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Sensitivity and Stability Analysis in DEA: Some Recent Developments

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Abstract

This papersurveys recently developed analytical methods for studying thesensitivity of DEA results to variations in the data. The focusis on the stability of classification of DMUs (Decision MakingUnits) into efficient and inefficient performers. Early workon this topic concentrated on developing solution methods andalgorithms for conducting such analyses after it was noted thatstandard approaches for conducting sensitivity analyses in linearprogramming could not be used in DEA. However, some of the recentwork we cover has bypassed the need for such algorithms. Evolvingfrom early work that was confined to studying data variationsin only one input or output for only one DMU at a time, the newermethods described in this paper make it possible to determineranges within which all data may be varied for any DMU beforea reclassification from efficient to inefficient status (or vice versa) occurs. Other coverage involves recent extensionswhich include methods for determining ranges of data variationthat can be allowed when all data are varied simultaneously for all DMUs. An initial section delimits the topics to be covered.A final section suggests topics for further research.

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References

  • Ahn, T., A. Charnes, and W. W. Cooper. (1988). “A Note on the Efficiency Characterizations Obtained in Different DEA Models.” Socio-Economic Planning Sciences 6, 253–257.

    Google Scholar 

  • Ahn, T., and L. M. Seiford. (1993). “Sensitivity of DEA to Models and Variable Sets in an Hypothesis Test Setting: The Efficiency of University Operations.” In Y. Ijiri (ed.), Creative and Innovative Approaches to the Science of Management. New York: Quorum Books.

    Google Scholar 

  • Andersen, P., and N. C. Petersen. (1993). “A Procedure for Ranking Efficient Units in Data Envelopment Analysis.” Management Science 39, 1261–1264.

    Google Scholar 

  • Banker, R. D. (1993). “Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation.” Management Science 39, 1265–1273.

    Google Scholar 

  • Banker, R. D., H. Chang, and W. W. Cooper. (1996). “Simulation Studies of Efficiency, Returns to Scale and Misspecification with Nonlinear Functions in DEA.” Annals of Operations Research 66, 233–253.

    Google Scholar 

  • Banker, R. D., A. Charnes, W. W. Cooper, and A. Maindiratta. (1988). “A Comparison of DEA and Translog Estimates of Production Frontiers Using Simulated Observations from a Known Technology.” In A. Dogramaci and R. Färe (eds.), Applications of Modern Production Theory: Efficiency and Productivity. Boston: Kluwer Academic Publishers.

    Google Scholar 

  • Boljuncic, V. (1999). “A Note on Robustness of the Efficient DMUs in Data Envelopment Analysis.” European Journal of Operational Research 112, 240–244.

    Google Scholar 

  • Boljuncic, V. (1998). “Sensitivity Analysis in the Additive Model of Data Envelopment Analysis.” International Journal of System Sciences 20, 219–222.

    Google Scholar 

  • Boljuncic, V., and L. Neralic. (1999). “Evaluation of Robustness of Decision Making Units Using Dual Multipliers.” Paper presented at 6th European Workshop on Productivity and Efficiency, Royal Agricultural University, Copenhagen, Denmark, 10/29 10/31/99.

  • Bulla, S. P., W. W. Cooper, K. S. Park, and D. Wilson. (2000). “Evaluating Efficiencies of Turbofan Jet Engines in Multiple Input-Output Contexts: A Data Envelopment Analysis Approach.” Journal of Propulsion and Power 6 (to appear).

  • Charnes, A., and W. W. Cooper. (1961). Management Models and Industrial Applications of Linear Programming. New York: John Wiley and Sons, Inc.

    Google Scholar 

  • Charnes, A., and W. W. Cooper. (1968). “Structural Sensitivity Analysis in Linear Programming and an Exact Product Form Left Inverse.” Naval Research Logistics Quarterly 15, 517–522.

    Google Scholar 

  • Charnes, A., W. W. Cooper, D. Divine, T. W. Ruefli, and D. Thomas. (1989). “Comparisons of DEA and Existing Ratio and Regression Systems for Effecting Efficiency Evaluations of Regulated Electric Cooperation in Texas.” Research in Governmental and Nonprofit Accounting 5, 187–210.

    Google Scholar 

  • Charnes, A., W. W. Cooper, B. Golany, L. Seiford, and J. Stutz. (1985). “Foundations of Data Envelopment Analysis for Pareto-Koopmans Efficient Empirical Production Functions.” Journal of Econometrics 30, 91–107.

    Google Scholar 

  • Charnes, A., W. W. Cooper, A. Y. Lewin, R. C. Morey, and J. J. Rousseau. (1985). “Sensitivity and Stability Analysis in DEA.” Annals of Operations Research 2, 139–150.

