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Neighborhood and Efficiency in Manufacturing in Brazilian Regions: a Spatial Markov Chain Analysis


  • Daniela Schettini
  • Carlos Roberto Azzoni
  • Antonio Páez


More competitive regions tend to present higher level of economic growth, with positive reflexes on social aspects. The different economic performances observed among regions are explained mainly by the spatial concentration of the economic activities. This paper aims to analyze the influence of space on the regional competitiveness behavior of the Brazilian manufacturing industry. In doing so, it uses a panel data of 137 mesoregions and industry sectors that are aggregated in four categories according to technology intensity, during 2000 to 2006. We apply the stochastic frontier of production methodology to obtain the measures of regional efficiency and the Markov spatial transition matrixes that analyze the dynamic of the transition of the regions among efficiency categories considering their local spatial context. We found evidences that there is a higher probability of the regions to become less competitive when the neighborhood is not considered. On the other hand, when considering spatial influences, we observed that the probability of a good neighborhood (more competitive) in stimulating the region's efficiency is higher than the probability of a bad neighborhood (less competitive) in lowering its efficiency. In other words, the pull effect of the neighborhood in the competitiveness of the Brazilian regions is stronger than the drag effect.

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  • Daniela Schettini & Carlos Roberto Azzoni & Antonio Páez, 2011. "Neighborhood and Efficiency in Manufacturing in Brazilian Regions: a Spatial Markov Chain Analysis," ERSA conference papers ersa10p1052, European Regional Science Association.
  • Handle: RePEc:wiw:wiwrsa:ersa10p1052

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    1. Ben Gardiner & Ron Martin & Tyler Peter, 2004. "Competitiveness, Productivity and Economic Growth across the European Regions," ERSA conference papers ersa04p333, European Regional Science Association.
    2. Afriat, Sidney N, 1972. "Efficiency Estimation of Production Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(3), pages 568-598, October.
    3. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    4. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    5. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

    1. M. Agovino, 2014. "Do “good neighbors” enhance regional performances in including disabled people in the labor market? A spatial Markov chain approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 93-121, August.

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