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Table of contents :
Cover......Page 1
Copyright......Page 4
Contents......Page 5
Contributors......Page 8
Preface......Page 11
Introduction......Page 13
PART I Concepts and Measurement: Innovation, Institutions and Change......Page 35
1. The new Argonauts, global search and local institution building......Page 37
2. Resilience, innovative ‘white spaces’ and cluster platforms as a response to globalisation shocks......Page 55
3. The Innovation Index: measuring the UK’s investment in innovation and its effects......Page 83
4. Territorial benchmarking methodology: the need to identify reference regions......Page 111
PART II Territories: Innovative and Evolving......Page 147
5. Innovative regions: strategic spaces for development......Page 149
6. New policy approaches to develop innovative territories: developing trust and behavioral additionality in Gipuzkoa......Page 162
7. New focus of economic reactivation in Spain: creative industries in the Basque Country......Page 178
8. Assessing country competitiveness: the case of Spain......Page 197
PART II IInnovation and Value Chains......Page 225
9. Outward FDI from developing country MNEs as a channel for technological catch-up......Page 227
10. Heterogeneous social capitals: a new window of opportunity for local economies......Page 242
11. China: beyond the global production line......Page 258
12. Are clusters the solution?......Page 286
PART IV Territorial Policies: Emerging from the Crisis......Page 305
13. Conceptualisations, relationships and trends between innovation, competitiveness and development: industrial policy beyond the crisis......Page 307
14. Regional policy: what (it seems) we have learned from some European experiences......Page 325
15. Cities in times of economic crisis: a challenge for local governments......Page 347
16. What public policies can and cannot do for regional development......Page 376
17. A participatory methodology for evaluating the cluster policy of the Basque Country......Page 394
18. For a resilient, sustainable and creative European economy, in what ways is the EU important?......Page 415
Bibliography......Page 441
Index......Page 493
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Innovation, Global Change and Territorial Resilience

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NEW HORIZONS IN REGIONAL SCIENCE Series Editor: Philip McCann, Professor of Economic Geography, University of Groningen, The Netherlands and Professor of Economics, University of Waikato, New Zealand Regional science analyses important issues surrounding the growth and development of urban and regional systems and is emerging as a major social science discipline. This series provides an invaluable forum for the publication of high quality scholarly work on urban and regional studies, industrial location economics, transport systems, economic geography and networks. New Horizons in Regional Science aims to publish the best work by economists, geographers, urban and regional planners and other researchers from throughout the world. It is intended to serve a wide readership including academics, students and policymakers. Titles in the series include: Migration and Human Capital Edited by Jacques Poot, Brigitte Waldorf and Leo van Wissen Universities, Knowledge Transfer and Regional Development Geography, Entrepreneurship and Policy Edited by Attila Varga International Knowledge and Innovation Networks Knowledge Creation and Innovation in Medium Technology Clusters Riccardo Cappellin and Rüdiger Wink Leadership and Institutions in Regional Endogenous Development Robert Stimson and Roger R. Stough with Maria Salazar Entrepreneurship and Regional Development Local Processes and Global Patterns Edited by Charlie Karlsson, Börje Johansson and Roger R. Stough Endogenous Regional Development Perspectives, Measurement and Empirical Investigation Edited by Robert Stimson, Roger R. Stough and Peter Nijkamp Media Clusters Spatial Agglomeration and Content Capabilities Edited by Charlie Karlsson and Robert G. Picard Spatial Scenarios in a Global Perspective Europe and the Latin Arc Countries Edited by Roberto Camagni and Roberta Capello Creative Knowledge Cities Myths, Visions and Realities Edited by Marina van Geenhuizen and Peter Nijkamp Societies in Motion Innovation, Migration and Regional Transformation Edited by Amnon Frenkel, Peter Nijkamp and Philip McCann Innovation, Global Change and Territorial Resilience Edited by Philip Cooke, Mario Davide Parrilli and José Luis Curbelo

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Innovation, Global Change and Territorial Resilience Edited by

Philip Cooke University Research Professor in Regional Development and Director, Centre for Advanced Studies, University of Wales, Cardiff, UK

Mario Davide Parrilli Senior Research Fellow, Basque Institute of Competitiveness, Orkestra and Associate Professor of Economics, University of Deusto, San Sebastian and Bilbao, Spain

José Luis Curbelo Managing Director, Basque Institute of Competitiveness, Orkestra and Deusto Business School, San Sebastian, Spain NEW HORIZONS IN REGIONAL SCIENCE

Edward Elgar Cheltenham, UK • Northampton, MA, USA

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© Philip Cooke, Mario Davide Parrilli and José Luis Curbelo 2012 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, electronic, mechanical or photocopying, recording, or otherwise without the prior permission of the publisher. Published by Edward Elgar Publishing Limited The Lypiatts 15 Lansdown Road Cheltenham Glos GL50 2JA UK Edward Elgar Publishing, Inc. William Pratt House 9 Dewey Court Northampton Massachusetts 01060 USA

A catalogue record for this book is available from the British Library Library of Congress Control Number: 2012930589

ISBN 978 0 85793 574 8

03

Typeset by Servis Filmsetting Ltd, Stockport, Cheshire Printed and bound by MPG Books Group, UK

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Contents List of contributors Preface

viii xi

Introduction Mario Davide Parrilli, José Luis Curbelo and Philip Cooke PART I

1

2

3

4

1

CONCEPTS AND MEASUREMENT: INNOVATION, INSTITUTIONS AND CHANGE

The new Argonauts, global search and local institution building AnnaLee Saxenian

25

Resilience, innovative ‘white spaces’ and cluster platforms as a response to globalisation shocks Philip Cooke and Arne Eriksson

43

The Innovation Index: measuring the UK’s investment in innovation and its effects Stian Westlake, Brian MacAulay, Peter Gratzke, Albert Bravo-Biosca and Hasan Bakhshi Territorial benchmarking methodology: the need to identify reference regions Mikel Navarro Arancegui, Juan José Gibaja Martíns, Susana Franco Rodríguez and Asier Murciego Alonso

PART II

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TERRITORIES: INNOVATIVE AND EVOLVING

5

Innovative regions: strategic spaces for development Antonio Vázquez Barquero

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New policy approaches to develop innovative territories: developing trust and behavioral additionality in Gipuzkoa Miren Larrea, Maria José Aranguren and James Karlsen

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Contents

New focus of economic reactivation in Spain: creative industries in the Basque Country Luciana Lazzeretti and Mario Davide Parrilli Assessing country competitiveness: the case of Spain Mercedes Delgado and Christian Ketels

PART III 9

10

166 185

INNOVATION AND VALUE CHAINS

Outward FDI from developing country MNEs as a channel for technological catch-up Alessia Amighini, Roberta Rabellotti and Marco Sanfilippo

215

Heterogeneous social capitals: a new window of opportunity for local economies Mario Davide Parrilli

230

11

China: beyond the global production line Philip Cooke and Fangzhu Zhang

246

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Are clusters the solution? Andrés Rodríguez-Pose and Fabrice Comptour

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PART IV

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TERRITORIAL POLICIES: EMERGING FROM THE CRISIS

Conceptualisations, relationships and trends between innovation, competitiveness and development: industrial policy beyond the crisis Patrizio Bianchi and Sandrine Labory

295

Regional policy: what (it seems) we have learned from some European experiences Juan R. Cuadrado-Roura

313

Cities in times of economic crisis: a challenge for local governments Manuel Perló Cohen

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What public policies can and cannot do for regional development Mikel Landabaso

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A participatory methodology for evaluating the cluster policy of the Basque Country Cristina Aragón, Maria José Aranguren, Cristina Iturrioz and James R. Wilson For a resilient, sustainable and creative European economy, in what ways is the EU important? Philip Cooke and Lisa De Propris

Bibliography Index

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Contributors Alessia Amighini, Lecturer, Faculty of Economics and Business Administration, University of Piemonte Orientale, Novara, Italy Cristina Aragón, Associate Professor and Coordinator of the Department of Strategy, Faculty of Economics and Business, University of Deusto, San Sebastian, Spain Maria José Aranguren, Associate Professor in Economics, University of Deusto and Head of Territory, Innovation and Cluster Unit, Basque Institute of Competitiveness, Orkestra, San Sebastian, Spain Hasan Bakhshi, Director, Creative Industries, National Endowment for Science, Technology and the Arts (NESTA), London, UK Patrizio Bianchi, Full Professor in Economics, University of Ferrara and Minister of Education and Labor Policy, Region Emilia-Romagna, Bologna, Italy Albert Bravo-Biosca, Senior Economist, National Endowment for Science, Technology and the Arts (NESTA), London, UK Fabrice Comptour, Researcher, College of Europe, Bruges, Belgium Philip Cooke, Professor and Director, Centre for Advanced Studies, Cardiff University, UK and Adjunct Professor, Aalborg University, Denmark Juan R. Cuadrado-Roura, Full Professor of Applied Economics, Institute of Economic and Social Analysis, University of Alcalá, Madrid, Spain José Luis Curbelo, Managing Director of the Basque Institute of Competitiveness, Orkestra and Deusto Business School, San Sebastian, Spain Lisa De Propris, Reader in Regional Economic Development, Birmingham Business School, University of Birmingham, UK Mercedes Delgado, Assistant Professor, Department of Strategic Management, Fox School of Business, Temple University, Philadelphia, USA viii

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Arne Eriksson, Organizational Learning Adviser, AEK Konsult, Nyköping, Sweden Susana Franco Rodríguez, Research Fellow in the Territory, Innovation and Cluster Unit, Basque Institute of Competitiveness, Orkestra and Deusto Business School, San Sebastian, Spain Juan José Gibaja Martíns, Associate Professor in the Faculty of Economics and Business Administration, University of Deusto, San Sebastian, Spain Peter Gratzke, Research Fellow, Innovation Index, National Endowment for Science, Technology and the Arts (NESTA), London, UK Cristina Iturrioz, Vice-Dean of Research and Associate Professor in Strategy, Faculty of Economics and Business Administration, University of Deusto, San Sebastian, Spain James Karlsen, Senior Researcher, Agderforskning, Norway and Senior Researcher, Basque Institute of Competitiveness, Orkestra and Deusto Business School, San Sebastian, Spain Christian Ketels, Principal Associate, Institute for Strategy and Competitiveness, Harvard Business School, USA, Senior Research Fellow at the Stockolm School of Economics, Sweden and Director of The Competitiveness Institute Sandrine Labory, Lecturer, Faculty of Economics, University of Ferrara, Italy Mikel Landabaso, DG Regio Head of Unit of Thematic Coordination and Innovation at the European Commission, Brussels, Belgium Miren Larrea, Senior Research Fellow, Basque Institute of Competitiveness, Orkestra and Lecturer at the University of Deusto, San Sebastian, Spain Luciana Lazzeretti, Full Professor in the Faculty of Economics and Business Administration, University of Florence, Italy Brian MacAulay, Director of the Innovation Index, National Endowment  for Science, Technology and the Arts (NESTA), London, UK Asier Murciego Alonso, Head of Information Technology Systems, Basque Institute of Competitiveness, Orkestra and Deusto Business School, San Sebastian, Spain

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Contributors

Mikel Navarro Arancegui, Full Professor in Economics, University of Deusto and Senior Research Fellow, Basque Institute of Competitiveness, Orkestra and Deusto Business School, San Sebastian, Spain Mario Davide Parrilli, Senior Research Fellow, Basque Institute of Competitiveness, Orkestra and Associate Professor in Economics, University of Deusto, San Sebastian and Bilbao, Spain Manuel Perló Cohen, Professor and Researcher in the Institute of Social Sciences, National Autonomous University of Mexico (UNAM), Mexico Roberta Rabellotti, Full Professor in Economics, Department of Political and Social Science, University of Pavia, Italy Andrés Rodríguez-Pose, Full Professor of Economic Geography, London School of Economics, UK and Research Professor at IMDEA, Madrid, Spain Marco Sanfilippo, Researcher, European University Institute, Florence, Italy AnnaLee Saxenian, Full Professor and Dean of the Faculty of Information, Department of City and Regional Planning, University of California (UC), Berkeley, USA Antonio Vázquez Barquero, Full Professor in Economics, Autonomous University of Madrid (UAM), Spain Stian Westlake, Executive Director, Policy and Research, National Endowment for Science, Technology and the Arts (NESTA), London, UK James R. Wilson, Senior Researcher in the Territory, Innovation and Cluster Unit, Basque Institute of Competitiveness, Orkestra and Lecturer in Economics, University of Deusto, San Sebastian, Spain Fangzhu Zhang, Lecturer, Bartlett School of Planning, University College, London, UK

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Preface In the current globalized economy a number of new challenges arise for local/regional economies as well as for broader national and community areas. In this context, territorial resilience is necessary and innovation represents the mechanism that may help to successfully sustain it. Indeed, innovation has become the critical driver for territorial development and competitiveness. The most advanced countries are those that invest the most significant percentage of their gross domestic product (GDP) in research and development (R&D) and other innovation activities. The globalization of markets has emphasized the need to respond quickly to such challenges, as large national economies have joined the international market on the basis of traditional cost advantages, but also on the basis of new advances made in technological and non-technological innovation. Many countries find themselves squeezed out of their former marketplaces. However, this situation not only creates threats and challenges, but also opens up opportunities that have to be exploited to create room for more balanced development between traditionally advanced economies, and transition, emerging and developing countries. It is a case of opening of new markets, but also potential for creating competent collaboration in research and development as well as in production and commercialization. As a result, the former market of 1–2 billion people in the advanced economies is getting too small; the new marketplace stretches well beyond that to incorporate a few more billion people who were previously left outside the production and consumption dynamics. In such a scenario nothing can be taken for granted, and significant work is needed in order to spread these wider options across a larger number of countries and territories, and to help them reap the related benefits. In this context, in September 2010, the Basque Institute of Competitiveness – Orkestra – organized the San Sebastian Meeting on ‘Competitive and Innovative Territories’ and invited a number of leading academics, international organization officials and international consultants who presented ideas and cases that were discussed and thoroughly analysed, with the most careful attention given to any useful policy implications and recommendations. In particular, the participants presented insights, models and hypotheses on innovation dynamics and activities in

xi

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Preface

present-day economics; other experts focused their work on methodological components, frameworks and tools that help to capture a more appropriate image of innovation dynamics, inputs and outputs. Another group of specialists focused on innovation processes along global value chains and global knowledge pipelines, including the social features of such exchanges that may contribute key insights into how local production systems can upgrade their competitive position within global production networks and markets. Complementing this, a group of experts focused on the policy implications of the whole discussion on innovation promotion in regional and national territories. Suggestions based on successful international experiences as well as on reflections that take into account the context specificity of policy are mirrored in this part that recognize the importance to add a practical discussion to the more academic part of this debate. Later, we included some chapters that addressed important aspects of contemporary globalization that had not been available for the meeting. Overall, the main message that flowed from the meeting and related debate is that in the current context of global change, local and national territories have to upgrade their resilience in terms of improving their capability to innovate and their competitiveness. Localized creativity, small high-technology entrepreneurship, related variety innovation platforms, social capital embedded in dynamically open territorial communities, and context-specific though continuously upgrading policy platforms are all means to face the new challenges and to promote increasing absorptive capabilities within local and national territories. These are much needed in the current globalized economy as a means to be sustainable and to offer continuously new opportunities to their inhabitants and agents. PC, JLC and MDP Cardiff and San Sebastian, June 2011

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Introduction Mario Davide Parrilli, José Luis Curbelo and Philip Cooke 1.

NEW GLOBAL COMPETITORS AND TERRITORIAL CHALLENGES

Today, numerous important challenges are catalysed by the spread of globalisation processes that offer critical opportunities for the exchange of cultures, knowledge, goods and services, as well as raising problems and threats, such as the powerful economic crisis 2007–12 and the uncertainties about the way out towards a sustainable development model for advanced and non-advanced economies. In this new situation, the traditional leadership of western countries in global markets is diminishing due to the increasing integration of many emerging countries, particularly Asian and Latin American in global markets. The market shares of western countries have drastically shrunk, whereas manufactured goods exports of a higher and lower level of sophistication show much bigger growth for these new world competitors, which challenge the competitive capacity of western countries in the medium to long term (United Nations, 2011). Achieving the process of economic catching-up, so much studied and desired by pioneers of the literature on economic development for many decades (Myrdal, 1956; Rostow, 1956; Hirschman, 1958; Seers, 1969; Wade, 1990; Lall and Teubal, 1998; Rodrik, 2004) has become reality for this wider group of countries, although this does not prevent them from displaying major internal inequalities in terms of access to resources, jobs and wealth. In spite of this, countries like Brazil, Russia, India and China (BRICs) have learned how to combine and profit from different factors (natural resources, workforce and proactive public policies) to build up new capabilities and competitive advantages that have allowed them to obtain notable growth in national and per capita income (Amsden, 1994). If these challenges represent potential threats for western local and national production systems, the current globalisation also offers potentials and opportunities that local production systems may exploit. These 1

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include easier access to national and international markets (for example, beyond Europe for European Union clusters), production chains and networks (for example, linked as specialized suppliers to multinational companies, or as niche producers to international consortia), institutional support (for example, joining framework programmes), and cultural and knowledge networks (for example, joining excellence or innovator networks, student exchange programmes) that offer high potentials for knowledge absorption and increasing the capabilities of local actors in such wider global landscapes. With regard to this context of global change and major challenges for different countries and territories, the participants in the first San Sebastian Meeting on Innovative and Competitive Territories, realized in September 2010, met to share analyses, reflections and orientations on the prospective roles of local and regional territories within global development processes. In this book we will refer to the need for a new perspective on many of the issues raised that considers change from the perspective of ‘resilience’ thinking. This involves assessing the quality and strength of two key features of any socio-economic system. These are its potential, measured in terms of the variety present in the economy, from the interaction among diverse entities, innovation emerges; the second element is its connectedness the degree to which it has interlocking systems of organisations, institutions and conventions assisting its governance. These two in combination allow for the capacity of a regional, or any other system, to face destabilising shocks, manage processes of creative destruction through innovation and recover. Recovery may mean forging a new pathway rather than continuing with an established regional path dependence (Folke, 2006).

2. ARE LOCAL TERRITORIES STILL RELEVANT? THE DILEMMA CLUSTERS VS NETWORKS In the past 30 years, together with the traditional macroeconomic and financial analysis centred on countries and macroregions, the role of local territories has also been much discussed. This has raised issues concerning the potential of industrial districts, clusters of firms, local production systems and, in general, regions as territorial and administrative territories endowed with some degree of homogeneity in terms of cultural traits, production profiles and entrepreneurial dynamics. Nonetheless, more recently, part of the international academic reflection has concerned whether these criteria are appropriate or sufficient given the recognised importance of firms to connect with global knowledge flows (Bathelt et

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Introduction

3

al., 2004; Hess, 2004). These act as means to break knowledge lock-ins through the integration of innovation inputs unavailable in local/regional territories. The consensus on the key role played in this ‘connective’ process by knowledge networks is wide enough to have led to the creation of different institutional programmes and mechanisms that favour their constitution, for example, within the European economic space. Examples include innovation networks measures within the European Union (EU) Framework Programme, and excellence networks and academic networks programmes in member countries, such as Projects of Research of National Interest (PRIN) in Italy and the research projects of Ministry of Science and Innovation (MICINN) in Spain, among others. Here, firms and private centres participate and collaborate to promote innovation, for example, technology centres in the Basque Country, Fraunhofer Institutes or Steinbeis Foundations in Germany, among many others. Notwithstanding policy attention towards the driving role of networks, the value of clusters and local/territorial production systems is further recognised for various reasons: on the one hand, the existence of these systems in the majority of countries creates opportunities for the promotion of local economies through joint actions and external economies – sharing the existence of skilled human capital, the flow of information and innovation, and the flow of clients, among others (Becattini, 1990; Schmitz, 1995). On the other hand, some have argued that the relevance of clusters and districts relies upon potential flows of knowledge that are hard to transfer through transnational networks due to their tacit knowledge basis built on experience, practice, collective action that, in addition, require face-to-face interaction and exchanges (Audretsch, 1998; Maskell and Malmberg, 1999; Lundvall, 2007b; Parrilli et al., 2010). In synthesis, territories (clusters, industrial districts, local production systems) and networks represent two crucial and complementary realities economic agents can utilise for the promotion of effective knowledge flows that contribute to the development of given territorial communities. These are their innovative potential and their governance connectivity which together represent their degrees of resilience. In addition, such territories often constitute significant parts of the national economy, in countries such as Spain, China, Italy, and Brazil, among others, where studies have identified hundreds of local production systems that take the form of clusters and industrial districts (Sforzi, 1992; Cassiolato and Lastres, 2001; Bellandi and Di Tommaso, 2005; Boix and Galletto, 2008).

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3. SPECIALISED AND CREATIVE TERRITORIES The debate on specialised and creative firms and territories is fundamental for the competitiveness of territorial systems of production because the effectiveness of external economies, knowledge networks, and interactive knowledge flows determines the development capacity of any territory. It is now known that where these components are missing, the competitive capacity of the local or regional economy is reduced as is its potential to integrate dynamically with national and international markets. As Schumpeter (1942) acknowledged, it is clear that the global market is a suitable fit for large firms to perform due to their economies of scale in production, research and development (R&D), marketing and commercialisation; however, small firms still play a crucial role, once connected to large firms as suppliers, and on other occasions for their capacity to specialise in market niches and activities of high added value, both in manufacturing production and knowledge-intensive service provision, in which the size of operations is less relevant than the quality of talent, skills and competences of research and technical personnel. For this reason, many different scientific-technological and production configurations can be found, for example, science and technology parks some of which have achieved world fame for the production of leading-edge knowledge as well as clusters of several firms producing in different technological fields (for example, Cambridge in the UK, Sophia-Antipolis in France, Oita and Kumamoto in Japan, Silicon Valley and Boston Route 128 in the USA, and Zamudio and Miramon in the Basque Country, among many others) (Cooke, 2001; 2005). These prospects are very much enhanced by the current development of new global demands related to both the more sophisticated consumption pattern of end-consumers as well as the requirements for environmental, social and economic sustainability. This is a sphere which economic development, innovation and globalisation thinking has almost completely neglected. In the sphere of economic practice too, global firms remain largely blind to their continued dependence on carbon-based energy. However, evidence has emerged that in a few smaller countries such as Sweden and Denmark, governments, regions and some clusters have begun broadening strategic niches in eco-production and eco-innovation while finding ready markets in the aforementioned BRICs (Cooke and Eriksson, Chapter 2 in this volume). Most of these countries are bad polluters and major contributors to global warming, but most have policies and production facilities, like Brazil-based bioethanol producer, Petrobras, India’s leading wind energy firm, Suzlon, or China’s equivalent, Goldwind, to seek to mitigate carbon emissions (Cooke and Porter, 2011).

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5

These are early examples of the ‘resilience effect’ as operators realise that eco-innovation is not only a social good but that significant global markets exist that are ready to invest in cleaner energy and production solutions. In addition to the newer eco-innovation poles and the more established technological innovation complexes, another pole of creativity has also been evolving and contributing to economic development processes at the city-regional level. In fact, the importance of cities has been acknowledged not only in terms of agglomerations of economic activities that benefit from diversification economies and the existence of centripetal forces (Jacobs, 1969; Audretsch, 1998), but also as centres of attraction of a multiplicity of people with distinct competences and cultures, which make it possible the existence of creative, artistic and industrial processes (Florida, 2002a; Saxenian, 2002; Cooke and Lazzeretti, 2008; Johnson, B., 2010). The creativity that can be found in geographies with a very high concentration of people with their own cultures, experience, competences and objectives – as occurs in the cities – cannot be found in other localities characterised by their traditional cultural, social, institutional and human homogeneity. In this literature and the related empirical evidence, diversity and the interaction among different actors are considered essential factors for the creativity of firms and territories.

4.

TERRITORIES AND MARKETS

On the basis of such considerations, in general the participants to the First San Sebastian Meeting on Innovative and Competitive Territories adopted an approach that underlines the economic importance of regional and local territories. Each saw them as key places in spurring the competitiveness of small and medium-sized enterprises (SMEs). They further recognised their role in the promotion of economic and social development as a means to respond to the challenges set by globalisation. Along with global economic recession, rising energy prices and climate change regions face massive destabilisation processes. Because of the neglect by multinational companies (MNCs) of seismic shifts in the economic landscape, local communities now confront and even question the standardised regime of mass consumption patterns that led to the empowerment of prevailing business groups and financial holdings operating in international markets. These large firms and their groups and holdings see the world market as spaces to exploit their large capacities and places in which they can pull together resources and benefit on the basis of global strategies and development plans. Contrariwise, regional territories and their local production systems face markets with a new approach, which aims at overcoming the distance

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that traditionally sets them apart from such markets. Although the traditional market of small firms is local and regional, wider spaces accrue every day for high-growth potential SMEs in industrial and service sectors that focus on wider, national and international markets. In particular, this refers to sophisticated markets that require personalised, high-quality and innovative applications that can help them integrate with global value chains and global production networks in a way that enhances their contribution to territorial innovation and development processes at the local and regional level. In the past thirty years many SMEs and their territories have penetrated wider markets, notably European firms in the EU market). Currently though, SMEs and their territories must take on new challenges and new objectives for which they have neither guarantees nor practice. Are Asian, Latin American and African markets accessible to western SMEs and to their territorial systems of production? Chinese SMEs are succeeding in such processes showing great capacity to reach global markets (Bellandi and Di Tommaso, 2005), mainly on the basis of a ‘low-road of development’ (Pyke and Sengenberger, 1992). However, even this is currently changing in specific segments and sectors (for example, energy, optical fibre, electronics and information and communication technologies (ICTs); see Cooke and Zhang, Chapter 11 in this volume) a feature that also needs to be taken into account by smaller western businesses in envisioning future global scenarios. Is it a solution that can be extended to other firms, particularly to western SMEs with high costs of production that are oriented to pursue the ‘high road of development’ based on innovation, quality and demand sophistication? This is the question that does not appear to have an easy and unequivocal answer; it rather requires attention to concrete phenomena occurring in global markets due to a lower demand in emerging markets for goods and services produced by technologically advanced SMEs (that is, at least in terms of the segments of population that in such markets show sensitivity and purchasing power for such goods and services). A research stream that needs to be explored and discussed in depth refers to the most appropriate mode SMEs and their territories can adopt to integrate competitively in global markets. This is a resilience question that emphasises the role that the territory and its firms can develop within global production networks in which many different actors come together. This includes multinational companies, specialised small and mediumsized companies, small and medium-sized capacity subcontractors, other local and/or national niche firms, and service providers for manufacturing companies (Ernst and Kim, 2002; Coe et al., 2008). What seems to be emerging from the system shocks dealt by ‘peak oil’, global warming,

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global financial meltdown and economic recession is a desire for change and even for more collaboration between such actors, turning sectors and clusters into platforms of innovation based on ‘related variety’ (Frenken et al., 2007). This is especially true of eco-innovation, which inevitably combines many technologies, but it has been true of ‘platform technologies’ like ICT and life sciences for some time. If the world production of consumer and capital goods is managed through global value chains and networks, what position can the small firms of a cluster assume in order to benefit from participating in these networks and, simultaneously, guarantee a durable and sustainable competitiveness? Also, what interactive learning processes are more likely to set in motion the national and regional innovation systems so as to promote a more competitive participation of clusters and territories in such networks and chains? The answers are neither automatic nor unequivocal since there can be various forms of insertion in global production networks; as a result academic and applied research as well as policy-making have to prioritise it in the agenda as a key topic for development.

5.

CULTURE AND SOCIAL HETEROGENEITY AS DRIVERS TO TERRITORIAL COMPETITIVENESS

There are territorial groups and communities that are characterised by a culture that is embedded in a common history that ease the development of their creativity, entrepreneurial spirit and innovation dynamics, what is not always available for all of the agents that operate in a country and its economy. As some of the contributions in this volume highlight, creativity can split between a traditional approach and a non-traditional approach, the latter responding to the requirements and features of ‘younger’ economies which search for a ‘creative’ positioning in markets, through a specific originality in the presentation of contents related to marketing, research and development, engineering, architecture, arts, software and the audiovisual sector, among others. Moreover, this is without excluding the creativity that is found in traditional sectors (for example, food and beverages) that helps identify a creative and innovative territory in the field of cuisine and restaurants (for example, star-ranked restaurants of the Michelin guide, as in the case of San Sebastian and, by extension, of the activities that are linked to cuisine in the Basque Country). As a few seminal contributors suggest (Saxenian, 2002; Gertler, 2003), creativity and the capacity to create new developmental pathways is not particularly the province of MNCs only. It arises also within specific communities, some having migrated to other territories, such as the Chinese

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and Indian high-technology communities in Silicon Valley in the USA. The culture (social capital) of a critical mass of agents within a territory constitutes a key factor to promote any territory, since it delivers a variety of inputs that include both aspects of human capital, that is, skills and standards. These are essential territorial assets in dealing with resilience issues, especially when a significant national investment has taken place in training and educating the people (as happened in many Asian countries over the past three or four decades, see Amsden, 1994). Further elements include tacit-to-explicit knowledge flows based on experience and interaction, and also values that characterise the business and social ethic of a community in the management and leadership of private and public companies and organisations (Lundvall et al., 2002). Finally, the attitude towards diversity is also essential for territorial progress, especially when the accelerated ageing phenomenon that occurs in many European countries, with their need to attract talent, skills and capabilities, converts into a priority issue or ‘Grand Challenge’ provoking a further ‘resilience response’ (Orkestra, 2011). This issue stresses the need for a new perspective on migration and, in general, even temporary movements of people across territories, especially across advanced economies, in which anxiety faced with apparently uncontrollable waves of people, which modify the social and cultural basis of any territory, promote tensions sometimes spurred by economic crisis. Most EU countries are facing this issue, some, like Italy, for the first time whereas in north America it is the foundation of their societies. A new approach based on resilience thinking is needed to take into account that such new situations also offer the bases for a new meeting among cultures, communities and values. These can enrich one another mutually through a peaceful exchange that helps build joint sustainable solutions to local and global problems that affect everyone (for example, global warming, environmental pollution, excess production in some territories alone, lack of human, and labour rights in advanced and developing economies). All this requires a creative cultural approach in its widest meaning, that is, conceiving the exchange as a conveyor of high returns to both parties, provided it is based on democratic management processes oriented to produce local, regional and national solutions to development issues (see Parrilli, Chapter 10 in this volume).

6. SCHEME OF THIS VOLUME This volume is structured in four core parts: the first is about conceptual and methodological aspects, the second centres on original accounts of

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innovative territories, the third is on innovation across countries and production networks, and the fourth focuses on regional policies as a means to drive resources and consensus to the promotion of innovative and competitive territories. These are parts of a coherent approach that helps analyse challenges and development prospects of local/regional production systems both internally, in terms of potential (for example, what industrial configurations seem most innovative), across territories (for example, what factors promote international integration in international markets and in global knowledge pipelines), and territorial connectivity that helps exploit opportunities for proactive policy actions, especially in the currently de-stabilised context in which the balanced allocation of resources and opportunities – particularly for SMEs – cannot be expected as the automatic result of the working of the market. Part I: Concepts and Measurement: Innovation, Institutions and Change In the first part, key conceptual aspects are set out. In particular, the openness of local and regional territories to global knowledge pipelines and/or global knowledge frameworks is discussed. These territories are no longer to be understood as self-contained poles of growth and development – as they tended to be in the 1980s – rather, they are impelled to cross numerous borders to attract talent, resources and opportunities not available locally or that belong to different local industries. The openness of these territories, not primarily in terms of exchange of goods and services, but rather of ‘related’ knowledge and innovation platforms are seen as key to promote the integration of territorial economies within dynamic global production networks and international markets. This discussion has methodological implications that need to be analysed and clarified in order to permit original, flexible and updated comparisons across territories. In this way benchmarking practices may deliver useful elements for the interpretation of ‘innovative and competitive territories’ in the current context of globalisation. In this first part, AnnaLee Saxenian’s (ALS) chapter is extremely meaningful as it looks at the growing importance of global search networks that firms and other actors rely upon to locate collaborators who can either solve a problem they face, or require a solution. On the basis of the success of new high-technology clusters, ALS suggests that local enterprises systematically look for collaborators – including clients – who can help them solve problems, rather than trying to elaborate comprehensive solutions on their own. The author focuses on highly skilled first-generation immigrant professionals in US technology industries who collaborated with their home country counterparts (Taiwan) to develop the context

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for entrepreneurial development. Her central argument is that these ‘new Argonauts are ideally positioned to search beyond prevailing routines to identify opportunities for complementary “peripheral” participation in the global economy and to work with public officials on the corresponding adaptation and redesign of relevant institutions and firms in their native countries’. AS argues that ‘Argonauts are the co-architects of further networks that extend and adapt to home-country conditions the web of relations they already know’. Overall, ALS’ argument is that the key contributions of skilled professionals to their home countries are more in the process of external search and domestic institutional reform than through direct transfers of technology or knowledge. In their chapter, Cooke and Eriksson (CE) describe innovation processes as being based on four complementary theoretical perspectives, which underscore some key elements. These are the regional ‘relatedness’ of industry, ‘path inter-dependence’ of socio-technical systems, ‘pre-adaptation’ and the ‘adjacent possible’. These key elements help explaining both incremental and radical innovation by means of emphasising the relevance of building knowledge in related knowledge fields as a means to benefit from pre-existent knowledge bases, while simultaneously breaking pre-existent lock-ins and path-dependence within the territory. Overall, these drivers help structure an evolutionary analysis of contemporary regional innovation processes. CE apply these concepts to the working of a representative set of regional innovation agencies in central Europe and contrast these with Scandinavian cases, sometimes showing ‘pre-adaptation’ of an innovation from one industry/cluster into a different one, and search practices for the ‘adjacent possible’ in others, with special reference to the region’s strategic response to two global ‘Grand Challenges’: the appropriate, ongoing solution of the climate change issue, and the provision of healthcare services in an ageing global society. In this context, sets of innovative clusters are integrated to maximise ‘potential’ on ‘sustainable cities’ and ‘personal healthcare’ in the shape of ‘interactive innovation platforms’. This discussion is then revised under the lenses of globalisation and the big challenge – for industries – posed by the rise of competition in ICT from Asian ICT competitors. A policy ‘transversality’ approach (connectivity) is proposed as a means to searching for innovation gaps among the interactive cluster elements of innovation platforms. Within the related methodological section of Part I, Westlake, MacAulay, Gratzke, Bravo-Biosca and Bakhshi focus on the need to elaborate a broader and more complete innovation index, which ‘provides a more up-to-date and comprehensive measure of UK innovation investment that reflects how businesses of all types develop ideas, take them to market, and profit from them’. It also links this investment directly

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to productivity growth, one of government’s most important economic priorities and, finally, provides new data on innovation across businesses and industries, and on wider innovation conditions in the UK. WMGBB suggest that it is important to measure a wider set of innovation indicators ‘than just R&D, patents, citations and the proportion of firms who are actively engaged in innovation’. Within this perspective the government should consider the level of investment in innovation as to involve more than R&D, for example, design, training in new skills, organisational innovation, developing new customer offering and brands, and copyright, which represent the seven new areas identified by the National Endowment for Science, Technology and the Arts (NESTA). This wider measure of innovation investment provides a much better indicator of the success of innovation policy in a large set of countries (for example, the UK) whose innovation pattern is based on these complementary areas of innovation. From a comparative perspective, Navarro Arancegui, Gibaja Martíns, Franco Rodríguez and Murciego Alonso (NGFM) develop a benchmarking analysis that is thought to facilitate the extraction of useful elements for development promotion by means of the formulation of a competitive innovation strategy for the regional territory, in so far as the follow-up and assessment of its performance. This work supplies an instrument to promote the development of three phases that are crucial in benchmarking exercises across regions, once the principle of comparability between equivalent entities (that is, regions) is assumed. The first phase is the identification of homogeneous spatial areas upon which to organise the territorial comparison; the second step is the identification of territories that achieve a better performance to facilitate explicit and useful comparisons; and the third benchmarking step is the analysis of the causes of such better or lower performance. This work then realises the comparison on the basis of quantitative indicators available in the main European database (Eurostat), though for further developments, qualitative and participatory analysis should also be included as a means to achieve a more complete comparative assessment. Part II: Territories: Innovative and Evolving The second part of the volume analyses concrete cases of regional and local territories within a world of crises, changes and challenges. Particular urban configurations (that is, technological and/or creative cities, polycentric regions) have become the main poles of innovation across larger national territories. A polarisation process has taken place around these centres of political, social and economic activities that should be complemented through appropriate dissemination practices as a way to favour a

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more equal distribution of resources and creative factors across regional and national territory. Within this approach, Vázquez Barquero (VB) describes the new international division of labour across global cities, each one specialised in a particular function (for example, politics, trade, finance and banking, knowledge and information, creativity, culture and amusements). In this connection, there are a number of polycentric urban regions that also polarize innovation and development processes (for example, Randstad in the Netherlands, Hansai in Japan, the Ruhr in Germany, Research Triangle Park in North Carolina and the Pearl River delta in China). The changes related to the intense use of ICT systems promote increasing exchanges across national and international markets. This process led to significant changes in the geography of production, that is, big cities and polycentric regions are leading rather unilaterally the economic dynamics in both advanced and developing countries. The process of economic and institutional change is not uniform across national territories but takes place in these city-regions that represent ‘innovative spaces’ with a lead capacity based upon their higher capacity to undertake strategic innovation through creative systems of organisation, interaction and production (potential). The capacity of these cities and regions to respond to the new challenges (for example, the current crisis) depends very much on the capacity of their firms in transferring innovation and of their institutions to support trust and actions oriented to increase productivity and competitiveness, and later disseminate it to other national and regional territories (connectedness). Larrea, Aranguren and Karlsen (LAK) focus on social capital as a driver to promote the construction of regional advantage. In the context of the Guipuzcoa province of the Basque Country, LAK argue that the generation of trust is a critical connectivity condition to create collective action between agents that are usually quite separated from one another (for example, innovation agents and firms, politicians and firms). Within such framework, proactive innovation policy could focus on generating behavioural additionality as a means to promote trust and cooperation. Of course, such additionality can be attained not only through measures set up by policy-makers, but also through a participatory economic development process where regional agents participate in the diagnosis and design of the relevant measures. Research can play a role here through the application of the action-research methodology, which can help promoting conceptual frameworks and a common language that help different stakeholders (social and economic agents) building a shared vision of the system in which they participate. It is a process that sets the bases to help building innovative and competitive territories (that is, exploiting ‘potential’).

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Lazzeretti and Parrilli (LP) discuss the relevance of creative industries in ‘creative cities’. Culture and creativity are regarded as resources for innovation that can activate new and different economic value chains. Culture has become a creative capacity that can rejuvenate products, sectors, professions and places. Recent studies shift the attention from the artistic and cultural heritage to the human factor and the creative class. Physical assets are not the key elements that justify the relevance of culture here; it is rather the creative capital, embodied in people, that enhances the opportunities delivered by such assets. Creative industries and the creative class are overwhelmingly concentrated in urban and metropolitan areas, which make wider use of new technologies, a by-product of creativity. In this chapter, LP emphasise the creative industries approach and extend it over creative local production systems. The authors offer a concrete application to the cities of Bilbao and San Sebastian (1 million and 200 000 inhabitants, respectively) in order to present a profile of how dynamic small cities can participate in a novel way in booming creative industries. These cities exhibit modern, though familiar configurations as ‘diversified creative local production systems’ that invests resources and people in non-traditional cultural activities such as marketing, R&D in engineering and architecture, and software production, among others. Within the wider discussion on competitive territories Delgado and Ketels (DK) analyse the measurement of competitiveness. DK document the main features of the diagnostic tool to assess country competitiveness they recently developed with Porter and Stern (Delgado et al., 2010a), and use it to assess Spain’s competitiveness. Their framework incorporates a wide range of these factors into an integrated structure with three main areas: endowments, macroeconomic competitiveness and microeconomic competitiveness. Macroeconomic competitiveness has two related areas: social infrastructure; and political institutions with macroeconomic policy. Microeconomic competitiveness includes the sophistication of company operations and strategies (potential) and the quality of the national business environment (connectivity). The contribution of these factors is influenced by multiple stakeholders, including different government agencies, companies and institutions for collaboration. While macroeconomic competitiveness sets general conditions that create opportunities for higher productivity, microeconomic competitiveness has a direct impact on the way these opportunities are actually translated into better performance. In contrast to previous contributions on the subject, DK propose a comprehensive model, in which the contemporaneous microeconomic environment matters for country prosperity, even after accounting for institutions and endowments. Within the context of Spain, DK strongly suggest that this country is facing a competitiveness challenge, in which

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some dimensions of microeconomic competitiveness have become serious constraints for higher economic performance. Part III: Innovation and Value Chains The third part of this volume opens up the space in which the local territories cultivate their innovation and competitive practices, in particular in connection with active and prominent global value chains and global production networks (for example, aircraft, automotive, ICTs and energy industries, among others), and more in general with international markets. The four contributions of this part highlight some of the international dynamics that have been implemented by multinational groups, for example, Chinese, in order to improve their connection to international markets. In addition, the importance of reaping the benefits of migrant communities – for their endowment of tacit knowledge and social capital – is stressed as a means to promote local development and innovation processes. The first contribution by Amighini, Rabellotti and Sanfilippo (ARS) explores one of the recent aspects of the globalisation process, that is, the rise and the increasing outward expansion of multinational enterprises (MNEs) from developing countries. Among the most promising effects of this phenomenon is a potentially positive impact of outward foreign direct investment (OFDI) through which developing country MNEs acquire new knowledge, which contributes to the technological catch-up of their home countries vis-à-vis the most advanced economies. This literature suggests that the features and global business environment of current emerging country MNEs is different from those of latecomer firms in earlier decades. ‘Modularity of production in an increasing number of sectors, combined with weak national innovation systems (NIS) in many developing countries explain why the sourcing of strategic assets – including technology and innovation – through OFDI has become such an important channel for technological catch-up.’ The modularisation of production in a growing number of sectors, favoured by information technology and technological progress has enabled the disintegration of production processes, allowing the outsourcing of several activities, including production, design and R&D. This is also analysed for digital media in Chapter 2. The phenomenon has two major consequences for the context in which developing country MNEs operate. First, developing countries are increasingly becoming the location of R&D and high-technology activities and not only of mature technology, as was the case in earlier decades. Secondly, strategic acquisitions provide a faster alternative to building technological capabilities in-house (more than through knowledge transfer). These

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options make it possible for firms in less developed countries (LDCs) to become acquainted with new technology at an earlier stage, and to learn from its application. These dynamics are analysed through Chinese investments in Italy that respond to strategic objectives in terms of catching up with European tastes and requirements, of quality of products, design and post-service assistance. Parrilli (MDP) analyses the relationship between global knowledge pipelines (GKP), tacit knowledge (TK) and social capital (SC). MDP stresses that GKPs are important not only as conveyors of codified knowledge, but also of TK. In addition to this, MDP extends the concept of TK to include systematically the concept of SC. Traditionally, TK tends to be conceived as individual experiential knowledge based on practice, as part of the human capital embodied in the highly skilled individual expert. Instead, TK also include collective pools of social knowledge otherwise called SC because TK can be created and later transferred by wider communities. In a second operation MDP develops an argument (Parrilli, 2007) on the richness of migrants’ codified and tacit knowledge by asserting that TK flows do not rely only upon highly knowledgeable economic agents such as scientists and engineers, but on a broader spectrum of collective agents who may be part of competitive GVC/GPN/GKP. This discussion has special importance for clusters and districts, where TK flows and SC are undergoing intense transformation processes that demand resilience. Thus there is a need for more thorough theoretical frameworks to represent these changing socioeconomic scenarios, as well as their real constraints and opportunities. These dynamics are then reviewed within two local production systems immersed in global knowledge pipelines: Silicon Valley and Prato. Within such contexts global knowledge pipelines (GKP) bring in TK flows that modify former social homogeneity, breaking common values and understanding though also offering new opportunities to enrich local buzz through new external knowledge. This process is promoted by the inflow of both skilled and unskilled people. MDP concludes with reference to a Krugman-type of approach to undertake a thorough and dynamic analysis of what may develop in such contexts. The overall process needs to be properly assessed by using a development approach that studies the ways in which some centripetal forces more than offset defiant centrifugal forces. Cooke and Zhang (CZ) study the role of China in stimulating global change in two distinct ways. This chapter briefly explores the manner in which leading Chinese firms penetrate western markets with the objective to base themselves in western R&D locations. CZ show that China seeks to evolve rapidly away from its ‘world factory’ reputation and that its attempt goes through the successful route to generating a significant ‘green

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technology’ platform; this endeavour will decide whether it is willing and able to make a global contribution to the environment’s ‘long emergency’ domestically and if by so doing it creates new, more affordable, means that can be replicated worldwide. After discussing three complementary theoretical perspectives (namely, transition, resilience and innovation) that seek to explain transition in the dominant production regime fuelling market-led development from one based on fossil fuels to one based on renewable energies, CZ argue that China’s global engagement has stimulated and been stimulated by all three. In the context of global and local changes, CZ propose an open and innovation-friendly perspective and a socially constructive approach as appropriate to promote change and innovation. Under conditions of proximate path interdependence, innovation is able to occur in line with the key complexity theory concepts of ‘pre-adaptation’ and the ‘adjacent possible’ earlier discussed. Overall, CZ refer to the continuing relevance of proximate knowledge and production geographies because innovative acts generally occur in specific localised spaces even if knowledge from many global locations is recombined to achieve them. Chapter 12 by Rodríguez-Pose and Comptour (RPC) takes a comprehensive approach to the capacity of clusters to innovation and competitiveness in the current globalised economy. They address a gap in the literature by studying the interaction of the presence of clusters with other factors deemed to promote innovation – such as investment in research and development (R&D), patent applications, or the presence of ‘innovation prone’ socio-economic environments – and economic growth across 152 regions located in 15 EU countries over the period 1995–2006. Using pooled cross-section regressions, RPC’s model intends to capture both the static and the dynamic connection between a series of innovation promoting factors grouped into three different composite variables or ‘innovation filters’ – the ‘R&D filter’, the ‘social filter’ and the ‘clusterisation index’ – specially designed in order to proxy the complex interaction among growth enhancing innovation variables. Part IV: Territorial Policies: Emerging from the Crisis This part is about the prospects of public policy stimulating local, regional and national production systems to take on the global ‘Grand Challenges’. It is argued this is best achieved by improving connectivity so that positive effects from such integration improve the innovative ‘potential’ of local economies. In the best possible world territorial policies are not needed because markets allocate efficiently and effectively resources and opportunities; however, in the concrete world with significant cost/price

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cross-country gaps in addition to current imbalances in terms of endowments, purchasing power, technology, human capital, institutional thickness across different regions and countries, the proactive role of public policy is crucial to re-stabilise economies and to offer new opportunities for catching up to laggard countries and regions, while guaranteeing options for continued growth to other territories that are on the right path. This discussion on public policy remains conscious of the need to acknowledge the individual path that each regional and national territory has the freedom to pursue on the basis of its own history, idiosyncrasies, endowments and capabilities. With a developmental purpose, Bianchi and Labory (BL) stress the importance of taking a broad view to growth that implies a long-term process of improvement that is simultaneously economic, social, political and cultural. Industrial policy choices must also be made holistically, considering the interdependencies between instruments and policy levels, between strategy and organisations, and between the organisations and their environment. BL develop a model centred on four main development levers, including innovation, territory, entitlements and provisions. This model creates an instrument (‘sundial’) to determine the appropriate policy mix for each specific context on the basis of the strength of these four levers. These latter can determine development through a virtuous spiral: innovation determines regional development and social improvements, which in turn enlarge entitlements, allowing more people to take part in the development process that enables further innovation. These elements and their links indeed determine possible learning mechanisms, which may be strengthened through appropriate mixes of four types of policies, that is, human capital policies, innovation policies, structural policies and social policies. BL apply the ‘sundial’ model to Brazil, South Africa and China in a way that shows how interrelated are industrial, policy and technological policies. These cases also exhibit the infinite set of actions that can be implemented within different policy levels, for example, town, province, region and nation, whose coherence can only be guaranteed by outlining a territorial strategy for long-term industrial development. Cuadrado-Roura (CR) focuses on lessons learnt from regional policies within the European context, where regional policies have often been a response to the existence of regional disparities in GDP per capita, unemployment and productivity rates, which have a profound effect on regional economic welfare. Other specific problems have also justified some types of regional policies, such as those aiming to solve problems of industrial decline or to slow down the concentration processes of economic activities in metropolitan areas. With regard to traditional regional policy, based

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upon offering subsidies to firms locating their new plant in designated assisted areas, the regional policies developed from the 1980s onwards represent a different phase in Europe’s evolution (when countries confronted resilience issues in the form of a deep recession process and budgetary constraints). The increasing importance of EU regional policy from the end of the 1980s to the present has made the difference in particular with reference to the main objectives and strategies of applied regional policies, which paid much more attention to efficiency problems than to the equityoriented policies. Perló Cohen’s (PC) contribution focuses directly on the effects of the global crisis on cities and towns and on the policies that can be set up to reactivate their economies. In his analysis PC stresses that large cities have a greater percentage of globalised sectors that are more vulnerable to recession but, at the same time, their economies tend to be more diversified and thus more resilient in absorbing shocks inside their wide-ranging urban economy. Small cities that are highly dependent on vulnerable sectors are in the most difficult position. Urban economies based on small and medium-sized companies operating locally demonstrate more resilience and more experience gained in former crises, and also less dependence on bank loans and less exposure to international markets. The Organisation for Economic Co-operation and Development (OECD) survey he quotes says that ‘Local economies with lower skills, lower employment rates, and lower levels of specialization are particularly exposed to the crisis because they offer limited compelling reasons to retain investment and jobs and are competing largely on price based factors which erode significantly in a downturn’. In such context, public policy matters, although over-specialisation caused regional problems in the past. Central governments may decide to cut their budgets heavily, for example, an action already seen in Ireland, Greece and the UK; this is going to affect the local governments, which experience a strong decrease in budget resources. Some of these cities will follow certain policies over others: some assign their resources to help rescue businesses (for example, most cities in Mexico); other local governments prefer using measures of a social nature, such a benefits for the unemployed and for the poorest part of the population; whereas yet other cities such as Rotterdam, Jyväskylä, and Turin choose developing comprehensive plans that combine actions in several areas. In conclusion, PC proposes that each city, with its own characteristics, resources and limitations, should generate its own response to combat the crisis. Landabaso (ML) argues that a more geographically balanced distribution of economic activity throughout the territory is likely to encourage its overall competitiveness, and promotes social cohesion by helping

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people work where they choose to live, preserving communities and a rich regional diversity that matters in an economy driven by innovation. Such an approach ‘may help liberate latent energies and unblock economic potential where they are currently most underutilized and needed, not to speak of diminishing environmental problems and external diseconomies in central regions and urban centres. Therefore it might be safe to assume that regional differences will progressively rest in the future on the way this intelligence is valorized or left idle’. For ML regional differences will depend more on having infrastructures and policies to effectively exploit this intelligence in the form of innovation and which are capable of attracting more talent from elsewhere than regions that do not, but nevertheless possess the raw material required in a latent form. The proposed regional development policies based on innovation are about ‘learning to fish’ and making the ‘cake’ grow (creation target) rather than on cutting it in pieces for those who need it (redistribution target) or simply providing people with fish (assistance target). It is about empowering regions to help themselves, and not about charity. ‘The capacity of key regional players to interact, share a vision and jointly commit efforts and resources is of paramount importance for the development prospect of a region.’ Aragón, Aranguren, Iturrioz and Wilson (AAIW) highlight the importance of taking a participatory approach to regional (cluster) policies. The Marshallian industrial districts together with Porter’s work to promote the competitiveness of industrial clusters have been especially influential among regional policy-makers who established clusters as a key focus for regional economic development. Indeed, given their prominent and widespread position on regional policy agendas, the evaluation of cluster policies is a critical yet under-examined issue. AAIW analyse the Basque Country Autonomous Community (BCAC) in Spain as one of the pioneers of regional cluster policy. The cluster policy today supports 11 priority clusters, nine of which have been supported since the 1990s, and five emerging ‘pre-clusters’. In recent years various evaluations have been made of different aspects of the policy, although it proved challenging to come to a holistic evaluation of policy as a means to achieving its ultimate aim of enhancing the competitiveness of the regional economy. Regional policies that centre on networks, social capital and local learning are not best suited to traditional methods of evaluation. They are characterised by intangible objectives and complex relationships; thus flexible and dynamic policy processes, requiring similarly systemic, dynamic and flexible evaluation processes seem more appropriate. With such objectives in mind, AAIW suggest that processes of ‘participatory evaluation’ are more effective for such policies and can additionally serve as a tool to mobilise communities and strengthen the learning capacity of the involved agents.

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In line with the creative destruction approach, Cooke and De Propris (CDP) take a ‘resilience perspective’ for which ‘a robust system is one that has the capability to withstand serious system-shock and re-stabilise itself albeit not exactly at the same equilibrium point as the status quo ante’. This position has recently been changing, especially in connection with studies of the implications of human abuse of the environment in the form of human-induced desertification, resource over-exploitation and climate change, which all seem the result of the failure of regulation in liberal market regimes. Within the EU crisis-response policy, CDP examine this proposition in two innovative areas: its record on climate change policy; and its response to the global financial crisis of 2007–09 by re-balancing the European economy towards ‘smart sectors’, notably the creative and cultural industries. On the first, CDP verify a shift from a purely responsive to a more agenda-setting policy mode at EU level, whereas on the second, they observe proliferating creative and cultural industries as a crucial component of the current technological shifts but poor attention by the EU to these industries as well as to eco-innovation sectors and clusters. This is because the EU’s traditional sector-based policy initiatives are clearly ‘way too narrow as the current crisis has set in motion a process of systemic resetting, whereby certainties are undermined and technoeconomic paradigms are flipped over’. For this reason, in the context of the current crisis and of the parallel weak strategic policy approaches, the way ‘out’ and ‘forward’ should be ‘on the technological platforms which will underpin the new techno-economic paradigm, namely, digital, design and cleantech’.

7. MAIN CONCLUSIONS This volume produces a set of relevant insights which might trigger further academic discussions as well as further policy orientations to face the revolutionary global changes that are happening currently, that impose epochal transformations in the objectives of production and consumption and, as an effect, in the way production is organised and the division and specialisation of labour takes place on a global scale. Within such a (resilience) perspective the importance of networks is now recognised and is to be associated in a complementary way to that of local production systems such as districts and clusters. The first breaks the borders, lock-ins and path-dependence of former closed local systems, and adds knowledge and other intangible assets, whereas the second reaffirms the value of proximity, especially when this proximity is more than geographical and includes relational, institutional, social and tacit knowledge aspects that fertilise

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the potential and connectedness of networks and local systems within globalised markets. The creative and cultural industries as well as the specialised hightechnology territories represent further key drivers for ‘innovative and competitive territories’ in that they supply knowledge and cultural inputs that are essential for territorial economies that want to maintain a leading edge over competitors or, at least, to keep working and managing a decent market share to satisfy workers’ demands for jobs and purchasing power. Large and small cities together with technopoles or science and technology parks crowded by high-technology innovative, including eco-innovative, firms as well as by knowledge-intensive business services and creative industries represent collective innovative agents and/or territories that offer these opportunities and responses to the new demand(s) of the globalised market. The capacity of local territories to develop such local/regional production and service systems is to be seen not only in terms of the internal capabilities that are created and renewed. In fact, these would not be enough to guarantee a sustainable territorial competitiveness. This objective requires the analysis and discussion on the positioning of local production systems within global value chains and global production networks. This discussion connects the strength and sustainability of production to dynamics and changes that are taking place outside the borders of the local system and even of the national territory. Multinational companies, big trading companies, large retailers, such as supermarkets, all play key roles in defining the margins for growth of the thousands of local production systems situated in the western world as well as in emerging markets. These dynamics take different forms, some of which are more favourable to local growth, whereas others imply significant bottlenecks and limitations. Policy matters in order to encourage the most appropriate options vis-à-vis the most inefficient and ineffective. The potentials of dynamic, innovative and eco-innovative production and service-based local systems depend not only on their nature and knowledge base, or on their connection to global value chains (GVCs) as market leaders or as specialiced first-tier suppliers for large world leading business groups; they also rely upon their capacity to be open and to integrate within their abstract knowledge borders the potential contribution of ‘heterogeneous social capitals’ that come with travelling skilled individuals as well as with wider migrant communities. These are not only useful and sustainable in political terms as the bases for more tolerant and peaceful ‘industrial atmospheres’, but also because they accompany a wealth of cultures, social capitals, tacit knowledge bases, and creative and entrepreneurial assets which represent strengths and activate important economic dynamics for the promotion of local and global development.

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PART I

Concepts and Measurement: Innovation, Institutions and Change

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The new Argonauts, global search and local institution building AnnaLee Saxenian

I.

INTRODUCTION

The emergence of technology entrepreneurship and innovation outside, but closely connected to the advanced core of the world economy is one of the most striking features of contemporary capitalism. Israel and Taiwan, both small, peripheral agricultural economies in the postwar period, became home to dynamic clusters of entrepreneurial experimentation in the 1980s and 1990s. Today Taiwan’s specialized producers define the state-of-the-art logistics and flexible manufacturing of low-cost, highquality electronic systems. Israel, with a population of just over 6 million, is home to more than a hundred Internet security and software-related technology companies listed on the National Association of Securities Dealers Automated Quotations system (NASDAQ), more than any other country outside North America. In both countries venture capital systemically encourages the proliferation of companies that in effect co-design specialized components or subsystems for firms in the core economies. The more recent emergence of clusters of, for example, software firms in mid-income developing economies like China and India is, if anything, more striking still. Vital urban hubs like Bangalore and Hangzhou are not only peripheral to the world economy, but also located in large national economies that – partial) liberalization of trade policy aside – lack most of the institutions economists view as pre-conditions for growth: the rule of law, secure property rights, good corporate governance, flexible labor markets, transparent capital markets, and so forth. If it is surprising that firms in the ‘periphery’ can co-design crucial components with firms in the core, then it is at least as surprising that institutions good enough to permit and sustain continuing growth can be built locally before such governance institutions are installed nationally, if at all. This chapter looks at yet another surprising, but less understood, aspect of these cases that grows directly from the connection of the first two: the growing importance of global, or external, search networks that firms and 25

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other actors rely upon to locate collaborators who can either solve (part) of a problem they face, or require (part of) a solution they may be able to provide.1 We focus here on the creation in emerging economies of publicly supported institutions – venture capital in particular – organized to search systematically for, and foster the development of, firms and industries that can in turn collaborate in specialized co-design. The emergence of venture capital in the periphery sheds light on current discussions in development economics of ‘self discovery’ – the search process by which an enterprise or entrepreneur determines what markets it can (come to be able to) serve (Hausmann and Rodrik, 2002). The success of the new high tech clusters strongly suggests that production is decomposable in ways that allow decentralized co-design of parts and their periodic reintegration into complex wholes. Enterprises in these clusters systematically look for collaborators who are already solving (parts of) the problems they face, rather than trying to elaborate comprehensive solutions on their own.2 At the same time as production is becoming more collaborative, relying more and more on co-design, so too is the process of self-discovery. Firms and entrepreneurs seeking to enter a new market must demonstrate not just the ability to produce a certain component or product, but also the ability to improve its design or the process by which it is produced in cooperation with the potential customer and their suppliers (Sabel and Zeitlin, 2004). Producers in less developed economies face distinct challenges when seeking to enter these partnerships, and increasingly require bundles of inputs or services – standards, certification, de facto property rights, and specific regulations – that only public authorities can provide. This means that self-discovery also typically entails collaborative search with (parts of) government for institutional solutions that will facilitate certain kinds of transactions. Thus understood, self-discovery shades into openended industrial policy: a process by which firms and governments collaborate in the identification and pursuit of promising opportunities for development.3 This chapter examines the creation of venture capital in emerging economies as an illustration of the way that public and private actors, building on networks they ‘find’, can construct an institution that systematically creates further networks to foster and monitor the progress of new firms and industries. We focus on the case of Taiwan, where highly skilled first-generation immigrant professionals in US technology industries collaborated with their home-country counterparts to develop the context for entrepreneurial development. The chapter refers to the members of these networks as the new Argonauts, an allusion to Jason and the Argonauts who centuries ago sailed in search of the Golden

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Fleece, testing their mythic heroism while seeking earthly riches and glory. While most of the evidence here is drawn from Taiwan, relevant aspects of analogue developments in Israel, India and China are considered as well. Our central argument is that new Argonauts are ideally positioned (as both insiders and outsiders at home and abroad) to search beyond prevailing routines to identify opportunities for complementary ‘peripheral’ participation in the global economy, and to work with public officials on the corresponding adaptation and redesign of relevant institutions and firms in their native countries. They are, in other words, exemplary protagonists of the process of self-discovery or open industrial policy – though surely there are in other contexts different institutional arrangements that are as exemplary as well. We argue further that in the cases considered here, the Argonauts’ contributions to domestic institution building crystallized most clearly in the development of domestic venture capital, one of, if not the most important, supports for technology entrepreneurship. Venture capital is itself a powerful search network: it is an institution for identifying and combining pieces of companies – finance, technical expertise, marketing know-how, business model, standard-setting capacity, and so on. Once integrated, these enterprises succeed by becoming nodes in the search networks for designing and building products in their domain. By supporting a diverse portfolio of ventures, and combining hands-on monitoring and mentoring with market selection, investors in developing countries are thus institutionalizing a process of continuous economic restructuring – and learning about how to improve restructuring itself – that transforms the domestic economy by linking it to the most demanding and capable actors in global markets. The new Argonauts are therefore at once the product of search networks among the professionals and companies for whom they have worked and with which they associate, and – in collaboration with parts of government and other domestic public institutions – the co-architects of further networks that extend and adapt to home-country conditions the web of relations they already know. Networks of overseas professionals are central to this story so we begin with the role of diasporas in development. Section II reviews the current debates to claim that the most enduring contributions of skilled professionals to their home countries are not direct transfers of technology or knowledge, but participation in the process of external search and domestic institutional reform. We argue that the focus on the high-skill diaspora as an asset has obscured processes of micro-level reform that, diffusing and cascading, can ultimately produce structural transformations. Section III illustrates this argument with the example of the creation

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of the venture capital industry in Taiwan, which provided the context for entrepreneurial growth in high-technology clusters. The following section (IV) situates search networks with respect to current debates about the structuring principles of the new, global economy. We show that these networks are based on and transmit knowledge that is more formalized than that circulating in the local networks typical of clusters (where knowledge is, at the limit, purely tacit), but less complete than the knowledge said to flow in modular global production networks (where knowledge is assumed to be fully explicit.) The final coda draws early conclusions for our understandings of the process of institutional reform and economic development.

II. DIASPORAS AND DEVELOPMENT In spite of the outpouring of research in the past decade, evidence that diaspora networks taken as various forms of intellectual capital or as ‘knowledge networks’ have a positive impact on economic development is limited. Diasporas are not new phenomena, nor is the interest of policymakers and scholars in their developmental potential.4 What is new, or relatively so, is the focus of recent research and policy on the highly educated (e)migrants who have long been viewed as a serious loss to poor economies (the brain drain.) Low transportation and communications costs now allow those who go abroad for further training or in search of work to interact and collaborate with their home country counterparts far more extensively than was feasible in earlier eras of emigration. A small but growing number of migrants have even become fully ‘transnational’ – with dual citizenship and residences in both their home and their adopted countries. Early research on diaspora contributions investigated remittances or direct investments, which can provide a stable source of finance and alleviate poverty, but typically have limited long-term impact. The recent literature, by contrast, suggests that skilled migrants can alter the development trajectory of a poor country through the diffusion of knowledge and/or technology transfers – as, for example, in the shift from a brain drain of talent away from the home country to ‘brain circulation’ between it and the core economies (Saxenian, 2002). Despite this attention to positive development impacts, much of the newer literature (and the public policies with which it is in dialog) continues to treat the diaspora as an asset, valuable insofar as it adds to the home country’s stock of capital not through remittances but in intellectual property or reputational capital or related forms of wealth. There is, however, little evidence that diasporas have contributed substantially to development in this way.

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The most direct mechanism for transferring intellectual capital to the home country would be for the highly educated migrants to return to work. Yet in spite of the aggressive recruitment efforts of home-country policy-makers, and some evidence of rising return rates (from a very low base) in places like India and China, there is no evidence that educated migrants to the USA and other advanced economies are substantially more likely to return permanently to their home economies than they were a decade or two ago. Nor is there evidence that the brain drain has abated, except in small countries that have experienced rapid growth, such as Taiwan.5 Some researchers suggest that there is a diaspora effect in scientific collaboration by documenting how knowledge, as measured by patent citations and co-authorship, flows disproportionately among members of the same ethnic community, even over long distances (Agrawal et al., 2004; Jin et al., 2007; Kerr, 2007, 2008). Yet efforts to demonstrate that diaspora scientific collaboration contributes to economic growth in the home country remain unconvincingly incomplete. Above all they have not identified a causal mechanism by which the findings of collaborative research are usefully transferred to firms and other domestic actors. Research in related areas has yielded similarly promising but incomplete findings. Studies have found, for instance, that ethnic networks in the USA increase trade with the home country, suggesting that a diaspora can help to reduce reputational and informational and barriers to trade (Lucas, 2005; Kapur, 2001; Rauch and Trindade, 2002). Similarly, case studies suggest that diaspora members can for the same reasons help direct corporate investments or contracts toward their home country. However, the most significant findings from both the quantitative studies and the extensive case study research come from a small number of Asian cases, particularly China and India (Lowell and Gerova, 2004; Lucas, 2005). As critics point out, there are many more cases of failed attempts to mobilize diaspora contributions to development, from Armenia to Argentina, which remain unexplained in current frameworks. The rise of dynamic clusters in the periphery, and the experience of the new Argonauts generally, suggest that the debate on diasporas and development has been misdirected. The increased salience of diaspora networks to economic development does not lie in the direct contribution of assets, but rather in their role in the design and construction of new institutions in their home countries. While these contributions are often incremental, thus difficult to detect and even more difficult to quantify, over time they have the potential to create a context that supports self-sustaining growth. In part because of the treatment of diasporas as assets, discussions focus on the macro-level: the relation of ‘the’ diaspora to ‘its’ home country.

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They overlook the internal heterogeneity of the diaspora, as well as the heterogeneity of the economy and the public sector in developing (as well as the developed) nations. The new Argonauts, for example, are only a subset of the diaspora, normally first-generation emigrants who work with ease in the institutions and environment of their home country, where they continue to have friends, family and colleagues. (Second or thirdgeneration immigrants, even if they speak the language of their country of origin, have greater difficulty doing business there because they lack both these personal connections and first-hand knowledge of local institutions and culture). The spatial differentiation of economic activity typically linked to industrial specialization (another manifestation of heterogeneity) means that a focus on national indicators and institutions can obscure critical transformations occurring at a sub-national level.6 Likewise the state, in developing as well as in developed counterparts, is not a unified whole, but rather consists of multiple, differently organized units with varying political and economic resources, jurisdictions and interests. Yet it is precisely this heterogeneity that permits innovation and growth within a generally hostile context (Kuznetsov and Sabel, 2007). The new Argonauts bring to their home countries expertise in specific industries that are located in a small number of urban areas or regions, and they collaborate only with a subset of domestic entrepreneurs and policy-makers. This means that economic and institutional change begins in certain locations and/or domains, and advances through partial and incremental (micro-level) reforms that only with time aggregate into larger-scale transformations. Only by disaggregating the diaspora and its interactions with (parts of) the equally differentiated public and private sectors it is possible to see whether and eventually how they are (re)building the institutions of economic development. A small example from India illustrates a micro-level reform can facilitate matching of collaborators, and how such reform can diffuse. In the early 1990s Indian products in general were suspect because of their reputation as low quality. Quality problems in software were an important obstacle to collaboration between local suppliers and customers in world markets. In software the problem was not particular to India: almost from the beginning of large software development projects, such as the operating system for the IBM 360 in the 1960s, it has been well known that quality problems can arise from the very partitioning of tasks which allows different groups to work on separate parts of programs simultaneously. Fixing performance specifications for each ‘chunk’ or module of the program introduces ambiguities which only come to light as defects when the parts are finally connected to each other. (Brooks, 1995) Long-range collaboration could

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only be expected to exacerbate a problem inherent to software production (and latent, as we will see in production and design generally). Anticipating this problem an Indian engineer from the Software Engineering Institute (SEI) at Carnegie-Mellon University traveled to Bangalore to speak at software firms about the Institute’s recently introduced Capability Maturity Model (CMM) for software engineering process improvement. The core of the CMM is a process of periodic peer review of development ‘pieces’ to ensure, by ongoing clarification of specifications, that the rate of error detection is higher than the rate of ‘error injection’. Many firms immediately picked up the idea and sponsored conferences and consultations on the topic. By the end of the decade virtually all large Indian software companies had adopted the CMM. Today India is widely recognized for its high-quality software development processes; the country has more SEI-CMM Level V (the top level) certified companies than any other. The development of a globally competitive software services and technology industry in Bangalore involved a multiplicity of similar micro-level reforms, both within the cluster and externally. In this case the best practice in software engineering processes was transferred to Indian firms as soon as they were being developed. Indeed, the most extensive and practical guide to the use of the quality model today is a study of its application and development at Infosys, one of India’s largest and most successful software firms, and published by the SEI (Jalotte, 2000). Such changes occur incrementally, and there is no guarantee that they will continue. But, as we will see in detail in the next section, when they accumulate, they have the potential to alter the institutional fabric of the economy.

III. INSTITUTIONALIZING VENTURE CAPITAL: THE TAIWAN CASE The collaboration of overseas Chinese professionals with government officials in Taiwan to create a venture capital industry exemplifies the contribution of global search to domestic institution building. The institutionalization of venture capital was a critical turning point for Taiwan. It insured that a few, isolated early entrepreneurial successes were followed by growing investment and collective learning in the electronics-related industries. Ultimately it supported the creation of a self-reinforcing cluster, or critical mass, of firms. The creation of venture capital in Taiwan also shows how such institution-building is enabled by, and helps encourage, new political alliances rooted in the incipient forms of cooperation that it fosters. The

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reform was initiated by an entrepreneurial ex-Finance Minister who leveraged both the search capabilities and the political influence of the diaspora to mobilize support for initiatives that were strongly opposed by older-line policy-makers and traditional industries. Last, but perhaps most importantly, the collaborative construction of venture capital in Taiwan shows how search networks can transform and give new meaning to the institutions they connect and to ‘import’. Venture capital in Taiwan was as much a means of reorienting the country’s emerging high-technology economy from competition to collaborative complementarity with Silicon Valley firms, and of redirecting investment by old-line industry and cautious commercial banks and family networks, as it was a tool for providing finance to start-ups that otherwise could not find it. In the 1970s Taiwan was a poor, agricultural nation. Its economy was controlled by a combination of state-owned enterprises (in finance and strategic industrial sectors) and risk-averse family-owned and run businesses.7 The ‘high-tech’ manufacturing sector consisted mainly of low-end, labor-intensive firms manufacturing calculators and electronic components almost exclusively for foreign customers. Intellectual property rights were notoriously disregarded, allowing in the early 1980s for the reverse engineering and production of ‘clones’ of the IBM PC and Apple’s MAC. Few would have predicted that entrepreneurs in this peripheral economy would compete in the most technologically advanced sectors of the world economy. Yet by the end of the 1990s Taiwan was a leading center of technology entrepreneurship; today its specialized semiconductor and computer related firms define the state-of-the-art logistics and manufacturing of low-cost, high-quality electronic systems. Scholarly accounts of the growth of Taiwan’s technology sector typically focus on a farsighted development strategy focused on industrial ‘catch up’, and particularly the transfer of leading-edge semiconductor technology through the creation of institutions like the Industrial Technology Research Institute (ITRI), a public-private research agency, and the Hsinchu Science-based Industrial Park (HSIP) (Amsden and Chu, 2003; Mathews and Cho, 1999.) Yet they leave a puzzle. How did domestic policy-makers manage to identify and supply precisely the institutional pieces required to support entrepreneurial growth in a highly competitive global economy – particularly when many other nations, often far better endowed, tried and failed to develop venture capital and technology industries in the same period? The answer to this puzzle is that the growth of the sector was only in part a planned or designed process; and the part that was designed was aimed less at moving Taiwan to a well-defined technology frontier than

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at creating institutions for identifying and pursuing appropriate economic opportunities – search networks. A plainly unplanned but crucial part was the decision by tens of thousands of Taiwan’s most talented university students to pursue engineering graduate degrees in the USA in the 1960s and 1970s. A majority took jobs in the USA after graduation because the professional and economic opportunities in regions like Silicon Valley far exceeded anything then available in Taiwan. Policy-makers complained bitterly about these losses and even sought to control them. None foresaw that the ‘brain drain’ might prove advantageous. The initial adjustment of the job seekers to their new environment was also spontaneous. As outsiders in Silicon Valley the immigrants created technical associations and alumni networks that allowed them to find one another, as well as to stay in touch with their counterparts at home. Some participated in government-sponsored policy discussions or gave talks at universities and technical conferences in Taiwan, but few considered returning home permanently. The decision not to return home was as self-evident as the decision to go abroad in the first place: Taiwan’s personal computer industry in the early 1980s was small and fragile, in spite of sizable public investments in higher education and technology research, and the efforts of the handful of entrepreneurs who did go back. The Hsinchu Science-based Industrial Park (HSIP) opened in 1980, but was unable to find tenants in spite of aggressive efforts to lure multinationals, including those run by Chinese. The turning point, and the beginning of a deliberate policy – in the sense of a strategy for building institutions to fix and revise strategies – came in the following years, when Minister without a Portfolio Kuo-Ting Li, formed an alliance with a group of foreign advisors, including members of the diaspora, to establish a venture capital industry in Taiwan. An engineer who headed both the Ministry of Economic Affairs (1965–69) and then Ministry of Finance (1969–1976), K.-T. Li is widely regarded as the architect of Taiwan’s technology strategy. He had met regularly with Chinese engineers and entrepreneurs in Silicon Valley during the 1960s and 1970s (many were his college classmates) to seek their advice on making Taiwanese industry more globally competitive. Li was especially impressed with the newly emerging US venture capital industry and the institutional support it created for entrepreneurship. While serving as the Minister of Finance, Li had hired a team of USeducated engineers to develop a plan for the creation and organization of private industrial investment companies in Taiwan. They concluded that Taiwan should import the venture-capital model from the USA; and their conclusions resonated with those of then Minister of Economic Affairs, Li-Te Hsu, as well as Stan Shih, the chief executive officer (CEO) of Acer,

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a leading personal computer (PC) maker, both whom had also visited the USA to study its new high-technology industries. During this period an IBM executive based in Silicon Valley, Ta-Lin Hsu, also used his status as a leading figure in the diaspora and an ‘outside’ expert to promote new policy measures to support technology entrepreneurship by contacting key individuals in various governmental units. By 1982 Li was able to convince the Ministry of Finance to introduce legislation to create, develop and regulate venture capital in Taiwan, including comprehensive tax incentives and financial assistance. The concept of venture capital, uncontroversial today, was foreign to the Taiwanese of the day, where family members closely controlled all the financial affairs of a business. Leaders of traditional industries such as chemicals and textiles opposed Li’s ideas. So did an influential consultant to the government, Dr Simon Ramo (a pioneer of systems engineering and a co-founder of the company that eventually became TRW), who argued that Taiwan lacked the capabilities to develop a venture-capital industry. Supporters of the project understood that venture capital would play a different role in Taiwan than in the USA, and that the difference would help redirect the developing economy in a crucial way. They argued that rather than trying to replicate the high-level research and technological innovation of places like Silicon Valley, Taiwan should exploit its own strengths: a supply of relatively low-cost, high-skilled engineers. In this view, Taiwan would position itself to develop commercial applications derived from US innovations, and lower-skill mass production could be carried out elsewhere. Li envisioned the HSIP as the place for Taiwanese entrepreneurs to undertake this commercialization, collaborating with each other and with foreign companies. The availability of venture capital, and the networking and mentoring that it provides in addition to finance, would be key to this strategy. Proponents of Li’s vision recognized that the conservatism of Taiwan’s established financial institutions was a major hindrance to the incubation of high-technology ventures. Most financial institutions at that time were commercial banks, which provided only mortgage or debt financing. The risk aversion of the government officials who managed the public ‘Development Fund’ and other financial-incentive programs limited the ability of these capital sources to spawn risky new technology enterprises. Only a publicly supported venture-capital industry would provide sufficient capital for such high-risk, high-return ventures. In addition Taiwan’s businesses were overwhelmingly (95 per cent) small- and medium-sized enterprises and most, as we have noted, were family-run. Family owned and managed enterprises of this type were typically oriented toward survival, rather than growth, and had little incentive

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to adopt modern management techniques. Policy-makers believed that a venture-capital industry could help promote the introduction of modern financial and management skills by institutionalizing the separation of ownership and control. Finally, proponents understood that the introduction of venture capital would entail the development of a public capital market that provided an exit option for investments in start-ups. Close scrutiny of the US experience had taught Li’s group both that Taiwan could profit from domestic venture capital, but also that the country lacked the relevant institutional know-how to start a venturecapital industry and the incentives to draw local actors into the process. Policy-makers therefore organized collaborations with large US financial institutions to facilitate the transfer of relevant financial and managerial expertise. For example, young Taiwanese were sent to the USA to be trained venture-capital management. The Ministry of Finance created tax incentives to encourage domestic firms to enter the venture-capital industry; 20 per cent of the capital invested in strategic (technology-intensive) ventures by individual or corporate investors was tax deductible for up to five years. The Ministry also offered substantial matching funds through a ‘Seed Fund’ with NT$800 million from the Executive Yuan Development Fund. In addition, regulation governing Security and Exchange was modified to support the development of a public capital market. But even with these incentives, development was hesitant. When Acer founded Taiwan’s first venture-capital firm in 1984 as a joint venture with the old-line Continental Engineering Group, there were at first no followers. K.T. Li invited the Overseas Chinese community to establish venture-capital businesses in Taiwan. In response Ta-Lin Hsu, a prominent diaspora member and policy advisor, set up Hambrecht & Quist (H&Q) Asia Pacific in 1986. Hsu reports that it was not easy to raise the initial $50 million fund: Li ‘twisted lots of arms’ to raise $26 million from leading Taiwanese industrial groups such as Far East Textile, President Enterprises, and Mitac. The balance (49 per cent) came from the government.8 The first general manager in H&Q Asia Pacific’s Taipei office, Ding-Hua Hu, was a classic returnee. After earning a PhD in engineering at Princeton in 1970, Hu had played a lead role in building Taiwan’s semiconductor industry as the first general director of the Electronics Research and Service Organization and as a Professor of Electrical Engineering at the elite Chiao Tung University. In 1987, two other Overseas Chinese engineers, Peter Liu and Lip-Bu Tan, responded to Li’s invitation as well, establishing Taiwan’s second US-style venture fund, the Walden International Investment Group (WIIG) as a branch of the San Francisco-based Walden Group. Both H&Q Asia Pacific and WIIG (along with Peter Liu’s spin-off firm, WI

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Harper) were able to raise capital for Taiwanese funds with relative ease from the networks of Overseas Chinese in Silicon Valley who were familiar with venture capital. It was only after these investments showed returns – after companies like Acer and Microtek (a scanner company started by an engineer who returned to Taiwan from the USA in 1980) were publicly listed on the Taiwan Stock Exchange in the late 1980s – that the venture capital industry in Taiwan took off. The ‘Seed Fund’ with matching grants for venture investments was depleted and the Executive Yuan committed a second fund of NT$1.6 billion that was also allocated quickly. Domestic information technology (IT) firms began to create their own venture funds, including D-Link, Macronix, Mosel, Taiwan Semiconductor Manufacturing Company (TSMC), SiliconWare, UMAX Data Systems, UMC, and Winbond. Old-line firms in traditional industries like petrochemicals that had been reluctant earlier to get involved in the ‘new economy’ also began investing in technology-related venture funds and businesses. The emergence of Taiwan’s venture-capital industry and the early successes of venture-backed start-ups attracted growing numbers of Overseas Chinese to return from the USA to start businesses. Miin Wu, a Stanford graduate who worked in Silicon Valley for over a decade before returning in 1988 to start Macronix International, one of Taiwan’s first semiconductor companies, in HSIP with funding from H&Q Asia Pacific, is a wellknown example. The availability of venture capital finally transformed HSIP into a fertile environment for the growth of indigenous technology firms. By 1996 over 2500 engineers and scientists had returned to work in the Science Park and 40 per cent of the 203 companies based in the park were started by returnees. The industry remained highly localized as it grew, with the personal computer industry in the greater Taipei region and semiconductor and component firms in Hsinchu, creating a corridor roughly the same size as the Silicon Valley cluster. The availability of venture capital in the 1980s also distinguished Taiwan from the rest of Asia: outside of Taiwan, capital was then available in the region only to large corporations with ties to governments or to wealthy families. One measure of the success of Taiwan’s venture-capital industry is the performance of venture-funded firms in public capital markets. Ten of the 32 new ventures started in the HSIP in 1996 received funding from local venture funds. By 1998 over 130 venture-funded companies were listed on the Taiwan Stock Exchange and some 40 were listed on NASDAQ. The new Argonauts have influenced policy in other developing nations, using best practices and models from Silicon Valley to lever open and animate discussion of institutional reform in their home countries. The

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experience of the coalition of policy-makers and overseas entrepreneurs and engineers that created Israel’s venture-capital industry from the mid-1980s to the mid-1990s is a striking example: in Israel, as in Taiwan, the introduction of venture capital linked together, in an economically viable way, the capabilities or firm fragments (for example, research outputs, managerial talent, engineering skill, market knowledge, and so on) – created by government’s earlier investment in national defense and technological development. In Israel these took the form of policy ‘experiments’ fostering commercial applications of military high-technology, and R&D cooperation between Israeli and foreign firms.9 As in Taiwan, early initiatives faced considerable opposition, and success grew from improvements on failures. Thus the first effort to institutionalize venture capital through a government insurance fund, Inbal, failed: under the program the state insured 70 per cent of initial investments, but in effect limited the investors’ rights to capital appreciation – and so attracted VCs more interested in minimizing risk than in increasing returns by selection and monitoring of porfolio firms. Inbal’s successor – Yozma – was a success: this time the state bought minority stakes in competing, private venturecapital firms, structured as limited partnerships between Israeli venture capitalists (VCs) and their foreign counterparts, thus insuring connections to global as well as local networks (Avnimelech and Teubal, 2004). Indian and Chinese Argonauts have similarly participated in the creation of institutions for venture capital in their home countries (Saxenian, 2006.) Each has transformed not only domestic institutions but also altered the development trajectory for those that followed. Policy-makers and entrepreneurs in Taiwan and elsewhere clearly learned from the Silicon Valley model; some even believed that they were replicating that model. But by solving problems of domestic economic development by adapting venture capital to domestic contexts, they changed both the model and the contexts themselves. Indeed, as the next section will show, they also helped transform Silicon Valley, in ways that suggest the broad generalizability of these experiences to other industries and settings.

IV. GLOBAL SEARCH NETWORKS AND CROSS-REGIONAL COLLABORATION In focusing on connections between the new Argonauts and Silicon Valley the discussion so far invites the objection that the construction of search networks is founded on, and therefore limited to the prior, ‘natural’ occurrence of tacit knowledge of technologies and persons associated with

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industrial clusters or professional and technical ‘communities of practice’ generally (Brown and Duguid, 2002; Lave and Wenger, 1991). Indeed, one pole in current discussion of links among firms in the emerging global economy sees that economy as a shift away from coordination by managerial hierarchies in vertically integrated firms towards informal coordination among networks of independent companies. These relations are said to be long term and grounded in ‘informal restraints on self-interested behavior’.10 This view generalizes to the economy at large the stylized experiences of the industrial districts or clusters, based on local cultures of trust, and the co-design relations among Japanese automobile firms and their subcontractor, based on an ethos of reciprocity, as these were understood in the 1990s. At the limit this view suggests the information needed to initiate, engage in and judge the performance of collaboration must be so deeply embedded in particular social relations that it is possible to foster collaboration institutionally only when social connections have become so dense and reliable that it is almost superfluous to do so. However accurate this view may have been of the tacit or ‘cultural’ coordination of flexible networks of firms in past decades, it ignores the extent to which formalization of key aspects of collaboration is not only possible but necessary to sustain the co-design relations prevailing today. Recall the CMM method of software engineering process improvement and its use of peer review of development ‘pieces’ to reduce errors. The CMM is just one of a wide array of similar devices for creating information pooling regimes in which cooperating firms can teach each other to be better collaborators even as they monitor one another’s capacities and intentions to do so.11 Thus it is routine in contracts between, for instance, producers of computers or automobiles and suppliers of key components to specify not only acceptable quality levels but target rates of price reduction, procedures for jointly and regularly reviewing progress towards all these goals, agreeing on joint action when necessary to achieve them and periodic consultation on emergent features of the next-generation components. Analogous regimes are common between firms co-developing new drugs or innovative computer hardware or software. These regimes do not of course eliminate the need for personal connections among buyers and sellers. But they do make a firm’s capacities and disposition to cooperate much more accessible not only to current but also potential partners than the informal, tacit view of linkages suggests. Because the regimes make it easier for firms to scan the world, they make it easier for the firm to find partners itself; because in scanning successfully the firm becomes known for its ability to search, the regimes make it more attractive to potential partners (Gilson et al., 2008). Thus the new nature of inter-firm networks facilitates rather than obstructs the creation of

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higher order search networks and open industrial policy, formalizing the information exchange that give rise to the metrics on which venture capital and like institutions depend in the monitoring performance of firms with which they are engaged. The prevalence of these collaborative, information-pooling regimes also casts substantial doubt on the modular view of inter-firm links at the opposite pole of current discussion of the global economy. In this view collaborative knowledge is not tacit and informal but rather fully explicit and formalized: new design and production tools allow development of technical standards and design rules that standardize the interfaces between organizationally separate stages of production. This standardization so drastically reduces the volume of information required for inter-firm coordination that products can be decomposed into distinct and further decomposable modules, each produced in virtual isolation from the others Langlois (2003) and Sturgeon (2002).12 Some codification of this kind is obviously necessary to allow specialist producers to focus on their specializations. But too much codification just as obviously becomes a barrier to systematic innovation, locking component manufacturers and those who combine their products into more complex wholes into potentially obsolete product architectures (Sabel and Zeitlin, 2004). Hence the prevalence, among all but the least sophisticated producers, of the information pooling regimes just noted, whose goal is the continuing elaboration of product and process specification, and the consideration of alternatives – not the clarification of fixed standards. So common are regimes of this type that their organization – the way in which quality control information is to be collected and evaluated – has itself been standardized. A more graphic demonstration of the limits of this view is the rapidly evolving relation between the economic core and periphery in general, and Silicon Valley and Taiwan and Israel in particular. The model of modular networks, with a relatively stable and hierarchical production chain dominated by global flagship producers, suggests that there is no potential for engineering improvements and innovation at any level of the supply chain but the top. In spatial terms there is no room in a fully modular world for indigenous entrepreneurship and innovation outside the core. Development in Taiwan demonstrates the opportunities for innovation in the periphery, even at the lowest level of the supply chain. By the early 1990s Taiwan had become a highly efficient and flexible producer of low-cost integrated circuits, components and motherboards – and left new product definition, high-end design and equipment manufacturing to Silicon Valley. Producers in both regions benefited from distinctive capabilities that allowed them to deepen their specialized expertise, in part by

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recombining it with that of other specialists. A decade later Taiwan’s firms had significantly upgraded their design and manufacturing capabilities; they were not only designing and making increasingly sophisticated and complex components such as liquid-crystal display (LCD) screens, microprocessors, and miniature optical components for cameras, but they were also responsible for the logistics and final integration of advanced products like laptop PCs and mobile devices. During the same decade, they moved virtually all of their high-volume manufacturing to the Chinese mainland, where they could exploit economies of scale and lower cost inputs. The semiconductor industry, in which Taiwan played an important role, corroborates the importance of venture capital to this process of technological upgrading. In the 1970s, vertically integrated independent device manufacturers (IDMs) based in the USA and Japan controlled the design, manufacturing, marketing and distribution of semiconductors. When Morris Chang returned to Hsinchu in the mid-1980s after decades of experience in the US semiconductor industry, he pioneered the ‘foundry’ model by focusing Taiwan Semiconductor Manufacturing Company exclusively on chip manufacturing.13 The availability and rapid growth of Taiwan’s contract foundry capacity coincided with the growth of venture capital, triggering a new generation of advanced chip packaging, assembly and materials firms in Taiwan and an unprecedented wave of new chip design start-ups in Silicon Valley. Investments over the next two decades by venture capitalists in both regions, sometimes joint, accelerated entrepreneurial experimentation (and learning from failure) and innovation. New semiconductor ventures identified still more highly specialized niches, such as the intellectual property components for chip design, or ‘design foundries’ with deep expertise in both fabrication technologies and design; and system start-ups incorporated the more complex, often cheaper and smaller components into new generations of computing products. And as US and Taiwanese producers became increasingly sophisticated, they ceded the lower end of their markets to new generations of entrepreneurs based in locations like China and India. In sum, open or external search networks, such as those that helped create venture capital in Taiwan, represent an intermediate form between the tacit networks of industrial districts and the fully explicit networks of modular production systems. Actors in these networks contribute, through intensive information exchange and comparisons, to the construction of shared, domain-specific, understandings and languages (or interpretations) that allow them to search for new models of products and of organizing production, even in distant localities, and to collaborate in incorporating these new possibilities into existing practice. This process

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blurs the boundaries among firms, industries and regional economies – and, perhaps most fundamentally of all, between linkages and organizations that arise or are ‘found’, and those that can by reflection and design be made.

