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Key Sectors, Industrial Clustering and Multivariate Outliers


  • Antonio Morillas
  • Barbara Diaz


In this paper a reflection is made on the problems that can arise in key sector analysis and industrial clustering, due to the usual presence of outliers when using multidimensional data related to the sectors in an input-output table. Multidimensional outliers are considered as being not only linked to the low number of clusters usually observed in this kind of study, but probably causing invalid results in most of the works involving multivariate statistical techniques, such as cluster and factor analysis. Actually, by comparing the key sectors of the Spanish economy obtained in Diaz et al. (2006) to the ones we get taking into account the problem the outliers pose, one can realize they greatly distort the results. On the other hand, it is shown that identification of outliers can be considered as a good and new procedure to help select the most important sectors in an economy.

Suggested Citation

  • Antonio Morillas & Barbara Diaz, 2008. "Key Sectors, Industrial Clustering and Multivariate Outliers," Economic Systems Research, Taylor & Francis Journals, vol. 20(1), pages 57-73.
  • Handle: RePEc:taf:ecsysr:v:20:y:2008:i:1:p:57-73
    DOI: 10.1080/09535310801890698

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    Cited by:

    1. García Muñiz, Ana Salomé & Ramos Carvajal, Carmen, 2012. "Linkages, contagion and resilience: an input-output scope from the demand and supply side," MPRA Paper 59369, University Library of Munich, Germany.
    2. Ana Salome GARCIA MUÑIZ & Carmen RAMOS CARVAJAL, 2015. "Input-Output Linkages And Network Contagion In Greece:Demand And Supply View," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 15(2), pages 35-52.
    3. João Lopes, 2011. "Industrial Clustering and Sectoral Growth: a Network Dynamics Approach," ERSA conference papers ersa11p637, European Regional Science Association.


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