Clusters and Competitive Advantage: The Turkish Experience

Clusters and Competitive Advantage The Turkish Experience

Özlem Öz

Clusters and Competitive Advantage

Also by Özlem Öz THE COMPETITIVE ADVANTAGE OF NATIONS: The Case of Turkey

Clusters and Competitive Advantage The Turkish Experience Özlem Öz Department of Business Administration Middle East Technical University (METU) Ankara, Turkey

© Özlem Öz 2004 All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission. No paragraph of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, 90 Tottenham Court Road, London W1T 4LP. Any person who does any unauthorized act in relation to this publication may be liable to criminal prosecution and civil claims for damages. The author has asserted her right to be identified as the author of this work in accordance with the Copyright, Designs and Patents Act 1988. First published 2004 by PALGRAVE MACMILLAN Houndmills, Basingstoke, Hampshire RG21 6XS and 175 Fifth Avenue, New York, N.Y. 10010 Companies and representatives throughout the world PALGRAVE MACMILLAN is the global academic imprint of the Palgrave Macmillan division of St. Martin’s Press, LLC and of Palgrave Macmillan Ltd. Macmillan® is a registered trademark in the United States, United Kingdom and other countries. Palgrave is a registered trademark in the European Union and other countries. ISBN 1–4039–3613–7 This book is printed on paper suitable for recycling and made from fully managed and sustained forest sources. A catalogue record for this book is available from the British Library. Library of Congress Cataloging-in-Publication Data Öz, Özlem. Clusters and competitive advantage : the Turkish experience / Özlem Öz. p. cm. Includes bibliographical references and index. ISBN 1–4039–3613–7 1. Industrial location—Turkey. 2. Strategic planning—Turkey. 3. Competition—Turkey. 4. Turkey—Economic conditions—1960– I. Title. HC492.O9275 2004 338.6c042c09561—dc22 2004045426 10 13

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Printed and bound in Great Britain by Antony Rowe Ltd, Chippenham and Eastbourne

To Kaya

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Contents List of Tables

ix

List of Figures

x

Preface

xi

Acknowledgements

xv

List of Abbreviations

1

2

3

4

5

xvi

Introduction: A Background to Clusters

1

Origins and milestones Clusters in the world economy Defining clusters: industrial districts, networks and clusters Clusters and competitiveness

1 5 8 11

Clusters in the Management Literature

20

An overview Porter-style geographic clusters

20 25

Industrial Clusters in Turkey

37

The Turkish business environment, past and present Turkey’s position in international competition Geographic concentration of Turkish industries Geographic concentration and competitiveness Finding a suitable methodology for the analysis of clusters Geographic clusters and competitiveness: which cases to study?

37 41 45 52 55

The Furniture Cluster in Ankara

60

Origins and historical developments Sources of international competitive advantage Reasons for geographic concentration Concluding remarks and future prospects

62 67 74 81

The Towel and Bathrobe Cluster in Denizli

84

Origins and historical developments Sources of international competitive advantage Reasons for geographic concentration Concluding remarks and future prospects vii

57

85 88 100 109

viii Contents

6

7

8

The Carpet Cluster in Gaziantep

114

Origins and historical development Sources of international competitive advantage Reasons for geographic concentration Concluding remarks and future prospects

116 119 128 132

The Leather Clothing Cluster in Istanbul

135

Origins and historical development Sources of international competitive advantage Reasons for geographic concentration Future prospects for the leather clothing cluster in Istanbul: stuck in the middle?

136 144 149

Conclusions

158

Theoretical implications Policy implications

158 168

155

Appendix 1 Top Five Industries in Terms of Location Quotient, by Province

172

Appendix 2 A Brief Description of Fuzzy-Set Analysis

183

Notes and References

184

Bibliography

197

Index

215

List of Tables 3.1 Percentage of Turkish exports by cluster and vertical position, 1992–2000 3.2 Top 100 Turkish industries, by C4EMP 3.3 The least concentrated Turkish industries, by C4EMP 3.4 The most populated Turkish provinces 3.5 Cumulative C4EMP totals for the industries examined 3.6 International competitiveness, fuzzy membership categories 3.7 Geographic concentration, fuzzy membership categories 4.1 Market shares of the leading furniture exporting countries 4.2 Provincial shares of employment in the Turkish furniture industry 4.3 Employment in Ankara, by economic activity 4.4 Concentration of furniture firms in Siteler, by street 4.5 Concentration of carpenters in Siteler, by street 4.6 Concentration of upholsterers in Siteler, by street 4.7 Concentration of polishers and varnishers in Siteler, by street 5.1 Employment in Denizli, by economic activity 5.2 Denizli exports of towels, bathrobes and closely-related items, 2002 6.1 Employment in the Turkish carpet industry 6.2 Employment in Gaziantep, by economic activity 7.1 Employment in Istanbul, by economic activity

ix

43 49 51 52 52 54 55 61 62 65 75 75 76 77 89 98 115 120 139

List of Figures 1.1 1.2 1.3 3.1 3.2 3.3 3.4 4.1 4.2 5.1 5.2 6.1 6.2 7.1 7.2 7.3

Examples of highly concentrated industries in the United States Examples of highly concentrated industries in Italy Examples of highly concentrated industries in India Exports and imports, Turkey, 1982–2000 Standard of living, Turkey, 1980–2001 Selected examples of highly concentrated industries in Turkey Methodological process followed in the study Internationally competitive subsectors of the Turkish furniture industry, plus related sectors and institutions Map of the furniture district in Siteler, Ankara Products made in the textile cluster in Denizli, plus related institutions Towns in the province of Denizli Traditional Anatolian carpet-weaving centres Products made in the carpet cluster in Gaziantep, plus related institutions and sectors The Istanbul leather industry, production and sales sites Leading centres of leather production and trade, Turkey, sixteenth and seventeenth centuries Products made in the leather clothing cluster in Istanbul, plus related institutions and sectors

x

6 7 8 39 40 53 56 72 78 97 101 115 126 136 137 146

Preface Economic and business life is conducted in space, and this geographic organization has an impact on how the economy functions, as well as on the process of creating and subsequently upgrading individual firms’ competitive advantages. Not surprisingly, then, the clustering of industrial activities in general and the geographic concentration of specific industries in particular have attracted considerable attention in the academic literature for a long time, the first influential contributions dating back to the nineteenth century. Studies investigating the relationship between location and competitive advantage, for instance, can be traced back to the contributions of Adam Smith, while Alfred Marshall brought the topic of geographic concentration of specific industries in districts to the attention of academics in the 1890s. The subsequent attempts to understand the extent of and underlying reasons for the clustering of economic activity resulted in an extensive body of literature. A wide range of disciplines – including economic geography, location theory, regional development and growth poles, urban economics and social networks – tackled different aspects of the issue and shed light on the phenomenon of clustering. With the ‘mid-century advent of neo-classical economics, however, location moved out of the economics mainstream’ (Porter, 1998, p. 206), and it was only recently that there was a revival of interest in the topic. Scholars of international trade, international business, industrial organization and business strategy have joined geographers and urban economists in investigating geographic concentration (Ellison and Glaeser, 1994). For example Krugman (1991a) contributed substantially to the field by discussing the role of geography in international trade (the role of geography is in fact implicit in any analysis of trade), after realizing that he had spent his ‘whole professional life as an international economist thinking and writing about economic geography, without being aware of it’ (ibid., p. 1). Similarly, growing interest in location-related issues in general and clusters in particular became evident in the management literature, a discipline that had previously shown minimal interest in the subject. The literature posits that very different circumstances, both economic and non-economic, might have an impact on the structure and competitiveness of clusters. Accordingly the foundations of success might be rather different in, say, the clusters in Baden-Württemburg where there is ‘Darwinian competitive pressure’ than in the clusters of ‘Third Italy’, where non-economic factors play a significant role (Staber, 1998). Distinct explanations of why a cluster might become competitive and sustain its competitiveness include those related to the organization of production (for instance in the form of xi

xii Preface

flexibly specialized small and medium-sized enterprises – Piore and Sabel, 1984), the social and political context (the role of tacit knowledge, institutions, the nature of work, trust and social capital – Putnam, 1993; Becattini and Rullani, 1996; Brusco, 1996) and relations within the cluster that pave the way for innovation, learning and untraded interdependencies (Camagni, 1991; Storper, 1999). There is also the possibility that both the initiation and the subsequent development of a cluster might be an accident of history, which is then locked into the region (Krugman, 1991a, 1991b). Although each of these explanations has undoubtedly improved our understanding of the competitiveness of clusters, none of them fully explains why certain clusters manage to become competitive whereas others do not. Contrary to what the flexible specialization perspective posits, for instance, there are competitive clusters that are not flexibly specialized and flexibly specialized clusters that are not competitive (Amin and Robins, 1990). Likewise, although the parts played by historical circumstances, the social and political context and strong collaboration among cluster participants might have contributed substantially to the success achieved by some clusters, such as those in Third Italy, these factors cannot explain the success of prominent clusters such as Silicon Valley (Saxenian, 1994). Similarly, explanations that tie the competitiveness of a cluster to innovation, learning and untraded interdependencies do not sufficiently elaborate why some clusters manage to become innovative and/or develop untraded interdependencies whereas others do not (Porter, 1998, 2000). So despite there being a rich variety of approaches in the literature, each of which has improved our knowledge about the clustering of economic activity, we still lack a comprehensive theory that can fully explain the competitiveness of clusters. One likely contributor for a more complete understanding of the competitiveness of clusters is a latecomer to the area, namely, the management discipline. The unique perspective offered by this discipline involves putting the firm at the centre of the analysis and trying to understand the phenomenon of clustering from that point of view. An interest in clustering has also been observed in the sub-disciplines of strategy and international business. International business scholars (for example Rugman and Verbeke, 2000) have focused on the geographic concentration/dispersion of foreign direct investment, and emphasized the two-way interaction between multinational enterprises and local clusters. Within the strategic management literature the contributions by Porter (1990, 1998, 2000) are noteworthy. Porter asserts that sources of advantage are local, and the impact of local conditions on the international competitiveness of clusters has become more pronounced, despite the increasing trend towards globalization. Understanding the competitiveness of clusters, according to Porter (1998, p. 208), requires embedding clusters in a dynamic theory of competition. Accordingly the basis of competitive advantage has shifted from static efficiencies (such as low input costs) to the ability to innovate and upgrade skills and

Preface xiii

technology. This has in turn brought about a radical change in the importance of location in that the capacity to innovate and upgrade draws heavily on the local business environment. Enduring competitive advantages, in other words, lie in the local environment. This study aims to clarify the link between geographic clustering and international competitiveness by examining Turkish experiences. The perspectives followed to achieve this are those offered by the approach adopted in the strategic management literature in general and by Porter (1990, 1998) in particular, since the purposes of this study are best served by that approach. Apart from the fact that Porter’s framework provides a good basis for highlighting the key sources of advantage in a geographically clustered industry, it also makes an explicit connection between the geographic concentration and international competitiveness of specific industries, which is the focal point of this study. This approach is supplemented by insights from several other perspectives, including those offered by the literature on path dependency, social networks and international business. Thus the implications of the key findings are discussed in respect of not only strategy but also the broader debates on clusters to provide a full account of what the Turkish experience, when looked at from the viewpoint of the strategic management discipline, offers to further thinking on clusters. The link between clustering and competitiveness has been a major area of investigation, but most researchers tend to assume that studying successful clusters is sufficient to understand sustainable competitiveness. Thus fundamental questions such as whether all clusters are competitive and which characteristics of competitive clusters differentiate them from uncompetitive ones have not received the attention they required. It is by no means guaranteed that clustering will automatically bring competitiveness, or that the typical characteristics of success stories (for example the dominance of clusters by flexible small and medium-sized enterprises) are the principal reasons why these clusters are competitive. Contributing to this relatively understudied aspect in the literature is another of the purposes of this study. In addition, by examining the competitiveness of clusters in Turkey, a country that is classified by the World Bank as a middle-income developing country, the study hopes to help overcome another shortcoming in the literature: evidence on the competitiveness of clusters has mainly been derived from analyses of clusters in developed countries, so very little is known about the competitiveness of clusters in the developing world (Nadvi, 1994), including whether the conditions that lead to the emergence and subsequent upgrading/ loss of competitive advantage are any different from those in developed countries. It is equally rare to see detailed discussions of uncompetitive cases, let alone addressing these issues together, especially from the viewpoint of the management literature. The contribution offered by the present study is therefore threefold: it examines the competitiveness of clusters in a developing country setting, it includes a detailed study of an uncompetitive cluster,

xiv Preface

and it conducts these analyses from the viewpoint of the strategic management literature. Although the analysis is restricted to Turkish experiences, a wider audience should find the book appealing given that the theoretical implications of the study are linked to the ongoing debates on competitiveness and geographic clustering in general. The book mainly addresses an academic audience, but as the subject matter is of vital importance to government policy makers and strategic planners in firms, it is likely to be of appeal in these circles as well. In fact the subject has been so important in policy-making circles that the World Bank alone has funded 266 cluster projects in recent years (Lundequist and Power, 2001). Researchers who are interested in the individual industries covered in the book (furniture, textiles and leather products) are also likely to benefit from the study. Needless to say, given that the investigation sheds light on the pattern of international competitiveness and geographic concentration of Turkish industries, as well as providing a comprehensive analysis of several key Turkish clusters, the book will be of interest to researchers and planners working in or writing about Turkey. Finally, the book may also be helpful to graduate and undergraduate students, especially those studying strategy, international business, international trade and economic geography. The structure of the book is as follows. Chapters 1 and 2 set the theoretical foundations for the study and introduce the reader to the main concepts through a review of the growing body of literature on clusters. Chapter 3 provides an overview of the Turkish economy and analyses industry and trade data to identify patterns of international competitive advantage and geographic concentration in Turkey. Chapters 4 to 7 are devoted to four in-depth cluster case studies: a furniture cluster in Ankara, a towel and bathrobe cluster in Denizli, a carpet cluster in Gaziantep and a leather clothing cluster in Istanbul. Chapter 8 discusses the key findings and the policy implications of the study. ÖZLEM ÖZ London and Ankara

Acknowledgements The theoretical part of this study was conducted in London in the summer of 2001 during my time as a research scholar at the London School of Economics (LSE), financial support for which was provided by the Turkish Academy of Sciences (TÜBA). The empirical part was conducted in Turkey (in 2001–4), with funding provided by METU (AFP 2001-04-02-02 and BAP 2003-04-02-01). I am grateful to these three institutions for their support. I thank Professor Michael Porter of the Harvard Business School for being an invaluable source of inspiration and granting permission to use the map presented in Figure 1.1. I also thank Professor Peter Abell (LSE) and Professor Ayda Eraydin (METU) for fruitful discussions on different aspects of the subject. I am indebted to my dear friends Dr Ioannis Konsolas, Dr S. Arzu Wasti and Dr Adil Oran for their help and support during various stages of the project. I also owe much to Funda Cantek, Yasemin Saatçioglu Oran and Özgür Nemutlu for providing me with special access to data and documents, as well as introducing me to the key informants. I am grateful to the numerous managers, state officials, industry representatives and academics who spared their time for interviews. I am particularly indebted to Nusret Özgünaltay (KOSGEB-Siteler), Baris Yeniçeri (IGEME), Dr Nese Kanoglu (SPO), Ismail Sengün (DSO-Ankara), Erhan Sarica (DSO), Abdülgaffar Nemutlu, Kürsat Göncü (GSO), Yusuf Ziya Iymen (GSO), Fahrettin Canpolat (Gaziantep Ihracatçi Birlikleri), Hüsnü Atzel (TDSD), Turgut Kosar (TDSD) and Onur Görgün (TDSD). I also gratefully acknowledge the help provided by a very special individual: Hasan Yelmen. My thanks also go to my research assistants, Yesim Özalp and Gizem Turan, for helping me with the drawings and figures during the preparation of the typescript, and to my students at METU, whose eagerness to discuss various aspects of the subject helped me to refine my ideas. Finally, I would like to thank Kaya Özkaracalar for his unstinting support and encouragement at every stage of the project. This book is dedicated to him. ÖZLEM ÖZ

xv

List of Abbreviations CIS DSO DTO EGS FDI GAP GSO ISIC IGEME ISO ITKIB KOSGEB KÜSGEM LQ METU MPM OAIB R&D SIS (DIE) SITC SPO (DPT) TDSD TSE TÜBITAK TÜSIAD US-AID

Commonwealth of Independent States Denizli Chamber of Industry Denizli Chamber of Commerce Aegean Garment Producers’ Association Foreign Direct Investment South-Eastern Anatolian Project Gaziantep Chamber of Industry International Standard Industrial Classification Export Promotion Centre Istanbul Chamber of Industry Istanbul Textile-Apparel Exporters’ Association Small and Medium-Sized Industry Organization Small Industry Development Centre Location Quotient Middle East Technical University National Productivity Centre Central Anatolian Exporters’ Union Research and Development State Institute of Statistics Standard International Trade Classification State Planning Organization Turkish Leather Industrialists’ Association Turkish Standards Institution The Scientific and Technical Research Council of Turkey Turkish Industrialists’ and Businessmen’s Association United States – Agency for International Development

xvi

1 Introduction: A Background to Clusters

This chapter introduces the concept of geographic clusters by first discussing the origins of cluster thinking and milestone contributions to the field. Selected examples of clusters in developed and developing countries are then provided in the second section. This is followed by a discussion on the definition of clusters, concentrating on their distinguishing characteristics compared with industrial districts and networks. The final section is devoted to the central issue of this study, that is, the link between clustering and competitiveness.