V. CONCLUSION The experience of the new Argonauts in creating venture capital in the peripheral locations such as Taiwan suggests that development today is a process of experimentation and learning in particular contexts. Economic decentralization creates possibilities for entrepreneurs almost anywhere in the world to identify promising market niches and opportunities at many points along supply chains. Diasporas, especially in the form of professional communities like the new Argonauts, can begin to connect suppliers and customers, producers and policy-makers.14 But even in the presence of the social bonds and trust that grow from shared ethnic identities, the challenges of self-discovery – of identifying appropriate partners in a decentralized economy, and of insuring the public inputs needed to work with them – remain substantial. The crucial step in reducing the obstacles to faster, more sustained growth occurs when individuals, firms and policy-makers jointly create institutions – or search networks – that extend the connections, not least by creating more nodes and links in the currently existing networks, and by connecting them to others. We have seen that venture capital can serve as a powerful search network in developing economies when the investors have global as well as local connections. By supporting a diverse portfolio of ventures, and combining hands-on monitoring and mentoring with market selection, they are institutionalizing a process of continuous economic restructuring – and learning about how to improve the institutions of restructuring – that transforms the domestic economy by linking it to the most demanding and capable actors in global markets. In other contexts such search networks have taken the form of publicly supported supply chain development and quality assurance programs. In essence, venture capital is a search network that helps transform the domestic economy by itself creating search networks. Put another way, search networks can help link partners in micro-level innovations in public institutions and the organization of production. Over time these changes can cumulate into, or inform programs for larger scale transformations that ‘endow’ the economy with institutions which, on some views of development, it would have needed to grow in the first place. Learning more about how this contemporary form of economic

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development was possible in places where – improbable at first – it has already occurred can teach how it might be done in settings where it today seems unimaginable.

NOTES 1. 2.

3. 4. 5. 6.

7. 8. 9. 10. 11.

12. 13. 14.

See Sabel (2005) which argues that search routines offer an alternative to the hierarchical decomposition of tasks as a solution to the problem of bounded rationality in organizations. If this were not the case it would be impossible for high-technology clusters to emerge in developing economies by specializing in complex components or special-purpose software, and to grow by collaborating more and more closely with their customers in the elaboration of successive, more sophisticated generations and generalizations of the original specialties. See Hausmann et al. (2007) and generally Rodrik (2007). See, for example, Brinkerhoff (2006), Kapur and McHale (2005), Kuznetsov (2006), Lowell and Gerova (2004), Lucas (2005) and Saxenian (2006). Ironically there is now concern in policy circles in Taiwan that they have lost the ‘bridge’ to Silicon Valley as a result – at least implicitly recognizing the importance of the diaspora as a search network. The literature on national institutions and development overlooks the evidence from India, China and many other cases suggesting that parts of economies grow rapidly and reliably even if the wholes to which they are connected do not have the institutions thought to be necessary for growth. The evidence suggests that the institutions of governance sufficiently ‘good’ to permit and encourage sustained growth can be built piecemeal, in particular sectors of the economy, and the regions in which they are located, in advance of comprehensive, national reform. No one looking only, say, at national legislation (or its absence) regarding property rights in China would have been able to predict the country’s growth. Taiwan’s per capita gross national product (GNP) in 1962 was US$170, on a par with Zaire and the Congo. Interview with Ta-Lin Hsu, San Francisco, CA, 1 June 1997. Avnimelech and Teubal (2004) speak explicity of ‘business experiments’ and ‘policy experimentation’ in this period (p. 88). Lamoreaux et al. (2003, p. 62). On such ‘pragmatist’ mechanisms such as benchmarking, simultaneous engineering, and ‘root cause’ error detection and correction see Helper et al. (2000). All of these generate information for collaborative improvement or design innovation by triggering ‘routine questioning of routines’. Langlois (2003, p. 374). This organizational innovation, which transformed the global semiconductor industry, is at direct odds with claims that Taiwan is not innovative. The new Argonauts have contributed actively to policy reform in India and China in the areas of telecommunications regulation, science and technology policy, and reform of educational institutions as well as capital markets (Saxenian, 2006).

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Resilience, innovative ‘white spaces’ and cluster platforms as a response to globalisation shocks Philip Cooke and Arne Eriksson

INTRODUCTION The newest and most fruitful insights into our understanding of innovation processes come where understanding is informed by one of four complementary theoretical perspectives. These are, respectively: the multi-level perspective on co-evolution (MLP); the resilience or panarchy perspective; complexity theory; and evolutionary economic geography (EEG). In this chapter these four complementary theoretical frameworks will be compared and contrasted. Then key elements are selected in ways that underline their value to a broadly evolutionary analysis of contemporary regional innovation processes. This is a prelude to exploring a theory of innovation that integrates the ideas of regional ‘relatedness’ of industry (Frenken et al., 2007), ‘path interdependence’ of socio-technical systems (Martin and Sunley, 2010; Geels, 2007), ‘pre-adaptation’ and the ‘adjacent possible’ as innovator moves that explain both incremental and radical innovation (Kauffman, 2008). The chapter then proceeds with accounts of regional innovation intermediation by innovation agencies in central Europe contrasted with a Scandinavian exemplar where ‘pre-adaptation’ or reincarnation of an innovation from one industry or cluster into a wholly different one (this is also known in evolutionary biology as ‘exaptation’ (Gould and Vrba, 2002) is found in the first two cases but search practices for the ‘adjacent possible’ are also found in the Scandinavian case. There then follows a lengthy, longitudinal analysis of a further Scandinavian case from the Skåne region in Sweden in which the quest for the ‘adjacent possible’ is pronounced, being structured around that region’s adoption of a strategic response to two societal ‘Grand Challenges’ of climate change, on the one hand, and healthcare, on the other (EC, 2010). In this two related sets of innovative clusters are integrated into the two master-narratives of ‘sustainable cities’ 43

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and ‘personal healthcare’ in the shape of interactive innovation platforms. The practices of these involve exploring relatedness by means of policy ‘transversality’ searching for innovative ‘white spaces’ among and between the interactive cluster elements of the single and double innovation platforms. Conclusions are drawn regarding the scope and nature of interactions provoked by the key Grand Challenges addressed and a further more pervasive one for regional and national industry posed by the rise of competition in information and communication technology (ICT) from Asia Pacific ICT competitors.

THEORETICAL SYNTHESIS To summarise, the multi-level perspective (MLP) on co-evolutionary transition is a comparatively simple but nevertheless useful perspective from the Netherlands addressed principally at the eco-innovation sphere where some 32 industrial sub-fields were selected for special attention in relation to their eco-innovation potential. Policy-makers worked over a number of years with eco-innovation academics to develop a three-tier macro-perspective on transition and the co-evolution of socio-political and economic sub-systems (socio-technical systems; STS) that define the needs driving eco-innovation (see, for example, Kemp, 2002; Geels, 2004; 2006). One of the important observations is that the challenge posed by the need to control human-centred global warming demands innovation of a far higher systems order than any preceding ‘technological paradigm’ in the world’s industrial history (Figure 2.1). This is because all preceding ‘long waves’ of transformative, radical technological innovation from mechanisation through the railroadisation, electrification, motorisation, and informatisation paradigms of global society, occurred under a macroregime or ‘landscape’ of reliance upon fossil fuels. Now the macro-regime and whatever technological paradigms emerge over time in the future should be set in a ‘post-hydrocarbon landscape’. Related to MLP is a richer and more integrated nature-society perspective on co-evolutionary transition known as ‘panarchy’ or the ‘resilience’ approach (Gunderson and Holling, 2002; Folke, 2006). Panarchy is a framework to account for the dual characteristics of all complex systems – stability and change. It shows how economic growth and human development depend on ecosystems and institutions, and how they interact. It arose from observation of failed attempts to manage regional ecosystems that often culminated in their degradation because of linear management efforts focused on a single variable, usually economic. By contrast, it demonstrates and models a multi-scalar adaptive cycle that promotes

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Carbon energy macro-regime and landscape (1770–2050?) 2

3

4

5

2050– 6

1770 1780 1790 1800 1810 1820 1830 1840 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060

1

45

Long wave

Mechanisation

Railroadisation

Electrification

Motorisation

Informatisation

Neuronanobio

Time period

1770– 1830

1820– 1880

1870– 1920

1910– 1970

1960– 2020

2010– 2060

New inputs

Water power

Steam power

Electric power

Oil

MicroBiochip, processor brain imaging

Driving industry

Textiles

Railways

Electricity

Automotive

MicroNanocomputers biotechnology

New industry

Canals, cotton mills

Steamship, telegraph

Motors, power tools

Aerospace

Software,

Neurotheranostics

Source: Based on Lynch, Z., www.neurosociety.com.

Figure 2.1

The waveform evolution of carbonised capitalism (1770–2050?)

eco-innovation alongside an MLP for institutional intervention in the process. Third, relatedly and concerned with complexity in systems of any kind, not especially ecosystems or economic systems, is complexity theory. This forms a bridge with the final approach to be outlined, namely evolutionary economic geography because of two of its key evolutionary insights, namely ‘pre-adaptation’ and the ‘adjacent possible’. These explain innovation (in this case, eco-innovation) in terms of fitting an unforeseen potential with the ‘adjacent possible’ application (Kauffman, 2008). Preadaptation takes already existing innovations from one industry setting and adapts them for wholly different industry solutions. The adjacent possible is a search process that seeks novel solutions, many being incremental innovations, relatively close to the existing state of the art. Such novelty becomes radical innovation when the knowledge recombination search reveals swiftly numerous related innovation possibilities and potentials. Complexity theory contains many more interesting and relevant concepts,

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some of which are shared with the other three approaches to be discussed. For example, it presumes complex economic systems display: dispersed interaction for example, regionally specialised knowledge domains; absence of a global controller (self-managing adaptive systems); cross-cutting hierarchical organisation (for example, multiple economic governance jurisdictions, including MLP); continual adaptation; permanent innovation; and ‘far-from-equilibrium’ (prone to crises) system dynamics (Arthur et al., 1997). Nevertheless, broadly speaking, although there are historic exceptions, discussed by the likes of Diamond (2006), economic and other systems have the self-adapting capabilities of ‘resilience’ although timescales that are quite lengthy, as also recognised in the MLP approach. It is further argued by Holland (1995) that the non-linearity and variety (diversity) of complex systems generates path dependence. This means ‘regional regimes’ of interaction facilitate innovation as the system evolves, allowing qualitative shifts in system dynamics (for example, hydrocarbons to a post-hydrocarbons macro-regime or ‘landscape’). This leads, fourth, to a brief introduction to key insights of relevance to this analysis coming from evolutionary economic geography (Frenken et al., 2007). Taking two key concepts from spatial economics and technological history it finds particular utility in the idea of ‘relatedness’, on the one hand, and path dependence, on the other. Relatedness arises from research into regional economic growth where it is found that economies with ‘related variety’ among industries perform better than those without it. This is called the ‘proximity’ effect superseding the ‘portfolio’ effect from the viewpoint of industrial structure. More related variety means more lateral ‘absorptive capacity’ from related ‘knowledge spillovers’. These can enhance the innovation potential of regions and EEG research goes further into this relationship and, as indicated, finds the element of ‘relatedness’ within the required variety to be the independent variable. Moving on, ‘path dependence’ at the regional level can explain stability but also system stagnation and inertia (Martin and Sunley, 2010). However, in contexts such as that of a regional economy with related variety, path interdependence can be envisaged where two or more economic trajectories may intersect in regional space, conceivably producing unforeseen innovations from their ‘revealed related variety’ or ex post relatedness. As the case material to be discussed below shows, much eco-innovation is of this speciation or mutation kind as indeed is much innovation. Recent research on ‘innovation biographies’ shows this also happens globally among distinctive ‘transition regions’ (Cooke, 2010). Table 2.1 lists the important elements from the foregoing review before these are marshalled into a conceptual model of the processes whereby regions may be stimulated or blocked by the dominant (national) socio-technical systems of consequence to the key

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Table 2.1

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Theoretical perspectives on multi-level innovation system interactions

Theoretical approach

Key innovation characteristics

Co-evolutionary, multi-level perspective

Non-cyclical scalar relationships Socio-technical systems (STS) – Interactive Strategic niche management Multi-level interactions (cyclical) Potential (high variety) Connectedness (robust endogenous institutions) Resilience (resistance to destabilisation; renewal) Pre-adaptation/exaptation – Cognitive reversal – Borrowing – Searching Adjacent possible Path dependence/path interdependence Relatedness/transversality Proximity

Panarchy

Complexity theory

Evolutionary economic geography

research, development and innovation (RDI) fields. Equally, there will be interest in the extent enlightened national institutional frameworks have stimulated regional innovation. As is evident, while the four theoretical perspectives are distinctive, they all adhere to broadly evolutionary principles, so there is a degree of overlap and associated redundancy. For example both the MLP and panarchy have clear multi-scalar structures. The cyclical nature of panarchy makes it richer and more relevant to a dynamic perspective and its emphases on potential (high variety), connectedness (institutional or regime robustness) and resilience (capability to resist shocks and exercise renewal/innovation) are central to the analysis. Nevertheless, the concepts of STS and strategic niche management are even more directly appropriate for innovation and eco-innovation analysis. Similarly, the complexity theory identification of pre-adaptation and the adjacent possible explains processes by which innovation proceeds through knowledge re-combinations related to proximate and non-proximate pathinterdependence and relatedness. These are highly complementary concepts of significance to the explanation of ‘value variety’. Accordingly, these largely complementary concepts may be arranged in the form of a conceptual model of innovation which is subsequently tailored to the analysis of comparative empirical case material to estimate the relative importance of regional–national interactions and determinations

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R E L PATH

A

T

E

D

DEPENDENT

STS 1

N

E S S

TRAJECTORIES

STS 2

Pre-adaptation

STS 3

Adjacent possible

Path interdependence

ECO-INNOVATION

Figure 2.2

Eco-innovation variant of STS path-interdependence

for eco-innovation. This model is provided in Figure 2.2. In the innovative territorial core of Figure 2.2 are path dependent socio-technical systems (STS), namely, regional industry paradigms and their associated sociotechnical regimes. Where these display relatedness, the triggering factors of pre-adaptation in one or other STS and/or the search and selection process for the ‘adjacent possible’ causes path interdependence of technologies (artefacts and/or organisations). Under the impetus of an initial shock to economic activity, resilient knowledge recombinations resulting in innovation occur. The adaptive regional innovation system is open and there are several multi-scalar links through networks to higher and lower governance or industry organisation levels as well as to relevant STSs elsewhere. In the space available, we will first summarise a number of stylised cases

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of ‘value variety’ in action in circumstances where regional relatedness is being stimulated by a ‘transversality’ policy approach to identify regional opportunities for innovation. In this regard, markets do not necessarily show themselves to be ‘small and fast’ adaptive resilience systems and public-private innovation agencies perform the path-interdependence role. In most of the live examples ‘preadaptation’ is being exploited, not least because it is easier to spot in the arenas or ‘research-based theatres’ such innovation intermediaries create (Pässilä and Oikarinen, 2010). In the more ambitious exemplar, discussed longitudinally (Region Skåne) the adjacent possible is being explored in a variety of ways.

PRE-ADAPTATION EXEMPLARS Bayern Innovativ Such a model is found in Bayern (Bavaria) Germany as summarised below and focused upon the platform-building activities of Bayern Innovativ a governance agency for regional development (Figure 2.3) based in Nürnberg. Here the agency identified key industries that were beneficiaries of cluster policy paid for by Bavaria’s resource windfall when it sold its share in the regional energy supplier. These were cross-tabulated New Technologies

s

Field of activity

gy

lo

o hn

ics

st

ng

nt

gi lo g/

i e d c er m erin te se ne iron ba ms ials gi ne r v n e gi t n e e ry /e ys mat en hn r/op led ist ical y s c g ic o e em ed er raff se now icr ew T ot Ch M N M T K IC Bi La En gy

o ol

BRANCHES Automotive Electric Mechanical engineering Nutrition Chemistry/pharma Building industry Plastics/timber Ceramic Textiles Metal processing

em

st

sy

s tic

ge

Note: The marked fields symbolise combinations of technologies and branches and change according to new tasks. Source: Bayern Innovativ, http://www.bayern-innovativ.de/, 2009.

Figure 2.3

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Bayern Innovativ: technology platforms

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against key technologies to find the inter-disciplinary and inter-industry innovation potentials of ‘related variety’ in the regional economy. Many innovations (on average 10 per cent) have ensued from the over 1000 per year ‘conversations’ facilitated between neighbouring sectors concerning technological co-operations, applications and resulting innovations. Part of the new platform thinking involved recognition of the importance of enhancing ‘preadaptation’ among clusters by creating an ‘innovation theatre’ where innovations from one industry could be shown to representatives of different industries for them to assess the knowledge transfer and recombination potential of specific technology applications. How does Bayern Innovativ’s proactive regional innovation policy work? Figure 2.3 gives an indication whereby matrix management of potential innovation opportunities at intersections between industries, some having been beneficiaries of earlier cluster programme investments, and technologies occur. These are points where conversations among distinct and by no means obviously neighbouring business sectors are facilitated. Accordingly, where these facilitate personal discussion between experts and customers, sustainable co-operation networks are developed. More than 1000 new co-operations are initiated annually. Examples of the roughly 10 per cent of marketed innovations arising from these cooperations include: ● ● ● ● ● ●

laser technology pre-adapted to beam nanoscale droplets onto microarrays for rapid bioanalysis; mechatronic systems for car engine management that have been transferred to bus steering systems; portable fuel cells that have  been pre-adapted in automotive electronics; plastic injection moulding processes from button manufacturing which have been pre-adapted in automotive plastic components; a logistics and transport company that has secured a contract with one of the world’s largest Internet suppliers; and a technical textile producer pre-adapted an automotive nanotechnology fabric to innovate in the field of medical uniforms.

Hence, Bayern Innovativ (BI) initiates business-driven project cooperations across disciplines and branches, taking into account the latest results from the scientific community. Over the past decade the agency has forged new pathways and created a portfolio of co-operation platforms and networks that have generated an extended, sustainable network structure. Both the platforms and the networks are in demand at regional, national and international levels.

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EcoPlus: Lower Austria It is worth noting that this approach may have been pioneered in the late 1990s in Upper Austria where a technology policy matrix cluster programme was first implemented. Unlike the BI approach that in Lower Austria is thematically formed into a matrix policy structure by infusing each member cluster with the common goal of enhancing ‘sustainability’. There are nowadays five key clusters evolving and receiving support around the theme of eco-innovation. These are, respectively: 1.

2. 3.

4.

5.

Green building – this is the economic hub of a network of ecologically aware firms in the region’s green construction industry. The cluster team includes architects, energy experts, building and interior design professionals. The cluster is coalescing towards energy and environmental technology fields Automotives – companies are supported in; internationalisation, qualification and co-operation with research facilities. Food cluster – supporting the regional food industry, from farm to fork. Food quality, safety, organic and regional products are supported and promoted. Logistics – this involves shippers, transporters, and logistics services to enhance transport bundling, reduction of empty journeys and more efficient transport and shipping. Plastics – an inter-regional cluster also involving the Salzburg region. This seeks innovation adjacencies in the development of bioplastics and fibre composites (biofibres). Expansion into medical technology and recycling is planned.

Finally, a further variation on matrix or transverse innovation methodology has long been practised in Värmland region, Sweden. Region Värmland’s Packaging Arena This is a user-driven and design-driven innovation platform model which is highly attuned to ‘transversality’ and empowering local small and medium-sized enterprise platforms to secure strong positions as innovative suppliers to global packaging users. In this respect it is one of the more interesting post-cluster complexes. It is the Swedish region of Värmland, home to the Packaging Arena – a complex cluster of packaging, paper, engineering, food and graphics firms that are indispensable for the multinational companies (MNCs) they supply, including

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in Asian markets. The Packaging Arena is a functioning cluster of 45 members that supplies services ranging from guidance and process support, to consumer testing, to innovation support. A strength is its engagement with consumer, paper and graphics research at Karlstad University and Broby College of Cross-Media in Sunne. The Packaging Arena is one of many clusters operating in the Värmland regional innovation system.  Importantly, this displays considerable relatedness among the clusters enabling knowledge spillovers and joint working to occur. This aspect of joint working is evident in the Packaging Arena’s strategic plan document. The process management team is well qualified and team members have distinct competence areas that result in the whole  group  being able to manage sometimes complex work-related tasks. Perhaps uniquely, the Packaging Arena displays a number of related facilities, notably the Packaging Media Lab, the Packaging Greenhouse, DoTank Design Studio, Swedish Flexography Institute and the Graphics Institute at Broby Cross-Media College. Consideration is being given to creating an incubator at the downtown Karlstad head office. The Packaging Arena adopts a modern, conceptualised approach to management. Members are allocated to a Value Star that covers each part of the supplier base. New memberships are encouraged mainly from candidates who offer functions that strengthen the Value Star. The chief executive officer (CEO) is male but the other seven staff are female, each with a particular sub-unit such as the Japan desk (Japanese national) or the Packaging Media Lab to manage. In this facility eye-tracking analysis is managed, a function which enables consumer eyelines to be tracked when confronted with substantial amounts of visual information as in a supermarket where choices are made over which products to buy. Such consumer information is made available to retail outlets who utilise the Packaging Media Lab as a living laboratory for testing out new product displays, for example. The Packaging Greenhouse, by contrast is a place where ideas can be proposed, discussed, analysed and adopted or rejected by members from retailing and the packaging supply chain. Inputs to such ideas sessions are enriched by the presence of representatives of the Service Research Centre at Karlstad University and the Graphics Institute at Sunne. The Japan desk is important because of the close knowledge transfer links established with the Japanese packaging industry. Representatives of the latter are regular visitors to several of the facilities of the Packaging Arena, as they are to major trade exhibitions such as TokyoPac.

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REGION SKÅNE’S WHITE SPACES AND RESILIENCE AMBITIONS (ADJACENT POSSIBILITIES) Research starting in the mid-2000s showed cluster strength in this region to be in agro-food production and services, including functional food based on biotechnology applications (health drinks) and organic food (farms, public canteens and restaurants) as well as conventional mass production using industrialised ‘productivist’ chemical, pesticide and other conventional control technologies. A once strong but now fading path dependence in Malmö was on shipbuilding but with the closure of the Kockums yard in the 1980s that led to redundancy and migration of shipyard workers. By early 2010 the western harbour area had been reinvented as a centre of ‘cognitive-cultural’ and other service activity. Other activity also assiduously promoted by a highly capable regional development agency includes mobile telephony (‘Mobile Heights’), electronic security, the Skåne film industry and new media, including computer gaming. An emergent clean-technology industry (‘Sustainable Hub’) and a systems resilience initiative (‘Training Regions’) are also beginning to be visible. The Skåne region development authorities prioritise cluster-building but also the exploration of value in the so-called ‘white spaces’ between cluster fields (‘adjacent possible’) where innovation opportunities are considered to lie. Accordingly, efforts have been successfully made to encourage crossover practices between the film industry (home of the Wallander detective series) and the tourism industry, resulting in a new €60 million ‘film tourism’ industry centred on Ystad in coastal southern Skåne. This also connects to the hospitality and culinary tourism aspects of the food-tourism platform. Another sphere in which the regime promotes both emergent clustering and transversality is in relation to clean food packaging with projects bringing joint research and testing of starch (from potatoes) as a degradable bioplastic and other variants of clean and reduced packaging. Lead packaging firm Tetrapak is faced with the imperative to innovate as consumer demand for cleaner and less packaging leads to reductions in market demand that resulted in reduction of overseas and domestic employment. The centrally funded Skåne Food Innovation Network (SFIN) has been a big promoter of functional food technology, receiving €10 million over ten years, to develop it and the food cluster more generally. In 2010 these efforts were met with considerable success when the main health drinks producer Skåne Dairies sold its main ProViva portfolio to French multinational Danone for €50 million. These clusters constitute specific regional STSs in line with the MLP perspective. Accordingly, Skåne Region is committed to giving greater identity and focus to its established and nascent industries by promoting its cluster

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policy which targets about eight fields. However, regime management builds upon advanced thinking and practice such as that advanced in Blackwell et al. (2010) about the evident advantages of filling regional ‘white spaces’ by stimulating the discovery of ‘revealed relatedness’ and promoting transversal or interface projects and initiatives among clusters. As it stands the clusters are mostly new and rather weak, except for food and film, but Skåne’s position on the Swedish periphery, but also a Scandinavian core due to its proximity to Copenhagen, means geographic proximity is important, something recognised in the status of the international Medicon Valley life science cluster between Skåne and the Danish capital. The Skåne Food Innovation Network Re-researching the regional innovation system in early 2011, the following had evolved. First, regarding the regional food cluster (SFIN) and its evolving strategy (regional paradigm and regime or STS), this had confronted the future shock of the ending of its core funding from Sweden’s central innovation agency by re-framing its trajectory. This involved three new initiatives. First SFIN’s core concept shifted significantly in perceiving opportunity in an ‘adjacent possible’ evolution as the regional innovation network selling and supplying innovation and entrepreneurship services to all regional clusters. Thus it would sit, close to market, beneath the regional economic development agency and the regional administration more generally. This advisory and consultancy function would grow out of capabilities developed over a ten-year period in managing both a complex food cluster and building ‘relatedness’ bridges to different regional and supra-regional (Baltic Sea Region) clusters and global networks with other food clusters. Second, and to assist this process, a multilevel perspective had been embarked upon involving lobbying central government ministries and politicians to raise standards by changing regulations and helping create new food innovation opportunities. The aim is no less than reinvention of the public procurement process, moving it away from an ingredients-led to a meal experience-led approach. One example of this concerned ‘Food for the Elderly’ whereby better quality would be required from rules and expectations regarding food from the health and social security authorities for hospitals and care homes. Having cluster entrepreneurs ready to demonstrate improved service quality to ministers strengthened the lobbying effort. A third aim is to raise innovation among large food companies by getting ‘silos and clusters’ to co-operate on innovation. This involves creating arenas where innovation demonstrations (‘pre-adaptation’) and exploration of innovation structural holes (known

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in the region as ‘white spaces’) might occur (‘adjacent possible’). Thus ‘green’ packaging, the ‘Sustainable Hub’ cluster, Life Sciences, Media Evolution and Mobile Heights (ICT) would be among innovative contexts introducing and exploring innovation with each other. Accordingly, from the food industry perspective, transversality of this kind would allow SFIN to spearhead a more robust export model, acting ‘guerrilla-like’ to bring entrepreneurs directly into export markets. In general, this ‘T’ model as deployed by SFIN works upwards in the food focus and outwards in the relatedness dimension, connecting fast-moving entrepreneur systems with slow-moving, large corporations to increase innovation and expand global markets. Mobile Heights This cluster constitutes a platform of large firms like Sony Ericsson, ST Ericsson, Ericsson Group and TeliaSonera and ICT start-ups in incubators and the MH Business Centre. As a cluster it is already well entrenched in a highly globalised ICT industry. Very rapidly, with major shocks to the hitherto Nordic predominance in mobile telephony, the global market has been invaded and expanded by Asian producers, notably South Korea and China. Thus the Swedish industry had responded with two action lines: first a lead-firm like Sony Ericsson would make decreasing amounts of hardware and instead place its focus upon managing global services, such as selling network services to service suppliers such as, in Scandinavia, Telenord and Telia, to whom they also sell the extra service of managing the network, Telia simply managing billing and cash flow. Accordingly Telia had been cutting employment since the mid-2000s and had not filed any patents for two to three years. It was also felt that ST Ericsson, the telephony infrastructure arm of the Ericsson Group would probably not survive in the future as a stand-alone company. The medium-term key competition for Sony Ericsson is seen in Mobile Heights to be Chinese firm Huawei, which recently opened an office in Lund, Mobile Heights’ home base, for the development of basic components for mobile phones. Huawei works with everything from base stations to mobile Internet modems and its own telephone handsets. This augments their earlier offices at Kista Science Park in Stockholm and Gothenburg, employing 250 engineers. Some Chinese Internet portals like Ali Baba and Ten Cents are assessed by Mobile Heights as larger than Google. The other keen competitor for the Ericsson Group is a leading specialist in mobile telephony infrastructure platforms, San Diego-based Qualcomm. Their list of customers includes Sony Ericsson, which uses the company’s platforms for their Android smartphones. The US firm also opened an office in Lund in 2010, seen

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partly as an effect of increased demand and cutbacks in the telecom sector in Lund that have made hundreds of qualified engineers available. The second resilience strategy being adopted in the industry, very rapidly in Sweden, was the adoption of ‘open innovation’ whereby intellectual property rights (IPR) are made available to SMEs and start-up businesses for exploitation. An early adopter of ‘open innovation’, the Dutch firm Philips had exhausted its unused IPR by outsourcing it to spin-out entrepreneurs, as a result of which it had experienced yet another round of employment downsizing. Sony Ericsson is now active in a major way in ‘open innovation’ relationships with innovative start-ups. Ericsson has been a classic ‘closed innovation’ firm but they have ample resources to buy from external suppliers and are actively seeking to contract to or acquire them. That there are quality entrepreneurial firms in the region is testified to by Research in Motion (BlackBerry) acquiring user-interface maker The Astonishing Tribe (TAT). Also Polar Rose, a Malmö start-up which built a facial recognition programme that linked into Facebook photographs, was bought by Apple for $29 million, both in late 2010. Other open innovation connections involve Mobile Heights start-ups contracting to AstraZeneca in the Life Sciences cluster for remote diagnostics telephony with discussions proceeding on biosensors. From the cluster perspective a key focus is on the MH Business Centre where start-ups are nurtured with enterprise support, contracts and IPR from larger firms. Lateral linkages are also in position with the Media Evolution (Nordic Game) and Open Health Alliance clusters nearby and the FPX (geographical information systems – GISs) cluster at Gaevle and the Baltic Sea Region cluster partnership. Media Evolution This cluster is concentrated on ‘convergent media’ or what is also known as ‘new media’. It promotes the emergence and growth of start-ups in the relevant fields. Most such new firms have entrepreneur leaders with at least two to three years past experience in larger companies, a minority came from Lund or Malmö University. An example would be Jan-Erik Solem, founder of Polar Rose (see above). Polar Rose grew out of computer vision research – the analysis of digital images and video – at the Universities of Lund and Malmö. Polar Rose entered the Teknopol MH Business Centre in 2004. Teknopol is a tailored business advice agency specialising in start-up activity, notably in regard to the Mobile Heights (MH) Business Centre, Cleantech in Sweden initiative, and Life Sciences Business Centre, each of which relates to Region Skåne’s cluster-platform programmes. Teknopol is not an incubator but connects to appropriate incubators at

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IDEON Science Park, Lund. It supplies other high-technology business services like investment, subsidies, customer identification and other enterprise support (see Teknopol account below). Polar Rose was given an initial loan of €30 000 as a Sony-Ericsson spin-out, to develop academically originated face-recognition software academic start-up. J.-E. Solem became the chief technical officer (CTO) of the company with Danish serial entrepreneur Nikolaj Nyholm as CEO. TAT (also discussed above), which was recently purchased by Research in Motion (RIM), responsible for BlackBerry smartphones, was only set up in 2002. TAT is a UX-UI, that is, user experience-user interface firm. The UX field has its roots in human factors and ergonomics, which since the late 1940s has been focusing on the interaction between human users, machines and the contextual environments to design systems that address the user’s experience. The term also has a more recent connection to user-centred design principles and also incorporates elements from similar user-centred design fields. As with the fields mentioned above, user experience design is a highly multidisciplinary field, incorporating aspects of psychology, anthropology, sociology, computer science, graphic design, industrial design and cognitive science. Depending on the purpose of the product, UX may also involve content design disciplines such as communication design, instructional design, or game design. The subject matter of the content may also warrant collaboration with a subject matter expert (SME) on planning the UX from various backgrounds in business, government, or private groups. TAT is set to enhance the BlackBerry PlayBook and smartphone platform. In 2009 Media Evolution established its own firm EDCO. The principal tasks of EDCO involve interacting with the broader Region Skåne Media Cluster (Figure 2.4), acting as a conduit of EU Structural Funds allocations through Swedish economic development agency Tillväxtverket (TVV, former Nutek), connecting Media Evolution’s more than one hundred ‘convergence-focused’ firm members (subscription SEK1–8000 per year), including Ericsson Multimedia, publishing companies, advertising agencies and a regional bank, connecting to the MINC incubator Malmö University Living Laboratories, the Nordic Game programme, and Film i Skåne initiative (Figure 2.4), the NetPort game business development organisation, which unites the interests of Karlshamn township, the games industry and Blekinge Institute of Technology, and ‘Learning Business’, a network of companies involved in media and e-learning. Among the ‘shocks’ confronting Media Evolution are the ICT corporate downsizing and entrepreneurship issues faced also in Mobile Heights and the rapid emergence of convergence requirements across Sweden’s Modern Times Group (MTG) with its multi-channel, satellite and cable demands and changing UX-UI and Internet protocol

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Figure 2.4

Ozma

TVV

Swedish TV (SVT)

Sony Ericsson

LTH (Faculty of Engineering)

MINC Incubator

Awnic

Region Skåne & Cluster Programme

Moving Media Southern Sweden (MMSS)

Skåne Film Producers Association

Malmö University, Department of Art, Culture & Communication (K3)

City of Malmö

Media Mötesplats (MMM)

KK Foundation (KKS)

Broader Skåne media cluster to which media evolution EDCO relates

Film i Skåne

Living Lab New Media

VINNOVA

Ericsson Mobile Platforms

Mobile Heights

MIT Teacher Education Programme

Agent ‘O’

Hermods Game Academy

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television (IPTV) expectations for downloading to iPads, iPhones and other digital devices. This is increasingly user-interactive with novel social media forms like ‘Crowdsourcing’ as described in Scott Page’s book The Difference, and even ‘Crowdfunding’ of anything from film projects to start-ups. Accordingly, Crowdsourcing also indicates the response of ‘open innovation’ to global, corporate competitive forces impinging upon large Swedish ICT incumbents. A larger shock, like Climate Change causes firms and Media Evolution itself to engage UK thinking with ‘experience economy’ customers like Exploria, Malmö’s incipient (2016) Theme Park and Science Centre with interests in sustainability, environment and in 2014 an Electronic Virtual Park. The process of ‘gamification’ (serious simulations as well as entertaining computer games) as recognised in disaster management cluster ‘Training Regions’ is part of the response to such changes. Thus, by connecting technologies, new business models and customer interaction, Media Evolution aims to promote new, convergent technology to make Skåne the interactive capital of the world. Teknopol As we have seen in the previous account, Teknopol is a business advice agency for start-ups dealing in subsidies and loans of between €30 000 and €500 000. Three ingredients guide advice for entrepreneurs; entrepreneurial skills enhancement; industry collaboration as an early customer base; and new ways of finding finance. Experience shows that having serial entrepreneurs like the one at Polar Rose is the optimal asset. Giving them six months on a project after they may have been downsized from Sony Ericsson with redundancy payments can make a 12-month salary, which with business experience is often sufficient to create a robust business. As noted, there are three cluster strands that Teknopol is active in promoting: Life Sciences; Mobile Heights and Sustainable Hub/Cleantech in Sweden. Building cross-cluster interactions between ICT and pharmaceuticals has worked well, as for example in the case of AstraZeneca’s requirement for an ICT solution for its remote diagnostics concept. Other interface projects supported have included digitisation of the regional newspaper and developing online loyalty and payment systems for IKES Bank. Other Life Sciences initiatives involve functional food collaborations around diabetes treatment and metabolism diagnostics again with diabetes treatment to the fore. In relation to renewable energy (Cleantech in Sweden) personal links were developed with E.ON which opened doors to their ideas portfolio, with a view to ‘open innovation’, which was then taken up with a different energy firm Vattenfall with main connections at CTO level. Mobile Heights’ new business plan (MH 2.0) directs

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future ICT activities forcefully towards ICT applications emphasising personal health and remote diagnostics and treatment. AstraZeneca gave access to their patient database to facilitate development of ICT based clinical testing of new treatments. In ICT Ericsson’s soft SIM-card is in development for machine-to-machine interaction (otherwise known as ‘Internet-of Things’) in combination with MH entrepreneurs. Other ICT outward innovation links include Qubulus, which is a system platform for indoor positioning (Figure 2.5) on which location-based services (LBS) can be developed by Qubulus or by an application developer community through a shared application programming interface (API). The platform aggregates positioning input from a wide range of proprietary Qubulus technologies ranging from web services and mobile applications to hardware installations. By using the best technology to fit the usage and purpose of the customer case Qubulus can meet tough demands and solve the problem of indoor positioning. Crowdsourced positioning activities are a focus in designing space syntax for people flows, shopper movements in retail malls, ‘product finder’ smartphone applications of the kind that Packbridge would like to develop (see Table 2.2). On this Mobile Heights is interacting with Gaevle’s FPX GIS/Global Positioning System (GPS) cluster supported by TVV. The overall mantra of Teknopol is that the ‘opposable mind grips the mystery and grasps the innovation’. Packbridge This cluster focuses on packaging innovation, especially for smaller, specialist firms like those engaged in ‘local food’ markets for whom it is difficult to get innovative packaging that show the values of the company on the package in small volumes. On this, Packbridge works with the Food Academy, a Lund University member of the SFIN regional food network. Packbridge was founded in 2010 and has conducted an industry survey, research review and a database exercise of key actors in the market for their kind of target businesses. In relation to ‘shocks’ of an environmental nature, the research showed such business customers seek lower-cost packaging, think recycling can be important for their markets and factor-in energy cost and type much more in their cost and ethical calculations. Alongside the climate change, energy and sustainable cities crises, health demographics influence Packbridge’s strategy for its members. Accordingly, as well as SFIN ‘relatedness’ to the ‘Sustainable Hub’ network is important. For example, bioplastics have been developed that are liquid-tight, a problem hitherto, and these can be introduced to organic product marketing in future. Then, in a food-packaging– cleantech interface with Media Evolution, Packbridge foresees interactive

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Figure 2.5

Qubulus crowdsourced indoor positioning platform

Crowdsourced positioning activities

Accurate Positioning of Mobiles

Developers API

Web Analytics

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Open innovation

Gamification, Training Regions, ‘Exploria’ Science Theme Park

Crowdsourcing, Crowdfunding etc.

Linkage

Technology

Corporate Downsizing

Shocks

Responses

New media convergence

Focus

Indicator

Apps software

Health, games, GIS

Open innovation

Asian Competition

Mobile ICT

Media Evolution; MH; SBHUB; Packbridge; Life Sciences ‘T’-model Inno-network and Lobbying

Green and healthy foods

Sustainability, losing subsidy

Food

SFIN

Apps; Internet of Things; crowdsourcing and positioning

Mobile Heights, Life Sciences and SBHUB

Green Market, ICT and Life Science down-sizing Open innovation

Start-up advice and funding

Teknopol

Media Evolution

Cluster

Mobile Heights

Matrix of regional innovation platform evolution elements

Table 2.2

Sustainable Hub; SFIN; Medicon Valley; Media Evolution; Training Regions Bioplastics; Biodegradability; GIS ‘Food Finder’

Green packaging

Climate change; personal health

Packaging innovation

PackBridge

Smart cities; eco-city design and engineering

Build energy and recycling platform SFIN; Packbridge; Mobile Heights

Systems optimisation in renewable energy and recycling Climate change; Energy crisis

Sustainable Business Hub

Arena of 60 disaster managers in Öresund region; serious games Gamification ‘simulation’; ‘theatre’

Systems integration

Environmental and Security disasters

Complex system integration

Training Regions

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packaging solutions involving mobile telephony for applications (‘apps’) in respect of payment, provenance, traceability and food recipes rising in importance. In relation to Skåne’s Life Sciences cluster and the broader Medicon Valley, relation-building is also on the Packbridge agenda. The idea of a ‘Food Finder’ app was also of interest using GIS/interior positioning platforms of a kind that, as noted, already exist as prototypes in the Qubulus family of platforms. Crowdsourcing is the key to achieving operability with this concept. This will evolve to the extent Packbridge meets its aspiration of engaging fully with the crowdsourcing platform, for example at the ‘Humanist Laboratory’ conference at Lund University. Related technologies of relevance in this dimension include nanotechnological surfaces, molecules and barriers in relation to sustainable packaging. Problems such as bacteria in liquids still occur in packaged goods, as a health scare at Ostersund regarding water indicated, and, in relation to disasters management, as when major system failures occur as in Haiti, or in relation to water supply after Northern Ireland’s unexpectedly hard winter, suggest ‘disaster stores’ that start renewable but when time-expired become biodegradable and require serious contemplation. Sustainable Hub The relatedness between this cluster concept and most of the preceding clusters is obvious, and in many cases has been recognised by the cluster agents and mentioned in the above accounts. The proximate ‘shock’ that led to the formation of this cluster initiative relates, naturally, to the combination of climate change and the energy crisis which invokes the strategic concept of smart, sustainable cities in which adaptive infrastructure systems are optimally integrated. There are economic as well as environmental benefits from this approach, which are seen in the business potential of the Middle East and China as well as, nearer home, upgrading the existing infrastructures in the Baltic Sea Region. Technologies like renewable energy and sustainable district heating are cases in point. Sustainable Business Hub (SBHUB) is a membership organisation of 120 global customer and local supplier firms, municipalities and research institutes throughout Sweden, but mainly based in Skåne. Their main focus and interest is in commercialisation of Swedish expertise in systems optimisation for renewable energy production, district heating and cooling, recycling, and water and waste management. The strongest expertise in SBHUB is water and waste management. However, there are other specialist clusters in the overall sustainable innovation platform. Thus SweHeat is the brand-name for energy production and district heating and cooling. VARIM is the industry association for water and water

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Figure 2.6

Innovation, global change and territorial resilience

Helsingborg piped biofertiliser. A Simatic S7-200 Micro Programmable Logic Controller (PLC) and a General Packet Radio Service (GPRS) modem is mounted on each local tank, which communicates with the central maintank

purification, and SymbioCity is the organisation promoting waste management. Because SBHUB works with systems optimisation aspects and export promotion, there are major synergies with the systems integration aspirations of the new ‘Training Regions’ cluster with its focus on infrastructure systems resilience (see next section). Demanding customers driving the SBHUB trajectory forward include Swedish municipalities and cities seeking to exploit scale economies in production and distribution. For example, the Skåne city of Helsingborg is a global leader in aspects of ‘industrial ecology’ whereby its waste management process produces inputs for biogas energy and biofertiliser production. For example, the Filborna recycling facility (Figure 2.6) used to transport its biofertiliser, some 35 000 tons, with trucks to farmers – at high transport costs and a considerable impact on the environment. Now the fertiliser is sent to the farms through pipelines. Thus 20 000 square metres of biofertiliser are pumped from the plant to four separate tanks over a radius of 12 kilometres. Each tank is monitored and controlled by the plant’s master system. Innovation such as this has stimulated cross-cluster interactions with SFIN (biogas energy optimisation and broader sustainability in the food chain), Packbridge (green packaging) and Mobile Heights (green ICT and transportation). Innovation arising from such

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interactions ensued at firm-level, including smaller new entrant businesses as well as more established firms in eco-innovation value chains. Creating arenas for knowledge transfer conferences and workshops around these and related issues is a key function of SBHUB. The shared interest (with ‘Training Regions’) and understanding of the imperative to improve systems management, integration and resilience is the main field requiring advanced knowledge sharing and practical innovation. A particular interest focuses on hospitals and improving their logistics, energy and wasterecycling in the SymbioCare project being conducted with the Region Skåne health authority. Training Regions This emergent cluster initiative was founded in December 2010; accordingly this account is programmatic rather than evaluative. It plans an Open Arena, run by logistics specialist company TR International charged with identification of needs, solutions, research and development (R&D) and policy expertise and targets for enhancing infrastructure system resilience in Skåne, Sweden, the Öresund region and internationally, especially where Swedish interests (for example, business) and capabilities (for example, disaster management) are involved. The increasing frequency of disasters in recent years and the scale of damage they bring, pose considerable challenges regarding the development of viable and effective approaches, to help mitigate the impact upon the population and the environment. The new threats faced, from developing technologies, globalisation and political tensions are increasing. These, together with the risks associated with climate change and threats to collective safety bring about a need for a co-ordinated approach to research and international collaboration on emergency management. Research leadership is provided by LUCRAM, Lund University’s Centre for Risk Assessment and Management. It hosts four research ‘clusters: Cluster for Emergency Response Research (CERR); Cluster for International Disaster Studies (CIDS); Human and Organisational Factors in Risk Management (HOFRIM); and the Leonardo da Vinci Laboratory for Complexity & Systems Thinking. The need for practical rapid response to system crises has been agreed with and supported by the Swedish Export Agency, centrally concerned with global commodity, asset and knowledge flows and structures. Global regions hosting large cities or export economies are highlighted (Figure 2.7). It is clear that emergencies, crises or disasters in any of the key nodes transceiving key infrastructure flows require optimal resilience. Training Regions’ January 2011 business plan focuses on interactions, leaderships and

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Figure 2.7

Global telecommunications flows, 2010

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resilience planning and management in relation to such multi-level flow structures, focused upon cities. This perspective echoes a quotation made by the famous Scottish town planner Patrick Geddes to the effect that ‘a city is more than a place in space; it is a drama in time’ (Geddes, 1915, p. 96). Armed with the theory that cities are processes not just geographical spaces, inter-city flows can be disrupted, for example, by bad weather, whereby medical staff may not be able to reach hospitals, not because they cannot be helicoptered in, but because they have to look after their children who cannot get to school. Normally municipal services and health services have different interests, systems and services but in emergencies they clearly need to be able to interact resiliently. Accordingly, network interface enhancement is a key instrument to improving system resilience. The interoperability of Apple PCs and smartphones is useful and accessible to millions, and semantic search engines demonstrate improvements in understanding user thoughts and needs. ‘Gamification’ of learning processes inspired by interfacing of the Tetris Blue Light gaming system is also inspirational, given it can easily be handled by children. Thus Open Arena will first assemble sixty or so representatives of the Öresund emergency and basic infrastructure services alongside private companies engaged in computer gaming to demonstrate how simulation ‘theatre’ can assist management, operation and technical solutions to Grand Challenge issues like climate changeinduced events, such as exceptionally bad weather or other emergencies, in an optimally resilient manner. ‘Gamification’ will upgrade typical civil protection training which relies on whiteboards and Post-it notes. Training is needed in Vector Command (VC), that is, where leadership is provided in emergencies, especially international ones, where different rules apply. In most countries police have VC status, but in Sweden and a few other nearby non-Scandinavian countries (for example, Russia, Poland) it is the fire brigade that exercises VC until it is established that a crime has been committed, such as terrorism, when the police take over. Some civil protection systems are highly procedurally driven, others are both more scientific and flexible. In the London terrorist attacks in 2005 the fire brigade frequently arrived first but hierarchical system ‘silos’ meant they could not override ambulance service regulations, as a consequence of which victims whose lives could have been saved died. A British firefighter receives 15 weeks’ training, while in Sweden it is two years of higher education involving preventative as well as rescue recovery, including cognitive skills like pattern recognition and so on. Accordingly, unified command (UC) systems require parametermapping, in developing knowledge of managing ‘systems of systems’. A priority is to make system information transparent to each Open Arena

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Figure 2.8

Innovation, global change and territorial resilience

Sweco design for Caofeidian eco-city, China

membership organisation as a basis for developing technical solutions together. Therefore, for Training Regions, which is a pedagogic initiative, methods of learning resilience management among complex systems is a priority, and the key Swedish international engineering consultancy, Sweco, won the contract to design the first phase of the new Chinese ecocity of Caofeidian some 200 km south-east of Beijing (Figure 2.8). Sweco defines the sustainable city or eco-city as a concept for sustainable urban development that can be applied on both a large and small scale. The idea is based on the use of a holistic approach to reduce emissions from entire urban districts. By planning according to the unique conditions of each site and proposing integrated system solutions for energy, transport, waste, landscape design and other factors, is it possible to create virtually climate-neutral cities. Accordingly, designing for resilience is evolving as a Sweco niche expertise as it changes its profile from a ‘silo-supplier’ of specific construction services to a ‘platform-supplier’ of integrated and sustainable services in multi-functional structures like eco-cities.

CONCLUDING REMARKS In the early parts of this chapter, we outlined an approach to regional economic change analysis that paid attention to the following key elements: multi-level relationships in governance and corporate structures and relations; resilience as an expression of a region’s capabilities in absorbing ‘shocks to the system’ and returning to the position before destabilisation,

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or more likely, engaging in some form of search behaviour for the adjacent possibilities of innovating after the creative destruction process had calmed down. The other, more common form of displaying resilience with the support of regional innovation agencies was to identify ‘pre-adaptive’ innovations that could be transferred from one industry or cluster to a different one. In some European regions like Bavaria and Lower Austria this is normal where in Värmland, in Sweden there is more of a search across a relatively narrow span of related regional industries for user and designdriven innovation in the ‘adjacent possible’. In the key comparative case of Skåne region, also in Sweden, this quest for the ‘adjacent possible’ is even more to the forefront across a broader span of industry clusters. In Table 2.2 the commonalities and divergences in this practice are summarised. It is clearer, perhaps, than in the other comparative case material that certain Grand Challenges have imposed shocks on the regional innovation system. These range from climate change imposing harsher winters on shared infrastructures in the Öresund region, deindustrialisation with the major closure of Kockums shipyard in Malmö and its rapid transformation into a ‘cognitive-cultural’ quarter housing the media, ICT and many other clusters as well as some regional development agency in close proximity. Downsizing of established large ICT corporations faced with massive global competition emanating from Asia, and China in particular has been another shock, resiliently responded to by ‘open innovation’. Finally, the demographics of ageing have also imposed strains on the system, recognition that services are deteriorating and efforts to respond with innovations that optimise infrastructure, ICT, food and healthcare expertise and capabilities in the region but serving Sweden and the wider world. Exploiting the region’s proximity and relatedness advantages has been key to the response made so far, indeed, each cluster is connected to a set of interlocking circles that represent two strategic Grand Challenges that the overall Region Skåne strategy is meant to address, as shown in Figure 2.9. The darker circles are existing cluster initiatives, the lighter ones are projected. In the remaining space, it is necessary to summarise the main ‘transversalities’ in each of the clusters in the Region Skåne innovation system discussed above, which is provided in Table 2.2. This shows recognition that regional industries have suffered significant shocks from climate change, energy scarcity, corporate ‘global shift’ and downsizing, and healthcare issues, such as ageing of the population. In different ways, as with SFIN and Mobile Heights, the response to destabilisation is to engage in some form of ‘platform-building’ among clusters to produce innovate solutions with regional industry neighbours. Linkage is particularly pronounced towards the ‘gaming’ technologies of Media Evolution, and to SFIN. The

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Production Technology Logistics

Tourism

Mobile Heights

Sustainable Cities SBHUB

Training Regions

Municipal Initiatives

Materials Science

Personal Health

Food Academy

Figure 2.9

Life Science

Healthcare

Media Evolution

Region Skåne’s two strategic ‘Grand Challenges’ innovation platforms

technology projects they are working on include: sustainable technologies; new kinds of ‘crowd’ or user-driven innovation; and the use of gaming and simulation to stimulate learning and change management in meeting the shocks imposed by Grand Challenges such as climate change.

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The Innovation Index:1 measuring the UK’s investment in innovation and its effects Stian Westlake, Brian MacAulay, Peter Gratzke, Albert Bravo-Biosca and Hasan Bakhshi

BETTER MEASUREMENT SHOWS THE IMPORTANCE OF INNOVATION – AND HELPS GUIDE POLICY Innovation sits at the heart of debates over economic growth, and encouraging an innovative economy is high on the wish-lists of many governments. The National Endowment for Science, Technology and Arts’ (NESTA’s) Innovation Index, the pilot form of which is presented in this report, is a major project to demonstrate the contribution of innovation to economic growth in the UK, and to complement this with a measure of growth at a company level and an assessment of the wider national conditions for innovation. Measuring innovation effectively is important because policy is affected by how we measure results. Lord Kelvin’s adage ‘if you cannot measure it, you cannot improve it’ has an important implication: if something needs to be improved, it must first be measured correctly. There is a particularly pressing need for good measures in the field of innovation. The most familiar and most widely accepted metrics of innovation still relate to a linear model of innovation based on science and technology and tailored to manufacturing industries. Despite the inclusion of aspects of non-technological innovation in surveys such as the European Union’s (EU’s) Community Innovation Survey, internationally agreed indicators such as expenditure on research and development (R&D), patent production, and numbers of science and technology graduates still loom large in public debate.2 This measurement bias has shaped innovation policy. After several

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decades of measurement of R&D,3 many countries have formulated policies to encourage more of it. The Lisbon European Council set a target that EU member states should spend the equivalent of 3 per cent of gross domestic product (GDP) on R&D.4 Having identified innovation as one of the five levers of productivity,5 and thus part of a key public service agreement (PSA) target, the UK government introduced R&D tax credits as a means of increasing it. NESTA’s Innovation Index is an attempt to provide a measurement of innovation that reflects how innovation really happens, and one that can both quantify the importance of innovation and act as a guide to better policy.6

THE INDEX The improved measures of innovation deployed in the pilot Index have highlighted a number of important phenomena: The UK invests more heavily in innovation than R&D measures would suggest. Private sector businesses invested £133 billion in innovation in 2007 (the most recent year covered by the Index), representing 14 per cent of private sector output. This compares favourably with the (admittedly more preliminary) data available for countries like France and Germany, and similar to the USA. This may be one reason why the UK has enjoyed higher productivity growth in recent years than France or Germany despite concerns over its investment in R&D.7 Most of this investment takes other forms than traditional scientific R&D. Traditionally, R&D expenditure has been used as proxy for innovation investment.8 However, R&D represents only 11 per cent of the investment in innovation measured by the Index, which includes a range of complementary investments needed to commercialise ideas, including product design, training in new skills, organisational innovation, developing new customer offering and brands, and copyright. The findings of the Index to date show that innovation may be responsible for the lion’s share of the UK’s productivity growth from 1990 to 2007. Two-thirds of UK private sector productivity growth between 2000 and 2007 (1.8 percentage points of productivity growth per year) was a result of innovation.9 These first three findings on innovation investment and productivity constitute the headline messages of the Index. Innovation is strongly linked to business growth across a range of sectors. It may come as no surprise that innovative software firms enjoyed a much faster growth rate than non-innovative ones (13 per cent average revenue growth per year compared to just over 0 per cent). But this relationship

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held true even in sectors like legal services, where innovative firms enjoyed average revenue growth of over 10 per cent, while non-innovative firms revenues shrank on average. The UK is a relatively good place to innovate, but has some important shortcomings. On the basis of available internationally comparable data, the UK appears to be a mid-table performer when it comes to the wider conditions for innovation compared to other leading economies (including the USA, France, Germany, Japan, South Korea and Finland). Although there is scope to develop these data further, they suggest that the UK performed less well on three important indicators: access to finance, demand for innovation (in particular the use of government procurement to encourage innovation) and skills for innovation.

THE INDEX BUILDS ON A RANGE OF EXISTING ATTEMPTS TO MEASURE INNOVATION NESTA is not alone in its desire to improve the measurement of innovation. The Index builds on a wide variety of research that has sought to measure national investment in innovation, how innovative firms are, and the innovation-friendliness of different countries.10 The task of defining innovation has been the subject of detailed work, in particular by the OECD in successive versions of the Oslo Manual.11 These definitions have expanded over time from a narrow focus on technological product and process innovation to include a much wider range of activities, including marketing and organisational innovations, and to take account of innovation in services and low-technology sectors. A recent definition proposed by the US Advisory Committee on Measuring Innovation in the 21st Century Economy is indicative of this broader definition; it describes innovation as ‘the design, invention, development and/ or implementation of new or altered products, services, processes, systems, organisational structures, or business models for the purpose of creating new value for customers and financial returns for the firm’.12 The OECD’s definition has played a central role in framing the Community Innovation Survey (CIS). This business-level survey, which has been conducted since 1991 by EU member states and Eurostat, asks businesses across the EU about their innovation activities. Six waves of the CIS have now been completed, providing an increasingly comprehensive view of innovation at the firm level, including product, process, organisational and marketing innovations. The European Innovation Scoreboard (EIS)13 further evaluates conditions for innovation at the national level. It is a composite of a large

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number of indicators used to rank EU countries (and a number of others, including the USA) in order of innovation-friendliness. Similar collections of indicators have been developed elsewhere, for example for the State of Massachusetts.14 Research has also taken place to address the wider question of the impact  of innovation on economic growth, as reflected in GDP. Particularly important to this have been macroeconomists’ attempts to measure investment in intangible assets and their impact on economic growth. Intangible assets have been described as investments in knowledge capital, as distinct from physical capital or labour, the two factors of production at the heart of the traditional growth accounting approach which is consistent with the national accounts. A way of measuring these investments was set out in 2006.15 Some early estimates have been made of the levels of intangible investment in developed countries.16 The exponents of the original research have argued that it offered a way of calculating the effect of innovation on the economy.17 These approaches are now being developed by the US Bureau of Economic Analysis.18 The initial idea for a UK index to measure innovation was proposed by the DTI Innovation Unit in 1994, but was not implemented. The Innovation Nation White Paper of 2008 tasked NESTA with designing an Innovation Index, taking into account a broad definition of innovation. This pilot Index represents the first substantive output of that work.

THE PILOT INDEX HAS THREE COMPONENTS The overall aim of the Innovation Index is to offer a significantly better basis for government policy that affects innovation. The pilot Index does this in four ways: 1.

2.

3. 4.

Its most important component is a measure of the amount of investment in innovation in the UK economy, and the effect that this has on economic growth and productivity. Its second component is a tool to understand innovation at the firm level that captures hidden innovation and reflects the different ways that innovation occurs in different sectors. Its third component is a set of metrics that can be tracked to assess how favourable a climate the UK is for innovation. Finally, it is intended to provide a measure of innovation in the public sector. The measurement of public sector innovation has not been carried out as part of the pilot Index, but will be included in the 2010 version of the Index.

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The remainder of this report considers the first three components in turn. In each case, the report identifies the work that has been done, the findings of the Index, and what will be done to develop the revised version of the Index.

COMPONENT 1: A MEASURE OF HOW MUCH THE UK INVESTS IN INNOVATION AND THE ECONOMIC IMPACT OF THIS This is the most important and ambitious aim of the Index. The impact of innovation investment on economic growth, and specifically productivity, is of central concern to the government. It is captured in the productivity Public Service Agreement Target shared by HM Treasury and the Department for Business, Innovation and Skills (BIS), the aim of which is to close the UK’s productivity gap with the USA, Germany and France. Innovation has been identified as one of the five levers to close this gap, but existing innovation metrics do not relate directly to productivity. The pilot Index has generated a good working figure for the size of UK investment in innovation from 1990 to 2007, and the contribution this makes to GDP. The next phase of work between now and November 2010 will refine the precise types of investment included, improve the underlying data – partly through primary data collection through a new intangible investments business survey – and provide a further year of data and a replicable process for generating future years’ figures.19 A.

What Was Done

A working definition of innovation investment as investment in new knowledge assets was adopted The first challenge is defining what investments to count as investments in ‘innovation’. Despite the work of the OECD and others to define innovation, existing definitions do not provide a simple distinction between ‘innovation-related’ and ‘non-innovation-related’ investments suitable for applying at the level of the national accounts;20 moreover, with the exception of the spending on software, the national accounts do not treat spending on innovation as an investment. For the purposes of the pilot Index, it was decided to define innovation investments as investments in knowledge, or, as macroeconomists would put it, intangible assets. This means that the Index measures not only scientific research and developments, but the downstream co-investments

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needed to commercialise and profit from new ideas. This definition has two advantages. First, it follows the approach for measuring innovation being considered by the US statistical authorities,21 and therefore increases the chances of obtaining directly comparable data from other innovation measurement exercises. Other developed countries have also undertaken some work to estimate the investment in intangibles, providing initial early points of comparison. Secondly, it includes a number of investments that relate to important aspects of ‘hidden innovation’,22 such as organisational innovation, the investment in skills needed to provide new services, investment in product design, and investment in branding necessary to take an innovative product or service to market. Many of these, such as training and skills development and organisational improvement, are particularly relevant for innovative services businesses, and constitute the bulk of their investment in innovative offerings. Box 3.1 provides a number of examples of the practical importance of these types of investments to innovative firms. Table 3.1 sets out the seven categories into which knowledge investment was divided while Box 3.1 provides a number of examples of the practical importance of these types of investments to innovative firms. The next phase of the development of the Index will re-examine this definition of innovation and if necessary improve on it. In particular, NESTA will consider (and invite comments on) whether some measure of innovative tangible capital (such as cutting-edge computers or high-technology machines23) should be included, how to include knowledge investments by the public sector (such as state-funded training), and whether some aspects of intangible investment should be excluded (in particular, some aspects of training and skill development, and some aspects of brand investment). The measurement of these has been significantly improved compared with previous estimates Previous attempts have been made to measure the amount of intangible investment in the UK24 and in other countries.25 The pilot Index builds on them but goes further by improving the UK data in several ways. First, it developed new estimates of investment in design for the UK, which capture both the design services bought from external providers but also that developed in-house by firms themselves. The latter was achieved by counting the hours spent by employees in a wide range of in-house design activities, from new engineering designs to the development of new

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BOX 3.1

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INNOVATION INVESTMENT IN PRACTICE

In the words of one economist, ‘the average businessperson knows that R&D spending is an investment in the future capacity of the firm. He/she also knows that innovation goes beyond the upstream discovery of new inventions and technologies by scientists and engineers, beyond the creation of new ideas and designs by other workers, and beyond the turning of those inventions and ideas into new products and services. Inventions, ideas, new products, and new services are worthless without a downstream process that turns them into something that convinces people and firms to become customers’.26 The seven types of investment in innovation described above can all be seen at work in practical business innovations. Consider, for example, the development by Bird’s Eye in the early part of the decade of a range of innovative frozen ready meals, the ‘Steam Fresh’ range, which could be steamed from frozen and which commanded a price premium in the otherwise commoditised frozen food market. Bird’s Eye’s investments to develop this product included, but went far beyond, activities we would recognise as R&D. In addition to food-science R&D, their investment included design (the product relied on innovative packaging to work properly) and a significant outlay on branding and marketing (both to demonstrate the need for the product in the first place and to make the case to retailers and consumers that it justified a price premium). Service industries often rely heavily on investments in training and organisational improvement when they innovate. Retail banking innovation, such as the deployment of online banking, typically involves a combination of software investments to process the new service, organisational improvement to support it, and training investments to ensure that staff are able to deliver it. Copyright is a particularly important investment for creative businesses, while investment in mineral exploration typically distinguishes more innovative oil and gas firms from less innovative competitors.27

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Table 3.1

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Investment in innovation was divided into seven categories

R&D

Design

Organisational improvement

Training and skills development

Market research and branding

Software development

Other (copyright development and mineral exploration)

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R&D is ‘classic’ innovation investment: scientific research and development that produces new knowledge in form of ideas or products that can be marketed by firms Investment in design has been described by some macroeconomists as ‘non-scientific R&D’. These designs may be critical in the innovation process, as they play an important role in new product and service development. This category is also assumed to include those investments aimed at developing new services and financial products Organisational innovation drives the efficiency and effectiveness of organisations. Investing in this type of knowledge is critical to stay competitive and be able to leverage innovative ideas and commercially exploit them Using our current definition, investment in workforce skills turns out to be the single most important source of investment in the UK. Therefore the investment in training and skills development is critical to the innovative capacity of firms; it is particularly important for service innovations: the most significant investment to realise these may be in human capital Market research is at the front end of innovation: to identify the market potential for new products companies must at the outset anticipate future demand. This category captures other investments made to develop brands in order to take products to market. Both are strategic elements of the innovation process Resources invested in developing software and databases creates a valuable asset that prior to the 2007 Blue Book were not treated as such in the UK’s national accounts Investment in new knowledge of exploitable mineral sources and copyrighted ideas both lead to assets that firms can commercially exploit and which are frequently capitalised in firms’ financial accounts. These two apparently dissimilar types of asset are grouped together to reflect the way they are treated in the national accounts, but represent the smallest category of investment measured

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financial instruments. Second, we collected new primary data28 that was used to test the robustness of the estimates for the different types of investment in innovation, with the result that the measures produced are more reliable.29 And last, but not least, we updated all the measures up to 2007, the latest year for which national accounts data is available.30 The quality of data will be further improved in time for the revised Index report in 2010 through the use of a detailed survey on investment into innovative assets by UK firms. A growth accounting approach was used to understand the effect of these on productivity growth The impact of these investments on productivity growth was then determined using the same growth accounting approach used to generate the national accounts. This provides a clear indication of the potential contribution of innovation investments to labour productivity growth. B.

Findings

The UK private sector invested approximately 14 per cent of private sector GVA in innovation in 2007 Figure 3.1 shows investment in innovation by the UK private sector in 2007, the most recent year for which the Index generated results. It is notable that investment in R&D is significantly lower than other types of investment in innovation. The breakdown of investment in innovation shows that R&D is only the fifth largest category of innovation investment, at 11 per cent of the total.31 UK firms invest most of their knowledge capital in training the workforce: training makes up a quarter of all innovation investment as currently defined. Organisational improvements represent the second largest share of innovative investment in the UK. Together with investment in training, this investment in ‘economic competencies’ makes up almost half of the total investment in innovation. Investment in design, ranging from architectural and engineering to the design of new financial instruments, is the third largest share of innovation investment, reflecting the importance design plays in product and service development. After losing importance after the end of the dot.com boom, investment in software has been accelerating since and is a key factor for the innovativeness of UK firms. United Kingdom firms invest as much in market research and advertising as they do on R&D. This is significant because this is the ‘strategic’ element of innovation – to determine the need for new products and services and exploit them commercially. Neither mineral exploration nor measured copyright investments play a quantitatively

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R&D

Design Organisational improvement

14.9

22.1

26.1

Training & skills development Software development Market research & advertising Mineral exploration & copyright development

Total

Figure 3.1

32.1

20.2

14.5

3.5

133.4

Investments in innovation (£bn, 2007)

significant role for the knowledge investments of the majority of UK firms, but it is worth noting that the measurement of copyright investments is particularly poor in existing sources, and one aim of the next version of the Index will be to improve this. These data are available for the time period 1990–2007, showing the changes in the different types of innovation investment over time (Figure 3.2). Since 1990, there have been considerable shifts in the importance of different types of investment in innovation. The largest share, investment in training has grown steadily since the mid-1990s, although its pace has slowed more recently. Perhaps partly as a result of the government’s R&D tax credits, investment in R&D has seen a marked increase since 2004, after declining through much of the 1990s. While the end of the 1990s saw a significant acceleration of investments in software, its share of market gross value added (GVA) has since been declining and only recently shown growth. Following growth throughout the 1990s, investments

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4%

Training 3%

Organisational capacity Design Software

2%

R&D Brand equity 1%

Mineral exploitation & copyright 2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

0%

Note: MGVA = market gross value added.

Figure 3.2

Investment by type of intangible assets (share of MGVA)

in organisational capacity, brand equity and design have all stalled and declined as a share of market GVA in the past five years. In total, investment in innovation has outpaced investment in physical capital and in 2007 stood at 14.1 per cent of market GVA (Figure 3.3). The end of the dot.com boom saw investment in physical assets declining, while total investment in intangible assets continued to grow (albeit as a slower pace). From 2006 to 2007, the share of intangible assets has decreased slightly, while the share of physical assets grew significantly. Internationally, as is well known, the UK appears to under-invest in R&D, although this may in part be a product of the UK’s industrial structure (Figure 3.4).32 However, if the full range of investment in innovation is taken into account, international studies surveyed by Barnes and McClure suggest that the UK compared more favourably in 2007 (Figure 3.5), the latest year for which international data is available. It is important to note that the figures reported by Barnes and McClure should be interpreted with great care, since they have not been gathered on a consistent basis and were not based on the same analysis as the UK figures presented in

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Innovation, global change and territorial resilience 15% 14% 13% 12% 11% 10%

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

8%

1990

9%

Note: Solid line is investment in innovation, dotted line is investment in physical capital.

Figure 3.3

Investment as share of MGVA

this report. It is possible that this relatively strong investment in innovation could be a cause of the higher rates of labour productivity growth enjoyed by the UK over the past five years, and may be an endorsement of a policy focus on innovation. Comparisons between countries in Figure 3.5 should be interpreted with caution. The chart shows investment in innovation as a share of the adjusted output (including investment in intangibles) of the sectors for which intangibles are measured. These differ for different countries. In the case of the UK, France and Germany this is the market sector, for the USA the data covers the non-farm business sector; for Finland it covers the non-financial business sector economy for Japan, the Netherlands and Canada output relates to the whole economy. UK output data is for 200733 and 2004,34 US 1998–2000, Japan 2000–05, Netherlands, Canada and Finland 2005, and France and Germany 2004.35 This investment was responsible for two-thirds of productivity growth The Index also identifies the contribution that innovation made to productivity growth in the period (Figure 3.6). The contribution of innovation was taken to be the sum of two elements: ●

The first of these is the direct contribution of the investments in innovation described above. This represents the value created by

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Figure 3.4

Software development Traditional innovation Hidden innovation

1.0%

R&D as a share of GDP, 2007

Netherlands

1.3%

France

1.7%

Germany

1.6%

1.9%

Canada

UK

2.0%

2.7%

Finland

US

2.8%

Japan

R&D as a share of GDP, 2007

9.0%

9.4%

10.1%

10.5%

12.6%

13.5%

Figure 3.5

Investment in innovation as a share of Market Sector Gross Value Added*

Includes R&D, Design and Mineral exploration and copyright development Includes Training and skills development; Organisational Improvement, Market research and advertising

Canada

Netherlands

Germany

Japan

France

US

13.0%

14.1%

UK 2007 UK 2004

14.6%

Finland

Investment in innovation as a share of Market Sector Gross Value Added*

84

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Total

2.73

Labour Capital deepening

TFP

6%

1.27

27%

New knowledge

0.54

47% 20%

Capital deepening

0.75

Knowledge

Labour

0.17

0

Figure 3.6



TFP

1

2

3

Productivity growth by contribution, 2000–2007 (%)

innovation investments that is captured by the businesses that make the investments, or the so-called ‘private’ benefits of innovation investment. The second component is what macroeconomists describes as total factor productivity (TFP). This is the measure of productivity growth that is not accounted for by the growth in factor inputs, such as physical capital or labour quality, and is generally attributed to better ways of doing things, including the broader benefits of technological advances and improved processes. In the approach used in the Innovation Index, in which the private benefits of investments such as R&D are captured separately, TFP includes the spillover benefits of innovation investment.

This methodology shows that between 2000 and 2007, labour productivity grew at an annual average of 2.7 per cent per year (Figure 3.7). Innovation contributed 1.8 per cent, or approximately two-thirds of the growth experienced. Figure 3.8 shows the impact of different types of innovation investment on productivity. Decomposing the contribution of individual components of innovation investment reaffirms the importance of investment in economic competencies of training and organisational improvement, which

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Figure 3.7

1990–1995

Capital New deepening knowledge

1.12

0.74

TFP

0.98

32%

Total

3.02

TFP

Total

1995–2000

Labour TFP

3.69

20%

27%

0.54

6%

2000–2007

TFP

1.27

47%

Labour

Capital New deepening knowledge

0.75

Knowledge

Labour

Capital New deepening knowledge

1.43

39%

TFP

Capital deepening

0.17

0.84

7%

Labour

0.25

1.17

Knowledge

23%

32%

Capital deepening

Average labour productivity increase per year (%)

Labour

0.18

25%

6%

Labour

Knowledge

37%

Capital deepening

Total

2.73

TFP

86

Innovation, global change and territorial resilience

R&D

90–95 95–00 00–07

0.06% 0.04% 0.04%

Design

90–95 95–00 00–07

0.14% 0.14% 0.11%

Organisational improvement

90–95 95–00 00–07

0.15% 0.13% 0.14%

Training & skill development

90–95 95–00 00–07

0.12% 0.17% 0.12%

Software development

90–95 95–00 00–07

0.18% 0.23% 0.09%

Branding & marketing

90–95 95–00 00–07

0.07% 0.13% 0.03%

Minerals & copyright

90–95 95–00 00–07

0.02% 0.00% 0.00%

TFP

90–95 95–00 00–07

Figure 3.8

While R&D is lower in terms of direct contribution its full effects will be captured within TFP

Investment in organisational improvement was the largest contributor to productivity growth between 2000 and 2007

Software development was the main driver of productivity growth between 1995 to 2000

Spillovers from firms’ private investments yield wider benefits to the economy that is reflected in TFP

0.98%

1.43% 1.27%

Innovation investment on productivity

between them contributed 0.26 percentage points to labour productivity growth between 2000 and 2007. While the contribution of R&D has been steady over the entire period since 1990, the contribution of software development peaked at a high level in the late 1990s, and has since declined significantly. It is important to note the overwhelming importance of TFP, which includes the spillovers from other innovation investments, for example R&D. Even though the direct contribution of R&D to productivity growth appears to be modest in the 17 years analysed, this overlooks the potentially significant benefits of R&D spillovers (which some research has suggested is more than double the private benefit of R&D).36 C.

Next Steps

The next phase of the Index will involve a survey of UK firms, administered by Office for National Statistics, to understand in more detail their investment in innovation. This Innovation Investment Survey will allow

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the estimates of investment in innovation to be considerably refined and will be of considerable interest not just national but internationally. In addition, the definition of innovation will be refined; the questions of whether to include investments in innovative physical capital (and if so, how) and whether to exclude some types of branding and training (and if so, which) will be considered; and the role of public sector intangibles factored in, along with any feedback received on this pilot. In the future, it is NESTA’s intention to design a parsimonious way of gathering annual data to indicate how levels of investment in innovation are changing. This may involve a survey or a panel, but ought to be smaller in scale and less resource intensive than the Innovation Investment survey that will be conducted in 2010. This will allow an updated Innovation Index to be published on an annual basis.

COMPONENT 2: A MEASURE OF INNOVATION AT A FIRM LEVEL The second aim of the pilot Index is to develop a firm-level innovation survey that reflects elements of ‘hidden’ innovation and is tailored to the innovation needs of specific sectors.37 A.

What Was Done

The researchers developed and tested a questionnaire based on a widely used model of how businesses innovate, that is based on academic research and has subsequently been used as a consulting tool in a range of businesses.38 This framework looks separately at firms’ ability to access innovation (develop ideas or obtain them for elsewhere), build innovation (turn ideas into products) and commercialise innovation (use innovative goods or services to make money). The questionnaire tested a number of areas not included in the Community Innovation Survey, and differed from other surveys in the sector-specificity of how the questions were designed and asked. An example of the extended scope of the survey is its coverage of the  process by which companies acquire knowledge, a key theme in the literature on open innovation. Thus while the CIS simply asks all firms the rate the importance they place on different broadly defined external sources of information, the Index survey goes into more detail, investigating the extent to which firms relied on a wide range of particular external knowledge sources that had been identified by prior research as relevant to their particular industry.

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Energy production Accountancy services Specialist design Consultancy services Construction Architectural services Software & IT services Legal services Innovators Non-innovators

Automotive –0.1 –0.05

Figure 3.9

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Sales growth, 2006–2009

At the same time, the structure of the questionnaire allowed comparability across sectors, so that it is possible to compare the relative innovation intensity of firms in different sectors. The questionnaire was administered by telephone to 1500 businesses across ten industries selected to provide a mixture of traditionally innovative sectors (such as the automotive industry) and sectors that were not usually associated with innovation (such as legal services). Each sector received a tailored version of the questionnaire, adapted to reflect dominant modes of innovation in the sector, as determined by interviews carried out by the researchers. For instance, survey questions regarding the sources of external knowledge used by firms were different for each sector to reflect variations across sectors. In automotive, for example, firms were asked about reverse engineering and recruitment from rival firms, while specialist design firms were asked about sources such as museums and observing people. B.

Findings

Innovative firms show higher sales growth than non-innovators (Figure 3.9). This holds true for every sector included in the pilot, although the effect was small in the energy production sector. Innovative accountancy firms grow twice as fast as non-innovative ones, and for law firms, innovation makes the difference between sales growth and decline. These findings

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have been tested in more depth in Business Growth and Innovation,39 demonstrating the relationship between innovation and growth through econometric analysis.40 The firm-level survey also demonstrates that measuring R&D investment does not capture investments in innovation in most sectors of the UK economy (Figures 3.10 and 3.11). The sectors chosen in the Index sample are mostly ‘low R&D’ sectors. In spite of this, the nine sectors we examined for the pilot Innovation Index exhibited significant levels of innovation. Much of this investment occurred in form of organisational, design or marketing innovation, rather than R&D. The importance of innovation varies for each sector. In some sectors, such as software and IT, innovation is widespread (a greater percentage of firms are innovators) and simply a condition for survival (the minority of non-innovative firms experience much slower growth). In others, such as legal services, firms are slower to innovate (there are fewer innovative firms), but innovative firms gain an enormous advantage over those that do not (sales growth is still higher among innovative firms). A number of industries surveyed show significant differences in the reported responses of small and large firms. Figure 3.12 shows the situation for legal services, where small firms are typically less innovative than medium and large firms. In other industries, such as consulting, the reverse is suggested by the survey. The survey also makes it possible to compare levels of innovation across sectors, either at an aggregate level, or in relation to specific innovation activities. Figure 3.13, prepared as part of the underlying research based on the survey, provides an overview of the relative levels of innovation in the nine sectors surveyed. Figure 3.13 shows the differences the levels of innovation between sectors and the degree to which firms in the same sector are different. The circle denotes the relative levels of innovation activity for the sector when compared to the leading sectors. Grey denotes lower relative levels of innovation while white denotes higher relative levels. The adjoining letter denotes the degree of variation between firms within a sector. An L (Low) is applied if firms within a sector are very similar, while a H (High) is applied if there is a higher level of variation between firms within the same sector. C.

Next Steps

The detailed answers to the 20 questions in the survey can be compared with firm-level performance information, such as revenue growth, to determine, using econometrics, which factors are most important for each sector.

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90

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Figure 3.11

40%

Figure 3.10

R&D spending by firms, % of turnover

Innovators in sector, % of firms

21%

26%

0.0%

Accountancy services

Construction

28%

0.0%

Legal services

0.7%

1.1%

Energy production

1.4%

49%

1.0%

Specialist design

Architectural services

50%

1.0%

0.7%

4.3%

Automotive

Consultancy services

Software and IT services

64%

64%

65%

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80 70 60 Most innovative firms are disproportionately larger

50 40 30 Least innovative firms are disproportionately the smallest

20 10 0

Small

Medium

Large

Note: The vertical axis represents a firm’s score for the building innovation phase of the innovation value chain (IVC). The score ranges from 0 to 100, where 100 is the most innovative; individual firms are ranked lowest score to highest score (left to right) on the horizontal axis.

Figure 3.12

Distribution of legal services firms by capacity for building innovation

Accountancy Architectural Automotive Construction Consultancy Energy services services production

Legal services

Software & IT services

Specialist design

Accessing knowledge Building innovation Commercialising innovation

Figure 3.13

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Relative levels of innovation in nine sectors

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NESTA has been approached by a number of sector bodies that have expressed an interest in conducting a survey of firms in their sector using this tool; it is proposed that this demand-led approach be used to extend the survey to new firms, rather than commissioning a very large follow-up survey of other sectors. The exception may be in one or more of the growth sectors of particular interest to the Department of Business, Innovation and Skills, where a more proactive approach will be considered. Finally, reflecting the international nature of many of the industries considered here, NESTA will consider ways of expanding the survey to obtain comparisons with leading firms in other countries.

COMPONENT 3: AN ASSESSMENT OF THE WIDER CONDITIONS FOR INNOVATION IN THE UK Although a number of measures of the framework conditions for innovation exist, notably the EIS, many are not based on a functional model of how innovation works, and NESTA believes there is room to improve on them. The assessment of the wider conditions for innovation was intended to be an exploratory tool that improved on existing measures without gathering new primary data. It is intended to capture neglected framework conditions (such as demand), to be rooted in factors that have been demonstrably linked to innovation and in a clear model of how innovation occurs, and to provide a time series of data and comparability across leading countries.41 A.

What Was Done

A literature review was undertaken to identify a wide range of factors influencing innovation, and existing indicators that described them. These were aligned to a four-part model of innovation designed by NESTA, and grouped into ‘conditions’. A question was included in the firm innovation survey asking businesses to rate the importance of the conditions for their own ability to innovate (Figure 3.14). This validated the chosen conditions. Detailed data on each indicator was then gathered (no new primary data collection was undertaken for this component of the Index, but a thorough examination of existing metrics was undertaken); the data was then presented and the UK’s performance assessed compared to a range of leading comparator countries. For some of these indicators, there are relatively well established international metrics used widely in performance evaluation; in other areas, such as public procurement or customer

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100% 90% 80%

23%

70%

35% 39%

36%

44%

46%

27%

60% 50% 40%

68% 52%

30%

52%

34%

20%

12%

10% Availability of talented people

Intensity of competition

Demand for new services or products

Very important

Figure 3.14

Quality of ICT infrastructure

Availability of finance

Public research

Fairly important

Importance of innovation ability

demand, available indicators are less complete. It is worth noting that these countries were deliberately chosen to be leading innovators, not a representative sample of all the world’s economies. An aggregate ‘traffic light’ score was calculated for each of the seven conditions identified, based on a combination of the UK’s current performance compared to leading economies, and based on the overall trajectory of this performance over time. B.

Findings

The report found that the UK performed relatively well, typically being in the middle of the group of comparator countries. This echoes the findings of the EIS which typically ranks the UK in the top group, but not at the very top of the table. A few areas of particular concern were raised: the UK underperformed competitor countries in its access to finance for growth companies, the use of demand to spur innovation (especially government procurement), and the availability of skilled workers (including level 3 and 4 skills, and to a lesser extent management skills). Figure 3.15 shows performance against these criteria mapped onto the functional model of innovation that links the seven conditions.

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Knowledge creation

Figure 3.15

Model of innovation

Key functional steps in the innovation process Key related framework conditions for innovation Areas of particular UK weakness

While spending on public research has grown in the UK, enabling research institutions to maintain their international standing, collaboration with industry continues to be low.

Public research

Connectivity and openness for innovative collaboration is mixed in the UK. While broadband speed is a weak spot, it is widely available and comparatively cheap.

Openness

Entreprenuership

A dynamic enterprise culture is essential for innovation. The higher birth rate of new businesses in the UK continues to drive competitive markets.

Entrepreneurship

Selection

Mobilising resources

Skills Although conditions have improved in recent years, firms still have a difficult time finding people with the right skills and talents in the UK.

Consumers in the UK are less keen on innovative products than consumers in other countries. This makes it difficult for firms to market innovative products.

Demand

Doing business in the UK is highly competitive. This is good for innovation, because firms have a high incentive to develop new products to stay ahead of competitors.

Competition

Innovation requires funding and the UK has a highly sophisticated financial sector, but access to credit and local equity markets is more restricted.

Access to finance

The Innovation Index

C.

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Next Steps

NESTA will look into the possibility of gathering relevant primary data to cast more light on the seven conditions (in particular the areas that seem to be problematic in the UK and where the available indicators are less comprehensive), and will incorporate feedback from the pilot.

IMPLICATIONS – FROM MEASUREMENT TO IMPROVEMENT The pilot Index provides a powerful framework that will act as a basis for better policy. First, it provides a more up-to-date and comprehensive measure of UK innovation investment that reflects how businesses of all types develop ideas, take them to market and profit from them. Secondly, it links this investment directly to productivity growth, one of government’s most important economic priorities. Finally, it provides new data on innovation at the level of businesses and industries, and on the wider conditions for innovation in the UK, that complements that available from other sources. There are a number of important implications of this work: 1.

2.

Policy-makers should track the success of innovation policy using these new and more representative measures. It is now possible to measure a wider definition of innovation than just R&D, patents, citations and the proportion of firms who are actively engaged in innovation. Rather than setting an ‘innovation’ target of spending 3 per cent of GDP on R&D, the government should consider the level of investment in innovation as more broadly defined to include design, organisational innovation and business investment in human capital. The fluctuation in this wider measure of innovation investment provides a much better indicator of the success of innovation policy. Tracking broader measures of innovation will encourage policymakers to appreciate better the different patterns of innovation seen across the economy, including those that do not rely heavily on R&D to innovate, such as many parts of the service sector. Rising levels of R&D investment over the past few years suggest that government policies to encourage R&D (such as tax credits) have had an effect. But these policies have had limited effect on sectors such as retail or oil and gas production. NESTA believes that focusing on a wider definition of innovation will encourage the development of new policies that will encourage innovation across the whole economy.

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3. Innovation policy should focus on areas where the UK lags behind other countries, and where improvement is within the grasp of government. Key conditions include access to finance, procurement, and skills; these are all important gaps, and in each area, the government has a role to play. The Index has a role to play in helping understand potential barriers and in identifying appropriate tracking measures. 4. These measures should be developed further and incorporated into headline economic indicators. In particular, the Office for National Statistics should consider how the measurement of innovation investments can be incorporated into national accounts; for example, by creating satellite accounts for important categories of innovation investment.

NOTES 1. 2.

3. 4. 5. 6.

7. 8.

9. 10. 11. 12.

This report was first published by NESTA in November 2009. The Oslo manual details a number of non-R&D-based forms of innovation. Organisation for Economic Co-operation and Development (OECD) (2005), Oslo Manual: Proposed Guidelines for Collecting and Interpreting Innovation Data, 3rd edn, Paris: OECD. OECD (2003), Frascati Manual 2002: Proposed Standard Practice for Surveys on Research and Experimental Development, Paris: OECD. European Commission (2002), More Research for Europe: Towards 3 Per Cent of GDP, Brussels: European Commission. See Department for Business, Enterprise and Regulatory Reform (BERR) (2008), ‘BERR’s role in raising productivity: new evidence’, BERR Economics Paper no. 1, February, available at http://www.bis.gov.uk/files/file44504.pdf. In this respect, NESTA welcomes the attention paid to non-R&D innovation in Innovation Nation, HM Government’s White Paper in which the intention to develop the Index was expounded. The Innovation Nation White Paper (2008) is available at: http://www.dius.gov.uk/reports_and_publications%20HIDDEN/~/media/pub lications/S/ScienceInnovation_web. For the years 2001–08; these are OECD figures not adjusted for intangibles. 2.0 per cent compared to 1.3 per cent and 1.1 per cent respectively. The UK Innovation Survey (the UK’s component of the Community Innovation Survey) has for some years asked about some types of non-R&D innovation, but has not drawn these together into a single number that is compatible with the national accounts. Based on a combination of the direct benefits of innovation investment captured by firms, plus the broader benefits of innovation as captured by total factor productivity. This methodology is described in more detail later in the report. Smith, K. (2005) ‘Measuring innovation’, in J. Fagerberg, D. Mowery and R. Nelson (eds), The Oxford Handbook of Innovation, Oxford: Oxford University Press, pp. 148–79. OECD (2005), Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, 3rd edn. Advisory Committee on Measuring Innovation in the 21st Century Economy (2008), Innovation Measurement: Tackling the State of Innovation in the American Economy,

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14. 15. 16. 17. 18. 19.

20. 21. 22. 23. 24. 25. 26. 27. 28.

29. 30. 31.

