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Value Efficiency Analysis for Incorporating Preference Information in Data Envelopment Analysis


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  • M. Halme
  • T. Joro
  • P. Korhonen
  • S. Salo
  • J. Wallenius


We develop a procedure and the requisite theory for incorporating preference information in a novel way in the efficiency analysis of Decision Making Units. The efficiency of Decision Making Units is defined in the spirit of Data Envelopment Analysis (DEA), complemented with Decision Maker's preference information concerning the desirable structure of inputs and outputs. Our procedure begins by aiding the Decision Maker in searching for the most preferred combination of inputs and outputs of Decision Making Units (for short, Most Preferred Solution) which are efficient in DEA. Then, assuming that the Decision Maker's Most Preferred Solution maximizes his/her underlying (unknown) value function at the moment when the search is terminated, we approximate the indifference contour of the value function at this point with its possible tangent hyperplanes. Value Efficiency scores are then calculated for each Decision Making Unit comparing the inefficient units to units having the same value as the Most Preferred Solution. The resulting Value Efficiency scores are optimistic approximations of the true scores. The procedure and the resulting efficiency scores are immediately applicable to solving practical problems.

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Bibliographic Info

Paper provided by International Institute for Applied Systems Analysis in its series Working Papers with number ir98054.

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Date of creation: Aug 1998
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Handle: RePEc:wop:iasawp:ir98054

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  1. Thanassoulis, E. & Dyson, R. G., 1992. "Estimating preferred target input-output levels using data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 56(1), pages 80-97, January.
  2. Charnes, A. & Cooper, W. W. & Golany, B. & Seiford, L. & Stutz, J., 1985. "Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions," Journal of Econometrics, Elsevier, vol. 30(1-2), pages 91-107.
  3. 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.
  4. Bell,David E. & Raiffa,Howard & Tversky,Amos (ed.), 1989. "Decision Making," Cambridge Books, Cambridge University Press, number 9780521368513, April.
  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.
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Cited by:
  1. M. Halme & T. Joro & M. Koivu, 1998. "Dealing with Interval Scale Data in Data Envelopment Analysis," Working Papers ir98112, International Institute for Applied Systems Analysis.
  2. P. Korhonen & A. Siljamaeki & M. Soismaa, 1998. "Practical Aspects of Value Efficiency Analysis," Working Papers ir98042, International Institute for Applied Systems Analysis.
  3. T. Joro & E-J. Viitala, 1999. "The Efficiency of Public Forestry Organizations: A Comparison of Different Weight Restriction Approaches," Working Papers ir99059, International Institute for Applied Systems Analysis.
  4. Marshall, Elizabeth P. & Shortle, James S., 2004. "Using Dea And Vea To Evaluate Quality Of Life In The Mid-Atlantic States," 2004 Annual meeting, August 1-4, Denver, CO 20198, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  5. Martin Eling, 2006. "Performance measurement of hedge funds using data envelopment analysis," Financial Markets and Portfolio Management, Springer, vol. 20(4), pages 442-471, December.
  6. Soushi Suzuki & Peter Nijkamp, 2008. "A Generalized Goals-achievement Model in Data Envelopment Analysis," Tinbergen Institute Discussion Papers 08-110/3, Tinbergen Institute.
  7. Halme, Merja & Korhonen, Pekka, 2000. "Restricting weights in value efficiency analysis," European Journal of Operational Research, Elsevier, vol. 126(1), pages 175-188, October.
  8. T. Joro, 1998. "Models for Identifying Target Units in Data Envelopment Analysis: Comparison and Extension," Working Papers ir98055, International Institute for Applied Systems Analysis.
  9. Özgür Özpeynirci & Murat Köksalan, 2007. "Performance evaluation using data envelopment analysis in the presence of time lags," Journal of Productivity Analysis, Springer, vol. 27(3), pages 221-229, June.
  10. George E. Halkos & Nickolaos G. Tzeremes & Stavros A. Kourtzidis, 2010. "An application of statistical interference in DEA models: An analysis of public owned university departments' efficiency," EERI Research Paper Series EERI_RP_2010_17, Economics and Econometrics Research Institute (EERI), Brussels.
  11. Inha Oh & Jeong-Dong Lee & Seogwon Hwang & Almas Heshmati, 2010. "Analysis of product efficiency in the Korean automobile market from a consumer’s perspective," Empirical Economics, Springer, vol. 38(1), pages 119-137, February.
  12. Korhonen, Pekka & Tainio, Risto & Wallenius, Jyrki, 2001. "Value efficiency analysis of academic research," European Journal of Operational Research, Elsevier, vol. 130(1), pages 121-132, April.
  13. Bogetoft, Peter & Hougaard, Jens Leth, 2001. "Rational Inefficiencies," Unit of Economics Working papers 24191, Royal Veterinary and Agricultural University, Food and Resource Economic Institute.
  14. Peter Nijkamp & Soushi Suzuki, 2009. "A Generalized Goals-achievement Model in Data Envelopment Analysis: an Application to Efficiency Improvement in Local Government Finance in Japan," Spatial Economic Analysis, Taylor & Francis Journals, vol. 4(3), pages 249-274.
  15. Jiménez, Fernando & Zabala Iturriagagoitia, Jon Mikel & Zofío, José Luis, 2007. "Efficiency in Public Research Centers: Evaluating the Spanish Food Technology Program," Working Papers in Economic Theory 2007/04, Universidad Autónoma de Madrid (Spain), Department of Economic Analysis (Economic Theory and Economic History).
  16. Bogetoft, Peter & Nielsen, Kurt, 2002. "Internet Based Benchmarking," Unit of Economics Working papers 24215, Royal Veterinary and Agricultural University, Food and Resource Economic Institute.


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