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Estimation of Technical Inefficiencies with Heterogeneous Technologies

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  • Ho-chuan Huang

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Abstract

This paper considers the measurement of firm's specific (in)efficiency while allows for the possible heterogeneous technologies adopted by different firms. A flexible stochastic frontier model with random coefficients is proposed to distinguish technical inefficiency from technological differences across firms. Posterior inference of the model is made possible via the simulation-based approach, namely, Markov chain Monte Carlo method. The model is applied to a real data set which has also been considered in Christensen and Greene (1976), Greene (1990), Tsionas (2002), among others. Empirical results show that the regression coefficients can vary across firms, indicating the adoption of heterogeneous technologies by different firms. More importantly, we find that, without considering this possible heterogeneity, the inefficiency of firms can be over-estimated. Copyright Kluwer Academic Publishers 2004

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Bibliographic Info

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

Volume (Year): 21 (2004)
Issue (Month): 3 (May)
Pages: 277-296

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Handle: RePEc:kap:jproda:v:21:y:2004:i:3:p:277-296

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

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Keywords: stochastic frontier; random-coefficient; Gibbs sampler; Metropolis–Hastings;

References

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  1. William H. Greene, 2000. "Simulated Likelihood Estimation of the Normal-Gamma Stochastic Frontier Function," Working Papers 00-05, New York University, Leonard N. Stern School of Business, Department of Economics.
  2. Efthymios Tsionas, 2000. "Full Likelihood Inference in Normal-Gamma Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 13(3), pages 183-205, May.
  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. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
  5. Yangseon Kim & Peter Schmidt, 2000. "A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data," Journal of Productivity Analysis, Springer, vol. 14(2), pages 91-118, September.
  6. Koop, G. & Osiewalski, J. & Steel, M. F. J., . "Bayesian efficiency analysis through individual effects: Hospital cost frontiers," CORE Discussion Papers RP -1245, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  7. Fernandez, Carmen & Osiewalski, Jacek & Steel, Mark F. J., 1997. "On the use of panel data in stochastic frontier models with improper priors," Journal of Econometrics, Elsevier, vol. 79(1), pages 169-193, July.
  8. 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.
  9. 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.
  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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Citations

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Cited by:
  1. Anup Kumar Bhandari & Pradip Maiti, 2007. "Efficiency of Indian Manufacturing Firms: Textile Industry as a Case Study," International Journal of Business and Economics, College of Business, and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 6(1), pages 71-88, April.
  2. Carta, Alessandro & Steel, Mark F.J., 2012. "Modelling multi-output stochastic frontiers using copulas," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3757-3773.
  3. Lambarraa, Fatima, 2011. "Dynamic Efficiency Analysis of Spanish Outdoor and Greenhouse Horticulture Sector," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114408, European Association of Agricultural Economists.
  4. Anis Bou Abid & Imed Drine, 2010. "Efficiency Frontier and Matching Process on the Labour Market: Evidence from Tunisia," Working Paper Series wp2010-123, World Institute for Development Economic Research (UNU-WIDER).
  5. Jorge Galán & Helena Veiga & Michael Wiper, 2014. "Bayesian estimation of inefficiency heterogeneity in stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 42(1), pages 85-101, August.
  6. Morrison Paul, Catherine J. & Sauer, Johannes, 2010. "Technologies And Localized Technical Change," 50st Annual Conference, Braunschweig, Germany, September 29-October 1, 2010 93963, German Association of Agricultural Economists (GEWISOLA).
  7. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
  8. Lambarraa, Fatima & Stefanou, Spiro E. & Gil, Jose Maria, 2009. "The analysis of irreversibility, uncertainty and dynamic technical inefficiency on the investment decision in Spanish olive sector," 2009 Conference, August 16-22, 2009, Beijing, China 51397, International Association of Agricultural Economists.
  9. Lambarraa, Fatima, 2012. "The Spanish Horticulture Sector: A dynamic efficiency analysis of Outdoor and Greenhouse farms," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126797, International Association of Agricultural Economists.
  10. Oum, Tae H. & Yan, Jia & Yu, Chunyan, 2008. "Ownership forms matter for airport efficiency: A stochastic frontier investigation of worldwide airports," Journal of Urban Economics, Elsevier, vol. 64(2), pages 422-435, September.
  11. Philippe K. Widmer & Peter Zweifel & Mehdi Farsi, 2011. "Accounting for heterogeneity in the measurement of hospital performance," ECON - Working Papers 052, Department of Economics - University of Zurich.
  12. Pavlos Almanidis, 2013. "Accounting for heterogeneous technologies in the banking industry: a time-varying stochastic frontier model with threshold effects," Journal of Productivity Analysis, Springer, vol. 39(2), pages 191-205, April.
  13. Hailu, Getu & Goddard, Ellen W. & Jeffrey, Scott R., 2005. "Measuring Efficiency in Fruit and Vegetable Marketing Co-operatives with Heterogeneous Technologies in Canada," 2005 Annual meeting, July 24-27, Providence, RI 19507, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  14. Yan, Jia & Sun, Xinyu & Liu, John J., 2009. "Assessing container operator efficiency with heterogeneous and time-varying production frontiers," Transportation Research Part B: Methodological, Elsevier, vol. 43(1), pages 172-185, January.

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