicate the flows of knowledge from one invention to another. Specifically, they compare the probabilities of patents citing prior patents with inventors from the same city against a randomly drawn control sample of cited patents. Their results suggest that citations are significantly more localized than the control group. The same methodology has been applied by Almeida and Kogut (1997) to study patenting in the semiconductor industry. The basic results agree: patent citations are highly localized, indicating that location and proximity clearly matter in exploiting knowledge spillovers. Researchers may patent their inventions as well. The incentives for researchers to collaborate with industry vary across researchers and disciplines. A number of studies have shown that researchers engage in university technology transfer to secure research grants, maximize their private income, get access to large laboratories, and secure jobs for their students (Bercovitz and Feldman 2008). Whatever their private motives may be, one can expect them to maximize their utility. In this light patents seem hardly the best way to achieve such utility maximization. Kenney and Patton (2009) show that the university ownership model of research patents that is now common in the United States and large parts of Europe leads to misaligned incentives and incurs additional costs to the inventors as they have to deal not only with probable acquirers of their knowledge but also with profit-maximizing and inefficient technology transfer offices (Siegel and Phan 2005; for Germany see Hülsbeck, Lehmann, and Starnecker 2013). Even if the researchers were to privately patent their inventions and thereby circumvent university bureaucracy, they would have to bear the costs of patenting and marketing their invention as well as face the risk of failure. In the end they would probably be forced to become involuntary academic entrepreneurs, as they might be unable to find interested research partners in incumbent firms and instead would have to turn to a venture capitalist (VC) to finance their ideas. For the vast majority of researchers who have selected themselves into an academic rather than entrepreneurial career, these highly increased transaction costs and uncertainty cannot be considered as maximizing utility (Dasgupta and David 1994; Tartari and Breschi 2012). While patents represent almost all codified knowledge, another important stream in the literature emphasizes the impact of tacit knowledge embodied in human capital within a geographic region. Malecki (1997) notes the importance of skilled labor as a mechanism for knowledge transfer in technology-based industrial clusters. It is also the case that for certain science-based industries, the location and preferences of scientists influence the geographical location of innovation. Zucker, Darby, and Brewer (1998) show that in biotechnology, an industry based almost exclusively on new knowledge and cutting-edge scientific discoveries, firms tend to cluster together in just a handful of locations, and the authors find that this is due to the location of star scientists—those individuals with high amounts of human capital who are able to appropriate their

224    Critical Drivers of Local Competitiveness knowledge through start-up firms. This finding is supported by Audretsch and Stephan (1996), who examine the geographic relationships of scientists working with biotechnology firms. The importance of geographic proximity is clearly shaped by the role played by the scientist. The scientist is more likely to be located in the same region as the firm when the relationship involves the transfer of new economic knowledge. However, when the scientist is providing a service to the company that does not involve knowledge transfer, local proximity becomes much less important. Research output of universities also enhances firm performance and regional competitiveness when high-skilled labor is a scarce resource and there is intense competition for human capital and other knowledge inputs (Porter and Stern 2001). Audretsch, Lehmann, and Warning (2005) show that firms tend to locate close to research-intense universities and especially those with a high output of well-educated students. It also seems that a close location to those basic resources positively enhances firm performance (Audretsch and Lehmann 2005a; 2005b). Schartinger, Schibany, and Gassler (2001) conclude that mobility of human capital is the main channel of knowledge transfers from universities to the business sector. Relational networks exist at multiple levels of analysis because they can link together individuals, groups, firms, industries, geographic regions, and nation-states. In addition, they can tie members of any one of these categories to members of another category. For example, Florida and Cohen (1999) and Feldman et al. (2002) demonstrate the ways in which research universities provide a link that facilitates knowledge spillovers in the form of recruiting talent to the region, transferring technology through local linkages and interactions, placing students in industry, and providing a platform for firms, individuals, and government agencies to interact. Similarly, Florida and Kenney (1988) examine the connections and special access to talent and resources that venture capital firms provide to link their new high-technology start-up clients. Manski (2000) considers that many of the interactions in R & D and human capital formation that are important to endogenous growth theory occur in nonmarket environments and are influenced by the expectations, preferences, and constraints of related economic agents. Moreover, social interactions have economic value in transmitting knowledge and ideas. Von Hippel (1994) explains that high-context, uncertain knowledge, or what he terms sticky knowledge, is best transmitted via face-to-face interaction and through frequent and repeated contact. Related to the translation of knowledge spillovers into economic knowledge is the mechanism of the knowledge filter. While in the endogenous growth theory (Romer 1990) it is assumed that knowledge spills over automatically, Acs et al. (2009), Audretsch, Keilbach, and Lehmann (2006), Braunerhjelm et al. (2010), and Acs et al. (2012) suggest that, instead, the automatic spillover of knowledge from its source is impeded by what they term the knowledge filter. A valuable attempt to formalize the concept of knowledge filter is in Acs and coauthors (2009). The authors model the link between the overall stock of knowledge in the economy, R & D exploitation by incumbents, and entrepreneurial activity. In their theoretical model, knowledge that spills over from its source can potentially be absorbed and commercially exploited by economic agents who are not the knowledge creators. Knowledge exploitation by prospective entrepreneurs through

The Role of Universities in Local and Regional Competitiveness    225 the creation of new ventures depends on their ability to penetrate the knowledge filter, which is the result of all the barriers inhibiting the conversion of knowledge produced in R & D laboratories of incumbent firms and in universities into commercialized knowledge. Several studies, both theoretical (Acs and Sanders 2013; Audretsch 2007) and empirical (Acs et al. 2009; Quian and Acs 2013) address the questions of absorptive capacity, the knowledge filter, and the vehicle to translate knowledge into growth (Carlsson et al. 2009). The knowledge filter prevents or at least impedes knowledge from automatically spilling over for innovation and commercialization. Regulations and legal restrictions account for some of the knowledge filter. Drawing from the institutional theory, Bruton, Ahlstrom, and Li (2010), Veciana and Urbano (2008), and Stenholm, Acs, and Wuebker (2013), among others, empirically analyze how regulative, cognitive, and normative institutions affect entrepreneurial activity triggered by knowledge spillovers. Acs and Sanders (2012) focus on the appropriability regime and theorize how various intellectual property rights regimes alter the incentives of both inventors and innovators. Others focus on the supportive role of the regional innovation system (Leyden and Link 2013) and the infrastructural endowment (Cumming and Johan 2010) in penetrating the knowledge filter and fostering knowledge spillover entrepreneurship. The knowledge filter (Acs et al. 2004) is “thin” when new ideas and innovations flow freely between academia and industry. In regions with industries lacking the ability to directly integrate new knowledge, the filter is “thick,” and academic research and knowledge cannot directly flow from universities to the local industry and researchers are forced to choose the second-best solution, signaling their know-how and willingness to engage in transfer activities via patents. These university patents may even slow down the pace of industrial innovation, as strong property rights hinder the free flow of knowledge.

5  Academic Policy and Regional Competitiveness Globalization is shifting the comparative advantage in the OECD countries away from being based on traditional inputs of production, such as land, labor, and capital, and toward knowledge. As comparative advantage has become increasingly based on new knowledge, public policy has responded by enabling the creation and commercialization of knowledge. The organization of knowledge activities within a region to better promote technological change and to cope with the challenges of globalization has been the subject of a heated debate for decades. A large and diverse literature exists that analyzes the role university commercialization plays in the regional and national innovation system (Mowery and Sampat 2005; Cosh and Hughes 2010; Bonardo, Paleari, and Vismara 2010), often highlighting the importance of separate organizational units to manage industry-university collaborations (Link et  al. 2008). Several

226    Critical Drivers of Local Competitiveness policy instruments have been identified that foster the strength of universities in providing knowledge spillovers to enhance firm and regional competitiveness, improving the national and regional innovation system and reshaping the focus at the institutional level toward structural changes within universities. The recognition of the importance of entrepreneurial firms and new venture creation for regional competitiveness has led policymakers to shift their lenses toward fostering entrepreneurial and innovation activities. The range of policy instruments is broad and varies from changes in the education system to tax incentives, improvements of the technological infrastructure, and changes in the regulations, like the Bayh-Dole Act (Link and Scott 2005; Wright et al. 2007; Link 2007). Within the different policy instruments and regulations discussed in the literature, the Bayh-Dole Act in 1980 and its implications have received broad interest in academia (Mowery and Sampat 2005). The striking success of the US policy of fostering university-industry links through the Bayh-Dole Act and thus the increasing success of entrepreneurial firms also led to rethinking and reformulation of European policy (Wright et al. 2007; Hülsbeck, Lehmann, and Starnecker 2013). This legislation gave academic institutions property rights to the results of federally funded research and thus shifted the incentives to promote and profit from the commercialization of inventions toward the university. This policy instrument reflects a shift, concentrating on enabling new venture creation and triggering national and regional growth and competitiveness (Audretsch and Beckmann 2007; Fritsch 2005). While often in the past economic policies were used and controlled at the federal or national level, entrepreneurial and innovation activities are generally applied at the decentralized level and thus directly shape regional and local competitiveness. Such policy instruments include support programs, instruments facilitating the institutionalization of regional cooperation and network building, instruments to stimulate start-ups out of universities, and investments in local infrastructure, regional capacities, or simply supporting future entrepreneurs (Audretsch and Beckmann 2007). Regional competitiveness is about the production, acquisition, absorption, reproduction, and dissemination of knowledge to foster local innovative activity. Public policy on the regional level is thus designed to promote and shape these activities. Even as scholars assembled the requisite theoretical frameworks and empirical analyses to reach conclusions with a high degree of confidence about the importance of geographic location, agglomerations, and clusters for competitiveness and growth, they began to question the role that institutions along with the organization and structure of economic activities play within spatially bounded regions. It has long been thought that when people work in close geographic proximity, the likelihood that they will collaborate together increases dramatically (Audretsch, Keilbach, and Lehmann 2006). But does it make a difference how economic activity is organized within the black box of geographic proximity and space? A heated debate has emerged in the literature about the manner in which the underlying economic structure within a geographic unit of observation might shape economic performance. Seminal economic concepts in the literature on agglomeration economies are those of externalities of the Marshallian type (Marshall 1920; Feldman and Audretsch 1999; Audretsch and Fritsch 1994) and Jacobian

The Role of Universities in Local and Regional Competitiveness    227 type (Jacobs 1969). While the former are due to economies of scale and scope and learning by doing, the latter are due to complementariness. In the first case, clusters should be rather homogenously and large, like biogenetic clusters, and knowledge spillover effects occur within the same industry. In the second case, clusters should be heterogeneous, and knowledge spillovers occur across different industries, as in biotechnology. The extent of regional specialization versus regional diversity in promoting knowledge spillovers is not the only dimension over which there has been a theoretical debate. A controversy involves the degree of competition prevalent in the region, or the extent of the local monopoly. The Marshallian model predicts that a local monopoly is superior to local competition because it maximizes the ability of firms to appropriate the economic value accruing from their investments in new knowledge. Glaeser et al. (1992) confirm this view, suggesting that an increased concentration of a particular industry within a specific geographic region facilitates knowledge spillovers across firms. The concentration of an industry within geographic space promotes knowledge spillovers among firms and therefore facilitates innovative activity. Lower costs of transaction in communication result in a higher probability of knowledge spilling over across individuals within the population. Knowledge externalities with respect to firms exist, but only for firms within the same industry. They realized economies of scale by homogenous clusters far outweigh the possible disadvantages of monopoly power and reduced competition. By contrast, Jacobs (1969) and Porter (1990) argue the opposite—that competition is more conducive to knowledge externalities than is local monopoly. Restricting knowledge externalities to the same industry may ignore an important source of new economic knowledge—interindustry knowledge spillovers. Griliches (1992, 29) defined knowledge spillovers as “working on similar things and hence benefiting much from each other’s research.” The most important source of knowledge spillovers is external to the industry in which the firm operates, and the exchange of complementary knowledge across diverse firms and economic agents yields a greater return on new economic knowledge. Both empirical (Feldman and Audretsch 1999) and anecdotal evidence (Hülsbeck and Lehmann 2007) favors the Jacobean model of diversity. Regional policymakers should thus focus on promoting heterogeneous clusters instead of focusing on one type of innovation or industry, for several reasons. First, innovations in high-technology industries serve as inputs and intermediate in multiple industries. Local proximity lowers the costs of communication and joint development in buyer-supplier relationships. Second, heterogeneous clusters are less prone to exogenous shocks and obsolescence. Third, future technologies and industries are almost always unplanned and arise by chance. The more heterogeneous the industries clustered within a region, the higher the likelihood that future developments are created within local proximity. A promising literature has emerged focusing policy instruments on the single university as the key incubator of knowledge that knows how to promote entrepreneurial activities and foster regional competitiveness (Shane 2003; Link and Siegel 2005; Wright et al. 2007). This literature analyzes academic entrepreneurship and focuses on the whole process of commercialization of ideas and knowledge. Academic entrepreneurship

228    Critical Drivers of Local Competitiveness since the 1990s has become the main focus and strategic orientation for policymakers and managers in the Anglo-Saxon higher education system (see Wright 2014). This encompasses not only individual characteristics, such as previous experience of the individual scientists, but also the academic and university environment. Such entrepreneurial universities adopt more and more roles of industrial R & D by engaging in university technology transfer (Link and Siegel 2005; Siegel, Westhead, and Wright 2003; Shane 2003; Wright et al. 2007). While there are several instruments fostering technology transfer, academic research has identified two mains issues promoting regional competitiveness: the establishment and performance of technology transfer offices, and academic spin-offs. The major role in the economic exploitation of university research is dedicated to the technology transfer offices (TTOs) of universities to promote the commercialization process of academic research (Bozeman et al. 2013; Hülsbeck, Lehmann, and Starnecker 2013). As intermediaries, they lower the transaction costs between the demand and supply side of innovations by reducing the search and negotiation costs for both parties and often serve as a third party to protect the inventor’s interests. TTOs were first established in the United States to foster and promote academic research into marketable products and services. The few empirical studies find overwhelming evidence for the importance and performance of TTOs in the United States (Siegel, Westhead, and Wright 2003), while empirical evidence for other countries is rather mixed (see Hülsbeck, Lehmann, and Starnecker 2013). Analyzing this unsatisfying and somewhat puzzling empirical result outside the United States, Siegel, Westhead, and Wright (2003) conclude that countries differ too much in the institutional arrangements of their TTOs, and thus empirical findings cannot be compared. With the establishment of TTOs, university spin-offs are becoming a significant global phenomenon (Shane 2003; Wright et al. 2007). While cutting federal and public funds for universities in general, governments are devoting increasing amounts of money to universities, with the goal of turning them into engines of economic growth through spin-off company formation and new venture creation. Academic literature focuses on university spin-offs as an important class of firms, an economically powerful and successful subset of high-tech start-ups (Shane 2003). In some industries, like biotechnology, university spin-offs are the dominant form of technology start-ups emerging from university research laboratories. Researchers have identified several policies that enhance the amount of spin-off activity, like licensing, taking equity in spin-offs, allowing faculty inventors leave of absence to found companies to exploit inventions, providing access to pre-seed-stage capital, and permitting the use of university resources to scientists and founders to develop the technology (Shane 2003, 69–70). While research has identified the important role that universities play in generating knowledge spillovers on the one hand and regional competitiveness on the other hand, the combined importance of both factors in transmitting knowledge spillovers still remains relatively unexplored. Analyzing the impact of institutional variables, such as university characteristics, on regional performance raises the issues of endogeneity, reverse causality, and co-evolutionary development. Reverse causality and the issue of

The Role of Universities in Local and Regional Competitiveness    229 endogeneity are given when richer regions can spend more resources to shape the performance of their local universities. Mobility of human capital, excellent students and academic researchers from more or less neighboring regions, would then lead to competitive advantages. While this phenomenon is discussed as the brain drain at the country level, it also holds on the regional level, as it is the case in several states in Europe, like Italy (south-north) and Germany (east-west and north-south). Geographical conditions, like climate and advanced leisure conditions, can also lead students and scientists to move toward more attractive places. An important question addresses the impact of regional endowment on competitiveness. Bae and Koo (2009) highlight the importance of knowledge relatedness and diversity. Knowledge relatedness refers to the extent to which knowledge bases in a region are complementary to each another, so that the value of pooling different knowledge sources is greater than the total value of the individual sources. Conversely, knowledge diversity refers to the extent to which knowledge in a region is dissimilar (either functionally or technically). Both knowledge relatedness and knowledge diversity positively impact new firm creation, but the effects of diversity are the strongest. The importance of knowledge diversity is acknowledged by Bishop (2012), who bases his insights on the broader diversity literature. In regards to heterogeneity across contexts, regional diversity in the exploitation of knowledge spillovers through entrepreneurship is analyzed empirically. Audretsch, Dohse, and Niebuhr (2010) distinguish between diversity at the industry (sectorial diversity) and individual levels (which the authors label cultural diversity). They find compelling evidence that cultural diversity facilitates the exploitation of knowledge spillovers from incumbent firms. Cheng and Li (2012) confirm these findings for the United States and show that either cultural or racial diversity has a positive effect on industry-specific new venture creation. Regional diversity also positively affects new firm creation in neighboring counties, in that knowledge exploitation for new firm creation strongly depends on localized competition, the “struggle for ideas and knowledge embodied in economic agents” (Plummer and Acs 2014, 6). Incumbents and new ventures compete for the same scarce resources of ideas and knowledge within a given region. Co-evolutionary processes occur when both regional attractiveness and institutional development act as complements, and effects and causalities cannot be separated. Excellent universities provide positive externalities for nearby locales beyond pure economic effects. Students, teachers, and researchers stimulate and enrich the cultural and societal value of regions and thus increase the attractiveness of cities and regions (Florida 2002). In addition, local policymakers can increase the attractiveness of places by investments in projects to increase the value of leisure, free time, and cultural life. This is particularly important since the location decision of firms and their growth strategy strongly depend on these local conditions. The higher the employment rate, the higher the tax payments and expenditures of people, and the more resources that can be spent on universities. In addition, private universities benefit from donations, joint research projects, and efforts. Feldman (1994) suggests that firms producing

230    Critical Drivers of Local Competitiveness innovations tend to be located in areas where there are necessary resources: resources that have accumulated because of a region’s past success with innovation.

6. Conclusion Historically, regions have differed in their long- and short-term performance expressed by variations in income, wealth, health, and other measures. Academic research has identified one major determinant in shaping regional performance—human capital. Regional competitiveness is about making places attractive to select people and providing the incentives to make local investments. Since the past millennium universities have played an important role as sources of knowledge, through acceleration of technologies, and by stimulation of social and cultural life within regions. Within the growing competition of the globalized world, universities, as the main source of trained knowledge workers, ideas, and knowledge spillovers from both basic and applied research activities, are seen as the key organizations and supporters in a national innovation system. Globalization today, with its increasing dynamic in the interrelations of markets and cultural and social life and decreasing costs of communication and mobility, drastically reshapes the landscape and importance of universities. The production, acquisition, absorption, reproduction, and dissemination of knowledge are the fundamental characteristics of contemporary competitive dynamics of regions today. To compete with the challenges raised by globalization, policymakers have tried to adopt conventional economic approaches to inter-university competition. While inter-university competition was a very important influence on the evolution of universities in the Anglo-Saxon countries, it has been limited in most national systems of higher education, in particular continental Europe. The dynamic of this competition is best reflected by rankings of universities, and the efforts made to compete for scarce resources to climb up in the rankings. The competition of universities also reflects different modes of political and economic systems, which shape local higher education institutions: the market-based Anglo-Saxon system, the centralized and governed system in most Asian countries, and the continental European Greco-Christian model of democracy and equality. Each system has to cope with the risks and challenges of globalization and the increased competition in the knowledge producing sector, fostering different strategies. Following their market-based system, the Anglo-Saxon countries proclaim academic entrepreneurship as the most promising strategy. Universities have to compete and act in an entrepreneurial spirit, seeking opportunities to generate and commercialize ideas and thus help transform the managed society into an entrepreneurial society. As a result, inequality among universities in both performance and resources is increasing in the Anglo-Saxon countries, leading to adverse effects for the low-performing regions. Adverse effects also arise through the excessive increases in tuition fees and student borrowing associated with a self-selection towards job with the highest earning premiums to cover the costs of graduation. This is associated with crowding-out effects of academic fields with a low

The Role of Universities in Local and Regional Competitiveness    231 earning premiums, in particular in arts and humanities. This leads to adverse effects on social and cultural living standards in the local community, shaping economic performance and welfare negatively in the future, including an increase of the inequality of income and wealth across regions. The universities in China are subject to governmental control and are far from autonomous, like the universities in the Anglo-Saxon countries or Europe. Universities are governed, controlled, and stimulated by large governmental programs and strong guidance by the government, in particular in research programs, intended to foster innovations and commercialization. University policy is primarily dedicated to developing the regional and national innovation system, leading, in the end, to short-term economic profit. The European Union has proclaimed a “new deal” in the higher education system: competition and mobility. Regardless of the historical paths of their universities, rooted in the Middle Ages, the most promising model in higher education seems to be the one from the Anglo-Saxon countries. Universities should compete for both students and academics to attract the most talented ones. Increasing mobility, by lowering the barriers to movement across countries, should be the means to keep up with the rest of the world.

References Acemoglu, D., S. Johnson, and J. A. Robinson. 2002. “Reversal of the Fortune: Geography and Institutions in the Making of the Modern World Income Distribution.” Quarterly Journal of Economics 117 (4), 1231–94. Acs, Z. J., D. B. Audretsch, P. Braunerhjelm, and B. Carlsson. 2012. “Growth and Entrepreneurship.” Small Business Economics 39 (2), 289–300. Acs, Z.  J., D. B.  Audretsch, and M. P.  Feldman. 1992. “Real Effects of Academic Research: Comment.” American Economic Review 82, 363–67. Acs, Z. J., D. B. Audretsch, and M. P. Feldman. 1994. “R&D Spillovers and Innovative Activity.” Managerial and Decision Economics 15, 131–38. Acs, Z.  J., D. B.  Audretsch, and E. E.  Lehmann. 2013. “The Knowledge Spillover Theory of Entrepreneurship.” Small Business Economics 41 (4), 757–74. Acs, Z. J., P. Braunerhjelm, D. B. Audretsch, and B. Carlsson. 2009. “The Knowledge Spillover Theory of Entrepreneurship.” Small Business Economics 32 (1), 15–30. Acs, Z. J., and M. Sanders. 2012. “Patents, Knowledge Spillovers, and Entrepreneurship.” Small Business Economics 39 (4), 801–17. Acs, Z. J., and M. Sanders. 2013. “Knowledge Spillover Entrepreneurship in an Endogenous Growth Model.” Small Business Economics 41 (4), 775–95. Almeida, P., and B. Kogut. 1997. “The Exploration of Technological Diversity and the Geographic Localization of Innovation.” Small Business Economics 9 (1), 21–31. Anselin, L., A. Varga, and Z. J. Acs. 1997. “Local Geographic Spillovers between University Research and High Technology Innovations.” Journal of Urban Economics 42 (3), 422–48. Arrow, Kenneth. 1962. “Economic Welfare and the Allocation of Resources for Invention.” In Richard Nelson, ed., The Rate and Direction of Inventive Activity. Princeton, NJ: Princeton University Press.

232    Critical Drivers of Local Competitiveness Audretsch, D. B. 1995. Innovation and Industry Evolution. Cambridge, MA: MIT Press. Audretsch, D. B. 2007. The Entrepreneurial Society. Oxford: Oxford University Press. Audretsch, D. B., and I. Beckmann. 2007. “From Small Business to Entrepreneurship Policy.” In D. B. Audretsch, I. Grilo, and R. Thurik, eds., Handbook of Research in Entrepreneurship Policy. Cheltenham: Edward Elgar. Audretsch, D. B., D. Dohse, and A. Niebuhr. 2010. “Cultural Diversity and Entrepreneurship: A Regional Analysis for Germany.” Annals of Regional Science 45 (1), 55–85. Audretsch, D. B., and M. P. Feldman. 1996. “R&D Spillovers and the Geography of Innovation and Production.” American Economic Review 86, 630–40. Audretsch, D. B., and M. Fritsch. 1994. “The Geography of Firm Births in Germany.” Regional Studies 28 (4), 359–65. Audretsch, D. B., M. Keilbach, and E. E. Lehmann. 2005. “The Knowledge Spillover Theory of Entrepreneurship and Technological Diffusion.” In G. Libecap, ed., University Entrepreneurship and Technology Transfers: Process, Design, and Property Rights. Boston: Elsevier. Audretsch, D. B., M. C. Keilbach, and E. E. Lehmann. 2006. Entrepreneurship and Economic Growth. New York: Oxford University Press. Audretsch, D.  B., and E. E.  Lehmann. 2005a. “Do University Policies Make a Difference?” Research Policy 34 (3), 343–47. Audretsch, D.  B., and E. E.  Lehmann. 2005b. “Does the Knowledge Spillover Theory of Entrepreneurship Hold for Regions?” Research Policy 34 (8), 1191–202. Audretsch, D.  B., and E. E.  Lehmann. 2005c. “Do Locational Spillovers Pay? Empirical Evidence from German IPO Data.” Economics of Information and New Technology 15, 71–81. Audretsch, D. B., E. E. Lehmann, and S. Warning. 2004. “University Spillovers: Does the Kind of Science Matter?” Industry and Innovation 11 (3), 193–205. Audretsch, D. B., E. E. Lehmann, and S. Warning. 2005. “University Spillovers and New Firm Location.” Research Policy 34, 1113–22. Audretsch, D.  B., E. E.  Lehmann, and M. Wright. 2014. “Technology Transfer in a Global Economy.” Journal of Technology Transfer 39 (3), 301–12. Audretsch, D, B., and P. E. Stephan. 1996. “Company-Scientist Locational Links: The Case of Biotechnology.” American Economic Review 86 (3), 641–52. Audretsch, D. B., and P. E. Stephan. 1999. “Knowledge Spillovers in Biotechnology: Sources and Incentives.” Journal of Evolutionary Economics 9 (1), 97–107. Audretsch, D. B., and R. Thurik. 2001. “What Is New about the New Economy? Sources of Growth in the Managed and Entrepreneurial Economies.” Industrial and Corporate Change 10, 267–315. Bae, J., and J. Koo. 2009. “The Nature of Local Knowledge and New Firm Formation.” Industrial and Corporate Change 18 (3), 473–96. Bercovitz, J., and M. P. Feldman. 2008. “Academic Entrepreneurs: Organizational Change at the Individual Level.” Organization Science 19 (1), 69–89. Bishop, P. 2012. “Knowledge, Diversity and Entrepreneurship: A Spatial Analysis of New Firm Formation in Great Britain.” Entrepreneurship and Regional Development 24 (7–8), 641–60. Bonardo D., S. Paleari, and S. Vismara. 2010. “The M&A Dynamics of European Science-Based Entrepreneurial Firms.” Journal of Technology Transfer 35 (1), 141–80. Braunerhjelm, P., Z. J.  Acs, D. B.  Audretsch, and B. Carlsson. 2010. “The Missing Link: Knowledge Diffusion and Entrepreneurship in Endogenous Growth.” Small Business Economics 34 (2), 105–25.

The Role of Universities in Local and Regional Competitiveness    233 Breznitz, S.  M., and M. P.  Feldman. 2012. “The Engaged University.” Journal of Technology Transfer 37 (2), 139–57. Bruland, K., and D. C.  Mowery. 2005. “Innovation through Time.” In J. Fagerberg, D. C. Mowery, and R. R. Nelson, eds., The Oxford Handbook of Innovation. New York: Oxford University Press. Bruton, G. D., D. Ahlstrom, and H.-L. Li. 2010. “Institutional Theory and Entrepreneurship: Where Are We Now and Where Do We Need to Move in the Future?” Entrepreneurship Theory and Practice 34 (3), 421–40. Carlsson, B., Z. J.  Acs, D. B.  Audretsch, and P. Braunerhjelm. 2009. “Knowledge Creation, Entrepreneurship, and Economic Growth: A Historical Review.” Industrial and Corporate Change 18 (6), 1193–229. Cheng, S., and H.-L. Li. 2012. “New Firm Formation Facing Cultural and Racial Diversity.” Papers in Regional Science 91 (4), 759–74. Cosh, A., and A. Hughes. 2010. “Never Mind the Quality, Feel the Width: University-Industry Links and Government Financial Support for Innovation in Small High-Technology Businesses in the UK and the USA.” Journal of Technology Transfer 35, 66–91. Cumming, D., and S. Johan. 2010. “The Differential Impact of the Internet on Spurring Regional Entrepreneurship.” Entrepreneurship Theory and Practice 34 (5), 857–83. Dasgupta, P., and P. A. David. 1994. “Toward a New Economics of Science.” Policy Research 23, 487–521. De Silva, D. G., and R. McComb. 2012. “Research Universities and Regional High-Tech Firm Start-Up and Exit.” Economic Inquiry 50 (1), 112–30. Diamond, Jared. 1997. Guns, Germs, and Steel: The Fates of Human Societies. New York: Norton. Feldman, M. P. 1994. “Knowledge Complementarity and Innovation.” Small Business Economics 6 (5), 363–72. Feldman, M.  P. 2000. “Location and Innovation:  The New Economic Geography of Innovation.” In G. Clark, M. P. Feldman, and M. Gertler, eds., Oxford Handbook of Economic Geography. New York: Oxford University Press: Oxford. Feldman, M.  P., and D. B.  Audretsch. 1999. “Innovation in Cities:  Science-Based Diversity, Specialization and Localized Competition.” European Economic Review 43 (2), 409–29. Feldman, M.  P., and P. Desroche. 2003. “Research Universities and Local Economic Development:  Lessons from the History of the John Hopkins University.” Industry and Innovation 10, 5–24. Feldman, M. P., I. Feller, J. Bercovitz, and R. Burton. 2002. “University Technology Transfer and the System of Innovation.” In M. P. Feldman and N. Massard, eds., Institutions and Systems in the Geography of Innovation. Boston: Kluwer. Florida, R. L. 2002a. “Bohemia and Economic Geography.” Journal of Economic Geography 2, 55–71. Florida, R. L. 2002b. The Rise of the Creative Class. New York: Basic Books. Florida, R. L. 2004. Cities and the Creative Class. New York: Routledge. Florida, R.  L., and W. Cohen. 1999. “Engine or Infrastructure? The University Role in Economic Development.” In L. Branscomb, F. Kodama, and R. Florida, eds., Industrializing Knowledge:  University-INDUSTRY Linkages in Japan and the United States. Cambridge, MA: MIT Press. Florida, R., and M. Kenney. 1988. “Venture Capital and High Technology Entrepreneurship.” Journal of Business Venturing 3 (4), 301–19.

234    Critical Drivers of Local Competitiveness Friedman, Thomas L. 2005. The World Is Flat:  A  Brief History of the Twenty-First Century. New York: Farrar, Straus and Giroux. Fritsch, M. 2005. “Editorial: Regionalization of Innovation Policy—Introduction of the Special Issue.” Research Policy 34 (8), 1123–27. Glaeser, E. L., H. D. Kallal, J. D. Scheinkman, and A. Shleifer. 1992. “Growth in Cities.” Journal of Political Economy 100 (6), 1126–52. Griliches, Zvi. 1979. “Issues in Assessing the Contribution of Research and Development to Productivity Growth.” Bell Journal of Economics 10, 92–116. Griliches, Zvi. 1984. R&D, Patents, and Productivity. Chicago: University of Chicago Press. Griliches, Zvi. 1992. “The Search for R&D Spillovers.” Scandinavian Journal of Economics 94, 29–47. Hall, B., A. Link, and J. Scott. 2003. “Universities as Research Partners.” Review of Economics and Statistics 85, 485–91. Harhoff, D. 2000. “R&D Spillovers, Technological Proximity, and Productivity Growth: Evidence from German Panel Data.” Schmalenbach Business Review 52, 238–60. Henderson, R., A. Jaffe, and M. Trajtenberg. 1998. “Universities as a Source of Commercial Technology: A Detailed Analysis of University Patenting 1965–1988.” Review of Economics and Statistics 65, 119–27. Hülsbeck, M., and E. E.  Lehmann. 2007. “Entrepreneurship Policy in Bavaria:  Between Lederhosen and Laptop.” In D. B.  Audretsch, I. Grilo, and R. Thurik, eds., Handbook of Research in Entrepreneurship Policy. Cheltenham: Edward Elgar. Hülsbeck, M., E. E. Lehmann, and A. Starnecker. 2013. “Performance of Technology Transfer Offices in Germany.” Journal of Technology Transfer 38 (3), 199–215. Jacobs, J. 1969. The Economy of Cities. New York: Random House. Jaffe, A.  B. 1989. “Real Effects of Academic Research.” American Economic Review 79 (5), 957–70. Jaffe A. B., M. Trajtenberg, and R. Henderson. 1993. “Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations.” Quarterly Journal of Economics 63, 577–98. Keller, W., and S. R. Yeaple. 2013. “The Gravity of Knowledge.” American Economic Review 103 (4), 1414–44. Kogut B., and U. Zander. 1992. “Knowledge of the Firm, Combinative Capabilities, and the Replication of Technology.” Organizational Science 3, 383–97. Kortum, S., and J. Lerner. 1997. “Stronger Protection or Technological Revolution: What Is behind the Recent Surge in Patenting?” National Bureau of Economic Research Working Paper No. 6204. Krugman P. 1991. Geography and Trade, Cambridge, MA: MIT Press. Lee, I. H., E. Hong, and L. Sun. 2013. “Regional Knowledge Production and Entrepreneurial Firm Creation:  Spatial Dynamic Analyses.” Journal of Business Research 66 (10), 2106–15. Leyden D.  P., and A. Link. 2013. “Knowledge Spillovers, Collective Entrepreneurship, and Economic Growth: The Role of Universities.” Small Business Economics 41 (4), 797–817. Liebeskind, Julia, Oliver L.  Amalya, Lynne G.  Zucker, and Marylin Brewer. 1996. “Social Networks, Learning, and Flexibility: Sourcing Scientific Knowledge in New Biotechnology Firms.” Organizational Science 7, 428–43. Link, A.  N. 2007. “Public Policy and Entrepreneurship.” In D. B.  Audretsch, I. Grilo, and R. Thurik, eds., Handbook of Research in Entrepreneurship Policy. Cheltenham Edward Elgar.

The Role of Universities in Local and Regional Competitiveness    235 Link, A.  N., F. T.  Rothaermel, and D. Siegel. 2008. “University Technology Transfer:  An Introduction to the Special Issue.” IEEE Transactions of Engineering Management 55 (1), 23–28. Link, A., and J. Scott. 2003. “The Growth of Research Triangle Parks.” Small Business Economics 20, 167–75. Link, A., and J. Scott. 2005. “Evaluating Public Sector R&D Programs:  The Advanced Technology Program’s Investment in Wavelength References for Optical Fiber Communications.” Journal of Technology Transfer 30, 241–51. Link, A.  N., and D. S.  Siegel. 2005. “Generating Science-Based Growth:  An Econometric Analysis of the Impact of Organizational Incentives on University Industry Technology Transfer.” European Journal of Finance 11 (3), 169–82. Kenney, M., and D. Patton. 2009. “Reconsidering the Bayh-Dole Act and the Current University Invention Ownership Model.” Research Policy 38, 1407–22. MacPherson, Alan D. 1998. “Academic-Industry Linkages and Small Firm Innovation: Evidence from the Scientific Instruments Sector.” Entrepreneurship and Regional Development 10, 261–76. Malecki, E.  J. 1997. “Technology and Economic Development:  The Dynamics of Local, Regional, and National Competitiveness.” Essex: Longman. Manski, C. 2000. “Economic Analysis of Social Interactions.” Journal of Economic Perspectives 14, 115–36. Marshall, Alfred. 1920. Principles of Economics. 8th ed. London: Macmillan. Reprint, 1994. Mowery, D.  C., and B. N.  Sampat. 2005. “Universities in National Innovation Systems.” In J. Fagerberg, D. C. Mowery, and R. R. Nelson, eds., The Oxford Handbook of Innovation. New York: Oxford University Press. Mowery, D. C., and A. A. Ziedonis. 2001. “The Geographic Reach of Market and Non-market Channels of Technology Transfer: Comparing Citations and Licenses of University Patents.” National Bureau of Economic Research Working Paper No. 8568. Nadiri, M. I. 1997. “Innovations and Technological Spillovers.” National Bureau of Economic Research Working Paper No. 4423. Nunn, N. 2009. “The Importance of History for Economic Development.” Annual Review of Economics 1 (1), 65–92. Olsson, O., and D. A.  Hibb. 2005. “Biogeography and Long-Run Economic Development.” European Economic Review 49 (4), 909–38. Plummer, L. A., and Z. J. Acs. 2014. “Localized Competition in the Knowledge Spillover Theory of Entrepreneurship.” Journal of Business Venturing 29 (1), 121–36. Porter, Michael E. 1990. The Comparative Advantage of Nations Chicago:  University of Chicago Press. Porter, Michael E. 2003. “The Economic Performance of Regions.” Regional Studies 37, 549–78. Porter, Michael E., and Scott Stern. 2001. “Innovation:  Location Matters.” MIT Sloan Management Review, Summer, 28–36. Putterman, L., and D. N.  Weil. 2010. “Post-1500 Population Flows and the Long-Run Determinants of Economic Growth and Inequality.” Quarterly Journal of Economics 125 (4), 1627–82. Qian, H., and Z. J.  Acs. 2013. “An Absorptive Capacity Theory of Knowledge Spillover Entrepreneurship.” Small Business Economics 40 (2), 185–97. Romer, P. M. 1986. “Increasing Returns and Long-Run Growth.” Journal of Political Economy 94 (5), 1002–37.

236    Critical Drivers of Local Competitiveness Romer, P. M. 1990. “Endogenous Technological Change.” Journal of Political Economy 98 (5), 71–102. Rothaermel, F. T., S. D. Agung, and L. Jiang. 2007. “University Entrepreneurship: A Taxonomy of the Literature.” Industrial and Corporate Change 16 (4), 691–791. Schartinger, D., A. Schibany, and H. Gassler. 2001. “Interactive Relations between Universities and Firms: Empirical Evidence for Austria.” Journal of Technology Transfer 26, 255–68. Shane, S. 2003. Academic Entrepreneurship Cheltenham: Edward Elgar. Siegel, D. S., and Ph. H. Phan. 2005. “Analyzing the Effectiveness of University Technology Transfer:  Implications for Entrepreneurship Education.” In G. Libecap, ed., University Entrepreneurship and Technology Transfer:  Process, Design, and Property Rights. Boston: Elsevier. Siegel, D. S., P. Westhead, and M. Wright. 2003. “Assessing the Impact of Science Parks on the Research Productivity of Firms:  Exploratory Evidence from the United Kingdom. International Journal of Industrial Organization 21 (9), 1357–69 Spolaore, E., and R. Wacziarg. 2013. “How Deep Are the Roots of Economic Development?” Journal of Economic Literature 51 (2), 325–69. Stenholm, P., Z. J.  Acs, and R. Wuebker. 2013. “Exploring Country-Level Institutional Arrangements on the Rate and Type of Entrepreneurial Activity.” Journal of Business Venturing 28 (1), 176–93. Stephan, P. 1996. “The Economics of Science.” Journal of Economic Literature 34, 1199–235. Tartari, V., and S. Breschi. 2012. “Set Them Free: Scientists’ Evaluations of the Benefits and Costs of University-Industry Research Collaboration.” Industrial and Corporate Change 21 (5), 1117–47. Varga, Attila. 2000. “Local Academic Knowledge Transfers and the Concentration of Economic Activity.” Journal of Regional Science 40, 289–309. Veciana, J., and D. Urbano. 2008. “The Institutional Approach to Entrepreneurship Research:  Introduction.” International Entrepreneurship and Management Journal 4 (4), 365–79. von Hippel, E. 1994. “ ‘Sticky Information’ and the Locus of Problem Solving: Implications for Innovation.” Management Science 40 (4), 429–39. Wright, M. 2014. “Academic Entrepreneurship, Technology Transfer and Society:  Where Next?” Journal of Technology Transfer 39 (3), 322–34. Wright, M., B. Clarysse, Ph. Mustar, and A. Locket. 2007. Academic Entrepreneurship in Europe. Cheltenham: Edward Elgar. Zucker, L., M. Darby, and J. Armstrong. 1998. “Intellectual Human Capital and the Birth of U.S. Biotechnology Enterprises.” American Economic Review 88, 290–306.

Chapter 11

T he Grand Cha l l e ng e Model of  R &  D Christopher S. Hayter

1 Introduction From the race to create an efficacious vaccine to prevent the spread of HIV/AIDS and mitigating the effects of the Fukushima nuclear disaster, to ensuring food security among refugees fleeing violent conflicts in Africa and Asia, regions worldwide are confronting increasingly complex, transnational problems. While the effects of these “grand challenges” are often felt locally, they are far too large for any one region or country, much less organization, to tackle. Further, the term has entered the policy lexicon of global leaders in describing grand challenge problems of their own. In the words of US president Barack Obama: We’re pursuing … grand challenges like making solar energy as cheap as coal or making electric vehicles as affordable as the ones that run on gas.… They’re ambitious goals, but they’re achievable. And we’re encouraging companies and research universities and other organizations to get involved and help us make progress. (April 2, 2013)

Society typically responds to complex challenges through science, the production and application of new knowledge created through research and development (R & D). Neoclassical economic theory views new scientific knowledge as a public good that automatically spills over into society (Romer 1986). Further, knowledge spills over within geographically bound regions, providing intellectual fuel for local innovators and social entrepreneurs, who often have few formalized R & D resources of their own (Jaffe 1989; Jaffe, Trajtenberg, and Henderson 1993; Feldman 1994; Audretsch and Feldman 1996). This localized “knowledge production function” has long been used to explain the economic and social dynamism of regions such as California’s Silicon Valley and San Diego region, Boston’s Route 128, and the Cambridge region of the United Kingdom (Wicksteed 1985; Saxenian 1994).

238    Critical Drivers of Local Competitiveness While the aforementioned regions have long attracted the attention of scholars, more recent work focuses on economic interdependences and social linkages among regions, often from the perspective of newly industrialized countries (Amsden and Chu 2003; Breznitz 2007; 2011). Much of this work has been written from an industrial development perspective emphasizing the role of regional and national governments in education, training, infrastructure, and other industrial policies required for regions to benefit from global market opportunities. Within this context, however, little attention is paid to the changing nature of science and its implication for regional competitiveness. Here we seek to address this auspicious gap in the literature by highlighting the emerging grand challenge model of R & D and its potential consequences for regions. The grand challenge model, defined as large-scale, multisector, solution-oriented R & D initiatives, is at once transregional: no single country (much less organization or sector) has the financial or scientific resources to address grand challenges on its own (Nowotny, Scott, and Gibbons 2001; Shrum 2007; 2010). Necessarily the grand challenge model reconceptualizes scientific collaboration as a global, strategically managed enterprise, but one whose impact is focused among local regions. Thus, regions must not only focus on aligning their “local” scientific, industrial, and educational assets, they must also understand how these assets fit into collaborative global grand challenge efforts. I explore the grand challenge model by briefly describing the emergence of the “big science” concept of collaboration in section 2. In section 3 I describe the grand challenge model of R & D, which has emerged through the pioneer work of the Gates Foundation, among several others. Section 4 explores several specific aspects of the grand challenge model that must be refined and developed for the model to succeed. Finally, in section 5, I explore the implications of the model for regional policy.

2  Conceptualizing Science: Small and Big Fundamental research is the seed corn for new knowledge, and, at least since the Enlightenment, science has been thought of as a lonely craft, driven by the sole scientist or inventor. Throughout much of the 19th and 20th centuries, “small science” flourished as both the conduct and (later) public funding of academic research focused primarily on the single investigator, at times accompanied by a small team of researchers, working within limited spatial scales (Nass and Stillman 2003; Zimmerman and Nardi 2010). The growth of science was accompanied by the rise of disciplinary structures—visible within most universities today—that allow researchers to specialize and create identities important for attracting students and obtaining resources (Crane 1972; Lewis 2010). World War II brought with it, however, an acute interest in mobilizing science to address the demands of global conflict. Scholars such as de Solla Price (1963) and Weinberg (1961; 1967) chronicle the emergence of “big science” within the United States, a concept that includes the exponential growth of scientific funding, a growing number

The Grand Challenge Model of R & D    239 of scientifically trained individuals, and establishment of large, mission-driven initiatives such as the Manhattan Project and creation of the European Organization for Nuclear Research (CERN) (Shrum, Genuth, and Champalov 2007; Vermeulen 2009). In particular, the big science concept emphasizes collaboration, shared large-scale equipment and materials, and the democratic culture of disciplines within the physical sciences, especially physics (Knorr-Cetina 1999; Weinburg 1967). More recent research on scientific collaboration has focused on the growth and increasingly international, multi-institutional, and multisector nature of copublication (Braun and Schubert 2003; Shrum, Genuth, and Champalov 2007; Porter and Rafols 2009; Hessels, de Jong, and Van Lente 2010), often reflecting the emergence of new interdisciplinary fields, such as systems biology (Calvert 2010) and ecology (Bocking 2010). A rich literature has also examined the emergence of interfirm research joint ventures (Hagedoorn, Link, and Vonortas 2000), public-private R & D partnerships (National Research Council 2003; Link 2006), and increasingly large research projects focused on building fundamental knowledge in the life sciences (Venter 2007; Vermeulen 2009; Parker, Vermeulen, and Penders 2010). What is often missing from conceptions of big (and small) science is specifically how new knowledge is disseminated, applied, and developed to address societal grand challenges, especially among research institutions and regions (Braunerhjelm et  al. 2010; Daar and Singer 2011). Armed with this understanding regions will be better equipped to (re)design policies, programs, and institutions in response to a rapidly changing world. The next section explores the emergent grand challenge model of R & D, a concept that may potentially revolutionize scientific collaboration.

3  The Emerging Grand Challenge Model of R & D The term grand challenge is not new. German mathematician Dr. David Hilbert is credited with elucidating what he viewed as the 23 most important unsolved grand challenges in mathematics. A few of “Hilbert’s Problems”—as they came to be known—were introduced during the International Congress of Mathematicians held in Paris in 1900, with the balance being translated into English and published by the Bulletin of the American Mathematical Society. By highlighting intractable grand challenge problems, Hilbert intended to spur collaboration within the entire international community of mathematicians while inspiring the next generation of scholars. Many of Hilbert’s problems were solved immediately, while others continue to be discussed among mathematicians today (Reid 1996). The term grand challenge has also been employed within a number of varying contexts throughout the 20th century, but its more recent usage reflects the aspirational nature of Hilbert’s Problems. Table 11.1 illustrates several examples of contemporary definitions of

Table 11.1  Example Grand Challenge Definitions Name

Organization type

Definition

The Belmont Challenge

Consortium of national research funding agencies Government

To deliver knowledge needed for action to avoid and adapt to detrimental environmental change including extreme hazardous events. [The] DARPA Grand Challenge is a field test intended to accelerate research and development in autonomous ground vehicles that will help save American lives on the battlefield. The Grand Challenge brings together individuals and organizations from industry, the R&D community, government, the armed services, academia, students, backyard inventors, and automotive enthusiasts in the pursuit of a technological challenge. [Grand Challenges] are the problems currently facing the world that require technological solutions and that will only be solved by the scientists, engineers, entrepreneurs, and policymakers of the future. A call for a specific scientific or technological innovation that would remove a critical barrier to solving an important health problem in the developing world with a high likelihood of global impact and feasibility. A grand challenge is a specific critical barrier that, if removed, would help solve an important health problem in the developing world, with a high likelihood of global impact through widespread implementation. In each of these broad realms of human concern—sustainability, health, vulnerability, and joy of living—specific grand challenges await engineering solutions. The world’s cadre of engineers will seek ways to put knowledge into practice to meet these grand challenges. Applying the rules of reason, the findings of science, the aesthetics of art, and the spark of creative imagination, engineers will continue the tradition of forging a better future. Smithsonian Grand Challenges Awards—a competitive, internal granting program—advance cross-disciplinary, integrated scholarly efforts across the Institution which relate to the Smithsonian Grand Challenges: Unlocking the Mysteries of the Universe, Understanding and Sustaining a Biodiverse Planet, Valuing World Cultures, and Understanding the American Experience. These awards encourage Smithsonian staff to advance research, as well as broaden access, revitalize education, strengthen collections, and encourage new ways of thinking that involve emerging technology. [Grand Challenges] promote significant scientific research that has the potential to solve major national or global problems … The aim of the Grand Challenge is to create ambitious but achievable goals that harness technology to solve important societal and health problems.

Defense Advanced Research Projects Agency (DARPA) Grand Challenge Program (2005 definition)

Georgia Institute of Technology

University

Grand Challenges In Global Health: Bill and Melinda Gates Foundation Grand Challenge Canada

Foundation

National Academy of Engineering

Non-profit honorific organization

Smithsonian Grand Challenge Consortia

Non-profit museum and research organization

New York University

University

Government

The Grand Challenge Model of R & D    241 grand challenges among disparate organizations ranging from universities (e.g. Georgia Tech) and foundations (Gates Foundation) to government agencies (e.g., DARPA [Defense Advanced Research Projects Agency]) and nonprofit, education, and research institutions (e.g., Smithsonian Institution). Not intended as an exhaustive list, organizations listed here frame grand challenges primarily in terms of research-based solutions to specific problems (e.g., “an important health problem in the developing world”) and, at times, articulate a specific method for addressing the problem (e.g., “brings together individuals and organizations from …”). Table 11.2 illustrates several characteristics of grand challenge programs listed in table 11.1 Established in 2003 by the Bill and Melinda Gates Foundation, the Grand Challenges in Global Health program is largely credited with the revival of the grand challenges term. The Grand Challenges in Global Health program defined grand challenges primarily in terms of health problems within the developing world and has made significant investments—more than $450 million over the past 10 years—to address them. Further, the program is collaborative, involving a number of partners including Grand Challenges Canada, the Wellcome Trust, and the Foundation for the (US) National Institutes of Health. Some individual programs, like Grand Challenges Canada, similarly focus on identifying and addressing grand challenges by awarding collaborative grants and working closely with awardees, while others, like the Belmont Forum, serve to coordinate the actions of disparate funding agencies but have little funding or management authority of their own. Universities use grand challenge grant programs as a way to change how students are educated, while promoting a collaborative, problem-based culture among faculty. Finally, the role of organizations like the National Academy of Engineering is limited to the identification and prioritization of the specific challenges themselves (in their case, relevant to engineering) in hopes of influencing the priorities, investments, and initiatives of governments, nonprofits, foundations, and individual philanthropists. Further, most grand challenge programs involve a significant number of administrative, technical, and funding partners; another Gates Foundation–sponsored grand challenge, Saving Lives at Birth, also involves Grand Challenges Canada, the Government of Norway, the United Kingdom Department for International Development (DFID), and the US Agency for International Development (USAID). Despite substantial differences among the aforementioned grand challenge programs, many program leaders view it as an effective approach for mobilizing science and society in response to the following core assumptions: 1. From global pandemics to food security and climate change, society’s challenges are (and will be) “mega-problems”: global, local, and interconnected. 2. No single country (much less organization) has the financial or scientific resources to address grand challenges on its own. 3. The ability to achieve solutions at scale, but within different geographic, cultural, and political contexts, will be the single most important aspect of addressing grand challenges—and the most difficult goal to achieve.

242    Critical Drivers of Local Competitiveness Table 11.2  Example Grand Challenge Elements Name

Established Purpose

Focus areas

Funding

The Belmont Challenge

2011

Climate change and the environment

Through member organizations

DARPA Grand Challenge Program

2004

‘Driverless’ robotic technology

Yes

Georgia Institute of Technology

2012

Grand Challenges 2003 In Global Health: Bill and Melinda Gates Foundation Grand Challenge 2011 Canada National Academy of Engineering

2011

Smithsonian Grand Challenge Consortia

2010

New York University

2013

A governance structure that seeks to align climate change-related research agendas among national funding agencies Team-oriented competition to Stimulate innovation in robotics Student-oriented learning program designed to encourage cross-disciplinary, problem-focused collaboration Solve problems in human health within the developing world Solve problems in human health within the developing world Identify and promote specific engineering challenges Encourage internal, goal-focused collaboration among Smithsonian staff (Initially) an internal competition designed to promote problem-focused collaboration among faculty researchers; (later) promote external partnerships

Student driven; Yes; for common themes collaborative include food, water, student health, and energy projects

Evolving; since establishment, problems of human health Human health

Yes

Yes

14 various No engineering challenges Learning and research Yes related to the Smithsonian’s education and research mission Open; faculty-driven Yes; two grants

On the last point, most grand challenge programs have learned that a regional perspective is not only required to understand the local nuances of a specific grand challenge, they have also learned that regions play an important role in the development and implementation of solutions. The efficacy of the grand challenge model will thus depend on two factors: strong (grand challenge) project governance that can direct and align the

The Grand Challenge Model of R & D    243 efforts of multiple regions and a reconceptualization of regions as key project intermediaries possessing unique knowledge assets, organizational capabilities, and responsibilities. The next section discusses several critical elements of the grand challenge model that both differentiate it from other forms of scientific collaboration and further provide an opportunity for regions in its implementation.

4  Conceptualizing the Grand Challenge Model The grand challenge programs listed above include a variety of objectives—and mechanisms for fulfilling those objectives; a normative definition does not yet exist. Yet these programs all focus on complex, yet surmountable global problems not well addressed by current scientific policies and institutions. Conceptualized within the context of a larger science, technology, and innovation ecosystem, the grand challenge model of R & D therefore represents a high-risk, high-reward collaboration mechanism for mobilizing existing—and in some cases undiscovered—knowledge assets to solve these entrenched problems. Further, a review of grand challenge programs yields at least seven separate but interrelated elements critical to the current and future success. These elements include the following:

1. Strategic focus 2. Solution orientation 3. Project governance 4. Defining, evaluating, and communicating impact 5. Private-sector engagement 6. “New” sources of knowledge 7. Global scale, regional focus The sections below explore each of these facets in greater detail.

4.1 Strategic Focus Strategy is one of the most critical elements of the grand challenge model. Strategy, at its core, requires prioritization of goals in order to achieve focus and scale; within the grand challenge context this means prioritizing and focusing on a small number of challenges, if not one single problem. While the importance of strategic thinking may seem straightforward, it is often difficult to achieve in creative research environments like those found in universities and research institutes. While the grand challenge programs listed above vary greatly in their structure and operation, they all embrace the importance of strategic focus. Examples of strategic

244    Critical Drivers of Local Competitiveness focus are evident in table 11.2. Most programs involve some type of process for scoping, identifying, and prioritizing specific grand challenges (Daar and Singer 2011). Other programs, like those at New York University and Georgia Tech, articulate general criteria and make the identification of a specific grand challenge by faculty and students, respectively, part of the overall process. Scoping and prioritization exercises help focus the attention of policymakers under the assumption that grand challenge problems also require leadership, political will, and significant resources. Further, a well-defined, prioritized problem provides a common objective around which disparate project partners can be unified. Discussed later, regions could easily design and deploy their own processes to scope, identify, and prioritize grand challenges of their own and/or adopt the objectives of other grand challenge programs.

4.2  Solution Orientation Prioritized, large-scale, transnational problems require both the creation and the application of new knowledge to address them, the latter of which is often missing from academic research projects. Stokes (1997) terms this application-oriented approach use-inspired research, which is at once fundamental and applied, based on the premise that the two are not mutually exclusive (Nowotny, Scott, and Gibbons 2001). Certainly, government mission agencies in health, energy, and defense necessarily take a solution-oriented approach to funding both fundamental and applied R & D to meet their respective mission requirements. As mentioned, all the example grand challenge programs listed above are solution-focused. Grand challenge solutions are a function of place. In many regions networks and governance structures already exist that enable companies, research institutions, foundations, and governments to collectively create scale within specific technological areas. Often cited institutional examples include San Diego’s CONNECT program, Taiwan’s Industrial Technology Research Institute (ITRI), and IMEC (Interuniversity Microelectronics Centre) in Belgium.1 While very different in their operation and structure, these regional platforms—along with forward-thinking institutional policies and culture—create an environment that enables scientists to take advantage of commercial opportunities represented by the grand challenge model. From the grand challenge perspective, regional platforms can play a critical role in mobilizing resources toward common social and industrial solutions not only within but also among regions, rather than relying on more traditional notions of individual scientific collaboration. The complementary nature of use-inspired research is also illustrated at the individual scientist and student level. Studies have shown that scientists who have experience working in industry, consulting, or establishing a new company are not only more likely to successfully develop new technologies, they are also more successful with regard 1 ITRI: https://www.itri.org.tw/eng/; CONNECT: http://connect.org/; IMEC: http://www2.imec.be/

be_en/home.html.

The Grand Challenge Model of R & D    245 to their academic publishing responsibilities (Zucker, Darby, and Armstrong 2002; O’Gorman, Byrne, and Pandya 2008; Gulbrandsen and Smeby 2005). Studies also show that application is also an important element of learning from preschool to graduate school and beyond (Boyer 1991; National Research Council 2000). Companies, universities, and mission agencies therefore use the grand challenge approach to motivate cross-departmental, cross-discipline (and often cross-sector) collaboration and stimulate long-term changes in organizational culture.

4.3  Project Governance A robust project management infrastructure enable grand challenge programs to effectively direct and coordinate the actions of multiple project partners under specific time and budget constraints. While project management has traditionally been less of a priority in high-trust, “democratic” scientific disciplines, such as high-energy physics, strong project management cultures can also coexist in an environment that prioritizes scientific discovery (Shrum, Genuth, and Champalov 2007). For example, the National Aeronautics and Space Administration (NASA) and European Space Agency (ESA) have long histories of managing large, collaborative, goal-driven projects. Within these projects, managers, engineers, and scientists work together—often among different national and international locations—to build scientific instruments that can effectively collect data while meeting the weight and size constraints of spacecraft within a specific time frame and budget (Shrum, Genuth, and Champalov 2007). Successful grand challenge projects similarly include project planning and management structures, supplemented by the data management, cofunding, and intellectual property policies that effectively guide disparate, geographically dispersed participants toward a common goal. Management structures should also enable partners to join and leave projects at different times based on the needs of the initiative while preserving the strategic intent and organizational memory of the enterprise (Shrum, Genuth, and Champalov 2007). For example, the global collaborative effort to map the rice genome—the Rice Genome Project—lasted for approximately four years but included over 32 partners from 10 different countries, five of whom left the project while four others joined over the life of the consortium (Halliday 2010). As mentioned, project management infrastructures could also account for regional intermediaries that organize and coordinate efforts among individual institutions. Grand challenge programs should include technical and implementation roadmaps that provide participants with collective understanding of what barriers will need to be overcome to achieve success at scale. Examples of implementation at scale can be found in industry: the international technology roadmap for semiconductors (ITRS) provides a blueprint for coordination within a highly sophisticated, geographically dispersed industry with high levels of technology and product differentiation. In this context, fundamental science and application coexist through close relationships among companies, universities, and government laboratories (Browning and Shelter 2000). R & D

246    Critical Drivers of Local Competitiveness roadmaps have already emerged in grand challenge-oriented initiatives for vaccines and climate change. The sophisticated nature of grand challenges will also require a project management structure that has embedded technical expertise important not only to manage projects, but also to mentor and develop capabilities among other, less developed regions. In many of the Gates Foundation grand challenge projects in India, for example, projects seek to collaboratively engage local partners to fund and, importantly, solve regional, in-country challenges. To do this, Foundation project managers must also mentor and frequently interact with Indian project partners, in stark contrast to “hands off ” approaches traditionally favored by granting agencies. Studies of various collaborative R & D initiatives show that effective project management leads to a number of benefits. First, in a public (and foundation) funding environment that seeks ever-higher levels of accountability, project management increases the likelihood that initiatives will finish on time and within budget (Shrum, Genuth, and Champalov 2007). Further, while concerns exist among scientists about the potential loss of independence, Shrum, Genuth, and Champalov (2007) find that effective management structures may actually protect the autonomy of scientists (compared to more ad hoc situations) by clearly articulating a specific division of labor and the specific rules under which data is shared and used.

4.4  Evaluating and Communicating Impact Given that the grand challenge model is a relatively recent innovation, evaluation is not only critical for understanding operational “best practices,” but also an important way to create awareness among policymakers and the general public of its impact (and potential impact). Many grand challenge programs use evaluation models such as the theory of change to map the various processes and elements needed to address a particular grand challenge. Granting agencies use solutions to a grand challenge as an end goal and work backward, collaboratively with project partners (grantees), to articulate and define specific milestones that need to be met in order to achieve program success. These mutually agreed-upon milestones in turn become the benchmarks against which project partners are evaluated. The solution orientation of the grand challenge model also affords, compared to traditional academic R & D grants, relatively more visibility and comprehension among policymakers and the general public alike. According to market research expert, Frank Luntz, the outcomes of science and education initiatives must make outcomes “tangible … crystal clear and specific, without being melodramatic” (Luntz 2007, 8). Effectively communicating impact is especially important in times of financial crisis when, relative to other priorities (e.g., primary and secondary education, pensions, etc.) society is more reluctant to support and fund scientific inquiry that does little to visibly improve society in the near term. As Shrum (2010, 250) argues “rarely … does any kind of societal ‘demand’ get expressed as ‘we need more fundamental research just for the sake of knowledge.’ ”

The Grand Challenge Model of R & D    247 Finally, an effective communications strategy will also tie the unique grand challenge approach to its impact. Doing so helps address a related concern that, according Behrens and Gross (2010, 141), “traditional ivory tower scientific practices” are no longer deemed capable of delivering solutions to complex problems. These perceptions are exacerbated as the scale, and therefore cost, of science grows, especially if projects are not perceived to live up to their expectations (Vermeulen and Penders 2010). For example, the Human Genome Project (HGP)—a nearly $3 billion project to sequence human DNA—was justified to policymakers for its potential to revolutionize and personalize medicine. While the HGP resulted in countless discoveries and provided invaluable “infrastructural knowledge,” along with new scientific tools and applications, many critics maintain that expectations were not well managed. Specifically, the project was undertaken without any clear understanding of what would be done with project data once the human genome was mapped (Vermeulen 2009). While science is inherently an open, uncertain enterprise, the political reality is that diminished expectations make subsequent “big science” projects increasingly less likely to be funded.

4.5  Private-Sector Engagement Private-sector involvement is critical to the long-term success of the grand challenge model. First, the industrial sector is an important source of new scientific knowledge. To take the United States as example, industrial sector R & D spending is nearly six times that of the federal government and academic public sectors combined.2 While certain industries, such as pharmaceuticals and semiconductors, perform significant levels of fundamental R & D, the primary focus of industry scientists and engineers is developing and scaling new technological solutions. In fact, studies show that private-sector involvement in R & D collaboration leads to better overall project performance, including the production of more reliable research results (Shrum 2007; Parker, Vermeulen, and Penders 2010). For example, tuberculosis, HIV/AIDS, and malaria afflict thousands of individuals in the developing world each year. While academic research institutions provide an important understanding of these disease targets, the bulk of capabilities needed to develop vaccines, including translational research, clinical trials, manufacturing, and distribution, lie within industry. Recent initiatives by the Gates Foundation and others have made important advances in vaccine development, but the most chronic diseases affecting the developing world are unlikely to be addressed without effective incentives and mechanisms to engage industry (US Vaccine Report). Recent conceptions of industrial clustering posit that large companies are drawn to regions in order to gain access to new knowledge created within scientific organizations, often manifest in high-tech start-ups. Start-ups can draw upon a variety of public and private funding sources, including federal government grants (e.g., Small 2 

http://www.nsf.gov/statistics/infbrief/nsf14307/.

248    Critical Drivers of Local Competitiveness Business Innovation Research [SBIR] program), regional funds, angel investors, and crowd-funding programs (e.g., Kickstarter), to further develop promising yet early stage ideas (Shane 2004; Daar and Singer 2011). Many individual institutions such as MIT (Deshpande Center), UC San Diego (von Liebig Entrepreneurism Center), and Georgia Tech (VentureLab) have established proof-of-concept programs to accelerate start-up development (Bradley, Hayter, and Link 2013b). Further, statewide efforts, such as the NYSERDA PoCC (New York State Energy Research and Development Authority Proof of Concept Center) and Ohio Third Frontier programs,3 view entrepreneurial assistance as a multiregional endeavor where much is to be gained by facilitating and supporting cross-regional collaboration. Similarly, grand challenge programs could not only provide resources to start-ups and relevant assistance programs, but also create cross-regional linkages among start-ups, institutions, and support programs with similar interests and complementary needs.

4.6  “New” Sources of Knowledge Open-innovation techniques and platforms, designed to find and learn from individuals from around the world with new and creative ideas, are an emerging component of grand challenge projects. One such approach, user-generated innovations, engages frequent and intensive (“leading edge”) users of particular products, from surf boards to open-source software, in order to understand how they have built new or modified off-the-shelf products to achieve better performance (Von Hippel 2005; Ye and Kankanhalli 2013). User-generated innovation has also emerged in the developing world, for example, with the use of mobile phones in Kenya for banking and government services, when traditional “bricks and mortar” infrastructure doesn’t exist.4 Another way for finding and engaging new knowledge communities is the emerging prize model of innovation. The prize model (also called crowd sourcing) provides grand challenge initiatives a way to find solutions to well-defined problems by tapping into “untraditional,” and often geographically dispersed, sources of knowledge. Solution seekers typically do this by posting specific challenges on an online platform (i.e., interactive website) and providing incentives—typically cash prizes and recognition—for individuals to solve a particular challenge. Procter and Gamble’s “Connect and Develop” model is typically considered the first crowd-sourcing platform, designed to solve a wide range of the company’s technical barriers such as, for example, reducing the environmental impact of the company’s extensive line of laundry detergents (Huston and Sakkab 2006). Solutions have come from a variety of solvers located all over the world. In addition to emergence of similar platforms at other companies (IBM, BMW, etc.), the X-prize is a nonprofit open innovation 3  NYSERDA PoCC program: http://htr.org/nyserda-announces-new-proof-concept-centers; Third Frontier: https://development.ohio.gov/bs_thirdfrontier/currentprograms.htm. 4  See, for example, http://www.economist.com/node/21560912.

The Grand Challenge Model of R & D    249 prize platform that seeks to involve larger teams through multiple stages to address specific, high-profile technical goals such as enabling commercial space flight, radically increasing automobile gas mileage, and building inexpensive ocean pH sensors.5 Further, third-party platforms such as Innocentive and Nine-sigma have also emerged, offering other companies, nonprofits, and universities access to crowd-sourcing tools to address a variety of challenges (Ye and Kankanhalli 2013). Finally, Nielsen (2012) chronicles the related emergence of “citizen science,” research conducted by amateurs or “nonprofessional scientists.” Compared to other forms of open innovation, citizen science is often viewed as the most democratic, frequently engaging the general public in data collection and analysis. For example, Galaxy Zoo is a project that seeks to harness citizen science to classify and analyze the huge amounts of data collected during the Sloan Digital Sky Survey. Galaxy Zoo does this through an online platform that provides what Nielsen (2012) calls the “architecture of attention,” focusing the efforts of the general public toward small tasks repeated among many individuals. Frequently repeated observations and analysis allow project scientists to rely on the law of large numbers: the average of volunteer responses should be close to the actual expected (expert) identification of a galaxy, a finding validated during evaluations of the project.6

4.7  Global Scale, Regional Focus Several factors are motivating the ever-expanding, global nature of grand challenges. Using the Census of Marine Life (CoML) as example, functions of nature—in this case marine biology—occur on a global scale but are affected by local conditions and contexts. Therefore, regional partners are needed to understand and address grand challenges: the CoML was established in the United States but later grew to include over 80 countries, a necessity to meet its ambitious goal of classifying and cataloging all life in the oceans (Vermeulen 2009). Local partners also provide a rich understanding of the cultural, economic, and political context of regions, important to grand challenge projects. For example, relating to global pandemics, HIV—the virus that causes AIDS—exists in nearly every corner of the world. Dozens of nations have made R & D investments in an effort to understand, prevent, and treat the disease. Given the complexity of the disease and its global scale, scientists and clinicians located among different regions must not only share data in real time, they must also consider vast epidemiological differences among populations from

5 See http://www.xprize.org/prize-development. 6 

The impact of effective citizen science projects is stunning: During the first year of Galaxy Zoo, more than 150,000 volunteers participated in the project, conducting more than 50 million classifications. Now, the second phase of the project, more than 60 million classifications have been made among 200,000 of the brightest galaxies, averaging roughly 300 classifications per galaxy. See http://www.galaxyzoo.org/#/ story.

250    Critical Drivers of Local Competitiveness Bangkok and San Francisco to Kinshasa. These variable factors include disease mutations and drug efficacy, along with the economic, political, and cultural context, which will affect the viability and efficacy of these solutions within a population of inquiry (Vermeulen 2009; Parker, Vermeulen, and Penders 2010). Grand challenge projects must also engage local communities to improve the efficacy of grand challenge projects; failing to implement an effective engagement strategy can spell disaster for well-intended projects. For example, Gilead Sciences, a California-based biopharmaceutical company, worked with at-risk populations in Cambodia to clinically test Tenofovoir, an oral antiretroviral drug designed to reduce the risk of contracting AIDS. Despite its acceptance and use in other parts of the world, the company failed to engage and gain buy-in from local populations for the project, leading to a high-profile rejection of the company and its drug by the Cambodian government (Daar and Singer 2010).

5  Discussion: Implications for Regions It is often said that today’s investments in science lead to the industries (and therefore economy) of tomorrow. We are reminded, however, that the primary purpose of public R & D is not economic development per se but rather to generate new knowledge and address the needs of society. Often missing from science policy discussions is how to structure R & D to maximize its social and economic impact and, for our purposes, how this relates to regional competitiveness. This chapter introduces the grand challenge model of R & D, which holds the potential to revolutionize how society organizes and mobilizes its scientific resources. The premise of the grand challenge model, as defined here, is that application, scale, and multisector, multidisciplinary partnerships are necessary to address complex, transnational problems confronting society. The emergence of the model has several implications for regional competitiveness. First, it is critical for regions to understand and mobilize the relevant knowledge assets among local companies, universities, research institutes, and the general public. Most regions have at some point undertaken an analysis of their industrial clusters, charting the relative size, concentration, and growth of employment within specific industries in the region. These analyzes should be complemented by emerging new techniques that seek to understand a region’s scientific capabilities, networks, and culture (Link and Vonortas 2013). Such methods will provide a better understanding for how well regions are positioned to take advantage of emerging grand challenge programs as well as illustrate interdependencies between industrial clusters and the scientific institutions within. Of course science is an inherently dynamic social enterprise that does not lend itself well to static analyses. Thus, regions can get a better understanding of their knowledge assets by concurrently establishing local challenge programs of their own. For example, when DARPA began its robotics challenge program in 2004, it sought to advance

The Grand Challenge Model of R & D    251 autonomous (driverless) vehicle technology first by getting a better understanding of where relevant expertise existed, which for the first round included Carnegie Mellon, Stanford University, and Osh Kosh Truck Company. Further, the competition allowed agency officials to identify and understand critical scientific and technical barriers faced by these teams that might be addressed in the long term, which it did with other, complementary R & D granting mechanisms and by involving other R & D partners (and future challenge participants), from universities and high school classes, to insurance companies, based on their expertise. Establishment of the DARPA Robotics Challenge was motivated by the goal of making approximately one-third military ground forces autonomous by 2015. Similarly, regions also need to scope, identify, and prioritize their own specific challenges. Most grand challenge programs select ambitious but—most importantly—achievable goals. Options for doing this, depending on the size and knowledge assets of the region, include embracing preexisting challenges (such as those identified by Grand Challenges Canada or the National Academy of Engineering), creating an organic process to generate and prioritize challenges of their own, or a combination of the two. In order to make challenges “grand,” regions will also need to have an understanding of what other regions (and their respective multisector partners) are doing within a challenge of interest. Regions will then need to construct the aforementioned management structures not only to guide local programs, but also to link local programs to other related initiatives thereby building scale. While a fundamental aspect of the grand challenge model is partnering, regional institutions can—like those in ­tables 11.1 and 11.2—also create challenge competitions of their own. Such challenge programs accelerate cultural change and prepare personnel to work within a collaborative, cross-disciplinary, solution-oriented context. For example, NYU recently completed a challenge competition that solicited ideas from internal faculty teams based on the “grandness” of the submitted challenge, relevance to social or industrial problems, the number of faculty involved, degree to which different disciplines were included, criteria for success, and feasibility. NYU not only awarded the two winning teams $250,000 each, officials are also planning several follow-up events where potential research partners from outside NYU, including companies, foundations, and other universities, will be invited to attend based on their interest and relevant activities pertaining to the winning project to discuss potential collaboration. Finally, national governments provide policy, regulation, and funding that may (or may not) encourage the collaboration necessary for the grand challenge model to exist. IP policy is especially critical: in the United States, the Bayh-Dole Act of 1980 streamlined IP policies among R & D granting agencies and assign universities title to IP stemming from federally funded research. While the law creates a powerful incentive for individual universities to transfer new technologies into society, critics maintain that the law has also made multi-institutional, multisector collaboration more difficult (Bradley, Hayter, and Link 2013a). The emergence of the grand challenge model will necessitate policies that enable rapidly shared IP so as not to slow innovation and the finding solutions to important social challenges.

252    Critical Drivers of Local Competitiveness National governments can also be strategic, devoting a larger percentage of their R & D portfolio to long-term grand challenge initiatives, while understanding the fundamental scientific and technical problems that underlie them. Indeed large governments, like the United States, already have experience with grand challenges but must now think about the strategy, policies, and institutions needed to address these at a global scale. As mentioned, scale requires a reconceptualization of scientific strategy, operations, and governance, but its eventual success hinges upon mobilization and interconnectivity among disparate global regions.

References Amsden, A., and W. Chu. 2003. Beyond Late Development:  Taiwan’s Upgrading Policies, Cambridge, MA: MIT Press. Audretsch, D. B., and M. P. Feldman. 1996. “R&D Spillovers and the Geography of Innovation.” American Economic Review 86, 630–40. Behrens, V., and M. Gross. 2010. “Customisation of Transdisciplinary Collaboration in the Integrated Management of Contaminated Sites.” In J. Parker, N. Vermeulen, and B. Penders, eds., Collaboration in the New Life Sciences, 139–60. Surrey, UK: Ashgate. Boyer, E. L. 1991. “The Scholarship of Teaching from Scholarship Reconsidered: Priorities of the Professoriate.” College Teaching 39 (1), 11–13. Bradley, S., C. S. Hayter, and A. N. Link. 2013a. “Models and Methods of University Technology Transfer.” Foundations and Trends in Entrepreneurship 9 (6), 571–650. Bradley, S., C. S.  Hayter, and A. N.  Link. 2013b. “Proof of Concept Centers.” Journal of Technology Transfer 38, 349–81. Braun, T., and A. Schubert. 2003. “A Quantitative View on the Coming of Age of Interdisciplinarity in the Sciences 1980–1999.” Scientometrics 58 (1), 183–89. Bocking, S. 2010. “Organizing the Field:  Collaboration in the History of Ecology and Environmental Science.” In J. Parker, N. Vermeulen, and B. Penders, eds., Collaboration in the New Life Sciences, 15–36. Surrey, UK: Ashgate. Braunerhjelm, P., Z. J.  Acs, D. B.  Audretsch, and B. Carlsson. 2010. “The Missing Link: Knowledge Diffusion and Entrepreneurship in Endogenous Growth.” Small Business Economics 34 (2), 105–25. Breznitz, D. 2007. Innovation and the State: Political Choice and Strategies for Growth in Israel, Taiwan, and Ireland. New Haven, CT: Yale University Press. Breznitz, D., and M. Murphree. 2011. Run of the Red Queen:  Government, Innovation, Globalization, and Economic Growth in China. New Haven, CT: Yale University Press. Browning, L. D., and J. C. Shelter. 2000. SEMATECH: Saving the U.S. Semiconductor Industry. College Station: Texas A&M Press. Calvert, J. 2010. “Systems Biology, Interdisciplinarity and Disciplinary Identity.” In J. Parker, N. Vermeulen, and B. Penders, eds., Collaboration in the New Life Sciences, 201–18. Surrey, UK: Ashgate. Crane, D. 1972. Invisible Colleges: Diffusion of Knowledge in Scientific Communities. Chicago: University of Chicago Press. Daar, A., and P. Singer. 2011. The Grandest Challenge: Bringing Life-Saving Science from Lab to Village. Toronto: Anchor Canada.

The Grand Challenge Model of R & D    253 de Solla Price, D. J. 1963. Little Science, Big Science. New York: Columbia University press. Feldman, M. P. 1994. The Geography of Innovation. Boston: Kluwer Academic. Gulbrandsen, M., and J. Smeby. 2005. “Industry Funding and University Professors’ Research Performance.” Research Policy 34, 932–50. Hagedoorn, J., A. N. Link, and N. Vonortas. 2000. “Research Partnerships.” Research Policy 29, 567–86. Halliday, L. 2010. “The Rice Sequence Collaborations:  Globalization, Innovation Networks and the Burgeoning Life Sciences in Asia.” In J. Parker, N. Vermeulen, and B. Penders, eds., Collaboration in the New Life Sciences. Surrey, UK: Ashgate, 219–44. Hessels, L.  K., S.  de Jong, and H.  van Lente. 2010. “Multidisciplinary Collaborations in Toxicology and Paleo-ecology: Equal Means to Different Ends.” In J. Parker, N. Vermeulen, and B. Penders, eds., Collaboration in the New Life Sciences, 37–62. Surrey, UK: Ashgate. Huston, L., and H. Sakkab. 2006. “Connect and Develop:  Inside Procter & Gamble’s New Model for Innovation.” Harvard Business Review, March. Jaffe, A.  B. 1989. “Real Effects of Academic Research.” American Economic Review 79 (5), 957–70. Jaffe, A. B., M. Trajtenberg, and R. Henderson. 1993. “Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations.” Quarterly Journal of Economics 108 (3), 577–98. Knorr-Cetina, K. D. 1999. Epistemic Cultures: How the Sciences Make Knowledge. Cambridge, MA: Harvard University Press. Lewis, J. 2010. “Collaboration in the Molecular Life Sciences: Matchmaking Mechanisms. Collaborative Arrangements in Proteomics and Bioinformatics.” In J. Parker, J., Vermeulen, and B. Penders, eds., Collaboration in the New Life Sciences, 179–200. Surrey, UK: Ashgate. Link, A.  N. 2006. Public/Private Partnerships:  Innovation Strategies and Policy Alternatives. New York: Springer. Link, A.  N., and N. Vonortas, eds. 2013. Handbook on the Theory and Practice of Program Evaluation. Northampton, MA: Edward Edgar. Luntz, F. 2007. “Americans Talk Innovation.” Prepared for National Governors Association, Washington, DC. Nass, S.  J., and B. Stillman, eds. 2003. Large-Scale Science:  Exploring Strategies for Future Research. Washington, DC: National Academies Press. National Research Council. 2000. How People Learn:  Brain, Mind, Experience, and School. Washington, DC: National Academy Press. National Research Council. 2003. Government-Industry Partnerships Project: Summary Report. Washington, DC: National Academy Press. Nielsen, M. 2011. Reinventing Discovery:  The New Era of Networked Science. Princeton, NJ: Princeton University Press. Nowotny, H., P. Scott, and M. Gibbons. 2001. Re-thinking Science: Knowledge and the Public in an Age of Uncertainty. Malden, MA: Polity. O’Gorman, C., O. Byrne, and D. Pandya. 2008. “How Scientists Commercialise New Knowledge via Entrepreneurship.” Journal of Technology Transfer 33, 23–43. Parker, J., N. Vermeulen, and B. Penders, eds. 2010. Collaboration in the New Life Sciences. Surrey, UK: Ashgate. Porter, A. L., and I. Rafols. 2009. “Is Science Becoming More Interdisciplinary? Measuring and Mapping Six Research Fields over Time.” Scientometrics 81 (3), 719–45. Reid, C. 1996. Hilbert. New York: Copernicus.

