INDUSTRIAL SPECIALIZATION AND REGIONAL CLUSTERS IN THE TEN NEW EU MEMBER STATES

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1 Paper to be presented at the DRUID Summer Conference 2006 on KNOWLEDGE, INNOVATION AND COMPETITIVENESS: DYNAMICS OF FIRMS, NETWORKS, REGIONS AND INSTITUTIONS Copenhagen, Denmark, June 18-20, 2006 Track E: Innovative Regions and Growth INDUSTRIAL SPECIALIZATION AND REGIONAL CLUSTERS IN THE TEN NEW EU MEMBER STATES Örjan Sölvell, Christian Ketels, Göran Lindqvist Center for Strategy and Competitiveness, Stockholm School of Economics Örjan Sölvell P.O. Box 6501, SE Stockholm, Sweden Phone: Fax: April 2006 ABSTRACT. This paper focuses on industrial specialization patterns within the ten new EU member states (EU10). Official employment statistics are analyzed across the industry dimension through 38 cluster sectors and across the geographical dimension through 41 NUTS 2 regions. Industrial specialization is analyzed in terms of absolute number of employees, specialization, and dominance. 19 three-star regional clusters, which display high values for each of these three parameters, are identified. The analysis also suggests that regional concentration in EU10 is lower than in the USA, and slightly lower than in the old EU member states. Keywords: Clusters, Industrial specialization, Regions, Employment, Europe JEL code: R12 - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade

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3 Industrial Specialization and Regional Clusters in the Ten New EU Member States ÖRJAN SÖLVELL, CHRISTIAN KETELS AND GÖRAN LINDQVIST Center for Strategy and Competitiveness, Stockholm School of Economcis, PO Box 6501, S Stockholm, Sweden ABSTRACT. This paper focuses on industrial specialization patterns within the ten new EU member states (EU10). Official employment statistics are analyzed across the industry dimension through 38 cluster sectors and across the geographical dimension through 41 NUTS 2 regions. Industrial specialization is analyzed in terms of absolute number of employees, specialization, and dominance. 19 three-star regional clusters, which display high values for each of these three parameters, are identified. The analysis also suggests that regional concentration in EU10 is lower than in the USA, and slightly lower than in the old EU member states. INTRODUCTION AND SUMMARY Recent research has shown that regional clusters of interlinked industries, organizations and institutions act as a fertile ground for innovation, entrepreneurship and upgrading of competitive advantage among firms (Porter 2003a; Porter 2001). This empirical paper focuses on industrial specialization patterns within Europe, with particular attention to regional specialization within the ten new member nations (EU10). Regional clusters in this paper are defined as bundles of co-located industries of a certain size and degree of specialization. Sectoral definitions follow the methodology used by Porter (2001) which is based on geographical specialization patterns with the U.S. economy. Within the traded industries (excluding natural-resource industries, local industries and public authorities) 38 cluster sectors categories are identified. Regions are defined according to the European NUTS 2 classification level, with a total of 41 regions within EU10. Regional cluster size and degree of specialization is measured along three dimensions: absolute number of employees (> jobs is used as cut-off for a regional cluster), degree of specialization (regional sector employment is at least two times expected levels) and degree of regional market labor dominance (> 3% of total employment in a particular sector). Each of these three measures of cluster size and regional specialization are classified with a star. This procedure identifies the following regional clusters within the 41 regions of EU10: 19 three-star regional clusters (above the cut-off on all three dimensions) 92 two-star regional clusters (two out of three dimensions) 313 one-star regional clusters (one out of three dimensions) The largest regional clusters in the EU10 include Warszawa Financial Services with an employment of and Warszawa Transportation & Logistics cluster with jobs. Budapest also has a large cluster in Transportation & Logistics. Katowice (PL) includes jobs in Processed Food. The Ostrava (CZ) Metal Manufacturing cluster includes over jobs. The Kosice (SK) Metal Manufacturing cluster is the largest cluster in Slovakia. Lithuania s largest cluster is in Heavy Construction Services, and Latvia s and Estonia s in Transportation & Logistics. Slovenia has a large cluster in Metal manufacturing. Cyprus and Malta have their main clusters in Hospitality & Tourism. Overall, the economies of EU10 exhibit a pattern of geographical concentration (Gini index) close to a random distribution, i.e. the process of industrial specialization and concentration is in a very early phase. Comparisons made with the U.S. shows that American levels of geographical concentration are much more pronounced than in the EU10 (and also in the EU15). In a few cases IT, Bio-pharmaceuticals, Communications equipment where the total size of the cluster is small and there is little historical legacy in Eastern Europe, the EU10 exhibits more geographical specialization than the EU15. If the European Union (EU) is to become the most competitive and innovative region in the world economy, policy makers at different levels must involve clusters in their thinking. Data on clusters in Europe

