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The Quantile Regression Approach to Efficiency Measurement: Insights from Monte Carlo Simulations

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  • ChunpingLiu
  • Audrey Laporte
  • Brian Ferguson

Abstract

In the health economics literature there is an ongoing debate over approaches used to estimate the efficiency of health systems at various levels, from the level of the individual hospital- or nursing home –up to that of the health system as a whole. The two most widely used approaches to evaluating the efficiency with which various units deliver care are non-parametric Data Envelopment Analysis (DEA) and parametric Stochastic Frontier Analysis (SFA). Productivity researchers tend to have very strong preferences over which methodology to use for efficiency estimation. In this paper, we use generated experimental datasets and Monte Carlo simulation to compare the performance of DEA and SFA in terms of their ability to accurately estimate efficiency. We also evaluate Quantile regression as a potential alternative approach. A Cobb-Douglas production function, random error terms and a technical inefficiency term with different distributions are used to calculate the observed output. The results, based on these experiments, suggest that neither DEA nor SFA can be regarded as clearly dominant, and that Quantile regression because it yields more reliable estimates, represents a useful alternative approach in efficiency studies.

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Bibliographic Info

Paper provided by HEDG, c/o Department of Economics, University of York in its series Health, Econometrics and Data Group (HEDG) Working Papers with number 07/14.

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Date of creation: Jul 2007
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Handle: RePEc:yor:hectdg:07/14

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Web page: http://www.york.ac.uk/res/herc/research/hedg/
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Keywords: Technical efficiency; data envelopment analysis; stochastic frontier estimation; quantile regression.;

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  1. Gong, Byeong-Ho & Sickles, Robin C., 1991. "Finite Sample Evidence on the Performance of Stochastic Frontiers and Data Envelopment Analysis Using Panel Data," Working Papers 91-12, C.V. Starr Center for Applied Economics, New York University.
  2. Jacobs,Rowena & Smith,Peter C. & Street,Andrew, 2006. "Measuring Efficiency in Health Care," Cambridge Books, Cambridge University Press, number 9780521851442.
  3. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
  4. Banker, Rajiv D. & Chang, Hsihui & Cooper, William W., 2004. "A simulation study of DEA and parametric frontier models in the presence of heteroscedasticity," European Journal of Operational Research, Elsevier, vol. 153(3), pages 624-640, March.
  5. Van Biesebroeck, Johannes, 2004. "Robustness of productivity estimates," Open Access publications from Katholieke Universiteit Leuven urn:hdl:123456789/253800, Katholieke Universiteit Leuven.
  6. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
  7. Newhouse, Joseph P., 1994. "Frontier estimation: How useful a tool for health economics?," Journal of Health Economics, Elsevier, vol. 13(3), pages 317-322, October.
  8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
  9. Cristina Bernini & Marzia Freo & Attilio Gardini, 2004. "Quantile estimation of frontier production function," Empirical Economics, Springer, vol. 29(2), pages 373-381, 05.
  10. Resti, Andrea, 2000. "Efficiency measurement for multi-product industries: A comparison of classic and recent techniques based on simulated data," European Journal of Operational Research, Elsevier, vol. 121(3), pages 559-578, March.
  11. 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.
  12. 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 435-44, June.
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