254    Critical Drivers of Local Competitiveness Romer, P. M. 1986. “Increasing Returns and Long-Run Growth.” Journal of Political Economy 94, 1002–37. Saxenian, A. 1994. Regional Advantage. Boston: Harvard Business School Press. Shane, S. 2004. Academic Entrepreneurship:  University Spinoffs and Wealth Creation. Northampton, MA: Edward Elgar. Shrum, W. 2010. “Collaborationism.” In J. Parker, N. Vermeulen, and B. Penders, eds., Collaboration in the New Life Sciences, 247–58. Surrey, UK: Ashgate. Shrum, W., J. Genuth, and I. Champalov. 2007. Structures of Scientific Collaboration. Cambridge, MA: MIT Press. Stokes, D. E. 1997. Pasteur’s Quadrant: Basic Science and Technological Innovation. Washington, DC: Brookings Institution Press. Venter, C. J. 2007. My Life Decoded: My Genome, My Life. New York: Viking. Vermeulen, N. 2009. Supersizing Science: On Building Large-Scale Research Projects in Biology. Boca Raton, FL: Dissertation.com. von Hippel, E. 2005. Democratizing Innovation. Cambridge, MA: MIT Press. Weinberg, A. M. 1961. “Impact of Large-Scale Science on the United States: Big Science Is Here to Stay, but We Have Yet to Make the Hard Financial and Educational Choices It Imposes.” Science 134 (3473), 61–164. Weinberg, A. M. 1967. Reflections on Big Science. Oxford: Pergamon Press. Wicksteed, S. Q. 1985. The Cambridge Phenomenon: The Growth of High Technology Industry in a University Town. Cambridge: Segal Quince Wicksteed. Ye, J., and A. Kankanhalli. 2013. “Exploring Innovation through Open Networks: A Review and Initial Research Questions.” IIMB Management Review 25 (2), 69–82. Zimmerman, A., and B. Nardi. 2010. “Collaboration in Ecology and the Environment—Two Approaches to Big Science: An Analysis of LTER and NEON.” In J. Parker, N. Vermeulen, and B. Penders, eds., Collaboration in the New Life Sciences, 65–84. Surrey, UK: Ashgate. Zucker, L. G., M. R. Darby, and J. S. Armstrong. 2002. “Commercializing Knowledge: University Science, Knowledge Capture, and Firm Performance in Biotechnology.” Management Science 48 (1), 138–53.

Chapter 12

C omm erciali z at i on or Engageme nt Which Is of More Significance for Regional Economies? Martin Kenney

Introduction In a global society within which the creative use of information transformed into knowledge is increasingly accepted as the major source of new value creation, it is natural that regional and national policymakers would ponder the role of the university, an institution dedicated to the creation and diffusion of information and knowledge.1 In every nation including the United States, the university’s role in knowledge creation has been overwhelmingly funded by public and nonprofit entities. Over the last four decades, interest in the monetization of this knowledge has resulted in the proliferation of offices dedicated to patenting, firm incubators, and even university-funded venture capital firms (Clarysse et al. 2005; Siegel et al. 2003). While this chapter does critique these new institutional mechanisms, it does not adopt the position of those that critique what they term “academic capitalism” (Slaughter and Leslie 1997). In contrast, this chapter suggests that the emphasis on direct monetization devalues the traditional channels of university knowledge transmission to the economy, or what Perkmann and coauthors (2012) describe as “academic engagement,” that have been very powerful regional development forces. Moreover, nearly all research agrees that these earlier channels continue to be of far greater importance than the newer, formalized channels (Perkmann et al. 2012). Further, in suggestive terms, it is argued that an emphasis on these formal

1  The author thanks Terttu Luukkonen for valuable comments and advice. Parts of this chapter were funded by the Research Institute of the Finnish Economy (ETLA), Project UNI (Universities, funding systems and the renewal of the industrial knowledge base) and Tekes (40422/11).

256    Critical Drivers of Local Competitiveness channels could disrupt the other traditional channels, thereby decreasing the contributions of university knowledge to the region and society as a whole. The literature on the role of universities exhibits a number of deep biases: First, it adopts the imagined “US model” described below as the single best model. Second, when considering the United States, the literature appears to privilege MIT and Stanford (Shane 2002; Agrawal and Henderson 2002), despite the fact that more than 68 percent of US research is undertaken at public universities. In fact, the University of California is the largest supplier of research. Third, the now accepted US model for technology transfer emerged from the commercialization of basic molecular biology research through university-patented molecules commercialized by venture capital-backed start-ups (see Kenney 1986 for one of the earliest statements on this process) and is probably more applicable to this field than other fields of university research. Rather than seeing the biotechnology pattern as industry-specific, it has become the dominant conceptual model (hereafter referred to as the biotechnology model). When combined with other biases, the biotechnology model profoundly misrepresents the regionally important economic contributions of the university in the knowledge economy.

The Economic Roles of the Research University Because the research university is a multipurpose institution, understanding its economic roles is difficult. However, nearly all observers agree that the two most economically significant roles are educating students and conducting research. In addition to imparting skills, the university certifies a certain level of capability among its graduates. Students are important not only in terms of skills, but also in the transfer of knowledge. Studies of the channels of information flow have found that after students, the most important channels of public research transmission are informal interaction, meetings, consulting, and for industry in particular, invariably publications (Meyer-Krahmer and Schmoch 1998; Bonaccorsi and Piccaluga 1994; Link, Siegel, and Bozeman 2007). Patents are viewed as very important only in “pharmaceuticals” (see, for example, Cohen et al. 1998; Cohen, Nelson, and Walsh 2002, 11; Klevorick et al. 1995). Summing up the findings of the academic literature, Agrawal (2001, 285)  concludes that for “knowledge transferred through the formal university technology transfer channel, patenting … represents only a small fraction of the total economically valuable transfer from universities.” Normally, when considering a market for knowledge in the form of patents (Arora, Fosfuri, and Gambardella 2004), the tacit dimension is forgotten (Agrawal 2001). And yet there is ample evidence that very often even after a firm licenses a patent from a university, it is important for the licensee to interact with the inventor (Jensen and Thursby 2001), because many university inventions are quite early stage and thus

Commercialization or Engagement    257 require significant further investment in bringing them to practice. This need for interaction makes localness of start-ups an important factor in easing the transfer of tacit knowledge. A significant issue that Agrawal (2001, 294) flags is that “the vibrant trade in scientific knowledge for commercial application that is not patented and does not flow through the university technology transfer office [has] been largely overlooked.” This is extremely important if research is correct in finding that only a small proportion of university knowledge diffusion occurs through technology transfer offices (TTOs) and other such administrative offices. If administrative centralization disrupts the existing and traditional informal channels of knowledge diffusion, then there will be a, likely unobservable, social loss.

The Biotechnology Model Described The biotechnology model took root in the recognition of the mid-1970s that some portion of the knowledge developed in molecular biology had matured sufficiently to be commercializable (Kenney 1986). In a remarkable period of less than a decade, the techniques of this branch of science became commercialized as a new industrial field termed “biotechnology.” The founding knowledge came directly from university research. What ensued was a “gold rush” within which both large pharmaceutical firms and small venture capital-financed firms rushed to university biology departments and medical schools to secure access to faculty members undertaking research on potentially valuable, patentable therapeutic compounds. There was initially a great deal of experimentation with different knowledge-commercialization models. However two became dominant: (1) The university knowledge was patented and then licensed to a large existing pharmaceutical firm. (2)  The university knowledge was patented and then licensed to a small, almost always local, venture capital-financed firm, often founded by the university researcher and possibly one or more postdoctoral students. In each of these models, the university monetized the research through patenting. The biotechnology model most closely resembles Vannevar Bush’s now deeply questioned linear model, whereby inventions generated in basic research flow to applied research and then product development (see figure 12.1). In the biopharmaceutical model, patents are considered vital for commercialization, which agrees with the common belief propagated by organizations such as the Association of University Technology Managers (AUTM). While most of the discussion about biotechnology technology transfer envisions proprietary pharmaceutical applications requiring extensive and expensive testing, a number of the largest biopharmaceutical income earners have been university-developed techniques such as the Cohen-Boyer recombinant DNA patent ($250  million) and Axel Co-transformation patents ($790 million). And yet for these patents there is no

258    Critical Drivers of Local Competitiveness University Lab

Patents on bioactive molecule VC-Funded Start-Up

New drug candidate Big Pharma

Product Time Horizon–8 years

Figure  12.1  Stylized Depiction of Biotechnology University Transfer  Model

credible argument that they would not have been used absent a patent, as they diffused in the research community long before the patents issued (Colaianni and Cook-Deegan 2009). The only contribution by the university technology-licensing office (TLO) was the creation of a licensing contract and the collection of effectively what was a tax on commercial users. In the case of the University of Utah–discovered BRCA breast cancer gene, which was licensed to a local start-up, there can be little doubt that the technology would have been used and that the expensive screening tests are precluding at-risk women without appropriate health insurance coverage from receiving the test (Dalpé et al. 2003; Paradise 2004). Effectively, women with this predisposition to cancer are being taxed to discover this. The use of University of Wisconsin–developed embryonic stem cells is similar. There is no doubt that the technology would have transferred, but the patents of the publicly funded research preclude their widespread use as the university and the private firm that secured the license operate to maximize their profits (Jain and George 2007; Murray 2007). In general, patents are most valuable in the specialty chemical industry and especially the pharmaceutical industry, where a firm can protect its molecules (Mansfield 1986). With the biotechnology revolution and the passage of Bayh-Dole, in the space of less than a decade, universities around the United States established technology-licensing offices and proceeded to patent increasing swaths of research results. With this development, what Rhoten and Powell (2007) term the “patent-grant” university model was born. The ability to own the patent was central for research universities, because, as nonprofit institutions, they are unable to practice their inventions. However, they are permitted to receive income from patent licensing. For this reason, through their technology-licensing offices, universities have been on the forefront of pressing for stronger patent protection. While the patent-grant

Commercialization or Engagement    259 university may be the icon of the 21st-century university, it is not a very accurate description of the many ways in which universities contribute information, knowledge, and technology to society.

Other University Technology Transfer Models Industries differ dramatically in their dynamics, structure, and sources of competitiveness and knowledge. Research, such as that by Cohen, Nelson, and Walsh (2002), demonstrates that industry characteristics affect the types of engagements firms have with universities. This section uses illustrations from a variety of knowledge fields to demonstrate the diversity of interactions with the goal of showing that no one model best describes the ways in which universities engage with society.

The Wine Industry and the University of California, Davis In the United States, the oldest organized technology transfer model is the land-grant public universities and their colleges of agriculture.2 In US agriculture there is an entire technology production and transfer system consisting of university researchers and publicly funded extension personnel who are charged with ensuring that a particular state’s farmers are aware of the technology being developed at the university. In the past, the research results were most obviously embedded in seeds and cultivars known in the vernacular as “college-bred” and provided to all interested parties for free (see, for example, Kloppenburg 1988). Despite the fact that there was no proprietary technology embedded in these seeds, they were widely diffused and adopted. In fact, some of the early and influential technology diffusion literature such as Rogers (1962) and returns-to-research studies such as Griliches (1958) were based on agriculture where commercially valuable research results were placed in the public domain. The history of the interaction between universities (and research institutes) and agriculture is well known (Evenson, Waggoner, and Ruttan 1979). However, it has been framed in unidirectional terms as universities develop new technologies such as seeds, pesticides, and new farm equipment, which are then transferred to farmers through extension activities or commercialized by farm input industries. The wine industry provides another perspective on the ways in which university research and training contributed to the creation of a high-value agricultural industry. In this section, I concentrate on the relationship between UC Davis and the Napa Valley, but draw upon the 2 

This section draws heavily upon Lapsley (2013).

260    Critical Drivers of Local Competitiveness much larger literature on the role of research in the development of fine wine industries in a number of nations. The relationship between scientific research and the wine industry goes back at least as far as Louis Pasteur’s research on wine fermentation (Debré 2000). More recently, others have documented the role of public institutions in providing skilled personnel and actionable knowledge to the wine industry (Giuliani and Arza 2009). After the repeal of Prohibition, the California wine industry, including the Napa Valley, produced low-quality sweet wines. However, in the 1950s a group of Napa Valley vintners in discussion with University of California professors came to believe that it was possible to produce fine wines in the Napa Valley (Lapsley 1996; 2013). In the immediate post–World War II period, Napa winemakers were dependent upon and eager to receive information from the university on how to upgrade regional production. To cement this commitment, in 1947, the Napa Valley vintners purchased a 20-acre vineyard site in the heart of the region and donated it to the university so that researchers could experiment close-by. The university also provided virus-free root stock to the region’s growers. During this early period, the Napa wine industry was technically unsophisticated and depended heavily upon university research and assistance. At that time, vintners and growers in the Napa Valley were mainly identifying problems for the university researchers to solve. As the wine industry matured, however, the information flow became bidirectional. As Americans began to consume more and better wines in the 1960s, the market for Napa wines also grew. Moreover, the emphasis on quality increased, creating greater demand for technically trained winemakers. As enrollment in the Department of Enology and Viticulture grew, it partnered with the self-supporting UC Davis University Extension to offer professional courses for those in the wine industry. Through students and extension courses, university knowledge diffused into the industry. Likely because they were so far behind the French wine industry, California and particularly Napa vintners were eager to adopt new technology to improve their production. As Lapsley (2013) points out, quality became their overwhelming goal. As the Napa wine industry matured, UC Davis research continued to be important, but it was the training in scientific winemaking that became paramount. Lapsley (2013, 204) quoted a prominent Napa winemaker as saying “somewhat rhetorically, ‘Can you think of a great winemaking region that doesn’t have a university associated with it?’ ” Wine is, perhaps, unusual in agriculture in terms of the level of interaction between local research and educational institutions and industry (see, for example, Giuliani and Arza 2009). Lately this interaction has become even more complex. Features of the wine industry in California, particularly the industry’s belief in the importance of university research and training, make generalization even to other agricultural fields hazardous, but it is suggestive of the rich variety of ways universities and local firms and industries interact. Applying the biotechnology model would prove disruptive to the successful wine industry model.

Commercialization or Engagement    261

Electrical Engineering and Computer Science In engineering, it is widely recognized that technology transfer is not a linear process, but rather can be seen as a long-term dialogue better modeled as a complicated set of interactions.3 Remarkably, the pattern of interaction in engineering has received far less attention than the biotechnology model, perhaps because of its complexity and the relative lack of importance of patents, which are so easily researchable and can generate revenue for the university through licensing income. This lack of attention is unfortunate because engineering provides an entirely different perspective on the patterns of interaction between industry and the university. This is emphasized by Agrawal and Henderson (2002) in a study of the interactions by 225 MIT engineering professors that found “a focus on patent citations or on licensing behavior may offer only partial insights as to the ways in which MIT interacts with the private sector.” Nearly half of their respondents had patented an invention, and a subsample believed that patenting accounted for only 7 percent of all of the technology transferred from their laboratories. The mechanisms for knowledge transfer are myriad, and there are many channels for interaction and mutual learning. This is illustrated in the ways in which UC Berkeley Electrical Engineering and Computer Science faculty interacted with personnel at the Bell Laboratories to develop an improved Unix that was released to the public for free under the moniker “Berkeley Software Development Unix” (BSD Unix). The development of BSD Unix followed a complicated interactive path. It began with a UCB professor becoming aware of Unix at a conference where he heard the Bell Laboratory inventors describe the program. One of the Bell Laboratory inventors was a former UCB PhD student. The UCB professor requested a copy of the program and its documentation. The University of California then negotiated a license from AT&T, the owner of the Bell Laboratories at the time. The original Bell Unix developer with his PhD from UCB returned and spent a one-year sabbatical at UCB teaching graduate seminars about Unix, thereby training UCB students. Still other UCB graduate students did internships at Bell Laboratories with Unix developers. These human relationships deepened the interaction and ensured a two-way transfer of technology. The research at the University of California was sponsored by the US Department of Defense Advanced Research Projects Agency, which was committed to Unix diffusion. The Berkeley Unix software team freely distributed their versions to the public. The UCB graduates that were employed by firms were important carriers of the software into the economy. Of particular importance was Sun Microsystems, which built its industry-changing workstations on a BSD Unix variant. Another output of the team developing BSD Unix was Sendmail, the Internet mail server program. Much later BSD Unix was integrated into the Apple operating system and also was the inspiration for the Linux operating system. This case study illustrates a number of points. First, openly published and freely provided university knowledge can 3 

See Kenney, Mowery, and Patton (2013) detailed discussion.

262    Critical Drivers of Local Competitiveness make enormous contributions to the public good and local (and global) economic growth. Second, though Berkeley Unix is only a single case, there is significant evidence that interactions between universities and firms are multifaceted and cannot be reduced to a process to be managed by a technology transfer office. In fact, inserting an intermediary into engineering relationships might weaken the technology transfer process.

Scientific Instruments Modern science depends upon new instruments for measuring and understanding physical phenomena. There is a long history of interaction between industry and academe that finds economic uses for machinery developed for research (Lenoir 1997; Mody 2006). There are many historical examples, such as Arnold Beckman’s establishment of Beckman Instruments to market a pH meter he developed when he was an assistant professor of chemistry at Caltech. Beckman Instruments would evolve into a large instruments firm later on (Simoni et al. 2003). In the case of nuclear magnetic resonance (NMR), which was actualized in scientific instruments, Felix Bloch, a physicist at Stanford, was a scientific pioneer in the area and would go on to work very closely with the local firm Varian Associates (Lenoir 1997). In the field of probe microscopy, university research led Vergil Elings, professor in the department of physics at UC Santa Barbara, to leave the university and establish a Santa Barbara firm to commercialize the technology (Mody 2013). In all of these cases, the entrepreneurial commercialization of the equipment first invented at the university does not end the interaction; rather it creates a new dynamic of interaction. As the firm experiments with and advances the university-derived technology, the relationship often becomes bidirectional and, if sufficiently powerful, can help improve the scientific status of the university laboratories where it was born. Some think of this process in terms of securing research funding for the university laboratory, but this may be a less important issue—as the most important source of funding for the majority of these laboratories is federal research funds. In fact, all of the extant research suggests that it is the collaboration and information sharing that is vital. For example, the ability of grad students to visit the firm, use new sophisticated equipment, secure spare parts, and interact with the corporate scientists seems to be a particularly large benefit (Mody 2013; Lenoir 1997). Because these instrument firms develop new applications at the cutting edge of physical phenomena, they can identify scientifically challenging problems, which university researchers can use in proposals to secure federal research funding. In most respects, these dynamics are not so different from those encountered in engineering, except the degree to which these close relationships might accelerate technology development and scientific research.

Commercialization or Engagement    263

Mathematics and Statistics For many, mathematics and statistics appear to be among the most “academic” of all departments and quite detached from the economic world. Naturally, there is good reason to accept this common-sense understanding. And yet there is a long history of commercial ventures spinning off from mathematics and especially statistics departments. To illustrate, North Carolina’s Research Triangle Park (RTP) has been hailed as an economic success, despite the fact that most of the largest biomedical operations in the region are branch operations for larger firms headquartered in other areas. Far fewer firms are indigenous entrepreneurial ventures established to commercialize university science, and of the local ventures few have become significant firms. The exception is firms specializing in data analysis. The two most important indigenous North Carolina technology firms are SAS and Quintiles, both of which are statistics department spin-offs. SAS was established by a North Carolina State University statistics graduate student and professor who developed a statistical program for analyzing agricultural data. The team created a firm to commercialize the program that has since grown to over 10,000 employees, the bulk of whom are located in the RTP region (SAS 2013). Remarkably, the other major regional university-derived entrepreneurial success story, Quintiles, was the progeny of Dennis Gillings, a statistics professor at the University of North Carolina, who began by consulting for pharmaceutical customers. Encouraged by his success, he joined with another UNC professor, Gary Koch, to establish Quintiles, which has become a pharmaceutical research consulting giant employing 27,000 persons globally. Quintiles never received venture capital and, as was the case with SAS, self-funded its growth. These are two salient examples, but there are a number of other firms founded by professors in mathematics and statistics. These firms are the outgrowth of successful consulting practices that were part of a professor’s normal activities. Without a doubt these firms have been important for RTP’s economic development, not only in terms of employment, but also in creating many further consulting opportunities for professionals in the region. More recently, with the rise of cloud computing and “big data,” mathematics and statistics are becoming more economically valuable, as start-ups are being formed to exploit the increasing amount of data available. The point of this section is not to argue that mathematics and statistics should be commercialized but rather to suggest that serendipitous economic benefits can emerge from an extremely wide variety of departments.

Discussion It is remarkable that on the basis of so little evidence European and other nations abandoned their previous models by which the university and industry engaged to adopt the

264    Critical Drivers of Local Competitiveness US patent-based biotechnology model (Baldini, Grimaldi, and Sobrero 2006; Grimaldi et al. 2011; Lissoni et al. 2008; Mowery et al. 2001). As more research is undertaken on the European model, there is increasing evidence of technology transfer that was concentrated in improving existing local firms. This transfer was not so visible because few new firms resulted. More recently, Sweden has decided that moving to the Bayh-Dole model might disrupt important channels of knowledge transfer and decided to retain the “professor privilege” model (Jacobsson, Lindholm-Dahlstrand, and Elg 2013). In Europe and other nations, the adoption of the biotechnology model has slowed because of recognition that a better goal than university commercialization may be university engagement, especially with smaller regional firms. Recently, some technology transfer professionals and academics have advocated directly considering professorial patenting and/or entrepreneurship in tenure and promotion decisions (Stevens, Johnson, and Sanberg 2011; see also Siegel et al. 2003), though in academic bioscience this does not yet seem to have occurred (Stuart and Ding 2006). Thus far, the movement toward including this in tenure has occurred among weaker US research universities (e.g., Texas A&M and Boston University). At this point, a university researcher’s decisions about commercialization are largely individual and, though there are financial and often personal rewards for success, it remains optional and not directly rewarded in the academic personnel system. In the United States, university inventors normally receive between 30 percent and 50 percent of the invention’s net income (divided among all the inventors). A successfully licensed invention thus can provide a significant income. There has been some movement toward more directly considering commercialization activities in the academic personnel system, and yet, in first-rank research universities it has been halting at best with many believing that the financial rewards for commercialization are sufficient to motivate those so inclined to undertake commercialization. In many cases, the technology-licensing office meant to encourage technology transfer may be creating barriers to transfer. If this is the case, then removing university technology-licensing offices from the path could encourage greater levels of commercialization (Kenney and Patton 2009; 2011). In terms of technology transfer of economically valuable research, this chapter argues that patents, in certain industries such as pharmaceuticals, may be of importance, but in many others, they are of less or even minimal importance. Moreover, in a patent-based transfer system in which patents are auctioned off to the highest bidder, smaller local firms are likely to lose out to large multinational firms that are almost certain to develop the invention extralocally and as a result contribute little to local capability development. In the case of BSD Unix, which was open access, arguably the greatest beneficiaries were local Silicon Valley firms, in terms of both the technology and the students that graduated. In the case of the Napa wine industry, there was a complex and bidirectional flow of knowledge, research questions, and individuals. Initially, the publicly funded research contributed enormously to the growth of the Napa wine industry, in later years it was the students, and, most recently, winery owners that have become generous contributors to the Davis campus.

Commercialization or Engagement    265 Assessing the value of the proliferation of technology transfer institutions on university campuses is difficult particularly because it is difficult to measure the social and regional benefit, which is different from measuring the income to the university. This observation is specific to this case and leads to the larger question of whether university efforts to increase patenting are rendering private ever greater amounts of what were previously freely available research results, thereby decreasing the knowledge commons. Technology transfer and the role of the university in regional economic growth has been the topic of this chapter, but the university contributes far more to local and global society. The university has a vital role as a social critic and home to the arts and humanities. While much of the discussion of the transfer of research results has focused upon private enterprise, as important or even more important is the transfer of research findings to society on issues such as poverty and global warming. These cannot be given a monetary value, and yet their social value is undeniable. Absent a university that values all forms of knowledge, vital outputs such as these might be lost. A university focused only upon economic outcomes is likely to result in an impoverished region and society.

References Agrawal, A.  K. 2001. “University-to-Industry Knowledge Transfer:  Literature Review and Unanswered Questions.” International Journal of Management Reviews 3 (4), 285–302. Agrawal, A., and R. Henderson. 2002. “Putting Patent in Context:  Exploring Knowledge Transfer at MIT.” Management Science 48, 44–60. Arora, A., A. Fosfuri, and A. Gambardella. 2004. Markets for Technology: The Economics of Innovation and Corporate Strategy. Cambridge, MA: MIT Press. Baldini, N., R. Grimaldi, and M. Sobrero. 2006. “Institutional Changes and the Commercialization of Academic Knowledge:  A  Study of Italian Universities’ Patenting Activities between 1965 and 2002.” Research Policy 35 (4), 518–32. Bonaccorsi, A., and A. Piccaluga. 1994. “A Theoretical Framework for the Evaluation of University-Industry Relationships.” R&D Management 24, 229–47. Clarysse, B., M. Wright, A. Lockett, E.  van de Velde, and A. Vohora. 2005. “Spinning Out New Ventures: A Typology of Incubation Strategies from European Research Institutions.” Journal of Business Venturing 20 (2), 183–216. Cohen, W.  M., R. Florida, L. Randazzese, and J. Walsh. 1998. “Industry and the Academy:  Uneasy Partners in the Cause of Technological Advance.” In R. G.  Noll, ed., Challenges to Research Universities. Washington, DC: Brookings Institute Press. Cohen, W. M., R. R. Nelson, and J. P. Walsh. 2002. “Links and Impacts: The Influence of Public Research on Industrial R&D.” Management Science 48 (1), 1–23. Colaianni, A., and R. Cook-Deegan. 2009. “Columbia University’s Axel Patents: Technology Transfer and Implications for the Bayh-Dole Act.” Milbank Quarterly 87 (3), 683–715. Dalpé, R., L. Bouchard, A. J. Houle, and L. Bédard. 2003. “Watching the Race to find the Breast Cancer Genes.” Science, Technology & Human Values 28 (2), 187–216. Debré, P. 2000. Louis Pasteur. Baltimore, MD: Johns Hopkins University Press. Evenson, R. E., P. E. Waggoner, and V. W. Ruttan. 1979. “Economic Benefits from Research: An Example from Agriculture.” Science 205 (4411), 1101–7.

266    Critical Drivers of Local Competitiveness Giuliani, E., and V. Arza. 2009. What drives the formation of “valuable” university-industry linkages? Insight from the wine industry. Research Policy 38, 906–21. Griliches, Z. 1958. “Research Costs and Social Returns: Hybrid Corn and Related Innovations.” Journal of Political Economy 66 (5), 419–31. Grimaldi, R., M. Kenney, D. S.  Siegel, and M. Wright. 2011. “30 Years after Bayh-Dole: Reassessing Academic Entrepreneurship.” Research Policy 40 (8), 1045–57. Jacobsson, S., A. Lindholm-Dahlstrand, and L. Elg. 2013. “Is the Commercialization of European Academic R&D Weak? A  Critical Assessment of a Dominant Belief and Associated Policy Responses.” Research Policy 42 (4), 874–85. Jain, S., and G. George. 2007. “Technology Transfer Offices as Institutional Entrepreneurs: The Case of Wisconsin Alumni Research Foundation and Human Embryonic Stem Cells.” Industrial and Corporate Change 16 (4), 535–67. Jensen, R., and M. Thursby. 2001. “Proofs and Prototypes for Sale: The Licensing of University Inventions.” American Economic Review 91 (1), 240–59. Kenney, M. 1986. Biotechnology:  The University-Industrial Complex. New Haven, CT:  Yale University Press. Kenney, M., D. Mowery, and D. Patton. 2013. “Electrical Engineering and Computer Science at UC Berkeley and Silicon Valley:  Modes of Regional Engagement.” In M. Kenney and D. Mowery, eds., Public Universities and Regional Growth. Stanford, CA:  Stanford University Press. Kenney, M., and D. Patton. 2009. “Reconsidering the Bayh-Dole Act and the Current University Invention Ownership Model.” Research Policy 38 (9), 1407–22. Kenney, M., and D. Patton. 2011. “Does Inventor Ownership Encourage University Research-Derived Entrepreneurship? A Six University Comparison.” Research Policy 40 (8), 1100–1112. Klevorick, A.  K., R. C.  Levin, R. R.  Nelson, and S. G.  Winter. 1995. “On the Sources and Significance of Interindustry Differences in Technological Opportunities.” Research Policy 24, 185–205. Kloppenburg, J.  R. 1988. First the Seed:  The Political Economy of Plant Biotechnology. Cambridge: Cambridge University Press. Lapsley, J.  T. 1996. Bottled Poetry:  Napa Winemaking from Prohibition to the Modern Era. Berkeley: University of California Press. Lapsley, J. T. 2013. “ ‘We Are Both Hosts’: Napa Valley, U.C. Davis and the Search for Quality.” In M. Kenney and D. Mowery, eds., Public Universities and Regional Growth. Stanford, CA: Stanford University Press. Lenoir, T. 1997. Instituting Science: The Cultural Production of Scientific Disciplines. Stanford, CA: Stanford University Press. Link, A. N., D. S. Siegel, and B. Bozeman. 2007. “An Empirical Analysis of the Propensity of Academics to Engage in Informal University Technology Transfer.” Industrial and Corporate Change 16, 641–55. Lissoni, F., P. Llerena, M. McKelvey, and B. Sanditov. 2008. “Academic Patenting in Europe: New Evidence from the KEINS Database.” Research Evaluation 17 (2), 87–102. Mansfield, E. 1986. “Patents and Innovation:  An Empirical Study.” Management Science 32, 173–81. Meyer-Krahmer, F., and U. Schmoch. 1998. “Science-Based Technologies: University-Industry Interactions in Four Fields.” Research Policy 27 (8), 835–51.

Commercialization or Engagement    267 Mody, C. 2006. “Corporations, Universities, and Instrumental Communities: Commercializing Probe Microscopy, 1981–1996.” Technology and Culture 47 (1), 56–80. Mody, C. 2013. “University in a Garage: Instrumentation and Innovation in and around UC Santa Barbara.” In M. Kenney and D. Mowery, eds., Public Universities and Regional Growth. Stanford, CA: Stanford University Press. Mowery, D. C., R. R. Nelson, B. N. Sampat, and A. A. Ziedonis. 2001. “The Growth of Patenting and Licensing by US Universities: An Assessment of the Effects of the Bayh-Dole Act of 1980.” Research Policy 30 (1), 99–119. Murray, F. 2007. “The Stem-Cell Market-Patents and the Pursuit of Scientific Progress.” New England Journal of Medicine 356 (23), 2341. Paradise, J. 2004. “European Opposition to Exclusive Control over Predictive Breast Cancer Testing and the Inherent Implications for United States Patent Law and Public Policy: A Case Study of the Myriad Genetics’ BRCA Patent Controversy.” Food and Drug Law Journal 59 (1), 133–54. Perkmann, M., V. Tartari, M. McKelvey, E. Autio, A. Broström, P. D’Este, and M. Sobrero. 2012. “Academic Engagement and Commercialisation:  A  Review of the Literature on University-Industry Relations.” Research Policy 42, 423–42. Rhoten, D., and W. W.  Powell. 2007. “The Frontiers of Intellectual Property:  Expanded Protection versus New Models of Open Science.” Annual Review of Law, Society, and Science 3, 345–73. Rogers, E. M. 1962. Diffusion of innovations. New York: Free Press. SAS. 2013. Company history. Retrieved from http://www.sas.com/company/about/history. html#s1=1. Shane, S. 2002. “Selling University Technology: Patterns from MIT.” Management Science 48, 122–38. Siegel, D. S., D. A. Waldman, L. E. Atwater, and A. N. Link. 2003. “Commercial Knowledge Transfers from Universities to Firms: Improving the Effectiveness of University-Industry Collaboration.” Journal of High Technology Management Research 14 (1), 111–33. Simoni, R. D., R. L. Hill, M. Vaughan, and H. Tabor. 2003. “A Classic Instrument: The Beckman DU Spectrophotometer and Its Inventor, Arnold O.  Beckman.” Journal of Biological Chemistry 278 (49), 79–81. Slaughter, S., and L. L. Leslie. 1997. Academic capitalism: Politics, policies, and the Entrepreneurial University. Baltimore, MD: Johns Hopkins University Press. Stevens, A.  J., G. A.  Johnson, and P. R.  Sanberg. 2011. “The Role of Patents and Commercialization in the Tenure and Promotion Process.” Technology and Innovation 13, 249–59. Stuart, T. E., and W. W. Ding. 2006. “When Do Scientists become Entrepreneurs? The Social Structural Antecedents of Commercial Activity in the Academic Life Sciences.” American Journal of Sociology, 112(1), 97–144.

Chapter 13

Phil anth rop y, C om petit i on, a nd L o cal C om pet i t i v e ne s s A Schumpeterian Conundrum Zoltan J. Acs

When I asked my publicist what makes bestsellers, she replied “four factors”: How well known the author? How interesting is the subject matter? How large is the book’s scope and reach? And what is the X factor? Applying this logic to Joseph A. Schumpeter’s 1942 book Capitalism, Socialism and Democracy (CSD), we note that the author was very well known. The subject was interesting. The book did have reach. Most importantly, the X factor turned out to be huge. CSD went on to become an international sensation for half a century. What was the X factor that catapulted CSD to become a bestseller almost a century ago? The book fed into a growing global debate about economics, specifically the long-term evolution of capitalism, inequality, the concentration of wealth, and the prospects for social stability. As World War II raged in 1942—the battles of Stalingrad and Midway and the carpet bombing of Axis cities—the world was concerned not so much with the battles, as with what the world would look like after the bombs stopped falling. Schumpeter provided a chilling and sober account of the state of the great debate. In CSD he made three predictions. First, he asked the question “Can capitalism survive?” and he responded, “I do not think so.” Second, to the question “Can socialism work?” he replied of course. Finally, to the question “Will socialism be democratic?” he punted. Well, Schumpeter was wrong on all three counts. Capitalism did survive, and flourish; socialism did not work, and it turned out to be authoritarian and not democratic. Of course that Schumpeter was so wrong is interesting, but all the great worldly philosophers, Smith, Malthus, Mill, Marx, Keynes, along with Schumpeter, were all wrong at some level, but it is the analysis that carries the day.

Philanthropy, Competition, and Local Competitiveness    269 To understand why Schumpeter was wrong we need to go back and look at the building blocks of modern society. In the 19th century the West invented capitalism, which brought the Industrial Revolution, jobs, and opportunity to millions. However, the institutional structure that sustained this development was much broader and had roots that go much further back. Capitalism, philanthropy, and democracy are the fundamental pillars of modern civilization. Democracy goes back to the 5th century b.c. in the Greek city state of Athens. Capitalism is from the 17th century and philanthropy was added in the 19th century. While capitalism is mostly governed by the market system and democracy by the political process, philanthropy is to a large degree independent of both forces. But it reinforces both democracy and capitalism by nourishing both processes by relying on the better side of human nature. In the 20th century the world lost patience in this Enlightenment idea and embraced socialism and communism. Communism and its variants spread to most of the world, and it rejected capitalism, democracy, and philanthropy by nationalizing capital, replacing the market with central planning, and exchanging democracy for totalitarianism. In effect a new world order was put in place. Only a few countries stood against this new world order. Even in the United States there were large sympathies for the communists, especially during the Great Depression. But at the end of the 20th century communism collapsed. It simply could not keep pace with the economic output of its competitors. In the 21st century the world is returning to its 19th-century roots of liberal democracy and market processes. Capitalism and democracy are flourishing all over the world. However, the world has not as of yet understood and fully appreciated that these two forces cannot survive and prosper without philanthropy. While capitalism is a cultural phenomenon and democracy has an institutional underpinning, philanthropy is a natural force that has always existed in all societies. It has taken different forms historically, but the need to look after each other is part of our DNA. Capitalism, philanthropy, and democracy are global forces that need to be woven together into a global system of opportunity and prosperity for all. The central mission of globalization is to help make this a reality. We need to create the dialogue among the wealthy, among research institutions, and among educational institutions. We need to bring the cultural, natural, and institutional aspects of humanity together to ensure our social survival into the 21st century. While government is looked to by many as the solution to our conundrum, and while others espouse the free market, it is philanthropy that holds the key to our future. The reason I suggest this is that philanthropy (moral capital) is a key to competition, and it is the key that unlocks regional competitiveness. The next section recasts Capitalism, Socialism and Democracy as “Capitalism, Philanthropy, and Democracy: to set the stage for where we are in the 21st century. The next section examines Schumpeter and the role of competition in economic development, and the third section applies the idea of competition and philanthropy to the American education story and suggests that it was philanthropy that provided much of the impetus to the competitiveness of American capitalism that propelled the system to triumph in the 20th century.

270    Critical Drivers of Local Competitiveness

Capitalism, Philanthropy, and Democracy The fall of the Berlin Wall and Fukuyama’s The End of History and the Last Man (Fukuyama 1989)  drove the final nail into the coffin of Capitalism, Socialism and Democracy. However, a new blueprint along the lines of CSD has not emerged with the work of Fukuyama or others in this intellectual space. The removal of trade barriers and capital flows after 1990 continues to create efficient markets but not equality of opportunity. The results are what economists call a Zipf distribution: a few big winners and lots of losers, where economic opportunity tends to concentrate and losers become frustrated. Turning for consolation to religious fanaticism or some substitute becomes the default. This is true in both developed and developing worlds. It was the subject of the last essay of the late Noble Laureate Paul Samuelson (Samuelson 2009). Both Marx and Keynes knew that efficiency and equity were important, and suggested that institutions need to be rigged in order to make capitalism work. Both, like Piketty, advocated eliminating or at least constraining the entrepreneur and private wealth. There are three broad approaches to social sustainability (1) lift the losers above some minimum level of mass consumption (illusions of fame trump economics); (2) restore order (ram it down their throats) at home and abroad; and (3) create structures that enable full use of people’s talent, self-expression, and entrepreneurship—in a nutshell, that create opportunity for all. [Some Americans define] economic freedom as an equal chance to become unequal.1 Of course the question today, as in 1942, is what the future of our society looks like. Is it the X factor that catapulted Piketty’s Capital in the Twenty-First Century to bestseller? Well, I believe, like CSD the book fed into a growing debate about economics, specifically the long-term evolution of capitalism, inequality, the concentration of wealth, and the prospects for social stability. From this perspective it is perhaps the first really important book in political economy in close to 100 years. The main drivers of inequality, according to Piketty are the tendency of returns on capital to exceed the rate of economic growth, r > g, where historically r is close to 5 percent and growth in OECD countries is below 2 percent. But the real X factor, I believe, was the policy prescription, a global tax on capital—that was embraced by the Left and lamented by the Right. Therefore, the real issue is whether capitalism can be both economically and morally robust. To understand the question “How does inequality matter for our economic well-being?” we need to examine the laws of capitalism. Here Piketty is brilliant. The first fundamental law of capitalism links the stock of capital to the flow of income from capital: α = r × β, where β is the capital/income ratio, r is the rate of return on capital, and

1 

Jennifer Hochschild (1981).

Philanthropy, Competition, and Local Competitiveness    271 α is the share of national income from capital. Piketty examines this accounting identity in great detail over the past two centuries. The second fundamental law of capitalism is β = s/g, where s is the savings rate and g is the growth rate. In the long run the capital/income ratio is related in a simple and transparent way to the savings rate s and the growth rate of national income g. Fundamentally, a country that saves a lot and grows a little will accumulate an enormous stock of capital relative to income. The law is asymptotic, meaning that it is only valid in the long run. The difference between the first law and the second is that the first law is an accounting identity, whereas the second law is the result of a dynamic process toward which the economy tends given the savings rate s and growth rate g. Piketty’s analysis is not without criticism, for example, David Soskice (2014) argues that his central analysis of the growth of contemporary inequality makes little sense because it is based purely on a neoclassical and mathematical analysis. Perhaps a more realistic approach is needed.2 Philanthropy matters in this debate because it (philanthropy) offers an alternative solution to the Piketty conundrum without relying exclusively on a wealth tax. So how does philanthropy solve the conundrum? The answer to the riddle is rather simple. You want to increase the growth rate of the economy g so as to mitigate the difference between r and g and reduce the share of income going to the owner of capital. Thinking of the two laws together, you want to maintain the dynamism of the system (efficiency) and solve the rising income inequality (equity), in part, by increasing growth and reducing the share of capital income going to the rich. Like taxes, the focus is on the capital/income ratio, but philanthropy focuses on the stock of capital (wealth) and not the flow of income. Philanthropy does not affect the stock of capital but redirects the flow of income to opportunity-creating activities. In other words, it turns a share of capital into moral capital. What we would expect to find is that moral capital has found its way into the universities, where opportunity for many is created. In the United States a half a trillion dollars sit in university endowments of the top 1,000 schools, each with an endowment of $500 million on average and land and buildings of equal value. The second source of moral capital is found in foundations. Over half a trillion dollars sits in the largest 100 foundations in the United States with over $30 billion in the Gates Foundation. Another half a trillion sits in smaller foundations. The third source of moral capital is in 2  Thomas Piketty's (2014) book, Capital in the 21st Century, follows in the tradition of the great classical economists, like Marx and Ricardo, in formulating general laws of capitalism to diagnose and predict the dynamics of inequality. They argue that general economic laws are unhelpful as a guide to understand the past or predict the future, because they ignore the central role of political and economic institutions, as well as the endogenous evolution of technology, in shaping the distribution of resources in society. We use regression evidence to show that the main economic force emphasized in Piketty's book, the gap between the interest rate and the growth rate, does not appear to explain historical patterns of inequality (especially, the share of income accruing to the upper tail of the distribution). They then use the histories of inequality of South Africa and Sweden to illustrate that inequality dynamics cannot be understood without embedding economic factors in the context of economic and political institutions, and also that the focus on the share of top incomes can give a misleading characterization of the true nature of inequality (Acemoglu and Robinson, NBER w20766, December 2014).

272    Critical Drivers of Local Competitiveness the churches, which have assets close to $2 trillion. A rough estimate is that total moral capital in the United States is close to $5 trillion, 5 percent of the total capital stock. This number may not be large enough by a factor of two, but it is much larger than in any other country in the world. Moral capital globally needs to be between 5 and 10 percent of global capital. Philanthropy has long been a distinctive feature of American culture, but its crucial role in the economic well-being of the nation—and the world—has remained largely unexplored (Zunz 2012). Philanthropy achieves three crucial outcomes: it deals with the question of what to do with capital—keep it, tax it, or give it away. By putting a part of your capital in a foundation for the public good you maintain the stock of capital and the capital/income ratio, but income flows to a privately created public good. It complements government in creating public goods. And by focusing on education, science, and medicine, philanthropy has a positive effect on long-run economic growth. For wealth to invigorate the capitalism system it needs to be “kept in rotation,” like the planets round the sun. Philanthropy strengthens capitalism in two ways: the first is that philanthropy, when targeted to universities, research, and other productive uses, lays the groundwork for new cycles of innovation and enterprise. The second way philanthropy strengthens capitalism is that philanthropy—like creative destruction—provides a mechanism for dismantling the accumulated wealth tied to the past and reinvesting it to strengthen the entrepreneurial potential of the future. When philanthropy is absent, capital remains concentrated, rent seeking flourishes, and innovation and entrepreneurship suffer. In other words, monopoly spreads (Acs 2013). But how could philanthropy be a part of capitalism? Capitalism, as Max Weber (1958) showed, is a cultural system, a relatively orderly system of institutions and incentives governed by the tractable logic of supply and demand. Philanthropy, by contrast, lacks a set of laws to explain its ebbs and flows. Like the art patronages in royal courts throughout European history, philanthropy is subject to the whims of the wealthy. Furthermore, philanthropy is not only largely ungoverned by economic principles but also relatively free from the checks and balances of democracy, such as elections, referendums, and recalls (Acs 2013). The answer to this puzzle is found in the writings of the moral philosopher Adam Smith, who wrote, “there are evidently some principles of [man’s] nature, which interest him in the fortune of others and renders their happiness necessary to him.” So philanthropy is governed by natural principles, embedded altruism, while capitalism is governed by culture and institutions (Acs 2013, 143–44). While philanthropy has been loyal to the institutions of capitalism, rarely is it understood as an entity intertwined with capitalism. Yet it has both emanated from the capitalist system and continually nurtured the system. It is the invisible, underappreciated force for progress in American-style capitalism, the secret ingredient that fails to get mentioned in economic accounts of capitalism, like Piketty’s Capital. Philanthropy does not interfere with the dynamics of capitalism. Individuals are free to accumulate capital, and the growth rate of the economy is not compromised with taxes, so the capital/income ratio can rise in the long run.

Philanthropy, Competition, and Local Competitiveness    273 I have argued that it is philanthropy, not just government and taxes that propels the basic machinery of capitalism. So in addition to well-functioning markets, property rights, contract law, capital markets, and the like, philanthropy—a little understood economic force—provides a superinstitutional element that serves to promote vital nonmonetary institutional forces necessary for achieving growth through technological innovation, promoting economic equality and cultivating economic security (Acs 2013). Philanthropy creates competition in the system by introducing new institutions. These institutions create opportunity for agents, and the opportunity leads to entrepreneurship and innovation. Innovation leads to productivity growth and regional competitiveness. While it is not the only source of regional competitiveness, it is an overlooked one.

Schumpeter and Competition In order to set the stage for the rest of this chapter, we need to take a careful look at the importance of competition in the working of the economic system. One of the hallmarks of socialism was the elimination of competition. Monopoly with state regulation and/or nationalization was viewed as superior for economic performance. In fact, ruinous competition was suggested as the cause of the Great Depression and other capitalist crises.3 Schumpeter himself argued in CSD that monopoly and not competition was the driver of innovation, as it was the large firm that had the technological and financial muscle to bring innovation to market. An army of economist spent the better part of half a century trying to come to grips with this prediction of the master. Study after study studied the role of firm size and monopoly, trying to figure out if indeed size was correlated with performance. It was not until the 1980s that economists using large databases started to unravel the system to discover that indeed it was competition that held the key to economic performance and growth. This was true first and foremost in the realm of innovation. Economists discovered that indeed monopoly hurt innovation and did not promote it. We saw this most clearly in the communist countries, where innovation came to an almost screeching halt by the 1980s. With respect to firm size, here the story turned out to be a little more complicated: it was important to have large firms, but the innovations emanated as much from new firms as it did from the large firms. But even in the capitalist countries, firm size turned out to be not that important for innovation (Acs and Audretsch 1990).

3  Today competition is strong in all of the innovation driven economies: United States, United Kingdom, Canada, and Australia. According to the Global Entrepreneurship Index (GEI 2015) competition is one of the strongest pillars of these countries.

274    Critical Drivers of Local Competitiveness It was the elimination of competition and therefore entrepreneurship that drove the Schumpeterian prognosis about the future of capitalism and the stationary state. However, what is still missing from this story is the lack of an explanation as to what turned the world around. For as we saw above, capitalism has had a fresh start, socialism has been relegated to the dustbin of history, and democracy is flourishing around the world in most countries. The answer to the question of turnaround is to be found, I believe, in the role of philanthropy and the role that philanthropy plays in promoting competition in the capitalist system. In order to offer some evidence on this point, I will examine one sector of the economy where philanthropy has promoted competition, a sector that in most countries has been the monopoly of the state—education. In fact, we will focus on university education, which is a state monopoly all over the world except in the United States.4 Finally, we need to ask the question, “Did Schumpeter understand the issue of entrepreneurship and capitalism as it related to the sustainability of the system?” In the “lost ­chapter 7” of the Theory of Economic Development [1911 (1937)] Schumpeter alluded to the problem when he spoke of the economy as a whole. He understood that the entrepreneur was involved in both an economic and a social process of reorganization that takes its cue from the entrepreneur-philanthropist. He understood that the social pyramid does not consist of economic building blocks. He wrote, “His position as entrepreneur is essentially only a temporary one, namely, it cannot also be transmitted by inheritance: a successor will be unable to hold onto that social position, unless he inherits the lion’s claws along with the prey” ( as quoted in Acs 2009, 320). In other words, a mechanism is needed for the social process to be held together. That glue, we argue, is philanthropy.

From Competition to Regional Competitiveness If philanthropy introduces competition into the economic model, it is the investment in place that leads to competitiveness: place can be a city, state, or country. Perhaps the most visible aspects of this is the moral investment made in education. Education in America started out by following the English model with its focus on classical education. However, education in the United States took an interesting turn in the 18th century with a break from the continental approach to education focusing on the classics, religion, and language. The shift to a more practical education was led the Benjamin Franklin and Thomas Jefferson. King’s College, which would become Columbia University, announced in 1754 that its curriculum would emphasize surveying, navigation, geography, and history. 4 

This is starting to change in several countries.

Philanthropy, Competition, and Local Competitiveness    275 Ben Franklin’s College of Philadelphia, which would later become the University of Pennsylvania, made even greater strides in this direction. In 1756, the college’s president proposed that “economic abundance” would require forming a succession of sober, virtuous, industrious citizens and checking the course of growing luxury. To carry out his vision, the president designed a course of study, approved by Franklin and the college’s trustees that increased the emphasis on practical studies and science. As the historian Frederick Rudolph puts it in his history of American colleges, “The King’s College prospectus of 1754 and the College of Philadelphia’s curriculum of 1756 may not have been the first shots in an exchange heard around the world, but they were nonetheless certain indications that the English colonies of North America were beginning to respond not to English needs but to American aspirations” (1990, 33). This experiment was made only possible by the growing largess of the new bourgeois wealth. However, this was not the only competition that set the stage for innovation. The federal government also moved into high gear. The land-grant universities were so called because of the Morrill Federal Land-Grant Act of 1862, sponsored by Congressman Justin Smith Morrill to support agricultural education. Morrill suggested that colleges in America should “top off a portion of the studies established centuries ago as the mark of European Scholarship and replace the vacancy—if it is a vacancy—by those of a less antique and more practical value (Lucas 1994, 147).” The public university system of Wisconsin is a prominent example. Like many of the emerging universities at the beginning of the 20th century, Wisconsin was experimenting with new methods and means of productivity that benefited society. Under the leadership of Charles Van Hise, its president from 1903 to 1918, the university set about innovating and expanding with the stated goal of serving the entire population of the state. While the federal government was funding and experimenting with the university, an equally large effort was underway funded by the private wealth of the new land. Johns Hopkins, John Rockefeller, and Leland Stanford founded three new research universities modeled after Humboldt University in Germany. These graduate institutions were funded with American fortunes to create three of the greatest universities in the world. Today, Stanford University is extraordinarily successful. It has a $18 billion endowment, enrolls roughly 15,000 students, and is ranked top in many fields, including engineering and technology, life and physical sciences, and health sciences. In creating Stanford University, Leland’s envisioned ways to provide a “practical education,” a vision that still guides the university more than 100 years later. In the fall of 1885, Stanford dedicated the founding grant for Stanford University at his country house, $8 million, and 10,000 acres of land. The key here is that this dual legacy of the American system of higher education, one private and one public has created a measure of competition between institutions that is both healthy and innovative. While many countries try to control the education system through a monopoly, making school responsible to civil servants and making professors civil servants, in America competition became the norm. It was made possible by the generous gift of the very wealthy and the millions of not so wealthy that gave back to their universities.

276    Critical Drivers of Local Competitiveness The story continues to this day. On Sunday, January 27, 2013, New York City mayor Michael Bloomberg announced his latest donation to Johns Hopkins University. His $350 million gift will be the largest in the university’s history. Over the last four decades Bloomberg has given $1.1 billion to his alma mater, a truly staggering amount of money. This makes him the most generous donor to any educational institution in the United States. Reading Bloomberg’s Giving Pledge letter sheds light on what he hopes to accomplish by giving money away. “If you want to fully enjoy life—give. And if you want to do something for your children and show how much you love them, the single best thing—by far—is to support organizations that will create a better world for them and their children (www.thegivingpledge.org).” Table 13.1 lists the top 50 endowments of American universities. While the list has both public and private schools, it shows that Americans have funded private universities to be on par or even surpass the state institutions. This creates a competition for faculty, research, and students that leads to a healthy and innovative system. Harvard tops the list with an endowment of $32 billion followed by Yale with $20 billion, the Texas system with $20 billion, and Stanford University with $18 billion. Table 13.1  Top 50 University Endowments in the United States

Rank

Institution

Endowment market value (millions)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Harvard University Yale University The University of Texas System Stanford University Princeton University Massachusetts Institute of Technology The Texas A&M University System University of Michigan Columbia University Northwestern University University of Pennsylvania University of Notre Dame University of Chicago University of California Duke University Emory University Washington University in St. Louis Cornell University University of Virginia Rice University University of Southern California Dartmouth College Vanderbilt University

32,334 20,780 20,448 18,689 18,200 11,006 8,732 8,382 8,198 7,883 7,741 6,856 6,669 6,377 6,041 5,816 5,652 5,272 5,167 4,837 3,868 3,734 3,673

Philanthropy, Competition, and Local Competitiveness    277 Table 13.1  Continued

Rank

Institution

Endowment market value (millions)

24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50

The Ohio State University Johns Hopkins University University of Pittsburgh The Pennsylvania State University New York University University of Minnesota Brown University University of North Carolina at Chapel Hill University of Washington Purdue University University of Richmond University of Wisconsin Foundation Williams College University of Illinois and Foundation California Institute of Technology Amherst College Pomona College Boston College Rockefeller University Indiana University and Foundation University of Rochester Georgia Institute of Technology Case Western Reserve University Michigan State University Swarthmore College University of Toronto Smith College

3,149 2,987 2,976 2,957 2,949 2,757 2,670 2,381 2,347 2,182 2,023 2,020 1,997 1,926 1,850 1,824 1,823 1,809 1,772 1,735 1,730 1,715 1,679 1,637 1,635 1,593 1,557

Source: 2013 NACUBO-Commonfund Study of Endowments.

The endowment of Harvard University is greater than the combined endowment of all European universities combined. The top 1,000 universities have an endowment of half a trillion dollars with an average endowment of $500 million. Most of these institutions are private. Even small colleges like Smith College, with 2,000 undergraduates, have an endowment of over $1 billion. Without this inflow of funds that generated over $50 billion a year in income, these universities would not be able to compete with one another. These figures understate the wealth of these institutions because they do not include the value of land and buildings that can run into the billions in urban areas. Each one of these investments led to competition in the education system that led to improvements in the productivity of the economy and local competitiveness. As economies improved, they became more competitive with other regions and countries.

278    Critical Drivers of Local Competitiveness Ranking universities is a fine art. Different lists will emphasis different aspects of research and student quality as well as other less tangible qualities. However, while the lists will vary, the leading schools are almost always more or less the same. Table 13.2 ranks the top 25 universities in the world using the Times Higher Education Survey: 18 of the top 25 are American. Of the top 10 seven are American and the other three are British. Both Cambridge and Oxford are the best-endowed European universities, with each having an endowment of around $5 billion. Almost half of the universities are private US institutions. It is the endowments of these institutions that allow them to compete with the state system and create the competition that drives the capitalist system. Without competition and philanthropy, most of these systems would be underfunded or of mediocre quality. Greater London is the education center of the world with four of the top twenty schools: Imperial College London, University College London, Oxford University and University of Cambridge with LSE a close second. Moreover, these institutions are local and lead to regional competitiveness. For example, Stanford University has been a major source of the competitiveness of Silicon Valley, and MIT and Harvard have made Cambridge, Massachusetts one of the most important economic engines in the world. The same is true of Johns Hopkins University in Baltimore, where Hopkins and the Johns Hopkins Medical Center is the regional engine of competitiveness. This is true for almost all of these regions that host a university with a large endowment. Table 13.2  The Top 25 Universities in the World 1 2 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

California Institute of Technology (Caltech) University of Oxford Harvard University Stanford University Massachusetts Institute of Technology (MIT) Princeton University University of Cambridge University of California, Berkeley University of Chicago Imperial College London Yale University University of California, Los Angeles (UCLA) Columbia University ETH Zürich–Swiss Federal Institute of Technology Zürich Johns Hopkins University University of Pennsylvania Duke University University of Michigan Cornell University University of Toronto University College London (UCL) Northwestern University The University of Tokyo Carnegie Mellon University University of Washington

United States United Kingdom United States United States United States United States United Kingdom United States United States United Kingdom United States United States United States Switzerland United States United States United States United States United States Canada United Kingdom United States Japan United States United States

94.9 93.9 93.9 93.8 93.0 92.7 92.3 89.8 87.8 87.5 87.4 86.3 85.2 84.5 83.7 81.0 79.3 79.2 79.1 78.3 77.6 77.1 76.4 76.0 73.4

Philanthropy, Competition, and Local Competitiveness    279 The Times Higher Education World University Rankings 2013–2014, powered by Thomson Reuters, are the only global university performance tables to judge world-class universities across all of their core missions—teaching, research, knowledge transfer, and international outlook. The top universities rankings employ 13 carefully calibrated performance indicators to provide the most comprehensive and balanced comparisons available, which are trusted by students, academics, university leaders, industry and governments. Table 13.3 lists the European universities by endowment. The top 50 universities present an interesting picture compared with the United States. Moral capital in European universities is far less than in the United States. While Oxford and Cambridge lead the field with close to five billion euros the list falls rapidly with the next university at around a billion euros. Each country seems to have one institution that is well endowed, for example the University of Lund in Sweden, the University of Oslo in Norway, the University of Helsinki in Finland, Heidelberg University in Germany, and the University of Vienna in Austria. But the fact remains while Europe is in some ways similar to the United States, the order of magnitude is far short of what should be invested in moral capital in Europe.

Table 13.3  List of European Universities by Endowment Institution

Country

University of Cambridge University of Oxford Swiss Federal Institute of Technology Zurich University of Copenhagen University of Zurich Utrecht University Lund University Central European University University of Oslo University of Helsinki University of Amsterdam University of Bern École Polytechnique Fédérale de Lausanne Ruprecht Karls University of Heidelberg Karolinska Institutet University of Groningen Uppsala University Technical University of Munich Technical University of Denmark University of Vienna Ludwig Maximilians University of Munich

UK UK Switzerland Denmark Switzerland Netherlands Sweden Hungary Norway Finland Netherlands Switzerland Switzerland Germany Sweden Netherlands Sweden Germany Denmark Austria Germany

Endowment 2010 (€m)

Endowment 2009 (€m) 5,354.6

4,284.0 1,100.6 1,003.8 960.4 749.0 724.5 656.62 652.8 613.5 584.3 595.5 579.2 576.1 576.0 549.6

1,058.5 941.5

624.0 597.9 600.4 535.2

548.0 546.7 493.6 485.4 (continued)

280    Critical Drivers of Local Competitiveness Table 13.3  Continued Institution

Country

University of the Basque Country Radboud University Nijmegen Aarhus University University of Tübingen Leiden University Erasmus University Rotterdam Vrije Universiteit University of Strasbourg Stockholm University Ghent University Royal Institute of Technology Pierre and Marie Curie University (Paris 6) Delft University of Technology Free University of Berlin (excl. Charité) University of Barcelona Université Catholique de Louvain Humboldt University of Berlin (excl. Charité) University of Basel University of Geneva University of Milan University of Lausanne Maastricht University Eindhoven University of Technology Albert Ludwigs University of Freiburg

Spain Netherlands Denmark Germany Netherlands Netherlands Netherlands France Sweden Belgium Sweden France Netherlands Germany Spain Belgium Germany Switzerland Switzerland Italy Switzerland Netherlands Netherlands Germany

Endowment 2010 (€m) 483.4 482.3

Endowment 2009 (€m) 461.7 480.3

479.0

433.6 432.0 417.7 410.0 403.8 400.0

477.8 470.0 420.1

382.7 380.0 379.3 370.0 352.0 351.4 349.1 323.9 323.0

292.3 341.0 322.1 293.7 268.3

Source: http://en.wikipedia.org/wiki/List_of_European_universities_by_endowment.

Table 13.4 lists the largest charitable foundations in the world. The most interesting observation is that most of the foundations are a rather recent phenomenon. The oldest foundation is the Rockefeller Foundation, created in 1913, followed by the Knut and Alice Wallenberg Foundation, 1917, and the Kresge Foundation, 1924. W. K. Kellogg, Wellcome, and Lilly Endowment were founded in the 1930s. The second observation is that here the United States is not such an outlier. The largest foundation is the Stichting INGKA Foundation in the Netherlands, founded in 1982. The Wellcome Trust is the third largest, with $14.2 in assets. The Mohammed bin Rashid Al Maktoum Foundation, founded in 2007, in the United Arab Emirates, has $36.7 billion in assets. What we see here is that philanthropy in the 21st century seems to be emerging as an international force. The evidence seems to fit our theoretical story that philanthropy is

Table 13.4  List of Wealthiest Charitable Foundations in the World Endowment (USD)

Endowment (home currency) Founded

Rank Organization

Country

Headquarters

22

Andrew W. Mellon Foundation Bill & Melinda Gates Foundation Calouste Gulbenkian Foundation David and Lucile Packard Foundation Ford Foundation

United States United States

New York City, New York Seattle, Washington Lisbon

$5.26 billion

1969

$34.6 billion

1994

United States

Los Altos, California

$5.8 billion

1964

United States

$11.0 billion

1936

Garfield Weston Foundation Gordon and Betty Moore Foundation Howard Hughes Medical Institute J. Paul Getty Trust

United Kingdom United States

New York City, New York London Palo Alto, California

$5.4 billion

2000

$16.1 billion

1953

$10.5 billion

1982

John D. and Catherine T. MacArthur Foundation Kamehameha Schools Knut and Alice Wallenberg Foundation Li Ka Shing Foundation Lilly Endowment

United States

Chevy Chase, Maryland Los Angeles, California Chicago, Illinois

$5.7 billion

1975

United States

Honolulu, Hawaii $7.3 billion

1887

Sweden

Stockholm

$5.3 billion

kr 32.7 billion 1917 (SEK)

Hong Kong

Hong Kong

$8.3 billion

United States

$7.28 billion

Mohammed bin Rashid Al Maktoum Foundation Realdania

United Arab Emirates

Indianapolis, Indiana Dubai

$64.4 billion 1980 (HKD) 1937

$10.0 billion

$36.7 billion 2007 (AED)

Denmark

Copenhagen

$3.5 billion

Robert Bosch Foundation Robert Wood Johnson Foundation Rockefeller Foundation

Germany

Stuttgart

$6.9 billion

€2.8 billion (EUR) €4.5 billion (EUR)

United States

Princeton, New Jersey

$9.0 billion

1972

United States

New York City, New York

$3.51 billion

1913

2 30 17

5 16 20

4 6 18

12 21

9 13 7

29 15 8

28

Portugal

United States United States

$3.5 billion

$6.5 billion

€2.8 billion (EUR)

£4.2 billion (GBP)

1956

1958

2000 1964

(continued)

282    Critical Drivers of Local Competitiveness Table 13.3  Continued

Rank Organization 1 27 10

31 25

23 19 26 14 3 11

24

Country

Stichting INGKA Netherlands Foundation The California United States Endowment The Church United Commissioners for Kingdom England The Kresge United States Foundation The Leona M. and United States Harry B. Helmsley Charitable Trust The MasterCard Canada Foundation The Pew Charitable United States Trusts Tulsa Community United States Foundation W.K. Kellogg United States Foundation Wellcome Trust United Kingdom William and United States Flora Hewlett Foundation William Penn United States Foundation

Headquarters

Endowment (USD)

Endowment (home currency) Founded

Leiden, Netherlands Los Angeles

$36.0 billion

1982

$3.7 billion

1996

London

$8.1 billion

Troy, Michigan

$3.0 billion

1924

New York City, New York

$4.1 billion

1999

Toronto, Canada $4.9 billion

2006

Philadelphia, $5.6 billion Pennsylvania Tulsa, Oklahoma $3.8 billion

1948

Battle Creek, Michigan London

$7.26 billion

1930

$22.1 billion

Menlo Park, California

$7.4 billion

£14.2 billion 1936 (GBP) 1967

Philadelphia, Pennsylvania

$4.4 billion

£5.2 billion (GBP)

1948

1998

Source: http://en.wikipedia.org/wiki/List_of_wealthiest_charitable_foundations.

in fact necessary for social progress and that this form of social organization seems to be spreading around the world. Finally, we want to present an estimate of the size of moral capital for a segment of the US educational establishment. Table 13.5 presents an estimate of the size of moral capital for the largest 25 universities in the United States. Using balance sheet data the table lists total assets for the 25 schools. The endowment data is from table 13.1. In addition, it lists Real assets that measures buildings and land. The top 25 universities have moral capital of $484,663,000 or almost half a trillion dollars. Endowments by themselves do not paint an adequate picture of the size of moral capital in universities and greatly underestimate the size of moral capital. For the top 1,000 colleges and universities in the United States it is over a trillion dollars.5 5  Some comments on moral capital: Assets devoted to the "production of higher education" ought rightfully to be included as portions of moral capital. At minimum, the long-term assets, or in particular,

Philanthropy, Competition, and Local Competitiveness    283 Table 13.5  Moral Capital for Top 25 Universities in the United States University

Total assets ($millions)

Real assets ($millions)

Endowment ($millions)

Harvard University Yale University University of Texas System (15) Princeton University Stanford University MIT University of Michigan, Ann Arbor Columbia University Texas A&M University System Northwestern University U Pennsylvania University of Chicago University of Notre Dame University of California System (10) Emory University Duke University Washington University in Saint Louis Cornell University University of Virginia Rice University University of Southern California Dartmouth College Vanderbilt University New York University Brown University Totals

74,209.807 31,265.211 54,112.7 22,754.06 37,987.903 17,719.84 16,435.216 14,728.942 9,078.661 10,917.16 16,017.853 12,525.477 10,329.366 53,356.279 11,456.053 15,536.933 9,807.33 11,505.819 8,978.546 6,686.951 8,790.257 6,182.484 7,605.896 12,258.579 4,415.343 484,662.666

5,793.371 4,347.257 13,144.6 3,227.763 5,994.616 2,516.264 5,369.4 3,068.544 9,078.661 1,683.639 4,369.373 3,733.388 1,350.192 26,179.885 2,777.055 3,276.533 1,901.786 3,544.465 3,097.929 1,183.159 2,537.902 944.327 1,781.293 5,481.727 10,19.875 117,403.004

30,435.375 19,345 18,263.85 18,200 17,035.804 10,149.564 7,691.042 7,654.152 7,638.555 7,118.595 6,754.658 6,570.875 6,329.866 5,962.906 5,816 5,555.196 5,225.992 4,946.954 4,788.852 4,418.595 3,488.933 3,486.383 3,399.293 2,755 2,624.332 215,655.772

the capital assets are directly devoted to the production of education—for the long term. In the same vein, the returns to university endowment funds, independent of how the money is actually invested, are utilized to fund the investments and other activities that are directly related to the production of education. Both of these long-term investments are inputs into the production of education process and are properly included in moral capital. It could be argued that short-term assets and the other remaining long-term assets of universities are also part of the moral capital. Consider, for example, working capital. If the university is to be productive in the education enterprise, it is necessary to fund the short term or day-to-day activities of that enterprise which means the working capital is a necessary investment in the enterprise. Consider some of the other assets: even if those assets are not directly related to the current education activities, those investments generate returns which are used in the production of education process, just as real assets are used. Taken together, the combined investments in real assets and endowed funds are a lower limit of any estimate of moral capital contributed by universities. Furthermore, the table states accounting values, which are likely to be below current market values. Consequently, if we accept the argument that ALL assets of a university are moral capital, even the total assets value may be an understatement of the value of that moral capital. The value of real estate may be especially understated when using book values given the physical location of many universities in urban centres’ where land has appreciated substantially in value. As a general accounting principle, there is an objective to state conservative values.

284    Critical Drivers of Local Competitiveness

Summary This chapter has taken a long-run view of the institutional structure of modern society and has argued that the pillars of it are advanced capitalism, philanthropy, and democracy and the key to the sustainability of the system is moral capital. Capitalism, philanthropy, and democracy are global forces that need to be woven together into a global system of opportunity and prosperity for all. The central mission of globalization is to help make this a reality. We need to bring the cultural, natural, and institutional aspects of humanity together to ensure our social survival into the 21st century. While government is looked to by many as the solution to our conundrum, and while others espouse the free market, it is philanthropy that holds the key to our future. The reason I suggest this is that it holds the key to competition, that is, the ability to introduce both efficiency and equity into the system. Philanthropy has been with us for millions of years while the cultural aspects of capitalism are no more than few hundred years old and have spread unevenly around the world. Democracy occupies an intermediate position in this structure, being a part of Western civilization for thousands of years. The chapter has presented evidence on the importance of moral capital and suggested that it is large, important, and spreading around the world. Moreover, when it creates competition, it contributes to regional competitiveness.

References Acs, Z. J. 2009. “Entrepreneurial Capitalism in Capitalist Development: Toward a Synthesis of Capitalist Development and the Economy as a Whole.” In Z. J. Acs, D. B. Audretsch, and R. Strom, eds., Entrepreneurship, Growth and Public Policy, 319–38. New York: Cambridge University Press. Acs, Z. J. 2013. Why Philanthropy Matters: How the Wealthy Give, and What It Means for Our Economic Well-being. Princeton, NJ: Princeton University Press. Acs, Z. J., and D. B. Audretsch. 1990. Innovation and Small Firms. Cambridge: The MIT Press. Acs, Z. J., L. Szerb, and E. Autio. 2015. The Global Entrepreneurship Index. Washington D.C.: The Global Entrepreneurship and Development Institute. Fukuyama, F. 1989. “The End of History?” National Interest, Summer, 3–18. Hochschild, J.  L. 1981. What’s Fair? Americas Beliefs about Distributive Justice. Cambridge, MA: Harvard University Press. Lucas, C. 1994. American Higher Education: A History: New York: St. Martin’s Press. Piketty, T. 2014. Capital in the 21st Century. Cambridge, MA: Harvard University Press. Rudolph, F. 1990. The American College and University:  A  History. Athens:  University of Georgia Press. Samuelson, P. 2009. “Advances of Total Factor Productivity from Entrepreneurial Innovations.” In Z. J. Acs, D. B. Audretsch, and R. Strom, eds., Entrepreneurship, Growth and Public Policy, 71–78. New York: Cambridge University Press. Schumpeter, J. A. [1911] 1937. The Theory of Economic Development. Cambridge, MA: Harvard University Press.

Philanthropy, Competition, and Local Competitiveness    285 Schumpeter, J. A. 1942. Capitalism, Socialism and Democracy. New York: Harper and Bros. Soskice, D. 2014. “Capital in the Twenty-first Century:  A  Critique.” The British Journal of Sociology, in press. Weber, M. 1958. The Protestant Ethic and the Spirit of Capitalism, trans. Talcott Parsons. New York: Scribner. Zunz, O. 2012. Philanthropy in America: A History. Princeton, NJ: Princeton University Press.

Chapter 14

L o cal P oli c i e s for High-Grow t h  Fi rms Erik Stam and Niels Bosma

Introduction: The Relevance of High-Growth Firms This chapter investigates the main features of local policies for high-growth entrepreneurial firms.1 Even though the focus is narrow in the sense that a specific type of entrepreneurship is concerned (high-employment growth firms) and that policies are to be aimed at the local dimension, we discuss the rationales, characteristics, and impact of these policies also in a wider perspective. We argue that without appreciating the broader context, well-intended local policies may prove to be unsuccessful. We make a distinction between enabling policies—removing barriers to new firm formation and business expansion—and policies that provide direct support to selected (potential) high-growth firms, for example, through training, mentoring, financing, and innovation support. The relevance of focusing on high-growth firms is hardly debated. Many local authorities’ principal concern is the creation of jobs, and several studies show that a minority of (young) firms is responsible for the lion’s share of new jobs created. For instance, the British National Endowment for Science Technology and the Arts (NESTA 2009) found that 6 percent of all UK firms with 10 or more employees could be seen as high-growth firms adopting the OECD (2008) definition, that is, firms with average annualized growth in employees greater than 20  percent a year over a three-year period. This 6 percent was responsible for more than half of the new jobs generated by the UK firms employing 10 or more employees. Canadian research showed that hypergrowth firms (those with at least 150 percent growth in employment over four years) accounted for 1 

This chapter is partly based on Bosma and Stam (2012).

Local Policies for High-Growth Firms    287 4  percent of continuing businesses between 1993 and 2003, but were responsible for 45 percent of net jobs created by continuing firms (Parsley and Halabisky 2008). This reflects older evidence (Storey 1994; Kirchhoff 1993) that about 4 percent of the new firms are responsible for more than 50 percent of the net new job creation in a cohort of firms. Next to these direct effects of high-growth firms on employment, there are perhaps even more important indirect effects on (regional) economic development (see Fritsch 2011). Young high-growth firms are an important driver of structural change (Bos and Stam 2014), and a stimulus for competition that is likely to increase productivity and employment levels in a region (Fritsch 2011).2 Policy efforts to promote entrepreneurship in the 1990s were often more focused on increasing the rate of entrepreneurship than on targeting particular types of entrepreneurship.3 These generally did not make sharp distinctions between promoting high-growth entrepreneurship and low-growth entrepreneurship. In contrast, current policy efforts in many OECD countries have an explicit aim to increase the number of “gazelles” (see Lilischkis 2011; Stam et al. 2012). Such programs tend to have a national, or at least “state level,” scope. Coherent and consistent local policies aimed at stimulating high-growth firms are still scarce, and there is an even more severe shortage of studies on policies for high-growth firms (with some exceptions, like Smallbone, Baldock, and Burgess 2002; Mason and Brown 2013).

Definitions High-Growth Firms Even though high-growth firms are central in this chapter, it makes sense to take into account the actors that started the process resulting in a high-growth firm, that is, ambitious entrepreneurs. Policy programs aimed at stimulating high-growth firms mostly try to connect to individuals who are (potential) ambitious entrepreneurs. Doing so acknowledges the emphasis on viewing entrepreneurship as a process, in line with the Shane and Venkataraman’s (2000) key message discussing the research field of entrepreneurship. In the same line of reasoning it is useful to split up the high-growth firm population into young (gazelles) and established firms. While dealing with high-growth firms, this chapter thus also specifically distinguishes between ambitious entrepreneurs, gazelles, and established high-growth firms.

2  Not all high-growth firms contribute to local competitiveness: some might react to changes in public spending or might exploit legal voids. 3  Entrepreneurship policy programs did focus on several groups of individuals, such as female entrepreneurship, immigrant entrepreneurship, and the unemployed.

288    Critical Drivers of Local Competitiveness Even though according to most definitions high-growth firms (for more details on common definitions see Bosma and Stam 2012) need to show continuous growth over a particular time period, this does not mean that they are likely to reveal a continuous growth path during their life course:  the opposite is more likely. Many studies have shown the erratic nature of firm growth (Coad and Hölzl 2009; Parker, Storey, and Van Witteloostuijn 2010; Garnsey, Stam, and Heffernan 2006): setbacks are the rule, continuous firm growth is the exception. However, high growth does not necessarily lead to instability; that is, high-growth firms are not more likely to go bankrupt than other types of firms (De Kok, Zhou, and Hartog 2012).

Stages toward High-Growth Firms Local policies for high-growth firms should take into account the transitions that need to be taken before (sustained) high-growth of the firm can be reached. Stam et al. (2012) assess the individual level and emphasize four transitions that precede the realization of substantial growth by ambitious entrepreneurs. The four key transitions that precede high-growth firms (see Stam et al. 2012): 1. Turn individuals into ambitious individuals, either with respect to performance ambitions or entrepreneurial ambitions 2. Transform (ambitious) individuals into (ambitious) entrepreneurs (in whatever organizational setting). This involves a two-step process: triggering entrepreneurial intentions and realizing the start of a new business. 3. Stimulate entrepreneurs to become ambitious entrepreneurs 4. Realize the creation of new value (e.g., jobs in high-growth firms) Ambition plays an important role in the first and third transition. According to the Oxford Dictionary, ambition is the “determination to succeed.” Sociologists Spenner and Featherman (1978) argue that ambition can be defined as a class of psychological orientations held with respect to two types of achievement: role-residing achievement and achievement as to performance. We have adapted this framework to include respectively entrepreneurial ambitions and (business) performance ambitions, in transition 1 and transition 3. The transitions are not linear; different paths can lead to the creation of new jobs through ambitious entrepreneurship. Each of the transitions is marked by different determinants at the levels of individuals and contexts, and therefore concerns different policy areas. The first transition relates to general social and education policy, targeting ambitions, while the second transition concerns traditional entrepreneurship policy, focusing on entrepreneurial behavior. As for the third and fourth transition, more dedicated business policies can be offered that are more directly tailored to growth ambitions and the creation of new value. These policies concern, respectively,

Local Policies for High-Growth Firms    289 Realized New Value Creation

Ambitious Entrepreneur Entrepreneur

Individual

Transition

Ambitious Individual

III

IV

Key policy areas

Social and education policy

I

Entrepreneurship policy

II

Entrepreneurship and industrial policy

Industrial policy and labor market policy

Key stimuli

Human talent and ambitions

Allocation of talent; entrepreneurship as career perspective

Allocation of entrepreneurship; growth attitude

Reduction or removal of growth barriers

Figure  14.1  Correspondence between Four Transitions and Key Policy  Areas

stimuli for human talent and ambitions, stimuli for entrepreneurship in general, incentives for the allocation of talent, incentives for the allocation of entrepreneurship, and removing the barriers for growth. Figure 14.1 summarizes this reasoning. It demonstrates that it is nearly impossible to address all key stimuli in all transitions at once.

Local Policies Local policies will be defined for the purposes of this chapter as policies that are designed and/or delivered by subnational governments and (semi)public organizations, including actions by regional and local governments and by state governments in federal countries, as well as policies that are designed by (supra)national governments but that have intended or unintended spatially uneven effects. This is a rather broad definition, but allows us to take into account nonlocal policies that have significant impacts on particular localities. Policies by regional governments might include labor market regulations (e.g., at the state level in the United States) and targeted programs for high-growth firms (e.g., in Scotland and Wales). Regional development agencies might be important providers of venture capital and training in the region (like the former regional development agencies in the United Kingdom). Local governments have been important investors in local incubators and accelerators. Science and innovation policies designed by national and supranational governments may have distinctive regional and local effects.

290    Critical Drivers of Local Competitiveness

Rationales and Reasons for High-Growth Firm Policies at the Local Level Policy support for high-growth firms is normally legitimized by market failures (e.g., too limited supply of capital, due to information asymmetries, or too low investments in R & D due to the public good nature of R & D investments), system failures (e.g., a lack of interaction between firms and knowledge institutes, leading to a suboptimal exploitation of new scientific knowledge) or by more broader public goals like employment creation. Most often there is an implicit assumption of market failure in the sense of support needs of high-growth start-ups not being adequately met by the private sector because of incomplete formation on both sides of the potential market for business services.4 The underlying reasoning is that an increase in high-growth firms is serving these public goals, or that a suboptimal number of high-growth firms is caused by these market and system failures, leading to inefficient allocation of resources in society (in the case of market failure) or suboptimal levels of value creation (in the case of system failures). The desired direct outcome of policy support could be a higher number of high-growth firms or faster growth (to a larger scale) of firms (see, e.g., Owen 2004). The ultimate outcome should be growth of market production (beyond the suboptimal level caused by market failures), more new value creation, measured as innovation and/ or higher productivity levels, and more employment. Increases in market production, productivity, and employment need not go together (Coad and Broekel 2012; Daunfeldt, Elert, and Johansson 2014). Another question is whether policies to support high-growth firms lead to an additional number of new jobs, or just reallocate jobs from established, slow-growing or non-growing organizations, to the set of high-growth firms (with potential productivity-enhancing effects). The limited amount of macroeconomic empirical research that is available suggests a net positive effect of an increase in the rate of ambitious entrepreneurship on aggregate national economic growth (measured as GDP growth; see Stam et al. 2011; Stam and Van Stel 2011) and on regional productivity levels (Bosma 2011). However, this in itself does not prove that policy support to stimulate ambitious entrepreneurship and/or high-growth firms will lead to improved aggregate economic performance. Policy support might also lead to substitution or deadweight effects (cf. Santarelli and Vivarelli 2007), having no positive influence on aggregate economic performance at best, and a negative influence at worst. The only way to examine the positive effects of policy support is a properly designed evaluation program to trace the targeted effects of the policies and the effectiveness and efficiency of the policies in terms of improving aggregate economic performance. Storey (2003) has proposed a hierarchy of evaluation methodologies from simple monitoring exercises to more advanced evaluation exercises that seek to match assisted and nonassisted 4 

Although private parties might disagree on this, emphasizing that private business service providers are much more important than governments in supporting high-growth firms (Fischer and Reuber 2003).