Origins and milestones1 Chapter 10 of Marshall’s 1890 classic, the Principles of Economics, is undoubtedly the most influential work on the geographic clustering of industrial activity. In this short chapter Marshall analysed the geographic concentration of specific industries in particular districts. However, recognition of such concentration went back centuries, and ‘even in [the] early stages of civilization, the production of some light and valuable wares [was] localized’ (Marshall, 1949; p. 222). According to Marshall, amongst the various reasons for the localization of industries, the chief ones were physical conditions such as the nature of the climate and the soil. He saw ‘the patronage of a court’ as another key rationale for localization since ‘the rich [folk] there assembled make a demand for goods of specially high quality, and this attracts skilled workmen from a distance, and educate those on the spot’ (ibid., p. 223). His ideas on the virtues of a local market for special skills are enlightening and inspiring in respect of the process of innovation: ‘good work is rightly appreciated, inventions and improvements in machinery, in processes and the general organization of the business have their merits promptly discussed: if one man starts a new idea, it is taken up by the others and combined with suggestions of their own; and thus it becomes the source of further new ideas’ (ibid., p. 225). He also touches on the likely contribution of industries that supply the core industry with implements and materials, as well as ‘the use of highly 1

2 Clusters and Competitive Advantage

specialized machinery’ and the importance of ‘the convenience of the customer’ (ibid., pp. 225, 227). If we simplify Marshall’s rich observations we can see that hidden in his analysis are some distinct reasons for localization: the availability of specific inputs, labour market pooling, information spillovers, local demand and related industries. Marshall also draws attention to the socioeconomic nature of clustering, which later emerged as one of the most vibrant areas of discussion in the literature. Since nearly all important knowledge has deep roots ‘stretching downwards to distant times’, the growth of a cluster is favoured by ‘the character of the people, and by their social and political institutions’ (ibid., p. 224). Moreover he foresaw that the ‘two opposing tendencies’ we now call globalization and localization can take place simultaneously. In his words, ‘every cheapening of the means of communication, every new facility for the free interchange of ideas between distant places alters the action of the forces which tend to localize industries’ (ibid., p. 227). These could be, for example, a lowering of tariffs or of the cost of transporting goods. As a result people can easily buy from a distance. On the other hand, the same forces that increase people’s tendency to migrate from one place to another bring skilled workers to practice their craft near the consumers. Finally, although it is often disregarded in the literature, Marshall identified some risks associated with clustering: ‘A district which is dependent chiefly on one industry is liable to extreme depression, in case of a falling-off in the demand for its produce, or of a failure in the supply of the raw material which it uses’ (ibid.) Nevertheless the overall benefits of clustering are very clear. For example when an industry is located in a district it is likely to stay there for a long time as the advantages associated with it are so great. As Marshall put it, ‘the mysteries of the trade become no mysteries; but are as it were in the air’ (ibid., p. 225). If the mysteries are in the ‘air’, among other things implying that sources of advantage in a specific locale interact in complex ways to form a system, it follows that it will be almost impossible to replicate it elsewhere, thus increasing the chance that the resulting advantage will be sustainable. Weber (1929) furthered Marshall’s pioneering ideas and concentrated on location theory in a milestone work that stressed the importance of the lower production and transportation costs associated with agglomeration. According to Weber, geographic patterns of production mainly depend on the location of inputs and markets. Weber’s theory triggered considerable debate, especially from the 1950s (Lösch, 1954; Isard, 1956; Smith, 1966). Lösch (1954, pp. 28–9), for instance, criticized Weber’s least-cost emphasis and argued that seeking the place of least cost is as ‘absurd’ as considering ‘the point of largest sales’ as the proper location. According to him, ‘every such one-sided orientation is wrong. Only search for the place of greatest profit is right.’ Lösch also showed that the existence of economies of scale is another viable reason for the geographic concentration of industry. During the 1950s and 1960s Lösch’s classic, The Economics of Location, provided the foundations for two disciplines,

Introduction 3

namely regional science and economic geography, the former subsequently becoming highly mathematical and the latter evolving into a more empirically oriented subject (Martin, 1999). The positivistic approach to economic geography, based on quantitative procedures, was the dominant paradigm until the 1970s (for example Smith, 1971; Amedeo and Golledge, 1975), although several economic geographers (for example Pred, 1967) had already begun to argue that there was a need to attribute a more central role to history and the social relations of production. As a result, economic geography underwent a vigorous expansion, incorporating ideas from French regulation theory, Schumpeterian models of technological evolution and the Marxian notion of uneven accumulation (Martin, 1999). Hence there was a major shift from neoclassical economics to political (especially Marxian) economy.2 As the 1980s progressed, however, Marxist theory came under intense criticism, ‘reinforced both by dramatic new developments in the nature and trajectory of capitalism, and by the emergence of new, post-structuralist and post-modern philosophical and epistemological movements’ (Bryson et al., 1999, pp. 9–10).3 As a result, local agglomeration economics and an emphasis on the relation between culture and economic sociology were reinstated as the focal points of economic geography (Martin, 1999). Meanwhile some studies continued to focus on the demand side, arguing that consumers prefer markets with a large number of sellers (Enright, 1990), since agglomeration minimizes the opportunities for arbitrage, as well as providing customers with greater variety and lower search costs in the case of differentiated goods. The literature on urban economics, regional development and international trade also provides useful insights into the distribution of economic activity within cities and across regions. First, regional theories of concentration and diffusion focus on the spread of regional economic growth, and on the mechanisms and policies that promote growth. Policy implications for backward regions are given special attention in the literature on these theories. Perroux’s (1950) theory of growth poles, Myrdal’s (1957) theory of cumulative causation, Hirshman’s (1958) notion of unbalanced growth and Friedmann’s (1972) core–periphery model are amongst the best known regional theories (Malizia and Feser, 1999, p. 103). Poles of growth contain firms and industries that attract other economic elements, and the theory emphasizes the part played by leading industries in fostering the development of particular regions (Perroux, 1950). Myrdal’s (1957) theory of cumulative causation supports the argument that market forces tend to increase disparities between regions. The origin of concentration, for instance, could be a historical accident and the subsequent growth could be at the expense of other regions because of ever increasing internal and external economies. Although Myrdal’s unit of analysis is the region, similar concepts can be applied to industries. At the industry level, the theory predicts that the advantages of geographic concentration might become so great that a single location might dominate an industry on a national or

4 Clusters and Competitive Advantage

even international scale (Enright, 1990). On the other hand, Hirshman’s (1958) notion of unbalanced growth suggests that some sectors will grow more quickly than others in a given environment, and thus some degree of interregional and international inequality of growth is inevitable. Finally, according to Friedmann’s (1972) core–periphery model, when industrialization begins in a spatial economy, investments tend to be concentrated in particular locations, resulting in an unequal distribution of economic activity between these core areas and those on the periphery. The theory predicts that, in the absence of government intervention, the core will dominate the spatial economy and there will be continued impoverishment of the peripheral areas (Malizia and Feser, 1999, p. 110) Second, the forces identified as responsible for urban concentration can aid our understanding of the geographic concentration of individual industries. The main themes of the literature on this subject are the conditions associated with economies and diseconomies of agglomeration in urban spaces.4 It has long been acknowledged by researchers (for example Hoover, 1937) that a distinction should be made between localization economies and urbanization economies in order to clarify the underlying causes of agglomeration. Accordingly the term localization is used to refer to the agglomeration of firms in closely related industries, and urbanization to refer to the agglomeration of firms in all kinds of industries.5 According to Henderson (1988, 2000), industries with extremely large economies of scale and large labour requirements will operate in large cities in order to utilize the productivity advantage of being located in a centre of activity. More recently Porter (1998) has argued that generalized urban agglomeration economies are diminishing in importance because of trade liberalization, advances in communications and transportation technology, and the availability of comparable infrastructures in more locations and countries. In his view, generalized urban agglomeration economies that are independent of firms and clusters tend to be most important for developing countries. Urban agglomeration diseconomies have also attracted considerable attention, reminding us that there are forces that limit the extent of agglomeration economies. Otherwise in the extreme case, agglomeration economies would extend to the point where all industries and people were concentrated in a single location (Enright, 1990). Finally, the location of industry is a central issue in the study of international trade. In fact the fundamental questions asked about international trade (such as ‘why do countries trade?’ and ‘what determines the international pattern of specialization?’) are directly related to location. In the literature on this subject the unit of analysis is usually the nation, mainly because of data restrictions, but it is possible to extend the same concepts to regions. This is evident even in early works on international trade. In this regard Ohlin’s (1967) seminal book Interregional and International Trade can be viewed as a study of location theory in which the theory of international trade is a special case (Enright, 1990). More recently the so-called ‘new trade theory’

Introduction 5

has paid explicit attention to the role of location in trade. Krugman (1991b), for instance, has developed a monopolistic competition model of international trade that emphasizes increasing returns. The rationale is that if there are economies of scale in specialization, there might be industries for which the world market can only bear a few centres of production. When increasing returns are introduced as a means to understand trade patterns, it becomes necessary to study regional economic concentration and specialization since the presence of increasing returns to scale forces firms to concentrate production in relatively few locations, and they are therefore confronted with the choice of where to operate. As Krugman (1991a) notes, the best evidence for the practical importance of external economies is the strong tendency of economic activity in general and industries and clusters of industries in particular to concentrate in a certain space. A substream of the international economics literature is concerned with the recent increase in ‘created’ location advantages for such activities as R&D (Midelfart-Knarvik et al., 2000). In addition the international economics literature has recently broadened its research area to include the functioning of multinational enterprises (Rugman and Verbeke, 2000). It should be noted that some scholars are sceptical about the revived interest in geography in the international economics literature. According to Martin (1999, p. 66), for instance, ‘although during the post-war period economists occasionally flirted with geography, they never seemed willing to commit themselves to any serious or permanent relationship’. But now, it seems, economists are at last rediscovering geography. In economic geographers’ view, however, the resulting theory – the so-called ‘new economic geography’ – is neither new nor geography but rather a reworking of economic geography proper. Martin also criticizes the preferred methodology and considers that the mathematical models they use – what Krugman (1995) calls ‘Greek letter’ economics – fail to produce novel results. For economic geographers, such models generate ‘a dull sense of déjà vu’ since geographers who analysed location in such terms back in the 1960s and 1970s had long recognized the importance of history in shaping the patterns of regional development. Besides, economic geography is committed to studying real places as well as the role of historical and cultural factors in the development of those places,6 rather than reducing locations to ‘points’ on a surface (Martin, 1999).

Clusters in the world economy Clusters in developed countries7 One of the most comprehensive studies of clusters in developed countries is that by Enright (1990), which presents abstracts of case studies of internationally competitive and geographically concentrated industries in Germany, Italy, Japan, Switzerland, the United Kingdom and the United States.8 Porter

6 Clusters and Competitive Advantage

Seattle-Bellevue-Event Fishing and fishing products Aerospace vehicles and defence Analytical instruments

Denver Oil and gas Power generation Processed foods

Chicago Processed foods Lighting and electrical equipment Plastics

Boston Education and knowledge creation Analytical instruments Footwear

San FranciscoOakland-San José Bay Area Information technology Communications Power generation

Los Angeles Area Aerospace vehicles and defence Entertainment Apparel

New York City Financial services Publishing and printing Jewellery and precious metals

Houston Oil and gas Chemical products Heavy construction services

Atlanta Entertainment Construction materials Transportation and logistics

Figure 1.1 Examples of highly concentrated industries in the United States Source: Reproduced with the permission of M. E. Porter, Cluster Mapping Project, Harvard Business School.

(1998) gives examples of clusters in the United States. As well as those shown in Figure 1.1 these are clusters of boat and ship building (Seattle), clocks (Michigan), automotive equipment and parts (Detroit), biotechnology (Boston), household furniture (North Carolina), carpets (Dalton), optics (Phoenix) and casinos (Las Vegas). The clusters in ‘Third Italy’ (the centre and north-east of the country) have perhaps received the most attention in the literature (Figure 1.2) not only because they are ‘the clearest and strongest’ examples of clustering (Pyke and Spengenberger, 1990) but also because of their dynamism, their ability to remain competitive and the fact that they have been able to maintain a satisfactory wage level (Crestanello, 1996). The remarkable resilience of the Italian clusters has attracted particular attention, given the pressure they have faced since the early 1980s from multinational enterprises and competitors in low-wage countries.9

Clusters in developing countries Clusters in developing countries have been relatively less investigated than those in developed countries (Bell and Albu, 1999). The studies that have been conducted usually focus on whether or not the associated conditions are any different in developing countries, and on policy lessons that can be learned from clusters in the developed world. According to Nadvi (1994) the ‘sparse and patchy’ literature on clusters in developing countries concentrates on the informal sector or small and medium-sized enterprises. Small firm clusters in developing countries usually enjoy ‘a historical tradition in the area with local enterprise in craft or artisanal workings of certain products

Introduction 7

Valpolicella Marble and building stone Piacenza Factory automation equipment Milan Factory automation equipment

Schio-Thiene Machinery manufacture

Bassano Ceramic art

Vicenza Jewellery Belluno Spectacles Montebelluna Sports shoes Ski boots

Brianza Furniture

Riviera del Brenta Footwear Murano Craft glassware Arzignano Leather tanning

Turin Factory automation equipment Sassuolo Ceramic tiles Ceramic art

Carrara Stones and stone work

Prato Textiles

Bologna Packing and filling machinery Monsummano Footwear Santa Croce Leather tanning Empoli Clothing

Arezzo Jewellery Poggibonsi Jewellery

Figure 1.2 Examples of highly concentrated industries in Italy

along side a custom of self-employment and entrepreneurship’ (ibid., p. 201). Meanwhile Schmitz (1999) states that artisanal clusters in developing countries range from those which show little dynamism and seem unable to innovate to those which have been able to improve their competitiveness. The main clusters in India are shown in Figure 1.3; examples in other countries include electrical fans (Gujrat in Pakistan), surgical instruments (Sialkot in Pakistan), farm machinery (Daska in Pakistan), shoes (Leon, Gadalajara and Mexico City in Mexico, Franca and Sinos Valley in Brazil, Trujillo in Peru and Pusan in South Korea), clothing (Lima in Peru), roof tiles (Karanggeneng in Indonesia), textiles (Daegu in South Korea) and cars (Ulsan in South Korea).10

8 Clusters and Competitive Advantage

Ludhiana Light engineering Delhi Cotton hosiery Okhla Garments

Kanpur Leather footwear

Agra Leather footwear Calcutta Cotton hosiery

Morvi Roof tiles

Bangalore IT/software Tiruppur Cotton knitwear

Figure 1.3 Examples of highly concentrated industries in India

One of the main purposes of the present study is to add to knowledge of and the literature on clusters in the developing world by examining the phenomenon of clustering in Turkey, which is classified by the World Bank as a middle-income developing country.