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A Report to the Secretary of Commerce, Washington, DC: Advisory Committee on Measuring Innovation in the 21st Century Economy. Maastricht Economic and Social Research and Training Centre on Innovation and Technology (UNU-MERIT) (2009), ‘European Innovation Scoreboard 2007, comparative analysis of innovation performance’, available at: www.proinno-europe.eu/ admin/uploaded_documents/European_Innovation_Scoreboard_2007.pdf. John Adams Innovation Institute (2009), Index of the Massachusetts Innovation Economy, Westborough MA: John Adams Innovation Institute. Corrado, C.A., Hulten, C.R. and Sichel, D.E. (2006), ‘Intangible capital and economic  growth’, NBER Working Papers 11948, National Bureau of Economic Research. Barnes, P. and McClure, A. (2009), ‘Investments in intangible assets and Australia’s productivity growth’, Staff working paper, Australian Government Productivity Division. Corrado, C. (2007), ‘Comment submitted to the Advisory Committee on Measuring Innovation in the 21st Century Economy’, available at: http://www.innovationmetrics. gov/comments/051107FederalReserveBoard.pdf. Aizcorbe, A., Moylan, C. and Robbins, C. (2009), ‘Toward better measurement of innovation and intangibles’, Bureau of Economic Analysis Briefing, available at: http:// www.bea.gov/scb/pdf/2009/01%20January/0109_innovation.pdf. This research was led by Professor Jonathan Haskel of Imperial College, London, and Tony Clayton of the Office for National Statistics. A detailed interim report has been published: NESTA (2009a), Innovation, Knowledge Spending and Productivity Growth: Interim Report for NESTA Index Project, London: NESTA. CIS data have been used as the basis for micro-econometric analysis by BIS and its predecessor departments, by NESTA and by others, but do not read across directly to the national accounts. Aizcorbe, Moylan and Robbins (2009), ‘Toward better measurement of innovation and intangibles’. See also the position set out in Corrado (2007), ‘Comment submitted to the Advisory Committee on Measuring Innovation in the 21st Century Economy’. NESTA (2007a), Hidden Innovation: How Innovation Happens in Six ‘Low Innovation’ Sectors, London: NESTA. Following Van Ark, B. and Hulten, C. (2007), ‘Innovation, intangibles and economic growth: towards a comprehensive accounting of the knowledge economy’, Conference Board Economics Program Working Paper Series, EPWP 07-02, December. Notably Giorgio Marrano, M., Haskel, J. and Wallis, G. (2007), ‘Intangible investment and Britain’s productivity’, Treasury Economic Working Paper No. 1. Barnes and McClure (2009), ‘Investments in intangible assets and Australia’s productivity growth’. Corrado (2007), ‘Comment submitted to the Advisory Committee on Measuring Innovation in the 21st Century Economy’. NESTA (2007a), Hidden Innovation: how innovation happens in six ‘low innovation’ sectors. This research was conducted by Professor Stephen Roper of Warwick Business School and Professor Jim Love of Aston Business School, and is published separately as Roper et al. (2009), Measuring Sectoral Innovation Capability in Nine Areas of the UK Economy, London: NESTA. See Barnett, D. (2009), UK Intangible Investment: Evidence from the Innovation Index Survey, London: Centre for Research into Business Activity (CERIBA). The Blue Book is the annual publication of Office for National Statistics (ONS) National Accounts. R&D expenditure data was derived from the Business Expenditure on Research and Development (BERD) survey. To avoid double counting of R&D and software investment, the R&D expenditure in the computer and related activities sector was subtracted from R&D in the financial sector.

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37. 38. 39. 40. 41.

Innovation, global change and territorial resilience Department of Trade and Industry (DTI) and HM Treasury (2005), ‘R&D intensive businesses’, DTI Economics Paper No. 11, London: DTI and HM Treasury, available at: http://www.berr.gov.uk/files/file9656.pdf. NESTA (2009a), Innovation, Knowledge Spending and Productivity Growth: Interim Report for NESTA Index Project. Giorgio Marrano, Haskel and Wallis (2007), ‘Intangible investment and Britain’s productivity’. Barnes and McClure (2009), Investments in intangible assets and Australia’s productivity growth. M. Ishaq Nadiri (1993), ‘Innovations and technological spillovers’, NBER Working paper 4423; C.I. Jones and J.C. Williams (1998), ‘Measuring the social return to R&D’, Quarterly Journal of Economics, 113 (4), 1119–35; Z. Griliches (1992), ‘The search for R&D spillovers’, The Scandinavian Journal of Economics, 94 (Supplement), Proceedings of a Symposium on Productivity Concepts and Measurement Problems: Welfare, Quality and Productivity in the Service Industries, pp. S29–S47. This research was conducted by Professor Stephen Roper of Warwick Business School and Professor Jim Love of Aston Business School, and is published separately as Measuring sectoral innovation capability in nine areas of the UK economy. Hansen, M.T. and Birkinshaw, J. (2006), ‘The innovation value chain’, Harvard Business Review, 85 (6), 121–30. NESTA (2009b), Business Growth and Innovation: The Wider Impact of RapidlyGrowing Firms in the UK City-Regions, London: NESTA. This research studied the majority of firms in the UK over the periods 2002–05 and 2005–08. This research was conducted by GHK, Technopolis and the University of Manchester; the functional model of innovation was set out in previous work by NESTA (‘An exploration of innovation systems’, NESTA, 2007b, unpublished report to Department for Innovation, Universities and Skills (DIUS)). The research and detailed lists of indicators is being published separately as Miles, N., Wilkinson, C., Edler, J., Bleda, M., Simmonds, P. and Clark, J. (2009), The Wider Conditions for Innovation in the UK: How the UK Compares to Leading Innovation Nations, London: NESTA.

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4. Territorial benchmarking methodology: the need to identify reference regions Mikel Navarro Arancegui, Juan José Gibaja Martíns, Susana Franco Rodríguez and Asier Murciego Alonso 1.

INTRODUCTION TO REGIONAL BENCHMARKING

The decisive role played by innovation in economic growth, productivity and competitiveness is widely recognised (Lundvall, 1992; Nelson, 1992; Nelson and Rosenberg, 1993; Verspagen, 1995; Archibugi and Michie, 1998). There is also common agreement that it is not sufficient to understand innovation and competitiveness as the fruits of the actions of individual agents; rather, they are social processes. Hence, the actions of innovation agents cannot be separated from the system of innovation in which they operate (Rothwell, 1994). Initially the literature focused on national and sector-based/technological systems, but later, influenced by economic geography, it also turned its attention to the regional sphere. Soon, the publication about regional innovation systems surpassed those that addressed national and sector-based/technological systems (Cooke, 1998; Carlsson et al., 2002). This reflects the growing acceptance that the key factors impacting competitiveness and innovation are largely determined systemically and at the regional level (Porter, 2003). All this has resulted in a confluence of industrial, technological and regional policies around competitiveness and innovation and on a shift from national to regional areas of application (Oughton et al., 2002). Yet while innovation can be regarded as a relevant competitiveness strategy for all regions (Asheim et al., 2007), a given region should not develop carbon copies of policies designed and used in other regions. The core competitive strategy of a region should establish a unique value proposition, which is likely to be influenced by the particular structural

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characteristics of the region (OECD, 2011). Even with similar structural characteristics, regions can set different strategies and goals (Niosi, 2002). Indeed, the literature on regional innovation systems has highlighted the vast richness and diversity of regional innovation patterns, showing that there are no ‘one size fits all’ policies (Nauwelaers and Reid, 2002; Tödtling and Trippl, 2005). Regional policies must pursue two goals: the development of unique regional strengths in some key areas of innovation and competitiveness (in Porterian terminology, ‘strategic positioning’); and a broad focus on the remaining competitiveness and innovation factors, avoiding the development of weaknesses that are too great in comparison with those of the other competing regions (for Porter, ‘operational efficiency’) (Porter, 1998b; 2003b). What is the role of benchmarking in this respect? Even though there is no universally accepted definition of benchmarking, it can be said that benchmarking is generally understood to be an improvement and learning method based on comparisons and the application of the knowledge generated from them (Huggins, 2008). Benchmarking can facilitate the formulation of a strategy and mission insofar as benchmarking analyses can help to identify the strengths and weaknesses of the organisation or territory being analysed (OECD, 2005a). Toward this end, benchmarking seeks to measure the levels of what Niosi (2002) calls ‘x-inefficiency’ (the gap or difference between the current performance in a particular area and the best performance) and ‘x-efficiency’ (the degree to which the mission is being accomplished). However, as pointed out by Huggins (2008), benchmarking exercises have been met with caution by many innovation analysts. This is because benchmarking exercises in the corporate environment are understood to entail the systematic comparison of one organisation with another organisation, in order to replicate their ‘best practices’ (Lundvall and Tomlinson, 2001). However, these optimum processes and general models are meaningless in the evolutionary theory that is the basis for innovation systems in contexts of uncertainty and high complexity (Edquist, 2001; Paasi, 2005). The literature on innovation systems considers that what is good or bad depends on the systemic context (Tomlinson and Lundvall, 2001) that is empirically determined rather than adjusted to the theoretical ideal (Balzat, 2006). In addition, excessive imitation is problematic because it reduces the diversity required by the system and goes against the very idea of strategy (Huggins, 2008). Even from an operational point of view, we have to consider that data are not perfectly comparable (Mairesee and Mohnen, 2009) and that lags to obtain data would delay the implementation of such strategy. This would result in a ‘lamb effect’, implying that

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practices that were considered appropriate at one time were deemed as obsolete later on. In summary, what may be valid for simple environments such as corporations is not applicable in environments as complex as innovation systems (Polt et al., 2001). However, as Huggins (2008) or Papaioannou et al. (2006) point out, benchmarking analyses have evolved substantially. Although the criticisms of the first benchmarking analyses, labelled by Lundvall and Tomlinson (2001) as naive and simplistic, are accurate, another type of ‘intelligent’ or ‘systematic’ benchmarking that accounts for context (Nauwelaers et al., 2003) has been developed. Instead of merely pursuing a ‘copy and paste’ approach, this type of analysis encourages the identification of ‘good’ practices (instead of ‘best’ practices), recognises relative strengths and weaknesses and examines performance areas using more cost-effective and efficient processes than those based on ‘trial and error’ (Nauwelaers et al., 2003; Paasi, 2005; Balzat, 2006). Hence, the first question regional benchmarking exercises should address is who to be compared with. There are three options: with targets set for oneself,1 with oneself along the time or with others (Edquist, 2008). In case of comparing one region with others, several options arise: regions can be chosen according to criteria such as location, economic structure or high performance. More simply, they can be regions that wish to enhance cooperation and learn from each other. Generally, benchmarking exercises have taken place according to an intraregional perspective (rather than an interregional approach) due, among other factors, to their requirement of fewer resources. However, advances towards more multi-regional and interregional benchmarking exercises are occurring (Huggins, 2008). If the option is to compare oneself with others, which is the one this chapter focuses on, a key requirement is what Papaioannou et al. (2006) call the comparison principle, that is, contrasts should take place among comparable entities. You can also learn from those who are very different, but the need to take into account the context, that was mentioned above, means that a comparison is likely to bring more valuable lessons when it is carried out between fundamentally equivalent entities (Archibugi and Coco, 2004; Archibugi et al., 2009). Thus, a first step is the identification of homogeneous areas in which the comparison exercise will be carried out. This has not been the norm in benchmarking exercises. Regions were compared to those that exhibited a better performance, whether they shared similar characteristics or not. In fact, early benchmarking exercises have been criticised for limiting comparisons to relative performances, merely providing lists or rankings without a proper analysis of the causes of those different performances (Polt, 2002; Papaioannou et al., 2006;

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Huggins, 2008). Once the identification of regions with similar structural conditions has taken place, it does make sense to prioritise those with better performances, since these will be the ones that will provide the best lessons. Nevertheless, it might also make sense to consider the others later on because, as Polt (2002) or Salazar and Holbrook (2004) point out, unsuccessful cases and those that do not achieve the best results can also provide information and be a source of learning. Good or bad performance cannot be determined according to theoretical rules, but must instead be established through empirical comparisons (Lall, 2001a; Balzat, 2006; Edquist, 2008). To sum up, the second step will be to identify, among the territories that share similar structural conditions, those that exhibit better performance It follows from the above that the third step of every benchmarking exercise should determine the causes of better or worse performances. As noted by Edquist (2001), a proper diagnosis consists of both the identification of performance problems and the analysis of their causes. Weak performing regions should reflect on how they differ in terms of framework conditions, activities or input indicators from regions with high performance (OECD et al., 2004). This chapter stops at the third phase because our main goal here is to provide an instrument to facilitate the development of the three above-mentioned stages in benchmarking exercises for European regions. However, we should bear in mind that there are further steps. In particular, such exercises are of no use if their implementation, policy assimilation, control and revision are ignored (Polt, 2002; Paasi, 2005; Balzat, 2006). Proper implementation, aside from requiring a full understanding of the changes required by the system, necessitates the involvement of policy-makers and stakeholders, their coordination and a permanent evaluation (Nauwelaers and Reid, 2002; Nauwelaers et al., 2003). It should also be noted that a complete benchmarking exercise requires both a quantitative and qualitative analysis, as quantitative indicators alone cannot encompass all key aspects of innovation systems (Lundvall and Tomlinson, 2001), soft elements (Huggins, 2008) or factors related to more tacit knowledge (Polt, 2002). However, owing to the limitations that generally exist, benchmarking exercises are usually restricted to the first phase of the analysis, merely dealing with quantitative data (frequently obtained from secondary sources). Our main contribution rests on this area, focused on the quantitative analysis. In what follows we will analyse more deeply how to identify reference regions. We will also select the variables that should be considered to identify the best-performing regions and to understand the activities that explain such better performance.

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2.

PROCEDURE FOR THE IDENTIFICATION OF REFERENCE REGIONS

(a)

Literature on the Identification of Reference Regions

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As noted above, while benchmarking was born in the business field, more recently it has been extended to also cover territories. Here the benchmarking exercise will be applied to regions. As statistical units to define regions we will use NUTS2 (Nomenclature of Territorial Unit for Statistics 2), except in Belgium, Germany and the UK, where NUTS1 will be instead used.2 Among all NUTS, we want to identify those that are homogeneous or share similar structural conditions to a given one, as it will be from these that we will learn more and their identification is not straightforward. Many authors and studies highlight the need to compare homogeneous entities according to a range of characteristics: industrial structure (Akerblom et al., 2008; Atkinson and Andes, 2008); economic structure and institutional framework (Andersson and Mahroum, 2008); relative patterns of innovation (Arundel and Hollanders, 2008); geographic, cultural and economic factors (Archibugi and Coco, 2004); size, income, infrastructure and human resources (Archibugi et al., 2009); social values, political goals and economic development (Balzat, 2006); geography (including latitude, longitude, extension, elevation, access to the sea and climate), demographics (including population density, ethnic groups and other types of classification), natural resources and history (Fagerberg et al., 2007; Fagerberg and Srholec, 2008); cluster structure (John Adams Innovation Institute, 2009); level of development (Lall, 2001a); economic specialisation, history, degree of openness, size of the economy, firms size, culture and social capital (Nauwelaers et al., 2003); institutional factors, industrial specialisation and size (OECD et al., 2004); industrial structure, policy context and geographic and cultural dimensions (OECD, 2005a); economic structure and development level, natural resources, size, culture and history (Paasi, 2005); and Gross Domestic Product (GDP) per capita (Schwab, 2010). However, despite the numerous studies arguing that comparisons or benchmarking exercises must be carried out with similar regions, or must correct and account for differences, very few have put this idea into practice. Perhaps one of the most significant cases in which this strategy was actually used is the Index of the Massachusetts Innovation Economy (John Adams Innovation Institute, 2009), in which the economy and innovation in the state of Massachusetts are only compared with those states that display an elevated concentration of employment in specific

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clusters. Many of the studies focusing on developing economies (for example, the Fagerberg papers cited above) incorporate a series of external variables in their regression analyses to investigate the influence of technological capabilities. These variables seek to control for the geography, demographics, natural resources and history of the different countries, and thus to correct for their heterogeneity. They also incorporate the composition of the economy (which would be equivalent to the industrial structure highlighted above) in the analysis. In reports such as The Global Competitiveness Report (Schwab, 2010), each of the sub-indexes that are combined in order to construct a composite index of competitiveness is given a different weight, according to the level of development of the country. In any case, with the exception of the Index of the Massachusetts Innovation Economy, we barely find in the literature attempts to identify homogeneous territories to base benchmarking exercises. An alternative for the identification of reference regions is resorting to groups arising from regional typologies of innovation undertaken through different initiatives. Regional typologies seek the identification of common patterns in the territories and therefore they might be considered an alternative instrument to identify common regions. Nevertheless, the problem rests in the variables chosen to construct the typologies. The review by Navarro and Gibaja (2009) points out that existing typologies include on the one hand variables similar to the above mentioned (for example, industrial specialisation), that would reflect the territories’ structural conditions, and on the other hand behavioural variables (for example, research and development (R&D) expenditure), that are greatly influenced by the structural conditions (since R&D expenditure is greater in the pharmaceutical rather than textile sector) and performance variables (for example, patents or productivity), that are influenced by the two previous types of variables. That is, existing typologies have not isolated the variables that are relevant for the identification of similar regions according to their structural conditions, even if, as we show below, it is possible to do so. (b)

Proposal of Variables for the Identification of Regions with Similar Structural Conditions

Of the factors mentioned in the literature as helpful in evaluating the degree of homogeneity of the territories under study, there are some that are not available from statistical secondary sources (for example, degree of commercial or productive openness). These are therefore left out of this study. The aim of this sub-section is to identify those indicators that may be considered as components of a region’s structural conditions and are

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publically available through Eurostat, the Organisation for Economic Cooperation and Development (OECD) or other regional databases. Taking into account such availability and considering their pros and cons, we will proceed to make recommendations. Nonetheless, we will start mentioning two indicators that, despite being cited by some of the above mentioned authors, we would rather not take into account: GDP per capita and business size. Per capita GDP levels have been used by many studies of economic development or by reports such as the World Economic Forum’s and is available in Eurostat. However, a problem is that the causal relationship between GDP per capita and innovative performance operates in two ways (Lall, 2001a). As Lall mentions, the majority of analysts consider the principal causal relationship to flow from innovation to technological and competitive performance. As the main goal of benchmarking is to improve innovative and competitive performance, a circular argument would be established if GDP per capita were placed among the factors that explain such performance. We therefore also leave out this variable. Among the structural statistics, Eurostat publishes data on the average size of local manufacturing units for most European NUTS. In principle, this indicator might be used as a proxy for business size, which Nauwelaers et al. (2003) mention. However, a detailed exam of such data uncovers strange patterns (particularly for German regions) that have made us to decide to avoid their inclusion. Leaving aside, due to the above-mentioned reasons, GDP per capita and business size, the rest of indicators that might be used to identify regions with similar structural conditions can be grouped in four blocks for operational reasons: (i) Size, demographic and location indicators The region’s size, mentioned by many of the studies we have cited, might be proxied through GDP and population. Both are available in Eurostat. In order not to multiply variables and for coherence with the other variables in the group, in this chapter we are only using population. Among demographic factors, there are two frequently used in innovation economics: population density and ageing rate (percentage of the population 65 years old or more). Both are available in Eurostat. Regarding geographic factors, there are some that encompass the synthetic effect of location on competitiveness: accessibility indexes. European Spatial Planning Observation Network (ESPON) (2009) has recently published multimodal indicators at NUTS3 level for the year 2006. It is possible to aggregate them for higher NUTS and use them here.

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(ii) The economy’s industry structure We consider the distribution of employment among the ten major sectors of Eurostat’s regional economic accounts (based on the new Nomenclature statistique des Activités économiques dans la Communauté Européenne (statistical classification of economic activities in the European Community) (NACE) rev2: Agriculture, forestry and fishing (Section A), Manufacturing (B, C, D and E), Construction (F), Trade, transportation, accommodation and food service activities (G, H and I), Information and communication (J), Financial and insurance activities (K), Real estate activities (L), Professional, scientific, technical, administration and support service activities (M and N), Public administration, defense, education, human health and social work activities (O, P and Q), Arts, entertainment, recreation and other services (R, S, T and U) (iii) Industrial specialization Even if the above allows a first approach to the economy’s industry structure, it is obvious that the disaggregation of the manufacturing industry is not satisfactory. Industrial sectors are more oriented towards exporting and less limited by the local market, which allows them to develop and specialise more. Based on the OECD’s STAN (STructural ANalysis) database classification, we divide industrial employment into 11 large sectors. The data were provided by Eurostat, upon a special request to extract this information from the Labour Force Survey.3 (iv) Especialización tecnológica Lastly, the technological areas of regional specialisation are defined according to the percentage distribution of European Patent Office (EPO) patents among the eight sections of the international patent classification (IPC).4 The source for this information is the OECD’s January 2010 EPO regional patent database. Given the small number of EPO patents in several regions, we have opted for adding the patents applied for over the period 2000–2008. (c)

Procedure to Obtain Reference Regions from Variables

Having defined a set of variables to identify reference regions, several transformations are required in order to obtain composite indices that measure the distance between a particular region and all others. First, the indicators are corrected for outliers, asymmetries and kurtosis using the usual statistical techniques. Secondly, in order to add them up, variables are normalised using the mini-max method, re-scaling them so

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all values fall between 0 and 100. Thirdly, distances are calculated between each NUT and all the others. There are different alternatives to assign weights to the variables. We have chosen the simplest option: equal weights are given to the variables within each of the above blocks and then equal weights are also assigned to each block to come up with the total distance between NUTS. Hence, total distance between two regions would be calculated through the following formula: k

d (i, ir) 5 a mj (xij 2 xirj) 2 j51 where j is the variable, i is the first region, i′ the second region and mj is the weight assigned to the variable. With the distance between each NUTS and all the others, we obtain a distance matrix. Based on this distance matrix, two different approaches may be followed: 1.

A typology of regions can be established via cluster analysis to identify groups of regions with similar structural conditions that will influence their economic and innovative performance. The row indicating the distances between the selected region and the other regions can be extracted from the distance matrix. Based on that row, those interested in analysing a particular region can order all other regions according to these distances.

2.

Each approach responds to different needs or interests. Obtaining a typology of regions is particularly interesting for policy-makers or analysts who work with regions at the European regional level as a whole, because it provides a collective vision of Europe’s regions. As we have mentioned above, there are already many typologies on regional innovation patterns. However the common flaw they share for benchmarking analysis is that they mix different types of variables: structural conditions, economic and innovation output variables and input variables. The typology we present here is only based on variables that reflect the structural conditions of the regions and, hence, it would not incur in the same flaw. The second approach is a better option for those who are interested in the benchmarking analysis of a particular region. This procedure has significant advantages over considering regions corresponding to groups determined using cluster analysis: ●

Given that the cluster analysis process does not reveal the distance between the centre of gravity of the group and each of its

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components, it is possible that those components most distant from the centre are in fact closer to some regions assigned to other categories than to some regions in its own group. Cluster analysis does not usually allow for direct visualisation of the distance between a given region and regions placed in other groups. From each region’s ordered row of distances, the number of NUTS to be compared with can be selected. In cluster analysis the number of regions varies among groups. The number of regions in which our target region is included might not be appropriate for our purposes.

3. REGIONAL PERFORMANCE (a)

Economic and Innovation Output Performance

Having identified reference regions for benchmarking, the following step consists in identifying, among them, those regions that exhibit better performances, as these are the ones we are more likely to learn from. As Edquist (2008) points out, the analysis of the innovation system should not get mixed up with the analysis of the whole economic system; economic performance is affected by innovative performance, but also by other factors. Thus, following Archibugi and Coco (2005, p. 177), ‘it is useful and necessary to separate the two concepts and find independent measurement tools for each of them’. The inclusion of production indicators among the measures of innovation would prevent us from exploring the effects of innovation on production, and vice versa. It is also interesting to distinguish between the performance that a territory has achieved in a moment in time (the last year with available data) and the evolution the performance has undergone over a period (five years, in our exercise). Given data availability from European regional databases, the following indicators might be used:5 ● ● ●

Level of economic performance: GDP per capita, employment rate and productivity. Variation in economic performance: annual percentage change of employment, productivity, real GDP and real GDP per capita. Level of innovation output: EPO patents (per million inhabitants), scientific publications (per million inhabitants), employment in high and medium-high technology manufacturing sectors (per cent) and employment in knowledge intensive services (per cent).

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Variation of the innovation output: percentage change of EPO patents, publications, employment in high and medium-high technology manufacturing sectors and employment in knowledge intensive sectors.

As with the variables used to identify reference regions, the values corresponding to the performance indicators are subjected to some treatments in order to correct potential asymmetries, kurtosis and outliers, and their values are standardised and weighted to obtain four composite indicators of output (namely, those of economic and innovative performance in a moment in time, and those of evolution of economic and innovative performance). (b)

Inputs for the Innovation Process

After having identified among regions with similar structural conditions those that exhibit the best performance, we want to explain the reasons for the disparities in performance. In particular, we want to identify which innovation activities have been undertaken by regions with top performances, in order to learn from them and reveal some of their key elements for success. Taking into account available data,6 we have selected three types of indicators regarding human resources, R&D and connectivity. The following indicators have been considered: ●









Level of human resources: human resources in science and technology (percentage of population), population aged 25–64 that has attained upper secondary and tertiary educational level (percentage of population aged 25–64), students in tertiary education (percentage of population aged 20–24) and population aged 25–64 taking part in lifelong learning (percentage of active population). Variation in human resources: percentage change in human resources in science and technology, population aged 25–64 that has attained upper secondary and tertiary educational level and population aged 25–64 taking part in lifelong learning. Level of R&D: business R&D expenditure (percentage of GDP), public R&D expenditure (percentage of GDP), business R&D personnel (percentage of employment) and public R&D personnel (percentage of employment). Level of connectivity: families with broadband access (percentage), patent co-invention (percentage of patents) and new foreign firm (per million inhabitants). Variation in connectivity: percentage change in patent co-invention.

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As in previous steps, after having selected the variables and estimated the missing values, data were subjected to some treatments – correction of asymmetries, kurtosis and outliers – standardized and weighted to construct composite indices.

4. ILLUSTRATION OF THE PROCEDURE: THE BASQUE COUNTRY CASE As mentioned above, the identification of reference regions can be carried out following two different approaches: ●



By an individual approximation that ranks regions by distance from any region of interest and allows us to consider any number of regions for comparison with the one we are interested in. By a cluster analysis that identifies groups of similar regions.

Table 4.1 shows the resulting reference regions for the Basque Country using both approaches: the left-hand column lists regions with the lowest distance to the Basque Country, and the right-hand column lists the regions included in the Basque Country’s group according to the cluster analysis. The identification of regions is quite different, depending on the approach that is chosen: in the Basque Country case, almost half of the 30 closest regions were not in the Basque Country’s cluster group and, conversely, some of the regions in that group were quite far from it according to the distance calculated from the structural conditions. The individual approach includes mainly German, Italian and Austrian regions, followed by French and Spanish regions and a couple of regions from Sweden and the UK. Previous exercises carried out to identify reference regions for the Basque Country come up with regions that are closer to those identified by the individual approach than the cluster approach. Nevertheless, and compared to previous attempts, the current exercise offers some advantages: apart from being based in a more objective and quantitative approach, it offers an array of regions for comparison. Despite being quite obvious, some of them had been previously ignored due to the lower visibility of their countries (for example, Austrian regions). Implicitly, as there are no regions from Nordic countries or from the Benelux among the closest regions, this approach warns us about the difficulty of importing experiences from such regions, despite having been the focus of attention from our innovation policies. Once the reference regions have been identified, the benchmarking

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NUTS name

Pais Vasco Niederösterreih Steiermark Oberösterreich Salzburg Tirol Vorarlberg Saarland Picardie Centre Lorraine Piemonte Lombardia P. A. Trento Veneto Friuli-Venezia Giulia Emilia-Romagna Umbria

ES21 AT12 AT22 AT31 AT32 AT33 AT34 DEC FR22 FR24 FR41 ITC1 ITC4 ITD2 ITD3 ITD4 ITD5 ITE2

0 209 233 189 332 336 240 243 286 335 307 185 298 309 196 215 281 333

DistPV

Individual approach

Basque Country’s reference regions

NUTS

Table 4.1

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Cluster ES21 AT12 AT22 AT31 AT32 AT33 AT34 DEC FR22 FR24 FR41 ITC1 ITC4 ITD2 ITD3 ITD4 ITD5 ITE2

NUTS Pais Vasco Niederösterreich Steiermar Oberösterreich Salzburg Tirol Vorarlberg Saarland Picardie Centre Lorraine Piemonte Lombardia P. A. Trento Veneto Friuli-Venezia Giulia Emilia-Romagna Umbria

NUTS name 0 209 233 189 332 336 240 243 286 335 307 185 298 309 196 215 281 333

DistPV

Cluster approach

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Cluster

112

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NUTS name

Baden-Württemberg Niedersachsen Nordrehein-Westfalen Rheinland-Pfalz Sachsen Sachsen-Anhalt Thüringen Cataluña Rhône-Alpes C. F. de Navarra Schleswig-Holstein Liguria West Midlands Midtjylland Östra Mellansverige

DE1 DE9 DEA DEB DED DEE DEG ES51 FR71 ES22 DEF ITC3 UKG DK04 SE12

290 284 292 333 198 319 250 229 304 323 221 253 259 334 319

DistPV

Individual approach

(continued)

NUTS

Table 4.1

2 2 2 2 2 2 2 2 2 4 6 6 6 7 7

Cluster AT21 ITF1 FR26 FR51 FR21 SE23 FR43 AT11 FR23 FR53 SE21 ITE3 ITE1 NL12 FR72

NUTS Kärnten Abruzzo Bourgogne Pays de la Loire Champagne-Ardenne Västsverige Franche-Comté Burgenland Haute-Normandie Poitou-Charentes Småland med öarna Marche Toscana Friesland Auvergne

NUTS name

353 393 394 401 410 434 449 460 469 485 487 497 501 529 583

DistPV

Cluster approach

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Cluster

Territorial benchmarking methodology

Table 4.2

113

Geo-demographic variables of the Basque Country’s reference group

NUTS NUTS name code AT12 Niederösterreich AT22 Steiermark AT31 Oberösterreich AT32 Salzburg AT34 Vorarlberg DE1 Baden-Württemberg DE9 Niedersachsen DEA Nordrehein-Westfalen DEB Rheinland-Pfalz DEC Saarland DED Sachsen DEE Sachsen-Anhalt DEF Schleswig-Holstein DEG Thüringen ES21 Pais Vasco ES22 C. F. de Navarra ES51 Cataluña FR22 Picardie FR41 Lorraine FR71 Rhône-Alpes ITC1 Piemonte ITC3 Liguria ITC4 Lombardia ITD2 P. A. Trento ITD3 Veneto ITD4 Friuli-Venezia Giulia ITD5 Emilia-Romagna ITE2 Umbria SE12 Östra Mellansverige UKG West Midlands Average of 30 closest regions Average of all 206 NUTS

Cluster Population Population Population Accessibility group density 65+ years old 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 3 6 6 7 2 2 2 1 2 2 2 2 7 6

1 600 830 1 206 213 1 408 534 528 335 366 721 10 749 631 7 959 464 17 964 843 4 036 997 1 033 461 4 206 501 2 397 172 2 835 817 2 278 491 2 137 400 610 380 7 264 172 1 904 750 2 341 500 6 136 500 4 416 919 1 612 443 9 692 541 516 579 4 858 944 1 226 499 4 306 891 889 336 1 540 058 5 396 500 3 780 814 2 407 231

83 74 118 74 141 301 167 527 203 402 228 117 179 141 295 59 226 98 99 140 174 297 406 83 264 156 195 105 37 415 194 296

18.4 18.6 16.9 16.0 14.9 18.7 20.0 19.7 20.1 21.6 23.1 22.5 20.4 21.6 19.1 17.6 16.6 14.8 16.6 15.8 22.7 26.8 19.9 19.2 19.7 23.2 22.5 23.2 18.2 16.5 19.5 17.2

112 97 104 116 108 136 121 152 137 130 108 100 111 108 93 75 114 112 103 113 119 114 135 85 119 92 110 83 81 126 110 86

exercise can attempt the characterisation of the group vis-à-vis all other regions regarding structural conditions. It can also attempt the characterisation of the Basque Country with respect to the regions in the reference group. Tables 4.2, 4.3, 4.4 and 4.5 have been produced with such purposes in mind. By comparing the two lowest rows in Tables 4.2–4.5, it is possible to characterise the Basque Country’s group of reference regions. Such regions

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Table 4.3

NUTS code

Employment distribution in the Basque Country’s reference group (%)

NUTS name

AT12 Niederösterreich AT22 Steiermark AT31 Oberösterreich AT32 Salzburg AT34 Vorarlberg DE1 Baden-Württemberg DE9 Niedersachsen DEA Nordrehein-Westfalen DEB Rheinland-Pfalz DEC Saarland DED Sachsen DEE Sachsen-Anhalt DEF Schleswig-Holstein DEG Thüringen ES21 Pais Vasco ES22 C. F. de Navarra ES51 Cataluña FR22 Picardie FR41 Lorraine FR71 Rhône-Alpes ITC1 Piemonte ITC3 Liguria ITC4 Lombardia ITD2 P. A. Trento ITD3 Veneto ITD4 Friuli-Venezia Giulia ITD5 Emilia-Romagna ITE2 Umbria SE12 Östra Mellansverige UKG West Midlands Average of 30 closest regions Average of all 206 NUTS

Cluster Agriculture Industry Construction Trade, group transport, hotels and restaurants 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 3 6 6 7 2 2 2 1 2 2 2 2 7 6

7.3 7.5 7.5 4.3 2.9 1.3 2.6 0.8 2.1 0.6 1.7 2.5 2.2 2.2 1.4 4.5 1.8 2.5 1.2 1.7 3.7 2.0 1.6 3.4 2.7 2.2 3.9 4.2 2.7 1.1 2.9 6.6

16.0 19.5 21.2 14.9 26.5 29.9 21.4 22.9 22.2 22.2 21.0 18.5 15.6 22.8 22.9 28.1 19.6 20.8 20.3 18.9 24.9 13.1 27.0 18.3 30.2 26.9 26.7 21.4 15.8 15.1 21.5 17.9

8.0 8.4 9.9 9.2 8.3 5.8 6.5 6.0 7.2 6.5 9.2 10.5 6.6 9.9 8.0 8.9 10.2 6.5 7.6 7.8 7.8 7.4 8.1 9.2 8.2 7.3 7.4 9.1 7.3 7.9 8.0 8.4

26.6 25.1 25.7 31.1 26.6 19.3 23.6 22.2 22.0 22.8 21.0 23.2 25.1 21.6 24.3 21.7 27.0 25.1 22.0 22.2 23.5 29.7 22.1 23.1 24.1 23.2 23.9 23.2 19.7 22.8 23.8 24.2

are characterised by their considerable size, aging population, good accessibility and a marked specialisation in manufacturing and knowledge intensive sectors (Financial and insurance activities; Professional, scientific, technical, administrative and support service activities; and Arts, entertainment and recreational activities). Within the industrial sector, they are specialised in Metals and Electrical, electronic, computing

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Information Financial Real estate Professional, Public Arts, and and activities scientific, administration, entertainment, communication insurance technical, admin education and recreation and services and support health other services service activ. 2.7 1.8 1.6 1.9 1.7 3.6 2.0 3.0 3.1 2.0 2.5 1.2 2.7 2.1 2.8 1.2 3.3 1.6 1.3 2.4 2.6 1.9 3.5 1.8 1.5 1.3 2.1 1.3 3.2 2.6 2.2 2.3

3.9 2.8 2.6 3.3 3.7 3.5 3.4 3.6 3.3 4.1 2.3 2.0 3.4 2.2 2.4 1.8 2.5 3.1 1.9 2.8 3.2 3.3 4.0 2.9 2.5 3.0 3.1 2.1 1.4 3.1 2.9 2.6

0.6 0.6 0.5 1.0 0.4 0.5 0.5 0.7 0.5 0.2 1.0 0.6 0.9 0.7 0.5 0.3 0.8 0.7 1.3 1.3 0.6 0.8 0.8 0.5 0.4 0.6 0.4 0.4 1.5 0.8 0.7 0.7

7.6 6.8 7.2 8.0 7.0 8.6 9.2 10.0 8.3 9.6 10.2 9.6 10.2 8.6 10.6 8.2 9.8 6.2 6.7 8.1 9.3 11.5 10.8 9.6 9.1 9.3 9.6 9.6 10.9 10.0 9.0 7.6

23.5 23.5 20.4 21.6 19.0 23.2 25.9 25.6 26.4 26.9 26.6 26.8 28.4 25.5 19.5 18.5 18.1 27.3 31.0 27.9 17.7 23.1 15.6 26.4 16.0 20.8 16.6 21.5 32.9 30.5 23.6 24.3

3.8 4.2 3.4 4.7 4.0 4.4 4.9 5.1 4.9 5.1 4.6 5.2 4.7 4.4 7.3 6.8 6.9 5.9 6.0 6.5 6.6 7.2 6.5 4.8 5.2 5.4 6.2 7.2 4.5 5.4 5.4 4.9

and optical equipment. In EPO patents they excel in the Performing Operations, Mechanical Engineering and Fixed Constructions sections. By comparing the values in the Basque Country row with those in the last two rows, we can characterise the Basque Country with regard to its reference group and all European NUTS, observing that the above features hold grosso modo for the Basque Country.

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Table 4.4

Industrial employment distribution in the Basque Country’s reference group (%)

NUTS NUTS name code

AT12 Niederösterreich AT22 Steiermark AT31 Oberösterreich AT32 Salzburg AT34 Vorarlberg DE1 Baden-Württemberg DE9 Niedersachsen DEA Nordrehein-Westfalen DEB Rheinland-Pfalz DEC Saarland DED Sachsen DEE Sachsen-Anhalt DEF Schleswig-Holstein DEG Thüringen ES21 Pais Vasco ES22 C. F. de Navarra ES51 Cataluña FR22 Picardie FR41 Lorraine FR71 Rhône-Alpes ITC1 Piemonte ITC3 Liguria ITC4 Lombardia ITD2 P. A. Trento ITD3 Veneto ITD4 Friuli-Venezia Giulia ITD5 Emilia-Romagna ITE2 Umbria SE12 Östra Mellansverige UKG West Midlands Average of 30 closest regions Average of all 206 NUTS

(a)

Cluster group

Mining and quarrying

2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 3 6 6 7 2 2 2 1 2 2 2 2 7 6

1.9 2.3 1.5 0.6 0.7 0.3 2.0 1.6 0.9 5.7 0.6 3.9 0.6 0.6 0.2 1.3 0.4 0.0 2.1 0.7 0.7 0.7 0.6 1.7 0.3 0.5 0.5 0.6 0.8 0.4 1.2 2.6

Food Textiles Wood, paper, products, textile printing and beverages products, publishing and tobacco leather and footwear 13.2 9.0 10.1 18.5 9.7 8.0 16.4 8.3 11.2 11.5 12.4 18.7 19.3 7.7 7.5 18.6 14.0 16.8 13.4 11.4 7.3 13.6 5.5 10.0 7.1 5.1 11.4 12.2 6.6 8.3 11.4 16.8

3.2 4.5 3.2 2.8 10.7 2.7 0.7 2.3 3.0 0.1 2.9 0.5 0.3 3.2 0.7 2.2 7.5 2.6 4.5 8.1 8.8 3.2 11.2 5.6 12.1 2.4 9.0 13.8 1.7 2.6 4.5 6.9

9.4 9.4 9.8 13.9 9.7 5.6 0.4 7.6 5.9 5.1 10.8 4.9 9.7 5.1 5.9 11.1 7.0 5.8 8.2 6.1 7.3 4.2 8.1 19.5 8.5 11.8 5.9 9.8 9.9 8.0 8.3 9.7

Economic and Innovation Performance

Even if we propose to compare regions with those that exhibit similar structural conditions, the data allow for performance comparisons with all other NUTS. However, if we want to follow the procedure described above, Table 4.6 presents a simple and direct way to assess the position,

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Chemical, Non-metallic Basic metals Machinery Electrical, Transport rubber, products and fabricated electronic, equipment plastics and metal computing and fuel products products optical equipment 6.8 4.5 10.8 6.7 7.6 10.0 10.6 12.5 26.9 7.4 4.3 10.3 12.0 7.1 7.8 9.7 15.8 23.9 11.3 13.8 9.1 11.6 13.7 10.6 8.4 5.8 7.1 7.0 10.0 9.2 10.4 10.4

5.4 5.2 3.4 3.1 2.2 0.8 2.3 3.4 3.4 0.7 4.5 4.8 0.9 8.9 2.7 4.9 3.5 4.6 3.1 3.2 2.6 2.5 3.0 6.5 4.2 5.1 8.7 11.7 2.7 4.6 4.1 4.9

21.4 22.1 17.9 13.3 26.6 12.5 14.0 23.9 10.5 32.4 21.6 26.8 16.5 16.8 28.8 15.7 13.3 16.2 20.1 18.1 15.9 19.9 19.9 17.0 18.6 15.6 15.0 15.4 22.3 17.6 18.9 14.5

7.9 12.0 6.6 7.0 9.3 11.3 6.7 8.2 4.7 3.9 6.5 4.9 9.6 10.1 10.0 5.0 6.3 6.4 5.6 12.8 7.9 6.8 9.8 4.8 8.7 12.5 7.0 4.7 9.5 6.1 7.8 7.4

11.3 10.0 14.8 10.5 11.5 17.9 10.0 15.9 15.2 11.3 13.0 10.1 10.7 15.8 14.5 7.8 7.7 7.9 10.1 8.1 10.9 7.0 11.8 10.9 12.1 10.7 20.3 8.7 16.8 11.7 11.8 7.0

6.4 9.7 10.0 5.5 4.4 22.8 28.3 9.3 11.3 12.8 18.0 6.4 14.9 16.4 14.2 17.6 11.9 7.1 14.8 6.0 20.1 12.9 5.9 2.2 3.9 4.8 5.1 3.3 11.6 20.7 11.3 8.8

strengths and weaknesses of a region’s performance with respect to its reference group and all the European NUTS. By comparing the two lowest rows in Table 4.6, we can characterise the output of the reference group with respect to all the NUTS. In this case we observe a superior level of economic performance, mainly caused by a higher productivity, and a superior innovation performance, mainly

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NUTS name

Niederösterreich Steiermark Oberösterreich Salzburg Vorarlberg Baden-Württemberg Niedersachsen Nordrehein-Westfalen Rheinland-Pfalz Saarland Sachsen Sachsen-Anhalt Schleswig-Holstein Thüringen Pais Vasco C.F. de Navarra Cataluña

AT12 AT22 AT31 AT32 AT34 DE1 DE9 DEA DEB DEC DED DEE DEF DEG ES21 ES22 ES51

2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 3

Cluster group

13.4 8.4 10.5 21.3 19.4 10.5 13.2 13.7 16.7 13.6 8.4 18.9 28.6 17.4 14.5 20.2 27.9

Human necessities

22.0 23.2 33.8 22.9 20.5 25.5 31.2 23.1 25.2 25.9 26.2 18.7 28.5 17.0 30.6 18.2 24.5

9.1 14.0 14.0 5.6 5.6 6.6 9.8 17.1 26.8 12.1 12.9 26.0 7.7 10.9 6.0 6.3 14.7

6.2 4.1 3.8 0.9 1.4 2.8 0.8 2.8 2.0 1.0 2.3 0.9 2.1 1.5 3.2 2.5 2.3

11.0 7.4 10.4 13.9 12.2 3.4 4.1 7.7 3.8 9.1 3.6 5.9 3.8 4.4 11.3 3.9 6.1

Performing Chemistry and Textiles and Fixed Operations Metallurgy Paper Constructions and Transporting

EPO patent distribution in the Basque Country’s reference group (%)

NUTS code

Table 4.5

13.0 11.8 14.5 12.0 17.6 20.2 13.8 13.3 9.4 16.8 8.4 9.0 11.4 7.3 15.9 26.2 7.2

Mechanical Engineering, Lighting, Heating and Weapons

10.6 14.3 7.2 17.9 6.8 16.7 14.9 10.1 8.7 14.0 16.3 12.7 10.7 27.3 10.0 13.9 7.2

Physics

119

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FR22 Picardie FR41 Lorraine FR71 Rhône-Alpes ITC1 Piemonte ITC3 Liguria ITC4 Lombardia ITD2 P. A. Trento ITD3 Veneto ITD4 Friuli-Venezia Giulia ITD5 Emilia-Romagna ITE2 Umbria SE12 Östra Mellansverige UKG West Midlands Average of 30 closest regions Average of all 206 NUTS

6 6 7 2 2 2 1 2 2 2 2 7 6

16.1 19.0 17.1 11.2 14.0 20.4 26.7 26.6 18.2 20.6 24.0 13.5 15.2 17.3 20.1

34.5 24.2 16.9 30.5 24.4 22.5 27.2 25.5 25.8 42.4 30.8 21.9 21.5 25.5 20.3

12.7 12.6 14.8 6.9 8.4 11.5 3.7 7.3 5.0 6.9 9.8 9.5 7.6 10.7 12.9

1.8 0.6 2.9 2.4 0.5 4.9 1.4 4.0 14.8 0.8 5.7 0.5 1.1 2.7 1.7

8.5 9.6 3.8 6.0 3.3 5.1 10.2 9.0 8.1 7.0 6.9 4.9 9.3 7.1 6.5

14.5 20.2 7.6 17.8 15.9 11.3 12.2 12.6 13.4 11.4 13.0 11.3 15.6 13.5 12.0

4.9 8.2 15.7 11.4 19.4 10.2 9.7 6.6 8.1 6.8 4.5 18.6 16.4 12.0 12.3

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Table 4.6

NUTS code

Level of economic and innovation performance of the Basque Country and its reference group

NUTS name

Level of economic output ranking

Level of economic output index

GDP per capita (thousand €)

Productiviy (thousand €)

AT32 Salzburg AT34 Vorarlberg DE1 Baden-Württemberg AT31 Oberösterreich SE12 Östra Mellansverige ITC4 Lombardia ITC5 Emilia-Romagna AT12 Niederösterreich ITD2 P. A. Trento FR71 Rhône-Alpes AT22 Steiermark DEB Rheinland-Pfalz DEA Nordrehein-Westfalen UKG West Midlands DEF Schleswig-Holstein DEC Saarland DE9 Niedersachsen ITD3 Veneto ES21 Pais Vasco ES22 C. F. de Navarra ITC1 Piemonte ITD4 Friuli-Venezia Giulia ITC3 Liguria DEG Thüringen DED Sachsen DEE Sachsen-Anhalt ES51 Cataluña FR41 Lorraine FR22 Picardie ITE2 Umbria Average of 30 closest regions Average of all 206 NUTS

25 29 34 38 46 49 50 51 53 54 55 57 58 61 62 66 67 68 76 82 83 87 92 97 99 100 102 104 105 112 69 104

68 67 65 63 61 59 58 58 56 56 56 55 55 55 55 54 54 54 52 51 51 51 50 49 49 48 48 48 48 45 55 45

37 34 33 32 31 34 32 27 31 30 28 26 29 29 25 29 26 31 31 30 29 29 27 21 22 21 28 24 24 24 28 23

65 71 65 62 69 70 63 62 66 72 57 57 61 63 58 60 57 63 59 52 61 61 64 48 48 51 54 67 66 54 61 51

caused for their better results in patents and employment in high and medium-high manufacturing, that compensate worse results in publications. Similarly, by comparing the Basque Country row with the two lowest rows we can infer that the Basque Country’s economic success, while real when we compare it with the European average, is not so bright when compared with the reference regions. Therefore, rather than being

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Employment Level of Level of Publications Patents Employment Employement rate (%) innovation innovation per per in H and in knowledge output output million million MH intensive ranking index inhabitants inhabitants technology sectors (%) (%) 75 74 74 74 71 66 69 72 67 65 71 72 68 68 71 67 70 65 64 67 64 63 64 72 71 70 64 62 62 63 68 64

97 95 1 83 4 57 41 105 91 24 45 18 21 51 48 31 27 86 85 74 63 70 92 53 36 62 80 64 93 100 60 104

50 50 79 53 74 57 60 47 52 65 59 67 66 58 59 62 63 53 53 55 56 55 51 58 61 57 53 56 51 49 58 46

571 119 1494 342 2426 768 1210 157 1116 1483 1202 854 964 877 966 1127 890 656 580 1126 545 665 885 894 1182 2031 1065 819 344 1038 761 872

168 361 513 205 226 141 169 130 45 213 162 262 226 56 134 132 157 124 56 84 133 123 60 103 85 40 66 58 74 49 116 87

3.5 6.8 16.8 7.7 6.8 9.0 9.3 4.4 3.7 5.8 6.4 10.5 9.3 5.9 6.8 6.5 9.9 8.3 9.1 8.4 10.4 7.9 3.8 8.1 8.3 4.2 6.7 6.7 6.5 4.4 7.4 6.3

34 31 38 31 48 32 30 38 40 41 35 39 39 46 37 42 38 28 33 29 31 33 39 36 38 40 32 41 38 33 36 32

the result of policies and behaviours of recent years, the success is largely due to its structural conditions (industrial and technological specialisation, accessibility). Additionally, it is also possible to identify the Basque Country’s strengths and weaknesses in economic and innovation performance. Its economic performance is negatively affected by a worse productivity and,

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Table 4.7

NUTS code

Variation in economic and innovation performance in the Basque Country and its reference group

NUTS name

AT22 Steiermark AT12 Niederösterreich AT32 Salzburg AT31 Oberösterreich AT34 Vorarlberg DED Sachsen DEC Saarland DEG Thüringen DE9 Niedersachsen SE12 Östra Mellansverige DE1 Baden-Württemberg DEE Sachsen-Anhalt ES22 C. F. Navarra DEB Rheinland-Pfalz ES21 Pais Vasco FR71 Rhône-Alpes DEF Schleswig-Holstein DEA Nordrehein-Westfalen ES51 Cataluña ITD3 Veneto FR41 Lorraine ITD5 Emilia-Romagna ITC3 Liguria UKG West Midlands ITE2 Umbria FR22 Picardie ITD2 P. A. Trento ITC1 Piemonte ITD4 Friuli-Venezia Giulia ITC4 Lombardia Average of 30 closest regions Average of all 206 NUTS

Variation of Variation of GDP economic economic per output output capita ranking index 55 56 58 59 71 73 76 78 96 97 99 109 110 115 122 135 136 141 149 157 163 171 177 179 184 186 187 188 189 197 126 104

61 60 60 60 58 58 58 58 55 54 54 53 53 52 51 48 48 48 45 42 41 39 39 38 37 35 37 35 35 32 47 54

3.1 2.9 3.0 2.9 2.4 2.8 3.0 4.1 1.9 2.5 1.8 2.6 2.0 1.7 2.9 1.5 1.1 1.2 1.1 0.8 0.9 0.3 0.5 1.3 0.1 0.7 0.0 0.4 0.6 0.0 1.7 2.7

GDP Productivity

3.3 3.5 3.4 3.3 3.1 2.2 2.5 2.2 1.9 2.9 2.1 1.5 3.5 1.7 3.3 2.4 1.3 1.2 3.3 1.9 1.1 1.5 1.0 1.6 1.2 0.9 1.2 1.1 1.1 1.1 2.1 3.0

2.4 2.5 2.1 2.0 2.1 1.9 2.4 2.3 1.5 2.6 1.7 1.7 0.2 1.1 0.7 1.5 1.1 0.9 −0.2 0.4 1.3 0.1 0.0 1.4 −0.7 0.9 0.2 −0.2 0.1 −0.2 1.1 1.9

after the adverse impact of the economic crisis on the region’s employment, by a lower employment rate. Its innovation performance is negatively affected by the bad results is patents and, to a lower extent, in publications. The comparison with the reference regions implies that these results cannot be explained, as it has been often proposed, by the Basque Country’s industry specialisation.

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Employment Variation Variation Publications Patents Employment Employment of of per per in H and in knowledge innovation innovation inhabitant inhabitant MH intensive output output index technology sectors ranking 1.3 1.2 1.3 1.4 1.3 1.7 1.2 1.0 1.9 0.6 1.7 1.5 1.1 1.8 0.1 0.7 1.7 1.7 0.5 0.7 0.7 1.1 1.2 −0.5 1.5 0.1 1.2 0.6 0.3 0.7 1.0 1.0

88 102 149 133 76 80 136 35 112 180 113 153 74 123 59 166 128 110 37 78 111 99 117 206 18 196 98 130 94 84 110 104

56 55 49 51 57 57 50 63 54 46 54 49 57 52 59 47 51 54 62 57 54 55 53 36 65 42 55 51 56 57 53 54

16 25 17 28 74 26 30 23 11 12 15 26 27 15 22 19 12 14 29 21 11 23 11 5 18 18 47 21 12 19 22 34

4 13 19 12 20 −5 −4 5 −7 5 0 −14 77 −3 50 2 −5 −5 26 19 −7 2 7 −24 37 4 −21 19 39 −2 9 23

2.8 −0.3 −1.6 −0.5 3.2 1.7 −0.2 4.8 2.3 −3.4 2.0 −0.9 −0.7 3.5 1.5 −1.9 0.9 0.7 1.5 1.4 2.3 1.0 0.3 −9.4 6.5 −2.0 −0.6 −1.9 1.1 −0.4 0.5 2.1

3.1 3.9 1.9 1.9 2.1 4.2 1.8 5.2 2.6 1.6 2.4 1.9 5.0 0.5 4.7 1.2 2.2 3.5 6.3 4.1 2.6 3.5 2.9 1.6 5.2 −1.5 5.1 2.9 3.5 5.3 3.0 2.7

In addition, to compare economic and innovation performances with regards to the level reached in the last available year, we can compare the variation in performance over the last five years. In Table 4.7 we can see that, in general, regions that improve more are those that started from lower levels. We also notice that better or worse performances are shared by regions in the same country, thus indicating that variations are largely

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due to national rather than regional factors. Consequently, benchmarking analysis that aim to identify regions that exhibit better evolutions in their performances should consider, in addition to similar structural conditions, the level the regions started from and the differential evolution with respect to other regions in its country. Focusing on the regions with similar structural conditions, Table 4.7 shows the good relative performance of the Basque Country in innovation output. However, when economic output is considered, the performance has not been so satisfactory. One of the reasons for this poor performance is that the analysis of the employment variable has been extended to 2009, showing the adverse effects of the crisis that is affecting Spain. The other reason is that the Basque Country shares with Spain the inadequate increase in productivity during the 2000 decade (even if the Basque Country’s increase has been higher than the Spanish average, or those experienced in Catalonia and Navarra, the other two NUTS in the reference group). (b)

Innovation Input

The third stage of benchmarking analysis consists in observing how the region behaved in a series of factors that might be considered as innovation input and that affect the innovation performance described above. We can see in Table 4.8 that, with respect to its reference group, the Basque Country shows an acceptable level of innovation input. In this respect, its main weaknesses (or areas for improvement) reside in the percentage of population aged 25–64 with secondary or tertiary education, clearly below the group’s and total averages, but above that of other regions in the group. Likewise, the Basque Country exhibits low levels in patent co-invention and new foreign firms. Lastly, the high values in broadband access that other regions exhibit show that there is also room for improvement in this area. In any case, the objective is not to increase the innovation input, but rather to increase the innovation output (which, in turn, should help to increase the economic output). In that sense, we should complete the comparison of the level of innovation with an indicator that reflects the efficiency in using this input. In this respect, Figure 4.1 shows that the Basque Country’s reference regions (with diamonds in the figure) exhibit better efficiency levels in their systems than the European average (with triangles), while the contrary is true for the Spanish regions (represented with squares, with the exception of those that are also in the reference group, that are represented with stars). During the last years the Basque Country (with a circle) has substantially corrected the efficiency problem that had been attributed to it over the last decade. Currently it practically

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sits on the adjusted line between innovation input and output for all the European regions. Finally, the comparison of innovation inputs may refer to variations, instead of levels. In this respect, Table 4.9 shows the Basque Country’s outstanding advance in innovation input, despite departing from an already high position compared to the reference group.

5.

SUMMARY AND CONCLUSIONS

Benchmarking can help on the formulation of the competitiveness and innovation strategy every territory should have. It can also help on the evaluation of the activities that have been carried out there. In order to do so, it is necessary to avoid simplistic approaches that do not take into account the territory’s context and are based on mere imitation that reduces diversity and goes against the very idea of a strategy. In fact, the first condition every territorial benchmarking exercise should accomplish is that comparisons should take place among homogeneous and comparable territories. Among them, particular attention should be placed on those regions with better performances. The analysis should not stop there: it should rather attempt to disclose the causes, analysing the activities and inputs that have led to such results. Even if the combination of quantitative and qualitative information and the active participation of territorial agents would be preferable, this work focuses on the analyses an individual analyst may undertake using publicly available secondary sources on regional data. Regarding the first stage of a benchmarking analysis, and despite the numerous authors who have underlined the need to compare homogeneous territories and have highlighted several factors that should be considered to assess homogeneity, existing territorial benchmarking analyses have frequently ignored such requirement and have used a very simple approach to determine homogeneity: grouping territories according to GDP per capita levels. Here we propose the identification of territories based on their structural conditions that have been grouped in four categories that are equally weighted: geo-demographic factors (population, population density, ageing rate and accessibility), the economy’s industry structure (distribution in ten large sectors), industrial structure (distribution in 11 sectors) and technological specialisation (distribution in international patent classification (IPC) patent sections). After subjecting the variables to a series of transformations (corrections and standardisation) and assigning relative weights (equal for indicators within the same group), we obtain a distance matrix among all EU-27 regions.

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Table 4.8

NUTS code

Level of innovation input in the Basque Country and its reference group

NUTS name

SE12 Östra Mellansverige DE1 Baden-Württemberg DED Sachsen AT22 Steiermark DE9 Niedersachsen FR71 Rhône-Alpes DEA Nordrehein-Westfalen DEG Thüringen DEB Rheinland-Pfalz ES22 C. F. de Navarra ES21 Pais Vasco UKG West Midlands AT32 Salzburg ES51 Cataluña DEF Schleswig-Holstein DEC Saarland DEE Sachsen-Anhalt AT31 Oberösterreich FR41 Lorraine ITC3 Liguria ITD5 Emilia-Romagna AT12 Niederösterreich ITD2 P. A. Trento ITD4 Friuli-Venezia Giulia AT34 Vorarlberg ITC4 Lombardia ITC1 Piemonte FR22 Picardie ITE2 Umbria ITD3 Veneto Average of 30 closest regions Average of all 206 NUTS

Level Level Human of of resources innovation innovation in science input input and ranking index technology 9 23 26 30 44 46 48 50 53 54 56 61 75 78 80 83 88 91 95 98 104 109 110 111 119 126 127 130 138 159 81 104

70 65 63 62 58 58 57 57 57 56 56 55 52 52 51 51 51 50 48 47 46 45 45 45 45 43 43 43 42 36 52 47

21 20 21 12 14 18 16 17 17 21 25 15 12 17 13 15 16 10 16 16 13 10 13 11 10 13 11 14 12 10 15 16

Population Tertiary aged 25–64 students with upper over secondary 20–24 or tertiary population 79 84 96 83 84 71 81 95 83 60 65 70 85 52 86 83 93 79 69 65 59 84 65 58 78 57 55 63 62 56 73 71

76 48 52 65 41 58 55 47 53 64 70 46 58 62 40 45 43 30 48 63 86 15 71 78 10 61 56 34 91 49 54 59

Based on this matrix two different approaches may be followed. On one hand, we can take the row of distances from the region we are interested in carrying out the benchmarking for, ordering all the other regions according to their distance to the benchmarked region and choosing the number of regions for a detailed comparison. On the other hand, a cluster analysis can be undertaken to obtain a regional typology based on the structural

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25–64 Business Business Public Public Families Patent co- New population R&D R&D R&D R&D with invention foreign in expenditure personnel expenditure personnel broadband firms lifelong access learning 22.9 8.8 7.0 13.5 6.5 6.3 7.0 8.1 7.4 12.9 13.3 18.4 13.5 9.8 8.1 7.1 6.4 13.2 4.5 7.3 7.0 11.9 8.9 7.1 14.3 5.8 5.1 4.3 7.2 6.1 9.3 9.1

2.4 3.7 1.4 2.8 1.8 1.6 1.2 1.0 1.4 1.3 1.6 1.0 0.7 1.0 0.6 0.4 0.4 2.2 0.5 0.7 0.8 1.2 0.3 0.6 1.3 0.9 1.5 0.9 0.2 0.3 1.2 0.8

0.86 1.65 0.59 1.30 0.70 0.95 0.58 0.48 0.62 1.06 1.27 0.62 0.48 0.71 0.27 0.28 0.21 1.07 0.34 0.58 0.69 0.54 0.22 0.47 0.88 0.65 1.00 0.70 0.18 0.32 0.68 0.45

1.6 0.8 1.3 1.2 0.8 0.8 0.7 0.9 0.5 0.6 0.4 0.3 0.4 0.6 0.7 0.7 0.8 0.2 0.6 0.6 0.5 0.1 0.8 0.7 0.1 0.3 0.4 0.2 0.7 0.3 0.6 0.5

0.6 0.5 0.6 0.5 0.4 0.6 0.4 0.4 0.2 0.8 0.4 0.4 0.3 0.6 0.3 0.5 0.4 0.1 0.5 0.5 0.5 0.0 0.8 0.7 0.0 0.3 0.3 0.1 0.7 0.3 0.4 0.5

82 75 67 63 81 66 79 68 74 59 63 69 64 67 81 77 65 62 64 48 51 62 57 51 65 53 48 55 51 54 64 58

73 69 62 77 65 80 63 65 46 86 93 69 78 87 64 68 55 76 66 80 88 64 82 78 84 85 88 60 74 88 74 31

71 34 18 114 22 136 43 14 29 53 61 416 478 88 19 19 15 202 108 21 36 253 26 26 334 101 34 90 8 33 97 177

conditions. The first approach is preferable for those interested in a particular region, since it allows for a finer selection of regions than the cluster analysis and the choice of the regions to be compared with. The second option is preferable for those interested in all European regions, since it provides a more complete picture. Having identified the regions with similar structural conditions, the

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0

10

20

30

40

50

60

70

80

90

0

10

Correlation coefficient +0.87

20

30

40 Innovation input index

50

CT PV

60

NA

70

80

Figure 4.1

Levels of innovation input and output in the European regions

Note: Symbols: Basque Country (circle), non-Spanish reference regions (grey diamonds), Spanish reference regions (black diamonds), other Spanish regions (squares) and other EU regions (triangles).

Innovation output index

129

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NUTS name

C. F. de Navarra P. A. Trento Pais Vasco Emilia-Romagna Cataluña Piemonte Lorraine Veneto Saarland Liguria Baden-Württemberg Friuli-Venezia Giulia Lombardia Picardie Rhône-Alpes Umbria Vorarlberg Rheinland-Pfalz

ES22 ITD2 ES21 ITD5 ES51 ITC1 FR41 ITD3 DEC ITC3 DE1 ITD4 ITC4 FR22 FR71 ITE2 AT34 DEB

12 23 32 40 41 49 60 61 72 73 84 88 96 117 123 124 126 132

Variation of innovation input ranking 71 65 63 62 62 60 58 57 55 55 53 53 51 48 47 47 47 46

Variation of innovation input index 4.2 12.0 4.5 6.9 1.7 7.1 7.8 3.6 5.6 4.3 5.1 4.8 3.8 2.6 3.5 0.0 2.1 3.7

4.7 3.4 3.5 3.3 4.0 2.9 2.8 3.8 1.8 2.5 1.8 2.0 2.4 3.6 1.7 3.0 1.3 0.5

Variation of Variation in human res. in population with science and upper secondary or technology tertiary educ.

Innovation input variation in the Basque Country and its reference group

NUTS code

Table 4.9

29.3 2.4 15.7 1.9 30.4 −0.2 −6.1 0.5 7.3 2.9 0.6 −2.5 −0.2 −7.3 −2.5 0.2 3.8 3.5

Variation in population in lifelong learning

86.1 −32.8 46.9 1.3 33.5 −2.3 12.2 26.6 −16.9 8.3 0.3 61.1 −8.0 4.4 3.9 38.9 25.2 −4.5

Variation in patent co-invention

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Variation of innovation input ranking 133 134 140 141 153 159 163 175 185 187 189 194 110 104

AT22 Steiermark UKG West Midlands AT32 Salzburg DEA Nordrehein-Westfalen AT31 Oberösterreich DEF Schleswig-Holstein DE9 Niedersachsen AT12 Niederösterreich SE12 Östra Mellansverige DEE Sachsen-Anhalt DED Sachsen DEG Thüringen Average of 30 closest regions Average of all 206 NUTS

(continued)

NUTS name

NUTS code

Table 4.9

46 46 46 46 44 44 43 41 38 37 36 35 50 51

Variation of innovation input index 2.8 1.2 1.9 3.9 2.1 1.1 2.3 0.2 3.2 1.6 2.8 −0.7 3.5 3.7

0.8 2.3 1.2 0.4 0.7 1.8 0.8 0.9 −0.5 −0.7 −0.5 0.1 1.9 2.0

Variation of Variation in human res. in population with science and upper secondary or technology tertiary educ. 3.2 4.3 1.9 0.5 3.4 1.9 0.3 1.4 −3.9 4.3 −3.7 0.4 3.1 3.2

Variation in population in lifelong learning

24.9 −17.9 33.6 −2.0 12.6 −14.9 −5.9 10.9 1.0 −17.5 −13.9 −1.6 9.8 28.1

Variation in patent co-invention

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following step consists in identifying those with better performance to determine the relative strengths and weaknesses of the region the benchmarking exercise is undertaken on. Regarding relative performance, it is advisable to distinguish between economic and innovation variables, as, even if they are closely related, the distinction allows the analysis of their dynamic interactions. Likewise, performance analysis should distinguish between achievements at a point in time, from variation or evolution analysis. In order to do so, several variables were selected: employment rate, productivity and GDP per capita for the level of economic performance; annual rate of employment growth, productivity, real GDP and real GDP per capita for its variation. With regards to innovation performance, the level was measured through EPO patents and publications per capita, and percentages of employment in high and medium-high manufactures and knowledge-intensive services; and the variation through the growth rates of the same four indicators. As before, the original variables were subjected to the usual treatments (correction and standardisation) and equal weights were assigned (within each level) to obtain composite indicators. The third stage of the benchmarking exercise consists in comparing the drivers that affect the above mentioned innovation performance. Three types of indicators have been selected: some linked to human resources (human resources in science and technology (percentage of population aged 25–64); population aged 25–64 that has attained upper secondary and tertiary educational level (percentage of population aged 25–64), students in tertiary education (percentage of population aged 20–24) and population aged 25–64 taking part in lifelong learning (percentage of active population); others linked to R&D (business and public R&D expenditure and personnel) and connectivity (families with broadband access, patent co-invention and new foreign firms). Similarly to performance indicators, they have been subjected to usual treatments and weights have been assigned to compute innovation input composite indicators. In order to verify its suitability, the benchmarking procedure has been tested on the Basque Country. The identification of reference regions depends on the approach chosen: the individual approach based on the row of distances taken from the distance matrix, or the collective approach based of the typology group from the cluster analysis. The observation of the Basque Country results seems to confirm the suitability of the individual approximation. Distinguishing between economic performance indicators and innovation performance indicators makes the detection of ‘innovation paradoxes’ possible. The analyses carried out also enable the identification of relative strengths and weaknesses in economic and innovation performances and

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the assessment on whether these are shared with the other regions in the reference group or quite specific to the region in question. The analysis of variation performance in order to identify those with top results has uncovered the need to incorporate the starting point of the region’s economic and innovation performance (given the convergence observed) and the differential behaviour with respect to the rest of the country (given the similarities in evolution with other regions in the same country). Finally, regarding innovation input, the benchmarking exercise we have proposed enables not only the characterisation of the reference group with respect to all the NUTS, but also the identification of strengths and weaknesses both with respect to all the NUTS and its reference group (for instance, the Basque Country’s weakness in upper secondary or tertiary education). In any case, having high innovation inputs is not a regional objective per se. Rather, high innovation outputs are pursued (and these, in turn, only if they enable a better economic performance). Hence, the comparative analysis of innovation input should be complemented with an indicator that captures the efficient use of such input. This can be achieved by comparing innovation input and output. Thus, for instance, the benchmarking analysis uncovers a certain efficiency problem in the Spanish innovation systems, since their output indicators are considerably worse than their input indicators. Lastly, in order to identify the regions to learn from due to their positive evolution in input performance, we should take into account the dynamics in the reference group, the starting point of the region’s innovation input and the behaviour of the region’s country.

NOTES 1. This would be the case, for instance, of a system’s x-efficiency measure. As we have seen before, Niosi (2002) used this term to assess the degree to which the mission is being accomplished, as opposed to x-inefficiency, that considers the difference between a particular area and others. 2. The choice between NUTS2 or NUTS1 has been based on the level where regional powers rest in each country. As Clarysse and Muldur (2001) and Baumert (2006) point out, NUTS usually reflect statistical units that differ in size and, in many cases, they do not coincide with the economic units. 3. The 11 large sectors are: Mining and quarrying (NACE rev2, codes 05–09), Food products, beverages and tobacco (10–12), Textiles, textile products, leather and footwear (13–15), Wood, paper, printing and publishing (16–18), Chemical, rubber, plastics and fuel products (19–22), Non-metallic products (23), Basic metals and fabricated metal products (24–25), Electrical, electronic, computing and optical equipment (26–27), Machinery (28), Transport equipment (29–30) and Other equipment (31–33). 4. The eight sections of the IPC are: Human Necessities (code A), Performing Operations and Transporting (B), Chemistry and Metallurgy (C), Textiles and Paper (D), Fixed

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Constructions (E), Mechanical Engineering, Lighting, Heating and Weapons (F), Physics (G) and Electricity (H). 5. All of these indicators can be obtained from Eurostat regional dababases, except for publications, that are available from Erawatch. 6. All of these indicators can be obtained from Eurostat regional dababases, except for coinvention, which is our own calculation from the OECD regional patent database; and new foreign firms, which is taken from ISLA-Bocconi.

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PART II

Territories: Innovative and Evolving

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5.

Innovative regions: strategic spaces for development Antonio Vázquez Barquero

1.

INTRODUCTION

Heightened competition in the markets – the result of increasing integration of the international economic system – is fueling major economic and industrial transformations. Companies and organizations respond to the challenges of globalization with strategies that encourage the forces of development in the more dynamic and creative cities and regions through the adoption and dissemination of innovations throughout economies and societies. Since mid-2008 the current economic and financial crisis has been affecting both advanced and emerging economies and is shifting the international division of labor and the system of international relations. For innovative regions – which, following the crisis of the 1970s, had driven the application of knowledge and innovations in the production system – the crisis has become an opportunity to expand the process of restructuring the production system, promote changes in the organization of production, and, consequently, renew the regional model. The purpose of this chapter is to show how company and regional strategies are driving the evolution and transformation of the production system in recent decades. In their quest to become more competitive in the markets, companies adopt new technologies, introduce new forms of organization and make decisions about the most efficient localization for their plants. These strategic decisions are made in an institutional  environment that is increasingly prone to adopting new forms of governance at the state, economic and social levels. Companies and organizations implement their investment and location decisions in the more dynamic and innovative regions, thereby making them strategic locations in processes of economic development and social progress.

137

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2. ENDOGENOUS REGIONAL DEVELOPMENT The interpretation given by Schumpeter (1934; 1939) regarding development processes is useful for understanding industrial transformations in the age of globalization. Schumpeter’s writings date back to the early twentieth century, a period in which the inventions and innovations that characterized the technological revolution brought about a major restructuring of production activity and in which economies became more integrated as international trade grew, capital flowed more freely and multinational companies expanded. The information revolution is now in full swing, and theoretical reflection focuses – as before – on enhancing productivity and mechanisms that foster the progress and productive transformation of economies (Castells, 1996). According to the concept that emerged after World War II (Arrow, 1962; Kuznets, 1966b; Solow, 1956), development refers to self-sustaining processes of growth and structural change that aim to meet the needs and demands of the population and improve its standard of living and, specifically, seek to stimulate economic and social progress and reduce poverty. Sustainable long-term development requires increased productivity in all productive sectors. Under this perspective, economic growth is made possible by the accumulation of capital and the application of knowledge and innovations in production processes. The generation of intellectuals, led by Romer (1986) and Lucas (1988), has made strides in understanding the behavior of productivity, arguing that diminishing output is just one possible consequence of the process of capital accumulation. Other avenues for economic growth exist when investment in capital goods, such as human capital, produces increasing output resulting from the dissemination of innovations and knowledge between companies and the creation of external economies. Nevertheless Schumpeter, an institutionalist economist in the end, proposed an interpretation of development from the bottom up, arguing that the determining factors in regional development processes are companies, innovations and institutions. Schumpeter maintained that the investments of industrial companies are what drive the development of economies, when they introduce changes and radical innovations that cause the production system, the economy and society to enter into a dynamic that entails a radical transformation in terms of functionality. In his article ‘Development’ from 1932, Schumpeter (2005) wrote that the difference between growth and development is that growth refers to variations in economic parameters such as production, employment, savings and investment, whereas development refers to changes in the endogenous

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mechanisms of the economic system which cause a breakdown in capital accumulation processes and transform them into other alternatives better suited for the new dynamics of the economy and society. As such, company strategies play an important role in economic development, since heightened competition in the markets and the search for return on investments lead them to adopt technologies that make better use of resources (including intangible resources) and the enticements (specific resources) of cities and regions. Therefore, development can be defined as a regional phenomenon in which the agents making investment decisions are immersed in the system of institutional, cultural and social relations that characterize each territory. In other words, economic and industrial transformations take place in creative regions where we find innovative companies and activities that drive development (Vázquez Barquero, 2006). The responses and results achieved when economies react to the new challenges vary from place to place according to the specific factors of endogenous development in each region and include the following: company strategies and entrepreneurial capacity, the dissemination of innovations and know-how throughout the industrial sector, the adaptation and changes in the standards and rules for meeting the needs and demands of organizations and the general population, and the integration of companies, cities and regions in competitive and innovative networks, on the national and international scale. The interaction of these mechanisms would explain the formation of increasing returns on investments and, thus, the sustained development of the territory (Vázquez Barquero, 2010). Globalization ultimately inspires the strategies of companies and regions, thereby fueling the transformations in production processes, which take place in particular environments and territories, in specific, cities regions and countries, which are differentiated by their human, technological, production and cultural resources, and by their competitive and creative capacities. Thus, we could say that the economic, technological and social processes of globalization are regional phenomena, led by creative companies and organizations from the innovative regions.

3.

ECONOMIC PROGRESS AND INNOVATION

The central factor in the economic and productive changes and transformations, generally speaking, has been the introduction of innovations, as demonstrated by the three major technological revolutions (Freeman and Soete, 1997): the industrial revolution, usually dated back to 1760,

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the year that the Wealth of Nations by Adam Smith was published; the technological revolution, which occurred between 1870 and 1914; and the information revolution, arriving in the 1980s and frequently made to coincide with 1989, the year marked by the toppling of the Berlin Wall and the emergence of broadband networks, built to transmit information. The industrial revolution was characterized by the production of new goods (textiles, iron) by small companies in small cities where new ways of organizing work had been introduced, aided by steam-driven energy, new machinery and capital goods (the spinning jenny in 1764; the steam engine in 1769; the steam-powered loom in 1785), and new materials (cotton). The technological revolution, meanwhile, introduced new goods (the automobile, the radio, the airplane, chemical products, and household utensils) produced by large firms, located in large cities (or in cities with mineral resources nearby), thanks to the use of new capital goods (electrical machinery) and electrical power (in the 1870s, Edison developed the generators and condensers associated with the electrical lighting system). The new information and communications technologies (ICT) are propelling the information revolution, with the emergence of new products (electronics, biotechnology, pharmaceuticals), and the introduction of knowledge in the production of traditional agricultural and industrial goods and of services, at companies of varying sizes that operate through networks, located in cities of varying sizes, and which also utilize renewable-energy sources. Some of the activities driving change in production systems include high-technology industries (for example, microelectronics, biotechnology, robotics and the aerospace industry), industrial activities that have been restructured and have introduced innovations (such as clothing or automobiles), agricultural activities that produce high-quality traditional goods (such as wine, fruit or wood), as well as genetically modified products (for example, soybean); advanced services for companies (that is, marketing, communications, technical support and new financial products); and leisure, culture and health services. As noted by Dunning (2001), the introduction of innovations, generated by the strategies of firms, follows a different kind of logic at each moment in history, as demonstrated by the type of resources given preferential use in each of the technological revolutions. It evolved from the use of natural resources (land, unskilled labor) in the industrial revolution, then progressed toward the use of tangible assets (machinery, capital goods, buildings) and financial assets in the technological revolution, and eventually gave way to intangible assets (such as knowledge and information) in the information revolution. Moreover, companies create

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and spread their innovations, preferably in cities and regions with skilled workers, training centers and institutions that engage in research and development (R&D) activities, with which they have relationships and interact. Finally, within the growth process and structural change that characterize the information revolution, we should point out the gradual development of the services sector and the role played in this process by the development and dissemination of information and communications technologies, most importantly in the advanced and emerging economies. The development and adoption of innovations in the realm of services for businesses, health, education, culture and leisure translate into increased use of information technology and the knowledge contained within the resources, which brings greater relative importance to the innovations in intangible assets and a relative decline in the dominant position of technological innovation.

4.

BUSINESS STRATEGIES AND ORGANIZATION

Striving to achieve the best results, companies adapt and shift their strategies according to the behavior of the markets. As such, the creation of the national, international and global markets has led to the emergence of new business strategies aimed at increasing efficiency in the production of goods and services. With that, companies have updated their production methods, utilized new energy sources, relocated to areas that facilitate access to the product and factor markets. To achieve that, they first had to transform the organization of the company itself. During the industrial revolution, the ‘putting-out system’ was replaced by the modern enterprise system, in which work was organized internally. This allowed companies to enhance their productivity and supply expanding national markets (Landes, 1969). Then came a specific mode of organization of production, as with the industrial districts that stimulate the external economies of scale as a result of the agglomeration of companies (Marshall, 1890). The mid-nineteenth century saw the emergence of big business in the steel and chemical industries, which would progressively expand out into new activities, including automobile manufacturing. It involved a form of organizing production that efficiently manages inputs to produce an increasing amount of goods for a wide market and introduce internal economies of scale. Business clustering allows the effects of internal economies of scale to be combined with those of external economies that result from locating companies in an industry in a single place and with those of urbanization economies, which are associated with

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concentrating various industrial activities and services in one city (Gordon and McCann, 2000; Hoover, 1948). Because of the information revolution, production is organized in an increasingly flexible manner due to changes in the way large firms are organized as well as how businesses network and also due to the proliferation of business-to-business agreements. In a world of intensifying competition in domestic and international markets, production is being reorganized in new ways spatially, industrial sites are being restructured and new centers are now rising up in both emerging and lateindustrialization countries, leading to strong interaction between clusters in all types of economies. Porter (1998a) maintains that companies tend to concentrate geographically, establishing ties with one another and creating a system of relations that improve their competitive position. However, heightened competition in the markets continues to prompt changes in the strategy and organization of companies. Perhaps one of the most significant is the internationalization of production through the subcontracting and relocalization of production (Rabellotti et al., 2009; Rullani, 2008). Occasionally, the more dynamic companies are forced to outsource part of their operations in order to reduce production costs; other times, the agglomeration economies of the industrial districts are not enough to make the local companies competitive, and so they relocate production to other places and regions. We also see cases of alliances and mergers that enable the local companies to increase their size and financial capacity, thanks to strategies involving forms of organization that make them more competitive, such as vertical integration and the horizontal differentiation of production. So, companies, clusters and industrial districts continue their transformation as new forms of organizing production constantly emerge (Schmitz, 2007; Scott and Garofoli, 2007). The globalization of the production system has led to greater variety in the types of exchange occurring between companies, such as the export of finished products, trading within the networks of multinational companies, the flows associated with subcontracting and outsourcing. These flows have gradually expanded as transportation costs have dropped and subcontractors networked with each other and brought production into line with international standards. Hence, economic integration connects companies, markets and regions, and value chains become more international in scope, linking production systems in various countries. Many different kinds of value-chain relationships are possible (Gereffi, 1999; Sverrisson, 2004). In some cases they are merely trade relations, whereas in others they entail relationships between companies of equal stature within a network, and in still others, there is unequal coordination

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of the production systems. Sometimes the leading companies control the production process as well as the marketing and distribution of the final production; there are also situations where the final production by local companies meets the design specifications of the buyers, who ultimately sell the finished goods under their own brand names; and it is also possible for the agreements between producers and buyers to be brokered by intermediaries and agents, who establish the trade conditions and thus also the inter-firm relationships. When value chains are internationalized by linking clusters and companies from different countries, the flow of goods between them depends on the systems of economic relations and the rules and institutions that enable them. New challenges arise when production is combined between companies in different areas and regions because it is essential to use a common technology, share the same corporate culture and have an institutional system that makes it viable. Governing the relations between business networks is increasingly complex, as companies have a hard time operating within the bounds of a global system of relations that is still under development and lacks specific regulation.

5.

ORGANIZATION OF SPACES FOR DEVELOPMENT

Development and urbanization are two inextricably linked phenomena. As mentioned above, business strategies are key factors in the process of adopting innovations, changing how businesses are organized and the siting of plants. Lasuen (1973) argued that the type of corporate organization (multi-site, networked, global) determines how widespread the innovations become and therefore affects development and urbanization processes. Throughout the industrial revolution, small industrial cities played a key role, since they drew rural populations and migration flows between the fields and the city, encouraged by the emergence of new companies and newly created jobs and by enhanced agricultural productivity. With the advent of the technological revolution, the development of transportation and the organization of production at large firms, large cities became the strategic location par excellence, to the extent that the development paradigm linking industrial development with investments by large firms in large cities (backed by government infrastructure investments) has been maintained for decades. Globalization has had a profound impact on the evolution of cities, giving a strong impetus to the formation and development of creative

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platforms to foster business competitiveness in the domestic and international markets. The new forms of spatial planning have taken on a variety of names, such as global cities and polycentric urban regions, each designed to highlight its characteristics and specific functions in the globalization process (Hall and Pain, 2006; Parr, 2004). The new international division of labor takes shape through the formation of the system of global cities, where each one has expertise in various functions and plays specific roles in the globalization process (Sassen, 2006; Taylor, 2004). In that regard, we should point out global economic centers such as Tokyo, London, Paris and New York; national capitals such as Berlin, Madrid, Rome, Cairo and Buenos Aires; and commercial cities such as Amsterdam, Barcelona, Frankfurt, Chicago, São Paulo, and San Francisco. Global cities are centers of political power, international trade and the banking and financial system; they are centers that specialize in the creation and dissemination of innovations and knowledge, centers where information is concentrated and transmitted; they are creative centers where the arts, leisure activities and culture are developed. Nevertheless, it is the initial models of population settlement and the structures they have engendered that determine urban development, which would explain the formation of polycentric urban regions (Batten, 1995). They are an assemblage of settlements and urban areas where the companies and activities comprising their production sector are located and where the population resides and works. The urban areas are interconnected through a transportation and communications system that facilitates the exchange of goods and the flow of people. These areas have their own culture and institutions, which have evolved over time and determine the spatial planning model. There are two notable examples of polycentric urban regions that have become classic, the Randstad in the Netherlands and the Kansai region in Japan. The Randstad comprises the cities of Amsterdam, The Hague, Utrecht and Rotterdam, along with other, smaller cities such as Delft, Haarlem and Zaanstad. The Kansai region comprises the former capital cities Nara and Kyoto, the port cities of Kobe and Osaka, and other cities such as Wakayama, Himeji and Otsu. Additional examples include the Ruhr Valley in Germany, the Research Triangle in North Carolina (around the downtown areas of Raleigh, Chapel Hill and Durham), the Pearl River Delta region in China (formed by Hong Kong, Macao, Canton, Shenzen and Guangzhou), and budding polycentric regions such as the Basque Country and Galicia. In today’s globalized world, enterprises, cities and regions can only become competitive if it is possible to assure accessibility between the

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region and the domestic market and connectivity to the international system over transportation and communications networks. The transportation and communications system has gradually evolved alongside the development of trade and commerce, and the transformation of the markets. It is composed of road systems, maritime and air routes, as well as telephony and energy infrastructures. And, as of 1989, it also includes broadband technology, which fosters the exchange of information and knowledge in the economic and social system. At this point, there are major regional differences as regards networking and nodes of communication (for example, airports and seaports) and the quality of services provided by transport companies. The integration of regional and urban economies and the improved competitiveness of companies in the national and international markets have benefited from the availability of efficient national and international connections, and dense transportation networks that are effective on a regional scale. International accessibility has steadily improved alongside the emergence of new connections capable of handling the increased international traffic and the rise of modal transport. And finally, over the past two decades, broadband has become one of the key infrastructures for the connectivity of companies and regions, since it offers more services than legacy networks like telephony, and is used by more than a quarter of the world’s population (Pepper et al., 2009). By the end of the first decade of the twenty-first century, broadband networks had become a basic infrastructure in all kinds of countries and regions because they are the preferred medium of the knowledge economy. The most creative, most dynamic cities have strengthened their position in the urban system precisely because information technology and knowhow have allowed them to achieve the critical mass needed to network and thus adopt innovations in their production systems. Moreover, broadband competitiveness has improved, since cities function as natural platforms for developing competitive strategies that use the power of broadband networks. These networks serve as a medium for conveying knowledge and information and for introducing and developing innovations in the production of goods and services, which fosters economic progress and social welfare (Ware et al., 2009). They also provide access to information, facilitate relations between companies, stakeholders and the public, create business opportunities in economies, and promote the use of education, health, and public services. It is not just another facet of ICT because the fast connectivity between companies, markets and people increases the productivity of companies, facilitates social interaction and drives innovation.

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6. INSTITUTIONS FOR DEVELOPMENT Both companies and economic and social actors make their investment and location decisions within the institutional and cultural context of each particular place and territory. As such, the development of the institutions linked to the functionality of the market, the organization of the state and the social networks, determines the economic dynamics. Indeed, it is an interactive process, since the institutional environment evolves gradually with the economy and society, as the strategies of firms are shaped through their investments, innovations and forms of organization (North, 1990). Economic evolution depends on the relationship between the economic and political institutions and on how they evolve together during each historical period (Caballero and Gallo, 2008). The new institutionalist approach makes it possible to identify the interaction and dynamics between the institutions derived from liberal democracy (separation of powers, independence of the judiciary, decentralization, political consensus), and the institutions that characterize the market economy model (right of ownership, free market, independence of the central bank, monetary and fiscal policy). When economic and political institutions create an atmosphere of trust, companies encounter a suitable environment for making their investment and location decisions, and taking the necessary risks to address the challenges entailed by increased competition. A good example of institutional dynamics can be seen in the experience of Spain in recent decades. Over the past 30 years, one emerging issue has been decentralization, which characterizes the current trend and transformation of the institutions in European countries. Decentralization and the devolution of powers to regions and municipalities have brought considerable change to the institutional system and strengthened the state’s local and regional levels of government. According to Bobbio (2002), the reduction of the internal national hierarchies has enabled greater participation of the civil society in decision-making processes, the proliferation of agreements between public organizations at various levels, and stronger cooperation between the public and private actors. In a situation like the present, in which companies have adopted strategies that improve their competitiveness, the institutional changes entailed by decentralization prompt a reconsideration of the relationship between the state and the market. In the decades following World War II, the state actively participated in the reconstruction of the economies affected by the conflicts, but also in those that initiated processes of dual transition, combining macroeconomic policy with industrial and

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regional policy. The political vision shifted with the onset of the energy crisis of the 1970s, as the door was opened to liberal policies and governments focused their efforts on maintaining a stable macroeconomic environment by developing fiscal and monetary policies capable of creating favorable conditions for companies to carry out their investment decisions. Over the past decade, the focus has returned to the issue of the relationship between the state and the market, due to the situation created by the repercussions that the changed business strategies have had on employment and economic and social progress. At a time in which market failures limit the use of development potential, and in which the financial crisis has negatively affected the real economy, we see the emergence of proposals that call for a change in economic policy. To those who claim that government intervention and market solutions are optional alternatives, one could argue that the market and the state are complementary, and therefore the entrepreneurial projects and industrial and infrastructure policies of the state can jointly stimulate the countries’ economic and social progress, within a stable macroeconomic framework (Meier, 2005). Moreover, as Ostrom maintains (2005; 2010), there are often other forms of regulation of economic and social activities based on selfregulation practices in polycentric systems that enable rules and standards to be adjusted to specific social and regional environments. These institutional arrangements can occur in a multiplicity of situations in which the general population, local entrepreneurs and the civil servants themselves provide, produce and manage production- and service-related activities, as well as resources used by the general population. These forms of government promote cooperation between users and organizations in the development of economic and social activities, which translates into improved well-being for people. The emergence of regional development policies in recent decades has been based precisely on the specificity and change of the institutions. In order to surmount economic and industrial inefficiencies, boost employment and facilitate social inclusion, the local actors define specific strategies for regions through initiatives that capitalize on the development potential and the capacities of the territory. Improving infrastructures, promoting the incorporation of innovation and knowledge in the production of goods and services, developing human resources and increasing marketing of local products are among the instruments that foster increased productivity and competitiveness.

