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Estimation of input‐oriented technical efficiency using a nonhomogeneous stochastic production frontier model

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  • Subal C. Kumbhakar
  • Efthymios G. Tsionas

Abstract

Technical inefficiency can be modeled as either input‐oriented (IO) or output‐oriented (OO). However, in the estimation of parametric stochastic production frontier models which use maximum likelihood method only the OO measure is used. In this article we consider a simple nonhomogeneous production function and estimate it with both IO and OO specifications. A sample of 80 Spanish dairy data (1993–1998) is used to estimate both models. We consider one output (liters of milk) and four variable inputs (viz., number of cows, kilograms of concentrates, hectares of land, and labor [measured in man‐equivalent units]). We find that returns to scale (RTS) and technical efficiency results derived from these models are different because either estimated technologies are different, or they are evaluated at different points. Using a Monte Carlo analysis we show that if RTS is close to unity differences in the estimates of RTS and technical efficiency are smaller. This holds true for estimates of both RTS and technical efficiency.

Suggested Citation

  • Subal C. Kumbhakar & Efthymios G. Tsionas, 2008. "Estimation of input‐oriented technical efficiency using a nonhomogeneous stochastic production frontier model," Agricultural Economics, International Association of Agricultural Economists, vol. 38(1), pages 99-108, January.
    • Handle: RePEc:bla:agecon:v:38:y:2008:i:1:p:99-108
    DOI: 10.1111/j.1574-0862.2007.00285.x
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    References listed on IDEAS

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    1. Caudill, Steven B. & Ford, Jon M., 1993. "Biases in frontier estimation due to heteroscedasticity," Economics Letters, Elsevier, vol. 41(1), pages 17-20.
    2. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
    3. 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.
    4. A. N. Halter & H. O. Carter & J. G. Hocking, 1957. "A Note on the Transcendental Production Function y=cx1a1eb1x1x2a2eb2x2," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 39(4), pages 966-974.
    5. Giannis Karagiannis & Peter Midmore & Vangelis Tzouvelekas, 2004. "Parametric Decomposition of Output Growth Using A Stochastic Input Distance Function," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1044-1057.
    6. Álvarez, Antonio & Arias, Carlos & Kumbhakar, Subal, 2003. "Empirical Consequences of Direction Choice in Technical Efficiency Analysis," Efficiency Series Papers 2003/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    7. 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.
    8. Fare, Rolf & Knox Lovell, C. A., 1978. "Measuring the technical efficiency of production," Journal of Economic Theory, Elsevier, vol. 19(1), pages 150-162, October.
    9. Caudill, Steven B & Ford, Jon M & Gropper, Daniel M, 1995. "Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 105-111, January.
    10. 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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    3. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2022. "Efficiency Analysis with Stochastic Frontier Models Using Popular Statistical Softwares," Springer Books, in: Duangkamon Chotikapanich & Alicia N. Rambaldi & Nicholas Rohde (ed.), Advances in Economic Measurement, chapter 0, pages 129-171, Springer.
    4. Bao Hoang Nguyen & Robin C. Sickles & Valentin Zelenyuk, 2021. "What do we know from the vast literature on efficiency and productivity in healthcare? A Systematic Review and Bibliometric Analysis," CEPA Working Papers Series WP092021, School of Economics, University of Queensland, Australia.
    5. Tsakiridis, Andreas & Mateo-Mantecón, Ingrid & O'Connor, Eamonn & Hynes, Stephen & O'Donoghue, Cathal, 2021. "Efficiency benchmarking of Irish and North Atlantic Spanish ports: Implications for blue growth," Utilities Policy, Elsevier, vol. 72(C).
    6. Alghalith, Moawia, 2010. "Preferences estimation without approximation," European Journal of Operational Research, Elsevier, vol. 207(2), pages 1144-1146, December.
    7. Moawia Alghalith, 2006. "Joint production and price uncertainty: hypothesis tests," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 49(3), pages 265-274.
    8. Bokusheva, Raushan & Kumbhakar, Subal C., 2014. "A Distance Function Model with Good and Bad Outputs," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182765, European Association of Agricultural Economists.

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