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A Fuzzy clustering approach to the key sectors of the Spanish economy

Author

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  • Barbara Diaz
  • Laura Moniche
  • Antonio Morillas

Abstract

The search for key sectors in an economy has been and still is one of the more recurrent themes in input-output analysis. When using clustering techniques, sectors can only belong to a group, having a particular performance. But, actually, the same sector could be important from different perspectives at the same time, to a different degree. So, a fuzzy clustering approach is needed. In this work we propose a multidimensional approach to classify the productive sectors of the Spanish input-output table for 1995, based on three groups of variables: those related to their productive integration, others measuring their specific weight in the economy and finally some showing their economic dynamic. We also incorporate into the analysis the technological level, which being a categorical variable presents special methodological problems. All these questions are tackled applying a robust and fuzzy clustering analysis, which gives as a result a classification of sectors illustrating the role that each one plays in the Spanish economy.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:ecsysr:v:18:y:2006:i:3:p:299-318
    DOI: 10.1080/09535310600844375
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    Cited by:

    1. João C. Lopes & Tanya Araújo & João Dias & João F. Amaral, 2010. "National industry cluster templates and the structure of industry output dynamics: a stochastic geometry approach," Working Papers Department of Economics 2010/20, ISEG - Lisbon School of Economics and Management, Department of Economics, Universidade de Lisboa.
    2. Juan Carlos Leyva Lopez & Diego Alonso Gastelum Chavira & Margarita Urias Ruiz, 2016. "A comparative approach of economic sectors in Sinaloa, Mexico, based on multicriteria decision aiding," EconoQuantum, Revista de Economia y Finanzas, Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas, Departamento de Metodos Cuantitativos y Maestria en Economia., vol. 13(1), pages 97-124, Enero-Jun.
    3. Francisco Benita & Serhad Sarica & Garvit Bansal, 2020. "Testing the static and dynamic performance of statistical methods for the detection of national industrial clusters," Papers in Regional Science, Wiley Blackwell, vol. 99(4), pages 1137-1157, August.
    4. Juan Carlos Leyva Lopez & Diego Alonso Gastelum Chavira & Margarita Urias Ruiz, 2016. "A comparative approach of economic sectors in Sinaloa, Mexico, based on multicriteria decision aiding," EconoQuantum, Revista de Economia y Negocios, Universidad de Guadalajara, Centro Universitario de Ciencias Economico Administrativas, Departamento de Metodos Cuantitativos y Maestria en Economia., vol. 13(1), pages 97-124, Enero-Jun.
    5. repec:thr:techub:10014:y:2020:i:1:p:316-327 is not listed on IDEAS
    6. Matías Piaggio & Vicent Alcántara Escolano & Emilio Padilla, 2012. "Economic structure and key sectors analysis of greenhouse gas emissions in Uruguay," Working Papers wpdea1204, Department of Applied Economics at Universitat Autonoma of Barcelona.
    7. Hristov, Jordan & Surry, Yves, 2013. "Considering the Effect of Uncertainty in an Input-Output Analysis of Water Consumption: A Fuzzy Approach Applied to Macedonia," 2013 Conference: Tools for decision support in agriculture and rural development, April 18-19, 2013, Krško, Slovenia 183913, Slovenian Association of Agricultural Economists (DAES).
    8. João Lopes, 2011. "Industrial Clustering and Sectoral Growth: a Network Dynamics Approach," ERSA conference papers ersa11p637, European Regional Science Association.
    9. García Muñiz, Ana Salomé, 2013. "Input–output research in structural equivalence: Extracting paths and similarities," Economic Modelling, Elsevier, vol. 31(C), pages 796-803.
    10. Caggiani, Leonardo & Ottomanelli, Michele & Dell’Orco, Mauro, 2014. "Handling uncertainty in Multi Regional Input-Output models by entropy maximization and fuzzy programming," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 159-172.

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