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Spatial Stochastic Frontier Models: accounting for unobserved local determinants of inefficiency

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  • Alexandra M. Schmidt
  • Ajax R. B. Moreira
  • Thais C. O. Fonseca
  • Steven M. Helfand

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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Bibliographic Info

Paper provided by Instituto de Pesquisa Econômica Aplicada - IPEA in its series Discussion Papers with number 1220.

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Length: 29 pages
Date of creation: Oct 2006
Date of revision:
Handle: RePEc:ipe:ipetds:1220

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  1. 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.
  2. 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.
  3. 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.
  4. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
  5. 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.
  6. Viliam Druska & William C. Horrace, 2004. "Generalized Moments Estimation for Spatial Panel Data: Indonesian Rice Farming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(1), pages 185-198.
  7. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
  8. Gamerman, Dani & Moreira, Ajax R. B., 2004. "Multivariate spatial regression models," Journal of Multivariate Analysis, Elsevier, vol. 91(2), pages 262-281, November.
  9. 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.
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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," Economics of Education Working Paper Series 0070, University of Zurich, Institute for Strategy and Business Economics (ISU).
  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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