    Google Scholar 

  • Charnes, A., W. W. Cooper, and S. Li. (1989). “Using DEA to Evaluate Relative Efficiencies in the Economic Performance of Chinese Cities.” Socio-Economic Planning Sciences 23, 325–344.

    Google Scholar 

  • Charnes, A., W. W. Cooper, and E. Rhodes. (1978). “Measuring the Efficiency of Decision Making Units.” European Journal of Operational Research 2, 429–444.

    Google Scholar 

  • Charnes, A., W. W. Cooper, and E. Rhodes. (1980). Appendix: “The Distribution of DMU Efficiency Measures.” In A. Charnes and W. W. Cooper. (1980). “Management Science Relations for Evaluation and Management Accountability.” Journal of Enterprise Management 2, 160–162.

    Google Scholar 

  • Charnes, A., W. W. Cooper, and R. M. Thrall. (1991). “A Structure for Characterizing and Classifying Efficiencies in DEA.” Journal of Productivity Analysis 3, 197–237.

    Google Scholar 

  • Charnes, A., W. W. Cooper, and R. M. Thrall. (1986). “Identifying and Classifying Scale and Technical Inefficiencies in Observed Data and Data Envelopment Analysis.” Operations Research Letter 5, 105–110.

    Google Scholar 

  • Charnes, A., S. Haag, P. Jaska, and J. Semple. (1992a). “Sensitivity of Efficiency Calculations in the Additive Model of Data Envelopment Analysis.” Journal of Systems Sciences 23, 789–798.

    Google Scholar 

  • Charnes, A., and L. Neralic. (1992b). “Sensitivity Analysis of the Proportionate Change of Inputs (or Outputs) in Data Envelopment Analysis.” Glasnik Matematicki 27, 393–405.

    Google Scholar 

  • Charnes, A., J. J. Rousseau, and J. H. Semple. (1996). “Sensitivity and Stability of Efficiency Classifications in DEA.” Journal of Productivity Analysis 7, 5–18.

    Google Scholar 

  • Cherchye, L., T. Kuosmanen, and T. Post. (2000). “New Tools for Dealing with Errors-in-Variables in DEA.” Discussion Paper Series DPS 00.06. Leuven, Belgium: Katholieke Universiteit Leuven, Dept. of Economics.

    Google Scholar 

  • Cooper, W. W., H. Deng, B. Gu, and S. Li. (2000). “Using DEA to Improve the Management of Congestion in Chinese Industries (1981–1997).” Socio-Economic Planning Sciences (to appear 2001).

  • Cooper, W. W., K. S. Park, and J. Pastor. (1999). “RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models and Relations to Other Models and Measures in DEA.” Journal of Productivity Analysis 11, 5–42.

    Google Scholar 

  • Cooper, W. W., L. M. Seiford, and K. Tone. (1999). Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Norwell, Mass.: Kluwer Academic Publisher.

    Google Scholar 

  • Dula, J. H., and B. L. Hickman. (1997). “Effects of Excluding the Column Being Scored from the DEA Envelopment Technology Matrix.” Journal of the Operational Research Society 48, 1001–1012.

    Google Scholar 

  • Gonzales-Lima, M. D., R. A. Tapia, and R. M. Thrall. (1996). “On the Construction of Strong Complementarity Slackness Solutions for DEA Linear Programming Problems Using a Primal-Dual Interior-Point Method.” Annals of Operations Research 66, 139–162.

    Google Scholar 

  • Grosskopf, S. (1996). “Statistical Inference and Nonparametric Efficiency: A Selective Survey.” Journal of Productivity Analysis, 161–176.

  • Joro, T., P. Korhonen, and J. Wallenius. (1998). “Structural Comparison of Data Envelopment Analysis and Multiple Objective Programming.” Management Science 44, 962–970.

    Google Scholar 

  • Neralic, L. (1997). “Sensitivity in Data Envelopment Analysis for Arbitrary Perturbations of Data.” Glasnik Matematicki 32, 315–335.

    Google Scholar 

  • Neralic, L. (2000). “Preservation of Efficiency and Inefficiency Classification in Data Envelopment Analysis.” Research Report Faculty of Economics, University of Zagreb, Kennedyev TRG 6, 1000 Zagreb, Croatia.

    Google Scholar 

  • Post, T. (1999). Finding the Frontier: Methodological Advances in Data Envelopment Analysis. Rotterdam: Tinbergen Institute Research Series. Erasmus University.

    Google Scholar 

  • Raab, R., D. Kotamrajv, and S. Haag. (2000). “Efficient Provision of Child Quality of Life in Less Developed Contries. Conventional Development Indexes vs. A Programming Approach to Development Indexes.” Socio-Economic Planning Sciences 34, 51–67.