Local Policies for High-Growth Firms    291 participants on observable differences or that make use of sample selection effects that control for observable and nonobservable differences between the two samples. Unfortunately, there are no such full-fledged evaluations of (local) policy programs for high-growth firms. There have been evaluations of other policy programs that might provide some useful insights into policy support for high-growth firms, for example, the microeconomic effects of the SBIR program (Lerner 1999: positive effect on the growth of SBIR-supported high-tech small firms) and the microeconomic effects of being located on a science park (Siegel, Westhead, and Wright 2003: no effect on firm performance). In practice, high-growth firm policies are often driven by benchmarking, with the assumption that having more high-growth firms is better for economic performance, that is, that lower rates of ambitious entrepreneurship, gazelles, and high-growth firms than benchmark countries is a sufficient reason for policy intervention; even more so when output indicators (e.g., with respect to [un]employment) are unfavorable. Ingram, Luo, and Eshun (2010) showed that in the case of US state-level business incubator programs, this is an adequate framework for predicting the number of (state policy supported) business incubator programs: relatively low regional economic performance (either historically, or in comparison to neighboring states) and the adoption of incubator programs by neighboring states were revealed to have a positive effect on the number of business incubator programs started in a state over the period 1980–2004. Such a behavioral theory of state action might explain a lack of positive effects on aggregate economic performance, as these programs are not based on “clear” economic rationales so much as on satisficing behavior and “arms race competitions” (or mimetic behavior in policymaking; see Dobbin, Simmons, and Garrett 2007). This latter type of behavior might even lead to locational tournaments in which regions compete for attracting investments by young or established firms.

Enabling High-Growth Firm Policies Gilbert, Audretsch, and McDougall (2004) argue that only since the 1990s has a new set of policies emerged that focused on enabling the start-up and viability of entrepreneurial firms rather than constraining existing firms and that this approach became especially relevant when it was established that knowledge was a major source of competitiveness in emerging industries. Regional and local examples identified in the United States include policy programs in the Research Triangle (North Carolina) and Austin (Texas). In this section we will more extensively discuss the role of education policy and labor market policy in enabling the emergence and growth of firms. We do not discuss the tax and other financial policies because these are most likely initiated at the national level, without very distinctive regional level effects.5 5  We also do not discuss RTD (research and technology development) policies here, because these policies are most often nationally initiated, and they are often implicitly or explicitly targeted at particular (high-tech) industries, and thus cannot be labeled as enabling policies. For example, R & D tax credits

292    Critical Drivers of Local Competitiveness

Education Policy As most education institutes are local in reach and impact, education policies are an important element of local policies for high-growth firms. Education policy is an important policy area for increasing the inflow of ambitious entrepreneurs in a society. The capacity to set high personal but obtainable goals, the so-called need for achievement (McClelland 1961) is an important input for the growth of firms. This need for achievement is not a given trait but can be developed, and this happens to be most important during adolescence and youth (Stam et al. 2012). This implies that the primary and secondary education system becomes more relevant in a broad sense—for example, by influencing younger people’s preferences, knowledge, and skills. This would also include securing the presence of (local) entrepreneurial role models in the educational program. In addition to the importance of early education in targeting the first transition stage of raising generalized ambition (see above), tertiary education is an important context to support the transitions toward ambitious entrepreneurship, and its effectuation in the third and fourth transition stages. The development of ambitions to grow, innovate, or internationalize heavily depends on individuals’ cognitive abilities (see c­ hapters 3 and 6 in Stam et al. 2012). Education has a strong positive effect on the probability of having growth ambitions as an entrepreneur (Bosma, Schutjens, and Stam 2009), and, on average, more highly educated entrepreneurs have better-performing firms. Indeed, entrepreneurs have even higher returns to education than employees (Hartog, Van Praag, and Van der Sluis 2010), and enrolment in tertiary education also has a positive effect on the number of fast-growing firms at the national level (Teruel and De Wit 2011). Moreover, meta-analyses have shown that human capital is important for venture success beyond self-employment, and that this relationship is stronger for human capital investments with high task-relatedness. Entrepreneurship education at universities and in professional education seems reasonable for promoting ambitious entrepreneurship as well.

Labor Market Policy Depending on the political system in a country, labor market policies and regulations are designed and implemented on the national, regional or municipal level. Labor market policy has significant direct and indirect effects on the growth of (new) firms. An important barrier to the employment growth of new firms is regulations that constrain the flexibility of labor markets, like strict employment protection legislation and noncompete agreements. Employment protection affects ambitious entrepreneurship by its impact on the opportunity costs of becoming an entrepreneur (or joining a fledgling new business). are sometimes taken to be horizontal or enabling policies, but in practice this kind of policy is targeted toward (high-tech, often manufacturing) firms that invest substantially in research and technology development.

Local Policies for High-Growth Firms    293 For ambitious employees, these may be relatively high in regimes with strong employment protection legislation: leaving their secure job for a highly insecure occupation as founder of a start-up may become less attractive in conditions of strong employment protection. Hence, ambitious entrepreneurship would benefit from more flexible labor markets. Moreover, employment protection will make ambitious entrepreneurs more reluctant to hire employees, as it may be hard to get rid of them in adverse situations, which is likely to be necessary in the very dynamic early phases of development of (potentially) high-growth firms (Garnsey et al. 2006). Thus, beyond being helpful in removing incentives that discourage prospective ambitious entrepreneurs from leaving their tenured jobs and creating new firms, a lower degree of employment protection would reduce the risks and impediments for new firms to create jobs and start growing. Domain-specific experience matters for ambitious entrepreneurship. In both the independent entrepreneurship and intrapreneurship literatures, we find that management experience enhances entrepreneurial behavior and willingness to grow. Likewise, industry experience has been shown to be important for growth and success. Growth-oriented entrepreneurs tend to be relatively highly educated and rather wealthy in terms of household income (see Stam et al. 2012, c­ hapter 5). This implies that not just any new entrepreneur is important, but that the focus should be on a special kind of individuals—that is, those who have much to lose when engaging in entrepreneurship, and accordingly face high opportunity costs. Rather than “necessity-driven” entrepreneurship (e.g., the transition to entrepreneurship by unemployed) policymakers should consider targeting experienced managers. Providing support and guidance to these potential high-growth entrepreneurs is merited. In the context of labor market institutions, labor market mechanisms should especially be made more flexible so that the individuals that are best positioned to grow a new venture will have more stimuli to do so. These individuals most likely face the highest opportunity costs for leaving their secure and well-paid jobs when embarking upon a high-risk, high-gain project. This means that making it more attractive for the best and the brightest to start a potentially high-growth enterprise is likely to be the most effective labor market policy action. An example can be to provide targeted support for talented people, for instance through creating awareness, training, and mentoring. This should not be seen as a discriminatory support program favoring those who are already well equipped for the job market, but merely as an additional tailor-made program that can exist next to other (targeted) programs for entrepreneurship; after all, the goal is the creation of jobs for all segments of the population. Noncompete agreements—contract clauses that inhibit employees to pursue (potentially) competing projects once the employee leaves the incumbent firm—make it hard for employees that want to pursue innovative ideas with their own business in the same or a related market of their employer. Empirical research has shown that the abolishment of these noncompete agreements takes away the barriers for innovative high potential start-ups (Fallick, Fleischman, and Rebitzer 2006; Gilson 1999; Garmaise 2011; Marx, Strumsky, and Fleming 2009; Samila and Sorenson 2011). However, one should be careful in implementing this as a one-size-fits-all policy. The regional context

294    Critical Drivers of Local Competitiveness is an important contingency in the effectiveness of these labor market policies: Fallick, Fleischman, and Rebitzer (2006) argued that the regional benefits of labor mobility in Silicon Valley (partly enhanced by California’s policy not to enforce noncompete agreements) depended on the benefits from shared tacit knowledge outweighing losses from reduced employer incentives to invest in human capital. The advantage of abolishing noncompete agreements thus might depend on local industry characteristics (i.e., a high density of similar or related industries). These labor market regulations sometimes originate from the national level (e.g., employment protection legislation), but can also originate from subnational levels (e.g., the US state level, in the case of regulation that prohibits the use of noncompete agreements). Broad-based exemptions from employment regulations based on firm size may support marginal businesses, but also put disproportionate burdens on firms beyond a certain firm size (Braguinsky, Branstetter, and Regateiro 2011; Garicano, LeLarge, and Van Reenen 2012). These legislations are established to secure employee rights that improve the quality of jobs in general. Providing exemptions for small firms might make sense as a generic entrepreneurship policy: they take away burdensome costs of compliance for start-ups, which are often relatively small. However, they also constrain the expansion of firms, and indeed might achieve the opposite effect of targeting high-potential firms (Shane 2009). Within the United States, there is substantial state and local variation in these kind of employment regulations (Feldman 2011). The main message here is not to abolish these regulations, but to harmonize them over regions, and to reconsider the phased nature of these regulations in order not to provide disincentives to grow businesses beyond a certain threshold.

Targeted High-Growth Firm Policies Truly high-potential ventures (and their entrepreneurs) tend to be well known in a limited industry circle, so it may be worth involving business angels, industry experts, and incumbent suppliers and/or customers to help identify ambitious entrepreneurs and connect them to each other. Next, some kind of mechanism is needed to screen and select those most promising individuals. For admittance, programs should require explicit orientation toward growth. Even though growth orientation cannot guarantee growth, growth in the absence of aspiration is extremely rare. Therefore, support programs should require explicit commitment to growth as a key criterion. Second, the longer a venture progresses in its development path, the more tangible proof of its growth potential should be required. In the early phases of new ventures, growth orientation and flexibility should be emphasized—corresponding with the third stage of our transition model. In the more advanced (fourth) stage, tangible proof of market acceptance may provide a feasible selection criterion.

Local Policies for High-Growth Firms    295 One type of targeted entrepreneurship policy that has already existed for several decades is so-called business incubators. In the next section we will discuss the more recent and focused business accelerator programs.

Business Accelerator Programs There is no single definition of incubators in the literature, but there are some common characteristics, including an element of space provision, shared services, on-site management with a business support function, a strong selection policy, and a supportive environment (cf. Hannon and Chaplin 2003). Business incubators are a diverse breed in that they have aimed at a broad variety of policy targets, from lowering unemployment, to neighborhood restructuring, to technology transfer, by lowering the barriers to entrepreneurial entry, and possibly to stimulate subsequent growth as well. There is no consensus on the business focus of the incubators: the focus is the result of the public and/or private interests involved in the business incubator. A recent type of high-growth firm policies that is attracting increasing attention is the so-called business accelerator programs (BAPs). BAPs have some similarities with business incubators, but BAPs have a more explicit focus on accelerating the growth of firms. There are two types of BAPs: virtual BAPs and location-based BAPs. Virtual BAPs most often target gazelles that want to make the transition toward high-growth firms, while location-based BAPs most often target ambitious entrepreneurs that aim to develop their (nascent) business into a gazelle. Virtual BAPs are most often initiated by governments at the national (and sometimes regional, or supranational)6 level that provide a platform for peer learning, coaching, and business services for owner-managers of gazelles that want to make the transition toward a high-growth firm. Virtual BAPs are primarily funded with public money, but participants often also have to pay a (relatively) small fee, in order to be committed to participation. The implementation of these virtual BAPs is often done by private organizations (e.g., business consultants, business angels), sometimes in coalition with civil servants or university staff that provide additional expertise. The virtual BAPs can be implemented on a regional level in order to ease the accessibility for participants, and can have a sectoral focus in order to tailor the program to sector-specific needs, problems, and opportunities. Virtual BAPs can have offices that provide meeting points or training sites for the program participants, but do not provide real estate services to locate firms. The location-based BAPs are based in distinctive premises, which provide offices and working space for the program participants, that is, the tenants of the accelerator. These location-based BAPs can be part of national policy programs, but are also initiated by private parties (e.g., the Google Campus in East London’s Tech City, and the Rockstart Accelerator in Amsterdam, initially located at the premises of TomTom—one of the few

6 

E.g., the Startup Europe Partnership.

296    Critical Drivers of Local Competitiveness “star” high-growth firms in the Netherlands). In practice most of them involve private and public organizations, and in that sense differ from the prior generation of business incubators that were often purely public organizations. Business accelerators provide different mixes of services, ranging from renting offices or workspaces, to educational services, to consultancy and financial services. The emphasis differs per program and also signifies the different (business) models these business accelerators pursue. There are hardly any evaluations of these business accelerator programs. Evaluating these programs is also more challenging than prior public programs because of the multistakeholder nature of the business accelerator programs. This means that next to the (different) targets of the (multiple) policy levels involved (national, regional, and municipal), targets of private parties should also be taken into account for evaluations.

Industrial Organization and High-Growth Firm Policies Multiple industrial policies have direct or indirect effects on high-growth firms. One category of industrial policies that is especially relevant here are policies that target particular industry-area combinations, also known as clusters or competence blocs. Entrepreneurship and in particular high-growth firms play different roles during the evolution of these clusters or competence blocs (see Braunerhjelm and Feldman 2006; Brenner and Mühlig 2007; Feldman, Francis, and Bercovitz 2005; Bos and Stam 2014). The recent literature suggests that the role of entrepreneurship is crucial in the very early stage of cluster formation (Feldman, Francis, and Bercovitz 2005; Avnimelech and Teubal 2006; Breznitz, O’Shea, and Allen 2008) even though it is not a necessary condition. The probability of entrepreneurial sparks being ignited at the roots of cluster formation is likely to be dependent on the regional institutional setting: regions with an entrepreneurial culture require less intervening attention and have higher probabilities of such entrepreneurial sparks, while other regions may require more active and selective policy (Breznitz, O’Shea, and Allen 2008). In addition there are also examples of firms that have not been very successful but that have been of key importance for cluster formation through spin-off processes (Buenstorf and Fornahl 2009; Garnsey and Heffernan 2005). Thus, even relatively “unsuccessful firms” (in terms of job creation) may prove to be important for cluster formation and high-growth firms at a later stage. Henrekson, Johansson, and Stenkula (2010) start from a competence bloc approach to analyze the phasing of policies that foster high-growth firms in particular industry-area combinations. The competence bloc approach emphasizes that the growth of (new) firms does not depend only on the entrepreneurial talents of the founder, but increasingly on the functioning of related capital, labor, and other markets, and the competences of actors in these markets. Henrekson, Johansson, and Stenkula (2010) distinguish four stages: business idea development, commercialization, rapid growth,

Local Policies for High-Growth Firms    297 and stagnation (and possibly decline and exit). They identify entrepreneurs, inventors, and venture capitalists as key players in the initial stage of entrepreneurial activity in a particular industry-area combination. In the next phases, skilled labor, actors on secondary markets, and industrialists come into play. The explicit role of the local government will be to enable the development of the competence bloc, but this necessitates targeting particular actors at particular phases of the firm life course: suppliers of risk capital and inventors in the phases of business idea development and commercialization, entrepreneurs and skilled labor in the phases of commercialization and rapid growth, and industrialists and actors on secondary markets during the phases of rapid growth and stagnation (and possibly decline and exit). A general local “blueprint” policy approach to high-growth firms is unlikely to be successful. Contexts will always matter and need to be appreciated for determining the most relevant accent at every stage of the process. Concerning the first phases Degroof and Roberts (2004), in their case of cluster policies in Belgian regions, point at the problematic factor of low growth orientation in weak entrepreneurial cultures. Kerr (2010) argues that migration, and particularly new immigration to the United States, may have facilitated cluster development directly after breakthrough innovations occur. However, from the case of Buenstorf and Fornahl (2003) it is rather unclear if there were any particular institutional settings that enhanced cluster formation in the region around Jena, as their evidence points at a large role of idiosyncratic spin-off events evolving around a pioneering (but unsuccessful) firm (cf. Klepper 2009; 2011). As far as the phase directly after the emergent phase of local industry development is concerned, Gertler and Vinodrai (2009) attribute an important role to anchor firms, industrial associations and civic entrepreneurship, but only if these serve as mechanisms for aligning the interests and resources of diverse stakeholders in the cluster. Buenstorf and Fornahl (2003) come up with similar arguments, but especially stress the spin-off mechanism. In line with the process models offered by Henrekson, Johansson, and Stenkula (2010) and Feldman, Francis, and Bercovitz (2005), Avnimelech and Teubal (2006) stress the role of the development of venture capital markets as a critical aspect of local industrial development. However, the emergence of a substantial and effective venture capital market is more likely to take place after a critical mass of high-growth firms has developed in a region rather than vice versa (Braunerhjelm and Feldman 2006; Casper 2007).

Policy Mixes Local-National Policy Mixes Local policies for high-growth entrepreneurship cannot be disentangled from policies at larger spatial levels. Table 14.1 illustrates that even though local policies obviously impact the local area as they are designed to do (some effects may be witnessed

298    Critical Drivers of Local Competitiveness Table 14.1  Multilevel Policy Sources and Effects Policy effects Policy source

Local

Local

Municipal business policies (e.g., incubators), land use regulations Regional development agencies, regional public venture capital

Regional

National

National science policies (affecting local university policies)

Supra-national European structural funds, European Investment Bank capital, European science and innovation policies (affecting local university policies)

Regional

US state level labor regulations (e.g., non-compete agreements) SBIR, industrial policies (e.g., biotech), cluster policies European structural funds (ERDF), European Investment Bank capital

National

National employment regulation

in neighboring areas), many regional, national, and supranational (such as EU) policies also will have implications at the local level. For instance, changes in national science policies may have severe implications in areas that rely strongly on one or more universities. Another example is the Small Business Innovation Research (SBIR) program implemented in the United States. Even though this was a national program, the activities stemming from this program were highly skewed across regions: the (already strong) regions in California and Massachusetts especially benefited from the SBIR initiative. Grimm (2011) reports that the Lisbon Agenda provided substantial opportunities for German local policy programs through European Structural Funds, more so than in the previous situation, where the entrepreneurial policymaking was implemented top-down. Local policies for high-employment growth firms are less likely to be less successful in areas where regional, national, and supranational policies are potentially conflicting with the proposed local policy. For example, local initiatives for fostering high-growth entrepreneurship may be more difficult where the national regulations protect employment (Henrekson, Johansson, and Stenkula 2010; Bosma and Levie 2010), or put disproportionate burdens on firms beyond a certain firm size (Braguinsky, Branstetter, and Regateiro 2011; Garicano, LeLarge, and Van Reenen 2012). One example in this respect is the underdevelopment of the high-tech industry in Ontario (Canada) in spite of high levels of R & D at local universities and firms, high flows of venture capital, and active support from the local government. According to Samila and Sorenson (2011, 25), “part of the answer may reside in the way common law in Canada effectively bars management-level employees from leaving to competing firms, even in the absence of actual non-compete clauses.” This observation also calls for more detailed studies on how the design

Local Policies for High-Growth Firms    299 and implementation of noncompete and related laws varies by jurisdiction (either regionally or nationally). It is essential to keep in mind that local policies should appreciate the local context in terms of resources, demography, cultural values, and industry structure. There is an abundant evidence of initiatives aiming to copy the Silicon Valley model in other regions (cf. Casper 2007; Hospers 2006). Most of these initiatives have not been successful because they could not capitalize on essential elements that were key to Silicon Valley’s success, such as the presence of “star” universities, a supply of skilled labor, a culture in which knowledge sharing was facilitated, institutions enabling flexible labor markets, and an excellent financial infrastructure (Saxenian 1994). Local policymakers also need to be aware of the strengths of close neighbors. Competing with them may be far less rewarding than collaborating (for example, through niches and stressing other regional amenities). Examples of this kind of policies are so-called locational tournaments in which regions compete in attracting (foreign direct) investments to their region. Such competitions are referred to as tournaments because payoffs are not awarded to all participants, but only to the winner. Participating in such a tournament involves substantial administrative and promotional costs for the regions involved, but only one of them can have a potentially positive outcome from the tournament. However, even this is not guaranteed for the winning region, as it can in the end be subsidizing new jobs at the cost of indigenous economic development. Competing with neighboring regions for investments in a specific industry could even lead to a net loss at a societal level.

Entrepreneurship Policy: Complementarities and Conflicts From the perspective of ambitious entrepreneurship, it is positive that policies are offered to influence people’s preferences for entrepreneurship, to enhance their knowledge and skills, to improve access to finance and labor, and to diminish the regulatory burden—at least to the extent that ambitious independent entrepreneurship is not possible without people willing to engage in self-employment first. In countries like Belgium and the Netherlands, these more generic entrepreneurship policies are well developed, and both countries also already offer growth-oriented policies. Policies for ambitious entrepreneurship, gazelles, and high-growth firms do not completely upset entrepreneurship policy thinking, but suggest that complementary interventions merit attention. However, especially in the third and fourth transition (see figure 14.1), policymakers have to be aware that the design of policy interventions should deviate from earlier transitions. To stimulate people’s ambition and lure them into self-employment, policies can be broad and untargeted—examples include general programs for entrepreneurship education, providing inspiration by means of role models and offering general tax deductions for the self-employed. Such policies can be labeled as “the more the better.”

300    Critical Drivers of Local Competitiveness Table 14.2  Generic Entrepreneurship Policy versus High-Growth Entrepreneurship Policy Policy goal

Generic entrepreneurship policy

High-growth entrepreneurship policy

Overall focus Entrepreneurs Entrepreneurial firms

Quantity Get more people to start new firms Increase the number of entrepreneurial ventures Facilitate SME entry and operation

Quality Get the right people to start new firms Improve the quality of entrepreneurial ventures Facilitate new firm growth

Mostly public A little to many Reduce VAT for small firms

Public and private partnership Much to a few Accommodate dramatic change over firm life course Expert advice on growth and internationalization

Operational environment Resources Resource distribution Fiscal Type of support

Standard advice for firm creation and operation

Source: Autio et al. (2007).

To stimulate the next transitions, however, policies should be much more selective. For high-growth policies, Autio, Kronlund, and Kovalainen (2007) summarize the main distinctions. Their summary is provided in table 14.2. Instead of focusing on quantitative aspects of entrepreneurship, to facilitate the third and fourth transition, policy should focus more on the qualitative aspects of entrepreneurship. Ultimately, welfare increases if the economy allows and rewards productive entrepreneurial initiatives, in whatever context. Whether this takes place in new or old, small or large firms, by self-employed or wage earners, is an empirical issue. Empirical evidence suggests that it is not self-employment or new (small) firms that drive economic growth, but that it is particularly ambitious entrepreneurship (Stam et al. 2009; 2011; 2012; Stam and Van Stel 2011; Wong, Ho, and Autio 2005) and the subsequent realization of gazelles and high-growth firms that positively affects economic growth. Stimulating ambitious entrepreneurship requires, unlike traditional entrepreneurship policies, concentrating policy resources on just a few “high potentials,” rather than many individuals who never make it beyond self-sufficiency. Given limited public funding, this requirement may actually cause conflict between ambitious entrepreneurship and traditional entrepreneurship policies. In addition, stimulating self-employment may even harm ambitious entrepreneurship, as the incentives to stay self-employed may deter these solo entrepreneurs from expanding their business with recruiting other personnel. At first sight, a group of solo self-employed may substitute for a high-growth start-up, especially when project forms of organizing are dominant (e.g., in the construction industry and in multimedia productions). However, when it comes to scale economies and large-scale innovations, a thousand solo self-employed cannot substitute for one Google or Facebook. New firms that want to change the economy and

Local Policies for High-Growth Firms    301 society are more likely to succeed with a large group of like-minded people that are committed to the collective endeavor.

Conclusions In this chapter we have identified the types of local policies that are conducive to generating high-growth firms. Even though it has become widely recognized that these firms are an important driver of economic development, locally and nationally, there is less insight into the need, effectiveness, and efficiency of policies that enable or directly stimulate these high-growth firms locally. We discussed the transitions that precede the realization of a full-fledged high-growth firm. Insight into these preceding transitions is necessary in order to increase the pool of potential high-growth firms and to target particular transitions in the path towards becoming a high-growth firm. Important enabling high-growth firm policies can be found in the areas of education policy and labor market policy. Even though these enabling policies are rather generic in nature, they can be made more effective by targeting particular groups that are more relevant for particular transitions toward becoming a high-growth firm. These policies often have specific local effects, because they are implemented on a regional level, and sometimes because they are even designed on a regional level. With respect to targeted local high-growth firm policies, we focused on the recent stream of business accelerator programs. These programs are often implemented with private parties, and sometimes they are even initiated by private parties, with only limited public support. In addition, there are many targeted industrial policies and regional cluster policies that also have direct implications for the presence of high-growth firms. Most countries have a mix of national and regional policies for high-growth firms. In many cases the national policy programs are implemented in a region-specific way. In order for local policies to be effective they should not conflict with national and supranational policies, and they should complement rather than compete with policies in neighboring regions. Recent findings seem to suggest that neighboring regions more often copy their neighbor’s policies than they learn from them and implement these policies in a way that fits the region-specific characteristics or decide to implement other policies that better fit the region. A more intelligent policy approach also suits the competence bloc approach (Henrekson and Johansson 2009) and the recent smart specialization strategy for European regions (Foray, David, and Hall 2011). In many cases generic entrepreneurship policies are complementary to high-growth entrepreneurship policies, because they increase the pool of potential ambitious entrepreneurs. However, there are several kinds of generic entrepreneurship policies that conflict with high-growth entrepreneurship policies, especially those policies that favor self-employed and small firms and in that way provide opportunity costs for entrepreneurs to hire employees (beyond a certain firm size).

302    Critical Drivers of Local Competitiveness

References Autio, E., M. Kronlund, and A. Kovalainen. 2007. High-Growth SME Support Initiatives in Nine Countries: Analysis, Categorization and Recommendations. Helsinki: Edita Publishing. Avnimelech, G., and M. Teubal. 2006. “Creating Venture Capital Industries That Co-evolve with High Tech: Insights from an Extended Industry Life cycle perspective of the Israeli experience.” Research Policy 35 (10), 1477–98. Bos, J., and E. Stam. 2014. “Gazelles and Industry Growth: A Study of Young High-Growth Firms in the Netherlands.” Industrial and Corporate Change 23 (1), 145–69. Bosma, N. S. 2011. “Entrepreneurship, Urbanization Economies, and Productivity of European Regions.” In M. Fritsch, ed., Handbook of Research on Entrepreneurship and Regional Development. Cheltenham: Edward Elgar. Bosma, N., and J. Levie. 2010. Global Entrepreneurship Monitor: 2009 Global Report. Global Entrepreneurship Research Association. Bosma, N., V. Schutjens, and E. Stam. 2009. “Entrepreneurship in European Regions: Implications for Public Policy.” In J. Leitao and R. Baptista, eds., Public Policies for Fostering Entrepreneurship: A European Perspective. New York: Springer. Bosma, N., and E. Stam. 2012. “Local Policies for High-Employment Growth Enterprises.” Organisation for Economic Co-operation and Development, Paris. Braguinsky, S., L. G. Branstetter, and A. Regateiro. 2011. “The Incredible Shrinking Portuguese Firm.” Nation Bureau of Economic Research Working Paper No. 17265. Braunerhjelm, P., and M. Feldman. 2006. Cluster Genesis:  Technology-Based Industrial Development. New York: Oxford University Press. Brenner, T., and A. Mühlig. 2007. “Factors and Mechanisms Causing the Emergence of Local Industrial Clusters: A Meta-study of 159 Cases.” MPI Working Paper No. 0723. Breznitz, S. M., R. P. O’Shea, and T. J. Allen. 2008. “University Commercialization Strategies in the Development of Regional Bioclusters.” Journal of Product Innovation Management 25 (2), 129–42. Buenstorf, G., and D. Fornahl. 2009. “B2C-bubble to Cluster:  The Dot-com boom, Spin-off Entrepreneurship, and Regional Agglomeration.” Journal of Evolutionary Economics 19 (3), 349–78. Casper, S. 2007. Creating Silicon Valley in Europe. New York: Oxford University Press. Coad, A., and T. Broekel. 2012. “Firm Growth and Productivity Growth: Evidence from a Panel VAR.” Applied Economics 44 (10), 1251–69. Coad, A., and W. Hölzl. 2009. “On the Autocorrelation of Growth Rates.” Journal of Industry, Competition and Trade 9 (2), 139–66. Daunfeldt, S.  O, N. Elert, and D. Johansson. 2014. “The Economic Contribution of High-Growth Firms: Do Policy Implications Depend on the Choice of Growth Indicator?” Journal of Industry, Competition and Trade 14 (3), 337–65. Degroof, J. J., and E. B. Roberts. 2004. “Overcoming weak entrepreneurial infrastructures for academic spin-off ventures.” Journal of Technology Transfer 29 (3–4), 327–52. De Kok, J., H. Zhou, and C. Hartog. 2012. “The Risk of Growing Fast.” Scales Research Reports H201119, EIM Business and Policy Research. Dobbin, F., B. Simmons, and G. Garrett. 2007. “The Global Diffusion of Public Policies: Social Construction, Coercion, Competition, or Learning?” Annual Review of Sociology 33, 449–72. Fallick, B., C. A. Fleischman, and J. B. Rebitzer. 2006. “Job-hopping in Silicon Valley: Some Evidence Concerning the Microfoundations of a High-Technology Cluster.” Review of Economics and Statistics 88, 472–81.

Local Policies for High-Growth Firms    303 Feldman, M.  P. 2011. “Inadvertent Infrastructure and Regional Entrepreneurship Policy.” In M. Fritsch, ed., Handbook of Research on Entrepreneurship and Regional Development. Cheltenham: Edward Elgar. Feldman, M.  P., J. Francis, and J. Bercovitz. 2005. “Creating a Cluster While Building a Firm:  Entrepreneurs and the Formation of Industrial Clusters.” Regional Studies 39 (1), 129–41. Fischer, E., and R. Reuber. 2003. “Support for Rapid-Growth Firms:  A  Comparison of the Views of Founders, Government Policymakers, and Private Sector Resource Providers.” Journal of Small Business Management 41 (4), 346–65. Foray, D., P. A.  David, and B. Hall. 2011. “Smart Specialization:  From Academic Idea to Political Instrument, the Surprising Career of a Concept and the Difficulties Involved in its Implementation.” MTEI Working Paper, EPFL, Lausanne. Fritsch, M. 2011. “The Effect of New Business Formation on Regional Development: Empirical Evidence, Interpretation, and Avenues for Further Research.” In M. Fritsch, ed., Handbook of Research on Entrepreneurship and Regional Development, 58–106. Cheltenham: Edward Elgar. Garicano, L., C. LeLarge, and J. Van Reenen. 2012. “Firm Size Distortions and the Productivity Distribution: Evidence from France.” Centre for Economic Performance Discussion Paper No. 1128. Garmaise, M.  J. 2011. “Ties That Truly Bind:  Noncompetition Agreements, Executive Compensation and Firm Investment.” Journal of Law, Economics, and Organization 27 (2), 376–425. Garnsey, E., and P. Heffernan. 2005. “High Tech Clustering through Spin Out and Attraction: The Cambridge Case.” Regional Studies 39 (8), 1127–44. Garnsey, E., E. Stam, and P. Heffernan. 2006. “New Firm Growth: Exploring Processes and Paths.” Industry and Innovation 13 (1), 1–20. Gertler, M. S., and T. Vinodrai. 2009. “Life Sciences and Regional Innovation: One Path or Many?” European Planning Studies 17 (2), 235–61. Gilbert, B. A., D. B. Audretsch, and P. P. McDougall. 2004. “The Emergence of Entrepreneurship Policy.” Small Business Economics 22, 313–23. Gilson, R. J. 1999. “The Legal Infrastructure of High Technology Industrial Districts: Silicon Valley, Route 128, and Covenants Not to Compete.” New York University Law Review 74, 575–629. Grimm, H. 2011. “The Lisbon Agenda and Entrepreneurship Policy: Governance Implications from a German Perspective.” Public Administration 89 (4), 1526–45. Hannon, P.  D., and P. Chaplin. 2003. “Are Incubators Good for Business? Understanding Incubation Practice—the Challenge for Policy.” Environment and Planning C: Government and Policy 21, 861–81. Hartog, J., C. M. van Praag, and J. van der Sluis. 2010. “If You Are So Smart, Why Aren’t You an Entrepreneur? Returns to Cognitive and Social Ability. Entrepreneurs versus Employees.” Journal of Economics and Management Strategy 19 (4), 947–89. Henrekson, M., and D. Johansson. 2009. “Competencies and Institutions Fostering High-Growth Firms.” Foundations and Trends in Entrepreneurship 5 (1), 1–80. Henrekson, M., D. Johansson, and M. Stenkula. 2010. “Taxation, Labor Market Policy and High-Impact Entrepreneurship.” Journal of Industry, Competition and Trade 10 (3), 275–96. Hospers, G.-J. 2006. “Silicon Somewhere?” Policy Studies 27 (1), 1–15.

304    Critical Drivers of Local Competitiveness Ingram, P., J. Luo, and J. P. Eshun, 2010. “Institutional Rivalry and the Entrepreneurial Strategy of Economic Development: Business Incubator Foundings in Three States.” In W. D. Sine and R. J. David, eds., Institutions and Entrepreneurship. Bingley: Emerald. Kerr, W. R. 2010. “Breakthrough Inventions and Migrating Clusters of Innovation.” Journal of Urban Economics 67 (1), 46–60. Kirchhoff, B. 1993. Entrepreneurship and Dynamic Capitalism: The Economics of Business Firm Formation and Growth. New York: Praeger. Klepper, S. 2009. “Spinoffs: A Review and Synthesis.” European Management Review 6, 159–71. Klepper, S. 2011. “Nano-economics, Spinoffs, and the Wealth of Regions.” Small Business Economics 37, 141–54. Lerner, J. 1999. “The Government as Venture Capitalist: The Long-Run Effects of the SBIR Program.” Journal of Business 72 (3), 285–97. Lilischkis, S. 2011. “Policies in Support of High-Growth Innovative SMEs.” INNO-Grips Policy Brief No. 2. Marx, M., D. Strumsky, and L. Fleming. 2009. “Mobility, Skills, and the Michigan Noncompete Experiment.” Management Science 55 (6), 875–89. Mason, C., and R. Brown. 2013. “Creating Good Public Policy to Support High-Growth Firms.” Small Business Economics 40 (2), 211–25. McClelland, D. C. 1961. The Achieving Society. Princeton, NJ: Van Nostrand. NESTA. 2009. The Vital 6 Per Cent:  How High-Growth Innovative Businesses Generate Prosperity and Jobs. London: National Endowment for Science, Technology and the Arts. OECD. 2008. Measuring Entrepreneurship:  A  Digest of Indicators. OECD-Eurostat Entrepreneurship Indicators Program. Paris: OECD. Owen, G. 2004. “Where Are the Big Gorillas? High Technology Entrepreneurship in the UK and the Role of Public Policy.” Entrepreneurship and Public Policy Project, Diebold Institute for Public Policy Studies, London. Parker, S. C., D. J. Storey, and A. van Witteloostuijn. 2010. “What Happens to gazelles? The importance of Dynamic Management Strategy.” Small Business Economics 35 (2), 203–26. Parsley, C., and D. Halabisky. 2008. “Profile of Growth Firms: A Summary of Industry Canada Research.” Industry Canada, Small Business Research and Statistics. Samila, S., and O. Sorenson. 2011. “Noncompete Covenants:  Incentives to Innovate or Impediments to Growth?” Management Science 57 (3), 425–38. Santarelli, E., and M. Vivarelli. 2007. “Entrepreneurship and the Process of Firms’ Entry, Survival and Growth.” Industrial and Corporate Change 16 (3), 455–88. Saxenian, A.-L. 1994. Regional Advantage: Culture and Competition in Silicon Valley and Route 128. Cambridge, MA: Harvard University Press. Shane, S. 2009. “Why Encouraging More People to Become Entrepreneurs Is Bad Public Policy.” Small Business Economic 33 (2), 141–49. Shane, S., and S. Venkataraman. 2000. “The Promise of Entrepreneurship as a Field of Research.” Academy of Management Review 25 (1), 217–26. Siegel, D. S., P. Westhead, and M. Wright. 2003. “Science Parks and the Performance of New Technology-Based Firms:  A  Review of Recent U.K. Evidence and an Agenda for Future Research.” Small Business Economics 20, 177–84. Smallbone, D., R. Baldock, and S. Burgess. 2002. “Targeted Support for High-Growth Start-ups:  Some Policy Issues.” Environment and Planning C:  Government and Policy 20, 195–209.

Local Policies for High-Growth Firms    305 Spenner, K.  I., and D. L.  Featherman. 1978. “Achievement Ambitions.” Annual Review of Sociology 4, 373–420. Stam, E., N. Bosma, A. Van Witteloostuijn, J. De Jong, S. Bogaert, N. Edwards, and F. Jaspers. 2012. Ambitious Entrepreneurship: A Review of the Academic Literature and New Directions for Public Policy. The Hague: Advisory Council for Science and Technology Policy. Stam, E., C. Hartog, A.  van Stel, and R. Thurik. 2011. “Ambitious Entrepreneurship and Macro-economic Growth.” In M. Minniti, ed., The Dynamics of Entrepreneurship: Evidence from the Global Entrepreneurship Monitor Data. Oxford: Oxford University Press. Stam, E., K. Suddle, J. Hessels, and A. van Stel. 2009. “High-Growth Entrepreneurs, Public Policies and Economic Growth.” In J. Leitao and R. Baptista, eds., Public Policies for Fostering Entrepreneurship: A European Perspective. New York: Springer. Stam, E., and A. van Stel. 2011. “Types of Entrepreneurship and Economic Growth.” In M. Goedhuys, W. Naudé, and E. Szirmai, eds., Innovation, Entrepreneurship and Economic Development. Oxford: Oxford University Press. Storey, D. J. 1994. Understanding the Small Business Sector. London: International Thomson Business Press. Storey, D. J. 2003. “Entrepreneurship, Small and Medium Sized Enterprises and Public Policy.” In D. B. Audretsch and Z. J. Acs, eds., The Handbook of Entrepreneurship. London: Kluwer. Teruel, M., and G. De Wit. 2011. “Determinants of High-Growth Firms:  Why Have Some Countries More High-Growth Firms Than Others?” EIM, Zoetermeer. Wong, P., Y. Ho, and E. Autio. 2005. “Entrepreneurship, Innovation and Economic Growth: Evidence from GEM Data.” Small Business Economics 24 (3), 335–50.

Chapter 15

Innovation Brok e rs Doug Henton and Jessie Oettinger

Investment in innovation infrastructure at the local level will be the deciding factor for regional and national prosperity over the next century. For the last few decades, the United States has been steadily losing jobs to emerging economies when competing on the cost of labor and natural resources. However, despite these losses, the United States currently maintains its position as a world leader in technology and other knowledge-based industries, capitalizing on national strengths such as a regulatory, academic, and cultural history of supporting innovation and innovators. Not to be left behind, emerging economies across the globe are simultaneously developing their own technological capacity, signaling the possibility for growing competition in innovation markets. Supporting innovation and innovators at national, state, and regional levels is key to maintaining global competiveness. Regional economies that leverage, support, and thrive off of local innovation are not limited to traditional high-tech hubs such as Seattle, Boston, and Silicon Valley; they are emerging all over the country. Innovative high-tech jobs are growing in new places like Boulder, Colorado, Huntsville, Alabama, and Wichita, Kansas (The Accelerators 2013). Federal initiatives like institutes for manufacturing innovation and state designation programs like iHubs (California) or Innovation Partnership Zones (Washington) signal that many stakeholders from governments to universities to private companies recognize the importance of investing in innovation (Muro 2013). But how can individual regions capitalize on this movement toward innovation economies? What actions should regions take to support innovation? From an economic development perspective, the task of spurring innovation is the creation and reinforcement of infrastructure—both tangible and intangible—that supports innovators and innovation. In 2008, Collaborative Economics published the “Innovation Driven Economic Development Model.” The paper outlines why innovation is important for regional development, how regional development relationships and existing social infrastructure might support innovation, and features a series of case studies on regions that have created a regional infrastructure that supports innovators and innovative companies. Investing in such infrastructure allows the region’s economy to grow by reinforcing

Innovation Brokers   307 Table 15.1  The Role of the Innovation Brokers Who

Individuals and institutions devoted to building innovation capacity at a regional level

What

•  Drive a regional innovation agenda •  Connect people, organizations, ideas, and resources •  Offer specialized innovation services

Where Why

At the hub of government, academia, and private sector To grow regional economies

the bonds, networks, and resources that promote the successful commercialization of ideas and research (Collaborative Economics 2008). To describe this infrastructure and the networks that connect people and businesses to resources, Collaborative Economics has coined the term “innovation broker.” An innovation broker is anyone who serves to connect people and ideas to resources (physical, capital, advisory) to propel the positive cycle of the innovation economy. As we will go on to describe (and is outlined in table 15.1), innovation brokers can be individuals or organizations, but what distinguishes them as brokers is their active role supporting regional innovation by driving the innovation agenda; connecting people, organizations, ideas, and resources; and ensuring that innovative people and businesses have the resources they need to flourish.

Supporting Regional Innovation Innovation-based economies have the potential to transform regions. There have been many attempts to analyze and break down what fuels regions with strong innovation economies because so many communities would like to replicate their successes. Common inputs such as strong research institutions and plenty of cash capital are just the beginning of the story. In an analysis of Silicon Valley (arguably the most powerful innovation economy in the world), Lee and coauthors break down the “Silicon Valley Habitat,” describing the characteristics of the Valley that make it so successful in driving innovation. Some of the characteristics such as strong research universities, federal laboratories, and a high-skill workforce are not surprising, but the list also alludes to a certain culture that makes the innovation economy flow: a collaborative and open environment that is supportive of risk taking, where people see working together on shared interests as an opportunity to grow the entire economy (Lee et al. 2000). Others have described Silicon Valley as an “innovation ecosystem.” As illustrated in figure 15.2, this vision of Silicon Valley depicts how various inputs, both tangible and intangible, create a complex, iterative, and self-reinforcing system. Ideas and capital are critical, but they need the proper networks, services, and infrastructure to flourish. Sources of ideas such as universities, incubators, and national labs are nurtured by being connected to specialty services such as intellectual property lawyers or start-up

308    Critical Drivers of Local Competitiveness Innovation Brokers /i nɘ vā shɘn brōkɘrs/ noun Organizations or networks that serve as the critical connectors in a network of individuals, organizations, and institutions to support entrepreneurs and companies at different stages of the business development process. Innovation brokers work across disciplines to ensure that a broad range of talent, technology and capital resources and expertise are accessible to businesses.

Figure  15.1  Innovation Brokers

Quality of life Appealing climate to live and work

Specialty Services Legal/IP Accounting IT

6.7

ye

ar

s

Capital Banks Angels VCs

Networks Mentors Peers

Exit: IPO/M&A Creates success for investors, VCs, employees, etc.

Corporate success

BUSINESS looking to reinvest GROWTH 10% make it out of this stage $

Incubators

IDEAS

Start-up get seed money Recycle Accelerators experiences, talent, ideas

Corporate Labs Universities

National Labs

Entrepreneurs

$ $

$

Reinvestment

$ $ $ $ $

Money, experience, connections enrich ideation pool

INNOVATION POOL rich with start-up nutrients

Figure  15.2  The Silicon Valley Ecosystem (Adapted from the Accenture Institute for High Performance’s Silicon Valley’s Lessons for CIOs and Innovators, 2013. Reproduced by permission of Chris DiGiorgio and Jeanne Harris, Accenture Institute for High Performance, 2013. www.accenture.com/siliconvalley.)

consultants as well as different sources of risk capital from venture capital firms to angel investors to banks. As these ideas and businesses mature, regardless of their commercial success they create knowledge and experience (and sometimes money!) that benefits the entire system. While the country’s most mature innovation economies, such as Silicon Valley, are up and functioning in the virtuous cycle depicted in figure 15.2, many communities are just getting started: identifying innovation assets, bringing together local leaders from industry, academia, and government, and crafting economic development plans that focus on innovation. Other emerging innovation economies are further along, actively engaged in efforts that support and drive their regional innovation economy. In the last few decades many regions have recognized that the industries that once upheld their local economy are in decline and that the future depends on a new kind of investment

Innovation Brokers   309 and a new kind of industry. Regional efforts to support innovation such as providing venture capital, building laboratories and business parks, facilitating opportunities to network, and offering formal mentorship and advisory services to local business can be seen all across the country. There is no one clear pathway to develop from an early to an intermediate to an advanced innovation economy, but there are a number of tools for supporting innovation economies and helping communities advance an innovation strategy. Central to this work is an understanding that to build a self-sustaining ecosystem, there needs to be some sort of intentional entity that builds networks, connects ideas, leverages federal, state, and private dollars, and provides specialty services that support innovative businesses. There needs to be a broker (or brokers) who facilitates and catalyzes these kinds of connections that define a true innovation economy.

Innovation Brokers in a Regional Economy Innovation brokers serve as the hub of an important network of businesses, capital, ideas, and people. They connect entrepreneurs and existing businesses to services, sources of capital, and to each other. Businesses—start-up and existing—are more readily able to launch or innovate as a result of this brokerage, and therefore can generate jobs, wages, and regional wealth. As illustrated again in figure 15.3, the regional innovation economy is reinforced by what flows from new and improved businesses—more capital for investment, more companies, more ideas, a workforce with exposure to new ideas and skills—and attracts even more investment and talent from other places to the region. Innovation is perpetually reinforced by the cyclical nature of investing in and supporting innovation. New markets, new skills, and increased capital created by this cycle attract high-quality talent to the region and spin out new companies, new ideas, and more capital to reinvest in other forms of innovation. In this vision of the innovation economy, the innovation broker takes its place in the cycle as the catalyst that drives deals. In mature innovation economies, there are many people, institutions, and networks serving as innovation brokers. A long-term effect of the functional innovation economy is that its products—wealth, businesses, knowledge, jobs, networks, and brokers—grow with time. For regions with a less established innovation economy, but where there has been an intentional move to promote innovation, there is often an institutional entity that serves as the broker. These institutional innovation brokers sit at the nexus of government, academia, and the private sector, connecting good ideas with the resources to launch them. In this capacity, innovation brokers can be many things: technology

310    Critical Drivers of Local Competitiveness OVATION BROKER INN DRIVES a regional innovation agenda CONNECTS raw innovation assets: people, organizations, ideas, and resources OFFERS specialized services for innovative organizations and people

Entrepreneurial talent Skilled workforce Scientists/Researchers Start-up businesses Existing businesses Research facilities R&D assets Intellectual Property

New products New markets New production and delivery methods Intellectual property Human capital

IN N

IN N

O V A TI O N A S S ETS

O V A TI

P O N O UT

UT

Spin off companies Staff turnover and idea flow across industry Wealth to invest in new businesses and ideas Strong networks that attract new entrepreneurs and inspire existing business Market for specialized innovation services IN N

OVA

TIO N E C O NO

MY

Figure  15.3  The Role of the Innovation Broker in a Regional Economy

transfer offices, public-private entities, nonprofits, industry organizations, or economic development agencies, to name a few. As the entity that helps firms and individuals achieve higher value and productivity by gaining access to appropriate innovation assets, they must be prepared to help with innovation at all points of the business cycle. For the purposes of this chapter, we will focus on what institutional innovation brokers do in this role to drive and support a regional innovation economy. Our work is informed by decades of assisting innovation brokers in all stages of the process and an ongoing, informal survey of innovation brokers across the United States and even abroad. What follows is a discussion of common innovation broker activities and profiles of brokers in growing innovation economies. The profiles

Innovation Brokers   311 and examples of innovation brokers demonstrate how diverse communities across the country have used innovation brokers to capitalize on their region’s innovation assets.

What Do Innovation Brokers Do? Innovation brokers play several key roles in the growth of an innovation economy. First and foremost, innovation brokers provide the leadership that drives an innovation agenda. In addition to providing direction, in many communities the innovation broker must also fill in the gaps between what an innovation economy needs to prosper and what is currently available. Especially in economies transitioning to support innovation, where basic innovation assets such as venture capital or specialty services, such as intellectual property lawyers, are in short supply, it is important for the broker to play the role of service provider. Innovation brokers also work with the larger community to create an innovation habitat, fostering the kind of culture described above where good ideas are more likely to gain traction. Finally, many think of innovation strictly in terms of start-up businesses, but innovation brokers know that existing businesses often have the most to gain from innovation assistance and thus make sure to connect local businesses to innovation efforts.

Driving an Innovation Agenda Leadership starts with assembling the right people to create a plan and continues indefinitely in the work of supporting innovation-specific endeavors and generally facilitating important connections between ideas, people, and resources. In our work with regions pursuing an innovation agenda, we have assisted brokers emerging from many places—tech-transfer offices at public universities or laboratories, economic development agencies, or civic organizations—and have found that including the right group of players in formative discussions and having them lead innovation activities is a decisive success factor. The most successful institutional innovation brokers have leadership that is comprised of entrepreneurs and innovation experts as well as established regional leaders who have the credibility, connections, and resources to aid implementation. In “Unraveling the Cultural and Social Dynamics of Regional Innovation Systems,” Walshok, Shapiro, and Owens confirm that the characteristics of the region, technology, industrial legacy, and leadership team have significant impact on the efficacy of innovation brokers. In particular, they found that the innovation broker must be entrepreneur focused (as opposed to simply business focused); that the leadership team must include researchers, scientists, and entrepreneurs; and that staffing and leadership must have

312    Critical Drivers of Local Competitiveness directly relevant skills and competencies to help innovators commercialize their ideas (Walshok, Shapiro, and Owens 2013). Governments and economic development organizations clearly have a stake in the success of driving local growth, and it can be tempting for the public sector to take on the tasks of the broker. However, the most sustainable brokers have found that leadership must come from the entities that directly benefit from the resulting collaboration and networking: the private companies and entrepreneurs. Government can still be an excellent partner in this process by providing funding and support for the processes that lead to strong relationships, addressing regulatory issues, and generally advocating for the innovation community. Once a diverse and relevant leadership team is assembled, the task is to articulate what the region needs to do to support innovation and how it can be accomplished. Many teams start with some sort of assessment to provide a deeper understanding of what is happening in the region.1 Based on regional assets and barriers, the broker team develops a customized plan to deliver on an innovation agenda. We have heard consistently from brokers that having a diverse and collaborative leadership team is particularly important during the planning stage. Often this work is spurred by the opportunity of a one-time grant, but thoughtful broker teams think about the long-term innovation economy as well as short-term goals. The work of building an innovation economy is a multiyear (if not multidecade!) effort. Long-term, sustained investments of time, capital, and human resources are needed to build physical and cultural innovation infrastructure.

Filling in the Gaps: Specialized Services and Capital Two key components of a healthy innovation ecosystem are specialized services for innovators and access to capital. For communities lacking in either area, an important role that the innovation broker can play is to provide those specialized innovation services and to connect businesses and start-ups with various forms of early-stage capital. These two key activities work hand in hand to assist companies and individuals with innovative ideas to secure financial and tactical support.

Services For brokers operating in early- and intermediate-stage innovation economies, service provision is an important function. As evidenced in the later profiles on Long Island, New York, and Fresno, California, the kind of services offered by an innovation broker is entirely dependent on what local businesses, entrepreneurs, and start-ups need in order to grow.

1  The Regional Innovation Acceleration Network (RIAN), a federally funded initiative to track innovation brokers, best practices, and innovative efforts, offers assessment materials on its website, http://regionalinnovation.org/.

Innovation Brokers   313 Examples of business services specifically pertinent to an innovation economy include the following: • Specialty consulting services for entrepreneurs or start-up businesses, such as business plan creation, market research, and product viability research • Legal firms specializing in intellectual property, business formation, and different kinds of investor relationships • Financial advisors who can help new businesses manage complex financial situations (with various forms of investment, debt, and equity relationships) as well as manage credit and cash flow • Technology transfer specialists who work specifically with scientists and researchers to find applied uses for more general research • Physical infrastructure, such laboratories or offices, where start-up businesses can share (often at reduced cost) office space, administrative services, and ideas. For example, a broker in the Tennessee Valley, Tech2020, built data storage capacity to propel research efforts in their region (Regional Innovation Acceleration Network 2011). • Headhunters who help start-up businesses find executive personnel that advise companies throughout various stages of growth Transitioning interesting ideas into viable businesses is challenging no matter how good the idea. Offering services tailored to entrepreneurs and start-up businesses can help those ideas come to fruition and attract investment. What seems to be an important success factor in broker-offered or broker-affiliated services is that the services are tailored specifically to high-growth, innovative businesses.

Access to Capital Capital is another instance where the maturity of the innovation economy is an important factor. In younger innovation economies, private venture capital and investment may be in short supply, so it is often up to the broker to provide capital. Many brokers start out by raising philanthropic, federal, and state grants to help innovators launch their ideas. However, as innovation economies move along the continuum from early to advanced, the role of the broker changes from being the main source of regional capital to providing connections to the community’s private capital firms. There are a variety of funding models for the dispersal of early-stage capital. Some brokers use their funds to provide grants or unsecured loans, while others become venture partners and favor other forms of investment like convertible notes. Many brokers offer a mix. For instance, Ben Franklin Technology Collaborative (BFTC) of southeast Pennsylvania supplies both loans and investments depending on the stage of the innovation. BFTC offers large ($100,000–$750,000) investments to more mature start-ups, and smaller direct loans for earlier-stage companies for things such as proof of concept, scalable prototypes, and field research (Regional Innovation Acceleration Network 2011).

314    Critical Drivers of Local Competitiveness Another means for financial support that an innovation broker can supply is access to individual, informal angel investors or formal venture capital firm investors. Some brokers serve as clearinghouses for angel investors who rely on connections with the innovation broker to introduce them to promising start-ups. We have also seen several examples of innovation brokers that manage community venture capital funds. TechColumbus in Ohio manages several “bands of angels” or funds of pooled angel money. While the angel leadership makes choices about who to invest in, the staff at the broker manages the day-to-day operations of the fund and makes sure investments/entrepreneurs are well supported. The angels will often coinvest with other TechColumbus ventures (Regional Innovation Acceleration Network 2011). In several innovation economies we’ve worked with, traditional venture capital was hard to come by at the beginning, despite the potential for innovation. This challenge was met in several ways. In San Diego, California, in the early years of the area’s budding biotech and communications industry, the broker (San Diego CONNECT) reached out to contacts in Silicon Valley. Silicon Valley venture capitalists funded many of the San Diego start-ups that went on to generate private wealth locally that could sustain region venture funding in later stages. In Long Island, the broker (Accelerate Long Island) found sources of venture capital already existing on the island and worked with them on an initiative to invest locally. The broker has also worked diligently to bring the community’s extensive portfolio of innovative companies and research facilities to the attention of the venture capital community across the water in New York City.

Fostering an Innovation Ecosystem All of the innovation brokers we have worked with provide opportunities for entrepreneurs, start-ups, and investors to network with each other. Different organizations host conferences, networking events, educational activities, and even awards ceremonies. These activities are not just about building a network of inventors, entrepreneurs, and innovative businesses; they are also about creating the innovation ecosystem. As discussed earlier, a culture of risk taking, idea sharing, and entrepreneurial thinking is one of the intangible qualities of an innovation economy. Mark Lesko, director of Accelerate Long Island, noted there is both a “horizontal” and a “vertical” approach to being an innovation broker. Accelerate Long Island works vertically with its portfolio companies, providing mentorship, services, and capital; but the broker also recognizes the importance of working horizontally across the region through its series of networking opportunities to build up an innovation ecosystem. Other examples of broker initiatives to extend their resources to the wider community include Pittsburgh’s Innovation Works and North Dakota’s Center for Innovation. Innovation Works, whose staff only has capacity to assist a limited portfolio of companies, makes resources publically available through its Entrepreneurs Toolkit blog. The blog includes a series of articles covering important topics for entrepreneurs such as business models, compensation, corporate governance, deals, founders’ issues,

Innovation Brokers   315 fundraising, management and marketing and sales. The Center for Innovation manages a venture competition that takes nine months and includes an entrepreneurial education curriculum and several boot camps along the way (Regional Innovation Acceleration Network 2011).

Serving Existing Businesses Innovation infrastructure resources appear to be largely directed at entrepreneurs and start-ups, despite the fact that the majority of new jobs come from existing business. However, there are several exceptions in our sample of innovation brokers. Pittsburgh’s Innovation Works, for instance, has a program that connects small manufacturing businesses with regional centers for excellence to provide fee-for-service research and development that assists the companies in developing new products and better manufacturing processes (Regional Innovation Acceleration Network 2011). In Cleveland, Ohio, the broker (NorTech) works with its existing anchor companies to introduce them to local researchers and innovators that might help them improve their competitiveness. In some ways, existing businesses need many of the same supports that start-ups do, but existing businesses also face challenges that start-ups do not, so it is important to work with local companies and assess their particular innovation needs. The innovation broker lens can be limited to entrepreneurs and start-ups, but this kind of thinking narrows sources of research and development, capital, and commercialization. In the past, important investment in research and innovation was much more prevalent in the private sector. The last few decades have seen a shift away from this practice, which has left some holes in the innovation landscape for existing companies (Massachusetts Institute of Technology Production in the Innovation Economy Taskforce 2013) that perhaps could be filled by innovation brokers. Strategic investment by existing firms in new (extra-organization) research, and innovation assistance for existing firms are two strategies that need to be further explored.

Profiles of Regional Innovation Brokers International Center for Water Technology, Fresno, California When one thinks of Fresno, California, the first thing that pops to mind might not be “high tech,” but some of the most innovative and important work in water and energy technology is happening in California’s San Joaquin Valley. Supporting, driving, and coordinating much of this innovation is the International Center for Water Technology

316    Critical Drivers of Local Competitiveness (ICWT), established as a collaborative venture between Fresno State University and the San Joaquin Valley Water Technology Cluster. The ICWT was created in 2001 as a public-private partnership to coordinate and propel action in the region’s growing water technology sector. The region is home to a significant number of leading water technology manufacturers, as well as the university’s world-class research program on water technology. Regional leaders saw an opportunity not just to help existing companies connect with established university research centers, but to use these assets to grow the entire regional economy by promoting innovation in water technologies. In an effort driven by local manufacturers and researchers, ICWT identified what was needed in the larger Fresno region to help existing companies grow and for entrepreneurs and scientists to commercialize promising research. Initially the partnership focused on major improvements the group could accomplish together. The list included developing a state-of-the-art hydraulic testing and research laboratory that would be utilized by multiple stakeholders; regional workforce development; and cooperative export development that would grow the market size for water cluster members and put Fresno on the map as the international hub of water technology activities. In 2007, ICWT partnered with Central Valley Business Incubator and opened the Water, Energy, Technology Center (WET Center), expanding the ICWT’s original focus on water to include a focus on the energy/water nexus. The WET Center, housed at Fresno State, offers hands-on training and lab space for entrepreneurs, but perhaps more importantly has become a “hub” for connecting students, faculty, local businesses, start-ups, and government agencies on innovation. Other accomplishments over the last decade include the following: • Delivering over 90 new technologies or critical water/energy seminars and presentations • Leading four successful export missions to Mexico and South America with 24 cluster companies • Generating over $5 million per year in export sales for 24 US firms • Holding five regional/international water technology conferences with nearly 1,500 attendees and over 180 exhibitors • Developing the www.icwt.net website and hosting quarterly meetings for the San Joaquin Valley Water Cluster • Having the WET Center recognized by the US Environmental Protection Agency in 2012 with the Efficient Water Infrastructure Award In its first decade, the ICWT accomplished most of what it laid out in its 2001 strategic plan. The broker recently returned to its group of stakeholders to direct the next phase of water and energy sector development. The three priorities for action moving forward are working more with regional energy and manufacturing clusters, increasing the membership of underrepresented members of the water community, and continuing to focus on services for their membership such as business support, workforce

Innovation Brokers   317 development, and resources to innovate technology. ICWT director David Zoldoske credits the group of stakeholders, their collaborative attitude, and a clearly laid-out plan as a crucial part of the ICWT’s success. With the help of the ICWT as an innovation broker Fresno has been able to capitalize on regional innovation assets (and the critical role of water on a global scale) to support growth in its regional economy.

Accelerate Long Island, Long Island, New York Long Island, New York, is home to a host of world-class research institutions including Brookhaven National Laboratory, Cold Spring Harbor Laboratory, Hofstra University, North Shore–LIJ Health System’s Feinstein Institute, and Stony Brook University. However, despite these strong research assets and a group of high net worth individuals and companies, Long Island does not have strong tradition of start-up companies, of entrepreneurship, or of attracting venture capital locally. In 2010, community leaders came together to identify how this wealth of over $1 billion in research could be better leveraged to spin out new companies and connect with private businesses on Long Island. Realizing they needed a central entity to coordinate and catalyze the connections for researchers and entrepreneurs, they created Accelerate Long Island. Accelerate Long Island is a unique collaboration among Long Island’s world-class research institutions and its business community to commercialize research and create an entrepreneurial ecosystem. Accelerate Long Island was created to help broker the transition from laboratory research to commercializable products. Leaders identified a dearth of services for entrepreneurs and researchers to take innovative business ideas and turn them into profitable businesses. The broker also recognized a need for the community to adopt a culture supportive of entrepreneurial activity. As the broker, Accelerate Long Island works in several areas to promote the growth of local businesses. They are directly involved with commercialization activities coming out of partner institutions, using industry domain experts to review and evaluate the potential of local research. They also provide services for start-up companies such as business mentorship, connections to start-up incubators, and technical support from industry experts. Another important start-up asset Accelerate Long Island assists with is connecting new ventures with investment capital. Working with a team of venture capitalists, angel investors, and its own funds, Accelerate Long Island helps regional companies find the right capital fit for new projects. An innovation economy is not just about specialized services, research, and capital. There is also a cultural aspect to foster in an innovation economy. Accelerate Long Island recognizes this as well and sees a third major prong of their work as helping promote an “entrepreneurial ecosystem” where individuals and companies are encouraged and supported in taking risks and generating new ideas. Entrepreneurial ecosystem activities include a significant amount of networking and idea sharing across institutions and individuals.

318    Critical Drivers of Local Competitiveness In its short life, the broker has brought important stakeholders together, mapped out where the region needs to go and has begun to act on their action plan. In 2013, Accelerate Long Island saw some of their first start-ups launch with venture capital investment from a combination of large firms, as well as the local angel investor network. A  partnership with Topspin Partners, the largest venture capital firm, on the island means that in 2014, the broker is even better poised to offer assistance to local start-ups. Accelerate Long Island realizes that the process of building an innovation ecosystem will take sustained effort over the long term. The broker and its supporters are invested in a long-term strategy to build this ecosystem on Long Island.

Conclusion: Creating Innovation Capacity Regionally to Increase Economic Resiliency Regions succeed over time by adapting to changing markets and new technologies. The innovation capacity of the region is a key factor in economic resiliency and is a direct reflection of the strength of the regional innovation habitat. Innovation brokers play a critical role in helping regions leverage, support and build innovation infrastructure. By working across institutions and industries to capitalize on tangible and intangible assets, innovation brokers help their communities adapt and thrive, even in times of change. Public and private leaders must take action to both invest in innovation assets and connect them in new ways to achieve higher economic performance. Innovation brokers will be key in building this innovation capacity and helping regions realize the full potential of the global innovation economy.

References The Accelerators. 2013. “Wall Street Journal: WSJ Blogs.” Debunking the myth of the startup hub. Wall Street Journal, February 3.  http://blogs.wsj.com/accelerators/2013/02/03/weekend-re ads-debunking-the-myth-of-the-startup-hub/ (accessed March 1, 2013). Collaborative Economics. 2008. “Collaborative Economics.” The Innovation Driven Economic Development Model: A Practical Guide for the Regional Innovation Broker. September. http:// www.coecon.com/innovdriven.html. Lee, Chong-Moon, William Miller, Marguerite Gong Hancock, and Henry S. Rowen. 2000. The Silicon Valley Edge. Stanford, CA: Stanford University Press. Massachusetts Institute of Technology Production in the Innovation Economy Taskforce. 2013. “Production in the Innovation Economy.” Report of the MIT Taskforce on Innovation and Production. http://web.mit.edu/press/images/documents/pie-report.pdf (accessed March 15, 2013).

Innovation Brokers   319 Muro, Mark. 2013. “Brookings.” Regional Innovation Clusters Begin to Add Up. Brookings. February 27. http://www.brookings.edu/blogs/up-front/posts/2013/02/27-regional-innovation-clusters-muro (accessed March 1, 2013). Regional Innovation Acceleration Network. 2011. “Profiles.” 2011. http://regionalinnovation. org/profiles.cfm (accessed October 1, 2012). Walshok, Mary L, Joshua D. Shapiro, and Nathan J. Owens. 2013. “Global Connect.” Unraveling the Cultural and Social Dynamics of Regional Innovation Systems. January. http://globalconnect.ucsd.edu/research/documents/UnravelingSocialDynamicsJanuary2013.pdf.

Chapter 16

Sw imm ing U p st re a m Why Regional Economic Development Depends on National Economic Competitiveness Robert D. Atkinson

Swimming Upstream: Why Regional Economic Development Depends on National Economic Competitiveness Since its inception after World War II, the practice of local and regional economic development has been largely divorced from national economic development policies. This is in part because the former was focused on addressing regional problems in what was assumed to be a healthy national economy. But as the US economy has suffered from serious structural decline, this relationship can no longer be taken as a given. To paraphrase Richard Nixon, “We are all economic developers now.” In this climate, it is time to more effectively link national and local economic development efforts if both are to be successful.

We’re All Economic Developers Now While some US cities and towns would engage in local promotion, it was not until the 1930s when the state of Mississippi developed its “Balance Agriculture with Industry” (BAWI) program that economic development in the United States came into its own (Hopkins 1944). Mississippi political and business leaders realized that if they wanted to have their state be anything more than a lagging poor agricultural economy, then they needed to industrialize. In order to do that, they crafted BAWI, an effort to attract

Swimming Upstream   321 northern industry to the South with the promise of low wages and generous incentives. Within a few decades virtually all the southern states had emulated the Mississippi model. Southern economic development officials trekked northward to cities like New York, Chicago, Cleveland, and Philadelphia to convince corporate executives to relocate or open new factories in the South. Dangling right-to-work laws, cheap labor, low taxes, and, in many cases, direct incentives in the form of tax breaks, infrastructure, or worker-training programs, these programs were remarkably successful. For example, manufacturing employment grew by 23 percent in North Carolina in the 1950s, compared to just 7 percent nationally. Of course, these programs built upon technological changes that were enabling the deconcentration of industry. With the building of arterial highways, followed by limited access parkways, and then finally interstate highways with the passage of the National Defense Highway System in 1956, metropolitan and rural regions across the nation were linked together. Jet air travel allowed managers to easily travel to dispersed factories. And just as IT is enabling many companies to locate anywhere in the world today, widespread electrification allowed industry much greater locational freedom, stimulating industrialization in the South and West. In addition, the development of air conditioning made living and working in hot southern and western climes more tolerable. All of this was taking place in an environment of economic prosperity and boom. In the 1950s and 1960s, US real GDP expanded by 51 percent and 55 percent respectively, with those gains being widely shared.1 Indeed, median personal income grew by 23 percent in the 1960s alone (contrast this with the 2000s, when median personal income fell by 1 percent).2 Yet some regions, such as Appalachia, and some parts of metropolitan areas were not enjoying the fruits of abundance. As a result, in the early 1960s, the focus on economic development shifted toward distressed regions and neighborhoods. President Kennedy made the development of the Appalachian region a priority and established the Appalachian Regional Commission. Likewise, President Kennedy’s and President Johnson’s efforts to boost lagging urban core economies, which suffered from “white flight” and the suburbanization of prosperous individuals and businesses, took off in the 1960s, embodied first in the 1950s by urban renewal efforts and in the 1960s by the War on Poverty and the establishment of the Department of Housing and Urban Development. This changed yet again in the mid-1970s, when whole regions began to suffer economically. The United States was enjoying the fruits of a 25-year postwar economic boom, during which real GDP per capita exploded, jobs were plentiful, and tens of millions of American households were vaulted into the middle class. But starting with the recession of 1969 (the longest since 1949) and then the much longer and deeper recession of 1974 1 

Bureau of Economic Analysis, National Income and Product Accounts (Real Gross Domestic Product, Quantity Indexes; accessed November 24, 2013), http://www.bea.gov/iTable/index_nipa.cfm. 2  Census Bureau, Income Data, Historical Tables (Table P-4: Race and Hispanic Origin of People, Both Sexes Combined, by Median and Mean Income; accessed November 24, 2013), http://www.census.gov/ hhes/www/income/data/historical/people/.

322    Critical Drivers of Local Competitiveness (the longest since the Great Depression), the robust economic performance began to falter, leading many to question if the good times were over. For America as a whole, the answer was an emphatic no. Things did keep getting better. Indeed, growth even accelerated from 1975 to 1985. But underneath this apparently healthy national growth was a troubling phenomenon—the emergence of two quite different economies:  a slower-growing industrial Midwest and Northeast and a faster-growing South and West. After World War II and until the end of the 1960s, these regions grew at about the same rate.6 But starting in the 1970s and through the mid-1980s, the former areas downshifted into slow growth, with a struggling industrial belt from western Massachusetts to northern Wisconsin and down to St. Louis.7 Portrayed in rock ballads like Billy Joel’s “Allentown” or Bruce Springsteen’s “My Hometown,” places that had grown to become industrial powerhouses in the 20th century, providing a path to the American dream for millions of workers, now faced shuttered factories, boarded-up homes, and shattered lives. But while these areas struggled, regions like the Rocky Mountains and the West now boomed, growing 37 percent and 27 percent faster, respectively than the nation as a whole.8 Books like Sunbelt, Snowbelt contrasted industrial decline in the Northeast and Midwest with industrial expansion in the South. The same dynamic occurred at the metropolitan level. Cities that had once powered America’s Industrial Revolution were now struggling for their economic lives. Take Buffalo, New York, for example. Buffeted by factories moving to the South and West, Buffalo’s total income grew at less than half the rate of Brownsville, Texas, from 1969 to 1986. While Brownsville saw its jobs grow by 75 percent, Buffalo saw its jobs decline by 1 percent (Atkinson and Ezell 2012, 4). Likewise, Syracuse, New York, home in the early 20th century to companies that manufactured more diverse products than New York City, saw its income grow just 53 percent as fast as that of Santa Fe, New Mexico, with jobs growing just 28 percent compared to Santa Fe’s 124 percent (Atkinson and Ezell 2012, 4). In short, entire regions never again experienced the robust growth rates they enjoyed in the century following the Civil War; they suffered deindustrialization, job loss, and fiscal crises. If you lived in Buffalo, Syracuse, or similar places, things probably weren’t so good. But if you lived in Brownsville, Santa Fe, or other growing places, things were good and getting better. Indeed, if the South had won the Civil War, economic historians might be writing about the economic decline of the United States after the 1960s and the boom of the Confederate States of America. Instead, they talked about modest overall US growth. There were a variety of reasons for the emergence of these two American economies, but a key one was that the production system enabled deconcentration of industry. With the completion of the Interstate Highway System in the 1970s, the emergence of jet travel, and nationwide electrification and telephone access, companies in traded sectors now had the freedom to locate almost anywhere in the United States. And they did so, with factories migrating away from the Northeast and Midwest to the South and the West. Combined with this was the emergence of new high-growth industries (e.g.,

Swimming Upstream   323 electronics, aviation, and instruments) that didn’t need to be located at the ports or rail spurs in the Midwest and East. Couple this with the high costs and lack of competitiveness of the Rust Belt, and the implications were clear. As a result, economic development efforts that were once concentrated in southern states trying to catch up and in some distressed regions and urban areas, now became much more widespread. These began after World War II, when states in the Northeast and Midwest woke up to the realization that their factories could be relocated anywhere in the country, as was increasingly occurring. To deal with these pressures, they began to compete fiercely with each other (and with states in other regions) to retain and attract geographically mobile investment. Emblematic of efforts of the day, a 1954 issue of Fortune magazine included a full-page ad from the state of Indiana that touted its benefits as a location of corporate investment, including attractors such as “no government debt,” a labor force that was “97  percent native” (with the implication that native-born workers were less likely to strike than immigrants), low taxes, and ample supplies of raw materials, calling itself “the clay capital of the world.” By the 1970s, states had ramped up their efforts, establishing economic development agencies with missions to go out and compete with an arsenal of tools ranging from tax breaks, to free land, to workforce-training programs. By the 1980s, in part reflecting the analysis of regional economists like Rees and Norton, who explained in “The Product Cycle and Decentralization of American Manufacturing” that higher-cost, older industrial regions had to specialize in early-stage innovation activities (Norton and Rees 1979), economic development practice, at least in some states and regions, began to shift its focus more toward supporting new firm creation and existing firm expansion. States such as Pennsylvania established programs like the Ben Franklin Partnership program to help firms and universities cooperate, and Ohio created its Thomas Edison program to support high-tech entrepreneurship. Many states and cities now developed business finance programs, worker-training programs, and other enterprise support efforts (see, e.g., Osborne 1988). But still, this was in a context of a growing national economy where the overall US economy was still reasonably strong and not confronted in any significant way with competitive challenges from other nations. This was an environment in which some state economies were booming while others languished. The latter, states like Pennsylvania, Ohio, and Michigan, were the ones that really ramped up their economic development efforts in an effort to address their competitive decline. But over the past few decades the economic performance of states has converged. Indeed, over the last three decades, as more and more states suffer weak economies, the variation in state GDP growth has narrowed. In the 1980s, US real GDP increased by an annual average of 2.9 percent. The average absolute value of deviations from this average was 1.12 percent. In other words, of the 50 states, the weighted average difference in rate of growth from the national growth rate was 1.12 percent. New Hampshire led growth with 6 percent annual growth, while North Dakota experienced no growth. In the 1990s absolute average deviations fell to 0.91 percent. And in the 2000s, the average deviation fell even more, to 0.70 percent. We see similar trends in state per capita growth

324    Critical Drivers of Local Competitiveness deviations, with the average increasing from 1.16 in the 1980s to 1.26 in the 1990s, but falling to 1.00 percent in the 2000s.3 In other words, not only did overall US growth slow, but many more places found themselves in the same—all too often leaky—boat. In short, today the United States has become the Great Lakes region of the 1970s and 1980s from a geo-economics perspective. “Rust Belt” is now “rust nation.” Santa Fe has become Syracuse, and Shanghai has become Santa Fe. Brownsville has become Buffalo and Bangalore, India, has become Brownsville. Places like North Carolina and Georgia, which benefited from the shift of manufacturing from the North from the 1940s to the 1970s, have seen their own textile, furniture, and other traditional factories move to lower-wage nations. Today, container ships, air freight, and the development of the Internet, cloud computing, and undersea fiber-optic cables have linked together not just state economies but also national ones. In essence, what was once a set of separate national economies in the 1970s has evolved into a single integrated global economy in the 21st century. And other parts of the world are now the economic engines, growing much faster than the United States (or Europe or Japan). Now its Southeast Asia, rather than the southeast United States, that is booming area.

Shooting for Fewer Birds We can see this change in the much tougher economic development environment states and regions face. An economic development official for the state of North Carolina once explained to me that the state’s strategy was to “shoot anything that flies, claim anything that falls.” Over the last two decades there are a lot more shooters, a lot less to shoot at, and fewer “birds” falling from the sky. If economic development prior to the 1990s was about some states, regions, and cities working to overcome certain weaknesses (e.g., structural underdevelopment, responses to plant closures and other disruptions, etc.), over the last two decades it has evolved into an imperative that virtually all political entities engage in. In other words, the demand for economic development has gone up dramatically. Virtually all political entities now seek to recruit new establishments to their borders, facilitate expansion of existing firms, and support the establishment of new firms. For example, Warner and Zheng find that business incentive use rose dramatically among US municipalities after the Great Recession (Warner and Zheng 2013). Likewise, one study of megadeals—incentive packages offered by states and cities with a total subsidy of more than $75 million—have doubled in recent years compared to the 1990s (Mattera, Tarczynska, and LeRoy 2013). And Kenneth Thomas has estimated economic development subsidies increased from $48.8 billion a year in 1996, of which $26.4 billion was for investment attraction, to almost $70 billion in 2005, of which $46.8 billion was for investment attraction (Thomas 2010). 3 

Bureau of Economic Analysis, Regional Economic Accounts (various tables; accessed November 8, 2013), http://www.bea.gov/iTable/index_regional.cfm. Author’s analysis.

Swimming Upstream   325 While demand has gone up, supply has gone down, in turn spurring even more demand. We can see this in a variety of indicators. We can start with manufacturing, long a staple of economic development efforts. From after World War II to the early 1980s, US manufacturing expanded at a robust pace. One indicator is investment in new manufacturing structures. From 1947 to 1982, manufacturing companies invested in new structures on average of 0.52 percent of GDP per year. Between 1983 and 2001, with increased competition from Europe, Japan, and the Asian tigers, that rate averaged just 0.31 percent of GDP per year. And from 2002 to 2012, with competition from China (and Mexico), that rate fell to just 0.14 percent of GDP (Atkinson et al. 2012, 37). This is a reflection of overall manufacturing trends. Between 2000 and 2010, manufacturing output, when measured properly, shrank by 11 percent (Atkinson et al. 2012, 37). From 2000 to 2010 all but one state (Alaska) saw manufacturing employment losses, with overall manufacturing employment declining by approximately one-third, compared to around 2 percent in the 1990s. Moreover, while losses in the 1990s tended to be concentrated in the Rust Belt, in the 2000s, losses were widespread. If anyone would have predicted in the mid-1990s that the state that would lose the second largest share of manufacturing jobs in the 2000s would be North Carolina, they would have been dismissed. Yet, North Carolina lost 43 percent of its manufacturing jobs, second only to Michigan. In fact, every southeastern US state lost more than 31 percent of its manufacturing jobs (Atkinson et al. 2012, 18) (see figure 16.1). We see the same dynamic with loss of manufacturing jobs in metro areas. Rust Belt metros like Buffalo, Rochester, and Detroit lost a significant share of manufacturing jobs, but so did “new economy” metros like San Jose and Charlotte (Atkinson et al. 2012, 19) (see figure 16.2). We also see this same dynamic in overall capital investment trends. Manufacturing investment in machinery, equipment, and software was 13 percent lower in 2011 than it was at its peak in 1998. Investment in manufacturing structures fared even worse, falling by over 54 percent between 2000 and 2011 (Stewart and Atkinson 2013). We also see the same loss of “supply” in the trends in major corporate relocations and new investments in the United States. From 1995 to 2000, the average number of new or expanded facilities per year was 5,139. From 2000 to 2005 these fell to 3,896 per year, and from 2005 to 2011, they fell even farther to just 2,824 per year.4 Foreign direct investment (FDI) in the United States also reflects this new, more competitive environment. For many decades from the 1960s through the 1990s, many states were able to get a not insignificant boost to growth from attracting foreign direct investment. From the Japanese car transplant factories located in the Midwest and upper South to European companies along the East Coast, FDI was a key part of US economic growth.

4 

“Editorial Archive,” Site Selection, March issues, 1999–2012, accessed September 26, 2012, http:// www.siteselection.com/pastissu.cfm; Atkinson and Correa 2007.

326    Critical Drivers of Local Competitiveness

–1.6%

–22.8%

–24.9%

–32.7%

–26.2%

–24.6%

–35.9%

–27.3%

–13.2% –10.8%

–46.7%

–12.8%

–34.6%

–32.6% –39.5% –39.5% –11.9%

–33.2%

–19.6%

–33.2%

–34.7% –34.2%

–35.4%

–32.7% –27.2% –30.7%

–30.3%

–37.5%

–38.5%

–19.9%

–18.8% –10.8%

–36%

–38.9%

–43.5%

–32.6%

–42.4%

–38.7% –38.4% –33.6%

–37.7%

–38.4% –38.4%

–34.6% Less than 20% Job Loss

–23.9% –20.7%

6.8%

–33.2%

20%–30% Job Loss 30%–40% Job Loss –31.9 %

Over 40% Job Loss

–2.1%

Figure  16.1  Manufacturing Job Change by State, 2000–2010 (Bureau of Labor Statistics, Quarterly Census of Employment and Wages (manufacturing employees by state; accessed March 15, 2012), http://www.bls.gov/cew/.)