4 2 Sölvell, Ketels & Lindqvist is thus of vital importance in the process of improving competitiveness policies. To date, no systematic mapping of European clusters has been done. This paper outlines a new, multi-dimensional methodology for statistical mapping of one-two-three star regional clusters, and presents data on all regional clusters within EU10. CONCEPTUAL FRAMEWORK AND CLUSTER MAPPING APPROACH Our approach in the mapping of clusters in the new EU member nations is based on the conceptual framework of competitiveness and clusters developed by Professor Michael E. Porter over the last two decades. 1 In this framework, the ability of regions to support high and rising levels of prosperity the final goal of economic policy is linked to the conditions at the location that enable companies to operate with high levels of productivity and innovation. While these conditions are also affected by the more general context defined by macroeconomic, political, legal, and social conditions at the national level, the focus of the framework is on the microeconomic foundations present at the regional level. As regions within a country tend to differ markedly in terms of economic performance, these microeconomic foundations require more attention than more traditional economic analyses have attached to them. A core element defining the quality of the microeconomic business environment is the presence of clusters. Clusters are groups of companies and institutions co-located in a specific geographic region and linked by interdependencies in providing a related group of products and/or services 2 (geographic region in this paper equals the NUTS 2 regional classification level). Because of the proximity among them both in terms of geography and of activities cluster constituents enjoy the economic benefits of several types of positive location-specific externalities. These externalities include, for example, access to specialized human resources and suppliers, knowledge spillovers, pressure for higher performance in headto-head competition, and learnings from the close interaction with specialized customers and suppliers. Clusters differ in many dimensions: the type of products and services they produce, the locational dynamics they are subject to, their stage of develop- 1 Porter (1996, 1990). 2 This definition builds on Porter (1996, 1990). For discussions see Doeringer/Terkla (1995), Rosenfeld (1997), and European Commission (2002). ment, and the business environment that surrounds them, to name a few. At a first level, clusters can be classified by the type of product and/or services they provide. There are clusters in automotive, in financial services, in tourism, in ceramic tiles, and many more. Our data set includes 38 such industry clusters. Early discussions focused on clusters with international importance and leading world market positions, such as the financial clusters in New York and London, the media cluster in Hollywood, the IT cluster in Silicon Valley, the automotive clusters in Southern Germany and Detroit, the telecom clusters in Stockholm and Finland, and the textile/fashion clusters in Northern Italy. This characterization of clusters has discouraged many regions with no realistic chance of achieving a similar level of performance in their clusters. More recent research indicated, however, that even within a given field there is room for many different successful clusters, each taking a unique, individual role. Clusters are differentiated by their specialization in a particular stage of their field s value chain, by their focus on specific geographic areas, or by targeting selected customer needs or market segments. Take the example of footwear: 3 Northern Italy is home to a very successful, high wage cluster, serving the world market and focusing on design, brand building, and high value production. Portugal is home to another cluster, focused on footwear manufacturing and short production runs serving fashionconscious markets in Europe. Timisoara, Romania, is emerging as another cluster, functioning as an offspring of the Italian cluster focused on production in the low- to medium value product range. And then there are the major clusters in Asia, China and Vietnam in particular, focused on high-volume contract manufacturing of low value footwear products for the U.S. and European markets respectively. While some footwear clusters grow and specialize over time, others diminish and close down, and as we will see in this paper, many of them located in the EU10. At a second level, clusters can be classified by the type of locational dynamics their constituent industries are subject to. 4 Industries differ by the extent to which they can choose locations. Two main reasons can limit that choice: many industries are tied to their location by the need to be close to their customers. These local industries are serving only local markets 3 This example draws on research by Claas van der Linde and student teams at Harvard Business School. 4 See Porter (2003a).

5 Industrial Specialization and Regional Clusters in the Ten New EU Member States 3 and are distributed across space roughly according to population (e.g. retail stores). They might cluster in a more narrow geographic sense like a part of a city - mainly due to complementarities in attracting customers - but these effects are not strong enough to influence the locational patterns across regions. Other industries are tied to their location by the need to be close to natural resources. These natural resourcedependent industries serve global markets and are concentrated across space according to the presence of natural resources. Finally, there are many industries that are essentially free to choose their location according to the quality of the cluster-specific business environment. These traded clusters serve markets in many regions and countries, and concentrate across geographies. The cluster effects in these industries are strong and their presence is a key part of the attractiveness of a specific location. Understanding the differences between these types of industries is important, because it affects the types of policies that are relevant to upgrade them. At a third level, clusters can be classified by the stage of development they have reached. The stage of development depends on two dimensions: First, it depends on the quality of the external business environment the cluster operates in. Researchers have looked at clusters in less developed economies 5 as well as in less developed regions of advanced economies, such as rural regions 6 or inner cities 7. This discussion is concerned with the question of whether clusters occur in environments that otherwise do not allow for a high level of sophistication in economic activities. Most of the theoretical literature suggests that clusters are a factor at every stage of economic development but that in weaker environments clusters will tend to be weaker and more narrow as well. Second, the stage of development depends on the progress the cluster has made in mobilizing the potential of its business environment through active cooperation and other internal activities 8. Researchers have focused on the role of cultural factors, institutions, and individual leadership. There is strong view in the literature that cluster dynamics do not always occur automatically, but that they depend on and can be reinforced by purposeful action. Clusters are important because they create tangible economic benefits. The benefits of a cluster come in three dimensions: 9 Companies can operate with a higher level of efficiency, drawing on more specialized assets and suppliers with shorter reaction times than they could in isolation. Companies and research institutions can achieve higher levels of innovation. 10 Knowledge spillovers and the close interaction with customers and other companies create new ideas and provide intense pressure to innovate, while the cluster environment lowers the cost of experimenting. The level of business formation tends to be higher in clusters. Start-ups are more reliant on external suppliers and partners, all of which they find within a cluster. Clusters also reduce the cost of failure, as entrepreneurs can fall back on local employment opportunities in the many other companies in the same field. Cluster Mapping Approach Cluster mapping is used as a term for describing the actual presence of clusters across geographies. There are a number of ways to develop definitions of clusters that can be used in empirical work. One approach relies on the ad-hoc allocation of industries to cluster categories. Based on the input of industry experts this approach can yield useful results. However, it is always biased to subjective judgments and makes it hard to create cluster definitions that are applicable across larger geographic regions and especially across all industries. A more systematic approach focuses on the critical importance of linkages between companies as the transmission mechanisms that enable cluster effects to occur. Researchers thus rely on cluster initiatives and/or input-output tables indicating supplier relationships between companies to identify the industries in a specific location that should be included in a cluster. While this approach works well in individual cases, it is again impractical for large empirical studies that attempt to create inclusive and comprehensive data sets of cluster presence across clusters and 5 See USAID (2003) and Fairbanks/Lindsay (1997). 6 See Landabaso (2001), Rosenfeld (2002b), and Porter/Ketels/Miller (2003). 7 See and Porter (1998b). 8 Enright (1996). 9 See Porter (1998a). 10 Because of the critical importance of innovation for advanced economies innovation clusters have become a particularly popular topic. See OECD (2001) and Monitor Company, Council on Competitiveness, and Michael Porter (2001).

