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The Evolution Of Industrial Clusters- Simulating Spatial Dynamics

Listed author(s):
  • Thomas Brenner, Niels Weigelt, -DISCUSSANT: Gianfranco Guilioni

In the last decade the regional aspect of economic activities has reoccurred in the economic debate. Under labels like "industrial district", "innovative milieu", and "regional innovative systems" it has been frequently analysed why certain regions are economically successful while others are not. The basis for these approaches are case studies of several successful regions, like Silicon Valley and the Third Italy, just to name two of the most prominent examples. On the basis of these case studies several authors have attempted to explain the specific reasons for the success of each of these regions. This paper deviates from most of the approaches in the literature in two ways. First, it does not aim to explain the success of industrial districts or the likes. Instead, it focuses on the evolution of industrial districts. The analysis done in this paper is concerned with the question of why industrial districts evolve, and when and where they come into existence. It is the aim to develop a life cycle theory for industrial districts, comparable to the life cycle of industries. Second, this paper does not focus on one or a few specific industrial districts. Instead, a general theory is developed, which focuses on the general features of the dynamics of industrial districts and ignores the specific features of single examples. It is intended to reach a general understanding of the evolution of industrial districts and similar phenomenon. Methodologically this approach is based on the concept of cellular automata, which allows to study the developments in several regions in a two-dimensional space. The unit of analysis in each of the regions are firms. Furthermore, each region is characterised by its research institutions, which are exogenously given, and the wages in the region, which are endogenously given. The variables of a firms are its capital, technology, and human capital all of which change endogenously. Furthermore, the firms are classified into types of industries. The dynamics of the variables are given, besides some standard economic dependencies, by the consideration of local external economies, spillovers between industries and firms, movements of the labour force to neighbouring regions, and the start-ups of new firms. With the help of simulations several aspects of these dynamics are studied. First, conditions that lead to the agglomeration of firms of the same industry are analysed. Furthermore, if there is an agglomeration of firms, the stability of these agglomerations is analysed. Second, the reaction of the system to changes of the demand market is studied. By this a kind of life cycle of industrial districts is created, due to the life cycle of industries. However, although these life cycles show some correlation, they are not necessarily identical. It is possible for industrial districts to survive the disappearance of the respective industry, dependent, as the simulations show, on the structure within the region and the features of the change in the demand market. Different changes in the demand market are exogenously imposed on the system and the reactions are analysed to obtain a better understanding of the stability of industrial districts and the reasons for the evolution of new industrial districts.

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2000 with number 284.

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Date of creation: 05 Jul 2000
Handle: RePEc:sce:scecf0:284
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CEF 2000, Departament d'Economia i Empresa, Universitat Pompeu Fabra, Ramon Trias Fargas, 25,27, 08005, Barcelona, Spain

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  1. Bent Dalum, 1995. "Local and global linkages the radiocommunications cluster in Northern Denmark," Industry and Innovation, Taylor & Francis Journals, vol. 2(2), pages 89-109.
  2. Adam B. Jaffe & Manuel Trajtenberg & Rebecca Henderson, 1993. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations," The Quarterly Journal of Economics, Oxford University Press, vol. 108(3), pages 577-598.
  3. Cani�ls; M.C.J. & Verspagen; B., 1999. "Spatial distance in a technology gap model," Working Papers 99.10, Eindhoven Center for Innovation Studies.
  4. Jonard, N. & Yfldizoglu, M., 1998. "Technological diversity in an evolutionary industry model with localized learning and network externalities," Structural Change and Economic Dynamics, Elsevier, vol. 9(1), pages 35-53, March.
  5. Krugman, Paul, 1991. "Increasing Returns and Economic Geography," Journal of Political Economy, University of Chicago Press, vol. 99(3), pages 483-499, June.
  6. Ellison, Glenn & Glaeser, Edward L, 1997. "Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach," Journal of Political Economy, University of Chicago Press, vol. 105(5), pages 889-927, October.
  7. Kiminori Matsuyama & Takaaki Takahashi, 1998. "Self-Defeating Regional Concentration," Review of Economic Studies, Oxford University Press, vol. 65(2), pages 211-234.
  8. Witt, Ulrich, 1986. "Firms' market behavior under imperfect information and economic natural selection," Journal of Economic Behavior & Organization, Elsevier, vol. 7(3), pages 265-290, September.
  9. Audretsch, David B, 1998. "Agglomeration and the Location of Innovative Activity," Oxford Review of Economic Policy, Oxford University Press, vol. 14(2), pages 18-29, Summer.
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