Data Envelopment Analysis

Part of the Springer Optimization and Its Applications book series (SOIA, volume 36)


In Section 1.2.9, the original model of data envelopment analysis (DEA), developed by Charnes, Cooper, and Rhodes [8], was introduced. With their study, DEA began as a new approach for efficiency and productivity analysis. They described DEA as a “mathematical programming model applied to observational data [that] provides a new way of obtaining empirical estimates of extremal relationships such as the production functions and/or efficient production possibility surfaces that are a cornerstone of modern economics” [34, p. 8].


Data Envelopment Analysis Undesirable Output Output Distance Function Input Distance Function Envelopment Surface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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References and Further Reading

  1. [1]
    A. I. Ali, Computational aspects of DEA, in A. Charnes, W. W. Cooper, A. Lewin, and L. M. Seiford, eds., Data Envelopment Analysis: Theory, Methodology, and Applications, Kluwer Academic Publishers, Boston, 1994, 63–88.Google Scholar
  2. [2]
    A. I. Ali and L. M. Seiford, Translation invariance in data envelopment analysis, Oper. Res. Lett., 9 (1990), 403–405.MATHCrossRefGoogle Scholar
  3. [3]
    T. Ahn, A. Charnes, and W. W. Cooper, Efficiency characterization in different DEA models, Socio-Econ. Planning Sci., 22 (1988), 253–257.CrossRefGoogle Scholar
  4. [4]
    P. W. Bauer, Recent developments in the econometric estimation of frontiers, J. Econometrics, 46 (1990), 39–56.CrossRefGoogle Scholar
  5. [5]
    D. Bouyssou, Using DEA as a tool for MCDM: Some remarks, J. Oper. Res. Soc., 50 (1999), 974–978.MATHCrossRefGoogle Scholar
  6. [6]
    R. D. Banker, A. Charnes, and W.W. Cooper, Some models for estimating technical and scale inefficiences in data envelopment analysis, Manage. Sci., 30 (1984), 1078–1092.MATHCrossRefGoogle Scholar
  7. [7]
    A. Charnes and W. W. Cooper, Programming with linear fractional functionals, Naval Res. Logist. Q., 9 (1962), 181–185.MATHCrossRefMathSciNetGoogle Scholar
  8. [8]
    A. Charnes,W.W. Cooper, and E. Rhodes, Measuring the efficiency of decision making units, Eur. J. Oper. Res., 2 (1978), 429–444.MATHCrossRefMathSciNetGoogle Scholar
  9. [9]
    A. Charnes, W. W. Cooper, and E. Rhodes, Short communication: Measuring the efficiency of decision making units, Eur. J. Oper. Res., 3 (1979), 339.CrossRefGoogle Scholar
  10. [10]
    A. Charnes, W. W. Cooper, B. Golany, L. M. Seiford, and J. Stutz, Foundations of data envelopment analysis for Pareto–Koopmans efficient empirical production functions, J. Econometrics, 30 (1985), 91–107.MATHCrossRefMathSciNetGoogle Scholar
  11. [11]
    W.W. Cooper, L. M. Seiford, and K.Tone, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References, and DEA-Solver Software, 2nd printing, Kluwer Academic Publishers, Boston, 2000.Google Scholar
  12. [12]
    W.W. Cooper, L. M. Seiford, and K.Tone, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References, and DEA-Solver Software, 2nd ed., Springer, NewYork, 2007.MATHGoogle Scholar
  13. [13]
    W. W. Cooper, R. G. Thompson, and R. M. Thrall, Extensions and new development in DEA, Ann. Oper. Res., 66 (1996), 3–45.MATHMathSciNetGoogle Scholar
  14. [14]
    G. Debreu, The coefficient of resource utilization, Econometrica, 19 (1951), 273–292.MATHCrossRefGoogle Scholar
  15. [15]
    W. E. Diewert and C. Parkan, Linear programming tests of regularity conditions for production frontiers, in W. Eichorn, R. Henn, K. Neumann, and R. W. Shephard, eds., Quantitative Studies in Production and Prices, Physica-Verlag, Würzburg, Germany, 1983.Google Scholar
  16. [16]
    H. Dyckhoff and K. Allen, Measuring ecological efficiency with data envelopment analysis (DEA), Eur. J. Oper. Res., 132 (2001), 312–325.MATHCrossRefGoogle Scholar
  17. [17]
    R. Färe and C. A. K. Lovell, Measuring the technical efficiency of production, J. Econ. Theory, 19 (1978), 150–162.MATHCrossRefGoogle Scholar
  18. [18]
    M. J. Farrel, The measurement of productive efficiency, J. R. Statist. Soc. Ser.A, 120-Part 3 (1957), 253–281.Google Scholar
  19. [19]