6.0%

25%

8.1%

20%

6.1%

7.1%

6.3%

15%

5.9%

5.5% 5.4%

5.2%

4.5% 4.3%

2000 2012

10% 5% sti sA n ng ele s Bu ffa lo Cl ev ela nd M ilw au Un ke e ite d St at es Lo

se Jo

Au

it

n

tro

Sa

De

tte lo ar

Ch

en id

ov Pr

Ro c

he

ste

ce

r

0%

Figure  16.2  Manufacturing Employment as Share of Total Employment (Bureau of Labor Statistics, Quarterly Census of Employment and Wages (manufacturing employees, total nonfarm employees, by metro area, seasonally adjusted; accessed March 16, 2012), http://www.bls.gov/cew/; Bureau of Labor Statistics, Current Employment Statistics (manufacturing employment, total nonfarm employment, seasonally adjusted; accessed March 16, 2012), http://www.bls.gov/ces/.)

While the relatively high level of FDI in the United States is sometimes touted as a bright spot amid the economic gloom, the reality is that the vast majority of this investment now takes the form of acquisitions of existing US businesses, rather than the establishment of new businesses—known as “greenfield” investment. From 1998 to 2008, acquisition investment grew at a rate of 2.9 percent per year, while greenfield investment declined at a 6.1 percent annual rate (Stewart 2012). By 2008, greenfield investment was just 7 percent of new FDI outlays.

Swimming Upstream   327

Causes of Weaker “Supply” Since the Great Recession was clearly a financially induced crisis, many believe that as bad mortgage loans and other troubled assets are worked out of the financial system and the banks stabilize, the US economy will return to a course of revitalized and sustained growth, just as it has for almost 250 years (Reinhart and Rogoff 2009). But while the US economy retains many strengths, what contributed to the Great Recession, and what the Great Recession itself has since masked—and further amplified—is a deeper and more serious problem: an unprecedented long-term structural decline of US economic competitiveness. To paraphrase Reinhart and Rogoff, authors of the 2009 book This Time It’s Different: Eight Centuries of Financial Folly, “This time it really is different.” In today’s global economy, nations must compete fiercely to retain and attract mobile investment. But in contrast to states competing by “smokestack chasing” 40 years ago, most nations now compete by “innovation chasing,” trying to grow and attract the highest-value-added economic activity they can: the high-wage, knowledge-intensive manufacturing, research, software, information technology (IT), and services jobs that power today’s global, innovation-based economy. Indiana is a case in point. It no longer touts its abundant clay, but now markets itself as a place “where innovation, discovery, and success are nurtured” and “that provides a pipeline of bright minds and new thinking” (Atkinson and Ezell 2012, 5). It is this intense race for global innovation advantage that most clearly distinguishes today’s global economy from the collection of regional and national economies that competed to attract “smokestacks” a generation ago. Nations around the world are establishing national innovation strategies, restructuring their tax and regulatory systems to become more competitive, expanding support for science and technology, improving their education systems, spurring investments in broadband and other IT areas, and taking a myriad of other pro-innovation steps. But unlike the competition between states in the United States, where they generally played by national rules established in the Constitution, many competitor nations have undertaken a new approach, “innovation mercantilism” (Ezell and Atkinson 2010). Innovation mercantilism can entail stealing intellectual property (IP), discriminating against foreign technology firms, requiring foreign firms to transfer technology for market access, or manipulating currency, and has become a mainstay of many nations’ game plans in the new global competition (Ezell, Atkinson, and Wein 2013). A large share of the decline of US manufacturing jobs and output has stemmed from the increase in the US trade deficit. While the United States has been running a trade deficit in manufacturing for more than three decades, it grew considerably worse after 2000. During the ensuing decade, the United States accumulated an astounding aggregate negative $5.5 trillion trade balance in goods and services with the rest of the world.5 5 

U.S. Census Bureau. Foreign Trade (U.S. trade in goods and services, balance of payments and basis), http://www.census.gov/foreign-trade/statistics/historical/gands.txt (accessed December 1, 2011).

328    Critical Drivers of Local Competitiveness In no year in the 2000s did the United States have a negative global trade balance of better than negative $360 billion; and in five of those years, the annual trade deficit topped $600 billion. To put this in perspective, during each of those five years, on average, each American household imported $5,450 in goods and services that was not matched by equivalent exports. In just five years, every American household got the equivalent of a new BMW essentially on credit, since we were not exporting an equivalent amount. Narrowing our view to just the goods deficit, the story is even worse: from 2006 to 2008, the United States accrued a trade deficit in goods of at least $823 billion annually. The goods trade balance for the 2000s decade was negative $7 trillion.6 Thanks to this, since 2000, the US share of world exports has declined from 17 percent to 11 percent, even as the European Union’s share held steady at 17 percent over that time period (Theil 2010). In fact, between 1999 and 2009, America’s share of world exports fell in almost every industry: by 36 percentage points in aerospace, nine in information technology, eight in communications equipment, and three in cars.7 Many Americans comfort themselves by thinking that the vast majority of the US trade deficit in goods is comprised of oil; cheap, low-value items, such as clothes, toys, or knickknacks; or the mass-market consumer electronics we have gotten used to importing from Asia. Surely, the United States must have a positive trade balance in advanced technology products from industries such as life sciences, medical devices, optoelectronics, information technology (IT), aerospace, and nuclear power. But no, the United States has run a deficit in advanced technology products since 2002. In fact, in the 10-year period from the beginning of 2002 to the end of 2011, the United States ran a trade deficit in advanced technology products every year tallying a $526 billion deficit in advanced technology products over that time period.8 And over that period the trend worsened virtually every year; indeed, the United States ran an $81 billion trade deficit in advanced technology products in 2010 and a $99 billion deficit in 2011.9 Notwithstanding the intensity of this new competition, as recently as 15 years ago, many nations did not even think they were competing. And if they did acknowledge a contest, they thought they were in last century’s quest for smokestack industries like steel mills, shipbuilding, textiles, and other labor- and/or capital-intensive industries. Today, however, most nations recognize that they have to be intense competitors if they are to be successful, as more and more firms can now produce goods and services virtually anywhere on the globe. And most nations also realize that high-wage innovationand knowledge-based industries play a key role in driving prosperity. There are now only a few nations still blind to these new realities, and unfortunately the United States is one. A bit like the old car rental commercial from the 1970s, the United States still thinks

6 Ibid. 7 

“This Time It’s Serious,” The Economist, February 18, 2012. U.S. Census Bureau, “Trade in Goods with Advanced Technology Products,” https://www.census. gov/foreign-trade/balance/c0007.html#2011. 9 Ibid. 8 

Swimming Upstream   329 of itself as Hertz (“We’re number one”), while most other nations think they are Avis, and as number two, they must try harder. The more difficult economic development environment in the United States decline has two underlying causes. The first is the deterioration domestically of fundamental sources of US competitiveness, from decaying industries and infrastructure to an erosion of US innovation capacity reflected by a weakened innovation ecosystem, a faltering education system, and a relatively poor environment for innovation and investment. The second cause is that foreign countries are competing more fiercely and strategically than ever to attain the standards of living and wealth that American citizens have come to take for granted. They want what we’ve got. This is not simply the rebalancing of global economic activity to a more even distribution as seen in the decades after World War II, as the US share of global GDP slid from 46 percent in 1946 to 24 percent in 2009 (Julius 2005). Such a rebalancing can happen without the United States losing millions of high-paying jobs in manufacturing and technology or with US growth rates being anemic. Rather, this is about the United States losing its presumptive leadership in many of the highest-value-added, often technology-based sectors of economic activity. It is a competition for the future, particularly for the kinds of jobs capable of sustaining the standards of living to which American citizens have grown accustomed.

Implications for Economic Development Policy In response to this tougher competition (more places doing economic development, less supply of business investment), doing more of the same will not be enough, for at their core US subnational economic development efforts are still premised on the assumption of a healthy national economy, within which states and regions are trying to better position themselves. As described, that is no longer the case. Subnational economic development efforts will need to evolve into a distributed system to drive national economic competitiveness. In the new global economy, many now claim that the actions of states and metropolitan areas will play the key role in determining innovation and national competitiveness. In fact, some even argue that much of economic policy should be decentralized to the regional level. To be sure, as urban agglomerations and knowledge sharing have become more critical components of a nation’s economic success, the actions of states and regions have assumed greater importance in determining national competitiveness. However, it is a dangerous illusion to believe that state or city policy actions alone can solve the US competitiveness challenge. Unless the federal government also acts and develops an effective national innovation and competitiveness strategy, all the state and city actions in the world will not be enough. In short, state (and to a lesser degree city) economic development policies play a necessary, but not sufficient, role in boosting and maintaining national competitiveness.

330    Critical Drivers of Local Competitiveness This is true for two key reasons. First, effectively addressing the competitiveness challenge will require considerably more public investment than states and cities can afford. The resources available to the federal government, even in an era of budget deficits, are considerably more than those available to the states and cities combined. While states might invest several billion dollars in R & D, the federal government invests approximately $140 billion annually (President’s Council 2012). While states might provide R & D tax credits and other corporate tax incentives, the federal corporate tax rate is approximately six times higher than the average state rate.10 Second, addressing the competitiveness challenge will require more than action in the United States; it will require action directed abroad to dramatically reduce unfair and protectionist foreign trade practices (Ezell, Atkinson, and Wein 2013, 4). Only the federal government can prosecute a more proactive trade policy that fights foreign mercantilist actions, including currency manipulation, closed markets, intellectual property theft, standards manipulation, high tariffs, forced offsets for market access, and other unfair trading practices. As such, we need a more fundamental transformation based on three key changes. First, more states and cities will need to transition their economic development efforts away from zero-sum (often in fact, negative sum) activities, to competitiveness-supporting, positive-sum activities. Second, the federal government will need to more effectively support positive-sum state and regional competitiveness efforts. And third, the federal government will need to develop its own competiveness agenda that will boost overall investment in the United States (Atkinson and Stewart 2012).

Shifting from Negative-Sum to Positive-Sum Activities By the mid-2000s states and cities were spending over $47 billion dollars annually on industrial recruitment efforts (Thomas 1988). Much of this support went to firms that were never going to consider locations outside the United States, and some went to firms, like retail stores, that are not even in traded sectors. States and regions could be more effective enablers of national competitiveness if they reoriented their efforts toward helping firms in globally traded sectors to better compete. States should start by taking steps to limit local communities’ within-state, zero-sum strategies—in other words, communities spending money to lure one firm from another community in the state. There are several ways to do this. States could develop tax-base-sharing proposals. These would require a portion of any increase in 10  OECD Tax Database, “Basic (non-targeted) corporate income tax rates” (Paris: Organization of Economic Co-operation and Development, 2013), Table II.1, http://www.oecd.org/ctp/tax-policy/ Table%20II.1-May-2014.xlsx.

Swimming Upstream   331 commercial and industrial property tax revenues to be shared, giving all communities an incentive to cooperate in the economic development of the region. If shared tax-base revenue collected from industrial and commercial property goes to schools and training, for example, it can lead to an increase in overall welfare. States could also make receipt of various state funds contingent on signing no-compete agreements stipulating that local governments will not provide incentives to in-state firms to relocate within the state. States can also make sure that any state programs (like state-owned industrial parks) are not used to support movement of firms from one community in the state to another. States should also work to reduce interstate zero-sum competition. Over the last several decades, states have occasionally considered interstate compacts or other agreements to collaborate more on economic development and engage in less zero-sum-based competition. But, generally, these efforts fail to make it through states’ legislative processes. Yet, given the critical need for such collaboration in these desperate economic times, there is hope that the field for these policies is now more fertile. Toward that end, regional state groups, such as the New England Governors’ Conference, and national organizations like the National Governors Association (NGA) could actively work on developing shared principles that states can sign onto to move more of their economic development efforts toward positive-sum efforts. They could start by agreeing to a one-year moratorium on financial incentives for firm relocation, except for US firms that would otherwise moves jobs outside of the United States or for foreign multinationals that require incentives to move jobs to America. While states and communities can reduce incentives on zero-sum competition, they can also expand incentives and programs to spur win-win results that benefit both the state and the nation. These include workforce-training programs, technical assistance for small and medium-sized manufacturers, entrepreneurial support programs, university technology commercialization programs, and other similar enterprise support programs. To date, unfortunately, the discussion of the state and federal role in competitiveness has largely been kept separate. States do their thing; the feds do theirs. Instead, it is time for a new state-federal partnership for innovation and competitiveness. Both parties bring valuable resources to the table. The federal government is able to marshal resources and drive incentives so that state actions benefit the entire nation, rather than simply redistributing economic resources within the nation. But in an economy where economic policy increasingly must focus on firms, industries, and knowledge-enhancing institutions, as opposed to simply managing the business cycle, states are ideally situated, as they are closer to firms, especially small and medium-sized enterprises, and have more control over some innovation infrastructure inputs (such as public higher education). However, an effective partnership will not be possible unless the federal government begins to see states and regions as important partners. All too often the feds believe that there is one uniform national economy where regional agglomerations are a sideshow at best. Moreover, to the extent states and regions even have a policy role, it is too

332    Critical Drivers of Local Competitiveness often to follow the federal government’s lead. A true partnership will require that federal decision-makers and program managers understand that states and regions can play an important role and that a top-down, one-size-fits-all federal approach will only stifle the most important role states and regions can play: generating policy innovations and developing policies and operating programs suited to the unique requirements of their regional economies. Given this new understanding, the federal government should maintain and even expand support for key programs such as the Manufacturing Extension Partnership, the Small Business Innovation Research program, and SBA’s Small Business Investment Company program. In addition, there are at least two new areas of partnership that would have important implications. First, the federal government should create a $250 million State Industry/ University Cooperative Research Center Program modeled after NSF’s IUCRC program that funds university-industry cooperative research centers. Second, Congress should create a “high road” state economic development matching fund of at least $2 billion per year. Such a “Winning Through Regional Innovation” fund that would provide matching grants to states to support their innovation-based, win-win economic development policies and programs/funding would be contingent on states reducing zero-sum, within-nation smokestack chasing and would have to be spent on activities that make the overall US economy more globally competitive. States that provide financial incentives to firms that simply move a job from one state to another would receive relatively less money from the WTRI fund compared to states that invest more in win-win, national competitiveness strategies.

Becoming Buffalo or Boston? So in an era of intense global competition for economic development, where does this leave the United States and, for that matter, older industrial regions like Europe and Japan? Looking back to the United States of the mid-1970s, it is important to note that not all Northeast-Midwest regions were fated for relative decline. Some, in fact, transformed themselves and thrived. A case in point is Boston, which like Buffalo, New York, lost much of its industry to the South, especially textile and shoe firms in search of cheap labor. Boston looked like it was on the same path to decline as Buffalo. But unlike Buffalo, Boston reinvented itself. With the growth of the Cold War and defense spending, Boston’s early success in electronics (much of it a spin-off from the Massachusetts Institute of Technology) grew into a thriving industry. Its long-standing strength in financial services provided a base for expansion. But by the mid-1980s, Boston’s future again looked troubled. Much of the region’s computer industry had placed its bets on the minicomputer, and firms like Data General, Digital Equipment Corporation (DEC), and Wang all went into bankruptcy with the emergence of the California-based personal computer (PC) industry, centered in the more dynamic Silicon Valley. But Boston would rebound again around its three long-standing pillars:  leading-edge research

Swimming Upstream   333 universities, a large number of talented and well-educated residents, and a venture capital industry willing to invest in the future. By the 2000s, the region’s IT industry had reinvented itself. Boston also became one of the world’s leading hubs of biotechnology. And it retained a strong financial services sector. Indeed, if Massachusetts were a nation, it would be the most innovative nation on earth, according to the Information Technology and Innovation Foundation’s (ITIF’s) Atlantic Century II report (Atkinson and Andes 2011). So if Boston could rebound to win the race, can the United States? Perhaps the single most important question confronting the United States (as well as Europe and Japan) is whether over the course of the next quarter-century it will become Boston—and rise from its decline through innovation and economic transformation—or Buffalo—and sink further into relative economic decline. “Becoming Boston” means moving aggressively into next-generation industries, including advanced IT, biotechnology, nanotechnology, robotics, and high-level business services, while at the same time maintaining a share of highly efficient and competitive traditional industries (such as autos, machine tools, chemicals, and so forth), and continually raising productivity in “nontraded” sectors such as retail and health care. “Becoming Buffalo” implies losing out in the competition for new, globally traded industries, continuing to lose shares in existing manufacturing industries, and experiencing slow productivity growth in nontraded sectors. Becoming Boston means putting in place an aggressive national innovation-based economic strategy, which includes both increased government investment in innovation and lower taxes on corporate investment in innovation. Becoming Buffalo implies doing what we’ve been doing:  cutting government investment in innovation while seeing our overall corporate tax system become less competitive compared to other nations’ as each year goes by. Becoming Boston means waking up to the crisis, becoming full-throated advocates—indeed, zealots—for innovation, and embracing a new kind of economics (“innovation economics”), which puts advancing innovation at the forefront of economic policy. Becoming Buffalo means continuing in our somnolence about the nature of the global race for innovation, erecting barriers to innovation, and placing our faith in a neoclassical economics dogma that holds that countries don’t compete, that innovation is “manna from heaven,” and that government action to spur innovation only makes things worse. To be sure, Boston’s academic infrastructure made the region ripe for innovation, but the fact remains that Boston and Buffalo took very different approaches, and this has made all the difference. And the United States can do the same—or not.

References Atkinson, Robert D., and Scott Andes. 2011. Atlantic Century II. Washington, DC: ITIF. http:// www.itif.org/files/2011-atlantic-century.pdf. Atkinson, Robert D., and Daniel K.  Correa. 2007. The 2007 State New Economy Index. Washington, DC: ITIF. http://www.itif.org/files/2007_State_New_Economy_Index.pdf.

334    Critical Drivers of Local Competitiveness Atkinson, Robert D., and Stephen J. Ezell. 2012. Innovation Economics: The Race for Global Advantage. New Haven, CT: Yale University Press. Atkinson, Robert D., and Luke A.  Stewart. 2012. The 2012 State New Economy Index. Washington, DC: ITIF. http://www2.itif.org/2012-state-new-economy-index.pdf. Atkinson, Robert D., Luke A. Stewart, Scott M. Andes, and Stephen J. Ezell. 2012. Worse Than the Great Depression:  What Experts Are Missing about American Manufacturing Decline. Washington, DC: ITIF. http://www2.itif.org/2012-american-manufacturing-decline.pdf. Ezell, Stephen J., and Robert D. Atkinson. 2010. The Good, the Bad, and the Ugly. Washington, DC: ITIF. http://www.itif.org/files/2010-good-bad-ugly.pdf. Hopkins, Ernest J. 1944. “Mississippi’s BAWI Plan: An Experiment in Industrial Subsidization.” Federal Reserve Bank of Atlanta. Julius, Deanne. 2005. “U.S. Economic Power:  Waxing or Waning?” Harvard International Review 26 (4), 14–19. Mattera, Philip, Kasia Tarczynska, and Greg LeRoy. 2013. Megadeals:  The Largest Economic Development Subsidy Packages Ever Awarded by State and Local Governments in the United States. Washington, DC: Good Jobs First. http://www.goodjobsfirst.org/sites/default/files/ docs/pdf/megadeals_report.pdf. Norton, R. D., and J. Rees. 1979. “The Product Cycle and Spatial Decentralization of American Manufacturing.” Regional Studies 13, 141–51. Osborne, David. 1988. Laboratories of Democracy. Boston: Harvard Business School Press. President’s Council of Advisors on Science and Technology. 2012. Transformation and Opportunity: The Future of the U.S. Research Enterprise. Washington, DC: Executive Office of the President. http://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast_ future_research_enterprise_20121130.pdf. Reinhart, Carmen M., and Kenneth Rogoff. 2009. This Time Is Different: Eight Centuries of Financial Folly. Princeton, NJ: Princeton University Press. Stewart, Luke. “The Sad Reality behind Foreign Direct Investment in the United States.” The Innovation Files (blog), October 23, 2012. http://www.innovationfiles.org/the-sad-realitybehind-foreign-direct-investment-in-the-united-states/. Stewart, Luke A., and Robert D.  Atkinson. 2013. Restoring America’s Lagging Investment in Capital Goods. Washington, DC:  ITIF. http://www2.itif.org/2013-restoring-americaslagging-investment.pdf. Theil, Stephan. 2010. “European Firms Beat American Rivals.” Newsweek, April 15. http://www. newsweek.com/european-firms-beat-american-rivals-70297. Thomas, Kenneth P. 2010. Investment Incentives and the Global Competition for Capital. London: Palgrave Macmillan. Warner, Mildred E., and Lingwen Zheng. 2013. “Business Incentive Adoption in the Recession.” Economic Development Quarterly 27, 90–101.

Pa rt  I I I

C OM P E T I T I V E N E S S AT  T H E L O C A L  L E V E L

Chapter 17

C ompetitive A dva ntag e s from Univ e rsi t y Research  Pa rk s Albert N. Link

Introduction A university research park is a cluster of technology-based organizations that locate on or near a university campus in order to benefit from the university’s knowledge base and ongoing research. The university not only transfers knowledge but expects to develop knowledge more effectively given the association with the tenants in the research park. … [Parks] are a mechanism for the transfer of academic research findings, a source of knowledge spillovers, and a catalyst for national and regional economic growth. —Link and Scott 2007, 661, 662

My point of departure from this encompassing definition of a university research park is a focus on the flow of knowledge—“a source of knowledge spillovers”—from research parks to both existing firms and nascent firms in the region of the park and elsewhere. It is this flow of knowledge that has the potential to generate competitive advantages. The flow of knowledge from a university research park is not a new theme in the academic domain. Scholars have emphasized the flow of knowledge from university research parks, but those that have focused on the prevalence of this phenomenon in the United States have been somewhat limited in the availability of data related both to the genesis of the park and to the performance of the park (i.e., the performance of its in-park tenants).1 1 

See Leyden and Link (2013), Link (1995, 2002, 2003, 2009), Link and Link (2003), and Link and Scott (2003a, 2003b, 2005, 2006, 2007).

338    Competitiveness at the Local Level The performance measures that I focus on in this chapter are two: patents received and scholarly publications emanating from the research conducted by in-park firms. To place those flows of knowledge in perspective, I compare such in-park firm performance to matched pairs of off-park firms in an effort toward quantifying the impact of a firm being located in a university research park. Other scholars have employed a matched pairs analysis, but this chapter represents the first report of findings from matched pairs analyses that are specific to US university research parks. With this background in mind, readers should view the arguments and findings below as both exploratory and preliminary. Clearly, the importance of the topic merits further investigation. As suggested above, there have been several previous park studies using a matched pairs analysis. For example, Lindelöf and Löfsten (2003, 2004) conducted a matched pairs analysis of 134 on-park and 139 off-park Swedish firms. These authors found that there are insignificant differences between on-park and off-park firms in terms of patenting and the development of new products. However, they found that firms located on science parks appear to have different strategic motivations than comparable off-park firms. More specifically, on-park firms seem to place a stronger emphasis on innovative ability, sales and employment growth, market orientation, and profitability than did off-park firms. Relatedly, Ferguson and Olofsson’s (2004) analysis of Swedish science-park firms found no significant differences between on-park and off-park firms in terms of their sales or their level of employment. Finally, Fukugawa (2006) analyzed the value added to firms by Japanese science parks. He found that firms located in these parks are more likely than observationally equivalent off-park firms to develop links with universities. The construction of the matched pairs of firms that I describe and analyze in this chapter is discussed in section 2. The two performance measures considered—patents received and scholarly publications—are defined in section 3, and descriptive statistics on these performance measures are also presented in that section. In section 4, the empirical findings from my comparative analyses are presented and discussed. This chapter concludes in section 5 with a brief summary interpretation of the findings and a call for universities to think about the benefits that their research park creates.

Matched Pairs Firms The matched pairs sample of research firms considered in this chapter consists of 172 firms, 86 on-park firms and 86 off-park firms. Matching of firms was based on three dimensions: technology area, size (i.e., R & D budget or number of R & D scientists),

Competitive Advantages from University Research Parks    339 and organizational structure (i.e., research laboratory of a larger firm or an independent research firm). The matching process that I employed is as follows. As of the end of 1997 there were 64 university research parks in operation with, in total, over 3,000 park tenants, not all of which are research firms. Based on published information about these parks and information from other sources, a sampling population of firms was established. The section criterion was that each research firm in the sampling population was so located for at least 10 years. With the assistance of either the operating officer, other key personnel, or scientific employees in each of these sampling population firms, each firm was then partitioned by the three dimensions noted above: technology area, size, and organizational structure.2 For each park, a balanced representative sample (i.e., representative in terms of the above three dimensions) of research firms was created. Through interviews, I arrived at a final sample of 86 matched on-park and off-parks firms from the population of 64 university research parks. Not all parks were represented equally in the final sample because of a lack of willingness to participate in the study. I obtained from the contact individuals in each firm in the sampling population of park firms information on the two performance measures of interest:  the number of patents received and the number of scholarly articles published between the 10-year window 1997 and 2007. These are the performance measures considered in the analysis below.

Performance Measures of Matched Pairs Firms The following model was considered: Research Performance = f (Innovation Resources, URP).

(17.1)

where Research Performance is a vector of firm innovation output measures, Innovation Resources is a vector of internal innovation inputs and capabilities of the firm, and URP is a vector of university research park characteristics. Table 17.1 shows the general categories of data that were collected from each of the 172 firms relevant to the estimation of equation (17.1). Each measure within each variable category is briefly defined, and caveats are noted in the footnotes to the table.

2 

The information sources for each firm differ based on access and the willingness of individuals within each firm to participate in the data collection process. Any error that this cross-firm characteristic of the data introduces into the model in equation (1) below is assumed to be random and to be captured by the error term in the model.

340    Competitiveness at the Local Level Table 17.1  Categories of Variables Relevant to Equation (17.1) Variable category

Variable measures

Description and comments

Patents received

Number of patents received per year over the years 1997 through 2007 that have been assigned to each firm Number of peer-reviewed papers published in a journal or in a conference proceeding per year over the years 1997 through 2007 by employed scientistsa

Research Performance

Publications Innovation Resources

URP

R&D

R & D/sales resources per year over the years 1997 through 2007b

On/off park

Binary variable for the firm being on/off a university research park (1 = on; 0 = off) Binary variable if the university has a medical school (1 = yes; 0 = no) Binary variable if the university has an engineering school (1 = yes; 0 = no)

Medical school Engineering school

a In many case cases, especially for smaller firms, publication information was not known exactly,

and thus it was approximated by a range. In those situations the mean of the range was used as the annual datum. b In many cases both R & D expenditures and R & D personnel were collected, but for the smaller

firms R & D personnel data were problematic because not all scientists were uniquely classified in a single employment category (i.e., the owner who was a scientist did R & D and also had managerial responsibilities). Thus, only expenditure data were used.

Table 17.2  Mean Values of Variables Used in Preliminary Estimation of Equation (17.1) Variable

On-park sample (n = 86)

Off-Park Sample (n = 86)

Avg. patents per year Avg. publications per year Avg. R & D/sales (%) Medical school Engineering school

2.099 3.896 0.946 0.362 0.487

0.990 3.231 1.070

Table 17.2 shows mean values for the variables used in the preliminary, and parsimonious, specification of equation (17.1). That said, the parsimonious specification of the model tells an interesting story, one that is appropriate for demonstrating the competitive advantages from university research parks with an understanding that future research on this topic is needed.

Competitive Advantages from University Research Parks    341 Patents and publications represent distinct, albeit limited, competitive advantages that are attributable to a university research park. On the one hand, patents represent an appropriable competitive advantage, one definitely realized by the patenting firm over time and perhaps also realized by the university if the underlying technology was licensed or if a faculty member was a co-assignee. On the other hand, publications represent a nonappropriable competitive advantage with public good characteristics that emanated from the park.3 In either case, these performance measures, as the data below indicate, are on average greater among on-park firms than among off-park firms, thus making a suggestive case that the environment created by a university research park confers competitive advantages in the form of new flows of knowledge.

The Empirical Findings With reference to table 17.3, there are three notable findings. First, on-park firms have received more patents than comparable off-park firms, and the scientists in the on-park firms have published more scholarly articles than scientists in comparable off-park firms, all else remaining constant. This conclusion is based on the positive and statistically significant coefficient on the binary variable On-park in the first specification in both column (1) and column (2). Second, the R & D intensity of the firm is not generally correlated with the firm’s research performance, all else remaining constant. The estimated coefficient on R&D/Sales is positive in each of the four specifications of equation (17.1), but the estimated coefficient is only marginally significant in the first specification for average patents received (column (1)). The estimated coefficient on R&D/Sales is not significant in either of the average publications per year specifications (column (2)). In other words, the relative scale of research of a firm, whether an on-park or off-park firm, is not a significant covariate with its research performance as measured herein. And third, note that a second specification of equation (17.1) was also estimated for each research performance variable. In that specification, the binary variable On-park is deleted and replaced by two binary variables denoting if the university has a medical school, Medical school, or an engineering school, Engineering school. On-park firms for which the attendant university has either a medical school or an engineering school patent more than comparable off-park firms, all else remaining constant. The presence of an engineering school is a significant covariate for publications, but the presence of a medical school is not.

3 

No attempt was made to determine the percentage of scholarly publications that were co-authored with university faculty.

342    Competitiveness at the Local Level Table 17.3  Negative Binominal Regression Preliminary Results (standard errors, n = 172) Variable

(1) Avg. Patents Received per Year

On-park

1.309 (0.368)***

R&D/Sales

0.321 (0.196)*

(2) Avg. Publications per Year 0.740 (0.385)**

0.301 (0.192)

0.120 (0.205)

0.063 (0.205)

Medical school

1.258 (0.435)***

0.281 (0.465)

Engineering school

0.827 (0.417)**

0.723 (0.445)*

Intercept Log Likelihood 2

χ

0.486 (0.391)

0.679 (0.348)**

3.043 (0.409)***

0.623 (0.329)**

–35.50

–35.11

–36.72

–36.90

21.92

21.30

23.99

24.33

* Significant at the .10 level; ** significant at the .05 level; *** significant at the .01 level or better.

Summary Remarks The empirical results from these empirical analyses are rather self-explanatory, but the implications may be more subtle, especially when viewed in the light of the theme of this Handbook: local competitiveness. As reported by Battelle (2013, 15): Looking to the future, although research parks are typically designed to accommodate significant growth on the original property, roughly one in five of the university research parks surveyed expect to max out their existing properties and plan on increasing the area of their parks within the next 5 years.

The implications of this forecast, given the competitive advantages from university research parks suggested from the exploratory findings in this chapter, are that universities with research parks might evaluate the “benefits” that they receive from the knowledge flows related to the environment created by their park. Figure 17.1 is a simplistic model that might guide a university as it assesses the benefits associated with its park, that is, the benefits of having its park continue to grow and remain research vibrant. Certainly, the environment of a research park confers benefits to the university itself in terms of an intellectual exchange among faculty and firm scientists. What is suggested by the empirical findings above is that the park benefits the on-park firms in terms of creating an environment that is reflected in their patenting behavior and publication

Competitive Advantages from University Research Parks    343

Host University

On-Park Firms

In-Migration of New Firms

Park

Local Community

Global Economy

Figure 17.1  Model for How a University Might Assess the Benefits of Having a Research Park

behavior. This is characterized by the vertical arrow from Park to On-Park Firms. But, as the excerpt from Battelle (2013) above suggests, if this relationship is important, and if other relationships exist (as shown in figure 17.1), universities might begin to think in terms of all of the competitive advantages associated with their park. Firm scientists might serve as adjunct faculty, and faculty and graduate students might consult with or be employed by the firms. These symbiotic relationships could lead to jointly assigned patents and coauthored publications. Note the two-way arrow between Park and Host University, and note the indirect relationship suggested by the dashed arrow from On-Park Firms to the Host University. Similarly, the park benefits the local community in terms of employment and overall economic growth. And as the local community grows, it might become attractive to the in-migration of new firms into the region, and possibly into the park. Note the arrows from Park to Local Community, from In-Migration to Local Community, and the indirect relationship that might exist between In-Migration and Park. Finally, the flow of knowledge from the Park to the Global Economy generates intangible benefits to the university not only in terms of reputation but also in terms of potential licensing of its technologies through its office of commercialization. In other words, the flow of knowledge from a university research park has broad ramifications for the university and the local community, and possibly for the global economy. Indeed, placing the empirical findings from the analysis presented in this chapter beside the cautionary warning from Battelle (2013), universities might indeed think strategically about how to grow their research park and maintain its research health.

References Battelle. 2013. Driving Regional Innovation and Growth: Results from the 2012 Survey of North American University Research Park. Columbus, OH. Ferguson, R., and C. Olofsson. 2004. “Science Parks and the Development of NTBFs: Location, Survival and Growth.” Journal of Technology Transfer 29, 5–17. Fukugawa, N. 2006. “Science Parks in Japan and Their Value-Added Contributions to New Technology-Based Firms,” International Journal of Industrial Organization 24, 381–400.

344    Competitiveness at the Local Level Leyden, D. P., and A. N. Link. 2013. “Collective Entrepreneurship: The Strategic Management of Research Triangle Park.” In D. B. Audretsch and M. Walshok, eds., Creating Competitiveness Entrepreneurship and Innovation Policies for Growth. New York: Edward Elgar. Lindelöf, P., and H. Löfsten. 2003. “Science Park Location and New Technology-Based Firms in Sweden: Implications for Strategy and Performance.” Small Business Economics 20, 245–58. Lindelöf, P., and H. Löfsten. 2004. “Proximity as a Resource Base for Competitive Advantage:  University-Industry Links for Technology Transfer.” Journal of Technology Transfer 29, 311–26. Link, A. N. 1995. A Generosity of Spirit: The Early History of the Research Triangle Park. Research Triangle Park: Research Triangle Foundation of North Carolina. Link, A. N. 2002. From Seed to Harvest: The Growth of the Research Triangle Park. Research Triangle Park: Research Triangle Foundation of North Carolina. Link, A. N. 2003. “University-Related Research Parks.” Issues in Science and Technology 79–81. Link, A. N. 2009. “Research, Science, and Technology Parks: An Overview of the Academic Literature.” In C. Wessner, ed. Understanding Research, Science and Technology Parks: Global Best Practice. Washington, DC: National Academies Press. Link, A. N., and K. R. Link. 2003. “On the Growth of Science Parks.” Journal of Technology Transfer 28, 81–85. Link, A. N., and J. T. Scott. 2003a. “The Growth of Research Triangle Park.” Small Business Economics 20, 167–75. Link, A. N., and J. T. Scott. 2003b. “U.S. Science Parks: The Diffusion of an Innovation and Its Effects on the Academic Missions of Universities.” International Journal of Industrial Organization 21, 1323–56. Link, A.  N., and J. T.  Scott. 2005. “Opening the Ivory Tower’s Door:  An Analysis of the Determinants of the Formation of U.S. University Spin-off Companies.” Research Policy 34, 1106–12. Link, A. N., and J. T. Scott. 2006. “The Economics of University Research Parks.” Oxford Review of Economic Policy 23, 661–74. Link, A.  N., and J. T.  Scott. 2007. “U.S. University Research Parks.” Journal of Productivity Analysis 25, 43–55.

Chapter 18

The C o-cre at i on of L o cally U se fu l Knowled ge by Bu si ne s s Scho ol s Simon Mosey, Paul Kirkham, and Martin Binks

The Historical Role of Business Schools The first business school was founded in Paris, where in 1819 a group of scholars and businessmen, including the economist Jean-Baptiste Say, founded the École Spéciale de Commerce et d’Industrie (now known as the École Supérieure de Commerce de Paris, ESPC). In common with many early business schools the primary role was upon teaching, and, more specifically for ESPC, language education was a central component to allow students to participate in the international trade prevalent in Paris at the time. ESPC also provided classes for local entrepreneurs, with Say developing one of the early definitions of an entrepreneur as someone who unites all the means of production (Say 2001, 61). Over time ESPC grew and developed a culture of research and today has 950 staff and over 4,000 postgraduate students. As in most leading business schools, the majority of the students are international, and the primary research focus is upon multinational businesses. While many students commonly state ambitions to work for blue-chip companies, the simple fact is that most of them will not. Some of the best and most dynamic will start their own businesses, often close to their alma mater. Many others will make their

346    Competitiveness at the Local Level livings in small and medium-sized enterprises, many of which will be family firms rooted in local economies. We therefore question the relevance of much of what is taught in modern business schools, particularly with reference to the needs of the local economy. It is difficult to see how theories based on such a narrow focus prepare students for what they will face in practice. This is reinforced by Starkey, Hatchuel, and Tempest (2004, 18), who report the case of the dean of a University of California management school who realized that her most useful knowledge “came primarily from engagement with and listening to practising managers, not from the scholarly work emanating in such quantities from the academy.” A longtime advocate of a broader, more dynamic approach is Roger Martin, former dean of the Rotman Business School, who argues: As a whole, MBA programs have taught students a suite of tested and trusted models, but left unanswered the problem of what to do when those models don’t apply or start to break down. As a result, we have left our graduates under-prepared to function in the ethically-murky, complex world outside of the classroom. (Martin 2010, 17)

Martin prescribes a significant change in research focus as necessary for business schools to survive. He proposes a transition toward understanding the creation of new business rather than the current obsession with describing the activities of multinational businesses. Yet business schools are self-satisfied and have little incentive to change their focus because of their phenomenal financial success. For instance, in the US business schools have exhibited massive growth in the last 100 years from 0 to 30 percent of the market by student numbers and from 0 to 50 percent by revenue (Martin 2013a). However, Martin confidently predicts the imminent and abrupt collapse of this market: “Half of US business schools will be out of business in five to seven years because of online disruption” (Martin 2013b). At the same forum Clayton Christensen, a management professor at Harvard Business School, warned: The advent of online learning, and the propensity of more and more companies to bring teaching of management in-house, versus outsourcing it, makes disruption a very big deal for business schools. (Christensen 2013).

The twin threats of internal redundancy and external disruption suggest it might be time to change. It would be somewhat ironic if business schools, with all their theoretical knowledge of entrepreneurship and creative destruction, were unable to rise to the challenge. Such a transition is profound, however, as it requires a paradigm change in the nature of the majority of research conducted, in terms of not only the content but also the method of study. Research considering international business is observational in nature, after which the findings are abstracted and disseminated to students. The direction of flow of knowledge in business school teaching is from the master to the

The Co-creation of Locally Useful Knowledge    347 apprentice. The nature of the knowledge imparted tends to be static; it is “ancient wisdom” to be passed on as a canon along with interpretation. Historically, in at least one school within the university, the flow does run both ways. In the case of a medical school the transaction is obvious—“I’ll cure you of that nasty little rash if you let my students have a look at how I do it.” This is fairly one-sided, as the patient is in no fit state to bargain, but the benefits are concrete. A first step for business schools may therefore be to build similar relationships with local organizations. They need to engage with small businesses in order to study them, but what do they offer in return? Research suggests that, in the rare instance, when business schools engage with the local firms, they have comparatively little to offer (Lucas 2011). A series of policy reports from the UK government have commented upon this disconnect: “There is still a gap between what the UK further and higher education system provides, and what manufacturers need” (Lucas 2011, 5). And there are “complaints that universities are often hard to work with, and the fact that too much research undertaken in the UK higher education does not find its way into viable British companies” (Lucas 2011, 17). According to the Confederation of British Industry, “only 10 percent of innovative enterprises in the UK cooperate with a university or another higher education institution” (Wainer 2009, 25). The UK government response has been to address this disconnect as a “market failure” and provide sources of funding to incentivize business schools and local businesses to work more closely together (Young 2013).

Opportunities for Business Schools to Engage Locally Wilson (2012, 23) summarized the potential value that universities can bring to the local economy in the UK: Universities are an integral part of the supply chain to business, a supply chain that has the capability to support business health and therefore economic prosperity. A thriving knowledge economy depends upon its universities in three critical dimensions: the application and exploitation of research capability; the enterprise and entrepreneurial culture that is developed amongst its students; and the applicability of the knowledge and skills of all its graduates.

Specifically considering business schools, Cox (2013) highlighted the main opportunities for engagement as being in skills development programs, knowledge transfer and consultancy, access to graduate talent, networking, strategic research, and development. However, she observes that medium-sized local businesses already had sophisticated training plans and were utilizing a blend of individual trainers, small businesses, and

348    Competitiveness at the Local Level internal trainers for their delivery. Business schools reported a relatively small market share of this business with less than 10 percent of such businesses opting for university providers. A more optimistic view of the potential of business schools to contribute toward local economic impact is proposed to be through the mobilization of students: There are currently in the region of 130 business schools in the UK, the majority of which belong to universities. They have been one of the biggest success stories of UK higher education over the past 60 years, enjoying a remarkable rise: in 2010 15% of all HE students in the UK were studying business and management in publicly funded UK universities (at foundation, undergraduate and postgraduate levels), and another 20,000 in private institutions. This arguably makes Business and Management Studies the largest academic provider of talent in the UK with an extensive skills and knowledge base. (Cox 2012, 5)

This resonates with the research of Salter and Martin (2001), who conclude that the largest economic impact that universities provide is through the provision of students with advanced knowledge and the capabilities to apply that knowledge to solve industry problems. In the following section we therefore consider examples of business schools from around the world that have recognized new opportunities to develop locally useful knowledge and built faculty and student capabilities to deploy that knowledge.

Developing Locally Useful Knowledge within Business Schools It is apparent that the development of locally useful knowledge is a significant challenge for modern business schools. Here we define useful knowledge, following Kuznets (1992), as being generated through the interaction of basic and applied research with societal challenges. Business schools are often criticized for their shortfall in this domain, yet business schools can and do develop such knowledge and use it to form the basis of two-way exchange with local companies. The ideal is for a business school to be a mediator and co-creator of knowledge (Lourenco, Jones, and Jayawarna 2013). The following case example illustrates the principle: Kay and Jonathan are in the floristry trade in a large provincial market town. Although they own the freehold to four shops, inherited from Kay’s father, life is increasingly tough. At an executive education workshop hosted by Nottingham University Business School, in the UK, they summed up their challenge thus: “How to compete effectively in a market with low profit margins.”

The Co-creation of Locally Useful Knowledge    349 Possible solutions fell into two categories—those that concentrated on more efficient use of their staff and premises, and those that sought to add value by expanding the services they could offer to differentiate them from the competition, using facilities and competencies that were unavailable to their rivals. Examples included diversification of the business into provision of flower-arranging classes, a cafe/teashop, and an expanded delivery service. It became obvious to observers that floristry was an extremely tough business. Profit margins were around 16 percent, while wastage in raw materials could easily top 10 percent. The competition came from both sides: large supermarkets, where economies of scale forced down prices, and also smaller, home-based enterprises with minimal overheads. This concrete example made it easy to introduce the concept of “Blue Ocean Strategy” (Kim and Mauborgne 2005) as well as research from Scott Shane that typical start-up “entrepreneurs” work longer hours for less money and that many such businesses start with around $25,000 worth of investment and only last for about two years (Shane 2007). And so it became clear that not only are Kay and Jonathan competing against the might of big business, they are also competing against individuals working out of their spare rooms and garages, a considerable number of whom, despite their best efforts, after a period of operating at a loss on the margins of legality, are statistically certain to go bust. When an inefficient business goes under, there is a knock-on effect as the remaining, more efficient competitors find their market swamped with bankrupt stock and secondhand machinery. Competition of this sort can easily drag down whole areas of business. The value realized from this transaction is that the participating faculty gained a new case study, the business owners learned some relevant and contextualized theory that they could put into practice, and all participants gained fresh insights into the day-today problems of local small businesses—real knowledge exchange and the co-creation of new, locally useful knowledge. This is particular case but not an isolated example. Lancaster University Management School, in the UK, has engaged in such activities for many years, and their research shows that when engagement occurs and useful local knowledge is exchanged, there is a measureable benefit for the local economy. They developed a program called Leading Enterprise And Development (LEAD). This was initially supported by funding from the local government for management school faculty to engage with local small business owner-managers, using pedagogical techniques such as experiential learning and providing additional support through mentoring and staff exchanges. The program grew, was adopted by other regional business schools, and demonstrated a significant return upon the public-sector investment. Over 1,000 high-growth businesses have been supported across the North West, and external evaluation has shown that every £8,000 invested generated average turnover growth per business of £200,000 (Cox 2012). The efficacy of faculty providing locally useful knowledge is clearly evident, yet such impact is ultimately limited by the number of faculty. As Salter and Martin (2001) suggest, perhaps even greater economic impact could be delivered locally through involving the much larger student body.

350    Competitiveness at the Local Level

Developing the Capabilities of Students to Deploy Locally Useful Knowledge It might be remarked that knowledge exchange with a florist is beneath an august institution like a business school and its privileged students but the vast majority of businesses in virtually all sectors are small and confront similar constraints, conditions, and opportunities. It is the richness of their experience in practice that is so valuable as a source of learning for students of business. “Live” case studies, preferably imparted by the businesses concerned, are a powerful and memorable source of understanding akin to laboratory studies in many of the pure sciences. The businesses gain as well. It is worth restating the obvious point that every large business started out as a small business. Business schools are, on the one hand, producing the future giants of business who all want blue-chip company internships on their CVs. On the other, they are producing the entrepreneurs of the future who only want to work for themselves. Yet statistically, most businesses lie in between these atypical extremes: “The UK’s 3 million family businesses represent two in three of all private sector firms and account for a quarter of UK GDP” Cox (2012, 21). Upon graduation, business school students employed in these firms may find themselves to be the only graduate on the payroll, and so it is essential that they gain experience at all levels of business. Many students attending UK business schools, perhaps a majority of overseas students, will end up working for a family business, where practical wisdom and dealing with ambiguity are as vital as accountancy knowledge. Yet the delivery of such practical wisdom requires a profound change in teaching and learning approaches. It requires an integrative learning experience “whereby knowledge is created through the transformation of experience” (Kolb 1984, 41). This builds on the assumptions that such learning is a process, not an outcome; requires individuals to resolve dialectically opposed demands; is holistic and integrative; and requires interplay between a person and the environment (Kayes 2002, 140). This is in stark contrast to business school practice where undergraduates sit in darkened lecture theaters and focus upon abstract theory and hone their skills at writing essays critically discussing such theory (Binks, Starkey, and Mahon 2006; Mintzberg 2004; Holcomb et al. 2009). It is notable, therefore, to observe leading US business schools pioneering practical knowledge-based pedagogical interventions. For example, at Stanford University students learn through immersion in actual rather than theoretical business. The module Technology Entrepreneurship and the Lean Startup has the goal to “Provide an experiential learning opportunity for engineers to see how entrepreneurs really build companies. In ten weeks, teach a four-person team how to transform a technology idea into a venture-scale business opportunity. Do it by having them get outside the classroom and test each element of their business model” (http://e245.stanford.edu/). On his website supervising professor Steve Blank states, “Congratulations to all the teams. They taught us a lot.”

The Co-creation of Locally Useful Knowledge    351 Another example is the Yale Entrepreneurial Institute, which started as a stand-alone program in 2007 to encourage students to start scalable new ventures. As YEI program director Alena Gribskov tells students: “There’s no substitute for talking to customers. It can be very anxiety-inducing, because you have to talk to strangers. But until you do, you will not have real data—you will only have assumptions” (http://yei.yale.edu/). It is interesting that both Stanford and Yale are pioneers of massive online open courses (MOOCs), demonstrating that internationalism and localism are not necessarily mutually exclusive. Such practice has also been adopted by US business schools based within areas suffering from economic decline, where a focus upon local issues has provided additional value for students. Alison Davis Blake, dean of Michigan Ross School of Business, argues that her students “need to discover their own purpose; we need to test the assumption that our students are adults and know why they are here. They need to learn how to create purpose and meaning for others with whom they work and learn how to use the power of the firm to address complex societal problems” (Davis Blake 2013). Similarly in the UK, faculty are recognizing the need for practical immersion in local business challenges by students. An ambitious approach has been created, developed, and refined over the last 10 years at Nottingham University Business School. Here every undergraduate takes a course presenting a formal analysis of entrepreneurship in theory and practice leading on to a consideration of creativity and business concept generation. The course concludes with the practical application of these theories and concepts in business planning and business concept presentation. Students are challenged to identify a problem, need, or opportunity and propose a business solution, be it a new product or service, to “pitch” to their assessors. It would be impossible to teach this course without the involvement of local entrepreneurs and businesspeople who take a central role in mentoring the students to help define societal challenges and evaluate new business ideas to address those challenges. From the examples above it is apparent that students can be mobilized to work on local business challenges and work with local business people to develop new business ideas, yet it is less clear how such approaches can be adopted to the extent suggested by the UK government (Witty 2013).

Recognizing the Opportunity for Business Schools to Engage with the Local Economy Of all the schools within the university, business schools should be best placed to recognize new opportunities within the market, a capability Kirzner (1985) argued to be the essence of entrepreneurship. First, business schools often maintain considerably

352    Competitiveness at the Local Level autonomy over resource allocation that other schools envy. Second, they are typically the home of the rapidly growing field of entrepreneurship education, with Katz (2003) reporting over 2,200 courses in entrepreneurship being taught worldwide. Yet there seems to be a disconnect between the teaching and the practice of entrepreneurship by business school faculty. It appears that in their haste to meet the increasing demand for entrepreneurship teaching, business schools have recruited adjunct faculty that are subsequently excluded from strategic decision-making by business school leaders (Hebert and Link 2006). Such leadership remains dominated by research professors intent upon preserving historical traditions. Moreover, even when entrepreneurial leaders are recruited to instigate change, they encounter considerable resistance from faculty. Roger Martin eloquently categorizes the barriers to change within the majority of business schools worldwide. He argues that it is difficult to change faculty attitudes, as they typically get paid double the salary of comparable social scientists elsewhere in the university. When leading Rotman School he encountered faculty that would “fight to the death if I took anything off them and asked them to try something new even if they ultimately would perish in the process” (Martin 2013). Martin observed, however, that younger faculty could be encouraged to work in a different way and could be supported by nontenured staff to help them to deliver small business and entrepreneurship offers. Through pursuing this strategy Martin grew the Rotman School from a $13 million to $130 million annual turnover. A different approach to achieve a similar outcome was seen in Italy, where Bocconi has created a competence center in public-private interaction, which acts as link between academe, local authorities, and businesses. Here faculty and students work on projects to help public-sector managers to encourage entrepreneurship in local schools, hospitals, and civil services. Nevertheless, Bocconi have encountered more difficulty than Rotman with scaling up this approach because of the lack of political stability and hence sustained external resources to grow the center (Cennamo 2013). A contrasting and more radical approach is seen in Canada, where Quebec Seeks Solutions (QSS) was launched in June 2010. This enterprise is a combination of online and face-to-face open innovation. It is a four-stage process: first is an online “call for problems” from local businesses and public-sector organizations. From these a selection is made by the organizers. The third stage is to describe and broadcast these problems and failed solutions through a web-based platform, in order to recruit the best problem solvers available from faculty and students. Finally a showcase event in the form of a conference is held to share insights and solutions. The most apparent gain for all was networking, but very real solutions have resulted. Further events have been held both at home in Canada and abroad, in Finland and Spain. The natural home for this sort of model of co-creation of new knowledge, relevant and supportive of the local economy, ought to be the local business school. Alongside the economic benefits and learning opportunities that this integration of students with their subject matter engender, the research potential is clearly invaluable and unique.

The Co-creation of Locally Useful Knowledge    353

Conclusions Business schools internationally stand at a crossroads. They face Clayton Christensen’s “innovator’s dilemma” of balancing the continuation of their legacy offer with developing a disruptive proposition (Christensen 1997). We highlight examples of business schools that are experimenting with disruption and see that by providing sustained leadership for the development of locally useful knowledge they can, somewhat counterintuitively, survive the short-term vagaries of local political support. Moreover, the key to scaling and sustaining such activity seems to be within the student body. If business schools reposition their teaching and learning toward more experiential methods where students work together with local leaders to address small business growth challenges, create valuable new ventures, and encourage entrepreneurship within the public sector, then this can provide a source of income that transcends the turbulence of the international student markets.

References Binks, M., K. Starkey, and C. Mahon. 2006. “Entrepreneurship Education and the Business School,” Technology Analysis and Strategic Management 18 (1), 1–18. Cennamo, C. 2013. “Sustainable Enterprise Models Innovation.” Academy of Management Conference, Orlando. Christensen, C. M. 1997. The Innovator’s Dilemma. Boston: Harvard Business School Press. Christensen, C.  M. 2013. “The Future of Management Education.” Thinkers 50 Forum, London. Cox, S. 2012. “Business School / Mid-Sized Business Collaboration: Supporting Growth in the UK’s Mid-Sized Businesses.” Report for Business Innovation and Skills, London. Davis Blake, A. 2013. “The Future of Business and the Role of Business Education.” Academy of Management Conference, Orlando. Hebert, R. F., and A. N. Link. 2006. “Historical Perspectives on the Entrepreneur.” Foundations and Trends in Entrepreneurship 2 (4), 261–408. Holcomb T.  R., R. D.  Ireland, M. R.  Holmes, et  al. 2009. “Architecture of Entrepreneurial Learning: Exploring the Link among Heuristics, Knowledge, and Action.” Entrepreneurship Theory and Practice 33, 167–92. Katz, J.  A. 2003. “A Chronology and Intellectual Trajectory of American Entrepreneurship Education.” Journal of Business Venturing 18, 283–300. Kayes, D. C. 2002. “Experiential Learning and Its Critics: Preserving the Role of Experience in Management Learning and Education.” Academy of Management Learning and Education 1 (2), 137–49. Kim, W. C., and R. Mauborgne. 2005. Blue Ocean Strategy: How to Create Uncontested Market Space and Make the Competition Irrelevant. Boston: Harvard Business School Press. Kirzner, I. M. 1985. Discovery and the Capitalist Process. Chicago: University of Chicago Press. Kolb, D. A. 1984. Experiential Learning-Experiences as the Source of Learning and Development. Englewood Cliffs, NJ: Prentice-Hall.

354    Competitiveness at the Local Level Kuznets, S. 1992. “Modern Economic Growth: Findings and Reflections.” In Assar Lindbeck, ed., Nobel Lectures, Economics, 1969–1980. Singapore: World Scientific Publishing. Lourenco, F., O. Jones, and D. Jayawarna. 2013. “Promoting Sustainable Development: The Role of Entrepreneurship Education.” International Small Business Journal 31, 841–65. Lucas, J. 2011. “Manufacturing for Export: Make or Break for the British Economy.” British Chambers of Commerce, August. Martin, R. 2013a. “The Future of Business and the Role of Business Education.” Academy of Management Conference, Orlando, 2013. Martin, R. 2013b. “The Future of Management Education.” Thinkers 50 Forum, London. Mintzberg H. 2004. Managers Not MBAs: A Hard Look at the Soft Practice of Managing and Management Development. San Francisco, CA: Berrett-Koehler. Salter, A.  J., and B. R.  Martin. 2001. “The Economic Benefits of Publically Funded Basic Research: A Critical Review.” Research Policy 30, 509–32. Say, Jean-Baptiste. 2001. A Treatise on Political Economy; or the Production, Distribution and Consumption of Wealth. Trans. C. R. Princep. Canada: Batoche Books. Shane, S. 2007. The Illusions of Entrepreneurship. New Haven, CT: Yale University Press. Starkey, K., A. Hatchuel, and S. Tempest. 2004. “Rethinking the Business School.” Journal of Management Studies 41, 1521–32. Wainer, R. 2009. Stronger Together:  Businesses and Universities in Turbulent Times. Confederation for British Industry Higher Education Task Force, UK. Wilson, T. 2012. A Review of Business-University Collaboration. Report for Business Innovation and Skills, London. Witty, A. 2013. Encouraging a British Invention Revolution:  Sir Andrew Witty’s Review of Universities and Growth. Report for Business Innovation and Skills, London. Young, L. 2013. Growing Your Business. Report for Business Innovation and Skills, London.

Chapter 19

Entrepreneurship and Sustainable Development The Relevance of Shaping Intertemporal Local Intangible Conditions José L. González-Pernía, Maribel Guerrero, and Iñaki Peña-Legazkue

Introduction What seems to matter is the capacity to build on early events or accidents through entrepreneurial actions and the construction of institutions. —Feldman 2014, 15

Over the past decades, the academic community has witnessed a long-lasting debate about the complex linkage between entrepreneurial activity and local economic development. The vast majority of theoretical contributions and empirical evidence provide support of the existence of a positive relationship between entrepreneurial activity and economic growth. Using metrics for “measureable” factors, the literature has reached a large consensus on the principle that business creation generates rents, jobs, new products, and often the emergence of new industries. Yet little is known on the relationship between the “invisible” factors embedded in a local community that favor entrepreneurship and economic development. The goal of this work is to underline the relevance of time (i.e., historical trajectory) and space (i.e., contemporary social capital) for building a local entrepreneurial context that contributes to enhanced prosperity and quality of life (i.e., regional development). With the advent of economic recession, the Basque region has experienced an undercapitalization of firms. The region has gone from a business fabric made up of more than 175,000 companies in 2008, to less than 154,000 companies in 2013. This phenomenon

356    Competitiveness at the Local Level is the result of the fact that more companies have closed than have been created during this recessionary period. In other words, the annual business start-up rate has not been high enough to compensate for the companies that shut down each year. This indicates that the current entrepreneurial activity is not strong enough to respond to the crisis and reverse this drop of the total number of Basque companies. During a period of economic downturn, an important goal of policymakers is to minimize the economic damage, getting back on the path to growth as quickly as possible. Incumbent firms, in addition to being fewer in number because of the crisis, are under pressure to be more productive and competitive in order to preserve the level of prosperity in Basque society (or have the least possible negative effect). Because there are fewer of them, to achieve this threshold of well-being, incumbent companies must be more efficient and competitive than in the past. This self-strengthening mechanism of incumbent firms will require new market entrants to exhibit a higher degree of competitiveness, which they will have to acquire during the critical early stages of their existence. In other words, entrepreneurs who enter the market must have the same competitive ability as, or greater competitive ability than, the incumbent companies that have best withstood the hardships of the crisis. In the precarious current environment, entrepreneurship takes on special importance: first, in order to battle the problems resulting from a growing trend of business closures, and second, to lay the foundations for a future business and institutional fabric that will make it possible to return to earlier rates of economic wealth (or even exceed them). If the local economy is to remain competitive, there is a need of more and better entrepreneurs. To promote entrepreneurship leading to economic development, a process of transforming local tangible and intangible conditions becomes crucial. This process depends, to some extent, on the historical trajectory and the space characteristics of the location. The Basque region has not yet been able to fully develop the entrepreneurial mindset and context required to alleviate the negative effects of the current crisis. This is partly due to the fact that in addition to experiencing an economic crisis, Basque society is also experiencing a crisis of entrepreneurial values, values like those instilled by the region’s entrepreneurs of five centuries ago. Interpreting the historical trajectory of local communities may help in discovering new opportunities (which are difficult to understand and identify for nonlocal individuals) and in replicating past successful behaviors of local residents (and entrepreneurs!). In essence, the transmission of entrepreneurial values among generations is an important regional asset, a tacitly inherited asset that may explain the prosperity of certain regions. When the social capital of a region reflects a living spirit of a community (i.e., rather than a dormant myriad of socioeconomic institutional shareholders), the transformation of a local area occurs more rapidly and effectively. Entrepreneurship is at the core of the constant change and refinement of the social capital embedded in a local community. To explain further these ideas, we will use an example that we know best: the Basque region.

Entrepreneurship and Sustainable Development    357

The Relevance of Time: Looking to Ancient Entrepreneurial Values A look back at the past suggests that the Basque area was once home of ambitious entrepreneurial enterprises. Some time ago, the Basque region was well known for its tradition and ability to hunt right whales. The right whale was a mammal that visited the Basque coast before continuing on to Newfoundland. Basque whalers put together expeditions (i.e., crews of 30–40 people) and when the opportunity presented itself, they pursued the cetaceans across the Atlantic Ocean, facing a range of challenges. Right whales were highly sought after by Basques, as the animal could be used to make a number of products: oil for lamps, bones to build houses, baleen to make umbrellas, and so on. Normally, the whale was captured in Newfoundland waters, and all the products obtained from the mammal were sold in major ports in North America and Europe, before whalers returned to Basque ports. In certain ways, these whaling enterprises were similar to today’s born-global companies, as Basque whaling ships took on supplies and sold their goods in foreign geographic areas. The vessels were provided with advanced technology. They required a wide range of sophisticated technology: marine instruments for proper navigation, fishing equipment and tools to capture the animal, special clothing to weather the ocean storms, food to survive and remain healthy during weeks at sea, and so. The British navy, the model of the age, wondered how Basque whalers managed to cross the ocean with so few losses among their crews. With all of this technology, Basque whaling enterprises could be considered akin to the high-tech companies we know today. Like hiring and provisioning a crew, building and maintaining a whaling ship was expensive. It should be remembered that we are talking about vessels crewed by 30–40 men. These were not microenterprises, but rather undertakings of considerable size. Indeed, this activity had characteristics similar to companies we now call high growth. These enterprises also involved many families on the Basque coast who supported the whale-hunting activity. These families represented a local specific social capital that helped nurturing and developing locally (and globally) this risky business. Moreover, without strong financial backing, it would be difficult to get a start in this business. Some Basque whalers used to raise money from noble families who lived in the Kingdom of Castile. They obtained financing from wealthy individuals who were willing to back the whaling enterprise, and most importantly, to make money from it upon the ship’s return, once they had cashed up after selling the whale products. We might even say that the relationship between whalers and financial backers would have been similar to the relationship between business angels and entrepreneurs today. In short, in discussing Basque whaling enterprises of five centuries ago, we are also alluding to concepts such as born-global, high-tech, and venture capital investment, all elements that characterize the entrepreneurial ecosystem enjoyed by certain regions that have experienced enviable regional development in recent decades (the Silicon

358    Competitiveness at the Local Level Valley model, for example). If the Basque coast had its own entrepreneurial ecosystem five centuries ago, what has happened to it? Many municipalities along the Basque coast still feature the image of a whale on their heraldry. These coats of arms are a tangible symbol that has remained for hundreds of years, but the “entrepreneurial values” that they may reflect have been forgotten and are buried along the Basque coast. The intangible dimension of this activity of hunting whales brought wealth and prosperity to the Basque coast: perseverance in the face of opportunity (whales did not appear every day; it was necessary to be patient and wait until the cetacean arrived in order to embark on an adventure in response to an opportunity that others could not, or simply did not, want to undertake), a risk-taking attitude (which involved not only financial loss to the enterprise, but also the very life of the whalers), knowledge of technological developments (modern instruments of all kinds for navigation, to hunt the animal, etc.), the skill to identify and strengthen strategic networks (new customers and suppliers in foreign countries, financial backers for the whaling enterprise in neighboring regions, etc.), and more. All of this reflects the intangible, multifunctional, collective capabilities required to build a region that helps cultivate and fuel entrepreneurial values. It is these entrepreneurial values that we need now, five centuries later, to help the Basque local region emerge from the economic crisis. From a time perspective, the awakening of the intergenerational absorptive capacity of a region to recover and enhance old entrepreneurial values is an important step in this upturn process of the economy.

The Relevance of Space: The Basque Country’s Contemporary Entrepreneurial Social Capital Throughout economic history, institutions have established the rules of society that shape human interaction (North 1990; 2005). In this sense, the building of institutions and the myriad of public/private decisions determines the character of a specific place (Feldman 2014). In each economic model,1 institutions are created and modified to facilitate the activity that serves as the driving force underlying economic growth and prosperity (table 19.1). This fact explains the main differences among types of economies (factor-driven, efficiency-driven, and 1 

Audretsch and Thurik (2004) and Audretsch and Keilbach (2004) identified two different economic models as the political, social, and economic response to an economy dictated by particular forces: the managed economy (the force is large-scale production, reflecting the predominant production factors of capital and unskilled labor as the sources of competitive advantage) and the entrepreneurial economy (the dominant production factor is knowledge capital as the source of competitive advantage, which is complemented by entrepreneurship capital, representing the capacity to engage in and generate entrepreneurial activity).

Entrepreneurship and Sustainable Development    359 Table 19.1  Economic Stages and Institutions Managed economy

Knowledge economy

Entrepreneurial economy

Economic stages Factor-driven Efficiency-driven Innovation-driven economy economy economy General Basic factor conditions A country’s advantage The ability to produce characteristics such as low-cost labor comes from producing innovative products and and unprocessed more advanced services at the global natural resources products and services technology frontier are the dominant highly efficiently. using the most advanced basis of competitive Heavy investment in methods becomes the advantage and exports. efficient infrastructure, dominant source of Factor-driven economies business-friendly competitive advantage. are highly sensitive to government An innovation-driven world economic cycles, administration, strong economy is characterized commodity prices, investment incentives, by distinctive producers and exchange rate improving skills, and a high share of fluctuations. and better access to services in the economy investment capital allow and is quite resilient to major improvements in external shocks. productivity. Institutional •  Basic institutions • Higher education and • Entrepreneurship characteristics •  Physical infrastructure training • Innovation • Macroeconomic stability • Efficient products and • Internationalization • Health and basic services market schooling • Efficient labor market • Sophisticated (reliable) financial markets • Curiosity about technology • International awareness Source: Own elaboration based on Audretsch and Thurik (2004), Porter, Ketels, and Valdaliso (2013) and Schwab, Sala-i-Martin, and Greenhill (2011).

innovation-driven). Proper coordination among institutions and agents (i.e., entrepreneurs, investors, policymakers, etc.) makes it possible to understand, build, and mature entrepreneurial ecosystems around the world (e.g., Silicon Valley, Tel Aviv, Boston, London, Moscow, Singapore, etc.). The development of entrepreneurial ecosystems has major consequences for the global economy and competitiveness, as well as significant implications for understanding how entrepreneurial ecosystems are built and how their odds of success can be improved in a given location (Herrmann et al. 2012). Under the space perspective, based on an exhaustive analysis of the Basque Country’s history, De Otazu and Díaz de Durana (2008) identified how Basque people have been involved in innovativeness, risk taking, and entrepreneurial activities inside and outside

360    Competitiveness at the Local Level the territory. Studies have also identified several elements, such as entrepreneurial mindset, ability to spot opportunities, a support infrastructure, foreign markets, and the attraction of talent/investors, that today characterize entrepreneurial ecosystems (Porter, Ketels, and Valdaliso 2013). Table 19.2 shows the main characteristics and factors involved in each evolutionary stage of the Basque Country’s entrepreneurial ecosystems, as well as the main elements of the contemporary entrepreneurial social capital (norms, reciprocity, trust, networks). For example, in the Middle Ages, the Basque economy was based on iron foundries and shipyards. As a result, the main public utilities (water, energy, etc.) and basic trade regulations were developed. Later, in the 1800s and 1900s, the economy was focused on modernizing those traditional sectors with greater investment in transport and communications infrastructure (ports, railways, etc.), educational systems (technical schools, business schools, etc.), and financial infrastructure (banks, stock exchanges, etc.), which attracted foreign investors, entrepreneurs, and also scientists. However, during the Franco years, the Basque Country attempted to maintain autonomy, but this was not possible. As a result, leaders, businessmen, investors, and skilled workers left the territory, while the remaining Basque firms had to cope with a climate of widespread labor turmoil, terrorist violence, and political uncertainty. These facts affected the sense of trust of the social and economic agents of Basque community to conduct all type of relationships. Afterward, with the coming of autonomy, the Basque Country’s strategies were planned by the government. An important element observed was an entrepreneurial leadership that allows rebuilding the system of social/economic linkages, the sense of trust of the community, and the standards of behaviors set from within the community itself. For instance, Navarro and coauthors (2013) describe three distinct stages in the modern history of the Basque region: (1) the 1980s, when the administration was created and was focused on industrial and economic restructuring based on investment in technological, educational, and physical infrastructure; (2)  the 1990s, focused on quality- and efficiency-driven competitiveness based on diversification, investment in infrastructure, human capital, partnership with several local, national, and European agents, and international expansion into Latin America; and (3) the 1999–2012 period, when government strategies were focused on R & D, innovation, diversification, and international expansion (i.e. Latin America, eastern Europe, and Asia), based on a range of actions such as investment in knowledge infrastructure, collaborations among agents, and education/training. The regional government has made a substantial effort to enhance the general business environment since the mid-1990s (Porter, Ketels, and Valdaliso 2013). Current support for entrepreneurship in the Basque Country clearly demonstrates two patterns: (1) actions aimed at reinforcing local competitiveness and economic development through programs to promote entrepreneurial culture, high-potential entrepreneurship, innovative technological growth, and corporate consolidation through the injection of smart capital; and (2)  actions designed to increase social inclusion through programs aimed at promoting self-employment, inclusion of minority groups (women,

Table 19.2  Evolution of the Basque Country’s Entrepreneurial Ecosystem

Economic stages

Managed economy

Knowledge economy

Entrepreneurial economy

Factor-driven economy

Efficiency-driven economy • 1980s • 1990s

Innovation-driven economy

Economic periods • Middle Ages •  1999–present •  1800s and 1900s • Franco years (constraints) Strategy •  Industrial revolution • Industrial restructuring • R & D based on innovation • Quality and and diversification efficiency-based • Cooperation and competitiveness competitiveness • Internationalization Main actors •  Entrepreneurs, investors •  Local, regional, •  Local, regional, national government, national and European entrepreneurs, skilled government, workers entrepreneurs, skilled workers, society Industry • Iron and shipbuilding • Traditional industries • Traditional industries industry (restructured and (restructured and •  Steel industry updated) updated) • Industrial diversification • Industrial diversification (aeronautics, ICT, (bio-tech, creative, cultural) nanotechnologies, renewable, energy, tourism) Entrepreneurial • Regulations and taxes •  Input-output analysis • Entrepreneurship ecosystem (free trade policy of no •  Explicit political support and programs elements tariffs) consensus on industrial (high-growth, women, • Physical infrastructure policies young, etc.) (utilities, transportation, •  Physical infrastructure • Financial support and communications) (finance, utilities, etc.) (business angel networks, • Attracting foreign • Technological venture capital, entrepreneurs/investors infrastructure loans, etc.) • Attracting European • Educational • Technological and scientists (wolfram was infrastructure scientific infrastructure discovered in 1783) •  Human capital • Local qualified human • Training and education improvement capital center (new technical •  Increasing appreciation • Entrepreneurship schools) = skilled of entrepreneurship in education and workers society training at different • Bilbao stock exchange educational levels and banks • Increasing support • Employer organizations and partnerships with other agents (local development agencies, cluster associations, universities) Source: Own elaboration based on Porter, Ketels, and Valdaliso (2013) and Navarro et al. (2013).

362    Competitiveness at the Local Level

Share of VC investment (% of total in Spain-Avg. 08–12)

immigrants, youth), pulse programs—projects to create jobs—and the creation of special sheltered employment centers. However, over the last decade, the evolution of budgets in the Basque Country showed a strong shift toward entrepreneurship programs aimed at fostering innovative entrepreneurship (IKEI 2011; Orkestra 2013). Based on this, the government’s main actions have been focused on knowledgegenerating activities. These were promoted first through strengthening corporate incentives and the creation of technological infrastructure. The government has created new kinds of research centers—Centers of Cooperative Research (CICs by the Spanish acronym)—for applied research, and Basque Excellence Research Centers (BERCs) for basic research. Three private universities and one public university in the region also contribute to generating new knowledge, while five business innovation centers (BICs) and other specific incubator centers (e.g., biotechnology and nanotechnology) support technology transfer activities via the creation of firms. At the same time, Basque firms have been encouraged to internationalize through new policies, including financial incentives and the involvement of the regional government through research and trade missions, as well as establishing representative offices abroad. As a result, Basque firms with facilities abroad and exports to foreign countries have grown over the last decade. Attracting foreign direct investment (FDI) has been also part of the development strategy followed by the regional government. In the late 1990s, it launched a major PR campaign in the global media to overcome the region’s negative image resulting from years of terrorism. By 2010, 6.8 percent of total regional employment came from the stock of inward FDI. In addition, in 2010, new public aid programs were launched to promote the formation of business angel networks. The government also has its own private equity firm, SGECR, which was created in 1985 and currently manages venture capital funds that provide financial capital for early-stage, high-potential, high-risk, growth start-up firms in the region. Apart from that, with an economy that represents 6.2 percent of Spanish GDP, the Basque region had a 4.9 percent share of total venture capital investment and 6.9 percent of all venture capital deals in Spain over the 2008–12 period (figure 19.1).

–5%

30% 25% 20% 15% 10%

Madrid

Catalonia

Basque Country

5% 0% 0% –5%

Andalusia 5%

10%

15%

20%

25%

30%

35%

Share of VC deals (% of total in Spain–Avg. 08–12)

Figure  19.1  Venture Capital Investment across Spanish Regions (Source: Own elaboration based on data from the Spanish Venture Capital Association (ASCRI in its Spanish acronym).)

Entrepreneurship and Sustainable Development    363 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 -

BC avg. rating 2004–2012 Spain avg. rating 2004–2012

Physical Commercial Intellectual Research and Social norms Taxes, Market dynamics Financing Educ.: primary Educ.: higher infrastructure infrastructure property development bureaucracy and secondary

Figure  19.2  Evaluation of the Basque Country’s Entrepreneurial Ecosystem (Source:  Own elaboration based on Peña-Legazkue et  al. (2012).)

All these actions and decisions from public/private agents have setting the current entrepreneurial and innovative character within the Basque Country. According to the Global Entrepreneurship Monitor (GEM) experts’ survey,2 in the Basque Country, the majority of entrepreneurial ecosystem elements were rated above the Spanish average for the 2004–12 period (figure 19.2). Nevertheless, there are some elements (the ability to identify opportunities, entrepreneurial values and culture, educational system, capital market, and bureaucratic barriers) that were rated less favorably than others (market dynamics, infrastructure, intellectual property system, minimization of bureaucracy). The role of policymakers in supporting the specific entrepreneurial ecosystem has been significant since 2000.3 The Basque government’s budget to promote entrepreneurship increased by more than seven times over 10 years (IKEI 2011). During the same period, the number of entrepreneurial projects that received public aid jumped from 653 per year to 1,565 per year. However, most of these were businesses with no growth potential. Start-ups with growth potential are fewer in number, but the amount of aid they received is around half of the annual public budget. Nevertheless, the Basque region has become a leading region in terms of local development and growth. In 1980, the region’s per capita income was 26 percent lower than the western European level. However, it had reached 136 percent of the EU-27 level by 2008 and ranked 23rd among the European regions with the highest per capita income. Despite the effects of the global recession from 2008 onward, the region has reported 2  The national experts’ survey is an instrument that makes it possible to gather the opinion of 36 experts on several environmental conditions that enable or constrain entrepreneurship by country/ region. In general, the answers are measured by a five-point scale, where 1 = completely false; 5 = completely true. For more details, visit www.gemconsortium.org. 3  Action in support of entrepreneurship in the Basque Country has been partly contingent on regulatory frameworks defined by (1) the European Commission to access the devices operated by the Directorate-General for Employment, Social Affairs and Equal Opportunities and the Directorate-General for Enterprise and Industry; (2) the Spanish state through the Ministry of Employment and Social Security and the Ministry of Industry, Energy and Tourism; and (3) the Basque government through the Industrial and Competitiveness Plans, the Science, Technology and Innovation Policy, and recently, by Law 16/2012 on support for entrepreneurs and small business.

364    Competitiveness at the Local Level the highest average GDP per capita and the lowest average unemployment rate in Spain over the last four years (see figure 19.3). Undoubtedly, becoming an entrepreneurial society requires a strong entrepreneurial ecosystem, significant commitment, and an entrepreneurial mindset among all stakeholders involved in this process with a common purpose (i.e. social, political, economic, etc.). This is one of the most important challenges facing the Basque Country. An initial step was taken in 2012 with the adaptation of the regional Law to Support Entrepreneurship, which was an important initiative to regulate and strengthen entrepreneurship in the Basque Country. In addition, at the national level, the Law to Support Entrepreneurs and Their Internationalization was passed by the lower house of Spanish Parliament on September 28, 2013. In general, the main strategic actions in both laws are aimed at supporting entrepreneurship, strengthening entrepreneurial culture, reducing taxes/ social security measures, and increasing fiscal incentives, financial support, and support for growth and internationalization. However, the effect of these regulatory actions in transforming local social capital (i.e., a contemporary social capital which improves the conditions to foster entrepreneurship) will only be seen in the middle or long term. From a space perspective, locally rooted collective actions designed and implemented by public-private partnerships are fundamental in shaping the social capital aimed at improving the conditions to enhance entrepreneurship and prosperity. The social capital embedded in a region must be built on intangible elements such as trust, altruism, and commitment, which will make a region distinctive and attractive to start-up firms.

Basque region

Basque region

€18,000 or less €18,001–€24,000 €24,001–€30,000 €30,000 or more GDP per capita (average 08–12)

12.0% or less 12.1%–18.0% 18.1%–24.0% 24.1% or more Unemployment rate (average 08–12)

Figure  19.3  Spanish Regions by GDP Per Capita and Unemployment  Rate (Source:  Own elaboration based on data from the Spanish Institute of Statistics  (INE).)

Entrepreneurship and Sustainable Development    365

Performance of Local Entrepreneurial Activity: An Alternate Shift of Regimes over Time? Despite its ancient legacy and efforts to build a contemporary entrepreneurial social capital, the performance of entrepreneurship in the Basque region does not appear to have been as outstanding as its improvement in local economic growth and its ability to sustain the level of wealth. Even though in terms of business creation the region was able to sustain a positive net entry rate over the decade from 1998 to 2008 (figure 19.4), its performance in entrepreneurial activity has experienced a decline in recent years. The number of business exits has exceeded the number of business entries since 2009. With an entry-to-exit ratio of less than 1:1, the total stock of businesses has decreased. The Total Entrepreneurial Activity (TEA) rate, which measures the adult population involved in entrepreneurial activities across countries and regions (Reynolds et al. 2005), confirms this negative trend, as fewer people in the region are currently entering into entrepreneurship (figure 19.5). A plausible explanation is that this reflects the effects of the economic recession in Spain. However, the decline in entrepreneurship experienced by the Basque region in the last years has been more pronounced than in Spain as a whole (including other Spanish regions) and the European Union. At first glance, these figures make policymakers concerned about the region’s capacity to become an entrepreneurial society in the way proposed by Audretsch (2007; 2009). Investment in a regional ecosystem for knowledge-based entrepreneurship has not been able to sustain economic growth since the beginning of the recession. Compared to other Spanish regions, the Basque Country continues to have the highest GDP per capital and lowest unemployment rate. Yet the impact of the recession on overall 20%

4

15%

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–5%

–1

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–2 Entry to exit ratio

Entry rate

Exit rate

Net entry rate

Figure  19.4  Business Demography in the Basque Country (Source:  Own elaboration based on data from the Basque Institute of Statistics (EUSTAT).)

366    Competitiveness at the Local Level 14% 12% 10%

TEA-GEM mean TEA-EU mean TEA-Spain TEA-Basque region

8% 6% 4% 2% 0% 2006

2007

2008

2009

2010

2011

2012

Figure  19.5 Total Entrepreneurial Activity (TEA) in the Basque Country and Other Economies (Source:  Own elaboration based on data from the GEM project (www.gemconsortium.org).)