6 4 Sölvell, Ketels & Lindqvist regions. In response, Professor Porter in 2000 launched a research effort at the Institute for Strategy and Competitiveness, Harvard Business School 11 to create a systematic mapping of clusters that would cover the entire economy and would be applicable across geographic regions. He could draw on detailed data on employment and wage levels at the regional level available for the entire U.S. He first identified industries that concentrate across geographies in terms of employment. This allowed local industries to be separated from traded industries competing across regional boundaries. Traded clusters in the U.S. account on average for about a third of regional employment, but record significantly higher wages, productivity levels, and innovation rates than the average of the economy. 12 Local industries serving local markets and present at similar densities across regions account for about two-thirds of regional employment, with below economy-wide average levels of wages, productivity, and innovation. Finally, natural resource-dependent industries accounts for less than one percent of U.S. employment but can be more significant in individual regions. To then identify cluster categories, Porter calculated the correlation of employment by industries across locations. Based on these correlations, industries were grouped into cluster and sub-cluster categories. Industries were assigned to one cluster as their primary association, giving rise to what we call narrow cluster definitions (the type used in this paper). Many industries, however, were also associated with other clusters so they created broad cluster definitions that took account of these relations as well. 13 These broad clusters show the overlaps that exist between clusters and allow new clusters to develop out of existing ones. With these definitions, the leading cluster locations in a cluster category could be identified and compared in size, profile, and performance. Outside of the U.S., these definitions of clusters have been applied in Canada 14 and in Sweden 15 to map cluster patterns in those countries. These efforts make the assumption that the U.S. economy provides a widely applicable benchmark in terms of the cluster effects that can be expected to occur among individual industries. In the United Kingdom, 16 a related effort assigned industries to specific clusters based mainly on a qualitative process of interviews. Regional clusters were then identified based on location quotients the national employment share of the region in an industry or cluster relative to the region s overall national employment share. Other empirical efforts have been done or are underway elsewhere, although most of them have either had a smaller geographic focus or used a less systematic approach to identify clusters. 17 A list of the 38 cluster sectors is given in Table 1. REGIONS AND INDUSTRY CLUSTERS WITHIN EU10 The analysis in this paper is based on employment data and covers three dimensions: Geography (national and regional level), industry (individually as industries or combined as cluster sectors), and regional clusters as the combination of geography and industry: National economies covering the ten nations: Cyprus, The Czech Republic, Estonia, Hungary, Lithuania, Latvia, Malta, Poland, Slovenia, and Slovakia. Regional economies covering a total of 41 regions (as specified by the NUTS 2 system) within the EU10. In the case of the six small nations - Cyprus, Estonia, Lithuania, Latvia, Malta, and Slovenia - the nation equals the NUTS 2 region. Industries as defined by the NACE 1.1 at three and four digit levels. In EU10, 6% of employment is found in public administration, 44% in local industries, and 13% in natural resource driven industries. The remaining 37% are found in traded industries and are the focus of this paper. They form cluster sectors covering 38 grouped sets of industries across the space of the EU See the web site of the Institute for Prosperity and Competitiveness for details, 11 For a detailed description of the methodology used see 12 This typology has been developed in Porter (2003a). 13 Note that these broad cluster definitions imply double-counting of industries that in their locational patterns show significant correlation with more than one cluster. 15 See Lindqvist, Malmberg & Sölvell (2003). 16 DTI (2001) 17 Interesting examples of cluster mapping can be found, for example, in Japan (regional cluster mapping under way) and Norway (clusters identified nationally but not broken down to regions). See Yamawaki (2002) and Reve/Jakobsen (2001).