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7. INNOVATIVE AREAS AND ECONOMIC PROGRESS In keeping with Schumpeter’s line of thought, we were able to identify innovations made over the past few decades in the production of goods and services, business organization, market and production geography, urban organization of territory, and the institutions that regulate the relationships between economic and political actors, business-to-business interactions, and power relationships. Interestingly, innovations have become progressively more widespread in service-related activities through the introduction of intangible assets and, in fact, technological innovations have lost ground when compared to service-related innovations. Furthermore, global value chains have become stronger, an impact that has increased international business and trade relations and redefined how businesses are organized. The rising use of broadband contributes substantially to the increased volume of exchanges, the strengthening of the national and international markets, and economic and social progress. The geography of production and markets is changing, as are the relations between cities and regions, resulting in the increased relative importance of global cities and polycentric urban regions, which lead the economic dynamics of the advanced and emerging countries. Consequently, the transformation and change process does not occur uniformly in the territory, but is instead localized in specific regions and downtown areas, which are considered strategic in the decision-making processes of the economic actors and the political and social organizations. They are innovative areas that lead the way in regional development processes because their companies and organizations respond more effectively to the challenges posed by heightened competition in the markets, since they are capable of formulating their strategic decisions through creative systems of production, organization and interaction. Innovative regions are characterized first and foremost by the fact that their production system is led by companies that adopt decisive strategies in innovation processes and are thus able to get a lead on other cities and regions. They are areas that also include a framework of economic and social relationships between the regional agents and actors, based on the fundamental rules and agreements that ensure the functionality of the exchanges of resources and know-how. Over time, these areas have seen the gradual formation of a system of technological externalities that facilitate the dissemination of innovations between local companies. Moreover, in recent decades there has been a surge in the innovative regions of governance systems, featuring the participation of the actors responsible for

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the investment and location decisions, namely companies, government agencies, and civil organizations. At present, advanced and emerging economies are experiencing major production and social shifts brought on by the effects of the financial crisis on the real economy. Nevertheless, such economies are recovering in the cities and regions where creative companies and organizations are using approaches that enhance the introduction and dissemination of innovations. Production growth and increased employment are sustained by the combination of actions aimed at boosting confidence in the financial institutions through measures designed to step up business productivity and competitiveness. Thus, regional development policies are playing a key role in cities and regions where the local actors make investment decisions based on consensus and negotiation, combine measures to promote physical development of the region with measures favorable to business creation and incorporation and to disseminating innovations between companies and organizations. The effect is particularly encouraging in creative cities and regions, as new opportunities are increasingly available to innovative companies in all types of operations.

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6.

New policy approaches to develop innovative territories: developing trust and behavioral additionality in Gipuzkoa Miren Larrea, Maria José Aranguren and James Karlsen

1. INTRODUCTION Innovation is nowadays conceived as a social and open process. This has brought significant attention to a systemic view of innovation and concepts as the national innovation system (Lundvall, 1992) or the regional innovation system (Cooke et al., 1997; Asheim and Isaksen, 2002) have been popular. Although intensive efforts have been developed in research to understand these systems, the process of how they change participants’ developing capabilities to innovate has remained to a great extent a black box. Concepts like constructing regional advantage (Asheim et al., 2006) point nowadays to the fact that it is possible to be proactive in such processes. This focuses attention on innovation policies that not only provide the system with its different elements, but also foster some processes where the different participants in the system can actively construct advantage. One of the approaches that can help reflect on these policies is that of system failure. The chapter focuses on such policies, and through a case study discusses some key elements that policy-makers in regions need to address in order to efficiently initiate this kind of process. To do so, it focuses on the concept of social capital and the role it plays in innovation policy. Two research questions guide the chapter: ● ●

How can system failure be detected? How can shared vision be developed to reduce system failure?

Section 2 presents the main concepts used, section 3 presents the context and the methodology for the empirical analysis and section 4 focuses on 150

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three main elements selected for discussion from the preliminary analysis of the data. Finally, section 5 presents the conclusions. The discussions in section 4 are based on the fieldwork developed in a project called Gipuzkoa Sarean. It is an ongoing project, where the first stage focuses on diagnosing social capital in the region of Gipuzkoa and is about to finish at the date of writing this chapter. Three of the target groups that have been participating in the process are firms, the science technology and knowledge system and politicians. So, although it is not a project that was originally designed to focus on the regional innovation system, the results obtained can be used to diagnose some of the problems faced by such a system. The project is now entering the stage where specific policy measures are going to be defined by the two government levels participating in the process, in the context of a public–private partnership project. This chapter argues that, given the goal of the process and the main challenges diagnosed, policies oriented to system level and focused on generating behavioral additionality (Nauwelauers and Wintges, 2002) should be considered among the adopted measures.

2.

REGIONAL INNOVATION SYSTEMS: CONSTRUCTING REGIONAL ADVANTAGE, SYSTEM FAILURE AND PARTICIPATORY APPROACHES TO GENERATE BEHAVIORAL ADDITIONALITY

As stated by the European Commission (2006b), Europe is characterized by large-scale and developmental diversity and there are no recipes for innovation policy. Past experiences, concrete cases, theories and models represent the background which policy-makers should work with in order to find their own (regional) solution, rather than the exact replication or cloning of more or less successful examples or regional policies from elsewhere. Copying best practice is almost impossible when it comes to intangible regional assets that are the results of long histories in particular regional contexts. As Tödtling and Trippl (2005) put it, there is no ‘onesize-fits-all’. In these contexts, the aim of this section is to present the concepts that give a framework for the discussion on how intervention in social capital can be part of innovation policy. This section develops three main arguments. First, why an active policy approach to the efficiency of regional innovation systems is needed; second, what are the critical elements of a new approach to policy that can help respond to the challenges of regional innovation systems (RIS) and,

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finally, that developing the right social capital can be a relevant goal of such policy approaches. 2.1

Regional Innovation Systems and the Construction of Regional Advantage

The regional innovation system is a concept profusely used but it lacks a definition that is accepted by all (Navarro, 2010). Navarro combines the approaches by Asheim and Gertler (2005), Cooke (1996; 2001) and Trippl and Tödling (2005) to describe the regional innovation system as the institutional infrastructure that supports innovation in the productive structure of a region. Such a system is integrated by two subsystems: on the one hand, the knowledge-generation subsystem or the regional support infrastructure comprised of public and private research laboratories, universities, technology transfer agencies, training centers, and so on; and, on the other hand, a subsystem of exploitation of the knowledge or the regional production structure, composed mainly of firms. There is a third subsystem that can influence the previous two; these are the governmental organizations and regional development agencies. All these subsystems are integrated in a shared regional socio-economical and cultural framework. This chapter focuses on the interrelation of this third subsystem with the previous two. The concept of RIS, that was first used by Cooke (1992), has inspired policy-makers’ decisions relating to different innovation policies. The results of such analysis have focused attention on among other things, what have been called innovation paradoxes. One of the cases that illustrate this is the Basque Country, upon which the later case study focuses. In this case, the paradox has been stated in terms of the position of the region related to innovation inputs measured mainly through R&D input indicator (R&D expenditure, human resources in R&D) being higher than  its position related to innovation outputs measured with indicators such as patents. Anyway, the performance indicator (gross domestic product per capita) is more positive than expected regarding innovation outputs. Such paradox is even stronger in Norway, and the reverse paradox can be found in Sweden, for instance. All these – at least apparent – paradoxical situations have focused interest on how the innovation systems are working. Literature on national as well as regional innovation systems has helped develop arguments to try to understand these situations and concepts as the different knowledge bases (Asheim and Gertler, 2005; Asheim et al., 2007) or modes of innovation (Jensen et al., 2007) have been used to argue that the mentioned paradoxes are not such, as they can be explained understanding how innovation really

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happens. The focus of these approaches in knowledge and learning underline the relevance of understanding the regional innovation system not as a static picture of what a region has and lacks in a specific moment, but as a process. In such a process, interactions among different participants in the system, and with other players outside the RIS, play a critical role. To better understand what policy-makers can do in relation to this process and interaction-oriented approach, the focus is on the theory of constructing regional advantage (European Commission, 2006b). This theory pays attention to the role and impact of the public sector in the economy and highlights policy support, preferably in public–private partnerships, by acknowledging to a greater extent the importance of institutional and economic complementarities in knowledge economies than do theories of comparative and competitive advantage. Instead of market failure, the rationale for policy intervention is the reduction of interaction or connectivity deficits. A regional innovation system approach, which is key to constructed advantage, sees such deficits as the core problem of innovation in the European Union (EU). The report presumes that true regional innovation system connectivity is not complete in most regions. When presenting ‘carriers’ of policies for constructing regional advantage, the European Commission (2006b) emphasizes the need for a more platform-oriented (not restricted to sectors) and system-oriented as well as more proactive innovation-based regional policy in order to construct regional advantage. A reorientation of what is called the target level of support, changing innovation policies from being firm oriented to a (regional) system-oriented perspective is also mentioned. The recommendation also includes concerns about the form and focus of support; changing from allocation of resources for innovation to focusing on innovative learning, aiming for generating behavioral additionality through pursuing a platform-oriented perspective. 2.2

Policy Approach to the RIS Challenges

Specific theories represent an important input for policy design, implementation and evaluation, as they lead to the adoption of certain approaches as opposed to others that may (or may not) justify government intervention. Two main theoretical approaches (that have been employed at different extent in different periods) are used to justify distinct forms of innovation policy: the neoclassical approach, where innovation policy responds to market failure, and the evolutionary approach, which emphasizes the system failure. The neoclassical theory is based on the assumption of perfect market competition so that only imperfect market competition justifies a policy

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action deemed necessary to re-establish the former equilibrium. Innovation is a knowledge output that is related to three factors: uncertainty of the innovation process, lack of appropriate knowledge (because of ‘public good’), and indivisibilities, which are the market failures that justify government intervention in innovation. In the evolutionary approach, government intervention can be justified by wider system failures rather than by market failures alone. Within this approach, the market is only one element of the system, which also includes institutions and networks. There can be the following system failures (Smith, 2000; Laranja et al., 2009): 1.

2. 3.

Network failure – innovation is an asset based on knowledge, which has tacit (inherent to human/personal relationships) and explicit knowledge components. Networks play an important role in innovation systems as they transfer (move) both types of knowledge needed to innovate. Network failure appears when an effective knowledge flow fails because firms are not well connected in the network or because absorptive capacity, and the consequent learning processes, is not adequate. Institutional failure refers to situations in which institutions are not facilitating or incentivizing the innovation process. Lock-in failure refers to the situation in which systems of innovation remain isolated and, for this reason, become slow to change path and move into new innovation paradigms.

The existence of these three types of failures justifies government intervention in the systemic approach. In the linear approach of innovation policy had to respond mainly to market failures and in the systemic approach of innovation, besides responding to market failures, it has to respond to systemic failures, which requires much more contextualized policies. As Nauwelauers and Wintjes (2002) say, in the systemic approach there is not a ‘one-size-fitsall’ policy portfolio, and regional differences in innovation capabilities call for a tailored mix of policy instruments, which means that more ‘policy intelligence’ is needed. Besides rationales, another key concept to justify public intervention and policy evaluation is the concept of ‘additionality’. Additionality refers to the subsidiary role of the state; its main principle is that government intervention can only be justified if it creates a complementary effect that would not have taken place without the policy (Geourghiou, 2002). This author recognized three types of additionality: ‘input additionality’ (the beneficiaries should add the same amount of resources that the ones they

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are receiving, so, they should be beneficiaries for an innovation process that they would not develop in absence of policy); ‘output additionality’ (captures the effects of the policy intervention in the outputs of the innovation process) and ‘behavioral additionality’ (effects produced in routines and processes within a firm as a consequence of state intervention, as, for instance, changes in business attitudes through public support and cooperation). The consideration of different types of additionality that innovation policy should generate is also very relevant to the design and implementation of innovation policies. It is not possible to separate each  kind of additionality in each kind of market or systemic failure, clearly, when there are market failures, policies are oriented mostly to generate input and output additionality, while, the in case of system failures, policies that generate behavioral additionality became key aspects. Since the 1980s the process of innovation has changed significantly. Before the 1980s it was understood as a linear process, more inputs (for example, research and development) equals more output (for example, more patents and new products). After the 1980s, innovation models have changed and have been conceived as interactive and systemic. Using this logic, innovation strategies and policy are oriented to develop mechanisms that help the creation, expansion and use of all type of knowledge that, as a consequence, generate collective learning and new capabilities (to innovate). In this model, innovation is a technical process and a social/interactive process of learning among firms and between them and their environment. A key aspect in this systemic approach to innovation is that firms do not innovate as isolated companies, but rather as parts of systems of close interactions with many other agents (Freeman, 1987; Lundvall, 1992; Nelson, 1993; Edquist, 1997). As Lundvall (1992) highlights, in this model ‘knowledge’ is the most important resource, ‘learning’ is the key process and ‘cooperation’ is a relevant innovation strategy. In this context innovation policies should consequently be designed from a systemic point of view in order to develop systemic capabilities and competences. This also means that policy-makers can no longer define their policies in isolation. New innovation policies must be defined and implemented in the context of public–private cooperation and exchange. In order to respond to systemic failures, there is a need for policy mix. This evolution from the lineal approach to the systemic approach to innovations supposes a change in the rationales for policy intervention and hence in the kind of additionality that is needed in order to respond to these failures.

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Designing and Implementing Innovation Policies with Practitioners

Nauwelaers and Wintjes (2008) proposed that a relevant and critical aspect of innovation policies is that they have to be contextualized, as innovation processes are specific to the context. This necessitates identifying what kind of firms, institutions and agents exist and operate in a geographical area, and what are their main features. In line with this, Edquist (2008) highlights the need for diagnostic analysis to identify the systemic failures (what he calls systemic problems) in order to define policies that respond to these problems. This diagnosis will give some clues about the failures that are in the system and the kind of additionality that is needed in order to improve the innovation processes in the system. If most developed economies are transiting to the innovation stage, and the systemic approach of innovation is crucial in their innovation processes, innovation policies should not be oriented only to improve the elements of the system (for example, firms, universities, technology centers) but also to upgrade the interactions among these elements (network failures) and the interactions with other systems (lock-in failures) (Parrilli et al., 2010). However, according to Nauwelaers and Wintjes (2008), current innovation policies are focused mainly on the institutions that foster innovation, that is, institutional failures, and do not respond to network and lock-in failures. For this, Nauwelaers and Wintjes (2002) propose an interactive mode of policy implementation, which means that services should be both designed and delivered in cooperation with the beneficiaries but also that the policy implementers can be partners in the supported action or project, so that learning can happen both ways – between policy implementers and firms. The case studied illustrates a case of network failure (Nauwelaers and Wintjes, 2008) and discusses the problems that arise when generating an interactive mode of policy implementation where beneficiaries participate in the process. To better understand the discussion in the case study, it is worth considering the Nauwelaers and Wintges’s (2002) proposal of the 40 innovation policy instruments analysed in 11 European regions along two dimensions: (a) the target level of support which could be firm-oriented or system oriented (the logic in the system oriented is based on the idea that the innovation capacity and the performance of the regional system may be larger than the ‘sum’ of the internal innovation capacity and the performance of the individual ‘members’ of the system); and (b) the form or focus of support, which could be focused on allocation of resources as inputs for innovation or focused on learning aimed at behavioral valueadded trying to act on behavioral aspects, such as the organizational

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Classification of policy instruments Form and focus of innovation support

Target level of support Firm-oriented

(Regional) System-oriented

Input resources (reactive tools allocating inputs for innovation) A type (traditional R&D subsidies, training subsidies . . . ) C type (mobility schemes, cooperative schemes, subsidy to promote cooperation . . . )

Behavioral value-added (proactive tools focusing on learning to innovate) B type (loans for competence development, management advice, ‘proactive innovation centers’ . . . ) D type (cluster policies, support for firm networking, local strategic plans . . .

Source: Nauwelauers and Wintges (2002: 209).

culture, innovation strategy, mentality, management. They propose the classification of innovation policies presented in Table 6.1. The conclusion of Nauwelaers and Wintjes (2002) is that policy tools are too concentrated in category A and that there are few instruments in category D. They propose that in order to conduct a change in perspective, it is necessary in most cases to first develop instruments of types B and C before the system and the agents are ready to implement and absorb D-type instruments. The hypothesis in this chapter is that generation of trust is a critical condition so that behavior can be changed in the context of systemic policies. One of the concepts through which trust and its role in regional innovation systems has best been analysed is social capital. One of the definitions of social capital where the focus on behavior is explicit is that of Portes and Sensenbrenner (1993, p. 1323), where they define social capital as those expectations of action within a collective that affect the economic goals and goal-seeking behavior of its members, even if these expectations are not oriented towards the economic sphere. Trust, which will be one of the elements that will be incorporated in the later discussion, appears in several definitions of social capital. For instance, Putnam et al. (1993) say that social capital has features of social organization, such as trust, norms and networks that can improve the efficiency of society by facilitating coordinated actions. Bowles and Gintis (2002, p. F419) say that social capital generally refers to trust, concern for ones associates, a willingness to live by the norms of one community and to punish those who do not. During recent decades, different researchers explained sustainable

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competitive advantage in terms of resources and capabilities (Barney, 1991). According to this view, successful organizations and/or regions count on unique capabilities and/or resources that provide them a competitive advantage in comparison to other firms and/or regions. As social capital is an intangible and socially complex phenomena (Westlund, 2006), and a valuable, scarce and non-marketable resource (for example, Bourdieu, 1986), the development of social capital can be a source of sustainable competitive advantage (Nahapiet and Ghoshal, 1998).

3. THE EMPIRICAL STUDY: CONTEXT AND METHODOLOGY 3.1

Geographical and Project Context

The Basque Country is one of the autonomous communities of Spain, with approximately 2 million inhabitants. It contributes 6 per cent of Spanish gross domestic product (GDP) and has an important amount of manufacturing (30 per cent of GDP). One of the characteristics of the Basque Country is the dense institutional framework. The Spanish government has mainly a regulatory role and some programs for very strategic projects. The Basque government has substantial autonomy to define industrial policy and is well known for its strong proactive industrial policy during the past three decades. The provincial councils (there are three provinces in the Basque Country – Bizkaia, Gipuzkoa and Alava) play a critical role, as they are in charge of collecting taxes. Finally, municipal authorities (especially in the bigger cities) and local development agencies promoted by them (in other areas) play a role in innovation policies in the context of their approach to local development, as they have the capacity to reach the final beneficiaries of such policies (specific firms). The research project where the data has been generated for the case study focuses on the province of Gipuzkoa. The project is promoted by the Provincial Council of Gipuzkoa and the City Council of San Sebastián, in the context of the public–private alliance Gipuzkoa Aurrera, led by the two mentioned institutions and Kutxa (Savings Bank), Adegi (the firm association of the region), Mondragón Corporación Cooperativa (the biggest group in the area, constituted by cooperatives) and the Chamber of Commerce of Gipuzkoa. It is important to underline this fact for the following discussion. On the one hand, the fact that the Provincial Council and the City council, led by politicians in different parties, are supporting the project is unusual, and is considered significant both in terms of policies having a systemic view as well as focusing on behavioral additionality.

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On the other hand, Gipuzkoa Aurrera, with its participants from public and private sectors, has been considered an interesting context in which to experiment with an interactive mode of policy implementation, as proposed by Nauwelaers and Wintjes (2002). The difficulties of both approaches are discussed in this chapter. From a research point of view, research teams from the Institute for Science, Innovation and Society at Oxford University, Mondragon University and Orkestra, Basque Institute of Competitiveness have participated. 3.2

Methodology for Data Gathering

The project has used various data sources; those described here are the ones used for the development of the argument in this chapter. Data have been extracted from two different phases of the project: 1.

2.

Diagnosis. The first phase of the process was to diagnose social capital in four collectives in Gipuzkoa: firms; organizations in the science, technology and innovation system; politicians; and organizations working in the sociocultural field (sport, voluntary activities, culture, aging, and so on). Owing to the focus on the regional innovation system of the chapter, data related to the first three will be used. Data have been taken from two different sources. First, in order to measure the degree of trust of citizens on the four analysed groups, personal interviews were held with 700 citizens of Gipuzkoa that were between 18 and 70 years old in December 2009 (see Ikerfel, 2010). Previously, four group meetings were held with sample groups to define the questionnaire. Second, in-depth interviews were held with 78 representatives of the four mentioned groups from May to July 2010. Definition of intervention areas on social capital. During April to June 2010 two workshops were held with those interviewed in each of the groups, where the main conclusions from the diagnosis were discussed and possible areas for intervention defined by the participants of each of the groups. In the case of the policy group only one meeting was held.

As mentioned in the introductory section, the empirical analysis has not been designed to specifically analyse the RIS in the region, but has a wider focus on the society of Gipuzkoa. From the discussions the research team had with different agents in the workshops, the authors have selected those that were more directly related to the RIS concept

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and exclusively present the data generated in the project that illustrates such discussions.

4. DISCUSSION ON RESULTS The goal of the project Gipuzkoa Sarean is to improve competitiveness in Gipuzkoa through enhancing social capital. More specifically, the program aims to improve intervention processes in certain fields where such capital will be developed. One of the main hypotheses that underlay the project is that the region has nuclear processes that are not operating at maximum efficiency because participants of such processes lack enough social capital. This chapter does not present the main results of the project. It just reflects a side issue in terms of how the data gathered could be interpreted in terms of the challenges of the RIS and how the intervention measures that are to be defined could find complementarities with innovation policies being developed in the region. In that sense, it is important not to consider the chapter as presenting the results of the project, but as just using some of the data generated to develop a complementary view of it. 4.1

Diagnosing System Failure

The design of the project and reactions of participants help illustrate the need of systemic approaches and the convenience of evolving beyond sectors towards platforms. The workshops to discuss the diagnosis developed in four distinct groups, three of which were: firms; science, technology and knowledge organizations; and politicians. In each of the groups variety was sought in terms of firms’ sectors and sizes, all universities being involved, technology centers with different specializations being included, and so on, but the discussion was developed within each group. The idea was that it was first necessary to understand each of the elements in the system in order to later proceed to work on the interactions. The response from participants was that by doing this the project was replicating some of the limitations that the existing system and policies have: not having a systemic approach from the beginning. The proposal was made that representatives should be mixed in later phases of the process. The process, as it was defined, has helped understand system failure in practice. Many of the contributions made by participants showed a positive perception of the RIS, mainly in terms of having taken important steps forward in the past. The following paragraphs focus on those

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contributions that help reflect on system failure. Firms considered themselves to be far ahead of the innovation agents in responding to market changes and felt they were not receiving the value they should from such agents considering the investment efforts made by the region in previous years. One of the expressions used was ‘universities don’t understand us’ or ‘for them publishing internationally is more a priority than our problems’. Some innovation agents on their side considered that many of the firms lacked the absorptive capacity to work with them, reflected in comments such as ‘they have not understood what they can get from us’. When asked about possible areas of intervention where developing social capital could improve competitiveness, both groups made proposals that could be considered related to the efficiency of the innovation system. One of the firm representatives said that it was necessary to ‘create a shared language between firms and university and technology centers’; such common language was defined in his discourse in terms of ‘a minimum agreement on what the main problems are and what the role of every agent is to solve them’. The group of innovation agents was less focused on this issue, but it appeared in the discussion in terms of a proposal to ‘create a new organizational structure in the region that would coordinate relationships between firms, technology centers and university’. There was another element that was not conceptualized in terms of the innovation system but probably gives a clue to policy measures that could improve the efficiency of the RIS. Both groups in parallel processes underlined the need of something that would be shared in the region. Firm representatives, mainly in individual interviews but also in the workshops, underlined the need for something shared that ‘would give cohesion to the different actions in the region’. Other words used were developing a ‘country culture’. In the workshops with the innovation agents the worry of the ‘diversity of goals in the system, understanding the system widely as university, technology centers, firms and public administration’ was shared. There was a proposal to start working to ‘share a vision on the main challenges faced by the region in terms of competitiveness and the way to face them’. It was also stated that ‘the shared vision should go beyond the generalist discourse used nowadays – which is worrying – and concentrate in specific issues’. The data extracted from in-depth interviews and workshops illustrates how system failures are reflected in the discourse of different agents in the RIS. The lack of a shared language and a shared view seem to be critical elements that are affecting more specific relationships. One of the challenges of system-oriented and behavioral value-added policies is to create processes where shared views will develop that give meaning to the interactions of the participants in the regional innovation system.

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Trust to Foster Shared Vision

Another key idea in the research process has been the role of politicians in the creation of the shared vision that should guide interaction and cooperation in the RIS. Public–private partnership has been mentioned as one of the ways to implement policies with a systemic vision that can construct regional advantage. From a social capital point of view, trust is a critical element that can enable such public–private partnership. The project has shown different perspectives on the issue. On the one hand, the fact that the project has been proposed from a public–private cooperation platform as Gipuzkoa Aurrera has created mixed reactions. Some participants have considered it as positive. Others have been critical because it is a platform where only some private agents have been included. This is an issue that is difficult to manage in this kind of public– private partnerships. It is not possible to interact with the whole system at the same time, and if learning processes are to be launched, groups that represent the system have to be selected. This can create the perception that others are being let out of the process. Communication within but also from these groups with the whole society becomes a critical issue. Although lobbying is something that appears as an issue every time different agents get together to develop a shared project, it is important that, when these processes are considered part of the agenda to improve regional competitiveness, the benefits for the system and not only for some of its components are made clear. This will be critical to develop the trust of agents in the RIS and, beyond that, of citizens in the process. But there is one kind of agent that has been especially at stake in the trust debate – the politicians. When interviews were held to peoples’ perceptions of the social capital of the four groups that were studied, they were presented with 15 values (principles, attitudes, behaviors) considered relevant for competitiveness of a region and asked whether they thought each of the groups did or did not have such values. In the case of political parties, 32 per cent of those interviewed attributed not a single value to the group (although this percentage was much lower when asked about town councils – 15 per cent – and the provincial council – 19 per cent). These percentages were even lower for the rest of the groups, 10 per cent in the case of universities and science and technology centers and 8 per cent in the case of firms and social organizations. The data illustrate that governments, and mainly politicians, need to recover trust from people if they are to construct a shared vision and policies that are oriented to create the right conditions for cooperation among the agents in the system. This message was also present in the workshops held by firm representatives and innovation agents in terms of the following question: ‘Who is going

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to lead these regional processes if the ones that represent us all are not perceived to have the values needed?’ On the other hand, one of the elements most positively valued by the participants in the process was that the Provincial Council of Gipuzkoa and the City Council of San Sebastian were actively cooperating in the project, giving an example of how trust could be developed that gives way to a shared language that will in the future be oriented to action. So, to the previous reflection on the need of a shared vision, this section adds the need for trust among the agents so that a shared leadership can emerge. In the case studied it was mainly the political parties that had lower rates, but trust is critical for the rest of agents too. 4.3

Heading Towards Behavioral Value-added in the RIS

The goal of this last section is to reflect on how intervention could be developed so that a step forward is taken in the experimentation of policies that address the efficiency of the innovation system from a systemic perspective and focus on behavioral additionality. One of the difficulties faced when approaching systems is that often the different participants in the system have very different degrees of awareness of the system, and quite different interpretations of what it is and what problems it faces. One of the consequences of interdisciplinary teams in the project has been the awareness that although the initial conceptual framework agreed on in the project orients the results, the different data gathered is being interpreted in a different way by different researchers, depending on the understanding they have of the system they are approaching. Two main views coexist nowadays in the project. On the one hand is the view the project was initially founded with, which could be described as a sociological view of the system, where Gipuzkoa is mainly perceived as a society facing the challenge of competitiveness. From this point of view, social capital is the main concept in the project, and competitiveness an effect of having social capital. On the other hand is the view presented in this chapter that it has been latent in the process by incorporating researchers in the competitiveness field. From this point of view, Gipuzkoa has also been conceptualized as an RIS. In this case, competitiveness was the driving concept. To be competitive it is necessary to innovate, and the new conception of innovation as a social process requires social capital. The concept of the system used to interpret the results and orient the intervention phase of the project is critical. Approaching the interpretation of data from different perspectives within the research team can be positive for understanding the diversity of views with which regional

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agents might interpret the project. That is why in this section a proposal is made in terms of developing innovation policy to improve the efficiency of the innovation system. This could be used as a by-product of the main approach to the project that could support innovation policies outside Gipuzkoa Sarean. The proposal is based on two main elements. Considering that one of  the approaches for the design and development of intervention discussed in the research team is action research, the democratic dialogue concept of Gustavsen (1992) is considered. It should be combined with the values that the participants in the process have said are critical to face their challenges. One of the main contributions of Gustavsen’s model (1992) is that he focuses on the relationship between change in communication and change in work organization. In the case of the RIS in Gipuzkoa it is important to experiment with how the common language required by firms could affect the efficiency of the system. The model connects change in patterns of communication with changes in which issues are defined as subject to development and, further, with change in work organization and change in selection and configuration of technological elements. The focus of the importance of language has also been known as the communicative turn in action research and working-life research (Johnsen 2001). The main argument is to link people to each other through a process of shared meanings where the research process is merged with a process of restructuring of language which encompasses those who have to understand the research (Gustavsen, 1992, p. 33). According to Gustavsen (1992) this can be done through a dialogue between groups of people where all participants share an interest in creating ‘a good language’. He admits that change in language and the use of words is not enough. The change in language must be connected to practice, through a process of understanding. Unless strictly necessary this process should not be oriented to creating a new structure. Another element in the system that has consistently appeared in the diagnosis is that there are too many structures originally created to foster cooperation that have not been able to develop such processes. So the research project could experiment with how to develop a dialogue process using already existing structures where representatives of firms, the innovation agents and policy-makers could generate ‘the good language’ mentioned by Gustavsen. The challenges would be how to take such language into action to generate change and how to gain scope with the language, so that the common understanding would not only be shared by a few in the region. The role of the researcher in the process would be to bring the concepts and method to create the shared language. The key success factor would be the adjustment of the process to

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the values underlined in the diagnosis phase by participants as the critical values for the future competitiveness of Gipuzkoa. These were honesty, transparency, compromise, shared leadership, effort, coherency (consistency between discourse and behavior), openness (connectedness to the rest of the world), focus on knowledge, focus on results, long-term perspective and optimism. A focus on a process that is coherent with such values is more critical for the generation of the mentioned behavioral additionality than the specific subject area selected for intervention.

5.

CONCLUSIONS

The chapter focuses on the context of challenges faced by RIS and the possibility of being proactive about them by constructing regional advantage. Using qualitative data, the need for a shared vision of the system and its challenges among the participants has been presented as a critical issue if effective cooperation is to be fostered. The role of policy approaches that can generate behavioral additionality has also been addressed. Such additionality can be attained not only through the measures adopted by politicians, but also through a participatory policy process where regional agents participate in the diagnosis and design of the measures. To do so, again using qualitative data, the relevance of trust among the agents in the system has been underlined. Examples illustrate situations of firms’ lack of trust in the innovation agents or different perspectives of lack of trust in politicians. This last case poses relevant questions on the leadership of innovation policies. A critical idea is the role of conceptual frameworks to help different collectives build a shared vision of the system they participate in. Research can play a role here. But beyond concepts and frameworks, researchers can contribute with research processes that help build a shared language that will be the path towards shared action and, consequently, shared vision. It is not discourse that creates a common understanding of situations, but action. To generate a shared vision of the system and its challenges it is necessary to choose the right concept, whether this is social capital in a region or a regional innovation system. But it is more necessary still that the process developed with agents should be an example of the values, attitudes and behaviors that generate trust in such a context.

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7.

New focus of economic reactivation in Spain: creative industries in the Basque Country* Luciana Lazzeretti and Mario Davide Parrilli

1. INTRODUCTION After 30 years of focus on industrial districts and clusters as centres of economic and social development, the attention of academics is currently more and more polarized by cultural economics, including the recognition of the dynamic role of the creative economy (OECD, 2007; UNCTAD, 2008), with its ‘creative cities’ (Landry, 2000) and ‘creative industries’ (Caves, 2000; DCMS, 2009) offshoots. The analysis of business models and networks of creativity (Belussi and Staber, 2011) spurs the academic debate in the area of creativity management (Richards et al., 2009), and underscores the importance of the creative process, the implementation of ideas, and the new product development. Related contributions increased significantly as the topic of creativity has become one of the most crucial for innovation economists as well as for scholars in regional economics, economic geography and local development. The main strands of the literature can be synthesized in two main interpretations: the first revolves around the consideration of culture and creativity as a means to generate ideas and innovations, rather independently from their impact on economic development (Power and Scott, 2004); culture and creativity are regarded as resources for innovation that can activate new and different value chains and filières.1 Culture has thus become a ‘creative capacity’ that can rejuvenate products, sectors, professions and places (Lazzeretti, 2009). The second interpretation fits within the most typical cultural district perspective, which regards culture and creativity as relevant assets for economic development (Santagata, 2002; Scott, 2005). In terms of the factors that lie behind the revitalization of such assets, studies on economic enhancement processes in arts and culture (Ginsburgh and Throsby, 2006; Cooke and Lazzeretti, 2008) shift the attention from the artistic and cultural heritage to the human factor and the creative class 166

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(Florida, 2002b), intended as the category of people devoted to the generation of ‘symbolic’ knowledge in its multiple forms (Asheim and Coenen, 2006). Within this interpretation, fixed/physical capital assets are not the key elements that justify the relevance of culture; it is rather the creative capital, which is embodied in the people that enhances the opportunities delivered by such assets. With regard to the geography, creativity tends to be part of clusters and/ or urban concentrations (De Propris et al., 2009; Boix et al., 2010). Creative industries and the creative class are preferably concentrated in urban and metropolitan areas, which make wider use of new technologies, a byproduct of creativity (Trullen and Boix, 2008). However, these phenomena can also extend to rural areas (McGranahan and Wojan, 2007) acquiring in some cases also a regional dimension (Cooke and Schwartz, 2007). The creative economy research strand has initially focused on American creative cities and their creative class, and only later it has replicated analyses within Europe (Florida and Tinagli, 2004). In this chapter, we assume an approach based on creative industries extended at the level of creative local production system (creative LPSs)2 (Lazzeretti et al., 2008) according to the industrial district approach (Becattini et al., 2001; 2009). Creative industries have become one of the most critical topics in the European debate that complements the discussion on creative cities and the creative class. There is no widespread consensus around its relevance; for example, in England it has become one of the focuses of revitalization of innovation policy based on the knowledge economy and new technologies (NESTA 2008); in Denmark attention has been drawn to the ‘experience economy’ side of it (Lorentzen, 2009); and in Italy and Spain the topic has been taken on with an approach that connects creativity to the economic valorization of the artistic heritage (Lazzeretti et al., 2008). In particular, the topic of creative industries is relatively new in Spain (Boix and Lazzeretti, 2011) since much has been said about cultural industries and innovation policies, whereas specific analyses on creative industries at the national and regional level are still missing. In this contribution we undertake a novel, preliminary analysis of this topic with reference to the Basque Autonomous Community, which is characterized by the high density of vital innovation networks and clusters that exhibit features that are typical of what is usually termed ‘creative economy’. In particular, the attention is focused on two creative clusters that are located around San Sebastian and Bilbao. This chapter starts with an introduction in which some of the main features of the creative industries approach are illustrated; in the second and third sections theoretical and methodological references are made to the creative industries in general and in relation to Spain. In the following sections, this phenomenon is analysed in the context of the Basque

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Country and, particularly, in two creative clusters: the audiovisual cluster in Bilbao and the cuisine industry in San Sebastian. A conclusion with some preliminary policy implications end this chapter.

2. CREATIVE INDUSTRY: KEY THEORETICAL ELEMENTS The term ‘creative industry’ was coined by the English Department for Culture, Media and Sport (DCMS, 2001) in the report The Creative Industries Mapping Document, to extend the definition of culture to include multimedia activities and to follow structural changes connected to the growth and development of new technologies. The DCMS (2001) classify the following creative industries: advertising, film and video, music, performing arts, publishing, software and computer services, research and development (in architecture, graphic design, fashion), and telecommunications. To take into account the possibility of differentiated geographical patterns of specialization, a comparison was made between the definition of ‘cultural industries’ (Towse, 2003) and the definition of ‘creative industries’ given in the mapping document (DCMS, 2001). In this way, it is possible to determine if these industries are widely related to culture or to a more specific notion of creativity. In fact, cultural activities might need to be distinguished between (a) creative cultural sectors (for example, software, films and videos) and (b) traditional cultural sectors (for example, museums, maintenance computer services), whereas a joint analysis might reveal insufficient to explain local creativity patterns as well as the growth potential of the related industries. According to Lazzeretti et al. (2008), the creative industries identified by the DCMS are separated into two specific groups. The first focuses on the ‘traditional cultural industries’; the second centres on ‘non-traditional creative industries’. Traditional creative industries include: visual arts (antiques and artisan), arts, antique markets and crafts; film and video; music and performing arts; publishing; television and radio; non-traditional creative industries include: advertising; architecture; graphic design; fashion design; software design; interactive leisure software; software and computer services.3 The first group represents traditional cultural industries that are connected to the cultural economics typical approach. The second group reckons the creative industries that are connected to the creative economy and the new technologies. This is an emerging topic in the context of Spain (Boix and Lazzeretti, 2011), where the study of cultural economics and the analysis of the so-called culture and leisure industry have been carried

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out (Herrero Prieto, 2009). It is also the case of surveys on the publishing industry (Palma and Palma, 2008), museums (Plaza, 2006), stage art (Bonet et al., 2008), the audiovisual sector (Pablo and Muñoz, 2001). However, the organic and systematic work that is being developed in other European countries has not been achieved yet. With this work, we try to launch the study of this phenomenon in the Basque Country, which is characterized by a clear policy focus on innovation and innovative clusters within a steady science and technology approach (Olarazan et al., 2009; Parrilli and Elola, 2011; Parrilli et al., 2010). Our objective is to consider the innovation that is linked to the so-called cultural and creative industries and to the policies that support such creativity.

3.

CREATIVE INDUSTRIES IN SPAIN

Over the past two decades, Spain experienced a steady growth process led by a few factors such as the effective utilization of European Structural Funds, the related and mutual push promoted by the construction industry and tourism, the investments of world-class financial corporations, and the economic transformation promoted in leading industrial regions such as Catalonia and the Basque Country. The growth process of the national economy stumbled with the crisis of the past two to three years, the reduction of aid from the European Union (EU), and the constraints set by the EU with regard to the construction of residential buildings and touristic resorts (EP, 2009). Within this new landscape, the country has to find solutions that can be extended beyond the classic poles of financial and economic development (that is, Madrid and Barcelona). The growth of creative industries represents one of these opportunities, in particular because it does not have to be based necessarily upon ancient cultural traditions; in fact, creative industries can grow out of new sectors too (non-traditional creative industries), and this opportunity opens the way to many local economies to develop new specializations and competitive advantages. As other European countries (for example, Italy and Greece), Spain relies on a variety of options that may benefit from the potential of traditional creative industries in regions with ancient cultural traditions (the Brava Coast around Tarragona), Andalucía with Seville, Granada and other towns with their cultural traditions, among others. However, Spain can also count on the potential of other towns and regions that do not have such ancient cultural traditions, which might develop non-traditional creative industries. This is the case of the Basque Country, among others. Former analyses (Lazzeretti et al., 2008) exhibited the development of creative industries in Spain, which reckoned about 673 000 employees in

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Table 7.1

Innovation, global change and territorial resilience

Traditional, non-traditional and diversified creative LPS in 2001 (LQ above 1 and minimum 250 employees by LQ) Number of LPSs

Employment Examples in creative industries

Traditional Creative LPS (1)

17

79 000

Non-traditional Creative LPS (2) Diversified Creative LPS (3)

0

0

8

359 000

25

438 000

Total

Valencia; Sevilla; A Coruña; Pamplona; Logroño; Santiago de Compostela; Girona; Vilafranca del Penedès; Tarragona; Manresa; Igualada; Seseña; Sant Sadurní d’Anoia; Estella; Ontinyent; Ibi; Capellades – Madrid, Barcelona, Bilbao, Sabadell, San Sebastián, Mataró, Guadalajara, La Garriga

Notes: (1) Specialized (LQ > 1) in only traditional cultural industries: publishing, architecture and engineering studios, and music, film, video and performing arts. (2) Specialized (LQ > 1) in only non-traditional creative industries: research and development (architecture, graphic design, fashion), software and computer services, and advertising. (3) Simultaneously specialized in traditional and non-traditional creative industries (both LQ are above 1). Source: Lazzeretti et al. (2008, p. 559).

Spain (4.1 per cent of total employment) in 2001 that is consistent with the usual range of between 4 and 6 per cent found in other studies (DCMS, 2001). Traditional creative industries add up to about 458 000 in Spain, which represent 68 per cent of total creative industries employment in Spain. Non-traditional creative industries had about 215 500 in Spain (32 per cent of total creative industries employment in Spain (Lazzeretti et al., 2008). In Lazzeretti et al. (2008), the method of identifying local production systems according to the industrial district approach (that is, calculating the location quotients on creative industries) was applied in order to map creative industries’ clustering in Italy and Spain. This enabled creative LPSs to be divided into three categories (traditional, non traditional and diversified – see Table 7.1). On this basis, 25 LPSs were identified in Spain

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Regional boundaries Traditional Non-traditional Diversified

Source: Lazzeretti et al. (2008, p. 561).

Figure 7.1

Creative local production systems in Spain, 2001 (LQ above 1 and minimum 250 employees by LQ)

(3.1 per cent of Spanish LPSs). Within the whole of the creative industry there were 17 traditional creative LPSs with about 79 000 employees in creative industries. In Spain, traditional creative LPSs are associated with both medium-sized cities as well as larger cities such as Valencia and Seville. Surprisingly, there were no creative LPSs exclusively specialized in non-traditional creative industries. Diversified creative LPSs are LPSs simultaneously specialize in traditional and non-traditional creative industries. There are eight diversified creative LPSs in Spain that add up to about 359 000 creative employees. This category contains the LPSs associated with the largest cities (Madrid, Barcelona, Bilbao, and so on, with the exception of Valencia and Seville). The map of creative LPSs shows patterns of spatial clustering (Figure 7.1). Creative industries are strongly concentrated in a few places, forming clusters around Madrid, Barcelona, Basque Country-Navarre-Rioja, and Galicia, as well as Valencia and Seville, but have a special concentration in some LPSs, particularly those of Madrid and Barcelona. In fact, Madrid’s LPS alone accounts for 30 per cent of the Spanish employment in creative industries and Barcelona’s for 15 per cent. Both have 45 per

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cent of the Spanish employment in creative industries and 69.5 per cent of the employment in creative LPSs. This provisional outcome might be interpreted as ‘history matters’ since the concentration of economic (and creative) activities in Spain is also related to the historic evolution of this country and its division in autonomous communities, each with its clear polarization around a specific urban concentration. In the next section the case of the Basque Country is discussed as a region that has been able to develop a modern diversified and creative economy on the basis of the local entrepreneurial spirit as well as of a very proactive regional policy approach.

4. THE BASQUE COUNTRY AND THE ROLE OF PROACTIVE INDUSTRIAL POLICY The Basque Country represents one of the main traditional poles of industrialization in Spain. From the first half of the twentieth century, and even earlier, went through an intense industrialization process based upon basic foundry and steel manufacturing, as well as the production of pulp and paper, and the shipbuilding industry. In the 1950s a strong concentration of machine tool producers also developed in the county of Urola (Elgoibar), who created their business association in an area where simultaneously were set the bases for the development of the world largest cooperative, Mondragon Cooperative Corporation, which successively boosted the creation of firms in a range of industrial sectors, including domestic appliances (for example, Fagor electrodomestics), automotive (with the plant of Volkswagen and Mercedes in the Basque territory), and more recently with a number of new industries and cluster agglomerations (for example, electronics and information and communication technologies (ICTs), aircraft industry, logistics, health and environmental service clusters, among others). In spite of the development of a strong, steady economy in the 1950s to 1970s, at the end of the 1980s the region plunged into a deep recession with high levels of firm closure and an unemployment rate soaring at around 20 per cent. The way out of this crisis was not easy, although it was identified through the proactive role taken by the autonomous community government and the reactivation of the regional economy in line with an effective exploitation of the potential for cultural and creative reconversion. The latter is superbly symbolized by the creation of the Guggenheim Museum on the Bilbao city river, just where the crisis-plunged shipyards were formerly settled (Azua, 2008). In particular, the government has promoted two types of policies that have been extremely successful in restoring the competitiveness of the

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region. The first was (and still is) the cluster policy, which was implemented from the early 1990s with the identification of a number of economic sectors that presented a substantial regional concentration (within Porter’s approach and with his own support). Twelve clusters were identified and promoted with the creation of cluster associations (as a sort of ‘soft policy’) that received a limited amount of funds (around 300 000 euros per year) delivered to promote cooperative initiatives around innovation, marketing and internationalization activities, among others (Aranguren et al., 2009). It has been a system that helped promote firm creation and cluster formation, growth and sustainability for 20 years (Orkestra, 2009). The second type of policy is a more traditional innovation policy based upon significant investments in science and technology. In particular, a dense network of infrastructures has been formed which now includes around four universities, twenty technology centres, ten excellence research centres, four science and technology parks and a large variety of business incubators. In addition, a large number of public programmes to support innovation activities within firms has been set up, including programmes for infrastructure formation, organization restructuring, research and development (R&D) operations, commercialization ventures, among others (for example, Aldatu, Innovaempresa, Saiotek, and so on) (Olarazan et al., 2009; Parrilli et al., 2010; Parrilli and Elola, 2011). In a way, these two types of policies helped develop new and creative industries, which in fact have expanded at an astonishing rate over the past ten years (see Table 7.2). The data displayed in Table 7.2 show the impressive growth of these heterogeneous creative industries over the past decade. Such creative employment represents 5.8 per cent of overall employment in the Basque autonomous community, which is quite high compared with international standards (Hall, 2000; DCMS, 2001). Of course, it is not the result of the aforementioned two types of policies that had, however, a role to promote the collective motivation towards firm creation in sectors related to the knowledge economy (for example, sector 731–2 on R&D in social and natural sciences and engineering show a threefold increase in both firms and employment). In the following sections we are describing the particular case of two medium-sized cities and their provinces, San Sebastian (about 200 000 inhabitants in the city and 600 000 in the province) and Bilbao (400 000 people in the city and 1 million inhabitants in the large metropolitan agglomeration). Both represent ‘diversified creative local production systems’ due to the fact that they did not develop these creative industries on the basis of particular ancient traditions and historic backgrounds, such as those that promoted these industries in Egypt, Athens or Rome and Florence. However, they have been able to mix the two different

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Table 7.2

Creative industries in the Basque Country (2000–2009)

NCEA

Activities

Number of firms (2000)

Number of firms (2009)

1 280

1 411

6 853

7 223

4 529

6 688

9 954

18 091

1 344

2 356

4 843

7 001

854

1 432

7 003

13 203

746 157

1 198 701

1 923 2 108

4 099 5 438

8 910

13 788

32 684

55 055

Traditional creative industries 221–2 Printing and publishing 742 Architecture and engineering 223,921–3 Film, video and performing arts Non-traditional creative industries 722–6 Software and computer services 744 Advertising 731–2 R&D in social sciences and in natural sciences and engineering Total creative industries Source:

Employment Employment (2000) (2009)

EUSTAT (2011).

types of industries (traditional and non-traditional) in a ‘creative mix’ in which each one feeds back the other in a sort of virtuous circle. It is the case that we will see in the following studies, which exhibit (among other interesting aspects) that creativity in the traditional cuisine industry in San Sebastian feeds back into the audiovisual industry by supplying content for audiovisual products, while the latter then feeds back into the cuisine industry by promoting its activity and success worldwide by means of specific audiovisual products. The case of San Sebastian shows another peculiarity: there are creative industries that are not even revealed in statistics based on the DCMS document because they imply a creativity that comes ‘from the backyard’, as in the case of creative cuisine in which San Sebastian has become a world-class leader. 4.1

A Diversified Steady Cluster: Bilbao

Bilbao is the main city in the Basque Country, although it is not the capital (Vitoria). For many decades, it has been the leading industrial capital

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in the North of Spain, capable of promoting significant industrial policies as well as intense waves of migration from the South (Extremadura, Canarias, Castilla-La-Mancha, among others). The key industries that led such important development process were heavy industries including steel production and the maritime industry. However, in the late 1980s and early 1990s both these industries and the city fell into a deep crisis from which they recovered through the aforementioned proactive and creative approach (Azua, 2008). In fact, the whole recovery was based upon the intention to value the traditional embeddedness of this province and region in its industrial roots. In the early 1990s the Guggenheim Museum was built in the regenerated area of the river ‘Ría’ where the former shipbuilding factories were based (now relocated closer to the estuary of the river). The Guggenheim itself is built of steel and titanium, among other metals and materials, as a means to connect very closely the culture of the city with its industrial tradition. Simultaneously, the cluster promotion policy was activated by the Basque government which, among others, spur the redevelopment of the same shipbuilding and forging and steel industries, which now have their own cluster associations. As a result, the regeneration of the centre of the city was based not on wiping off the former industrial traditions; they are rather built upon the attempt to reactivate such industries, together with other sources of growth and innovation (Azua, 2008; Orkestra, 2009). The creative industries in the province of Bilbao also show a significant growth over the past couple of decades, as Table 7.3 shows. Table 7.3 shows the impressive growth of creative industries in the province of Bilbao. It is outstanding in a similar way to San Sebastian (see the following case). This result shows the relevance of the effort put in place within the province. In the case of Bilbao, the creative industries are positioned differently from the cousin city of San Sebastian, as ‘architecture and engineering’ activities are not yet the most developed here (compared with San Sebastian), but Bilbao shows a much stronger presence of ‘software and computer services’ that also represent around 30 per cent of total employment in creative industries, whereas the other sectors represent less than 10 per cent each. Research and development activities are much weaker here than in San Sebastian – where dynamic science and technology parks are based – both in absolute and percentage terms; in spite of these data, a growing science and technology park (Zamudio) is located beside the city with hundreds of relevant R&D companies, technology centres, excellence research centres, among other organizations. Overall, it is worth noting that Bilbao exhibits the same proportion of traditional creative industries (57 per cent and 60 per cent) compared with nontraditional (43 per cent and 40 per cent) of San Sebastian, which differs

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Table 7.3

Bilbao province as a diversified LPS (2000–2008)

NCEA

Activities

Number of firms (2000)

Traditional creative industries 221–2 Printing and publishing 742 Architecture and engineering 223,921–3 Film, video and performing arts Non-traditional creative industries 722–6 Software and computer services 744 Advertising 731–2 R&D in social sciences and in natural sciences and engineering Total creative industries

Number of firms (2008)

Employment Employment (2000) (2008)

662

710

3 908

3 748

2 295

3 351

6 027

10 701

617

1 117

2 535

3 712

482

912

4 472

9 314

429 66

679 310

1 117 767

2 523 1 907

4 453

7 079

18 826

31 905

Source: EUSTAT (2011).

from other known centres of cultural activities such as Florence, Madrid and Barcelona, in which non-traditional creative activities attain a lower proportion (30 per cent). Overall, the province of Bilbao shows a few other interesting features. For example, creative employment is 6.7 per cent of the overall provincial employment with outstanding growth compared with in 2000 (it was 5 per cent). As in the case of San Sebastian, this information shows the proactive and creative approach taken by firms and entrepreneurs in this province over the past few years. The knowledge economy is not an empty concept, but an effective call for a change towards more dynamic economic sectors. In the following subsection we present the case of the creative audiovisual cluster in Bilbao, as an example of the strength in the development of these creative industries in just a few years time. 4.1.1 The creative audiovisual industry in Bilbao This creative cluster is divided in two main productions that include television products as well as the film industry. The overall cluster includes

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around 200 firms based in the Bilbao province, although they would be 400 if video and CD shops are added. From the point of view of the governance dynamics, it is a hub-and-spoke (or a state-anchored cluster, following Markusen classification, (Markusen, 1996)) cluster which has a natural hub in the public television company, EITB, and a few other leading companies, such as EUVE, Euskaltel (telephony) and Vicomtech (technology centre) beside or leading other smaller, although innovative, companies such as 3Comma, Pausoka, Bynet, K2000, Baleuko, among others. A large number of micro and small companies operate in the cluster, especially devoted to services such as mobile camera teams or the design of specific products, software, and so on. The competitiveness of these firms and of the cluster as a whole relies upon good product quality, sometimes innovative, that is supported by the capacity to produce at a moderate price. The cooperation among small firms is an important asset to produce these quality products at such efficient prices. The innovativeness of these companies benefits significantly from the collaboration established with some hubs such as EUVE (virtual engineering company) and Vicomtech (technology centre), that support the creation of original and innovative products, for example, the design of new software for automatic dubbing for deaf people or other applications targeting specific health issues, which help local firms reach and respond to new market demands. Overall, this cluster employs 2300 people and produces turnover of around 170 million euros, 4 per cent of which is exported. In global terms it is not a big cluster (in its birth, following Swann’s lifecycle theory of clusters); however and interestingly, it is a cluster that has survived the crisis quite well as its turnover increased during the past couple of years, whereas the number of firms and employees remained stable. The export market is rather small, although increasing through a number of products (that is, cuisine and history documentaries, and amusement products) realized by very innovative companies such as 3Comma, and mainly sold across Europe to Germany, France and the UK. The role played by EITB in setting quality standards has been important, and had the effect of triggering original capabilities in small specialized companies; EITB also works as a powerful buyer of products that it later sells in international markets to large original equipment manufacturers OEMs (for example, Endemol). Apart from this, international integration of smaller companies depends a lot on their participation in international film festivals (Venice, Cannes, Berlin). This limitation indicates that a big step is currently needed to pass from a ‘craft’ approach to audiovisuals to an ‘industrial’ approach that has higher objectives in terms of series or volumes of production, and scope for greater market reach. Currently, the whole market for television and film products is passing

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through a process of concentration, at least at the national level (that is, Filmax in movies, ESPN in television), with the attempt by these large companies to control the best product and service providers. In this way they have the power to destabilize the whole local industry, which can lead to the elimination of entire value-chain phases (for example, in product commercialization such as music). For many years this trend has been counterbalanced through the proactivity of the regional government, which selected this cluster as one of the 12 potential clusters to promote through specific finance for the setting up of a cluster association – EIKEN (2004) – which sustains the collective work of the firms interested in organizing relevant joint initiatives (participation in international festivals and fairs, joint projects with technology centres). Would such public support be enough to continue counterbalancing the market concentration trends. Up to now, public support has been coordinated by the department of ‘culture’ rather than from the department of industry, which explains the less ‘industrial’ approach to the growth of these activities, which are still considered only as cultural activities, with little or no economic impact on the regional economy. As a result, policy-makers did not promote needed powerful actions targeting, for example, the financing of export consortia or marketing initiatives oriented to the recognition of the brand ‘Basque Audiovisual’ promoted by the cluster association. This constitutes a priority for a cluster that wants to be more visible internationally, more independent from international hubs, and that also want to have a higher economic impact in the region. 4.2

San Sebastian: A Booming Diversified LPS in Spain

If cities like Rome and Florence are well-known centres imbued with history and culture, in a way, they represent less challenging cases than other cities that might be currently discovering their propensity for cultural and creative activities. This is the case of San Sebastian, which won the recognition of ‘2016 European Capital City of Culture’ (Ayuntamiento de San Sebastian, 2010). In competition with well-known cities such as Cordoba, Burgos, Zaragoza, Malaga and Pamplona, San Sebastian strived on the basis of long-standing effort – 20 years – to modernize the city, in practice from the deep crisis into which the Basque Country plunged and from which it emerged through the joint effort of the local private sector together with very proactive regional governments. San Sebastian can be examined as a LPS of diversified creative industries; it represents a tiny black area in the upper right part of Figure 7.1 (see section 2). Table 7.4 shows the strong performance of the traditional and non-traditional creative sectors within this economy between 2000

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Table 7.4

San Sebastian province as a diversified LPS (2000–2008)

NCEA

Activities

Number of firms (2000)

Traditional creative industries 221–2 Printing and publishing 742 Architecture and engineering 223,921–3 Film, video and performing arts Non-traditional creative industries 722–6 Software and computer services 744 Advertising 731–2 R&D in social sciences and in natural sciences and engineering Total creative industries

Number of firms (2008)

Employment Employment (2000) (2008)

476

529

2 179

2 613

1 566

2 285

2 577

4 247

522

937

1 674

2 492

274

419

1 581

2 675

221 51

365 282

568 891

1 192 2 452

3 110

4 817

9 470

15 671

Source: EUSTAT (2011).

and 2008. These data present astonishing results, which justify the decision to analyse this case in depth, and as a benchmark and an instrument to propose similar analyses in other less known cities and urban centres. In spite of the clear leadership of Barcelona and Madrid as the main creative centres in Spain, the province of San Sebastian becomes a noteworthy reality. In a few years (from 2000 to 2008), these industries in San Sebastian increased from 9500 jobs to around 15 600 creative jobs, with an increase higher than 50 per cent on the total number of jobs and firms (EUSTAT, 2011). Table 7.4 presents San Sebastian as a ‘diversified LPS’ since it involves both traditional and non-traditional creative industries. The table shows a balanced spread between the two types of activities, although it seems that the non-traditional creative industries show a special feature with regard to the size of the enterprises as their size is significantly larger (six employees per firm versus three in the traditional industries). In comparison to well-known cities such as Barcelona, San Sebastian

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does not exhibit an outstanding concentration of economic activities with regard to national creative industries; this depends on the size of the town, which is 20 times smaller than Barcelona and 40 times smaller than Madrid. With regard to the specialization within the overall local employment, creative industries in San Sebastian occupy around 5.0 per cent which represents a lower level than Barcelona’s (7.4 per cent of total local employment). More importantly however, it passed from 3 per cent in 2000 to the current 5 per cent (2008) in a few years, showing a very strong trend in the growth of these industries over the recent past. In contrast to this and other well-known cities, San Sebastian exhibits a different combination of creative industries. The share of total creative employment represented by traditional creative industries is more important in Barcelona (68 per cent), whereas it is much more balanced in the case of San Sebastian, in which it spreads between 60 per cent for traditional industries and 40 per cent for new creative industries (increasing from 27–28 per cent in 2000). This information helps drawing the special profile of the Basque city as its creative skills and capabilities are not rooted in ancient origins, traditions and artistic monuments, but more on new aptitudes and, possibly, on more ‘industrial’ activities, which reflect the historic development of this town and its surroundings. In comparison to Barcelona, where the activities of printing and publishing are very important for local creative employment (38 per cent), San Sebastian is focused on a wider range of activities in which architecture and engineering are the most relevant (27–28 per cent), though software, printing and publishing, R&D and films, videos and performing arts also represent effective strengths with around 13–14 per cent each (EUSTAT, 2011). What is also relevant in the context of San Sebastian is that these activities occur in several of the identified regional clusters (for example, electronics and ICTs, aerospace, automotive, logistics and transportation, environment, among others), which shows that they also represent the typology of the knowledge-intensive business services (KIBS) that are very much needed in modern industrialized economies as a means to add knowledge and value to the basis of industrial products in the territory (Orkestra, 2011). In general, the growth of creative industries has been very intense as they are part of a process of local innovation promoted by both policy-makers and the private sector, which is not only represented by a large number of new firms, but also by a relatively large number of agents within the regional innovation system (for example, technology centres, excellence centres, universities, R&D departments in large companies) that have thrust the creation of a much larger number of small R&D units or KIBS, such as those operating in architecture and engineering or those operating in natural sciences and technology. The trend is very

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likely to be maintained and even enhanced since the Basque economy (San Sebastian, Bilbao and Vitoria) has assumed a clear drive towards the knowledge and innovation economy. 4.2.1

Traditional non-creative clusters versus the cuisine creative cluster in San Sebastian Statistics are always a partial measure of socio-economic life. In fact, there are sectors that, although are not strictly considered as part of the creative economy, are extremely creative in nature. In San Sebastian and its province, eight restaurants are recognized within the Michelin guide for star-awarded restaurants, totalling 15 Michelin stars. This is the densest per capita concentration of star-awarded restaurants in the world. This recognition comes on the basis of outstanding quality and creativity in the preparation of food as well as in practices related to customer assistance and satisfaction. A brilliant atmosphere of cooperation and competition is in place in the area (as well as in the Bilbao province), which leads to peculiar events, such as the culinary ‘research’ meetings organized by the most recognized ‘chefs’ of the local cuisine, in which they study and explore new products, flavours and recipes. As a result, they are later invited to present their masterpieces in television programmes that will be later broadcast on television channels and in quality documentaries. This local effort is being promoted by a number of institutions and public initiatives such as the research ‘Basque Culinary Centre’ that is currently starting in San Sebastian and that will include an international faculty in gastronomic sciences and a centre of research and innovation in food and gastronomy that is led by the University of Mondragon together with the well-known Basque chefs. The annual conference ‘San Sebastián Gastronomika’ is also part of these initiatives that add tradition and prestige to the city and that, in addition, benefit from a number of synergies developed with the software and audiovisual cluster. Many other restaurants are involved in this local collective effort to improve quality and originality of food products and services, which enhance local competition and cooperation for further improvements. The software/audiovisual sector, which is also part of the creative industries (see former subsection), complements this effort by producing a number of informatics and software products that convey updated and appealing information about the outstanding capacity of this apparently traditional ‘non-creative industry’. Further refinements to the theory of creative industries are called for in order to better represent the skills and capabilities that are being created in this apparently traditional production. All this is part of a broader ‘industrial atmosphere’ or even of a particular social capital that find their roots in the tradition of the ‘gastronomic

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circles’ (‘sociedades gastronómicas’) where a number of families become members of a neighbourhood association and share a common building oriented to the specific usage of weekend meetings and collective friendly dinners. These circles have their own routines and implicit regulations, such as only men can enter the kitchen to cook, whereas women can ‘only’ enjoy the tasty food. It is a tradition that is strongly embedded in the local community, and that, curiously, is not challenged by women despite this society being essentially ‘matriarchal’ (that is, meaning that such traditions are accepted in a rather flattering way). Overall, this industry is neither considered part of traditional creative industries nor of non-traditional creative industries. This information makes it clear that creative industries can rise in a rather implicit, subtle way, and remain quite hidden in terms of statistics, at least for a significant period of time. And in spite of such neglect they work and produce relevant outputs so as to become an important source of jobs and economic growth for the local community. This case shows how creativity can arise from traditions and find new evolutions that promote new industries and new options for local economic development.

5. CONCLUSIONS AND POLICY IMPLICATIONS This chapter takes advantage of the creative industry approach and methodology (Lazzeretti et al., 2008) in order to develop a relevant application to Spain and to some specific local economies in that country. In particular, this chapter covers two objectives. The first aims to open a window of opportunity for countries like Spain that need to find novel solutions to restore their path to competitiveness after a global crisis that is likely to require fresh patterns of sustainable growth. Secondly, this work presents the case of medium-sized cities, which are betting on the growth of creative industries by pursuing their own peculiar strategies. These local economies (Bilbao and San Sebastian) seem to centre on a ‘strategic mix’ that promotes together traditional and non-traditional creative activities as they occur in ‘diversified local production systems’. Both Bilbao and San Sebastian represent a balanced model of creative industries that include both traditional and non-traditional forms of creativity with both industries showing a very strong firm and employment creation over the past decade. The proactive policy approach adopted by the Basque government at a macro level, and the urban planning and initiatives organized by the respective municipal governments at the micro level, seem to have played a key role in the promotion of creative industries in these cities and in the

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region as a whole. Cluster and innovation policies have promoted a form of collective entrepreneurship, and the stemming and coordination of new types of economic activities based on both traditional and non-traditional creative industries. Simultaneously, the efforts put in place by the municipality of Bilbao in the 1990s (and in recent years), and those developed in these years by the municipality of San Sebastian in order to compete for ‘European Capital of Culture 2016’, also contribute to motivate large numbers of would-be-entrepreneurs to create new firms and activities in creative industries, and to connect these efforts with relevant investments in the knowledge economy that represent opportunities to build up new strengths and competitive advantages for these local economies. Within this context and prospects, urban reactivation policies become fundamental for the growth and sustainability of creativity. Three sets of policy implications can be underlined here: 1.

2.

3.

Urban development policies focused on creativity are more likely to be efficient and effective when they take into account the different typologies of creativity expressed by the territories in terms of traditional and non-traditional creative industries (for example, the development of arts and traditional creative industries in Athens, Florence and Rome, that of non-traditional creative clusters such as cuisine and R&D in San Sebastian and Bilbao). In this way, they may build on pre-existing strengths and critical mass of actors and resources. In this respect, the presence of clusters of creative agents focused on either artistic, cultural, human or natural resources may be equally substantial. They all offer opportunities that the local economy may enjoy and exploit, although in different ways and to different extents. The case of ‘diversified creative local production systems’ in Bilbao and San Sebastian show a further opportunity that cities based on more traditional or non-traditional creative industries alone do not offer: the integration and mutual or complementary reinforcement that they provide to one another, such as in the case of the creation and commercialization of documentaries on Basque cuisine. The pool of local collective creativity may benefit from this fertile ‘strategic mix’ of resources and capabilities. The current statistics on creative industries – based on the DCMS report (2001) – are an important measurement device for the promotion of urban planning and development processes, although they are not the only ones. As shown in the case of the cuisine cluster in San Sebastian, it becomes necessary to integrate quantitative and qualitative knowledge and analysis in order to comprehend more deeply this complex creativity phenomenon that frequently utilizes subtle and/or

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implicit mechanisms and trajectories, and that undergoes continuous changes and tranformations.

NOTES *

The authors acknowledge the very useful comments and information delivered by Kepa Korta, San Sebastian Municipality, and Itziar Mena, EIKEN. 1. An industrial filière is a chain of subsequent economic activities starting from raw materials to final products. 2. We extend the notion of local production system (LPS) to the creative economy and we define creative LPSs as LPSs specialized in creative industries according to the industrial district approach Then we separate them according to their specialization in traditional and non-traditional creative industries. 3. Starting from the ‘ATECO 2002’ classification of Italian economic activities, the relevant figures for each code identified of creative industries were extrapolated from the Florence Chamber of Commerce databank in 2004. In this case the category of ‘Trade, Craft and Antique Market’ is included in the analysis. In the following studies, data are not available for this category and therefore it is omitted. See also note 4.

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8.

Assessing country competitiveness: the case of Spain* Mercedes Delgado and Christian Ketels

1.

INTRODUCTION

There is increasing consensus about the need for context-specific policy advice to improve country prosperity (see, for example, Rodrik, 2007). In recent work we have developed a robust and novel diagnostic tool to assess country competitiveness, grounded in the latest academic research and designed to provide practical insights to policy-makers (see Porter et al., 2008; Delgado et al., 2010a). In this chapter we document the main features of the diagnostic tool and use it to assess Spain’s competitiveness and develop policy recommendations. There are many things that matter for country prosperity. Our framework incorporates a wide range of these factors into an integrated structure with three main areas: endowments, macroeconomic competitiveness and microeconomic competitiveness (see Figure 8.1). Macroeconomic competitiveness has two related areas: social infrastructure and political institutions (SIPI) and macroeconomic policy (MP). Policies in both of these areas are controlled mainly by central governments. Microeconomic competitiveness (Porter, 1990; 2003a; Porter et al., 2007) includes the sophistication of company operations and strategies (COS) and the quality of the national business environment (NBE). These factors are influenced by multiple stakeholders, including different government agencies, companies, and institutions for collaboration. While macroeconomic competitiveness sets general conditions that create opportunities for higher productivity, microeconomic competitiveness has a direct impact on the way these opportunities are actually translated into better performance. Building on the Business Competitiveness Index (for example, see Porter, 2003a; Porter et al., 2007) and on the recent literature on composite indices (see, for example, Kaufmann et al., 2004; Nardo et al., 2008; Høyland et al., 2009), we construct a robust competitiveness diagnostic tool that uses the best available methodology and draws on and 185

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Innovation, global change and territorial resilience Microeconomic Competitiveness Quality of the National Business Environment

Sophistication of Company Operations and Strategy

State of Cluster Development

Macroeconomic Competitiveness Social Infrastructure and Political Institutions

Macroeconomic Policies

Endowments

Figure 8.1

Foundations of country prosperity

contributes to the rich academic research on country prosperity. Data is drawn from a mix of public sources and the annual Executive Opinion Survey of the World Economic Forum (WEF). Our work defines national competitiveness as the expected level of output per working-age individual, given the overall quality of a country as a place to engage in productive economic activity. The inclusion of all potential workers in based on the realization that both the productivity of all employed workers and the ability to employ a large share of the available labor force influence prosperity. Prior work focuses on the role of institutions in country differences in prosperity, with scant attention to microeconomic factors. In contrast, we specify a comprehensive model that simultaneously accounts for microeconomic and macroeconomic competitiveness and inherited endowments. We find that the contemporaneous microeconomic environment matters for national competitiveness, even after accounting for institutions and endowments. Consistent with other studies, we also find that the quality of institutions has a positive influence on competitiveness (for example, see La Porta et al., 1998; Hall and Jones, 1999; Acemoglu et al., 2001). We use our competitiveness framework to examine Spain’s economic performance and briefly illustrate the type of policy analysis that can be implemented. Our diagnostic tool allow us to set country-specific contemporaneous action priorities and define comprehensive policies for sustainable prosperity. The analysis strongly suggests that Spain is facing

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a competitiveness challenge, not only a severe cyclical downturn. We identify some dimensions of microeconomic competitiveness that have become serious constraints for higher economic performance. The remainder of the chapter is organized as follows. We begin by describing the competitiveness framework. Section 3 explains the data, and section 4 offers a brief overview of the estimation method to construct the competitiveness diagnostic tool. Section 5 then uses the diagnostic tool to assess the competitiveness of Spain and discuss policy implications. A final section concludes.

2.

DRIVERS OF COUNTRY COMPETITIVENESS

Our competitiveness framework captures the factors that influence the productivity level that an economy can sustain. Productivity is the prime factor in explaining country prosperity differences, not unequal stocks of factor skills or capital inputs (Hall and Jones, 1999; Caselli, 2005). In our work competitiveness is defined as the expected level of output per working-age individual, given the location as a place to engage in productive economic activity (Delgado et al., 2010a). There is a large literature identifying the drivers of prosperity across countries.1 Theoretical work initially concentrated on the role of capital accumulation in economic growth (Solow, 1956). Over the past decades, they have focused on the role of knowledge in country productivity (Lucas, 1988; Romer, 1990; Warsh, 2006). Empirical work has examined a wide range of factors, including the role of institutions, openness to trade and investment, geographic location, and the quality of the business environment. While there is broad agreement on some general findings (for example, the importance of institutions), no consensus model has emerged on the specific drivers of prosperity. Results tend to be highly dependent on the specifics of the sample of countries chosen and the actual measures used (for example, Barro, 1991; Mankiw et al., 1992; Sala-i-Martin, 1997; Easterly, 2001; Sala-i-Martin et al., 2004). These mixed findings have led to debate about policy implications (World Bank, 2005; 2008a; Rodrik, 2007; 2008; Easterly, 2008). While prior work tends to focus on a minimum number of ultimate causes that explain the differences in prosperity levels across countries, such as a nation’s colonial past, our goal is to identify a rich set of contemporaneous drivers of competitiveness that are amenable to change through policy actions. Building on the prior literature, we provide an integrative framework with three overall building blocks of country prosperity: endowments, macroeconomic competitiveness, and microeconomic

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competitiveness (Figure 8.1). Endowment can affect prosperity directly through inherited natural resources, geographic location, or a large home market. Since we are interested in country competitiveness we control for country endowment. By doing this, we can focus on the microeconomic and macroeconomic competitiveness factors that influence country productivity. Macroeconomic Competitiveness We distinguish two broad areas of macroeconomic competitiveness: macroeconomic policy and social infrastructure and political institutions. These two areas reflect key strands in the macroeconomic literature. Macroeconomic policy has dominated the debate for a long time (Fischer, 1993). Recent literature has put more emphasis on the strong and long-term effect of institutions (Hall and Jones, 1999; Acemoglu et al., 2001; 2003). Macroeconomic factors are heavily the province of central governments. Macroeconomic policy is normally seen as most relevant for managing short-term fluctuations in economic activity, but it can also have longerterm effects on productivity. While ‘moderate’ levels of inflation and debt may have no effect on sustained growth, high (and persistent) inflation and debt could hinder growth (Levine and Renelt, 1992; Temple, 2000; Barro, 2002b; Reinhart and Rogoff, 2010). Macroeconomic policy decisions and effects are influenced by the quality of institutions. For example, fiscal policy is related to the efficiency of government expenditure and the extent of the informal economy. Social infrastructure and political institutions have become the focus of much academic analysis and policy attention in the last two decades (North, 1990). In its wake, the policy debate shifted towards an ‘augmented’ Washington consensus, combining solid macroeconomic policy with a stronger focus on social infrastructure and political institutions (Rodrik, 2006). Our analysis captures three related dimensions of SIPI, each of which has been the focus of different types of research: basic human capacity, political institutions, and the rule of law (see Table 8A.1 in the appendix to this chapter). Basic human capacity, such as access to basic education and health care, is necessary to enable individuals to effectively engage in economic activity (Sachs, 2005; Lorentzen et al., 2008; Weil, 2007). The nature of political institutions is important for the effectiveness and credibility of the rules and regulations that set the overall context of the economy. However, the literature finds no simple relationship between specific political systems and economic outcomes (Barro, 1996; Tavares and Wacziarg, 2001; Brunetti, 2002; Persson, 2005; Persson and Tabellini, 2007). Finally, the

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rule of law, especially the existence of property rights and the ability to defend legal rights against private and public interests, has an important influence on the incentives to engage in economic activity (La Porta et al., 1998; de Soto, 2000). Corruption and crime can weaken property rights by making it harder to establish them in a court of law (Shleifer and Vishny, 1991; Mauro, 1995; Pelligrini and Gerlagh, 2004). Microeconomic Competitiveness Building on the Business Competitiveness Index (Porter, 1990; 2003a; Porter et al., 2007), we distinguish three related areas of microeconomic competitiveness: sophistication of company operations and strategies, business environment quality, and the state of cluster development (see Figure 8.1).2 Microeconomic factors are influenced by multiple stakeholders. Government, companies, universities, and platforms for collaboration are essential in defining the microeconomic environment in which business takes place. Thus, a richer and more complex set of policy tools are available to shape country prosperity. Microeconomic factors are numerous, multifaceted, and highly interrelated. In any given country at a particular point in time, however, a smaller number of microeconomic conditions represent the most pressing barriers to reaching higher levels of productivity (Porter, 1990; Hausmann et al., 2005; Fagerberg et al., 2007). Improvements in some areas of microeconomic competitiveness will have little effects, unless the binding constraints to productivity are removed. Company sophistication, the nature of company strategies and operational practices, is an area that has been largely neglected in the traditional literature on economic growth. Yet the productivity of a country is ultimately set by the productivity of its companies (for example, Alfaro et al., 2008). Researchers, often from a management or industrial organization perspective, have found significant differences in management practices across countries (Porter and Ketels, 2003; Bloom and Van Reenen, 2007; Bloom et al., 2009; Freeman and Shaw, 2009). Moving to more sophisticated company operations and strategies depends on simultaneous improvements in the quality of the national business environment. For example, poor management practices are more prevalent when product market competition is weak (Porter, 1990; 1996; Nickell, 1996; Lewis, 2004; Porter and Sakakibara, 2004; Carlin et al., 2005; Bloom and Van Reenen 2007). More productive company strategies and operating practices require more highly skilled people, improved infrastructure, lower administrative burden, better suppliers, more advanced research institutions, and so on (see, for example, Rajan

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and Zingales, 1998; Calderon and Serven, 2004; Aghion et al., 2007; Ciccione and Papaioannou, 2007; Council on Competitiveness, 2007). In our model, the national business environment is understood in terms of four interrelated dimensions: the quality of factor (input) conditions, the context for firm strategy and rivalry, the quality of local demand conditions, and the presence of the related and supporting industries. Because of their graphical representation, the four areas have collectively become referred to as the Diamond (see Porter, 1990; Porter et al., 2007; 2008).3 The influence of one part of the business environment depends on the state of others. Lack of improvement in any dimension can lead to a plateau in productivity growth and stalled development. Clusters provide the link between the general business environment quality and firm strategies. Clusters are geographic agglomerations of companies, suppliers, service providers, and associated institutions in a particular field, linked by externalities and complementarities of various types (Porter, 1990; 1998; 2000b). Clusters, such as wine in Australia or sport cars in Italy, are often concentrated in a particular region within a nation. Clusters foster externalities of various sorts: specialized knowledge, skills, sophisticated demand, and supporting and complementary industries. National economies tend to specialize in a subset of clusters, in which they develop a particularly favorable business environment. Higher levels of regional cluster specialization are associated with improvements in job creation, wages, innovation and business creation (see, for example, Feldman and Audretsch, 1999; Porter, 2003b; Delgado, 2007; Delgado et al., 2010a; 2010b). For an economy to advance, the sophistication of clusters must grow to support more advanced activities (within clusters and in related clusters) in the nation. Many public and private initiatives have developed in countries at all stages of development to support cluster development (Porter et al., 2001; Sölvell et al., 2003; Cortright, 2006; Ketels et al.; 2006; Mills et al., 2008). The nature and depth of clusters varies with the state of development of the economy. In developing economies, firms perform relatively less advanced activities in the cluster (Ketels and Sölvell, 2006), in part because they tend to lack many supporting industries and institutions. Successful economic development is a process of successive upgrading, in which a nation’s business environment and clusters evolve to support and encourage more sophisticated and productive ways of competing by firms located there. Nations at different levels of development face different competitiveness challenges for sustainable prosperity. As nations develop, their modes of competing move through several stages, though rates of progress and the specific path will vary by country (Porter, 1990).

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Assessing a country’s competitiveness is a challenge because of the large number of highly correlated factors influencing national productivity. We build on the recent literature on composite indices to confront this challenge. In the next two sections we briefly explain the data and the method to aggregate numerous factors into composite indicators that capture meaningful dimensions of the microeconomic and macroeconomic environment. We then use our framework to assess the competitiveness of Spain.

3.

DATA

The main source of data for the construction of the competitiveness index  is the Executive Opinion Survey (EOS) developed by the World Economic Forum in collaboration with the Institute for Strategy and Competitiveness (Harvard Business School). The EOS data are used to evaluate the competitiveness of countries as reported in the Global Competitiveness Reports (for example, see Schwab, 2010). Survey respondents are executives of companies, capturing the informed judgments of the actual participants in the economies of the countries examined. Respondents evaluate each question using seven-point Likert-scale ratings. The data used for the development of the competitiveness model covers 130 countries for up to eight years (2001–08). Sector-weighted country-year averages for each indicator are computed using more than 48 000 individual responses (see Browne et al., 2008). The model uses 118 EOS survey indicators as well as 29 non-EOS hard data and survey indicators from other internationally recognized data sources (see Table 8A.1). The non-EOS datasets include the World Bank Governance Indicators that are used to compare regulatory quality and institutions across countries (Kaufmann et al., 2008); the World Bank Doing Business indicators that capture multiple factors influencing the costs of doing business in a country (World Bank, 2008b); the World Bank World Development Indicators that measure many aspects of the macroeconomic environment; the World Health Organization database; the International Monetary Fund (IMF) World Economic Outlook database; the World Telecommunication/ICT Indicators database; the United States Patent and Trademark Office (USPTO) patent database; the United Nations (UN) Comtrade export dataset, the Center for International Development database at Harvard University that provides a rich set of geography indicators; and the UN Gender-related Development index that measures inequalities between men/women.

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4. ESTIMATION OF THE COMPETITIVENESS DIAGNOSTIC TOOL Analysing country competitiveness is challenging because of the sheer number and variety of factors affecting productivity. We build on the recent literature on composite indices to confront these challenges (Kaufmann et al., 1999; 2004; Nardo et al., 2008; Høyland et al., 2009). In this section we briefly explain the steps in the estimation method that we use to calibrate the diagnostic tool. A detailed explanation is available in Delgado et al. (2010a). Choice and Allocation of Competitiveness Indicators Candidate indicators are those that capture dimensions suggested by both theory and policy debate. We choose mainly input and policy indicators (versus output indicators) available for a large number of countries. Each indicator is tested for a significant relationship to GDP per capita, controlling for endowments (see Table 8A.1). The competitiveness index groups the many competitiveness indicators into categories that capture different mechanisms of influence. In contrast with other indices, categories are organized hierarchically so that it is clear how each part of the index sums to the final competitiveness score. Figure 8.9 shows an overview of the index categories and subcategories, and Table 8A.1 reports the individual indicators grouped within each category. At the first level, the Competitiveness Index score (CI) incorporates all factors that influence competitiveness. At the second level, rankings are presented on macroeconomic competitiveness and microeconomic competitiveness (MICRO). At the third level, rankings are reported on four broad categories. In the macroeconomic area, macroeconomic policy (MP) captures fiscal and monetary policies. Social infrastructure and political institutions (SIPI) collects indicators of general human conditions and institutions. In the microeconomic area, the two categories are the sophistication of company operations and strategy (COS) and the overall quality of the national business environment (NBE). At the fourth level, the SIPI category is decomposed into three subcategories: basic human capacity (basic health and education), political institutions (decision-making and efficiency of the executive), and the rule of law (safety, corruption and efficiency of the legal process). In the NBE category, indicators are divided into the four elements of the Diamond framework (Porter, 1990): factor conditions, context for strategy and rivalry, demand conditions, and supporting and related industries and clusters.

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At the fifth level, some areas of microeconomic competitiveness are further differentiated into narrower subcategories to better target a policy response. For example, under factor conditions, indicators are grouped by logistical infrastructure, communications infrastructure, administrative infrastructure, capital market infrastructure, and science and innovation infrastructure. Under COS, indicators are grouped by strategy and operational effectiveness, organizational practices, and internationalization of firms. In most cases, the allocation of individual indicators to categories is clearly given by their nature. We also assess whether indicators within a category group well by using factor analysis (see Delgado et al., 2010a). Estimation of Country Competitiveness The competitiveness index is generated in a two-step process using a panel data for over 130 countries up to eight years (2001–08). In the first step, we aggregate indicators using principal component analysis (PCA) and obtain a score for the different categories and subcategories. In the second step, we aggregate MICRO, SIPI and MP into the final index using regression analysis to determine the weights. In contrast with other indices (for example, the WEF Global Competitiveness Index), we use regression analysis to estimate a country’s competitiveness score. We specify a comprehensive regression that uses (log of) gross domestic product (GDP) adjusted for purchasing power parity (ppp) per working-age individual (15–64 years old) as the dependent variable, with scores for MICRO, SIPI, MP for each country-year as the main explanatory variables and controlling for endowments and year dummies. The estimated coefficients for MICRO, SIPI, and MP are normalized into weights that add to 1; and the predicted competitiveness score for a particular country c in year t is then calculated by summing its weighted microeconomic and macroeconomic components: IndexˆScorec,t 5 w^ MicroMICROc,t 1 w^ SIPISIPIc,t 1 w^ MPMPc,t.

(8.1)

The core finding is that all broad categories of competitiveness are statistically significant and important, even after controlling for the others. Prior work has tended to argue that macroeconomic competitiveness, notably institutional factors (SIPI in our model), predicts differences in prosperity (La Porta et al., 1998; Hall and Jones, 1999; Acemoglu et al., 2001; Rodrik et al., 2004). Our results strongly support an independent role of each category (see Delgado et al., 2010a).

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In constructing conceptual categories and composite indices, it is important to examine the robustness of the resulting scores and rankings. The categories and subcategories’ scores and rankings are robust with respect to the random exclusion/inclusion of indicators. Importantly, the regression weights are also robust to changes in the set of countries, years and endowment controls. In the rest of the chapter, we use the competitiveness framework (and preliminary index rankings and scores) to diagnose Spain’s economic performance and illustrate the type of policy analysis and insights that can be delivered with our diagnostic tool.

5. ASSESSMENT OF SPANISH COMPETITIVENESS Prosperity levels have been increasing during the last decade in Spain until the Great Recession of 2008–09. Prosperity growth reached a maximum of 5.6 per cent in 2006 (based on GDP per capita purchasing power parity (ppp)-adjusted), but by 2009 Spain and most European countries experienced a large contraction of GDP (see Figure 8.2). Spain

8.0%

EU-15 (median) EU-27 (median)

5.6%

6.0% 4.8%

4.9% 3.7%

4.0%

4.8%

4.2% 3.5%

2.0% 1.2% 0.0%

–2.0% –3.7% –4.0% 2001

2002

2003

2004

2005

2006

2007

2008

2009

Notes: Authors’ calculation based on IMF GDP data (accessed September, 2010).

Figure 8.2

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Annual prosperity growth rate (GDP per capita ppp-adjusted)

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Table 8.1

195

Labor productivity (GDP per hour)

Year

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 Avg 2000–09

GDP per hour (in 2009 EKS$) United States

EU-15 (median)

Spain

Spain/EU-15 (%)

47.6 48.7 50.2 51.8 53.0 53.8 54.2 55.0 55.8 57.2 52.7

44.3 45.6 46.5 47.3 48.6 49.3 50.4 51.5 50.5 50.0 48.4

40.3 40.6 40.9 41.2 41.5 41.9 42.3 43.0 43.6 45.3 42.1

91.1 89.0 87.8 87.1 85.4 84.9 83.8 83.4 86.3 90.6 87.0

Notes: Authors’ calculations. Source: The Conference Board Total Economy Database, January 2010, http://www. conference-board.org/economics/database.cfm.

Much of Spain’s pre-crisis growth was based on rising levels of labor mobilization. Growing domestic demand, especially in the burgeoning construction sector, created many employment opportunities for lower-skilled, often immigrant workers. While labor productivity has been improving during 2000–09, it continues to lag relative to other European Union countries. For example, Spain’s GDP per hour was around 91 per cent of the median value for the EU-15 countries in 2009 (the GDP per hour was 45 EKS$4 in Spain versus 50 in EU-15; see Table 8.1).5 Spain has also underperformed in total factor productivity (TFP). The country experienced a negative TFP growth during the last decade.6 These are danger signs. While higher labor mobilization can provide a short-term boost to prosperity, sustained prosperity growth depends on rising productivity. Such productivity growth is the result of innovation, and this is an area where several indicators suggest a weak Spanish position. The levels of patenting, R&D spending, and high-technology exports are all relatively low, suggesting that Spain is not yet in an innovation-driven stage of development (Table 8.2).7 In what follows, we apply the competitiveness diagnostic framework introduced above to develop some insights into Spain’s economic performance. First, Spain’s competitiveness has worsened over the last decade, especially during the last three years prior to the crisis. Figure 8.3 shows

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Table 8.2

Selected indicators of innovation capacity (average over 1998– 2007; rank versus 87 countries in parenthesis) High-tech Mfg exports mfg exports per capita (current (% mfg US$) exports)

R&D USPTO Researchers per million (% of GDP) annual people patents granted per million people

Spain 6.823 (46) 285 897 (27) 6.527 (25) 2152.501 (26) 1.027 (28) EU-15 16.289 (27) 831 967 (11) 61.377 (14) 3076.668 (19) 1.811 (15) (median) EU-27 11.918 (35) 471 477 (18) 6.527 (25) 2235.345 (25) 1.086 (26) (median) Spain/EU-15 0.419 0.344 0.106 0.700 0.567 Spain/EU-27 0.573 0.606 1.000 0.963 0.946 Notes: Authors’ calculations based on World Bank World Development Indicators (High-tech exports, Researchers per capita, and R&D indicators) and USPTO dataset (patents).

1.6

1.4

EU-15 competitiveness (median)

1.399

1.204

1.2

1.017

1

EU-27 competitiveness (median)

0.8 0.711

0.673 0.6

0.546 Spain competitiveness

Note:

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

0.4

Based on competitiveness index calculation in Delgado et al. (2010a).

Figure 8.3

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Competitiveness score 2001–10 (and 90 per cent confidence interval)

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2 1.5

2010 competitiveness score (and 90% confidence intervals)

1.0 0.5 0

–1

Sweden Finland Netherlands Denmark Luxembourg Germany Austria United Kingdom Belgium France EU-15 Cyprus Ireland Estonia Malta EU-27 Spain Czech Republic Portugal Slovenia Poland Italy Slovak Republic Lithuania Hungary Latvia Greece Romania Bulgaria

–0.5

Note: Based on competitiveness index calculation at Delgado et al. (2010a).

Figure 8.4

European Union countries’ competitiveness score in 2010 (and 90 per cent confidence intervals)

the competitiveness score of Spain (and its 90 per cent level confidence interval), and the median scores of EU-15 and EU-27 countries over 2001–10. The last few years have in many ways been a lost era for Spain’s competitiveness, at least relative to the efforts taken by its peers. Figure 8.4 reports the competitiveness score of each of the EU-27 countries in 2010. Spain’s competitiveness remains higher than that of most Eastern and Southern European EU members, but Spain underperforms compared to most EU-15 countries. Indeed, Spain’s competitiveness score relative to the EU-15 (median) score has declined significantly in the last three years, becoming 54 per cent lower than the EU-15 estimated score (Figure 8.5). The relatively low competitiveness seems mainly driven by the weakness of the microeconomic environment, which ranks 33rd by 2010. Second, Spain’s prosperity is above what would be predicted by the competitiveness factors. While the country’s prosperity remained broadly stable during the last decade, its competitiveness deteriorated. The prosperity rank is 23 while the competitiveness rank is 29 (six ranks worse) as of 2010 (Figure 8.6).8 To further assess these differences between prosperity and competitiveness, Figure 8.7 shows a country’s actual prosperity and the prosperity predicted by its competitiveness.9 Consistent with our prior analysis, Spain (and other EU countries, such as Greece, Italy, and Portugal) has a significant positive gap between actual and predicted

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Spain score/EU-15 (median) score

1.00 0.90 0.87 0.80

MICRO

0.70

SIPI

Competitiveness Index

Prosperity (GDP pc ppp)

0.60

0.51 0.46

0.50 0.40 0.30

0.33 2001

Figure 8.5

2002

2003

2004

2005

2006

2007

2008

2009

2010

Spain performance relative to the EU-15, 2001–10

Rank (1 means top) 35 MICRO

33

33

31

Competitiveness Index

29

29 SIPI

27 25

Prosperity (GDP pc ppp)

23

23

21 19 2003

2004

2005

2006

2007

2008

2009

2010

Note: Based on competitiveness index estimation in Delgado et al. (2010a).