12% 10% 8% 6% 4% 2%

a oj

try un

Ri La

rre va

cia

rid

Ba

sq

ue

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a

ia

ad M

lic

ur ad

Ga

cia tre

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len

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lo ta

Va

Ex

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sti

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Ca

-M

d an le

sti Ca

an

Le

ón

ria ab nt

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an

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ry

Isl

Isl Ca

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ds

ds

s

an

ria tu

ric

on

lea

As

sia

ag Ar

lu da

An

Sp

ain

0%

Entry rate (Avg. 09–12)

Exit rate (Avg. 09–12)

Figure  19.6  Entry and Exit Rates across Spanish Regions (Source:  Own elaboration based on data from the Spanish Institute of Statistics  (INE).)

entrepreneurship performance seems to be greater than in other regions. In all Spanish regions, the exit rate for the 2009–12 period was higher than the entry rate for the same period, but the difference between these rates was more significant in the case of the Basque Country (figure 19.6). Looking at this phenomenon in depth, we can distinguish between businesses without employees and businesses with employees, and from this view the picture is different. The decrease in the number of Basque businesses over the 2009–12 period is concentrated in those businesses that have no employees. As figure 19.7 shows, unlike other regions, most of the negative net entry rate in the Basque Country comes from the difference between entries and exits of businesses without employees. In addition, compared to other regions, the Basque Country has a high number of businesses per people in the labor force, greater than many other regions and Spain as a whole. The change in the ratio of businesses without employees per people in the labor force over the 2009–12 period was the second most negative change after Extremadura, whereas the change in the corresponding ratio for businesses with employees was greater than Spain as a whole and many regions such as Catalonia, Madrid, Aragon,

Entrepreneurship and Sustainable Development    367

a oj Ri

La

Sp ain An da lu sia Ar ag on As t Ba ur ias lea ric Isl a Ca nd na s ry Isl an ds Ca nt Ca ab sti ria le an Ca d sti Le león La -M an ch a Ca ta lo ni a Va len cia Ex tre m ad ur a Ga lic ia M ad rid M ur cia Na Ba va rre sq ue Co un try

1.0% 0.5% 0.0% –0.5% –1.0% –1.5% –2.0% –2.5%

Net entry (without employees)

Net entry (with employees)

Figure 19.7  Net Entry Rate Broken Down into Businesses without Employees and Businesses with Employees (Source:  Own elaboration based on data from the Spanish Institute of Statistics  (INE).)

5%

VAL

0%

RIO

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ARA CNT

2% 1%

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Spain

AND CLM CAN

–2%

Businesses with employees/100 people in the labor force (Var. 09–11)

Businesses without employees/100 people in the labor force (Var. 09–11)

4%

BAL

–3% EXT

–4% –5%

7

8

Basque Country 9

10

Businesses without employees/100 people in the labor force (2009)

11

–2%

AST CNT

–4% –6% –8%

AND

–10%

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RIO NAVMAD EXT

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ARA CYL

VAL

Basque Country

CAT BAL

CLM

Spain

MUR –12% –14% 6.0

6.5

7.0

7.5

8.0

Businesses with employees/100 people in the labor force (2009)

Figure  19.8 Dynamics of the Stock of Businesses per People in the Labor Force across Spanish Regions (Source:  Own elaboration based on data from the Spanish Institute of Statistics  (INE).)

and Andalusia (figure 19.8). Another sign of the quality of business projects is the entrepreneur’s motivation to start up a business in the Basque Country. Only 14.6 percent of the TEA rate during the 2006–12 period was driven by necessity, while 79.8 percent was driven by opportunity.4 This places the Basque Country among the Spanish regions with relatively better entrepreneurial activity (figure 19.9). Thus, the performance of entrepreneurial activity in the Basque Country seems to be comparatively poor overall, but not in the case of business creation activity that is directed toward 4 

The remaining 5.6 percent correspond to those entrepreneurs that are driven by reasons other than pure necessity or opportunity.

368    Competitiveness at the Local Level 84% NAV MAD

% TEA driven by opportunity (Avg. 2006–2012)

82%

ARA

CAT 80% 78%

Basque Country

RIO

AST BAL EXT

CAN GAL

CNT VAL MUR

76%

CYL

74%

AND CLM

72% 70% 14%

16%

18% 20% 22% % TEA driven by necessity (Avg. 2006–2012)

24%

Figure  19.9  TEA Driven by Opportunity and Necessity across Spanish Regions (Source:  Own elaboration based on data from the GEM project (www.gemconsortium.org).)

creating jobs. Despite this fact, the decrease in the number of Basque businesses becomes a major challenge for the transition from a managerial economy to an entrepreneurial society (Audretsch and Thurik 2001). It is known that this transition is driven by a change from a technological regime that favors large incumbent firms generating stocks of knowledge that are not easily transferable, to one characterized by knowledge spillover that favors entrepreneurs and new businesses (Acs, Audretsch, and Feldman 1994; Acs et al. 2009). When new businesses make a significant contribution to growth, it is said that the region is under an entrepreneurial regime, whereas when economic growth depends on an enterprise structure with a predominance of large and incumbent businesses, the region is under a routinized regime (Audretsch and Fritsch 2002). In contrast, regions characterized by low economic growth and high business creation are under a revolving-door regime, while regions characterized by low growth and low business creation are under a downsizing regime. These regimes vary not only across regions, but also across time periods. In the case of the Basque Country, it seems that the region was under an entrepreneurial regime until 2008, when business creation was accompanied by positive economic growth. However, the region currently seems to be under a routinized regime, because regional economic growth does not seem to depend much on business creation.5 This recent path of local entrepreneurial performance shows that local regimes are not stagnant. While

5  Indeed, a recent report by Orkestra (2013) provides evidence that, in the Basque Country, entry rates by industry (according to the NACE classification at the three-digit level) are more closely related to exit rates of businesses in existence for five or fewer years than exit rates of businesses older than five years. This suggests that the contribution of business creation to regional growth in recent years has been weak.

Entrepreneurship and Sustainable Development    369 entrepreneurial regimes exceptionally lead to new economic landscapes, local regimes in general react to changes of business cycles. Instead of following a lineal process (i.e., the transition from a managed to an entrepreneurial regime), the regimes seem to alternate back and forth over time in a given region.

Conclusion There is no one recipe to foster regional economic development, as trajectories and local conditions are heterogeneous across regions. Nonetheless, a good understanding of three regional elements becomes of critical importance in transforming a local economy: the time dimension, the space dimension, and the monitoring of regional performance. From a time standpoint, leaders of local institutions must understand the roots of ancient and recent history of their regions. A good sense of the historical trajectory may help in developing distinctive local core competences (i.e., a “history based” absorptive capacity of a region). The local space is an area full of networks. The characteristics of the connection among all sort of local actors are unique and region-specific. Public and private institutions are ruled by laws and habits that give vigor and energy to the local social capital. A shared (long-run) strategic vision and the shaping and deployment of these two locally embedded intangible elements, time and space, by local leaders is a fundamental condition to sustain regional development and to enhance local prosperity. These are relevant elements that will contribute to nurturing a self-reinventing process of a region. Feldman (2014, 17) holds that “[o]‌ften when the conventional factors do not account for differences in the economic performance of places, we attribute the difference to culture. But culture, rather than fixed and imputable, is socially constructed and defined. Cultures also change over time.” The Basque region needs to recover the entrepreneurial culture and its character of place. Unfortunately, the Basque region shows several weaknesses in all these fronts. A weak history-based absorptive capacity, a fragile connection among local actors, and the lack of a self-monitoring and reacting system by local policy leaders explains to some extent the insufficient entrepreneurial vigor, in comparison to that which would reverse the recent negative economic trend. There are several reasons that may explain why efforts to build an entrepreneurial ecosystem and investment in knowledge-generating activities in the Basque Country have not produced better results in recent years in terms of entrepreneurship performance. First, the development of an entrepreneurial ecosystem in the Basque Country has been led by the regional government (i.e. the Basque government and other local authorities). Since 1979, the Basque government and the region’s three provincial governments have had considerable autonomy, including setting tax rates and collecting their own taxes.6 6 

While the Basque Country and Navarre have tax autonomy, the other Spanish regions do not have such autonomy.

370    Competitiveness at the Local Level This, together with strong economic activity and efficient tax collection, has resulted in a public spending capacity that is greater than in other regions (Porter, Ketels, and Valdaliso 2013). As a consequence, most of the funds devoted to supporting entrepreneurship and incentives for new knowledge generation in the region come from the public sector. There is a need of a shared vision built and deployed through collective action (i.e., public-private partnership). Second, as a consequence of this public spending capacity, the regional governments have financed many initiatives designed to support entrepreneurship. The Basque government estimated that by 2010, there were around 300 supporting agents in the region (a region with approximately 2.1  million inhabitants and 160,000 firms). The public spending devoted to over 1009 programs to support entrepreneurship during year 2013 is over 60 million euros. Often, this overwhelming amount of local actors and programs results in an overlap in the aid intended for entrepreneurs. More coordination among the agents supporting entrepreneurship in the Basque Country is required to ensure that public spending has a greater impact on entrepreneurial performance. The local social capital can be more efficient and effective. Third, the participation of the private sector in leadership of the Basque entrepreneurial ecosystem is limited to using public funds and participating in forums for public-private dialogue. By engaging the private sector more, governments can build entrepreneurial ecosystems that are profit-driven and self-sustaining (Isenberg 2010). The involvement of the private sector means not only having access to its opinion when designing and implementing entrepreneur-friendly policies and programs, but also engaging it in financing new firms and the ecosystem. There are some cases in the Basque Country in which private partners have coinvested with public funds in new ventures, but this practice is not yet widespread in the region. However, a support platform known as CRECER+ does foster this sort behavior. Fourth, in addition to the lack of coordination among supporting agents, there is very little evaluation of Basque entrepreneurship policies and programs. Monitoring is needed. One of the main challenges associated with the use of public funds is the need to use them efficiently, and to measure additionality to confirm whether public policies and programs have a real impact on the performance of the recipient entrepreneurs and new businesses (Norrman and Bager-Sjögren 2010). Without effective public policy evaluation, the allocation of more public spending to activities aimed at fostering entrepreneurship will not necessarily result in better performance for entrepreneurial activity, or in a better entrepreneurial ecosystem. Lastly, entrepreneurial activity will have an impact on economic and social growth to the extent that it improves the quantity and/or quality of entrepreneurial projects. If a region has a limited number of poor-quality projects (i.e., projects with very few employees), the economic impact on the region will be low. On the other hand, if a region has many entrepreneurs with high-quality projects, the impact will be that much greater. Some geographic spaces have many entrepreneurial projects in which the majority of the businesses are of an individual nature (i.e., self-employed,

Entrepreneurship and Sustainable Development    371 self-subsistence entrepreneurial activity). In this case, the contribution to economic progress is virtually nonexistent, sustaining and maintaining the status quo rather than local development. In contrast, there are regions where fewer firms are created (i.e., there are few entrepreneurs), but these include companies of a larger size (i.e., more employees in their businesses). These employees receive paychecks, consume, pay taxes, and so on. In short, they contribute to economic growth (but not necessarily to economic transformation). In order to contribute to the economic growth of a region, it is possible to create more new companies, even if they are single-person; fewer companies but larger in size (i.e., with employees); or best of all, more companies that are larger in size. And if they are also innovative, develop new technologies, expand internationally, and have an ongoing drive to continue expanding, their contribution not only to economic growth, but also to the transformation and diversification of the region’s business fabric, will be that much greater. We believe that this process relies to a large extent on the development of local intangible factors that contribute to improving the conditions for entrepreneurship (e.g., absorptive capacity of an entrepreneurial legacy, and empowerment of local social capital for entrepreneurship). Improving local competitiveness requires strategic coordination of agents in an entrepreneurial ecosystem that makes it possible to adapt to the environment and to collectively transform the environment in a more dynamic and effective way. The goal of accomplishing a more entrepreneurial and competitive local territory will lead us to reclaim the values and habits of the region’s whalers of five centuries ago.

References Acs, Zoltan J., David B.  Audretsch, and Maryann P.  Feldman. 1994. “R&D Spillovers and Recipient Firm Size.” Review of Economics and Statistics 76 (2), 336–40. Acs, Zoltan J., Pontus Braunerhjelm, David B.  Audretsch, and Bo Carlsson. 2009. “The Knowledge Spillover Theory of Entrepreneurship.” Small Business Economics 32 (1), 15–30. Audretsch, David B. 2007. The Entrepreneurial Society. New York: Oxford University Press. Audretsch, David B. 2009. “The Entrepreneurial Society.” Journal of Technology Transfer 34 (3), 245–54. Audretsch, David B., and Michael Fritsch. 2002. “Growth Regimes over Time and Space.” Regional Studies 36 (2), 113–24. doi:10.1080/00343400220121909. Audretsch, David B., and A. Roy Thurik. 2001. “What Is New about the New Economy: Sources of Growth in Managed and Entrepreneurial Economies.” Industrial and Corporate Change 10 (1), 267–315. Audretsch, David B., and A. Roy Thurik. 2004. “A Model of the Entrepreneurial Economy.” International Journal of Entrepreneurship Education 2 (2), 143–66. De Otazu, Alfonso, and José Ramón Díaz de Durana. 2008. El espíritu emprendedor de los vascos. Madrid: Silex Ediciones. Feldman, Maryann P. 2014. “The Character of Innovative Places:  Entrepreneurial Strategy, Economic Development, and Prosperity.” Small Business Economics 43 (1): 9–20. doi:10.1007/ s11187-014-9574-4.

372    Competitiveness at the Local Level Herrmann, Bjoern Lasse, Max Marmer, Ertan Dogrultan, and Danny Holtschke. 2012. Startup Ecosystem Report 2012:  Part One. Telefónica Digital partnered with Startup Genome. http://cdn2.blog.digital.telefonica.com.s3.amazonaws.com/wp-content/uploads/2012/11/ Startup-Ecosystem-Report-2012.pdf. IKEI. 2011. Análisis de las Acciones de Emprendimiento Realizadas entre 2.000/2.010 en el País Vasco. San Sebastián, Spain: IKEI and SPRI–Sociedad para la Transformación Competitiva. http://www.parlamento.euskadi.net/irud/09/00/032359.pdf. Isenberg, Daniel J. 2010. “How to Start an Entrepreneurial Revolution.” Harvard Business Review 88 (6), 40–50. Navarro, Mikel, Jesús M. Valdaliso, Mari Jose Aranguren, and Edurne Magro. 2013. “A Holistic Approach to Regional Strategies: The Case of the Basque Country.” Science and Public Policy, November, sct080, doi:10.1093/scipol/sct080. Norrman, Charlotte, and Lars Bager-Sjögren. 2010. “Entrepreneurship Policy to Support New Innovative Ventures: Is It Effective?” International Small Business Journal 28 (6), 602–19. doi:10.1177/0266242610369874. North, Douglass C. 1990. Institutions, Institutional Change and Economic Performance. Cambridge: Cambridge University Press. North, Douglass C. 2005. Understanding the Process of Economic Change. Princeton, NJ: Princeton University Press. Orkestra. 2013. The Basque Country Competitiveness Report 2013: Productive Transformation for Tomorrow. Bilbao, Spain:  Basque Institute of Competitiveness—Deusto Foundation; Deusto Publications. http://orkestra.deusto.es/images/publicaciones/archivos/Executive_ summary_2013.pdf. Peña-Legazkue, Iñaki, José L.  González-Pernía, Maribel Guerrero-Cano, Saioa Arando-Lasagabaster, Jon Hoyos-Iruarrizaga, María Saiz-Santos, and David Urbano-Pulido. 2012. Global Entrepreneurship Monitor:  Comunidad Autónoma del País Vasco. Informe Ejecutivo 2011. San Sebastián, Spain: Eusko Ikaskuntza; Orkestra. http://orkestra.deusto.es/ images/publicaciones/archivos/InformeGEM2011.pdf. Porter, Michael E., Christian H.  M. Ketels, and Jesús M.  Valdaliso. 2013. “The Basque Country: Strategy for Economic Development.” Harvard Business School Case No. 713-474. Reynolds, Paul, Niels Bosma, Erkko Autio, Steve Hunt, Natalie De Bono, Isabel Servais, Paloma Lopez-Garcia, and Nancy Chin. 2005. “Global Entrepreneurship Monitor: Data Collection and Implementation (1998–2003).” Small Business Economics 24, 205–31. Schwab, Klaus, Xavier Sala-i-Martin, and Robert Greenhill. 2011. The Global Competitiveness Report, 2011–2012. Geneva: World Economic Forum.

Chapter 20

I n Search of New C ompetitive A dva ntag e Japan’s Local Firms in Sustainable Business Hideki Yamawaki, Hiro Motoki, and Kayo Hirai

1 Introduction Since the burst of the “bubble” in 1991, Japan has been struggling to revitalize its economy and regain industrial competitiveness in the world market. The remarkable record of Japan’s economic success from the late 1950s through the early 1990s has been overshadowed by the success story of China in the last 20 years. Although Japan is currently at a juncture to rethink its economic and management model, it is still vivid in our memory how Japanese corporations successfully penetrated the world market and competed against Western corporations from the 1960s through the 1980s (e.g., Yamawaki 2007). Japan’s industrial competitiveness in machinery, electronic and electric equipment, cameras, office equipment, and transportation was the envy of many industrialized countries during this period. It is thus quite remarkable to witness how once-strong Japanese industries have been struggling to maintain their international competitiveness and losing their market positions gradually to newly emerging competitors in the past 20 years.1 During Japan’s high-growth era, practitioners as well as academics tried to understand the underlying economic and organizational factors that contributed to Japan’s strong industrial performance in the marketplace. There is a long list of structural elements and business practices in place that are identified as being quite distinctive to

1 

According to the World Economic Forum Global Competitiveness Index, Japan’s ranking further dropped from sixth in 2010–11 to ninth in 2011–12.

374    Competitiveness at the Local Level Japanese corporations and industries. In particular, the Japanese corporate model was considered a critical source of Japan’s competitive advantage by many Western and Japanese scholars (e.g., Aoki 1986; 1990; Abegglen and Stalk 1985). As is well documented elsewhere, the key elements of Japan’s distinctive management model include lean production and just-in-time systems (e.g. Womack, Jones, and Roos 1991; Clarke and Fujimoto 1991; Fujimoto 1999), labor and human resource practices such as lifetime employment, job rotation, and skill formation (e.g. Koike 1988; Odagiri 1992), long-term goals and R & D policy (e.g. Rosenbloom and Abernathy 1982; Abegglen and Stalk 1985; Odagiri and Goto 1996), strong interfirm relationships and corporate groups (e.g. Imai 1989; Odagiri 1992), and the role of small enterprises and subcontracting (e.g., Caves and Uekusa 1976; Kawai and Urata 2002). Beginning in the 1960s and throughout the 1980s, the market and technological environments under which Japanese manufacturing firms in machinery, electrical equipment, and automobiles operated were changing quickly but continuously without major disruption. In such environments, many Japanese firms were able to develop their competitive advantage incrementally with continuous learning through their organizational model and to implement their distinctive business and labor practices. As a result, the Japanese corporate organization became quite effective in sophisticated assembly-type industries where users and consumers demand a wide variety of products, their demands shift from one variety to another quickly, users demand swift deliveries of their orders, and innovation is measured in terms of continuously improving quality and lowering costs (e.g., Abegglen and Stalk 1985; Porter, Takeuchi, and Sakakibara 2000). The ways in which Japanese corporations engage in such practices have significantly changed, and their managerial importance has been reduced (e.g., lifetime employment, interfirm relationships, and corporate groups) to adapt to the quickly changing economic and business environments of the last 20 years. On the other hand, Japan’s distinctive organizational model and the basic notion upon which this model was built remain at the core of many Japanese corporations. The Japanese business model is built on the assumption that a company can shift the production frontier outward with continuous improvement in process innovation and operational efficiency. Through pursuing such innovation, the company can achieve higher productivity and push the frontier of best-practice, quality-cost locus outward. While this strategic position worked well from the 1960s to the 1990s, the emergence of new competitors who built their competitiveness upon lower-cost positions and yet another group of competitors with strong differentiation advantages, made the competitive position of Japanese corporations that pursue high quality and low cost simultaneously less tenable. The positioning of Japanese corporations has exposed them to the major competitive threats in recent years that are driven by the increasing rate of technological change, the intensifying degree of global competition, and the emergence of a new generation of highly diverse global customers. In particular, Japan’s small- and medium-sized firms (SMEs) have been hit hard in this adjustment process. Their performance has been lagging behind that of their larger counterparts. As is well documented, Japan’s SMEs have played a key role in Japan’s manufacturing system as

In Search of New Competitive Advantage    375 subcontractors and OEM suppliers for manufacturers and formed the hierarchically structured relationship between manufacturers, first-tier suppliers, and second-tier suppliers (e.g., Caves and Uekusa 1976; Uekusa 1987; Asanuma and Kikutani 1992). Large enterprises that had engaged in international operations quickly adapted to the emerging new business environment by rationalizing their domestic operations, shifting their production locations to low-cost countries, and reducing the scope and scale of existing relationships with their domestic subcontractors and suppliers. It has not been an easy task for Japan’s SMEs to find business opportunities and implement new business models in new market spaces, as that requires a fundamentally different notion of how business is designed and executed from the traditional model that the Japanese model was built upon. Using the new longitudinal data at plant and enterprise levels for 1982–2007 in Japanese industries, Fukao (2012) indeed finds that the growth rates of total factor productivity (TFP) increased steadily for large plants and large enterprises, while the TFP growth rates for small plants and SMEs slowed down in the 2000s. Despite such an unfavorable situation for many of Japan’s SMEs, a growing number of local SMEs have been searching for new business opportunities and exploring new nonconventional business models. Some of them have shown some success in the marketplace, although their performance has not yet reached the critical level to alter the general trend of declining competitiveness. The purpose of this chapter is to focus on the new generation of local firms in Japan and to examine their characteristics and strategies. The remainder of this chapter is organized as follows. The next section provides a brief overview of the role and characteristics of SMEs and local clusters during the high-growth era in Japan. Section 3 presents new data of 110 firms in sustainable business in Japan and describes their general characteristics. Section 4 provides case studies of five firms from the sample used in the previous sections to describe their strategies in detail and to shed some light on their distinctive features. Finally, section 5 summarizes the key findings and concludes the chapter.

2  Japan’s Small and Medium-Sized Enterprises from the 1950s through the 1990s It is a well-known fact that Japan hosted the largest number of SMEs in the 1990s among industrialized countries. When SMEs are defined as those enterprises with fewer than 300 employees or less than yen 100 million in capital, more than 99 percent of all enterprises in Japanese manufacturing were classified as SMEs in 1994. The share of Japan’s SMEs in the total number of establishments in manufacturing remained virtually unchanged from the late 1950s through the 1990s. When the SME size was measured by employment, its share even increased from 73.5 percent in 1957 to 74.1 percent in 1996

376    Competitiveness at the Local Level (Kawai and Urata 2002). Japan’s SMEs traditionally played an important role in Japan’s manufacturing system as subcontractors for larger firms. The main features of SMEs in this system, among others, include cooperative and risk-sharing relationships with parents, their continuous efforts to improve quality and reduce costs, and their capabilities to deliver outputs for just-in-time production (Uekusa 1987). Another important feature is that SMEs have been an integral part of regional industrial clusters in Japan. According to the 1996 survey of the Small and Medium Enterprise Agency, a total of 537 industrial clusters were reported to exist throughout Japan. While the economic significance of these clusters varies widely, generally speaking, the emergence of Japan’s clusters is attributed to several factors, including historical circumstances, prior existence of large manufacturers in the region, prior existence of related and supporting industries in the neighboring regions, transportation costs, and government policy. Japan’s clusters have supported a large number of SMEs with specialized capabilities and skills and organized market structures that encourage interfirm linkages and facilitate the dissemination of knowledge among firms in the cluster (Yamawaki 2002). While small enterprises in Japan, on average, recorded lower productivity growth rates and levels than their larger counterparts in the same industry,2 they were by no means inefficient from the standpoint of technical efficiency. Addressing the question of whether the industry’s production process is inefficient if it employs a larger amount of inputs than the minimum level that is required to attain the actual output, Torii (1992) does not find any evidence that supports the hypothesis that large plants have a large advantage in technical efficiency over small plants in Japan. Finally, the subcontracting system in Japan contributed to strong competitiveness by enhancing TFP levels and growth rates in its industries (Urata and Kawai 2002) and raising the efficiency frontier in Japan relative to the frontier in the United States (Torii and Caves 1992). Thus, the existence of large number of efficient SMEs and the wide use of subcontracting arrangements within regional clusters were an important element in the Japanese manufacturing industries from the 1950s through the 1990s.

3  Toward New Business Models: Japan’s Small and Medium-Sized Enterprises To the extent that Japan’s manufacturing system and business model worked brilliantly in its high-growth era, its adjustment process to adapt to the new environment has been very slow, as Japanese firms’ past success created slackness in organizations and posed threats to sustained competitive advantage and entrepreneurship. In addition, their 2 

Urata and Kawai (2002) find evidence that in some machinery sectors, small firms achieved higher TFP growth and levels.

In Search of New Competitive Advantage    377 past investments in highly specialized assets and resources that had focused primarily on their domestic business environment and customers prevented them from acting according to the urgent needs of the new global strategy. While corporations anywhere in the world face management challenges arising from an accelerated rate of technological change, intensifying global competition, the increasing importance of knowledge and creativity in value creation, and the emerging importance of diverse customers and markets, Japanese firms are challenged additionally by a set of economic, societal, and environmental constraints—an aging and shrinking population and sustainability. Is there any bright spot that we can find in this grim reality that Japanese firms are currently facing? The purpose of this section is to describe the new directions that some of Japan’s local firms are pursuing and present five cases of their new business models in sustainable business. For this purpose, we use the corporate database compiled by E-Square3 in 2010 and examine a subsample of 110 Japanese firms that take sustainability at the heart of their business concepts and that engage in business experimentation. Although our sample is by no means complete in its coverage and hence susceptible to omissions, its industry distribution shows that 38 percent of the sample firms are in manufacturing, 18 percent of them in service, 15 percent in retail and distribution, and 14 percent in agriculture.4 An interesting finding is that among those classified in manufacturing, only 15 percent of them are classified primarily in Japan’s key industries from the high-growth era, such as electric and electronic equipment, transportation, and machinery. On the contrary, the majority of firms classified in manufacturing are in the industries that are not clearly defined by the standard two-digit industry classification systems and hence labeled miscellaneous. It is also worth noting that 14 percent of the sample firms are from the agricultural sector, which reflects the emerging trend in recent years among new generations of entrepreneurs to start new businesses with a vision to revitalize Japan’s agriculture. Based on the data gathered through interviews, site visits, and other primary sources of information supplemented by secondary data, we asked, first, if Company X actively engages in innovation, creates results, and focuses on innovation at the core of its strategy. If the answer is yes, we then asked, what is innovation at X? For descriptive purposes, we defined innovation categorically in five areas: technology; product/service; process; channels; and business model. Of the 110 firms examined, we find 28 percent of them are firms that actively engage in innovation in products/services and launch new products and services, and 29 percent of them are firms that engage in innovation in new 3 

www.e-squareinc.com. The majority of firms in this sample can be classified as small- or medium-sized enterprises and operate in and for regional areas in Japan. Of the 110 firms, 83 percent employ less than 300 employees, and 45 percent employ less than 50 employees. Sixty-three percent of the sample firms limited the geographic scope of their activities within regional boundaries in Japan at the time of data collection. Thirty-one percent of the sample firms were established before 1960 and have been continuously in business over 50 years. On the other hand, 24 percent of the firms in the sample are relatively young and were established after 2000. If combined them with those firms established during the 1990s, 37 percent of our sample firms were established in the last 25 years. 4 

378    Competitiveness at the Local Level business models and implement them in the marketplace. On the contrary, we find only 5 percent of the sample firms are classified as firms that focus on process and production innovation, and 7 percent of them focus on technology. The data, most importantly, suggest that Japan’s SMEs are focusing more on developing new products/services and creating new business models in nontraditional sectors. It is important to note that this pattern contrasts with the traditional model of Japanese SMEs described in the previous sections of this chapter. Indeed, it appears that this emerging pattern reflects the underlying driver of change in the business environment and firms’ traditional relationships with large manufacturers and their roles as OEM suppliers. We also examined if Company X actively engages in innovation across the board over the five categories (technology, processes/production, products/services, channels, business models) or over a subset of these categories. While only one firm was found to engage in an integrated approach through the five categories, 21 of the 110 sample firms focus on two or three categories of innovation and try to infuse more integrated approaches rather than simply engaging in innovation in one area. Finally, we examined if Company X’s experimentation was a success in the marketplace and if its success was driven by distinctive advantages in strategy, innovation, leadership, and execution. Our initial screening based on business success eliminated the majority of firms from the pool, but those firms that presented evidence of market success show that innovation and leadership are the most important drivers of their success. As mentioned earlier, innovation in products/services and business models are the most important drivers of success for the new generation of Japan’s SMEs that engage in sustainable business. Our data suggest, not surprisingly, that the successful firms are more likely to have leaders who have a vision, go against conventional and mainstream wisdom, demand excellence, pay attention to details, and encourage innovation from their employees.

4.  Case Studies 4.1 Ikeuchi Towel Imabari City in Ehime Prefecture grew as an important industrial cluster of cotton weaving and towels after imported technology in towel production was transferred to the region in 1872. The towel industry in Imabari continued to prosper after World War II through the 1980s. However, its domestic market share declined significantly during the 1990s as the neighboring countries in Asia started offering substitutes at much lower prices. With the help of import restrictions imposed by the government during this period, many companies in Imabari tried to cut costs and increase operating efficiency to compete against the low-cost competitors from Asia. On the contrary, Ikeuchi Towel took a different approach to revitalize its competitive position in the industry and shifted its focus to highly differentiated products and moved away from OEM production.

In Search of New Competitive Advantage    379 The vision of Keiji Ikeuchi, the second-generation CEO of the family-owned business, is to offer environmentally safe and high-quality towels to mothers who have babies and young children. Instead of competing on price, he wants to increase the customer’s willingness to pay by offering quality towels with high values. Although this strategy is nothing new to US and European companies, it is rather uncommon for a Japanese company to pursue product differentiation, as the strategic priority is traditionally placed on achieving the goals of high operating efficiency and high quality simultaneously, as described in the previous section. The key features of Ikeuchi Towel’s revitalization strategy are (1) creating competitive advantage only in selective areas of the industry value chain (product development, weaving, and final product quality control); (2) using subcontracting for other activities within the value chain (cotton threads, dying, printing, embroidery, finishing, and shipping) in the regional ecosystem; (3) focusing on product innovation and launching “miracle soft” towels; (4) branding in international markets rather than in the domestic Japanese market by winning international awards (e.g., Best New Product Awards at Home Textile Show in New York in 2002); (5) making a serious commitment to environmental protection and investment in sustainability technology (the wastewater treatment facility that satisfies the most stringent standard at the moment and the 100 percent use of wind energy); and (6) creating a private fund, the Cotton Nouveau Fund, to promote and procure organic cotton produced in Tanzania. Ikeuchi Towel (30 employees in 2013) has distribution in Japan, the United States, and Europe, and the company’s revenue increased from 400 million yen in 2010 to 600 million yen in 2013.

4.2  FP Corporation Established in 1962 in Fukuyama in Hiroshima Prefecture, Fukuyama Pearl Paper Manufacturing Corporation (FPCO) started its business as a specialist manufacturer of disposable food containers for use in supermarkets, groceries, and other stores. Its business in formed PS thermoforming food containers, wood food containers, and other packaging supply grew steadily since the firm’s inception in the 1970s. As early as 1980, the company identified potentially large problems with the disposal of food trays and launched a tray collection program. During the 1990s, with the emergence of convenience stores throughout Japan and the rising popularity of precooked foods sold at convenience and other stores, the disposal of used trays and packages became a serious concern among consumers. Pursuing the development of recycling technology for food trays, FPCO launched the “Tray to Tray” system in 1990. Under this innovative system, disposed trays are collected by participating stores and shipped to FPCO’s facilities to recycle them and convert them to new trays. FPCO was one of the pioneers in the world to launch such a recycling system. The number of participating stores increased significantly from 1,000 in the early 1990s to 8,150 in the early 2010s, and the tonnage of recycled trays grew from 800 tons in 1992 to 7,000 tons in 2012. Currently, FPCO’s Eco Tray accounts for 25 percent of all types of trays distributed in Japan. The company launched

380    Competitiveness at the Local Level a recycling system for transparent containers in 2008 and located 3,200 recycle collection boxes throughout Japan in 2011. Sales were 158 billion yen with 712 employees in 2013. Several key elements stand out as the source of competitive advantage: (1) focusing on product development and design that addresses customer needs and raises user experience; (2) holding FPCO fairs regularly since 1976 to offer suggestions to distributors, supermarkets, and other retailers to use the latest products and services to develop new food products or to create more aesthetically pleasing sales displays; (3) focusing on solution system innovation that involves not only product innovation but also system innovation in collection and recycling of disposed trays; and (4) establishing strong distribution and collection networks to support the recycling system.

4.3 Ecohai Since its establishment in 2007, Ecohai grew rapidly and reached out to 100,000 customers by 2011. Ecohai picks up and delivers small items by primarily using bicycles and electric vehicles, hence the company name, Ecohai—economy and ecological delivery. The company specializes within large cities such as Tokyo, Nagoya, and Osaka, but its delivery networks allow it to handle intercity shipments as well. For example, Ecohai charges 350 yen for shipping a standard size bag (32 cm × 40 cm × 11 cm; 14 liter) within the Tokyo area for next-day delivery. The key features of Ecohai’s business model are (1)  concentration in three large metropolitan areas (Tokyo, Osaka, and Nagoya); (2)  the use of standard size bags and envelopes; (3) a simple structure for the product and service; and (4) the use of bicycles. The first three features enable Ecohai to increase its operating efficiency and hence offer lower prices—40 percent lower than its conventional competitors within the three metropolitan areas for an item of equivalent size. The use of bicycles naturally contributes to environmental protection because it achieves zero emissions in comparison to the conventional industry standard of 321.1 grams of CO2 emission per parcel in 2011. An additional advantage driven from the use of bicycles is that the company does not need to hire workers with appropriate driver’s licenses to drive delivery and pickup trucks. This allows Ecohai to tap into a low-cost and flexible workforce that conventional competitors could not use for their operations—students and homemakers. While the origins of companies such as Ikeuchi Towel and FPCO are family-owned small and medium-sized enterprises typical in Japan, Ecohai started as a venture firm and is managed by professional venture capitalists and managers. Ecohai had obtained 1.3 billion yen in capital by 2013 and is expected to undertake an IPO in 2015. It has 36 service locations, employs 301 full-time employees and 221 part-timers, and owns 213 bicycles and 180 K-cars to serve 100,000 customers.

In Search of New Competitive Advantage    381

4.4 Kaiho Sangyo In 1969 Norihiko Kondo, at the age of 22, founded Kondo Motors in Kanazawa City in Ishikawa Prefecture. His company originally specialized in dismantling automobiles and selling scrap steel, aluminum, and copper. The turning point of his business came in 1991 when he sold 20 tons of used engines and suspension parts to a customer from Kuwait. In 1992 Kondo renamed his company Kaiho Sangyo and shifted his business from a simple automobile dismantler to a recycling reseller of used automobile parts. Although many scrapped vehicle parts resellers had not been aware of the potential importance of establishing an industry standard for used-parts quality, Kaiho decided to invest in environmental management systems and product quality management systems in early 2000s. It obtained ISO14001 for environmental management systems in 2002 and ISO9001 for product quality management systems in 2005. And, Kaiho introduced JRS (Japan Reuse Standard) for the first time for its exports of recycled parts in 2011 with the aim to convert it to an ISO standard in the future. One of the key features of Kaiho’s business is the company’s focus on the maintenance of high-quality standards for its recycled parts. Kaiho’s computerized KRA system allows the company to manage quality control and inventory control with its extensive use of barcodes attached to individual parts. The barcode system identifies the origin, history, and specs of individual parts and provides vital information to customers. Kaiho opened a training center in 2007 (International Recycling Education Center, IREC) to improve the skills of automobile recycling workers for its own employees as well as for alliance partners in Japan and foreign countries. Kaiho’s operating capacity in 2011 was 1,200 vehicles per month, and it exports over 20,000 engines annually to 71 countries with joint ventures in Thailand, Kenya, Nigeria, Ghana, and Singapore. Revenue grew rapidly from 715 million yen in 2003 to 2.1 billion yen in 2008, and expected to recover to 2.6 billion yen in 2013 after a dip in 2009–10. Japan’s Ministry of Economy, Trade, and Industry awarded the IT Management Award for Small and Medium Enterprises to Kaiho in 2008.

4.5 My Farm After graduating from the University of Kyoto’s Agricultural Department and working several years for an IT marketing company, Kazuma Nishitsuji founded My Farm in 2007 in Kobe City in Hyogo Prefecture. My Farm’s vision is to create a community where people produce agricultural products for their own consumption and to provide solutions to revitalize idle agricultural lands in Japan. My Farm finds the owners of idle agricultural lands who are willing to lease their lands near metropolitan areas, prepares and reconditions those lands for agricultural use, and leases them to individuals who would like to

382    Competitiveness at the Local Level try farming. This rent-a-farm business started with a small number of lands in the Kansai area of Japan, but grew to 10 locations by 2009 and 60 locations nationwide by 2013. Although there are other rental businesses for land in Japan, My Farm distinguishes itself from the others by offering a better user experience by providing additional services in rental agricultural equipment, farming instruction and hands-on coaching, soil safety checkups for organic farming, and various family events. Prior to opening a rental farm, My Farm conducts testing for potential contamination of soil, prepares the lots, and builds restrooms and small storage houses for equipment. Since My Farm offers all the administrative work for landowners, their opportunity costs to enter the rental business become much lower than if they had done all the work themselves. A typical rental contract starts around 1,980 yen per 15 m2 plus an up-front annual fee of 10,500 yen (3,750 yen after the second year) for a contract term of one year. While My Farm is a small enterprise with 10 full-time employees and 45 part-timers, its revenue is 170 million yen in 2012 and is expected to grow in 2013. In 2010 the company launched an educational program, My Farm Academy, which is now integrated with its own Agri Innovation College. This government-accredited agricultural school offers courses on farming know-how and skills, together with agribusiness skills, to generate a new generation of farmers.

5 Conclusions Based on our analysis of the sample of Japanese firms, we conclude that the key drivers for their success are (1) focusing on developing and designing products to address the needs of customers and offering superior customer experience; (2) concentrating on solution system innovation that involves not only product development but also systems innovation to offer solutions to societal and environmental problems with a business perspective; (3) experimenting with new business models that have a clear customer value proposition, revenue/profit formula, and investment plans for key resources and capabilities; and (4) the presence of leaders with entrepreneurial spirits who are willing to engage in business experimentation. Japanese corporations are in transition from their successful organizational and business model in the high-growth era of the 1960s through the 1980s, to new models to adapt to the emerging business environment of the last 20 years. The adjustment process has been slow, but is providing new opportunities for the new generation of entrepreneurs who are willing to engage in business experimentation in new products, services, and business models. Although it is still premature to predict that the current efforts by the innovative firms will become the mainstream in Japan’s industrial structure in the coming years, it is important to note that their efforts are gaining traction in the marketplace and certainly creating new sustainable values.

In Search of New Competitive Advantage    383 During Japan’s high growth era, the existence of SMEs with specialized skills and capabilities was the major driving force for Japan’s industrial competitiveness. Japan’s SMEs created agglomeration in their regional clusters that supported large numbers of local suppliers and organized market structures that encouraged inter-firm linkages and facilitated the diffusion of knowledge among them. While it is not contradictory to this traditional model of local competitiveness, this chapter finds most importantly that the new types of SMEs in Japan are likely to operate outside the traditional ecosystem of regional clusters. Japan’s emerging innovative firms are trying to create new ecosystems suited for their new business models and redefine the source of local competitiveness from the traditional model of supplier-based export cluster to a new model of user-oriented value creation.

References Abegglen, James C., and George Stalk Jr. 1985. Kaisha: The Japanese Corporation. New York: Basic Books. Aoki, Masahiko. 1986. “Horizontal vs. Vertical Information Structure of the Firm.” American Economic Review 76, 971–83. Aoki, Masahiko. 1990. “Towards an Economic Model of the Japanese Firm.” Journal of Economic Literature 28, 1–27. Asanuma, Banri, and Tatsuya Kikutani. 1992. “Risk Absorption in Japanese Subcontract ing: A Microeconometric Study of the Automobile Industry.” Journal of the Japanese and International Economics 6, 1–29. Caves, Richard E. and Masu Uekusa. 1976. Industrial Organization in Japan. Washington, DC: Brookings Institution. Clarke, Kim B., and Takahiro Fujimoto. 1991. Product Development Performance:  Strategy, Organization and Management in the World Auto Industry. Boston:  Harvard Business School Press. Fujimoto, Takahiro. 1999. The Evolution of a Manufacturing System at Toyota. New York: Oxford University Press. Fukao, Kyoji. 2012. Ushinawareta 20nen to Nihonnkeizai [The lost 20 years and the Japanese economy]. Tokyo: Nihonkeizaishinbun shuppansha. Imai, Kenichi. 1989. “Kigyo Group” [Corporate groups]. In Kenichi Imai and Ryutaro Komiya, eds., Nihon no kigyo [The Japanese corporation]. Tokyo:  Tokyo Daigaku Shuppankai. Kawai, Hiroki and Shujiro Urata. 2002. “Entry of Small and Medium Enterprises and Economic Dynamism in Japan.” Small Business Economics 18, 41–51. Koike, Kazuo. 1988. Understanding Industrial Relations in Modern Japan. London: Macmillan. Odagiri, Hiroyuki. 1992. Growth through Competition, Competition through Growth: Strategic Management and the Economy in Japan. Oxford: Oxford University Press. Odagiri, Hiroyuki, and Akira Goto. 1996. Technology and Industrial Development in Japan. Oxford: Oxford University Press. Porter, Michael E., Hirotaka Takeuchi, and Mariko Sakakibara. 2000. Can Japan Compete? London: Macmillan.

384    Competitiveness at the Local Level Rosenbloom, Richard S., and William J.  Abernathy. 1982. “The Climate for Innovation in Industry:  The Role of Management Attitudes and Practice in Consumer Electronics.” Research Policy 11, 209–25. Torii, Akio. 1992. “ ‘Dual Structure’ and Differences of Efficiency between Japanese Large and Small Enterprises.” In Richard E. Caves, ed., Industrial Efficiency in Six Nations. Cambridge, MA: MIT Press. Torii, Akio, and Richard E.  Caves. 1992. “Technical Efficiency in Japanese and U.S. Manufacturing Industries.” In Richard E.  Caves, ed., Industrial Efficiency in Six Nations. Cambridge, MA: MIT Press. Uekusa, Masu. 1987. “Industrial Organization: The 1970s to the Present.” In Kozo Yamamura and Yasukichi Yasuba, eds., The Political Economy of Japan, vol. 1, The Dynamic Transformation. Palo Alto: Stanford University Press. Urata, Shujiro, and Hiroki Kawai. 2002. “Technological Progress by Small and Medium Enterprises in Japan.” Small Business Economics 18, 53–67. Womack, James P., Daniel T.  Jones, and Daniel Roos. 1991. The Machine That Changed the World: The Story of Lean Production. New York: HarperCollins. Yamawaki, Hideki. 2002. “The Evolution and Structure of Industrial Clusters in Japan.” Small Business Economics 18, 121–40. Yamawaki, Hideki. 2007. Japanese Exports and Foreign Direct Investment:  Imperfect Competition in International Markets. Cambridge: Cambridge University Press.

Chapter 21

Assessing Stat e - L ev e l Science and T e c h nol o g y P olici e s North Carolina’s Experience with SBIR State Matching Grants John Hardin, Lauren Lanahan, and Lukas C. Brun

Introduction State governments play a critical, yet understudied, role in supporting research and development (R & D) activity in the United States. Although the federal government makes up the vast majority of public support for R & D, state government R & D expenditures increased 11.3 percent between fiscal years 2010 and 2011, while federal expenditures increased by only 1 percent.1 State governments have directed increasing attention to R & D and innovative activities through science and technology policies and, more broadly, economic development policies to leverage economic benefits within their local jurisdictions. Recent research has found that innovative activity is spatially proximate—which is to say that innovation depends on nearby resources and produces intellectual and economic benefits that are concentrated within a geographic area (Hall, Jaffe, and Trajtenberg 2000; Greenstone, Hornbeck, and Moretti 2010). These local benefits of knowledge spillovers and agglomeration economies catalyze innovation and competitiveness, which have direct implications for regional economic activity and therefore are of growing interest to state 1 Sources: http://www.nsf.gov/statistics/infbrief/nsf14300/ and http://www.nsf.gov/statistics/infbrief/

nsf14307/#tab1.

386    Competitiveness at the Local Level governments. “This has triggered a fundamental shift in public policy … towards a new set of enabling policies, implemented at the regional and local levels” (Audretsch 1998, 18). A recent 2013 report, Top Trends in State Economic Development,2 published by the National Governors Association, echoed this theme, emphasizing the role and capacity of state economic development strategies to bolster the building blocks for competitiveness and economic growth, with the foundation premised on entrepreneurs and innovation, workforce, investment climate, business support, and a stronger connection between universities and the economy.3 While considerable attention has been placed on public investment in R & D, often conflating the terms public with federal investment, this chapter focuses on the increasing role of US states in R & D policy. Increased attention on state government activity offers great promise for this line of scholarship, given that state governments have greater flexibility to experiment with a range of policies to spur innovative activity. Improved understanding of state R & D actions places state policymakers in a fortuitous position to design R & D programs relevant to their states’ local capabilities, regional variation, and proximate research and economic climates (Feller 1997). This broad range of actions that spans both science and technology policies and economic development policies is not only a critical component of a region’s innovation system, it also offers a valuable lens for understanding the breadth and scale of the public’s role within the innovation process. This chapter discusses the increasing role of United States state government policy in supporting R & D activity, paying particular attention to a small business innovation program in North Carolina (NC). This program provides support for promising early-stage R & D activity among small businesses, with the expectation that the investment will bolster innovation and regional development. The next section highlights the growing role of state governments in this capacity by providing an overview of both state science and technology policies and economic development policies that aim to promote innovation and competitiveness. The third section provides a brief background on the federal Small Business Innovation Program (SBIR) program, which the NC small business program is designed to complement, and reviews a broad range of complementary government actions to the SBIR program at the federal and state levels. The fourth section focuses on the One North Carolina Small Business Program, a state program designed to complement the SBIR program and promote early-stage innovation among small businesses. The state program serves as an illustrative policy to highlight the role and impact of state-level policies on innovation. Section 5 offers reflective conclusions about state R & D policy and regional competitiveness.

2 Source: http://www.nga.org/files/live/sites/NGA/files/pdf/2013/1308TopTrendsinStateEconDevPa per.pdf. 3 Source: http://www.nga.org/cms/home/nga-center-for-best-practices/center-publications/ page-ehsw-publications/col2-content/main-content-list/top-trends-in-state-economic-dev.html.

Assessing State-Level Science and Technology Policies    387

Relevant Literature Although the literature on state-level R & D policy is relatively scant, Sapolsky (1971) was among the first to discuss state governments within this context. Notably, in reaction to a series of events, including Sputnik, the subsequent federal influx of R & D investment, and the creation of federal-level science policy advisory positions, state governments demonstrated initial interest in R & D in the late 1950s and early 1960s by establishing state science advisory bodies. New York was the first in 1959; within the following decade, 46 states followed suit (1971). During the 1980s, and throughout the next two decades, theoretical and policy attention on strategies that stimulate the innovative capabilities of regions accelerated (Oerlemans, Meeus, and Kenis 2007). The “geography of innovation” literature (Feldman 1994; Audretsch and Feldman 1996) investigates both why and the extent to which knowledge spillovers are bounded by geography. This line of literature finds that knowledge produced by a small group of firms and universities investing in research and technology development is transmitted through regionally proximate face-to-face communication, facilitating the innovation efforts of other organizations, and stimulating regional economic growth (Oerlemans, Meeus, and Kenis 2007). During this time, associated literatures on new industrial districts (Markusen 1996), innovative milieu (Aydalot and Keeble 1988), new industrial spaces (Storper 1995; 1997), regional innovation systems (Lundvall 1992), and the learning region (Morgan 1997) also studied how co-located actors organized regional networks to enhance competitiveness. Policy strategies seeking to improve the innovative capabilities of regions also emerged during this period. These “territorial innovation models” (Moulaert and Sekia 2003) sought ways to develop human capital, infrastructure, regional educational and regulatory institutions, and quality of production factors as the main ingredients for regional growth (Oerlemans, Meeus, and Kenis 2007). Complementing these regional economic development initiatives, state-level science and technology programs were part of the policy portfolio seeking to improve the capabilities of regions. Plosila (2004) advanced this discussion in his historical overview of state science and technology (S & T) programs, classifying the evolution of state programs into three stages. The first, in the 1960s–1970s, placed emphasis on bolstering S & T programs; the second, in the 1980s, marked a notable shift toward increased linkages between state S & T programs and economic development policies; and the third, in the 1990s, marked an increased interest in forming technology alliances and trade associations in addition to a stronger linkage between S & T and economic planning. Subsequent research on state science policy has traced the continued attention and strategic state policymaking to promote regional competitiveness and economic development. State policies directed toward innovation have ranged widely from R & D tax incentives (Wilson 2009), to nanotechnology-focused programs (Woolley and Rottner 2008), to a broad portfolio of university-based R & D programs (Feldman, Lanahan,

388    Competitiveness at the Local Level and Lendel 2013; Feldman and Lanahan 2013; Hearn and Lacey 2014), and open innovation policies among states with lagging R & D capacity (Mayer 2010). This growing body of scholarship recognizes the critical role that state governments have come to play to promote local competitiveness by investing in factors stimulating innovation and R & D. R & D benefits may certainly extend beyond state bounds, yet state interest is tied to the geographically concentrated component of innovation and its subsequent economic benefits. In sum, while there has been considerable discussion over the role of federal R & D for innovation, state governments arguably also have a strong responsibility to invest in R & D. This is a topic that the literature is beginning to explore.

The Federal SBIR Program Established by the Small Business Innovation Development Act of 1982 and reauthorized multiple times since, the SBIR program requires federal agencies with annual extramural R & D budgets in excess of $100 million to set aside 2.8 percent of their R & D funds for the program for FY 2014.4 Eleven agencies5 participate in this program, offering competitive grant funds to small businesses via two phases of funding that support R & D having high potential for commercialization: Phase I—Proof of Concept and Phase II—Development.6 The size of award increases by phase from $150,000 for the Phase I award to approximately $1,000,000 for the Phase II award. Only firms that have secured a Phase I award are eligible to apply for the larger Phase II funding.7 Among federal programs to support US small business early-stage innovative activity, the federal SBIR program stands as the largest, totaling over $30 billion in awards as of 2012.8 4  https://www.federalregister.gov/articles/2012/08/06/2012-18119/small-business-innovation-researchprogram-policy-directive. 5  The 11 federal agencies that participate in the SBIR program include Department of Agriculture, Department of Commerce (National Oceanic and Atmospheric Administration and National Institutes of Standards and Technology), Department of Defense, Department of Education, Department of Energy, Department of Health and Human Services, Department of Homeland Security, Department of Transportation, Environmental Protection Agency, National Aeronautics and Space Administration, and National Science Foundation. 6  This includes a third phase, Phase III—Commercialization; however no federal funds are obligated for this portion of the program. Additional information about the SBIR and STTR programs is available at SBIR.gov. 7  Small Business Technology Transfer (STTR) is another program that expands funding opportunities in the federal innovation research and development arena. Established in 1992 and modeled after the SBIR program, STTR is a smaller program, with five participating federal agencies, and a number of awards and funding level that vary, by year, typically between 10 and 15 percent of the SBIR level. The unique feature of the STTR program is the requirement for the small business to formally collaborate with a research institution. For the purposes of this chapter, the STTR program is not discussed separately, though occasionally, as discussed below, it is grouped with the SBIR program. 8  Derived from data on awards available at sbir.gov.

Assessing State-Level Science and Technology Policies    389 The purpose of the SBIR program is to “strengthen the role of the small, innovative firms in federally-funded research and development, and to utilize Federal research and development as a base for technological innovation to meet agency needs and to contribute to the growth and strength of the Nation’s economy” (1982 Statute).9 Public financial support in this capacity has been justified as a response to the underinvestment from the private sector, given the uncertainty and inherent riskiness with innovation (Arrow 1962). The SBIR’s programmatic attention to small firms is particularly warranted because the innovation spillover effects are “likely to be more severe for small firms [because] they do not have the same level of market power or legal resources to protect the economic returns to new innovations” (Cooper 2003, 148). The SBIR program has received notable accolades in identifying small firms with innovative potential (Lerner 2000; Audretsch 2003; Toole and Czarnitski 2007; Wessner 2008; Ege 2009; Link and Scott 2010; Keller and Block 2012). Benefits include increased sales, employment, follow-on financing, innovative output, the attraction of talent, a “certification effect” signaling to potential investors high-quality research and technology, a “demonstration effect” inducing others to innovate, and enhanced property rights protection. However, the positive outcomes of the SBIR program are not uniformly distributed across all projects, but rather are contingent on a series of factors, including regional levels of venture activity, the presence of star scientists on the project, and prior ownership of intellectual property.10

Programs Complementing the SBIR Program Within the United States, three broad types of federal and state programs have emerged to complement these SBIR program—federal programs, federal and state partnerships, and state initiatives.11 We briefly review, in turn, each type of policy complementing the federal SBIR program.   9  The SBIR was one in a cluster of legislation in the early 1980s seeking to address American competitiveness, in particular the concern that the United States was losing its historic advantage in developing and commercializing new technologies necessary to maintain global leadership in productivity. The Bayh-Dole Act of 1980, the R & D tax credit of 1981, the SBIR program of 1982, and the National Cooperative Research Act of 1984 were created to address the malaise in the American economy by providing incentives to spur innovation, particularly for small businesses. Link and Scott 2010 explore in greater detail the history of the SBIR program than can be developed here. 10  While the balance of the evidence points to the program’s ability to promote innovation, Wallsten (2000) finds that growing firms tend to secure SBIR awards, rather than attributing firm growth to the SBIR program. 11  The impacts of the program have been documented in the scholarship and extend further into the policy realm. Countries like Sweden, Russia, the United Kingdom, the Netherlands, Japan, Korea, and Taiwan have followed suit and adopted SBIR-like programs to cultivate an innovation system and promote economic competition (Wessner 2008).

390    Competitiveness at the Local Level Federal programs: A cohort of subsequent federal programs12 have been designed to complement the federal SBIR program and assist small businesses. These programs include the Department of Energy’s Clean Energy Alliance Partnership13 and the Industry and Growth Forum,14 the National Aeronautics and Space Administration’s Space Alliance Technology Outreach Program (SATOP),15 the National Institutes of Standards and Technology’s “Nanofab” Lab,16 and the National Institutes of Health’s Niche Assessment Program.17 While each program is unique in design, they overlap in their shared mission to provide assistance to small businesses in terms of incubator access, technical assistance, and access to resources. The NIH Niche Assessment Program, in particular, provides support for SBIR awardees, while the others offer services for a broader set of small businesses. Federal and state partnerships: The second cohort of policies includes federal and state policies that stem largely from two federal programs that require state-level participation to ensure greater equity of the distribution of federal SBIR awards. These include the Small Business Administration’s Federal and State Technology Partnership (FAST) and the Rural Outreach Program (ROP). Both were established in 2000,18 although ROP no longer has funding. The FAST program specifically aims to strengthen the technological competitiveness of small business concerns in every state, providing initial funds that must be met with a nonfederal match (often from the state government).19All states are eligible to participate in the program; however, competitive funds vary based on the level of SBIR activity in the state.20 The ROP was similar to FAST, yet only “eligible states,” as determined by their lagging SBIR performance, qualified for the program’s cooperative agreements. While the FAST and ROP programs have been subject to tenuous funding since 2000, these programs have served as a catalyst for state governments to offer outreach programs for their own SBIR community. Programs like Hawaii’s High Technology Development Corporation, Indiana’s 21st Century Fund, Mississippi’s FAST Program, and even New Hampshire’s NH Inspires Innovation program provide support services in terms of technical expertise, one-on-one mentoring, and workshops for SBIR applicants. Additionally, programs that include Louisiana’s SBIR/STTR Phase Zero Part I program, Missouri’s Technology Incentive Program, and South Carolina’s Phase 0 Program provide seed funding for the SBIR proposal preparation stage. While not an exhaustive 12 

http://www.sba.gov/community/blogs/community-blogs/small-business-cents/help-start-ups-p art-3-technical-assistance-prog. 13  http://cleanenergyalliance.com/. 14  http://www.industrygrowthforum.org/. 15  https://www.spacetechsolutions.com/letmeknowform.asp. 16  http://www.cnst.nist.gov/nanofab/nanofab.html. 17  http://grants.nih.gov/grants/funding/nap.htm. 18  Established by the Small Business Innovation Research Program Reauthorization Act of 2000, Public Law 106-554. 19  All proposals must include a letter of endorsement from the governor or governor’s designee to be eligible. 20  Leading states qualify for a 1:1 match of nonfederal funds; lagging states qualify for a 1:2 match; and intermediate states qualify for a 3:4 match.

Assessing State-Level Science and Technology Policies    391 review of all the state programs, this brief list illustrates state interest outside of the federal domain. State-level initiatives: The third set of policies includes state-level initiatives designed to complement the federal SBIR and directly support local innovation. Programs like North Carolina’s One NC Small Business Program, the Kansas Bioscience Authority, and Kentucky’s SBIR/STTR Matching Funds program offer a state noncompetitive match ranging from $30,000 to $150,000 to successful Phase I  recipients to aid the technical aspects of the program and assist them as they compete for the larger Phase II award.21 These SBIR matching programs stand in contrast to the state programs mentioned above, given that they are more aggressive state programs that provide direct investment for small firm early-stage innovative activity. In total, 14 states have established a publicly funded matching program offering noncompetitive grants to successful Phase I recipients (Lanahan and Feldman 2015). These states offer grants that are intended to support technical aspects of an innovative venture for firms who have demonstrated potential by securing the federal SBIR award. New York was the first state to adopt the program in 1984, followed by Hawaii and Oklahoma in 1989. It was not until after the two most recent federal SBIR reauthorizations in 2000 and 2011, respectively, that state governments increased the rate of policy adoption. Figure 21.1 highlights the 14 states with a publicly funded matching program, indicating the active policy years for each state. While these matching programs share the feature of providing noncompetitive funds for successful Phase I recipients, the size of the match varies not only between states but annually as well. Kentucky’s SBIR/STTR Matching Funds Program and New York’s NYSTAR offered up to a 1:1 match, while the Kansas Bioscience Authority Program and Illinois’ Department of Commerce and Economic Opportunity were limited to a 1:2 match. Connecticut requires supplemental funding from a third party, and the One NC Small Business Program ranges from $30,000 to $100,000, contingent on availability of funds. Despite the range in the size of these matches, this state policy activity is illustrative of a growing commitment among states to support programs that directly bolster innovation and local competitive advantage. Moreover, as of 2014, additional states, including Alabama, Nevada, and Rhode Island, have publicly expressed interest in establishing a matching program in the near future. Scholarly attention on the impacts of small business innovation programs has been generally directed to the federal SBIR program, with few exceptions (Lanahan and Feldman 2015; Lanahan 2015). In 2012, however, North Carolina completed a comprehensive assessment of its state matching program. The next section reports the findings of an in-depth survey undertaken in 2012 to assess the impacts of the One NC Small

21  Most of these programs provide matching funds for both SBIR and STTR grants. Because the differences between the SBIR program and the STTR program are not relevant for the purposes of this chapter, the term “state matching program” is used here to refer to programs that provide matching funds for SBIR grants, STTR grants, or both.

392    Competitiveness at the Local Level

2012 2008–2011 1984–1991 2011

2005–2009

2007– 2008 2003

1989 2004 1989

2006

2012

2012 2006–2011

Figure  21.1 Map of 14 States with a Publicly Funded Matching Program, Indicating the Active Policy Years for Each  State

Business Program. This survey presents the most comprehensive assessment to date of a state matching program.

One NC Small Business Program In 2005, North Carolina established the One NC Small Business Program (NC General Statute §143B-437.81). In doing so, lawmakers modified an existing grant program focused on recruiting larger companies to the state—the One NC Program—by broadening and realigning it to include aggressive and comprehensive financial support for small high-technology businesses already within the state. Modeled after similar SBIR matching programs in other states, the One NC Small Business Program has four core objectives, as outlined in its statutory guidelines22: 1. Increase the amount of federal research dollars received by NC small businesses 2. Help NC small businesses bridge the funding gap period between the final Phase I payment and the first Phase II payment in the federal SBIR/STTR program 3. Increase the intensity of the research conducted under Phase I, making NC small businesses more competitive in the competition for Phase II funds 4. Encourage the establishment and growth of high-quality, advanced technology small businesses in NC 22 

http://www.nccommerce.com/scitech/grant-programs/one-nc-small-business-program.

Assessing State-Level Science and Technology Policies    393 To accomplish these objectives, the program, which is administered by the North Carolina Board of Science, Technology & Innovation (BSTI), provides state matching grants up to a maximum of $100,000 to a NC business that received a federal SBIR/ STTR Phase I award. Matching grants are disbursed in two stages. Stage 1 awards 75 percent of the total match, and is disbursed when proof of Phase I award is provided to the BST. Stage 2 awards the remaining 25 percent of the total match, and is disbursed upon completion of the Phase I project and submission of the Phase II application to the federal agency.23 This two-stage award structure is designed to provide sufficient funding to strengthen the Phase I project work, and then to reserve a portion to assist the small business during the transition period between the completion of the Phase I project and the initiation of the Phase II project. During spring 2012, as part of a legislatively required organizational and programmatic review, BST staff conducted a comprehensive survey of the program’s grantees to assess the impact of the state’s SBIR matching funds.24 The BST staff were expressly interested in the independent impacts of the state matching grant above and beyond the initial impacts of the federal SBIR/STTR grant. Thus, while the survey was modeled after the survey methodology used in An Assessment of the SBIR Program (Wessner 2008), a congressionally mandated study of the federal SBIR program, conducted by the National Research Council of the National Academy of Sciences, its questions were explicitly designed to measure, to the extent possible, the independent impacts of the state funding.25 The findings presented below—drawing upon the program’s baseline information, routine reports, and the 2012 assessment survey—offer valuable insights into the impacts that a state-level matching-grant program can have on promoting local competitiveness.26

23 

The program’s guidelines require that the applicants must also fulfill several other requirements, such as be a for-profit small business with its principal place of business in NC, meet all federal SBIR/ STTR program eligibility requirements that are applicable to the relevant federal solicitation, have received an official notification of federal SBIR/STTR program Phase I SBIR or STTR award, certify that at least 51 percent the activity conducted under the Phase I research and subsequent Phase II effort, if awarded, will be performed in NC, etc. For additional detail, see http://www.nccommerce.com/scitech/ grant-programs/one-nc-small-business-program. 24  http://www.nccommerce.com/Portals/6/Documents/Resources/OST_Continuation_Review_ Report_Final.pdf. 25  Efforts to measure the independent impact of the state funding took two forms: (1) Likert-Scale questions on which grantees rated their level of their agreement or disagreement with statements focused on the extent to which the program achieves its stated objectives and (2) preprogram and postprogram counts of relevant factors (e.g., employment, follow-on funding, collaboration with universities) paired with corresponding Likert-Scale questions on which grantees rated their level of their agreement or disagreement with statements addressing the extent to which the state matching grant contributed to increases in those factors, independent of the federal funding. 26  Baseline demographic data are available for all 245 matching grants awarded between 2006 and 2012; responses to the 2012 survey are available for 200 (82 percent) of the matching grants.

394    Competitiveness at the Local Level

Program Impacts The NC small businesses receiving the state matching grants expend the funds in a number of ways. The majority of the funds, 53 percent, is used to cover the wages and salaries of the small businesses’ employees. Twelve percent of the expenditures support research-related equipment, 10 percent for supplies, 6 percent each for facility rental and consultant fees, and 2 percent for computer software. The remaining 11 percent cover other costs, such as patent and legal fees or consultant fees; these are expenses not allowable under the SBIR programs as shown in table 21.1 below. Of note, many grantees cite the greater flexibility of the state funds in contrast to the restrictions placed with the federal SBIR funds as one of the state matching program’s most important benefits. It enables them to pay for a broader array of product and services that are vital to their commercialization efforts. In terms of impacts, at the broadest level, the grantees indicate the program’s funds are highly effective in enabling them to achieve the program’s four core objectives. As indicated in Figure 21.2, with respect to the program’s first objective—increasing the amount of federal research dollars received by NC small businesses—73 percent of the grantees strongly agree, and 14 percent moderately agree, that the state matching funds helped them receive federal research dollars. As for the program’s second objective—helping bridge the funding gap between Phase I and II funds—83 percent of grantees strongly agree, and 10 percent moderately agree, that the state matching funds helped them bridge the funding gap. Turning to the program’s third objective—increasing the intensity of the research conducted under Phase I to make them more competitive for Phase II funding—77 percent of businesses strongly agree, and 13 percent moderately agree, that the state matching funds helped them be more competitive. With respect to the program’s fourth objective—encouraging the establishment and growth of high-quality, advanced technology small businesses in NC—82 percent of businesses strongly agree, and 12 percent moderately agree, that the state matching funds encourage the development and growth of advanced technology firms. Table 21.1  One NC Small Business Program, Use of Funds, FY 06-11 Use

Matching $

Wages and salaries Equipment Supplies Facility rental Consultant fees Computer software Other

$8,862,142 $1,984,994 $1,637,191 $1,000,130 $941,646 $370,991 $1,843,635

Total

$16,640,729

Source: Hardin 2012.

% of Total 53% 12% 10% 6% 6% 2% 11% 100%

Assessing State-Level Science and Technology Policies    395 Objective 1: Increase the Amount of Federal Research Dollars Received by NC Small Businesses 1. Strongly Disagree 0%

1. Strongly Disagree

2. Moderately Disagree 0%

3. Slightly Disagree 1%

5%

5. Slightly Agree 6. Moderately Agree

0%

2. Moderately Disagree 1%

3. Slightly Disagree 0% 4. Neutral

Objective 2: Help NC Small Businesses Bridge Funding Gap Period between Final Phase I Payment and First Phase II Payment

8% 14%

7. Strongly Agree

2% 3%

6. Moderately Agree 73%

Objective 3: Increase Intensity of Research Conducted under Phase I, Making NC Small Businesses More competitive in Competition for Phase II Funds

10%

7. Strongly Agree

83%

Objective 4: Encourage Establishment and Growth of High-Quality, Advanced Technology Small Businesses in NC 1. Strongly Disagree 0% 2. Moderately Disagree 1%

1. Strongly Disagree 0% 2. Moderately Disagree 0% 3. Slightly Disagree 0% 4. Neutral 1% 5. Slightly Agree 9% 6. Moderately Agree 13% 7. Strongly Agree

4. Neutral 5. Slightly Agree

3. Slightly Disagree 0% 4. Neutral 5. Slightly Agree 6. Moderately Agree 77%

7. Strongly Agree

2% 4% 12% 82%

Figure  21.2 Grantee Assessment of One NC Small Business Program’s Effectiveness in Meeting Program’s Four Core Objectives

In terms of additional quantifiable impacts of the state matching grant, above and beyond the program’s four core objectives, the survey identified four in particular. They are summarized in table 21.2 and described below. Increased Phase II Award Rate: Prior to the program’s inception, the Phase II award rate for North Carolina small businesses was 50 percent or lower. Among the small businesses receiving matching grants and completing the survey, however, the Phase II award rate was 56 percent, or at least six percentage points higher, resulting in a total of $73,313,803 in follow-on Phase II SBIR/STTR awards. Because the composition of the set of preprogram businesses and the size of the federal SBIR program are not identical to the composition of the set of businesses participating in the program, the increased Phase II award rate may not be due entirely to the impact of the program. However, 68 percent of the grantees strongly agree, and 15 percent moderately agree, that the state matching grant helped their business be more competitive in the competition for Phase II funds, indicating the importance of the state matching grant.27 27 

This question was distinct from the question above addressing the extent to which the program achieves its second core objective. Specifically, this question asked the grantees to rate the extent to which state matching grant helped their company be more competitive in the competition for Phase II funds. Additionally, in two related questions, 68 percent of grantees strongly agreed, and 17 percent moderately agreed, that the state matching grant helped them to complete the Phase I project on time, and 74 percent of grantees strongly agreed, and 15 percent moderately agreed, that the state matching grant helped them to achieve the goals of the Phase I project, respectively.

396    Competitiveness at the Local Level Table 21.2  Summary of One NC Small Business Program Impacts • Increased Phase II award rates by approximately 6 percent • Increased employment by approximately 10 percent • Increased collaboration with institutions of higher education for a majority of grantees • Increased follow-on funding across several sources Source: Hardin 2012.

Increased employment: To a limited but notable degree, the program also contributed to increasing the number of jobs the small businesses create and retain. Specifically, the survey respondents noted that the state matching grants contributed to the creation of a total of 241 new jobs and retained 246 exiting jobs.28 In total, the estimated net wemployment impact of the state grants is 487 jobs, or 20 percent of the small businesses’ workforce at the time of award. Given that the small businesses devote 53 percent of the grant funds to wages and salaries, that makes the cost per job created/retained slightly more than $18,000, a competitive amount among state-level economic development programs as shown in table 21.3 below.29 Improved collaboration with  institutions of  higher education: The program also improves the small businesses’ collaborations with higher education research institutions. This is a notable finding, as research indicates that collaborative activity bolsters spillovers and agglomeration economies that can promote competitive advantage (Chesbrough 2006; Gulbranson and Audretsch 2008; Block and Keller 2009). Of the 59 percent of grantee businesses that collaborated with a university or college, 49 percent strongly agreed, and 22 percent moderately agreed, that the state matching funds enhanced the scope or quality of their partnership with a university or college. Of those partnerships, 91 percent were with a college or university within North Carolina, suggesting additional local benefits of the state matching grant. Additional funding received: Collectively, the 200 small businesses completing the survey indicate receiving more than $85 million dollars of follow-on funding to directly support the development and commercialization of the products and services they developed with the initial federal and state funding. This is in addition to $73,313,803 in follow-on Phase II SBIR/STTR awards noted above and captured elsewhere in the survey.30 Thus,

28 

The exact wording of the survey question was as follows: “How many jobs per category, if any, did your business create with the state matching grant? How many jobs per category, if any, did your business retain with the state matching grant? Do not include jobs created or retained by the original Phase I SBIR/ STTR award or other funding.” 29  This figure is derived by dividing the total amount of state matching grant funds devoted to wages and salaries ($8,862,142) by the total number of jobs created or retained (487). 30  The SBIR/STTR funding listed in table 21.4 pertains to other SBIR/STTR funds, such as a Phase I grant that resulted from the work of the Phase I grant originally matched by the One NC Small Business Program.

Assessing State-Level Science and Technology Policies    397 Table 21.3  Number of Employees in Businesses Awarded Matching Grants under the One NC Small Business Program Type Professional/scientific Management Technical/technician Skilled labor Unskilled labor Other Total

# of employees (at time of award)

# of employees (at end of award period)

1,278 441 417 176 21 60

1,398 458 475 195 40 68

2,393

2,634

Source: Hardin 2012.

Table 21.4  One NC Small Business Program: Additional Funding Directly for Technology Developed during Project, FY 06-11 Additional funding Colleges/universities Foreign investment Nonprofit Non-SBIR/STTR federal Other domestic company Other private equity Personal funds of company SBIR/STTR federal funding State or local governments US venture capital Your own company Total

Count 83 76 62 103 116 97 97 95 88 87 102 1,006

Dollars $600,000 $125,000 $435,000 $21,326,825 $10,948,403 $11,613,000 $905,084 $28,608,219 $1,807,250 $6,700,000 $2,237,500 $85,306,281

Source: Hardin 2012.

combined with the follow-on Phase II SBIR/STTR funding noted above, the total amount of additional funding the businesses received amounts to $158,620,084. For every $1 the state provides to the small businesses with matching grants,31 they receive an additional $9.5 dollars from other sources. While not all of that follow-on funding can be attributed to the state matching grants—notably, the Phase I federal funding contributes to the follow-on funding as well—the grantees report that the state grants play a significant role. Specifically, 54 percent of grantees strongly agree, and 11 percent moderately agree, that the state matching grant helped them receive 31 $16.6 million total.

398    Competitiveness at the Local Level additional funding from one or more additional sources, indicating the additional impact of the state matching grant, independent of the federal SBIR grant. The survey also captured hundreds of testimonials from the program’s grantees, of which the following three are representative: I launched my company specifically because I knew that if we landed one federally funded Phase I effort, the state would match it. This early-stage support helped us pay personnel, achieve all of the goals of the Phase I project, and generate preliminary data that helped us obtain the larger Phase II grant. It also helped us establish business infrastructure. Our company couldn’t have proceeded without the state matching monies. To do the R&D, input from consultants in the field is needed but could not be budgeted for in the federal grant, so the NC match grant helped us obtain the guidance needed to be more competitive in the Phase II application.

These testimonials underscore the supplemental, targeted impact of the state matching grants, attesting to the value of state support for local technology commercialization efforts.

Conclusion This chapter focused on state government innovation policies, paying particular attention to the range of state policy programs complementing the federal Small Business Innovation Research (SBIR) program. Focusing specifically on one such state-level SBIR matching grant program, the One NC Small Business program, it presents the results from a recent survey assessing the efficacy of that program. The survey finds evidence that the state matching grants offer notable benefits for the local economy, including increased award rates, greater employment among small businesses, enhanced follow-on funding, and improved collaborations with institutions of higher education. While the literature has generally focused on federal R&D policy activity, this chapter offers a unique and more detailed perspective on how states can promote economic development by supporting science and technology programs that increase regional spillover effects.

References Arrow, Kenneth. 1962. “Economic Welfare and the Allocation of Resources for Invention.” In The Rate and Direction of Inventive Activity:  Economic and Social Factors. Princeton, NJ: Princeton University Press. Audretsch, Bruce. 1998. “Agglomeration and the Location of Innovative Activity.” Oxford Review of Economic Policy 14 (2), 18–29.

Assessing State-Level Science and Technology Policies    399 Audretsch, David B. 2003. “Standing on the Shoulders of Midgets:  The US Small Business Innovation Research Program (SBIR).” Small Business Economics 20 (2), 129–35. Audretsch, David B., and Maryann P. Feldman. 1996. “R&D Spillovers and the Geography of Innovation and Production.” The American Economic Review 86 (3), 630–40. Aydalot, Philippe, and David Keeble, eds. 1988. High Technology Industry and Innovative Environments: The European Experience. New York: Routledge. Block, F., and Keller, M. R. 2009. “Where do Innovations Come From? Transformations in the US Economy, 1970–2006.” Socio-Economic Review 7 (3), 459–83. Chesbrough, Henry William. 2003. Open Innovation:  The New Imperative for Creating and Profiting from Technology. Boston: Harvard Business Press. Chesbrough, H. W. 2006. “The Era of Open Innovation.” Managing Innovation and Change 127 (3), 34–41. Ege, Metin. 2009. “How Do Grants Influence Firm Performance? An Econometric Evaluation of the SBIR Program at NIH.” PhD diss., Rutgers University, New Brunswick, NJ. Feldman, Maryann P. 1994. The Geography of Innovation. Vol. 2. Boston: Kluwer Academic. Feldman, Maryann P., and Lauren Lanahan. 2013. “State Science Policy Experiments.” In Adam B. Jaffe and Benjamin F. Jones, eds., The Changing Frontier: Rethinking Science and Innovation Policy. Chicago: University of Chicago Press. Feldman, M.  P., L. Lanahan, and I. Lendel. 2013. “Experiments in the Laboratories of Democracy:  State Scientific Capacity Building.” Economic Development Quarterly, doi: 0891242413490018. Feller, Irwin. 1997. “Federal and State Government Roles in Science and Technology.” Economic Development Quarterly 11 (4), 283–95. Greenstone, Michael, Richard Hornbeck, and Enrico Moretti. 2010. “Identifying Agglomeration Spillovers:  Evidence from Million Dollar Plants.” National Bureau of Economic Research Working Paper No. w13833. Gulbranson, Christine A. and David B.  Audretsch. 2008. “Proof of Concept Centers:  Accelerating the Commercialization of University Innovation.” Journal of Technology Transfer 33 (3), 249–58. Hall, Bronwyn H., Adam B. Jaffe and Manuel Trajtenberg. 2000. “Market Value and Patent Citations: A First Look.” National Bureau of Economic Research Working Paper No. w7741. Hardin, John W. 2012. “Office of Science & Technology: Continuation Review Report.” North Carolina Department of Commerce, Raleigh. Hearn, J.  C., T. A.  Lacy, and J. B.  Warshaw. 2014. “State Research and Development Tax Credits: The Historical Emergence of a Distinctive Economic Policy Instrument.” Economic Development Quarterly, doi: 0891242413517135. Keller, Matthew R., and Fred Block. 2013. “Explaining the Transformation in the US Innovation System: The Impact of a Small Government Program.” Socio-economic Review 11 (4), 629–56. doi: 10.1093/ser/mws021. First published online: September 30, 2012. Lanahan, Lauren. 2015. “Multilevel Funding for Small Business Innovation: A Critical Review of US State SBIR Match Programs.” The Journal of Technology Transfer. Forthcoming. Lanahan, L., and M. P. Feldman. 2015. “Multilevel Innovation Policy Mix: A Closer Look at State Policies that Augment the Federal SBIR Program.” Research Policy. Accepted. Lerner, Josh. 2000. “The Government as Venture Capitalist: The Long-Run Impact of the SBIR Program.” Journal of Private Equity 3 (2), 55–78. Link, Albert N., and John T.  Scott. 2010. “Government as Entrepreneur:  Evaluating the Commercialization Success of SBIR Projects.” Research Policy 39 (5), 589–601.

400    Competitiveness at the Local Level Lundvall, Bengt-Ake. 1992. “User-Producer Relationships, National Systems of Innovation and Internationalisation.” In National Systems of Innovation: Towards a Theory of Innovation and Interactive Learning. London: Pinter. Markusen, Ann. 1996. “Sticky Places in Slippery Space: A Typology of Industrial Districts.” Economic Geography 27 (3), 293–313. Mayer, Heike. 2010. “Catching Up: The Role of State Science and Technology Policy in Open Innovation.” Economic Development Quarterly 24 (3), 195–209. Morgan, Kevin. 2007. “The Learning Region: Institutions, Innovation and Regional Renewal.” Regional Studies 41 (S1), S147–S159. Moulaert, Frank, and Farid Sekia. 2003. “Territorial Innovation Models: A Critical Survey.” Regional Studies 37 (3), 289–302. National Research Council (US) Committee for Capitalizing on Science, Technology, and Innovation. 2008. An Assessment of the Small Business Innovation Research Program. Ed. C. W. Wessner. Washington, DC: National Academies Press. Oerlemans, Leon, Marius Meeus, and Patrick Kenis. 2007. “Regional Innovation Networks.” In Rutten Roel And Frans Boekema, eds., The Learning Region: Foundations, State of the Art, Future. Cheltenham: Edward Elgar. Plosila, Walter H. 2004. “State Science- and Technology-Based Economic Development Policy: History, Trends and Developments, and Future Directions.” Economic Development Quarterly 18 (2), 113–26. Sapolsky, Harvey M. 1971. “Science Policy in American State Government.” Minerva 9 (3), 322–48. Storper, Michael. 1995. “Regional Technology Coalitions: An Essential Dimension of National Technology Policy.” Research Policy 24 (6), 895–911. Storper, Michael. 1997. The Regional World:  Territorial Development in a Global Economy. New York: Guilford Press. Toole, Andrew A. and Dirk Czarnitski. 2007. “Biomedical Academic Entrepreneurship Through the SBIR Program.” Journal of Economic Behavior & Organization 63 (4), 716–38. Wallsten, S. J. 2000. “The Effects of Government-Industry R&D Programs on Private R&D: The Case of the Small Business Innovation Research Program.” RAND Journal of Economics 31, 82–100. Wessner, C.  W., ed. 2008. An Assessment of the SBIR Program. Washington, DC:  National Academies Press. Wilson, D. J. 2009. “Beggar Thy Neighbor? The In-State, Out-of-State, and Aggregate Effects of R&D Tax Credits.” Review of Economics and Statistics 91 (2), 431–6. Woolley, Jennifer L., and Renee M. Rottner. 2008. “Innovation Policy and Nanotechnology Entrepreneurship.” Entrepreneurship Theory and Practice 32 (5), 791–811.

