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Data Envelopment Analysis: An Overview

Listed author(s):
  • Subhash C. Ray

    (University of Connecticut)

Over the past decades Data Envelopment Analysis (DEA) has emerged as an important nonparametric method of evaluating performance of decision making units through benchmarking. Although developed primarily for measuring technical efficiency, DEA is now applied extensively for measuring scale efficiency, cost efficiency, and profit efficiency as well. This paper integrates the different DEA models commonly applied in empirical research with their underlying theoretical foundations in neoclassical production economics.

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File URL: http://web2.uconn.edu/economics/working/2014-33.pdf
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Paper provided by University of Connecticut, Department of Economics in its series Working papers with number 2014-33.

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Length: 59 pages
Date of creation: Nov 2014
Handle: RePEc:uct:uconnp:2014-33
Contact details of provider: Postal:
University of Connecticut 365 Fairfield Way, Unit 1063 Storrs, CT 06269-1063

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Web page: http://www.econ.uconn.edu/

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  1. Kerstens, Kristiaan & Vanden Eeckaut, Philippe, 1999. "Estimating returns to scale using non-parametric deterministic technologies: A new method based on goodness-of-fit," European Journal of Operational Research, Elsevier, vol. 113(1), pages 206-214, February.
  2. Banker, Rajiv D. & Thrall, R. M., 1992. "Estimation of returns to scale using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 62(1), pages 74-84, October.
  3. Murty, Sushama & Russell, R. Robert, 2010. "On modeling pollution-generating technologies," The Warwick Economics Research Paper Series (TWERPS) 931, University of Warwick, Department of Economics.
  4. Banker, Rajiv D., 1984. "Estimating most productive scale size using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 17(1), pages 35-44, July.
  5. 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.
  6. Fare, Rolf & Knox Lovell, C. A., 1978. "Measuring the technical efficiency of production," Journal of Economic Theory, Elsevier, vol. 19(1), pages 150-162, October.
  7. Banker, Rajiv D. & Chang, Hsihui & Cooper, William W., 1996. "Equivalence and implementation of alternative methods for determining returns to scale in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 89(3), pages 473-481, March.
  8. 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 435-444, June.
  9. Murty, Sushama & Robert Russell, R. & Levkoff, Steven B., 2012. "On modeling pollution-generating technologies," Journal of Environmental Economics and Management, Elsevier, vol. 64(1), pages 117-135.
  10. Luenberger, David G., 1992. "Benefit functions and duality," Journal of Mathematical Economics, Elsevier, vol. 21(5), pages 461-481.
  11. Silva Portela, Maria Conceicao A. & Thanassoulis, Emmanuel, 2005. "Profitability of a sample of Portuguese bank branches and its decomposition into technical and allocative components," European Journal of Operational Research, Elsevier, vol. 162(3), pages 850-866, May.
  12. Subhash C. Ray, 2010. "A One-Step Procedure for Returns to Scale Classification of Decision Making Units in Data Envelopment Analysis," Working papers 2010-07, University of Connecticut, Department of Economics.
  13. Banker, Rajiv D. & Cooper, William W. & Seiford, Lawrence M. & Thrall, Robert M. & Zhu, Joe, 2004. "Returns to scale in different DEA models," European Journal of Operational Research, Elsevier, vol. 154(2), pages 345-362, April.
  14. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
  15. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
  16. Rolf Färe & Shawna Grosskopf, 2000. "Theory and Application of Directional Distance Functions," Journal of Productivity Analysis, Springer, vol. 13(2), pages 93-103, March.
  17. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
  18. P. Byrnes & R. Färe & S. Grosskopf, 1984. "Measuring Productive Efficiency: An Application to Illinois Strip Mines," Management Science, INFORMS, vol. 30(6), pages 671-681, June.
  19. 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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