Figure 8.6

Spain’s prosperity-competitiveness gap (ranks versus 94 countries. Higher rank means worse performance)

prosperity. Even after controlling for endowments, Spain’s predicted prosperity (from estimating equation 8.1) is lower than the actual prosperity in 2010 (see Figure 8.8). In other words, Spain is over-performing given its competitiveness and endowments. Prosperity levels unexplained by

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199

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5.5

6.5

7.5

8.5

9.5

10.5

11.5

–2

–1.5

–1

Italy Portugal

SPAIN

–0.5 0 0.5 Competitiveness score, 2010

Greece

1

1.5

Denmark Germany

Belgium

Sweden

2

Finland Netherlands

Luxembourg Austria

France

Ireland UK

Competitiveness and prosperity

Authors’ calculations based on Delgado et al. (2010a).

The graph does not depict the impact of endowments, which will explain some of the gap between actual and estimated prosperity.

Figure 8.7

Source:

Note:

Ln GDP per capita (ppp) 2009

200

Innovation, global change and territorial resilience

2.5 Competitiveness Score, 2010

Under performance

Overperformance

2

Norway Germany Denmark 1.5 Belgium UK 1

France SPAIN Malta Portugal

0.5

Slovak Republic

0

Italy Greece

Romania –0.5 –1 –0.8 –0.6 –0.4 –0.2

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

(Actual minus predicted GDP per capita (ppp))/predicted GDP per capita (ppp)

Figure 8.8

Actual prosperity relative to predicted prosperity (given competitiveness and endowments)

competitiveness or endowments are a cause for concern. They may be the consequence of short-term growth spurts with, for example, consumption or rates of investment in areas such as real estate that are not sustainable. Spain seems to be in such a situation. It is not only facing a cyclical challenge to reinvigorate economic activity, but also a competitiveness problem of increasing productivity to the level necessary for sustaining current prosperity. Third, Spain’s competitiveness profile indicates that the country has achieved a solid position in some areas on which policy action has focused, while it has fallen behind in others. Figure 8.9 shows the competitiveness profile of Spain.10 We use two benchmarks to assess the relative performance of Spain: the actual prosperity level in Spain (as measured by the GDP per capita ppp-adjusted ranking), and the (median) rankings and scores of EU-15 and EU-27 countries (Figure 8.10). Spain has achieved a leading position in logistical infrastructure (driven by the use of EU structural funds), solid levels of human development (basic health and education), and a relatively advanced structure of supporting and related industries and clusters (consistent with policies in some Spanish regions). However, a number of key weaknesses in microeconomic factors reduce the benefits Spain is gaining from these advantages: ●

In terms of the Factor (input) conditions, there are two subcategories of alarming concern. First, the administrative infrastructure ranks 70th (see Figure 8.11). Second, and particularly important for innovation, Spain has a relative weak science and innovation

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201

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Strategy (34)

Related and supporting industries and clusters (23)

Human development (20)

Rule of law (30)

Political institutions (38)

Social infrastructure and pol. institutions (29)

Macroeconomic policy (34)

Macroeconomic competitiveness

Spain competitiveness profile 2010 (ranks versus 94 countries; higher rank means worse performance)

Innovation (36)

ICT/energy (32)

Capital (36)

Figure 8.9

Skills (37)

Logistical (12)

Administrat. (70)

Factor input conditions (30)

Organization (57)

Internationalization (39)

Context for strategy and rivarly (41)

Demand conditions (33)

Company sophistication (41)

Business environment quality (32)

Microeconomic competitiveness (33)

Index (29) GDP pc ppp (23)

202

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Strategy (11/31)

Related and supporting industries and clusters (13/28)

Human development (12/22)

Rule of law (16/28)

Political institutions (19/36)

Social infrastructure and pol. institutions (16/27)

Macroeconomic policy (34/35)

Macroeconomic competitiveness

EU-15/EU-27 competitiveness profile 2010 (median ranks versus 94 countries)

Innovation (17/31)

ICT/energy (16/25)

Capital (21/35)

Figure 8.10

Skills (18/28)

Logistical (12/28)

Administrat. (23/29)

Factor input conditions (16/28)

Organization (17/33)

Internationalization (16/33)

Context for strategy and rivalry (14/28)

Demand conditions (15/28)

Company sophistication (14/34)

Business environment quality (16/28)

Microeconomic competitiveness (16/28)

Index (15/28) GDP pc ppp (16/24)

Assessing country competitiveness: Spain Rank (1 means top) 85

203

Administrative infrastructure, 2010 (Spain 70) 80

79 74

75

67

Spain

65

EU-15

55

EU-27

45 35

34

25 15

10

Figure 8.11





Paying taxes – number of payments

Number of days to start a business

Number of procedures to start a business

Easiness of starting a new business

Burden of government regulation

Custom procedures

5

Administrative infrastructure in Spain and EU, 2010 (ranks versus 94 countries; higher rank means worse performance)

infrastructure. Spain underperforms in seven (out of nine) indicators of science and innovation infrastructure relative to EU-15 and EU-27 (see Figure 8.12). Spain is particularly lagging in the quality of its educational system and in indicators of innovation capacity (especially in the quality of scientific research, the university–industry R&D collaboration, and the number of international patents per capita). Third, the analysis suggests that there are some weaknesses in the capital market infrastructure, especially the low availability of venture capital funds and the difficulties to access credit. In terms of the context for strategy and rivalry, Spain ranks 41st, while EU-15 and EU-27 rank 14th and 28th, respectively. One key problem is the rigidity of the labor market, including the imbalances between wages and productivity and the poor ability of companies to flexibly manage their workforce and quickly hire and fire employees (as measured by the World Bank Doing Business, Rigidity of employment index). Another problematic dimension is the distortive effect of taxation and subsidies, and state-owned firms (Figure 8.13). In terms of the sophistication of company operation and strategies, Spain ranks 41st, while EU-15 and EU-27 rank 14th and 34th, respectively. Spain underperforms in all (but one) COS indicators

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Figure 8.13

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FDI and tech transfer

Rank (1 means top) 95 80 85 74 75 66 65 52 55 45 35 25 15 5 72

55

EU-27

32 21 28 58

25

Low tariff rate

65

Regulatory quality

53

Employment flexibility

Spain EU-15

University/industry research collaboration

42

Investor Protection

85 Quality of scientific research institutions

38

Efficacy of corporate boards

44

Low market disruption from state-owned enterprises

23

USPTO patents per million

Low brain drain

35

Effectiveness of antitrust policy Low market dominance by business groups

25

Impact of rules on FDI

Availability of scientists & engineers

Rank (1 means top) Skills (Spain 37)

Intensity of local competition

32

Low trade barriers

Quality of management schools

45

Prevalence of foreign ownership

Figure 8.12

Auditing/accounting standards

15

Openness to capital flows

76

Quality of math & science education

Quality of educational system

75

Distortive effect on incentives to work/investment of taxes Distortive effect on competition of taxes/subsidies Intellectual property protection

5 Tertiary enrollment

25

Pay and productivity

Cooperation labor-employer

204 Innovation, global change and territorial resilience Innovation (Spain 36)

Spain

EU-15

41

EU-27

17 28

8

Science and innovation infrastructure in Spain and EU, 2010 (ranks versus 94 countries; higher rank means worse performance)

Context for strategy and rivalty, 2010 (Spain 41)

72 79

45 56

24 6

Context for strategy and rivalry in Spain and EU, 2010 (ranks versus 94 countries; higher rank means worse performance)

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Assessing country competitiveness: Spain Companies operations and strategies, 2010 (Spain 41) 69 57

54

55

EU-27

53

41 36

39

35 23

25

48

47

43

45 41

Spain EU-15

43

42

Breadth of international markets

65

Control of international distribution

Rank (1 means top) 75

205

27

25

Figure 8.14

Extent of regional sales

Foreign tech licensing

Incentive compensation

Reliance on professional management

Willingness to delegate authority

Staff training

Customer orientation

Extent of marketing

Production process sophistication

Capacity for innovation

Value chain breadth

Company R&D

Firm technology absorption

5

Unique Product/Process

15

Sophistication of companies operations and strategies in Spain and EU, 2010 (ranks versus 94 countries; higher rank means worse performance)

relative to the EU-27 (see Figure 8.14). Organizational practices (such as the extent of staff training, willingness to delegate authority, extent of incentive compensation, and reliance on professional management) are particularly weak. Fourth, there are important systemic interactions across the different dimensions of Spanish competitiveness. For existing companies, the labor market regulations have created incentives to hire temporary workers with limited skills rather than to invest in employees’ skills. A low-skill workforce combined with weaknesses in the innovation infrastructure, especially in the linkages between universities and companies, hinder companies’ ability to move to innovation-based strategies. And without such strategies, it appears rational for many Spanish companies to continue to use less sophisticated operations and management methods. For start-ups, the combination of restrictive labor market regulations and cumbersome administrative procedures, especially when starting a business, have made entry of (potentially innovative) new firms unattractive. Market distortions and the presence of state-owned firms in some markets creates additional problems for new entrants. These barriers for existing and new companies explain why Spain has not reaped the full productivity benefits from the significant advantages it has, especially in logistical

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infrastructure and clusters, but also in narrow areas like management education. These findings based on the competitiveness framework we presented earlier in this chapter (and in Delgado et al., 2010a) have significant policy implications. Spain is facing a competitiveness challenge, not only a severe cyclical downturn in conjunction with overheating. If Spain does not address this competitiveness challenge, its current level of prosperity might very well be under threat, even if the business cycle improves. Meaningful improvements in Spanish competitiveness will depend on shifting the policy focus to those dimensions of microeconomic competitiveness that have become binding constraints11 for higher economic performance. These dimensions – in particular, labor market regulations, the regulatory environment for companies (especially start-ups), and the innovation system – have not been policy priorities in the past. If public policy starts to address these areas, there is room for companies to compete on innovation-based strategies. Clusters, drawing on the significant experience that some Spanish regions already have in this area,12 can play an important role in mobilizing companies to take advantage of new market and innovation opportunities.

6.

CONCLUSION

Policy-makers looking for robust, action-oriented advice on strategies to improve competitiveness face currently two, often equally unsatisfactory, choices. First, the traditional country prosperity literature, especially the empirical growth studies, has focused on identifying macroeconomic factors that are critical to explain long-term prosperity differences across countries (for example, the role of the colonial past and the geography of a country). This literature is relevant but provides little actionable policy advice: most relevant choices seem to have been made long ago. Second, the policy-oriented literature of generic growth plans (Organisation for Economic Co-operation and Development) and competitiveness rankings (for example, World Economic Forum) provides useful ideas and rich data on individual policy areas. However, these studies tend to be based on fragmented evidence from individual pieces of policy-specific research rather than on integrated models that allow systemic interactions. These studies often focus on providing general checklists or simple benchmarking of relative strengths and weaknesses instead of identifying a set of country-specific priorities for sustained prosperity. This chapter documents a new competitiveness diagnostic framework that aims to bridge the gap between these two individually beneficial but

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Assessing country competitiveness: Spain

207

incomplete approaches. On the one hand, our framework is firmly rooted in the traditional economic literature but shows (Delgado et al., 2010a) that a broader set of dimensions matter for country prosperity than the ‘deep’ causes of institutions and/or geography otherwise highlighted in the literature. In particular, the contemporaneous microeconomic environment of a country matters, even after accounting for institutions and endowments. On the other hand, we draw on many of the indicators used in the policy-oriented literature but integrate them into a robust conceptual framework that is econometrically tested (ibid.). We then use the competitiveness framework to diagnose Spain’s economic performance and illustrate the type of analysis and policy insights that can be delivered. While some of the insights are broadly consistent with the results of more traditional analysis, our diagnostic tool allow us to better set priorities for Spain today, and define the appropriate sequencing of individual action steps. Spain needs a comprehensive policy approach to upgrade competitiveness, not just fiscal austerity and labor market deregulation. This policy approach needs to focus on the dimensions of microeconomic (and macroeconomic) competitiveness that are hindering sustainable prosperity. The collaboration between government, companies, universities and other institutions will be crucial to transition to an innovation-driven stage of development.

NOTES *

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

This body of work represents joint efforts with Michael E. Porter (Harvard University) and Scott Stern (MIT). The authors would like to acknowledge Albert Bravo-Biosca and participants at the Orkestra’s Meeting on Innovative and Competitive Territories for very helpful suggestions. For a detailed literature review see Porter et al. (2008) and Delgado et al. (2010a). The state of cluster development is conceptually important but data limitations preclude independent measurement. Cluster development variables are covered as part of the business environment. See Porter et al. (2008) for a detailed discussion on each of the four Diamond components and the related literature. Table 8A.1 in the Appendix shows the set of indicators that are grouped within each category. EKS is the multilateral method developed by O. Elteto, P. Koves and B. Szulc. EU-15 refers to the 15 countries that were members of the European Union before the enlargement on 1 May 2004; and EU-27 refers to the current 27 members of the European Union. Spain’s TFP (value added based) growth (with 1995 = 100) declined from 96.2 in 2000 to 92.1 in 2007 (based on EU KLEMS database, November 2009 release). For instance, high-technology manufacturing exports accounted for ~7 per cent of total manufacturing exports on average during 1998–2007, while the median of this variable was ~16 per cent in the EU-15. Rank versus a constant sample of 94 countries available 2003–10. Higher rankings means worse performance.

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208 9. 10. 11. 12.

Innovation, global change and territorial resilience Specifically, Figure 8.7 plots the 2010 competitiveness scores against 2009 (log of) GDP per capita (ppp adjusted). We do not use 2010 GDP data because of data limitations. The reported rankings are based on a constant sample of 94 countries available during 2003–10. The idea of ‘binding constraints’ to country performance has been introduced by Hausmann et al. (2005). For example, see Elola et al. (2010) study of the array of clusters in the Basque Country (including ICT, electronics, and clean-technology clusters).

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0.35 0.39 0.40 0.47 0.43 0.57 0.51 0.40

0.39 0.34 0.39 0.26

0.34 0.38

Strategy and operational effectiveness Firm-level technology absorption Company spending on R&D Nature of competitive advantage Value chain breadth Capacity for innovation Production process sophistication Extent of marketing Degree of customer orientation

Organizational practices Extent of staff training Willingness to delegate authority Extent of incentive compensation Reliance on professional management

Internationalization of firms Prevalence of foreign technology licensing Control of international distribution

Coef.

0.72 0.75

0.75 0.72 0.71 0.69

0.73 0.75 0.77 0.80 0.78 0.83 0.79 0.76

R-sq.

Communications infrastructure Quality of telephone/fax infrastructure Internet access in schools Mobile cell subscribers per 100 inhabitants Personal computers per 100 inhabitants Internet users (%) Fixed telephone lines per 100 inhabitants

Logistical infrastructure Quality of roads Quality of railroad infrastructure Quality of port infrastructure Quality of air transport infrastructure quality Quality of electricity supply Quality of transport network: business

Factor (input) conditions (NBE)

Extent of regional sales Breadth of international markets

MICROECONOMIC COMPETITIVENESS (MICRO)

0.43 0.54 0.69 0.46 0.44 0.60

0.43 0.39 0.39 0.39 0.55 0.45

0.37 0.49

Coef.

0.79 0.83 0.84 0.79 0.77 0.86

0.78 0.76 0.75 0.75 0.82 0.79

0.73 0.79

R-sq.

Relationship of indicators to (log) GDP per capita, ppp-adjusted (controlling for endowments and year fixed effects)

Company operations and strategy (COS)

Table 8A.1

APPENDIX

210

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0.32 0.45 0.26 0.39 0.39 0.25 0.20 0.14 0.39

0.36 0.39 0.34 0.40

Capital market infrastructure Regulation of security exchanges Financial market sophistication Soundness of banks Ease of access to loans Venture capital availability Financing through local equity market Protection of minority shareholders’ interests Getting Credit Legal rights index Domestic credit to private sector

Science and innovation infrastructure Quality of scientific research institutions University/industry research collaboration Availability of scientists and engineers Low brain drain

Coef.

0.73 0.75 0.72 0.75

0.72 0.77 0.69 0.74 0.75 0.69 0.68 0.67 0.76

0.76 0.68 0.69 0.69 0.71 0.70

R-sq.

Availability of latest technologies Supplier quantity Supplier quality Availability of process machinery Availability of specialized research and training State of cluster development Extent of collaboration in clusters Extent of cluster policy

Supporting and related industries and clusters (NBE)

Gov procurement of advanced tech. products Gov success in ICT promotion Laws relating to ICT Buyer sophistication Presence of demanding regulatory standards Stringency of environmental regulations

Demand conditions (NBE)

Utility patents per capita Quality of the educational system Quality of math and science education Quality of management schools Tertiary school enrollment

MICROECONOMIC COMPETITIVENESS (MICRO)

0.40 0.20 0.24 0.22 0.26 0.23

(continued)

Administrative infrastructure (Low) Burden of custom procedures (Low) Burden of government regulation Easiness of starting a new business (Low) # of procedures required to start a business (Low) Days required to start a business Paying Taxes -(Low) Payments numbers

Table 8A.1

0.49 0.45 0.52 0.50 0.46 0.37 0.39 0.25

0.26 0.20 0.45 0.47 0.50 0.40

0.56 0.32 0.30 0.35 0.42

Coef.

0.79 0.77 0.80 0.79 0.78 0.74 0.75 0.69

0.70 0.68 0.78 0.78 0.80 0.75

0.84 0.72 0.71 0.72 0.76

R-sq.

211

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Human capacity Quality of primary education Quality of healthcare services Accessibility of healthcare services

Social infrastructure and political institutions (SIPI)

Cooperation in labor-employer relations Pay and productivity FDI role in technology transfer Impact of taxation on incentives to work/invest Low distortive effect of taxes/subsidies on competition Intellectual property protection Restrictions of capital inflows/outflows Strength of auditing and accounting standards Absence of trade barriers Prevalence of foreign ownership Impact of rules on FDI Intensity of local competition Effectiveness of antitrust policy Low market dominance by business groups Efficacy of corporate boards Low market disruption from state enterprises Investor protection Low rigidity of employment Regulatory quality Low tariff rate (applied rate, simple mean)

Context for strategy and rivalry (NBE)

0.43 0.41 0.45

0.21 0.27 0.11 0.13 0.26 0.43 0.31 0.35 0.44 0.19 0.19 0.37 0.37 0.35 0.21 0.25 0.17 0.08 0.59 0.34

Coef.

0.77 0.76 0.76

0.68 0.70 0.65 0.65 0.69 0.77 0.71 0.73 0.74 0.67 0.67 0.74 0.74 0.73 0.67 0.70 0.67 0.66 0.84 0.72

R-sq. 0.24 0.58 0.44 0.46 0.59 0.26 0.54 0.78

0.27 0.30 0.25 0.29 0.27 0.24 0.24 0.29 0.40

0.34 0.31 0.25 0.34 0.33 0.40 0.41

Health expenditure Life expectancy Low prevalence of malaria Low incidence of tuberculosis Low infant mortality rate Primary school enrollment Secondary school enrollment Gender-related development index (UN) Political institutions Effectiveness of law-making bodies Public trust of politicians Government spending efficiency Lack of favoritism in decisions of gov officials Gov. effectiveness in reducing poverty/inequality Transparency of government policy-making Decentralization of economic policy-making Freedom of the press Voice and accountability (WB) Rule of law Safety – Reliability of police services Safety – Low business costs of crime/violence Safety – Low impact of organized crime Judicial independence Efficiency of legal framework Property rights Infrequency of diversion of public funds

Coef.

0.74 0.72 0.69 0.73 0.73 0.76 0.76

0.71 0.71 0.69 0.71 0.70 0.69 0.69 0.71 0.75

0.69 0.81 0.77 0.76 0.80 0.69 0.80 0.89

R-sq.

212

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(continued)

0.50 0.42 0.38 0.52 0.52

Coef. 0.80 0.76 0.75 0.81 0.82

R-sq.

Gov. surplus/deficit (prior 3 years moving avg) Gov. net debt (prior 3 years moving avg) Inflation (prior 3 years moving avg)

Macroeconomic policy (MP)

MICROECONOMIC COMPETITIVENESS (MICRO)

0.13 0.06 0.30

Coef.

Notes: Table reports the standardized coefficients. Pooled OLS estimation using a panel of countries over 2001–08; and controlling for endowments and year fixed effects.

Infrequency of irregular payments by firms Low business costs of corruption Ethical behavior of firms Control of Corruption (WB) Rule of Law (WB)

Table 8A.1

0.65 0.64 0.72

R-sq.

PART III

Innovation and Value Chains

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9.

Outward FDI from developing country MNEs as a channel for technological catch-up* Alessia Amighini, Roberta Rabellotti and Marco Sanfilippo

1.

INTRODUCTION

An increasingly important aspect of globalization is the growing number of developing country multinational enterprises (MNEs).1 This is demonstrated by the annual Fortune ‘Global 500’ ranking of the top 500 MNEs across the world: in 2009, 86 companies in the list were from developing countries, compared to 69 in 2007 and only 19 in 1990. These companies are small relative to the world’s largest MNEs, they are owned by developing country nationals (in some cases with government a major capital shareholder), and operate on a global basis through subsidiaries, outsourcing and integration in global value chains (GVCs) and global production networks (GPNs) (UNIDO, 2006). According to the United Nations Conference on Trade and Development  (UNCTAD, 2009), outflows of foreign direct investment (OFDI) from developing and transition economies reached 19 per cent of world total in 2008. Asia has the highest level foreign direct investment (FDI) outflows, but this trend is spreading to all regions. In terms of stocks, developing countries account for more than 15 per cent of the world total, with the following regional composition: Asia 65.7 per cent of total stock, followed by Latin America with 21.7 per cent, the transition economies with 8.7 per cent and Africa with 4 per cent. Within each region, a few countries play the leading role: China, India and the Association of South East Asian Nations (ASEAN) countries in Asia; Mexico and Brazil in Latin America; Russia among the transition economies; and South Africa in Africa. With regard to the sectors involved, the concentration of FDI is high in services and, more recently, natural resources. The typology of investments varies widely across countries and sectors. Emerging country MNEs usually invest through mergers and acquisitions 215

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Table 9.1

Innovation, global change and territorial resilience

Top 100 MNEs worldwide and from developing countries Top 100 MNEs worldwide 2007

Assets ($ billion) Foreign Total Foreign as % of total Sales ($ billion) Foreign Total Foreign as % of total Employment (thousands) Foreign Total Foreign as % of total

Top 100 MNEs from developing countries

% change 2006–07

2007

% change 2006–07

6 116 10 702 57.0

16.60 15.80 0.40

767 2 186 35.0

34.3 29.0 1.4

4 936 8 078 61.0

21.00 14.00 3.60

737 1 617 46.0

21.8 24.0 −0.8

8 440 14 870 57.0

−1.66 −3.40 0.98

2 638 6 082 43.0

22.6 15.9 2.4

Source: UNCTAD (2009).

(M&A) in industrialized countries to get access to technologies, knowhow, skilled human capital, globally recognized brands and market opportunities. Greenfield investments are frequent in other developing countries, with the notable exception of a large number of direct investments in the natural resources sectors, where joint ventures with local players and acquisitions are more common (UNCTAD, 2007). The United Nations Conference on Trade and Development (UNCTAD,  2009) compares the 100 largest non-financial MNEs with the top 100 from developing countries, based on some key indicators and degree of internationalization. Table 9.1 reports some of these indicators showing that, although differences are still large, the international profiles of MNEs from developing countries is increasing, especially when foreign assets and employment are taken into account. As a consequence, overall level of internationalization, which UNCTAD measures through the composite transnationality index (TNI), shows rapid improvement among developing country MNEs and, in mature sectors, such as electrical and electronic equipment, is above the level of the top 100 MNEs worldwide (UNCTAD, 2009). The rapid rise of MNEs from emerging countries has attracted the attention of the business and economics literature, with an increased number of contributions and special issues of journals such as Journal of

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217

International Business Studies (2007), Journal of International Management (2007), International Journal of Technology and Globalization (2008) and Industrial and Corporate Change (2009), appearing after publication of the 2006 UNCTAD World Investment Report which first documented this new phenomenon. The aim of this chapter is to review this theoretical and empirical literature with a special focus on emerging MNEs as a channel for technological catch-up by their home countries. The literature on technological catch-up stresses that firms acquire technological capability through a combination of internal R&D efforts and access to external knowledge (Lee and Lim, 2001). The channels for accessing external knowledge are diverse and include informal learning, licensing, strategic alliances and FDI. The increasing importance of OFDI from emerging country firms, as reported above, makes a review of this literature interesting to derive empirical evidence on how such firms contribute to technological catch-up. The chapter is organized as follows. Section 2 summarizes the theoretical and empirical backgrounds to developing country MNEs. Section 3 explores how OFDI contributes to technological catch-up in emerging countries. Section 4 concludes and provides some directions for future research.

2.

WHAT IS SO SPECIAL ABOUT MNES FROM DEVELOPING COUNTRIES?

The literature on the international activities of firms is based mainly on observation of MNEs from the so-called triad (that is, the USA, the EU and Japan). Scholars such as Lall (1983), Tolentino (1992) and Wells (1983) investigated the first MNEs from developing countries (mostly Latin American and Asian), which appeared in the international market between the end of the 1970s and the beginning of the 1990s, but no ad hoc theories were developed. It is only recently, following a rise in OFDI activity by developing and transition economies, that a strand of literature has emerged arguing that some appropriate theory needs to be elaborated (among others see Child and Rodrigues, 2005; Goldstein, 2007; Matthews, 2002a; Sauvant, 2008). Traditionally, MNE theory has addressed such questions as why firms internationalize (Buckley and Casson, 1976; Vernon, 1966), why they do it through FDI (intra-firm) rather than through inter-firm modalities such as trade or licensing agreements (Hymer, 1976), and which modalities are favored along their internationalization processes (Johanson and Vahlne, 1977).

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The most influential approach to studying the international activities of MNEs is represented by the eclectic paradigm, originally proposed by John Dunning (1981). According to the so-called ownership–locationinternalization (OLI) framework, the decision of firms to expand their activities abroad via FDI depends on three kinds of advantages: ownership advantages, which represent the ownership of firms’ specific resources to be exploited externally; location advantages, which depend on the characteristics of the host country (for example, natural resource endowments); and internalization advantages, which depend on the opportunity to internalize firm specific advantages rather than exploit them in the market through arm’s length transactions. The OLI framework includes no specific provision explaining the pattern of internationalization of developing country MNEs and this has been criticized on two different grounds. First, because firms from developing countries might not possess the same competitive advantages as firms from developed countries and, thus: ‘If they invest abroad, it is not on the basis of “O”, and the parameters that determine the degree of “I” in their foreign operations are different’ (Goldstein, 2007, p. 81). According to this asset exploration view, firms internationalize in order to get access to the strategic resources they need, being motivated by ‘learning objectives that allow these firms to overcome the initial resource hurdles arising due to technological gaps and late mover disadvantages in international markets’ (Aulakh, 2007, p. 237). Moon and Roehl (2001) define these as unconventional FDI, that is, strategic investments in order to strengthen rather than to exploit the set of resources owned by the firm. Thus, internationalization becomes a strategy aimed at strengthening the firm itself based on the accumulation of resources previously not available.2 Second, and related to the first point, the OLI framework is a (comparative) static model, that takes into account only the existing advantages prior to the FDI decision, but does not explain the opportunities for the development and evolution of firm capabilities over time based on accumulated experience in the international market. The main criticisms of this view, are based on the knowledge based (Kogut and Zander, 1993) and dynamic capabilities approach (Teece et al., 1997), both of which are extensions of the resource based theory of the firm (Barney, 1991). Based on these criticisms, Mathews (2002a) proposed an ad hoc theoretical framework, founded entirely on the observation of a group of dynamic firms originating in the Asia-Pacific region, referred to as the ‘Dragon Multinationals’, and which are recognized by several international organizations such as UNIDO (2003; 2006) and the Organisation for Economic Co-operation and Development (OECD, 2006). In a number of successive

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works Mathews (2002a; 2002b; 2006a; 2006b; 2006c; 2006d) focuses on the adoption of a resource based analysis of what – in his opinion – is not explained by the existing theories (and especially the eclectic paradigm). To take account of the fact that MNEs from emerging countries often do not possess stocks of domestic assets that can be exploited abroad, but rather that international expansion is aimed at the search for new resources, Mathews (2002a) proposes the so-called ‘linkage, leverage, and learning’ (LLL) framework. Linkages, such as joint ventures, strategic alliances and other forms of collaboration in global value chains with foreign companies (the incumbents) represent a fast and efficient way to access the resources that emerging MNEs lack. Once linked, ‘latecomer’ firms use their global connections to leverage their resources and particularly their cost advantages, and to learn about new sources of competitive advantage and how to operate internationally. Within this framework, the global economy is described as a set of resources available to firms, and internationalization is defined more broadly as: ‘the process of the firm’s becoming integrated in international economic activities’ (Mathews, 2006b. p. 16). Unlike the predictions of the OLI framework, the first phase of MNE formation is most likely to be motivated by asset-exploring rather than with asset-exploiting reasons. Moreover, in the early stages, this internationalization process is often interlinked to inward FDI activity at home (Li, 2007), which provides local firms with the unique chance to enter into established global production networks, enhancing their capabilities (Chen and Chen, 1998; Hitt et al., 2000; Makino et al., 2002). Luo and Tung (2007) stress the capacities of emerging country MNEs to take advantage of inward FDI (via original equipment manufacture, joint ventures or participation in global value chains, which, in turn, allow firms to develop their own capabilities and to become more competitive abroad through experiential learning. This depends on the capacity of firms to leverage external resources, which is dependent on the extent to which foreign firms are willing to share their resources, and on domestic ‘absorptive capacity’, defined as the ability of the firm to identify, assimilate and exploit external knowledge (Cohen and Levinthal, 1990). According to Zhang (2009), the role of foreign MNEs through technological spillovers, knowledge transfers and the establishment of forward and backward linkages, is a sound opportunity to enhance absorptive capacity during the ‘pre-catching up’ stage. Indeed, empirical analyses of the determinants of emerging country OFDI find that inward FDI play a significant and positive role in explaining the internationalization of local MNEs (Banga, 2009; Pradhan, 2009). An excellent example of this process is Asian subcontractors in the information technology (IT) and the electronics sectors, which:

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have prospered as contract manufacturers, most visibly in the fields of information technology and consumer electronics. In the process, through their own learning and innovation efforts, many of them are becoming original design manufacturers (ODMs) and original brand manufacturers (OBMs), in a pattern of development and internationalization. (UNIDO, 2006, p. 18)

The innovative contribution of the LLL framework for the analysis of emerging country MNEs has been widely debated in the literature. The main criticism is that the focus is almost exclusively on firms originating in the fast growing countries in the Asia Pacific region, making it difficult to extend it to developing countries generally (Narula, 2006). Also, based on the growing empirical evidence, it seems that some latecomer firms might possess certain unique, different from the traditional, competitive advantages that explain their internationalization strategies (Dunning, 2006). Dunning et al. (2008) acknowledge a relative lack of firm-specific O-advantages in developing country firms and highlight the importance of country specific ownership advantages in determining these outward FDI activities. Moreover, Dunning and Lundan (2008) recognize the importance of institutions as an essential component in the internationalization process of firms and, consequently, have incorporated some institutionally related variables into the three initial components of the eclectic paradigm. According to the literature on latecomer firms, the role of home country institutions and particularly government, is key to shaping the process of internationalization of domestic firms (Ramamurti, 2008), and especially in the case of Asian firms (Buckley et al., 2008). In the case of China, the role played by government has been stressed repeatedly in the literature since many Chinese MNEs are state-owned enterprises (SOEs); at the same time, the Chinese government has also supported some selected private firms through instruments such as preferential loans, easier and cheaper access to capital, favorable tax regimes, selection of international partners for joint ventures in order to make them internationally competitive (Athreye and Kapur, 2009; Buckley et al., 2007; Child and Rodrigues, 2005; Li, 2007; Liu and Tian, 2008). Reporting on the case of Haier, Duysters et al. (2008) outline the importance of the support provided by central government through direct financial contributions and its role as ‘supporter and organizer of technology networks’ to enhance the company’s technological capabilities. Yiu et al. (2007) provide an empirical assessment of the rise in international venture activities in a sample of Chinese firms. They include in their analysis institutional variables such as linkages with domestic institutions (that is, central and local governments, financial institutions, trade associations, research centers)

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and participation in business networks. These variables play a statistically significant role in the internationalization process. On the basis of their empirical findings, they conclude that, for firms in countries at an early stage of development, the presence of institutional network ties represents an outstanding ownership advantage on which to base international activity. Analysing the case of Huawei, Zhang (2009) finds that one of the main determinants of its global success was the strong network of alliances that the firm was able to create with local universities, which, in turn, contributed to enhancing the company’s absorptive capacity. State support and formal and informal institutional network ties represent a competitive resource for the international activities of domestic companies in a number of other countries, see Goldstein and Pananond (2007) on Singapore and Thailand, Kim and Rhe (2009) on Korea, and Kalotay and Sulstarova (2008) on Russia. Finally, for the Indian pharmaceutical sector, Athreye and Godley (2009) and Chittoor and Ray (2007) stress the relevant role of the Indian government in promoting the establishment of many MNEs in the high-technology sectors, through investment efforts and regulatory activities. With regard to other specific advantages of emerging MNEs, Mathews points out that the same condition of being a latecomer in the international market may represent an advantage in itself for firms engaged in the process of internationalization. This is related to access to low cost labor and, in some cases, low cost access to natural resources (for example, Brazil and Russia), but also, for instance, to the opportunity to access advanced technologies and innovations (through imitation) and, thus, to catch-up more rapidly (Mathews, 2006b). Cuervo-Cazurra (2007) stresses that developing country MNEs enjoy greater competitive advantage compared to MNEs from developed countries, in the more difficult institutional environments, such as characterize the group of the least developed countries. According to these authors, developing country MNEs are able to take advantage of their familiarity with a context with poor institutions, and turn their relative disadvantage into advantage. Also, developing country MNEs possess the technological capabilities useful for operating in a developing country context, as highlighted in a study by Kumar (2008) on India, in terms of ‘frugal engineering’ endowing the ability to manufacture low-cost versions of goods for mass markets. From what it has been said so far, we see that the internationalization process of companies in developing countries is characterized by some very relevant peculiarities with respect to what is proposed by the traditional framework for studying MNEs. Acknowledgment of these peculiarities combined with increasing empirical evidence on this phenomenon, is generating a new and interesting stream of literature. In the next section,

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we focus on how this literature contributes to enhancing the knowledge on emerging MNEs as a channel for technological catch-up.

3. INTERNATIONALIZATION AS A STRATEGY FOR TECHNOLOGICAL CATCH-UP 3.1

Technological Catch-up in Developing Countries

Technological catch-up has always fascinated economists. The spectacular performance of the newly industrialized countries (NICs) in Asia animated  debate and encouraged novel conceptualizations of economic growth and structural change. The Asian experience cannot be explained as the result of the import and adoption of technologies and organizational models developed in advanced countries, as implied by the theory of economic growth that prevailed in the 1950s and 1960s. A large body of investigations on Asian NICs is challenging the view that catching up is basically a question of relative speed, in a race along a fixed track, in which latecomers take advantage of mature technologies, forerunners’ experience and reduced market uncertainty (Perez, 1988). The very broad literature on technological catch-up has shifted the emphasis from resource endowments and comparative advantage, to institutional variables, building up of capabilities and dynamic creation of competitive advantage.3 While the role of government versus market was central to some of the earliest studies on latecomer Asian firms (Amsden, 1989), later work emphasizes the important role of other factors than institutional setting and government in the catch-up model. In particular, the innovation system (IS) approach makes it clear that technological change is affected by firm-specific efforts and systemic interactions with other firms, technology organizations, universities, research and development (R&D) laboratories, research institutes and financial institutions. It has also been shown that the IS approach needs enrichment by the international dimension (Bunnell and Coe, 2001; Carlsson, 2006) and, in developing countries, this argument becomes even stronger (Pietrobelli and Rabellotti, 2009). Indeed, the extra-national influences on the innovation process are particularly crucial given that new frontier innovation is rarely created in developing countries and the bulk of knowledge and technology has to be imported. Technology imports played an important role in the technological catch-up of the earlier latecomer firms in Japan, South Korea and Taiwan, and are playing a similar role in current latecomer developing country firms’ catch up. However, in terms of the attitude towards imported

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technology, there are important differences in the catch-up models in the current developing country MNEs and earlier latecomer firms, for example, from South Korea. The Korean catch-up model can be described as a three-stage model (Kim, 1997): the first stage is acquisition of mature technology from developed countries; in the second stage, firms acquire process development and product design capabilities; and in the third stage, firms develop their own product innovation capabilities through significant R&D investments. Korean firms invested heavily in assimilating imported technology, much of it originating from Japan. Also, a specificity of the Korean model is that government restricted FDI in favor of foreign technology licensing and government procurement policies. In the words of Liu (2005, p. 8): ‘they imported foreign technology but did not innovate together with foreign companies. They focused on in-house R&D to be able to improve imported and “mature” foreign technology gradually; and did not simply rely on foreign technology for their new products’. Compared to the experiences of Korean firms in the past, current developing country MNEs (especially Chinese companies) are putting less effort into the assimilation of foreign technology and more into innovation (Liu, 2005). In the case of China, although companies have relied on reverse engineering as a learning and development strategy, the fragmentation between technology users and technology within the NIS is one of the main reasons why Chinese firms have not been able to master and innovate based on imported technology, as rapidly as their earlier counterparts in South Korea (Liu and White, 2001). The catch-up model of Chinese MNEs is described by Liu (2005) as two-stage. In the first stage, firms acquire technology from abroad (mainly through imports or inward FDI) and exploit it to pursue market-oriented product innovation, benefitting from lower production costs. In particular, and in contrast to the Korean experience, China has relied heavily on FDI to access foreign technology, admitting foreign firms conditional on their signing up to joint-ventures with domestic firms on order that the latter can benefit from interacting with more advanced technology suppliers. In the second stage, Chinese companies are trying increasingly to improve their technological capabilities through international technology alliances and M&A with firms in developed countries. Another important specificity of the catch-up model of current developing country MNEs compared to earlier latecomer MNEs, is related to the global context in which firms operate. Compared to the 1960s and 1970s when Korean firms started to expand, the current business environment is radically different. The modularization of production in a growing number of sectors, favored by information technology and technological progress

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has enabled the disintegration of production processes, allowing the outsourcing of several activities, including both production and design and R&D. This phenomenon has two major consequences for the context in which developing country MNEs operate. First, developing countries are increasingly becoming the location of R&D and high-technology activities and not only of mature technology, as was the case in earlier decades according to the product life cycle theory (Vernon, 1966). This makes it possible for firms in developing countries to become acquainted with new technology at an earlier stage, and to learn from its application. Secondly, it is not necessary for developing country firms to master the entire production process from R&D to manufacturing of components, assembly, logistics, marketing and after-sales service; they can decide to specialize in just one activity. This strategy enables latecomer firms to outsource abroad those activities (usually the most skill and technology intensive) for which they lack the necessary capabilities. Therefore, strategic OFDI in developed countries is a frequent option for many latecomer MNEs. The next section provides a review of the literature on OFDI from developing country MNEs, to investigate its importance for accessing knowledge and enhancing learning and innovation. 3.2

How OFDI Can Contribute to Technological Catch-up: Some Empirical Evidence

In developing countries, access to external knowledge is considered a key factor for technological catch-up and OFDI is becoming a popular strategy for speeding up this process. Lee and Lim (2001) propose an interesting model to explain how Korean industries have been able to catch-up technologically on the basis of a combination of their existing knowledge base and their technological effort. With particular reference to the case of D-Ram production, Lee and Lim stress the key role played by access to external knowledge through R&D outposts in Silicon Valley. Mu and Lee (2005) apply this model to the telecommunication industry in China, again emphasizing the role played by external strategic alliances in technological catch-up. The position of ‘latecomer’ MNEs within global and regional networks is stressed by Mathews (2006a) as one of the peculiar ‘ownership’ assets characterizing companies from developing countries in their internationalization process. According to Ramamurti (2008), a group of ‘global first-mover’ developing country MNEs, operating mainly in the high-technology industries (for example, Embraier in the aircraft industry, Huawei in telecommunications, Suzlon Energy in wind power) has been able to jump some technological stages and grow fast by adopting

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a strategy of greenfield investment in emerging countries, and M&As in developed countries. Strategic acquisitions provide a faster alternative to building technological capabilities in house, and, especially for developing country firms, allows access to more advanced resources through direct transfer of knowledge (Pradhan and Singh, 2008). Empirical evidence confirming the acquisition of strategic assets through foreign acquisitions at earlier stages of development is provided by Niosi and Tschang (2009) for Indian and Chinese software firms. And in a study of a sample of Indian firms, Elango and Pattnaik (2007) show that rather than building capabilities for international operations following a sequential process (as suggested by the Uppsala model of internationalization), these companies have been able to enter the international market more quickly through extensive exploitation of foreign partnerships in established networks of firms. This pattern of rapid internationalization characterizes the several well known MNEs such as Acer from Taiwan and Cemex from Mexico (Mathews, 2002a), Samsung from Korea (Lee and Slater, 2007), Tata from India (Goldstein, 2008) and the three Chinese ‘global champions’ in the electronic industry – Haier, Lenovo and TLC (Li, 2007). Similar to other Chinese companies, Haier based on its strategic capacity to participate in more advanced networks of firms and its level of absorptive capacity, has been able to ‘leapfrog’ some of the stages of internationalization (Li, 2007). Bonaglia et al. (2007) also describe an ‘accelerated’ internationalization pattern of three MNEs in the white goods sectors of China, Mexico and Turkey. In a study on the Indian pharmaceutical sector, Athreye and Godley (2009) stress the importance of foreign acquisitions to tap into more advanced resources missing in the home market. The acquisition of strategic assets, such as technology, know-how, managerial and marketing skills, recognized brands and reputation, is one of the classical motivations for OFDI, and is dominant among MNEs from developing countries that invest in developed countries (UNCTAD, 2006). These OFDI aimed at sourcing assets not fully developed at home are reversing the traditional direction of knowledge flows (that is, from parent to subsidiary) (Narula, 2010). Some recent empirical evidence on Chinese OFDI, in countries such as the UK (Buckley et al., 2007; Cross and Voss, 2008; Liu and Tian, 2008), Italy (Pietrobelli et al., 2010) and Germany (Schüler-Zhou and Schüller, 2009), confirms the relevance of strategic asset seeking motivations. Based on a survey of Chinese companies in the UK, Cross and Voss (2008) find that the main reasons for internationalization are the need to acquire new and advanced management skills and to tap into pools of knowledge. Further empirical evidence on these motivations is provided by case studies on Chinese MNEs such

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as Haier, Lenovo, BOE and TCL (Li, 2007; Liu and Buck, 2009). For evidence on other countries, several studies stress the importance of strategic asset seeking motivations by MNEs from Taiwan (Makino et al., 2002), Mexico, Poland and Romania (Hitt et al., 2000) and Brazil (Carvalho et al., 2010). In a recent study of Chinese investments in Italy, Pietrobelli et al. (2010) show that Chinese investments in this country are motivated by market seeking given that Italian consumers are considered very demanding and particularly sophisticated. In sectors such as domestic appliances, Italy is seen as a test market for products that will be suitable for the European market in general. Location in Italy is strategic in terms of catching up with European tastes and requirements, of quality of products, design and post-service assistance. In interviews conducted by Pietrobelli and colleagues the authors, Chinese managers stressed the importance of being close to consumers in order better to understand their needs and their culture and to receive feedback. The importance of being embedded in an industrial area with an established old manufacturing tradition was one of the reasons for Haier’s choice to locate its European headquarters in Varese. The area of Varese is well known for its white goods production and is home to other important companies, such as Philips and Whirpool, and firms specialized in components and intermediary phases. The agglomeration of several specialized firms generates positive externalities arising from the presence of a pool of specialized workers and suppliers and specialized knowledge on markets and technologies. These agglomeration advantages attracted Haier and influenced its decision about where to establish its European headquarters (Duysters et al., 2008). Haier in Italy has made two acquisitions: the Meneghetti refrigerator plant in 2001 and Elba cooking appliances in 2009. Another case of Chinese acquisition in Italy is Benelli, an established motorcycle producer which, at the time of its acquisition (2005) by Quianjiang, was in serious financial trouble. Alongside the desire to acquire a well-known brand, the deal was aimed at the acquisition of Benelli’s manufacturing and R&D facilities and it has become Quianjiang’s European R&D centre for highquality production (Pietrobelli et al., 2010). The strategy of M&A is becoming increasingly common among emerging MNEs. The intensification of cross-border M&A activities is primarily motivated by the desire to rapidly obtain and control strategic assets. This is confirmed by Tata’s main acquisitions discussed in Goldstein (2008), who points out that they were aimed at improving the company’s position in higher value activities in some of its operational sectors, and gaining a foothold in more advanced markets. Focusing on the case of Tata’s

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automotive division and another big Indian automotive group, Amtek, Pradhan and Singh (2008) show that OFDI represent a source of crossborder knowledge flows. In a succeeding empirical analysis the authors show that Indian OFDI is a significant determinant of the domestic R&D performance of Indian automotive firms, especially when directed to developed countries. Based on case studies of companies such as Lenovo, Huawei, Haier and TCL, Deng (2009) and Rui and Yip (2008) analyse the rationale for foreign acquisition activity, emphasizing that it offers a means to compensate for competitive disadvantage and is a low-cost way of leveraging advantages in production capabilities (for example, the case of Lenovo) and the institutional support received for these operations. Rui and Yip (2008) rightly stress the difficulties involved in these operations and the importance of culture and management capabilities for their success. Referring to the well known cases of Lenovo and Huawei, they emphasize that the capacity to integrate and combine Chinese culture with world class Western management systems, is key to the success of these acquisitions. Therefore, although many firms in developing countries hold considerable amounts of financial resources which makes it relatively easy for them to acquire advanced country companies that find themselves in financial distress, some difficulties with respect to managerial styles and business culture might represent a constraint to the rapid acquisition of knowledge and capabilities and, therefore, to technological catch-up.

4.

CONCLUSIONS

The significant increase in internationalization among firms from developing economies has attracted the attention of business scholars and economists. In this chapter we focused on how outflows of foreign direct investment from developing countries, particularly directed to developed countries, can contribute to technological catch up. Outflows of foreign detect investment do indeed represent an increasingly important channel to access knowledge and to build key capabilities in fields such as technology, design, management and marketing. The empirical evidence is growing and shows that much OFDI from countries such as China and India, is based on strategic asset seeking motivations and the need to acquire direct knowledge about more sophisticated markets in developed countries. Emerging MNEs, through greenfield investments but increasingly through acquisitions, undertake early internationalization in order to tap into technological, managerial and market knowledge and human

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capital that is available in the developed countries, to acquire the resources that are lacking or in short supply in their home countries. The literature includes a number of case studies showing the relevance of this channel for catch up. However, this line or research is new and we can draw no definite conclusions. Moreover, there are some biases because many analyses are focused on a few selected case studies of successful companies, from a limited number of countries, and a limited number of sectors. More robust empirical evidence and collection of appropriate data are needed. There is also an urgent need for robust empirical research on the determinants of the different internationalization strategies through outward FDI by developing country MNEs. These determinants are likely to vary depending on a number of factors including industry and country characteristics. First according to the sector in which they operate and the degree of modularity of production, as pointed out by Lee and Lim (2001), the nature of the innovative activities of firms trying to catch-up depends on the technological regime in their industries. Regimes where innovation is more predictable and frequent are thought to give latecomers more opportunities to catch up. However, given that this prediction is based on the Korean experience, which followed a different path of catch up with respect to the current emerging countries, it might be that outward FDI might allow firms to bypass the characteristics of the technological regimes of their industries. Moreover, modularity of production may be making it possible for latecomer firms to catch up in sectors with a higher technology content and where innovation is less predictable. More research is needed to address this question. Second, according to the characteristics of the innovation systems of their home countries, the opportunities for catch-up through OFDI may change. It is possible that countries with more developed IS are less motivated to enter foreign involvement than countries with weaker or less efficient NIS. But it could also be that a well developed IS is a condition for building domestic technological capability and, therefore, generating MNEs with a sufficient level of absorptive capacity. This, link between IS and OFDI would make another interesting line of research.

NOTES *

Financial support from Compagnia di San Paolo, CASCC (Centro Alti Studi sulla Cina) and PRIN 2007 is gratefully acknowledged. 1. Although most of these MNEs originate from emerging economies, throughout this chapter we use the terms ‘developing’ and ‘emerging’ country MNEs interchangeably. 2. This point has been widely stressed in the literature. See among others, Chen and Chen

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(1998); Child and Rodrigues (2005); Li (2007); Luo and Tung (2007); Makino et al. (2002); Yiu et al. (2007). 3. It is not possible to review this very rich literature here, we note the IS approach (for a recent focus on IS in developing countries see Lundvall et al., 2009) and Sanjaya Lall’s major contribution to this field (see among others Lall, 1992; 1993; 2001b).

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10. Heterogeneous social capitals: a new window of opportunity for local economies Mario Davide Parrilli 1. INTRODUCTION In this chapter we build on the seminal work of Bathelt et al. (2004) who suggest that the frequently used concept of global knowledge pipelines embodies not only the concept of codified knowledge flow, but also of tacit knowledge flow. In particular, we add two main propositions: the first is about the relation between tacit knowledge (TK) flow and social capital (SC); TK is not only about individual technical and experiential knowledge transfers but also about collective values, norms and attitudes of the people and workers that compose the local production system. In this sense, we benefit from the knowledge typology developed by Blackler (1995) that introduces the concepts of ‘encultured knowledge’ and ‘embedded knowledge’ to express these aspects of collective knowledge bases. Our second argument is that these strengths can be reaped not only from highly skilled human capital (that is, scientists and engineers), but also from a broader spectrum of workers some of whom belong to the local production system whereas others migrate from other locations. As a consequence, in our view global knowledge pipelines (GKP) bring in TK flows that modify former social homogeneity (Becattini, 1990; Maillat, 1995), breaking common understanding of issues but also offering new opportunities to enrich the local buzz (the dense exchange of information and knowledge that takes place at the local level between neighbours, economic agents and the like) through new sources of external knowledge. This process is facilitated with the inflow of both skilled and unskilled people. This opportunity needs to be explored in detail, which is what we attempt in this exploratory chapter.

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THE CONTEXT: LOCAL PRODUCTION SYSTEMS AND THEIR GLOBAL CHAINS/NETWORKS

The basic subject of our analysis refers to local production systems (LPS) such as clusters and industrial districts. It is 30 years at least since they were recognized as fundamental collective agents of local and, in some cases, national development. The literature on industrial districts (Piore and Sabel, 1984; Becattini, 1990, among others) and on clusters (Porter, 1990; Schmitz, 1995) has displayed the immense advantages of being part of dynamic clusters for social, institutional and economic development (for example, reaping benefits from cooperation and external economies). However, it is about ten years since some academics and specialists realized the limitations of the analysis of LPS such as districts and clusters once it because clearer that they are part of powerful globalized markets (Humphrey and Schmitz, 2004). A broader perspective needs to be taken on board as development dynamics are strongly influenced by the participation and position that these LPS play in global markets. In fact, the most novel and relevant knowledge is not fully contained in the locality; rather it is produced in several locations, thus local agents have to be anxious to absorb it wherever it is produced (Bathelt et al., 2004; Isaksen and Karlsen, 2010). In addition, the LPS and its firms are not always market leaders; they may assume different positions in the global market, that is, they may be specialized suppliers, market leaders or satellite platforms (Markusen, 1996; Guerrieri and Pietrobelli, 2004) or even marginal actors such as survival clusters (Altenburg and Meyer-Stamer, 1999; Parrilli, 2007). This is why a number of academics studied these relationships and developed new economic concepts, such as that of global value chains – GVC – (Humphrey and Schmitz, 2004; Gereffi and Korzeniewicz, 1994) or that of global production network – GPN – (Ernst and Kim, 2002; Yeung, 2009) with a specific objective of capturing the participation and potential development trajectory of LPS within such broader economic frameworks/landscapes. Each of the above-mentioned concepts have their specific rationale; the first concepts of the French ‘filière’ (ADEFI, 1985) and the American ‘subsector’ (Boomgard et al., 1992) were rather focused on the inward-oriented production chain, with little focus on their foreign linkages. The objective was to analyse what local actors participate in the chain, their capacity to add value across phases, and what actors have the leading role within it (including foreign buyers). The subsequent formulation of the global commodity chains and the global value chains put a strong emphasis on the international linkage to final markets and strong players within such markets such as multinational companies and/or hub industries (Gereffi

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and Korzeniewicz, 1994; Humphrey and Schmitz, 2004). Within this approach the governance of the system is very important because it determines the room for development and/or upgrading that local players (for example, clusters) can play in such global value/commodity chains. The characterization of the global production network goes beyond the conceptualization of adding and controlling value across the chain; it prefers emphasizing the changing geography of production across the world, the different elements that participate in such networks that include relevant cultural and social drivers; the substitutability of participants, and the new dynamics and strategies developed as a means to conquer more competitive positions (Ernst and Kim, 2002; Coe et al., 2008; Yeung, 2009). The latter concepts (GVC/GPN) proved to be quite flexible as they offer the opportunity to go beyond production and trade exchanges to include also knowledge flows. The Nordic/Scandinavian literature on innovation stresses the importance to associate these concepts with that of ‘global knowledge pipelines’ (GKP) as conveyors of knowledge that enrich the local production and innovation dynamics, which may otherwise get too inward oriented and stagnant, that is, locked-in (Saxenian, 2002; Bathelt et al., 2004; Lundvall, 2007b; Lundvall and Lorenz, 2012). We consider that the contribution of GKP needs to be enlarged in order to approach the wider dynamics that operate in GVC/GPN. Many authors, especially those linked to the industrial district literature, stress the importance of absorbing scientific and/or technical knowledge abroad, mostly as codified knowledge, whereas tacit knowledge being stickier is available in situ (for example, Becattini, 1990; Guerrieri and Pietrobelli, 2004). Following Bathelt et al. (2004), we argue that also tacit knowledge (TK) can be conveyed through global knowledge pipelines and that this international knowledge can have important effects on local economies. These aspects also raise issues and challenges for the sustainability of localized pools of TK and the related SC. However, we suggest broadening the spectrum of the relevant TK flows across countries and systems. The definition of TK includes a set of experiential knowledge that is not automatically transferable from one person to another since this has to be seen, practised, discussed and improved little by little through further practice (Polanyi, 1966); simultaneously, the concept of SC refers to experience-based values, traditions, norms, routines, practices that are socialized across agents and people in a specific environment and that set the frame for trust and cooperation that may help deliver economic transactions in an efficient way (Putnam et al., 1993). The connection between the two concepts is critical in our view. Both concepts reflect experiential knowledge based on practice; the difference can be found in the individual versus collective capability of the two; this is why we suggest

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considering SC as a meaningful root of TK, the collective and interactive part of TK, whereas TK may be represented as the individual part of SC, the individual practical knowledge of routines and forms of operating in the economic environment (Lundvall, 1992, Lundvall et al., 2002). In this work we discuss whether and how this knowledge can be constructively transferred across GKP. In Bathelt et al. (2004), TK exchanges across knowledgeable agents are the ones that matter; in their view, ‘the processes behind the establishment and maintenance of global pipelines must be predesigned and planned in advance, and they require specific investments. This involves a complex and costly process. One of the first decisions to be made here is the selection of external partners’ (p. 43). In a way, this represents a rather selective approach to TK flows (see also Gertler, 2003, p. 81). In our view instead, most agents, including blue-collar workers and ethnic minorities that are part of the LPS, have the capacity to participate in GKP in a way that enriches the related TK flows and capital. In this regard Gertler’s proposition seems to be quite adequate (2003, p. 82): ‘the ability to appropriate tacit knowledge in the workplace depends on the social relations surrounding production’, which stress the suggested, implicit connection between SC and TK. With this project in mind we thus propose a view to connect TK with SC and its changing conditions in the current global economy. This approach helps us extending the concept of the knowledge economy to an economy that involves not only scientific knowledge roots, but also tacit knowledge bases that change over time, and that need to be taken into account to plan further developments of the global society.

3.

TACIT KNOWLEDGE FLOWS AND CHANGING SOCIAL CAPITAL

Contemporary studies that focus on the flowering knowledge economy complement former above-mentioned analyses that were developed within streams of development economics. These recent contributions (Asheim and Isaksen, 2002; Saxenian, 2002; Bathelt et al., 2004; Asheim and Coenen, 2005) underline the importance of ‘global pipelines’ as conveyors of key knowledge inputs, often codified – though not only – analytical knowledge inputs that represent a means to increase the capacity of local economies to produce higher value-added products, designs, brands, competitive processes and organizational forms. In their view, if the local buzz ‘generates opportunities for a variety of spontaneous and unanticipated situations where firms interact and form interpretative communities . . .

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the advantages of global pipelines are instead associated with the integration of multiple selection environments that open different potentialities and feed local interpretation and usage of knowledge hitherto residing elsewhere’ (Bathelt et al., 2004, p. 42). They emphasize the importance of finding a right balance between working through ‘local buzz’ and ‘global pipelines’ because both of these knowledge pools can be part of a ‘tradeoff between a too much inward-looking and a too much outward-looking organizational structure. In the first case, knowledge is easily transmitted throughout the firm, but the new external sources can be difficult to comprehend’ (2004, p. 46). Overall, an approach that pulls them adequately together is thought to maintain and even sustain the regional competitive advantage based upon specific competences and capabilities developed in local production sectors and/or clusters (Cooke, 2006b; Asheim et al., 2008). Within this framework, relatively less attention has been given to the flow of people that embody knowledge, both codified and tacit. In a restrictive sense, knowledge is codified and technical knowledge based on scientific and technological personal competences. However, knowledge is much more than this. As Lundvall and others showed (Johnson and Lundvall, 1994; Lundvall et al., 2002; Jensen et al., 2007), there is a whole part of TK that is hardly reducible to scientific and technological knowledge, since it entails not only principles and technicalities, but also social capabilities, trust and/or social capital (that is, ‘org-ware’ and ‘soc-ware’ in Lundvall, 2007b). The latter include constructive values, norms and attitudes as well as the capacity (of the local people) to trust each other, and to work together to achieve common objectives and goals. Thus TK is not only about technical knowledge (that is, know-how) incorporated through learning-by-doing and by-using practices, and it is not only accumulated individual knowledge, but it is also very much social knowledge, social embeddedness and social relations (the ‘know-who’ part of knowledge, and the ‘collective pool of experiential knowledge’) that may also help producers to design collective development projects at the meso and macro level. In a way, it is consistent with the typology of knowledge developed by Blackler (1995) who distinguishes between embrained, embodied, embedded and encultured knowledge. Within this conceptualization embrained and embodied knowledge tend to be individual types of knowledge bases, the first being based on conceptual skills and cognitive abilities, and the second on practical thinking based, among others, on learning-by-doing; encultured and embedded knowledge tend to be collective pools of knowledge being based on shared understanding arising from socialization and bounded to contextual factors respectively, thus highly local-specific.

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Conceptual skills and cognitive abilities

Practical skills and learning-by-doing

Socially based shared understanding

Context-based social knowledge

Individual codified knowledge

Individual tacit knowledge

Collective tacit knowledge

Collective tacit knowledge

Human capital

Social capital

Source: Own elaboration.

Figure 10.1

Codified and tacit knowledge flows as part of human and social capital pools

In a way, if embrained knowledge responds to the definition of codified knowledge, the other three types are more in line with the definition of TK. Two of these meanings focus on and stress collective aspects of TK (strictly connected to the definition of SC) that are hardly taken into account in the conceptualization of TK flows across GKP (Bathelt et al., 2004). Figure 10.1 shows the correspondence that we envisage between the four types of knowledge identified by Blackler (1995) and the drivers of human and social capital for local development. Embrained knowledge is a type of individual codified knowledge, which differs from embodied knowledge that is more a tacit, although yet individual, knowledge capital. Blackler incisively identified two other forms of tacit collective

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knowledge capitals, encultured and embedded knowledge, which go beyond the individual and stress the significance of collective pools of knowledge that are the key components of the concept of SC within most approaches (Becattini, 1990; Putnam et al., 1993; Dasgupta, 2002). This correspondence points to the relevant role of SC within flows of knowledge across GKP and, implicitly, underscores the role of some (nonskilled) actors that are not usually considered as vectors of significant knowledge flows across GKP. The question that we want to address is related to the change of SC in the context of globalized markets/production networks. Local production systems are no longer islands; they are production systems inserted in wider contexts, in wide GPN in which they compete with many other production systems and (large) firms to maintain or to improve their position from the satellite, substitutable third- or fourth-tier supplier (that is, satellite platforms in Markusen, 1996) to that of specialized supplier and up to that of market leader. The global market is changing everyday; relationships are less stable than expected on the basis of traditional production (Coe et al., 2008). These changes affect the same social bases, since human resources are moving, thus introduce significant variations in the original homogeneous social fabric (Parrilli, 2009). Social atmosphere is currently more heterogeneous since important waves of migration take place and introduce new cultures and new production modes in former homogeneous environments. People from China, India, Eastern Europe, Northern Africa, Latin America and people from Europe are modifying the European social fabric generally as well as in specific local production systems. The famous district of Prato has already ‘embodied’ 30 000 citizens from China and 1000 firms owned by Chinese citizens (Smyth and French, 2009). Is it the same Prato district that it was 20 years ago? Can we talk again about trust and low transaction costs, verbal contracts or shall we rather accept the challenge of living within new social environments that need new kinds of ‘social contracts’, new values, new attitudes and new forms of coexistence in order to re-create the conditions for a constructive interaction across the local and global agents that participate in the production of this local system and, with this, in the production developed across GVC/GPN in which clusters and districts participate? As stated above, TK and SC are strictly interconnected since SC is both TK capital and the fluid that makes knowledge contents easier to flow and work more effectively and efficiently. Most analyses on knowledge flows across GKP seem to stress the importance of absorbing individual codified and tacit knowledge as these enrich the company knowledge pool and help the local system to upgrade its capabilities (Bathelt et al., 2004;

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Giuliani, 2005). In contrast, heterogeneous social fabric is politically and popularly considered a threat for local systems that might need to be limited through a number of local, regional, national laws and barriers that reduce this possibility (Alesina and La Ferrara, 1999; Lodigiani et al., 2003). However, this increasingly heterogeneous social fabric involves two aspects that have to be taken into account: (1) inevitability of change; (2) collective TK flows and capital. First, the changes that are taking place in western countries as in the global environment are inevitable. Too many global and local imbalances have been magnified by the increasing globalization of information and communication about lifestyles, welfare, market opportunities, which attract/catalyse important flows of population from poor countries to rich countries. Thresholds and control are being significantly enhanced with the involuntary effect of increasing the interest that various ‘lobbies’ have in the traffic of clandestine migrants that will be later exploited in traditional manufacturing, for example textiles and furniture, as well as subjugated within different ‘industries’ that include drug-trafficking and prostitution. The second aspect refers to a more thorough concept of knowledge. The increased heterogeneity of the (local) social fabric – due to SC enrichment of the local system inserted in specific GVC/GPN and GKP – involves a component of collective TK in terms of values, norms, traditions, routines, attitudes and aptitudes that may offer new development opportunities to local communities. An adequate reflection is needed on this aspect, which is too easily dismissed by many on the basis of sensitive protectionist demands, and in general not analysed in depth. Currently, this flow of people from different countries, cultures and social capitals are allowed with increasing intolerance in most western countries. These people tend to create their own communities that limit interaction with local people to the minimum in order to avoid social problems, fulfill current regulations, produce and sell their goods, and/or to obtain advantages derived from the variety of social services available in these societies to both incumbents and latecomers. In some cases, this poor interaction may even resemble the situation of closed communities. In the most advanced case, the new international mobility of human resources is analysed from the perspective of the ‘quantity’ of ‘human resources’ that are needed to substitute the elderly that are retiring from the labor market making the pension system sustainable in the future (Razin and Sadka, 2000), or that might be accepted to cover gaps in the labor market (for example, unpleasant jobs such as cleaners, carers for the elderly, and construction workers). Is it the whole opportunity offered by the exchange of human and social capital in globalized markets? Probably not; this is why we address this aspect here.

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4. TACIT KNOWLEDGE FLOWS AND SOCIAL CAPITAL: A FRAMEWORK FOR ANALYSIS The literature on GKP is discussed and merged with the conceptualization of TK flows. The literature on SC and social embeddedness is discussed as a new relevant input for this discussion; in particular we stress the importance of analysing SC not only as a basis for trust and cooperation, but also as a means to convey more easily codified knowledge and TK across global value chains and within local production systems. Here we merge this discussion with the need of local economies for a broader flow of workers and people that is today analysed from two limited perspectives: (1) the skilled human capital needed to increase the codified knowledge inputs into the local economies as a means to enrich the local innovation dynamics (Saxenian, 2002; Williams, 2007); (2) the young workforce needed to substitute the elderly that are retiring at a higher rate and that are jeopardizing the sustainability of the social security system for the elderly and the system as a whole (Razin and Sadka, 2000). Little attention is instead paid to (3) the heterogeneous social capital as a ‘potential’ catalyst of (tacit) knowledge flows and, as a consequence, of change and innovation. Instead of being perceived as a threat to the local tradition and social homogeneity (see, for example, Alesina and La Ferrara’s (1999) interpretation of heterogeneity and income inequality as barriers to participate in social activities), a richer, multifaceted SC could be perceived as a catalyst of change that needs to be understood, discussed, integrated and put in prospect as an opportunity for the local economy to introduce new wealth, rich features of different traditions that can be integrated only in specific social atmosphere or ‘creative knowledge environments’ based on tolerance as well as on valuing diversity and connectivity as assets rather than threats (Asheim, 2010). The case of Silicon Valley well represents this situation (Saxenian, 2002). The local production system (for example, the typical industrial district) is wrongly perceived as the basis of positive social capital only, that is, trust and cooperation among local socially homogeneous agents (Putnam et al., 1993; Lodigiani et al., 2003), whereas it should be conceived and analysed more thoroughly on the basis of the balance of positive and negative forces, since some of them represent a positive oil/fluid that eases transactions, while others represent a negative/ sticky glue that impedes wider exchanges and cooperation (Anderson and Jack, 2002). This aspect is particularly relevant in many local production systems across western countries that do not tend to recognize the limitations implied by the excessive homogeneity of social capital that may lead to the lock-in of the local system.

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In this sense it may be useful to adopt Krugman’s framework regarding centripetal and centrifugal forces (1998) and apply these to understand what forces strengthen the process of knowledge absorption and what forces weaken it. In terms of ‘centripetal forces’, the flow of knowledge, both codified and tacit, goes in tandem with the flow of people from other countries that may introduce a number of potential benefits such as: (a) different though enriching cultural values and routines (for example, Southern Europeans and Latin Americans bring in a stronger appreciation of social life that opens up new markets for leisure and services, North Americans exhibit a stronger attitude to risk bearing and entrepreneurship; Scandinavians and Northern Europeans in general bring in a deeper sense/capacity of collective responsibility that mirrors positive effects on the management of public resources, Chinese, Japanese and Asian in general show a stronger attitude to collective work for a common aim such as the development of infant industries or the reconversion of declining industries); (b) new social institutions that may contribute to build up a richer pool of collective knowledge, for example, the strength of the cooperative system in the rural environment in several European and Latin American countries, the strength of the relationship between university and industry in Anglo-Saxon contexts and of the triple-helix relation in Scandinavian countries, the capillarity of non-conventional credit institutions in some Asian and Latin American countries, the historic development of Middle-East and North-African trading practices turned around bazaars and the likes; (c) the presence of new masses of consumers that activate an increasing sensitivity to demand and promote a more diversified and personalized production capacity. In terms of ‘centrifugal forces’, this flow can also introduce: (d) high remittances abroad with lower local consumption pattern (however, it could be good in global terms as it reduces poverty elsewhere that promotes migrations), (e) disrespect for local values that may lead to both inefficient economic practices (for example, when delivery times are not respected) and elevate the risk of social clashes, (f) destructive competition inside among incumbent firms and newcomers with losses of jobs in the local youth population; (g) overutilization and/or improper use of public services, as social security may be exploited without correspondent contributions. Blackler’s cognitive, ‘embrained knowledge’ (1995) is possessed to a higher extent by highly skilled workers (for example, scientists, engineers and managers) who are the top priorities of many public programs and private corporations (for example, ‘brains return’ programs) to promote their own scientific and technological upgrading. However, forms of ‘encultured and embedded knowledge’ are carried not only by these highly skilled workers; these forces/knowledge bases are also carried and

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transferred by more ‘common’ workers involved in ordinary jobs. This happens because TK and SC are a wide, extended repository of collective knowledge that benefit also from a larger number of agents (and people) involved in day-to-day interactions and transactions. For example, the well-known Notting Hill carnival in London takes place because of the presence of large international communities of Caribbeans, Latin Americans, Africans, among others. On these bases, a market for goods and services develops, which could never grow on the basis of the isolated presence of a few high-talented individuals from those regions. This Krugman-type of approach helps us move away from a simplistic interpretation of falling social capital in western local production systems and undertake a more complex, thorough and dynamic analysis of what may develop in such contexts. In fact, the overall outcome of the assessment process cannot be taken for granted; it needs to be properly assessed instead of following inappropriate judgment criteria (for example, opportunism, power control, nationalism). This assessment may be a partial instrument as it describes the situation as it is, in a static form, rather than presenting possible development scenarios, which represent a more dynamic view of the balance between centrifugal and centripetal forces. A development approach needs to take on the challenge and examine the ways in which the key centripetal forces more than offset extant centrifugal forces.

5. HETEROGENEOUS SOCIAL CAPITALS: A WINDOW OF OPPORTUNITY The case of two local (and global) production systems can help us apply more concretely these concepts and the related development opportunities: Silicon Valley in the USA and Prato in Italy. The multiples studies by Saxenian (1994; 2002) and Dei Ottati (2003) represent critical references that help us to draw some factual conclusions on the importance of pulling together ‘heterogeneous social capitals’ in specific local production systems. The analyses by Saxenian illustrate the importance of the migrant force of skilled human capital as a driver of the local entrepreneurial spirit in Silicon Valley. What is most interesting in her analysis is that although she focuses on the importance of codified and tacit knowledge flows coming with the skilled international experts, she also describes in great detail the relevance of specific communities of skilled experts in the region, that is, Chinese and Indians, who compose more than 50 per cent of total entrepreneurs in Silicon Valley. In her account of the contribution of these communities, Saxenian (2002) stresses that it does not come

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by skilled individuals alone; in contrast she identifies a set of important business, ethnic and cultural associations as a means to coagulate forces of dispersed Chinese and/or Indians and to deliver them not only technical services (for example, information, training, finance), but even with a space for inter-ethnic meeting that help these people feel part of a bigger cohesive community in a sometimes threatening, unknown world. This kind of social meeting point adds the strength of the collective power and sense of belonging to that of the important individual codified and tacit knowledge bases. The interpretation that the recent economic growth of the American community seems to have been quite disconnected from SC contributions does not stop the consideration of SC itself as a critical driver for local development processes (Putnam, 2000). What is more crucial in this argument is that the introduction of this lever through the collective, institutional work of key communities such as the Indian and the Chinese (in addition to other minority groups in the region) adds an insightful element in the discussion on the contribution of social capital to economic development. The heterogeneity of such groups points to the heterogeneity of their social capitals. The indirect impact of such heterogeneity seems to be quite positive as all these groups and their social capitals did contribute to the continuous development of Silicon Valley over time. In a sense this constitutes a revolution in economic thinking on the value of social capital in local production systems. For many years, experts identified the homogeneity of social capital as a key asset for local communities (Becattini, 1990; Lorenz, 1992; Fukuyama, 1995); here we suggest that homogeneity of social capital may currently be less important than the heterogeneity of SC, which is to be taken as a means to intensify the sources of both creativity (Florida, 2002a) as well as collective strength, endeavor and social absorptive capacity. Of course, this approach does not reject the need to identify to what extent, and under what conditions, heterogeneous social capitals contribute to development, instead of becoming an obstacle via little mutual understanding of economic agents and excess transaction costs. The second case may help us focus on the situation of several hundred local production systems that feel threatened today by the increasing waves of migration and that, for this reason, do not benefit yet from such forces as those that we have just highlighted in the context of Silicon Valley. The case of Prato is very special in this regard. In fact, as Smyth and French (2009) indicate, as in many other Italian industrial districts, the workforce as well as the entrepreneurial class has changed as a result of the important wave of migration from China. Today, 30 000 Chinese laborers are currently occupied in about 1000 local Chinese-owned firms and in another 10 000 local small and medium firms in a town that accounts

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for about 170 000 inhabitants. This means that the former homogeneous social capital has changed. Former trustful relationships across local producers and workers are probably less common both for the worldwide competition that local Tuscan producers have been facing since the early 1990s, and for the new competitors that settled down in their own locality, introducing new values, practices, firms and competition inside the district. To a larger extent it represents a process that can be observed in several traditional districts that relied upon homogeneous SC for local development over many years and, yet, currently face a changed situation that may jeopardize the economic prospects of these localities (Parrilli, 2009). Many local agents are not happy with the situation since these newcomers represent low-level type of competitors who jeopardize their own sustainability. Is it the best approach to the sustainable competitiveness of this and other local production systems? Echoing the US Silicon Valley case, we could take a rather different approach and start considering the newcomers as a force rather than as a threat, and to take this approach not on narrow economic bases, which already offer some opportunities (for example, new firms that may open distant markets, such as in China), but by recognizing the relevance of taking a broader socio-economic approach that appreciates the value, richness and innovativeness that the current higher heterogeneity may add through new communities open to discuss, plan and work for local development and growth. Some may counter-argue that in Silicon Valley a large number of skilled workers (engineers, scientists, computer experts, and so on) came together and that this factor helped the local system succeeding in such an astonishing way; this is perhaps true, but what happens in Prato may not be structurally different, as not only unskilled workers moved there from their rural underdeveloped areas, but also several rather skilled workers and entrepreneurs who have been able to set up several hundred workshops and factories in which they compete by means of imitation, cheap work, as well as continuous quality upgrading and productivity growth (Smyth and French, 2009). In addition, sons and daughters of these workers grow up better prepared in just a few years, and will add new human capital to the current system and to the novel SC. Complementarily, the history of Japan teaches another interesting lesson that concurs with the argument that a non-average workforce can catch up and become a real asset: the Japanese used imitation as a golden path to learn new practices and internalize new capabilities so that they now manage the most innovative companies (for example, register of patents in the US patent office, see Carlton and Perloff, 2005). China and India are likely to follow a similar path, and are already starting to invest significant finance in new technologies, with or without

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western partners (UNCTAD, 2008b). These are indications that need to be thoroughly and prospectively considered in order to take a more flexible and dynamic approach to global and local development processes.

6.

NEW ISSUES FOR POLICY

This chapter focuses mainly on local territories immersed in global networks. Our main attempt is to show and discuss the changes occurring in local social capital. These changes are seen as a worrisome for obvious reasons, not least for the more difficult comprehension of newcomers and the higher transaction costs that ensue on the basis of such more complex communication among economic agents. Notwithstanding these difficulties, within the context of current inevitable changes, the novel heterogeneous SC is also creating crucial opportunities for knowledge enrichment based not only on the much searched flows of skilled human capital, but also on collective tacit knowledge flows brought in by critical masses of people migrating from other geographical areas. This analysis has a meaning for the whole debate on urban economies promoted by Jacobs (1969), and later by Krugman (1998), Scott (1998), Florida (2002a), B. Johnson (2010), among others, that are themselves centers of diversity, creativity and, potentially, of connectivity. In a similar way, the surge of heterogeneous SC becomes a subject which extends beyond the context of circumscribed localities (that is, clusters and cities) to include the case of several regions that display characteristics and traditions of significant internal homogeneity. In the current context of economic crisis, strong pressures are rising to assume a protectionist approach towards inflows of people from other geographical areas in order to protect the wealth that has been produced over the past few decades and that is tied to the collective work of homogeneous social communities accepting and recognizing themselves in common attitudes, norms, routines of work and management of the wealth for the sake of the community. What is not fully appreciated is that in many of these regions the migration of different and heterogeneous populations is not lost in history, but very recent. For example, in the case of the regions of Lombardy and Piedmont, in Italy, several hundred thousand workers moved up from southern regions from the early 1900s to the 1970s to work in large industries, in a trend that was supported by national policies (for example, subsidies and fiscal incentives to industry, the creation of infrastructures). Similar situations may be found in big cities such as London, Paris, Frankfurt, Madrid as well as in regions such as the Midlands, Flanders and the Basque Country, among many other

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territories, which attracted millions of workers from neighboring regions and/or countries, and so industrialized. The wealth and the creativity of these territories owe a lot not only to the low cost of this workforce (Sassen, 1988), but also to the implicit, TK and SC flows brought in these regions from distant communities (Portes, 1995). The current protectionist approach in search for a narrow identification of homogeneous local populations, however comprehensible, limits enormously the possibility of opening the local system to a wider and deeper interaction that arises not only on the bases of the attracted talents and codified knowledge flows, but also of the richer flow of TK/SC that come when large heterogeneous social groups approach a locality; this phenomenon, often catalysed by migration waves, may promote noticeably the local innovation insights, capabilities and efforts. Within this approach, some may think to protect the wealth accumulated in specific localities, regions and countries by means of ‘closing’ more or less explicitly the borders of that country (that is, protectionism versus trade, capitals and other factors of production); however, the problem is hardly going to be solved in this way. It is quite clear that in the past 20 years the market quota of several countries and regions shrunk due to the incursion of many newly industrializing countries in the global market (World Bank, 2010), which is not going to end soon. However, closing national and/or local markets is not an option in a globalized market for several reasons, among which: (1) production is organized in global networks, and any strong local firm searches for cheaper locations for production and other supply and service operations (Yeung, 2009); (2) key agents such as commercial and investment banks and other financial institutions operate on a global scale in search for higher returns and benefit from such an ‘open’ approach (Ocampo and Stiglitz, 2008); (3) less developed countries will always search for new options and markets for their products, especially when they realize they are small; and (4) poor people will always search for better job and life opportunities for themselves and their families (Williams, 2007). In this sense, the solution requires more originality and creativity, and, perhaps, the working of several groups of people (heterogeneous local communities, regions and/or countries) together in order to identify new equilibriums worldwide. This argument does not pretend to exhaust the whole problem of balanced global development; macroeconomic analyses and macro global policies (for example, for the implementation of worldwide environmental, social security, labor and health standards) are required, but of course this broader, complementary discussion goes far beyond the scope of the present chapter. The only objective of this work is to stress the relevance of opening the debate on the importance of new heterogeneous sources of

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SC within local (and global) production systems. This novelty demands a more shared strategy to help localities, regions and countries to benefit from the whole richness of GKP, including its TK/SC parts that migrant populations, with their skilled and unskilled workers, bring into the western local production system. In this effort, a methodology built around the Krugman-type of approach (centripetal versus centrifugal forces) might help by taking an in-depth and thorough analysis of the opportunities offered by heterogeneous SCs that are developing in former homogeneous local production systems and urban agglomerations. Inappropriate judgment criteria (for example, opportunism, nationalism) need to be removed, whereas the new options are to be considered within a development approach that studies the ways in which the centripetal forces more than offset the centrifugal forces over time. In this way, development scenarios and trajectories can be drawn and help capture more throughly the dynamic nature of local economic development.

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11. China: beyond the global production line Philip Cooke and Fangzhu Zhang INTRODUCTION In this chapter, we will study the role of China in stimulating global change in two distinct ways. The first, which appears in the second section, refers to aspects of China’s engagement in the highly globalised information and communications technology (ICT) industry. This account briefly explores the manner in which China, or rather leading Chinese or Chinesebased non-western firms like Huawei, not only penetrate western markets but also base themselves in western research and development (R&D) locations as these are vacated through competitiveness failures. There follows a section based on an account of a large industrial platform which embodies ICT and other applications (for example, batteries) in ‘green’ technologies like solar energy, light emitting diode (LED) lighting, electric vehicles, renewable energy, construction and the design of eco-cities. This signifies recognition in China that critiques from the West about excessive fossil fuel emissions in China and the contribution these make to global warming has some practical purchase. As China seeks to evolve rapidly away from its ‘world factory’ reputation, its success in innovating a significant ‘green technology’ platform will decide whether it is willing and able to make a global contribution to the environment’s ‘long emergency’ domestically, and if by so doing it creates new, more affordable, means whereby the world may follow suit in the years to come. In the first section attention is devoted to three complementary theoretical perspectives that seek to explain transition in the dominant production regime fuelling market-led development from one based on fossil fuels to one based on renewables. The first of these adopts a multi-level perspective regarding the destabilisation and resilience to ‘shocks’ of complex adaptive systems such as socio-technical systems (STS). The second focuses on the manner in which complex adaptive systems resolve destabilisation shocks by means of innovations associated with recovery from creative destruction events. The third is the evolutionary economic 246

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geography (EEG) perspective which by its emphasis on path dependence, path-interdependence, relatedness and proximity helps synthesise a theoretical framework connecting regional development to these three changedrivers, namely, transition, resilience and innovation. It will be argued that China’s global engagement has stimulated and been stimulated by all three. Path dependence has had an overly equilibrium perspective (David, 1985), tending to emphasise negative ‘lock-in’ issues in favour of a more open and innovation-friendly perspective (Martin and Sunley, 2006; 2010). Moreover, in tune with this more positive view, a prevailing reliance on ‘chance’ explanations for innovative events (Arthur, 1994) is questioned. In its place a more socially constructive approach reflective of Garud and Karnøe’s (2001) notion of ‘mindful deviation’ by social agency to effect change is introduced. This aligns to an important EEG concept, namely, ‘proximity’, where path interdependence may occur. Under conditions of proximate path interdependence, innovation is able to occur in line with the key complexity theory concepts of ‘preadaptation’ and the ‘adjacent possible’. Accordingly, as we shall see, despite geographically dispersed ‘relational’ proximities within, for example, multinational corporate structures, innovative acts generally occur in specific localised spaces even if knowledge from many global locations is recombined to achieve them. The exception is the phenomenon of ‘the multiple’ where the same innovation occurs simultaneously in different global locations (Johnson, 2010).

THREE PERSPECTIVES ON CO-EVOLUTIONARY TRANSITION The Multi-Level Perspective (MLP) Starting at the simplest level, MLP is a one-dimensional dynamic representation of the progress of innovation from an initial, competitive, niche-market situation through a trajectory that leads to it becoming a ‘dominant design’ or a small circle of diverse dominant designs within the socio-technical system (STS). The STS comprises the main sub-systems of modern society, including: industry, markets, science, technology, culture and policy sub-systems. For a dominant deign from a previous technological paradigm to be challenged by an innovative dominant design and eventually displaced, there has to be a high degree of ‘buy-in’ by each sub-system within the STS. This is the essence of ‘co-evolution’ in the sense that STS sub-systems evolve at their own pace in relation to evolutionary movement by the accompanying sub-systems. During this process

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interactive loops from the array of sub-systems give stimulus or feedback to the ‘emergent’ eco-innovation paradigm and its ‘dominant designs’. Over lengthy time periods from innovation initiation (in some well-known cases like wind turbine energy this can take at least 30 years), the preceding dominant design has been destabilised and the eco-innovation (for example, renewable energy) design combination (for example, wind, solar, marine, biogas, biomass) triumphs as the main source of energy supply at ‘landscape’ (that is, nationally and globally pervasive) level. The process is represented in Figure 11.1. At the niche level are candidate innovations of the kind listed in Figure 11.1 including also innovations in systems, components, parts and services. These compete robustly either as single firms or firm networks. Such competition is likely to receive stimulus from a process called ‘strategic niche management’ comparable to ‘infant industry’ protection, whereby national and/or regional regulation, subsidy and incentive structures protect emergent technologies according to politically set priorities. Different parts of the STS may be influential at different times. In some cases ‘culture’ can be important in the articulation of a discourse of critique and renewal of political priorities. At other times ‘science’ or ‘technology’ may be to the forefront as discoveries or innovations evolve solutions to hitherto intractable problems. If these enable innovations to be competitive in ‘markets’, they will begin to articulate consumer preferences which ‘industry’ should gear up to fulfil. If not, but niche eco-innovations provide desirable public goods, ‘policy’ may be to the fore with an appropriate subsidy regime. This approach is designed to address the lengthy processes attending the transition of the global economy from non-renewable energy to renewable energy. In that event, at the level of the socio-technical regime, destabilisation will not begin until dominant designs enter the market, attract customers and begin to displace, for example, fossil fuel energy in favour of leading renewable energies or conventional cars with renewably fuelled cars of various kinds. It is clear that MLP contains useful theoretical and practical guidance in relation to envisaging and envisioning a process by which eco-innovation can be stimulated. The notions of ‘eco-innovation niche’ and ‘strategic niche management’ are interesting and important. At the STS level, it is also important to recognise the co-evolutionary nature of the distinctive sub-systems that need to be in some degree of stable alignment to bring about regime-level change at least in a potentially ‘lighthouse’ region, city or preferably country. Finally, it is important also to recognise the strong element of feedback or stimulus given by national legislation that creates conditions whereby eco-innovation may become ‘emergent’ through incentives, regulatory instruments and subsidies. The weaknesses of this approach are threefold; first, despite its origins in the policy-world

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Nicheinnovations

New regime influences landscape

Elements become aligned, and stabilise in a dominant design. Internal momentum increases.

New configuration breaks through, taking advantage of ‘windows of opportunity’. Adjustments occur in socio-technical regime.

Small networks of actors support novelties on the basis of expectations and visions. Learning processes take place on mulitiple dimensions (co-construction).

Socio-technical regime is ‘dynamically stable’. On different dimensions there are ongoing processes.

Landscape developments put pressure on existing regime, which opens up, creating windows of opportunity for novelties

Multi-level perspective on evolution of eco-innovations

Geels (2006).

Figure 11.1

Source:

Culture

Science

Technology

Policy

Industry

Markets, user preferences

External influences on niches (via expectations and networks)

Sociotechnical regime

Socio-technical landscape (exogenous context)

Increasing structuration of activities in local practices

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it does not give much guidance on eco-innovation stimulus governance. Rather it tends to rely rather heavily on a possibly naive belief that ecoinnovation only comes from small firms operating in highly competitive niche markets. Secondly, it lacks a notion of destabilisation or crisis as a motivator for speeding up eco-innovation processes, tending to take a benign perspective on system innovation (compared with, for example, Schumpeter’s ‘creative destruction’ concept of innovation). Finally, it lacks dynamism in general and specifically in regard to issues of space (for example, ‘lighthouse’ or ‘transition regions’) and time (that is, slow versus fast changes in conditions for eco-innovation). These questions are treated in a more sophisticated but also quite complex manner in the ‘Panarchy’ perspective. The MLP Resilience Model Resilience is a related but more dynamic theoretical approach that facilitates understanding of the source and role of paradigm and regime change in adaptive systems. This seeks to understand transition in terms of both gradual and episodic change, on the one hand, and local and global change, on the other. It thus shares with MLP the interest in STS, multilevel interactions and co-evolution but it promises to overcome MLP deficiencies in space, time and dynamism. It seeks to recognise but not be theoretically dominated by Simon’s (1962; 1973) seminal thinking on the hierarchical nature of complex adaptive systems by taking into account the cross-scale, interdisciplinary and dynamic nature of change theory. Of key importance to understand resilient responses to a major endogenous or exogenous shock to the system are two key variables: ●



System potential sets the limits to what is possible – the number and kinds of future options available (for example, high variety of industry provides more future options than low variety). System connectedness determines the degree to which a system can control its own destiny through internal controls, as distinct from being influenced by external variables (for example, a region with high legislative and taxation control in its multi-level system demonstrates high connectedness).