Chapter 22

Clusters, C ommu ni t i e s , and C om peti t i v e ne s s An Emerging Model from America’s Midwest David Lawther Johnson

“Creating jobs is our Job Number One!” For any president or member of Congress in postrecession America, the impassioned language of job creation is now a fact of everyday life and, seemingly, of every speech. The truth remains, however, that most jobs still get “created” where economic activity takes place—at the regional and community level. For many communities across the United States, the standard approach to job creation for over a decade now has involved the pursuit of technology-intensive regional “clusters” of related and specialized economic activity. Cluster initiatives typically engage interconnected businesses, suppliers, vendors, and other service providers and related institutions, all focused on a specific market. Such efforts seek to advance particular sectors of 21st-century opportunity that promise high-paying and high-skilled employment oriented to the future, rather than more traditional, and less strategic, “elephant-hunting” pursuits for company attraction. In his recent book The New Geography of Jobs, UC Berkeley economist Enrico Moretti estimates that nearly $60 billion is spent every year by federal and state governments on place-based policies to promote the creation and expansion of these new economy, technology-intensive economic clusters (Moretti 2012, 187).

The Pursuit of Cluster Opportunities Regional cluster initiatives have arrived as a particular saving grace for many of America’s Midwestern communities, since clusters give policymakers and community leaders something to do with economic assets they already have. In the last decade of the 20th century and the first decade of the 21st, when manufacturing operations—the

402    Competitiveness at the Local Level foundation for so many local Midwestern economies—increasingly moved south or offshore altogether, Midwestern states were left in search of better ways to redeploy workers and address a changing and increasingly remote world of opportunity. Books such as Caught in the Middle, published in 2008 by the journalist Richard Longworth, spoke to (and perhaps, even rationalized) the overwhelming problem of turning traditional economies based on hard work, heavy production, and nonspecialized skills into something far nimbler, more entrepreneurial and creative (Longworth 2008). Cluster strategies pointed an important way forward, by allowing—and urging—communities to focus more strategically on industrial assets already in place, seeking to build a more skill-intensive future for their survival. Importantly, cluster strategies also took hold as a measure of regional, private-sector self-help at a time when public resources for economic development attraction were growing scarce. Beginning in the 1990s and accelerating during the recession of the following decade, state and local governments lacked many of the conventional tools (financial incentives, tax credits, signature investment programs like the efforts to attract German car manufacturers to the South or aircraft manufacturers to the Midwest), at least in conventional volumes, to close on major economic attraction transactions. Tax increases through the issuance of debt to fuel major economic development efforts became more challenging as economic cycles grew more volatile, and government grew more suspect. By 2011, as America began to emerge from recession, major long-term public financing commitments for projects like Research Triangle Park in North Carolina; biosciences development in Florida and Kansas; or the “Third Frontier” in Ohio, each entailing hundreds of millions of dollars in public-sector funding, looked like heroic efforts from a bygone era—capital outlays on a scale unlikely to be seen soon again. Regional innovation clusters have thus helped fill a growing public funding gap by turning to nonpublic sources for investment and growth, especially strategic philanthropy and corporate investment. Led by regional development intermediaries, such as Bio STL (a St. Louis–based industry and academic coalition to advance regional strengths in plant sciences and medical technology) and Nortech Ohio (a northeastern Ohio collaboration seeking regional revitalization through technology developments in alternative energy, electronics, and water), Midwestern cluster strategies have attracted additional—and substantial—private-sector investment by building on existing business assets, rather than starting from scratch. Such collaborative efforts then seek out signature sectors or subsectors—such as plant science, animal health, defense, electronics, logistics, or medical technology—to drive capital to opportunities for national, or even global, competitiveness. In addition, federal and state policymakers have applauded cluster-based approaches for their deliberate selectivity and focus, in a way that most government-sponsored programs, usually sensitive to appearances of industrial policy or preferential politics in favoring certain economic or geographic sectors over others, could never be. State research and land-grant universities, one of the single greatest legacy assets found in many Midwestern states today, have a special role to play in cluster strategies as well, with their promise of technology transfer and licensing of

Clusters, Communities, and Competitiveness    403 innovation from the academic research bench to the hoped-for start-up companies that can fuel the next generation of growth. The rationale for promising, high-technology clusters continues to make sense as a cornerstone for prioritizing economic development. Indeed, the accomplishments of many Midwestern cluster-based strategies (e.g., BioEnterprise to advance investments in medical technology in Cleveland; the Animal Health Corridor for animal health sciences development across Kansas; the BioGenerator for plant sciences investment and development in St. Louis) are impressive. Cluster strategies, with their sharpened focus on particular industries and the investments of human and financial resources required to build them, often make particularly effective use of messaging and branding tools that call attention to a particular region, and thus provide a reputation benefit that can helpfully spill over into wider community and state marketing efforts for talent attraction, investment, and growth. Still, over time, certain policy and practical limitations of cluster strategies have become increasingly apparent to many of us in the field.

Problems in Cluster Pursuit: The Innovation-Addition Problem Though economic development clusters are classically defined around existing economic drivers, the concept of advancing regional industry clusters has grown increasingly intertwined, to the point of becoming synonymous, with the far more ambitious goal of pushing traditional industrial assets to stimulate new company growth through the development of “regional innovation clusters.” In this composite concept, innovative activity joins the mix to offer the appealing promise of economic transformation through commercializing new discoveries and advancing a host of small companies on the industrial landscape as old-line pharmaceutical companies theoretically give rise to a new burst of biotechnology start-ups, and all manufacturing becomes, by necessity, “advanced” manufacturing. The difficulty with this attempted transformation-by-linkage of traditional industry to “innovation” is that innovation is typically expensive, risky by definition, and requires sustained investments over many years (and often, substantial good luck) to come to fruition—all challenges to public-sector economic development policies that are inherently time- and cash-limited and, at least in the eyes of the public officials who promote them, rewarded primarily when they produce the prized end-product of new jobs. Communities seeking to build the next Silicon Valley in software technology or the next biotechnology capital along the ever-changing frontiers of biological sciences must be prepared to embark on complex strategies of investment, risk taking and talent attraction that can, and probably will, require decades for true implementation and the achievement of measurable success in producing new jobs—if indeed this ever happens

404    Competitiveness at the Local Level at all. To public officials for whom vocational success is typically measured out in fouror even two-year election cycles, such a notion of starting and sticking to a highly expensive and risky strategy of long-term investment, with any payoff likely only in the “out years,” can quickly lose appeal.

Problems in Cluster Pursuit: The Relentless Jobs Metric Even when successful in producing jobs, regional innovation cluster strategies (especially the most popular ones in leading technology areas like life sciences and information technology) often yield only a relative few. Innovation’s earliest metrics typically involve “inputs” like financial investment and translation to more commercial settings; they rarely lead to immediate opportunities for employment on any significant scale. Those looking to build the next Pfizer or Merck from every promising new biotech start-up are understandably disheartened to learn that a payroll of 15, after five years, is trumpeted as a hallmark of success. Indiana provides a case in point, with its two publicly traded homegrown pharmaceutical companies:  Eli Lilly and Company, around since 1876, with its 35,000 highly skilled employees; and Endocyte (perhaps even more remarkably, around and still going since 1995, but still seeking its first FDA-approved product for marketing and sale), with invested capital of nearly $300  million and a workforce of barely 70. Such start-ups, when they succeed, do indeed provide important and helpful signs of hope; but they rarely serve as a satisfactory jobs strategy to put meaningful numbers of citizens into productive work in the foreseeable future. Particularly for Midwestern states, which have shed automobile and steel manufacturing jobs by the hundreds of thousands over the past two decades, adding jobs by the occasional handful looks like a woefully deficient strategy for economic recovery.

Problems in Cluster Pursuit: The Credible Claims Challenge Public champions of cluster strategies often bring the aspirations (and metrics) of politics to the business of industry-building. Cluster advocates regularly choose to describe their goals in terms of becoming “the best,” the “most,” or at the center of “global competitiveness” for their communities. While such competitive goals can be useful, and even necessary to justify public focus and major investment, they are typically elusive. Such hyperbole also becomes a danger all in itself to efforts to build credible concentrations of talent and opportunity. In the life sciences sector, for example, where seemingly

Clusters, Communities, and Competitiveness    405 every American state and major metropolitan area has the goal of becoming the next global hub for biotechnology or medical technology development—and tens of millions of public and private dollars have been expended over the past decade in pursuit of such goals—a map of the locations for America’s major regional centers of pharmaceutical, medical device, and biotechnology innovation today looks remarkably similar to one charting those very same centers a decade or two ago (Plosila 2011). This result is not especially surprising: clusters by their very nature build on existing assets and arguably (particularly for expensive clusters such as life sciences, nanotechnology, and biotechnology), the most successful regions will be those starting with such a rich asset pool of companies, established funding sources, supply chains, and talent that others are unlikely to be able to make the jump to catch up. Moreover, falling short of global competitiveness does not, of course, mean a particular cluster strategy is not worthwhile. Again in the field of life sciences, there are many communities across the Midwest today (e.g., Kansas City, Memphis, Cleveland, Madison) that may not rival San Francisco or Boston or Minneapolis for sector dominance, but have nonetheless achieved significant growth and are thereby developing real promise for the future of their respective regions. Still, the way public officials and private advocates and investors view these clusters, and the words we use to describe them, are important. The policy language of clusters must be kept credible for real success, and for the assurance of continued public support and private investment.

Problems in Cluster Pursuit: The Benefit Gap Finally, regional industry or innovation clusters are by their very nature selective, rather than inclusive of broad segments of the traditional workforce. Again, the central point of genuine cluster development is deliberate specialization around a core of assets demanding specific talent, relentless focus, and targeted investment for growth. Such a strategy, especially if successful, can lead to a “spiky” landscape of high opportunities for a few communities in a single state, or selected industry components within a particular community, but seemingly minimal opportunities for broader participation by others. Efforts to quantify cluster strategies’ larger contributions to the general welfare through constructs like job multipliers and expansive discussions of wealth creation certainly help (and often have the benefit of being true, of course). Yet again, for statewide elected leaders and policymakers responsible for all citizens, clusters that focus—and, arguably, must focus—on the activities and elite talents of a relative few bring real challenges when it comes to showing a plan offering hope across the board. How do communities work with these challenges to achieve success at a time when the global competition for talent and financial resources makes effective cluster strategies increasingly urgent? Our experience in Central Indiana may provide some useful insights.

406    Competitiveness at the Local Level

The Central Indiana Response (CICP) For reasons shrouded in history and rich in controversy, Indiana has long been known as “the Hoosier State,” and its residents called “Hoosiers.” It is a state that is relatively compact (fewer than 7  million people) with one major metropolitan area—Indianapolis—serving at the same time as the geographic heart, political capital, and principal business center. Four interstate highways pass through Indianapolis, long ago dubbed the “Crossroads of America” for its central location and making it a logical focal point for a cluster strategy based around logistics; and indeed, many trucking and other transportation companies have had their start there. Moreover, for a state its size, the Hoosier State is favored with a legacy of globally traded and large privately held companies, including defense and logistics in the northeast around Fort Wayne; vans and recreational vehicles in Elkhart; diesel engines in Columbus; nutraceuticals in Evansville; cardiovascular devices in Bloomington; a third of the world’s orthopedics implant makers in tiny Warsaw (a town of 17,000, 7,000 of whom are employed in the orthopedics industry—a true economic cluster if there ever was one); pharmaceuticals, diagnostics, major healthcare providers, insurance, Internet marketing services, motor sports, and global defense companies in Indianapolis; and major automobile manufacturers and supply chain facilities located throughout Indiana’s central and southern portions. Indiana is a leading exporter of high-value-added industrial products, including diesel and aircraft engines, automotive electronics, and medical devices and pharmaceuticals. Indiana is also home to nearly 40 private and public colleges and universities, including three nationally ranked research institutions (Indiana University, Purdue University, and the University of Notre Dame), as well as the nation’s second largest medical school, the IU School of Medicine, located in downtown Indianapolis. Despite these substantial pockets of strength, Hoosier prosperity is highly uneven, and the state faces significant challenges. By concentration of employment, Indiana is America’s most manufacturing-intensive state (16.4  percent of the workforce).1 This remains true despite the fact that, like those of its Midwestern neighbors, Indiana’s traditional heavy manufacturing industries, especially automotive and steel, have lost tens of thousands of jobs in recent years. Per capita income has slipped from 21st among 48 states in 1950 to 40th out of 50 states today (Hicks et al 2013, 2). While its many noteworthy colleges and universities often outperform those of other states in turning out graduates with four-year degrees—14th in the nation, according to a recent report—the percentage of Hoosiers actually holding and using such a degree in the workplace is depressingly subpar at 44th in the nation, translating into less than 20 percent of the workforce (Faulk et al. 2012, 1). A recent study finds these factors converging to make the state a net exporter of educated talent, thereby calling into question 1  U.S. Bureau of Economic Analysis, cited by National Association of Manufacturers and accessed November 15, 2013 at http://www.nam.org/mfgdata/statedata.

Clusters, Communities, and Competitiveness    407 Indiana’s continuing capacity to offer the prospect of relative, middle-class prosperity for its citizens in a rapidly shifting global economy (Battelle Technology Partnership Practice 2012). As drivers for much of the state’s high-growth activity, Central Indiana’s business and community leaders understand their outsized responsibilities to address these long-term structural issues with better strategies for putting more people to work, in more highly skilled, better-compensated employment. Finding such strategies is not easy, but the region does start with a strong history of highly engaged and strategic philanthropy, genuine business collaboration, and productive public-private partnerships at the grass-roots level, forged initially through the shared pursuit of amateur and professional sports enterprises and events beyond the iconic Indianapolis 500 mile race. Acting on this collaborative tradition, corporate and philanthropic leaders, along with public university presidents, came together 15  years ago in Indianapolis to form a new type of civic organization—moving beyond the strategy of sports and sports-inspired regional tourism—built around a clear, executive-level business leadership structure, the Central Indiana Corporate Partnership (CICP). CICP draws on a sophisticated nonprofit architecture that significantly leverages philanthropy, especially the Lilly Endowment and other major regional foundations. CICP itself was established as a 501(c)(6) membership organization, with membership limited initially to 50 (later, expanded to 60) CEOs and presidents, respectively, of the region’s dominant companies and leading research institutions. To effectuate that philanthropic leverage, CICP is in turn supported by a dedicated charitable foundation, the CICP Foundation, designed to be able to receive both charitable and public funds to augment capabilities and advance appropriate projects for the qualified, federally tax-exempt objectives and activities of CICP itself. Such programs include the important promotion of innovation and development through university engagement and technology transfer, as well as the establishment of new types of collaborations to advance specific goals and develop key talent. While organizations similar to CICP in other communities are often based on substantial public-sector participation and funding, CICP is staunchly private. CICP works closely with both state and local governmental leaders, but no elected officials serve on CICP’s board, and none of the organization’s core funding comes from public dollars. For Indiana, with its tradition of relatively minimal state-level economic development funding based on a deep culture of fiscal conservatism and an unusually broad constitutional prohibition against state-incurred debt, CICP’s privately funded structure is particularly appropriate—and important.2

2  Ind. Const. art. 10, sec. 5, bars the State of Indiana from incurring or issuing virtually any form of public debt, including general obligation bonds for economic development. This provision was added to Indiana’s restated constitution of 1851, after the state defaulted on bonds issued to finance a failed series of canals attempting to link the Great Lakes to the Ohio River through Indiana’s growing cities.

408    Competitiveness at the Local Level CICP’s original strategy, which remains its underlying approach today, was to survey and then build on existing economic strengths holding the greatest promise for high-skilled, high-wage jobs for regional growth. This strategy, informed by a series of expert third-party consultant studies, led to CICP’s focus on establishing credible cluster initiatives around noteworthy assets in life sciences, high-skilled manufacturing and logistics, and information technology. To implement this focus, over the past decade CICP has created a series of separate, visibly branded sector initiatives that remain legally a part of the consolidated CICP organization, even though each is separately organized and invested in by stakeholders that include but also extend considerably beyond the membership of CICP itself. Thus today, the CICP organization is home to the BioCrossroads initiative for life sciences and healthcare development; TechPoint for IT and other forms of digital technology; and Conexus Indiana for advanced manufacturing and logistics. Additional technology clusters have sprung up opportunistically under the CICP umbrella, specifically around energy and clean-tech (Energy Systems Network) and agricultural innovation (Indiana Food and Agricultural Innovation Initiative). Each of these cluster initiatives has developed a recognizable brand—in some cases, a brand deliberately stronger than CICP’s own. Each has also succeeded in engaging impressive boards of investors and stakeholders, along with strong executive management teams and project professional talent. None has hesitated to pursue investments and development opportunities wherever they may be found, frequently beyond the nine-county boundaries of Central Indiana. Three of CICP’s nonprofit initiatives (BioCrossroads, TechPoint, and Energy Systems Network) have set up for-profit subsidiaries to engage more directly in new company development, either through the direct formation of new enterprises or the establishment of angel or seed-stage investment funds to advance promising sector start-ups. Each has also evolved its own, appropriately flexible strategy to address sustainability, metrics, and growth. And all have achieved impressive and, importantly, measurable results, even though the guiding strategies for each initiative differ. For purposes of this brief chapter, it is not feasible to attempt a detailed review of the records of these four separate cluster initiatives. But a brief evaluation of two—the innovation strategy of the BioCrossroads life sciences initiative, and the talent development strategy of the Conexus initiative for advanced manufacturing and logistics—does offer at least some encouraging evidence of progress in light of the pitfalls to effective cluster development noted earlier, specifically #1—The Innovation-Addition Problem: addressing the unavoidably higher costs and risks when goals for success in innovation are combined with the agenda of building clusters on the base of traditional assets; #2—The Jobs Metric: succeeding in moving expectations beyond perennial jobs goals to include other significant indicators of success; #3—The Credible Claims Challenge: advancing distinctive cluster strategies through realistic messaging in light of national competition; and #4—The Benefit Gap:  ensuring cluster programs are sufficiently inclusive to build broad support and drive growth across the diverse population of an entire region or state.

Clusters, Communities, and Competitiveness    409

BioCrossroads and Innovation in Biotech and Medtech CICP’s BioCrossroads initiative has eagerly embraced challenges #1 and #3, largely sidestepped challenge #2, and struggled with challenge #4. Established through substantial philanthropic and industry contributions in 2002–3, BioCrossroads was designed to forge key industry-university collaborations, analyze and brand the particular assets underlying Indiana’s strong but fragmented life sciences sector, address chronic shortages of angel funding and venture capital to build new companies, and create a more engaging setting for the considerable talent required to advance this promising portion of Indiana’s economy. From the start, BioCrossroads has been all about innovation and, thus, finding sustainable ways to finance the expense and shoulder the risks of the “innovation-addition problem.” Accordingly, the organization has sponsored two corporate and fiduciary-funded venture capital funds dedicated to life sciences investments (totaling $130 million) and, through its for-profit affiliate, BC Initiative, has also assumed active management of two similarly funded seed-stage venture vehicles (totaling a more modest $15 million) to launch and attract even more capital to new biotech and medtech enterprises. Results are still a work in progress, but BioCrossroads can claim that genuine innovation really can take root in Indiana today. Through BioCrossroads’ efforts, over 30 Indiana-based life sciences start-ups have attracted a total of over $400 million in venture funding and, in many cases and despite the Great Recession of 2008–10, continue to represent promising new companies on the Hoosier landscape. Through its nonprofit component, BioCrossroads has raised another, nearly $200 million in philanthropic and private funding to launch an array of innovative healthcare collaborations (e.g., the nationally recognized Indiana Health Information Exchange for community-wide gathering, analysis, and transmittal of healthcare data; the Datalys Center for Sports Injury Research; and the Indiana Biosciences Research Institute to attract international talent, opportunity, and funding to major sponsored research programs in metabolic disorders, nutrition, and obesity). Other than the Indiana Biosciences Research Institute, which has received an early commitment of $25 million in state funding, all of the capital required for these innovation-related activities has come from corporate, philanthropic, and fiduciary sources. To raise investments and philanthropic grants on this scale, BioCrossroads has been required to demonstrate to its funders and other stakeholders that Indiana has a set of healthcare assets that merit such investment—and thus, has needed to confront head-on the “credible claims” challenge to stake out those areas where Indiana truly presents world-class opportunities. To do this, BioCrossroads has been able to assemble (thanks to major philanthropic grants from Lilly Endowment) and deploy credible data to determine opportunity and judge success, opting to commission its own sector database using the definitions and metrics for life sciences companies established for all 50 states by BIO, the U.S. biotechnology industry association, in collaboration with the Battelle

410    Competitiveness at the Local Level Memorial Institute. BIO/Battelle’s data, tracked over more than a decade now, confirm that life sciences is indeed a competitive field of opportunity for Indiana: with annual healthcare product exports of over $10 billion, Indiana ranks behind only California as the nation’s largest exporter of life sciences products;3 and in a 2012 BIO/Battelle survey of all 50 states, Indiana has emerged as one of only five in “Tier I” (alongside more usual suspects California, Massachusetts, New Jersey, and North Carolina) in terms of overall number of life sciences enterprises and citizens employed in them.4 Using this same database, BioCrossroads published, in anticipation of its 10th anniversary in 2012, a review of progress over a decade employing metrics such as new company and venture capital formation, increases in patent filings and federal funding, and employment growth in the sector.5 Most of these findings have been effectively captured for broader presentation in the local media through BioCrossroads’ considerable marketing efforts, which have begun to drive a message of credible opportunity for Hoosier talent and investment. BioCrossroads has been less concerned, and thus less successful, in addressing other cluster challenges. Despite its ability to demonstrate rising employment numbers in local healthcare companies or occasional successes in working with state or local officials to recruit new companies to the region, BioCrossroads has elected to focus on economic-activity factors such as new enterprise formation and capital investment, rather than attempting to set a “jobs metric” as its standard for success. Even more problematic for BioCrossroads has been confronting the fourth cluster challenge—the “benefit gap”—in a state where fewer than 10 of 92 counties can claim any credible concentration of life sciences companies or employment, and in a sector in which the best career opportunities increasingly require the kind of significant post-high school (and even postbaccalaureate) credentials that are generally in short supply in the Hoosier State. Mitigating some of the problem here is the basic fact, again demonstrated by BioCrossroads’ Battelle/BIO data, that Indiana’s life sciences sector simply generates such an outsized statewide impact that it becomes important to everyone, despite its selective geographic concentrations: the life sciences industry today makes a $50+ billion annual contribution to the Indiana economy, paying the more than 55,000 Hoosiers employed in this sector an average salary ($88,000) that is more than twice the usual Indiana wage.6 Another helpful factor has been the expansion of BioCrossroads’ focus, beyond innovation and start-ups, to include an emphasis on contract manufacturing and industry supply chain. The region has more than 50 contract research organizations 3  Indiana Business Research Center 2013, accessed at http://www.biocrossroads.com/Newsroom/ INLifeSciencesSector/ExceptionalResults. 4  Battelle/BIO State Bioscience Industry Development 2012, accessed at http://www.bio.org/articles/ battellebio-state-bioscience-industry-development. 5  Walter H. Plosila, Ph.D., Indiana’s Life Sciences Industry: 2002–2010—Tracking Progress and Charting the Course for Continued Success (June 2011), accessed at http://www.biointellex.com/reports/indiana-l ife-sciences-sector/. 6  Indiana Life Sciences Exceptional Results (2013), accessed at http://www.biointellex.com/reports/ indiana-life-sciences-sector/.

Clusters, Communities, and Competitiveness    411 and manufacturers, already employing over 8,000 well-compensated employees, that likely represent some of the best chances for lasting job growth at a time that major pharmaceutical and device companies are turning to a more distributed network of outside developers and manufacturers as a core element of business strategy.

Conexus and Jobs in Manufacturing and Logistics As a complement but also in contrast to BioCrossroads, CICP’s Conexus initiative to promote advanced manufacturing and logistics is focused far less on innovation and investment in new enterprises, than on using Indiana’s documented national leadership in manufacturing as a still-vibrant asset to be advanced, rather than an outmoded base to be put aside. In this context, Conexus has deliberately sought to address cluster challenges #3 and #4 (credibility and inclusiveness), largely avoided challenge #1 (innovation costs), and arrived at an effective response to challenge #2 (job creation). Logistics is included in the Conexus agenda because of the shared Indiana linkage from distribution to production, sectors that together comprise over a third of the state’s economy and 25 percent of all Hoosier jobs. Conexus has worked directly with Indiana’s industry leaders and community college network to develop a comprehensive, two-year manufacturing and logistics curriculum for high school students, using a mix of materials including online lessons and hands-on projects. Completion of this “Hire Technology” curriculum gives students an immediate opportunity to earn up to 15 college credits and various industry-recognized credentials and, essentially, puts them on a faster track to higher education and early, higher-skilled employment in Indiana’s manufacturing and logistics companies.7 The curriculum is already employed in nearly 100 high schools in nearly 50 communities all across the state, with both the State of Indiana, industry, and philanthropy contributing additional funds for expanded and more rapid curriculum deployment. Paired with the Hire Technology curriculum is a matching program for participating schools with so-called A+ Partners in the local manufacturing or logistics business community, working to provide students and their schools with even more direct exposure to career opportunities. All of these efforts are pitched to involve Indiana’s substantial number of manufacturing and logistics companies more directly in building the base of technology-intensive talent that will increasingly be the hallmark of sector success. Surrounding the Conexus curriculum efforts are industry-specific councils, in aerospace and defense, automotive and logistics, further designed to bring sector leaders together to address specific issues of talent, training, and opportunity for growth. 7 

The “Hire Technology” program is described in detail on the Conexus website, accessed at http:// www.conexusindiana.com/hire.

412    Competitiveness at the Local Level Unlike BioCrossroads, Conexus has no for-profit subsidiary focused on finding new start-ups or the capital to build them. But also unlike BioCrossroads, Conexus embraces a deliberate jobs strategy throughout every region and across the entire state of Indiana, focusing not on building new companies but on retaining manufacturing capacity by developing the skills and talent across Indiana’s student population that are increasingly required to fill the estimated 11,000 jobs in manufacturing and logistics that become available in Indiana each year. Not surprisingly, the State of Indiana has readily sought out Conexus as its partner in better understanding and addressing the core manufacturing and logistics sectors, funding substantial amounts of curriculum development and contracting with Conexus’s various industry councils for the development of key strategies and collaborations. With regard to the “credible claims” cluster-development challenge (#3), Conexus addresses this issue directly as part of a broader effort to confront the notion of manufacturing as a bygone American strength. The result is a communications strategy that both generally articulates the high-tech realities of manufacturing and logistics in the 21st-century global marketplace, and then specifically establishes metrics to compare Indiana’s outsized manufacturing and logistics sectors with those of other states. Conexus delivers these metrics through its annual “Manufacturing and Logistics Report Card” prepared by an outside economic policy consultant. Among other things, this 50-state report tracks postrecession sector growth by state; comparative wage levels; exports, foreign investment and other elements of sector global reach; and growth of human capital. Some in other states are skeptical of this report card since the consultant preparing it is based at an Indiana university. Still, Indiana fares well in some categories of the report (overall sector health, rate of postrecession recovery, extent of global reach) but relatively poorly in others (overall improvement in human capital), adding to the report’s integrity.8 More fundamental, Conexus is given appropriate credit for taking the initiative to define and grade and measure progress in a sector that, for too many states in the Midwest, has been prematurely abandoned as a continuing source of opportunity and growth.

Conclusions Strategies such as those embraced by the Central Indiana Corporate Partnership and its industry sector initiatives like BioCrossroads and Conexus demonstrate the challenges as well as the opportunities that come in pursuing long-term cluster development as a primary engine for regional growth. At their best, such cluster efforts capture the attention, resources, and essential involvement of strategic philanthropic, industry, and university partners to advance success in an era of rising technological complexity 8  “Indiana Maintains Enviable Position in Manufacturing, Logistics: Report Shows,” June 2013, accessed at http://www.conexusindiana.com/news-release/2013/06.

Clusters, Communities, and Competitiveness    413 and declining public funding for meeting the challenges of job creation. Such strategies also require substantial and expert professional guidance, continuing and honest assessment, and constant retooling to keep abreast of new opportunities and to avoid being trapped in outdated assumptions. Success is neither linear nor rapid—and never assured. But Central Indiana does show what a collaborative community, committed to a realistic and long-term cluster development approach, can do when it comes together and stays engaged to meet today’s challenges of creating some of tomorrow’s best opportunities.

References Battelle Technology Partnership Practice. 2012. Indiana’s Competitive Economic Advantage: The Opportunity to Win the Global Competition for College Educated Talent. Battelle Technology Partnership Practice and Biotechnology Industry Organization. 2012. Battelle/BIO State Bioscience Industry Development 2012. Hicks, Michael J., Srikant Devaraj, Dagney Faulk, Dick Heupel, and Sharon Canaday. 2013. The Causes of State Differences in Per Capita Income:  How Does Indiana Fare? Ball State University Center for Business and Economic Research. Longworth, Richard C. 2008. Caught in the Middle: America’s Heartland in the Age of Globalism. New York: Bloomsbury USA. Moretti, Enrico. 2012. The New Geography of Jobs. Boston: Houghton Mifflin Harcourt. Plosila, Walter H. 2011. Indiana’s Life Sciences Industry:  2002–2010—Tracking Progress and Charting the Course for Continued Success.

Chapter 23

Lesson s on M icroente rpri se Devel opment from a Universi t y- Base d Microlendi ng Devel opm en t Pro g ra m Paul Miesing, Brad Watts, Donald S. Siegel, and Katharine Briar-Lawson

Economic development is widely regarded as the key to growth and prosperity. However, the social development that must be in place to support such growth is often overlooked. Thus, integrating social and economic development is viewed as a best practice internationally (Midgley 1997). Such integrative practices and investment in the “ecology” of innovation and enterprise formation have been less well developed in the United States. The Small Enterprise Economic Development (henceforth, SEED) program, an innovative microloan pilot program, has been operating in the New York State (NYS) Capital Region since 2011 (http://www.albany.edu/seed/). SEED is a public-private collaborative effort between the Albany Small Business Development Center (henceforth, SBDC) and the University at Albany School of Business and School of Social Welfare to provide funding, training, and support to entrepreneurs in New York’s Capital District. A large statewide credit union originally provided $2.5 million for a revolving loan fund. The first two years of financial support for program operations was provided through a start-up grant from the New  York Empire State Development Corporation to offer a character-based microloan program that assists new and existing small businesses that are underserved by traditional funding sources.

Lessons on Microenterprise Development    415 This chapter provides an overview of this unique program and its successes and challenges to date. We present preliminary findings on the extent to which character versus traditional cash, credit, and collateral predicts effective lending and small business success. In addition, we test the integration of social and economic development and examine the ability of SEED to meet some of its initial goals of promoting entrepreneurship among underrepresented groups in the region.

Local Context In New  York State, 287,840 of all 443,992 firms (65  percent) have four or fewer employees (US Census Bureau 2014b). Thus, policymakers view entrepreneurs—not big business—as the key drivers of economic development and job creation, with small business development a key feature of a diversified and vibrant economy (Birch 1987). Not only do small businesses create more jobs than large firms, they also help stabilize and revitalize distressed communities by utilizing local resources and strengths. Moreover, developing small businesses and microenterprises also complements the state’s efforts to attract larger corporations to upstate New York. According to US Census Bureau data, New York’s Capital Region has developed into communities of haves and have-nots: urban poverty prevails alongside a booming Tech Valley. That is, the suburbs and high-profile city neighborhoods have prospered, while inner-city neighborhoods continue to suffer economically. Despite the region’s technological and economic growth, there appears to be no trickle-down effect for poorer households. Census data provide stark evidence of higher levels of poverty in the city. For example, the Albany County poverty rate was 13.7 percent in 2010, compared to 28.2 percent in Albany city (US Census Bureau 2010). Data on surrounding cities provide similar evidence of persistent inner-city poverty.

Character-Based Microlending That old joke that banks will loan you money only if you can prove you don’t need it is especially true for underserved populations. Standard criteria place lower-income individuals who seek funding for business loans at an overwhelming disadvantage. Current lending policy requires personal capital, high credit scores, and collateral at levels that are not available to most low-income individuals. These barriers to accessing capital can be insurmountable. Research from international lending for microenterprises demonstrates that revolving loan funds are highly successful. For instance, the Grameen Bank, led by the acknowledged granddaddy of microfinance and Nobel Peace Prize winner professor Mohammad Yunus, boasts a 99 percent repayment rate (Yunus and Jolis

416    Competitiveness at the Local Level 1999). Such high repayment in turn fosters incremental leveraging that leads to successful implementation of business plans. There is a strong need for new programs that make loan funds available using a different methodology to assess the candidate’s ability to repay loans and provide social and business supports. It is safe to say that commercial loans are greatly reduced in distressed areas if traditional lending analysis is used that depends on essentially wealth indicators. In short, an alternative criterion and methodology beyond using the traditional requirements of cash, credit, and collateral can be applied to stimulate economic growth. An alternative model that predicts a loan candidate’s ability to generate revenues and profits (and hence, has the capacity to repay the loan) is based on establishing the character of the candidate, as well as the validity of the business model. Examples of character traits include the entrepreneur’s socioeconomic environment, personal commitment to transparency and accountability, completion of entrepreneurial training, entrepreneurial drive, personal integrity and honesty, and active involvement with an assigned mentor.

Social and Business Supports A microenterprise fund can support microentrepreneurs in their initial stages of development, help them to expand an existing business, or support survival during difficult economic conditions. Microenterprise programs not only provide access to credit, but also offer technical assistance in supportive environments that reinforce initiative and achieving entrepreneurial goals. The Grameen Bank found that social supports through peer-based lending and related initiatives were essential to success (Yunus and Jolis 1999). While such peer-lending decisions are not integrated into the SEED program, the development of peer and professional social supports is a key feature of the program. SEED offers mentoring and guidance along with trouble-shooting to help microentrepreneurs overcome obstacles to success. SBDC counselors, interns from the UAlbany School of Business and the UAlbany School of Social Welfare, and student and alumni volunteers serve as mentors and provide ongoing technical assistance and social support to the entrepreneur. All of these are intended to increase the odds of success for otherwise high-risk ventures. The program is administered by the Albany SBDC, which interacts with over 1,000 entrepreneurs every year. While the majority of these aspiring entrepreneurs have developed a valid business model and feasible business plan, slightly more than 20 percent get financing largely because the vast majority of clients never apply for financing. Many engage with SBDC for advice on such matters as marketing, financial analysis, the Empire State Development’s Division of Minority and Women’s Business Development procurement certification program, technology development, and other areas. Many who seek financing do not apply for funding, because of the risk involved or because they have not formulated a complete business plan. Some of these entrepreneurs enter the SEED program.

Lessons on Microenterprise Development    417 SBDC staff note that many of the local working poor they meet with would like to start a small business to break out of the cycle of poverty. While this population might want to start their own small business, their lending options have been limited. On the other hand, some lenders speculate that small enterprise lending could be a profitable market segment for financial institutions especially when the microenterprise grows and requires additional funding. The SEED program was established with a goal of promoting and fostering small businesses and microenterprise development within distressed communities. In ways rarely seen in regional development initiatives, this project draws on the resources of diverse sectors, including social services and economic development, to provide the necessary supports that will advance entrepreneurship and improve opportunities for individuals within distressed communities.

SEED Program At its core, the SEED program consists of the following components: the application process, training and business plan development, loan award, and ongoing peer support. Initially, prospects fill out an application, provide character references, and undergo an interview with a program staffer. Unlike many microlending programs that operate in developing countries, the SEED program does rely on some formal criteria, including criminal background checks, a review of the applicant’s credit report, and a general finance review. An additional tool used to assess applicants is the Entrepreneurial Assessment metric, which has applicants rate themselves on 20 different factors thought to be associated with business success. Applicants are also asked to explain any financial issues, though credit-scoring measures are not used in the decision-making process. Individuals who make it past the application process are required to complete an eight-week training program offered by SBDC to learn the key components of becoming a successful small business owner. Workshop training sessions include strategic planning, legal issues, marketing, and financial management. Applicants also receive technical assistance in developing a business plan and the loan application. Counseling and assistance is also available on such specific topics as budgets and marketing through the SBDC as needed. Finally, at the end of the process all applicants make a business plan presentation to three loan officers from the credit union, which makes the final decision on awarding the loan. Up to $35,000 is available for viable candidates who successfully complete the SEED screening and training process. All loans incur a 7.0 percent interest rate for seven years. Figure 23.1 provides an overview of the program elements and partners.

Loan Administration The credit union administers and services the loans. Most small business loans of this size tend not to be profitable for banks because of the prohibitive costs of originating,

418    Competitiveness at the Local Level

SEED Client

Individual Meetings

Individual Meetings & Peer Support Group

8 Week Training & Education

School of Business Interns*

School of Social Welfare Interns*

SBDC

*Pre- & Post-Loan Approval Supports

Business Plan Development

Loan Application

Loan Approval

Business Mentors

SBDC

SEFCU

SEFCU

School of Business*

Figure  23.1  SEED Partners and Process

processing, and servicing such transactions. The small size of microloans means that bank fees, which are typically based on a percentage of the loan principal, do not yield sufficient revenues to justify extending such loans. Fortunately for the SEED program, a large statewide credit union has decided to provide financial support as part of a commitment to the larger community. However, for financial supporters of microlending in general, there are also long-term incentives for making the investment. The main objective of a microlender is to develop this crucial niche market with the overall social goal of providing financing, technical assistance, and opportunities to underserved customers. Through microlending initiatives such as the SEED program, low-income borrowers obtain financing that enables them to start or expand their business, gain experience, and establish a demonstrable track record. For successful microloan businesses, this translates into larger loans from conventional lenders in the future and hence future business opportunities for lenders.

Developmental Evaluation To assess the initial implementation of the SEED program and to build in continuous quality improvement, a developmental evaluation was conducted by UAlbany’s Center for Human Services Research on the process and initial outputs of the program during

Lessons on Microenterprise Development    419 roughly the first one-and-a-half years of operation. The evaluation consisted of two major components: An analysis of SEED program data and a survey of program applicants. The purpose of the analysis of program data was to identify strengths and weaknesses of program implementation, including the recruiting and screening process, the nature and consistency of program delivery, and the performance of the initial groups of applicants. The intent of the survey of program applicants was to examine participant satisfaction, gauge the quality and effectiveness of program components, and identify early outcomes such as hiring and business sustainability. Although comprehensive long-term evaluation of the SEED program will ideally examine such outcomes as business success, job creation, and community social and economic impact, this study provides an early review of how the program has been implemented and its success in generating the desired outputs of program completers and business start-ups.

Findings from Initial Analysis of SEED Data This section details the findings from an analysis of files for individuals who applied to the SEED program between its inception during the summer of 2011 and the end of 2012. The files included applicants who had entered and completed the SEED program, as well as applicants who were rejected, decided not to enroll, dropped out, or otherwise left the program. During this analysis period there were 101 applications in program files, out of which 25 were confirmed as having been awarded loans. Table 23.1 shows demographics of SEED participants.

Applicant Personal Characteristics One goal of most microlending programs is to reach underserved populations; on this goal it appears that the program had some success. The data indicate that SEED applicants and program completers are more diverse than the profile of the typical small business owner. Both the applicant pool and the group that has completed the program were roughly half female. Additionally, nearly 60 percent of SEED applicants and 40 percent of those that completed the program were nonwhite. In comparison, nationwide only 21.9 percent of all non-publicly owned businesses had majority nonwhite ownership in 2007, and females were the sole or majority owners of only 29.6 percent of all nonpublic US businesses (US Census Bureau 2014c). However, the demographic data also suggest that the likelihood of completing the SEED program differs across demographic groups. Most striking is the difference between the racial composition of all SEED applicants and those who complete the program and receive funding. Black entrepreneurs represented the largest single racial group of applicants at 43.9 percent; however, only 28 percent of the individuals that completed the program and received funding were black. Conversely, white entrepreneurs represented 41.8 percent of applicants and 43.2 percent of those that went through the interview process, but ultimately represented 60 percent of those that completed the SEED program and received loan funding. Put another way, the odds of being selected

420    Competitiveness at the Local Level Table 23.1  SEED Demographics by Application Status Applicants N Total applicants Gender Male Female Major race or ethnic group Black White Hispanic Other Type of business Start-up Existing Industry of business Professional service Other Retail Food service Construction Manufacturing Personal services

Interviewed %

101

N

Completed and funded %

83

N

%

25

55 44

55.6 44.4

44 38

53.7 46.3

13 12

52.0 48.0

43 41 5 9

43.9 41.8 5.1 9.2

34 35 4 8

42.0 43.2 4.9 9.9

7 15 1 2

28.0 60.0 4.0 8.0

45 49

47.9 52.1

35 44

44.3 55.7

10 14

41.7 58.3

24 18 15 14 7 7 5

26.7 20.0 16.7 15.6 7.8 7.8 5.6

20 15 14 12 5 6 2

27.0 20.3 18.9 16.2 6.8 8.1 2.7

6 3 6 6 1 1 1

25.0 12.5 25.0 25.0 4.2 4.2 4.2

Note: Numbers do not calculate to total amounts because of missing data.

for the SEED program and completing the program to receive a loan was better than one in three for white applicants but only about one in six for black applicants. Also, it appears that some types of businesses may be more likely to receive funding than others. For example, businesses in the food service and retail industries represented half of the funded SEED awards despite constituting approximately one-third of applications. SEED program applicants who fell into the “other” industries category—which includes such businesses as training and educational activities, transportation companies, agriculture, and tourism—also are less likely to complete the program and receive funding than other applicants.

Applicant Financial Characteristics As mentioned previously, although “character based,” the program does give some consideration to the applicant’s personal and financial circumstances. Still, the assessment of early applicant data showed that the majority of applicants did not have a major issue on their application. The most common issues were a prior bankruptcy, which was reported by over 20 percent of applicants; and a criminal conviction, which was

Lessons on Microenterprise Development    421 reported by about 15  percent of applicants. Instead of major event-based barriers, it seems that applicants may more commonly face barriers associated with limited income or employment. The average monthly personal income reported by applicants equated to an annual wage and salary earnings level of approximately $44,300, which is below the $67,981 single-worker family average for the local metro area (US Census Bureau 2014a, mean income in the last 12 months).

The Entrepreneurial Assessment The drive and ability of an entrepreneur is a trait that may be more important to success than any characteristic measured by the traditional lending process. The SEED program developed and implemented its own Entrepreneurial Assessment instrument, a self-assessment on 20 questions that address factors that are purportedly related to one’s ability to succeed as an entrepreneur. A five-point rating scale ranging from “strongly agree” at the low end to “strongly disagree” at the high end was used. Unfortunately, the program’s experience highlights a gap in the development of valid measures of entrepreneurial ability. For instance, the average scores of applicants who successfully complete the SEED program and receive funding were not significantly different from the scores of applicants who fail to complete the program or who are denied funding. Hence it is not useful for making admission decisions or predicting program completion. The program eliminated the tool from the application, but has been unable to identify a replacement—a situation that other microlenders will certainly face.

Defining and Identifying Character: Interview and References Admission into the SEED program is largely determined by two factors: references and an in-person interview. Once it has been determined that the applicant meets the basic criteria for admission (as demonstrated in the application) the applicant is interviewed by a member of the program staff followed by a reference check. The interviews, which are conducted in a face-to-face setting at the SBDC offices, provide an opportunity for assessing the character of the applicant as well as for confirming details about the proposed business start-up or expansion. The interview session and the reference calls provide one of the only opportunities for the SEED program to gauge the character of the applicant. The protocols for the interview and the reference contacts each contain a question that directly addresses how “good character” should be defined. Specifically, the questions are the following: 1. “What is your definition of ‘good character’?” (applicant interview) 2. “How has the applicant demonstrated ‘good character’?” (reference contact) Because the SEED program itself does not explicitly define character, the responses of applicants and references (as well as whether or not those applicants ultimately were admitted into the program) can shed some light on how good character is perceived by all parties involved in the process. In total, documentation was examined for 32 completed or partially completed reference interviews and 69 completed applicant

422    Competitiveness at the Local Level interviews. During the interviews, applicants tended to describe themselves based on internal factors and work ethic. The most universal themes applicants used to describe good character were “honesty,” “integrity,” and “hard working.” The responses recorded during the reference contacts commonly defined character through the applicant’s behavior toward customers and employers. For example, “interact[s]‌with all employees, customers, vendors with integrity [and] courtesy” and “communicates well, [has] high integrity.” Other references described the character of the applicant as reflected in their attitude: “very positive and upbeat,” “very peaceful,” and “always very helpful.” Additionally, reliability was another factor references associated with character as demonstrated by statements such as “very reliable” and “always there, always on time.”

Point of Exit from the SEED Program In addition to assessing the process the SEED program uses to recruit and accept applicants, the evaluation analysis also examined the flow of applicants into the program to determine where most exit. Figure 23.2 illustrates the number of applicants who remain at each stage from application through training and loan attainment. The application stage consists of the applicant contacting SEED and filling out an application; most of those that inquire about the program but do not finish the application process are not included in the program files, although this is likely a point where many would-be entrepreneurs are turned away. The staff review is when SBDC staff review the applications, conduct interviews, and contact references—most applicants that do not complete the program leave at this stage. Finally, the program phase consists of the training course, mentoring, counseling, support services, and completion and presentation of the business plan. Around one-third of those who made it through the reference calls did not

Flow Through the SEED Process Application Phase

101 documented applications to SEED

99 complete applications

2 incomplete

SEED Staff Review Phase

83 complete in-person interview

43 documented ref calls 4 pending status

16 declined or quit

36 declined or quit program

SEED Program Phase 27 complete courses 3 pending status 13 leave program

Most SEED applicants are declined or quit following the application or following the interview: 51.5% of applicants; 70.3% of all who don’t make it.

Figure  23.2  Flow of SEED Applicants from Application to Program Completion

25 pass SEFCU board 2 rejected

Lessons on Microenterprise Development    423 go on to compete the training; however, among those that completed the main program activities during the first 18 months of the program, only two were explicitly rejected by the board.

Ongoing Supporting Services for Entrepreneurs All graduates of the SEED program are asked to participate in an ongoing peer support network provided by the School of Social Welfare. Bringing together both new and experienced entrepreneurs to discuss business challenges offers individual and group support for SEED clients. Master of social work students (MSW) offer individual and family support and develop the peer group agenda based on the client needs assessment. Peer support includes weekly group meetings after each business training class. These meetings give the opportunity to build stronger ties between the clients and for them to discuss their stresses, goals, fears, hopes, and issues. Prior to each training class, MSW students have both scheduled individual meetings with SEED clients and open office time to discuss individual issues in a private setting. Peer Support also involves quarterly meetings for all current SEED clients and those that have received a loan. These meetings include an educational component based on expressed client interests, such as health exchanges, website development, Occupational Safety and Health Administration (OSHA), and minority- and/or women-owned businesses. There is also peer support sharing and discussions of issues important to them, including hiring good employees, stress reduction, and service networking. The evaluation found that individuals who completed the program and obtained business funding made good use of the counseling services, using 9.4 contact hours on average. The most popular primary area of counseling was for “business start-up or business expansion.” A few applicants received counseling on marketing, financial analysis, government procurement, and other activities.

Loan Repayment and Sustained Business Operations The primary outcomes for the SEED program are long term in nature. Determining program success requires sufficient time to determine what portion of loans is repaid and how many businesses survive beyond start-up or remain operating at the same level following an expansion. At the time of the initial evaluation study, even the earliest completers of the program could not have been operational and in loan repayment for significantly longer than one year, which limits the ability to draw strong conclusions about the success of the SEED approach. According to SEED staff, two of the loans made through the program had entered default during the initial 18-month period. Default rates for commercial lenders are difficult to obtain, but estimates published using a sample of commercial loan data between 1983 and 1998 show an average annual default rate of 3.5 percent for SBA-guaranteed loans during the period (Glennon and Nigro 2005). Small business loans with a SBA guarantee have some similarity to SEED loans in that they tend to be smaller and higher risk than commercial loans as a whole, although logically SEED loans are riskier since applicants have already been turned down for other lending options, which should

424    Competitiveness at the Local Level include traditional and SBA loans. Ultimately, it is too early to draw any strong conclusions about the default rate and SEED’s success as a method of screening borrowers. For one, there is not yet enough evidence to know how SEED loans will perform over the expected repayment period. Additionally, the two loans that have defaulted represent far too small a number to assess for possible traits associated with loan failure.

Survey of SEED Applicants In August 2013, a survey of program applicants was conducted to ask about program experiences during and after applying to SEED. The purpose of the survey was to ask applicants about the application process as well as the training, financing, and follow-up support for those that were granted loans. In addition to inquiring about the satisfaction of applicants, the survey also addressed what applicants—including those that were not admitted to SEED—have done since they first applied. The survey group consisted of all individuals that made a formal inquiry about SEED application from the program inception until July 2013; in total, 205 valid contacts were identified and sent surveys via mail and email. This group is much larger than the group examined during the analysis of applicant file data and includes both later applicants (who applied after December 2012) and applicants who did not get far enough into the application process to warrant the creation of a file. Ultimately, 45 valid surveys were completed, a 22.9 percent response rate. The findings from the survey provide some insight into the satisfaction and success of SEED applicants that could not be answered through the review of documents conducted during the first stage of the evaluation. 1. Are applicants satisfied with the program? The survey results indicate that applicants are satisfied with the program overall. A  large majority (79.4  percent) affirmed that they would recommend the SEED program to other applicants. 2. What are strengths and weaknesses of the training and support aspects of the SEED program? Half of the respondents found the meeting and interview process to be “extremely helpful/essential,” and approximately two-thirds of the respondents who had experienced the eight-week entrepreneurship course and the process of preparing and presenting a business plan also gave these activities the top rating. Conversely, the mentorship program and the peer network (distinctly different program elements) were combined into one measurement item and were rated poorly by respondents, with approximately one-third indicating that these activities were “not at all helpful.” 3. Do applicants who do not make it into SEED go on to start businesses? The survey results indicate that those applicants who had been accepted into the SEED program were much more likely to have already started a new business than those who were not accepted. Only about a quarter of applicants who did not complete SEED had already managed to start or expand a business on their own.

Lessons on Microenterprise Development    425 4. Is the loan size adequate? The size of the SEED loan does not in most cases appear sufficient to support small business start-ups or expansions on its own. A majority of all entrepreneurs reported utilizing alternate sources of financing and less than half of those that completed SEED described the size of the loan as being sufficient.

Hiring as a Result of Business Creation Survey respondents were asked about whether or not they hired new employees (excluding themselves) as a result of opening or expanding a business. For the majority of respondents, the answer was either “no” or “N/A” because they had not started or expanded a business. About one-third of respondents had hired workers as a result of their business expansion. Respondents reported hiring an average of 5.5 workers as a result of their business creation or expansion, ranging from 1 to 20.

Implications Given the potential utility of character-based lending for the region and state, the goal will be to scale up to other regions if successful. Also, other populations could benefit from SEED or similar programs, such as recent flood victims, returning veterans, and victims of domestic violence. Thus, the need is greater than ever to ensure that a reliable approach to microlending in the United States can be developed and scaled up. Our example of the implementation of the SEED program illustrates some initial successes and challenges in this regard. The SEED program is still relatively new, which means that it is too early to measure most long-term outcomes such as loan repayment or business sustainability. Still, there are some encouraging signs. For one, the evaluation found that the program has succeeded in attracting a diverse and nontraditional group of applicants who may have had difficulties getting conventional loans because of financial or other hurdles. Despite being from underserved or disadvantaged populations, roughly one in four applicants succeeded in the program, received a loan, and started his or her own business. Additionally, a large portion of those surveyed indicated that they had hired additional employees to work at their business, which offers some hope for the larger economic development potential of the approach. Still, the SEED program faced many challenges that will need to be resolved to successfully expand and thrive. For example, the evaluation found that SEED had not developed strong criteria for identifying entrepreneurs with good character. The review of SEED program files found that the Entrepreneurial Assessment used in the application process is unrelated to outcomes such as program enrollment or completion. Both the applicant survey and discussions with SEED program staff suggest that program admission is primarily determined by the interview process and the individual’s credit report—the former is difficult to replicate, while the latter is not in line with

426    Competitiveness at the Local Level typical microlending or alternative-lending practices. Capacity building is obviously key to supporting novice entrepreneurs and was taken advantage of by SEED applicants; however, many voiced disappointment with the mentoring and peers support networks that were major components of the original program design. Finally, there are challenges to ensuring continuing funding support for administration and capacity building, which risks the sustainability of the model regardless of business success and loan repayment rates. One solution is further investment in research and program development within the field. New curricula and programs can be developed at universities; indeed, the University at Albany’s Schools of Business and Social Welfare are already collaborating on new courses in social entrepreneurship and hiring two new tenure-track faculty members in social entrepreneurship. Faculty, students, and alumni in both schools are excited about this initiative. In addition, new services can be offered through the nonprofit sectors, as few at this time have microlending opportunities for those they serve. Optimally, new microbusinesses can be sustained that satisfy consumer demand. Ideally, this work will evolve into a model program that will make a difference in lower-income neighborhoods in the greater Capital Region and further enhance regional economic development and local competitiveness. This program can easily be replicated at other public and private universities, especially in regions where universities play an important role in economic development (such as in New York State’s Capital Region).

Conclusions While the microlending movement has been heralded internationally as a means for the poor to escape poverty, few models have been tested in the United States, especially those using the character-based lending approach. Even though the United Nations declared 2005 as the International Year of Microcredit (United Nations 2014), there has been very little attention to this inclusive financial strategy in the United States. Programs such as SEED offer a model to advance such international goals in some of the highest need communities in the nation. This chapter introduced the SEED program model and provided the results of an assessment of the program based on the first two years of program operations. As we have described in this chapter, SEED is an innovative $2.8 million social entrepreneurship program, which provides microloans and technical/business assistance to local entrepreneurs in distressed areas of New York State’s Capital Region (Albany, Schenectady, and Troy). The program is innovative for two key reasons: (1) it is a unique public-private partnership, involving the School of Business, School of Social Welfare, the business school’s Small Business Development Center, a major credit union (the private partner), Empire State Development Corporation,

Lessons on Microenterprise Development    427 and numerous community partners, and (2) SEED is the first university-based, character-based microloan program which will provide additional opportunities for ongoing research and evaluation. The ability of the SEED program to attract nontraditional entrepreneurs and help create small businesses serves to demonstrate that there is an unmet demand for both capital and training. It also constitutes a new and interesting form of university technology transfer/entrepreneurship (see Link, Siegel, and Wright 2015 for other interesting examples of university-based entrepreneurship programs). However, additional investment will be required to further develop a replicable model for the United States and to empirically assess its value added, in terms of economic development and job creation. In addition, SEED may prove to be a fertile ground for testing ways to more systematically eliminate some of the disparities revealed in the evaluation. These include the fact that minority microentrepreneurs are less likely to attain a SEED loan than nonminorities. Moreover, the evidence that either mentoring or peer supports are not yet as helpful as they might be (they were evaluated as if one program when in fact they are quite distinct) suggests that inventive new consumer-guided innovations need to be developed so that the guidance and supports involving both the business and nonbusiness aspects of entrepreneurial success are more effectively addressed. It is important to note that our evaluation did not tease out the differential effectiveness of mentoring versus peer services. Both programs used different strategies, students, and supports. These may have differential returns and levels of success. Thus, in future research, we will separately evaluate the two interventions of peer-based versus mentoring supports rather than combining them in one item for measurement of satisfaction. Finally, it should be noted that while Grameen Bank–based programs loan funds only to women (both internationally and in the United States), our research and experience suggests that men are more worthy investments, in microlending programs such as SEED. It is also important to quantify the impact of SEED on regional economic development and local competitiveness. Although the program is relatively new, it has already generated some strong results, in terms of promoting regional economic development. In just two years, SEED has invested $1.2 million in the Capital Region, in 37 new businesses, and created or saved 150 jobs, at a cost of only $1,000 per job. This is a very high “rate of return” for an economic development initiative. In sum, SEED may be a useful model for universities (and their stakeholders) seeking to (1) extend credit to worthy entrepreneurs who are underserved by traditional funding sources; and (2) enhance local competitiveness by stimulating economic development in inner-city neighborhoods. Unlike conventional economic development initiatives, which are typically targeted to high-tech firms, SEED is targeted to the “base of the pyramid.” This unique program, which leverages the resources of the university and its stakeholders, can be adopted by other universities seeking to promote regional economic and social development.

428    Competitiveness at the Local Level

References Birch, David G. W. 1987. Job Creation in America: How Our Smallest Companies Put the Most People to Work. New York: Free Press. Glennon, Dennis C., and Peter J. Nigro. 2005. “Measuring the Default Risk of Small Business Loans: A Survival Analysis Approach.” Journal of Money, Credit, and Banking 37 (5), 923–47. Link, Albert N., Donald S.  Siegel, and Mike Wright. 2015. Chicago Handbook of University Technology Transfer and Academic Entrepreneurship. Chicago: University of Chicago Press. Midgley, James. 1997. Social Welfare in a Global Context. Thousand Oaks, CA: Sage. Prahalad, C.  K. 2005. The Fortune at the Bottom of the Pyramid. Upper Saddle River, NJ: Wharton School Publishing. US Census Bureau. 2010. “State & County Quick Facts.” Web. May 9, 2014. http://quickfacts. census.gov/qfd/states/36/36001.html. US Census Bureau. 2014a. “2007–2011 American Community Survey 5-Year Estimates for per capita income.” Web. May 9, 2014. http://quickfacts.census.gov/qfd/meta/long_INC910209. htm. US Census Bureau. 2014b. “2007 Statistics about Business Size.” Web. 9 May 2014. https://www. census.gov/epcd/susb/latest/ny/NY—.HTM. US Census Bureau. 2014c. “2007 Survey of Business Owners (SBO).” Web. May 9, 2014. http:// www.census.gov/econ/sbo/. United Nations. 2014. “U.N. International Year of Microcredit 2005.” Web. February 9, 2014. http://www.un.org/events/microcredit/ Yunus, Muhammad, and Alan Jolis. 1999. Banker to the Poor:  Microlending and the Battle against World Poverty. New York: Public Affairs.

Chapter 24

A Region in Tra nsi t i on Bottom-Up Economic Transformation in Postconflict Northern Ireland Mary Lindenstein Walshok and Steve Orr

Introduction The substance of this chapter differs from others in this collection in that it is a local and most especially a “personal” story of some of the critical factors and individuals contributing to the economic transformations essential to regional economic transformation in a unique and fascinating place, Belfast, Northern Ireland. For students and researchers interested in the dynamics of local economic transformation as well as practitioners charged with helping to “make something happen” in their communities, the Belfast case elucidates a number of principles of practice worthy of replication. Thus, this chapter is structured to include the “work” of the observant social scientist, on the one hand, Mary Lindenstein Walshok, and that of the civic entrepreneur, Steve Orr, on the other. Our goal is to establish the context in this brief introduction, tell the story in the main part of the chapter, and tease out some of the key principles of practice and lessons learned in our conclusion. The Belfast case is also a story of how practices and experiences with proven results can be transferred and/or adapted successfully in another region. In this case the success of the CONNECT program in helping San Diego, California, transform from a primarily defense and tourism economy to one that today supports a half-dozen globally traded technology-based clusters such as wireless, biotech, electronics, and software proved to be the inspiration for a young entrepreneur working in a global company based in San Diego who was about to return to his homeland Belfast, Northern Ireland. The CONNECT organization was created in 1984 as a community partnership anchored in the University of California, San Diego, explicitly to accelerate the rate of science- and technology-based start-up companies in the region as well as support

430    Competitiveness at the Local Level high-growth S & T companies. The clear goal was the seeding of new economic clusters that would create high-wage jobs and new forms of wealth to ensure a sustainable, competitive regional economy. Global market shifts, new supports for local innovation such as Bayh-Dole, SBIR, and the deregulation of the banking industry as well as the end of the Cold War economy drove this development. CONNECT was one of the earliest intermediary organizations in the United States to recognize that innovation and entrepreneurship depend upon a unique business culture, not just scientists and engineers interested in technology transfer and commercialization. Many of the scientists who moved to the region throughout the 1950s, 1960s and 1970s, drawn by the world-class basic research institutions building on the free land deeded by the city on what today is known as Torrey Pines Mesa, came from entrepreneurial research institutions such as MIT and Stanford or had worked in federal labs (i.e., Los Alamos) or corporate R & D (i.e., Bell Labs). The “seed corn” for an innovation economy was growing by leaps and bounds. However, the knowledge transfer mechanisms, capital, and business know-how essential to grow an innovation economy were absent. Attorneys proficient in real estate, wills and trusts, and civil litigation abounded, but IP attorneys and corporate lawyers were essentially absent. Commercial and mortgage bankers as well as personal financial planners were in great supply, while angel and venture capital were nonexistent. Residential and shopping mall developers were flourishing, while industrial and science park developers were in short supply. Marketing firms, accounting firms, even executive search firms were adept at helping hoteliers and the tourism industry, local banks, and retail and residential housing developers, but “clueless” when it came to globally focused science- and technology-based products and services. Thus, the CONNECT organization from day one focused on changing the local business culture into a more globally savvy, S & T–anchored entrepreneurial culture simultaneous with supporting scientists and engineers with technology that could be translated into business-building and job-creating products and services. CONNECT over a 15-year period created so many events, capital workshops, science briefings, and celebratory showcasing programs involving business services, professionals, and scientists that by 2000, San Diego was “on the global innovation economy map.” Today, the tech clusters represent 25 percent of the regional payroll, with median wages approaching $100,000 annually. It is among the top 10 centers of angel and venture capital in the world and is home to dozens of global law firms and marketing companies. The once “start-up” research institutions represent collectively $3 billion annually in basic and applied research in the region. Michael Porter at Harvard and many others have acknowledged the catalytic role of CONNECT, and CONNECT’s own reports on the successful regional start-ups assisted and the revenues and jobs they have created are nothing short of astounding. It was the regional outcomes associated with CONNECT activities as well as the numerous personal success stories that inspired Steve Orr, the Northern Ireland–born and Northern Ireland–bred entrepreneur about to return to his homeland, a place once the center of global shipbuilding and engineering “know-how” that had not adapted to

A Region in Transition    431 changing economic imperatives and subsequently was ravaged by the Irish Catholic/ Protestant “Troubles.” The Belfast to which he was returning was characterized by a risk-averse business culture, heavily government dependent with the public sector employing nearly one in three workers, compared with less than one in five in Britain, resulting in an annual budget deficit estimated at 33% of GVA—in other words, an economy not too dissimilar from the highly military-/defense-contracting-dependent economy of San Diego up to the 1980s. But this is where the “real story” needs to take over.

Overview Today, nearly every region around the world dreams of transforming its economy into “an innovation ecosystem.” Politicians dream of a locale the tax returns from the many high-value jobs in direct and indirect employment, students graduating university have access to employment opportunities on their doorstep without having to leave, and research scientists can see their work impact the world through innovative products and services. The current emphasis in economic development is to develop regional capabilities that can create a perpetual pipeline of new indigenous companies where some succeed, a few really succeed, and those that fail, creatively reassemble and go again. Building an “ecosystem” is a critical element of developing that capability, but how do you do that? In a short space of time, Belfast, Northern Ireland, has developed a strong innovation ecosystem, a community uniting all of the stakeholders in one team committed to sharing networks and collaborating to deliver the support to make the region’s entrepreneurs more successful. The region has made an impressive start, but significant challenges lie ahead if Northern Ireland is to go from one of the worst-performing regions in Europe to one of the best. This section shares the story of building that ecosystem so far, records where the region is on that journey, and provides an outline of the challenges that must be met in order for the region to secure a sustainable innovation economy.

The Key First Question: Who Should Lead? In his excellent book Startup Communities, one of the leading authors on technology ecosystems, Brad Feld, underscores that efforts to build these communities must be led by entrepreneurs. This resonates with the experience of the San Diego region in Southern California, the story of how CONNECT was formed in the mid-1980s and the economic transformation that unfolded. Empowered by UCSD chancellor Richard

432    Competitiveness at the Local Level C. Atkinson, masterminded by Dean Mary Lindenstein Walshok, and led by entrepreneur Bill Otterson, CONNECT is one of two organizations credited with playing a leading role in San Diego’s transformation1. As an entrepreneur living in San Diego, Steve Orr became inspired by the story and felt compelled to explore if something similar was possible at home in Northern Ireland. The more he learned about the San Diego story and the parallels to Northern Ireland’s challenge, the more obsessed he became. Consistent with Brad Feld’s belief that entrepreneurs must lead and with the San Diego story, Orr believed that these things only get started if there is some idiot who is prepared to jump off the cliff first, and he was ready to be that “idiot.”

2006, 2007: Learning about the San Diego Transformation and Initiating Contact Back Home In 2006, while Orr was taking a break from an entrepreneurial endeavor and evaluating what, if anything, to do with the rest of his life, a fellow board member and venture capitalist friend, Rob Ayling, recommended that Orr meet with the folks at CONNECT in San Diego (specifically Mary Lindenstein Walshok and Greg Horowitt), suspecting that the San Diego story might be something that could resonate with Northern Ireland. With the help of the CONNECT team, over the following 18 months Orr came to understand the principles, the model, the history, and the programs. Around this time serendipity played a critical role when a colleague put Orr in touch with Norman Apsley, the CEO of Northern Ireland Science Park (NISP). NISP had been established in 1999 as an independent nonprofit organization to incubate promising technology companies and foster commercialization programs like those CONNECT had started in the 1980s. NISP’s governance structure formally integrated the knowledge economy’s key stakeholders in one team, and its independent status gave it the flexibility to generate its own ideas rather than procure the ideas of government or a small group of elites. The buildings had a contemporary feel, and Queens University had located its research institute for electronics communications and IT there, realizing the vision of the world-class researcher Professor John McCanny. It seemed like an ideal place to replicate many of the key principles of CONNECT in the Northern Ireland context. Additionally, there was a key difference between NISP and other science parks across Europe, and that was Norman Apsley, another Northern Ireland native who had returned home from years leading research institutions in the UK. Norman’s vision and integrity proved to be critical factors in the success of NISP today. The CONNECT 1 

Michael Porter, Clusters of Innovation Initiative: San Diego, US Council on Competitiveness, May 2001.

A Region in Transition    433 philosophy and principles resonated with Apsley. Using unexpected funds from a grant from Belfast City Council provided for a prior organization, Investment Belfast, Apsley personally championed the development of a new program with a wide range of stakeholders and continues to do so today.

The Northern Ireland Context Since the signing of the Good Friday Agreement in 1998, the envisioned peace dividend has yet to be realized in Northern Ireland, and the economic picture today appears stubbornly similar to 1998: an economy dependent on the public sector and gross domestic product has been stuck at 80 percent of the UK average for decades. For a number of years now, the economy has been the number one priority in Northern Ireland’s Programme for Government with the creation of “a dynamic, innovation led economy.” However, despite the best intentions and numerous creative efforts, the economy has not changed. How can a region that once led the world in five industries leave the shackles of its recent past behind and rediscover its capability, confidence, and ambition? Northern Ireland is a postconflict region. It is not the same as other First World regions trying to create a knowledge economy. While elected officials are expected to magically transform the economy, their attention is continually diverted back to issues from the past. It is a terrible position to be in, and in postconflict Northern Ireland the public’s growing expectations of an improved quality of life and access to good jobs increases the pressure for change. One of Northern Ireland’s greatest advantages is its enduring history of technical competence, traceable to more than a century ago, when it was the center of shipbuilding in the Western world. This culture of excellence and engineering is still thriving today. In contemporary Silicon Valley terms, Belfast resembles the old Valley more than the new Valley: with technology strengths that are closer to that of the South Bay of Silicon Valley (intellectual property, engineering, enterprise software, scientific discovery similar to Cambridge, UK) than the fast-moving digital explosion in the city of San Francisco, similar to Tech City in the East End of London.

2007: Getting Started—Arrival in Northern Ireland—“Culture? How Do You Measure Inputs and Outputs?” Relocating from San Diego, California, in summer of 2007, with the support of a key institution and six months’ worth of partial funding, Orr first met Northern Ireland’s

434    Competitiveness at the Local Level most successful entrepreneurs. When the idea of a new support model for entrepreneurs was mentioned, people’s eyes often glazed over because of the number of disparate programs that already existed to do just that. Panos Lioulias, former CEO of Queens University’s investment organization, QUBIS, was familiar with and believed in the idea of CONNECT. He introduced Orr to his network of Northern Ireland’s top entrepreneurs: Denis Murphy, J. G. Doherty, Frank Graham, Bryan Keating, and Brian Baird. All grasped the idea and pledged their support and personal time to help make it happen. Without the support of some of the most credible high-tech entrepreneurs in Northern Ireland at the outset, the initiative could not have gotten started. Within weeks the challenges facing Northern Irish entrepreneurs became clear: geographic isolation; a small number of science and technology companies; a heavily subsidized public sector that did not empathize with new entrepreneurs looking for funding; and tight market access with limited opportunity to commercialize their technology. Another thing that was evident early was how siloed the institutions and industry organizations were. Public funding bodies dominated the entrepreneurial support space, and entrepreneurs consistently whispered their discontent with the support that they provided, careful not to upset their next grant, not to “rock the boat.” But the advantages were clear too: Northern Ireland is part of a large market in the UK and enjoys tremendous goodwill from the United States, and the people of Northern Ireland have a very strong sense of place and willingness to contribute toward change. And as mentioned earlier, it is a region that is really good at science and tech. The CONNECT model was also validated and recommended by Lord Sainsbury in his influential October 2007 Review of UK Government’s Science and Innovation Policies, which caught the attention of the Department of Enterprise Trade and Investment in Northern Ireland.

2008: Pilot Resources Northern Ireland’s private sector was too small to fully support NISP CONNECT as a nonprofit organization, as had been the case in San Diego. Thus, achieving public support was important to getting started. Encouraged by Northern Ireland’s regional development agency that financial support was probable, the team learned 13 months after their first engagement that financing would not be available for NISP CONNECT because they were already supporting a different initiative that was too similar. This nearly killed the project before it started. Plans were quickly scaled down, and the model revised into a two-person pilot. Luckily, some other important leaders from Northern Ireland’s knowledge economy understood the CONNECT model, how it was differentiated from other programs, and why it could be a key part of developing a commercialization ecosystem in Northern Ireland. Northern Ireland Science Park board members Queens University vice chancellor Professor Sir Peter Gregson, pro vice chancellor Professor Gerry

A Region in Transition    435 McCormack, University of Ulster vice chancellor Professor Richard Barnett, and commercialization director Tim Brundle, forcefully put their support behind the initiative. Franklin Adair, then chairman of the Northern Ireland Science Park, had a personal belief that a CONNECT-type service was critical to the Northern Ireland Science Park and championed underwriting the program. The board unanimously agreed to underwrite a pilot of NISP CONNECT, and a pilot was launched in April 2008.

2008–2011: Pilot Progress After securing the endorsement and support from some of the region’s top tech entrepreneurs, the next priorities were these: • Get the professional services firms engaged and participating. They are the stakeholders who will benefit the most if the economy is transformed, they are connected, they have a lot of knowledge of the commercialization process, and they want to help. • Get started doing great things, CONNECT-style organizing events, forums, helping entrepreneurs to get access to whatever they need. The focus is that whatever NISP CONNECT does, it has to be so good (and different) that it gets people talking. An emphasis is placed on getting lots and lots of stakeholders involved in working the model. At the end of the first pilot year, Matrix, the science industry panel for Northern Ireland, commissioned an independent review of NISP CONNECT by Mary Lindenstein Walshok and Greg Horowitt of Global CONNECT at UC San Diego that confirmed that the ingredients to build something special were in place. The headline findings of that review were as follows: The community values the work and role of NISP CONNECT. Ninety-six percent of all community members (entrepreneurs, research professionals, professional service providers, investors) who had attended a NISP CONNECT event or volunteered support said that they would “recommend NISP CONNECT to a friend or family member” in a survey conducted in March 2009. There is strong demand from the community for the services of NISP CONNECT. A significant number of quality early-stage ventures and promising science and technology product developers engaged in NISP CONNECT’s activities between April 2008 and March 2009: • Over 200 unique early stage companies and “wantrepreneurs” attended a NISP CONNECT event or forum or received customized support. • 71 proposals were received from the research base to commercialize science or technology in the 2008, 25K awards program.

436    Competitiveness at the Local Level There was a strong willingness from the community to volunteer support: • 26 venture capital funds engaged • 23 professional service firms engaged and provided pro bono support • 25 entrepreneurs in residence pledged to support and mentor promising inventors/ entrepreneurs There was a new willingness within the community to work together: • NISP CONNECT produced 30 events and forums in 2008/2009. • There were over 1,100 total attendees at events (+ an additional 350 online). • NISP CONNECT events raised private-sector sponsorship of £40,000. The culture of collaboration and openness was developing quickly, and a community was starting to come together. The challenges also were clear in 2010; a large majority of Northern Ireland’s start-up ventures were embryonic (preseed and seed stage), the region traditionally had not enjoyed success in the conversion of embryonic start-ups into fast-growth companies, and there was very little institutional funding for prerevenue product development. During this time Northern Ireland Science Park also took on the ownership of the management of the angel investor network for Northern Ireland. Under former-entrepreneur Alan Watts’s leadership, Halo, the angel network, developed very quickly into one of the best-performing networks in the UK.2 Integration of networks between Halo and NISP CONNECT proved critical in both directions for investors and mentors and still does today. Even with a two-person pilot, it was clear that there was an important role for CONNECT in Northern Ireland, and thus began a two-year journey to raise some expansion funding for the following five years. Championed personally by Enterprise Minister Arlene Foster with support from her Department of Enterprise, Trade and Investment and the science industry panel, Matrix, funding was made available to develop an expanded version of NISP CONNECT that went live at the beginning of 2011. The funding model enabled a team of four full-time staff. The funding model was as follows: • 50 percent private funding from membership and sponsorship • 50 percent public funding (this would decline each year) • By year 5 a charitable endowment of £5  million would need to be established, the investment income from which would replace the public funding to enable independence

2 

Deloitte, on behalf of the UK Business Angel Society, Taking the Pulse of the Angel Market found that Northern Ireland was responsible for 6 percent of the UK’s total angel investment in 2012–13 with just 2.9 percent of the population.

A Region in Transition    437

2011: NISP CONNECT 2.0 Many regions in the UK have attempted to develop CONNECT-like models, but they were rather poor imitations using the name and success of the US model to pursue government funding contracts. As CONNECT purists, the Belfast team decided to stay close to the model and the principles, adapting where necessary to the Northern Ireland context even though that was by far the most difficult way to develop the model. Both private-sector financing and volunteer member involvement have been pivotal to the success of CONNECT in San Diego, and these are the pieces most often missing from other adaptations. Belfast implemented a membership model at three levels of contribution: the most expensive at Platinum in return for a seat on the board and then Gold and Silver for participation in the direction and delivery of the programs. The response from the community to fund and participate in the new model was excellent, and results exceeded all early targets. Next, the team had to mobilize the community of members and stakeholders (entrepreneurs, executives, investors, academic research leaders, and professional services firms), many of whom were unfamiliar with NISP CONNECT, and set up governance structures that were inclusive, participatory, and rewarding in order to ensure better support for entrepreneurs. This was tricky, but they got there by the end of 2011. Building a wider circle of support became a major focus in 2012. Throughout 2012 NISP CONNECT facilitated a series of workshops with participation from over 170 people: “The Tiger Teams.” This analysis resulted in the most comprehensive understanding of what Northern Ireland needed to do to achieve the vision of growing an innovation economy. Following the October 2011 publication of the Northern Ireland Knowledge Economy Index (KEI): Baseline Report, NISP CONNECT set out to crowdsource the following: • The Vision—communicate our collective 20-year ambition to Northern Ireland’s key knowledge economy stakeholders • The Targets—what must be achieved over the next 5, 10, 15, and 20 years in the key metrics? • The Barriers—what are the three biggest things holding us back? • The Solutions—what must be done to solve the barriers, now? The Vision Northern Ireland can and will transform itself one of the most entrepreneurial knowledge economies in Europe by 2030. The Targets—what will this look like? While Northern Ireland can take inspiration and learn from other regions, it now has set its own path. By 2030, as shown in table 24.1 Northern Ireland aspires to achieve the following: They are using the goals and metrics shown in table 24.2 to assess their progress.

438    Competitiveness at the Local Level Table 24.1  Northern Ireland Economic Targets 1

Target

2009

2030

Knowledge Economy Employment

30,600 4.4%

71,250 10%

Table 24.2  Northern Ireland Goals and Metrics

2 3 4 5 6 7 8 9

Target

2009

2030

Venture Capital invested per annum M&A per annum (# of deals) Publicly-traded companies Total R&D Number of Science Graduates Start-ups per annum required (innovation based) Total number of innovation businesses Number of R&D employees

£12M 30 3 £500M 57,300 200 2,080 6,500

£90M 77 24 £1.05bn 71,250 500 7,000 15,130

The Barriers The following three major challenges have been identified: • Culture o  Northern Ireland must change the aspirations of its youth so they aspire to careers in the knowledge economy (including sales) not just government positions and the professions. o  Northern Ireland has to scale the culture of collaboration within the business community. • Talent o  Northern Ireland needs stronger founding teams of new start-up companies earlier. o  Northern Ireland needs to increase the supply of technical and commercial talent to help companies grow. • Risk Capital o  Northern Ireland must evolve the right model and scale of seed and growth capital to enable products to be developed and companies to get to grow. In 2013, findings from the stakeholder analysis (the Tiger Teams) became the basis of a sustained advocacy program to help educate elected and public officials on the vision and the kinds of interventions that will be necessary to achieve it. The challenge was to develop a narrative with a direct connection between intervention and high-quality jobs for their constituents instead of the jargon of the technology

A Region in Transition    439 community. This process has been well received. Nonetheless, it will take a long time and depends on establishing real trust. Increasingly, elected officials accept that the business community can get on with a transformation on its own, but this could take 40 years; if public policy can be aligned behind it, the transformation could occur in 20 years. NISP CONNECT programs are annually reviewed against the targets and the blockers to make sure that they were directed effectively at that time. Another fundamental milestone happened at the beginning of 2013 when Chris Horn, one of Ireland’s most successful technology entrepreneurs, agreed to become the chairman of NISP CONNECT as a volunteer. His involvement speaks volumes about the level of support and respect the CONNECT program in Belfast has achieved, and the most recent performance data continue to indicate progress and value to the Northern Ireland economy in the 12 months to March 2013: • 467 early stage ventures and wantrepreneurs engaged in our programs • 28 entrepreneurs in residence (EiRs), the highest-quality pool of mentors ever established in Northern Ireland • 54 events and forums • £880,000 of value in pro bono contribution ($1.4 million) • £234,000 in private funding ($375,000 from 20 member companies and 20 additional companies sponsoring events) • From its first building going up in 2003, Northern Ireland Science Park today has 2,200 employees working on site and 110 companies and has been 100  percent occupied for the last four years, the peak of the Great Recession. • NISP CONNECT is now a representative on the UK’s Tech City Cluster Alliance, integrating clusters and providing policy advice to central government.

From Bottom-Up Movement to Royalty In January 2013 HRH Prince Andrew, the Duke of York, visited the Northern Ireland Science Park and was so taken with what he saw and the progress made that in May 2013 he agreed to become the patron of the Northern Ireland Science Park Trust, the home of the CONNECT program championed by returning expat Steve Orr in 2007.

Evidence of Progress As Table 24.3 Shows, Northern Ireland Has the Second Fastest-Growing Knowledge Economy in the UK The Knowledge Economy Index was developed by Richard Johnston of the Northern Ireland Centre for Economic Policy to provide a barometer of the health and

440    Competitiveness at the Local Level Table 24.3  Knowledge Economy for the United Kingdom

North East Northern Ireland West Midlands Scotland East Midlands Yorkshire & the Humber North West Wales South West London UK South East East

2009

2010

2011

2012

2013

2014

100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

102.0 111.5 132.9 115.7 101.6 101.9 110.4 102.1 103.1 105.8 103.7 100.7 103.8

124.6 122.8 120.1 108.8 114.2 99.5 105.4 105.1 99.7 107.5 104.3 101.0 96.1

127.2 130.3 120.3 120.2 104.8 110.3 105.7 103.3 104.6 103.5 104.5 97.2 92.9

136.2 133.0 127.3 125.7 107.8 109.3 106.5 106.1 104.7 104.4 105.5 96.3 94.3

138.3 135.2 130.9 129.9 111.6 110.6 110.2 108.6 108.1 106.3 106.1 96.7 94.9

Note: UK regional Knowledge Economy Indices (2009 = 100). The index is based on 21 indicators, including employment, businesses, R & D, investment, innovation, and patents.

development of the knowledge economy in Northern Ireland for NISP CONNECT. It is a composite index, comprising of 21 indicators including employment, businesses, R & D, investment, innovation, patents and allows the progress of the knowledge economy to be tracked against the UK and other UK regions.

