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Neighborhood and Efficiency in Manufacturing in Brazilian Regions

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  • Daniela Schettini
  • Carlos R. Azzoni
  • Antonio Paez

Abstract

This paper analyzes the geography of regional competitiveness in manufacturing in Brazil. The authors estimate stochastic frontiers to calculate regional efficiency of representative firms in 137 regions in the period 2000–2006, in four sectors defined by technological intensity. The efficiency results are analyzed using Markov Spatial Transition Matrices to provide insights into the transition of regions between efficiency levels, considering their local spatial context. The results indicate that geography plays an important role in manufacturing competitiveness. In particular, regions with more competitive neighbors are more likely to improve their relative efficiency (pull effect) over time, and regions with less competitive neighbors are more likely to lose relative efficiency (drag effect). The authors find that the pull effect is stronger than the drag effect.

Suggested Citation

  • Daniela Schettini & Carlos R. Azzoni & Antonio Paez, 2011. "Neighborhood and Efficiency in Manufacturing in Brazilian Regions," International Regional Science Review, , vol. 34(4), pages 397-418, October.
    • Handle: RePEc:sae:inrsre:v:34:y:2011:i:4:p:397-418
    DOI: 10.1177/0160017611403141
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    Cited by:

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    2. 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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    5. Eduardo A. Haddad & Jesús P. Mena-Chalco, Otavio J. G. Sidone, 2015. "Scholarly Collaboration in Regional Science in Developing Countries: The Case of the Brazilian REAL Network," Working Papers, Department of Economics 2015_12, University of São Paulo (FEA-USP).
    6. Massimiliano Agovino & Massimiliano Cerciello & Aniello Ferraro & Antonio Garofalo, 2022. "A Regional Perspective on Social Exclusion in European Regions: Context, Trends and Policy Implications," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 8(2), pages 409-433, July.
    7. Eduardo A. Haddad & Jesús P. Mena-Chalco & Otávio J. G. Sidone, 2017. "Scholarly Collaboration in Regional Science in Developing Countries," International Regional Science Review, , vol. 40(5), pages 500-529, September.
    8. Sergio J. Rey & Wei Kang & Levi Wolf, 2016. "The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics," Journal of Geographical Systems, Springer, vol. 18(4), pages 377-398, October.

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