Defining clusters: industrial districts, networks and clusters Although the variety of clusters makes it difficult to define the concept precisely, there is no shortage of definitions in the literature. According to Hill and Brennan (2000, p. 66), for instance, a cluster is ‘a geographic concentration of competitive firms or establishments in the same industry that either have close buy–sell relationships with other industries in the region, use common technologies or share a specialized labor pool’. This is similar to the definition adopted by Rosenfeld (1995, 2000), who sees a cluster as a geographically bounded agglomeration of related firms that together are able to achieve synergy. Redman (1994, p. 37) includes institutions as well and defines clusters as a ‘pronounced geographic concentration of production chains for one product or a range of similar products as well as linked institutions that influence the competitiveness of these concentrations’. For ‘industrial districts’, a term that is sometimes used interchangeably with ‘clusters’ in the literature, Pyke and Spengenberger (1990, p. 2) provide

Introduction 9

the following definition: ‘[Industrial] districts are geographically defined productive systems, characterized by a large number of firms that are involved at various stages, and in various ways, in the production of a homogeneous product’. According to them, small and often family-owned firms, innovativeness and entrepreneurial spirit, interfirm cooperation and flexible productive networks are common features of such districts. Another definition comes from the new industrial districts (NIDs) literature, which focuses on the set of locational characteristics implied by flexible specialization: a district is a spatially concentrated cluster of sectorally specialized firms, with a strong set of forward and backward linkages, a common cultural and social background linking economic agents and creating a behavioural code, sometimes explicit but often implicit, and a network of public and private supporting institutions (Rabelotti, 1995). Biggiero (1999), on the other hand, defines industrial districts as ‘regional hyper-networks’, or ‘networks of firm networks’. Similarly, industrial districts are defined as a network of small and medium-sized enterprises within geographically defined production systems (Asheim, 1994). Meanwhile for Brusco (1990, pp. 14–15), industrial districts comprise ‘a cluster of firms producing something which is homogeneous in one way or another, positioning themselves differently on the market. Thus, the district could be defined as being a cluster, plus a peculiar relationship amongst firms.’ As a final example, Markusen (1996a) sees an industrial district as a spatially delimited area of trade-oriented activity with a distinctive economic specialization. Markusen classifies ‘sticky places’ (districts that demonstrated resilience in the postwar period in advanced industrialized countries) into four broad categories: Marshallian industrial districts (with an Italian variant); hub and spoke districts (such as Boeing in Seattle and Toyota in Toyota City); satellite industrial platforms (such as the US Research Triangle Park); and state-anchored industrial districts (such as a military base or a university influence on the development of some districts). According to Markusen, many localities exhibit elements of all four models. Silicon Valley, for instance, can be considered an industrial district in electronics, but it also has several important hubs (including Hewlett Packard and Stanford University) as well as hosting large branch plants of US, Japanese, South Korean and European companies (including IBM, Hyundai, Samsung and NTK Ceramics). Furthermore it is the fourth largest recipient of military contracts in the country. In Markusen’s view, therefore, it is wrong to concentrate solely on Italian-type, small-firm industrial districts as ‘sticky places’ are the complex product of multiple forces, including corporate strategies, industrial structures, profit cycles, state priorities and local and national politics. From the definitions reviewed above, two broad questions emerge: is it a necessary condition for a cluster to be a small-firm agglomeration, and is it a necessary condition for a cluster to be geographically concentrated? These questions have to be answered in order to differentiate clusters from industrial

10 Clusters and Competitive Advantage

districts and networks. Amin and Thrift’s (1999) analysis of two districts – Santa Croce in Tuscany and the City of London, the former specializing in leather shoes and bags and the latter in financial services – is of special relevance in respect of the first question. Santa Croce is a successful small-firm district that targets the fashion-conscious end of the market. Meanwhile the Marshallian structure of the City of London has undergone considerable changes since the 1960s in that large corporations – including many multinationals – now have offices there. Importantly, however, the concentration of financial services has persisted, which – along with many other examples, such as car production in Detroit – signals that geographic concentration is not peculiar to small and medium-sized enterprises. Another interesting study that reinforces this point is Saxenian’s (1994) work, which compares the high technology cluster on Route 128 near Boston with Silicon Valley in California. The former is dominated by vertically integrated, large companies that prefer to maintain control over their technology and innovations. This is manifest in the fact that companies along Route 128 are located in large, self-contained, campus-like areas. In contrast Silicon Valley has a history of independent, ‘garage-based’ entrepreneurs. It is necessary to remember at this point that the definitions of industrial districts cited above usually emphasize the predominance of small and medium-sized enterprises, although it is not clear in the literature whether this is a necessary condition for industrial districts. With regard to the question of geographic concentration, many researchers, starting with Marshall (1949), have cited geographical proximity as a key characteristic of districts (for example Pyke and Spengenberger, 1990; Asheim, 1994; Van Dijk, 1994). According to Asheim (1994, pp. 93–4), ‘what distinguishes an industrial district from other industrial agglomerations with strong external economies such as the Perrouxian development poles or the Japanese just-in-time production systems is precisely the existence of agglomeration economies’. The latter is therefore a common feature of industrial districts and geographic clusters, but not necessarily of networks. Instead a network is defined in more general terms as a set of high-trust relationships that are usually contractual and explicit. ‘In contrast to clusters, networks are generally based on a group of firms with restricted membership and specific, often contractual, business objectives . . . The members of the network choose each other; they agree explicitly to co-operate in some way’ (Brown and McNaughton, 2002, p. 27). Based on this definition, it can be argued that network relations are usually more cooperative in nature than are relations among cluster participants, for which competitive forces are also emphasized. For example Porter (1998) points to the intense rivalry in clusters. According to him, clusters offer transaction cost advantages without imposing the inflexibilities of vertical integration or the management challenges of creating and maintaining formal linkages such as networks, alliances and partnerships (ibid., p. 214). What can be deduced from the above is that a network usually involves explicit and formal links among firms that are often cooperative in nature. Whether

Introduction 11

or not these are necessary conditions for networks, however, is again not entirely clear. What is clear is that firms in a network are not necessarily tied to the same location, while a cluster is a form of network that is situated in a particular geographic location. Following this rationale, clusters can be seen as ‘localized networks’ (Van den Berg et al., 2001) involving geographically concentrated firms from a particular sector with links that can be both cooperative and competitive in nature. Defined as such, it appears that clusters are a form of network, whereas industrial districts are a form of cluster. Although there are certainly network relations amongst firms that are not geographically restricted, the focus of the present study is on the role of the local environment in shaping competitive advantage in geographically concentrated industries. In this regard Porter’s (1998, 2000) definition exactly matches the purposes of this study, and hence we shall define clusters as geographic concentrations of interconnected companies and institutions in a particular field (Porter, 1998, p. 197).11 They include specialist suppliers, specialized infrastructure, other service providers and associated institutions (including universities, standards agencies and trade organizations), and also extend to customers and firms in related industries.12 As a final note, in many of the definitions provided in the literature, clusters are implicitly seen as dynamic, successful and competitive. This brings us to the focal point of this study: the theorized link between clustering and competitiveness.

Clusters and competitiveness The debate on the competitiveness of locations begins with the fundamental question of whether all clusters are successful. Surprisingly this question has received little attention in the literature. One notable exception is Amin (1994), who investigates the attributes of successful versus unsuccessful clusters based on two Italian case studies: Santa Croce in Tuscany and Stella in Naples. Santa Croce is a competitive cluster where fashionable leather shoes and bags are made, while Stella is a footwear cluster in an area with high unemployment and widespread poverty. Interestingly, Amin argues that many of the characteristics of the Stella firms are similar to those which have made the clusters in Third Italy so successful. For example the firms’ owners are master craftsmen, the production process can respond quickly to changing market signals, the existence of family businesses and community ties permits labour flexibility, the lack of job opportunities mean cash savings, and agglomeration and product specialization attract buyers and sellers of raw materials and machinery. According to Amin, however, the specialist shoe makers in Stella are not competitive since they have not formed themselves into a locally networked economic system (ibid., p. 62). Instead, and despite their agglomeration, they are isolated from each other so there are no exchanges of ideas, spin-offs and economies of scale through specialization. Rather the artisans of Stella carry out ‘the tasks of the whole corporation

12 Clusters and Competitive Advantage

internally, but without any scale advantages or resources’ (ibid., p. 62). In contrast Santa Croce has a typical Marshallian industrial structure, with entrepreneurial, institutional and social interdependencies. According to Amin, firms in successful clusters act like a collective brain, although what contributes to this and why unsuccessful clusters persist are not made clear in his analysis. What is clear is that the same conditions that are associated with internationally competitive clusters are also typical of some rather unsuccessful ones. The main reason why these are not covered in the literature is simply that they have had little publicity. The answer to the question posed at the beginning of this section is therefore ‘no’ – not all clusters are competitive. The following subsections will discuss alternative views on how and why some clusters manage to become competitive while others do not.

Flexible specialization: a sure route to competitiveness? A stream of research called ‘the flexible specialization approach’ focuses on the organizational features of the regional economy and highlights the embeddedness of economic relations in broader social and political contexts. The main argument (Piore and Sabel, 1984) is that there has been a transition from Fordism to post-Fordism over the past two decades and a new post-Fordist landscape has emerged: new industrial spaces containing small firms with specialized, flexible production.13 This perspective has fuelled studies on the adoption of flexible manufacturing techniques and the link between industrial organization and agglomeration. Proponents of flexible specialization argue for vertically disintegrated and locationally fixed production, derived from examples such as Silicon Valley (high-tech), Third Italy (semirural) and Hollywood (inner city) (Amin and Thrift, 1999). In light of his study of industrial districts in Emilia-Romagna, Capecchi (1990) argues that the definition of industrial districts should include flexible specialization as a necessary condition, together with the presence of small and medium-sized enterprises (SMEs). There are, however, strong criticisms of the view that flexible production is a sure route to competitiveness. According to Amin and Robins (1990, p. 199), for instance, this view represents ‘a kind of anti-Fordist utopia’ and ‘an imposing orthodoxy’, since ‘we are being asked to believe that the very laws of capitalist development are becoming, as it were, Marshallian (as opposed to Fordist)’. In their view Fordism has far from disappeared, and the tendency for localized agglomerations is in fact paralleled by a countervailing tendency for transnational networks. It is therefore multinational corporations that are the real shakers and shapers of the world economy. According to this rationale, it is possible that clusters such as ‘Santa Croce will come to perform only specific tasks in an internationally integrated value-added chain, thus risking a shake out of firms dependent upon tasks no longer performed locally’ (Amin, 1994, pp. 59–60). In response to Amin and Robins’ (1990)

Introduction 13

criticisms, Sabel et al. (1990, p. 230) state that proponents of flexible specialization have never claimed that all agglomerations can be viewed as flexible production systems. Sabel et al. cite the Prato cluster as an example: this cluster ‘has survived, even flourished, on the ashes of a number of crises in its history. The current crisis – competition by large firms in some markets served by the district – might simply push the district into doing what it has done several times in the past: move up the price-performance curve by specializing in higher quality items and leave the middle-range products to the large firms’ (ibid., p. 234). Thus the argument that larger corporations and multinationals are of determining importance is regarded as overrated. As suggested by Bellini (1996), the experience of Third Italy seems to offer irrefutable evidence of the possibility of an alternative, socially progressive path of capitalist growth, providing an intellectual base for a number of microinterventions at the territorial level. In the early 1990s, however, there was a shift in the focus of research when some potentially negative effects of flexible production were brought to light. Harrison (1994), for instance, pointed to the adverse effect that corporate flexibility might have on labour practices in light of evidence that the use of child labour and the exploitation of immigrants were becoming more common in some small and medium-sized enterprises in Third Italy as global competition intensified. Malizia and Feser (1999, p. 224), on the other hand, underlined that cooperation between contracting firms did not necessarily imply an even playing field between partners. It is possible to bring the transaction costs approach to bear on the flexible specialization debate. This perspective focuses on the activities of the typical firm and presumes that if transaction costs are high the firm will choose to internalize its operations. Also, in a region that is relatively underdeveloped the firm may have no choice but to handle most of its basic functions in-house since the market may not be large enough for other companies to focus exclusively on producing the intermediate inputs or services needed. The flexible specialization approach, on the other hand, builds on the fact that there are dynamic external economies in districts, and the related benefit is manifested in reduced costs, enhanced productivity and superior innovation. These opposing views are likely to continue to compete in shaping not only the debates in academia but also the organization and location of production. To conclude, the flexible specialization perspective has a normative dimension in that industrial districts have effectively been defined as places where the dominant industries employ flexible production methods and are highly competitive. On the methodological side, what this means is that few researchers have conducted studies on the incidence of flexibly specialized clusters that are struggling in terms of performance. In the absence of such empirical studies, one might gain the impression that the type of industrial structure and organization highlighted in the flexible specialization literature is a guaranteed route to sustained competitiveness. This is of course a rather

14 Clusters and Competitive Advantage

limited picture (Malizia and Feser, 1999, p. 235). In the United States, for instance, there are strong, resilient clusters that are not flexibly specialized (Porter, 1998, 2000). Such clusters have been understudied, as have ones that are flexibly specialized but not competitive. Storper (1999) thinks that although the flexible specialization debate is theoretically powerful, empirical investigation covering a wider sectoral base is needed to determine whether or not the experience is specific to Italy. In Storper’s view there are deep historical roots associated with the Italian districts that are difficult to generalize to other competitive cultures, such as the Anglo-American ones. Overall, although it is by no means certain that localized flexible specialization is a sure route to competitiveness (Malizia and Feser, 1999, pp. 237–8), it is undeniable that the flexible specialization perspective has enabled us to develop a more sophisticated understanding of clusters, where not only markets and industries but also industrial organization, interfirm business relations and the social situation can be instrumental in success (see Chapter 2 for a discussion of this issue from the viewpoint of the management literature). An approach that shares a common thread with the flexible specialization perspective is the socioeconomic approach, which emphasizes the specific roles played by the social situation and politics. We shall consider this in the following subsection.

The parts played by the social situation, trust and politics This perspective is concerned with social, cultural and institutional influences on the competitiveness of clusters. Brusco (1996), for instance, argues that the importance of knowledge accumulation exceeds that of capital accumulation. According to him, two types of knowledge are of particular importance in this respect; namely codified knowledge (scientific and technical knowledge in scientific journals, technical reviews and textbooks, whose conventions and language are universal and known to the scientific community) and local or tacit knowledge (embedded in the minds, imagination and skill of people who live side by side and swap news and experiences when working together). The latter type of knowledge is acquired by seeing how other people do things, a process that is better managed in a local system (ibid.) Becattini and Rullani (1996, p. 167) attribute special importance to a local system’s ability to integrate codified and contextual knowledge, and argue that this makes the cluster concept essentially socioeconomic and thus a ‘disciplinary hybrid’, combining economics, sociology, geography and industrial organization. In a similar vein, and building on the fact that phenomena that persist over time possess some internal logic that cannot yet be explained in full, Becattini (1990) calls for a ‘socioeconomic notion’ that will link neoclassical, Marshallian and Marxian thinking. According to him, ‘better equilibrium in analysis cannot be reached without the direct contribution of the non-economists’ (ibid., p. 38). Only then can we understand what leads to the construction of a strong image that evokes feelings of identification and

Introduction 15

belonging, ‘giving substance to expressions such as “the response of Prato” to the lira devaluation’ (Becattini and Rullani, 1996, p. 172). In contrast to this line of thinking, Lissoni (2001) points to the possibility that even in clusters dominated by small and medium-sized firms, knowledge may be highly codified and firm-specific, rather than flowing freely throughout the cluster. The idea that economic action is embedded in the structures of local social relations paved the way for a related body of work on the role of social capital in reducing transaction costs and facilitating network formation (Granovetter, 1985). According to this literature, close social networks and thus proximity can help social capital to develop (Brown and McNaughton, 2002). A leading scholar in this field, Putnam (1993), argues that areas with low levels of social capital, which in the case of Italy are concentrated in the south, have slower rates of economic development than those with high levels of social capital, which are concentrated in the central and northern parts of the country. According to Putnam, repeated interaction and trust enhance social capital, and it is likely that there will be a high demand for law enforcement in areas with low levels of social capital as a result of heightened distrust. Of course it is possible that other communities will exhibit a different type of social capital (Flora and Sharp, 1997). Cohen and Fields (1999), for example, examined social capital networks in Silicon Valley and found that the understanding of social capital influenced by Putnam’s (1993) ideas (which refer to the complex of local institutions and relationships of trust among economic actors that evolve from unique local cultures) does not fit the situation in Silicon Valley. Specifically, ‘networks of civic engagement’, which Putnam sees as facilitating the activities of politics, production and exchange, have played little role in Silicon Valley. For instance it is not possible to say that business relations in Silicon Valley are embedded in family structures, given that Silicon Valley is a world of strangers and newcomers. Moreover there is no deep history to speak of. For Cohen and Fields, Silicon Valley is an economic space built on a very different kind of social capital, where the pursuit of economic objectives relates specifically to innovation and competitiveness and there is virtually nothing in the history of Silicon Valley to connect these networks of innovation to civil society. The high incidence of lawyers, accountants and auditors is presented as an indicator of the limited degree of informal, familial and communitarian trust in Silicon Valley, while the rapid turnover of employees reveals a commitment to innovation rather than to any particular company. Cohen and Fields conclude that there is trust in Silicon Valley, but it is of a specific kind. This commercially valuable and performance-focused trust is the building block of Silicon Valley’s ‘particular brand of social capital’. Another dimension is the role of work and politics in the competitiveness of clusters. According to Brusco (1996), competitiveness and worker participation are closely linked. He argues (rather dramatically) that the clusters in Emilia-Romagna show that cluster firms have at least solved some of the

16 Clusters and Competitive Advantage

key problems of large companies, that is, ‘How to involve workers and indeed all production people generally in the productive process, how to secure the participation of workers and technicians, how to storm world markets with products that are the creation not only of the hands but also of the heads and hearts of those that have made them’ (ibid., p. 154). Trigilia’s (1990) emphasis is on the related issue of local political subcultures. Following the institutional perspective, he examines two locales with similar socioeconomic but different political structures: the ‘red’ Valdelsa (furniture and glass) and the ‘white’ Bassano (shoemaking). The results of Triglia’s research show that not only social components such as the extended family and the local community, but also specific political components such as industrial relations and the activities of governments in respect of the Catholic and communist subcultures have played a part in the continuing competitive success of the clusters studied. Regardless of its being ‘red’ or ‘white’, a political movement that actively defends the collective interests of the local society seems to matter.