Northern Ireland Is Achieving the Trajectory Targets in Key Knowledge Economy Index Metrics Northern Ireland Is the Leading UK Region in Attracting Foreign Investment A report published by UKTI—Inward Investment Report 2013/143—has shown that Northern Ireland has outperformed the rest of the UK regions in attracting foreign investment. Alastair Hamilton, chief executive of Invest Northern Ireland said: “Northern Ireland saw growth of 32 percent on the previous year in the number of FDI projects reported. When you compare this to the performance of the other regions—England (including London) 11 percent growth, Scotland 10 percent, Wales 18 percent—this is a great result and is testament to the hard work that has gone into developing our offering to investors.” 3  Northern Ireland Knowledge Economy Infographic 2013, http://www.nispconnect.org/kei/. https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/341601/UKTI_Inward_ Investment_Report_2013-2014.pdf. Northern Ireland cancer treatment success: http://www.bbc.co.uk/news/uk-northern-ireland-26016428.

A Region in Transition    441

Cancer Survival Rates in Northern Ireland Have Moved from the Bottom of the UK League Table to Near the Top Queen’s University Belfast president Professor Patrick Johnston, a world-renowned cancer specialist, is now leading the fight to improve cancer survival rates in Europe, launching the European Cancer Patient’s Bill of Rights. The bill of rights is the result of two years’ work by the European Cancer Concord (ECC). Professor Johnston cochairs the ECC, which involves 17 European countries and represents more than 1,000 national organizations and millions of cancer patients and survivors. It aims to address the disparities that currently exist in cancer care from one European country to the next.

Challenges Ahead: Can Do If? Starting a CONNECT organization from scratch was not easy. It caused more sleepless nights and had more uncertainty than starting a fast-growth company, according to Orr. Thankfully, Northern Ireland had so many great leaders and volunteers who helped make it happen in the beginning and now all day every day. The team at CONNECT Northern Ireland approaches 2015 cognizant of many challenges moving forward: • Can they constantly evolve the NISP CONNECT model to react to the changing local and global trends, staying ahead by constantly differentiating from the many other initiatives that are out there? • Can they evolve the roles for member and community participation to achieve a real return on involvement? • Can they keep the model together when on the one hand they have to foster a candid environment where all stakeholders “confront the brutal truth” and on the other hand institutional stakeholders are sensitive to criticism, no matter how constructive? • Can they mobilize the resources and leadership to change the culture? • They will always have too few resources, so can they continue to be opportunistic? • Can they unite and aggregate key knowledge economy stakeholders to speak with one voice to government instead of 100 disparate voices that are easily ignored? Can they then convince government to share the same trajectory of ambition and implement riskier interventions that will be necessary to achieve the rewards? • Will philanthropy in Northern Ireland start to support economic development?

442    Competitiveness at the Local Level

Conclusion: What Are the Lessons from the Belfast Experience? Critical to this development are a number of cultural and social factors that may be essential to effective competitiveness-focused transformations in most locales. • Readiness for economic change If communities do not perceive that their existing industries or ways of doing business are no longer appropriate, regional change simply cannot happen. Community means different things in different places. In old industrial cities with established corporate interests, old families, and a long history of labor unions, the stakeholders who have to recognize the need for change are highly diverse and often siloed. In newer communities such as one finds in the Southeast and Southwest of the United States with less corporatized and bureaucratic economies and a higher dependence on small business and often collaborative mechanisms, there may be different paths and timelines to recognizing the need for change. In more rural, less industrialized regions, global shifts may be experienced in still different ways. Whatever the context, there needs to be a readiness among the key stakeholders in that context to change. • Passionate champions of new approaches to realizing economic goals As best we can assess from our research across America, through this comparative study of Belfast and San Diego, as well as evaluative work being done in Sweden on regional initiatives, in every place where you begin to see new institutional mechanisms and new social dynamics that lead to new economic outcomes, there are identifiable champions catalyzing that change. An individual with great ideas and passion is critical but not without being able to enroll leadership from the various stakeholder groups that will be affected by the change. Both the San Diego and Belfast cases underscore this reality. • Institutional platforms of legitimacy and credibility Change initiatives are greatly enhanced by early validation and endorsement by an established institution. Established institutions can rarely lead such efforts, but they can become a host for them, a validator and an endorser of them. The perceived risk of change, new ideas, and new people in leadership roles can be minimized in the eyes of key stakeholders and skeptics if this is in place; UC San Diego for CONNECT and the Northern Ireland Science Park for NISP CONNECT.

A Region in Transition    443 • Willingness of previously siloed groups to align objectives, collaborate, and share resources Because regional prosperity is increasingly dependent on innovation, whether of the breakthrough or incremental type, it of necessity requires systemic challenge and opportunity development. No one institution can lead the change or make the change alone. Change in the economy requires participation and support from the business sector, from the workforce, and from the public sector influencing both policy and resource allocations affecting the regional economy. In many contexts it requires support by civic leadership and philanthropic leadership. As a result, intermediary organizations that can bring together all of these groups and align objectives for collaboration and for sharing resources to move forward can be extremely catalytic in improving regional competitiveness options. • A source of seed capital to launch potentially change-catalyzing initiatives Networking organizations are essential to bring new knowledge into the community, create new networks of collaboration and sharing, and ensure that the “know-how” and capital needed for change are in place. This cannot happen without some platform from which a small, energetic group can do the catalytic work necessary. This in turn requires seed capital. In many contexts such as Silicon Valley; Austin, Texas; and San Diego, California, the vast majority of that seed capital has come from the private sector and/or foundations. In other contexts such as upstate New  York in the United States, regions across Sweden, and Belfast, Northern Ireland, such early capital came from the government and, on occasion, foundations. In all cases there were sufficient start-up funds to get the momentum going to build the stakeholder base and, ultimately, to broaden the investor base to ensure multiple sources of revenue for sustainable change initiatives. • A significant coinvestment of resources and volunteer time from the private sector The importance of substantial time commitment and resource commitments from diverse sources, most especially the private sector, relates ultimately to what will be the measures of success and where accountability lies. Programs that are exclusively funded by the public sector may have a narrower range of metrics to which they are accountable and may miss much of the wisdom and experience the private sector can bring to accountability measures and ultimately, strategies building economic success over time. In the 21st-century innovation economy, knowledge is anchored in multiple sectors. That knowledge needs to be engaged and integrated around shared goals and metrics, and without the investment of volunteer time and private-sector money, that knowledge is often not put to work on behalf of regional competitiveness.

444    Competitiveness at the Local Level • A commitment to developing and regularly collecting shared metrics as a way to assess both the return on investment and involvement The issue of metrics is a profound one. Typically in government programs and often foundation programs, the metrics are not about the collective impacts on a region, that is, do school dropout rates go down, does external investment go up, are new businesses started, are jobs and wealth created. Too often metrics are based on participation rates in programs and near-term employment or opportunity gains for individuals rather than a broader community-wide assessment of impacts. Collaborative mechanisms that engage multiple stakeholders from day one are in a position to identify and build a consensus about what regional competitiveness would look like and how to measure it. The most successful programs of which we are aware in places across the United States and Europe have engaged in this collaborative metrics development and assessment process. In both the San Diego case and in Belfast 6,000 miles away on another continent with a very different social and cultural context, these social dynamics were essential to transformation. In San Diego, the end of the Cold War created a sense of urgency about the need to diversify the economy just as the end of “the Troubles” in Northern Ireland enabled a new focus on global economic competitiveness. Passionate champions, in the San Diego case migrants from Stanford and MIT, and in Belfast returning expats, were critical to catalyzing institutional mechanisms enabling positive change. In San Diego’s case, the region’s University of California was the initial platform for CONNECT, as was the Science Park for CONNECT in Belfast—both respected forces in their regions. Collaboration was achieved in both instances through leadership and governance models, programs, and events deliberately inclusive of the “voices” of business services, scientists, and regional entrepreneurs. In each case, early investment—in San Diego from the private sector, in Belfast, from government—helped launch the enterprise, build the stakeholder base, and eventually grow a significant group of private-sector investors and volunteers. And, perhaps most important, in both cases an economic outcomes focus using agreed-upon measures of success developed early. Data-driven accountability and decision-making focused on incremental indicators of regional change and growth, not simply on event attendance numbers, and so on. Based on these parallels between two such disparate regions, we believe there may be some general principles vis-à-vis the social dynamics of competitiveness in the new innovation economy that can be useful to regions around the globe.

Chapter 25

The 2008 Ec onomi c Cri sis and Its I mpac t on U niver si t i e s ’ C om petiti v e ne s s Shiri M. Breznitz and Paige A. Clayton

1 Introduction Over the years, much of universities’ output was the result of national and local government funding (Acs, Audretsch, and Feldman 1992; Jaffe, Trajtenberg, and Henderson 1993; Stephan 2012). In particular, studies show connections between university revenues and commercial output, faculty-student ratios, and university prestige. Hence, the funding changes due to the 2008 economic crisis provided a case study for evaluating the economic impact of universities on commercialization. Because of the decline in state appropriations for higher education, the ability to evaluate the economic effect of universities while distinguishing between public and private institutions is important. This chapter analyzes the impact of university funding, due in particular to the 2008 financial crisis, on the competitiveness of local universities (measured by number of students, commercial output, and research excellence), which in turn influences local competitiveness. The analysis combines existing studies with current and historical data on three research-intensive universities in the city of Atlanta. The chapter proceeds as follows: Section 2 reviews existing literature on technology transfer, economic geography, and the economics of higher education, resulting in five hypotheses. Section 3 explains the methodology; section 4 provides the results of the analysis; and the final section is the conclusion.

446    Competitiveness at the Local Level

2  Universities and Economic Development: Input and Output Studies show that university research has a direct impact on local economic growth (Acs, Audretsch, and Feldman 1992; Jaffe, Trajtenberg, and Henderson 1993). Unlike private businesses, universities can conduct basic research in which failure can be considered a positive result as much as success would be. Moreover, studies show that universities have strong spillover on their regional economies (Stephan 2012). In the late 1980s and early 1990s, the development of the knowledge economy led to increased reliance on universities’ contributions to financial economic development by focusing on the outcomes of academic research (Etzkowitz and Leydesdorff 1997; Goddarrd and Chatterton 1999). As a result, universities have played a central role in contributing to regional and national economies. In some countries, such as the United States, universities have long histories of economic engagement, while in others, like the UK, it was a later development (Breznitz 2011; Rahm et al. 2000). The mission statement of a university offers a window into the commercial culture and organization of the institution. Especially in technology commercialization, mission statements indicate an institution’s commitment to economic development in general and technology and research commercialization in particular (O’Shea et al. 2005). The original role of the university, teaching and research, was reflected in its mission statement. However, over time, many institutions have updated their mission statements to reflect their expanding roles (Scott 1977). At most universities, those added roles focus on knowledge transfer and technology commercialization (Breznitz and Feldman 2012b). The organization’s cultural base, influenced by the organization’s history and the history of the decision-makers in the organization, affects the way in which it makes decisions about issues such as strategy, outlook, and cooperation with other players in the local economy (Schoenberger 1997). The impact of a university on a regional economy can be measured in two ways. The classic or “short run” method is by determining the institution’s contribution to the annual flow of regional economic activity. The “long run” method focuses on the contribution of the institution to the continuous growth of human capital in the region (Beck and Elliott 1995). The short-run impact—actual dollars flowing into a region because of the mere presence of the university—can be measured by the purchases made by the university in the region: office supplies, rent, food, services, and salaries for employees, some of whom live and spend their wages in the region. Outside funds like donations, grants, and state and federal government funding to the university are also considered in determining a university’s economic effect. In this way, a university is measured only by direct input and output. University contributions that are not measured in dollar amounts, such as graduate students’ firms and firms’ products based on university research, are not taken into consideration.

The 2008 Economic Crisis and Its Impact    447 The long-run impact measures “the future income stream of graduates who stay to work in the area” (Beck and Elliott 1995, 246) and the economic impact of graduate students’ firms and firms with products based on university research and patents, all of which contribute to local competitiveness. Measuring the long-run impact provides a way to calculate the return on taxes invested in higher education. Studies have proven that having a higher education leads to higher levels of income. In urban areas, the presence of universities seems to affect the growth rates, earnings, and composition of employment. Hence, the ability of a university to patent, license, and spin out firms has a direct impact on the long-run economic development of a region (Jaffe, Trajtenberg, and Henderson 1993; Stephan 2012). The commercial output of universities is based on academic prestige, research collaborations, and funding availability (Shane 2004; Roberts 1991; Zucker, Darby, and Peng 1998; Clark 1998; Lockett and Wright 2005; Kenney and Goe 2004; Goldfarb 2008; Payne and Siow 2003). Studies have identified a positive correlation between the amount of research grants and publications and patents (Payne and Siow 2003; Zucker et al. 2007). Specifically, studies have found that the level of federal research funding is an indicator of technology transfer performance at universities, especially by start-up creation (Powers 2004). Moreover, and as a result, academic prestige, in the form of publication records, has been shown to lead to an increase in funding (funding brings funding) as well as an increase in the quality of publications. At the same time, research collaboration has been shown to be one of the most important factors in the local innovation system that leads to economic growth. Federal R & D budgets have become a substitute for decreasing state budgets (Powers 2004). Thus, specifically following the 2008 financial crisis and reduction in state funding, it would be interesting to know how federal funding contributed to total university funding. Furthermore, in order to maintain their academic prestige, to fund hiring packages, and to cover indirect costs cuts, more and more higher education institutions are self-funding research. Institutional funding of research shows the direct positive impact of federal contributions. By 2009 more than 20 percent of research funds came from the universities themselves (Stephan 2012). Thus, a reduction in federal R & D increases the likelihood of universities’ research self-funding. In particular, Powers (2004) found that growth in institutional R & D is an indicator of university entrepreneurship and that institutional funding of R & D grows with reductions in state appropriations and federal R & D. When we review the literature on the economics of higher education we find that universities allocate their funding according to their source. Thus, in line with institutional theory, studies have found that university funding from grants and contracts will be spent on research, while funding from tuition or state appropriation will be spent on teaching. That said, administrators at research-intensive universities prioritize research (Leslie et al. 2012). Hence, because of the economic crisis and its impact on universities, if research support is reduced, universities will compensate for it by tapping other revenue sources, including those normally allocated for teaching. This is especially true at public universities, which in times of crises accept more students than

448    Competitiveness at the Local Level private universities (Leslie et al. 2012). Thus, since the 2008 financial crisis, universities have been diverting funding from tuition to research. The transfer of tuition funding to research at a time of increasing enrollments will lead to an increase in the ratio of faculty per student in public universities and will lead to an increase in tuition at private institutions of higher education (Leslie et al. 2012; Erenberg, Rizzo, and Jakubson 2007).

3 Methodology To assess the impact of funding on universities and their output, this chapter analyzes the effect of the 2008 economic crisis on three universities in Atlanta, Georgia. In particular, the chapter focuses on the following research question: • How did funding changes affect universities’ research, teaching, and commercialization output? The previous section’s review of the literature illustrates the importance of universities to local economic development—specifically, their contribution through labor force training and technology commercialization. In particular, the section highlights the importance of research and research funding on this output and the connections between university revenue and output. Thus, the chapter analyzes the following hypotheses: • Hypothesis 1: The level and combination (what kind) of research funding will affect the university publication level and commercial output.1 • Hypothesis 2: The reduction in state appropriations will increase the institutional support for research at public universities. • Hypothesis 3: The increase in institutional funding will positively affect commercial output. • Hypothesis 4: Because they are public institutions with a mandate to support economic development, public universities will enroll more students than private universities will, a trend expected to be evident after 2008. • Hypothesis 5:  Because of the economic crisis, the ratio of faculty per student will increase at public universities and tuition will increase at private universities. This chapter uses a case-study approach to evaluate the competitiveness of three universities in the midst of the economic crisis. In an attempt to control for factors other than the funding streams for universities, we analyze three universities located in the same city. This approach allows us to control for historical and environmental factors, 1 

Commercialization output is measured by patents, licenses, and number of spin-out companies. Research funding refers to all sources.

The 2008 Economic Crisis and Its Impact    449 especially at the federal, state, and city level. Moreover, because the literature highlights the importance of intensive research, we focus on the three research-intensive universities: the Georgia Institute of Technology, Georgia State University, and Emory University (Carnegie Foundation for the Advancement of Teaching 2012). We analyze both public and private universities because of the importance of their different resource allocations and output mentioned in the literature. Atlanta was chosen based on the fact that it has three research universities, one private and two public, within its metropolitan area. For comparison purposes, this setting allows us to focus on the specifics of each institution, and not worry about differences that may result due to regional or national differences. The study focuses on quantitative analysis. The main data sources for our quantitative analysis are the universities’ factbooks or reports to the president, the National Science Foundation (NSF), the Association of University Technology Managers (AUTM) database, and direct information from university administrators via email or from an interview.

4  Results and Discussion In this section, we use data on our three universities to investigate basic claims regarding the relationships of university research funding and commercialization as well as particular impact that the 2008 economic crisis may have had. Interestingly, all three universities show that total research expenditures have continued to grow, without any evidence of an impact from the 2008 economic crisis. While we cannot see much of a relationship between research funding and patents, we do see its negative impact on the total number of publications. At all three universities, regardless of the continuous growth in total research expenditures (see fi ­ gures 25.1, 25.2, and 25.3), the number of publications has declined by 30–50 percent. For Georgia Tech and Emory, the decline started in 2011, whereas at Georgia State it started in 2009. In addition, the data reveal that total R & D has not declined drastically thanks to growth in institutional funding of research. However, that is not enough to counteract the reduction in publication, since even institutional funding grew more slowly after 2008. Thus, the results support H1. The reduction in federal R & D had a direct impact on the publication level of the studied institutions. Hence, the source of funding, that is, federal versus institutional, impacts the level of publication. However, the source of funding showed no direct impact on university commercialization. When we compare the three universities, we find that the main reduction in funding sources for Emory and Georgia Tech following the crisis was state and local funding as well as industry and institutional funding (in decreasing order of importance), while for GSU it was mainly a reduction in state funding. For Georgia State, the reduction in state support in 2009 was critical (more than 400 percent).2 2 

This could be a due to an unexplained large increase in state funding that the university received in 2008 (growth of 80 percent).

GSU Publications & Patents as Funding Output 2,500

100,000,000 90,000,000 80,000,000 70,000,000 60,000,000 50,000,000 40,000,000 30,000,000 20,000,000 10,000,000 0

2,000 1,500 1,000 500 4

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Figure  25.1  Georgia State University, Publications and Patents as Funding  Output (Source:  Microsoft Academic Search (2013); National Science Foundation (NSF) (2013a; 2013b).)

Georgia Tech Publications & Patents as Funding Output 10,000

700,000,000 600,000,000 500,000,000 400,000,000 300,000,000 200,000,000 100,000,000 0

8,000 6,000 4,000 2,000

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Figure  25.2  Georgia Institute of Technology, Publications and Patents as Funding  Output (Source:  Microsoft Academic Search (2013); National Science Foundation (NSF) (2013a; 2013b).)

Emory Publications & Patents as Funding Output 8,000

800,000,000

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Figure  25.3  Emory, Publications and Patents as Output Funding (Source:  Microsoft Academic Search (2013); National Science Foundation (NSF) (2013a; 2013b).)

The 2008 Economic Crisis and Its Impact    451 Is H2 supported? As can be seen from ­figures 25.4 and 25.5, it depends on the university. The two public universities examined here, Georgia State and Georgia Tech, had their state appropriations reduced starting in 2009 (Georgia Tech) and 2010 (Georgia State). At Georgia State, which does not enjoy the same level of federal funding as Emory and Georgia Tech, there was a drastic increase in institutional funding for research. However, at Georgia Tech, where federal research funding continues to grow, and even at a higher rate, no growth is seen. On the contrary, during the crisis years, institutional support for research appears to have declined. Thus, where—if at all—do we see the impact of the 2008 economic crisis on these universities? Since studies indicate that a reduction in state funding will increase institutional support and, therefore, positively affect a university’s entrepreneurship and

Georgia State: Institutional Support for Research and State Appropriations 60,000,000

250,000,000

50,000,000

200,000,000

40,000,000

150,000,000

30,000,000

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03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11

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Figure  25.4  Institutional Support (Source:  Georgia State University (1980–1994; 1995–2003; 2005–2009); National Science Foundation (NSF) (2013a; 2013b).)

Georgia Tech: Institutional Support for Research and State Appropriations

19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11

450,000,000 400,000,000 350,000,000 300,000,000 250,000,000 200,000,000 150,000,000 100,000,000 50,000,000 0

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Federal

Figure  25.5  Georgia Institute of Technology Institutional Support (Source:  Geogria Institute of Technology (2011); Georgia Institute of Technology (1993–2011); National Science Foundation (NSF) (2013a; 2013b).)

452    Competitiveness at the Local Level commercialization, we analyze the relationship between institutional funding and commercialization output at the public universities. The reduction in state appropriations is felt more strongly at Georgia State, and, hence, the institution reacted by increasing institutional funding. However, this increase still does not help with the short-term commercialization output in the form of applied patents. Patents and income from licenses, which are a result of the previous year’s funding, are still growing. As stated above, at Georgia Tech, institutional funding after 2008 slightly declined and, in addition to publications, the only commercial output that suffered from the change is spin-out formation as shown in ­figures 25.6 and 25.7 below. Hence, we find that H3 is supported. The increase in institutional funding will positively affect commercialization output. Georgia State: Institutional Support & Commercialization 70

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Figure  25.6  Georgia State, Institutional Support and Commercialization  Output (Source:  Georgia State University Office of the Vice President for Research and Economic Development (2013); National Science Foundation (NSF) (2013a; 2013b).)

Georgia Tech: Institutional Support & Commercialization 100

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Licenses (#) Patents (USPTIS) License income: Gross Received (LIRECD)

Figure  25.7 Georgia Institute of Technology, Institutional Support and Commercialization Output (Source:  Association of University Technology Managers (2013); National Science Foundation (NSF) (2013a; 2013b).)

The 2008 Economic Crisis and Its Impact    453 When we examine enrollment, we find, not surprisingly, that the financial crisis of 2008 affects the three universities in a way that fits their mandate. This supports H4. Hence, during funding crises public universities do enroll more students than private universities. Georgia State, which behaves like a teaching university and always had a larger number of students than Emory and Georgia Tech, accepted more students and showed an increase of 13 percent in the number of students between 2008 and 2011. At the same time, Georgia Tech, a public institution with strong support from privately funded research, and Emory, a private university, show only single-digit percentage increases in the number of students as shown in ­figure 25.8 below. Interestingly, it is Emory that increased its enrollment by 9 percent and not Georgia Tech, whose increase was 8 percent. However, is H5 supported? Do we see a difference between public and private universities in regards to faculty-student ratios and tuition increases? The short answer is yes. All three institutions increased the student-faculty ratio and tuition (Georgia Institute of Technology 1993–2011; Georgia State University 1980–94; 1995–2003; 2005–9; University 1997–2011; Emory University Office of the Registrar 2013a). Notably, these changes were made in accordance with the identity and character of each university. Emory, the private university, could not increase its tuition much more from its already high level. Georgia Tech tried not to increase its student-faculty ratio much, but it is a smaller school that experienced a reduction in state appropriations. Hence, the majority of the increase is in its tuition. Georgia State had already higher student-faculty ratios, so it could not greatly increase those. However, it did not suffer from as much reduction in state appropriations, and its large student body allowed it to increase its tuition only slightly. At Emory, the average faculty member teaches 4.48 students. This ratio fell to 4 students per faculty member in 2008 and then continuously grew and by 2011 reached 4.52 students per faculty member. Comparing the trends, we see that tuition and the student-faculty ratio both increased by 10 percent. Hence, as anticipated by studies, at Enrolled Students by University, 1980-2011 35,000 30,000 25,000

Emory Students

20,000

Georgia Tech Students

15,000

Georgia State Students

10,000 5,000 0 80 982 984 986 988 990 992 994 996 998 000 002 004 006 008 010 2 2 2 1 1 1 1 1 2 1 1 1 1 2 2

19

Figure  25.8  Total Number of Enrolled Students by University 1980–2011 (Source:  Emory University Office of the Registrar (2013b); Georgia Institute of Technology (1993–2011); Georgia State University (1980–1994; 1997–2011).)

454    Competitiveness at the Local Level a time of financial constraints, the private university’s tuition increased, but at the same time the student-faculty ratio grew as well, in fact at about the same pace. Both represent a very small increase. This behavior fits the profile of a private university attempting to keep a low student-faculty ratio. Emory, a university that does not lack for federal funding, did not have it reduced, and does not receive state funds, did not need to supplement a reduction in state research funding by as much as the others. Georgia State, which on average enrolls more students than Emory or Georgia Tech, has an average of 30 students per faculty member. However, in 2008 the average was 27 students and in 2011 that grew to 29 students. This represents a growth rate of 8 percent during the crisis years, while tuition grew by 26 percent. Thus, Georgia State increased its student-faculty ratio but not more than its average and hence increased its research funding from tuition only slightly. Again, this behavior fits the profile of Georgia State—a university that focuses on teaching and does some research but always at a lower level of funding than Emory and Georgia Tech. Moreover, Georgia State had only a 4 percent reduction in state appropriations. So the growth in its student-faculty ratio and tuition increase is more than fitting for the university with the largest number of students. The institution with the largest tuition increase was Georgia Tech, at 48 percent. This increase is disproportionate to both Emory and Georgia State. However, the nature and size of the institution and the 19 percent reduction in state appropriations can explain it. Georgia Tech is an engineering school and is slightly bigger than Emory. The average student-faculty ratio is 21.25. In 2008 the ratio was slightly higher, at 21.29, and in 2011 that grew to 22.79—total growth of 7 percent.

5. Conclusions This chapter set out to analyze the impact of university funding on the competitiveness of universities, which has a direct result on local competitiveness. The role of universities has been an important topic in debates on local economic development, but the 2008 economic crisis and the reduction in state funding for universities made this investigation even more critical. In order to focus on the impact of state and federal funding, this chapter analyzed three universities in one city: Georgia State, Georgia Tech, and Emory, in Atlanta. The 2008 economic crisis was global. However, one of the major results of the crisis was a reduction in state funding, in particular, a reduction in state appropriations, which has an impact on public universities, as well as a reduction in state funding for research, which has an impact on both public and private universities. The most evident impact of the crisis is the reduction in university publications. This result is not surprising, since existing studies indicate a strong correlation between research funding and publication. With regard to the education and training role that universities play in local economies, this study finds that all three universities accepted more students. The number of students accepted at each university

The 2008 Economic Crisis and Its Impact    455 fits with its role in the Georgia economy. Georgia State accepted the majority of students without raising tuition very much, while Emory, as a private university, tried to keep its student-faculty ratios low. The rise in the student-faculty ratio and the tuition increase were not high enough to prevent the reduction in publications. In fact, the increase in the student-faculty ratio may have reduced the time available for faculty to conduct research and hence contributed to the total reduction in publications. Moreover, and critically for future research, we cannot see an impact on university commercialization and hence on local competitiveness. However, technology transfer studies have pointed out that there is a lag between research and commercialization. Another way to think about these results is that publication is the first outcome of any research. Even research that needs to be patented will go through some form of publication first, while the patent process can take years. Thus, this study finds that the 2008 financial crisis had a direct impact on university research. However, future analysis is needed to evaluate whether the same can be said about commercialization and local competitiveness.

References Acs, Z.  J, D. B Audretsch, and M. P.  Feldman. 1992. “Real Effects of Academic Research: Comment.” American Economic Review 82 (1), 363–67. Association of University Technology Managers. 2013. Statistics Access for Tech Transfer (STATT). Beck, R and D. Elliott. 1995. “Economic Impact Studies of Regional Public Colleges and Universities.” Growth & Change 26 (2), 245. Breznitz, S. M. 2011. “Improving or Impairing? Following Technology Transfer Changes at the University of Cambridge.” Regional Studies 45, 463–78. Breznitz, S. M., and M. P. Feldman. 2012. “The Engaged University.” The Journal of Technology Transfer 37, 139–57. Carnegie Foundation for the Advancement of Teaching. 2012. 1994 Edition Data File. http:// classifications.carnegiefoundation.org/resources/. Clark, B. R. 1998. Creating Entrepreneurial Universities:  Organizational Pathways of Transformation. Oxford: Pergamon Press. Emory University. 1997–2011. President’s Annual Report. Emory University Office of the Registrar. 2013a. Total number of faculty. Emory University Office of the Registrar. 2013b. Total number of students. Erenberg, R.  G., M. J.  Rizzo, and G. H.  Jakubson. 2007. “Who Bears the Growing Cost of Science at Universities?” In P. E. Stephan and R. G. Ernberg, eds., Science and the University. Madison: University of Wisconsin Press. Etzkowitz, H and L. Leydesdorff. 1997. Universities and the Global Knowledge Economy: A Triple Helix of University-Industry-Government Relations, Science, Technology and the International Political Economy. London: Pinter. Georgia Institute of Technology. 1993–2011. Georgia Institute of Technology Fact Book Archives. http://www.irp.gatech.edu/fact-book-archives/.

456    Competitiveness at the Local Level Georgia Institute of Technology. 2011. “Fact Book Online.” Georgia State University. 1980–94. Georgia State University Fact Book. Georgia State University. 1995–2003, 2005–9. Georgia State University original budget for year Ending June 30, various years. Georgia State University. 1997–2011. Enrollment reports. Georgia State University. 2004. “Information Digest, 2005–2006.” http://www.usg.edu/ research/digest/2006/financial0506.pdf. Georgia State University Office of the Vice President for Research and Economic Development. 2013. “Commercialization Output.” Goddarrd, J., and P. Chatterton. 1999. “Regional Development Agencies and the Knowledge Economy: Harnessing the Potential of Universities.” Environment and Planning C: Government and Policy 17, 685–99. Goldfarb, B. 2008. “The Effect of Government Contracting on Academic Research: Does the Source of Funding Affect Scientific Output?” Research Policy 37 (1), 41–58. doi:http://dx.doi. org/10.1016/j.respol.2007.07.011. Jaffe, A. B., M. Trajtenberg, and R. Henderson. 1993. “Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations.” Quarterly Journal of Economics 108 (3), 577–98. Kenney, M. and R. W.  Goe. 2004. “The Role of Social Embeddedness in Professional Entrepreneurship: A Comparison of Electrical Engineering and Computer Science at UC Berkeley and Stanford.” Research Policy 33, 691–707. Leslie, L. L., S. Slaughter, B. J. Taylor, and L. Zhang. 2012. “How Do Revenue Variations Affect Expenditures within U.S. Research Universities?” Research in Higher Education 53 (6), 614–39. doi:10.1007/s11162-011-9248-x. Lockett, A and M. Wright. 2005. “Resources, Capabilities, Risk Capital and the Creation of University Spin-out Companies.” Research Policy 34 (7), 1043–57. Microsoft Academic Search. 2013. Organization Search. Available from http://academic. research.microsoft.com/. National Science Foundation (NSF). 2013a. “Academic R&D Expenditures 1993–2009.” Ahttp://www.nsf.gov/statistics/rdexpenditures/. National Science Foundation (NSF). 2013b. “Higher Education Research and Development (HERD) 2010–2011.” http://www.nsf.gov/statistics/herd/. O’Shea, R.  P., T. J.  Allen, A. Chevalier, and F. Roche. 2005. “Entrepreneurial Orientation, Technology Transfer and Spinoff Performance of US Universities.” Research, Policy and Planning 34 (7), 994–1009. Payne, A. A., and A. Siow. 2003. “Does Federal Research Funding Increase University Research Output?” Advances in Economic Analysis & Policy 3 (1), 1–22. Powers, J. B. 2004. “R&D Funding Sources and University Technology Transfer:  What Is Stimulating Universities to Be More Entrepreneurial?” Research in Higher Education 45 (1), 1–23. Rahm, D., J. Kirkland, and B. Bozeman. 2000. University-Industry R & D Collaboration in the United States, the United Kingdom, and Japan. Dordrecht, The Netherlands, Kluwer Academic Publishers. Roberts, E. Baer. 1991. Entrepreneurs in High Technology:  Lessons from MIT and Beyond. New York: Oxford University Press. Schoenberger, E. 1997. The Cultural Crisis of the Firm. Cambridge, MA: Blackwell. Scott, P. 1977. What Future for Higher Education? London: Fabian Tracts.

The 2008 Economic Crisis and Its Impact    457 Shane, S. 2004. Academic Entrepreneurship:  University Spinoffs and Wealth Creation. Cheltenham: Edward Elgar. Stephan, P. 2012. How Economic Shapes Science. Cambridge, MA: Harvard University Press. Zucker, L.  G., Michael R.  Darby, J. Furner, R. C.  Liu, and H. Ma. 2007. “Minerva Unbound:  Knowledge Stocks, Knowledge Flows, and New Knowledge Production.” Research Policy 36 (6), 850–63. Zucker, L. G., M. R.  Darby, and Y. Peng. 1998. Fundamentals or Population Dynamics and the Geographic Distribution of U.S. Biotechnology Enterprises, 1976–1989. Cambridge, MA: National Bureau of Economic Research.

Chapter 26

Smart Specia l i z at i on an d Eu ropean Re g i ona l Devel opm e nt  P ol i c y Dominique Foray, Philip McCann, and Raquel Ortega-Argilés

The Need for Good Policy Prioritization: The Background to Adopting Smart Specialization as a Key Element of EU Cohesion Policy The reforms to the regional and urban development policy of the European Union, known specifically as EU Cohesion Policy, have involved a great deal of reflection and reconsideration on the basis of expert advice from numerous scholars. All policies operate in a particular political economy context, and in the case of EU Cohesion Policy, the programs and interventions undertaken as part of the policy operate across 28 different countries with heterogeneous institutional and governance systems, and enormous interregional variations in terms of economic performance and development. For promoting local and regional development, a single “one size fits all” type of approach to policy design dictated from Brussels can never be workable in such a context and instead the policy needs to be designed in a way that ensures that local policymakers make the most realistic and also the wisest decisions possible. For this to be the case a much greater role is required for tailoring policies to the local context. At the same time, one of the key issues that has emerged from the policy-reform debates was the need for a shift away from policies based largely on “hard” infrastructure provision and a greater emphasis to be placed on “soft” investments aimed at enhancing entrepreneurship and innovation. Policy-tailoring with a focus on

Smart Specialization and European Regional Development Policy    459 enhancing local entrepreneurship and innovation has therefore become a keystone of the new EU Cohesion Policy architecture and regulations. The smart specialization agenda is central to this effort, because it provides a narrative and a way of thinking about policy-prioritization that had previously been lacking in Cohesion Policy. All stakeholders will wish prioritize their own needs and demands, and the setting of policy priorities often depends on the power of certain elites groups (Acemoglu and Robinson 2012). At the same time, all political decision-makers aim to ensure that they receive some of the benefits or rents from policy actions that they can then show to their constituencies as evidence of success in the political bargaining processes. The problem here is that any attempts at the concentration of resources on a few key strategic priorities tend to be subverted by these institutional issues unless a means of overcoming these blockages can be identified. As Rodrik (2014) has recently argued, one of the key aspects of new ideas is the ability to help overcome institutional opposition from entrenched elites, and in this respect, the smart specialization approach has also helped to motivate institutional reflection and to galvanize policy reforms in many EU regions aimed at driving new regional trajectories. The original smart specialization concept (Foray, David, and Hall 2009) emerged out of discussions led by a team of expert analysts investigating the growing “Transatlantic Productivity Gap” (Ortega-Argilés 2012)  and the puzzling lack of innovative and entrepreneurial dynamism in many parts of Europe in the light of the potential market opportunities offered by newly emerging technologies. Entrepreneurial dynamism varies greatly across EU regions, and the reasons for these variations differ between places.1 However, one widespread feature of European regions appeared to be a lack of alignment of many of the institutional mechanisms and influences potentially facilitating entrepreneurial and innovative activities (Foray, David, and Hall 2009) such that the context often appeared to mitigate rather than facilitate such activities. Linkages between sectors, between policies, between levels of government, and between objectives were often severely lacking, and this was particularly apparent in terms of public-/ private-sector engagement. In many EU regions the governance environment for innovation was rarely characterized by smooth and coordinated systems, and innovative and entrepreneurial activities were often having to work against the grain in order to make headway (McCann 2015). At the same time, it became apparent from the widespread debates regarding the need for reforms to EU Cohesion Policy (Barca 2009; McCann 2015) that across the EU arena there needed to be renewed efforts toward promoting economic development at the local and regional scale that not only best suited the local context but also consequently offered the strongest opportunities for realizing scale advantages. The entrepreneurial and innovative challenges raised by the smart specialization line of thinking dovetailed closely with the emerging place-based consensus underpinning the EU Cohesion Policy reforms (McCann and Ortega-Argilés 2013a; 2013b; 2013c). Furthermore, the 1 

http://bookshop.europa.eu/en/redi-the-regional-entrepreneurship-and-development-in dex-pbKN0214462/?CatalogCategoryID=cKYKABsttvUAAAEjrpAY4e5L.

460    Competitiveness at the Local Level imperative to update innovation policy at the regional level so that it is consistent with the latest theoretical analysis and empirical evidence has increased the urgency for developing a new narrative that engages with wide-ranging audiences (Rodrik 2014). The smart specialization agenda has therefore become the main approach being adopted for those regional development activities related to entrepreneurship and innovation operating under the EU Cohesion Policy framework. Moreover, the much greater shifts of EU regional policy resources currently taking place away from expenditure on “hard” infrastructure such as transport investment and toward “soft” infrastructure aimed at promoting local entrepreneurship and innovation has greatly increased the profile of the smart specialization approach within the overall EU Cohesion Policy reforms. The changes wrought by both the overall Cohesion Policy reforms and in particular the smart specialization agenda require a major change of mindset by policymakers and also a shift in understanding of their own roles on the part of many actors involved with EU regional development policy. Importantly, these required changes and shifts are needed just as much within the private and civil society sectors as within the public sector.

The Chapter Logic The aim of this chapter is therefore to explain the arguments underlying the smart specialization way of thinking and to explain how this approach is being adopted in the context of the EU Cohesion Policy reforms. The arguments in this chapter draw extensively on the insights addressed by a set of papers already published on various aspects of this topic (Foray, David, and Hall 2009; Coffano and Foray 2014; McCann and Ortega-Argilés 2013b; 2013c; McCann and Ortega-Argilés 2014a; 2014b ; 2014c), and the rest of the chapter is organized as follows. The section titled “Entrepreneurial Discovery” discusses the conceptual underpinnings of the smart specialization way of thinking and for reasons of expositional clarity does so in part by way of various examples. The section “Entrepreneurial Discovery as a Factor of Structural and Industrial Changes” discusses why these approaches are relevant for regional policy, and then the section “Entrepreneurial Knowledge and Economic Knowledge” explains how this approach is being implemented with the EU Cohesion Policy reforms. The following sections provide examples of European regions that are embarking on this process, and the concluding section provides some final thoughts and conclusions relevant for other arenas beyond the EU.

Entrepreneurial Discovery The simple idea of smart specialization is that regions—in particular the less advanced and transition regions—need to build capabilities—not only generic capabilities but

Smart Specialization and European Regional Development Policy    461 also capabilities within specific fields, technologies, and subsystems in order to build competitive advantages in a few market niches. The idea is neither to narrow down the development path of a region nor to produce some sort of technological monoculture. Smart specialization strategies reflect rather the capacity of an economic system (a region, for example) to generate new specialties through the discovery of new domains of opportunity and the local concentration and agglomeration of resources and competences in these domains. Such a capacity is needed to initiate structural changes in the form of diversification, transition, modernization, or the radical foundation of industries and/or services. A key contribution of the smart specialization literature has been to emphasize the centrality of entrepreneurial discovery to animate such a process.

Entrepreneurial Discovery as a Factor of Structural and Industrial Changes The fundamental act underlying industrial and structural changes is most often an entrepreneurial discovery. It precedes the innovation stage and consists of the exploration and opening up of a new domain of technological and market opportunities, potentially rich in numerous innovations that will subsequently occur. In 1796 in the region of Morez—a small town on the border between France and Switzerland—Pierre Hyacinte Caseaux discovered that from his production of nails he could switch to the production of glasses (spectacles) using the same techniques and capabilities. Very soon other nail producers started to manufacture glasses, leading to the creation of many factories within the next 20 years and the opening of a technical school to train apprentices. Morez became a world-class center for the manufacture of glasses. Indeed, this is a simple story! However it includes the three main episodes of a smart specialization process: entrepreneurial discovery and spillovers (the discovery is the fact that it is possible to move from nails to glasses on the basis of a similar set of engineering capabilities and techniques); entry and agglomeration of similar and complementary businesses (cluster formation); and structural changes (in the form of transition from an old business to a new one). In the 1930s, Anibal H. Abrantes created the first mold-manufacturing company in Portugal, the main market for which was glassmaking. But the latter was declining, and Abrantes very quickly saw the economic potential offered by the new plastic products market. He observed the rapid development of “plastic firms” in a large number of sectors (toys, electrical equipment, household utensils, and articles). He traveled all over Europe and brought back all sorts of plastic products manufactured by injection molding for which he wanted to design and produce the molds. He then explored the possibility of a major diversification of his companies by converting the production tooling. This entrepreneurial discovery was to have two effects (Sopas 2001): providing an exceptional boost to the mold-manufacturing industry, in which the Marinha Grande cluster

462    Competitiveness at the Local Level still plays a very important role today, and encouraging the setting up of a large number of firms producing plastic articles in the same region. As in Morez, the sequence is infallible and the industrial dynamic very virtuous: entrepreneurial discovery, entry and agglomeration, structural change! As a result of a crisis situation faced by traditional markets in the silk industry that began to decline in the 1960s, a dozen firms broke away from the Lyon factory to explore ways of orchestrating a fundamental transition from silk to technical fabrics (Houssel and Houssel 1992). They were silk manufacturers who had discovered that the Americans were using glass fiber in the aeronautics sector and these firms worked on the integration of these new materials (glass fiber and then composite materials) into their processes. “This marriage between textile and chemistry opens the way to a multitude of products for new outlets in aerospace and transport equipment, sports, protection and decoration items, medical prostheses and geotextiles” (Houssel and Houssel 1992, 190). In the big Lyon chemical complex, firms found the specialists they needed to resolve complex knowledge integration problems relating to the spinning of glass fiber, to resolve warping problems, and to master the adhesion of the resin to the glass fiber. The nose of the Concorde supersonic airliner, the tail fin of the Airbus 330, and the sails of some of the boats participating in the America’s Cup are products symbolizing this successful transition. Here again entrepreneurial discovery, agglomeration, and structural changes characterize this dynamic that leads to the construction of very strong competitive advantages, realized by the creation of over 2,000 jobs between the early 1970s and end of the 1980s. In Finland, a group of companies in the pulp and paper industry were exploring the potentials of some new applied science and technologies to improve the operational efficiency of manufacturing processes in this traditional industry (Nikulainen 2008). A few Finnish entrepreneurs viewed nanotechnology as a promising source of valuable applications, and firms in this industry were taking steps to assess this potentiality. Some firms responded to these opportunities by increasing their R & D spending to explore recent advances in nanotechnology in order to develop applications for their own sector. The emergence of a new R & D collaboration network—involving incumbents, research institutions, specialized suppliers, and universities—was a critical step for the assessment of the usefulness and value of developing nanotechnology applications for pulp and paper processes. Once again we see an entrepreneurial discovery process at work that assembles different actors and will lead to the development of a new activity—at the crossroads between a new technology and a traditional sector—and structural changes (modernization and diversification). It is clear that the entrepreneurial discovery, which lies at the origin of each of the historical dynamics presented, does not only amount to routinized innovation—although it increases its probability. It does not just amount to a basic research phase either, as it is essentially oriented toward the market and applications. It is the demonstration that something is possible—for example, moving from the manufacture of nails to glasses; developing from traditional silk manufacture to a production of technical fabrics; integrating nanotechnologies into the wood pulp production process. Entrepreneurial

Smart Specialization and European Regional Development Policy    463 discovery is the essential phase, the decisive link that allows the system to reorient and renew itself. The recent work of Hausmann and Rodrik (2003) has brought the concept of “discovery” to a wide audience, but to the best of our knowledge, the earliest economic analysis of the concept of “discovery” as distinct from innovation is provided by Hirshleifer (1971). Hirshleifer developed a formal expression for what he termed discovery information as a compound event A consisting of the joint happenings: “state a is true (something is possible)” and “this fact is successfully exploited (what is possible is created).” The first event has a probability ∏a, while the second event has a probability ∏A with ∏a > ∏A. The discovery process provides information about ∏a: something is possible that will happen with a probability ∏A. The discovery A may be about the potential of a general-purpose technology application to transform processes in a traditional sector; or it may be about the possibility of a diversification path based on the exploitation of potential economies of scope and internal pullovers; or it may be about the possibility of a transition path from a low-productivity area to a higher one (Foray 2015). An entrepreneurial discovery is a new area of structural change that opens up, into which a whole segment of an industry can move to explore and generate numerous innovations.

Entrepreneurial Knowledge and Economic Knowledge The various historical cases mentioned above place the notion of entrepreneurial knowledge at the center of the process. Entrepreneurial knowledge—composed of vision and integration between different bodies of knowledge—plays an essential role in the discovery of a new domain; it is the driver of the discovery process. Entrepreneurial knowledge involves much more than knowledge about science and techniques. Rather, it combines and relates such knowledge about science, technology, and engineering with knowledge of market growth potential, potential competitors, and the whole set of inputs and services required for launching a new activity. From the policy point of view that will be introduced later in this section, entrepreneurial knowledge is thus a precious input to generate relevant information during the priority-setting process. It would be a mistake to think that the entrepreneurial discovery process generates only technological knowledge—what works from a technological point of view. No! The discovery focuses especially on economic knowledge—the knowledge of what works (and does not work) economically, as elaborated by Hayek, and central to the general theory of economic dynamism developed by Phelps (2013). The entrepreneurial discovery process is basically economic experimentation with new ideas, which, of course, will to a great extent emanate from scientific and technological inventions. A case from Portugal will help to illustrate the process of structuration of entrepreneurial knowledge and the subsequent developments (entrepreneurial discovery,

464    Competitiveness at the Local Level economic experimentation, and smart specialization). This case is that of the footwear industry, which has undergone profound renewal in a context of frantic global competition. The entrepreneurial knowledge enabling the development of new forms of flexible automation in the footwear industry in Portugal is based on integration of engineering knowledge from the University of Porto (INESC); skills of companies specialized in industrial machinery, tools, and software; and the entrepreneurial vision of a few footwear-manufacturing firms that understand very well the urgent need for revival via innovation. The integration of this knowledge facilitates the discovery and exploration of the potential of the automation associated with advanced cutting tools to increase the flexibility and quality of production. Economic experimentation with these technological developments determines a new business model. The latter is based on an increase in the variety of models and the capacity to rapidly respond to small orders. This development has led the footwear industry to bypass global competition and become the second most important European producer in terms of exportation and added value.

The Locus of Entrepreneurial Discovery The processes of entrepreneurial discovery and exploration of new domains of potential innovations usually require the integration of divided and dispersed knowledge. This is why the organizational forms most appropriate for entrepreneurial discovery are the network, association, and partnership, forms allowing the integration of knowledge originating from firms, research laboratories, specialized suppliers, and clients. The research laboratory-backed start-up can also fall into this category, of course. We also observe the presence of more horizontal associations, allowing, for example, the collaboration of small firms that share certain infrastructures and services for collective exploration of a new domain. However, the large integrated company is also a possible locus since it is by definition capable of assembling very diversified knowledge and carrying out risky discovery projects by financing its projects with its own resources. In her recent work, S. Berger (2013) gives numerous examples of German companies that create new industries through an internal entrepreneurial discovery process:  “What (we) saw in company after company was the repurposing of key technologies to develop wholly new products and services. . . . New businesses are being created, not usually through start-ups—in contrast to the American model—but through the transformation of old capabilities and their reapplication and repurposing for new ends” (Berger 2013, 134–37). Berger’s book is brimming with examples of companies, moving from autos to solar modules, from semiconductors to solar cells, from machines to make spark plugs to machines that make medical devices like artificial knees (Berger 2013, 137). These are very illuminating cases of entrepreneurial knowledge structuring (often thanks to relations between the large company and one of its clients that poses a very

Smart Specialization and European Regional Development Policy    465 specific industrial problem), exploration of the new domain (for example, the application of core wet chemistry technologies to solar cell equipment), and economic knowledge production (via the implementation of new equipment at the client company) (see Berger 2013, 134). The organizational characteristics of the large integrated company enable all this to be accomplished. Therefore, numerous organizational forms are possible for integrating divided and dispersed knowledge and managing the risks of entrepreneurial discovery projects, from the research laboratory-backed start-up to the large integrated firm, and all sorts of forms of networks in between.

Spillovers, Firm Entry, and Structural Changes Discoveries are characterized by a strong learning dimension. The social value of the discovery is that it informs the whole system that a particular domain of R & D and innovation is likely to create new opportunities for the regional economy. This is not the standard model, whereby an innovator excludes others from the use of the innovation in order to appropriate the largest fraction of the benefits. Discoveries and subsequent emerging activities have the potential to provide learning spillovers to other agents in the regional economy. Entrepreneurial discovery precedes the stage of innovation and therefore the incentive structures supporting the former will be different from those supporting the latter. Thus, as Rodrik (2004) argues, the reward for entrepreneurial discoveries (if it is needed, i.e., in case of informational externality problems) has to be structured in such a way that it will maximize these spillovers. While entrepreneurial discovery signifies the opening up of exploitation opportunities, entry constitutes the confirmation that others see this discovery as meaningful. If an initial experiment and discovery are deemed to be successful and the information about this is publicly diffused, then a range of other agents or actors are induced to shift investments away from older domains in which they were operating with less growth potential to the new one with the higher expected possibilities. Hirshleifer (1971) argues that this public information about the discovery (about ∏a) is socially valuable in redirecting productive decisions. Entry is a key ingredient of smart specialization, and the discovery of a potential domain in which a region could become a leader should very quickly result in multiple entrants to the new activity (Coffano and Foray 2014). The potential success of discoveries and new activities that aim at exploring and opening up a new area of opportunities will ultimately translate into some kind of structural changes within the economy, and the outcome of the process is thus much more than a “simple” technological innovation, but rather a structural evolution of the whole regional economy. This facilitates the onset of the clustering or networking phase of a smart specialization process in which agglomeration, clustering, or network externalities in related fields and activities may be realized.

466    Competitiveness at the Local Level Different logics of structural transformations can be identified (Foray 2015): • Transition is characterized by a new domain emerging from an existing industrial commons such as a collection of R & D, engineering, and manufacturing capabilities that sustain innovation. The case of silk/textile firms in Lyon exemplifies such a transition pattern from traditional technologies for old declining markets to new technologies allowing these firms to enter new markets. • Modernization is manifest when the development of specific applications of a general-purpose technology produces a significant impact on the efficiency and quality of an existing (often traditional) sector. A good case in point is the example mentioned above of the development of nanotechnology applications to improve processes and products in the pulp and paper industry. There are many other examples, such as the development of ICT applications in tourism and the exploration of biotech potentials in the food industry. In all these instances, the intersection between the development of applications of a general-purpose technology and a mature sector defines a space of opportunities in which entrepreneurs’ experiments and discoveries can be expected to produce socially useful knowledge. • Diversification, in a narrow sense, is a third pattern. In such cases the discovery concerns potential synergies (economies of scope) that are likely to materialize between an existing activity and a new one. Such synergies make the move toward a new growing market attractive and profitable. Our example of the molding company in Marinha Grande is a good case in point. • Radical foundation is a fourth pattern. In this case, a new domain is founded with no direct link with existing structures. It is difficult to identify an ex ante role for public policy in the case of radical structural and technological changes, as these are by definition almost impossible to predict and are the largely the arena of multinational corporations or of specialist private investors. However, we see from these three first patterns that the essence of entrepreneurial discovery and the development of subsequent new activities typically involves exploring the adjacent possible, to borrow a scientific analogy. The adjacent possible captures both the present limits of, and also the potential for, innovation and transformation of the existing structures. We can see from the cases above that, in general, entrepreneurial discoveries relate to existing structures and local knowledge, and all of these cases involve the generation and exploitation of related variety opportunities (Frenken, Van Oort, and Verburg 2007) whereby “Regions diversify by branching into industries that are related to their current industries” (Neffke, Henning, and Boschma 2011, 261; Boschma and Iammarino 2009; Boschma and Frenken, 2011; Boschma, Minondo, and Navarro 2012). Modernization, diversification, and transition are forms of evolution whose point of departure is existing productive capabilities, which are determined by local technological and productive contexts and stimulated by the integration of new knowledge. All the cases described above exemplify the processes of transformation that link the existing productive structures to new domains of potential competitive advantages.

Smart Specialization and European Regional Development Policy    467

Why Entrepreneurial Discovery Is Important in the Design of Regional Innovation and Development Strategies Why is the principle of “entrepreneurial discovery,” coming from Hausmann and Rodrik (2003), so important for regional innovation and development strategies? Indeed entrepreneurial discovery matters twice, and the importance of entrepreneurial discovery lies in fact in the association of the two words: entrepreneurial and discovery.

“Discovery” We are talking of entrepreneurial discovery, not entrepreneurial innovation, and the distinction between “innovation” and “discovery” is central. The notion of discovery emphasizes the importance of opening new domains of opportunities, exploring new fields, or generating new options. That is the profound sense of “a discovery” as compared with an innovation, in that a discovery does represent the critical starter of a dynamics of structural change. In many cases it needs to be supported and facilitated, and engendering and enhancing this discovery process provides a possible role for public policy in the smart specialization arena. However, this role is quite distinct and rather more nuanced than has been previously understood in traditional industrial policy approaches, and there are several aspects to this. The centrality of the notion of discovery leads immediately to the understanding of what is the appropriate level of aggregation to set smart specialization priorities. The level at which those priorities (new domains, new fields) are identified, assessed, and supported is neither the sectorial level nor the individual/firm level. The relevant level is that of “midgrained” granularity. At this level: • New activities/projects involve groups of firms and other (research) partners. • The aim is to explore a new domain of (technological and market) opportunities. • There is potentially a certain weight and a high significance in relation to the regional economy (in terms of the kind of structural changes it is likely to generate). An example is the companies exploring the potentials of nanotech to improve the operational efficiency of the pulp and paper industry (Finland). In such a case, the priority is not the pulp and paper sector as a whole, but rather the activity involving the development of nanotech applications for the pulp and paper industry. What governments would support in this and the other cases is neither whole sectors nor single firms but the growth of new activities. The notion of a new activity is somewhat fuzzy. Of course

468    Competitiveness at the Local Level economic activities take place at firm level, but the essence of smart specialization—as well as of any kind of new industrial policy—is not to favor one particular firm but to support the development of collective action and experience aimed at exploring, experimenting with, and discovering new opportunities.

“Entrepreneurial” Second, the principle of entrepreneurial discovery emphasizes a new policy’s logic in which discovering what has to be done is fully part of the policy process. Indeed, prioritizing certain technologies or domains always entails a risk because this implies predicting the future development of technologies and markets. Horizontal policies might be difficult to achieve, but the likelihood of being wrong is minimized, that is, the identification of what to do is not so difficult: everybody knows about the direct and indirect framework conditions that foster innovation (see, for instance, Aghion 2006). In contrast, the identification of desirable areas of intervention in a more vertical fashion—what technology, what subsystems—is extremely difficult and entails great risk. Discovering the right domains for future specialization in the knowledge economy is no trivial matter, especially when we abandon the representation of the omniscient central planner who knows beforehand what should be done: the government does not have innate wisdom or the ex ante knowledge about future priorities. We must guard against the intellectual logic imposed by the principal-agent model, according to which the principal (the government) knows from the start which specialties should be developed and therefore confines itself to setting up the incentives for private industry to carry out the plan! (Rodrik 2013). “What if, as I and many others assume, there are no principals … with the robust and panoramic knowledge needed for this directive role?” (Sabel 2004, 3). So the discovery process—discovering which domains of R & D and innovative activities a region should move into to construct its future in the knowledge economy—is an issue in its own right. In that case, the discovery and collective experimentation process forms an integral part of political action and must be carried out within the framework of strategic interactions between the government and the private sector. This is the essence of entrepreneurial discovery.

Entrepreneurial Discovery and the Policy Space There has been a long history of policies setting priorities and objectives that were very much top-down, centralized, and bureaucratic but they generated a lot of inefficiencies. Entrepreneurial discovery, however, is a unique concept in that it reconciles the idea that policies take things in hand again by shaping the regional system through priority-setting and the idea that market processes are central in producing information

Smart Specialization and European Regional Development Policy    469 about the best domains for future priorities. Successful smart specialization dynamics are rooted in an entrepreneurial discovery process, and in many cases government intervention is needed to address the problems of underinvestment in entrepreneurial discovery or insufficient capabilities to undertake entrepreneurial discoveries. Putting it very candidly, the emphasis on entrepreneurial discovery as the main process for generating information to identify priorities means that the policy is not about telling people what to do, but rather about helping stakeholders to discover what to do and then implementing the necessary sequence of policies according to what has been discovered. It should be clear, however, that the emphasis on entrepreneurial discovery as a decentralized and bottom-up process of producing information about potential priorities should not result in narrowing the scope of policy intervention. Emphasizing the role of entrepreneurial discovery is for us not a plea in favor of a laissez-faire policy, and the constraints we have placed on the process should not result in some kind of shrinkage of policy scope to exclude all governmental actions as being too top-down! By the way, the familiar top-down/bottom-up dichotomy is itself not very helpful in capturing the complexity of the policy process, and policy programs fostering smart specialization need to be more sophisticated than thinking within the confines of this dichotomy will allow. The novelty of such a policy is that while addressing the issue of supporting the development of new specialties and activities through preferential interventions, it will try to promote and support the decentralized decisions of entrepreneurs concerning R & D, innovation and structural changes. In other words, this policy is an attempt to reconcile two logics of political action that are usually considered as being in potential conflict. The first logic involves encouraging regional governments to develop strategic actions through the setting of priorities—not horizontal priorities such as improving human capital, developing good universities, or building an effective intellectual property rights system—but vertical ones regarding particular fields and technologies as well as particular sets or networks of actors. The second logic is less controversial; it involves decentralized entrepreneurial initiatives and competitive entries—in other words the set of factors now recognized as the true engine for innovation and economic growth (Baumol 2002; Phelps 2013). The only way to reconcile these two logics is for policymakers seeking for vertical priorities to rely on an entrepreneurial discovery process. In other words the search for and identification of priorities will be carried out from the grass roots, from the bottom up, and not just from the top. These ideas closely follow the logic of the development policy arguments outlined by Rodrik (2007).

Implementing the Smart Specialization Approach within EU Cohesion Policy In suggesting these policy design principles, particularly that of entrepreneurial discovery, the European Commission has opted to set up a process in each region that will

470    Competitiveness at the Local Level enable it to generate new evolution dynamics by opening up new domains of opportunity, without any danger of uniformizing the system or petrifying existing structures. On the contrary, what must be put in place is a decentralized dynamic process that should ensure the continuous transformation of productive structures via research and innovation, a transformation that goes far beyond the high-tech domain and concerns the entire regional economy. In order to make practical headway in this regard from the point of view of the policy process, translating the smart specialization logic into one that is workable in a geographical context and connects directly with the regional agenda involves reinterpreting the spatial concepts of a “domain,” “relevant size,” and “connectedness” (Foray, David, and Hall 2009), as the explicitly spatial concepts of “embeddedness,” “relatedness,” and “connectivity” (McCann and Ortega-Argilés 2014a). Once the discussion is translated into these economic geography terms, it becomes very apparent how powerful and pertinent the whole smart specialization agenda is to the case of European regions. The basic argument is that many aspects of economic policy should not only aim to foster local entrepreneurship and innovation but to do so in particular technological trajectories, namely those activities that have the best chance of galvanizing the existing industrial fabric. Information and data on these dimensions are readily available at the regional level for many EU regions, and the availability of such data is also increasing rapidly in many other regions. Moreover, a range of new analytical inputs designed to support policymaking are also becoming available (McCann and Ortega-Argilés 2014b). In this rich context, regions are better prepared to undertake these types of evidence-based policies than at any previous juncture. There are obviously always unforeseen events and unforeseen technological breakthroughs in any innovative field, and analysts are largely unable to predict these. Moreover, public policymakers have no specific advantages over private-sector decision-makers in identifying where technological breakthroughs may arise, and in many cases can be argued to have less specific knowledge. On the other hand, public policymakers do often have a broader range of information and knowledge inputs than other actors, which helps to provide a broader framework for discussion, while private-sector decision-makers tend to have more specific knowledge relating to particular sectors or technologies. Therefore, finding ways to combine private knowledge with public knowledge so as to better utilize private-sector knowledge in a public policy-prioritization framework is likely to help ensure that policymaking is placed on a stronger footing. In order to do this, a forum needs to be established in which private-sector actors can play a more central role in guiding policy interventions in a manner that helps to build trust between sectors, institutions, and different arenas of governance. Such forums are often easier to establish and develop at a local or regional levels, precisely because so many aspects of innovation are inherently local (Hughes 2012; Moretti 2012), and also fostering widespread and ongoing engagement in such forums on the part of small and medium-sized actors as well as large actors and institutions is also much easier at the local and regional levels than at the national level. Social capital and institutional capabilities are in part built by engagement, interaction, and experience; they are also built

Smart Specialization and European Regional Development Policy    471 by incentive-alignment and agreements between diverse parties regarding objectives, partnership roles, and coordination arrangements. The regional case of Extremadura outlined below is a good example of this approach in action. The smart specialization approach explicitly recognizes that all of these issues regarding objectives, goals, incentives, institutional alignment, and coordination are central to establishing a “knowledge ecology” context fit for purpose. This is because developing policy settings that are aimed at fostering local entrepreneurship and innovation while also being carefully tailored to the local context requires a great deal of commitment by various different actors. The type of entrepreneurial and innovation agenda aimed at by smart specialization involves a great deal of self-reflection, data-gathering, and the development of a policy prioritization logic based on clear goals and objectives, and these goals and objectives are to be clearly derived from the existing economic context. For many regions involved in Cohesion Policy activities, in previous years the specific goals and objectives of the policy actions or interventions were often rather vaguely defined, or alternatively there were too many goals. Either case precludes proper monitoring and evaluation, and the need for indicators to help drive the smart specialization logic became clear at an early stage (Foray 2015). Similarly, many regions mimicked other regions or simply borrowed “off the shelf ” prescriptions from other cases, without any serious consideration or recourse to the local context. In many cases, such policy approaches had little chance of being successful in the long run because the technological and skills requirements in these borrowed models were unlikely to be already heavily embedded in the local economy. Moreover, even where such policies appeared in hindsight to have been successful, if local policies had not been originally based on sound data and evidence-gathering allied with ongoing monitoring and evaluation exercises, then such outcomes were often likely to have been largely a matter of luck. This is because the chance of policy successes increases and the risks of policy failures fall when policies are explicitly designed from the outset with clear intended objectives and agreed priorities, and based on the best available evidence. The regional cases outlined below of Lower Austria, Flanders, Navarra, and Limburg are all very good examples of where regions have explicitly designed policies on the basis of detailed data analyses. They demonstrate that part of the smart specialization agenda is to develop a culture in which objectives, priority-settings, and evidence are all aligned. Many of the elements inherent in the smart specialization concept were already discussed in the literature, but in rather fragmented manner and widely scattered across various different research literatures (OECD 2013a). The result of this was a certain amount of duplication and a lack of any overall coherent narrative. The smart specialization concept gave an overall structure to these discussions and one that also provided a workable approach to the policy prioritization challenge (Fraunhofer ISI 2013). Both the timeliness and institutional origins of the emergence of the smart specialization discussions were very important in that they were initially provoked by research and expert advisory work being undertaken in the European Commission Directorate responsible for research and innovation rather than anything specifically related to geography and regions. However, the ideas that emerged from these discussions became increasingly

472    Competitiveness at the Local Level relevant for regional policy discussions, at a time when EU Cohesion Policy needed a workable policy-prioritization methodology that was applicable to innovation-related activities but also appropriate for the place-based reforms of EU Cohesion Policy. Thus, the issues raised, the possibilities outlined, and the requisite emphases highlighted by the smart specialization approach, all became increasingly understood as being very relevant pertinent for local and regional policymaking. While smart specialization is just one component of the reforms to EU Cohesion Policy, albeit a very important component, the way in which the smart specialization agenda has been driven in Europe is also consistent with the overall approach to the reforms. Although the smart specialization concept originally focused on new technologies, the smart specialization approach can be easily applied to addressing environmental issues and skills-related issues as we well as more orthodox technology-related issues. Thus, the approach is well framed for helping Cohesion Policy to respond to each of the smart, sustainable, and inclusive growth challenges faced outlined by the Europe 2020, the growth strategy of the EU. The new EU Cohesion Policy regulations require that regions undertake detailed outward-looking evaluations regarding their economic and institutional structures and systems, their strengths and weaknesses, and their opportunities and threats, all of which must be based on the most comprehensive and detailed evidence available. A system of outcome indicators—or results indicators in EU parlance—that is to be used for monitoring and tracking the progress of the policy must be established in order to guarantee long-term stakeholder engagement. As Rodrik (2004) points out, the use of outcome indicators is not because the outcomes are known in advance, but precisely because they are not. These results or outcome indicators are required to be constructed according to very clear principles (Barca and McCann 2011), and they are required because in an environment of uncertainty, identifying what works in each setting requires a certain amount of experimentation and self-discovery (Hausmann and Rodrik 2003). All of these various features are entirely consistent with the smart specialization logic. The promotion of partnership principles is also an important aspect of the Cohesion Policy reforms, whereby the different roles played by different stakeholders in the policy design, policy delivery, and policy evaluation aspects of the policy cycle are clarified in a manner that is intended to ensure the maximum engagement of diverse stakeholders at various levels. The coordination and alignment of policy systems is also an important feature of a smart specialization approach, and this is also a central feature of all good multilevel governance systems. The smart specialization agenda therefore also forces policymakers to consider not only the technological, innovation, and entrepreneurial features of their regions but also the institutional and governance coordination context, and to identify what needs to be adjusted or adapted in order to improve the policy settings. In order to facilitate the move toward a smart specialization approach to policymaking the European Commission has set up a facility or forum known as the S3 Platform2 that allows different EU regions to receive expert advice and analysis and

2 

www.s3platform.jrc.ec.europa.eu.

Smart Specialization and European Regional Development Policy    473 also to undergo peer review processes in partnership with other EU regions. When this was first established in 2010 there were only a dozen or so regions participating in this scheme. There are now over 150 EU regions and 12 EU countries participating in these programs, and a range of official publications and guidance material (Foray et al., 2012; Foray and Goenaga 2013; Foray and Rainoldi 2013) are now available dealing with all aspects of the new smart specialization policy agenda, all of which can be downloaded from the S3 Platform website. This learning-by-doing philosophy is also a central part of the place-based logic to development policy, which posits that institutional engagement and interactions between actors are essential in order to foster the trust and mutual awareness necessary to develop locally tailored policies. Smart specialization emphasizes the opening up and sharing of knowledge, and these peer review processes greatly help policymakers to achieve this. In order to facilitate transparency and mutual learning the activities of the different regions are also publicized via the RIS3 website along with large quantities of guidance material designed to aid policymakers in their policy prioritization discussions and negotiations. Thus, the engagement, sharing, and dissemination activities that are so important in smart specialization logic are encouraged right from the start as a form of learning-by-doing on the part of policymakers.

Examples of the Implementation of Smart Specialization at the Regional Level There are many sources of evidence and ideas regarding the different types of policy initiatives that might form part of a smart specialization agenda. The RIS3 Platform is repository of a large body of both guidance material and also case study experiences from which policymakers are able to draw. In this spirit, the following section will discuss some real case study examples from Europe that have followed the logic and principles of smart specialization in the elaboration of their regional innovation and smart specialization strategies (RIS3).

Promoting Stakeholder Involvement in the Elaboration of Regional Policy The region of Extremadura in Spain has been following the place-based logic in which enhancing social engagement and participation have been critical factors for the elaboration and development of the new regional economic strategy. The region used public forums, an interactive online platform, and a continuous information system to

474    Competitiveness at the Local Level disseminate the main results of the ex-ante regional profiling exercise and to start defining the prioritization process of strategic policy actions under the framework of the new regional innovation and development strategy. “Forum One”3 has acted as a focal point for the sharing of the new regional innovation strategy with the local community. The open forum event was attended by more than 450 regional agents along with the most of the relevant regional and local political leaders and public officers. The one-day forum started with the president and vice president of the region giving a public presentation based on the final results of a detailed SWOT (strengths and weaknesses, opportunities and threats) analysis including the main regional strengths, weaknesses, opportunities, and threats as well as needs for the economic development of the region. The presentation was followed by a discussion panel, with experts coming from academia, consultancy, and government (national, supranational, and regional) backgrounds, on the potentialities of the new regional strategy for the future development of the region. Finally, the open forum was divided into four parallel discussion panels where regional stakeholders could debate and comment on the results of the analysis with the regional government. The forum offered interactive opportunities for policy ideas and suggestions. Stakeholders interested in continuing being informed and involved in the process of development and implementation of the strategy will be contacted periodically and monitoring and evaluation reports will be available in the future. This case is a clear example of a new way of doing policy, providing publicity, and increasing trust in the government by fostering transparency and accountability. Moreover, it is also a good example to explain the importance of mobilizing public and private agents and platforms in order to create learning spillovers to other agents in the regional economy.

Assessing the Impacts of the Regional Innovation Strategies Many regions in Europe are already fully aware that only the existence and regular application of an impact assessment system will allow a continuous improvement of the need oriented regional innovation policy with the aim to increase the firms’ and regional competitiveness, to create new high quality jobs and thus to increase the regional welfare. The Lower Austria region has a long experience in the process of monitoring and evaluating its regional policy strategies. Among others, the Lower Austria region has implemented the following methodologies to evaluate and monitor its regional strategy: • The Balanced Scorecard (BSC) methodology is the monitoring process of the regional innovation strategy in Lower Austria. Since 2008, it has been systematically 3 

http://one.gobex.es/.

Smart Specialization and European Regional Development Policy    475 rolled out for all innovation services and the respective intermediaries as service providers. The results of the monitoring process indicate the development of the region in relation to other provinces in Austria as well as to the Austrian average. In combination with the program, BSC shows the contribution of the regional economic and innovation policy to regional development. The data are gather externally but treated internally by the regional government. • State aid scheme: external postevaluation. In this methodology, the target groups of monitoring are companies supported by the state aid scheme, the “Innovation Assistant.” This ex post check evaluates the whole “Innovation Assistant” by aggregating the results of the individual projects. The evaluation consists in sending an ex post questionnaire to all the companies that have participated in the program, 1 or 1.5 years after completion of their funded project. The processing of gathering, analyzing, and evaluating the data is done externally. The results of the analysis allow the regional government to see whether the regional state aid scheme fulfills its targets. Additionally, the insights of the analysis help to identify potential for further improvement and further adaptation of the existing scheme. • Large-scale questionnaire. The aim of the large-scale questionnaire is to help in the monitoring of Lower Austria’s regional strategy. It is addressed to approximately 5,000 companies with a response rate of approximately 10 to 14 percent. The questionnaire monitors future perspectives of the companies, such as strategic key activities and need for innovation support, knowledge, usage. It is used to identify gaps and overlaps. Further issues are the relevance of innovation partners, transparency of offered services, and companies´ economic and innovative performance. With these data the Regional Government of Lower Austria can draw conclusions about the impact of regional innovation policy and the importance of service providers. The Lower Austria region can be considered a pioneer and a best-practice example for many other regions in developing a long-term successful regional development strategy through the implementation of monitoring and evaluation techniques that allow for adaptation and modification of the policy implementation process. In last years, other European regions have also developed further their monitoring system; an example is Navarra in Spain.4

Decentralization of Entrepreneurial Activities, SMEs’ Innovation Initiatives, and Competitive Entries Smart specialization implementation strategies have emphasized the need for a more direct connection with the citizen in the areas of entrepreneurship in order to better 4 

Moderna plan: http://www.modernanavarra.com/el-plan-moderna/indicadores/.

476    Competitiveness at the Local Level match supply and demand. Governments have also shown a growing interest in empowering various types of users with the skills, knowledge, and platforms necessary for them to become more effective players in the labor and innovation markets. Several initiatives have appeared to promote entrepreneurship at the local level, such as the Capital Investment Fund in the Polish region of Malopolska. Malopolska Capital Investment Fund is set up to channel research from laboratory to market, forging the link between local researchers and entrepreneurs. Another example is the Barcelona Activa in Spain. Barcelona Activa is an entrepreneurship center established by the local government to serve as a reference point to entrepreneurs, as well as a hub that boosts entrepreneurship through its activities and resources. In the same line, other regions are implementing courses and seminars to encourage entrepreneurial initiative at earlier stages such as implementing entrepreneurship activities in school or universities, as is the case with the Centre for Amsterdam Schools of Entrepreneurship in the Netherlands. A number of regions have implemented SME support measures with the aim of engaging them in R & D and innovation activities providing innovation vouchers that allow them to access the necessary in-house financial and human capital resources. That is the case in the Bavaria region in Germany, the West Midlands in the UK, and Opolskie in Poland, among many other cases (McCann and Ortega-Argilés 2013a). Meanwhile other programs have focused on the stimulation of use-driven innovation in companies and in public-sector institutions through grant schemes. This is the case of the Danish policy that focuses on the three dimensions needed to spur user-driven innovation in organizations: (1) helping companies to integrate customer experiences and needs in their product process development; (2) facilitating companies’ access to the skills and competencies necessary to assess customer needs; and (3) providing firms with the means to make accurate use of surveys (OECD 2011).

Promoting Cross-Country Cooperation One of the characteristics of the smart specialization regional strategies is centered on avoiding the duplication of similar activities, with the aim of encouraging a region to find its own regional competitive advantage that allows the region to differentiate itself from others. Connected to the avoidance of duplication is the need to develop synergies with other locations that can offer multiplicative effects by providing complementary efforts. Following this reasoning, several regions belonging to different countries in the center of Europe have created a transnational network that facilitates the cooperation in different aspects of the regional innovation and development process. The Top Technology Region / Eindhoven-Leuven-Aachen Triangle (TTR-ELAt), constituted by six subregions belonging to three different countries, the Netherlands, Belgium, and Germany, promotes cross-border cooperation in different science and technology

Smart Specialization and European Regional Development Policy    477 areas. The area in which the TTR-ELAt is located has a long history of cross-border policy efforts, with cooperation projects since the 1970s (with the Euregio Meuse-Rhine) among the most relevant cross-border functional efforts in technology and innovation policy support (OECD 2013b). The TTR-ELAt area has many strengths and opportunities to be a potential nest for discovery and innovation activities. Among the strengths, there is a high innovation performance as a result of it being constituted by a group of regions considered to be “innovation leaders” in Europe; and a strong presence of innovative enterprises, universities, research centers, and highly skilled people that allows direct interactions between actors, along with the presence of the public sector, which allows “triple helix” activities. The regions that constitute the TTR-ELAt share similar aspects, such as sociocultural context and industrial and knowledge specializations that facilitate understanding and create complementarities between actors of similar backgrounds. The drivers that present a stronger relevance in the cross-border cooperation are basically the economies of scale gained from the combinations of regional and local resources in terms of investment, labor accessibility, and accessibility to a wider community of businesses and knowledge actors; the complementarities built on a diversity of assets in terms of research and technology, as well as supply chain linkages; and the macro-regional branding that increases the recognition of the cross-border area and acts as an attraction for international investment and skilled labor. Many of the projects in the area are bilateral actions between two countries, and among the most interesting initiatives are bottom-up programs combining funding sources on the various sides of the border, such as the Holst Centre,5 a joint research infrastructure cofunded by the Dutch and Flemish authorities. Among the policy instruments used in the TTR-ELAt region we can find strategy and policy development instruments such as analytical and profiling exercises (BAK Basel Economic Reports); R & D support instruments such as cross-border private R & D funding programs (GCS Cross Border Cluster Stimulation project, which offers grants for cross-border R & D projects involving SMEs); technology transfer and innovation support instruments such as cross-border innovation advisory services (TeTTRA, which promotes academia-SME linkages and SMEs recruiting in nonurban areas); and human capital policy instruments such as talent attraction with informational points for border commuters. The TTR-ELAt also develops policy initiatives based on the creation of innovation networks developing cross-border science and technology parks and cross-border clusters initiatives.

Conclusions As we have seen, the emergence of these smart specialization ideas coincided with the momentum that was building for a wide-ranging reform of EU Cohesion Policy, the regional and urban development policy of the European Union. The smart specialization agenda originated from something like a technology-lead approach, which tended 5 

http://www.holstcentre.com/.

478    Competitiveness at the Local Level to link easily with largely sectorial narratives. However, over time the smart specialization debate had shifted toward incorporating regional issues and increasingly emphasized the importance of promoting entrepreneurship and innovation in the local and regional context. This shift was both fortunate and timely for the debates regarding the much-needed EU Cohesion Policy reforms, as it threw the spotlight on the role played by entrepreneurship and innovation in fostering regional development. In addition, the convergence of thinking between researchers working on the economics of knowledge and technology along with a wide range of regional economists and economic geographers produced a confluence of ideas and insights that was very broadly based (McCann and Ortega Argilés 2014a; 2014b; Boschma 2014). In earlier periods much of the policy focus had been on the provision of infrastructure, including transport and land infrastructure as well as capital investment for R & D, the majority of the time with a “narrow” vision of the innovation process focused mainly on research and development support with a clear “picking winners syndrome.” Often the research and innovation regional policies were associated with “blind” duplication that avoided potential collaboration opportunities between public-private agents and other local and regional governments. However, over time the academic debates surrounding innovation and regional development increasingly emphasized the roles played by “soft” capital in enhancing local entrepreneurial and innovative activities. These softer forms of capital included activities related to business advice and mentoring, better access to knowledge networks, participation in pilot projects, and engagement with various policy experiments. Capitalizing on the insights associated with these shifts in thinking became central to many of the debates surrounding the reform of EU Cohesion Policy because this offered not only a way to reorient much of the policy on the basis of the best evidence and the latest thinking but also to develop a framework whereby regions and localities explicitly reflect and build on their existing capabilities. The smart specialization agenda has galvanized this process within Europe, and recently there has been wider interest from across the OECD (OECD 2013a) considering how these lessons can be applied in a much broader set of national and regional contexts. Policies aimed at promoting entrepreneurship and innovation are now central to Europe’s regional and urban development policy framework, and smart specialization is the core component of this new realigned agenda.

References Acemoglu, D., and J. A. Robinson. 2012. Why Nations Fail: The Origins of Power, Prosperity and Poverty. London: Profile Books. Aghion, P., 2006, “A Primer on Innovation and Growth,” Bruegel Policy Brief, No. 6, Brussels. Barca, F. 2009. An Agenda for A  Reformed Cohesion Policy:  A  Place-Based Approach to Meeting European Union Challenges and Expectations. Independent Report Prepared at the Request of the European Commissioner for Regional Policy, Danuta Hübner, European Commission, Brussels.