7 Industrial Specialization and Regional Clusters in the Ten New EU Member States 5 Table Clusters Sectors Cluster sector Aerospace Analytical Instruments Apparel Automotive Building Fixtures, Equipment & Services Business Services Chemical Products Communications Equipment Processed Food Agricultural Products Distribution Services Education & Knowledge Creation Entertainment Heavy Machinery Financial Services Fishing & Fishing Products Footwear Forest Products Furniture Heavy Construction Services Hospitality & Tourism Information Technology Jewellery & Precious Metals Leather Products Lighting & Electrical Equipment Construction Materials Medical Devices Metal Manufacturing Oil & Gas Products and Services Biopharmaceuticals Plastics Power Generation and Transmission Production Technology Publishing & Printing Sporting, Recreational & Children's Goods Textiles Tobacco Transportation & Logistics Examples of industries Aerospace industry, aerospace engines Measurement instruments, process control Clothes Motor vehicles, components Kitchen furnishing, plaster Management consultancy, rental of office machinery Chemicals, nuclear fuels, industrial gases TVs, Cable, telephony equipment Beer, dairies, glass packages/wrapping Sugar, agricultural services, alcoholic drinks Mail order, wholesale trading Universities, libraries Video- and music recording, sport events Forest machinery, tractors, locomotives Banks, insurance companies Fishing, hunting Shoes Paper machines, pulp Furniture, laminated boards Construction businesses, rental of construction machineries Hotels, taxies, amusement parks Electronic components, computer manufacturing Jewellery, cutleries Bags, furs Lamps, electricity distribution's equipment Scrap, ceramic sanity fixtures Medical equipment, wheelchairs Rolling mills, casting, tools, screws Refineries Pharmaceuticals Plastics, colours Generators, isolators Bearings, tanks, machine tools Publishing services, printing Bicycles, toys Fabrics Cigarettes, snuff Inventories, air transports Regional clusters covering each of the 38 sectors within the borders of one of the 41 regions This means that the term cluster sector refers here to one group of industries without any geographical connotation, while regional cluster is a combination of industry (a cluster sector) and geography (a NUTS 2 region). All data has been collected at EUROSTAT, except in the case of Estonia, Latvia, Slovenia and Cyprus where data is drawn from the respective National Statistics Institutes. For an overview of the methodology used in this paper, please refer to Appendix 1. Overview of the 41 Regions The 41 regions covered in this paper are specified in a map below. The size of each region varies from Malta with a population of some to Warszawa with more than 5 million inhabitants. For convenience, in this paper we have assigned a label for each NUTS 2 region instead of using the official NUTS names. For countries, we are using the English name of the country, and for other regions we refer to the largest city in he region. The map of the 41 regions is shown in Figure 1. For a list, see Appendix 2. General Levels of Industrial Specialization Within EU10 In advanced economies, economic activity tends to concentrate around metropolitan areas and special-

8 6 Sölvell, Ketels & Lindqvist Figure 1. EU10 Divided into 41 Regions According to NUTS 2 ized industrial districts, i.e. regional clusters. Driving forces of concentration involve both enhanced efficiency (e.g. economies of scale and lowered transportation costs) and enhanced innovation. Some clustering is more general in nature, whereas other clustering is more technology bound. An important measure to describe the process of geographical concentration is the Gini coefficient (a short note on sources is presented below Table 2). A Gini coefficient of 0 implies that economic activity in a certain industry is spread out proportionally among a set of regions according to size of each region. The more geographically concentrated the industry is the higher the Gini value. Purely random patterns of geographical dispersion lead to measures of around 0.3. Therefore, it is fair to say that clusters within advanced economies with some mobility of factors and firms between regions should reach Gini values of above 0.3. Our data shows that the U.S. economy as measured through 50 regions (States) and the 38 industry clusters reach a consistent pattern of Gini values of around The EU10 based on 41 regions exhibits Gini coefficients between (see Table 4). Interestingly enough, within EU15 where the four freedoms have been in place for quite some time, regional clustering is closer to the EU10 levels than the U.S. The average Gini in EU15 is 0.39, ranging from 0.84 in Footwear to 0.17 in Transportation & Logistics and Heavy Construction Services. In a few cases IT, Biopharmaceuticals, Communications equipment the EU10 is more geographically specialized than EU15. The most concentrated clusters in the EU10 include Tobacco (0.75) and Aerospace & Defence (0.72), both of which are small in absolute size. For natural reasons Fishing (0.66) is also concentrated. Oil & Gas products and services (0.61), Footwear (0.59), Analytical instruments (0.55), Lighting & Electrical Equipment (0.53) and IT (0.51) are other concentrated clusters. In traditional clusters like Textiles, Jewellery and Precious metals, Furniture, Forest products, Agricultural products and Processed food the EU10 has a long way to go to reach U.S. levels of concentration. The EU10 economy exhibit extremely dispersed patterns for industry clusters such as Heavy Construction Services, Transportation & Logistics, Distribution Services and Building Fixtures, Equipment & Services. Processed Food and Hospitality & Tourism are dispersed industry clusters. A list of Gini values for each cluster sector in the US, EU15 and EU10 is given in Table 2.