Innovative milieu and untraded interdependencies14 Schumpeter (1934) stressed the importance of the past trajectory of a locale as a sign of its future innovative capacity. Accordingly the likelihood of the next wave of an innovation to take place in its original area of development is quite high. In this regard, Lagendijk and Charles (1999) call for a distinction to be made between scholars who emphasize the role of networking in a particular sociocultural context, as captured in the term ‘innovative milieu’, and those who adopt the more institutional concept of ‘regional innovation systems’. For the former, the growth of a locally embedded innovation system is essential in shaping the social routines and strategies of actors in the regional economy, whereas the latter pay more attention to the development of and interaction between specific technology-oriented organizations such as universities and research centres (ibid., p. 129). Borrowing from Storper (1997, p. 16) however, there are a large number of universities around the world but ‘there is a much smaller number of Silicon Valleys’, which suggests that there must be other necessary conditions for the development of innovative clusters (Brown and McNaughton, 2002). An innovative milieu supports the development of such conditions. Specifically, based on the concept of knowledge spillovers in clusters, the milieu approach focuses on the ways in which a local socioeconomic network creates favourable conditions for innovation and competitive capacity: ‘local factors such as business services, public support, infrastructures, skilled labor and venture capital must be successfully woven together in order to sustain and support innovation’ (McDonald and Vertova, 2002, p. 45). A closely related concept is the ‘learning region’, in which a collective learning process by firms takes place via social and business networks and becomes embedded in the region (Camagni, 1991). This can also be linked to knowledge creation

Introduction 17

and the importance of tacit and codified knowledge in this process, as discussed above. According to this approach, globalization has triggered a shift in the sources of competitive advantage towards innovation and thus knowledge-based economic activity. In turn, knowledge-intensive economic activities have a high propensity to cluster within a geographic region since knowledge is generated and transmitted more efficiently in a local system (Audretsch, 1998). According to Storper (1999), this approach is paralysed by the circularity involved in its analysis: innovation occurs because of a milieu, and a milieu exists in regions where there is innovation. Instead, what really generate region-specific assets and thus govern the sustainability of a cluster’s competitiveness are ‘untraded interdependencies’. Storper argues that a region can be seen as ‘a nexus of untraded interdependencies’ among three systems: the labour market, the input – output system and the knowledge system. A process of becoming specific that is in operation in such regions, imply that ‘there is only one Silicon Valley if one wants to be “in the know” for the most advanced innovations in semiconductor technology’ (ibid., p. 213). The same logic can be extended to explain why some clusters persist and maintain their competitiveness over time. That is, there are webs of user– producer relations and untraded interdependencies, and localization of these is frequent. The region is then key in the supply architecture for learning and innovation (ibid., p. 214). The literature, however, is inconclusive about the conditions that lead to the emergence of an innovative milieu or untraded interdependencies in a locale (see Chapter 2 for the management literature’s view on how clusters foster innovation).

Is it just an accident of history? Path dependency and the lock-in phenomenon This approach investigates how and why a cluster emerges in a given location and looks at the conditions associated with its subsequent development. Understanding how certain standards or technologies persist despite the fact that they might not be optimal has been the special concern of some researchers (David, 1985; Arthur, 1985). Regarding the initiation of a cluster, several authors (Marshall, 1949; Myrdal, 1957; Scott, 1988; Krugman, 1991a) emphasize the role of ‘historical accident’. That is, the initial pattern may simply be an accident of history, and once established a self-reinforcing loop might occur (Martin, 1999). Thus the initial pattern becomes locked into an area for economic and sociocultural reasons.15 Once a locale has become a centre of activity the lock-in effect comes into operation, and even if exogenous circumstances change (perhaps reducing the attractiveness of the site) economic agents may not want to move away and forgo the benefits of agglomeration. History, then, might be a determining factor in the spatial pattern of economic activity. A connected idea is related to sunk costs and how these bear upon

18 Clusters and Competitive Advantage

the spatial configuration of the firm, and thus upon the geography of economic activity (Clark and Wrigley, 1997). Another dimension of the issue is how the location of an agglomeration is determined, given that it is typical for many locations to be candidates for hosting an agglomeration. In this regard the possibilities range from pure accident and the existence of small initial differences (Henderson, 2000), to chance events that are partly shaped by the conditions prevailing in the business environment offered by a location (Porter, 1990). Porter argues that even inventions, which might change the competitive prospects of an industry as well as the location in which it is concentrated, are more likely to occur in places that are conducive to their development, the conditions of which are specified in the diamond framework (see Chapter 2 for a detailed discussion of this). Hill and Brennan (2000), on the other hand, argue that it matters little why an industry locates in a certain place; what is crucial is whether or not the conditions prevailing in the earliest stage of its development begin to generate cluster economies. In short, despite the fact that many studies conclude that ‘history matters’ (Arthur, 1986), it is not clear which industries will become locked in and which will not, and hence whether history does or does not matter (Enright, 1990). Rauch (1993), for instance, asks, whether history matters only when it matters a little.

Negative externalities and the possible dissolution of clusters This final subsection considers a rather overlooked aspect of the competitiveness of clusters: negative externalities. So far we have concentrated on the positive externalities associated with agglomeration. Pulling in the opposite direction, however, are forces of dispersion. The main concern in this respect has traditionally been the impact of congestion on firm costs and performance (Brown and McNaughton, 2002, p. 24). The effects of an increase in the cost and supply of immobile factors (rents and labour costs in particular) might be serious enough to cause firms to relocate. There is also a possibility that a cluster might be locked into the rules and routines of the past, thus missing any new chances of upgrading. What might be even more damaging is that such a situation, which Porter (1998) calls ‘groupthink’, might be exacerbated by a tendency to exclude newcomers or outsiders, as suggested in the social capital literature (Brown and McNaughton, 2002, p. 25). It has been argued that there is a tendency for clusters to disperse as they grow (ibid.) Relatedly, Dei Ottati (1994) emphasizes the destructive aspects of price competition, which are more evident in a cluster environment than elsewhere. She suggests that formal institutions should intervene to prevent competition from degenerating into destructive forms. Otherwise the resulting disequilibrium might produce a chain reaction, and if the destructive forces are sufficiently intense and prolonged a change of organizational model (that is, dissolution of the cluster) might be unavoidable. Yet another force that might contribute to the dissolution of clusters is the globalization of

Introduction 19

economic activities in general and the impact of multinational enterprises in particular (Amin and Robins, 1990). Overall, what the above discussion reveals is that very different circumstances, both economic and non-economic, might have an impact on the structure and competitiveness of clusters. Accordingly the foundations of success are rather different in, say, the clusters of Baden-Württemburg, where there is ‘a Darwinian competitive pressure’, than in the clusters of Third Italy, where there is a significant role for non-economic factors as well (Staber, 1998).16 The various explanations of why a cluster might become competitive and sustain its competitiveness include those related to the organization of production (for example flexibly specialized small and mediumsized enterprises), the social and political context (for example the part played by trust, social capital and institutions) and the existence of relations within the cluster that pave the way for innovation, learning and untraded interdependencies. There is also a possibility that both the initiation and the subsequent development of a cluster might be an accident of history, with the cluster then being locked into the region. Finally, the opposite forces of dispersion should also be taken into account. None of these explanations, however, can explain fully why certain clusters are competitive while others are not. Specifically, contrary to what the flexible specialization approach envisages, there are competitive clusters that are not flexibly specialized and flexibly specialized clusters that are not competitive, as discussed above. On the other hand, although the part played by the social and political situation in general and extensive collaboration among cluster participants in particular might be important to the success of some clusters, such as those in Third Italy, this cannot explain the success of prominent clusters such as Silicon Valley. Similarly, the explanations that tie the competitiveness of a cluster to innovation, learning and untraded interdependencies do not sufficiently clarify why some clusters manage to become innovative and/or develop untraded interdependencies while others do not. Hence despite the fact that the numerous approaches in the literature have improved our knowledge of the clustering of economic activity, we still lack a comprehensive theory that can explain the competitiveness of clusters in full. One likely contributor to a more complete understanding of the competitiveness of clusters is a relative latecomer to the area; namely the unique perspective offered by the management discipline, which is the subject matter of the next chapter.

2 Clusters in the Management Literature

This chapter provides a review and discussion of recent debates on clusters in the management literature in order to complete the background on clusters presented in the previous chapter. An additional purpose of the chapter is to highlight the unique contribution made by the management literature in this regard. As Porter’s (1990, 1998) approach directly parallels the central concern of this study due to its focus on the link between clusters and competitiveness, the discussion will pay special attention to this approach and the literature it has spawned, after considering some recent studies in the general management literature that focus on different aspects of the issue.

An overview As with the management literature in general, the literature on organization theory is becoming richer in respect of cluster-related studies. Neoinstitutional theory, for instance, offers a distinctive explanation of why organizations cluster and argues that to be perceived as legitimate, organizations must conform to the rules and requirements in a given institutional environment. According to this rationale the organizations in a common institutional environment converge in response to similar regulatory and normative pressures or as they copy successful organizations (Baum and Haveman, 1997). Meanwhile ecological models predict that the addition of an organization to a population is likely to have stronger competitive effects on neighbouring organizations than on those further away (Hannan et al., 1995). In a related work, Hannan and Freeman (1977) outline a model in which competition among the members of an organizational population is localized with regard to size. They infer that organizations of different sizes use different strategies and structures, and similarly sized organizations compete most intensely. However Baum and Haveman’s (1997) analysis of location decisions by the Manhattan hotel industry provides evidence in support of a combined perspective in which hoteliers locate new hotels sufficiently close to established hotels that are similar in terms of price and therefore benefit 20

Clusters in the Management Literature 21

from agglomeration economies, but differ in terms of size, thus avoiding localized competition and creating complementary differences. In this study, information externalities and the reduction of consumer search costs are seen as the primary reasons for clustering. Ecological models of localized competition have much in common with Hawley’s (1950) model of competitive processes, which asserts that localized competition eventually leads to differentiation since less fit organizations are transformed through functional or territorial differentiation as a result of competitive pressures. Similarly, according to White (1981), entrepreneurs try to avoid invading not only competitors’ product/client niches but also their geographic niches. This approach parallels a line of work from the area of marketing, which particularly focuses on the competitive effects of brand location in product space (Hauser and Simmie, 1981). In fact marketers have developed techniques such as multidimensional scaling to find ‘ideal points’ for new product entries (Baum and Haveman, 1997). Another stream of research is the study of networks and interorganizational relationships. These mostly concentrate on buyer–supplier relationships and pay little attention to location. Studies that compare ‘global Toyotaism’ with ‘global Fordism’ (for example Fujita and Hill, 1999) are a notable exception in this regard. Their main conclusion is that global Toyotaism localizes more of the production process than does global Fordism. The just-in-time (JIT) supply system used by Japanese car makers encourages suppliers to locate nearby to avoid the risk of delivery delays (Van Dijk, 1994). A frequently cited example of this is Toyota City, an immense complex of interlinked suppliers that have grown up around Toyota’s main assembly plants. A similar situation exists in the case of Japanese automotive factories in North America in that component companies tend to be located within a short driving distance of the factories. ‘Just-in-time and in one place’, therefore, is a common phenomenon, but is not necessarily inevitable. Hudson (1992) argues that a historical analysis of the patterns of investment by Japanese automotive companies in North America versus those in Europe provides an illustrative case in this regard. The fact that American car producers have internalized a considerable proportion of component production has left their Japanese counterparts with little choice but to ensure that new component companies are set up to supply them, and it is rational for these companies to be located in close proximity. However a very different pattern is observed in Europe, where the majority of the main European component producers are significant international players and their production is rationalized on a European scale. Therefore it is no surprise that new Japanese assembly plants have plugged into the existing system. The introduction of JIT production organization, therefore, may or may not lead to the spatial concentration of production, depending on the specific historical circumstances and the cost of coordination and transportation, which may be high enough to necessitate close proximity (Malizia and Feser, 1999, p. 233).1

22 Clusters and Competitive Advantage

Paralleling the network literature in general, the study of local networks, which has only recently secured a prominent place in the research agenda (Porter, 1998), focuses on trust and identity. In this literature the functioning of a positive feedback loop in a local network is summarized as follows: the more that people trust one another the more they strengthen their group (network) identity, and the more they perceive themselves as a group (network) the more they trust one another (Biggiero, 1999). Geographic proximity reinforces this process (Porter, 1998). The transaction costs approach has brought additional insights in this regard. According to some researchers (for example Storper, 1999), agglomeration is the result of efforts to minimize transaction costs. Specifically, the assertion that vertical integration has transaction cost advantages over the market form is challenged by the cluster approach since clusters are seen as having an organizational form that lies in between markets and hierarchies, given that some disadvantages of the market form are diminished by geographic proximity. The cluster environment, for instance, is conducive to reputation building since the cluster participants usually live in the same area and are likely to have direct social and economic exchanges over a long period of time, thus making it possible to monitor their behaviour. Visser (1999) argues that spatial clustering promotes the development of networks by lowering transaction costs in at least two ways: the high density of related economic activities facilitates the screening and selection of business partners on the basis of local information and established reputations, and proximity between agents facilitates the monitoring of behaviour and enforcement of contracts. In these circumstances, local business customs are developed to such an extent that violating them may result not only in the withdrawal of cooperation by other parties but also in social sanctions (Dei Ottati, 1994).2 The notion of clusters as an organizational form relates to the connections between location and industrial organization, which are just beginning to be explored (Enright, 1990). In a related work, Steinle and Schiele (2001) argue that clustering is more relevant for industries with a divisible production process and a transportable product. Another interesting piece of research is that by Saxenian (1994), who provides a historical analysis of the development of Silicon Valley and Route 128. This comparison of the two clusters shows that Silicon Valley has exhibited resilience when facing challenges, whereas Route 128 has been rather slow to respond. The main reason for this, according to Saxenian, is related to industrial organization. Specifically, Silicon Valley is a network-based industrial system whereas Route 128 is dominated by a small number of relatively integrated corporations. There is a risk that this conclusion might be misinterpreted as it seems to suggest that we should rethink the large firm as an organizational form. It is therefore necessary to point out that Saxenian’s emphasis is on the importance of local networks rather than the size of firms in a cluster: ‘The contrasting experiences of Silicon Valley and Route 128 suggest that industrial systems built on regional networks

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are more flexible and technologically dynamic than those in which experimentation and learning are confined to individual firms’ (ibid., p. 161). She also acknowledges that there might be large as well as small firm variants of network-based systems, as the Japanese experience suggests. Another noteworthy finding of her study is that although collaborative practices in Silicon Valley are pronounced, the region has long been dominated by individual achievement, pointing to the value of competition as well as collaboration in this cluster. Finally, the idea that ‘industrial organization matters, although the type of organization that is most successful in particular places may vary’ (Malizia and Feser, 1999, p. 236) is perhaps the only point of consensus in this literature, and the organizational aspects of a cluster of independent but informally linked firms and institutions, which constitutes a robust organizational form in the continuum between markets and hierarchies, are still underexplored (Porter, 2000). The main contribution of international business scholars (for example Dunning, 1995, 1998; Rugman and Verbeke, 2000) has been to analyse the geographical concentration/dispersion of FDI conducted by multinational enterprises, which may strengthen the location advantages of the countries in which they operate through spillovers to local networks. A competitive local cluster is in turn beneficial for the multinationals, since it enables them to gain access to leading-edge ideas and specialist talents (Bartlett and Ghoshal, 1991). With regard to new entrants to US industries, for instance, there is evidence that foreign firms locate new plants in states with a high concentration of similar activities (Head et al., 1995). When examining the characteristics of foreign-owned subsidiaries in leading industry clusters, Birkinshaw and Hood (2000) found that these subsidiaries gradually develop the same characteristics as other firms in the clusters, since they have to become insiders in order to reap the associated benefits. According to Rugman and Verbeke (2000, p. 29), it is not only possible that multinationals might alter a cluster’s attractiveness but also that they might act as a conduit for international exchanges and spillovers, and thus contribute to the global diffusion of knowledge. Finally, since the structure and content of the location portfolios of multinationals have recently become more crucial to their global competitive position, more attention needs to be paid to the importance of location per se as a variable that affects the global competitiveness of firms (Dunning, 1998, p. 60). An interesting but overlooked aspect of agglomeration economies has been addressed by Shaver and Flyer (2000): firms contribute to externalities in addition to benefiting from them. This suggests that if firms are heterogeneous they will differ in the net benefits they receive from agglomeration. According to Shaver and Flyer, firms with the best technologies, human capital, training programmes, suppliers and distributors will gain little but suffer much when their technologies, employees and access to supporting industries spill over to competitors. Therefore, unlike firms with weak