Together, these determine system resilience or how vulnerable a system is to unexpected disturbances and surprises that can exceed or break that control. System resilience also emphasises the interconnectedness of levels between the smallest and the fastest and the largest and the slowest (Folke, 2006). The large, slow cycles set the conditions for the smaller,

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faster cycles to operate. But the small, fast cycles can also have an impact on the larger, slower cycles. Thus, in respect of innovation, a national and/ or supranational regime may set favourable conditions for innovation. A case in point would be the ‘shock’ to China’s command economy system by Deng Xiao Ping’s internal reforms that paved the way for regional economic experimentation with marketization at provincial or city-regional levels. In principle, though, a region may anticipate its slow-moving institutions and begin swiftly innovating independently, expressing local collective demand or proto-market building by technologically advanced or interested firms. Either way, resilience can be seen to be a precondition for restoring adaptive system stability. Complexity Theory Complex systems display: dispersed interaction (for example, regionally specialised knowledge domains); absence of a global controller; crosscutting hierarchical organisation (for example, multiple economic governance jurisdictions, including MLP); continual adaptation; permanent innovation; and ‘far-from-equilibrium’ (prone to crises) system dynamics (Arthur et al., 1997). In Kauffman (2008) it is demonstrated that key features of complex systems are scientifically ‘lawless’ in that they cannot be reduced to the level of understanding provided by physics. This is due to two features that are of especial interest to the understanding of ecological and economic development namely ‘preadaptation’ and the ‘adjacent possible’. The ‘autopoeisis’ (self-organization) and ‘autocatalysis’ (self-energising) characteristics of complex ecological and economic systems can be understood in terms of their key characteristics of ‘preadaptation,’ on the one hand, and pursuit of the ‘adjacent possible’ on the other. In the economic sphere, Kauffman (2008) describes a case of preadaptation in the economy. It concerns the invention of the tractor, the massive engine of which continually broke its chassis when mounted. An engineer, noting the scale and rigidity of the engine block, suggested it could form the chassis too; ‘And indeed that is how tractors are made’ (Kauffman, 2008, p. 152). Well, not all tractors: probably the reference is to Henry Ford’s Fordson Model F which was completed in 1916 and was the first lightweight, mass-produced tractor in the world. Ford engineer Eugene Farkas successfully designed the engine block, transmission, and axle housings bolted together to form the basic structure of the tractor. By eliminating the need for a heavy separate chassis, costs were reduced and manufacturing was simplified. With the small size and innovative frame of the first Fordson, the tractor was well suited

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to mass production and mass agricultural markets (Klancher et al., 2003). Hence the Ford philosophy of satisfying affordable mass market demand drove the innovation, which nevertheless advanced the industry standard. The ‘adjacent possible’ refers to the fulcrum of evolution, connecting the restless character of economic (or ecological) life to progress beyond the current status quo ante. It is a cumulative capacity in which the more variety the system displays, ‘the easier is the creation of still further novelty’ (Kauffman, 2008, p. 151). However, because the further out from the present human capability for prediction dramatically decays, such novel moves are generally fairly short-range but adjacent. Adjacency means ‘close at hand’ but it implies no particular directionality. Thus it can be straightforward, or an angle forwards, sideways or, interestingly, backwards. This captures the Schumpeterian notion of innovation being intimately bound up with new combinations of knowledge, including recombinations of old knowledge as well as of combinations of new and old and even, conceivably, new and new knowledge. Evolutionary Economic Geography Earlier it was possible to see more clearly the element of ‘path interdependence’ introduced by Martin and Sunley (2010) that defines key spatial forces underlying and influencing inter-organisational relations. They mean it largely in terms of the economic geography dimension, including inter-dependent technological paradigm interaction. This can be explored further in terms of ‘relatedness’ conjoined to ‘transversality’. This moves the discourse closer to that of regional regime/paradigm interaction because ‘transversality’ is the policy correlate of relatedness. Policy – whether by government, public-private governance, or private governance by intermediary or lead-firm initiative – may be active where market failure means that potentially complementary firms or industries in geographical proximity never meet to discuss possible innovations. If policy is not active, then innovative ‘structural holes’ (Burt, 1992) will remain unidentified unless and until firm ‘search’ of the selection environment eventuates, possibly due to the rise or entry of new incumbents. High market uncertainty in a context that values ‘innovation’ as the highest virtue of the accomplished firm (and region) owing to its overwhelming contribution to productivity and growth, means regional regimes increasingly assist such search for structural holes by inducing a speed-up in the process, as we shall see. The parallel concept to path dependence of importance to EEG is related variety or, more generally, ‘relatedness’. The advocates of

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‘relatedness’ indicate the pivotal position occupied by the idea of ‘related variety’ in evolutionary economic geography. Research effort is expended in relation to path dependence and relatedness in seeking to assess the relative importance of each in understanding the evolution of agglomerations or clusters, the core problematic of economic geography. In doing this, light is cast on the role of numerous other of the key process elements of interest to evolutionary economic geography, such as: innovation, technology, knowledge spillovers, learning and the creation of new regional developmental pathways. Foremost, authors apply these perspectives to issues of externalities and regional growth, on the one hand, and technological change in new path creation, on the other. With respect to externalities and regional growth Boschma (2005) and Frenken et al. (2007) note that a key research question has been the extent to which firms in agglomerations benefit most, if at all, from ‘Romer externalities’ of localisation (Romer, 1991) or ‘Jacobs externalities’ of urbanisation (Jacobs, 1969). Specialisation and diversification are the key differentiating dynamics respectively of these two perspectives on growth and agglomeration. However, while under perfect market conditions specialisation would logically require less inter-industry knowledge transfer effort because similar specialist technologies were being utilised and lateral absorptive capacity among incumbents would be accordingly high, such is seldom the case. Therefore, the gains from efforts, not least by intermediary agencies, to assist knowledge transfers among different industries might yield a greater regional reward than awaiting intermittent market signals for firms to react to. Beyond sectoral relatedness, evolutionists also place strong emphasis on technological relatedness, even among diverse industries, as being a necessary but not sufficient condition for cognitive proximity, meaning clarity of understanding the other’s business model, processes and potential, possibly leading to innovation-led profitability (Kaplan, 2008). Their empirical research shows advantage accrues from the absorption of knowledge spillovers from regional (and extra-regional) industry that is cognitively relatively proximate in some way (technological, inputs, skills) whereas gains from Romer externalities are less so. We are now in a position to summarise the key elements of the foregoing review that are of value in guiding the following analysis of regionalnational interactions on eco-innovation. Table 11.1 lists the important elements from the foregoing review before these are marshalled into a conceptual model of the processes whereby eco-regions may be stimulated or blocked by the dominant (national) socio-technical systems of consequence to the sustainable energy and mobility fields. Equally, there

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Table 11.1

Innovation, global change and territorial resilience

Theoretical perspectives on multi-level innovation system interactions

Theoretical approach

Key innovation characteristics

Co-evolutionary, multi-level perspective

Multi-level Interactions (cyclical) Interactive socio-technical systems (STS) Potential (high variety) Connectedness (robust endogenous institutions) Resilience (resistance to destabilisation; renewal) Preadaptation/exaptation – Cognitive reversal – Borrowing – Searching Adjacent possible Path dependence/Path inter-dependence Relatedness/transversality Proximity

Complexity theory

Evolutionary economic geography

will be interest in the extent enlightened national institutional frameworks have stimulated regional innovation, including eco-innovation. As is evident, while the theoretical perspectives are distinctive, they all adhere to broadly evolutionary ecological and economic principles, so there is a degree of overlap and associated redundancy (high variety), connectedness (institutional or regime robustness) and resilience (capability to resist shocks and exercise renewal/innovation) are central to the analysis (Figure 11.2). Nevertheless, the concepts of STS, potential and connectedness as explicanda of resilience and strategic niche management are directly appropriate for ‘creative destruction’ followed by innovation analysis. Similarly, the complexity theory identification of preadaptation and the adjacent possible explain processes by which innovation proceeds through knowledge recombinations related to proximate and non-proximate path-interdependence and relatedness. These are otherwise also known as ‘strange attractors’ in complexity theory (Urry, 2003). Hence Table 11.1 assembles highly complementary concepts of significance and value to the explanation of innovation, including eco-innovation. Accordingly, these largely complementary concepts may be arranged in the form of a conceptual model of innovation which is subsequently tailored to the analysis of selected cases of relevance to the twin transitions involving China moving, first, from a locked-in command economy to a global market competitor and, second, from a fossil fuels industrial age polluter to a post-hydrocarbons eco-innovator.

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Figure 11.2

small and fast

RENEWAL

RESILIENCE

STS 1

PATH

ECO-INNOVATION

STS 3

TRAJECTORIES

E S S

Adjacent possible

Path interdependence

STS 2

DEPENDENT

A T E D N

Preadaptation

R E L

Multi-level co-evolutionary system adaptation, resilience and innovation



large and slow

Institutional Cycles

SHOCK e.g. Peak Oil

LOCAL

OTHER REGIONAL

STS 5

STS 4

NATIONAL

GLOBAL

SYSTEM INTERACTIONS

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CHINA AND ASIA PACIFIC: RAPID EMERGENCE OF INNOVATIVE REGIONAL INDUSTRIES ICT: Global Platforms for Desktops, Laptops and Smartphones The economic geography of the interaction between the West and Asia Pacific around IT (that is, personal computers, laptops, Netbooks, and so on) is only different in certain specifics from the one that will be presented for ICT (mobile telephony, smartphones, tablets, and so on) because it is even more asymmetrically evolved towards Asia Pacific (for example, the rise to dominance of Lenovo, Acer, Asus, Samsung, Toshiba and Asianassembled Western badges like Dell, Apple or Hewlett Packard). In the West, leading competitors have experienced debilitating shocks. Thus in 2010 Dell closed its desktop computer manufacturing plants in WinstonSalem, North Carolina, costing 905 jobs, and in Lebanon, Tennessee. Dell’s remaining US manufacturing plants are in Miami (Alienware) and Austin (servers; Round Rock). It bought Perot Systems for some $3.9 billion in cash, to make the company its global services delivery division. Dell moved production of computers for customers in Europe, the Middle East and Africa from Limerick in Ireland to Lodz, its Polish operation. Dell also owns production facilities in Asia at Penang, Malaysia and Xiamen, China. Besides owning plants, Dell also gets its products made by third-party manufacturers, like Quanta and Compal in Taiwan for notebooks or its standard SmartStep PC by Taiwan’s Mitac, which is manufactured in China. For some specific components and peripherals, the locations are as follows: 1. 2. 3. 4. 5.

Monitors – Europe and Asia (Phillips, Nokia, Samsung, Sony, Acer) PCBs – Asia and Eastern Europe (Sanmina-Singapore/Malaysia, Celestica-Dongguan, China) Drives – Asia, mainly Singapore (Seagate/Maxtor-Suzhou, Western Digital-Shenzen) Box builds – Asia and Eastern Europe (Hon Hai/Foxteq-Foxconn; Taiwan/China) Chassis – Asia and Eastern Europe (Hon Hai/Foxteq-Foxconn; Taiwan/China).

Taiwan-based Foxconn Technology Group, which includes Hon Hai Precision Industry, supplies a constellation of global ICT brands including Nokia, HP, Apple and Dell. Most of that production comes from its plants in Shenzhen, in the Pearl River Delta area, one of the three major Chinese coastal manufacturing hubs, along with the Yangtze River area

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around Shanghai and near Beijing. It has now moved inland to Henan province, 1600 km (1000 miles) from Shenzhen, where wages are lower and workers more plentiful, keeping mostly higher-value, engineering and R&D work in China’s coastal areas. Foxconn will have as many as 1.3 million workers in China by the end of 2011, up from 920 000 in 2010. This kind of tough global competition involving significant shocks to traditional hardware manufacturers as Asian competitors rise, to inland populations soon to be recruited to the world’s largest factories and to managements charged with charting new ways forward into knowledgeintensive services, occurs far more swiftly than it did when the new international division of labour was first being mapped out. It demands a new ‘complexity geography’ of ‘complex adaptive systems’ that can capture the destabilisation and re-stabilisation effects of such massive and significant moves. In only ten years the ‘world factory’ model of massively scaled production platforms in locations like Shenzen, on the one hand, and monopolistic embedded software and systems platforms like Cambridge (UK) for chip design or Malmö, Sweden, Ontario and Silicon Valley for smartphone ‘apps’, on the other, have reconfigured the global production framework from a ‘diffused network/cluster’ model to a more focused ‘distributed platforms’ one (Figure 11.3). Europeans and North Americans are seeking clear water with little competition by continuing to emphasise their evolving advantages in knowledge-intensive services, particularly in the ‘closed’ versus ‘open’ worlds of smartphone and other digital software, systems design and services. They also, of course, continue to embed their reliance upon Asian, especially Chinese, ‘world factory’ capabilities for the production of hardware. South Korea, notably Samsung, is innovative in handset design and software, systems and services. However, China’s giant Huawei known in the early 2000s as a low-cost, telecommunicationsequipment maker that has been increasingly competing with larger western rivals is challenging global markets and positions itself in western technology and innovation strongholds like Sweden, also selling global systems for mobile communications (GSM) services to Finland. By 2009 it was the third largest mobile infrastructure seller in the world after Ericsson and Nokia Siemens Networks, having displaced Alcatel-Lucent. As indicators of this industry’s global dynamics, earlier and in an easterly direction, for example, Nokia has R&D sites in seven countries: Finland, in Asia China and India, as well as Kenya, Switzerland, the UK and the USA. Nokia operates a total of 12 manufacturing facilities outside Finland: Manaus in Brazil; Beijing, Dongguan and Suzhou in China; Farnborough in the UK; Komárom in Hungary; Chennai in India; Reynosa in Mexico; Jucu in Romania and Masan in South Korea. Litigation among these global suppliers is not uncommon. In early 2011,

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Policy

Governance System

Environment, Health & Safety, Trade

Culture

Outcomes

Conventions

‘Rules of the Game’

Suppliers

Foreign Buyers

Foreign Competition

Agent: e.g. Advanced Embedded Software & Systems @ ARM, Autonomy CSR, & Cambridge Platform

Source: Cooke (2012) for OECD.

Sub-Agents: EMS(Electronic Contract Manufacturing), e.g. PCBs, Cables, PC Parts, Metals

Sub-Agents: ODM (Original Design Manufacturers) e.g. Compal, Arima, Hong Hai @ Taiwan

Agent: e.g. ‘World Factory’ Contract Manufacturing Platform@Foxconn, Celestica, Sanmina, Flextronics & Shenzen, Dongguan platforms

Principal: e.g. Apple, SonyEricsson, HP, Nokia

Agent: e.g. ‘Apps’ UX-UI, LBS @TAT, Polar Rose & Malmö, Ottawa, Silicon Valley Platforms

Open Innovation Model

From global production networks to global production platforms

Cooke (2002) for UNIDO.

Figure 11.3

Source:

Supply Chain

Formal Rules

Core Producer

Local Cluster

Local Cluster

MNC Firms

Global Value Chain

Funding Programmes

Strategy

Knowledge, Skills, Technology

Factor Institutions

Buyer

Local

Buyer

Global

Global Cluster

MNC-LDC Cluster Model

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Huawei asked its former partner Motorola to stop the sale of its wireless network unit to Nokia Siemens. The company claimed that it had provided the phone maker with $878 million worth of equipment and technology for wireless networks that it had developed. Nokia Siemens’ $1.2 billion purchase of Motorola’s wireless network business, therefore, amounts to an illegal transfer of Huawei’s intellectual property, alleged the company. Huawei also said that the deal would cause harm to its business interests as Nokia is its competitor. Nokia first filed suit alleging that Apple had infringed Nokia products regarding technologies used on the iPhone, iPad and iPod Touch. Apple has filed a lawsuit, challenging one of the seven patents Nokia filed against it at the end of 2010. Apple is already involved in disputes with other Android device makers HTC and Motorola, so Nokia would have continued to be in the firing line had it partnered with Google rather than Microsoft in 2011 for its future smartphone operating system. Motorola, once a global innovator and market leader now also uses the Google Android software platform for its handsets. Other former top competitors that succumbed to faster-moving rivals include Ericsson, Palm, Siemens and Alcatel. Ericsson, which competed with Motorola and Nokia in the top three throughout the 1990s, combined its phone unit with that of Sony in 2001 to help regain lost market share; SonyEricsson now ranks sixth. Siemens and Alcatel, both in the top five a decade ago, never recovered from market-share losses and ended up selling or giving their mobile-phone businesses to Asian rivals. Motorola’s handset business, which occupied second spot globally as recently as 2007, has fallen to seventh, and was spun off in early 2011 from the rest of the company in a bid to recover. Motorola Mobility offers tablet devices with 7- and 10inch screens. In the increasingly crowded tablet market, the larger device would compete with the iPad and a smaller device would compete with RIM’s planned PlayBook tablet and Samsung’s Galaxy Tab, which also uses Android. As can clearly be seen, the ICT inside these convergent communication devices is now a cheaply produced, commodified technological input (chips, pirated-circuit boards, and so on) assembled in locations such as Shenzen, China, by giant overseas contract manufacturers such as Foxconn of Taiwan. The value of the products shipped lies almost entirely in the software, system and services supplied on smartphones and the innovative applications (‘apps’) increasingly produced by start-up businesses in the West. In the chapter, an account is given of the main innovative elements of this rapidly evolving industry, demonstrating how the division of labour among tasks has been divided up over the globe. In this, the West retains the leading edge in software, systems, services to some extent, and ‘apps’ but Asia Pacific dominates hardware and in

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South Korea hardware engineering and design there remain innovative applications to be exploited. Thereafter, a different account is given of the utilisation of commoditised IT and ICT componentry in new applications that frequently utilise ICT technologies as part of a new demand for ICT-enabled devices in new markets; the one concentrated upon in the second half of the chapter is eco-innovation, including renewable energy, electric and other mobility (vehicles) and related areas experiencing growing demand in the West, China and South Korea especially; the other, mentioned briefly where linked to eco-innovation, concerns personalised healthcare for which there is growing demand in the West and Japan. These accounts are preceded by a theoretical section which frames the evolutionary economic geography-influenced analysis of global ICT in relation to innovation interactions between the West and Asia Pacific. The global power of the smartphone ‘apps’ platforms is testified to in the following narrative. Thus in addition to the smartphone litigiousness discussed above, Apple and Google have recently ended a ‘phoney war’ to engage in an all-out contest, the victor in which will be the one who can attract the most desirable apps for the smartphone and tablet platforms that use their proprietary operating systems. Industry experts expect Google to prevail because of its open source and open innovation model, which means the quantity (if not the quality) available on its Android system overtakes Apple ‘apps’ in mid-2012 (LMS, 2011). A counter-argument favouring Apple is that ‘apps’ entrepreneurs who want to make profits rather than experience the glory of publication on Google will prefer Apple’s closed innovation model (iOS system) because of its superior intellectual property rights (IPR) regime which allows for contractual appropriation by suppliers of income streams (for example, digital newsprint). Apple’s newsprint ‘app’ scheme charges 30 per cent of subscription fees and disallows data sharing (for example, subscriber addresses). Google’s model charges publishers only 10 per cent of subscription fees and subscriber information will be passed along. It is basically a scope versus scale contest in which Apple’s App Store runs on tight control, high vetting and censoring of apps, while inducing high customer loyalty. Google’s approach is more liberal but also less quality-minded since Android has been an open source project from the start. Thus customers buy Android through buying an HTC, Huawei, Motorola or SonyEricsson smartphone rather than from Google itself. Thus China, Taiwan and South Korea have delivered successive shocks to the recent Nordic hegemony in mobile telephony. During the decade after 2000, first markets, then production home base, were invaded by rapidly expanding Asian producers from South Korea (Samsung) and

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China (Huawei). This resilience ‘shock’ (Gunderson and Holling, 2002; Folke, 2006) led to three major shifts in a key innovator economy in mobile telephony, namely, Sweden (as described in Chapter 2). First, globally influential hardware producers like SonyEricsson abandoned in-house hardware production, going instead for the Android platform, and re-focused upon managing global network and network management services for telcos. Vacating research and production markets released talent pools in Swedish regions that were rapidly occupied by vertically integrated behemoth Huawei. Second as well as moving heavily into services, SonyEricsson and others evolved ‘open innovation’ relationships with innovative start-ups moving to partner or acquire them. Other globally leading ‘smartphone’ firms like RIM (BlackBerry) and Apple for $29 million were also swiftly in the same acquisition market in 2010. Elsewhere, new open innovation ‘apps’ markets evolved for start-ups presaging a new ‘tech-boom’ based on inflated stock-market value estimations. In the North American ‘apps’ heartlands of Silicon Valley and Ontario (Ottawa, Waterloo, Toronto) most new ‘apps’ start-ups contract with the Apple Apps Store and to a lesser extent RIM. However Nordic ingenuity in this burgeoning field means some niches have global precedence, interesting the likes of these large incumbents. Use of social networking, ‘positioning’ and ‘visualisation’ expertise sites are increasingly user-interactive with novel space and flows valuation methodologies like ‘crowdsourcing’ (Page, 2006; Howe, 2009; Shirkey, 2010). Accordingly, and third, it can clearly be seen that the external ‘shock’ to the mobile telephony system in Sweden has provoked ‘endogeneity’ in the mobilisation of resilience ‘potential’. Recall, this is the concept that addresses the capacity of complex adaptive systems to respond to shocks by drawing upon internal related variety or ‘relatedness’ of industries through the interaction of which innovation may be forthcoming. Such ‘potential’ has been swiftly mobilised at the lower level in the multigovernance hierarchy of systems support policy – in a medium-sized reconverting city – rather than by decree of multinational capital or central government. The key mediating role is performed by alert regional development agencies, by a platform-building policy of pursuing innovative ‘strange attractor’ cross-cluster knowledge recombinations, assisting firms to find innovative ‘white spaces’ (Johnson, M., 2010) (or ‘structural holes’; Burt, 1992) among the cluster-platform elements. China’s initiation of market shock processes began in 1992 with economic reforms, thereafter firms have been enabled to grow rapidly by serving the large internal market and then attacking selected western markets with more advanced products, utilising talent released from advanced technology research

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by the onset of Chinese (Huawei) and other Asian (HTC, Samsung) competition in ICT. Eco-innovation China has experienced rapid economic growth in the past 30 years, but the same fast economic growth means China is now facing major challenges regarding resource and environmental issues associated with rapid development. Learning from early experimentation with ‘industrial symbiosis’ (more commonly known nowadays as ‘industrial ecology’) evolved in Nordic cities like Kalundborg, Denmark, and Örnsköldsvik, Sweden (Cooke, 2010a), China has been one of the more assiduous global practitioners of this approach. It is a system of productively recycling industrial waste according to a ‘closed loop’ input-output waste-to-new product methodology. Interest in industrial ecology began in China in the 1980s and early experimentation began in that decade in Tianjin (see below), China’s State Environmental Protection Administration (SEPA) promoted the eco-industrial parks (EIP) concept and initiated a pilot programme for eco-industrial parks in 1999. Guigang Eco-industrial Park was one of the earlier of these new demonstration sites in China. It is located in Guanxi Zhang Autonomous Region in coastal southern China adjacent to Vietnam. The park was initially managed by the Guitang Group, a state-owned enterprise with over 50 years of agro-food history. The economy of Guigang was traditionally dependent upon sugar refining and processing which declined rapidly during the 1990s. The EIP initiative was to transform the declining Guitang Group from a conventional sugarproducing industrial system to an eco-industrial system. The Guitang Group set up the eco-industrial complex based on sugar production at what became the largest sugar refinery in China. The complex includes sugarcane farms, sugar-making plant (it produces 120 000 tonnes of sugar annually), an alcohol plant (10 000 tonnes), a pulp and paper mill (85 000 tonnes), a calcium carbonate plant (8000 tonnes), a cement plant (330 000 tonnes), and a fertilizer plant (30 000 tonnes). The paper mill uses sugar slag generated from sugar production, while the cement mill uses another by-product, sugar sludge, as a raw material input for the production of cement (Fang et al., 2007). Guitang Group is responsible for managing the whole eco-industrial system at the EIP. The material and by-product exchange occur primarily within the same complex, significantly improving the efficiency of its many processing plants (Fang and Lifset, 2008). Subsequently the group was privatised and has extended its exchange network to receive by-products from other sugar producers allowing increased production. This Guitang Group success inspired the city of

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Guigang to adopt a five-year plan to become an eco-industrial city. By 2007, there were 24 national EIPs established in China. Most of them are organised and managed by the administrative commissions of the development zones and the governments at city and county level, while others are under management by private enterprise. Now, the concept of ecoindustrial development has expanded from park, community-level, and city-level, to provincial level such as Liaoning Province, a demonstration province for the circular (‘closed loop’) economy (Fang et al., 2007). As noted, SEPA was one of the few worldwide seriously to promote the concept of the closed loop economy with a specific programme to assist model eco-industrial parks across the country, another of which, Tianjin, is located on China’s north-east coast 150 km from Beijing. It is China’s sixth largest city and one of four administered directly by central government. Following development of its EIP, Tianjin also became a forerunner and demonstrator in the development of the eco-city concept. The Tianjin Economic-Technological Development Area (TEDA) exemplifies an existing industrial region with developed industrial symbiosis linkages among key facilities. The TEDA was formed in 1984, and provides a utility sharing infrastructure including electricity, gas, steam, water and wastewater treatment, for all regional facilities including reuses of rubber, ash, metals, and organic materials. Unlike some Chinese ecocities, notably Dongtan, which has suffered from the expense of its design and difficulties over land assembly among municipal and private owners, Tianjin has a working eco-city neighbourhood built by the Sino-Singapore Tianjin Eco-city partnership, a strategic cooperation project between China and Singapore to improve the living environment and build an ‘eco-culture’, as stated in the official announcement. Located in Tianjin’s ‘Binhai New Area’ a Special Economic Zone, home to global businesses like Rockefeller, Tishman-Speyer, Motorola and EADS Airbus, the ecocity has a planned population for 2020 of 350 000. As an official eco-city and home to Tianjin Qingyan Electric Vehicle Co., Tianjin hosted 2010’s New Energy Vehicle Technology and Investment Congress that assembled over 120 industry executives and experts from the new energy vehicle value chain to discuss best practice, global market trends and industry outlook. In line with its green credentials Tianjin is also home to Tianjin Lishen Battery, one of the world’s largest manufacturers of lithium-ion batteries. These are the energy source for Coda Automotive’s US-badged, Chinese-built Hafei Saibo electric saloon car, which in 2010 began selling in California where its price of $45 000 can be offset by $10 000 from a federal tax credit and state incentives. Two other city-regions that display less central state governance structures than Tianjin but where city prefectural initiative has stimulated

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Table 11.2

Innovation, global change and territorial resilience

Leading enterprises in the Yangzhou LED lighting value chain

Enterprise category Upstream (substrate; epitaxial wafer) Midstream (chip) Downstream ‘packaging’ (assembly) Downstream (lighting application)

Percentage 20% 20% 20%

Employment Number of enterprises 80–100 80–100 260–300 300

6 5 7 151

Source: Jingan (2010).

the rapid evolution of Chinese Eco-Industrial Clusters are Yangzhou on China’s east coast and Shenzen, near Hong Kong in the south. Yangzhou is home to China’s official, Ministry of Science and Technology affirmed (2007) leading semiconductor lighting cluster, otherwise light emitting diode (LED) lighting. In 2009 it was designated the national LED pilot city by the same ministry. The industry began in 2003 with the founding of Darewin Opto, which was joined by six other companies, one manufacturing semiconductors, two specialising in assembly and four producing lighting applications. On this basis, the city prefecture of Yangzhou determined to be the key local driver of the industry, committing $5 million for research, applications and testing in three new industries; new light sources, new energy and new materials. By 2009 a full supply-chain involving base material-epitaxial-wafer-chip-assembly-application interconnections had evolved, reaching $1 billion output value. In 2009 the city announced an enhanced LED technology fund of $3 million to buy foreign metal-organic chemical vapour deposition (MOCVD) equipment for advanced LED epitaxial wafer production. This unprecedented city-level investment attracted leading firms Canyuan and Rainbow from elsewhere in China to augment the cluster to over 30 firms with a $2 billion output value. Table 11.2 gives a detailed breakdown of the Yangzhou LED cluster as of 2010. This successful city-regional strategy has been to gain advantage in the wafer fabrication phase of the value chain to control the LED and a burgeoning photovoltaics value chain in proximity. The solar energy competence has developed as an ‘adjacent possible’ from the epitaxial wafer base since silicon is the key material in both technologies. To that end the prefecture has also funded a 2 km2 Silicon Industrial Park in the Yangzhou Development Zone, a semiconductor lighting R&D centre, as part of the branding strategy of ‘Yangzhou LED City’. Shenzen’s eco-city is Pingdi, located to the east of the main city and one of the three large cities of the Pearl River Delta alongside Guangdong and Hong Kong. It is planned and approved ready for development to begin

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265 $3.5

Fueling Growth A long-time battery maker, BYD Co. has been ramping up its car business

3.0 2.5

Parts

Total sales, in millions

2.0

Batteries 1.5

Autos

1.0 0.5 1999

2000

’01

’02

’03

’04

’05

’06

’07

0

Note: Converted from Chinese yuan at the current rate. Source: BYD Co.

Figure 11.4

Business transition of BYD Co., 1999–2007

in 2011. In the city-region a key industry set is ICT, electronic equipment and automotives. A key firm is BYD China’s (and the world’s) largest producer of lithium-ion batteries. This firm made strategic use of ‘relatedness’ and the ‘adjacent possible’ to innovate in electric vehicles from its origins in (laptop) batteries. Founded in 1999 the company has developed its own iron-phosphate-based lithium-ion (LFP) battery following over ten years’ R&D. The core battery technology can be applied in all the main types of electric vehicles and has a lifetime of over ten years with a charge time to 50 per cent of its capability in ten minutes. The company started by supplying batteries to mobile telephony companies such as Nokia and Motorola. In 2003 BYD made the acquisition of Qinchuan Motors of Xian which gave it the opportunity for the company move from part and battery supplier to car marker (Figure 11.4). In 2008, BYD purchased SinoMOS Semiconductor of Ningbo to facilitate its upstream value chain and accelerate its development of electric vehicles. It attracted $230 million from global billionaire investor Warren Buffett through his MidAmerican Energy Holding Co. for a 10 per cent investment stake. This investment strategically helped BYD extend its markets for electric vehicles from China to global. In its corporate strategy, BYD plans to sell some 9 million electric vehicles by 2025 to surpass the leading global automakers in electric vehicle (EV) technology. Pingdi is located in proximity

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to the BYD value chain and is part of the wider, related variety electrical and electronics industry platform. The design of two large, open campuses and high valuation of local ecological areas, with fully protected rare flora and fauna, make Pingdi highly attractive for eco-innovation. One open campus, close to the existing Gaoquiao Industrial Park is dedicated to eco-innovation. Key innovative platforms at Gaoquiao include digital applications to energy (smart grids), transport (information systems), water, waste, green buildings and eco-cities. Shenzen region, with BYD batteries and electric vehicles (EVs), also has the Chinese State Grid’s Electric Power Research Institute (EPRI) and other R&D institutes specialising in vehicle charging (V2G) requirements and standards alongside development of EV charging stations. Related to the evolution of the eco-city concept is the construction industry, which is China’s fourth largest industry, contributing 9 per cent of GDP. The Chinese government has set the detailed goal to reduce energy consumption. In the Eleventh Five-Year Plan (2006–10) the target was also set to reduce building energy consumption by 50 per cent; to improve energy efficiency of government institutions by saving 10 per cent energy per unit construction area and per capita; and to reduce electricity consumption of appliances by 29 billion kWh (Osterkorn, 2008). The Ministry of Housing and Urban-Rural Development, the Ministry of Finance, and the National Development and Reform Commission have passed numerous laws and regulations on building standards in recent years (Table 11.3). To promote energy efficiency, six key areas are focused upon. State and local government supply financial support for a range of demonstration projects in some major cities (The Climate Group, 2009). EMC, shown as a financial mechanism in Table 11.3 is an active private company in China which has been involved in developing energy-efficient green buildings. With the rapid rise of a Chinese middle class, energy consumption by residential building is expected to grow by 5 per cent annually, more than doubling by 2020 (McKinsey & Company, 2007). There are several barriers to improve energy efficiency in buildings in China, including lack of rigorous enforcement of building energy codes; lack of incentives to save energy due to a fixed rate price of heating energy and out-dated heating system design with coal-fired, heat-only boilers (WBCSD, 2009). As shown in Figure 11.5, building insulation comparisons indicate that there is a significant gap in insulation efficiency between Chinese and comparator countries and cities. At least one third of buildings in China need an energy performance upgrade. Such energy retrofits in existing buildings could exceed US$380 billion at average cost of 200 Yuan (US$29) per m2 (The Climate Group,

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Table 11.3

267

Government policies on green building and energy efficiency in China

Legal environment

Six key areas

Demonstration projects

Financial mechanism

Plan:

China medium and long term energy conservation plan Laws: ● Renewable energy law ● Energy conservation Law Regulations: ● Energy conservation regulation for civil buildings ● Energy conservation for state-funded institutions Support measures: ● Special funds ● Standards ● Labelling ● Assessment ● Quality control ● 50% energy conservation design standard for new buildings ● Heating system metering and retrofitting in North China ● Energy conservation in government and public buildings ● Solar and geothermal renewable ● New building materials ● Building energy auditing and assessment ● Energy conservation and retrofit demonstrations for government and public building in 24 provinces and municipalities ● Heating system metering and retrofit demonstration for existing building in 15 provinces and municipalities in North China ● 212 demonstrations and promotion projects for renewable energy applications in building in 25 million m2 ● 100 demonstration projects for green building and 100 demonstration projects for low-energy-consuming buildings ● Energy efficiency auditing and labelling for civil buildings in 18 provinces and municipalities ● Solar roof plan Government ● Renewable application in buildings support special ● Energy conservation for government funds: and public buildings ● Heating system metering and retrofit for existing buildings in North China ● Renewable energy-saving building materials Private sector: ● Such as EMC

Source: The Climate Group (2009).

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Building-standards comparsion – limitation of heat leakage in wall/window/roof W/m2 Window Roof Wall Beijing

1.16

Germany Beijing temperature equivalent areas

0.50

United Kingdom United States 1 Beijing 1 Canada 1

North Japan

N.A. double deck required

0.45

2.0

0.44

0.52 0.42 0.32

Denmark

0.30

Canada 2

0.27 0.17

0.80 0.11

0.46

Russia 2

Stockholm

1.5

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2.8

0.36

Harbin

Harbin temperature equivalent areas

4.0

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2.9

0.40

2.5

0.50

2.3

0.23

2.4

0.24

2.9

0.15

2.2 2.0

0.31 0.12

Source: McKinsey & Co. (2007).

Figure 11.5

Building standards comparison among several countries and cities

2009). Constructing new buildings to world-class insulation standards and installing energy-efficient heating and cooling packages would help capture 8 quadrillion British thermal units (QBTU) of savings, contributing 6 per cent of the global energy productivity opportunity (McKinsey & Company, 2007). Total floor space in China is currently 40 billion m2 and is expected to reach 70 billion m2 by 2020. According to the estimation by The Climate Group (2009), the market for new green buildings would be worth between US$220–400 billion, depending on the application scale of green buildings among new buildings. The Chinese government also has been encouraging upgrading of heating systems in China, particularly in the colder north, which would cost about US$30–44 billion (The Climate Group, 2009). In total, market volume for green buildings including both new buildings and retrofits could reach trillions of US dollars (Figure 11.6). In parallel with the promotion of green construction in China, renewable energy in the form of wind power has been growing faster than the government had planned in recent years, having more than doubled each year since 2005. In late 2005, the Chinese government increased the official wind energy target for the year 2020 from 20 GW to 30 GW (Lema and

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TOTAL MARKET VOLUME: IN THE TENS OF TRILLIONS OF YUAN (TRILLIONS OF US$)

ENERGY CONSERVATION IN OPERATIING SYSTEMS: 200–300 BN YUAN (US$30–44 BN)

ENERGY RETROFITS IN EXISTING BUILDINGS: 2.6–8 TN YUAN (US$0.4–1.2 TN)

ENERGY EFFICIENCY IN NEW BUILDINGS (TO 2020): 1.5–3 TN YUAN (US$220–440 BN)

Source: The Climate Group (2009).

Figure 11.6

Potential market volume of green buildings in China by 2020

Ruby, 2007). The industry reached the original goal of 5 GW for 2010, three years ahead of schedule. Policy-makers doubled their wind-power prediction for 2010. The government announced an initiative to build a 1000-megawatt wind farm in Hebei, near Beijing, for completion in 2020. Goldwind has emerged as the leading Chinese wind turbine manufacturer and has begun to export Chinese turbines and components globally. It currently holds about 3 per cent of market share in global wind turbine sales and captured some 30 per cent of sales within China in 2006. By 2007, over 40 Chinese firms were manufacturing wind turbines commercially; many of these firms were engaged in prototype development and testing (REN21, 2008). In some cases these were engineering firms diversifying into this

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rapid growth market, for example China South Railway Locomotive workshops at Zhuzhou Industrial Park, near Changsha in central China. It was predicted that China would become the world wind-power leader by 2010 (Watts, 2008). National Developments in Eco-innovation China is both one of the world’s largest consumers of non-renewable energy as well as one of its largest producers of non-renewable energy. To put this into perspective, the Chinese government invested $34 billion in clean energy in 2009. The strength of this industry has been augmented by the framework of strategies and incentives provided by the central government. With the dissolution of the Energy and Industry Department in 1993, renewable energy policy has been managed through several organisations, such as the Ministry of Commerce and the National Development and Reform Commission (NDRC). In 2001, the State Economic and Trade Commission had proposed its Tenth Five-Year Plan for Sustainable Development which included a section on New and Renewable Energy Commercialisation Development. The goal of this plan was to curtail carbon emissions through developing renewable energy alternatives such as hydropower, biomass, solar power, wind power and geothermal energy. During this five-year plan, the NDRC’s Centre for Renewable Energy Development drafted the Renewable Energy Law (2005) which stated the reduction targets as well as the incentives to meet those targets. This law authorised feed-in tariffs for wind power, biomass. The Eleventh Five-Year Plan, 2006–2010, also stressed greater emphasis on green energy through increasing energy efficiency by 20 per cent by the end of the plan, namely, through developing efficient fans, pumps, boilers and lower energy intensity steel and cement. The NDRC established the National Energy Administration in 2008, but this was officially judged inefficient, which paved the way for the State Council to establish the National Energy Commission in 2010. The commission is responsible for drafting the national energy development plan, reviewing energy security and major energy issues and coordinating domestic energy development and international cooperation. Detailed incentive policies and programmes included Promoting the Wind Electricity Industry (2006), offering preferential policies for wind power development and the Golden Sun Programme (2009) which provided subsidies, technology support and market incentives to facilitate the development of a solar power industry. The Golden Sun Programme (GSP) focused on solar photovoltaic (PV) installation through 2011 on a project-by-project basis. Off-grid installations

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received 70 per cent capital subsidies while grid-connected installation (300 kW capacity plus) received 50 per cent subsidies. A separate part of the programme, funded by the Ministry of Finance and Construction, provides additional subsidies for building-integrated PV. Similar incentives are available for wind power. Along with the wind power feed-in tariff, the GSP had its subsidies reduced by the 2010 amendment of the Renewable Energy Law. This also placed more responsibility and planning interaction among regional and local entities to ensure grid connection, augmenting the Ministry of Finance’s renewable energy fund and guaranteed purchases of renewable energy power generated by electric utilities. It is expected that the Twelfth Five-Year Plan, 2011–2015, will establish an environment tax. Beyond the NDRC, other government organisations provide subsidies and conduct research on renewable energy. The Ministry of Science and Technology (MOST) has also provided subsidies for renewable energy research ($3.4 million), as well as funding two high-technology R&D programs ($25 million): the 863 program (commercialisation of new technologies) and the 963 program (research in basic science). In addition, the Department of Resource Conservation and Utilisation provides low-interest loans for supporting industrial development of renewable energy. Moreover, the Department of Agriculture, amongst other government institutions, has run renewable-energy based ‘living lab’ projects throughout the country since the mid-1990s. These range from the ‘Rural Marsh Gas Projects’ to ‘Use of Crop Stalk as Energy Source’ projects. Finally, the Ministry of Finance released an EV subsidy policy in mid-2010. Unlike EV subsidies in other parts of the world that provide cash-back incentives to customers, the Chinese subsidy will be made directly to vehicle battery vendors in an effort to reduce claims of class discrimination. The subsidy will run from 2010 to 2012 in five pilot cities: Shenzhen, Shanghai, Changchun, Hefei and Hangzhou. In late 2010 it was announced that China was planning on launching a new EV strategy entitled ‘One Thousand Electric Cars’. This initiative intends to put 1000 EVs in ten Chinese cities per year. The government believes this initiative would make 1 per cent of all vehicles in China electric by 2012. From the point of view of the automotive industry, such subsidies are catalysts for change and innovation; however, infrastructure to support the EVs is not mentioned in policy and it is equally important for the sustainability of the product. This is where again, the firm Better Place which, it will be recalled, provides infrastructure for EV as well as EV-based consumersolutions, comes in. Noticing the rising demand in China, Better Place and Chery Automobile Co., China’s largest independent auto producer, are collaborating to provide both good quality EV cars on the market

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alongside the infrastructure (charging stations/battery switch points) to support them.

CONCLUSIONS China is consolidating valued aspects of its ‘world factory’ reputation by facilitating expansion of enormous low-wage production platforms in contexts like the Pearl River delta. However, embedded within this trend are two other counter trends. The first of these involves the move upmarket of its leading mobile telephony to smartphone producer Huawei, whose penetration of western markets has begun as has its location in talent pool locations in leading Nordic smartphone design and ‘apps’ locations in, for example, Sweden’s three leading cities. The second counter-trend has been a rapid plunge into large swathes of the eco-innovation supply chain from renewable fuels to electric vehicles, green construction and the building of new eco-cities. Many of these technologies such as solar photovoltaics and LED lighting embody silicon technologies and no expense has been spared acquiring state-of-the-art wafer fabrication equipment. A different move has been exploration of adjacent possibilities from battery production, supplying the world’s energy storage requirements for laptop computers and mobile phones. In these industry-specific and varied responses, Chinese firms have met the shock of market liberalisation and become global scale producers in important current and future global markets. A second major conclusion is how well the theory outlined in the first section of the chapter explains complex adaptive system evolution from the regional to the global scale. The MLP perspective of scalar interactions in time–space relations is crucial to understanding both destabilising shocks (Deng’s Chinese economic reforms; Asian impact on EU telephony leadership; EU ‘Grand Challenges’ to recover from crises) and restabilising regulatory and industry responses (new national frameworks; Europe 2020, open innovation; rise of ICT KIBS). Western and Chinese platforms are looking outwards from the known by exploring adjacent possible-related variety to capture innovations (‘apps’) or ‘preadapting’ existing technologies for new uses (ICT for bioelectronics; silicon for LED then solar energy). In these cases, innovations occurred as novelties or as less-cost solutions often with the intersection of path dependent trajectories in specific regions or cities. This points to the extreme importance of geographical proximity compared to say non-geographical ‘relational’ proximity in locating innovation ‘pumpstations’ in the world. Thus Cambridge (UK) dominates global chip-set design while China’s main river deltas accumulate mega-platforms of EMS and ODM parts or badge

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manufacturing. Meanwhile, Apple, RIM, Android and HTC act as principals commissioning these various agency platforms according to ‘design driven innovation’ norms of ‘changing socio-cultural regimes’ (Verganti, 2006; Shirkey, 2010) ‘crowdsourcing’ (Page, 2006), ‘positioning’ and ‘eco-living’.

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12. Are clusters the solution?* Andrés Rodríguez-Pose and Fabrice Comptour 1. INTRODUCTION One of the traditional advantages associated with clusters of firms has been their capacity to engender greater innovation and to transform this innovation into economic growth (Porter, 2000c). Groups of firms working in the same or in closely related sectors are deemed to generate agglomeration economies and knowledge spillovers. These spillovers, in turn, are at the root of self-reinforcing processes of innovation and growth (Capello, 1999). Physical proximity among firms is considered to facilitate the emergence of interaction and the formation of interpersonal and firm networks leading to the genesis of complex collective learning mechanisms (Melachroinos and Spence, 2001; Storper and Venables, 2004). Knowledge spillovers and collective learning mechanisms thus help transform mere clusters of firms into ‘neo-Marshallian industrial districts’ (Becattini, 1987), ‘new industrial spaces’ (Scott, 1988), ‘innovative milieux’ (Aydalot, 1986), ‘learning regions’ (Morgan, 1997), or ‘regional innovation systems’ (Cooke et al., 1997; Cooke and Morgan, 1998), where firms and the territories they are located in – together with their intrinsic social and structural characteristics and interactions – are put at the centre of the innovation process and of the generation of economic growth. Hence, local social structures, interaction, and collective learning processes within clusters are viewed as making firms located in close physical proximity more innovative and more dynamic than isolated firms (Baptista and Swann, 1998). The link between clusters of firms, innovation, and economic growth has generally been based on a large number of case studies where the learning processes of firms in dense institutional environments are documented. However, as Martin and Sunley (2003, p. 22) acknowledge – possibly because of the constant resort to what can be considered as favourable cases – the positive connection between the presence of clusters and innovation and economic growth is far from well documented. There are relatively few studies that address the link between clusters, innovation

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and growth from a comparative perspective, and even fewer that try to venture into quantitative analyses of a large number of territories, in order to assess whether the positive relationship between clusters, innovation, and growth found in specific cases stands the scrutiny of including not only successful clusters, but also areas a priori less prone to the emergence of collective learning process. This chapter addresses this gap in the literature by studying the interaction of the presence of clusters with other factors deemed to promote innovation – such as investment in research and development (R&D), patent applications, or the presence of ‘innovation prone’ socio-economic environments – and economic growth across 152 regions located in 15 European Union (EU) countries over the period 1995–2006. Using pooled cross-section regressions, the model intends to capture both the static and the dynamic connection between a series of innovation promoting factors grouped into three different composite variables or ‘innovation filters’ – the ‘R&D filter’, the ‘social filter’ and the ‘clusterisation index’ – specially designed in order to proxy the complex interaction among growth enhancing innovation variables.

2.

FROM CLUSTERS TO INNOVATION AND GROWTH

Clusters or ‘the geographic concentrations of interconnected companies, specialized suppliers, service providers, firms in related industries, and associated institutions’ (Porter, 2008, p. 213) have been at the centre of much of the literature aiming to understand and describe the link between innovation and economic growth. This literature has tended to highlight the importance of the presence of agglomerations of firms, organisations, and institutional actors, located in close geographical proximity and interlocked in intricate systems of cooperation, competition, and knowledge diffusion, for the genesis and the spreading out of innovation and, subsequently, economic growth. There has certainly been no shortage of high-quality research dealing with the implications of clusters for innovation and economic growth (Cheshire and Malecki, 2004). Among this research, qualitative case-study analyses abound. Most of these studies have focused on a handful of cases, including a limited number of well-known technology clusters, such as Cambridge (for example, Keeble et al., 1999), or of industrial clusters in the Third Italy or Baden-Württemberg. However, while many of these analyses provide deep insights into the internal and external relationships that may – or may not – make clusters hotbeds of innovation and

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growth, there is always the uncertainty of whether we have been simply observing the lushest trees, while, at the same time, overlooking the overall condition of the forest. More systematic analyses, trying to map out clusters across Europe have been few and far between. Crouch et al. (2001), in perhaps the most ambitious attempt to date, have mapped local production systems across France, Germany, Italy, and the UK, however the analysis has been confined to national borders, generally avoiding dynamic quantitative analysis. This noticeable absence of robust quantitative evidence is without doubt the result of problems with measuring the intricate interactions, the institutional linkages and the complexity of the collective learning processes happening within clusters, learning regions, or regional innovation systems. But this absence of more systematic analysis flies in the face of recent improvements in databases measuring clusters and of the importance clusters have acquired in policy circles. The belief that clusters, in general, and regional systems of innovation, in particular, are key drivers of innovation and growth has become widespread among academics and policy-makers alike. The diffusion of the cluster concept by leading management academics such as Michael Porter and the impetus that research on regional innovation systems has acquired in recent years have lead to the extensive implementation of cluster policies as a means to achieve economic dynamism. But many questions remain unanswered. Do clusters have a greater influence over innovation than investment in R&D? Is the role of education greater than that of R&D and the presence of regional systems of innovation in generating economic growth? These are questions which have been overlooked or, at most, addressed tangentially by the literature studying innovation and economic growth and which have been mainly examined in case studies. 2.1

Operationalising the Model

That the questions presented in the previous section have been somewhat neglected can be largely put down to the difficulties in defining – and, consequently, operationalising – most of the concepts involved in this type of analysis. In particular, the concept of what a regional system of innovation is is far from straightforward. The most commonly accepted definition is that by Cooke et al. (1998, p. 1581), who consider that when ‘firms and other organisations are systematically engaged in interactive learning through an institutional milieu characterised by embeddedness’ they make up a regional innovation system. The interaction between the production and the institutional structure generates territorially embedded networks which determine the genesis, import capacity, diffusion,

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and assimilation of knowledge within any given cluster (Howells, 1999; Evangelista et al., 2002). These networks generate, in turn, a governance and a business structure within the cluster (Braczyk et al., 1998). The governance dimension involves the ‘soft infrastructure of enterprise innovation support’ (Cooke, 2006a, p. 6), such as ‘public policy, institutions, and knowledge infrastructure’ (ibid.). The business dimension includes the ‘industrial base: . . . the type of firms, the level of R&D investment, the level of linkages’ (ibid, p. 7). Most of these networks, institutions and dimensions are idiosyncratic and dependent on the context on which every cluster is placed. As the characteristics of each region and locality are unique, operationalising clusters in a quantitative manner is virtually impossible (Iammarino, 2005). It is often the case that regions with, on paper, very similar socioinstitutional structures diverge (often wildly) in terms of their innovative capacity. These differences underline that ‘there is no single model that is able to generalize the dynamics of successful regional innovation systems’ (Doloreux and Parto, 2005, p. 138) and question whether the regional innovation framework can be really applied beyond the identification of ‘stylized regional innovation systems’ (Iammarino, 2005), that is, purely theoretical concepts with no clear equivalent on the ground. Despite these gargantuan difficulties, some authors have embarked on the heroic task of trying to identify clusters and/or design cluster policies in Europe on a large scale. This is, for example, the case of the pioneering work of Jacobsson et al. (2006), who, using functional analysis, aim to identify and measure the different functions of a cluster and the different steps in its creation. In a more systematic way, the European Commission has used the INNOVA initiative to gather best practices from European clusters and to promote them (EC, 2006a). But it is possibly the European Cluster Observatory (ECO) the organisation, which has made the greatest effort in order to systematically identify, measure, and map clusters in Europe. Their measures – not exempt, as any such measure, of controversy – are used in this chapter in order to assess clusterisation across the regions of Europe. Operationalising other constituents of innovation and growth, such as R&D and, in particular, ‘social filters’ is also problematic. But the indicators behind the construction of this type of variables tend to generate, by and large, greater consensus. Bearing in mind these caveats, in this section we now define the variables included in the analysis. In order to do this we follow previous empirical work and, in particular, the work of Rodríguez-Pose and Crescenzi (2008), who resorted to a series of parameters to measure ‘social filters’ across the regions of Europe.

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The dependent variable is perhaps the most straightforward and widely accepted of all the variables included in the analysis: the growth of the logarithm of the regional GDP per capita. The explanatory variables deserve, by contrast, much greater attention. Following the three key strands presented in the theoretical section (linear model, ‘context’, and clusters and regional systems of innovation), in order to analyse the link between (regional) economic growth and the factors that generate innovation in Europe’s regions, we resort to three basic explanatory variables, which we call the three filters. These are the ‘R&D’ filter, the ‘social’ filter and the ‘clusterisation’ filter. R&D filter The ‘R&D filter’ is directly derived from the basic principle of the linear model of innovation. We create a composite index using the two basic input and output variables of this approach. The former is represented by the regional expenditure in R&D as a percentage of gross domestic product  (GDP), whereas the latter is depicted by the number of patent applications per million inhabitants in any given region. Despite the controversy surrounding patent applications as a measure of innovation outputs – not all sectors patent in the same way, not all patents lead to true innovation, and not all patents lead to short-term economic returns – the inclusion in the analysis of the number of patent applications responds to its value as a proxy for the capacity of a region to absorb and generate knowledge and its correlation with regional economic growth. Research and development expenditure and patent application are given equal weight in the resulting ‘R&D filter’ index. As could be expected, the higher the R&D expenditure and the higher the patent applications per capita, the higher the value of the R&D filter. Social filter The concept of ‘social filter’ aims at building a composite index reflecting the socio-economic conditions that make a region innovation prone or innovation averse. This filter reflects the ‘territorially embedded’ character of innovation as often presented in regional innovation systems approaches. Multiple aspects can play a role in the emergence of innovation. Among these we highlight: (a) local market rigidities, (b) demographic aspects, (c) education, skills, and human capital, and (d) the scientific base of the region.1 The ‘social filter’ variable used in this chapter is based on that of Rodríguez-Pose and Crescenzi (2008), including some additional variables, in order to reproduce better the socioeconomic setting in which innovation and growth take place. The first aspect covered – that of market

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rigidities – refers to the local use of resources. The variables covered by this domain include long-term unemployment (long-term unemployment) as a means of measuring the degree of rigidity in the local labour market and, at the same time, as a potential indication of the share of the active population with inadequate or insufficient skills. The second variable is agricultural employment (agricultural employment), used as a proxy to partially measure levels of ‘hidden unemployment’, especially prevalent in some of the new members of the EU. These two parameters are also indirectly linked with the productivity level of the labour force. The last variable in this domain is the level of corporate tax rate (corporate tax rate). The rationale for the inclusion of this variable is based on the complaints often raised by entrepreneurs and other economic actors. A high level of corporate taxation is said to diminish the investment capacity of firms (especially in R&D) and to be a disincentive for location in certain regions. The second aspect covered by the ‘social filter’ relates to the demographic characteristics of a region. It is assumed that the total population of a region (total population) may have an impact on its innovative capacity and thus on its growth potential. Indeed, in regions with large populations, the presence of a large market pool will make it easier for a company to find workers with the right skills and knowledge. Moreover, a larger population may be at the source of both greater diversification (Jacobs type) and specialisation (Marshall-Arrows-Romer type) externalities. The influence of the number of people living in a region on innovation and growth is complemented by the average age of the population (percentage of young). The impact of this variable on economic growth is difficult to predict theoretically. On the one hand, a young population is often associated with less risk aversion and greater openness to innovation. On the other, if a large percentage of the young is still studying or in full-time training, their immediate impact on economic growth is bound to be limited. The third domain refers to the education and skills level of the population. Education is widely regarded as a key source for innovation and economic growth. Two variables are included in this domain: the share of the population with a higher education degree (education population) and the percentage of adults participating in lifelong learning activities (lifelong learning). The final domain in our ‘social filter’ index reflects the importance of the presence of scientists in the innovation process. The variable included is the share of employed in science and technology (human resources in science and technology), as a proxy for the human resources devoted directly to the generation of new knowledge. A strong scientific community in a region

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can be considered as a competitive advantage for innovation and growth. This aspect was not included in Rodríguez-Pose and Crescenzi’s (2008) operationalisation of ‘social filter’. Principal components analysis (PCA) is used in order to create the resulting composite variable ‘social filter’. Clusterisation index The third and final filter represents an approximation – given the complexity of the task – at capturing the ‘clusterisation’ effects which are, according to the literature on clusters and regional innovation systems, believed to be directly behind the economic dynamism of a region. The logic for including this index is based on the importance of proximity in the generation of innovation as explained in the theoretical section of the chapter. The variables included try to measure the propensity of firms to cluster – or concentrate geographically – in similar or related industries. As mentioned earlier, such dynamics are expected to create important internal flows of knowledge and a strong potential to innovate. The three variables used in this ‘clusterisation index’ stem from data collected by the European Cluster Observatory (ECO). The ECO has identified clusters in the 27 members states of the EU, sorting them by region and assessing them through a detailed methodology.2 Using different criteria, the ECO develops a series of regional indices. The index of cluster specialisation (specialisation) exploits employment data in order to create a specialisation quotient representing the employment intensity of a given regional cluster sector compared to the employment intensity in general for this region. The Focus index captures the share of a region’s total employment represented by a specific cluster. If this share is large, this means that the ‘clusterisation’ effects for that sector in this region are strong. The last variable included in this index intends to control for the diversification of clusters in a region (diversification), that is, the presence of economic cluster activities in different industries. If a region is characterised by the existence of several clusters in various sectors (even if these clusters are relatively small in comparison to those in regions with only one large cluster), it can expect to benefit from diversification or Jacobstype externalities, likely to foster greater innovation and growth. As in the previous filter, the three variables are combined into a composite one using PCA. 2.2

Data and Geographical Coverage

The analysis covers 152 regions in 15 EU member states for the period 1995–2006.3 The economic analysis is conducted at NUTS24 regional level

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for most of the countries – Austria, Czech Republic, Finland, France, Italy, Hungary, Poland, Portugal, Slovakia and Spain. NUTS1 regions have been used for Belgium, Germany, Greece, the Netherlands and the UK, both for reasons of data constraints and as a need to reflect – at least in the case of decentralised countries – similar tiers of government and levels of decision making capacity.5 The data used in the chapter stem from two main sources: the European Statistical Office (Eurostat) and the European Cluster Observatory. The names, definitions, and sources of the 14 variables included in the analysis are presented in Table 12.1.

3.

THE MODEL AND EMPIRICAL ANALYSIS

3.1

The Model

The econometric model used in the empirical analysis adopts the following form: D ln GDPpci,(t11) 2t = a 1 b1 ln GDPpci,t21 + b2RDFilteri,t 1 b3SocFilteri,t 1 b4ClusterIndexi,t 1 b5ND 1 e

(12.1)

Where: D ln GDPpci,(t11) 2t

is the growth of GDP per capita in region i during the period of analysis;

a

is a constant;

ln GDPpci,t21

ND

represents the natural logarithm of GDP per capita in region i at the beginning of the period of analysis; denotes the R&D filter conditions in region i and time t; represents the social filter conditions in region i and time t; denotes the degree of clusterisation in region i and time t; are a series of national dummies;

e

is the error term.

RDFilteri,t SocFilteri,t ClusterIndexi,t

The specific characteristics of the data included in the ‘Clusterisation index’ – the use of a ‘reference year’ by the European Cluster Observatory – constrain us to estimating the model by means of

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Table 12.1

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Definition of the variables and data sources

Variable

Definition

Sources

Dependent variable Growth of GDPpc

GDP PPS per inhabitant

Eurostat

Percentage of GDP Applications per million inhabitants

Eurostat Eurostat

Eurostat

Human Resources in Science and Technology

Percentage of total unemployment Percentage of total employment Percentage of corporate benefits (national proxy) People aged 15–24 as percentage of total population Percentage of national population Percentage total population with tertiary education (levels 5–6 ISCED 1997) Percentage of adults (25–64) participating in education and training Percentage of active population

Cluster Index Specialisation

See text

Focus

See text

Diversification

Number of clustered industries in the region per 100 000 employees

European Cluster Observatory European Cluster Observatory Authors’s calculation based on European Cluster Observatory data

R&D Filter R&D expenditure Patents Social Filter Long-term unemployment Agriculture employment Corporate tax rate Percentage of young

Total population Education

Lifelong learning

Eurostat Eurostat Authors’s calculations based on Eurostat data Eurostat Eurostat

Eurostat

Eurostat

heteroskedasticity-consistent pooled ordinary least square (OLS) regressions. VIF tests have been conducted for all the variables in the model, with no multicollinearity having been detected. In order to account for unobserved national fixed effects, a set of national dummies (variable ND) is included in the model.

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The analysis is conducted in two steps. First, a static picture is presented in Table 12.2. In this table the results of running 14 different regressions are reported. These regressions aim to capture both the aggregate connection between each composite variable representing the different approaches to the analysis of innovation or filters (regression 1), as well as the individual correlation of between each individual variable included in each of the three filter variables (regressions 2 to 9 for the Social Filter, regressions 10 and 11 for the R&D Filter and regressions 12 to 14 for the Clusterisation Index), on the one hand, and economic growth in the regions of Europe, on the other. The three composite filter variables – R&D filter, social filter and clusterisation index – have been standardised in order to make it possible to compare their effects on regional growth. The standard errors are presented (in italic) under the value of the coefficient. In each regression tests have been conducted in order to account for the good specification and goodness of fit of the model. Second, the dynamic dimension of the relationship is reported in Table 12.3. This table includes seven pooled HC-OLS regressions, with the dynamic effect achieved by regressing regional per capita growth on the initial GDP per capita and lagged filters, where the number of lags is n [ [ 1; 6 ] . While this econometric approach enables us to give a global picture of the dynamics of the model, it has the drawback of reducing the number of observations after each lag. In any case, even after six annual lags, the number of observations (n = 796) remains relatively large. As in the case of the static analysis, the potential presence of spatial serial correlation is controlled for. No multicollinearity is detected in the model. 3.2

Static Analysis

The first fact that can be underlined in the static analysis is the goodness of fit of the model (Table 12.2). A very high proportion of the variance in regional growth is explained, implying that the combination of the more traditional variables of innovation, with the social filter and its components, and the different indicators aimed at identifying the presence of clusters have a powerful association with regional economic growth. Most variables are significant and tend to remain so despite the introduction of different controls. This is the case of the initial GDP per capita of a region, which is positively and robustly associated with regional economic growth in all 14 regressions (Table 12.2). When the three composite filter variables are considered together, the social filter and the clusterisation index have a positive and significant relationship to economic growth, but the R&D filter variable is not significant (Table 12.2, regression 1). This, in principle, represents a confirmation of the

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Table 12.2

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Pooled (HC-OLS) regressions of regional log GDP/capita 1

Constant Log GDPpc R&D Filter Social Filter Clusterisation index National dummies Social Filter Long-term unemployment Agriculture employment Corporate tax rate Percentage of young Total population

2

2.157*** 0.955*** 0.350 0.256 0.757*** 0.947*** 0.038 0.027 0.009 0.034*** 0.008 0.006 0.049*** 0.005 0.013** −0.004 0.005 0.004 x x

3 1.288*** 0.2674 0.901*** 0.0297 0.036*** 0.006 0.002 0.003 X

4(*) 0.691*** 0.132 0.988*** 0.014 0.006 0.006 −0.003 0.003

5 2.048*** 0.261 0.896*** 0.029 0.027*** 0.007 −0.007 0.004 x

6 0.561*** 0.195 0.979*** 0.021 0.038*** 0.006 −0.003 0.003 x

−0.002*** 0.001 −0.007*** 0.001 −0.010*** 0.001 −0.045*** 0.005 −0.001 0.001

Education Lifelong learning HR in science & techno R&D Filter Patent application R&D expenditure Clusterisation Index Specialisation Focus Diversification R2 F Number of observations

0.925 614.21 1756

0.883 1012.33 1760

0.885 546.29 1760

0.898 1737.74 1760

0.895 493.00 1760

0.880 1313.86 1760

Notes: *,**,*** indicate significances at 10 per cent, 5 per cent and 1 per cent respectively. (*) this regression has been run without national dummies since a national proxy has been used for the corporate tax rate.

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7

8

9

10

11

12

13

14

1.459*** 0.292 0.854*** 0.035 0.019** 0.008

1.086 0.154 0.9101*** 0.006 0.024*** 0.006

2.165*** 0.281 0.722*** 0.030 0.003 0.008

2.327*** 0.346 0.738*** 0.0373

2.032*** 0.367 0.770*** 0.040

2.008*** 0.340 0.769*** 0.037 0.008 0.008 0.048*** 0.005

2.067*** 0.356 0.766*** 0.039 0.008 0.008 0.049*** 0.005

0.000 0.003 x

0.0494*** 0.0046 0.016*** 0.014** 0.004 0.005 X x

2.012*** 0.329 0.770*** 0.036 0.004 0.009 0.049*** 0.005 X

X

X

0.010* 0.006 X

0.051*** 0.005 0.011** 0.005 x

0.025*** 0.004 0.047*** 0.005 0.019*** 0.001 0.000** 0.000 −0.009 0.007 0.0126* 0.0065 0.011* 0.006 0.011 0.007 0.914 533.36 1760

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0.905 1104.96 1760

0.916 418.44 1756

0.926 538.64 1756

0.925 773.24 1756

0.924 561.30 1756

0.924 705.68 1756

0.924 517.31 1756

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Note:

0.925 614.21 1756

0.932 582.94 1596

1.956*** 0.314 0.785*** 0.034 0.015** 0.007 0.043*** 0.005 0.011** 0.005

Lag 1

0.940 630.35 1436

1.689*** 0.282 0.820*** 0.031 0.017*** 0.006 0.037*** 0.004 0.009* 0.005

Lag 2

0.947 705.77 1276

1.683*** 0.243 0.853*** 0.028 0.017*** 0.006 0.031*** 0.004 0.006 0.005

Lag 3

*,**,*** indicate significances at 10 per cent, 5 per cent and 1 per cent respectively.

R2 F Number of observations

2.157*** 0.350 0.757*** 0.038 0.009 0.008 0.049*** 0.005 0.013** 0.005

Lag 0

Dynamic analysis

Clusterisation Index

Social Filter

R&D Filter

Log GDPpc

Constant

Table 12.3

0.956 763.65 1116

1.283*** 0.253 0.874*** 0.027 0.016*** 0.006 0.030*** 0.004 0.004 0.005

Lag 4

0.964 927.23 956

1.159*** 0.256 0.891*** 0.027 0.014** 0.006 0.025*** 0.004 0.002 0.005

Lag 5

0.968 1281.37 796

1.392*** 0.248 0.899*** 0.028 0.015** 0.007 0.024*** 0.005 0.002 0.005

Lag 6

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views of those strands of research which have highlighted importance of both the presence of clusters and complex regional innovation systems, on the one hand, and the basic socio-economic conditions on which these networks and systems can be constructed, on the other, for economic growth. Indeed successive regressions (regressions 2 to 9) reveal the close interaction between the presence of clusters and of favourable socioeconomic conditions. When the composite social filter variable is excluded from the analysis, the coefficient of the clusterisation index becomes generally insignificant (regressions 2, 3, 4, 5, 6 and 8, Table 12.2). This also points to the fact that the association of clusters with regional economic growth is closely related to the presence of a good level of education in the population (Regression 7), with an emphasis on lifelong learning (regression 8) and, essentially, with the existence of a good pool of researchers (regression 9). The presence of a relatively ‘high-technology’ labour force thus seems to play a major role in the settlement of innovation-enhancing socio-economic conditions and, more globally, in the economic growth of a region. This may be a confirmation of some of the basic characteristics associated with regional innovation systems. In these complex systems the existence of a pool of researchers surrounded by a highly educated workforce will naturally tend to form a community where innovation is generated, diffused, and absorbed in the workplace. This is, in essence, the ‘local learning process’, as defined by Doloreux and Parto (2005). If companies in a region are, in addition, geographically clustered, this is likely to increase intra-regional knowledge flows between hightechnology workers and educated people. Therefore, clusterisation, on the one hand, and the presence of a high density of researchers and of a well-educated labour force, on the other, will reinforce each other in the generation of innovation and growth. The greater the density of clusters in any given region, the easier the knowledge flow between innovative firms and the rest of the production fabric, facilitating the diffusion and absorption of knowledge. This renders the impact of clusters significant to economic growth. The absence of these conditions, in contrast, makes clusters almost irrelevant for growth. Factors such as the presence or absence of long-term unemployed, of greater or lower levels of agricultural employment, of a younger or older population, or the overall dimension of the region neither enhance, nor reduce the potential relationship between clusters and economic growth. In fact, they contribute to make them irrelevant (Table 12.2). Extracting the social filter from the analysis renders the more traditional R&D variables of R&D expenditure and patent applications positive and significant (regressions 2 through 9, Table 12.2), with the exception of when the R&D filter is considered in combination with corporate tax rate

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(regression 4) and the regional human resources devoted to science and technology (regression 9). Turning to the individual variables included in each filter – while controlling for other filters – exposes other interesting associations. First, the decomposition of the R&D filter variable into its two components brings to the fore a significant and positive correlation between the number of patent applications and growth (regression 10) whereas, investment in R&D turns out as non significant (regression 11). The weak association between R&D expenditure and economic growth, at least in the short term, comes in support of the views which highlight the relative irrelevance of policies dominated by public investment in R&D in environments associated with inadequate or weak socio-economic conditions and in the presence anaemic networks and systems to absorb it (Cooke, 2001). However, the results regarding patent applications are in line with the linear approach to innovation. The different regressions including individual social filter variables (regressions 2 to 9) give more detailed information about the socioeconomic conditions which may matter for innovation and growth in the regions of Europe. Among the socio-economic variables that have a positive and significant association with economic growth, the educational parameters clearly stand out. Both coefficients of the level of education of the population and of a lifelong learning dimension in the workforce are strongly positive and significant and of great importance for growth (regressions 7 and 8). The human resources devoted to science and technology go in the same direction (regression 9). By contrast, the level of long-term unemployment, that of agricultural employment, the corporate tax rate, and the percentage of young (regressions 2 to 5) are negatively and significantly associated with regional economic growth. The demographic size of a region is completely dissociated from growth, once the R&D and clusterisation indices are included in the analysis (regression 6, Table 12.2). Finally, of the variables making up the clusterisation index, specialisation and focus are positively and significantly – albeit at the 10 per cent level – correlated with regional economic growth (regressions 12 and 13, Table 12.2), The coefficient of the variable representing the diversification of clusters is, however, not significant (regression 14). In brief, the static analysis exposes the very strong, positive, and robust association between the social filter of a region and its economic growth. The strength of this relationship is significantly stronger than that of the other two filters with regional growth. The link between R&D and patents and growth, on the one hand, and the presence of clusters, combining both specialisation and diversity externalities, and growth, on the other,

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is contingent on their interplay with the presence or absence of adequate social filters. The R&D variable only becomes significant when the social filter is not taken into account, while the relevance of the existence of clusters in a region for economic growth only comes to the fore in areas with adequate social filters (Table 12.2). The capacity by economic actors to absorb innovation across European regions depends on the overall combination of social conditions and, more specifically, on the educational endowment of the population and on the existence of a ‘high-technology literate’ labour force. Clusters also matter, but their importance for growth is contingent on the existence of adequate social filters. Weak or rigid social filters – characterised by factors such as the prevalence of longterm unemployment, low productivity employment and high levels of corporate taxation – may damage significantly the innovation potential of a region and render the association between clusters and economic growth irrelevant. Adequate social filters (that is, those featured by well-educated populations, a high-technology labour force and limited market rigidities) combined with the capacity to transform R&D into patents quickly, and to develop clusters both specialised and focused – relative to those in other regions – are at the base of the formation of innovative and economically dynamic regions. 3.3

Dynamic Analysis

The dynamic analysis in an up to seven-year horizon is presented in Table 12.3. It adds a series of interesting nuances to the relationship between the key factors behind innovation and economic growth, outlined in the static approach. The most relevant finding is the enduring importance of an adequate social filter for regional economic growth in Europe. The social filter is the only composite variable to remain significant throughout the whole period of analysis, despite the fact that the strength of its relationship with regional economic growth wanes in time. The association of the social filter with the variation of regional economic growth in Europe is only half as strong when considering a six-year time lag as when no time lags are considered (Table 12.3). Another important finding is the contrasting trajectories of the relationship between the R&D filter, on the one hand, and the clusterisation index, on the other, and regional economic growth. As highlighted in the static analysis, the presence of a greater specialisation in and focus on clusters in favourable socioeconomic environments is connected to higher growth in the short term. This positive relationship is, however, short-lived. The strength and the significance of the coefficient starts to wane quickly and becomes non-significant beyond three years (Table 12.3). The R&D

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filter, by contrast, is insignificant in the first year considered, but becomes significant after one year. The strength of this association remains more or less intact during the remaining years. The importance of this association also increases over time, especially as the intensity of the connection between the social filter and regional economic growth starts to decline (Table 12.3). This may be a signal that, at least in the European case, the importance of clusters and innovation systems for regional economic growth may have been somewhat overstated. Conversely, hard R&D indicators may have a greater sway over short- and medium-term economic performance than admitted by some recent strands of literature.

4. CONCLUSION The objective of the chapter has been to assess through the use of an econometric model with a static and a dynamic dimension the association between the different factors that promote innovation and economic growth across the regions of Europe. In particular, we have analysed the role that the presence of clusters within regions play in this relationship. Three primary conclusions can be extracted from the analysis. First and foremost is the importance of having a favourable socioeconomic setting in order to foster innovation and growth. Much more than the presence or absence of clusters, having a good level of education, a strong endowment of skills in the population or a workforce with sufficient high-technology skills is not just crucial in order to generate and absorb innovation, but also as a way of ultimately promoting greater economic growth. Having a good employment/unemployment balance is also equally important for innovation and economic growth. Fiscal incentives can also become useful in fostering innovation, if they help attract companies with a high innovative potential. These socio-economic conditions weave a complex substratum that allows certain territories to become more innovation prone than others. Second, regional clusters have a strong association with economic growth in the static model, especially when they help increase the knowledge flow in already highly integrated communities well endowed with firms, skilled workers, researchers and scientists. However they appear only as ‘second-best factors’ in relation to the social filter. The results also highlight that the association between the presence of clusters, innovation, and economic development in the regions of Europe is (a) contingent on the presence of adequate social filters that would help make the transition from a mere cluster of firms into a real regional system of innovation, and (b) less relevant in time than the socio-economic substrata on which the

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clusters are based. Clusters seem to matter when they become the hub for regional systems of innovation, but this tends to happen only when they are located in innovation-prone environments with adequate social filters, and even in these cases, their influence seems to be weaker than, for example, investment in R&D. Hence, the influence of clusters for economic growth may be lower than what many think. What really matters for economic growth is setting up in every territory adequate conditions for innovation, including greater education and lifelong learning opportunities, a better and more efficient use of human resources, a better matching of investment in training and innovation to local production fabric, and more emphasis on science and technology. The third conclusion is the limited short-term association between R&D investment and patent applications and economic development across the regions of Europe. However, the presence of adequate social conditions helps improve the returns on R&D investment and patents over time. The research presented here probably sends a message of warning against the adoption of one-size-fits-all and even ‘mesmeric’ types of cluster policies for local economic development (Taylor, 2010). Policies aimed at fostering or encouraging the agglomeration of firms may, without paying attention to local conditions and potential, end up yielding lower results – if at all – than expected. Indeed, the analysis points towards the need to address local social filter bottlenecks as a precondition for achieving greater returns in R&D and in cluster policies. However, neither all clusters have the same transactions costs and internal relations characteristics, nor the same technological regimes and knowledge features (Iammarino and McCann, 2006). This implies a need to make greater distinctions in policy-making among different types of clusters, as different clusters in different contexts may require different types of intervention (Gordon and McCann, 2000). In any case, while the analysis presented here provides a springboard for some potential practical policy implementations and recommendations, it also calls for further research, and in particular of research trying to better reproduce and capture the effects of different types of clusters.

ACKNOWLEDGEMENTS The authors are grateful to Sharmistha Bagchi-Sen, the editor of the journal, two anonymous referees, Jacob Jordaan, Riccardo Crescenzi and participants at seminars held in Groningen, Jönköping, La Coruña, San Sebastian, Utretch, and Valencia for their insightful suggestions to earlier versions of the manuscript. They would also like to acknowledge

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the generous financial support of a Leverhulme Trust Major Research Fellowship and of the PROCIUDAD-CM programme. The chapter is also part of the research programme of the independent UK Spatial Economics Research Centre funded by the Economic and Social Research Council (ESRC), Department of Business, Innovation and Skills, Communities and Local Government and the Welsh Assembly Government. The views expressed are those of the authors and do not necessarily represent the views of the funders.

NOTES * 1. 2.

3. 4. 5.

This is a reduced and modified version of the paper ‘Do clusters generate greater innovation and growth? An analysis of European regions’, published in the journal Professional Geographer. Another aspect is local institutions, which are, however, hard to measure at the regional level for the whole of Europe. The detailed methodology is available directly on the website: www.clusterobservatory.eu. Only clusters with at least 1000 workers are taken into consideration in order to ‘prevent the appearance of very small insignificant clusters’ (Cluster Observatory website, in Methodology: Evaluation of regional cluster strength). Bulgaria, Cyprus, Denmark, Estonia, Ireland, Latvia, Lithuania, Luxembourg, Malta, Romania, Slovenia and Sweden were excluded because of lack of sufficient and/or reliable regional data on R&D expenditure. Nomenclature of Territorial Unit for Statistics as defined by the European Commission on http://ec.europa.eu/comm/eurostat/ramon/nuts/home_regions_en.html. In addition, some specific regions have been excluded because of lack of data. This is the case of all the French Overseas Departments and Territories, and of the regions of the Åland islands (Finland), Açores and Madeira (Portugal) and the African enclaves of Ceuta and Melilla (Spain).

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PART IV

Territorial Policies: Emerging from the Crisis

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13. Conceptualisations, relationships and trends between innovation, competitiveness and development: industrial policy beyond the crisis Patrizio Bianchi and Sandrine Labory 1.

INTRODUCTION

This chapter provides some thoughts on industrial policy beyond the crisis, which are based on a long-range research by the authors (Bianchi and Labory, 2011).1 This research suggests that the crisis reveals longterm structural changes in the economy, which firms in all sectors have to face and which require government intervention. Important structural changes are occuring in organisations, hence the division of labour, with deep implications not only from an economic point of view but also from a social and political point of view. We suggest that adequate industrial policies, in the broad sense of intervention to sustain firms’ competitiveness and re-structuring, have as a consequence to be defined in a holistic manner.

2.

A LONG-PREPARED CRISIS

We have analysed the crisis in depth elsewhere (Bianchi and Labory, 2010) and shown that what the crisis mainly reveals is the inadequacy of the world regulation system defined in Bretton Woods, although adjusted through time. The world has changed, the main characteristic of which is the emergence of new economic and political powers, especially that of China. The supremacy of the USA remains, but is brought into question by its financial dependence on China. The dominant regulation model has been liberal, the main rules of which can be found in the Washington Consensus. The crisis is due to a burst bubble of the American economy, the financial sector booming to exponential levels while private and public debt

295

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10 8 6 4 2 2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

–2

1980

0

–4 Advanced economies Emerging and developing economies World Note: 2010–2014 are forecasts. Source: IMF statistics (www.imf.org).

Figure 13.1

Real GDP growth since 1980 (annual percentage change)

were rising at unprecedented levels. Financial markets worldwide have boomed without any correspondence in the real economy. As Keynes taught us, excessive speculation leads to crises. Parallel to that, firms in all sectors were making organisational changes, setting up global value chains or global production networks. World economic growth has experienced ups and downs since the 1980s, the analysis of which helps us to understand the roots of the crisis. Figure 13.1 shows real gross domestic product (GDP) growth for the world, for advanced economies and emerging and developing countries, since 1980. From the mid-1980s there is a first economic cycle characterised by growth up to 1988, followed by a reduction of real GDP up to the period 1992–94. A new cycle follows, of growth up to 2000 (apart from the 1997 crisis), followed by a sudden fall in 2001, and then the actual development phase until the current crisis. The most striking point illustrated by Figure 13.1 is the growing distance between emerging countries that settle at growth levels that are well beyond that of advanced countries. The latter countries are most hit by the actual crisis since they almost go back to the growth levels of the early 1980s. Thus, world growth is now entirely driven by the growth of emerging countries. The growing tendency of emerging countries to drive world growth can be dated back to about the early 1990s. This corresponds to the fall of the Berlin Wall and the end, from a political point of view, of bi-polarism

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(the dominance of two world powers, namely, the USA and USSR). The world progressively becomes multi-polar in the 1990s, with the emergence of new world powers, namely, the BRIC (Brazil, Russia, India and China) countries. Meanwhile, many changes occurred. The European Union (EU) deepened integration with the monetary union and the adoption of a common currency between a core of member countries, and at the same time enlarged its borders to the east. Such enlargement has consolidated the growth of these transitional countries, not only attracting capital from abroad, but also acquiring productive activities from Western Europe, thereby generating export economies that could re-structure, allowing the extension of supply networks throughout Europe. The rapid growth of Eastern European countries gave impetus to the growth of Western European countries, so that benefits were shared among old and new members of the EU. Regarding other emerging countries, growth was very high during the whole period, especially in China. The Chinese economy opened up to international trade, although the political regime is still limiting freedom. Other Asian countries experienced high growth rates in the period, although they are confronted by a crisis around the mid-1990s. Latin America experienced problems too, again mainly due to the disparity between the financial and productive spheres of the economy. Indeed, the search for short-term profits leads the financial markets, if not regulated, to rapidly expand, attracting all savings, at the expense of the productive sectors, which have difficulty in investing, and of the government sector, whose deficit tends to increase. Such disparity grows to the point where financial assets are too overvalued and the bubble explodes. Various crises with a similar scenario took place in the world, namely, the development of weak economies accelerated by the development of financial and housing activities which crowded out the sectors of the real economy. Meanwhile, international institutions imposed rigid and inappropriate measures to deal with these economic difficulties. They enacted a drastic reduction in the role of the state, with a reduction in interest rates, so that the capacity of the government to intervene with monetary policy was reduced, together with privatisation and liberalisation of financial markets as a factor for opening up trade and rapid integration of capital markets. The problem became acute when the crisis hit the American economy. The US financial sector rapidly and exponentially boomed without any corresponding increase in the productive sector. Wages were stagnating but internal demands were artificially sustained by rapidly rising private debt. Much of this consumption was due to imported products (investment in the manufacturing sector reduced from the end of the 1990s), so that the balance of payments kept worsening. Public debt

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also experienced dramatic growth, due to military spending in the fight against terrorism and the wars in which the USA engaged, in Iraq and in Afghanistan. The main problems revealed by the crisis are problems shared by politicians making economic policy decisions and economists advising them: 1.

2.

3.

Short term views prevailed, highlighting the short-term benefits of sustained consumption and profits in the financial sector, without regard to the worrying rise in private and public debt and to the lack of long-term investment in productive sectors. Partial views also prevailed, in the sense of isolating economic phenomena from political and social aspects; for instance, the political consequences of the emergence of new economic and hence political powers did not seem to be explicitly considered, and the high risks incurred by highly indebted households with relatively low wages but high consumption did not seem to be worrying; The crisis is also generated by myopic and individualistic views whereby self-interest and own profits and returns prevail, without regard to the community.

The crisis has therefore called for a return to the consideration of the long term, the future, not only from the point of view of individuals, but also from that of collective actions and effects. We therefore suggest in this book a return to the concept of development and not just growth, whereby development means a long-term process of growth and improvements not only from an economic point of view, but also from social, political and cultural points of view. As stressed by Sylos Labini (2006), economic development is a means to reach the wider aims of cultural and social  development; Dahrendorff (1988; 2008), also highlights that economic development cannot arise without civil development.

3.

NEW APPROACH TO INDUSTRIAL POLICY AFTER THE GLOBAL CRISIS

Given the above considerations, we propose a holistic approach to industrial policy, with the idea that the properties of the industrial system cannot be explained by its component parts alone. The whole industrial system has to be considered, together with the underlying society and polity. The unit of analysis of the industrial system should be the division of labour. Production organisation is determined by the firm’s characteristics, its internal and external environments, social and political

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institutions, the extent of the market and products requirements. In turn, production organisation has implications for the social, economic and political characteristics of the economic system in which the firm is embedded. In other words, the division of labour determines the productive power of labour (productivity) that has to be defined according to the extent of the market, but it also determines working conditions and the living standards of workers, hence their access to education and to cultural development. Inventions and technical progress in turn depend on the culture and knowledge that originate from experience. It is in this respect that industrial policy has to be determined in a holistic approach: industrial development is determined by, and in turn influences, the characteristics and evolution of the society and its cultural development. Therefore, industrial development policies must take account not only of available resources and technologies, but also of the social characteristics of the territory, the training of human resources that simultaneously determine social and economic evolution. We represent this in a framework for the definition of industrial policies, a ‘sundial’ of industrial development. Two important elements of industrial development are resources and rules. Resources include knowledge and human capital, as well as raw materials and infrastructures. Rules are both social (in some societies some categories of people are excluded from economic activities, such as some castes in India, or women in Islamic societies) and institutional (law and regulation that determine, for instance, access to education or the procedures to create firms). Countries have different sets of rules and resources that determine different production organisation capacities and hence different paths in industrial development. Rules and resources are not fixed through time. On the contrary, they constantly evolve as demographic, cultural and political trends occur. Entitlements open up choices in our purchase of commodities, in our use of the education system, in our voice in the political system, and so on. The set of choices (in the sense of objects from which to choose, not of the act of choosing) opened up by entitlements are ‘provisions’. Both entitlements and provisions are key determinants of the development of nations. Some countries have levels of provisions and entitlements that are quite balanced and lead to a balanced path of industrial development, which is the path chosen by the European Union in the economic integration process. Some have high levels of provisions but low levels of entitlements, which is the case in China. Others have low levels of provisions but high levels of entitlements, which is, for instance, the case in South Africa. Provisions and entitlements determine the capacity to organise production, and simultaneously the efficiency and equity of a system. We can also relate this framework to the discussion of industrial

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policy in the current post-crisis context we have carried out so far. What globalisation has essentially meant is a rise in the diversity of players in world exchange, and therefore in the complexity of industrial development. New players have emerged, with different culture and social characteristics, basing their development on particular provisions and entitlements. Whereas before the 1990s world trade was dominated by trade between developed countries, which had similar provisions and entitlements (culture, labour regulation, and so on), nowadays world trade is characterised by an intense competition where old players (developed countries of the western world, such as the USA, Europe and Japan) have progressively lost significant market shares relative to new players, namely, the ‘emerging’ countries such as China and India. These countries are characterised by specific provisions and entitlements. Entitlements and provisions are essential elements of industrial policy. In addition, we identify two other dimensions or levels of development, namely, innovation and territory. Entitlements determine the rights of individuals to take part in development as well as in productive and competitive processes; provisions determine their knowledge, competencies and resources, and thus their capability to take part in these processes, together with other resources necessary to perform economic activities. Innovation is the capacity to create knowledge and to apply this new knowledge to production processes; it is a dynamic element that sustains development. The territorial dimension highlights the importance of the embeddedness of productive processes in territories, where they can gain from local resources such as social capital and infrastructure. Sustainable industrial policy should aim at extending all four levers, coherently defining and implementing measures at the different levels of government, that is, regional, national and supranational. 3.1

Industrial Policy as a Vision of Industrial Development

The consideration of industrial policy as a vision of industrial development implies the explicit analysis of the complementarities between instruments and between implementation levels. Defining such an industrial policy allows all policy fields and all levels to share the same objective of industrial development (and development in a broad, classic sense). Once this view is adopted, two issues arise. First, the design of industrial policy requires the identification of industries’ current and future expected trends, of crisis industries, and of the technologies of the future and the possible applications of such technologies. This analysis should allow for the identification of possible scenarios of future industrial development which policy-makers have to choose. The choice of the industrial

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development path to favour is both very risky (choosing a technology that is revealed to be useless because it is rapidly surpassed by a new technology) and very costly (resources required to identify scenarios and above all to invest in the new technologies). However, the potential gain for the country as a whole, in terms of industrial and civil development or in terms of wealth of nations, is very high. The industrial policy choice is also very political. Stakeholders have to be involved in order for the vision to be shared and approved. In dictatorial regimes, such as Italy in the 1930s, industrial policy choices are imposed. In democracies, consultation with all interested parties is necessary in order to increase the probability of success, both because consulting interested parties allows policy-makers to better access relevant information and so make better-informed and more appropriate choices, and because consensus indicates that implementation will be more closely followed. The need for consensus on economic policy confirms the fact that economic policy implies value judgements on which all stakeholders have to agree. Industrial policy choices must also be made holistically, considering the interdependencies between instruments and policy levels, between strategy and organisations, organisations and their environment, and so on. The economic system is complex and industrial development is determined by complex interdependencies and processes. Industrial policy as a long-term vision of industrial development implies the abandonment of methodological individualism by considering the specific industrial development problem within the overall system of which it is a part. Just as individual actions within societies are largely explained by the characteristics of the whole society, especially its social norms and culture, firms’ strategies and organisations are determined by the characteristics of the economic system of which it is a part: the institutions, the extent of its market and the characteristics of other related markets. Industrial policy as a long-term vision of industrial development, leading to and backed by a civil society, can therefore be represented as shown in Figure 13.2. 3.2

A Framework for Industrial Policy as a Long-term Vision

Entitlements and provisions are essential elements of industrial policy. Using our reflection on industrial policy in the twenty-first century (Bianchi and Labory, 2006a; 2006b; 2009), we identify two other dimensions or levels of development, namely innovation and territory. Entitlements determine the rights of individuals to take part in development as well as in productive and competitive processes; provisions determine their

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Innovation: I

Entitlements: E

Provisions: P

Territory: T

Figure 13.2

Four levers of industrial development

knowledge, competencies and resources, hence their capability to take part in these processes, together with other resources necessary to perform economic activities. Innovation is the capacity to create knowledge and to apply this new knowledge to production processes; it is a dynamic element that sustains development. The territory dimension highlights the importance of the embeddedness of productive processes in territories, where they can gain from local resources such as social capital and infrastructure (Figure 13.2). All levers are important and do not substitute for each other. Sustainable industrial policy should aim at extending all four levers, coherently defining and implementing measures at the different levels of government, that is, regional, national and supranational. The levels of government are represented by the fifth dimension of the picture, namely, the bold line, thereby obtaining a sundial. All four elements determine both the cumulativeness and the sustainability of industrial development: cumulativeness in the sense of providing a dynamic process; sustainability essentially in the sense of compatibility of the economics, social and political spheres. The dimensions are not substitutable but complementary, and have to be simultaneously developed in order for industrial development to be sustainable from both social and environmental points of view. In fact, the four levers can determine

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I Human capital policies

E

303

Innovation policies

P

Social policies

Structural policies T

Figure 13.3

The sundial of industrial policy: the EPIT framework

development in a circular process, or virtuous spiral: innovation determines regional development and social improvements, which in turn enlarge entitlements, allowing more people to take part in the development process that enables further innovation. The scheme is therefore essentially dynamic, not representing the development process itself but both the elements to take into account in the policy decision-making process as well as their links. These elements and their links indeed determine possible learning mechanisms. The scheme – entitlements, provisions, innovation and territory – comprises all possible public policies, as shown in Figure 13.3. The overall area defined by the four elements in Figure 13.3 can be divided along the different axes drawn, thereby determining areas that show different focuses of policies. The IOP area is that of traditional industrial policies, consisting in measures to develop resources, such as infrastructure, financing for firms, and so on, and measures to promote innovation. The IOT area is that of social policies, since people are primarily part of territorial communities and their social well-being is intimately linked to it. New social policies also include the provision of learning capacity to workers and therefore extend to the whole IET area. Entitlements are another key element of not only social policies, but also human capital policies in the sense of training and education. Providing the population with the relevant skills is indeed essential to industrial

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and economic development. The ETP area is that of urban and environmental policies. If the axis leans towards the provision pole, away from entitlements, the risk is to reach what can be called a ‘Chinese syndrome’, namely, growth of provisions without entitlements, hence growth that is unsustainable from a social point of view because economic growth is not supported by redistributive and participative justice. In contrast, if the axis is shifted towards entitlements, without simultaneously developing provisions, we have a high social demand that cannot possibly be fulfilled, given the lack of resources. If the axis is pushed greatly towards innovation, the risk is that of a non-embedded change, while pushing excessively towards the territory risks leading to conservation without the ability to manage change. The sundial therefore expresses the necessity of coherence between the different levers. It is not necessary that all countries be perfectly in equilibrium, but if the sundial leans towards one pole or another it can indicate necessary policy adjustments and possible actions for policy-makers so that their long-term vision and, consequently, implemented instruments become more coherent and effective. These are industrial policies after the crisis: the capacity to view in the long term the complexity of society and to define policies that could guarantee their economic, social and human development. The axes can also change over time or according to new emerging priorities. For instance, the terms implicit in the EU Lisbon Strategy may be better termed as knowledge instead of innovation (because of its emphasis on the knowledge-based economy where not only innovation, that is, knowledge creation, is essential but also the transfer and accumulation of existing knowledge), and environment (stress on environmental concerns) instead of territory. Instead of entitlements, related to the right of access to collective goods, the concept of capabilities, defined by Amartya Sen (1982), is in the sense not only of access rights but also the capacity and the competency necessary to execute the rights. Provisions become human capital, which is key to activate the other three elements. Development depends both on the extension of the basis, that is, of the four elements (knowledge, environment, capabilities and human capital), and on the coherence between the instruments that the state, at different levels (regional, national and supranational), implements in order to develop and coordinate the four key elements. The sundial therefore represents a holistic approach to industrial policy, trying to understand the mechanics of the whole in which industry is a component part in order to favour sustainable development, that is, industrial development that is compatible with social development and equity and environmental preservation for future generations.

