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Sensitivity analysis of efficiency rankings to distributional assumptions: applications to Japanese water utilities

  • Yane, Shinji
  • Berg, Sanford

This paper examines the robustness of efficiency score rankings across four distributional assumptions for trans-log stochastic production-frontier models, using data from 1,221 Japanese water utilities (for 2004 and 2005). One-sided error terms considered include the half-normal, truncated normal, exponential, and gamma distributions. Results are compared for homoscedastic and doubly heteroscedastic models, where we also introduce a doubly heteroscedastic variable mean model, and examine the sensitivity of the nested models to a stronger heteroscedasticity correction for the one-sided error component. The results support three conclusions regarding the sensitivity of efficiency rankings to distributional assumptions. When four standard distributional assumptions are applied to a homoscedastic stochastic frontier model, the efficiency rankings are quite consistent. When those assumptions are applied to a doubly heteroscedastic stochastic frontier model, the efficiency rankings are consistent when proper and sufficient arguments for the variance functions are included in the model. When a more general model, like a variable mean model is estimated, efficiency rankings are quite sensitive to heteroscedasticity correction schemes.

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File URL: http://mpra.ub.uni-muenchen.de/32892/1/MPRA_paper_32892.pdf
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 32892.

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Date of creation: 2011
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Handle: RePEc:pra:mprapa:32892
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  1. Wei Siang Wang & Peter Schmidt, 2007. "On The Distribution of Estimated Technical Efficiency in Stochastic Frontier Models," CEPA Working Papers Series WP022007, School of Economics, University of Queensland, Australia.
  2. Kaddour Hadri & Cherif Guermat & Julie Whittaker, 2003. "Estimating Farm Efficiency in the Presence of Double Heteroscedasticity Using Panel Data," Journal of Applied Economics, Universidad del CEMA, vol. 0, pages 255-268, November.
  3. 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 105-11, January.
  4. Hadri, Kaddour, 1999. "Estimation of a Doubly Heteroscedastic Stochastic Frontier Cost Function," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 359-63, July.
  5. Tim Coelli & Antonio Estache & Sergio Perelman & Lourdes Trujillo, 2003. "A Primer on Efficiency Measurement for Utilities and Transport Regulators," World Bank Publications, The World Bank, number 15149.
  6. 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.
  7. Peter Schmidt & Antonio Alvarez & Christine Amsler, 2004. "Interpreting and testing the scaling property in models where inefficiency depends on firm characteristics," Econometric Society 2004 Far Eastern Meetings 520, Econometric Society.
  8. 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.
  9. William Greene, 2003. "Distinguishing Between Heterogeneity and Inefficiency: Stochastic Frontier Analysis of the World Health Organization’s Panel Data on National Health Care Systems," Working Papers 03-10, New York University, Leonard N. Stern School of Business, Department of Economics.
  10. Haney, Aoife Brophy & Pollitt, Michael G., 2009. "Efficiency analysis of energy networks: An international survey of regulators," Energy Policy, Elsevier, vol. 37(12), pages 5814-5830, December.
  11. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, 01.
  12. Kristof De Witte & Rui C. Marques, 2008. "Capturing the environment, a metafrontier approach to the drinking water sector," Center for Economic Studies - Discussion papers ces0804, Katholieke Universiteit Leuven, Centrum voor Economische Studiën.
  13. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
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