IDEAS home Printed from
   My bibliography  Save this paper

Industrial Clustering and Sectoral Growth: a Network Dynamics Approach


  • João Lopes



Cluster analysis has been widely used in an Input-Output framework, with the main objective of uncover the structure of production, in order to better identify which sectors are strongly connected with each other and choose the key sectors of a national or regional economy. There are many empirical studies determining potential clusters from interindustry flows directly, or from their corresponding technical (demand) or market (supply) coefficients, most of them applying multivariate statistical techniques. In this paper we follow a different strategy. Since it is expected that strongly (interindustry) connected sectors share a similar growth and development path, we will try to uncover clusters from sectoral dynamics, by applying a stochastic geometry technique, based on the yearly distances of industry outputs. An application is made, comparing these growth based cluster templates with interindustry based ones, using Portuguese input-output data. Identifying regional clusters and its dynamics can be a useful extension of the methods proposed in this paper.

Suggested Citation

  • João Lopes, 2011. "Industrial Clustering and Sectoral Growth: a Network Dynamics Approach," ERSA conference papers ersa11p637, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa11p637

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Edward Feser & Edward Bergman, 2000. "National Industry Cluster Templates: A Framework for Applied Regional Cluster Analysis," Regional Studies, Taylor & Francis Journals, vol. 34(1), pages 1-19.
    2. Barbara Diaz & Laura Moniche & Antonio Morillas, 2006. "A Fuzzy clustering approach to the key sectors of the Spanish economy," Economic Systems Research, Taylor & Francis Journals, vol. 18(3), pages 299-318.
    3. Frederic Rychen & Jean-Benoit Zimmermann, 2008. "Clusters in the Global Knowledge-based Economy: Knowledge Gatekeepers and Temporary Proximity," Regional Studies, Taylor & Francis Journals, vol. 42(6), pages 767-776.
    4. Antonio Morillas & Barbara Diaz, 2008. "Key Sectors, Industrial Clustering and Multivariate Outliers," Economic Systems Research, Taylor & Francis Journals, vol. 20(1), pages 57-73.
    5. Sara Cruz & Aurora Teixeira, 2010. "The Evolution of the Cluster Literature: Shedding Light on the Regional Studies-Regional Science Debate," Regional Studies, Taylor & Francis Journals, vol. 44(9), pages 1263-1288.
    6. Fidel Aroche-Reyes, 2003. "A qualitative input-output method to find basic economic structures," Papers in Regional Science, Springer;Regional Science Association International, vol. 82(4), pages 581-590, November.
    7. Alex R. Hoen, 2002. "Identifying Linkages with a Cluster-based Methodology," Economic Systems Research, Taylor & Francis Journals, vol. 14(2), pages 131-146, June.
    8. Christina M. L. Kelton & Margaret K. Pasquale & Robert P. Rebelein, 2008. "Using the North American Industry Classification System (NAICS) to Identify National Industry Cluster Templates for Applied Regional Analysis," Regional Studies, Taylor & Francis Journals, vol. 42(3), pages 305-321, April.
    9. Vilela Mendes, R. & Araújo, Tanya & Louçã, Francisco, 2003. "Reconstructing an economic space from a market metric," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 323(C), pages 635-650.
    10. Sedef Akgüngör & Nese Kumral & Aykut Lenger, 2003. "National Industry Clusters and Regional Specializations in Turkey," European Planning Studies, Taylor & Francis Journals, vol. 11(6), pages 647-669, September.
    11. Mirko Titze & Matthias Brachert & Alexander Kubis, 2011. "The Identification of Regional Industrial Clusters Using Qualitative Input-Output Analysis (QIOA)," Regional Studies, Taylor & Francis Journals, vol. 45(1), pages 89-102.
    Full references (including those not matched with items on IDEAS)

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wiw:wiwrsa:ersa11p637. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gunther Maier). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.