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Divide and Conquer? Decentralisation, Co-ordination and Cluster Survival

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  • Kerstin Wolter

Abstract

This paper develops a simulation model of the behaviour of clusters in the face of bifurcation events in their environment. Bifurcations are understood as the regional equivalent to Schumpeterian creative destruction. The model investigates the role of decentralisation and co-ordination for the likelihood of successful adaptation by comparing adaptive performance of clusters exhibiting different degrees of decentralisation and alternative modes of co-ordination. Using Kauffman’s (1993) N/K model, it is found that there is an optimum degree of decentralisation with respect to cluster adaptability while different co-ordination mechanisms face a trade-off between speed and cluster-level optimality of results. In doing so, the model sheds light on an empirical controversy regarding the role of both factors for adaptation that has emerged between the Silicon Valley – Boston 128 comparison on the one and the Italian Industrial District experience on the other hand. Moreover, the identification of the roles played by decentralisation and co-ordination for cluster adaptability in changing environments could serve as guidance for future empirical research as well as policy initiatives.

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Bibliographic Info

Paper provided by DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies in its series DRUID Working Papers with number 05-12.

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Date of creation: 2005
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Handle: RePEc:aal:abbswp:05-12

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Web page: http://www.druid.dk/

Related research

Keywords: Clusters; Bifurcations. N/K model;

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  1. Glenn Ellison & Edward L. Glaeser, 1994. "Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach," NBER Working Papers 4840, National Bureau of Economic Research, Inc.
  2. Giovanni Dosi & Daniel Levinthal & Luigi Marengo, 2001. "Bridging Contested Terrain: Linking Incentive-Based and Learning Perspectives on Organizational Evolution," LEM Papers Series 2001/20, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  3. Marengo, Luigi & Dosi, Giovanni, 2005. "Division of labor, organizational coordination and market mechanisms in collective problem-solving," Journal of Economic Behavior & Organization, Elsevier, vol. 58(2), pages 303-326, October.
  4. Bent Dalum & Christian Ø.R. Pedersen & Gert Villumsen, 2002. "Technoligical Life Cycles Regional Clusters Facing Disruption," DRUID Working Papers 02-10, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.
  5. Bresnahan, Timothy F & Gambardella, Alfonso & Saxenian, AnnaLee, 2001. "'Old Economy' Inputs for 'New Economy' Outcomes: Cluster Formation in the New Silicon Valleys," Industrial and Corporate Change, Oxford University Press, vol. 10(4), pages 835-60, December.
  6. Fleming, Lee & Sorenson, Olav, 2001. "Technology as a complex adaptive system: evidence from patent data," Research Policy, Elsevier, vol. 30(7), pages 1019-1039, August.
  7. Timothy F. Bresnahan & Franco Malerba, 1997. "Industrial Dynamics and the Evolution of Firms' and Nations' Competitive Capabilities in the World Computer Industry," Working Papers 97030, Stanford University, Department of Economics.
  8. Dan Johansson & Dilek Cetindamar & Bo Carlsson & Pontus Braunerhjelm, 2000. "The old and the new: the evolution of polymer and biomedical clusters in Ohio and Sweden," Journal of Evolutionary Economics, Springer, vol. 10(5), pages 471-488.
  9. Brusco, Sebastiano, 1982. "The Emilian Model: Productive Decentralisation and Social Integration," Cambridge Journal of Economics, Oxford University Press, vol. 6(2), pages 167-84, June.
  10. Ottati, Gabi Dei, 1994. "Trust, Interlinking Transactions and Credit in the Industrial District," Cambridge Journal of Economics, Oxford University Press, vol. 18(6), pages 529-46, December.
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