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INDUSTRIAL POLICIES AS LONG-TERM STRATEGIES: SOME EXAMPLES

We wish to illustrate our industrial policy framework using a few examples. These examples show the necessary coherence between the main pillars of the sundial we have drawn above, but are not used to identify a single optimal path. In fact, it is important to stress that there is no single path to long-term, sustainable industrial development but different possible routes, one of which may be chosen. We suggest that this choice can be helped by the sundial that expresses the required coherence for development to be sustainable and contributing to civil as well as economic development. We illustrate this view taking three country cases. We start with China and Brazil. The growth of the emerging countries is indeed, as already emphasised in this book, the most important aspect of the twenty-first century. Since the 1990s, these countries are guiding the growth of the world economy. For China, we focus on a particular region, Guangdong and the area of Shenzhen within it. The third country we mention is South Africa, because it is an interesting example of lack of entitlements for a long time, followed by a sudden extension of rights that continue creating tensions owing to the lack of equal distribution of provisions. China In China, Shenzhen is the city which grew most rapidly in the past 30 years. In 1978, Shenzhen was a fishing village, closed behind the frontier that separated the Chinese People’s Republic and the booming British colony of Hong Kong. The Shenzhen district had then 20 000 inhabitants and was the southern part of the Guangdong province, namely, the wide area on the delta of the Pearl River, whose capital Guangzhou, Canton, had been, in the eighteenth century, the port of entry of the western world into the Chinese empire, characterised by corruption and political dependence about which Adam Smith, in the Wealth of Nations, mentions the depravation. However, during the Mao era Guangdong became the area with most rapid industrialisation of the People’s Republic, based on heavy industry. At the end of the 1970s the policy of economic zones was started in China, and the first of these zones was created in Shenzhen in 1980. Economic zones are special areas where specific rules are implemented in order to support high growth rates. The main objectives are to attract foreign capital and to favour the creation of manufacturing activities not only thanks to low-cost labour but also to normative and fiscal conditions

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specific to the zones that make their product internationally competitive. The economic zones can thus manufacture products that are exported to the rest of the world, on the basis of a sort of social dumping that can only be temporary. Shenzhen was chosen by Deng Xiao Ping as the first economic zone with the idea that it could constitute, thanks to its geographical location, the productive basis for the island of Hong Kong. From then on, the Shenzhen economic zone has grown at very rapid rates, reaching 13.5 million inhabitants and attracting investments amounting to about $30 billion. The Shenzhen Stock Exchange has operated since 1990 and illustrates the strong development of the financial sector in the city and region. From an urban point of view the city expanded in two directions, one towards Canton, with the development of satellite cities increasingly integrated thanks to a railway and metro network that link the whole delta, and the other towards Hong Kong. The integration process is so strong that a unique metropolitan context has being considered since 2007 and would constitute the third largest metropolitan area in the world, after New York and Tokyo. From an economic point of view the development of Shenzhen is supported by a strong concentration of high-technology investment, in particular by multinationals in the electronic sector, together with commercial activities in the port which is integrated with those of Canton and Hong Kong. The extremely high growth has generated numerous problems, despite the regulation provided by the necessity to hold internal work and residence permits – so-called hukou – that limit the residence period within the economic zone. However, it is estimated that about 70 per cent of the Chinese workers employed in the zone do not hold hukou, so that they have to go back to the countryside after finishing their working contract. Parallel cities for temporary workers are also developing. Such a complex urban development experienced two crucial periods. The first period is the period of the definition of the special economic zone and of the first housing development centred on the growth of industrial areas with rather precarious residential areas. In the second period it has been necessary to structure the city through urban interventions that articulated service and residential areas when the economic zone was becoming more and more high technology. In particular, new universities were created, such as the Shenzhen University, the Shenzhen Polytechnic, the Shenzhen Graduate School of the Beijin University, the Shenzhen Institute of Information Technology and the Southern University of Science and Technology. The very rapid growth of the urban area inevitably created social problems, in particular regarding criminality. Another important type of

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problem has been created as regards the environment. Such a rapid and extensive growth has created many pollution problems, not only air pollution but also water and soil pollution. The Shenzhen government therefore decided to face the social and environmental problems by designing a long-term strategy of industrial development. International experts2 and business representatives, including representatives of foreign multinationals, were invited to identify the long-term development issue of the area and suggest solutions. The reflection carried out led to the identification of three critical issues: 1.

2. 3.

The need to increase the quality of production and the required adjustments in education and research, hence the relationship between industrial, technological and educational dynamics. The definition of environmental priorities and the redefinition of urban areas in order to address pollution and modernisation problems. The need to address social problems and to extend entitlements, in particular freedom of expression, pushed by the media in Hong Kong which are still relatively free.

The case of Shenzhen therefore shows how the issues of industrial policy, urban policy and technological policy are interrelated (lesson 1). It also shows the infinite set of actions that can be implemented at the different levels of policy, from town to province, region and nations, the coherence of which can only be guaranteed by the definition of a strategy for long-term industrial development (lesson 2). A third lesson to draw from this case is that a long-term vision of industrial development can only be sustainable if entitlements are developed together with provisions. The whole country of China is trying to sustain development on the basis of the growth in provisions, without regard for entitlements, namely, civil and political rights of citizens. Political elites of the Communist Party indeed take all decisions at all decision-making levels. This development, exclusively based on provisions without regard to entitlements, has a high cost, that is, the obligation to maintain high growth rates in order to avoid social unrest. China after the crisis will be a more technological, more dynamic and more educated country, but also a country with higher internal inequalities and therefore more unstable, although the country will have to show more responsibility and participation in global economic and political stability since it is one of the foremost world powers. This extremely high contradiction will have to be resolved. In terms of our sundial, Chinese development can be represented by the arrows, which will have to be moved to the left and towards the bottom in order to become more sustainable (Figure 13.4).

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I

E

P

T Figure 13.4

Sundial of Chinese development and necessary changes (arrows)

Brazil Brazil is today part of the BRIC, that is, the countries which inverted the dynamics of the world economy at the beginning of the twenty-first century. In 2009 Brazil was the world’s eighth largest economy in terms of GDP, and ninth in terms of purchasing power parity (World Bank). After a long history of colonisation and political instability after independence, Brazil has been a democratic government since 1985, after a period of dictatorship. Political and economic stability have been ensured since then. President Cardoso governed the country between 1994 and 2002 and ensured a return to economic stability; Lula da Silva has been governing the country since 2002, implementing an interestingly balanced strategy of industrial development. The Lula government courageously pushed public spending and sustained the poorest families’ income by raising minimum wages and pensions. This implied a significant rise in internal demand both from households and from the state. Investments also increased so that overall the economy experienced a boom.

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Without going into details in this short overview, it is enough to say that Brazil provides an example of balanced industrial development in the sense  that both effective rights (entitlements) and provisions rise simultaneously. In addition, entitlements and provisions contribute to development in a complementary way, so that development is robust and sustainable in the long term. This synergetic development of entitlements and provisions not only comprises social cohesion but also regional cohesion, which is a concern of the Lula government from the start. Regional cohesion means that the North-Eastern States must develop in order not only to eliminate the historical social injustice of the country, but also in order to generate a new level of economic development that could benefit the whole country. This strategy however requires a long-term vision where investment in large infrastructures, such as the Porto Suape, are viewed not only as development gears able to generate the growth of the areas around the port, but also become the basis for the development of trade with the rest of the world, thereby turning these regions into worldwide hubs of exchange. Hence the Brazil of Lula seems to represent an attempt to simultaneously develop rights and resources, ensuring cohesion between economic development and democracy (Figure 13.5). South Africa The new South Africa started in 1994, with the end of apartheid and the change in the Constitution. The history of the country originates with the arrival of Dutch colonists in 1652, whose objective was not to create provinces of their home country abroad, but to create new independent states. Fighting against local populations, the Dutch colonists (Boers) expanded along the coastal area around Cape Town and Natal Province. When Great Britain attempted to put these areas under British dominance, lured by the discovery of diamonds and later gold, the Boers were pushed further inside the country, crossing the Vaal river, where they founded the Transvaal and the Orange Free State. After a new war with the British, these states were conquered and the Union of South Africa was created, which got independence in 1910, taking the name of Republic of South Africa. Segregation became institutionalised and the government established three classes of racial stratification: white, coloured and black. The Republic was a state of the white, which reached standards of living comparable to western countries’ standards, while coloured people were discriminated against but involved within democratic life, and blacks were left as class without rights. After World War II, foreign governments started to boycott South African

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E

P

T Figure 13.5

Sundial of Brazilian development

products in protest against apartheid. Within the country, the black population started to organise and movements against apartheid expanded, of which Nelson Mandela, incarcerated for 27 years, remained a reference and leader. At the beginning of the 1990s, in the extraordinary period of history where the Berlin Wall and bi-polarism ended, even apartheid came to an end. The government took the first step towards this in 1990 by lifting the ban on the African National Congress and other political organisations. Nelson Mandela was freed and soon became Head of State. Civil and political rights were recognised for the whole of the population, but the economy remained in the hands of the white population. Today South Africa remains imbalanced since entitlements have been provided to the whole population but provisions are still unequally distributed, raising discontent among the poor population. Here the problems are essentially social and show that entitlements cannot be extended if provisions are not, hence the necessary balance between the two (Figure 13.6).

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I

E

P

T Figure 13.6

5.

Sundial of South African development and necessary changes (arrows)

FINAL REMARKS

Industrial policies therefore essentially constitute a vision of the future. Different visions are possible, but each choice involves opportunities and risks. There is a coherence issue in defining the vision and in making the specific policies. Coherence between policy actions and between policy levels requires policy-makers to have broad view of the economic system in which the particular development problem arises (holistic approach). This, in turn, requires going back to the basics of production organisation (to understand complexity of the economic system and identify appropriate gears of development). Rethinking industrial policies today means to redefine the basic concepts of collective actions and to put them in a dynamic perspective.

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NOTES 1. The above-mentioned research and volume has been condensed in this chapter version, which was presented during the San Sebastian meeting on ‘Innovative and Competitive Territories’, 11–13 September 2010. 2. One of these experts was Patrizio Bianchi who was Economic Advisor to the Governor of the Guangdong province in 2000.

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14. Regional policy: what (it seems) we have learned from some European experiences* Juan R. Cuadrado-Roura 1.

INTRODUCTION

Most European countries – from the UK and the Scandinavian countries to Italy and Greece – have a long experience in implementing regional policies. Generally, these policies have been a response to the existence of regional disparities in a wide range of variables (gross domestic product per capita, unemployment and productivity rates, and so on), which have a profound effect on the economic welfare of a nation region’s. Other specific problems have also justified some types of regional policies, such as those aiming to solve problems of industrial decline or to slow down the concentration processes of economic activities in the metropolitan areas. Traditional regional policy, based upon offering subsidies to firms locating their new plant in designed assisted areas, became rather popular in many European states until mid-1970s (Albrechts et al., 1989; Armstrong and Taylor, 1994). Government investments in the infrastructure of the lagging regions have also had an important role in the regional policies applied, and the location of state-owned companies was also employed as a tool to push the development of less developed areas or regions having problems of industrial decline. A different phase in the evolution of regional policies in Europe started at the end of the 1970s when these policies were set at a low priority owing to the fact that the countries were confronted with a deep recession process and budgetary constraints. Nevertheless this practically coincides with the period of rapid expansion of the European Community’s own regional policy. The increasing importance of the European Union’s regional policy since the end of the 1980s has been welcome in that it has occurred when regional policies of individual European states have been under restrictive fiscal constraints. Actually, the European Union (EU) has not taken over 313

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completely the member states regional policies. Individual states continue to implement regional policies of their own. What has really changed are the main objectives and strategies of the regional policies applied, paying much more attention to efficiency problems than to the equity-oriented policies mainly searching to diminish regional disparities in terms of income per capita. The aim of this chapter is to offer a panoramic approach to the regional policies operated in Europe from the 1960s to date, paying particular attention to the changes introduced in their objectives, the strategies pursued and the principal instruments employed in different phases. To do so, three periods are going to be differentiated. The first comprises from the end of the 1950s to the end of the 1970s, when the effects of the oil crisis knocked the economies and their macroeconomic equilibriums. The second refers to a period including from the 1980s to the mid-1990s, when regional policies passed to a low level of priority in the agenda of above all European countries, but the EU regional policy started to operate. And the third period starts from the mid-1990s and runs practically until now, mainly under the leadership of the European Union. The chapter adopts a very synthetic approach, according to the purpose of offering a panorama of the regional policies operated in Europe for more than five decades. The research supporting this chapter includes not only the use of documents and reports of different type produced by some European countries as well as the European Commission, but the personal contacts and experiences accumulated as consultant, member of teams charged to prepare reports for the European Commission, as well as adviser of some high authorities.1 The chapter is organized as follows. Section 2 gives a first panoramic approach to regional policies practiced in Europe, paying particular attention to the arguments used to support them. Section 3 is the central part of the chapter focused on the analysis of the basic characteristics of the regional policies operated in Europe – particularly in the European Union of 15 countries – during the three periods previously described. Section 4 answers the question of the influence of ‘theories’ in the orientation of regional policies. Finally, section 5 offers a number of specific points which can be drawn from the experiences analysed. They are not conclusions. Surely not really ‘lessons’ we have learned from the  European regional policies, as the chapter’s title suggest. But, the points underlined in this final section suggest a good number of aspects which should be taken into account to implement efficient regional policies.

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2.

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REGIONAL POLICIES IN EUROPE AND THE ARGUMENTS GENERALLY ACCEPTED TO SUPPORT THEIR NEED

Since the 1920s, many European countries have had some type of regional policies as part of their Government’s agenda. Initially, regional policy emerged as a matter of contingency, as part of policies to confront the global economic crisis of 1929.2 As the crisis passed, regional policy evolved into a genuine policy for structural change in the economy (Armstrong and Taylor, 1994) rather than merely ‘fire-fighting’ (Bleitrach and Chenu, 1982). The cases of the UK, France, Germany, Italy and the Netherlands are good examples of this; individual states pursued their own regional and/or territorial policies under different objectives and names, as ‘territorial arrangements’, ‘local and regional activities’, ‘correction of regional imbalances’, ‘development of lagging areas’, and others. From the mid-1950s to the early 1970s most West European governments developed some types of regional policy, trying to reduce the regional disparities existing inside their countries. In some cases, as in the UK and France, problems of congestion in the main metropolitan areas (Greater London; Paris-Region) have also been included as part of regional policies. Fiscal and monetary incentives and disincentives were widely employed to attain the objective of a much more territorially equilibrated economic growth, but other figures (growth poles, metropolis of equilibrium, reconversion areas, and so on) have also been put in practice. Interesting experiences, but not always sufficiently successful, were developed in Italy (the ‘Mezzogiorno’ policy), France (under the DATAR), the Netherlands (having a long tradition of physical planning from the early 1950s), the UK (with a particular emphasis a confronting the declining industrial areas and the development areas), Germany (a much more decentralized country, as federal state) and Spain (growth poles and new industrializing areas). From the end of the 1970s, as a consequence of the so called ‘oil crisis’, lower priority was given to regional policies by most of the member states of the European Community. The deep recession, the specific crisis of some industrial activities, the problems of unemployment and, particularly, the budgetary constraints determined a cutting back of the resources previously allocated to regional policies. Nevertheless this was also the period of taking off of the regional policy of the European Union (EU), which from the late 1980s3 until now has greatly strengthened to become one of the two most important policies of the EU, absorbing around 40 per cent of the EU budget, that is, a 0.45 per cent of the EU-15 gross domestic product (GDP).

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One of the reasons for the existence of regional policies in Europe is that less faith was put on the benefits of the free market than in some other countries, such as the USA. The development of the welfare state had its origins in this idea and in the criticisms made of the pure market as unable to solve certain social problems and needs. Thus, we talk about the ‘socially oriented’ free market economy and not just of the ‘free market economy’. In Europe, as in other parts of the world, the objective of wealth redistribution, and not only the attention paid to the population’s collective needs, also ranks high in economic policy, and it is clear the role the state often took in the economies for supplementing the market, promoting industrial development, improving the infrastructures or trying to solve labor market problems. Looking at the historically extended existence of regional policies in many European countries, some questions come to any observer or analyst. Why have regional policies had and do they continue to have such an important position in the economic and social policies practiced in Europe? What have been the arguments used to support them? There is agreement that regional policies have been justified not only by economic reasons, but also because of political and social reasons, and even by making reference to potential ‘historical debts’ for certain areas in a country. Among the economic arguments, the reasoning is rather lineal: 1.

2.

Generally, they started from one fact: economic imbalances do exist at territorial level. Wealth and growth do not take place in a harmonious way, and still less in a homogenous way. The reasons for this can be attributed to many different causes: the differences in the resources available, the geographical location of a region or territory (more or less isolated from the most dynamic centers); the existence of a number of crowding-in effects (the advantages of large capital cities, the weather); or to delocalization and reorganization processes of production sectors which were very concentrated, due to technological changes or to the rules of the market. Will these differences increase or decrease with time? Theories clearly disagree on this point. According to neoliberal and neoclassical model principles (see Table 14.1), the existence of such disparities should not be a cause for concern. The exchange market-based system itself will take care of adjusting prices and quantities (from Borts and Stein, 1964, to the most recent contributions, applied to regional trends by Hulten and Schwab, 1984; Harris and Trainor, 1997; and Barro and Sala-i-Martin, 1992; 1995). According to this approach it would be better if any type of regional policy were to be practiced,

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Key elements of neoclassical theory

Key assumptions ● Perfect information (same technology across countries), constant returns to scale and full divisibility of all factors leads to a world of perfect competition. ● Trade based on factor endowments (labor and capital). ● Within countries, factors of production (labor and capital) are perfectly mobile across industries.

Key driving factors Trade (move from autarky to free trade) provide an engine for growth (static gains from trade).



Implications for (regional) competitiveness All countries have a role in the division of labor based on their relative factor proportions. But if factor proportions are the same across countries (regions) then there is no basis for trade. Theory is most relevant for North–South or developed–developing country trade (i.e. where nations display major differences in factor proportions). ● Factor price equalisation implies convergence of returns to capital and labor. ● Given (universal) perfect competition, the notion of ‘competitiveness’ is essentially not relevant in the long run. ●

because it should mean a restriction or impediment to free-market movements. Convergence will really be the result of successive adjustments through the market reducing or cancelling any distortion to its operative capacities. However, other approaches have been much more critical of the market capacity to solve the disparities existing at the departing period and their future evolution. Arguments have been developed by Myrdal (1957), Hirschman (1957; 1958), Kaldor (1970) and Kuznet (1966a; 1971), among others, and are also supported by some conclusions of the modern theories of economic growth (endogenous growth theories). The cumulative economic development model (Table 14.2), for example, states that ‘the play of the forces in the market normally tends to increase, rather than to decrease, the inequalities between regions’ (Myrdal, 1957). Once particular regions have by virtue of some initial advantage moved ahead of others (Clark, 1966), new increments of activity and growth will tend to be concentrated in the already expanding regions, because of their derived advantages, rather than in the remaining areas of the country. So, the convergence among countries and regions is not something that must be taken for granted (Kaldor, 1970; Dixon and Thirlwall, 1975)

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Table 14.2

Key elements of cumulative development economics

Key assumptions (observed facts) ● Incomes do not necessarily converge over time ● Some countries develop more successfully than others ● Economics policy plays an important role in determining this success

Key driving factors (observations) ● Move from agriculture to higher value added sectors ● Openness to trade ● Foreign direct investment (FDI) ● (Foreign) development funds

Implications for (regional) competitiveness ● ‘Central’ regions with initial productive advantages are likely to maintain their lead over less productive ‘peripheral’ regions ● Catch-up productivity between regions is likely to be a slow process ● Policies should take into account a region’s stage of development ● Policies are needed to promote ‘spread effects’, e.g. through FDI or development funds

Agreeing with some points of this line, the contributions of the modern theory of international trade and the supranational integration processes also support the idea that it is possible that regional disparities not only disappear but that they will increase in size. There will be more advantages for the ‘central’ regions/nuclei, for the localization of new enterprises in and out of different territories, for an increase in competition in the markets, all of which will have an adverse effect on the least efficient producers or on those who are less able to react (Asuad-Sanen and QuintanaRomero, 2010; EU, 1987; 1991). Based on some of these last ideas and prospects, regional policies have generally found support in a set of essential economic arguments: 1. 2. 3. 4. 5.

Efficiency is not fully guaranteed by the market and by its adjustment mechanisms. The lack of adaptation and the backward state of some regions entail the misuse or the underutilization of their resources. The market is blind in terms of wealth redistribution. Population movements (migrations) have costs (economic, moral, cultural) which the market does not evaluate. The development of a backward territory can benefit the whole country, since it promotes a better use of resources, prevents the abandonment of facilities and equipment, and so on.

But it is also true that a number of ethical reasons and arguments have weighed in favor of the regional policies. The equity and solidarity

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principles support the need to progress towards greater equality of living conditions for all and the sharing of profits among the inhabitants of a country. The existence of wide economic and living standards differences among a country’s regions (or among a set of integrated countries) is neither politically nor socially admissible. It is not acceptable either that a proportion of the inhabitants are forced to leave their places of origin and their roots in order to obtain a job and enough income for living, sometimes which comes at a high price in personal and family terms. There are also political reasons supporting the need for regional policies aimed at balancing the social and economic situation of a country or of a group of countries. From a political point of view, it is argued that it is usually difficult to maintain the stability and the social and political cohesion of a country when there are territorial conflicts or serious inequalities. It will not be easy either ‘believing’ in the advantages and benefits of a supranational integration process (such as the European Union) or those resulting from the intensification of free trade through different global agreements (for example, the North American Free Trade Agreement and Mercosur – the South American Common Market), when one of the countries or the regions affected by such integration feels that it does not benefit in any way from the advantages attached to such integration. Finally, among the possible arguments for justifying a regional policy, recourse has occasionally been made to historical reasons or facts, alleging that some past decisions (localization of a country’s capital; the railway network; trade preferences for a seaport; the existence of international borders, which sometimes influence the growth of the regions on the border) have caused the current imbalance, which should be corrected or ‘offset’. To sum up, the arguments in favor of regional policies are supported by economic, ethical and political reasons, which usually are intertwined in the documents on which those policies were put forward. In the case of the European Union the main argument supporting a common regional policy is linked, in particular, to the concept of ‘cohesion’, first introduced in the Maastricht Treaty of 1992, which states that: ‘In order to promote its overall harmonious development, the Community shall develop and pursue its action leading to the strengthening of its economic and social cohesion. In particular the Community shall aim at reducing the disparities between the various regions and the backwardness of the least favored regions, including the rural areas’ (art.130 a).4

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3. EUROPEAN REGIONAL POLICIES: THE EVOLUTION FROM THE 1960S TO THE MOST RECENT FEATURES AND PROFILES Above all it must be emphasized that the experiences of regional policies in Europe show a wide range of possibilities and cases, both with regard to their objectives and the instruments used to reach them, or the institutional structure supporting their implementation, whether through centralized (national) regional policies or decentralized ones, as in federal states. Nevertheless some common points can be identified. First, the reduction in the regional disparities of GDP per capita has regularly been the main objective of regional policies, which have tried to promote the growth and solve the problems of the more backward regions in each country. But this objective has gone hand in hand with other more specific objectives: the industrial restructuring of an area in decline due to sector-wide changes; the structuring of the territory through railway or expressway networks; even to stop the excessive growth of some metropolitan areas, as was implemented during the 1960s and the early 1970s in France, as in the case of Paris.5 Secondly, there are no doubts that, in general, the approaches taken by regional policies have been clearly ‘conditioned’ by the social, political and economic context in which they were designed. Sometimes the design was also influenced by some theoretical ideas, to which mention will be made below, and in quite a few cases by the ‘fashions’ regarding the actions which might be taken, based on the ideas and policies already essayed in other countries, which had not always been checked. Finally, the phase of the business cycle in which the national economies found themselves has always been important and has conditioned those policies. It is clear that there have always been much more attention and funds devoted to regional policies in times of economic prosperity than in times of economic crisis, such as at the end of the 1970s and the early 1980s. Starting from this unquestionable variety, and without elaborating further on the details, our objective is to review the most significant features of the types of regional policies implemented from the 1960s to mid1970s, the changes brought about by the international crisis developed from the end of the 1970s and the predominant approaches in regional policies that can be observed in the past few years. To that end, special attention will be paid to the following items: the main problems and environment of regional policies practiced; the theoretical models used to support them; the central objectives pursued and the strategy adopted; the main instruments used; and the agents finally responsible for implementing the policies.

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Regional policy Key problems

Regional disparities

Policy criteria

Equity [Efficiency]

Objectives

Lagging regions Income Redistribution and Compensation

• Reduction of regional disparities • Development of backward regions • Regions having sectorial problems

Incentives (fiscal, monetary, commercial) Main instruments

321

Industrialization Strategy Polarized development

Infrastructures of transport Public enterprises Controls and un-incentives

Agents

Central Administration

Source: Author’s own elaboration.

Figure 14.1 3.1

Regional policies until the 1980s

Main Characteristics of the Regional Policies from the 1960s to the Early 1980s

Until the late 1970s, western countries’ economies grew at very high rates. This enabled large amounts of resources to be directed to regional objectives and policies during almost two decades. According to a scheme going to be used for each period, Figure 14.1 summarizes the problem considered as the most important to be solved by the regional policies until the 1980s, the policy criteria adopted, the objectives and main instruments, and the principal agent responsible for implementing and practicing the policy. What was regarded as the most relevant problem in this period? Without any doubt, that of interregional disparities in terms of GDP per capita and the existence, in particular, of lagging regions, apparently not able to progress to an economic take-off phase. Besides this focal problem, some policies also included specific rural regions and those regions which suffered an industrial decline as a result of the problems faced by very specialized and highly localized industries (traditional mining, steel and

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shipbuilding industries, basic chemicals and pharmaceuticals and others). In more isolated cases (France), policymakers considered the possibility of ‘applying the brakes’ to the growth of the large metropolitan areas (Paris). The dominant criteria on which regional policies were based at the time was that of the ethical/solidarity type. In fact, regional policies were justified on the basis of the policies for income redistribution and/or as a ‘compensation’ in favor of the more backward regions, in part due to the bad social conscience existing at the time. This equity principle almost always prevailed over the efficiency principle, in the well-known conflict between both criteria, and has always been at the heart of the discussions regarding political and social policies. The main objective to be achieved by regional policies was to reduce the existing interregional disparities and, at the same time, to try to develop those areas which were especially backward as a result of geographical reasons (peripheral regions; rural areas) or to try to help regions with specific problems. A feature which stands out in this overview of the regional policies implemented during this period is the type of prevailing strategy to achieve those objectives. In general, although exceptions existed, this was based on promoting the ‘industrialization’ of those areas, attracting new investments from abroad, the location of new factories and the development of infrastructures to support economic growth. In a high number of cases, growth was identified with ‘industrialization’. This was equally based on the principles of ‘polarized growth’; creating one or more focus on a region through large investments which could act as growth instigators and which would be expected to act as the engines of the region’s economy. Undoubtedly, on the theoretical level were the ideas of those in favor of the cumulative growth processes (as Hirschman, 1957 and 1958; Myrdal, 1957; and Kaldor, 1970, had pointed out) and the need to create unbalancing elements in some areas in order to offset the backward effects. But there were also present some relatively abstract ideas about the advantages of the concentration of industrial activities (or, in particular, a big key industry) and its backward linkage effects in the region (Perroux, 1955; Boudeville, 1966), or those of the center-periphery model (Prebisch, 1950; 1959; Friedmann and Alonso, 1964; Friedmann, 1966) and the need to ‘break off’ such a dependent relation. The range of instruments used, always oriented, above all, to ‘industrialize’ and to raise investments and new activities, whether national, local or foreign, was really very wide. Let us remember the analysis on tools carried out by the Organisation for Economic Co-operation and Development (OECD) or by the team from the University of Strathclyde

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(Yuill and Allen, 1981). In general, those tools were in some way related to the Keynesian concepts prevailing at the time; more spending, making investment cheaper, a belligerent attitude on the part of the state; and so on. The abundance of tools constitutes, in any case, one of the basic features, with a predominance of a wide range of incentives (fiscal, monetary, loans, commercial, and so on) or using reductions, subventions, discounts, as well as the provision of public land for establishing new businesses. Besides this, another widely used tool consisted in investing in physical infrastructures (roads, railways, airports, ports). Generally its effects were slow to materialize, and sometimes it even provoked overinvestment (Biehl, 1975; Biehl et al., 1986). Another tool used has been state-owned enterprises, which generally were focused on the basic industrial sectors (iron and steel industry, chemicals and shipbuilding) and located so that they promoted the economic activity of underdeveloped areas (the INI – National Industry Institute in Spain; the IRI – Instituto per la Ricostruzione Industriale in Italy; the industrial de-location practiced in France). Their long-term effects have been very much in doubt. In some cases they have been dubbed as ‘white elephants’. In other cases, it has been shown that the investments made in them and their own activity had indirect effects which favored the more developed regions in the country, as has been demonstrated by Rosenstein-Rodan in Italy. Finally, among the tools there also appear the more occasional use of controls, regulations and discouragement concerning the set up of new industries in those metropolitan areas which already suffer serious concentration and congestion problems. The case of France and the deconcentration policy from Paris towards other province capitals is a good example of the use of these instruments, despite their poor results. From the point of view of the regional policies responsibility and the institutional framework, a dominant feature is that such policies were almost always designed from the ‘center’ (that is, by the national or central government), and were controlled and managed also from that center (that is, in France, through the DATAR; in Spain directly linked to the Ministry of Planning, Commissariat of Plan; in Italy, through the Cassa per il Mezzogiorno). So, referring to this rather long period (mid-1950s to the end of 1970s) it is possible to talk of ‘regional policies . . . without the regions’. 3.2

Changes in Regional Policies in the 1980s

The brusque irruption of the so-called ‘oil crisis’ (1973 and 1979), which in turn provoked the apparition of serious problems of oversupply,

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together with the development of new technologies and the need to carry out industry-wide reorganizations, provoked significant changes in the regional policies applied (Cuadrado-Roura, 1988). These changes consisted of the following: 1.

2.

3.

4.

An increase in the number of problem regions, so that the regions which demanded state aid were not restricted to the more backward, but also included some of the wealthier which faced serious unemployment rates and needed industrial restructuring processes (UK, Belgium, France, Germany, Italy, Spain). Interruption of a number of those actions which until then had been regarded as useful (incentives) and positive (state-owned enterprises, focused actions). Irruption, with a growing force, of neoliberal approaches, in favor of the market and its adjustments, criticizing the welfare state and with arguments in favor of rolling it back and applying fiscal orthodoxy, and so on. The result was that regional actions were sidelined to a large extent. There was a clear predominance of macroeconomic approaches and problems (unemployment, industrial reorganization, petrodollars, and so on). As a consequence of all this, the efficiency criterion became more important than that of equity, owing to the need to modernize the economies and making them more productive. Figure 14.2 shows in a very simplified way the impact of the international crisis on the key problem, the policy criteria, the objectives and instruments of regional policies applied in many countries, particularly in Europe. At the same time, and as a fact which stands out, the relatively good performance of some intermediate regions, based on small and medium enterprises (SMEs), whose innovation and export capacity caused regional economists to turn their attention towards some ‘new’ ideas, which are not easily applied, such as regional policy (RP): endogenous development; millieux innovateurs (Aydalot, 1986; Maillat, Quevit and Senn, 1993); spread of innovation; learning regions; industrial districts; and so on. Most of these ideas offer a bigger role to regional and local authorities, and pay more attention to local matters than in the past.

From a strategic point of view, the reorientation of regional policies was already expressed in three great guiding principles: 1. 2.

Priority was given to efficiency over equity. Greater importance given to the regions’ endogenous growth potential.

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Regional policy Key problem

Policy criteria

Objectives

Economic crisis 1970s/1980s

325

Lagging regions + Problem-regions

Efficiency & sectorial reconversion over Equity

Endogenous potentialities Strategy

Sectorial adjustments ‘Modernization’

National versus Regional Coordination

• Old incentives ... but much more selective Main instruments

Agents

• New policies: -Business services -Technological infrastructures -SMEs finance -Innovation policies

Central Authorities Regional Authorities

(Gov´ts) + (Reg. Gov´ts)

Source: Author’s own elaboration.

Figure 14.2 3.

The impact of the international crisis on regional policies

The need of greater coordination of the national/regional activities with the aim of employing as best as possible a lower volume of resources than in the past. As a result, appeared different bodies linked to the promotion of SMEs (such as regional development agencies), with technological development (scientific and technological parks); financing (mutual guarantee companies; venture capital companies, and so on).

In Europe it is possible to notice that during these years there was a ‘contagion’ and ‘imitation effect’ among countries and regions. Some ideas became ‘fashionable’, as it was the case when creating numerous technological parks, scientific parks, regional development agencies, ICT programs, and so on. In quite a few cases, these initiatives had very limited or no success and they often demanded the achievement of short-term objectives, without using all the means for succeeding.

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Regional Policies, 1995–2007

During the period 1995–2007 there have been a number of significant changes in the orientation of regional policies: 1.

2.

3.

4.

Regardless of the fact that the solidarity and equity principles continue to be in force, it is clear that the efficiency objective (under the guise of ‘competitiveness’) has taken again a predominant position in all regional policies. This has resulted, on the one hand, from the influence of neoliberal ideas, which have dominated the ideological scene during the past few years (growing trust in the market, horizontal and not industry-oriented measures) and, on the other, from the economies’ globalization, which has caused regions to be more exposed to international competition and with fewer protective measures available than in the past. Any region is practically forced to try to be competitive against the other regions in the country and the rest of the world, instead of being ‘protected’ or ‘subsidized’. To all this must be added the fast development of information technologies (IT) and of the knowledge society, which demand special efforts on the part of more backward regions for bridging what is called the technological gap. The above has led many countries and groups of countries to decide that regional policies must be much more ‘selective’ about the regions eligible for receiving state aids. At the same time, it is thought that the resources assigned to regional actions must always include messages which reinforce the idea of developing the territories taking into account their sustainability and capacity for competing in the future. That is, they must promote those factors which can allow a region to modernize and have efficient production structures within their potential specializations (Cuadrado-Roura and Parellada, 2002; Cuadrado-Roura, 2010). Thus, we cannot be surprised that competitiveness and efficiency today occupy a key position in regional policies (see Figure 14.3), and from that result the objectives which those policies pursue: making the territories competitive, ensuring that they contain elements which contribute to that area’s companies having a competitive ‘plus’, besides their own competitiveness. In strategic terms there is a belief that what is needed is to create in the regions a competitive environment, and for this it is necessary not only to support the industry (as in the past) but also the options provided by service activities and the development of IT. Many of these ideas are supported by some of the theories and

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Regional policy Key problems

Globalization: Different regional impacts

Policy criteria

Competitiveness and efficiency

Objectives

Competitive territories Modernization Efficient productive structures Human capital

327

Selection of regions to be supported

Internationalization Internat. competitive

Creating a Competitive environment

Strategy

Particular attention to services and ICT

Infrastructures (physical but particularly technological)

Main instruments

Advanced services (Business services + Innovation) SMEs potentialization Entrepreneurship Other horizontal measures to increase territorial competitiveness

Agents

Coordination

Central authorities (gov´ts) + Regional authorities (reg. gov´ts) + [Supranational administration]

Source: Author’s own elaboration.

Figure 14.3

Regional policies from the mid-1990s to now

interpretative models of growth and its causes. This is the case of the approximations of the ‘modern’ neoclassical model with the conditionality of the b convergence due to the existence of a number of factors which slow down or prevent progress and which determine a stationary state which differs from that of the more dynamic regions). Equally, the social capital theory has paid more attention to the influence of cultural differences, social attitudes, reciprocity norms, civic commitment networks and institutions in every development process. Besides, the ‘new economic geography’ has reintegrated the role of the buildup and agglomeration economies and of the intangibles in every economic concentration process. The models of exporting competitiveness (Porter, 1985, 1990; Kay 1994) raise the possibility of reaching interregional convergence through ‘virtuous’ accumulative causation processes, based on a region’s exporting capacity, particularly when this region enjoys good production factors (human capital with specific skills); a strong set of interrelated activities (clusters); enterprises which operate in competitive environments; and a significant local demand.

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When considering regional policy instruments, it is clear that not only are there some new instruments, but also that there has been a change in the emphasis placed on the role of many of them. In this sense, investments in human capital (training at all levels, having a skilled workforce) occupy a prominent place. The following are also important: investments in physical infrastructures, especially in those of a technological character and those which promote innovation (R&D); the promotion of modern and advanced services (which bind together enterprises and innovation); SMEs promotion (reducing the importance of direct state aid, but improving the support provided and promoting closer cooperation among enterprises); the revalorization of the entrepreneur role (who must be pampered, in this sense, due to his key role); and, last but not least, other horizontal promotion actions like specialized rural promotion, development of tourist areas and environmental conservation policies. To attain the objectives of creating new opportunities of regional development some new entities have been fostered in many European countries, such as regional development agencies, technology parks and technical laboratories generally oriented to help the SMEs already existing in the region as well as to attract new investments and initiatives. A proliferation of these kinds of entities have been created by the states and/or the regional governments in almost all European countries. Some of them are successful (for example, the regional development agencies of EmiliaRomagna, Italy; Valencia, Spain; and Ireland, for example), but there are also examples of failure or of very poor results, mainly in the case of the technology parks It is obvious that one of the basic objectives actually assigned to regional policies is that of improving the regions’ competitive capacity, which is placed ahead of the mere reduction of interregional income disparities. But what competitiveness are we talking about? Competitiveness has microeconomic and macroeconomic components. At the micro level, it is stated that an enterprise is competitive when it is able to grow, to acquire new markets and achieve profits at its level. Thus, competitiveness consists in the enterprise’s capacity to produce, in a coherent and profitable way, those products which meet the requirements set by an open market in terms of price, quality, innovation, and so on. Any enterprise must meet these requirements if it wants to stay in business, and the more competitive an enterprise is against its competitors, the more market share it will be able to win. Those who are uncompetitive will lose market share, and they will be able to survive ‘artificially’ only through official aid and protection, which does not make much sense. From a macroeconomic perspective, the competitiveness concept is more fluid, but it means that the enterprises ‘environment’ should promote

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their competitive capacity and that it should also solve their problems for retaining their competitiveness or increasing their capacity to do so (skilled labor, supplementary services, collaboration between companies, and solutions for specific problems in certain sectors (from packaging to export routes or information regarding the regulations in force in the export markets, and so on). ‘The competitiveness of a country/region can be measured by the degree in which it can, in a free and competitive market, produce goods and services which meet the test of international markets, and at the same time expand the inhabitants real income’ (World Bank, 2008c). In this sense, the sixth periodical report about the EU regions (European Commission, 1999) defines regional competitiveness as ‘a region’s capacity to produce goods and services which meet the test of international markets, while at the same time maintaining high and sustainable income levels; or in a more general way, as the regions’ capacity to generate relatively high income and employment levels while being exposed to external competition’.

4.

HAVE GROWTH THEORIES HAD ANY INFLUENCE ON THE REGIONAL POLICIES APPLIED?

Apparently, growth theories have really had an influence on the regional policies applied in Europe, at least, on some of them, as has been pointed out at section 2. Some ideas and models supporting regional policies implemented in Europe in the three periods previously analysed have also already been quoted in section 3. However, after analysing many of the regional policies’ experiences and approaches, it can be pointed out that the influence of the theories has not been particularly significant. Recourse has been made to them, of course, but with the purpose of ‘justifying’ specific actions (politicians always seek prestigious external support, which suits them, as authoritative arguments), but a gap existed between the theories/models and the policies. To better understanding this statement, it is necessary to begin acknowledging that many of the theories on regional growth and its causes are, above all, ‘indebted’ to broader models and theories which make reference to national economies. In many cases there is not a highly ‘regional’ specificity (Cuadrado-Roura, 1995). It might even be argued that some of the supposedly ‘regional’ theories pay almost no attention to the ‘space’ or the ‘territory’ as a significant variable, and that they have not been able to integrate it into the models. In reality, quite a few such

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theories are clearly ‘subordinated’ to macroeconomic theories of a more general nature. Nevertheless, it must be admitted that the progress recorded in the field of growth theories has supplied regional policies with new ideas. Some of them have contributed to ‘clarify’ the factors which seem to be most important to achieve economic growth. Others, have highlighted that territorial imbalances require the authorities’ direct intervention and that, without it, the market will not be capable of solving such disparities (unless one thinks that population movements in the form of migrations have no costs and that the readjustment between factors, prices and costs is made by the market, which is not the case, nor is it guaranteed that it can take place within an acceptable period; on the contrary, the period involved is quite long). And, finally, some rather limited or modest contributions have provided regional policies with new ideas. This is the case with the theory of the export basis, of the theory of the innovative means, of the aspects which highlight the ideas of endogenous development (not to be confused with the endogenous growth theories), the thesis of the ‘industrial districts’ and the idea about the clusters, which have also provided elements used in designing regional policies from the 1990s up to now. However, it can be established that economists and geographers have had a clearer influence on improving regional policies in the field of applied analysis. Specifically, the studies that became increasingly sounder and deeper in respect of the regions’ behavior and referred to concrete countries and regions, have proved to be very useful. They have made it possible to identify weaknesses and strengths the latter of which have been included in the regional policy programs and approaches. Equally, the use of some analytical tools (input-output tables, regional accounting, social accounting, estimates of social capital, comparative studies of technological development, and so on) have made it possible to make better regional diagnosis, contributing in this way to give sounder support to the ideas and programs to be developed. The evaluations of the effects of regional policies which have been put into practice in many countries (particularly in the European Union) are also having an extraordinary influence. This has made it possible to evaluate the real contribution of the regional funds and actions for development, handling the ‘counterfactual’ evolution, both in the estimate of macroeconomic effects (investment, growth, productivity and employment), as in more specific policies (training for those unemployed, providing support to enterprises for tackling their technological and export problems, technological development, and so on). Finally, in quite a few countries, the growing interest about the regions has led them to produce regional statistics which did not exist before, and

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which now allow them to make real comparisons both within one country and among different countries. This is a fundamental step in the progress achieved in the last few years, which has enabled many decisions to be rationalized and the objectives to be achieved quantified and to evaluate their rationality ex ante and the results of their application ex post. Thus, two points can be made. First, theories have had a lesser influence than it might have appeared at first sight. Second, the progress recorded and the greater depth of regional studies have contributed to provide a greater support to regional policies.

5.

WHAT HAVE WE LEARNED?

Analysis of the European experiences in regional policy and their evolution gives sufficient evidence to succinctly highlight what we ‘have learned’. In some cases these consist of recommendations which should be made to any authority that wants to start a regional development program. The first that must be pointed out is that in regional policies experiences have varied greatly in terms of success or of little or no success. Some years ago, Maurice Dobb put as an example of the search for developing the backward countries and areas the image of the zigzagging path followed by a dog seeking a buried bone. The examples available of the European regional policies indicate learning ‘trial and error’. Second, regional policies are not, and must not be, mere ‘redistributive’ policies. They cannot be presented as such (as politicians sometimes do); they must include much more demanding objectives than just the distribution of ‘subsidies’, ‘state aids’ and ‘gifts’, in short, as a sort of ‘Santa Claus’ state. To grant subsidies to a region on a permanent basis is the worst decision that can be taken for that region. Any regional policy must include specific deadlines for reviewing whether the regions included must ‘exit’ the program. Third, there are some principles which seem to be definitively consolidated as issues which we have learned from the RPs experiences. Four in particular can be highlighted: 1.

Need to program for the medium to long term. Under no circumstance can a regional policy be included in government programs with shortterm objectives. As with all the actions aimed at promoting economic growth and development, their objectives and results are only visible in the long term, provided the policies have been maintained, even when no immediate results are apparent. The failure of many regional policies can be attributed to a premature change in policy, which

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causes the abandonment of the actions taken before; to the fact that no programming is made for the medium/long term; and to the consequences of a change in government and in the authorities before being able to see the potential rewards. In this regard, the EU has set up action ‘programs’ for its regional policy with a six- to seven-year horizon, which are agreed with governments – national, regional or local – and which cannot be altered despite the political changes which might result from fresh political elections or eventual changes in the government. Concentration of the actions. This means that the regional policies cannot be confused with policies for distributing privileges to all regions of a country, under the pressure exercised by the different politicians and representatives. The RPs must be, above all, selective. A region’s ‘eligibility’ must be the result of certain minimum terms set beforehand (that is, that its per capita GDP is below the 60 or 70 per cent mark of the national average). As a result, only some areas or regions must be the object of the regional policy. Principle of additionality. This requires that regional programs do not include actions and investments which would have been programmed in any case by the state, regional governments or state-owned enterprises. Regional policies must contain investments and actions which ‘would not have been made’ if they were not programmed as part of the regional policy. Participation and joint participation in the preparation and implementation of regional policies. It is essential that the regions and their main agents have a clear role both in the preparation of the programs and in their implementation. There are enough examples of successful development agencies, whose success is due to the fact that they have really ‘integrated’ entrepreneurs and other regional agents (representatives of chambers of commerce, autonomous organizations and so on) within themselves and within the technological support institutes for SMEs.

2.

3.

4.

Besides the above, there are several ideas which we have also learned from the most positive experiences: ●



To limit the subsidies and aid to the first time an enterprise sets up its business. Many of these ideas do not regard subsidies as effective. They often promote ‘rent-hunting’. To leave direct aid measures in favor of those which help enterprises to solve their problems and can become more competitive at the national/international level (technological improvements;

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solving export problems; cooperation among enterprises; innovation promotion). A definitive ‘no’ to the role played by state-owned enterprises as means for promoting development. Of course, there can be exceptions, but, in general, authorities and civil servants take the wrong decisions, based on a knowledge which ignores the changes, or which is based on non-replicable cases. To grant the utmost importance to ‘entrepreneurs’ as the agents for development, either those in SMEs or in micro-enterprises. Programs promoting them: first launch support; simplification of the bureaucratic procedures; joint ventures with foreign enterprises; and so on. The institutions work when the agents apply themselves to their working. This can seem like an obvious thing to say, but it is true. There exist clear examples about this. The protection of the Santa Claus State is not essential. No regional development can take place if this is not sustainable through time. That is why it is necessary to set up productive structures and units which are efficient and competitive, and which offers several fronts on which to act. Regional policy must be designed to bring about concrete results. So, regional policies must help to finance actual projects for regions, towns and their inhabitants. But the idea is to create potential, so that the regions can fully contribute to achieving greater growth and competitiveness. Regional policies must be subjected to a regular ‘evaluation’. This is a very important point. The European experience is, in this sense, very positive. Ex ante evaluation in order to determine whether what is intended to do is feasible within the programming period (six- to seven-years); ongoing evaluation for changing any imbalance which might have arisen; and, above all, ex post evaluation for effectively justifying whether the resources employed have been put to good use. Usually, politicians do not like being evaluated. But they must be, and not for preventing frauds or the diversion of resources, but to have elements to be considered in future policies and programs.

NOTES *

A first version of this chapter was given by the author as a keynote lecture at the 1st Congress of the Regional Science of the Americas, Cartagena (Colombia), in 1999. This is a much revised version of it. References to the case of some Latin American countries have been passed over. 1. Anyway, the responsibility of this chapter is exclusively that of the author.

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2. Some previous experiences could also be quoted as historical precedents, mainly in the cases of the UK, France, the Netherlands, Spain and in the Scandinavian countries. 3. The great reform of the EU Regional Policy was decided upon in 1988 and started from 1989. A set of major reforms were introduced which were extremely far-reaching and altered the nature of the previous European Community regional policy. The creation of the European Regional Development Fund in 1975 had been the departing point of EC regional policy, but its purpose and relevancy changed completely with the new rules approved in 1988. 4. This compromise has also been introduced into the most recent ‘Lisbon Treaty’ (2007). 5. The ideas and the methods of J.F. Gravier (1958) were adopted by the Délégation interministévielle à L’aménagement du territoire et à l’attractivité régionale (DATAR) (French Agency for Territorial Planning and Development) (1963) to implement a policy oriented to stop the strong growing and centralizing process of Paris.

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15. Cities in times of economic crisis: a challenge for local governments Manuel Perló Cohen INTRODUCTION The global economic crisis, which began officially on 15 September 2008 with the bankruptcy of the investment bankers Lehman Brothers, has spread throughout a wide-ranging group of countries and regions of the world. It has penetrated rural areas and cities, has simultaneously taken over large metropolis and small urban centers, and has caused devastation in neighborhoods as well as in central districts. In short, it has spread over the most diverse geographies. However, the devastating effect of this phenomenon differs considerably between large regions, countries, cities and neighborhoods, when a comparative analysis of the particular effects of the crisis is performed. In the case of urban localities – this study’s central theme – we can identify cities whose main macroeconomic indicators (employment, production, investment, consumption, public-sector spending) have suffered considerable deterioration. However, we see at the same time, that some urban localities have been able to mitigate the most adverse effects, and still others have done enough to get out of the crisis and have found a sustained path for growth. There are many factors involved in such a variety of results and, although some of them are completely outside the control of the authorities in urban areas, others are related to their economic structure and financial situation, in addition to being related to the way that local governments have reacted to the crisis. One particular aspect is the way they have reduced the most negative effects possible and, in some cases, have carried out actions seeking recovery in the medium and long term. The participation of local governments in combating the crisis seems natural and uncontroversial. However, it is a relatively new and quite complex action which has led to questions being asked, such as: are local governments endowed with the power, resources and capacity to develop and put in place anti-crisis plans and programs? Can their actions really 335

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be effective, can they make a difference and counteract the effects of the recession? Is it not a distraction to be thinking that local governments can ‘solve’ the current situation when it is known that powerful forces are to be found in other places and that other more powerful economic and political actors have the last say in this process? Our point of view concurs with the idea that local governments should and can get involved actively and responsibly in combating the crisis but, at the same time, we understand that this intervention has its limits, and these limits should be identified. It is a sphere of action that requires local government to have real and effective powers and capacities. Finally, the instruments that are going to be applied should be the right instruments and should be designed to achieve precise and specific objectives. In this study, we will review the main effects that the world economic crisis has had on cities in different regions around the world; also, we seek to learn about the response of local governments to the crisis and make an initial evaluation of their effectiveness. In particular, we are interested in identifying those governments that are committed to policies forming human capital, urban innovation and strategic development planning. This interest derives from what appears to be an important consideration, still subject to systematic and rigorous proof, that is that the cities that have best responded to the challenge of the crisis are precisely those cities that have the best human capital, that have invested the most in innovative urban projects and that have a political-institutional leadership with a long-term outlook. We do not seek to offer solutions or recommendations. Each city, with its own characteristics, resources and limitations, should generate its own response, be it individual or collective, to combat the crisis. However, we want to emphatically question the idea that it is not possible for local governments to make significant contributions to combating the current recession. Thinking that the ‘crisis will soon be over’, that ‘it is not with in my power’, or that ‘someone else has to do the work’ will only aggravate the problems and put off recovery of growth. This particular historical climate marks a major moment of definition for local governments. The immediate future of each city will depend on what they do, or fail to do, at this time. There are several reasons underlying the need to analyse the topics treated in this chapter: some are of a theoretical and methodological nature, but the most important reason is a practical one: the crisis is still not over and we have to find the best way of combating it. In reality, despite the fact that the end of the crisis has officially been decreed, in truth, we are still quite far from having overcome it. In his testimony

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before the US Congress on 21 July 2010, the Chairman of the Federal Reserve, Ben Bernanke, pointed out that the ‘economic outlook remains unusually uncertain’ (Bernanke, 2010). On the other hand, the outlook for 2011 is for less growth than that recorded in 2010 (The Economist, 2010). But, even if the march of the economy were to restart in a sustained manner in the medium term, the effects of the crisis, such as unemployment, insolvency, loss of housing and others, will remain for years.1 This is the ‘new normality’ with which we will have to live in the near future. This is an opportune moment to review many commonly accepted theses, to review history and theory, and to put new hypotheses to the test. Perhaps these efforts may be of some use in orienting the action of citizens, companies and governments in combating the first great economic recession of the twenty-first century.

I.

HEURISTIC FRAMEWORK

This chapter does not undertake a deep and systematic theoretical exploration of what a crisis is. However, we must accept that a heuristic framework can be very useful in helping guide the particular research that one wants to work on. Therefore, I will quickly review the existing literature in this section and will submit an analogy on order to give the general heuristic principles which will be applied throughout the text. The phenomena of economic crises have received a significant amount of attention within economic sciences. The Great Economic Depression of 1929 has been studied by renowned contemporary economists from different schools of thought. Some of the most outstanding research was carried out by Galbraith (2009), by the Monetarism School represented by Friedman and Schwartz (1963), by the Austrian school represented by Murray Rothbard (1963), Ben Bernanke’s research (2000), and research with a neoclassic point or view from Cole and Ohanian (2007). On later crises, there is a broad range of work of all sorts such as the work by Castells (1978), Eichengreen (2002), Harvey (1982; 2010), and Kindleberger (1989), to name a few from a vast group of authors. Much has been written on the current economic crisis. Thomas Bourke (2010) compiled, for the European University Institute, a bibliography of books that were published for the first time, as well as a smaller list of reprints between January 2008 and November 2010. The result is that 407 books have been printed in less than two years. Now, in case this is not enough, we are facing a phenomena that has captured the attention of many historians, including Clavin (2000), Constatine (1983), Rauchway (2008), culture specialists such as Dickstein (2009),

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public policy analysts Stuart and Stack Jr (2008), and many more pieces of work from just about all branches of social and humanistic knowledge. In spite of such a large array of books, and I have not even begun to touch the surface of academic articles, it is hard to find a more general theorization of the phenomenon, something along the lines of ‘A general theory of crisis’ that groups different takes on the issue while it goes beyond sectorial approaches. The absence of such an approach is felt, especially when it comes to the subject we cover in this chapter. Our interest is to explain the differential impact of the crisis on the territory and the contrasting responses by local governments. Because we would like to have guiding principles for our work, principles with a more general and heuristic approach, what we have done is to help ourselves with an analogy to create some of the principles and questions that will guide our analysis.2 We will use an analogy from the field of natural disasters, specifically earthquakes. Obviously we are facing very different fields of knowledge and phenomena (one being from earth sciences and the other one from social sciences). Their causes and effects reflect many differences (that is, an earthquake may not be avoided in spite of the vast amount of scientific knowledge, but a crisis can be avoided, or at least lessened through actions and human intervention.) Yet, there are similarities too, as crisis and earthquakes are extraordinary events that are not part of our daily living, and their impacts have significant differences for which we may be more or less prepared. The points described next represent some of the similarities between both phenomena. (1) The causes of phenomena (earthquakes and crises) are structural and inherent to the system they will affect. One comes from tectonic plate dynamics which create energy subduction and release processes, and the other comes from features belonging to the capitalist system, such as uncertainty, speculation and imbalance in different economic cycle phases. (2) We have the technical and scientific knowledge to know that they will occur. We know they can reach a certain degree of intensity and know they will have a given impact. We even know that they possess precursory phenomena which announce the proximity of the event, but we cannot know for sure when or where they will occur. (3) Although they can have a broad radius of impact, their intensity depends on the proximity to the epicenter and on the form of dispersal. (4) Even though there are elements within the same range of affectation, they will suffer damage unequally, depending on their degree of vulnerability. (5) Even if the phenomenon’s critical phase may last briefly, its destructive effects may be felt for a long time. (6) A history of past events is very important to identify patterns of recurrence, impact and society response. (7) Although

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having records is important, no event is a repetition of any other. They are unique and have their own characteristics. (8) The mental representation and the memories we keep of phenomena, as a society and as individuals, tends to diminish and to become erased with time. Those generations that did not live through such phenomena will tend to think that they are situations they will not be living through. (9) In spite of the destructive effect such events entail, there is always the opportunity to rebuild in order to decrease damages and vulnerability, helping us face future events in a better way. Describing these heuristic principles or research paths is important for this investigation, since many authors have used either different or even opposing principles. I will not develop each one in this section, since that is not the purpose of this work. We are not undertaking theoretical questioning, and these principles will appear in other sections. In a recent interview, economist John Roemer (2009) pointed out that an economic crisis brings with it a process of homogenization of risk and loss of wealth among the population. My point of view is just the contrary: a crisis exacerbates differences and produces heterogeneity among the population and within the territory. The difference in reactions depends on their place on the crisis map, and on their own internal conditions, to wit, on their degree of vulnerability. Another example can be drawn from one of the most recurring subjects on the history of crises: the denial or the reluctance to accept that they can happen again. Such a reaction has been analysed and criticized by Reinhart and Rogoff (2009), in one of the most comprehensive books ever written on crisis in recent times. They state that: The essence of the ‘this-time-is-different’ syndrome is simple. It is rooted in the firmly held belief that financial crisis are things that happen to other people in other countries at other times; crisis do not happen to us, here and now. We are doing things better, we are smarter, we have learned from past mistakes. The old rules of valuation no longer apply. Unfortunately, a highly leveraged economy can unwittingly be sitting with its back at the edge of a financial cliff for many years before chance and circumstance provoke a crisis of confidence that pushes it off. (Ibid., p. 1)

Reinhart and Rogoff worked exhaustively to document crises in China and during Medieval times in Europe from times reaching as far back as the twelfth century. But they specifically worked on crises from the 1800s onwards, attempting to identify how many have occurred, to locate their epicenter or place of origin, and define their nature. Of the book’s multiple contributions, one includes finding the common elements between different types of economic crisis, in addition to recognizing the fact that

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each crisis has its very own special physiognomy. One of their conclusions is that the 2008 crisis, which they called ‘Second Grand Contraction’, is a financial global crisis with the following components: (1) one or more global financial centers are mired in a systemic (or severe) crisis of one form or another; (2) the crisis involves two or more distinct regions; (3) the number of countries in crisis in each region is three or greater. We will apply some of these trains of thought in the following sections.

II. THE UNEQUAL EFFECT OF ECONOMIC CRISES ON TERRITORIES AND CITIES When an economic crisis takes place, be it national, regional or global, its effect on the territory tends to be uneven. The history itself of crises in the twentieth century provides us with important evidence in the sense that its effects are felt differently in the territory. For example, in the case of the crisis of 1929, its impact spread to the entire world. However, some countries were much more affected than others, both because of the depth and the duration of the crisis. We have a group of countries that were more adversely affected, such as the USA, Canada, Germany, Holland, Australia, Poland and Austria (Clavin, 2000). These nations underwent unemployment rates greater than 30 per cent, plummeting industrial and raw-material production, massive bankruptcies, and a million people without housing. In Latin America, all countries were hard hit, especially those depending heavily on exports of raw materials and agricultural products. However, unemployment never reached the proportions it did in the case of more developed countries, since it was a fundamentally rural region. Industry had a presence that was of little significance within the economic structure (Urquidi, 2005). In other countries such as Great Britain, France, Poland and Czechoslovakia, although the crisis hit hard initially, economic recovery began earlier. In Japan, after the initial sharp drop, recovery began and, by 1933, the country was out of the depression, undergoing strong industrial growth, as is shown by Eichengreen and O’Rourke (2010). Within countries themselves, the crisis hit differently in regions and cities. While average unemployment in the USA for 1933 was 25 per cent, in the cities of Cleveland and Toledo (Ohio), rates of 60 per cent and 80 per cent, respectively, were reported. Old industrial centers such as Pittsburg and Gary (steel), and Detroit (automobiles), as well as cities and small localities closely associated with mining fields, railway centers and small industrial plants, underwent more significant drops than other cities in the country.

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The work of Rauchway (2008) shows that, during the Great Depression in the USA, conditions in some agricultural regions were much more severe than in others (as were climatic and environmental factors that aggravated conditions) and this led to major migrations toward regions and cities with better conditions for survival. In Canada, the most affected cities were the heavy-industry centers of southern Ontario and the manufacturing provinces of Toronto, Tulbury and Windsor. In Ontario, unemployment reached 45 per cent. In England, average unemployment rates were 20–25 per cent. However, the internal differences were major, as can be seen in the work of Constantine (1983). In the localities and cities linked to heavy industry, textiles, shipyards and coal (Yorkshire, Sheffield, Lancashire), unemployment levels were higher than in other regions. In the Northeast of England, unemployment reached 70 per cent. In less industrialized parts (the Midlands), in the South and in the Greater London area, the effects were less severe. After the Second World War, a large number of countries underwent a long period of unprecedented economic expansion and that caused attention to shift to questions related to growth, one of the concerns being to explain why different regions had different rates of growth (Richardson, 1979). Perhaps, because of this, the topic of unequal impact from the crisis remained quite forgotten. The 1970s, however, marked the beginning of economic problems in several regions of the world, opening the way for crisis in some countries. As David Harvey (2010) points out, ever since 1973, the world has known hundreds of financial crises, compared to the few that were recorded between 1945 and 1973. Some industrialized countries began to undergo sharp economic contraction tied fundamentally to the process of deindustrialization, while in other countries (particularly, Latin America), the appearance of crisis was tied fundamentally to the incapacity to pay their huge foreign debts, to sudden devaluations, and to galloping inflation (Reinhart and Rogoff, 2009). The crisis that hit some developed countries attracted the attention of researchers interested in delving further into the differential impact of this phenomenon within the countries affected. In 1980, Frank Stilwell published a book in which he analyses the impact of the crisis on Australia, showing the existence of marked differences in unemployment rates in cities and also in the interior of the same. While average unemployment in Sydney reached 5.4 per cent, the suburbs in the north of that city did not exceed 2 per cent and, in other areas of the city, went beyond 7 per cent and, in still others, exceeded 10 per cent. In the 1980s, many countries in Latin America, Africa and, to a lesser degree, Asia underwent a crisis of insolvency in debt payment, devaluations

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and high inflation. In 1997–98, Southeast Asia, Russia, Ukraine, Colombia and Brazil were affected by a crisis in their banking systems. However, at the same time that some regions and countries recorded severe crises, in other places, economic growth has continued accelerating. Once again, emphasis was placed more on growth and on its inequalities. It is not until the global crisis of 2008 that, once again, attention was given to the unequal effects on the territory in eras of economic contraction, the topic we deal with below.

III. THE IMPACT OF THE CRISIS OF 2008 ON CITIES Undoubtedly, the current crisis shares many similarities with the Great Depression of 1929 and with later events; yet the differences are also quite significant. Many are directly related to the fact that since 1929 the world has changed considerably from the economic, technical, and demographic point of view. One of those changes is the planet’s urbanization. Today, more than half of the 6.8 billion inhabitants live in cities; 40 mega/regions concentrate 18 per cent of the world’s population where 66 per cent of the economic activity is found, and where 85 per cent of scientific and technical innovation are created (United Nations-Habitat, 2010). Just for the sake of providing a national example, when the 1929 crisis occurred, Mexico had approximately 16.5 million inhabitants; 82.8 per cent of them lived in the countryside. Preliminary 2010 census information shows that Mexico today has 112.3 inhabitants, and 77 per cent of them live in urban locations; 41.2 million live in 11 metropolitan areas where most of the country’s production, wealth, investment, culture and power is concentrated. Undoubtedly, the prosperity of countries depends largely on their cities’ capacity to generate wealth, increase productivity, and to be the driver behind innovation. It is also clear that cities are the origin and the epicenter of a crisis that will manifest itself in its most virulent and persistent form. Not surprisingly, from the beginning of 2009 to date, a variety of research has appeared in which the impact of the crisis on cities has been analysed. Some tackle sectorial aspects such as the real-estate market (Rodriguez Lopez, 2010), the phenomenon of housing foreclosures (Davis, 2010), or the financial debacle of urban areas (Paulais, 2009; Heuton, 2010). Others have centered on the economic and social impact of the crisis in English cities (Lee et al., 2009), US cities like Florida (2009) and (Glaeser, 2009). Still others have sought to analyse the response of

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local governments in light of the economic disaster, such as Clark (2009) and Perlo et al. (2009) and, finally, some have been interested in finding out about the relationship of the phenomenon to the structural dynamics of the capitalist system (Harvey, 2010). In addition to this work, ever since 2009, a series of surveys and case studies have been carried out, ordered by international organizations (Clark, 2009), international associations and national local governments (CEMR, 2009; UCLG, 2009; The United States Conference of Mayors, 2010), and private research institutions (Lee et al., 2009; URBACT, 2010a; Wial and Shearer, 2010), which offer material of enormous value in bringing together a wide view, fundamentally of a descriptive nature, revolving around the effects that the crisis has had on cities and the response that local governments have given. We have decided to base ourselves on all the information mentioned before to generate a wide-ranging and extremely summarized view of the most outstanding impacts undergone by a large number of cities in different parts of the world due to the crisis. 1.

The Majority of the Surveys of Cities Report Severe and Varied Negative Impact from the Crisis

All the surveys and studies show the depth and range that the great economic contraction of 2008 has had on the lives of cities. Its negative impact and its damages are only comparable, notwithstanding their enormous quantitative and qualitative differences, to the Great Depression of 1929. Economic life The survey done by the URBACT program titled Cities and the Economic Crisis (2010a), which encompasses 131 cities in the 25 member countries making up the OECD, found that 80 per cent of the cities that responded to the survey felt a greater impact on its economic activities, which applied both to large transnational companies as well as to small and mediumsized companies. The effects have very diversified manifestations such as the closing and bankruptcy of many companies, the slowdown in economic activity in all its branches, a decrease in investment, restriction of loans, reticence of companies to take risks in times of uncertainty, decrease in the number of new start-ups and a decline in exports. Unemployment and underemployment One generalized conclusion between the different surveys and analyses is that, although the crisis manifested itself negatively in economic, social,

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political and other aspects, the most serious and worrisome are those linked to unemployment. The URBACT (2010a) survey found that 80 per cent of the cities surveyed reported a rise in unemployment, both in the private sector as well as in the public sector. Likewise, the Organisation for Economic Co-operation and Development (OECD) survey (Clark, 2009) concluded that, of all the impacts, unemployment and job loss has been the most widespread up until now. Only 12 of the 41 urban economies analysed did not report this as its main problem. In some cities in the less developed countries, it is an even more serious problem because, to unemployment, has to be added the large number of people who are underemployed or working in the informal sector. For example, in Mexico, while open unemployment in the 32 most important urban areas of the country reached 6.6 per cent of EAP in the month of September, the national underemployment rate was 8.5 per cent during the third quarter of the same year. Unemployment in several cities reached very high levels, such as in Guadalajara, Saltillo and Tlaxcala-Apizaco where rates reached 11.08 per cent; 14.9 per cent and 201 per cent, respectively (INEGI, 2010). Urban poverty In many of the surveys and studies, it is clear that poverty, to different degrees and expressed in various ways, has grown visibly and notoriously. The URBACT (2010a) survey points out that the situation has contributed to the appearance of different forms of poverty and that the increase in the number of people requesting food assistance, clothing and a place to sleep is alarming. In the city of Vilna, in Lithuania, for example, the number of applicants for support from the soup kitchens increased 6.5 times in one year: from 800 in 2008 to 5234 in 2009. It also points out that, in many Spanish cities, early manifestations of social exclusion and poverty have been reported from the beginning of the crisis. Housing foreclosures This is a widespread topic in many countries (Spain, Ireland) and cities, but has been manifested most in some metropolitan areas of the USA. According to Morris Davids (2010), while, during a period of 27 years (between 1979 and 2006), 7.5 million foreclosures occurred. In just three years (2006–09), there were 6 million. According to the firm RealtyTrac, Inc. (2010), between January and June 2010, the number rose to 1.7 million and it is expected that, by the year end, it will reach another million. The highest index of foreclosures occurs precisely in those states and cities in which unemployment shows the highest rates and where the

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price of housing has plummeted (giving rise to the value of the mortgage being greater than that of the property). Cities showing the highest rates, quite above the national average for the USA, are: Detroit (5.0 times more than the national average), Las Vegas (4.6 times), Riverside (3.8), Sacramento (3.8), Atlanta (3.3), Denver (3.2.), Dallas (2.6) and Miami (2.6). Financial crises of local governments According to the survey of the organization United Cities and Local Governments (UCLG), the impact of the global crisis on local governments (2009), one of the major consequences of the world fiscal crisis, has led to a serious deterioration of the fiscal situation of many local governments around the world. Owing to revenue decreases thanks to the slowdown in economic activity, the reduction of the fiscal value of property and the simultaneous increase in expenditures to face social demands, many cities have found themselves in what some analysts call the worst ‘fiscal crunch’ in decades. Even some municipal governments lost the major assets that they had invested in risk funds and banks that collapsed during the crisis. The municipal associations of European countries indicated that 61 per cent of those surveyed saw a drop in their own revenues, and 55 per cent responded that they also received fewer transfers from upper levels of government, especially from central government. In Japan, in 2009, the city of Tokyo underwent the largest cut of any metropolitan government in the world. 2.

Not All Economic Sectors within the Cities have been Equally Affected

On this topic, there are different points of view. The OECD survey (Clark, 2009) found that the two most widespread impacts locally are the drop in the real-estate market and construction. However, some localities reported that the financial restrictions of the local governments had the most negative impact (Clark, 2009). Another point of view is presented by the URBACT (2010a) survey, which concluded that, although cities reported that the construction sector turned out to be the most affected in terms of closing down companies and bankruptcies, it is in the industrial/manufacturing sector where the greatest loss of employment was recorded. In his opinion, this discrepancy may be explained by the fact that the construction sector uses many workers coming from the informal market, and this does not show up in official statistics.

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In those cities where local finances have undergone a sharp drop and which have opted to make major spending cuts, the public sector has been greatly affected (Riga in Latvia, Antwerp in Belgium). 3.

Within a Single Country, there are Cities Presenting Quite Varied Situations

The information we have about European cities in the current crisis also indicates a considerable difference between cities in the area of unemployment. The survey carried out by URBACT (2010a) found that, as a consequence of the economic recession, the unemployment rate went up in 80 per cent of the cities that responded to the surveys, although, in some cities, no increase was recorded and, in others, it went down. Very high unemployment rates were found in the cities of Valencia, Tallin, including in the city of Czestochowa, located in Poland, a country that recorded less impact compared to others, where high rates were recorded. In contrast, Warsaw only recorded a small increase in unemployment due to a slowdown in manufacturing activity, construction and real estate, since, at the same time, the number of people working in commerce, travel industry and food services increased. In 2009 in Berlin, the number of unemployed grew only 1.4 per cent, while, in Munich, it remained stable. In some cities, unemployment even went down. This was the case of Leipzig which, from 2008 to 2009, went from 15.5 per cent to 14.8 per cent, and Halle, whose absolute number of unemployed went down in these same years, going from 17 444 to 15 967. The importance of the differences in the levels of unemployment can be seen by looking at the evolution of unemployment rates in a group of cities from the USA and Mexico before the crisis and up until the second half of 2010. In Tables 15.1 and 15.2, we can see which cities maintained major differences in unemployment levels, but, due to the crisis, those differences have grown. In the USA, the crisis increased unemployment levels in all cities, but, in some the increase was much greater. When comparing 2009 and 2010, it was observed that, in some cities, the slow decrease in unemployment had already begun, while in others it had stayed steady or even gone up (Oklahoma, Huston, Miami, Riverside, Las Vegas). In Mexico, a similar polarization of unemployment rates has occurred due to the crisis, which has continued to the present day. During the first quarter in 2010, Campeche recorded an unemployment rate of 3.2 per cent, and Toluca of 4.3 per cent, while in Chihuahua it rose to 7.7 per cent and reached 8.7 per cent in Saltillo. For the same period, underemployment

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Table 15.1

347

Evolution of unemployment rates in ten metropolitan areas of the USA, 2007–10*

Metropolitan area Minneapolis Oklahoma Austin Houston New York Chicago Los Angeles Miami Riverside Las Vegas

2007

2008

2009

2010*

4.3 4.1 3.7 4.3 4.4 4.9 4.8 4.1 5.8 4.8

5.1 3.7 4.4 4.9 5.2 6.2 6.9 6.1 8.2 6.7

7.8 5.9 6.9 7.6 8.8 10 10.9 10.2 13.3 12

6.7 6 6.8 8.2 8.5 9.4 11.8 12.1 14.8 15

Notes: * Corresponds to September 2010. 2010 not seasonally adjusted. Source: Bureau of Labor Statistics (2007–2010).

Table 15.2

Evolution of unemployment rates in ten metropolitan areas of Mexico, 2007–10*

Metropolitan area Campeche Acapulco Morelia Colima Toluca Guadalajara Mexico Monterrey Chihuahua Saltillo

2007

2008

2009

2010*

2.3 2.4 3.8 3.3 3.3 3.7 6 4.8 4.3 6.5

2.1 2.3 3.9 3.4 3.6 3.8 5.8 4.7 5.3 6.4

2.9 3.9 4.7 4.4 5.1 5.9 7.3 7.6 7.8 9.5

3.2 3.4 3.9 4.1 4.3 6.2 6.4 7.5 7.7 8.7

Note: * Corresponds to the 1st quarter of 2010. Source: Own elaboration based on the Encuesta Nacional de Ocupación y Empleo, INEGI, 2007 to 2010.

rates showed deeper differences: while Colima, Cuernavaca and Tampico did not go over 3.50 per cent, 4.23 per cent and 4.39 per cent, respectively, in Guadalajara, Saltillo and Tlaxcala-Apizaco levels skyrocketed reaching rates of 11.08 per cent, 14.9 per cent and 20.1 per cent.

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If we were to extend this type of comparison to headings such as local gross domestic product, consumption, investment and other macroeconomic indicators, we would possibly find polarization similar to that found in the case of unemployment. 4.

Despite the Majority of Cities Registering Negative Effects, One Group of Cities Reported Positive Effects

Although the balance leans clearly toward negative effects, almost all the surveys indentified a series of positive effects, many of which take place simultaneously with the negative effects. The survey done by the OECD (Clark, 2009, p. 28) reports that: It is important to note that the global recession can produce and is producing beneficial impacts in a variety of local economies. Indeed, a number of localities experienced many positive impacts, suggesting that positivity in one sector catalyses or is catalyzed by others. The localities which experienced the largest number of positive impacts are Warsaw and Pittsburgh.

In his analysis, the author of the survey pointed out that cities such as Paris and Warsaw were benefitting by the return of a ‘skilled dispora’ that local economies such as Budapest, Turin and Miami have undergone tangible profits in areas such as trade and tourism, while Toronto reported good performance in its financial services and rates of growth. The URBACT (2010a) survey found that almost 20 per cent of the cities that responded to its questionnaire, mainly Greek and Polish cities, reported that the economic crisis did not affect the execution of their projects in 2009. These localities explained this effect by the fact that their local finances were relatively less affected and that they had loans previously committed from banks – funds from the European Union. In the case of Poland, they consider that the reduced severity of the crisis on the cities is due, to a good degree, to the country feeling the crisis less strongly than other European countries. One survey done in Mexico in 2009 (Perlo, Paredes and Gonzalez) found that, of the 35 cities answering a questionnaire (sent to 90 cities with more than 100 000 inhabitants), nine stated that, besides the negative effects generated by the crisis, there were also positive effects on touristic activities and in exporting certain products. This was due to the new exchange parity resulting from the devaluation of the Mexican pesos vis-á-vis other hard currencies.

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IV. EXPLANATIONS OF THE DIFFERENCES 1.

City Size

The results we have found in surveys and in analytical studies lead us to different conclusions and, to a certain degree, contrary ones. Upon analysing the case of cities that were less affected by the crisis (23 of the 131 included), the URBACT (2010a) survey reached the conclusion that size, in and of itself, does not explain the differences, since the same are found in large capital cities as well as in small provincial ones. However, the survey done by OECD (Clark, 2009) recognizes that, although there is no iron-clad relationship between the size of the locality and the impact of the crisis, and that there are cases of cities of the same size population and same economic size that have been affected quite differently, the variable size can have an influence on certain processes. According to the same survey, evidence suggests that disproportionately large economies felt more recessive conditions from the beginning of the recession than did the small and medium-sized local economies, owing to the fact much of what happened in those economies was global in nature. This same study points out, however, that large cities have greater opportunities to initiate recovery earlier, since they are capable of attracting more talent, which gives them better opportunities to mitigate the worse effects of the recession and to position themselves adequately for long-term recovery. This point of view is shared by Paul Soto (2009, p. 5), who emphasizes the importance existing between the size of the city, sectorial diversification and globalization: Big cities have more probability of having a greater percentage of globalized sectors that are more vulnerable to recession but, at the same time, their economies tend to be more diversified and, because of their nature, are more capable of absorbing the shock inside their wide-ranging urban economy. Small cities that are highly dependent on vulnerable sectors are in the most difficult position.

2.

The ‘Country Effect’

The URBACT (2010a) survey has referred to the ‘country effect’, that is, to the fact that the effect of the crisis in cities will depend on the depth to which the country as a whole has been affected by the crisis. Given that the current crisis presents significant differences between countries, notwithstanding its character so spread out over all regions of the world, it might well help to explain the differences in the impact of the crisis on the cities.

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This is clearer when the cities of different countries are compared. For example, Poland is a country that did not suffer, in so negative a way, the effects of the crisis, and its cities show relatively lower unemployment rates than the other countries in which the impact of the crisis has been greater (Spain, the USA, Ireland, the Baltic countries). However, the ‘country effect’ is not very useful in explaining why significant differences exist within the countries or why some cities of highly affected countries have indicators of impact similar to those of countries that were less affected by the crisis. 3.

The State of the Local Economy

The URBACT (2010a) survey argues that the local economy has enormous relevance in explaining different situations. By conditions of the local economy, we refer to activities that provide a foundation for a city’s needs and those of its regional surroundings. A local economy is formed by small and medium-sized companies operating on a self-financing basis, and depending less on bank credit, with a broad and diversified services sector, and is less connected to the international economy and depends on internal demand. Twenty-three cities, especially from Germany, Poland and Sweden, reported that the impact of the crisis on business and employment was relatively small and concluded that, although the national context is relevant to explain the relative isolation of those cities, local economic characteristics are also key. Among the factors pointed out by the local governments surveyed by URBACT (2010a) and that have played a role in reducing the impact of the economic crisis, are the following: the service sector is less affected; urban economies based on small and medium-sized companies operating locally demonstrate more resistance; economies based on small businesses that self-finance themselves or that do not depend on bank loans are less affected, as are economies less connected to international markets; and experience gained and the restructuring of the local economy due to previous crises also play a part. The prior economic behavior of a city also has an influence. If, before the crisis, there was already a negative trend, it is probable that it will be accentuated during the crisis (Detroit in the USA, Saltillo in Mexico). High levels of unemployment, little investment and, local deficit finances undoubtedly cause many cities to feel the brunt of the crisis more strongly. However, we also find the contrary situation: cities that had undergone sustained growth and, when the crisis began, their economies plummeted (Las Vegas, Miami).

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4.

351

Strong Economic Specialization Linked to the Sectors Hit the Hardest by the Crisis

Cities with a high degree of specialization in sectors of the economy affected by the world crisis, such as manufacturing, automobiles and construction (Chihuahua, Aguascalientes, Hermosillo in Mexico), speculative real estate activity (Miami, Las Vegas), and financial services (Chicago) suffered greater decreases than cities that are more diversified and that depend less on less dynamic sectors (Mexico City, New York). The report on North American metropolitan economies, presented by the United States Conference of Mayors (2010), states that cities most affected by the crisis are in states such as California, Michigan and Florida, where unemployment spiked due to the real-estate bubble and a strong dependence on the manufacturing sector. However, this explanation has its limitations as it tries to explain the reasons why large urban centers with an ample sectorial diversity, such as Mexico City, Monterrey and Guadalajara (services, industry, education and government), felt such a strong impact, while cities highly specialized in tourism, such as Puerto Vallarta or Acapulco were less affected. 5.

Labor Qualification

Research undertaken by Lee et al. (2009) on the behavior of 12 cities affected by the crisis in the UK, suggests that labor qualification has been fundamental to help establish a difference in job employment between cities: the lower the qualification profile in an area, the higher the crisis. They studied 12 cities (Newcastle, Manchester, Bristol, Glasgow, Swindon, Cardiff, Birmingham, Liverpool, Derby, Oxford, Belfast and Chelmsford) and the only exception to this was Swindon. The OECD (Clark, 2009) survey also shares this conclusion. The survey says that ‘Local economies with lower skills, lower employment rates, and lower levels of specialization are particularly exposed to the crisis because they offer limited compelling reasons to retain investment and jobs and are competing largely on priced based factors which erode significantly in a downturn’ (p. 13). Edward Glaeser (2009) reached a similar conclusion about the USA. In his opinion, and in light of the huge gap between skilled and unskilled labor unemployment levels, it is not surprising to see that skills provide the best explanation to current country unemployment rates. For Glaeser, the ratio of adults with college degrees by itself explains about half the variations in unemployment rates.

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However, both the OECD (Clark, 2009) report as well as Glaesers’ (2009) clearly state that worker classification has to be analysed using other variables of equal standing. In the first case, local economies that fared the best are those with the highest level of skilled workers, the lowest initial unemployment rates, and top economic specialization levels. The latter case says that other variables, such as the weight of heavy manufacturing (the higher the weight the higher the unemployment) and the degree of metropolitan centralization (the higher the centralization the lower the unemployment) must also be taken into account. The next section will discuss different responses to the crisis and action programs by local governments.

V. GOVERNMENTAL POLICIES AT DIFFERENT GOVERNMENT LEVELS (CENTRAL, STATE AND LOCAL) FOR CITY CRISIS Public policies applied by the public sector to the crisis in urban contexts are undoubtedly significant in determining the situation a given city will find itself in. However, we do face a condition that is difficult to evaluate for three reasons. First, very little time has passed since the onset of the crisis, making their efficiency hard to evaluate. Second, is that there is a broad array of measures that different levels of government have applied, complicating the possibility of a specific rigorous analysis of the state of the cities. Third, is that thousands of local governments exist around the world and we have information from only a few.3 Regarding the first difficulty, all research and published surveys agree that one must have had enough time elapsed, and sufficient statistical information, to work on a comparative analysis of local policy impact and efficacy. As to the second difficulty, we have created the chart (Table 15.3) to give a schematic view of the variety of governmental responses, taken basically from the UCLG (2009) and the OECD (Clark, 2009) surveys. We have tried to solve the third difficulty through a practical method – we have picked out readily available information generated by governments themselves, either local and/or national, and by international organizations. Before going into specific cases, we must point out that many local governments have not developed a crisis-specific policy. We do not know how many have such a policy, but we do have sufficient evidence to lead us to think that the ratio is high – maybe even falling into the majority. The surveys record a sizeable proportion of cities that did not answer the questionnaires. This may show either a lack of interest, or may be

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indicative of their inability to answer, or may even allude to the fact that cities do not have anything to report in terms of actions. URBACT’s survey was sent out to 190 cities; only 131 answered. Of those who did reply, 30 per cent said they had not taken any steps, or were in the process of finishing their plans, or simply did not provide information. The survey was applied to local governments in Mexico between March and April 2009 and included 90 cities with over 100 000 inhabitants. The response rate was 39 per cent, a lower rate than that reported in URBACT II (URBACT, 2010a). Of the 35 cities that did answer the survey, 28 had returned to old plans and measures which had been labeled as anticrisis programs, and only seven – Celaya, Ciudad Juarez, Manzanillo, Matamoros, Mexico City, Puerto Vallarta and Tula – drafted new programs with specific measures to help face the crisis (Perlo et al., 2009). The second survey, which was applied in July–September 2010, elicited an even lower rate of reply; only 29 of the 90 cities answered (Perlo, 2010). Another important subject has to do with the resources that central governments manage for cities. This issue is critical because part of the maneuvering margin local governments can use in light of the crisis depends precisely on tax resources from central governments. URBACT’s (2010a) conclusion in this regard strengthens the importance of national policies at the local level: Firstly, the response at the city level is obviously heavily dependent on the national reaction to the local crisis. In some countries such as Spain, the initial response has included large injections of local investment to compensate for the decline in private construction (Plan E). In others such as Ireland and Latvia major cuts in public expenditure were already affecting local authorities. It is clear that the size, nature timing of national policies for public expenditure have a strong effect on the urban response to the crisis. (p. 33)

Paradoxically, cities that depend on their own income, and who manage more independently are the very same cities who have experienced most problems (cities in North America), while cities with a more centralized regime (England, Holland, Mexico) to date have been able to preserve their income. For example, in Mexico, public ingress from the main metropolitan areas grew in 2009 (the crisis’s worst year) 2.45 per cent in real terms against that of 2008 (Perlo, 2010). This was due basically to the fact that the federal government, who contributes with about 90 per cent of municipality income, increased the amount of fiscal resources put aside for municipal governments. Yet, this situation is a long way off from being fixed. If central governments decide to cut their budgets heavily – an action which we have

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354

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Unemployment

Labor market

Local economy

Decrease in tax resources allocated to local governments

National public expenditure and distribution of tax resources

Stabilization of real-estate markets

Drop in real-estate prices

Drop in tourism

Reactivate businesses Finish projects that were begun Reactivate construction

Protection against unemployment

Maintain or increase programs directed to public works, education and social security Maintain or increase tax participation to local governments Create employment

Objectives

Business bankruptcy Cancelling growth plans

Underemployment

Problems

Local government actions regarding the economic crisis

Subject

Table 15.3

Promote the decentralization of social programs and infrastructure structure New job training Unemployment Insurance and benefits Study grants New study programs for youth Temporary employment Freeze and/or reduce taxes Simplify procedures to open up new businesses Small and medium-sized company support programs Real-estate purchasing

Emerging programs on tax aid to local governments

Instruments

355

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Source:

Growing demand of social services and benefits Dispossession of housing Drop in housing prices Lack of resources to keep providing urban services Increase in urban poverty Increase in insecurity Loss of political strength with central governments and international agencies Decrease in the ability to attract new investment and long-term development projects

Planning crisis

Financial crisis Social unrest

Own elaboration based on UCLG (2009) and Clark (2009).