24 Clusters and Competitive Advantage

endowments, these firms have little motivation to cluster. Shaver and Flyer found supportive evidence of their arguments by examining the location choice and survival of new foreign investments in US manufacturing industries. Specifically, their results show that large entities are less likely to agglomerate, which in their view explains why some studies have identified the existence of agglomeration mechanisms but many large-sample studies have not found a superior performance among agglomerating firms. One body of research in the strategy literature has investigated how clusters affect innovation (Bergeron et al., 1998). With regard to the localized character of innovation in the UK, for instance, Baptista and Swann (1998) have found that firms in strong clusters are more likely to innovate. Another interesting piece of research is that by Beal and Gimeno (2001), who have found that technological knowledge spillovers matter little for US software firms. What really matters are marketing spillovers in that cluster firms gain an edge in finding markets and customers, as well as tailoring products for them. This finding, which at first seems counterintuitive, highlights two important issues. First, although technological innovations are without doubt very important, sometimes it is possible to gain access to them relatively easily, as in the case of new software technologies. Second, a broader understanding of innovation is necessary, rather than reducing the concept to ‘scientists in laboratories making breakthrough inventions’ (Porter, 1990). As Beal and Gimeno’s (2001) study illustrates, innovation in marketing can be of determining importance as well, and the related information (for example on industry trends, market niches and customers’ needs) might be difficult to codify and is thus easier to acquire in a cluster environment. Following this rationale, it is not unlikely that a non-agglomerated firm will make a breakthrough innovation but mistime its entry into markets (Yu, 2002). As the above discussion has revealed, management scholars have addressed numerous aspects of clustering, including the organization of production, transaction costs, the nature of localized competition, the role of firm strategies and marketing, firm-specific perceptions of the costs and benefits of clustering, the part played by multinationals and the interaction between global and local networks. The unique approach of management scholars involves putting the firm at the centre of the analysis and trying to understand the phenomenon of clustering from that point of view. This micro approach to the issue has without doubt enhanced our understanding of clusters. However, despite the growing interest in location-related issues in management research in recent years, relatively little management thinking is directly connected to location (Porter, 1998). In fact Porter (1994) argues that in the strategy literature, the part played by location has been seriously neglected, particularly in respect of competitive advantage. Meanwhile profound changes have been taking place in the world economy in that globalization and the intensification of knowledge have greatly altered the role of location in competition. Understanding this new role ‘requires embedding

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clusters in a broader and dynamic theory of competition’ (Porter, 1998, p. 208). Porter’s (1990, 1998, 2000) work on this has triggered a revival of interest in the subject and prompted a lively debate in the literature. Apart from furthering our understanding of clusters, Porter’s contributions are of particular relevance to this study since they specifically focus on the relationship between clustering and competitive advantage. The rest of this chapter will therefore be devoted to a discussion of these contributions and the debate they have spawned in the literature.

Porter-style geographic clusters The main focus of Porter’s book The Competitive Advantage of Nations (1990) was on sources of international competitive advantage at the industry level. Based on a study of more than one hundred industries in ten countries, he developed his diamond framework, which rests on the idea that sources of advantage are local. In this framework, four attributes of the local environment – factor conditions, demand conditions, related and supporting industries, and context for firm strategy and rivalry – play a major part in enabling domestic firms to gain and sustain competitive advantage. These factors interact with each other to form a complex, mutually reinforcing system, which makes the resulting advantage very difficult to replicate elsewhere, and hence more sustainable. The dynamic character of the system is magnified by the effects of domestic rivalry and the geographic concentration of industry. Domestic rivalry prompts improvements in all the other determinants, while geographic proximity amplifies the interaction between the sources of competitive advantage. Pressure and challenge are of particular importance in the emergence and sustainability of competitive advantage, and both are driven by intense domestic rivalry and felt more heavily when firms are in close physical proximity. In his subsequent works Porter (1998, 2000) argues that the existence of clusters is a manifestation of diamond theory. In his view the beginnings of a geographic cluster can often be traced to historical circumstances or an unusually sophisticated local demand (Porter, 1998). The prior existence of related industries and/or one or two innovative companies might also provide the seed for a new cluster, the latter echoing the growth pole approach. Alternatively a chance event (such as an invention) may create advantages that foster cluster development. Once a cluster begins to form a self-reinforcing cycle promotes its growth since talented individuals are attracted by success stories, specialist suppliers emerge, information accumulates and local institutions develop specialized training programmes, research facilities and infrastructure. It can take a decade or more to develop a sustainable competitive advantage, but it is also possible that some clusters will lose their competitive edge because external (for example technological discontinuities)3 and internal forces (for example ‘group-think’ among cluster participants, which

26 Clusters and Competitive Advantage

can result in an overly inward-looking cluster) cause shifts in relative competitive positions. In Porter’s (1994) view the basis of competitive advantage has shifted from static efficiencies (such as low input costs) to the ability to innovate and upgrade skills and technology. This has brought about a radical change in the importance of location in that the capacity to innovate and upgrade draws heavily on the local environment, and the related advantages are difficult to tap from a distance. Enduring competitive advantages thus lie in the local environment and distant rivals cannot easily replicate them. In other words the microeconomic quality of the local business environment determines the sophistication with which companies compete. Clusters affect competition in three broad ways: by increasing productivity, by driving the direction and pace of innovation (which means future productivity growth) and by stimulating the formation of new businesses (Porter, 1998). Local rivalry boosts productivity since the presence of successful rivals puts pressure on firms to innovate and break their dependence on less sustainable sources of advantage – say, basic factors – to which their rivals also have access. Close proximity facilitates constant comparison, and rivalry among local firms often transcends the economic to become emotional and personal. In fact the motivation-driving nature of a cluster can be amplified by pride and a desire to look good in the local community. On the other hand, interactions among cluster participants are likely to reflect longer-term interests because the easy spread of information can affect reputation and individuals’ desire for standing in the local community (Porter, 2000). Word soon spreads if one cluster participant takes advantage of another or provides shoddy products or services (Enright, 1990). Moreover, since assets and skills are readily available in the location and would-be entrepreneurs can benefit from established relationships, clusters are conducive to new business formation. These factors, together with the presence of local firms that have achieved success, reduce the perceived risks of entry. Customers’ demands can also be better exploited in a cluster environment since firms can tune into customers’ changing needs with a speed that is difficult to match by companies located elsewhere. Relationships and face-to-face contacts in a cluster enable companies to learn early about evolving technology and marketing concepts. Since the opportunities for innovation are more visible and therefore firms are prompted to act rapidly, clusters can remain centres of innovation for decades (Porter, 2000). This approach to clusters has implications both for economic development (it calls for attention to microeconomic factors in government policy) and for companies (for instance in respect of a location’s chance of attracting FDI). Porter (ibid.) argues that the seeds of most clusters germinate independently of government action and that a market test must be passed before development efforts are justified. What is required of the government changes as a cluster develops. In the early stages the government should focus on improving the

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local infrastructure and eliminating disadvantages. In the later stages, it should concentrate on removing obstacles to innovation. In short, the government’s role is to improve the microeconomic foundations for competition, which will ultimately determine the competitiveness of the cluster. According to Porter, since knowledge and innovation are key factors in cluster development, the incidence of clusters tends to increase with economic development. In developing countries, which usually lack well-developed clusters, diverse economic activities tend to be concentrated in one or two large cities.4 Promoting clusters in such countries, Porter (1998) argues, should start with the basics: improving capital markets, institutions, education and skill levels, and technological capacity. Only then should cluster-specific investments be made. As mentioned earlier, Porter’s ideas on the importance of the local business environment for creating and sustaining competitive advantage became the focus of an intense debate in the academic literature.5 In addition to the ten countries studied in the original work (Porter, 1990), the approach was applied to hundreds of industries in more than 40 countries, and the academic literature on the topic grew to more than 30 book reviews and around 50 published articles (Davies and Ellis, 2000). The following pages will provide examples of the application of Porter’s approach in different contexts, plus a review of the major debates in the literature. Of particular relevance for our purposes are critiques and comments on issues related to geographic clustering.

Applications and empirical tests The many applications of Porter’s approach include ones that replicated his 1990 study in other countries. Some of these replications were conducted by groups headed by Porter himself (for example in Canada, New Zealand and several Latin American and Middle Eastern countries). A commonality among these studies was that they took the value of Porter’s (1990) framework for granted, and thus the primary purpose of the exercises was to identify the sources of advantage/disadvantage in particular industries. The results of the analyses were then used to suggest policy remedies for their weaknesses and strategies to build upon their strengths. Replications conducted by other researchers (such as those in Turkey by Öz, 1999, and Greece by Konsolas, 2002) were aimed at determining whether the diamond concept would work in different contexts. There have also been empirical tests of different aspects of Porter’s model. For example O’Donnellan (1994) has tested, among other things, the geographical concentration hypothesis. The study concludes that in Ireland there is a general preference among industries for large urban areas such as Dublin and Cork, and that there is little association between sectoral clustering and industrial performance. O’Malley and Van Egeraat (2000) have also investigated the link between industrial performance (in this case in terms

28 Clusters and Competitive Advantage

of growth) and clustering, and concluded that there is limited evidence of Porter-type clusters in Irish industry and no clear association between the occurrence of such clusters and the growth of indigenous manufacturing. Yet another study of Ireland is that by Clancy et al. (2001), which integrates the principal findings of three Irish case studies on dairies (O’Connel et al., 1997), popular music (Clancy and Twomey, 1997) and software (O’Gorman et al., 1997). The overall conclusion is that although various elements in Porter’s diamond have contributed to the competitive advantage of the industries studied, there is also support for critics of Porter, especially those who question the importance of a home base in small, open economies such as Ireland, as well as the part played by foreign multinationals in fostering competitiveness. However it should be noted that Clancy et al. (2001) do not focus on industries that are both competitive and indigenous, which they themselves acknowledge is a weakness of their study. This is a reasonable self-critique, and a counterfactual speculation is possible. This does not, however, change the fact that although it lacks clear examples of Porter-style clusters, Ireland has performed well economically in recent years. This leaves two possible explanations. First, critics of Porter may be right that his approach to clusters is not relevant for a small, open economy. Alternatively, it may be that Ireland is an exceptional case, with growth occurring from a low starting point, and it is possible that it will prove difficult to sustain indigenous growth in the long term unless strong clusters emerge. In another empirical test, Yetton et al. (1992) have re-examined the analyses conducted by Porter in Canada and New Zealand and supplemented them with an original analysis in Australia. They conclude that competitive industries usually lack strong diamond elements in their home base in these countries. Similarly, and again focusing on Australia, Ellis and Pecotich (1996) have found evidence to refute the importance of strong home-base diamond elements. The test results obtained by Cartwright (1993) in his attempt to test the whole model in light of the New Zealand finding show that the Porterideal model can be associated with industries that are characterized by moderate competitiveness and static/declining profitability, rather than ones characterized by strong competitiveness and growing profitability. A similar result has been produced from a study on Hong Kong’s competitiveness, in that Hong Kong’s most competitive industries do not have strong demand conditions or favourable factor conditions in their home base. Finally, based on their study in the Netherlands, Jacobs and De Jong (1992) conclude that the importance of the home base varies from sector to sector. Having summarized the country applications and specific tests of the hypotheses put forward by Porter (1990, 1998, 2000), we shall now consider some regional investigations of Porter-style clustering. In their abovementioned study of clusters in Ireland, Clancy et al. (2001) look at the geographical location of dairies, popular music and software. While all three are spatially concentrated, the dairy cluster is the only one that is both indigenous and

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very competitive. The authors argue that proximity has facilitated information flow within this cluster. Interestingly, for the dairy cluster (like many other clusters in Ireland) most of the important downstream and related industries are foreign-owned. This is linked to a much-debated aspect of Porter’s approach that will be discussed in detail in the next subsection. In his application of the diamond framework in London, Hamilton (1991) provides a historical analysis of the capital and argues that during the past 30 years the area known as the City has been transformed from a predominantly materials-based economy to a mainly information- and financial-based one. He then investigates London’s international competitiveness with the help of Porter’s diamond model, which is assumed to be valid and is used as an organizing framework to examine competitiveness. According to Healey and Dunham (1994), who apply Porter’s analysis to another British local economy, Coventry, some changes are required when the diamond is applied to subnational units since a number of the tools available to the national government (such as exchange rate control and interest rates) are not available to regions. Moreover factors of production, especially labour and capital, are more mobile within than between countries. Having discussed such concerns, Healey and Dunham use Porter’s approach to investigate why Coventry’s position changed from relative competitive disadvantage during the 1970s and early 1980s to relative competitive advantage during the remainder of the 1980s. They conclude that Porter’s (1990) analysis provides a useful framework to examine the competitiveness of local economies, since each of Porter’s four determinants has had some bearing on the change in Coventry’s competitive advantage. Kaufman and Gittel (1994) have applied the diamond framework in New Hampshire (United States) to evaluate Porter’s (1990) hypotheses on the effect of geographical concentration on industrial performance. To identify New Hampshire’s competitive industries they used export shares (following Porter’s methodology) plus two additional indicators: productivity and wage levels. A historical analysis of New Hampshire’s four leading industries – fabricated metals, industrial machinery and equipment, electronic and electrical equipment, and instruments – was supplemented by a survey that provided interesting finding. When asked whether proximity promoted rivalry, for example, New Hampshire companies stated that they did not compete with one another, nor did they generally compete with other firms in the region. Kaufman and Gittel’s interpretation of this is that firms in each of the leading industries were operating in different market niches, thus avoiding direct competition. Similarly, with regard to proximity to customers, half of the respondents stated that this was of little importance since the majority of primary products were sold to customers located elsewhere in the country. However there was some evidence of suppliers being geographically concentrated. Thus the survey results indicate that several of Porter’s conclusions do not hold for New Hampshire. Kaufman

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and Gittel’s overall conclusion is that although Porter’s model provides a useful tool for analysing a region’s economic activity, the New Hampshire economy differs in some very important ways from the predictions of the model. As well as the applications and empirical tests summarized above, other studies have taken elements of Porter’s approach. For instance Padmore and Gibson (1998) have designed a model to describe and assess the strengths and weaknesses of regional industrial clusters using some dimensions of Porter’s diamond and applying the model at the regional level in British Columbia (Canada). Dobkins (1996) has modelled locales that produce goods for trade outside their boundaries, assuming monopolistic competition. This model was inspired by Porter’s (1990) story of the Montebelluna (Italy) ski boot industry, in which externalities, economic growth, and historical and spatial considerations were all present. The most significant contribution of these two studies has been to put some of Porter’s key ideas into a mathematical model. In another study, Shilton and Stanley (1999) trace the survival and growth of the headquarters of publicly listed firms in the United States. Their finding supports Porter’s thesis that firms cluster for competitive advantage. Finally, Doeringer and Terkla (1995) explore the economic foundations of business clusters in the United States and their link to development and competitive advantage. Drawing on interview-based field research, they warn that intense local rivalry among firms within production channels can be counterproductive.

The debate on Porter-style clusters There have been various criticisms of Porter’s (1990, 1998) approach. Stopford and Strange (1991) criticize Porter’s lack of formal analytic modelling, while Bellak and Weiss (1993), Dunning (1992) and Grant (1991) question the originality of the framework. Porter has also been criticized for his treatment of macroeconomic policy (Daly, 1993), for his failure to clearly define determinants and several key terms (Grant, 1991) and for paying insufficient attention to modern trade theory (Bellak and Weiss, 1993) and the role of national culture (Van den Bosch and Van Prooijen, 1992). His methodology has also attracted criticism, including his heavy reliance on world export shares as a measure of international competitiveness (Grant, 1991; Cartwright, 1993; Rugman and D’Cruz, 1993), his inadequate treatment of relatively less competitive industries (Yetton et al., 1992) and his treatment of multinationals and foreign direct investment (Rugman, 1991; Bellak and Weiss, 1993; Dunning, 1993; Hodgetts, 1993; Rugman and D’Cruz, 1993; Rugman and Verbeke, 1993). Criticisms of his diamond framework relate to the undue importance attributed to the relationship between domestic rivalry and international competitiveness (Smith, 1993) and the indirect role envisaged for the government (Stopford and Strange, 1991; Van den Bosch and de Man, 1994).

