Industrial specialization and regional clusters in the ten new EU member states

Industrial specialization and regional clusters in the ten new EU member states Purpose – The purpose of this paper is to provide an analysis of regional concentration patterns within ten new European Union (EU) member states, EU10, and make comparisons with EU15 and the US economy. Design/methodology/approach – Industrial specialization and clusters are measured as employment in the intersection between a sector (three‐digit NACE data) and a particular region (NUTS 2 level), with a total of 38 sectors and 41 regions within EU10. Regional cluster size and degree of specialization is measured along 3D: absolute number of employees (>10,000 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 per cent of total employment in a particular sector). Each of these three measures of cluster size, specialization and labor market focus are classified with a “star”. The largest and most specialized clusters receive three stars. Findings – EU10 exhibits 19 three‐star regional clusters, which display high values for each of the three measured parameters. In addition, there are 92 two‐star regional clusters and 313 one‐star regional clusters. The analysis also suggests that regional concentration in EU10 is clearly lower than in the USA, and slightly lower than in the old EU member states. In a few cases – IT, biopharmaceuticals and communications equipment – where the total size of the cluster is small, and there is little historical legacy in Eastern Europe, the EU10 exhibits higher geographical concentration than EU15. Research limitations/implications – Overall, the economies of EU10 exhibit a pattern of geographical concentration close to a random distribution, i.e. the process of regional concentration and redistribution of industry is in a very early phase. If Europe is to build a more competitive economy, industrial restructuring towards larger clusters must be allowed and pushed by policy makers both at the national and EU levels. Practical implications – Policymakers must be well informed about geographical concentration patterns of industry. The research offers a consistent methodology of mapping regional clusters and geographical concentration patterns across sectors. Originality/value – This paper is the first in measuring regional concentration patterns in Europe at this fine level, and is based on a new methodology developed by Professor Michael E. Porter at Harvard University. The paper has also introduced a new method of ranking clusters according to the star model. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Competitiveness Review: An International Business Journal incorporating Journal of Global Competitiveness Emerald Publishing

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Publisher
Emerald Publishing
Copyright
Copyright © 2008 Emerald Group Publishing Limited. All rights reserved.
ISSN
1059-5422
DOI
10.1108/10595420810874637
Publisher site
See Article on Publisher Site

Abstract

Purpose – The purpose of this paper is to provide an analysis of regional concentration patterns within ten new European Union (EU) member states, EU10, and make comparisons with EU15 and the US economy. Design/methodology/approach – Industrial specialization and clusters are measured as employment in the intersection between a sector (three‐digit NACE data) and a particular region (NUTS 2 level), with a total of 38 sectors and 41 regions within EU10. Regional cluster size and degree of specialization is measured along 3D: absolute number of employees (>10,000 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 per cent of total employment in a particular sector). Each of these three measures of cluster size, specialization and labor market focus are classified with a “star”. The largest and most specialized clusters receive three stars. Findings – EU10 exhibits 19 three‐star regional clusters, which display high values for each of the three measured parameters. In addition, there are 92 two‐star regional clusters and 313 one‐star regional clusters. The analysis also suggests that regional concentration in EU10 is clearly lower than in the USA, and slightly lower than in the old EU member states. In a few cases – IT, biopharmaceuticals and communications equipment – where the total size of the cluster is small, and there is little historical legacy in Eastern Europe, the EU10 exhibits higher geographical concentration than EU15. Research limitations/implications – Overall, the economies of EU10 exhibit a pattern of geographical concentration close to a random distribution, i.e. the process of regional concentration and redistribution of industry is in a very early phase. If Europe is to build a more competitive economy, industrial restructuring towards larger clusters must be allowed and pushed by policy makers both at the national and EU levels. Practical implications – Policymakers must be well informed about geographical concentration patterns of industry. The research offers a consistent methodology of mapping regional clusters and geographical concentration patterns across sectors. Originality/value – This paper is the first in measuring regional concentration patterns in Europe at this fine level, and is based on a new methodology developed by Professor Michael E. Porter at Harvard University. The paper has also introduced a new method of ranking clusters according to the star model.

Journal

Competitiveness Review: An International Business Journal incorporating Journal of Global CompetitivenessEmerald Publishing

Published: May 23, 2008

Keywords: European Union; Regional development; Geographic regions; Economic development; Cluster analysis

References

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