Political presence in domestic and international affairs

Local living conditions

Local governance

To strengthen leadership and institutional capacities in local governments

Protect and help owners settle Urban service maintenance Support the poor and vulnerable population Social peace preservation

Strengthen cooperation and association efforts

Satisfy new social demands

Increase budget resources

Joint action programs to face the crisis Exchange of information and technical advising

Sustainable urban innovation programs Formation of political alliances with other local governments

New financing instruments Expenditure rationing Increase in urban efficiency and productivity Sustainable urban renewal Enter into agreements and accords with urban actors Create an anti-crisis program Advising on strategic plans Debtor aid programs Legal advice to debtors New programs to fight poverty

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already seen in Greece and England – then it will be the local governments who will experience a strong decrease in budget resources. On the other hand, US municipal governments have shown a strong bargaining capacity with President Obama in order to receive federal funds. We saw this in the stimuli package called ‘America Recovery and Reinvestment Act, 2009’. We will have to see if municipal governments will be capable of gaining more funds in the future to help compensate the drop in their own resources. On the other hand, even if many cities were to continue receiving resources from the central government, they would not necessarily be able to spend them freely, because transfers are often ‘earmarked’ in many centralized tax regimes, meaning that funding has to be spent in line with items predetermined by the central government. This leads us to another issue with important implications, that is, understanding the legal powers and capacities that local governments have. URBACT (2010a) describes this correctly: The legal powers for cities to act in certain fields vary enormously between countries. In some countries, cities have a long tradition of involvement in local economic development, entrepreneurship and training. In other countries, these responsibilities are managed by national or regional bodies. One cannot expect cities to develop innovative solutions where they have no competences. (p. 3)

In this sense, there are many different situations. Dutch cities, for example, depend strongly on funds from the central government, yet they do have a strong margin of autonomy to program their spending and use it for local development. This has allowed cities, such as Rotterdam, to design and apply comprehensive and efficient anti-crisis policies. However, this is not the case in most cities in Latin America, Africa or Asia, which belong to centralized tax regimes and have to distribute income from the central government pursuant to pre-established criteria, objectives and use. Even in countries with a long strong local government tradition, such as the USA, very often the legal structure and rules are not designed to enable cities, within state-defined limits, to pursue a vision of their future, as authors Frug and Barron (2008) thoroughly documented. The last point we will cover in this section is on the way cities have integrated a wide variety of policies such as those described in Table 15.3. We have cases of cities that prefer certain policies over others and will direct their resources to them. Some have assigned their resources to help rescue businesses, as most cities in Mexico have done, as the measures to help weather the crisis (Perlo et al., 2009). Others have emphasized using measures of a social nature, such as benefits for the unemployed and for the poorest part of the population, while other cities such as Rotterdam,

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Jyvaskyla and Turin, among others, have developed more comprehensive plans that combine actions in several areas. Moreover, several cities have integrated their crisis-fighting programs and actions into the strategic plans that they had prior to 2008, such as Turin with its ‘2nd Strategic Plan for a Knowledge Society’. Several cities have developed plans or programs intended specifically to face the crisis, such as Rotterdam (‘Rotterdam Offers Perspective’), and Newcastle (‘Ten Point Response to the Economic Downturn’). Evaluations (Perlo et al., 2009; Rivas, 2010; URBACT, 2010a) have been researched in an attempt to determine if formal anti-crisis plans exist, that is, approaches integrated into a document containing all elements of a program with a comprehensive vision covering different aspects for the short-, medium- and long-term views. Yet, the experience in the cases studied by surveys shows that we have to be careful not to think that the existence of ‘formal’ comprehensive plans with a long-term approach will guarantee that things will be done better than cities that do not have such plans. Actually, many cities that have been especially active and have been efficient in fighting the crisis have strategies that are exactly right for such an undertaking, even if they do not have formal plans drafted to fight the crisis, such as Jyvaskyla in Finland, Obidos in Portugal, Barnsley in the UK, and Enguera in Spain (Rivas, 2010).

VI. POLICIES DIRECTED AT HUMAN CAPITAL TRAINING, URBAN INNOVATION, AND STRATEGIC POSITIONING OF CITIES We stated earlier that there are many differing responses by local governments to deal with the crisis. These range from those who are unwilling to change a thing, to those who have done something, to those – albeit a few – who have designed special plans for the present situation. While some cities address social and economic reactivation policies, others have a broader outlook. They have focused on forming human capital, on providing urban innovation, and on acting with a strategic vision for the future. 1.

Education and Human Capital Formation

The city of Rotterdam, in the Netherlands, has developed a very interesting approach to help fight youth unemployment by relating it to education and to a networking building that will help youth when they enter the labor

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market (URBACT, 2010d). The program targets the group hit hardest by unemployment: youth. In general terms, we are talking about the population with the lowest and most vulnerable qualifications to access the labor market. By using a preventive focus and funding made available from the national government, Rotterdam recently created a youth unemployment action plan called ‘Just Keep Going’, designed at keeping young people in school or returning them to school. It also seeks to improve cooperation between employers, educational institutions, employment services, and the youths themselves. Other interesting examples exist about the programs local governments have set in motion in education and human capital training. The city of Jyvaskyla in Finland established a ‘Structural Change Working Group’ whereby businesses, the University of Jyvaskyla, and the Ministry of Employment and the Economy converge to provide educational opportunities for high-skilled labor to continue their education through PhD studies (URBACT, 2010b). 2.

Urban Innovation

What have local governments done in the arena of urban innovation to fight the crisis? The city of Turin, undoubtedly hard hit by the crisis, has also adopted short-term measures to face the most direct and immediate effects of the recession (such as relocating displaced workers into new jobs, creating new jobs, financial aid to the unemployed and to poor families). At the same time, the city has created a series of strategic initiatives designed to reach recovery in the long term. Within this approach, innovation has received widespread attention. The city has started to broaden its innovation system in order to be ready for the future urban economy it expects in the not too distant future. Infrastructure renewal has been taken into account in local and regional authority plans which acknowledge the existence of old and inefficient infrastructure. The idea is to make available infrastructure that will confer high added value to actors in the process of innovation (URBACT, 2010e). One can find programs that explicitly and tacitly hold innovation at the heart of their approach, such as the New York City Center for Economic Opportunity from the New York City government. The city recently launched an initiative to create the Federal Fund for Urban Innovation, a program that targets new ways to fight poverty (Center for Economic Opportunity, 2008). Another innovation program is the ‘Economic Resilience Plan’ (City of Edinburgh Council, 2008) promoted by the city of Edinburgh (UK) designed to help businesses diversify their services. We have located other innovation experiences such as those that give

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priority to projects with a high potential to create jobs with energyefficient standards in Edinburgh (UK), or to projects with low carbon emission transportation schemes in Graz (Austria), or to plans by cities that have resorted to improving their administrative work by improving their tax-collecting procedures like in Dobrich (Bulgaria). Upon a first review of the subject on innovation, which must be submitted to rigorous validation, we can conclude that innovation does not appear as a policy of priority in local government agendas in today’s crisis situation. Programs or innovation measures do not seem to stand out from the hundreds of actions, programs and policies that local governments have adopted to weather the crisis (URBACT, 2010a). Yet, the subject of innovation is more relevant than ever (Castells, 2009) in a context full of tax crises of many local governments, in which resources are short. In this sense, the crisis is an opportunity to do things differently, to try out new ideas, and to take advantage of opportunities. If local governments do not drive innovation and try to implement it, the other levels of government will hardly do so. 3.

Planning and Medium- and Long-term Views

In the URBACT II survey, 70 per cent of cities said they had applied some type of plan or measure to fight the crisis. However, only four cities said they had a formal plan of recovery. Cities, such as Rotterdam, Rennes Metropole, Newcastle, Birmingham, Eidhoven, Jyvaskyla, Gothenburg, and Obidos actually said they had explicit long-term strategies. In light of the number of businesses closing (including the emblematic Nokia Center for Research and Development), and of the drop in income tax, the City Council of Jyvaskyla in Finland adopted a new urban strategy in March 2010 for the years 2010–13 (URBACT, 2010b). Anti-crisis actions have centered on helping highly skilled labor to develop their own businesses, and/or even to carry on with their postgraduate work. This has all been done in close cooperation with universities and the private sector. Using the experiences from the severe recession of the 1990s, the city has resorted to its planning and strategic planning development capacity. Another example is found in the city of Newcastle in the UK. With growing unemployment that has affected the elderly and professionals, and with the drop in construction and in real-estate prices, the city’s Municipal Council drafted, in June 2009, a ten-point plan known as the ‘Economic Downturn Response’. The city used its wealth of experience gathered during the severe crisis it experienced in the 1980s, and during its ‘urban rebirth’, to create the plan which contains a mixture of shortterm actions seeking to reduce the social consequences of the economic

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recession and stimulate housing. The city’s long-term actions are directed towards placing the city that it predicts will emerge in 2030. One of the most interesting long-term strategic approaches that is described in the Newcastle–Gateshead 1 Plan (published in January 2010) is the way the city combined special planning, urban regeneration, and a more sustainable urban economic design based on science and knowledge (URBACT, 2010c). Through new financial instruments, such as the Accelerated Development Zone Pilot for Tax Incremental Funding (to purchase debt to buy locations on the basis of the tax income they will generate in the future), and by availing themselves of their excellent universities and research centers, the Municipal Council is choosing parts of the city where it is purchasing real estate (taking advantage of low prices) to locate businesses (low carbon and sustainable industries), research centers (International Centre for Life), and clusters (Science City) in strategic areas in the city in order to include the urban center and other zones, which will lead to a better integrated and more compact city. Other cities have come up with similar plans and proposals. The city of Rotterdam has a comprehensive plan called ‘Rotterdam Offers Perspective’. The plan includes joint measures to counteract the impact of the crisis by allotting a significant portion of its budget to construction projects to keep and create jobs in the construction industry. This proactive position can also be seen in other cities where their mayors and/or municipal councils have shown a driving leadership (Mexico City, Celaya in Mexico, San Francisco, Newcastle, Rotterdam). The same applies to Austin’s mayor, Lee Leffingwell, recently called a ‘Model for Hope’ by Time Magazine, 2010: This is the time to proactively and aggressively diversify Austin’s economy. We’ve been talking about this long enough that I think there is general agreement about where our economic focus should be moving forward: renewable energy, creative media, and medical technology. These industries are the future of Austin’s economy. They can, and will – if we play our cards right – form a new foundation of sustainable growth into the next generation. (Leffinwell, 2010)

Political activity has also been part of the strategies adopted by local governments, especially in large metropolises. Local governments have been active in promoting meetings, agreements and pacts that allow the strengthening of negotiating and decision capacities by cities with national governments and multilateral bodies. International bodies grouping cities, such as United Cities and Local Governments (UCLG), Metropolis and other groups, have pushed for agreements such as the ‘Barcelona

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Declaration’, the ‘Athens Declaration’, and most recently the ‘Mexico City Declaration’. An important world conference was held in Mexico City in November 2010. Over 1000 mayors from around the world attended this meeting, and the main issue discussed by them was combating the effects of climate change. This meeting paved the way to draft the Mexico City Declaration. Increasing negotiating capacities is important for many reasons, particularly because it allows cities to get resources from national authorities, a noteworthy issue because there are cities that depend heavily on these resources. Insufficient time has passed to prove if measures and actions undertaken by local governments have any real effect on their local economies, or if they are a factor of importance to explain the differences that we have described between cities. This set of actions will have to be submitted to rigorous measuring, correlating such actions with variables such as employment and economic growth, and isolating factors that may be equally important.

4. 1.

2.

3.

CONCLUSIONS Cities across the world have been severely hit by the crisis. Indeed, they are the epicenter of what has now been called the Great Contraction of 2008. In spite of the general negative impact, when we compare cities, the recorded differences were significant. There is not a lineal or simplistic explanation to the origin of these differences, but their existence is undeniable, and understanding such differences can provide us with important keys in discerning why some cities are more developed, productive, innovative, and more capable of pulling through crises than others. In this difficult situation, one of the big novelties has been the strong participation and presence of the world’s mayors, especially from large metropolises. They have shown timely participation, have offered alternatives, and have developed proposals to fight the crisis. This all contributes to placing cities as a fundamental actor that must be taken into account. Yet, this participation should not remain at the rhetorical level. A strong position must go hand in hand with measures, actions, programs, investment, and budget. Otherwise alternatives are ineffective and they lose credibility. What is truly important is what cities, their local governments, and their communities do to fight the crisis. There are successful and encouraging examples in different regions around the world where

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5.

6.

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cities – some of them small or medium-sized urban centers – have placed their best strengths and creativity forward to help survive a critical situation. Nevertheless we have also found many cities that are just waiting for the crisis to blow through and for problems to magically disappear. Local governments may not be able to change the course of large events. They do not control the large macroeconomic variables (monetary, credit, tax, expenditure, international trade policies), and frequently do not even have enough tax resources or the legal powers to decide how to spend them. On the other hand, they do have many advantages, such as a final say on the destiny of the territory, understanding territory not only as a mere physical space, but as a complex web of history, of an immense built capital, political and institutional agreements, and social relations. The main advantage is that they are in touch with the people. This is what allows them to do many things, for instance, to lessen the effects of the crisis and to lay the basis of their future development. A lesson provided to us by cities that have put in motion highly advanced programs is that the solution to current problems must be made by building the future. This can be achieved by creating new jobs, building modern, sustainable, and technologically advanced infrastructure, and by promoting innovation, human capital formation, and improving urban management. Local governments have these factors within their grasp, especially those that have human and material resources and who have accrued administrative experience in local economic development management and strong political leadership. An economic crisis is a devastating event; it affects millions of people and marks an entire generation, in addition to leaving deep social, political and cultural consequences. Nonetheless, it is also an opportunity to do things anew, to open up the best resources, and to allow imagination to run rampant. History teaches important lessons, again. Notwithstanding the profound bleakness, and political and social aberrations after the Great Depression of 1929, it was also a time to promote extraordinary social reforms. Portentous pieces of infrastructural works were built, and the most astounding displays of artistic and cultural works flourished. We will eventually get out of this crisis; undoubtedly we will have new growth periods followed by recessions. It has been so for centuries, especially in the recent history of capitalism. But when recovery begins, the cities that will fare the best and have a more solid basis, will be those that did their job during this crisis.

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NOTES 1. On the announcement made about the increase in unemployment rate that jumped from 9.6 per cent to 9.8 per cent in November 2010, Ben Bernanke himself said ‘It could be four, five years before we are back to a more normal unemployment rate. Somewhere in the vicinity of say five or six percent’. Interview with the Federal Reserve Chairman Ben Bernanke in 60 Minutes, with Scott Pelley, www.cbsnews.com/stories/2010/12(03/60 minutes. 2. Holyoak and Thagard (1995), have identified three constrains that must be satisfied by a good analogy: (1) similarity – the source of the analogy and the target must share some common properties; (2) structure – each element of the source domain should correspond to one element of the target domain, and there should be an overall correspondence in structure; (3) purpose – the creation of analogies is guided by the problem-solver´s goal. Analogies are not fixed forever. 3. As per United Nations-Habitat (2010) there are 592 cities around the world with over 750 000 habitants. If we scroll down to those with 100 000 habitants, the number of urban locations increases considerably, reaching about 4500 cities. Considering that many of them are built by multiple political-administrative units, the number of local governments must be in the tens of thousands.

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16. What public policies can and cannot do for regional development Mikel Landabaso* Innovation is one of the most fundamental processes underpinning economic growth .  .  . The innovation process requires significant and appropriate public  policy support to secure the social benefits it can deliver. (OECD, 2010, p. 15)

Most economists, when confronted in the field with the challenge of designing regional economic development policies, would acknowledge the sheer difficulty of the task due to its complexity and multifaceted character. Most would focus on one particular aspect of the economic development process with the result being an economic analysis and policy choices based on a few interconnected economic problems. Most of the time we are aware of the limitations of our efforts and we feel somehow frustrated by our inability to apprehend the entire problem and provide a satisfactory response which will ‘solve it’. This begs the question: is calling for more labour market flexibility, deregulation, lower salaries and tax rebates to attract foreign direct investment, in regions with high levels of unemployment and little or no influence over macroeconomic policy tools, the best we can do to create jobs and stimulate economic growth? Are we in reality, simply asking the unemployed to emigrate, explaining, of course, that in time the market will adjust and that development will eventually take place in those regions they have left behind? This school of thought argues that after some unavoidable adjustments these regions will start catching up faster, surpassing others, and now ranked, in line with their comparative advantage (see for example World Bank, 2009). Graphs will show ‘curves’ nicely crossing each other again in the right places. Some development economists would have a harder time accepting this act of economic faith. They might be unable to silence the echoes in their minds from pioneer development economists like Myrdal or Hirschman, or even impatient ones like Keynes or Krugman, who pessimistically argued that ‘curves’ may run wild opening up ever widening disparities

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REGIONAL POLICY MIX LINKED TO ECONOMIC DEVELOPMENT LEVELS: WHY NO ONE-SIZE FITS ALL?

Within a given (healthy) macroeconomic framework, regional development depends primarily on two types of meso-economic conditions which can be influenced by regional policy: ‘necessary conditions’ linked to physical and human capital minimum requirements: basic infrastructures – air, road, sea transport, telecommunications, energy, waste and water treatment, risk prevention – and a workforce over a certain threshold of skills level and training opportunities. If these basic conditions are lacking or do not reach a minimum level, there is little point in going any further in our policy reflection. ‘Sufficient conditions’ based on ‘intangibles’ and related to ‘structural competitiveness’ – efficient regional innovation systems, business culture which promotes entrepreneurship, quality of management, innovation business services, and so on – and an institutional framework which encourages public–private and inter-firm co-operation, directly linked to social capital and institutional thickness. ‘[T]he competitiveness of a firm certainly depends on its own forces but in no less extent on the quality of its environment, sometimes referred to as “structural competitiveness”’ (Chabbal, 1992, p. 5). Neither macroeconomic policies nor inward investment policies on their own can be very effective in creating sustained (lasting) and sustainable development trajectories. On the one hand, to try to solve the problem of microeconomic competitiveness with an overdose of macroeconomic policies is self-defeating and highly inefficient (Segura, 1993). On the other hand, direct foreign investment is less available to less favoured regions in the European Union (EU), and foreign-owned manufacturing plants operate for a limited period of time and then go east, and further east, as recent history shows. Regional policy is better placed to tackle the challenge of microeconomic competitiveness, including by helping rooting foreign direct investment in the regional economy. In order to do so, an effective regional policy must help to establish both types of conditions simultaneously, ‘necessary’ as well as ‘sufficient’ conditions through the right policy mix. If either is missing

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NECESSARY CONDITIONS (NC) NC

NC Economic development level

SC

SC Extremadura (Ri)

Figure 16.1

P O L I C = Rn (NC * SC) Y M I X

SUFFICIENT CONDITIONS (SC) Baden-Württemberg (Re)

Regional policy mix linked to economic development levels

or both are not planned in a complementary fashion, policy effectiveness will be severely adversely affected. The policy mix for these conditions will be different for each region depending, among other things, on its level of development, since the higher the region’s level of development the more important intangible factors become (Figure 16.1). Nevertheless, in all regions we always need a mix of hardware and software, although in different quantities and combinations, precisely because the hardware alone cannot work (for example, new road links in less favoured regions by themselves are often more a means to siphon off local markets than to stimulate endogenous growth). All the above also implies that a regional policy which supports and stimulates development efforts will necessarily have to change and evolve over time to become more effective, focusing increasingly on intangibles linked to regional innovation capacities, including the institutional framework and public–private interaction which underpins this capacity; a difficult transition from opening roads (with big budgets) to opening minds (with smaller budgets but much more difficult to design and implement). Thus governance, both horizontal across traditional institutional boundaries among ministries and professional intermediaries (for example, regional development agencies), as well as vertical through intelligent subsidiarity among local, regional and national authorities addressing ‘functional’ regions, becomes of

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paramount strategic importance as we move forward in the developmental path to design the right policy mix. In short, regional policy is a moving target which is relevant for all types of regions as a means to liberate latent developmental capacities through appropriate policy mixes – the most effective means to ensure cohesion through the ‘overall harmonious development’ established in Article 174 of the Lisbon Treaty.

among regions through ‘absolute’ disadvantage and that time is only a relative concept for those that can afford to wait. In Europe, we see that the limits to workers’ mobility in a European Union (EU) with an incomplete internal market with more than 20 different languages and nearly 300 regions in 27 countries is very different from a more homogeneous market, institutional and cultural framework in the USA, where many of these orthodox recipes come from. In fact, it is most interesting to see how the perception of regional economic context and its diversity, including economic, institutional, historical and cultural features, have largely influenced the recent debate on the relevance of place for regional policy, between those that think that regional policies should be people centred as opposed to place based (Gill, 2010) and those that think that these policies need to be place-based in order to be people centred (Barca and McCann, 2010; Garcilazo et al. 2010). At the centre of the debate is, of course, the concept of agglomeration and its role in speeding up development as well as the extent to which it can (or is desirable) to be influenced by public policy. Arguably, what is in question are not the benefits of agglomeration processes themselves (economies of scale, concentration of the brains of industry in globally connected high-technology hubs, and so on) but its scale, pace and connectedness to its wider geography, including its limits in terms of environmental (pollution), economic (inflationary pressures), social (security) and accessibility considerations, not to mention ‘beyond GDP’ issues in terms of ‘human dimension’ and ‘shared community values’. In terms of policies, one could argue that in the early stages of development (for example, some of the new EU members from south-eastern Europe) public policies should accompany the process of agglomeration while ensuring that ‘trickle down’ effects in its wider hinterlands do take place in order to maximise growth countrywide. In more advanced stages, public policies should turn their efforts to develop a balanced multipolar growth structure and ensuring connectivity both among these poles and

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between them and the global economy in order to develop a harmonious development process which does not leave any region behind and exploits overall economic capacities to their full, wherever they are to be found. Regional development policy should not become a social policy in the form of charitable fiscal transfers if it is to have a truly lasting development impact. Regional policy is about sharing growth efforts and helping regions help themselves, not about redistribution per se. In terms of agglomeration processes, no one can deny the advantages of a more flexible and therefore mobile labour market, in particular in those advanced economies which work with the safety nets of low unemployment and generous social security systems. In these economies people may be able to choose to emigrate in search of better job opportunities, enrich themselves by exposure to multicultural experiences or take chances with their present jobs by trying more promising avenues. Moreover, the economic adaptation to an ever-changing market demand fuelled by an accelerated process of ‘creative destruction’ will be much facilitated by this flexibility and, in the long run, will probably help limit and minimise its social costs. Moreover, this adaptation might be particularly useful in a context of globalisation and open war for ‘talent’ of highly educated people. But in developing economies, the price paid by most of those forced to emigrate, in terms of changing culture, leaving behind family, loss of identity, isolation and plain human distress, is difficult to measure in economic terms. As economists, we also have to consider the deserted underdeveloped regions left behind and deprived of their younger and more entrepreneurial people or of those latent economic capacities left unexploited. Here history and geography matter, even if they are quite difficult to fit as such in the neoclassical growth function. Nowadays, with a new Rio Grande in the making over the Mediterranean Sea and the US free trade agreement with its neighbours, including thousands of kilometres of high gates which allows trade but not people to pass, these issues will come more and more to the fore of economic and political debate. In order to reap the full benefit of economic integration between regions and countries at different levels of economic development there needs to be regional development policies based on solidarity among trade partners. In short, one of the public policy dilemmas for many governments comes down to two main questions. On the one hand, either they help exploit underutilised economic resources in backward regions by proactively building capacities – intentionally creating competitive advantage – in order to maximise overall national economic potential to its full, or they are likely to face a suboptimal and unbalanced geographical distribution

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of economic activity in their territories which misses out latent economic opportunities. On the other hand, either they help underdeveloped regions and countries develop or they are doomed to confront higher unwanted migration pressures which can create all sort of political tensions if, as is the case today, integration efforts do not keep pace. The dilemma is even more complex since the European Union cannot afford to lose its working population, needed to sustain increasingly expensive health and pension bills of an ageing population.1 One might think out of developmental considerations that a more geographical balanced and connected distribution of economic activity throughout the territory might militate in favour of overall economic competitiveness and, among others, more social cohesion by helping people work where they choose to live. This way we can also contribute to preserving communities and a rich regional diversity that counts positively in an economy driven more and more by innovation. Such an approach may eventually help unleash economic potential where it is currently most underutilised, including reducing environmental problems and external diseconomies in central regions and urban centres (for example, price of housing, traffic congestion). One simple interesting argument that militates in favour of this consideration revolves around the nature of the ‘new knowledge based economy’ in the making, with intelligence as the key raw material of economic activity and regional competitiveness. Fortunately, and in contrast with the type of raw materials on which past industrial revolutions depended, intelligence is the one economic resource that is geographically evenly distributed. Therefore it might be safe to assume that regional differences will progressively rest in the future on the way this intelligence is valorised or left idle. In other words, regional differences will depend more and more on regions having the infrastructures (schools, vocational training centres, universities and technology centres and R&D laboratories), policies and business environment to effectively transform and exploit intelligence in the form of innovation, and which are capable of attracting more talent from elsewhere. The so-called ‘digital divide,2 and similar other new ‘technology and education-related divides’ we will soon be hearing about are the offspring of this situation. Another way of looking at this is that we will all be better off if we help develop those that need it most where they are in order to prevent unwanted social, economic and political problems in our own (developed) countries and regions. Put simply, the cake can be made bigger; there will be more to redistribute, leading to a win-win situation for both richer and poorer regions. As European regional policy has demonstrated, those that give to help others stand on their (economic) feet do get value for

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their money. In time, their economies will benefit through increased, more stable and diversified demand for goods, know-how and a market for higher value-added products for which they have consolidated competitive advantage (European Commission, 2010b, p. 10). So the key question becomes: can we do something (else) about underdevelopment in less favoured regions? Or should we continue to promote faster, more balanced and environmentally friendly developmental trajectories in advanced regions and countries? How should it be done? More fundamentally, is there any sense in developing public action in the form of regional policy? Many economic policies are based on the assumption that public action in other realms of economic life, away from macroeconomic policy management, health, education, and basic public infrastructures, might not only be inefficient but even counterproductive, notably by ‘crowding-out’ private investment and other undesired macro-economic effects of budget deficits.3 In other words, doing nothing is better than doing something, in the form of industrial or regional policy, since the invisible hand was always better than what an intelligent public mind could do; possibly because economic intelligence and the public mind could sometimes be considered as a contradiction in terms! Thus, planners and politicians in particular, have often been left with very few alternatives. They are told to pursue an orthodox economic policy which is better left in the hands of a small group of high-level economists (do not touch it if you do not understand it), often sitting in national ministries, reputed international consultancies, or even better, independent organisations such as central banks (the least you touch it the better) usually located in the capital cities and leading financial centres of central regions. Monetary and fiscal policies have therefore concentrated most of the debate, and the role of the public sector in these fields is widely acknowledged and well established. And quite rightly so because a reasonably healthy macroeconomic framework, inflation under control and lower interest rates in particular, is a necessary pre-condition for any regional or industrial policy to succeed, even if the latter can, in turn, contribute to achieving the former objectives. The real issue here is about the ‘else’ part: what else should be done once the macroeconomic picture is more or less right? Is there anything else that can be done through public intervention to promote economic development? The issue is not to oppose macroeconomic policies and meso-economic policies (regional policy) as excluding alternatives. The problem arises when all public economic policy is based exclusively on the first type of policies and the second is discarded on the grounds of orthodox economic doctrine. By pursuing this route, possible synergies are, at best, not maximised, and at worst, simply lost.

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Moreover, our problem increases when our political masters look for proven standard economic development recipes (non-contested economically or politically) that are easy and fast (within their political cycle) to implement. An inward investment policy, based on subsidies or tax rebates, to attract foreign multinational companies which can create a significant number of clearly identifiable new jobs ‘the day after tomorrow’ and bring new technology with them is a good example of what it is meant by the latter. Unfortunately the scarcity of this type of foreign direct investment opportunities for Europe’s less favoured regions is making policy-makers look for more complex policy options. Finally, the really big problem starts when economists simply proclaim that there are no easy recipes and that proposed solutions are long, never easy and require deep pockets. Moreover, they may get very nervous if we tell them that trying to replicate Silicon Valley throughout is not only not possible but might well be a waste of time and resources. All this usually tends to confirm the politician’s suspicion that the only really serious economists (and economic policy options) are those who talk about interest/ exchange rates, money printing or taxes with a clear-cut advice on an x per cent increase/decrease of such and such monetary or fiscal variable. No wonder it has been called the dismal science. Fortunately, there are still those that advocate that ‘the most important job for economic policy is to create an institutional environment that supports technological change’ (Romer, 1994, p. 5). Therefore, we would still like to think that we can do something more through regional policy. In a nutshell, the argument goes as follows: in all regions, but in those of more developed countries in particular, we need a new regional development policy driven mainly by the promotion of innovation which is based on strong public–private partnerships with inclusive planning and implementation processes, including a shared vision for the region. That is, a regional development policy driven by innovation and based on smart specialisation strategies (Barca, 2009; Foray et al., 2009). In order to be able to argue for a new regional innovation policy, we must first identify in which way this policy, with new objectives, instruments and forms of implementation, differs from traditional industrial policies. In this sense it might be worthwhile to summarise why traditional regional policies do not respond adequately to developmental needs in less favoured regions in particular. The old industrial and regional policies have been subject to heavy criticism for their cost-effectiveness, having often been based on several of the following (the list is not exhaustive) (Landabaso and Mouton, 2005):

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Selecting and positively discriminating in favour of a few winners (businesses, sectors or technologies) by means of public grants from the national/regional public authorities. These are awarded on the basis of administrative decisions made by officials and/or politicians without adequate entrepreneurial or technological know-how. There is a lack of a long-term strategic approach and insufficient consultation with other key economic actors in the region. Excessive reliance on direct individual subsidies rather than the establishment of financial engineering instruments and the provision of collective services to groups of firms (for example, technology centres and clusters). Moreover, entrepreneurship promotion has often been neglected in favour of more short-term economic ‘success’ of old established businesses. In this sense, big industry in traditional sectors has often received more attention than homegrown small and medium sized enterprises (SMEs), including newly created ones in promising sectors. Protecting domestic industry from international competition with artificial barriers, often non-tariff barriers, that provided a breathing space that was only occasionally used to lay the foundations for sustained growth in the sector concerned, by actively identifying and reinforcing new factors for regional competitiveness. Concentrating efforts on attracting direct foreign investment from big multinational companies using tax policy in competition with other regions rather than attracting talent and promoting bottom-up endogenous growth in SMEs. Almost all regional development agencies in Europe have placed great emphasis and money on attracting foreign investments, which have been found to be highly volatile, sometimes bringing more problems when they are withdrawn than benefits when they arrive. This is particularly the case if they are branch plants with no or little research and development (R&D) and technological internal capacity based on exploiting low labour costs and there are no active accompanying measures to ‘root’ the investments in the region, which requires more than offering low wages and taxes. Horizontal, automatic and non-discriminatory public aid schemes intended to reduce business costs temporarily without substantially changing firms’ strategic behaviour. In addition, they have in certain cases created clientelistic business networks that tend to become self-perpetuating and generate a market of specialised consultancy on public aid. On the business side, the excessively bureaucratic and complex nature of the application procedures and the time taken to evaluate

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applications and grant aid have made firms regard aid more as rebates on the cost of investments they would have made anyway rather than as real incentives to make high-risk investments they would not have made without aid. In other words, the principle of ‘additionality’ of public aid has often not been applied because of administrative complexities. Sometimes, industrial policies have actually been just reconversion policies with an exclusively redistributive accent, designed more to compensate ex post negatively affected social groups with lobbying powers in the short term than to promote economic development of territories in the long term.

More generally, these policies have been criticised because of their shortterm nature, short-sightedness in terms of the wider economic context and their sometimes limited anticyclical impact. Thus, new regional development policies based on innovation must be considered neither as a lesser evil nor as a miracle cure but, rather, as stable, inclusive and incremental policies, taking into account interrelated historical, economic, cultural and sociological aspects, including business culture and existing public institutions in a given region or country. Some of these policies depend and are built on territorial capital. Moreover, by their very nature the effects of these policies take time and deep pockets, which make them hard to sell politically. The new regional development policies based on innovation are much more about empowerment by helping regions help themselves. It is also about helping regions reach their full potential whatever their level, rather than establishing a standard goal for all or creating rankings within a single narrow developmental path. These policies are much more about levelling the playing field at the start than equalising the outcomes at the end. In the context of the less developed regions, regional innovation policies are faced with a special difficulty, the so-called regional innovation paradox (Oughton et al., 2000). The regional innovation paradox refers to the apparent contradiction between the comparatively greater need to invest in innovation in the less developed regions to be able to withstand global competition, and their relatively smaller capacity, compared with more developed regions, to absorb public funds earmarked for the promotion of innovation. That is, the more a region needs innovation to maintain and improve the competitive position of its businesses in an increasingly globalised economy, the more difficult it is to invest effectively and to absorb public funds to promote innovation in these regions. In other words, one might expect that once the need (the innovation

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gap) is acknowledged/identified and the possibility exists, through public means, to respond to it, such regions would have a greater capacity to absorb such resources, since they are starting at a very low level (‘with everything still to be done’). However, experience shows that these regions have serious difficulties in absorbing the public money available for innovation and, often, new available funds are captured by well-established lobbies (for example, ‘science academies’) which divert their use away from innovation into basic, sometimes second-rate, R&D efforts. The main reason for this apparent paradox is not that public funds are not available for innovation in the less favoured regions. The explanation lies instead in the nature of the regional innovation system and the institutional capacity in these regions, including the availability of professional intermediaries (Grillo, 2010) such as regional development/innovation agencies, technology centres with private participation or technology transfer organisations. One way of understanding this problem might be the following: within new growth theory, the main explanation for increasing returns to production factors, which is key to explain cumulative processes of economic growth, is knowledge spillovers. The latter has been progressively recognised to be spatially bounded, not least because of the ‘tacit’ and ‘sticky’ nature of certain types of knowledge and the territorial nature of much of the process of knowledge accumulation and learning, once the circular and systemic model of innovation, rather than the linear model, is used for regional policy planning as a more realistic guide of what goes on in reality in the business world. The latter is particularly true in the case of less developed regions, with relatively lower rates of formal R&D investments and personnel, and where innovation is more incremental, often based on the adoption and adaptation of generic technologies, and less ‘radical’ and science based, as may be the case of leading regions. The latter concentrate most of the ‘excellence’ of research, technological development and innovation (R&TDI) infrastructures and leading universities and they are naturally where high-technology industries and knowledge-intensive services are overwhelmingly located, thus producing the highest number of patents. Within this framework, ‘intra-regional knowledge spillovers are considered to arise when actors involved in the innovation process such as universities, the business sector and the government sector tie close links leading to fertilizations and feedback relations’ (Greunz, 2003, p. 34). In short, knowledge spillovers are generated and further developed within efficient regional innovation systems, which facilitate ‘links’ and ‘crossfertilisation’ among the key innovation players, not least by connecting SMEs to a responsive R&TDI knowledge base. Thus, public–private

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co-operation, business networks, university–enterprise connections and clusters can be of outmost importance in generating these spillovers and therefore in increasing the knowledge generation, diffusion and absorption capacities in a region. This is precisely what is most lacking in less favoured regions, and therefore largely explains the regional innovation paradox mentioned before. From a policy perspective, it also means that action-oriented, open, consensus-based and participative strategic planning processes led by regional authorities with a sufficient degree of autonomy/power and acting within a ‘development coalition’ (Asheim, 2001) with the private sector and the knowledge base can become key ingredients of the process of generating knowledge spillovers. This includes an innovation-friendly business environment underpinned by sound regional governance structures, including the collective capacity of key socio-economic players in the region (for example, companies, local or regional authorities, research centres, business support agencies, universities, and so on). The key is to form and effectively use networks or other forms of cooperation on the basis of shared interest, trust and reciprocity in order to enable and accelerate the process of regional learning and knowledgebased development. Thus, institutions and the way in which regional key players learn to interact among themselves matter (Bellini and Landabaso, 2007). This explains why some regions shoot ahead through innovation while others are left behind in the developmental race, even in the presence of similar endowments of those factors that have been traditionally considered by economists as key to economic development efforts. Thus, the most important long-term keys to economic development are not the availability of strategic raw materials, proximity to central markets, transport/ logistics axis or providential foreign investment. No one really knows how the virtuous circle of higher productivity and better quality jobs is sparked and maintained. However, increasing investment capacities and open markets, better educated human capital, enhanced attractiveness for foreign direct investment, new technology-based companies, an entrepreneurial business culture, and so on cannot by themselves explain regional economic development. Neither can Richard Florida’s famous 3Ts (technology, talent and tolerance) (Florida, 2002) which very elegantly and intelligently captures important parts of the complexity of knowledge-driven economic development. Indeed, it is difficult to distinguish which of the above are inputs or outputs of the process of economic development. Possibly a fourth T, in the form of territorial capital, would give us better insight. This explains why the word ‘region’ is important when we talk about public policies to

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promote economic development: regional policy is neither national policy at the smaller scale nor public policy applied to a territory which has been artificially identified as a region. Territorial capital cannot fit into administratively determined boundaries. In this next section, the chapter examines what these new policies should look like, based on lessons learnt in the field from the experiences of European regional policy over the past 20 years. This can be summed up in three general considerations.

1. INNOVATION POLICY IS NOT ONLY ABOUT SUPPORTING R&D POLICY, SIMPLY BECAUSE R&D IS NOT INNOVATION Promoting innovation-led regional development is not first and foremost about increasing R&D excellence and R&TD infrastructures (supply push) but is also about new partnerships where efficient innovation systems (demand pull) mobilise the intellectual and entrepreneurial capacities to create an innovation-friendly business environments, for SMEs in particular, in all regions and covering all sectors (not just high technology). This is not to say that R&D excellence is not important or that regional innovation policies should not concentrate scarce public resources in a few promising fields which fit each region’s competitive assets, both in terms of sectors and research and innovation (R&I) capacities. On the contrary, the only R&D efforts which are worth supporting through regional policy are excellence ones, which by their own nature are international in perspective. Regional is not parochial. Regional innovation policy cannot and should not be considered as supporting second-rate R&D efforts of those not able to reach the top rankings. Instead, complementary efforts in R&D and innovation are needed, with a particular emphasis on the latter in less favored regions, as the OECD (2010, p. 16) has put it: Policies need to distinguish clearly between a few highly innovative and high growth potential firms and the great majority of SMEs, reflecting the different ways in which they innovate. The different needs can be characterized by a distinction between Science, Technology and Innovation mode of innovation on the one hand, focused on R&D and breakthrough innovation and Doing, Using and Interacting mode of innovation on the other, focused on incremental innovation in the ‘ordinary’ SME. Both must be encouraged.

This actually coincides with how business associations (UEAPME,4 2010, p. 15) see the process of innovation in SMEs:

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Innovation processes in crafts and SMEs in Europe are characterized more by on-going permanent processes and less by linear technical-driven inventions. In most SMEs the innovation process is based on available technologies, which are used in a new and sophisticated way; based on the experiences and the knowledge of persons in and around the company; supported by highly qualified employees and the entrepreneurial spirit of the business owner; based on highly flexible, but long lasting, customer and supplier relations and supported by existing networks and clusters.

Thus, R&D excellence and regional innovation are complementary and both are required: exploiting agglomeration and economies of scale is important (centripetal forces) within the European Research Area to ensure a competitive edge vis-à-vis our trade competitors. However, the diffusion and absorption mechanisms based on regional innovation potential (centrifugal forces) is also necessary in order to disseminate R&D efforts throughout the entire territory as well as the opportunities to link into R&I international partnerships. Thus, from a policy perspective it is important to emphasise that the linear model (from R&D to the market) is much less relevant for policy design than the systemic or interactive model: not just patents but economic exploitation of talent and new ideas – not just manufacturing industry and big firms with R&D but also innovation in services and open innovation. This is precisely because regional innovation capacities are much more about personal commitments, institutions, networks, co-operation,5 including social capital, than it is about narrowly focused science and technology efforts. In short, reinforcing through the policy triple helix – knowledge triangle, clusters and university–enterprise is key for regional innovation. Regional innovation for most regions in the EU is basically about knowledge absorption (education and training, advanced business services) and diffusion (technology transfer, information and communication technology, entrepreneurship) than about knowledge generation (science efforts). Moreover, since innovation has a strong territorial dimension (OECD, 2009) largely based on tacit knowledge exchanged through a networked economy, there is no ‘one size fits all’ innovation policy: regional diversity is an asset that advocates different routes to growth through innovation. In this sense, ‘regional diversity calls for different policies, in order to maximize the potential of the variety of regional knowledge economies in Europe’ (Wintjes and Hollanders, 2010).

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2. IN ORDER TO TACKLE THE REGIONAL INNOVATION PARADOX – BIG DEMANDS, BIG MONEY AND LIMITED CAPACITY IN LESS FAVORED REGIONS – IT IS OF UTMOST IMPORTANCE TO PREPARE THE GROUND BEFORE ANY PUBLIC MONEY IS INVESTED Moreover, this is even more important with the current crisis, which has led to increases in the innovation gaps between developed and less developed regions, or leading ‘to a reversal of the convergence between EU countries in innovation performance, .  .  . with firms which are already more innovative being less likely to cut back on innovation expenditure’ (Kanerva and Hollanders, 2009, p. 8). Preparing the ground involves the strengthening of planning capacities, the identification of policy intermediaries for the implementation of the policy (public or semi-public innovation or development agencies) and establishing appropriate co-operation with the private sector and the knowledge base simultaneously. In other words, the design of smart specialization strategies should be a pre-condition for any public investment in this field. Regions should develop integrated and tailor-made strategies pursuing ‘smart specialisation’ by defining a few research and innovation priorities based on the European objectives and on their needs and potentials, identified in partnership with stakeholders, and concentrate earmarked EU resources on these identified priorities. In this sense, place-based regional innovation strategies and action plans integrating multilevel governance (international–national–regional) and horizontal (inter-ministerial) co-operation are a necessary first step. Grassroots ownership of the strategy is of paramount importance. This means that consultants and international expertise are useful but they should not be in the driving seat of the design of smart specialisation strategies, as the evaluation of the first wave of regional innovation strategies launched by the European Commission during the 1990s clearly shows (Charles et al., 2000).

3. THE KEY QUESTIONS ABOUT REGIONAL INNOVATION POLICIES ARE NO LONGER ABOUT WHAT TO DO OR WHY, BUT CHIEFLY ABOUT HOW AND WHO (EU COMMISSION, 2010A)? These policies need experimentation and institutionalised learning processes in the form of (ongoing) evaluations, including appropriate input

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as well as output indicators. Such indicators should go well beyond R&D expenditure and patents. Admittedly, the necessary indicators for properly characterising regional innovation potential or measure policy impact are still lacking, with much work remaining to be done in this area. In terms of experimentation, these policies should favour a culture of public policy risk taking and allow for tests through pilots and failure. This could be seen as a sort of public venture capital which invests in latent innovation capacities to make them happen and support them in their early stages. Innovation policies require risk-taking, trial and error and sound evaluation as well as funding and long lead times, with political consensus as a plus. Since ‘innovation cannot be dictated but it can be cultivated’ (Sallet et al., 2009, p. 5), policy-makers need to encourage business as well as professional intermediaries such as regional development agencies, technology centres, technology parks and incubator managers, and technology transfer offices, as in many ways being the soldiers on the front line. In this sense, it is important that the public sector understands its role as providing shared leadership and vision, rather than control, and catalyses economic development by promoting new ideas and partnerships with the private sector. In other words, ‘regional governments and its development related agencies play a key role in animating regional innovation systems to stimulate the learning processes’ (Runiewicz-Wardyn, 2009). It is important to underline that innovation policies cannot be ‘for them but without them’. This is also why innovation support schemes must be stable, understandable and readily accessible by SMEs. Administrative simplification and short awarding times are not an option in this field, but an absolute requirement for policy efficiency and business recognition. Policy-makers should aim at matching business demand, as the starting point, with R&TD supply and not the other way round. Innovation is a market phenomenon which transforms creativity, ideas and knowledge into development opportunities. Research and development policy takes the same path in the opposite direction: innovation earns money by transforming new or existing knowledge into business; R&D spends money to generate new knowledge. In terms of policy tools, innovation policy requires new and better forms of support over and above direct individual subsidies, which ensure stronger financial discipline on the side of firms. Venture capital, business angels, soft loans, guarantees, equity participation and other forms of financial engineering are better than grants and tax incentives, although in most cases there is need for combination (EU Commission, 2010a). Furthermore, a

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broad menu adapted to the characteristics of a particular innovation investment which is generally characterised by being risky, long term and based on intangibles is also needed. These three characteristics are not particularly welcome by traditional financial institutions, which on top of this often lack the expertise to properly assess the innovation projects submitted to them. This market failure, with serious developmental consequences, can justify timely public action to support these types of investments. Furthermore, public procurement (green and innovation driven) is also an important tool to consider given the sheer amounts at stake (16 per cent of GDP in Europe) which ‘is potentially one of the most powerful levers for effecting behavioural change amongst private sector suppliers’ (Morgan, 2010). Finally, it is also worth considering among ‘new’ policy tools building up territorial capital through regional branding and image (for example, the Guggenheim effect), which is key to cultivate, retain and attract the talent which fuels innovations.

4. CONCLUSIONS While public policy can make a difference in the pace and direction of regional development, current policy practice needs a fundamental rethink in terms of objectives, instruments and policy delivery systems. In terms of objectives, regional policy has to move on from a narrow interpretation of cohesion in terms of redistribution and disparities into supporting latent growth capabilities wherever these are to be found. In this sense, it is essential for decision-makers to understand the role played by innovation and technological development in regional development trajectories. Microeconomic and mesoeconomic competitiveness problems cannot be efficiently tackled by overdoses of macroeconomic or sector-based policies but by integrated, place-based regional policies which focus on the promotion of innovation. Place-based regional policies should not mean parochial or inward looking. Place based means understanding institutions, history and business culture, which are precisely the key features that define a region. Regions are neither artificial administrative constructs nor independent institutional frameworks, but links in a governance chain where public policy can sometimes be more effectively developed, because of its closeness, to understand economic needs and mobilise capabilities. The objectives of these policies, with their feet solidly rooted in their territories should be to have regional heads above the clouds or nearby valleys and into the global economy, where they should aspire to become competitive players on their own terms.

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In terms of instruments, public policy effectiveness is about the right policy mix adapted to each regional context: there are no recipes, and good practice exists only for those willing to learn and experiment. The capacity of key regional players to interact, share a vision and jointly commit efforts and resources is an integral part of any public policy trying to promote regional development. Regions which are able to develop the institutions and policies which promote these interactions through public– private partnerships and across the governance ladder are likely to be successful in developing a process of sustained and sustainable growth, no matter their current development level, natural endowments or inherited comparative advantage. Public policy to promote regional development cannot be left solely in the hands of the national or regional ministries of the economy. It requires horizontal and vertical governance structures cutting across administrative boundaries and power structures. Budgetary, audit and accounting considerations should only be at the service of strategic planning capabilities and not the other way round: ideas and co-operation first, public money after. In terms of policy delivery systems, regional policy cannot continue to be treated as simply a funding strand but as a public investment framework with specific requirements. It requires much stronger conditionality and a healthy degree of competition to access scarce public funds. In conclusion, the best thing one could probably say about public policy for regional development is that it can make economic development a nonzero-sum game where all stand to gain.

NOTES * 1. 2.

3.

4. 5.

The responsibility for the accuracy of the analysis and for the judgement expressed lies with the author alone and these do not constitute policy positions of the EU Commission. BEPA (Liddle, 2006): ‘By 2030 age-related public expenditure on pensions, health and long term care is forecast to rise from 18 per cent of GDP at present to over 20 per cent’. ‘Digital divide’ refers to the differences in access, skills and connectivity to information technology and the Internet among different income groups (for example, according to 2000 data, in the USA 80 per cent of households with an income of $75 000 or above have computers, compared to 16 per cent of households earning from $10 000 to $15 000. Forty-seven per cent of white households have computers, compared to 23 per cent of African-Amercian and 26 of Hispanic households (Gore and Lieberman, 2000, p. 69). Interestingly enough, some of the most radical ‘orthodox’ would not even agree to this list, the last items in particular, and still see it as quite natural to support huge (very public) defence expenditures which are mostly channelled through very restrictive national markets. Association of European crafts, trades and SMEs at EU level with 12 million company members accounting for 55 million jobs. ‘In EU 27, 52 per cent of enterprises reported innovation activity between 2006 and 2008. Among them 34 per cent cooperated with other enterprises, universities and public R&D institutes’ (Eurostat, 2010, p. 2).

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17. A participatory methodology for evaluating the cluster policy of the Basque Country Cristina Aragón, Maria José Aranguren, Cristina Iturrioz and James R. Wilson I.

INTRODUCTION

Policies that aim to support clusters of firms are now well-established tools of regional policy agencies. Since the 1980s, and rooted in research with a much longer pedigree, a large literature has emerged around clusters, motivated by the apparent success of Marshallian-type industrial districts in Italy (Becattini, 1978; 1991; Becattini et al., 2003; Piore and Sabel, 1984; Pyke et al., 1990) alongside a range of other experiences (Christerson and Lever-Tracy, 1997; Saxenian, 1994; Schmitz and Musyck, 1994). Porter’s (1990; 1998a; 2004) promotion of industrial clusters in the context of the competitiveness of particular locations has been especially influential among regional policy-makers, establishing clusters as a key focus for regional economic development. Among the growing obsession with clusters, however, there has been some scepticism (Martin and Sunley, 2003; Sugden et al., 2006) around the way in which the concept has frequently been seen as a policy panacea. Indeed, given their prominent and widespread position on regional policy agendas, the evaluation of cluster policies is a critical yet under-examined issue. The Basque Country Autonomous Community (CAPV) in Spain was one of the pioneers of regional cluster policy, applying Porter’s framework from the early 1990s (Aranguren et al., 2006). The cluster policy today supports 11 priority clusters, nine of which have been supported since the 1990s, and five emerging ‘pre-clusters’. In recent years various evaluations have been made of different aspects of the policy, but it has proved challenging to arrive at a holistic evaluation of the policy in terms of success in achieving its ultimate aim of enhancing the competitiveness of the Basque economy. As argued by Diez (2001; 2002), regional policies that centre on networks, social capital and local learning are not best suited to traditional 382

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methods of evaluation. They are characterised by intangible objectives and complex relationships. Moreover they are by nature systemic, flexible and dynamic policy processes, requiring similarly systemic, dynamic and flexible evaluation processes. It is suggested that processes of ‘participatory evaluation’ are more effective for such policies and can additionally serve as a tool to mobilise communities and strengthen the capacity for learning among the involved agents (Diez, 2001). This is also related to the model of ‘total ownership’ with regard to change processes proposed by Sullivan and Stewart (2006), for example. This chapter develops the rationale for applying a participatory evaluation approach to the cluster policy of the Basque government, arguments that we suggest can be generalised to similar policies elsewhere. Based on this rationale we propose and discuss the application of a methodology designed for pilot application with the Basque aerospace cluster. We begin in Section II with some context on Basque cluster policy and an analysis of the evolution of attempts to evaluate this policy over recent years. Building on these experiences and drawing on appropriate literature, in Section III we argue that a participatory evaluation approach, rooted in an action research framework, is the appropriate response to overcoming some of the limitations of earlier analysis. Section IV proposes a methodology for this participatory approach, and in Section V we present the process of a pilot application of this methodology to the Basque aerospace cluster. Finally, Section VI draws some conclusions.

II. CLUSTER POLICY AND ITS EVALUATION IN THE BASQUE COUNTRY At the end of the 1980s the Basque Country economy was in a process of decline resulting from the loss of its traditional industry competitive advantages. Until then most industries had been competing on price, but new challenges such as the common European market were threatening this model. There was therefore recognition that it was necessary to develop new, specialised and sustainable advantages to compete in international markets, and a firm called Monitor Company was contracted to study the future competitive situation of Basque industry in the world economy. This study based its analysis on the newly presented work of Porter (1990) on the competitive advantage of nations. From this basis, and recognising growing perceptions on the relevance of clusters for territorial competitiveness, the Basque Country became a pioneer in Europe (alongside Catalonia and Scotland) in establishing a targeted, cluster-based industrial policy during the early 1990s. Taking

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into account Monitor’s study, the Basque government established a competitiveness programme in 1991 that supported workgroups in nine priority clusters. The workgroups were constituted by business leaders, government representatives, industry association leaders and representatives from other institutions (education, training, basic and applied research). They worked to define priority improvement areas and action proposals in the nine clusters, which then evolved into a dedicated cluster policy. The mission of this policy is stated as the improvement of competitiveness of firms and the region responding to strategic challenges through cooperation in three main areas: technology, quality management and internationalisation. This is operationalised by financial support for cluster associations (CAs), institutions whose main objective is to improve each cluster’s competitiveness by facilitating and fostering cooperation among their members, who include firms, research and development (R&D) centres, universities, and so on. Since 2000 the Basque government has maintained a stronger relationship with the CAs, which are obliged to present strategic plans each three to four years and annual action plans. Today the policy supports 11 priority clusters, nine of which have been supported since the 1990s, and five emerging ‘pre-clusters’. First efforts to evaluate the policy were made in 1998, when the Basque government decided to carry out a policy reflection process. What had been designed as a report evolved towards a formal evaluation (Ahedo, 2004). Three main conclusions were obtained by Ahedo (2004) from interviews with those involved in the process: (1) it had been an adequate industrial policy for prioritising public resources; (2) there had been a low level of mergers and strategic alliances but an important increase in interfirm relations; (3) the associated firms were not inclined to self-finance the whole budget of the cluster activities, and therefore public aid was still regarded as necessary. Some years later a new evaluation approach was developed by Aranguren and Navarro (2003), which has subsequently stimulated a series of further research projects (Aranguren et al., 2006; 2008) that have been defined in cooperation with the Department of Industry, Trade and Tourism and some of the CAs. The root of these developments was an initial exercise to extract nine good practices from European studies on cluster policy and to describe how such issues had been considered in the policy process in the region (Aranguren and Navarro, 2003). One of the main conclusions was that although direct effects of the policy were difficult to measure in terms of competitive upgrading, two positive effects were detected. First, the adaptation of other policies to the real needs of

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Table 17.1

Basque policy evaluation projects: correspondence with Storey’s ‘six steps to heaven’

Step

Description

Evaluation projects

Monitoring Step 1

Take up of schemes

Planning and budget control Basque government

Step 2 Step 3 Evaluation Step 4 Step 5 Step 6

385

Recipients opinions Recipients. Views of the difference made by the assistance Comparison of the performance of ‘assisted’ with ‘typical’ firms Comparison with ‘matched’ firms Taking account of selection bias

Project I

Project II

Source: Storey (2000) and own elaboration.

firms and, secondly, improvement in the level of knowledge firms had about public policies. The lack of explicit evaluation in the policy process was also clearly detected. While this could not be considered an evaluation process in traditional terms – on which see Turok (1990), Raines (2002) or Storey (2000; 2004), for example – it was a preliminary study that made it possible to focus subsequent questions. The next project (Project I) was the application of an evaluation tool designed to measure the perceptions of CA firms on the extent to which outputs (networking, social capital and cooperation in strategic projects) and results (improvements in firm competitiveness) were being reached. This was applied to the Paper Cluster Association, and results are reported in Iturrioz et al. (2005) and Aranguren et al. (2008). The key finding was that CAs appeared to be helping to develop social capital, but with little perceived impact on the competitiveness of their members. Following Storey (2000; 2004), both the internal evaluation process developed by the Basque government in terms of planning and budget control, and the external evaluation project developed by the authors in Project I should be categorised as monitoring rather than evaluation. More specifically, the planning and budget control processes would be related to Storey’s step 1 (see Table 17.1), where characteristics and nature of take-up of the scheme are identified. Project I is related to step 3, as the process relies exclusively on a survey where opinions of policy recipients were collected.

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Thus a challenge was identified to proceed through steps 4, 5 and 6, essentially comparing performance of assisted firms with the performance of non-assisted firms, ensuring that the control group of non-assisted firms matched the assisted ones, and that selection bias was considered. A new project (Project II) was developed in response to this challenge (Aragón et al., 2008). Although the general goal was to try to understand whether the existence of a cluster association improves the performance of associated firms, a more specific goal was established from a methodological point of view: to compare the performance of assisted firms with a control group of non-assisted ones, something that had not previously been attempted with regards to this policy. This evaluation process was applied in two cluster associations: the Paper CA (PCA) and the Electronics, Computer and Telecommunications CA (GAIA) and it focused on the three main areas defined by the cluster policy as priorities: technology and innovation, quality management and internationalisation. It was found that while firms belonging to CAs demonstrated better performance in indicators for innovation, quality and internationalisation, this did not translate into being more profitable. Moreover no cause–effect conclusions could be obtained, as it was difficult to determine whether the associations had helped firms improve in these areas or firms with a higher performance profile joined the CA. In summary, while Project I enabled an evaluation of firm-level perceptions of the CA’s effect on competitiveness, Project II facilitated a measurement of the differences in behaviour and performance of assisted and non-assisted firms. Nevertheless, difficulties in deriving clear conclusions from the evaluation processes were clearly apparent in both projects. These difficulties are directly related to some of the characteristics implicit to clusters as a policy focus, and well-acknowledged in the clusters literature: the mixture of tangible and intangible objectives; their systematic nature; and the complexity of cause–effect relationships. A conclusion, therefore, was that there remains a need to develop methodologies better suited to the specific characteristics of an essentially ‘soft’, relationshipcentred policy designed to generate tangible results, but through less tangible processes of co-operation. Indeed it can be argued that the systemic nature of the policy requires an evaluation process that is long-term, strategic and flexible, and as such able to contribute itself to dynamising the co-operative relationships around which the policy is structured. As a response to these concerns, the remainder of this chapter develops a participative evaluation methodology, the rationale for which is set out in the next section before detailing and applying the methodology in the subsequent sections.

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III. RATIONALE FOR PARTICIPATORY CLUSTER POLICY EVALUATION As the term suggests, participatory evaluation is a process integrating the participation of all implicated parties. Specifically, it implies the development of a consensus around the criteria used to evaluate a particular policy among all of the stakeholders affected by that policy. Hence the determination of criteria for evaluation is not undertaken ex ante; rather the engagement of actors in reaching consensus on these criteria is integral to the overall evaluation process. Given these characteristics, the term ‘participatory evaluation’ in fact covers a wide range of more specific approaches that all share a common denominator: they involve the implicated parties (stakeholders) in a periodic evaluation analysing the relevance, efficiency and impact of an activity/policy/programme. In order to achieve this, the process must involve these actors directly, including stages of planning and development, so that the construction of consensus and mutual understanding is facilitated as a central tenet of the evaluation. A contrast with conventional policy evaluation processes is set out in Table 17.2. Thus participatory evaluation is centred on institutions and on people. The stakeholders in a particular policy become the principle actors in the process of evaluation, rather than merely the objects of evaluation. This brings a number of important advantages over conventional evaluation Table 17.2

Conventional versus participative policy evaluation

Conventional

Participative

Who?

External experts.

What?

Success criteria and information necessities are pre-determined. Evaluation by objectives. Distance from the evaluation team and other participants. In general, when the policy or programme is finished. Summative evaluation. Should the policy or programme be continued?

Beneficiaries, business people, policy-makers, evaluation team. Participants identify their own information necessities and determine their own success criteria.

How? When? Why?

Shared methods and results from the involvement of participants. Frequently, throughout the duration of the policy. Continuous evaluation. Formative evaluation to generate actions of improvement. Continual learning.

Source: Diez (2001).

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approaches, in particular with regards opportunities for dynamic learning processes, leading to potential policy improvements. The opportunity for policy stakeholders and the evaluation team to analyse problems, restrictions and obstacles together has potential to lead to new solutions that emerge from the exchange of ideas and perspectives, potentially improving policy design from an early stage. At the same time the development of common understanding of the issues increases the viability of making necessary changes, and such interaction is likely to help participants construct shared capacity for reflection, analysis and effective implementation of actions. More basically, the bringing together of information held by different (and often isolated) actors around issues related to the policy and its aims can bring significant benefits for the evaluation process. Participatory approaches respond to the necessity of rooting policy and evaluation in existing reality; they promote a better understanding of the social and political environment and can assist in working towards meeting the information requirements of potential policy users. Moreover, participation as a central tenet of the evaluation process supports recognition among actors of the plurality of values and interests in society, in turn directly addressing a key characteristic of all evaluation processes; that by definition they involve value judgements, in a society in which there is no one unique system of values. By tackling this issue head on, participatory evaluation approaches can help to facilitate: legitimisation of the diversity of present interests in the specific policy/programme; respect for the opinions and concerns of different implicated actors; recognition of the inherent multiplicity of perspectives, and that there are likely to be varying views on what constitutes successful policy implementation and impacts. As such, stakeholder participation within evaluation processes can build horizontal linkages, reinforce mutual trust, foster collective learning processes, and support empowerment goals and institutional capacity. Moreover, such approaches can play a role in the democratisation of the policy process, closing the gap between strategic decision-making and the aims and objectives of the actors among whom key decisions are felt (Sugden and Wilson, 2002). Diez (2001; 2002) suggests that participatory evaluation is particularly appropriate for new regional policies that emphasise the importance of networks, social capital and local learning. Cluster policies currently form a core component of these new regional policies and are characterised by precisely the features – intangible objectives, complexity of relationships, systemic nature, dynamism and flexibility – that pose significant problems for conventional evaluation approaches. Moreover, cluster policies are themselves premised on the benefits of co-operation among

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actors towards common goals, and as such seek to generate dynamic cooperative learning among those actors. This presents a compelling rationale for evaluating such policies using processes that parallel the types of participation that the policies themselves are seeking to stimulate and/or consolidate. Indeed, participatory evaluation is committed to the development of changes or improvements that are interactive, contextualised and directed to knowledge building, responding to Turok’s (1990) challenge of incorporating understanding and explanation. From a broader public policy research perspective, this rationale is coherent with the proposal made by Cooke (2007) that research must cease jumping to policy implications from theoretical modelling, a tradition that produces unrealistic advice to understandably sceptical policy actors. Rather, implications should be subject to ‘proof of concept’ testing in negotiated stakeholders discourse to establish the appropriateness or otherwise of such policy implications. Indeed, participatory evaluation sits within the framework of ‘action research’ in the sense of being ‘not so much a methodology as an orientation to inquiry that seeks to create participative communities of inquiry in which qualities of engagement, curiosity and question posing are brought to bear on significant practical issues’ (Reason and Bradbury, 2008, p. 1; original emphasis). In this sense action research is a form of inquiry that supports practical change in real situations, while at the same time producing new knowledge that enhances our understanding of it (Svensson and Nielsen, 2006). In the Basque Country cluster context the rationale for participatory evaluation is strongly supported by the evolution of evaluation attempts during the past 15 years, as recounted in section II. In particular, evaluating the policy’s mission of improving competitiveness by responding to strategic challenges through co-operation has proved challenging using convention methodologies. Following the above rationale, we suggest a synergy between the challenges inherent in evaluating this policy and the advantages that are argued to stem from participatory evaluation approaches. Such an approach in the context of this specific policy would involve developing a consensus approach to the definition of a set of indicators and their interpretation that is shared (in particular) by three groups: the policy-makers (and their technical teams); the management and technical teams at the cluster associations; and the representatives of those firms/institutions belonging to the implicated clusters. Building on earlier experiences, we suggest that the development of an appropriate methodology to address this challenge is an important step forward in the policy evaluation process. In the next section we set out a specific methodology designed to take this step.

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IV. A METHODOLOGY FOR PARTICIPATORY EVALUATION Following Gilliam et al. (2002) and Diez (2002), in this section we describe a methodology developed to set in motion a participatory evaluation approach with regard the Basque cluster policy. The specific characteristics of this cluster policy raise methodological challenges that have important implications in the evaluation design. First, the cluster policy involves a range of very different agents, including policy-makers, firms of different sizes and activities, universities, technology centres, and so on. Owing to this variety, the methodology needs to be simple and flexible, in order to integrate each context and reality. Second, the limited number of people working in the CAs and the predominantly small size of the majority of their members require an efficient process that is not too time-consuming and that demonstrates a concrete programme of activities and results. Third, the current cluster policy is an ongoing reality and its management has been established for over ten years. The evaluation methodology must therefore be integrated into this management process, sensitive to its evolution and past experiences, and able to profit from the synergies arising between them. Taking into account these characteristics and building on European Commission (2006b) guidelines, we have adapted a four phase evaluation process proposed by Gilliam et al. (2002; see Table 17.3). Table 17.4 summarises our adaption of these phases for a proposed participatory evaluation framework for cluster policy in the Basque Country. Below we set out each of these phases and steps in detail. Phase 1:

Planning and Model Development

This phase ranges from defining the scope and objectives of the evaluation to finalising a proposal of data collection tools that will ultimately be used for preparing implementation activities. In particular, this first phase includes the following five steps. Step 1: Present a participatory evaluation project In order to assure from the beginning the participatory nature of the evaluation it is necessary to contrast interest in the project among cluster policy stakeholders, presenting them with the opportunity to actively participate where there is interest. This open call for participation helps avoid one of the main weaknesses of participatory evaluation, that is, the lack of commitment of the involved agents.

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Table 17.3

391

Gilliam et al.’s phases of participatory evaluation

Phase

Activities

Phase 1: Planning

1. Define goals and objectives 2. Identify evaluation team members (stakeholders) 3. Establish roles and responsibilities for identified evaluation participants, including a vehicle for ongoing communication 4. Plan logistical and administrative arrangements 5. Develop framework for the evaluation 6. Develop a set of evaluation questions 7. Develop data collection instruments 8. Finalise sample of data collection sites and interviews/focus groups 9. Organise/train evaluation team members 10. Facilitate the communication process 11. Conduct interviews/surveys/focus groups, etc. 12. Analyse information/data collected 13. Summarise evaluation findings 14. Discuss evaluation findings including issues of technology transfer and translation 15. Formulate lessons learned from the evaluation process and outcomes 16. Develop recommendations based on each evaluation question or objective 17. Summarise lessons learned and recommendations for program improvement 18. Write evaluation report 19. Distribute evaluation results via multiple media

Phase 2: Implementation

Phase 3: Development of action plan/lessons learned

Phase 4: Finalisation and dissemination of results

Source: Gilliam et al. (2002).

Step 2: Define a common view of the aims and scope of the evaluation process The purpose of this second step is threefold. First, to share among stakeholders the general aims of the cluster policy before developing the evaluation tool. Second, to identify and agree the potential objects of evaluation.

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Phases of participatory evaluation for cluster policy in the Basque Country

PHASE 1: PLANNING AND MODEL DEVELOPMENT Step 1. Present a participatory evaluation project to the cluster policy agents Step 2. Define a common view and the scope of the evaluation process Step 3. Define the key evaluation questions Step 4. Define a proposal of the indicators and data collection methods Step 5. Approval of the complete evaluation framework PHASE 2: DATA COLLECTION AND PROCESSING PHASE 3: REFLECTION AND ACTION Step 1. Analyse data/information collected and formulate lessons learned Step 2. Develop action plans and assess the evaluation process PHASE 4: DISSEMINATION Source: Own elaboration from Gilliam et al. (2002).

At this early stage only a general definition of the evaluation objects are discussed and agreed by the stakeholders; the final definition will be determined during steps 3 and 4. Finally, within this step it is necessary to organise the process and to define the chronogram of evaluation. This contrast allows planning of the evaluation in a participatory manner to ensure that stakeholders buy into the process. At this point, co-ordination of the evaluation process with the strategic reflection process of the CA can also be considered so as to maximise synergies. Step 3: Define the key evaluation questions In this step, all stakeholders are involved in identifying and selecting the key questions to which the evaluation process should respond. In particular common objectives are defined as a starting point to develop an evaluation tool in terms of the specific results that stakeholders seek in order to improve the competitiveness of the cluster. The key evaluation questions must comply with the mission of the cluster policy: they must be strategic competitive challenges, or key elements of these challenges, that have to be confronted in cooperation or within the scope of the CA. It is necessary for all stakeholders to participate in this step in order to guarantee inclusiveness in the identification of key questions, and that the final outcome of the process is agreed by all. However, the motivation to evaluate cannot be taken for granted, even among those who might be expected to benefit from evaluative outputs. Selecting only crucial challenges in which a majority of the agents involved agree can be one way of

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providing an additional incentive for stakeholders to take the evaluation process seriously. Step 4: Define a proposal of the indicators and data collection methods A set of indicators are defined to answer the key evaluation questions previously determined. In the policy evaluation context, an indicator can be defined as a measurement of the success of a policy that produces information to help stakeholders communicate, negotiate or make decisions. Applying some of the criteria of the European Commission (2006b), the indicators selected must: be closely linked to the key evaluation question; be measured preferably on an independent basis and based on reliable data; provide clear information, simple and easy to communicate and understand. From the key evaluation questions agreed in step 3, the research team identifies a structured set of potential indicators to respond adequately to the defined evaluation needs. For each indicator, it is important to detail the value it gives to the question it addresses. Moreover, the roles and responsibilities of evaluation participants must be established, in particular in terms of providing feedback and comments on the availability of data, methods and frequency of data collection, clarity of instructions and record keeping, disclosure, reporting formats, and on the data elements to be reported. Step 5: Approval of the complete evaluation framework In this final step of the planning process, all stakeholders meet to discuss and contrast the framework of indicators proposed by the research team. The aim is to agree the final selection of indicators, together with the data collection methods, and establish consensus around the timetable for the implementation of the ongoing evaluation. Thus the complete evaluation model will be approved and ready to be implemented. Phase 2: Data Collection and Processing Based on the evaluation indicators collectively identified and the information collection process defined, in this phase data will be generated, collected, and organised in order to prepare for the reflection and action of the next phase. The evaluation team will lead the development of necessary data collection tools and the organisation of the collection process. The method chosen to collect data should be subordinated to the usefulness of the information. In this sense, Diez (2002, p. 302) argues that ‘the final quality of an evaluation does not depend upon the methods used, but rather on the usefulness of the information produced and its

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capacity for offering up valuable answers’. In the case of cluster policy, the indicator structure designed in prior steps will be the reference to collect information in the most valuable form. The sources of data are likely to include: ● ● ● ●

CA annual reports; statistical data provided from various sources (CA, official sources); interviews with key stakeholders; and evaluation surveys of stakeholders.

Generally, quantitative data will not be sufficient to make a complete evaluation of this type of policy; qualitative information directly obtained from the agents is likely to be important. Thus interviews between the research team and key cluster policy stakeholders such as the CA manager will be required to obtain additional information. The persons interviewed may not be confined to the CA management, CA members or policy-makers, but might also include external, indirect stakeholders (for instance, a CA member’s client) whose opinion is interesting to measure the impact of the policy. Phase 3: Reflection and Actions In this phase, the agents involved in the evaluation process will come together to reflect on indicator results and analyse the reasons for failure or success. Based on these findings they will develop future action plans. Finally, they will give feedback about the evaluation process. This third phase includes two steps: Step 1: Analyse data/information collected and reflect on lessons The data must be processed and structured in order to generate valuable and interpretable information, from which the stakeholders will discuss the results and ultimately determine the format of the final report to disseminate information and facilitate further learning processes. Besides sharing results, these action planning meetings are a forum for discussion and feedback to interpret the results, address further arising issues, and provide recommendations for the future. The evaluation team will facilitate the process. Based on the work of the previous phases they will provide an overall summary of the evaluation. This summary will facilitate a review of the original purpose of the evaluation, the procedures and methods used, and the results obtained. The objective is to capture problems encountered, lessons learned, solutions, and first recommendations for future action.

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Step 2: Develop action plans and assess the evaluation process The evaluation is undertaken within the framework of an ongoing policy, and thus evaluation results should be directly applied to the programme for overall improvement. In keeping with the concept of participatory evaluation, as the agents involved work towards a better understanding of the causal effects in the results, they can have an input into changes in future action plans. Feedback about the evaluation process will be elicited from the stakeholders. The conclusions reached in the previous step should then be integrated in the CA strategic definition process and concretely in their future action plans. The learning can also be translated to strategic decisions in the firms belonging to the CA, so that findings feed into multiple specific programme activities. In principle, these areas will be closely related to the expected uses of the evaluation results initially identified. Phase 4: Dissemination The objective of this final phase is to socialise the evaluation of the policy. The evaluation team will prepare a draft report that reflects on evaluation objectives, methods, results, recommendations for strengthening the network, and plans for conducting ongoing evaluations. In keeping with the concept of participatory evaluation, the draft report will be circulated among stakeholders to review for accuracy and clarify any further issues. The recommendations from this evaluation are likely to be both generic and specific for enhancing the performance of policy with respect to the specific cluster in question. The evaluation team will distinguish and organise these different recommendations in a final version of the report, ensuring confidentiality issues are dealt with effectively. This report will be shared internally with stakeholders: policy makers, CA director and cluster members. Finally, presentations will be made at international and regional conferences and workshops with a dual purpose: first, to share the case study methods and framework for evaluation; and, second, to highlight the benefits of this approach for other clusters that will enable a smooth extension of the initial pilot project.

V. APPLICATION TO THE BASQUE AEROSPACE CLUSTER: SOME RESULTS While the evaluation of cluster policies is a complex and long-term process, here we report on initial results from an application of the methodology set out in section IV to a specific CA in the Basque Country. The

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design and application of this innovative methodology has taken place over a three-year period. From the beginning the research team has interacted with the agents involved in this cluster policy – those in the Basque government responsible for the cluster policy and the directors of the CAs – contrasting ideas not only about the content of the evaluation process but also about the evaluation methodology itself, so as to ensure that participation is central in the process from the outset. Selection of the specific cluster for this study was guided by two main criteria. First, the motivation of the management team of the CA to be involved in an innovative pilot evaluation process. Secondly, the timing of the project in terms of the cycle of activities of the CAs that demonstrated their interest, in particular to benefit from synergies with their own strategic reflection processes. This led to the selection of Hegan, the Basque aeronautics cluster, a medium-sized cluster (35 members) established in 1997. Following the methodological proposal defined in section IV, the evaluation process has been developed in four main phases. Results from the process corresponding to each of these phases are set out below. Phase 1: Planning and Model Development Step 1: Presentation of the participatory evaluation project The project was presented at a preliminary workshop to which the director and technical team of the Basque government cluster policy and all directors of the existing CAs were invited. The main characteristics of the project, its preliminary programme, and the main advantages and risks of participatory evaluation were presented and opened for discussion. One of the key messages to explain to all stakeholders was the novelty and benefits of an evaluation approach focused on learning rather than control, to support social capital improvement and facilitate greater understanding and commitment among cluster stakeholders with regards the policy and its purpose. After this workshop, CAs were invited to propose themselves to participate in the pilot project. Approximately half of the existing CAs expressed their availability and interest in participating. The research team then selected Hegan, the Aerospace CA, for the pilot experience, making clear to the other associations that the intention of both the Basque government and the research team was to extend the process to all interested CAs following the pilot. Step 2: Define a common view and the scope of the evaluation process First, the potential stakeholders in the evaluation process were identified: the Basque government cluster policy director and team; the Hegan CA director and team; and representatives from each of Hegan’s members.

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Secondly, as a base for the evaluation process a common view of the general aims of the cluster policy was developed among the first two groups of stakeholders through meetings with the evaluation team based around semi-structured interviews. Two key themes emerged from these discussions: 1.

2.

The focus of the policy on fostering competiveness through collaboration. The difficulties in isolating and measuring the impact on competitiveness of any policy forces us to define concrete problems linked to competitiveness where the policy may potentially impact. In general, questions related to strategic competitive challenges that the industry as a whole must confront in co-operation were raised. Social capital was identified as a key element in the policy development. In this sense, a policy-maker argued that: ‘It is an intelligent environment that, as opposed to an environment of isolated agents, can build on what already exists to create the conditions for generating a virtuous circle.’ Moreover it was argued that ‘the intensity of the policy depends on the degree of acceptance (the voluntary adscription to the cluster policy). If a cluster really exists it is because the members want it to, and if they stop believing (in the cluster policy) it will disappear . . . The weakness of the model is the weakness in the relations of the network.’ A member of the CA management team also stressed the importance of the ‘team mentality and cohesion’, pointing out that this is something that should exist prior to the involvement of the CA, but that in reality often needs this policy stimulus.

Potential objects of the evaluation were agreed among this group of stakeholders. At this early stage only the general definition of the evaluation objects was discussed and accepted, which corresponded to general strategic challenges previously identified by the CA that could be addressed through cooperation. A preliminary project proposal and chronogram was then agreed, and in this case the evaluation process was scheduled to correspond with the strategic planning process of the CA so as to maximise synergies. Step 3: Definition of the key evaluation questions In terms of the mission of this specific cluster policy, the key evaluation questions complied with the following condition: they were strategic competitive challenges, or key elements of these challenges, that had to be confronted in co-operation and within the scope of the CA. A document outlining the key points agreed in step 2 provided a starting point from which a specific set of strategic challenges could be identified in

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a participative process as a basis for developing an evaluation framework for the cluster. This was achieved through an interactive workshop, which incorporated several group-dynamic techniques in order to assure broad agent participation and, at the end, obtained an agreed selection of key strategic challenges: ● ● ● ● ● ● ● ● ●

qualitative growth of the cluster to consolidate the value chain; innovation and generation of new products; adaptation to new technologies; training and identification of ‘best practice’; financing of projects; deepening of the cluster philosophy among members; internationalisation and search for new clients; improvement of communication between the CA and members; and strengthening the design of government support programmes.

Step 4: Defining a proposal of the indicators and data collection respondents/methods Responding to the outcomes of step 3 a proposed evaluation framework was constructed by the research team, guided by the following considerations: 1. 2. 3.

4.

5.

The definition of indicators must be shared among all the stakeholders. The indicators should be not only quantitative but also qualitative, to foster evaluation as reflection and learning, not only control. The indicators need not be exhaustive; the aim of the evaluation is not to control every activity, but to reflect on the key impacts and the elements behind them. The dynamic character of the indicators is important, because they are conceived not only to evaluate the present situation but also to guide the evolution of the cluster. The indicators must be quantifiable and easily available.

The proposed framework was structured around four sets of indicators: 1. 2.

Drivers: designed to act as an overall thermometer of the ‘cluster philosophy’ or ‘associative maturity’ among the cluster members. Facilitators: designed to reflect the social capital and common interest among the cluster stakeholders that are basic conditions for effective co-operation and related to the strategic challenges of improved communication and programme design.

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4.

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Results: Designed to capture achievement in specific desired strategic outcomes for members as a result of their participation in the CA, including consolidation of the value chain, innovation and technological adaption, training, finance and internationalisation. Impacts: Designed to reflect evolution in the overall impact of the cluster in the Basque economy, including measures of productivity, growth and critical mass.

Step 5: Approval of the complete evaluation framework The evaluation framework proposal developed in step 4 was presented for discussion in a second open workshop, where representatives of the Basque government, cluster association management, and firm associates reflected on and modified proposals using group-dynamic techniques. The aim of this workshop was to agree the final selection of indicators, together with the data collection methods, and to establish consensus around the timetable for the implementation of the ongoing evaluation. Thus as a result of this activity the complete evaluation model was approved and ready to be implemented. As a result of this process of workshops, the final evaluation framework created includes three groups of indicators, with some minor modifications to those proposed in step 4: 1.

2.

Network policy outcomes (NPO): a development of ‘drivers’ in the initial proposal, NPO indicators measure the level of accomplishment of the specific behavioural changes that the cluster policy aims to promote. The objective of the policy implies the development of strategic actions in cooperation among cluster agents. Two indicators are employed to measure this. First, ‘associative maturity’, as a reflection of the degree of advancement in the development of strategic projects in cooperation. We measure it using a 12-item question designed to reflect where each participant is situated in the key progress stages to reach the final cluster policy purpose of working together in strategic cooperation projects. Our second measure of NPO captures the observed projects in co-operation among the network, including the number of projects, their nature and the perceived value generated by them. Social capital (SC): a refinement of ‘facilitators’ in the initial proposal, these indicators measure the existing social capital among CA members. The evaluation centres on the three dimensions of social capital identified by Nahapiet and Ghoshal (1998): relational, structural and cognitive. In the relational dimension we measure trust, reciprocity and commitment, using an adaptation of previously published scales for these constructs. The cognitive dimension is measured using

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questions that capture shared vision, the perception of the agents on the commonalities of goals in the network, and reasons for belonging to the network. Finally, in the structural dimension we measure the network itself, in particular the actual and desired relationships declared by each CA member. Results and impact (RI): these two groups are combined due to the longer time scale with which data will be collected and their coherence with the parallel and ongoing strategic reflection process of the CA. The first sub-group measures the level of achievement or results of the key strategic objectives that the cluster association has established in its strategic plan, in line with the results indicators in the initial proposal. The second sub-group measures the overall impact of the activity of the CA in the development of the region, again in line with the indicators defined in step 4.

Phase 2: Data Collection and Processing Data to construct the NPO and SC indicators was collected through an ad-hoc evaluation survey addressed to all cluster associates through a simple online software application.1 In this first stage of data collection there was a 60 per cent response rate. The resulting data was processed and organised by the research team to construct the indicators corresponding to the agreed evaluation framework. Phase 3: Learning Process Step 1: Analyse data/information collected and reflect on lessons At this stage the results obtained from the data were presented to stakeholders to continue the participative reflexion process begun in the earlier workshops. The research team provided an overall summary of the evaluation results, which was first presented to CA management to share interpretations on the resulting indicators and to begin to analyse the reasons for the emerging patterns. Based on these findings the CA management and research team identified some broad issues for the future development of the CA as a basis for action plans to overcome the present situation and improve CA outputs and results. The results along with these reflections were then presented by the CA management to all members at the annual general meeting of the CA. Step 2: Develop action plans and assess the evaluation process The second step of phase 3 is currently in process, in parallel with phase 4 (see below). The conclusions reached following the meetings and

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presentations of the previous step are now being integrated into CA’s strategic definition process and concretely in their action plans. In particular, the CA management has started the definition of several strategies designed to build on the strengths and address the weaknesses uncovered by the network performance and social capital indicators. For example, this has involved the identification of groups of firms with shared weak or strong characteristics, to be targeted either for special assistance or to learn from good practice. It is envisaged that the development of action plans based around the NPO and SC indicators will be further strengthened as insights from the planned collection of RI indicators become evident later in the long-run evaluation process. Phase 3: Dissemination In parallel with the CA’s actions in the previous step, the research team has produced a draft evaluation report, reflecting on the whole process. This was first presented to and contrasted with the CA and policy-makers in the Basque government, and was then the focus of a workshop in which the rest of the Basque country cluster associations were invited to participate. This dissemination aimed to highlight the benefits of this approach to policy evaluation to other clusters that will enable a smooth extension of the initial pilot project. As a result of this workshop a further six CAs have signed up to a series of sessions at which the research team will train CA staff so that they can implement the methodology themselves. Finally, in this dissemination phase the research team is currently developing papers based on the initial sets of results for presentation at academic conferences and journal publication.

VI. CONCLUSIONS After almost two decades since the cluster policy of the Basque government was defined following Porter’s methodology, various efforts have been made to evaluate the results obtained. While the evaluation methods employed have uncovered some important findings that have been used to assist the ongoing clusters policy, they have demonstrated difficulties in deriving clear conclusions. We have argued that these difficulties are directly related to some of the characteristics implicit to clusters as a policy focus: the mixture of tangible and intangible objectives; their systematic nature; and the complexity of cause–effect relationships. Building on this long-term evolution of evaluation approaches, we have developed and applied a new methodology better suited to the specific characteristics of

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an essentially soft, relationship-centred policy designed to generate tangible results, but explicitly through processes of co-operation. Specifically, we have developed a rationale for the use of participatory evaluation techniques in an action research framework. The actor-centric nature of such approaches we suggest makes them ideally suited to a policy that is geared towards encouraging effective co-operation between a range of cluster actors with an overall aim of improving their (individual and collective) competitiveness. We have therefore presented a detailed methodology designed for specific application to the cluster policy of the Basque Country government. This methodology is by nature long term, integrated as it is with the ongoing dynamics of the policy process itself. As an example of the application of this participatory evaluation methodology, a pilot application to the case of the Basque aeronautics cluster is presented. While this application is not yet complete – only the first sets of indicators that emerged from the participative sessions have been collected – our initial exploration of how the process has unfolded in this case offers encouraging signs for the potential for such an evaluation methodology both to re-enforce the policy’s aims in facilitating co-operation and to generate policy learning. We therefore propose this as an example to stimulate discussion around the applicability, practical implementation and usefulness of this methodology in different place and policy contexts.

NOTE 1. The application was designed so that it can be extended at a later stage in the process to collect data pertaining to the RI indicators.

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18. For a resilient, sustainable and creative European economy, in what ways is the EU important? Philip Cooke and Lisa De Propris 1.

INTRODUCTION

From a ‘resilience’ perspective a robust system is one that has the capability to withstand serious system-shock and re-stabilise itself, albeit not exactly at the same equilibrium point as the status quo before (Folke, 2006). Creative destruction is normally thought of as involving some degree of necessary hardship before a more benign regime evolves in which innovation brings increasing economic returns, and improved life conditions and living standards. Seldom has it been the case that ‘creative destructions’ are viewed from the other perspective of a failure of the innovations that may have destabilised a system to have a beneficial effect. But that position has been changing of late, especially in connection with studies by ‘apocalyptic’ geographers like Jared Diamond (2006) or former planner James Kunstler (2005) of the implications of human abuse of the environment in the form of human-induced desertification, resource over-exploitation and climate change. While Diamond (2006) sees curious parallels between the over-consumption rituals of Vikings in Greenland, Polynesians on Easter Island and modern-day farmers in Montana, Kunstler (2005) envisages planetary system-collapse induced by a ‘perfect storm’ of over-reliance on fossil fuels and associated interactions among climate change, pandemic diseases, water scarcity, desertification and industrial pollution. For both, however, it is the failure of regulation, especially in liberal market regimes stressing freedom from rather than freedom to (Bailey and de Ruyter, 2007) as an inalienable right, that caused system destabilisation. However, neither offers regulatory guidance. Kunstler retreats into a romantic localism –‘The model is obvious. Since we will need to live an intensively local existence, we will have to re-establish those local webs of economic relations and occupations that existed all over America until the last decades of the twentieth century’ 403

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(Kunstler, 2005, p. 259) – while Diamond sees the problem as one of collective human intelligence failure: ‘Human societies and smaller groups make disastrous decisions for a whole sequence of reasons: failure to anticipate a problem, failure to perceive it once it has arisen, failure to attempt to solve it after it has been perceived, and failure to succeed in attempts to solve it’ (Diamond, 2006, p. 436). Accordingly, it is for the policy-making community to assess the value of ‘apocalyptic’ analyses such as these, determine the appropriate response and fall back on their established ways of formulating an affordable response. However, it is likely, and indeed palpably evident from the record so far, that traditional policy deliberation and determination methods may be insufficiently resilient to make significant indentations in the complex, interconnecting system destabilisations described above. In this chapter, we examine the power of this proposition by reference to the evolution of European Union (EU) crisis-response policy in two innovative areas: first, attention will be devoted to its recent record on climate change policy; second, by reference to the thinking expressed in Europe 2020 (EU Commission, 2010c), which is its response to the global financial crisis of 2007–09, its strategy on re-balancing the European economy towards ‘smart sectors’, notably the creative and cultural industries. To advertise the results of this study beforehand, after a very belated start, effectively only since 2007, some significant initiatives to induce modest regime change and socio-political prioritisation of what had hitherto been a Cinderellalike treatment of climate change could be discerned. This can be argued to signify a shift from a purely responsive to a more agenda-setting policy mode. On re-balancing, the burgeoning value creation economy (including the creative and cultural sectors) is a crucial component of the technological shifts that – alongside clean technology (cleantech) – we are witnessing; however, along with eco-innovation, it has received scant attention. This can be considered a regression from imaginative policy-scanning to a narrowly ‘productivist’ policy regime, conceivably because of a combination of faith in ‘technology’ (for example, information and communications technology (ICT), nanotechnology and possibly biotechnology) as a crisis-moderator and inadequate creative/cultural interest expression from below by either governmental or associational lobbies. Sector-based policy initiatives are now seen to be way too narrow as the current econoeco crisis has set in motion a process of systemic resetting, whereby certainties are undermined and techno-economic paradigms are flipped over. Rather than sectors, the focus ought to be on the technological platforms which will underpin the new techno-economic paradigm, namely, digital, design and cleantech.

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Before, first, elaborating the fields and, second, equating these with policy ‘emergence’ and ‘closure’, there follows a brief introduction to key ‘resilience’ concepts and, particularly, their application in the policy context.

2.

RESILIENCE THEORY AND POLICY RESILIENCE

Resilience theory (otherwise known as panarchy) studies the impact of change that destabilises human or natural systems. In its ecological home base it was stimulated by the realisation that many ecosystem changes were triggered by regional ecosystem management itself. This was often unconsciously driven by a linear model of decision-making focused on managing a primarily economic variable. System degradation often occurred slowly and unnoticed over decades and more until incremental impacts triggered an abrupt change (for example, forest became infested, river polluted, or fish stocks collapsed). The inference from this type of event is that change and time are both multi-scalar with change being ‘episodic’, regulated by interactions between fast and slow variables and different scale levels concentrating resources and potential in different ways. Non-linear processes reorganise resources across levels leading to a key characteristic of ecosystems, which is that they do not have a single equilibrium; multiple equilibria are common. Stability is maintained by productivity and resource reorganisation cycles (for example, maintaining or slowly improving efficiency) while destabilising processes provide diversity, resilience and opportunity. Effective management systems account for these dynamic features of ecosystems being flexible, adaptive and experimental at scale levels compatible with the levels of critical ecosystem functions. Resilience determines how vulnerable a system is to unexpected disturbances and surprises that c