9 Industrial Specialization and Regional Clusters in the Ten New EU Member States 7 Table 2. Overall Levels of Geographical Specialization (Gini coefficients) Cluster sector US50 EU15 (7) EU10 Cluster sector US50 EU15 (7) EU10 Fishing and Fishing Products cont. Footwear Construction Materials Oil and Gas Products and Services Medical Devices Aerospace Vehicles and Defence Chemical Products Textiles Metal Manufacturing Aerospace Engines 0.72 N/A N/A Production Technology Jewellery and Precious Metals Analytical Instruments Tobacco Hospitality and Tourism Motor Driven Products 0.57 N/A N/A Processed Food Information Technology Education and Knowledge Creation Apparel Leather Products Prefabricated Enclosures 0.54 N/A N/A Plastics Furniture Transportation and Logistics Automotive Entertainment Heavy Machinery Power Generation and Transmission Biopharmaceuticals Publishing and Printing Forest Products Building Fixtures, Equipm. and Serv Agricultural Products Financial Services Lighting and Electrical Equipment Business Services Sporting, Recr. and Childr. Goods Distribution Services Communications Equipment Heavy Construction Services Average Median Standard deviation Sources: EU15 is measured on 119 NUTS 2 regions in 7 nations (France, Spain, Italy, Portugal, Germany, Luxembourg, Austria). EU10 is measured on all 41 NUTS 2 regions. U.S. Data from ISC Cluster Project at Harvard. EU data from LFS Data from Eurostat and National Statistics Institutes. U.S. data is for 2000 (50 States), EU data for 2003/2004. Cluster sectors in EU10 In the EU10 total employment in the 38 cluster sectors ranges from Processed Food with almost 1 million jobs to Tobacco with just under jobs (Table 3). Total employment in cluster sectors in the EU10 is around 10 million jobs. Some cluster sectors are more service oriented for example Hospitality & Tourism, which is the fifth largest sector, ahead of sectors such as Automotive and twice the size of IT. Table 3. Clusters Sectors in EU10 (2004) Cluster sector Employment Cluster sector Employment Processed Food Heavy Machinery Heavy Construction Services Chemical Products Transportation and Logistics Communications Equipment Financial Services Agricultural Products Hospitality and Tourism Plastics Metal Manufacturing Biopharmaceuticals Education & Knowledge Creation Lighting & Electrical Equipment Building Fixtures, Equipment & Services Construction Materials Apparel Footwear Automotive Power Generation & Transmission Textiles Jewellery & Precious Metals Forest Products Fishing & Fishing Products Entertainment Oil & Gas Products and Services Information Technology Medical Devices Furniture Leather Products Production Technology Sporting, Recreational & Children's Goods Publishing & Printing Analytical Instruments Distribution Services Aerospace Business Services Tobacco Sum

10 8 Sölvell, Ketels & Lindqvist REGIONAL CLUSTERS IN EU10 Regional clusters are defined using three measures: the absolute size in terms of employment, the degree of specialization (size relative to expected levels given the size of the region), and the degree of dominance in the region (a sector s share of total regional employment within the cluster sector). A regional cluster can thus score on one, two or Table 4. The 50 Largest Regional Clusters in EU10 Region Cluster sector Employment Specialization Quotient % of Total Employment Total Employment of Region Warszawa; PL Financial Services % Katowice; PL Processed Food % Warszawa; PL Transportation & Logistics % Lodz; PL Apparel % Poznan; PL Processed Food % Warszawa; PL Processed Food % Ostrava; CZ Metal Manufacturing % Warszawa; PL Education & Knowledge Creation % Lodz; PL Processed Food % Lithuania Heavy Construction Services % Budapest; HU Transportation & Logistics % Katowice; PL Metal Manufacturing % Katowice; PL Automotive % Budapest; HU Financial Services % Bydgoszcz; PL Processed Food % Kraków; PL Processed Food % Budapest; HU Education & Knowledge Creation % Katowice; PL Heavy Construction Services % Gdansk; PL Transportation & Logistics % Warszawa; PL Hospitality & Tourism % Lithuania Apparel % Katowice; PL Education & Knowledge Creation % Lithuania Transportation & Logistics % Lithuania Processed Food % Poznan; PL Building Fixtures, Equipm. & Serv % Katowice; PL Transportation & Logistics % Lublin; PL Processed Food % Latvia Transportation & Logistics % Brno; CZ Heavy Construction Services % Kraków; PL Heavy Construction Services % Lithuania Education & Knowledge Creation % Budapest; HU Heavy Construction Services % Kraków; PL Education & Knowledge Creation % Katowice; PL Financial Services % Budapest; HU Hospitality & Tourism % Slovenia Metal Manufacturing % Praha City; CZ Financial Services % Warszawa; PL Heavy Construction Services % Liberec; CZ Automotive % Katowice; PL Hospitality & Tourism % Kosice; SK Metal Manufacturing % Szeged; HU Processed Food % Liberec; CZ Processed Food % Rzeszów; PL Processed Food % Poznan; PL Heavy Construction Services % Estonia Transportation and Logistics % Brno; CZ Processed Food % Lithuania Textiles % Budapest; HU Entertainment % Budapest; HU Information Technology %

