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Key Parameters and Efficiency of Mexican Manufacturing: Are There Still Differences between the North and the South? An Application of Nested and Stochastic Frontier Panel Data Models

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  • Frauke G. Braun
  • Astrid Cullmann

Abstract

This study explores the prevalence and nature of the regional divide for the Mexican manufacturing production across sub-national regions. We utilize a unique panel of municipality-level data from the manufacturing sector. An important contribution is the use of different panel methods to account for latent regional characteristics and the computation of performance indicators for each municipality which will enable detailed regional rankings. Firstly, we apply nested panel methods to estimate regional production functions and to analyze production characteristics and scale economies. Subsequently, we use stochastic frontier analysis methods to test for productivity and efficiency differences in manufacturing throughout the country. Our results suggest that the economic structure and productivity of southern Mexico is considerably different from the centrally located manufacturing belt and the north. Remarkably, rankings based on nested panel and stochastic frontier estimations confirm very similar regional patterns. Nevertheless, efficiency varies strongly within states, indicating that 'islands of excellence' prevail in otherwise highly inefficient and lagging states.

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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.88646.de/dp816.pdf
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Bibliographic Info

Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 816.

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Length: 31 p.
Date of creation: 2008
Date of revision:
Handle: RePEc:diw:diwwpp:dp816

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Keywords: Mexico; Manufacturing; Efficiency Analysis; Stochastic Frontier Analysis; Panel Data Models;

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  1. 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, Springer, vol. 20(2), pages 325-32.
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  3. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, Elsevier, vol. 19(2-3), pages 233-238, August.
  4. Héctor Salgado Banda & Lorenzo E. Bernal Verdugo, 2007. "Multifactor Productivity and its Determinants: Al Empirical Analysis for Mexican Manufacturing," Working Papers, Banco de México 2007-09, Banco de México.
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  7. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, Elsevier, vol. 13(1), pages 57-66, May.
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  11. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, Elsevier, vol. 46(1-2), pages 141-163.
  12. 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, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-44, June.
  13. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, Elsevier, vol. 46(1-2), pages 201-211.
  14. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, Elsevier, vol. 126(2), pages 269-303, June.
  15. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, Elsevier, vol. 9(1), pages 43-64, August.
  16. Yu Hsing, 1996. "An empirical estimation of regional production functions for the U.S. manufacturing industry," The Annals of Regional Science, Springer, Springer, vol. 30(4), pages 351-358.
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