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Several of the contributors to a volume compiled by the OECD (1999) have incorporated Porter’s approach to clustering into their analysis. For example Drejer et al. (1999) assess Porter’s Danish studies, Peneder (1999) focuses on the case of Austria, and Rouvinen and Yla-Anttila (1999) first evaluate Steinbock’s (1998) study of Finland and then apply Porter’s approach to Finland, but with a number of changes.6 While these studies are generally positive about Porter’s approach, Davies and Ellis (2000, p. 1189) offer a rather negative evaluation of Porter’s 1990 book: ‘While it was enormously rich in its range and scope it fell far short of the claims made for it.’ According to them, the assertions at the heart of the study can be refuted because strong diamonds are not in place in the home bases of many internationally successful industries. Amongst the numerous proposals for improvement, Grein and Craig (1996) suggest that the diamond should have three elements instead of four – ‘infrastructure/demand’, ‘competitive investment’ and ‘education’ – and O’Shaughnessy (1997) adds ‘custom’, ‘history’ and ‘politics’. When considering the South Korean case, Cho (1994) suggests that the four-cornered diamond should be replaced by a nine-factor model consisting of four physical factors (endowed resources, business environment, related and supporting industries, domestic demand), four human resources (workers, politicians and bureaucrats, entrepreneurs, professional managers and engineers) and chance. However many scholars have acknowledged the value of Porter’s approach, and ‘even the most hostile [have] praised the richness of Porter’s cases’ (Davies and Ellis, 2000). For instance Greenaway (1993) is of the opinion that Porter’s approach serves as an excellent complement to mathematical analyses of competitive advantage. Similarly Smith (1993, p. 404) believes that Porter’s firm-oriented approach represents an original contribution to development theory: ‘The alternative of simply applying an unalloyed paradigm of neo-classical analysis plus the assumption that government failure is always greater than market failure seems weak by comparison.’ Holt (1998) especially celebrates Porter’s emphasis on local rivalry and considers that his analysis of how competitive pressure in clusters drive innovation and prosperity is ‘one of the glories of his approach’. According to Gray (1991) and Dunning (1992), Porter’s extensive and rich field research has advanced our knowledge of why corporations in some locations have successfully penetrated foreign markets in some product areas but not others, and of why some countries have been able to attract foreign-owned firms to participate in some value-added activities but not others. According to Grant (1991), Porter’s assertion that innovation and upgrading are central to the creation and maintenance of competitive advantage constitutes a step towards the reformulation of the strategy model in a dynamic context. One implication of this is that when determining the sources of competitive advantage and formulating strategy, it should be remembered that the resource base of a firm is determined not only by its own past investments

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but also by the conditions for resource supply and resource creation in its proximate environment (Öz, 1999). We shall now look at the most relevant debates for the purposes of this study.

Geographical unit of analysis and applicability to every context A very interesting debate in the literature is on the most appropriate geographical unit of analysis to apply Porter’s approach. In his 1990 study, Porter argues that many of the determinants of advantage are more similar within a country than across countries. However, because the geographic concentration of competitive industries is so important he questions whether the country is the most appropriate unit of analysis since competitive advantage often seems to be localized in an area within the country. International business scholars, however, tend to take the opposite position. Regarding the EU, for example, Dunning (1993) argues that national diamonds should be replaced by ‘supranational diamonds’ in order to capture the true competitive advantages of the EU. Jacobs and De Jong (1992), on the other hand, argue that there is a dialectic relationship between divergence and convergence, and concur with Porter’s (1990) idea that globalization paradoxically leads to more emphasis on local conditions and creates an opportunity for firms to take advantage of them. Others (for example Rugman, 1991; Hodgetts, 1993; Rugman and D’Cruz, 1993; Rugman and Verbeke, 1993) share the idea that double and/or multiple-linked diamonds would reflect the sources of competitive advantage better than Porter’s (1990) single diamond framework does for smaller countries that are highly dependent on one or more of the major blocs (Europe, North America and Japan). At the micro level the issue is further complicated by the existence of cross-border clusters (Saner and Yiu, 2000). Relatedly, some researchers consider that Porter’s approach cannot be used for all countries. For instance Rugman (1991) believes that while most of Porter’s (1990) analysis would work for managers based in the EU, the United States or Japan, much of it could not be applied in Canada. The main reason for this, according to Rugman, is that Porter’s study does not incorporate the true significance of multinational activities, an issue that will be discussed below. Similarly, in Hodgetts’s (1993, p. 44) view, ‘since most countries of the world do not have the same economic strength or affluence as those studied by Porter, it is highly unlikely that his model can be applied to them without modification’. Porter’s emphasis on home markets and local firms, according to Bellak and Weiss (1993), may be justified in the case of large countries but is of little relevance for small ones. Narula (1993) and Yetton et al. (1992) make a similar point when arguing that since it is based on and applied to them, the diamond is most relevant for mature, manufacturing-based economies and cannot be used to explain the international competitiveness of developing countries. Similarly Davies and Ellis (2000) argue that since Porter generalizes inappropriately from the American

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experience, developing countries are inadvertently encouraged to pursue policies that might be harmful.

Sources of advantage: global versus local Whether sources of advantage are local, as suggested by Porter (1990, 1998), is another issue that has been subject to severe criticism. Porter’s (1990) treatment of multinationals and foreign direct investment in particular has been widely criticized. According to Rugman (1991), the narrow understanding of foreign direct investment is a major conceptual problem with Porter’s model. Relatedly, Davies and Ellis (2000) argue that it is not surprising that Singapore was not included in Porter’s 1990 book, eventhough it had been studied by him: ‘If Singapore’s prosperity were determined by the activities of firms for whom Singapore is a home base its residents would be poor people, but they are not.’ According to Dunning (1993), to suggest that the competitiveness of multinationals rests only on their access to the diamond of competitive advantage in their home countries is ludicrous, regardless of whether or not their initial foray overseas was based on such advantages. The geographical dimension of the criticisms of Porter’s attitude towards FDI is the focus of a work by Lagendijk and Charles (1999), who emphasize the importance of foreign assets in clustering and suggest that at the regional level the issue of multinationality becomes an issue of multiregionality.7 Rugman and Verbeke (1993, p. 72) challenge ‘Porter’s allegation that the core competencies of large MNEs and the innovative processes occurring within these firms necessarily need to depend upon the characteristics of a single home base’. They argue that multinationals from small countries may rely on a host nation to such an extent that it becomes difficult to make a distinction between the home base and the host country or countries.8 According to Rugman and Verbeke (2001), a major problem with Porter’s approach is that he concentrates solely on non-location-bound, firm-specific advantages (FSAs) developed by companies in their home country prior to engaging in FDI, which is only one of many possible combinations that can be observed empirically in respect of the locational determinants of competitive advantage. For example one alternative is for non-location-bound FSAs to be created jointly by subsidiaries located in various countries and exploited throughout the network. Here we have an increasingly complex and blurred picture of the relative contribution of FSAs versus CSAs (countryspecific advantages) and home CSAs versus host CSAs to overall multinational competitiveness. Another point of disagreement concerns the identification of the home base of a multinational. According to Rugman and Verbeke (ibid.), it is necessary to define a threshold percentage of core assets, competencies and strategic decision-making power, below which a firm would be viewed as functioning with several home bases. In addition, if a firm is able to enhance its accumulated competencies through interactions with location advantages

34 Clusters and Competitive Advantage

in host countries, it will again be viewed as functioning with several home bases. This implies that most multinationals will have several home bases, which is in sharp contrast to Porter’s approach. It should be remembered that Porter (1990) uses the world export shares of industries as a proxy to measure international competitiveness at the industry level. An industry is also considered to be competitive when domestic firms in the industry are engaged in substantial outward FDI. With regard to inward FDI, a methodological problem arises when a country has an internationally competitive sector (measured by world export share) that is dominated by foreign companies. What Porter does in such cases is to try to locate the source of advantage through in-country research. This requires determining whether the firms in the industry operate as branches of a multinational company or can be clearly associated with the host country. In the former case the industry is excluded, and in the latter case it remains on the list of competitive industries. This is confusing but logical, and the real challenge is to locate the source of advantage. What is even more confusing is Porter’s (1990) argument that ‘inward FDI is not entirely healthy’, especially when examples of relatively prosperous countries such as Singapore, Canada and Ireland, which host considerable inward FDI, are taken into account. Dunning (1993) argues that Porter’s interpretation of the link between FDI and competitiveness rests on the idea that outward FDI reflects the possession of firm-specific tangible assets that give a competitive edge prior to undertaking the FDI. While this is a valid explanation of why individual firms are able to engage in FDI, it does not follow that inward FDI has a negative effect on the competitiveness of the recipient economies (Davies and Ellis, 2000). Recently Lin and Song (1997) have taken up Dunning’s (1995) extension of the diamond framework, which adds ‘multinational business activity’ as a determinant of competitive advantage. Applying the model to China, Lin and Song conclude that the country’s recent success owes much to inward FDI. Similar findings are available for other countries, including Mexico (Hodgetts, 1993) and Singapore (Chia, 1994). The crucial point here is that foreign investors might and do choose competitive locations because the environment offered by a particular industry cluster acts as a magnet for other firms in the industry, so both national and foreign firms gravitate to favourable cluster locations even if corporate ownership is based elsewhere.9 This being the case, there is no reason why inward FDI should be considered ‘unhealthy’. In summary, many international business scholars (Rugman, 1991, 1992; Rugman and D’Cruz, 1993; Jacobs and De Jong, 1992; Yetton et al., 1992; Bellak and Weiss, 1993; Cartwright, 1993; Dunning, 1993; Hodgetts, 1993; Rugman and Verbeke, 1993; Yla-Anttila, 1994) have found fault with Porter’s (1990) insistence that firms’ ability to compete depends on the strength of the diamond in their home base. As Davies and Ellis (2000) point out, however, this difficulty with the diamond goes deeper than these researchers realize

Clusters in the Management Literature 35

since the argument can be extended to suggest that not only multinational companies but also other companies that are exposed to international influences in one way or other (for instance via exporting) may sharpen their advantages as a result of such interactions. If, however, ‘firms in one country are able to draw upon diamonds in another, the concept of the national diamond is stripped of its content’ (ibid., p. 1204), since the whole concept of the diamond is based on the hypothesis that the sources of competitive advantage are local. Porter thinks that such criticisms mainly stem from an unnecessary confusion: the geographic scope of competition and the geographic locus of competitive advantage are two different things. In his view, competition can be global but the sources of advantage are local (Porter and Amstrong, 1992). It is therefore clear that the two sides of the debate, that is, Porter versus the international business scholars, are arguing for two competing hypotheses: that the sources of advantage are local, or that advantages can be sourced globally. The point made by international business scholars, in other words, is in fact a counter-hypothesis rather than a criticism. Needless to say the burden of proof lies on both sides when there are two competing hypotheses, and this calls for further empirical research. This book hopes to contribute to this by investigating not only the local circumstances of but also the global linkages associated with the Turkish clusters.

The debate on policy issues Another noteworthy debate focuses on regional policy issues. According to Markusen (1996b), agglomeration effects are largest for industries that are high-tech, knowledge-intensive, innovative and young. She implies that developing countries need these industries because they support a higher standard of living. Porter (1996), however, believes that this perspective may be misleading, and that the productivity of an industry matters more than its being high-tech. Markusen also challenges Porter’s argument that industrial clusters are the most significant unit of analysis for investigating regional economic advantage. According to her, this is an empirical question and far from self-evident. As an example she cites Seattle, the dynamism of which is explained by the presence of five distinct sectors: shipping, forestryrelated activity, aircraft, software and biotechnology (Markusen, 1996b, p. 91). Porter agrees with Markusen’s view that the significance of generalized versus cluster-specific agglomeration economies is an empirical question. With regard to the Seattle example, Porter underlines that he is not suggesting that all clusters in a regional economy have to be connected. Another major point of divergence for the two researchers is that Markusen supports government targeting of particular industries, which in her view is appropriate and effective, whereas for Porter, the whole premise of targeting is flawed. Porter and Markusen also disagree on the types of regional policy that should be followed. Markusen favours a traditional formulation of regional policy that includes broad incentives for firms to locate in less developed regions,

36 Clusters and Competitive Advantage

whereas Porter thinks that such measures are doomed to failure. According to him, cluster formation can only be encouraged ‘by locating specialized infrastructure and institutions in areas where factor endowments, past industrial activity, or even historical accidents have resulted in concentrations of economic activity’ (ibid., p. 88). Moreover in Porter’s view there are strong arguments for the greater decentralization of economic policy to subnational regions, marking yet another area in which he disagrees with Markusen.10 Another dimension of policy issues that has been subject to debate is the revitalization of inner-city areas.11 Based on his approach to the locational determinants of competitiveness, Porter (1995a) argues that this task can only be done through private initiatives based on economic self-interest and competitive advantage. In the associated debate in the literature, Blakely and Small (1995) state that Porter’s analysis is incomplete, while Johnson et al. (1995) argue that Porter has devoted too little attention to the role of the business community in revitalizing such areas. In their view, Porter’s assertion that the private sector – in exchange for a more business friendly environment – will step in to fill the gap is not convincing given that this has rarely happened in the past. Businesses need steady customers and reliable employees, and people who are ‘ill-housed, ill-fed or just plain ill’ cannot be either (Lowery, 1996, p. 64). Overall the critics agree that Porter’s (1995a) approach can serve to supplement other efforts, but it can never be an all-inone solution or as important as affirmative action. In his reply to his critics, Porter (1995b, p. 304) insists that many of the criticisms indicate a misunderstanding of his arguments. According to him, as a general principle it is necessary to view the disadvantages suffered by inner-city areas as an economic problem and the result of poor strategies and obsolete public policies. It is therefore necessary to develop a new strategy for each area, tailored to its unique characteristics and building on its advantages (ibid., p. 333). With regard to the role of government, Porter (1990) believes that clusters often emerge and grow naturally so there is only an indirect role for the government. This is one of the most criticized aspects of his approach. Several scholars (for example Stopford and Strange, 1991; Van den Bosch and de Man, 1994; Öz, 1999) are of the opinion that in developing countries a more active part should be played by the government as poor countries cannot afford the luxury of letting market forces determine outcomes. In his later work Porter (1998) continues to argue that the essential role of government is to challenge and press industries, and that too much help can undermine the industries’ success. A detailed discussion of the ideal level of government intervention is beyond the scope of this study. However the discussions in this book on the part played by the government in shaping the sources of competitive advantage of the Turkish clusters examined may provide some insights into to the role of government in cluster development.

3 Industrial Clusters in Turkey

The Turkish business environment, past and present During the first ten years of the newly established Republic of Turkey (1923–32), state involvement in economic activities was rather limited. This was mainly because (1) the basic principles adopted in the Izmir Economic Congress (1923) committed the government to the establishment of a private enterprise economy, and (2) some economy-related provisions in the Lausanne Treaty (1924) considerably restricted the area in which the government could operate. For instance the country was bound to apply the Ottoman tariffs for another five years. Over this period little was achieved in terms of industrialization since the private sector lacked the necessary technological competence and capital. These factors, combined with external ones such as the Great Depression, were enough to convince the policy makers that the private sector could not be entrusted with the task of leading the country’s economic development. This marked the beginning of a new period (1933–45) in Turkish economic history called ‘etatism’, during which the government heavily intervened in the production of goods and services. The First Five Year Industrialization Plan (1934–38) placed strong emphasis on the industrialization process, particularly in the case of textiles, iron and steel. As a result of the related policies the pace of industrialization accelerated, with industry’s share of GNP rising from 14 per cent to 18 per cent during the period in question (Kepenek and Yentürk, 1997). Between the end of World War II and 1960, some attempts were made at liberalization, shaped by a new type of etatism in which the government supported the private sector. The transition to a multiparty regime and the provisions of the Marshall Plan are considered to be the major reasons for this policy shift. Significant investment in energy and motorways as well as a boom in the housebuilding sector associated with rapid urbanization created a considerable demand for construction firms, thus promoting the development of the Turkish construction industry. Another feature of the period was that special emphasis was placed on agriculture in accordance with the Marshall 37