    Google Scholar 

  • Seiford, L. M. (1994). “A DEA Bibliography (1978–1992).” In A. Charnes, W. W. Cooper, A. Lewin, and L. M. Seiford (eds.), Chapter 22 in Data Envelopment Analysis: Theory, Methodology and Applications. Norwell, Mass: Kluwer Academic Publishers.

    Google Scholar 

  • Seiford, L. M., and J. Zhu. (1998a). “Infeasibility of Super-Efficiency Data Envelopment Analysis Models.” INFOR 37, 174–187.

    Google Scholar 

  • Seiford, L. M., and J. Zhu. (1998b). “Sensitivity Analysis of DEA Models for Simultaneous Changes in All of the Data.” Journal of the Operational Research Society 49, 1060–1071.

    Google Scholar 

  • Seiford, L. M., and J. Zhu. (1999). “Sensitivity Analysis of Returns to Scale Classifications in DEA.” Journal of Productivity Analysis 12, 55–75.

    Google Scholar 

  • Seiford, L. M., and J. Zhu. (1998c). “Stability Regions for Maintaining Efficiency in Data Envelopment Analysis.” European Journal of Operational Research 108, 127–139.

    Google Scholar 

  • Sexton, T. R., R. H. Silkman, and R. H. Hogan. (1986). “Measuring Efficiency: An Assessment of Data Envelopment Analysis, New Directions for Program Evaluations.” In R. H. Silkman (ed.), Measuring Efficiency: An Assessment of Data Envelopment Analysis. Publication No. 32 in the series New Directions for Program Evaluations. A publication of the American Evaluation Association. San Francisco: Jossy Bass.

    Google Scholar 

  • Simar, L., and P. W. Wilson. (1998). “Sensitivity Analysis of Efficiency Scores: How to Bootstrap in Nonparametric Frontier Models.” Management Science 44, 49–61.

    Google Scholar 

  • Simar, L., and P. W. Wilson. (2000). “Statistical Inference in Nonparametric Frontier Models: The State of the Art.” Journal of Productivity Analysis 13, 49–78.

    Google Scholar 

  • Thompson, R. G., P. S. Dharmapala, J. Diaz, M. D. Gonzalez-Lima, and R. M. Thrall. (1996). “DEA Multiplier Analytic Center Sensitivity Analysis with an Illustrative Application to Independent Oil Cos.” Annals of Operations Research 66, 163–180.

    Google Scholar 

  • Thompson, R. G., P. S. Dharmapala, and R. M. Thrall. (1994). “Sensitivity Analysis of Efficiency Measures with Applications to Kansas Farming and Illinois Coal Mining.” In A. Charnes, W. W. Cooper, A. Y. Lewin, and L. M. Seiford (eds.), Data Envelopment Analysis: Theory, Methodology and Applications. Norwell, Mass.: Kluwer Academic Publishers, pp. 393–422.

    Google Scholar 

  • Thrall, R. M. (1989). “Classification of Transitions Under Expansion of Inputs and Outputs.” Managerial and Decision Economics 10, 159–162.

    Google Scholar 

  • Thrall, R. M. (1996). “Duality, Classification and Slacks in DEA.” Annals of Operations Research 66, 104–138.

    Google Scholar 

  • Thrall, R. M. (2000). “Measures in DEA with an Application to the Malmquist Index.” Journal of Productivity Analysis 13, 125–138.

    Google Scholar 

  • Wilson, P. W. (1995). “Detecting Influential Observations in Data Envelopment Analysis.” Journal of Productivity Analysis 6, 27–46.

    Google Scholar 

  • Zhu, J. (1996a). “DEA/AR Analysis of the 1988–1989 Performance of Nanjing Textile Corporation.” Annals of Operations Research 66, 311–335.

    Google Scholar 

  • Zhu, J. (1996b). “Robustness of the Efficient DMUs in Data Envelopment Analysis.” European Journal of Operational Research 90, 451–460.

    Google Scholar 

  • Zhu, J. (1999). “Super Efficiency and DEA Sensitivity.” Working Paper, Department of Management, Worcester Polytechnic Institute, Worcester, Mass. 01609.

    Google Scholar 

  • Zlobec, S., R. Gardner, and A. Ben-Israel. (1981). “Regions of Stability for Arbitrarily Perturbed Convex programs.” In A. Fiacco (ed.), Mathematical Programming with Data Perturbations. New York: Marcel Dekker, pp. 69–89.

    Google Scholar 

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Cooper, W.W., Li, S., Seiford, L.M. et al. Sensitivity and Stability Analysis in DEA: Some Recent Developments. Journal of Productivity Analysis 15, 217–246 (2001). https://doi.org/10.1023/A:1011128409257

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