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

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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. Neste texto, analisamos a produtividade de estabelecimentos agrícolas localizados em 370 municípios da região Centro-Oeste do Brasil. Propomos um modelo de fronteira estocástica de produção com estrutura espacial latente que representa os determinantes não-observados da ineficiência da produtividade da agropecuária. Esse componente espacial condiciona a distribuição da ineficiência. Usamos o paradigma bayesiano para estimar os modelos propostos. Foram exploradas duas distribuições diferentes para este termo, a normal truncada e a exponencial, e utilizamos duas especificações para a variável latente, suposta independente entre os municípios, ou dependente dos municípios vizinhos segundo um modelo auto-regressivo espacial. O procedimento de inferência considera explicitamente todas as incertezas quando incluímos o termo espacial. Como a distribuição a posteriori não tem uma expressão analítica, utilizamos técnicas estocásticas da simulação para obter a
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Suggested Citation

  • Alexandra Schmidt & Ajax Moreira & Steven Helfand & Thais Fonseca, 2009. "Spatial stochastic frontier models: accounting for unobserved local determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 31(2), pages 101-112, April.
  • Handle: RePEc:kap:jproda:v:31:y:2009:i:2:p:101-112
    DOI: 10.1007/s11123-008-0122-6
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    References listed on IDEAS

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    1. 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.
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    5. 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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    7. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    8. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    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;Sociedad de Estadística e Investigación Operativa, vol. 13(2), pages 263-312, December.
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    Cited by:

    1. 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;Western Regional Science Association, vol. 47(2), pages 353-371, October.
    2. Anthony J. Glass & Karligash Kenjegalieva & Robin Sickles, 2012. "The Effects of Efficiency and TFP Growth on Nitrogen and Sulphur Emissions in Europe: A Multistage Spatial Analysis," Discussion Paper Series 2012_11, Department of Economics, Loughborough University, revised Oct 2012.
    3. Orea, Luis & Álvarez, Inmaculada C., 2017. "A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms," Efficiency Series Papers 2017/04, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    4. Maria Olivares & Heike Wetzel, 2014. "Editor's Choice Competing in the Higher Education Market: Empirical Evidence for Economies of Scale and Scope in German Higher Education Institutions," CESifo Economic Studies, CESifo, vol. 60(4), pages 653-680.
    5. Francisco José Areal & Kelvin Balcombe & Richard Tiffin, 2012. "Integrating spatial dependence into Stochastic Frontier Analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 56(4), pages 521-541, October.
    6. repec:eee:eneeco:v:64:y:2017:i:c:p:373-383 is not listed on IDEAS
    7. Pavlyuk, Dmitry, 2011. "Efficiency of broadband internet adoption in European Union member states," MPRA Paper 34183, University Library of Munich, Germany.
    8. Gude, Alberto & Álvarez, Inmaculada C. & Orea, Luis, 2017. "Heterogeneous spillovers among Spanish provinces: A generalized spatial stochastic frontier model," Efficiency Series Papers 2017/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    9. Areal, Francisco Jose & Balcombe, Kelvin & Tiffin, Richard, 2012. "Integrated spatial dependence into Stochastic Frontier Analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 56(4), December.

    More about this item

    Keywords

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

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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