11 Industrial Specialization and Regional Clusters in the Ten New EU Member States 9 three of these measures. Here, we will refer to regional clusters as one-, two- or three star clusters. A three star cluster has at least jobs, has an employment twice the size compared to expected levels given the size of the region (specialization quotient of 2 see discussion below), and has 3% or more of total employment in the region (this corresponds to roughly 10% of employment in the traded industries). Large Regional Clusters In total there are 314 regional clusters with jobs or more in EU10. The 50 largest regional clusters are presented in Table 4 above. Large Transportation & Logistics clusters are located in Warszawa (60 000), Budapest (47 000), Gdansk (40 000), Lithuania (39 000), Katowice (36 000), Latvia (34 000) and Estonia (30 000). Processed food is another large cluster sector. Poland clearly has the largest concentration of food processing clusters (Katowice, Poznan, Warszawa, Lodz, Bydgoszcz, Kraków, Lublin and Rzeszów) with almost employees. The Lithuanian cluster has some employees, and Szeged in Hungary and Liberc in the Czech Republic have some employees each. Leading Metal manufacturing clusters are found in Ostrava in the Czech Republic (52 000), Katowice in Poland (47 000), Slovenia (32 000) and Kosice in Slovakia (31 000). Leading Automotive clusters are located in Katowice (45 000), and the three Czech clusters Liberc (31 000), Praha region (26 000) and Plzen (15 000). Wroclaw (23 000) and Poznan (23 000) in Poland are other leading Automotive clusters. The Nitra cluster in Slovakia has employees. Heavy construction clusters are spread out, the largest being Lithuania with almost employees. Apparel and Textile clusters can be found in all regions. Large apparel clusters can be found in Poland (Lodz, Poznan, Katowice, Bydgoszcz, Warszawa and Wroclaw, all with over employees). Other clusters are in Lithuania (39 000), and Nitra and Kosice in Slovakia. Lithuania also has a large Textile cluster (29 000). The second largest is in Lodz and third largest in the EU10 is in Liberc in the Czech Republic. The Education & knowledge creation clusters are naturally centred in large cities. Warszawa has over employees. Budapest has around , Katowice , Lithuania , Kraków , and Praha city and Poznan both with around The two Baltic countries Latvia and Estonia have quite large Education & knowledge creation clusters. As an example, the University in Tartu in Estonia was established already in The smallest clusters are found in Cyprus with only and Malta with employees in Education & knowledge creation. Other city-oriented clusters include Financial services, Entertainment and Hospitality & tourism. The largest Financial services clusters are Warszawa (74 000), Budapest (43 000), Katowice (32 000) and Praha city (32 000). The same cities also have the largest entertainment clusters. Hospitality & tourism clusters can be found in all countries. The leading regional clusters in EU10 include: Warszawa, Budapest, Katowice, Lithuania, Gdansk, Kraków, Praha City, Poznan, Cyprus, and Slovenia. Specialized Regional Clusters Apart from employment size, cluster concentration can be measured by the specialization quotient (SQ) where a value of 1 for a certain cluster is the expected value given the size of a region. Values above 1 signal a regional specialization in a certain cluster. For example, a value of 2 means that the regional cluster has twice the expected level of employment. Table 5 below presents the 50 most specialized clusters. Typically small clusters such as Tobacco and Oil & Gas products and Services tend to have high SQs. SQ values also allows us to statistically detect clusters in smaller regions. Dominant Regional Clusters The most dominant regional clusters (measured as a cluster s share of total employment within the region) are presented in Table 6 below. For example, the Ostrava Metal Manufacturing cluster accounts for 10% of total employment in the region. Hospitality & Tourism in both Malta and Cyprus accounts for more than 6% of total regional employment. The use of the dominance measure allows us to detect clusters in smaller regions. Using the three dimensions of describing a regional cluster we have found 111 regional clusters satisfying at least 2 of the 3 criteria (size, specialization and dominance, see further Appendix 3). 19 of these regional clusters are rated three-star regional clusters; The Czech Republic has seven, Hungary four, Slovakia four, Poland two and Cyprus two.