Smart Specialization and European Regional Development Policy    479 Barca, F., and P. McCann. 2011. “Outcome Indicators and Targets: Towards a New System of Monitoring and Evaluation in EU Cohesion Policy.” DGRegio, July. http://ec.europa.eu/ regional_policy/information/evaluations/guidance_en.cfm#1, http://ec.europa.eu/regional_ policy/sources/docgener/evaluation/doc/performance/outcome_indicators_en.pdf. Baumol, W. 2002. The Free-Market Innovation Machine. Princeton, NJ:  Princeton University Press. Berger, S. 2013. Making in America: From Innovation to Market. Cambridge, MA: MIT Press. Boschma, R. A. 2014. “Constructing Regional Advantage and Smart Specialisation: Comparison of Two European Policy Concepts.” Italian Journal of Regional Science 13 (1), 51–68. Boschma, R.A., and K. Frenken. 2011. “Technological Relatedness and Regional Branching.” In H. Bathelt, M. P. Feldman, and D. F. Kogler, eds., Dynamic Geographies of Knowledge Creation and Innovation, London: Taylor and Francis, Routledge. Boschma, R.  A., and S. Iammarino. 2009. “Related Variety, Trade Linkages and Regional Growth.” Economic Geography 85 (3), 289–311. Boschma, R. A., A. Minondo, and M. Navarro. 2012. “Related Variety and Regional Growth in Spain.” Papers in Regional Science 91 (2), 241–56. Coffano, M., and D. Foray. 2014. “The Centrality of Entrepreneurial Discovery in Building and Implementing a Smart Specialisation Strategy.” Italian Journal of Regional Science 13 (1), 33–50. Foray, D. 2015. Smart Specialisation:  Opportunities and Challenges for Regional Innovation Policies. New York: Routledge. Foray, D., P. David, and B. Hall. 2009. “Smart Specialisation:  The Concept.” Knowledge Economists Policy Brief No. 9, June. http://ec.europa.eu/research/era/publication_en.cfm. Foray, D., J. Goddard, X. Goenaga, M. Landabaso, P. McCann, K. Morgan, C. Nauwelaers, R. Ortega-Argilés, and F. Mulatero. 2012. RIS3 Guide. European Commission Directorate General for Regional Policy and Institute for Prospective Technological Studies, Joint Research Centre, Smart Specialisation Platform, Seville. Foray, D., and X. Goenaga. 2013. “The Goals of Smart Specialisation.” S3 Policy Brief Series No 01/2013, European Commission Joint Research Centre IPTS, Seville. Foray, D., and A. Rainoldi. 2013. “Smart Specialisation Programmes and Implementation.” S3 Policy Brief Series No 02/2013, European Commission Joint Research Centre IPTS, Seville. Fraunhofer ISI. 2013. “Smart Specialisation Approaches: A New Policy Paradigm on Its Way from Policy to Practice.” http://www.isi.fraunhofer.de/isi-media/docs/p/de/vortragsfolien/ regionen_cluster/S3_Projekt_final.pdf. Frenken, K., F. G. van Oort, and T. Verburg. 2007. “Related Variety, Unrelated Variety and Regional Economic Growth.” Regional Studies 41 (5), 685–97. Hausmann, R., and D. Rodrik. 2003. “Economic Development as Self-Discovery,” Journal of Development Economics 72 (2), 603–33. Hirshleifer, J. 1971. “The Private and Social Value of Information and the Reward for Inventive Activity.” American Economic Review 61 (4), 561–74. Houssel, A., and J. P. Houssel. 1992. “L’évolution de la fabrique lyonnaise de soieries.” Revue de Géographie de Lyon 67 (3), 187–98. Hughes, A. 2012. “Choosing Races and Placing Bets: UK National Innovation Policy and the Globalisation of Innovation Systems.” In D. Greenaway, ed., The UK in a Global World: How Can the UK Focus on Steps in Global Value Chains That Really Add Value? BIS e-book, CEPR, ESRC, and Department for Business, Innovation and Skills. http://www.nottingham.ac.uk/ gep/documents/reports/dgebookcomplete.pdf.

480    Competitiveness at the Local Level McCann, P. 2015. The Regional and Urban Policy of the European Union: Cohesion, ResultsOreintation and Smart Specialisation. Cheltenham: Edward Elgar. McCann, P., and R. Ortega-Argilés. 2013a. “Modern Regional Innovation Policy.” Cambridge Journal of Regions, Economy and Society 6 (2), 187–216. McCann, P., and R. Ortega-Argilés. 2013b. “Redesigning and Reforming European Regional Policy: The Reasons, the Logic and the Outcomes.” International Regional Science Review 36 (3), 424–45. McCann, P., and R. Ortega-Argilés. 2013c. “Transforming European Regional Policy: Smart Specialisation and a Results-Driven Agenda.” Oxford Review of Economic Policy 29 (2), 405–31. McCann, P., and R. Ortega-Argilés. 2014a. “Smart Specialisation, Regional Growth and Applications to EU Cohesion Policy.” Regional Studies. http://dx.doi.org/10.1080/0034340 4.2013.799769. McCann, P., and R. Ortega-Argilés. 2014b. “Smart Specialisation in European Regions: Issues of Strategy, Institutions and Implementation.” European Journal of Innovation Management 17 (4), 409–27. McCann, P., and R. Ortega-Argilés. 2014c. “The Role of the Smart Specialisation Agenda in a Reformed EU Cohesion Policy.” Italian Journal of Regional Science 13 (1), 15–32. Moretti, E. 2012. The New Geography of Jobs. New York: Houghton Mifflin Harcourt. Neffke, F., M. Henning, and R. Boschma. 2011. “How Do Regions Diversify over Time? Industry Relatedness and the Development of New Growth Paths in Regions.” Economic Geography 87 (3), 237–65. Nikulainen, T. 2008. “Open Innovation and Nanotechnology:  An Opportunity for Traditional Industries.” ETLA Working Paper, Research Institute of the Finnish Economy, Helsinki, April. OECD. 2011. Demand-Side Innovation Policies. Paris: Organisation for Economic Growth and Development Publishing. OECD. 2013a. Innovation Driven-Growth in Regions:  The Role of Smart Specialisation. Organisation for Economic Growth and Development, December. http://www.oecd.org/sti/ inno/smartspecialisation.htm. OECD. 2013b. Regions and Innovation: Collaborating across Borders. Paris: Organisation for Economic Growth and Development Publishing. Ortega-Argilés, R. 2012. “The Transatlantic Productivity Gap: A Survey of the Main Causes.” Journal of Economic Surveys 26 (3), 395–419. Phelps, E. S. 2013. Mass Flourishing. Princeton, NJ: Princeton: Princeton University Press. Rodrik, D. 2004. “Industrial Policy for the Twenty-First Century.” Working paper, Kennedy School of Government, Harvard University. Rodrik, D. 2007. One Economics Many Recipes:  Globalization, Institutions and Economic Growth. Princeton, NJ: Princeton University Press. Rodrik, D. 2013. “Green Industrial Policy.” School of Social Science, Institute for Advanced Studies, Princeton. Rodrik, D. 2014. “When Ideas Trump Interests:  Preferences, Worldviews, and Policy Innovations.” Journal of Economic Perspectives 28 (1), 189–208. Sabel, C. 2004. “Beyond Principal-Agent Governance:  Experimentalist Organizations, Learning and Accountability.” Wetenschappelijke Raad Voor Het Regeringsbeleid, Amsterdam, July. Sopas, L. 2001. “The Portuguese Mould Industry and Plastic Clusters.” First meeting of the Portuguese session of the Society of Plastic Engineers, Marinha Grande.

Index Figures and tables are indicate by "f " and "t" following page numbers. Abbeys and monasteries, 216 Abrantes, Anibal H., 461 Abu Dhabi, 196n6 Academic capitalism, 255 Academic engagement, 255 Academic policy, 225–230. See also Universities Accelerate Long Island (New York), 314, 317–318 Accountability and transparency, 28, 168, 193, 196, 206, 212, 246, 416, 443, 444, 473, 474 Acs, Zoltan J., 7, 55n2, 220, 224, 225, 268 Adair, Franklin, 435 Adjacent possible, 466 Advanced Micro Devices (AMD), 19 Advanced technology. See also Biotechnology; Nanotechnology need to focus on, 333 trade deficit in, 328 Aerospace industry, 73n29 Agglomeration economies, 329, 331, 385, 465 Agrawal, A. K., 256, 257, 261 Ahlin, Lina, 176, 176n26 Ahlstrom, D., 225 Air conditioning, 105 Akron (Ohio), 132 Albany Small Business Development Center, 414, 416–417 Albouy, David, 44 Alexeev, M., 194 Algeria, Arab Spring in, 200, 201 Almeida, P., 223 al-Qaeda, 199n10 Alto Livenza's furniture industry, 114–119 education system and, 114–115 financial system and, 115–116 globally fragmented production and, 115 internationalization drive and, 116

local government policies and, 115 overview, 103–104, 114–115, 114nn1–2 product chain and, 117–119 specialized suppliers and, 119 Altruism, 15, 272, 364 Ambition, 288, 292, 293 Amenities of place, 43–44 "Anatomy of Detroit's Decline" (New York Times), 145 Ancient Greece, education in, 216 Andersson, Åke E., 42 Andersson, Martin, 7, 145, 176, 176n26, 178, 179 Andersson, Thomas, 191 Angel investors and networks, 65n24, 88, 93, 161, 248, 294, 295, 308, 314, 317, 318, 357, 362, 408, 409, 430, 436 Animal Health Corridor (Kansas), 403 Appalachian Regional Commission, 321 Apple, 261 Apsley, Norman, 432 Arab Gulf natural resources, 198–207 Arab Spring and, 198–201, 200t curse of, 192–195 disparate outcomes, 195–198, 198n8 future recommendations for, 207 Oman and, 201–206 overview, 191–192 Arab League of Nations, 198, 198n8 Arab Spring, 7, 191–192, 198–206, 200t Aristotle, 216 Armington, Catherine, 55n2 Arrow, K., 218 Artifact complexity, 60–61 Aspen Institute, 55 Aspen Network of Development Entrepreneurs, 55 Association of University Technology Managers (AUTM), 257

482   Index AT&T, 261 Atkinson, Robert D., 8, 320, 431–432 Atlanta (Georgia), funding of universities in. See University research funding, impact of economic crisis of 2008 on Atlantic Century II report (ITIF), 333 Attachment to place, 36, 45 Audretsch, David B., 6, 13, 14, 27, 31, 55n2, 65, 155, 220, 224, 229, 291, 358n1 Auerswald, Philip E., 6, 54, 59–60, 62n15, 63, 66n24 Augmentation Research Center (ARC), 92 Austin (Texas), 23, 291 Australia competition in, 273n3 natural resources in, 195 Austria, innovation strategy of Lower Austria region, 471, 474–475 Autio, E., 300 Automobile industry, 105, 129 Axel Co-transformation patents, 257 Ayyagari, Meghana, 68f, 69 Bacolod, Marigee, 41 Bae, J., 229 Bahrain, Arab Spring in, 200 "Balance Agriculture with Industry" program (Mississippi), 320–321 Balanced Scorecard methodology to monitor regional innovation, 474–475 Balkans, off-shore furniture production in, 117 Banche di Credito Cooperativo, 115 BAPs. See Business accelerator programs Barcelona Activa (Spain), 476 Barnett, Richard, 435 Basque region, business environment in, 355–372 ancient entrepreneurial values from whaling industry, 357–358 business angel networks, 362 contemporary entrepreneurial social capital, 358–364, 359t, 361t, 362–363f CRECER+ support platform, 370 crisis of entrepreneurial values and, 356 entrepreneurial ecosystem, 360, 361t, 363, 363f foreign direct investment (FDI), 362 government budget to promote, 363

performance of local entrepreneurial activity, 365–367f, 365–369 recessionary period, 355–356 regulatory frameworks, 363n3 SGECR (government private equity firm), 362 social capital, 364, 370 Total Entrepreneurial Activity (TEA) rate, 365–367, 366f, 368f unemployment rate, 364, 364f Bathelt, H., 134 Battelle, 342, 343, 410 Baumol, William J., 154, 155 Bayh-Dole Act of 1980, 221, 226, 251, 258, 264, 389n9, 430 Beck, Thorsten, 68f, 69 Becker, Gary, 37 Beckman, A., 262 Beckman Instruments, 262 Behrens, V., 247 Bekar, C. T., 127 Belfast. See Northern Ireland's economic transformation Belgium cluster policies in, 297 Top Technology Region/ Eindhoven-Leuven-Aachen Triangle (TTR-ELAt), 476–477 Bell Laboratories, 261 Belmont Forum, 241 Benefit gap of cluster strategies, 405 Ben Franklin Partnership program (Pennsylvania), 323 Ben Franklin Technology Collaborative (Pennsylvania), 313 Bercovitz, J., 297 Berger, S., 121, 464 Berkeley (California), 137 Berkeley Software Development Unix (BSD Unix), 261–262, 264 Berlin Wall, 270 Bertrand, Marianne, 168 Berwick, Donald, 74, 76 Bettencourt, L. M., 133 Bhidé, Amar, 177 Bill and Melinda Gates Foundation. See Gates Foundation

Index   483 Billionaire entrepreneurs, 149n5, 151, 152f, 176. See also Entrepreneurs Binks, Martin, 8, 345 BIO (US biotechnology industry association), 410–411 BioCrossroads (Indiana), 408, 409–411 BioEnterprise (Cleveland), 403 BioGenerator (St. Louis), 403 Biopharmaceutical model, 257. See also Biotechnology Bio STL (St. Louis), 402 Biotechnology, 127, 128, 136–137, 223–224, 227, 228, 404–405, 409–411 model, description of, 256, 257–259, 258f, 260, 264 Birdseye, C., 130 Birdzell, L. E. J., 105 Bishop, P., 229 Blake, A. D., 351 Bloch, F., 262 Bloomberg, Michael, 276 Blue Ocean Strategy, 349 Blum, Bernardo S., 41 BMW, 248 Bologna (Italy), 173, 217 Bologna University, 214, 216 Boschma, R., 131 Bosma, Niels, 7, 172, 286 Boston, 5, 151, 332–333. See also Route 128 Boston University, 264 BP, 202 Brain drain, 214, 229 Branscomb, Lewis, 66n24 Braunerhjelm, Pontus, 131, 220, 224 Breast cancer gene (BRCA), 258 Brenner, Sydney, 77 Bresnahan, T., 25 Brewer, Marylin, 223 Breznitz, Dan, 6, 102, 104, 109 Breznitz, Shiri M., 9, 445 Briar-Lawson, Katharine, 9, 414 Britain. See United Kingdom British National Endowment for Science Technology and the Arts (NESTA), 286 Brokers. See Innovation brokers Brown, J., 128 Brownsville, Texas, 322

Brun, Lukas C., 385 Brundle, Tim, 435 Bruton, G. D., 225 Buciuni, Giulio, 6, 102, 109 Buffalo, New York, 322, 332–333 Bulletin of the American Mathematical Society, 239 Bureaucracy. See Regulations Bureau of Labor Statistics, 38 Burrell, G., 128 Burt, Ronald S., 63, 64 Bush, Vannevar, 257 Business accelerator programs (BAPs) for high-growth firms, 291, 295–296 location-based, 295–296 virtual, 295 Business climate entrepreneurship and, 66–67 people climate and, 45–46 Business incubator programs. See Business accelerator programs Business model innovation, 105 Business schools, 8, 345–354 developing locally useful knowledge within, 348–349 developing students' capabilities to deploy locally useful knowledge, 350–351 economic impact of, 347–348 historical role of, 345–347 knowledge development within, 348–349 opportunity to engage locally, 347–348 opportunity to engage with local economy, 351–352 overview, 345–347 student development in, 350–351 California defense contracts in, 73n28 entrepreneurs in, 151, 174 Gold Rush, 135 Callon, M., 133 Caltech, 262 Cambodia, 250 Cambridge (Massachusetts), 43, 136–137, 278 Cambridge (UK), 237 Cambridge University, 217, 278, 279

484   Index Canada business schools' role in, 352 competition in, 273n3 natural resources in, 195 underdevelopment of high-tech industry in, 298 "Can Detroit Find the Road Forward?" (Glaeser), 174–175 Capital, access to, 25, 116, 135, 313–314 Capital in the Twenty-First Century (Piketty), 270, 271n2, 272 Capitalism evolution and history of, 34, 171n16, 268–269 laws of, 270–271, 271n2 philanthropy and, 270–274 Capitalism, Socialism and Democracy (Schumpeter), 268, 269, 270, 273 Carlaw, K., 127 Carnegie Mellon, 251 Carrier branches, 127 Caseaux, Pierre Hyacinte, 461 Casson, Mark C., 148n4 Castilla, Emilio J., 65 Caught in the Middle (Longworth), 402 Caves, Richard E., 42 Census of Marine Life, 249 Center for Innovation (North Dakota), 314–315 Central Indiana Corporate Partnership (CICP), 406–413 BioCrossroads initiative and, 408, 409–411, 412 Conexus Indiana initiative and, 408, 411–412 overview, 406–408 Centre for Amsterdam Schools of Entrepreneurship, 476 Centro Studi FederLegnoArredo, 116 Champalov, I., 246 Charitable foundations endowments of, 280–282, 281–282t grand challenges and, 240t, 241, 242t, 247 philanthropy and, 272 Chatterji, Aaron, 75 Cheng, S., 229 Chile, Startup Chile program in, 55 China

entrepreneurship in, 74 Ministry of Industry and Information Technology certification, 111 UPS production in, 108. See also Dongguan's UPS industry Chinese Academy of Science, 109 Christensen, Clayton, 346, 353 CICP. See Central Indiana Corporate Partnership Cities. See also specific cities by name agglomeration economies and, 133 central role as production hub, 41–43. See also Talent college graduates moving to, 176 consumer cities, attractiveness of, 180 creativity and, 42, 44, 47 diversity and, 27, 65 economic inequality correlated to metro size, 46–47 entrepreneurs and. See Entrepreneurship knowledge spillovers and, 38, 55n2 quality of place, 43–45, 46. See also Place regulations. See Regulations tolerance of diversity and, 44–45 zoning. See Zoning regulations Clark, Terry N., 44 Clayton, Paige A., 9, 445 Clean Energy Alliance Partnership, 390 Climate change, 265 Cluster initiatives, 94 Clusters, 84–101, 401–406 benefit gap, 405, 410 Central Indiana Corporate Partnership (CICP), 406–413 characteristics of, 86–87 cluster commons, 6, 84–101 construction of a commons, 91–92 credible claims challenge, 404–405 dynamic cluster, 89, 89f entrepreneurship and, 173, 296–297 free riders and, 96–97 grand challenges and, 247 incentives for construction, 94 industrial cluster genesis, 131–132 innovation-addition problem, 403 innovation and, 85–87 job metrics and, 404, 410

Index   485 knowledge spillovers and, 84–88, 90, 97 manufacturing districts and, 103, 110 as meeting place, 87–88, 87f Midwestern states, 401–403 model of seven cluster gaps, 88–91 new industries and, 135 opportunities presented by, 401–403 organized construction of, 94–96, 96f overview, 84–85, 401 policy role, 97–98 self-help strategies of, 402 Silicon Valley, 25, 92–93 spatial inequality and, 46–48 spatial structure and, 25 static cluster with internal gaps, 91, 91f talent and, 42–43 universities and, 226–227 Cluster theory, 35–36 Coase, Ronald, 63 Coasean theory of the firm, 59, 63–64 Codified knowledge, 219, 223 Cohen, W. M., 224, 259 Cohen-Boyer rDNA technique, 127, 257 Cold War, 23 Collaboration among states, 331 clusters and, 88, 89, 91–95, 97 Dongguan and Alto Livenza local institutions, 113, 120 innovation and, 136, 307, 312, 317 local officials with industry leaders, 104 microenterprise development and, 414, 426 natural resources management, 194, 195n3, 202, 205 in Northern Ireland's economic development, 436, 438, 442–444 R & D initiatives and, 238–239, 241, 243–248, 251, 462 regional innovation clusters and, 402, 407 of small firms, 464 universities with industries, 223, 225, 226, 262, 396, 409 Collaborative Economics, 306–307 College of Philadelphia, 275 Colombia, entrepreneurship in, 74 Columbia University, 274 CommerceNet, 93

Commodity prices of natural resources, 196, 196n7 Commons, 84–101. See also Clusters; Tragedy of the commons Communism, 269, 273 Community venture capital funds, 314 Competition/competitiveness cities. See Cities clusters, effect of. See Clusters creativity as essential for competitive advantage, 23–24, 34, 377 enablers and barriers to regional competitiveness, 4–5 entrepreneurs and. See Entrepreneurship globalization's effect. See Globalization linkage of competition and competitiveness, 1 look of success, 5 monopoly power. See Monopoly national economic competitiveness. See National economic competitiveness new industries, role of. See New industries R & D role in. See Research and development (R & D) regional. See Regional development state level. See State-level science and technology policies universities' role in. See Universities university funding, economy's effect on. See University research funding, impact of economic crisis of 2008 on university research parks as spur to. See University research parks zero-sum competition, 331 The Competitive Advantage of Nations (Porter), 25 Complete production chain, 109 Computer science in research universities, 261–262 Conexus Indiana, 408, 411–412 Confederation of British Industry, 347 Confederation of Swedish Enterprise (CSE), 168–170, 169n14, 170f CONNECT program Northern Ireland. See Northern Ireland's economic transformation San Diego, 314, 429–430, 432–434

486   Index Conrad, R., 194 Consultants, 295, 308, 313, 398, 412 Copublication, 239 Cotton garment industry, 129 Cotton gin invention, 135 Council for Regional Engagement and Economic Development (CREED), 22 Courtial, J., 133 Cowhey, P., 104 Cox, S., 347 Creative-class workers, 23, 24, 27, 30, 36, 37f, 38–40, 47 Creative destruction, 103n1, 125, 272, 346 Creativity. See also Creative-class workers diversity and, 134 entrepreneurship and, 67, 126 essential for competitive advantage, 23–24, 34, 377 new industries and, 44, 47, 126, 131 occupational, 38–40 open-innovation techniques and platforms and, 248 place and, 23–24 social conditions conducive to, 67 universities and research institutes and, 243, 351 Credible claims challenge, 404–405 Credit pools, 110n6 CREED (Council for Regional Engagement and Economic Development), 22 Crowd sourcing, 248 CSE. See Confederation of Swedish Enterprise Cuellar Mejia, Maricol, 166 Culture capitalism and, 272 entrepreneurship and, 147, 170–174, 171n16, 296, 297, 317, 360, 363 grand challenges and, 245 innovation economy and, 307, 311, 314, 369 natural resources and, 194 in Oman, 202n11, 204 philanthropy as part of US culture, 272 quality of place and, 43–45 Curse of Arab Gulf natural resources, 192–195. See also Arab Gulf natural resources Darby, Michael R., 223 Darwin, Charles, 1

Data General, 332 Deal-flow of entrepreneurship, 68–74 De alio firms, 128 Deeside (UK), 146 Defense Advanced Research Projects Agency (DARPA), 241, 250–251 Degroof, J. J., 297 Delgado, M., 25 Demand-pull factors, 132 Demirguc-Kunt, Asli, 68f, 69 Democracy history of, 268–269 philanthropy and, 270–273 Deng Xiaoping, 74 Denmark social insurance policy and entrepreneurship in, 161 user-drive innovation in, 476 De novo firms, 128 De Otazu, A., 359 Department of Energy's Clean Energy Alliance Partnership, 390 Department of Housing and Urban Development (US), 321 De Silva, D. G., 221 de Solla Price, D. J., 238 Destructive entrepreneurship, 54–55 Detroit decline of, 145 entrepreneurship in, 174–175 Diamond, Jared, 213, 215 Diamond, Rebecca, 47 Díaz de Durana, J. R., 359 Diffusion of ideas. See also Knowledge spillovers GPTs and, 127–128 new industries and, 126 Digital Equipment Corporation (DEC), 332 Disembodied knowledge, 219 Disk drive industry, 128n2 Disruption, 73 Diversification, 129–134, 195, 201, 349, 360, 371, 415, 444, 461, 466. See also New industries Diversity entrepreneurship and, 65, 65n22, 68–74, 145 new industries and, 134 tolerance of, 44–45

Index   487 universities and, 229 "Divided We Stand: Three Psychological Regions of the United States and their Political, Economic, Social and Health Correlates" (Rentfrow), 29 Dohse, D., 229 Dongguan's UPS industry, 107–114 innovation and, 108–109 labor market and, 111–113 local government and, 111–113 overcapacity and, 113–114 overview, 103–104, 107–108, 107n3 social capital and, 110n6 universities and, 113 Dot-com firms, 128 Dual use, 136 Dubai, 196n6 Duranton, G., 130 Dynamism, 68–74, 161. See also Entrepreneurship East Asia, natural resources in, 195, 195n5 East Company, 108, 113 East Germany, entrepreneurship in, 174 Ecohai case study (Japan), 380 École Spéciale de Commerce et d'Industrie (École Supérieure de Commerce de Paris), 345 Economic crisis of 2008, 74, 324, 327 Dubai and, 196n6 university competitiveness and, 445–457. See also University research funding, impact of economic crisis of 2008 on Ecosystem Basque country's entrepreneurial ecosystem, 360, 361t, 363, 363f entrepreneurship compared to. See Entrepreneurship innovation. See Innovation ecosystem manufacturing districts and, 106, 116 Edquist, C., 136 Education. See also Business schools; Universities Alto Livenza's furniture industry and, 114–115 creative-class workers and, 39–40 GCC countries, future recommendations for, 406–407

high-growth firms and, 292 human capital and, 38 natural resources management and, 193, 194 in Oman, 202, 203 philanthropy and. See Philanthropy Egypt, Arab Spring in, 200, 201 Electrical engineering, 261–262, 261n3 Eli Lilly and Company, 404 Elings, V., 262 Embodied knowledge, 219 Embryonic stem cells, 258 Emory University, 449–454, 450f, 453f Employment Invention Act 2002 (Germany), 221 Endeavor (global organization), 70 Endocyte, 404 The End of History and the Last Man (Fukuyama), 270 Endowments of universities, 276–277t, 276–279, 279–280t, 282, 282–283n5, 283t. See also Philanthropy Energy Systems Network (Indiana), 408 Enlightenment, 238, 269 Entrepreneurial commercialization, 262 Entrepreneurial discovery, 460–463 discovery and collective experimentation process, 468 discovery vs. innovation, 467 guarding against principal-agent model, 468 importance in design of regional innovation and development strategies, 467–468 locus of, 464–465 policy role and, 468–469 preceding stage of innovation, 465 smart specialization and, 460–461, 464–465, 467–468 Entrepreneurial knowledge, 463–464 Entrepreneurship, 6, 7, 8, 54–83, 145–190. See also Entrepreneurial discovery academic, 227–228 Arab Spring and, 200, 201 business climate and, 66–67 clusters and, 296–297 complexity and, 60–63 creativity and, 39, 67, 126

488   Index Entrepreneurship (Cont.) definition of, 148–152 diversity, dynamism, and deal-flow of, 68–74 ecosystem of, 54, 56–58, 60, 62–63, 67, 71–73, 73f, 75–76 further research on, 182–183 high-growth firms and, 70, 287, 293, 299–301, 300t importance of local institutions for, 56, 78, 145, 162–165 informal institutions, 170–174 innovation and, 55–56, 55n2, 67 institutions at national level, 155–156 knowledge spillovers and, 26, 55n2, 62 labor market institutions, 158–160 local institutional framework for, 164–165, 165f, 174–181 local policy, 162–181 local regulations, 167–170, 168n13, 169n14, 170f local taxes, 165–167 market failures and, 63–66 missing middle and, 68–69 national policy, 155–162 national taxes, 156–158 new combinations as, 59–60 Oman and, 204–205 overview, 54–59, 145–148 philanthropy and, 273–274 policy, 152–154, 153f, 155–162, 158n9 political stability and, 67 productive, 54–55, 150, 152, 156, 158, 161–162, 164–165, 167, 174, 178–179, 182 rainforest, compared to, 69, 70f, 72 randomized controlled trial (RCT) and, 74–77 rule of law and property rights protection, 156 SMEs vs., 68–69, 68f, 152–154, 153f social insurance system, 160–161 spatial proximity and, 18 strategic management and, 72 supply and direction of, 154–155 sustainable development and, 355–372. See also Basque region, business environment in

universities and, 292 Environmental sustainability, 119 Escalator regions, 176–177 e-science, 195, 195n3 Eshun, J. P., 291 E-Square, 377 Etzioni, Amitai, 171 Eurasia, environmental advantages in, 215 European Cancer Patient's Bill of Rights, 441 European Commission Directorate responsible for research and innovation, 471 European Space Agency, 245 European Structural Funds, 298 European Union Cohesion Policy, 9, 21, 458–460, 469–473 creative-class workers in, 40 labor market regulations in, 159–160 Smart Specialization Policy. See Smart specialization Exit, Voice, and Loyalty (Hirschman), 15 Ex post bias with new industries, 130 Extremadura (Spain), 471, 473–474 Fairchild electronics, 19–20, 93, 173 Fallick, B., 294 Family businesses, 350 FAST (Federal and State Technology Partnership) program, 390 FDI. See Foreign direct investment Featherman, D. L., 288 Federal and State Technology Partnership (FAST) program, 390 Feld, Brad, 431 Feldman, Maryann P., 6, 27, 65, 125, 127, 128, 129, 131, 136, 224, 229, 297, 355, 369 Ferguson, R., 338 Feser, Edward, 41 Fielding, Tony, 177 Finance and Private Sector Development Network, 55 Financial advisors, 313 Finland entrepreneurs and R & D spending in, 462 natural resources in, 195, 195n4

Index   489 Firm incubators, 255 Firm stage-specialization, 114–115 Flat world effect, 212 Fleischman, C. A., 294 Flexible business models, 104 Florida, Richard L., 6, 14, 23–24, 29, 34, 36, 38–39, 40, 41, 43, 44, 46, 47, 65n22, 224 Footwear industry in Portugal, 463–464 Foray, Dominique, 9, 458 Forbes, D. P., 128 Forbes on billionaires, 151 Ford capitalism and, 36 manufacturing districts model and, 102, 105, 106 Foreign direct investment (FDI) in Basque region, 362 in Northern Ireland, 440 in United States, 325–326 Foshan (China), UPS production in, 108. See also Dongguan's UPS industry Foster, Arlene, 436 Foundations. See Charitable foundations 4Potentials, 177n28 FP Corporation case study (Japan), 379–380 France labor market regulations in, 160 regulations for entrepreneurship in, 168 universities in, 217 wine industry in, 260 Francis, J., 297 Franklin, Benjamin, 274, 275 Frauenhoffer Institutes, 24 Freeman, Christopher, 125, 127, 128 Free riders, 96–97 Fresno State University, 316 Friedman, Thomas L., 31, 41 Fritsch, Michael, 172, 174 From Seed to Harvest: The Growth of Research Triangle Park (Link), 29 Fukao, Kyoji, 375 Fukugawa, N., 338 Fukuyama, F., 270 Furniture industry Alto Livenza, 114–118 Uno Contract, 118 Valcucine, 119

Gabe, Todd M., 39–40, 41 Galaxy Zoo, 249, 249n6 Gallup-Knight survey, 45 Gambardella, A., 25 Gans, Joshua S., 156 Garicano, L., 160 Gassler, H., 224 Gates, Bill, 39, 180n31 Gates Foundation, 238, 241, 242t, 245, 247, 271 GCC. See Gulf Cooperation Council Geertz, Clifford, 68 GEM. See Global Entrepreneurship Monitor Gene-culture-evolution, 216 General Dynamics, 73n29 General purpose technology (GPT), 127–128, 133, 136 A Generosity of Spirit: The Early History of the Research Triangle Park (Link), 29–30 Genetic distance, 215 Genuth, J., 246 Geographic factors. See Place Geography of innovation, 387 Georgia Institute of Technology, 244, 248, 449–454, 450–451f, 453f Georgia Research Alliance, 24 Georgia State University, 449–455, 450–453f Germany brain drain in, 229 entrepreneurship in, 172, 172n17 Industrial Revolution in, 211 innovation vouchers in, 476 legal mandate for economic performance in, 21–22 manufacturing clusters in, 105 national-local programs for entrepreneurs in, 298 Top Technology Region/ Eindhoven-Leuven-Aachen Triangle (TTR-ELAt), 476–477 universities in, 7, 217 Gertler, M. S., 297 Gilbert, B. A., 291 Gilead Sciences, 250 Gillings, D., 263 "The Giving Pledge" (Bloomberg), 276 Glaeser, E. L., 13, 26, 27, 43, 65, 75, 174–175, 222, 227

490   Index Global Entrepreneurship Congress (2014), 68 Global Entrepreneurship Index, 273n3 Global Entrepreneurship Monitor (GEM), 55n2 experts' survey, 363, 363n2 Global Innovation Index, 204 Globalization cities' roles in, 35, 41–42 economic competitiveness and, 324, 327 grand challenge and, 249–250 industrial, 34, 102–104 knowledge and, 225 philanthropy and, 269, 284 pressures of, 2, 3, 31, 145, 225 regional effect of, 212, 213 universities and, 212, 230 Globally fragmented production, 106–107, 106n2 Alto Livenza furniture industry and, 115, 115n12 Global pipelines, 134 Gnosjö spirit, 173 González-Pernía, José L., 8, 355 Google, 177 Google Campus (London), 295 Gottlieb, J. D., 13 GPT. See General purpose technology Grameen Bank, 415, 416 Grand challenge model, 7, 239–250 conceptualization of, 243 definition of, 239–241, 240t evaluating and communicating impact of, 246–247 global scale and regional focus of, 249–250 model of, 239–243, 240t, 242t new sources of knowledge and, 248–249 private-sector engagement, 247–248 project governance of, 245–246 solution orientation of, 244–245 strategic focus of, 243–244 Grand Challenges Canada, 241, 251 Grand Challenges in Global Health program, 241 Great Depression, 273 Great Recession. See Economic crisis of 2008 Gregson, Peter, 434 Gribskov, Alena, 351

Griliches, Z., 128, 221, 227, 259 Grilo, I., 155 Grimm, H., 298 Gross, M., 247 Guan, J., 133 Guerrero, Maribel, 8, 355 Gulden, Tim, 41 Gulf Cooperation Council (GCC) countries Arab Spring in, 201 countries participating in, 191n1 future recommendations for, 206–207 natural resources in, 191, 191n1, 192, 195–196 Halo (angel network), 436 Haltiwanger, John, 151 Hardin, John, 385 Harvard University, 276, 277, 278 Hassink, R., 128 Hatchuel, A., 346 Hausmann, Ricardo, 64, 65, 65n21, 66, 76, 463, 467 Hawaii's High Technology Development Corporation, 390 Hayter, Christopher S., 7, 237 Headhunters, 313 Health insurance, 161 Hedonic price functions, 221 Hegemony in industrial capitalism, 35–36 Heidelberg University, 279 Henderson, R., 223, 261 Hendry, C., 128 Henrekson, Magnus, 7, 145, 150, 151, 155, 296, 297 Henton, Doug, 8, 306 Herb, M., 194 Hesiod, 216 Hess, Jack, 14 Hidalgo, César A., 64, 65, 65n21, 66 Hierarchy of needs (Maslow), 45 High-growth firms, 7–8, 286–305 business accelerator programs for, 295–296 definition of, 286, 287–288 education policy and, 292 entrepreneurship and, 150–151 entrepreneurship policy and, 70, 287, 293, 299–301, 300t industrial organization and, 296–297

Index   491 labor market policy and, 292–294 local policies and, 289–301 multilevel policy, 297–299, 298t overview, 286–287 stages toward, 288–289, 289f targeted local policies and, 294–296 High-impact entrepreneurship, 147, 151–152, 173, 173n21, 177, 181 Highway system, effect of, 321, 322 Hilbert, D., 239 Hilbert's Problems, 239–240 Hirai, Kayo, 8, 373 Hirschman, A. O., 15, 67 Hirshleifer, J., 463, 465 HIV/AIDS, 247, 249–250 Holst Centre (joint Dutch-Flemish research infrastructure), 477 Homebrew Computer Club, 93 Home region, entrepreneurship and, 176, 176n24, 180n31 Hommen, L., 136 Hopkins, Johns, 275 Horizontal priorities, 314, 468, 469 Horn, Chris, 439 Horowitt, Greg, 432, 435 Howkins, John, 42 HSK Power, 108 Huawei (China), 113 Huazhong University of Science and Technology, 113 Hukou regulations (China), 112–113, 112n7, 112n8 Human capital high-growth firms and, 292 innovation and, 194, 214 natural resource curse and, 193 place, strategic management of, 27–30 talent and, 37, 39–40, 42 universities and, 216, 223–224, 229 Human Genome Project, 247 Humboldt University, 275 Hybrid corn, 126, 128 Hydrocarbon sector in Oman, 201–202 IBM, 93, 106, 248 ICT. See Information and communication technologies

Identity and image, 29 iHubs (California), 306 Ikeuchi Towel case study (Japan), 378–379 Illinois' Department of Commerce and Economic Opportunity, 391 Immigration, 44, 111, 112–113, 112n7, 112n8 Imperial College London, 278 "Inc. 500," 177 Incentives Alto Livenza's furniture industry and, 115 for construction of cluster commons, 94 to deflect firm relocation, 331 Dongguan's UPS industry and, 110–113 Missouri's Technology Incentive Program, 390 Incremental and process innovation (I & P), 105, 109 Incumbent firms, 69–71, 177–180, 177n29, 182, 221 India entrepreneurship in, 77 grand challenges in, 245 Indiana Central Indiana Corporate Partnership (CICP), 9, 406–413 Food and Agricultural Innovation Initiative, 408 21st Century Fund, 390 Indiana University, 22 Industrial Age, 34 Industrial cluster genesis, 131–132 Industrial organization. See also Clusters entrepreneurship and, 59, 61 high-growth firms and, 296–297 manufacturing districts and, 102–103 Industrial Revolution, 125, 126, 127, 211, 217, 269 Industrial Technology Research Institute (Taiwan), 244 Industrial working class, 36, 38 Industry and Growth Forum, 390 Industry life cycles, 131, 134 Inequality dynamics, 270–271, 271n2 Informal institutions entrepreneurship and, 170–174, 182 universities and, 216

492   Index Information and communication technologies (ICT), 102, 106, 196, 197f, 199 in Dongguan, China. See Dongguan's UPS industry Information Technology and Innovation Foundation's (ITIF's) Atlantic Century II report, 333 Inglehart, Ronald, 44 Ingram, P., 291 Innocentive, 249 Innovate Indiana Network, 22 Innovation. See also Creativity; Research and development (R & D) Alto Livenza's furniture industry and, 117–118 brokers and. See Innovation brokers citizen science and, 249 clusters and, 85–87 Dongguan's UPS industry and, 108–109 economic development relying on innovation-based strategies, 332–333 entrepreneurship and, 55–56, 55n2, 67, 146 firm size and, 273 GCC countries, future recommendation for, 206 geography of, 387 GPTs and, 127–128 grand challenges and, 248 human capital and, 194 incremental and process, types of, 105 knowledge filters and, 220 manufacturing districts and, 103n1, 104–107, 121 new industries and, 125, 127, 129, 130, 134 Oman and, 204–205 open-innovation techniques and platforms, 248 philanthropy and, 273 procurement and, 136 smart specialization and, 474–475 universities and, 214, 217–218, 222–223, 225–226, 275 Innovation-addition problem of cluster strategies, 403–404 Innovation brokers, 8, 306–319 Accelerate Long Island (New York), 317–318 agenda of, 311–312

capital and, 313–314 definition of, 307, 308f economic resiliency and, 318 economy and, 309–311 existing companies and, 315 fostering innovation ecosystem, 314–315 increasing economic resiliency, 318 International Center for Water Technology (Fresno, California), 315–317 networking opportunities and, 314–315 offering specialized services, 312–313 overview, 306–307 profiles of, 315–318 providing access to capital, 313–314 in regional economy, 309–311, 310f regional innovation and, 307–309 role of, 307, 307t, 311–315 serving existing businesses, 315 Innovation chasing, 327 "Innovation Driven Economic Development Model" (Collaborative Economics), 306–307 Innovation economics, 333 Innovation ecosystem, 307, 308f, 314–315, 329 Innovation mercantilism, 327 Innovation Partnership Zones (Washington), 306 Innovation vouchers, 476 Innovation Works (Pittsburgh), 314, 315 Integrated circuits, 129 Intel, 19, 93 International Center for Water Technology (Fresno, California), 315–317 International Congress of Mathematicians, 239 International Exhibition of Components, Semi-finished Products and Accessories for the Furniture Industry, 116n13 Internationalization drive, 116 International technology roadmap for semiconductors, 245 International Year of Microcredit (2005), 426 Internet (dot-com) firms, 128 Interuniversity Microelectronics Centre (Belgium), 244 Intrapreneurship, 149–150 Inward Investment Report 2013/14 (UKTI), 440 Italy

Index   493 Alto Livenza's furniture industry in, 114 brain drain in, 229 labor market regulations in, 160 universities in, 217 Jacobian externality, 131, 134 Jacobs, J., 26, 27, 42, 65, 134, 226–227 Jaffe, A. B., 218, 220, 222, 223 Japan, business environment in, 8, 373–384 agricultural sector, 377 assembly-type industries, 374 Ecohai case study, 380 FP Corporation case study, 379–380 Ikeuchi Towel case study, 378–379 Kaiho Sangyo case study, 381 My Farm case study, 381–382 overview, 373–375 small- and medium-size enterprises (SMEs), 374–378, 377n4 Jarmin, Ron S., 151 Jefferson, Thomas, 274 Jihad, 199n10 Job creation, 158–159, 159f high-growth firms and, 287, 290 postrecession, 401 SEED and, 425 Jobs elimination, 73, 73n29 Johansson, Dan, 150, 296, 297 Johns Hopkins Medical Center, 278 Johns Hopkins University, 276, 278 Johnson, David Lawther, 9, 401 Johnson, Lyndon B., 321 Johnston, Patrick, 441 Joint Venture: Silicon Valley Network, 93 Judaism, 171n16 Kaiho Sangyo case study (Japan), 381 Kansas Bioscience Authority, 391 Katz, J. A., 352 Kauffman, Stuart, 59, 60, 62n15, 65 Keilbach, M. C., 220, 224, 358n1 Kennedy, John F., 321 Kenney, Martin, 7, 223, 224, 255 Kentucky's SBIR/STTR Matching Funds program, 391 Kenya, user-generated innovation in, 248 Kerr, W., 75

Kewang, 113 Keynes, John Maynard, 268, 270 Kickstarter, 248 King's College, 274, 275 Kirch, D. A., 128 Kirkham, Paul, 8, 345 Kirzner, Israel M., 148, 351 Klepper, S., 19–20, 128–129, 173, 174, 178, 179 Knowledge, types of, 219, 229 Knowledge assets, 250 Knowledge development and production. See also Knowledge spillovers business schools and, 348–349 grand challenges and, 243 natural resources and, 194 universities and, 212, 213–214, 216–217, 218–221, 226 Knowledge diversity, 229 Knowledge ecology, 471 Knowledge Economy Index, 437, 439–440, 440t Knowledge filters, 220, 224–225 Knowledge gaps, 90–91 Knowledge production function, 237 Knowledge relatedness, 229 Knowledge spillovers clusters and, 84–88, 90, 97 entrepreneurial discovery and, 465 entrepreneurs and, 26, 55n2, 62, 62–63n17 grand challenge and, 248–249 new industries and, 132, 133–134 smart specialization and, 465 spatial proximity and, 18 state-level R&D and, 385 universities and, 24, 216–225 university research parks and, 337 Knowledge workers, 36, 38 Knut and Alice Wallenberg Foundation, 280 Koch, G., 263 Kogler, D. F., 129 Kogut, B., 219, 223 Kolko, Jed, 166 Koo, J., 229 Kovalainen, A., 300 Kramarz, Francis, 168 Kresge Foundation, 280 Kronlund, M., 300

494   Index Krugman, Paul, 1–2, 219–220 Kurlansky, M., 130 Kuznets, S., 348 Labor market. See also Creative-class workers Alto Livenza's furniture industry and, 111–112 Dongguan's UPS industry and, 115 entrepreneurship and, 65n23, 158–160, 159–160, 159f, 164, 164n11 high-growth firms and, 292–294 institutional framework and, 176–177, 176n26, 177n29 in Oman, 203–205 regulations, 294 skilled vs. unskilled labor, 23 Lanahan, Lauren, 385 Lancaster University Management School (UK), 349 Landry C., 42 Lapsley, J. T., 260 Latin America's natural resources, 195, 195n5 Laville, F., 133 Law to Support Entrepreneurs and Their Internationalization (Spain), 364 Leadership, 29–30 Basque Country's entrepreneurial leadership, 360, 370 grand challenge model and, 241, 244 of innovation brokers, 311–312 regional investment in innovation assets and, 318 sunk costs and, 16 Leading Enterprise And Development (LEAD) program, 349 Lederman, D., 194 LED lighting production, 108 Lee, Chong-Moon, 307 Lehmann, Erik E., 7, 211, 220, 224 LeLarge, C., 160 Lendel, I., 128 Lesko, Mark, 314 Levin, Simon, 60 Levi Strauss, 135 Li, H.-L., 225, 229 Libya, Arab Spring in, 200, 201 Life cycle model of industries, 131–132 Lilly Corporation, 16

Lilly Endowment, 280, 407 Lindelöf, P., 338 Link, Albert N., 8, 15, 29–30, 337–344 Linux, 261 Lioulias, Panos, 434 Lipsey, R. G., 127 Lisbon Agenda, 298 Litan, Robert E., 155 Loans for microenterprise lending. See Small Enterprise Economic Development Program (SEED) Lobo, Jose, 59, 62n15 Local buzz, 134 Local institutions. See also Cities; Regional development capitalism and, 272 culture and, 147 entrepreneurship and, 146–147, 164–165, 165f, 174–183 in Oman, 204 philanthropy and, 273 Location. See Place Locational tournaments, 299 Location paradox, 41 Löfsten, H., 338 London as education center, 278 escalator region of, 177, 177n27 London School of Economics and Political Science, 278 Longworth, Richard, 402 Louisiana's SBIR/STTR Phase Zero Part I program, 390 Lowe, N., 136 Lucas, Robert E., 42, 175n23 Luntz, F., 246 Luo, J., 291 Magis, role in furniture industry, 117–118 "Making Sense of the Competitiveness Debate" (Krugman), 1 Malaria, 247 Malecki, E. J., 223 Malmberg, A., 134 Maloney, W., 194 Malopolska Capital Investment Fund (Poland), 476

Index   495 Manski, C., 224 Manufacturing districts, 6, 102–124 Alto Livenza's furniture industry, 114–118 Dongguan's UPS industry, 107–114 literature review, 105–107 Magis, role in furniture industry, 117 overview, 102–105 Uno Contract, role in furniture industry, 118 Valcucine, role in furniture industry, 119 Manufacturing Extension Partnership, 332 Manufacturing sector. See also Manufacturing districts; specific countries relocation and job loss in, 321–322, 325, 326f, 402 Mao Zedong, 74 Maré, David C., 43 Marine biology, 249 Marinha Grande cluster, 461–462 Market failures entrepreneurship and, 58, 63–66 globalization and, 107 high-growth firms and, 290 localization and, 17–20 targeted subsidies or tax breaks to fix, 153 UK incentivization of business schools and, 347 Marshak, Jacob, 61 Marshall, Alfred, 35–37, 77, 77n30, 103–104, 103n1, 109, 126, 131, 134, 177n27, 211, 217, 226–227 Marshall-Arrow-Romer model, 25–26, 84 Martin, B. R., 348, 349 Martin, R., 346, 352 Marx, Karl, 268, 270, 271n2 Maskell, P., 134 Maslow, A. H., 45 Massachusetts, entrepreneurs in, 151 Massachusetts Institute of Technology (MIT), 256, 261, 278, 332 Deshpande Center, 248 Production in the Innovation Economy Taskforce, 315 Matched pairs firms, 338–339 performance measures of, 339–341, 340t Matera (North Carolina), furniture industry in, 117

Mazzucato, M., 136 McCann, Philip, 9, 458 McCanny, John, 432 McComb, R., 221 McCormack, Gerry, 434–435 McDougall, P. P., 291 McGranahan, David, 40 Meeting places, cluster commons as, 87–88, 87f Megadeals, 324 Mellander, Charlotta, 6, 34, 40, 41, 43, 47 Michelacci, Claudio, 55n2 Microenterprise development program. See Small Enterprise Economic Development Program (SEED) Microsoft, 177, 180n31 Middle East, 191, 195, 195n5, 197, 198n8. See also Arab Gulf natural resources Midwestern states clusters, 401–406 local competitiveness of. See Indiana Miesing, Paul, 9, 414 Migrant labor, 111–112 Mill, John Stuart, 268 Mincer, Jacob, 38 Ministry of Industry and Information Technology (China) certification, 111 Minniti, Maria, 171, 172 Miranda, Javier, 151 Missing middle, 68–69 Mississippi's FAST Program, 390 Missouri's Technology Incentive Program, 390 MIT. See Massachusetts Institute of Technology MNCs. See Multinational corporations Mobility, 88, 89, 294 Model of seven cluster gaps, 88–91. See also Clusters Model T, 105 Modernization, 360, 461, 462, 466 Mohammed bin Rashid Al Maktoum Foundation (UAE), 280 Molecular biology, 256 Monopoly, 25–26, 227, 272, 273, 274–275 Moore, Gordon, 19, 178n30 Moore's Law, 178n30

496   Index Moral capital philanthropy and, 269, 271–272 universities and, 279, 282–283n2, 282–284, 283t Moretti, Enrico, 401 Morocco, Arab Spring in, 200, 201 Morrill, Justin Smith, 275 Morrill Federal Land-Grant Act of 1862, 275 Moscow protests, 198n8 Mosey, Simon, 8, 345 Motoki, Hiro, 8, 373 Motoyama, Yasuyuki, 71 Multilevel policy of high-growth firms, 297–299 Multinational corporations (MNCs) business school programs and, 345, 346 entrepreneurship and, 177, 178 globalization and, 35 manufacturing districts and, 103 Oman and, 202 radical structural and technological changes and, 466 technology transfer and, 264 Multiplier effects, 180, 182 Murphree, M., 104 My Farm case study (Japan), 381–382 Nanotechnology, 133, 333, 362, 387, 405, 462, 466, 467 Napa Valley, 259–260, 264 National Academy of Engineering, 241, 251 National Academy of Sciences, 393 National Aeronautics and Space Administration (NASA), 93, 245 Space Alliance Technology Outreach Program (SATOP), 390 National Cooperative Research Act of 1984, 389n9 National Defense Highway System, 321 National economic competitiveness, 8, 320–334 causes of weaker supply, 327–329 implications for economic development policy, 329–330 loss of manufacturing jobs, 325, 326f megadeals and, 324 recent state economic performance, 323–324

recommendations to form partnerships, 332 shifting from negative-sum to positive-sum activities, 330–332 southern economic development, 321–322 National Governors Association (NGA), 331, 386 National Institutes of Health, 241 Niche Assessment Program, 390 National Institutes of Standards and Technology's "Nanofab" Lab, 390 National Research Council of National Academy of Sciences, 393 National Semiconductor, 19 Natural resource curse, 192–195 Natural resources. See Arab Gulf natural resources Navarra (Spain), 475 Navarro, M., 360 Nelson, Richard R., 56, 127, 259 Neoclassical economics, 237 Neoclassical theory of production, 59 Neoeconomics, 129 Neolithic Revolution, 215 Neo-Schumpeterian theory, 59 Netherlands Centre for Amsterdam Schools of Entrepreneurship, 476 creative-class and, 40 labor market regulations in, 159 natural resources and, 193 Top Technology Region / Eindhoven-Leuven-Aachen Triangle (TTR-ELAt), 476–477 universities in, 217 Networks and linkages, 28. See also Angel investors and networks; Collaboration entrepreneurship and, 65, 65n24 innovation brokers and, 314–315 relational networks and universities, 224 sunk costs and, 16 Neumark, David, 166 New England Governors' Conference, 331 New firms, 151, 159–160, 173–174, 177–179, 182, 229 The New Geography of Jobs (Moretti), 401 New growth theory, 220

Index   497 New Hampshire's NH Inspires Innovation program, 390 New industries, 6, 125–141 emergence of, 128–130 empirical challenges of, 129–130 future trends, 137 overview, 125–126 regional context for, 132–137 regional-specific push factors for, 133–134 technology-pull factors for, 134–137 theoretical challenges of, 130–132 transformative potential of, 126–129 value to place and, 18 New York (state) defense contracts in, 73n28 Empire State Development Corporation, 414 Energy Research and Development Authority Proof of Concept Center (NYSERDA PoCC), 248 NYSTAR program, 391 New York City, escalator region of, 177 New York University, 244, 251 NH Inspires Innovation program, 390 Niche supplier firms, 109 Niebuhr, A., 229 Nielsen, M., 249 Nine-sigma, 249 Noncompete agreements, 174, 293, 298, 331 Nonentrepreneurship, 149–150, 149f Nonexcludable, 62, 62n15 Nonpecuniary spillover. See Knowledge spillovers Nonprofit organizations. See also Charitable foundations strategic management of place, 22 Nonrival, 62, 62n15 NorTech Ohio, 315, 402 North, Douglass C., 146 North American Classification System (NAICS), 128, 129 North Carolina's One NC Small Business Program, 8–9, 391–398 additional funding received, 396–398, 397t administered by North Carolina Board of Science & Technology (BST), 393

collaboration with institutions of higher education, 396 increased employment, 396, 397t increased phase II award rate, 395 program impacts, 394–398, 394t, 395f, 396t statutory guidelines, 392 survey and measurement of, 393, 393n25, 396n28 North Carolina State University, 263 Northern Ireland Knowledge Economy Index (KEI): Baseline Report, 437 Northern Ireland's economic transformation, 9, 429–444 context post-peace agreement (1998), 433 evidence of progress, 439–441, 440t foreign direct investment in, 440 lessons learned, 442–444 NISP CONNECT 2.0, 437–441, 438t Northern Ireland Science Park (NISP), 432, 434–439 Orr's role in, 433–434 overview, 431 pilot programs, 434–436 Norton, R. D., 323 Norway grand challenges in, 241 natural resources in, 195 Nottingham University Business School (UK), 351 Novel-product innovation, 109 Noyce, Robert, 19 Nuclear magnetic resonance, 262 Obama, Barack, 237 Occupy Wall Street, 198n8 OECD countries economic growth in, 270 globalization and, 225 high-growth firms and, 286, 287 innovation in, 133 smart specialization agenda and, 478 Oettinger, Jessie, 8, 306 Offshore production, 2, 102, 117, 402 Offshore tax havens, 158 Ohio Third Frontier programs, 248 Olofsson, C., 338 Olson, Mancur, 65n22, 66, 69

498   Index Oman Arab Spring and, 201–206 competitiveness ranking of, 202t Eighth Five-Year Plan of Oman, 203, 203t natural resources and, 196 One NC Small Business Program, 392–398 impacts of, 394–398 overview, 392–393 Open windows of location opportunity, 131 Optics, as example of GPTs, 128 Optics Society of America, 128 Ordnungspolitik, 21 Organisation for Economic Co-operation and Development. See OECD countries Organizational reproduction and heredity, 129 Orkestra, 368n5 Orr, Steve, 9, 429, 430–434 Ortega-Argilés, Raquel, 9, 458 Osh Kosh Truck Company, 251 Otterson, Bill, 432 Overcapacity, 113 Owens, Nathan J., 311–312 Oxford University, 217, 278, 279 Page, Scott E., 44 Pandemics, 249–250 Papke, Leslie E., 166 Pareto efficiency, 63 Paris escalator region of, 177 universities in, 217 Pasteur, Louis, 260 Patent-grant university model, 258–259, 264 Patent protection history of, 217 importance for economic growth, 156 knowledge production and, 220–221 research universities and, 256–258, 261, 263–264 technological change and, 129–130 universities and, 222–223 Patton, D., 223 Pearl River Delta (PDR) cities, 108, 112, 113. See also Dongguan's UPS industry Peña-Legazkue, Iñaki, 8, 355 Pension funds, 161 People climate, 46

Perez, Carlota, 127 Perkmann, M., 255 Persson, Lars, 156 Peters, M., 133 Pharmaceuticals, 256 Philanthropy, 7, 268–285 capitalism and, 270–273 overview, 268–269 Schumpeter and, 273–274 universities and, 274–283, 276–280t, 283t Photonics Spectra, 128 Pigou, Arthur Cecil, 37 Piketty, T., 270, 271, 271n2, 272 Pisano, Gary, 62 Place, 2, 6, 9, 13–33 entrepreneurial discovery and, 464–465 human dimension, 27–30 importance of, 3, 16–20 long-term effects for universities, 214–216 mandate, 14–22 new industries and, 132, 133 overview, 3–4, 13–14 participants, 20–22 policy role, 30–31 quality of place, 43–46 resources and factors, 3–4, 22–24 short-term effects for universities, 216–218 spatial structure and organization, 25–27 Plato, 216 Plosila, Walter H., 387 Poland innovation vouchers in, 476 Malopolska Capital Investment Fund, 476 Policy role cluster commons, 97–98 economic development and, 329–330 entrepreneurial discovery and, 468–469 entrepreneurship and, 150 GCC countries, future recommendations for, 206 local entrepreneurship policy, 111–113, 115, 162–181, 163–164f, 163n10 national entrepreneurship policy, 155–162 natural resources and, 193 new industries and, 131, 136–137 Oman and, 202, 204–206, 204n12 place, strategic management of, 30–31

Index   499 in radical structural and technological changes, 466 smart specialization and, 468–469 state-level policies. See State-level science and technology policies universities and, 225–230 Political stability, 67 Polymer research, 132 Pordenone (Italy), 116, 116n14 Porter, Michael E., 1, 25, 26, 41, 60, 227, 430 Portugal footwear industry in, 463–464 labor market regulations in, 160 Poverty, 47, 56, 265, 321, 415, 417, 426 Powell, W. W., 258 Power and Prosperity (Olson), 66n25 Powers, Joshua B., 447 Principles of Economics (Marshall), 77n30 Private equity firms, 158 Private property rights, protection of, 156 Private sector grand challenge and, 243, 247–248 Oman and, 205 Prize model of innovation, 248 Probe microscopy, 262 Procter and Gamble, 248 Procurement, 136 Product chain, 117–119, 257, 258f "The Product Cycle and Decentralization of American Manufacturing" (Rees & Norton), 323 Product cycle theory, 35 Production recipes, 59–62, 62n16, 64–65 Productive entrepreneurship, 54–55, 150, 152, 156, 158, 161–162, 164–165, 167, 174, 178–179, 182 Project governance grand challenge and, 245–246 grand challenge model and, 243 Property rights, protection of, 156. See also Patent protection Protestant ethics, 171n16 Protor and Gamble, 248 Public-private partnerships in SEED program, 426 Puga, Diego, 130 Pull factors for new industries, 132–133, 134–137

Push factors for new industries, 126, 132, 133–134 Putnam, Robert D., 28–29, 171n16 Qatar, natural resources in, 196 Qingxi (China), UPS production in, 109 Quality of place, 43–45, 46 entrepreneurship and, 70 Quebec Seeks Solutions (QSS), 352 Quigley, John M., 65n22 Quintiles (data analysis firm), 263 Radical foundation, 466 Radner, Roy, 61 Rainforest, compared to entrepreneurship, 69, 70f, 72 R & D. See Research and development Randomized controlled trials (RCTs), 74–77 Real time challenges of new industries, 129–130 Rebitzer, J. B., 294 Recombinant DNA technology, 127, 129 Rees, J., 323 Regional Advantage (Saxenian), 27–28 Regional development. See also specific locations differences of place, 3–4 of high-growth firms, 289 innovation brokers in, 309–311 for manufacturing districts. See Alto Livenza's furniture industry; Dongguan's UPS industry national economic competitiveness and, 320–334. See also National economic competitiveness of new industries, 132–137 smart specialization and, 473–475 universities and, 7, 213–214, 255–267. See also Universities Regional Innovation Acceleration Network (RIAN), 312n1 Regulations entrepreneurship and, 147, 167–170, 168n13, 169n14, 170f zoning, 167–168, 168n13, 180 Reinhart, Carmen M., 327 Relocation to southern states, 320–321

500   Index Rentfrow, P. J., 29 Rentier mentality, 204, 207 Rent-seeking, 193 Research and development (R & D), 237–254 Bayh-Dole Act of 1980, 251 big science and, 238–239 Dongguan's UPS industry and, 111, 113 financing, 247 grand challenge, 7, 239–250. See also Grand challenge model manufacturing districts and, 104 Oman and, 205 overview, 237–238 regional implications of, 250–252 small science, 238 state-level, 385–400. See also North Carolina's One NC Small Business Program tax credit of 1981, 389n9 universities and, 221, 222–223, 224–225, 228 Research funding of universities, 262, 445–457. See also University research funding, impact of economic crisis of 2008 on Research Triangle (North Carolina), 15, 29–30, 291 Research Triangle Park (RTP), 263, 402 Research universities. See Universities Residency laws, 112–113, 112n7, 112n8 Resiliency innovation brokers increasing, 318 of manufacturing clusters, 120 Saxenian's analysis of Silicon Valley and Route 128 and, 172 Review of UK Government's Science and Innovation Policies (Sainsbury), 434 Rhoten, D., 258 Ricardo, David, 22, 271n2 Rice Genome Project, 245 Rigby, David L., 129 RIS3 Platform, 473–475 The Rise of Creative Class (Florida), 23–24 Roback, Jennifer, 43 Roberts, E. B., 297 Robotics, 250, 333 Rockefeller, John, 275 Rockefeller Foundation, 280 Rockstart Accelerator (Amsterdam), 295

Rodrik, Dani, 71, 459, 463, 465, 467, 472 Rogers, E. M., 259 Rogoff, Kenneth, 327 Romer, Paul M., 42, 62, 220 Romer model, 26 Rosen, Sara Thomas, 43 Rosenberg, N., 105 Rotman School, 352 Route 128 (Boston), 27–28, 43, 147, 172–173, 173n19, 237 Rudolph, Frederick, 275 Rule of law, 156 Rural Outreach Program (ROP), 390 Ruttan, V., 136 Rwanda, entrepreneurship in, 74 Sachs, J., 194 Salone Internazionale dei Componenti, Semilavorati ed Accessori per l'industria del Mobile (SICAM), 116, 116n13 Salter, A. J., 348, 349 Samila, S., 298 Samuelson, Paul, 270 Sanandaji, Tino, 151 Sanders, M., 225 San Diego (California), 73n29 CONNECT program, 314, 429–430, 432–434 knowledge production function in, 237 San Francisco, 135 San Joaquin Valley Water Technology Cluster, 316 Santa Fe (New Mexico), 322 Sapolsky, Harvey M., 387 SAS (data analysis firm), 263 Saudi Arabia Arab Spring and, 200, 201 natural resources in, 196 Saving Lives at Birth, 241 Saxenian, A., 27–28, 65, 65n23, 147, 172, 173n19 Say, Jean-Baptiste, 345 SBA. See Small Business Administration SBIR program. See Small Business Innovation Research (SBIR) program Schartinger, D., 224 Schibany, A., 224 Schmitz, J. A., Jr., 55n2

Index   501 Schmookler, J. A., 126, 132 Schmookler's scissors, 126, 132 Schneider Electric's MGE, 108 Schramm, Carl J., 155 Schrank, A., 121 Schultz, Howard, 175–176 Schumpeter, Joseph A., 7, 55n2, 58, 59, 66, 69, 103n1, 104, 105, 125, 146, 148, 173, 180, 182, 268, 269, 273–274 Science. See also Biotechnology; Research and development (R & D) big science, 238–239 citizen science, 249 small science, 238 state-level, 385–400. See also North Carolina's One NC Small Business Program Scientific instruments and research universities, 262 Scott, Allen J., 41 Scott, J. T., 337 Scuola Professionale del Mobile, 114, 115 Seattle, 177, 180n31 Second welfare theorem, 63 SEED. See Small Enterprise Economic Development Program Self-employment entrepreneurship and, 149–150, 149f, 152–153, 160, 182 taxes and, 158, 158n9, 166 Self-serving agents, 148 Semiconductor industry, 173 Semipublic goods, 103, 106, 106n2, 118, 120, 129 Sendmail, 261 Service class workers, 38 Shane, S., 287, 349 Shannon, Claude, 77 Shapiro, Jesse M., 44 Shapiro, Joshua D., 311–312 Shared production assets in manufacturing districts, 104 Shearman, C., 128 Shell, Karl, 59, 62n15 Shenzhen (China), UPS production and, 108. See also Dongguan's UPS industry Shrum, W., 246 Shuen, Amy, 62

Shura, Majlis, 201 Siegel, Donald S., 9, 228, 414 Silicon Manufacturing Valley Group, 93 Silicon Valley, 5, 19–20, 23, 25, 27–28, 30–31, 43, 44, 65, 65n23, 73, 87, 89, 92–93, 97, 103, 125, 129, 147, 151, 172–174, 173n19, 177, 214, 237, 264, 278, 294, 299, 307–308, 308f, 314, 332 Simon, Herbert A., 61 Simonton, Dean Keith, 44 Sleeper, Sally, 178 Sloan Digital Sky Survey, 249 Small and medium-size enterprises (SMEs) Arab Spring and, 200, 201 clusters and, 89–90, 95 Dongguan's UPS industry and, 111 entrepreneurship vs, 68–69, 68f, 152–154, 153f in Japan, 374–382 Oman and, 204–205 smart specialization initiatives to promote, 476 Small Business Administration (SBA) Federal and State Technology Partnership (FAST), 390 Small Business Investment Company program, 332 Small Business Innovation Development Act of 1982, 388, 389n9 Small Business Innovation Research (SBIR) program, 9, 247–248, 291, 298, 332, 388–392, 388nn5–6, 430. See also North Carolina's One NC Small Business Program federal and state partnerships, 390–391 federal programs, 390 grand challenges and, 247–248 state-level initiatives, 391, 391n21, 392f Small Business Technology Transfer (STTR) program, 388n7, 391n21 Small Enterprise Economic Development Program (SEED), 9, 414–428 applicants' assessment, 421 applicants' characteristics, 419–421, 420t applicants' survey, 424–425 character-based microlending and, 415–418 components of, 417 entrepreneurial assessment for, 421 evaluation of, 418–425

502   Index Small Enterprise Economic Development Program (SEED) (Cont.) findings of study, 419–424 implications of, 424–425 interview and references of study protocol, 421–422 job creation and, 425 loan administration of, 417–418, 418f loan repayment and sustained business operations, 423–424 local context of, 415 overview, 414–415 point of exit from, 422–423, 422f social and business supports for, 416–417 support services for participants, 423 Small word networks, 65–66n24 Smart specialization, 9, 458–480 clustering phase of, 465 cross-country cooperation and, 476–477 decentralization and, 475–476 decentralization of entrepreneurial activities and, 475–476 entrepreneurial discovery and, 460–463, 467–468. See also Entrepreneurial discovery entrepreneurial knowledge and, 463–464 entry as key in, 465 within EU Cohesion Policy, 469–473, 477 firm entry and, 465 implementation of, 473–477 initiatives to promote SMEs and, 476 innovation strategies of, 474–475 knowledge spillovers and, 465 large-scale questionnaire on regional innovation strategy, 475 locus of entrepreneurial discovery and, 464–465 overview, 458–460 policy role and, 468–469 private-sector actors' role, 470–471 at regional level, 473–475 RIS3 Platform, 473–475 S3 Platform, 472–473 stakeholder involvement and, 473–474 state aid scheme, 475 structural and industrial changes and, 461–463, 466

SMEs. See Small and medium-size enterprises Smith, Adam, 1, 37, 42, 126, 268, 272 Smith College, 277 Smithsonian Institution, 241 Social capital Basque Country regional development and, 358–364 in Basque region, 364, 370 definition of, 194 Dongguan's UPS industry and, 110, 110n6 entrepreneurship and, 171–172, 171n16 importance of, 194 place and, 28–30 Social embeddedness, 170–172 Social environment, 146 Social insurance system, 160–161 Socialism history of, 268–269 philanthropy and, 270, 273–274 Social legitimacy, 7, 181 Socrates, 216 Solar panel industry, 130, 133 Solow model, 23, 26 Solution orientation, 244–245 Sölvell, Örjan, 6, 84 Sombart, W., 171n16 Sorenson, O., 298 Soskice, David, 271 South Africa, inequality in, 271n2 South Carolina's Phase 0 Program, 390 Southern economic development, 321–322 Space Alliance Technology Outreach Program (SATOP), 390 Spain. See also Basque region, business environment in Barcelona Activa's entrepreneurship center, 476 Extremadura regional economic strategy, 471, 473–474 Law to Support Entrepreneurs and Their Internationalization, 364 Navarra regional economic strategy, 475 unemployment in, 21 universities in, 217 Spatial inequality as challenge, 46–48 Spatial sorting, 175 Spatial structure and organization, 25–27

Index   503 Specialization, 27, 118, 119, 126, 134 Spencer, Herbert, 57 Spenner, K. I., 288 Spin-off processes, 114, 173–174, 177–179, 178n30, 179, 179f, 228 Spolaore, E., 214, 215 Stam, Erik, 7, 286 Standard Industrial Classification (SIC) Codes, 128, 129 Standortpolitik, 21 Stanford, Leland, 275 Stanford University, 92–93, 256, 262, 275, 276, 278, 350–351 Stangler, Dane, 74, 150 Starbucks, 175–176 Starkey, K., 346 Startup America (program), 55 Startup Chile (program), 55 Startup Communities (Feld), 431 Start-ups entrepreneurship rates, 163–164, 163–164f, 163n10, 174, 178n30 grand challenges and, 247–248 hours of work, 349 research universities and, 258 sharing physical space and services, 313 universities and, 221 State-level science and technology policies, 8–9, 385–400 federal SBIR program and, 387–392 literature review, 387–388 One NC Small Business Program, 392–398. See also North Carolina's One NC Small Business Program overview, 385–386 Stenholm, P., 225 Stenkula, Mikael, 155, 296, 297 Stephan, P. E., 224 Stern, S., 25 Sternberg, Robert, 39, 128 S3 Platform, 472–473 Stichting INGKA Foundation (Netherlands), 280 Sticky knowledge, 224 Stokes, D. E., 244 Stolarick, Kevin, 40, 43 Storey, David, 150n6

Strategic management entrepreneurial ecosystems and, 61 as field of study, 1, 14 of place, 13–33. See also Place Strumsky, D., 129 STTR. See Small Business Technology Transfer (STTR) program Student development in business schools, 350–351 Succession entrepreneurship and, 72, 73f and progressive change, 57 Sunk cost, 15–16 Sun Microsystems, 261 Sustainable development. See Basque region, business environment in; Japan, business environment in Sweden creative-class workers in, 39 entrepreneurship in, 173 entrepreneurs in, 168–170, 169n14, 170f inequality in, 271n2 local income taxes in, 166–167, 166n12 natural resources and, 195, 195n4 social insurance policy and entrepreneurship in, 161 spin-offs in, 179, 179f start-up entrepreneurship rates in, 163–164, 163–164f university graduates in, 176, 176nn25–26, 177n28 Swedish National Encyclopedia, 173 SWOT (strengths and weaknesses, opportunities and threats) analysis, 474 Syracuse (New York), 322 Syria, Arab Spring in, 200, 201 Tacit knowledge, 219, 221, 222, 256–257 Talent, 6, 34–53 from business climate to people climate, 45–46 central role of city, 41–43 clustering of, 42–43 education, 38 entrepreneurship and, 70, 154, 177, 179 from firms to talent, 36–37 hegemony in industrial capitalism, 35–36

504   Index Talent (Cont.) human capital, 37 occupation, 38–40 overview, 34–35 people climate and, 46 quality of place, 43–45 skills, 41 spatial inequality as challenge, 46–48 Tangxia (China), UPS production in, 110 Tannenwald, Robert, 167, 168 Tansley, Arthur George, 54, 57, 65 Tavassoli, Sam M., 6, 125 Taxes capitalism and, 270–271 entrepreneurship and, 156–158, 165–167, 166n12 offshore tax havens, 158 philanthropy and, 271–272 TechColumbus (Ohio), 314 Technical universities (TU), 211, 217 Techno-economic paradigm, 127 Techno-fetishism of novelty, 104 Technological regime, 127 Technology. See Biotechnology; Nanotechnology; Research and development (R & D) Technology-licensing offices (TLOs), 258, 264 Technology transfer institutions, 256, 264–265 Technology transfer models, 7, 259–263 Technology transfer offices (TTOs), 228, 257 Technology transfer specialists, 313 TechPoint (Indiana), 408 Tech2020, 313 Teece, David J., 62 Tempest, S., 346 Tencent, 113 Tenofovoir, 250 Texas A&M, 264 Texas university system, 221, 276 TFP. See Total factor productivity Theory of Economic Development (Schumpeter), 274 Third Frontier (Ohio), 402 This Time It's Different: Eight Centuries of Financial Folly (Reinhart & Rogoff), 327 Thomas, Kenneth P., 324 Thomas Edison program (Ohio), 323

Thompson, Wilbur R., 38 Thulin, Per, 176, 176n26 Thurik, A. Roy, 148, 150n6, 155, 358n1 Tie industry, 129 Times Higher Education Survey, 278 Times Higher Education World University Rankings (2013–2014), 279 Tolerance of diversity, 44–45 Topspin Partners (New York), 318 Top Technology Region/ Eindhoven-Leuven-Aachen Triangle (TTR-ELAt), 476–477 Top Trends in State Economic Development (National Governors Association), 386 Total factor productivity (TFP), 193, 193n2, 375 Toyota, 146 Tragedy of the commons, 6, 96, 97 Trajtenberg, M., 223 Transaction costs, 63–64 Transition, 466 Transparency. See Accountability and transparency Trust, 29, 36, 62, 62n17, 65n22, 71, 86, 95, 110–111, 110n6, 171n16, 360, 364, 473 Tsipouri, L. J., 136 Tuberculosis, 247 Tunisia, Arab Spring in, 200, 201 Turbine manufacturers, 131–132 Twenty First Century Fund in Indiana, 24 Uninterruptable power supply production (UPS) Chinese manufacturers of. See Dongguan's UPS industry overview of, 107–108, 107–108n4, 108n5 United Arab Emirates (UAE), natural resources in, 196 United Kingdom competition in, 273n3 Department for International Development (DFID), 241 gap between education and job skills in, 347 high-growth firms in, 286 Industrial Revolution in, 211 innovation vouchers in, 476 United States competition in, 273n3

Index   505 corporate tax variation across, 166 creative-class workers in, 39 diversity in, 229 foreign direct investment (FDI) in, 325–326 GPTs in, 136 high-growth firms and, 70 labor market regulations in, 159–160, 294 manufacturing districts in, 103 performance of a place in, 22 philanthropy in, 271–272 R&D spending in, 247 regulations in, 167 research universities and, 256, 264 Startup America, 55 trade deficit of, 327–328 TTOs in, 228 universities and, 255 Universities, 7, 211–236, 255–267. See also Business schools academic policy of, 225–230 biotechnology model and, 257–259 clusters and, 89–90 Dongguan's UPS industry and, 113 economic crisis of 2008, impact on competitiveness of, 445–457. See also University research funding, impact of economic crisis of 2008 on economic development and, 446–448 electrical engineering and computer science at, 261–262 entrepreneurship and, 151, 292 grand challenge model and, 241 history (US) of, 274–275 knowledge spillovers, 24, 218–225 long-term effects of geographic factors and, 214–216 mathematics and statistics at, 263 microlending development program. See Small Enterprise Economic Development Program (SEED) mission statements of, 446 overview, 211–213, 255–256 patents and, 263–264 philanthropy and, 272, 274–283, 276–280t, 283t regional development and, 7, 213–214, 255–267

research parks. See University research parks science growth and, 238 scientific instruments and, 262 short-term effects of geographic factors and, 216–218 strategic management of place, 22 technology transfer institutions at, 264–265 as technology transfer models, 7, 259–262 wine industry and, 259–261 University at Albany School of Business and School of Social Welfare, 9, 414, 416, 426 University College London, 278 University of California, 7, 256 University of California, Berkeley, 261 Electrical Engineering and Computer Science, 261 University of California, Davis University Extension, 260 wine industry and, 259–260 University of California, San Diego, 429 von Liebig Entrepreneurism Center, 248 University of California, Santa Barbara, 262 University of Helsinki, 279 University of Lund, 279 University of North Carolina, 263 University of Oslo, 279 University of Pennsylvania, 275 University of Porto, 464 University of Utah, 258 University of Vienna, 279 University of Wisconsin, 258 University research funding, impact of economic crisis of 2008 on, 9, 445–457 economic development and, 446–448 Emory University, 449–454, 450f, 453f Georgia Institute of Technology, 449–454, 450–451f, 453f Georgia State University, 449–455, 450–453f methodology to assess, 448–449 overview, 445 results and discussion of study, 449–454 self-funding, 447 University research parks, 8, 337–344 benefits of, 342–343 definition of, 337 empirical findings of study, 341, 342t

506   Index University research parks (cont.) matched pair analysis of, 338–339 matched pairs firms, 338–341, 340t model for how university might assess benefits of, 342–343, 343f overview, 337–338 performance measures of, 339–341 performance of, on-park vs. off-park, 341–342 R & D intensity of firms, 341 University spin-offs, 228. See also Spin-off processes Unix, 261 Uno Contract, role in furniture industry, 118 Unproductive entrepreneurship, 54–55 "Unraveling the Cultural and Social Dynamics of Regional Innovation Systems" (Walshok, Shapiro, & Owens), 311–312 UPS. See Uninterruptable power supply production Urban economics, 38 Urbanization economies, 133 Urbano, D., 225 US Agency for International Development (USAID), 241 US Bureau of Labor Statistics O*NET database, 41 US Census of Manufacturers (1900), 126–127 "The Use and Abuse of Vegetational Concepts and Terms" (Tansley), 57 User-based innovation, 135 User-generated innovation, 248 US Patent and Trademark Office, 129–130 Vaccine development, 247 Valcucine, role in furniture industry, 119 Van der Knaap, G., 131 Van Hise, Charles, 275 Van Reenen, J., 160 van Stel, André, 150n6 Varga, A., 55n2 Varian Associates, 262 Veciana, J., 225 Venkataraman, S., 287 Venture capital firms, 158, 255 Venture capital funds, 314, 362, 362f Vernon, Raymond, 35

Vertical place-based policies, 182 Vertical priorities, 314, 468, 469 Vinodrai, T., 297 von Hippel, E., 224 von Humboldt, Alexander, 24 Wacziarg, R., 214, 215 Wadhwa, Vivek, 44 Wallsten, S., 30–31 Walmart, 149n5 Walsh, J. P., 259 Walshok, Mary Lindenstein, 9, 73n29, 311–312, 429, 432, 435 Walton, Sam, 149n5 Wang, 332 Warner, A. M., 194 Warner, Mildred E., 324 Warning, S., 224 War on Poverty, 321 Wasta, 197 Water, Energy, Technology Center (WET Center, California), 316 Watts, Alan, 436 Watts, Brad, 9, 414 The Wealth of Nations (Smith), 37, 126 Weber, Max, 171n16, 272 Weinberg, A. M., 238 Weiner, Norbert, 77 Weitzman, Martin L., 55n2, 65 Wellcome Trust, 241, 280 Wennekers, Sander, 148 West Germany, entrepreneurship in, 174 Westhead, P., 228 Whaling and Basque economy, 357–358 Whitford, J., 121 Whitney, E., 135–136 Wietzman, M. L., 129 Williamson, Oliver E., 170 Wilson, Charles Erwin, 73, 73n28 Wilson, T., 347 Wine industry, 259–261, 264 Winter, Sidney G., 56, 59, 127 Wisconsin university system, 275 W. K. Kellogg Foundation, 280 Wojan, Timothy, 40 Wood, M., 128 Working classes, 38

Index   507 World Bank, 28, 71, 192 Innovation, Technology, and Entrepreneurship Practice, 55 World Competitiveness Report, 196 World Economic Forum, 71 World War II, 238, 268 Wright, M., 228 Wright, Sewell, 60 Wuebker, R., 225 Wuhan University, 113 Wyrwich, Michal, 172 Xenophon, 216 X-prize, 248 Yale Entrepreneurial Institute, 351 Yale University, 276

Yamawaki, Hideki, 8, 373 Yemen, Arab Spring in, 200, 201 Yoon, J. W., 127 Yunus, Mohammad, 415 Zander, U., 219 Zero-sum competition, 331 Zhao, Q., 133 Zheng, Lingwen, 324 Zhicheng Champion, 108, 109, 113 Zhongshan (China), 108. See also Dongguan's UPS industry Zipf distribution, 270 Zoldoske, David, 317 Zoning regulations, 167–168, 168n13, 180 Zucker, L., 223