38 Clusters and Competitive Advantage

Plan, which brought modern practices to the sector. The government was clearly committed to encouraging the private sector and therefore pursued pro-business policies. However this fostered rent-seeking activities, which subsequently became an increasingly deep-rooted problem. Interestingly, since the pro-business policies did not bring stability, both politicians and business people started to question whether it was possible to achieve stability and liberalization at the same time. In this respect it is worth mentioning that even Prime Minister Menderes, who was very sceptical about planning, had a change of mind and took certain steps to prepare a development plan in his last year in office, prior to the military intervention in 1960. The disappointing results of liberalization, together with the tendency elsewhere in the world for greater government intervention, caused the military government of the early 1960s to introduce a 20-year import-substitution development strategy for a mixed economy, to be implemented via five-year plans. During this period there were improvements in the growth rate of overall output and industrial production. Big businessmen were also in favour of a planned approach and stressed the importance of having a long-term economic strategy to reduce the uncertainty in the economic environment. The need to clarify the boundaries of private sector activity was another factor in this. The sense of responsibility felt by the newly emerging bourgeoisie for the economic development process resulted in the establishment of influential business associations such as TÜSIAD (Bugra, 1994). The period 1960–80 was a time of unusual political turmoil and there were three military interventions (in 1960, 1971 and 1980). After these interventions, concern about the position of the private sector was soon replaced by concern about the instability generated by the regimes’ macroeconomic policies. In the 1970s two additional developments, the oil shock and the Cyprus crisis, exacerbated the already bleak scene. The coincidence of an unfavourable global economic environment with the political instability in Turkey led the country into a major crisis in the late 1970s, resulting in another military takeover in 1980. In that year the ‘January 24 Resolutions’ introduced a comprehensive stabilization programme under the auspices of the IMF and the World Bank. The structural adjustment policies adopted in accordance with the programme were intended to shift the economy from an inward to an outward orientation, with an emphasis on export-led growth. Reforms were conducted in a number of key areas, one of which was trade policy, with the introduction of extensive export promotion measures and the gradual liberalization of imports. The results were impressive in terms of exports in general and manufactured exports in particular, although the increase in exports was matched by a boom in imports (Öz, 1999). In the second half of the 1980s there was a considerable reduction in export subsidies. Tariffs and quotas, and therefore the level of import protection, were also reduced. With the unexpected but comprehensive financial liberalization achieved by making the Turkish lira convertible in 1989,

Industrial Clusters in Turkey 39

the main policies of the liberalization programme were completed. The immediate result was a worsening of the trade deficit, mainly stemming from the increase in imports rather than a decrease in exports, which actually continued to increase gradually (Figure 3.1) and Turkey’s world export share remained fairly stable. It is argued that the frequent and unexpected changes to key policies created a chaotic business environment in Turkey in the 1980s and 1990s (Bugra, 1994). Under the circumstances it was essential for business people to have good state contacts so that they would at least have a vague idea about what was going on. In fact, they often complained not about the changes themselves but about the way they were handled. What was worse, however, was that such an environment offered considerable opportunities for abuse. Allegations about tax rebates for exports, for instance, caused some scholars to question the export success achieved by Turkey in the post-1980 period, and to ask whether the export figures were fictitious (see Arslan and van Wijnbergen, 1990). While the 1980s are associated with major reforms, the 1990s are often considered ‘lost years’ in Turkish economic history (Kumcu and Pamuk, 2001). With regard to the key events that shaped the 1990s, the first was the Gulf crisis in the beginning of the period, which damaged Turkey’s economic relations with Iraq. In 1994 Turkey faced yet another economic crisis, due mainly to mismanagement of a programme to reduce interest rates. The customs union between Turkey and the EU, which had been in effect since January 1996, brought challenges as well as opportunities for Turkish industry. 60 000 50 000 40 000 30 000 20 000 10 000

19 82 19 83 19 84 19 85 19 86 19 87 19 88 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00

0

Exports

Imports

Figure 3.1 Exports and imports, Turkey 1982–2000 (US$ 000s) Sources: SIS (2000); ITC (2002).

40 Clusters and Competitive Advantage

Towards the end of the decade the Asian crisis broke out, affecting many parts of the world. The impact of this on the Turkish economy was indirect and occurred after a one-year lag, but the Russian crisis caused considerable damage to the construction and leather sectors, whose main trading partner was Russia. In 2000 the government introduced a disinflationary programme, but this collapsed in February 2001. Finally, Turkey implemented yet another stabilization programme, under the auspices of the IMF and the World Bank and aimed at ‘empowering the Turkish economy’. Turkey is classified by the World Bank as a middle-income developing country. It has close ties with the EU, including a customs union agreement. It occupies a very advantageous geographical position, constituting a natural link between West and East, and recently it has started to take greater advantage of this, especially in respect of trade and tourism. Turkey’s standard of living, as measured by GDP per capita, has gradually increased (Figure 3.2) but is still rather low at US$ 2200–6080, based on purchasing power parity (PPP) (2001 figures, SPO, 2002). The average annual growth rate of the economy, as measured by the rate of growth of real GDP, on the other hand, averaged about 4 per cent in the post-liberalization period. This rate, though fluctuating widely, was slightly above the average attained by middle-income countries (around 2–3 per cent) during the same period (World Bank, 1999). However, although overall domestic production and per capita income have been increasing at above average rates compared with other middle-income developing countries, inequalities in income distribution remain significant. Persistently high inflation rates and external debts, when taken together with Turkey’s ‘grey’ economy, present a bleak outlook for the country’s macroeconomic future. This is further complicated by the continuing political uncertainty. Such an environment is preventing firms from improving their

7000 6000 5000 4000 3000 2000 1000 01

00

20

99

20

98

19

97

19

96

19

95

19

94

GDP per capita (PPP)

Figure 3.2 Standard of living, Turkey, 1980–2001 (US$) Source: SPO (2002).

19

93

19

92

19

91

19

90

GDP per capita

19

89

19

88

19

87

19

19

85

86

19

84

19

83

19

82

19

81

19

19

19

80

0

Industrial Clusters in Turkey 41

competitive advantages. Given this picture it is not surprising that Turkey has failed to attract much FDI, the annual average being less than US$ 1 billion in recent years, a figure that compares unfavourably with those achieved by other emerging economies (SPO, 2002). An examination of the broad characteristics of the Turkish business environment shows that small and medium-sized enterprises account for more than 90 per cent of Turkish firms, but larger firms’ contribution to value-added and exports are much higher (Taymaz, 1997). Big corporations are a relatively new phenomenon in Turkey: of the 405 TÜSIAD member companies, only 22 were incorporated before 1950 (Bugra, 1994, p. 55). The 1950s were an important decade for many of the largest Turkish companies, reflecting the government’s shift to more liberal policies. Many of today’s leading Turkish construction firms, for example, were either established or made an important turn in their business during that decade (Öz, 1999). Family-dominated management of firms of all sizes is a common phenomenon in Turkey as there is a lack of confidence in salaried managerial personnel. Educating young members of the family in top universities, integrating a professional manager into the family via marriage, and strong relationships established over the years between family members and professional managers, making the latter ‘part of the family’, appear to be common ways of achieving a delicate balance between professionalization and family control (Bugra, 1994). According to Bugra (ibid., pp. 68–9), all Turkish business tycoons have certain characteristics in common, including family support in commercial activities at the start of their career, the arbitrary – and rather opportunistic – choice of their initial area of activity, heavy engagement in unrelated diversification as the business grows, and good connections especially in state circles. Rent-seeking behaviour is common, and real-estate speculation is particularly widespread. The high degree of state involvement in business activity, be it in the form of subsidized credits, input supply or output demand, has been detrimental to the Turkish business environment. Given the key role of government in the economy, good connections in government circles have contributed significantly to business success. The slow bureaucracy and unexpected changes in key policies, on the other hand, have caused problems for Turkish business people.

Turkey’s position in international competition This section provides an overview of the evolution of the competitive structure of Turkish industry. The analysis is conducted with the help of Porter’s (1990) methodology. The basic measure used to determine the international competitiveness of an industry is its share of world exports, which is defined as a country’s exports for an industry divided by total world exports for that industry in a given year. All industries defined in the Standard International

42 Clusters and Competitive Advantage

Trade Classification (SITC) are then sorted by world export share at the lowest possible level of disaggregation (in five-digit detail). Next the cut-off rate is calculated by dividing the total exports of a country by total world exports. Those industries which have world export shares above the cut-off rate constitute the relatively more competitive industries of the country. The list of these industries is then modified according to additional criteria. For example, industries with a world export share that lies between the cut-off rate and twice its value are checked to exclude ones with a negative trade balance. Also, industries that are among the top fifty in terms of their country’s export share (which is defined as the share of an industry in the country’s total exports) but below the cut-off rate in terms of their world export share are included in the list of relatively more competitive industries, provided they have a positive trade balance. If there is considerable outward foreign direct investment in an industry, this industry is also included in the list. Finally, with the addition of the internationally competitive service sectors the list is completed for that particular year and country (Öz, 1999). The list of competitive industries is used to produce cluster charts. These reveal the connections between and interrelationships amongst the country’s competitive industries, and hence the country’s competitive pattern. All competitive industries are classified into three broad groupings, each of which includes different clusters. The first group consists of ‘upstream industries’, whose primary products are inputs to the products of other industries. The clusters included in this category are semiconductors/computers, materials/ metals, petroleum/chemicals and forest products. The second group, ‘industrial and supporting functions’, comprises clusters of multiple businesses, transportation, power generation and distribution, office, telecommunications, and defence. The last group is ‘final consumption goods and services’, which contains the food/beverage, textiles/apparel, housing/household goods, health care, personal, and entertainment/leisure clusters. The industries in each cluster are further classified into four groups, revealing the vertical relationships among industries and the depth of national clusters. These four groups are primary goods, the machinery used to produce these goods, the special inputs required and the related service industries (Öz, 1999). We shall now apply the above methodology to recent data on Turkish industries. Table 3.1 shows the percentage of exports by cluster and vertical position in 1992–2000. Turkey’s share of world exports in 2000 was 0.52 per cent, and six clusters of industries had a share above that figure, namely materials/ metals (from the upstream industries group), food/beverages, textiles/apparel, housing/household, personal and entertainment/leisure (all from the final consumption goods and services group). Of these, textile/apparel had the highest share with an impressive 2.4 per cent. Turkey exports a great variety of items in this category, mainly primary goods and special inputs. The importance of the cluster for the Turkish economy is considerable, given that it accounts for around 37 per cent of the country’s total exports. While it has

Table 3.1

Percentage of Turkish exports by cluster and vertical position, 1992–2000

Materials/Metals

Primary goods Machinery Special inputs Total

Petroleum/Chemicals

Forest products

Upstream industries

Semiconductors/Computers

SC

CSC

SW

CSW

SC

CSC

SW

CSW

SC

CSC

SW

CSW

SC

CSC

SW

CSW

SC

SW

8.3 0.2 0.5 9.0

3.4 0.2 0.5 2.7

0.8 0.1 0.4 0.6

0.1 0.1 0.4 0.1

0.3 0.0 0.0 0.3

0.3 0.0 0.0 0.3

0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0

1.6 0.0 0.0 1.6

0.2 0.0 0.0 0.2

0.1 0.0 0.0 0.1

0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0

10.0 0.2 0.5 10.7

0.2 0.1 0.3 0.2

Multiple businesses

Transportation

Power generation & distribution

Office

Telecommunications

Indus. & support functions

Defence

SC CSC SW CSW SC CSC SW CSW SC CSC SW CSW SC CSC SW CSW SC CSC SW CSW SC CSC SW CSW SC Primary goods Machinery Special inputs Total

0.2 0.2 0.0 0.0 3.7 2.5 0.2 0.2 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.3 0.4 0.4 0.0 0.0 4.9 2.8

Food/Beverage SC Primary goods Machinery Special inputs Total

CSC SW CSW

0.2 0.1 2.3 0.7 0.0 0.0 0.0 0.0 0.2 0.1 0.0 0.0 0.2 0.1 2.3 0.7

Textiles/Apparel SC

0.2 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.1 0.0 0.0 0.0

Housing/Household

Health care

0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0

0.0 0.0 0.0 0.0

Personal

0.0 0.0 0.0 0.0

0.1 0.1 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1 0.1 0.2 0.2

Entertainment/ Leisure

CSC SW CSW SC CSC SW CSW SC CSC SW CSW SC CSC SW CSW SC CSC SW CSW

9.7 6.9 0.9 0.1 33.1 1.6 2.7 0.7 7.5 2.7 0.9 0.4 0.2 0.2 0.0 0.0 2.0 2.0 0.5 0.5 3.1 1.4 0.6 0.5 0.3 0.3 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.6 2.5 0.2 0.4 3.4 0.2 1.6 1.0 0.8 0.6 0.3 0.4 0.0 0.0 0.4 0.4 1.3 0.7 1.2 1.8 0.0 0.0 0.0 0.0 10.6 9.1 0.7 0.1 36.5 1.8 2.4 0.9 8.3 2.1 0.8 0.3 0.2 0.2 0.0

0.0 3.3 1.3 0.6 0.3 3.1 1.4 0.6 0.5

6.3 0.2 1.2 7.7

SW 0.1 0.1 0.2 0.1

Final consumption goods & services SC

SW

56.0 0.3 6.1

1.2 0.2 0.7

62.4

1.1

43

Notes: SC share of country’s total exports (2000); CSC change in share of country’s exports (1992–2000); SW share of world cluster exports (2000); CSW change in share of world cluster exports (1992–2000).

44 Clusters and Competitive Advantage

always been important in terms of world market share its performance improved remarkably after liberalization (Öz, 1999). The second most important cluster is housing/household goods, which includes a variety of processed products and some special inputs. Turkey’s strong position in carpets, glass, ceramics and cement products is especially noteworthy. With regard to the food/beverages cluster, Turkey holds significant positions at all vertical stages, including related machinery, although primary goods dominate. Within the primary goods category there has been a move towards processed foods. The materials/metals cluster holds the highest world export share in the primary goods category, together with special inputs and machinery. Competitive industries in the entertainment/leisure cluster, on the other hand, exclusively produce primary goods and have virtually no presence in other vertical categories. Despite the considerable rise in this category’s position in the world market in the 1990s, the range of competitive industries in the cluster is rather limited. The personal cluster has a different structure from the ones outlined above as its strength mainly lies in special inputs. In fact a single item, unprocessed tobacco, is largely responsible for the high export share of the cluster. Thus like the entertainment/leisure industry, the personal industry is hardly a strong contributor to the Turkish economy. In addition to these six leading clusters, some competitive positions are held in the transportation, power generation and distribution, and defence clusters, although their world export shares are rather low at around 0.2 per cent. Turkey’s position is weak in categories such as forestry products, semiconductors/computers, multiple businesses, office, telecommunications and health care. Finally, there have been a few isolated successes, such as that by the construction services sector in the otherwise uncompetitive multiple business cluster.1 The most striking finding of the examination of the competitive structure of Turkish industry over time is that there has been little change in terms of the types of industry in which Turkey is internationally competitive. Although it increased its overall exports after the 1980 liberalization and improved its strength in the existing clusters, it failed to establish itself in other ones. As a result the economy still depends on four major clusters: materials/metals, textiles/apparel, food/beverages and housing/household goods. Turkey also has a strong advantage in primary goods and to as lesser degree in special inputs, but its position in the machinery category is rather weak (Öz, 1999). Although some improvement can be observed in a few additional clusters (Table 3.1) it would be premature to assert that these will join the four major clusters. Three of the four leading clusters (textiles/apparel, food/ beverages and housing/household goods) are in the final consumption goods and services group, where a concentration of competitive industries is considered typical for a developing country.

Industrial Clusters in Turkey 45

Geographic concentration of Turkish industries In the previous section we looked at patterns of export competitiveness in Turkish industry, outlining the changes that have taken place over time. We shall now switch our focus to the location of industries and investigate which are concentrated spatially, and where they are concentrated. First, however, we shall discuss the methodology that will be followed to identify geographic clusters.