    R. Färe, S. Grosskopf, and C. A. K. Lovell, The Measurement of Efficiency of Production, Kluwer Academic Publishers, Boston, 1985.Google Scholar
  20. [20]
    R. Färe, S. Grosskopf, C. A. K. Lovell, and C. Pasurka, Multilateral productivity comparison when some outputs are undesirable: A nonparametric approach, Rev. Econ. Statist., 71 (1989), 90–98.CrossRefGoogle Scholar
  21. [21]
    R. Färe, S. Grosskopf, and D. Tyteca, An activity analysis model of the environmental performance of firms: Application to fossil-fuel-fired electric utilities, Ecol. Econ., 18 (1996), 161–175.CrossRefGoogle Scholar
  22. [22]
    R. Färe and D. Primont, Multi-Output Production and Duality: Theory and Applications, Kluwer Academic Publishers, Boston, 1997.MATHGoogle Scholar
  23. [23]
    H. O. Fried, C. A. K. Lovell, and S. S. Schmidt, eds., The Measurement of Productive Efficiency and Productivity Growth, Oxford University Press, NewYork, 2008.Google Scholar
  24. [24]
    E. G. Gomes and M. P. E. Lins, Modeling undesirable outputs with zero sum gains data envelopment analysis models, J. Oper. Res. Soc., 59-5 (2008), 616–623; advance online publication, 2007.Google Scholar
  25. [25]
    B. Golany,Y. Roll, and D. Rybak, Measuring efficiency of power plants in Israel by data envelopment analysis, IEEE Trans. Eng., 41-3 (1994), 291–301.CrossRefGoogle Scholar
  26. [26]
    F. Hernandez Sancho, A. Picazo Tadeo, and E. Reig Martinez, Efficiency and environmental regulation, Environ. Resource Econ., 15 (2000), 365–378.CrossRefGoogle Scholar
  27. [27]
    T. C. Koopmans, Analysis of production as an efficient combination of activities, in T. C.Koopmans, ed., Activity Analysis of Production and Allocation, Cowles Commission Monograph 13, Wiley, NewYork, 1951, 33–97.Google Scholar
  28. [28]
    P. J. Korhonen and M. Luptáčik, Eco-efficiency analysis of power plants: An extension of data envelopment analysis, Eur. J. Oper. Res., 154 (2004), 437–446.MATHCrossRefGoogle Scholar
  29. [29]
    C. A. K. Lovell and P. Schmidt, A comparison of alternative approaches to the measurement of productive efficiency, in A. Dogramaci and R. Färe, eds., Applications of Modern Production Theory: Efficiency and Productivity, Kluwer Academic Publishers, Boston, 1988.Google Scholar
  30. [30]
    M. Luptáčcik, Data envelopment analysis as a tool for measurement of eco-efficiency, in E. Dockner, R. F. Hartl, M. Luptáčcik, and G. Sorger, eds., Optimization, Dynamics and Economic Analysis, Physica-Verlag, Würtzburg, Germany, 2000, 36–48.Google Scholar
  31. [31]
    S. C. Ray, Data Envelopment Analysis: Theory and Techniques for Economics and Operation Research, Cambridge University Press, Cambridge, UK, 2004.Google Scholar
  32. [32]
    R. R. Russell, Measures of technical efficiency, J. Econ. Theory, 35 (1985), 109–126.MATHCrossRefGoogle Scholar
  33. [33]
    F. Scheel, Undesirable outputs in efficiency valuations, Eur. J. Oper. Res., 132 (2001), 400–410.MATHCrossRefGoogle Scholar
  34. [34]
    L. M. Seiford and R. M. Thrall, Recent developments in DEA: The mathematical programming approach to frontier analysis, J. Econometrics, 46 (1990), 7–38.MATHCrossRefMathSciNetGoogle Scholar
  35. [35]
    J. K. Sengupta, Data envelopment analysis for efficiency measurement in the stochastic case, Comput. Oper. Res., 14 (1987), 117–129.MATHCrossRefGoogle Scholar
  36. [36]
    R.W. Shephard, Cost and Production Functions, Princeton University Press, Princeton, NJ, 1953.MATHGoogle Scholar
  37. [37]
    R. W. Shephard, Theory of Cost and Production Functions, Princeton University Press, Princeton, NJ, 1970.MATHGoogle Scholar
  38. [38]
    D. Tyteca, On the measurement of the environmental performance of firms: A literature review and a productivity efficiency perspective, J. Environ. Manage., 46 (1996), 281–308.CrossRefGoogle Scholar
  39. [39]
    D. Tyteca, Linear programming models for the measurement of environmental performance of firms: Concepts and empirical results, J. Productivity Anal., 8 (1997), 183–197.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York 2010

Authors and Affiliations

  1. 1.Department of EconomicsVienna University of Economics and Business AdministrationViennaAustria

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