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Industrial Clusters: Concepts and Empirical Evidence from East-Central Europe

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Clusters in Automotive and Information & Communication Technology

Abstract

Industry clusters (ICs) have attracted much attention in the recent past. Besides the ever-growing academic interest, ICs have become primary targets of development policy, similarly to foreign direct investment (FDI) attraction and the development of local linkages (e.g., supplier networks) of foreign investment enterprises (FIEs). Both structures, ICs and widespread supplier networks, have common features. Most importantly, both need a sufficient number of potential collaborators. Both can be developed most successfully in regions where economic activity is vivid and enterprising and cooperation has traditions. It is therefore of special interest to learn what should and could be the relationship between the two cooperation systems, what are their common features, and what are the differences. These are the issues examined in this chapter, both from theoretical and empirical points of view, this latter based on the example of the Hungarian and other CEE economies’ experience and with special regard to the automotive and ICT sectors.

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Notes

  1. 1.

    EC (2003); EC (2007); EC (2008a), (b).

  2. 2.

    Michael Porter’s original definition for clusters is as follows: “Clusters are geographic concentrations of interconnected companies, specialized suppliers and service providers, firms in related industries, and associated institutions (for example universities, standards agencies, and trade associations) in particular fields that compete but also co-operate” Porter 1990, (1998). The main aim of this cooperation is enhancing competitiveness of regions and actors in the region.

  3. 3.

    For more details on supplier network promotion programs in Hungary, see Sass and Szanyi (2004); ICEG (2006); Sass et al. (2009).

  4. 4.

    One main precondition of successful cluster operation is the presence of a fairly large and diverse pool of economic agents specializing on similar or supportive activities. The sufficiently large, specialized local economic activity is crucial for knowledge generation and transfer, for the internal stability of cluster organizations, for the “visibility” of clusters, and for the self-sustaining development of cluster activities. For more general descriptions and about various interpretations of the cluster concept, see Porter (1998); Sölvell et al. (2003); ICEG (2007); Sölvell (2008); EC (2008b); Szanyi (2008b).

  5. 5.

    Sölvell et al. (2003) run the first major questionnaire-based empirical survey on clusters worldwide. Using the survey results, they described a typical or best practice cluster type: the most common appearance of clusters. Because of overrepresentation of clusters from developed market economies, this model, which they called “dynamic cluster,” reflected basically those characteristics, cooperation forms, and structures that were found typical in more-developed economies. Later research (e.g., Ketels and Sölvell 2005; Ketels et al. 2006) revealed the fact that in emerging market economies or developing countries, clusters may substantially differ concerning their focus of activity and working models.

  6. 6.

    See for details: ICEG (2006).

  7. 7.

    See http://data.isc.hbs.edu/isc/index.jsp.

  8. 8.

    See http://www.clusterobservatory.eu.

  9. 9.

    For a thorough description of the traded cluster category, see http://data.isc.hbs.edu/isc/index.jsp.

  10. 10.

    The design of the locational quotient is similar to Bela Balassa’s RCA measure (revealed comparative advantage). It expresses the relative weight of one single sector in a region to the total weight of the region, compared to either the national economy or a larger geographical area. The calculation is as follows:

    \( {\hbox{L}}{{\hbox{Q}}_{{ij}}} = \frac{{{e_{{ij}}}/{E_i}}}{{{e_j}/E}} = \frac{{{s_{{ij}}}}}{{{x_j}}}, \) where

    e ij : number of employees in area j in branch i,

    e j : the total number of employees in area j,

    E i : number of employees in branch i in the whole country (spatial unit of comparison), and

    E: total number of employees in the whole country (spatial unit of comparison), and therefore

    S ij : shows the share of area j in total employment of branch i, and

    x j : shows the share of area j in total employment.

  11. 11.

    The database allowed even deeper NUTS-4 level calculations.

  12. 12.

    We also evaluated the branch-comitat pairs at a lower 30% level.

  13. 13.

    We must notice here again that spatial concentration is just one important condition of cluster formation. Hence, even if we call the observed concentrations clusters or potential clusters, it does by no means mean that there is an actual cluster organization present. HBS documents, as well as the European Cluster Observatory, also uses the term “cluster” for spatial activity concentrations.

  14. 14.

    For car industry and the role of PANAC, and the Hungarian automotive cluster, see Grosz (2006).

  15. 15.

    For evidence and case studies, see Szanyi (2008a, b).

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Authors and Affiliations

  1. Institute for World Economics, Budapest, Hungary

    Miklós Szanyi

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  1. Miklós Szanyi
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Correspondence to Miklós Szanyi .

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  1. Europäisches Institut für Internationale, Wirtschaftsbeziehungen (EIIW) e.V., Bergische Universität Wuppertal, Rainer-Gruenter-Straße 21, Wuppertal, 42119, Germany

    Paul J.J. Welfens

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Szanyi, M. (2012). Industrial Clusters: Concepts and Empirical Evidence from East-Central Europe. In: Welfens, P. (eds) Clusters in Automotive and Information & Communication Technology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25816-9_3

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