Identification of geographic clusters This subsection reviews alternative approaches to identifying clusters. One well-known index is the Gini coefficient, which compares a distribution against a profile. When the profile represents a country the coefficient is called the ‘coefficient of localization’. The ‘location quotient’ is another frequently used measure of spatial concentration. This linear scale transformation is obtained by dividing each occurrence by a constant, enabling the occurrences to be compared against a norm (Üser, 1983). The range of the quotients indicates the relative degree of concentration of a certain activity in a region.2 Enright (1990) has adapted the indices used to measure industrial concentration in the literature on industrial organization. Accordingly, C4EMP and C8EMP are defined as the shares of employment in the leading four and leading eight provinces in a given industry.3 Enright warns that these indices record clusters of firms that are spread across provincial borders as two different clusters, thus understating the extent of geographic concentration. However it would be wrong to merge the provinces in question as this would render the indices non-comparable (ibid., pp. 4–9). Ellison and Glaeser (1994, 1997) propose a ‘dart-board approach’, which is based on a dart-throwing metaphor. The term localized is used to describe industries whose degree of concentration goes beyond that which would have prevailed if firms had chosen the locations of their plants in a completely random manner. Ellison and Glaeser’s main index measures concentration of employment, adjusted for the plant size. The method, however, requires a substantial data filling procedure necessitated by the limitations of census data. Maurel and Sedillot (1999) offer a slightly different index that measures the location decisions of two business units in the same industry.4 Midelfart-Knarvik et al. (2000, p. 2) offer another measure of spatial dispersion that takes into account the relative locations of clusters of industries. In their comprehensive analysis of the location of European industry, they first investigate the degree of specialization in EU countries. For each country they calculate the share of industry k in that country’s total manufacturing output. Next they calculate the share of the same industry in the production of all other countries. It is then possible to measure the difference between

46 Clusters and Competitive Advantage

the industrial structure of a country and all other countries by taking the absolute values of the difference between these shares, summed over all industries. They call this the Krugman specialization index (following Krugman, 1991a). It takes the value of zero if country i has an identical industrial structure to the rest of the EU, and the maximum value two if it has no industries in common with the rest of the EU. They calculate this as a four-year moving average for the period 1970–97 to remove spurious fluctuations due to the differential timing of country and sectoral business cycles. Next they use the Gini coefficient of concentration of the variables for all manufacturing to measure the concentration of manufacturing industries in the EU. Like Enright (1990), Midelfart-Knarvik et al. (2000) discuss the challenges imposed by geographic boundaries when measuring concentration. With their index, two industries may appear to be equally geographically concentrated, when in fact one is predominantly located in two neighbouring countries and the other is split between two geographically separated countries. Since distinguishing such patterns can provide additional insights, they propose ‘an index of spatial separation’, which can be thought of as a supranational index of geographic location, as a complement to the traditional concentration indices (ibid., p. 28). The spatial separation index incorporates a measure of the distance between two locations. It should be noted that Midelfart-Knarvik et al.’s units of analysis are countries (rather than provinces) within a supranational entity – that is, the EU – which works to their advantage in terms of the availability of detailed time-series data. Among the many other ways of measuring geographic concentration are ‘nearest neighbour’ analysis, which takes account of the spatial separation of the observed units; general harmonic mean distance variation, which measures the concentration of each sector in respect of the spatial distribution of employment among provinces, calculated as the average distance between the occurrences; and peak potential, which measures the average distance from the occurrences to their peak potential. Feser and Bergman (2000) have developed a ‘spatial-economic test’, which uses a case control design to test whether certain types of manufacturing firm are more spatially concentrated than might be expected given the general geographic pattern of all firms in the locale. All plants in a given industry are used as a case, and a matched sample of all other manufacturing firms is used as a control. The difference in concentration between the two, measured by means of standard statistical geography techniques, provides evidence of spatial concentration or dispersion at different spatial scales for the firms in the cluster (ibid., pp. 258–9). Finally, Shilton and Stanley (1999) use a modified form of the location quotient, designated as the ‘growth quotient’. It is obvious from the above discussion that there is no consensus in the literature on the best means of measuring geographic concentration. This study will use the concentration indices proposed by Enright (1990), supported by location quotients (LQs). Data-related considerations, comparability

Industrial Clusters in Turkey 47

across industries and ease of interpretation favour the use of these indices. Moreover some of the alternative indices are designed in such a way that an industry is not considered to be localized if employment is concentrated in a small number of plants. This approach underestimates the localization of oligopolistic industries (such as the automotive industry in Detroit), which are as interesting as small firm concentrations for the purposes of the present study.5 It should be noted that problems can emerge if a purely statistical approach is used to identify geographic concentration (Brusco et al., 1996). This is mainly because the definition of clusters itself is not easily quantifiable, given that it involves social relations and value systems as well as production relations. Consequently a purely statistical approach can fail to spot places that are clearly concentrated, the most typical example being Silicon Valley. In fact many of the indices cited above have failed to identify the concentration in Silicon Valley due to the absence of the finely detailed data required to uncover this cluster statistically. Qualitative evaluations should therefore be used to complement the quantitative measures. Identifying the boundaries of a cluster is another crucial issue since industrial clusters do not necessarily conform to political boundaries, as emphasized by Padmore and Gibson (1998, p. 627): ‘A successful cluster may crowd into one corner of a province, span several cities and suburbs, or straddle an international border.’ Saner and Yiu’s (2000) study of a cluster in the Upper Rhine Valley region, which encompasses neighbouring provinces in Switzerland (Basle), France (Alsace) and Germany (Baden), is an illustrative case in this respect. The ceramic goods cluster spanning the border between the provinces of Kütahya and Bilecik in Turkey is another example. It is also possible for a cluster to enlarge over time and spread into neighbouring provinces, as has happened with the textile cluster in Gaziantep, which has extended north-westward to reach Kahramanmaras. The choice of geographic unit of analysis is further complicated by the fact that provinces can differ substantially in size and population. Despite these concerns, the most appropriate geographic unit of analysis for the present study is still the province, since the data are fairly complete at the provincial level for Turkey. Defining the scope of a cluster in terms of the industries it embodies is equally difficult since the distinction between cluster firms on the one hand and related and supporting firms on the other can be fuzzy. In general a narrow definition is preferred since in a broadly defined cluster, linkages are likely to be less strong and less complete. In the end there should indeed be a limit before the cluster is defined as ‘the whole economy’. Otherwise, in the extreme case, it would be possible to define a cluster encompassing the whole economy (Padmore and Gibson, 1998, p. 630). Enright (1990, pp. 4–3) also argues that highly aggregated classifications cannot be used to develop an index of geographic concentration since the true pattern is distorted by aggregation, which tends to ‘average out’ industry location. Such concerns

48 Clusters and Competitive Advantage

clearly favour a disaggregated data set, so this study uses a data set that covers all Turkish industries at the four-digit (ISIC) level. A summary of the results is presented in the next section.6

Spatial patterns in Turkish industry In the present study,7 geographic concentration indices (C4EMP, C8EMP and LQs) are calculated for all Turkish provinces and the 231 sectors for which the necessary data are available at the four-digit level.8 The top 100 Turkish industries, as ranked by the C4EMP indices, are listed in Table 3.2, while Table 3.3 lists the 25 least geographically concentrated Turkish industries.9 Since it is not possible to include the full list of the LQs calculated for all industries and provinces due to space limitations, only the top five industries are reported for each province (see Appendix 1). However the full list of LQs that are greater than 1 will be presented for the provinces and clusters chosen for detailed examination in each of the relevant chapters. Finally, Tables 3.4 and 3.5 show the proportion of the total Turkish population in the four (C4POP) and eight (C8POP) most populated provinces, plus the cumulative totals. The population figures provide a base line to compare the geographic concentration of Turkish industry. Analysis of the information provided in the tables shows that on average Turkish industries are far more geographically concentrated than is the population (with a p value of 0.0191). If the C4EMP and C4POP values are compared, it can be seen that 225 of the 231 industries are more geographically concentrated than is the population. A comparison of Table 3.2 and Table 3.3 provides some rough information on the nature of the most and least geographically concentrated industries. Among the highly concentrated industries are those dominated by a small number of firms, such as the manufacture of tobacco products, which is located in a single province (Izmir). The manufacture of watches and clocks (Eskisehir) and financial intermediation (Istanbul) are highly concentrated despite there being a larger number of firms. Among the least concentrated industries are wholesaling and retailing, plus restaurants, hairdressers, the manufacture of cement and builders’ carpentry. Figure 3.3 shows a selected number of highly concentrated industries. To conclude this section we shall briefly review the evolution of industrial activity in Turkey. In the 1970s the growth rate attained in the less developed regions of eastern Turkey remained below the national average, whereas those achieved in the relatively more developed parts of the country enjoyed a rise. In this decade the Istanbul metropolitan area accounted for almost 45 per cent of national employment, while the relevant figures for the Izmir, Adana and Ankara metropolitan areas were 11 per cent, 5.5 per cent and 5.5 per cent respectively (Eraydin, 2002a).10 In the more liberal environment that prevailed in the 1980s, Istanbul enhanced its position as the top location of choice for Turkish industrial establishments,

49 Table 3.2

Top 100 Turkish industries, by C4EMP

Rank

ISIC

Industry

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

1010 1030 1542 1600 2213 2421 2430 3710 4020 4550 5251 6210 6591 7122 7413 7495 8022 6412 7492 7230 3330 7320

23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

6599 7422 3150 7111 5190 7220 6712 6110 6220 2310 9211 3220 3000 2412 5150 6420 3694 7290 9213 3320 1110 7210 2927 1712 1730

Mining and agglomeration of coal Extraction and agglomeration of peat Manufacture of sugar Manufacture of tobacco products Publishing of recorded media Manufacture of pesticides and other agrochemical products Manufacture of man-made fibres Recycling of metal waste and scrap Manufacture of gas; distribution of gaseous fuels Hiring out of construction or demolition equipment Retail sale via mail order houses Scheduled air transport Financial leasing Hiring out of construction and civil engineering machinery Market research and public opinion polling Packaging activities Technical and vocational secondary education Courier activities other than the national postal service Investigation and security activities Data processing Manufacture of watches and clocks Research on and experimental development of SSH (Social Sciences and Humanities) Other financial intermediation n.e.c. (not elsewhere classified) Technical testing and analysis Manufacture of electric lamps and lighting equipment Hiring out of land transport equipment Other wholesale Software consultancy and supply Security dealing activities Sea and coastal water transport Non-scheduled air transport Manufacture of coke oven products Motion picture and video production and distribution Manufacture of television and radio transmitters, etc. Manufacture of office and computing machinery Manufacture of fertilizers and nitrogen compounds Wholesale of machinery, equipment and supplies Telecommunications Manufacture of games and toys Other computer-related activities Radio and television activities Manufacture of optical and photographic equipment Extraction of crude petroleum and natural gas Hardware consultancy Manufacture of weapons and ammunition Finishing of textiles Manufacture of knitted and crocheted fabrics and articles

CR4EMP 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 0.9935 0.9921 0.9906 0.9880 0.9744 0.9715 0.9712 0.9677 0.9630 0.9613 0.9570 0.9569 0.9565 0.9535 0.9500 0.9447 0.9356 0.9353 0.9333 0.9321 0.9265 0.9264 0.9264 0.9262 0.9225 0.9156 0.9146 0.9099 0.9092 0.9086

50 Table 3.2 (Continued) Rank

ISIC

Industry

48

6719 Activities auxiliary to financial intermediation n.e.c.

0.9065

49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94

9303 8021 1723 5131 2423 1912 2892 8010 3693 3130 3512 6601 2919 3691 7414 3210 1711 9220 1820 3230 7010 2413 2101 3699 7493 2912 2109 5142 2102 3110 6120 6304 1310 5149 3592 7430 3312 2610 2926 1532 3190 3591 2422 5139 2929 2923

0.9032 0.8900 0.8851 0.8796 0.8780 0.8775 0.8710 0.8701 0.8675 0.8657 0.8642 0.8618 0.8602 0.8506 0.8503 0.8500 0.8463 0.8401 0.8396 0.8383 0.8381 0.8342 0.8338 0.8330 0.8323 0.8288 0.8275 0.8232 0.8225 0.8183 0.8165 0.8163 0.8094 0.8094 0.8066 0.8059 0.8056 0.7964 0.7883 0.7879 0.7859 0.7851 0.7829 0.7786 0.7782 0.7769

95

2922 Manufacture of machine tools

Funeral and related activities General secondary education Manufacture of cordage, rope, twine and netting Wholesale of textiles, clothing and footwear Manufacture of pharmaceuticals, medicinal chemicals etc. Manufacture of luggage, handbags etc. Treatment and coating of metals; mechanical engineering Primary education Manufacture of sports goods Manufacture of insulated wire and cable Construction and repair of pleasure and sporting boats Life insurance Manufacture of general purpose machinery Manufacture of jewellery and related articles Business and management consultancy activities Manufacture of electronic valves, tubes etc. Preparation and spinning of textile fibres News agency activities Dressing and dyeing of fur; manufacture of fur articles Manufacture of television and radio receivers, etc. Real estate activities with own or leased property Manufacture of plastics in primary form Manufacture of pulp, paper and paperboard Other manufacturing n.e.c. Building cleaning activities Manufacture of pumps, compressors, taps and valves Manufacture of articles of paper and paperboard Wholesale of metals and metal ores Manufacture of corrugated paper, paperboard etc. Manufacture of electric motors, generators and transformers Inland water transport Travel agencies, tour operators etc. Mining of iron ore Wholesale of intermediate products, waste and scrap Manufacture of bicycles and invalid carriages Advertising Manufacture of instruments for measuring etc. Manufacture of glass and glass products Manufacture of machinery for textile and leather production Manufacture of starches and starch products Manufacture of other electrical equipment n.e.c. Manufacture of motorcycles Manufacture of paints, varnishes etc. Wholesale of other household goods Manufacture of other special purpose machinery Manufacture of machinery for metallurgy

CR4EMP

0.7759

Industrial Clusters in Turkey 51 96 97 98 99 100

2511 2913 2732 4520 6309

Table 3.3

Manufacture of rubber tyres and tubes Manufacture of bearings, gears, gearing and driving elements Casting of non-ferrous metals Building of complete constructions or parts; civil engineering Activities of other transport agencies

0.7704 0.7703 0.7688 0.7661 0.7641

The least concentrated Turkish industries, by C4EMP

Rank

ISIC

Industry

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

5520 5232 5220 5233 9302 2811 5234 7494 2930 1533 5260 2695 2921 5211 8520 2010 1541 5020 5050 5040 6022 1410 1531 4010 2022

Restaurants, bars and canteens Retail of textiles, clothing, footwear and leather goods Retail of food, beverages and tobacco in specialist shops Retail of household appliances, articles and equipment Hairdressing and other beauty treatments Manufacture of structural metal products Retail of hardware, paint and glass Photographic activities Manufacture of domestic appliances n.e.c. Manufacture of prepared animal feeds Repair of personal and household goods Manufacture of concrete, cement and plaster articles Manufacture of agricultural and forestry machinery Non-specialized retail shops Veterinary activities Sawmilling and planing of wood Manufacture of bakery products Maintenance and repair of motor vehicles Retail of automotive fuel Sale, maintenance and repair of motorcycles and parts Non-scheduled passenger land transport Quarrying of stone, sand and clay Manufacture of grain mill products Production, collection and distribution of electricity Manufacture of builders’ carpentry and joinery

CR4EMP 0.3959 0.3892 0.3851 0.3830 0.3745 0.3743 0.3657 0.3615 0.3580 0.3545 0.3459 0.3397 0.3278 0.3270 0.3168 0.3122 0.3095 0.3093 0.3018 0.2967 0.2780 0.2589 0.2547 0.2542 0.2026

whose export orientation increased enormously. In general, regions that could survive the test of international competition prospered, while the less developed areas became even less attractive. In the 1990s the share of the Istanbul metropolitan area in national employment rose to almost 50 per cent and, as could be expected, diseconomies of urbanization began to emerge. As a result, export-oriented industries chose to move to nearby areas – Tekirdag in particular. In the last two decades, several new locations, including Denizli, Gaziantep, Kayseri, Konya and Çorum, have attained impressively high growth rates. Overall, however, Istanbul and its

52 Clusters and Competitive Advantage Table 3.4

The most populated Turkish provinces

Province

Population

Istanbul Ankara Izmir Konya Adana Bursa Içel Samsun Manisa Antalya Hatay Diyarbakir Gaziantep Sanliurfa C4POP C8POP

7195773 3236378 2694770 1752658 1549233 1546327 1267253 1161207 1154418 1132211 1109754 1096447 1010396 1001455

Table 3.5

0.127420 0.057308 0.047718 0.031035 0.027433 0.027382 0.022440 0.020562 0.020442 0.020049 0.019651 0.019415 0.017892 0.017733 0.263481 0.361298

Cumulative C4EMP totals for the industries examined

C4EMP range 1.000 0.9–0.999 0.8–0.899 0.7–0.799 0.6–0.699 0.5–0.599 0.4–0.499 0.3–0.399 0.2–0.299

Share of total

Number of industries

Cumulative total

17 32 36 40 30 30 20 20 6

17 49 85 125 155 185 205 225 231

environs have historically been the leading location for industrial activity in Turkey, followed by Izmir, Ankara, Bursa and Adana. In fact the geographical concentration of economic activity in major metropolitan areas and regional centres has become more pronounced in recent years (ibid.).

Geographic concentration and competitiveness Although the information provided in Tables 3.1–3.3 is helpful, a more detailed analysis is needed to establish a link between geographic concentration and international competitiveness. Our data set has allowed us to conduct simple statistical tests to investigate whether internationally competitive

Istanbul Textiles/apparel Leather/fur Jewellery Glass Financial services Media and entertainment

Ankara Construction Furniture

Bolu Leather tanning

Bartin Ship building Sinop Fish and fish products

Bursa Textiles Furniture

Tekirdag Wine

Trabzon Fish products Ship building

Çanakkale Fish and fish products Ceramics Kütahya Ceramics Usak Leather tanning Carpets

Adiyaman Carpets

Mugla Construction and repair of boats Tourism Denizli Textiles Antalya Tourism

Gaziantep Carpets

Isparta Carpets Afyon Ceramics

Nevsehir Ceramics Wine

Kayseri Carpets Furniture

Figure 3.3 Selected examples of highly concentrated industries in Turkey

53

54 Clusters and Competitive Advantage Table 3.6

International competitiveness, fuzzy membership categories

Raw score in (%)

Membership position

Fuzzy membership score (F)

>2.08 1.05–2.08 0.53–1.04 0.52 0.26–0.51 0.13–0.25 0.8 0.6–0.8 0.6 0.4–0.6