12 10 Sölvell, Ketels & Lindqvist Table 5. The 50 Most Specialized Clusters According to Specialization Quotient Region Cluster sector Employment Specialization quotient % of Total Employment Total Employment of region Bratislava; SK Oil & Gas Products and Services % Bialystok; PL Tobacco % Gdansk; PL Fishing & Fishing Products % Nitra; SK Footwear % Praha City; CZ Aerospace % Malta Sporting, Recr. & Childr. Goods % Rzeszów; PL Aerospace % Estonia Oil and Gas Products & Services % Debrecen; HU Tobacco % Pécs; HU Leather Products % Estonia Fishing & Fishing Products % Slovenia Analytical Instruments % Ostrava; CZ Metal Manufacturing % Malta Information Technology % Nitra; SK Communications Equipment % Latvia Fishing & Fishing Products % Székesfehérvár; HU Information Technology % Szczecin; PL Fishing & Fishing Products % Györ; HU Lighting & Electrical Equipment % Malta Medical Devices % Praha Region, CZ Tobacco % Lublin; PL Tobacco % Györ; HU Information Technology % Székesfehérvár; HU Communications Equipment % Rzeszów; PL Oil & Gas Products and Services % Miskolc; HU Tobacco % Debrecen; HU Medical Devices % Olomouc; CZ Aerospace % Lodz; PL Apparel % Praha Region, CZ Aerospace % Liberec; CZ Jewellery & Precious Metals % Praha Region, CZ Automotive % Miskolc; HU Lighting & Electrical Equipment % Györ; HU Footwear % Pécs; HU Sporting, Recr. & Childr. Goods % Liberec; CZ Automotive % Debrecen; HU Footwear % Bydgoszcz; PL Analytical Instruments % Szczecin; PL Construction Materials % Malta Hospitality & Tourism % Gorzów Wielkopolski; PL Footwear % Bydgoszcz; PL Aerospace % Estonia Communications Equipment % Olomouc; CZ Power Gen. & Transmission % Cyprus Hospitality & Tourism % Warszawa; PL Tobacco % Budapest; HU Analytical Instruments % Praha City; CZ Entertainment % Budapest; HU Biopharmaceuticals % Kosice; SK Metal Manufacturing %

13 Industrial Specialization and Regional Clusters in the Ten New EU Member States 11 Table 6. The 50 Most Dominant Clusters Based on Share of Total Employment Region Cluster sector Employment Specialization quotient % of Total Employment Total Employment of region Ostrava; CZ Metal Manufacturing % Malta Hospitality & Tourism % Cyprus Hospitality & Tourism % Szeged; HU Processed Food % Malta Transportation & Logistics % Bratislava; SK Financial Services % Gdansk; PL Transportation & Logistics % Kosice; SK Metal Manufacturing % Lodz; PL Apparel % Bydgoszcz; PL Processed Food % Praha City; CZ Financial Services % Olsztyn; PL Processed Food % Estonia Transportation & Logistics % Cyprus Financial Services % Praha Region, CZ Automotive % Lodz; PL Processed Food % Liberec; CZ Automotive % Poznan; PL Processed Food % Brno; CZ Heavy Construction Services % Liberec; CZ Processed Food % Debrecen; HU Processed Food % Bialystok; PL Processed Food % Praha City; CZ Education & Knowlede Creation % Zilina; SK Heavy Construction Services % Bratislava; SK Education & Knowledge Creation % Rzeszów; PL Processed Food % Györ; HU Processed Food % Plzen, CZ Heavy Construction Services % Olomouc; CZ Heavy Construction Services % Malta Information Technology % Olomouc; CZ Metal Manufacturing % Estonia Heavy Construction Services % Praha City; CZ Transportation and Logistics % Szczecin; PL Processed Food % Lublin; PL Processed Food % Olomouc; CZ Processed Food % Praha Region, CZ Transportation and Logistics % Kosice; SK Heavy Construction Services % Székesfehérvár; HU Information Technology % Brno; CZ Processed Food % Katowice; PL Processed Food % Latvia Transportation and Logistics % Budapest; HU Transportation and Logistics % Székesfehérvár; HU Processed Food % Warszawa; PL Financial Services % Slovenia Metal Manufacturing % Szczecin; PL Transportation and Logistics % Cyprus Transportation and Logistics % Opole; PL Processed Food % Ústi nad Labem; CZ Heavy Construction Services %

14 12 Sölvell, Ketels & Lindqvist Three-star regional clusters include: Praha Region (CZ) Automotive Liberec (CZ) Automotive Székesfehérvár (HU) Automotive Györ (HU) Automotive Olomouc (CZ) Metal Manufacturing Ostrava (CZ) Metal Manufacturing Kosice (SK) Metal Manufacturing Székesfehérvár (HU) Information Technology Györ (HU) Information Technology Gdansk (PL) Transportation and Logistics Liberec (CZ) Textiles Lodz (PL) Apparel Kosice (SK) Apparel Cyprus Hospitality and Tourism Cyprus Financial Services Praha City (CZ) Financial Services Bratislava (SK) Financial Services Praha City (CZ) Education and Knowledge Creation Bratislava (SK) Education and Knowledge Creation From the data we can see that Automotive exhibit the most prominent regional clusters within EU10, including four three-star clusters in The Czech Republic and Hungary: Praha Region (CZ), Liberec (CZ), Székesfehérvár (HU), and Györ (HU). In addition two-star clusters are found in Plzen (CZ), Katowice (PL), Bratislava (SK) and Nitra (SK). Some of these clusters survive from the era of Soviet style planning, whereas some are new. In the Czech Republic regional clusters one can find Skoda, Citroen, Peugeot and Toyota as lead firms. Hungary includes Audi, Suzuki and Ignis. The two Slovakian clusters are home to VW, Audi, Citroen (planning stage) and Kia. Opel, Fiat and VW all manufacture cars in the Polish cluster. DISCUSSION AND CONCLUSION The phenomenon of regional industrial concentration, or agglomerations, has been studied for more than a century, but over the past couple of decades interest has increased both from academics and policy makers. However, our understanding for the phenomenon is hampered by theoretical and methodological obstacles. First, a range of theories have been presented, suggesting several potential economic benefits related to agglomeration. Examples of such effects include benefits derived from intra-industry specialization, inter-industry specialization, specialized labor pools, down-stream demand size, vertical transaction costs, search costs, exit barriers, rivalry, and knowledge spillovers. When testing these theories, it is important to note that some effects are best operationalized by using counts (absolute numbers) while others are more related to concentrations (relative numbers). For example, the benefits of a specialized labor pool is arguably primarily a function of the absolute number of persons, whereas the knowledge spillovers are also driven by the concentration of industry people. So to capture all facets of industrial agglomeration, we need to employ multiple measures, both absolute and relative. In this paper we have chosen to apply three separate measures. Second, the fact that different studies employ different measures of agglomeration makes it difficult to compare results between regions and industries. In this study we have tried to apply the same methodology across multiple industries and across ten countries. This kind of consistent methodology makes it possible, for example, to make maps indicating regional clusters for each of the studied cluster sectors. Figure 2 gives one example of such a map indicating regional clusters in Automotive. For future research, the aim is to extend this study in several dimensions. By analyzing time series it should be possible to study how regional clusters evolve over time, and see which ones are on the rise and which are on the fall. Another extension is to include performance in the study, such as measures for export activity, productivity, patents, and other output measures. Finally, the study can be extended to include all of EU s 25 member states.

