The Quantile Regression Approach to Efficiency Measurement: Insights from Monte Carlo Simulations
AbstractIn 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper 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.
Date of creation: Jul 2007
Date of revision:
Contact details of provider:
Postal: HEDG/HERC, Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom
Phone: (0)1904 323776
Fax: (0)1904 323759
Web page: http://www.york.ac.uk/economics/postgrad/herc/hedg/
More information through EDIRC
Technical efficiency; data envelopment analysis; stochastic frontier estimation; quantile regression.;
Other versions of this item:
- Chunping Liu & Audrey Laporte & Brian S. Ferguson, 2008. "The quantile regression approach to efficiency measurement: insights from Monte Carlo simulations," Health Economics, John Wiley & Sons, Ltd., vol. 17(9), pages 1073-1087.
- NEP-ALL-2007-10-27 (All new papers)
- NEP-ECM-2007-10-27 (Econometrics)
- NEP-EFF-2007-10-27 (Efficiency & Productivity)
- NEP-HEA-2007-10-27 (Health Economics)
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.:
- 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.
- Jacobs,Rowena & Smith,Peter C. & Street,Andrew, 2006. "Measuring Efficiency in Health Care," Cambridge Books, Cambridge University Press, number 9780521851442, November.
- 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.
- Cristina Bernini & Marzia Freo & Attilio Gardini, 2004. "Quantile estimation of frontier production function," Empirical Economics, Springer, vol. 29(2), pages 373-381, 05.
- 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.
- Koenker,Roger, 2005.
Cambridge University Press, number 9780521845731, November.
- 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.
- 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.
- Johannes Van Biesebroeck, 2004.
"Robustness of Productivity Estimates,"
NBER Working Papers
10303, National Bureau of Economic Research, Inc.
- Gong, Byeong-Ho & Sickles, Robin C., 1991.
"Finite Sample Evidence on the Performance of Stochastic Frontiers and Data Envelopment Analysis Using Panel Data,"
91-12, C.V. Starr Center for Applied Economics, New York University.
- 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.
- 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.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Jane Rawlings).
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.