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Measurement of technical efficiency in the case of heterogeneity of technologies used between firms - Based on evidence from Polish crop farms

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  • Jerzy Marzec

    (Department of Econometrics and Operational Research, Institute of Quantitative Methods in Social Sciences, Cracow University of Economics, Kraków, Poland)

  • Andrzej Pisulewski

Abstract

In the present study, we have investigated several competing stochastic frontier models which differ in terms of the form of the production function (Cobb-Douglas or translog), inefficiency distribution (half-normal or exponential distribution) and type of prior distribution for the parameters (hierarchical or non-hierarchical from the Bayesian point of view). This last distinction corresponds to a difference between random coefficients and fixed coefficients models. Consequently, this study aims to examine to what extent inferences about estimates of farms' efficiency depend on the above assumptions. Moreover, the study intends to investigate how far the production function's characteristics are affected by the choice of the type of prior distribution for the parameters. First of all, it was found that the form of the production function does not impact the efficiency scores. Secondly, we found that measures of technical efficiency are sensitive to distributional assumptions about the inefficiency term. Finally, we have revealed that estimates of technical efficiency are reasonably robust to the prior information about the parameters of crop farms' production technology. There is also a resemblance in the elasticity of output with respect to inputs between the models considered in this paper. Additionally, the measurement of returns to scale is not sensitive to model specification.

Suggested Citation

  • Jerzy Marzec & Andrzej Pisulewski, 2021. "Measurement of technical efficiency in the case of heterogeneity of technologies used between firms - Based on evidence from Polish crop farms," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(4), pages 152-161.
    • Handle: RePEc:caa:jnlage:v:67:y:2021:i:4:id:347-2020-agricecon
    DOI: 10.17221/347/2020-AGRICECON
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    1. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    2. Lukas Cechura & Aaron Grau & Heinrich Hockmann & Inna Levkovych & Zdenka Kroupova, 2017. "Catching Up or Falling Behind in European Agriculture: The Case of Milk Production," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(1), pages 206-227, February.
    3. 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.
    4. Lajos Baráth & Imre Fertő & Štefan Bojnec, 2018. "Are farms in less favored areas less efficient?," Agricultural Economics, International Association of Agricultural Economists, vol. 49(1), pages 3-12, January.
    5. 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.
    6. Feng, Guohua & Zhang, Xiaohui, 2012. "Productivity and efficiency at large and community banks in the US: A Bayesian true random effects stochastic distance frontier analysis," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1883-1895.
    7. 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), pages 1-21, December.
    8. 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.
    9. Grigorios Emvalomatis, 2012. "Productivity Growth in German Dairy Farming using a Flexible Modelling Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 63(1), pages 83-101, February.
    10. Skevas, Ioannis & Emvalomatis, Grigorios & Brümmer, Bernhard, 2018. "Productivity growth measurement and decomposition under a dynamic inefficiency specification: The case of German dairy farms," European Journal of Operational Research, Elsevier, vol. 271(1), pages 250-261.
    11. Lukáš ČECHURA, 2010. "Estimation of technical efficiency in Czech agriculture with respect to firm heterogeneity," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 56(4), pages 183-191.
    12. Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian efficiency analysis through individual effects: Hospital cost frontiers," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 77-105.
    13. Stefan Bojnec & Laure Latruffe, 2009. "Determinants of technical efficiency of Slovenian farms," Post-Communist Economies, Taylor & Francis Journals, vol. 21(1), pages 117-124.
    14. Xueqin Zhu & Alfons Oude Lansink, 2010. "Impact of CAP Subsidies on Technical Efficiency of Crop Farms in Germany, the Netherlands and Sweden," Journal of Agricultural Economics, Wiley Blackwell, vol. 61(3), pages 545-564, September.
    15. Jerzy Marzec & Andrzej Pisulewski, 2017. "The Effect of CAP Subsidies on the Technical Efficiency of Polish Dairy Farms," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 9(3), pages 243-273, September.
    16. Eric Njuki & Boris E. Bravo-Ureta & Christopher J. O’Donnell, 2019. "Decomposing agricultural productivity growth using a random-parameters stochastic production frontier," Empirical Economics, Springer, vol. 57(3), pages 839-860, September.
    17. Kalirajan, K P & Obwona, M B, 1994. "Frontier Production Function: The Stochastic Coefficients Approach," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 56(1), pages 87-96, February.
    18. Laure Latruffe & Kelvin Balcombe & Sophia Davidova & Katarzyna Zawalinska, 2004. "Determinants of technical efficiency of crop and livestock farms in Poland," Applied Economics, Taylor & Francis Journals, vol. 36(12), pages 1255-1263.
    19. Marzec, Jerzy & Pisulewski, Andrzej, 2019. "The Measurement of Time Varying Technical Efficiency and Productivity Change in Polish Crop Farms," German Journal of Agricultural Economics, Humboldt-Universitaet zu Berlin, Department for Agricultural Economics, vol. 68(1), March.
    20. Luis Orea & Subal C. Kumbhakar, 2004. "Efficiency measurement using a latent class stochastic frontier model," Empirical Economics, Springer, vol. 29(1), pages 169-183, January.
    21. Ioannis Skevas, 2019. "A Hierarchical Stochastic Frontier Model for Efficiency Measurement Under Technology Heterogeneity," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 513-524, September.
    22. 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.
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    Cited by:

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    2. Marta Arbelo-Pérez & Pilar Pérez-Gómez & Antonio Arbelo, . "Profit efficiency and its determinants in the agricultural sector: A Bayesian approach," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 0.

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