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Spatial stochastic frontier models: accounting for unobserved local determinants of inefficiency

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  • Alexandra Schmidt

    ()

  • Ajax Moreira
  • Steven Helfand
  • Thais Fonseca

Abstract

In this paper, we analyze the productivity of farms across n = 370 municipalities located in the Center-West region of Brazil. We propose a stochastic frontier model with a latent spatial structure to account for possible unknown geographical variation of the outputs. This spatial component is included in the one-sided disturbance term. We explore two different distributions for this term, the exponential and the truncated normal. We use the Bayesian paradigm to fit the proposed models. We also compare between an independent normal prior and a conditional autoregressive prior for these spatial effects. The inference procedure takes explicit account of the uncertainty when considering these spatial effects. As the resultant posterior distribution does not have a closed form, we make use of stochastic simulation techniques to obtain samples from it. Two different model comparison criteria provide support for the importance of including these latent spatial effects, even after considering covariates at the municipal level.

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File URL: http://hdl.handle.net/10.1007/s11123-008-0122-6
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Bibliographic Info

Article provided by Springer in its journal Journal of Productivity Analysis.

Volume (Year): 31 (2009)
Issue (Month): 2 (April)
Pages: 101-112

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Handle: RePEc:kap:jproda:v:31:y:2009:i:2:p:101-112

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Web page: http://www.springerlink.com/link.asp?id=100296

Related research

Keywords: Bayesian paradigm; Conditional autoregressive priors; Monte Carlo Markov chain; Stochastic frontier models; Spatial econometrics; C01; C11;

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References

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  1. Tsionas, E.G., 2001. "Stochastic Frontier Models with Random Coefficients," Athens University of Economics and Business 130, Athens University of Economics and Business, Department of International and European Economic Studies.
  2. Viliam Druska & William C. Horrace, 2002. "Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming," Econometrics 0206004, EconWPA, revised 11 May 2003.
  3. Gamerman, Dani & Moreira, Ajax R. B., 2004. "Multivariate spatial regression models," Journal of Multivariate Analysis, Elsevier, vol. 91(2), pages 262-281, November.
  4. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
  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.
  6. Helfand, Steven M. & Levine, Edward S., 2004. "Farm size and the determinants of productive efficiency in the Brazilian Center-West," Agricultural Economics, Blackwell, vol. 31(2-3), pages 241-249, December.
  7. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
  8. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika van der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639.
  9. Alan Gelfand & Alexandra Schmidt & Sudipto Banerjee & C. Sirmans, 2004. "Nonstationary multivariate process modeling through spatially varying coregionalization," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 13(2), pages 263-312, December.
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Cited by:
  1. Pavlyuk, Dmitry, 2011. "Efficiency of broadband internet adoption in European Union member states," MPRA Paper 34183, University Library of Munich, Germany.
  2. Maria Olivares & Heike Wetzel, 2011. "Competing in the Higher Education Market: Empirical Evidence for Economies of Scale and Scope in German Higher Education Institutions," Working Paper Series in Economics 223, University of Lüneburg, Institute of Economics.
  3. Mari Maté-Sánchez-Val & Antonia Madrid-Guijarro, 2011. "A spatial efficiency index proposal: an empirical application to SMEs productivity," The Annals of Regional Science, Springer, vol. 47(2), pages 353-371, October.

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