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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.

Suggested Citation

  • Frauke G. Braun & Astrid Cullmann, 2008. "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," Discussion Papers of DIW Berlin 816, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp816
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    References listed on IDEAS

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    1. Hanson, Gordon H., 1998. "Regional adjustment to trade liberalization," Regional Science and Urban Economics, Elsevier, vol. 28(4), pages 419-444, July.
    2. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    3. Yu Hsing, 1996. "An empirical estimation of regional production functions for the U.S. manufacturing industry," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 30(4), pages 351-358.
    4. Krugman, Paul, 1991. "Increasing Returns and Economic Geography," Journal of Political Economy, University of Chicago Press, vol. 99(3), pages 483-499, June.
    5. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
    6. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    7. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    8. Kumbhakar, Sabul C., 1993. "Production risk, technical efficiency, and panel data," Economics Letters, Elsevier, vol. 41(1), pages 11-16.
    9. Héctor Salgado Banda & Lorenzo Bernal Verdugo, 2011. "Multifactor productivity and its determinants: an empirical analysis for Mexican manufacturing," Journal of Productivity Analysis, Springer, vol. 36(3), pages 293-308, December.
    10. H. Baltagi, Badi & Heun Song, Seuck & Cheol Jung, Byoung, 2001. "The unbalanced nested error component regression model," Journal of Econometrics, Elsevier, vol. 101(2), pages 357-381, April.
    11. Garcia-Mila, Teresa & McGuire, Therese J & Porter, Robert H, 1996. "The Effect of Public Capital in State-Level Production Functions Reconsidered," The Review of Economics and Statistics, MIT Press, vol. 78(1), pages 177-180, February.
    12. 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, vol. 19(2-3), pages 233-238, August.
    13. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    14. Chambers,Robert G., 1988. "Applied Production Analysis," Cambridge Books, Cambridge University Press, number 9780521314275, April.
    15. 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.
    16. 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.
    17. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
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    More about this item

    Keywords

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

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure

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