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The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations

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Author Info
Chunping Liu (Department of Economics, University of Guelph, Guelph, Ont., Canada)
Audrey Laporte (Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ont., Canada)
Brian S. Ferguson (Department of Economics, University of Guelph, Guelph, Ont., Canada)

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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 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, depending on the quantile estimated, the quantile regression approach may be a useful addition to the armamentarium of methods for estimating technical efficiency. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/hec.1398
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Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

Volume (Year): 17 (2008)
Issue (Month): 9 ()
Pages: 1073-1087
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Handle: RePEc:wly:hlthec:v:17:y:2008:i:9:p:1073-1087

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January. [Downloadable!] (restricted)
  2. 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. [Downloadable!] (restricted)
  3. 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. [Downloadable!] (restricted)
  4. Johannes Van Biesebroeck, 2004. "Robustness of Productivity Estimates," NBER Working Papers 10303, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  5. Cristina Bernini & Marzia Freo & Attilio Gardini, 2004. "Quantile estimation of frontier production function," Empirical Economics, Springer, vol. 29(2), pages 373-381, 05. [Downloadable!] (restricted)
  6. Gong, Byeong-Ho & Sickles, Robin C., 1992. "Finite sample evidence on the performance of stochastic frontiers and data envelopment analysis using panel data," Journal of Econometrics, Elsevier, vol. 51(1-2), pages 259-284. [Downloadable!] (restricted)
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  7. 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. [Downloadable!] (restricted)
  8. 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. [Downloadable!] (restricted)
  9. 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. [Downloadable!] (restricted)
  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. [Downloadable!] (restricted)
  11. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall. [Downloadable!] (restricted)
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