15 Industrial Specialization and Regional Clusters in the Ten New EU Member States 13 Figure 2. Regional Clusters in Automotive

16 14 Sölvell, Ketels & Lindqvist APPENDIX 1. METHODOLOGY The Industry Dimension Based on Porter s system we have translated the American SIC system into the NACE system used in the EU. Unfortunately there is no simple translation key between the American SIC system and the European NACE, and as a consequence the translation has to go through the UN based ISIC system. The translation between NACE and ISIC is simple. However, between ISIC and SIC there exists a many-to-many relationship, meaning that one ISIC category can be assigned to many SIC categories, and one SIC category can be assigned to many ISIC codes. The translation from SIC to NACE requires some adjustments and simplifications of the cluster definitions. This work, translating SIC into NACE, was conducted by Lindqvist, Malmberg and Sölvell (2002). With a few exceptions described below, this paper follows their translation of SIC to NACE. It should be noted that this translation is not perfect. However, the level of detail of the various classification systems differs to such an extent that any translation always will cause problems of adjustments. There is an ongoing project to harmonize the American and the European classification systems, which will eventually enable more simple and accurate comparisons between European and U.S. industry data. The translation from SIC to NACE brings three major changes in the cluster sector definitions. The SIC system includes industry categories for Aerospace engines and Aerospace, vehicles and defence respectively. To obtain a fit with the NACE system these two clusters have been consolidated to one. Moreover, the clusters Prefabricated enclosures and Motordriven products are affected by the translation in a way that their relevance can be questioned. These two clusters are therefore not included in the analysis. Taken together, we introduce 38 clusters compared to the original 41. The number of industries that are included in a cluster varies among clusters; at the most as many as 37 codes and at the least only one economic activity is forming the clusters (narrow cluster definitions are used throughout this paper, see discussion under Cluster mapping approach ). The Geographic Dimension Regions in Europe are divided according to the NUTS system, a nomenclature of territorial units for statistics. As a hierarchical classification, the NUTS subdivides each Member State into a whole number of NUTS 1 regions, each of which is in turn subdivided into a whole number of NUTS 2 regions and so on. In this paper we use the concept of NUTS 2 regions, including 41 regions in the 10 studied countries. There are a total number of 254 Nuts 2 regions in the EU, where Germany alone has 41 regions. Different criteria may be used in subdividing a national territory into regions. These are normally split between normative and analytic criteria. Normative regions are the expression of a political will; their limits are fixed according to the tasks allocated to the territorial communities, according to the sizes of population necessary to carry out these tasks efficiently and economically, and according to historical, cultural and other factors. Analytical (or functional) regions are defined according to analytical requirements; they group together zones using geographical criteria (e.g. altitude or type of soil) or using socioeconomic criteria (e.g. homogeneity, complementarity or polarity of regional economies). Six out of the 10 new member states only have one NUTS 2 region, meaning that this region equals the whole country. There are many advantages to using the concept of NUTS 2 regions. First of all, NUTS 1 regions would result in regions too large for cluster analysis. The biggest regions in Poland do already include millions of persons on a NUTS 2 level. Using NUTS 3 regions on the other hand, there would be a significant difference between the size of regions in small countries like Malta and Cyprus compared to other regions. Moreover, there is no data on a NUTS 3 level that is detailed enough, and if it where, it would most probably suffer from confidentiality problems. Data Professor Porter s definition of clusters use 4-digit SIC codes on a regional level. Overall it has shown to be very difficult to obtain reliable and comparable data for the 10 countries at this level of detail (and for all other European countries as well). EU employment data is collected from two different sources: from the Labour Force Survey (LFS) and from the Structural Business Statistics (SBS), both administrated by Eurostat. LFS is a quarterly survey given to a sample of the population living in private households. The LFS includes data on at most 3-digit NACE level for most, but not all, NUTS 2 regions. SBS statistics are mainly sourced from business registers and include structural data over the economy. On NUTS 2 regional level Eurostat only administers data