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Data Envelopment Analysis – a method for measuring the relative technical efficiency

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  • Anna Domagala

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    (Katedra Ekonometrii, Akademia Ekonomiczna, Poland)

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    Abstract

    In the article, the author presents Data Envelopment Analysis, which is a method for measuring relative technical efficiency. The idea of DEA method is illustrated by a simple example, where objects characterized by one input and one output only are considered. Next, a the basic DEA model (CCR model) is described and conditions are given, based on which a unit under examination can be considered efficient. The author also presents a short review of some basic CCR model modifications, which, e.g., allow the existence of variable returns to scale (in CCR model, it is assumed that returns to scale are constant), and simultaneous input and output model orientation – basic CCR model is input-oriented or outputoriented. The modifications may also consist in introducing nondiscretionary variables into DEA efficiency analysis or additional constrainsts to the basic CCR model.

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    File URL: http://www.ioz.pwr.wroc.pl/boid/artykuly/3-4-2007/art-2-domagala.pdf
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    Bibliographic Info

    Article provided by Wroclaw University of Technology, Institute of Organization and Management in its journal Operations Research and Decisions.

    Volume (Year): 3-4 (2007)
    Issue (Month): ()
    Pages: 21-34

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    Handle: RePEc:wut:journl:v:3-4:y:2007:p:21-34

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    Related research

    Keywords: Data Envelopment Analysis (DEA); technical efficiency; efficiency frontier;

    References

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    1. 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.
    2. Galanopoulos, Konstantinos & Aggelopoulos, Stamatis & Kamenidou, Irene & Mattas, Konstadinos, 2006. "Assessing the effects of managerial and production practices on the efficiency of commercial pig farming," Agricultural Systems, Elsevier, vol. 88(2-3), pages 125-141, June.
    3. 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.
    4. Allen N. Berger & David B. Humphrey, 1992. "Measurement and Efficiency Issues in Commercial Banking," NBER Chapters, in: Output Measurement in the Service Sectors, pages 245-300 National Bureau of Economic Research, Inc.
    5. 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.
    6. Jacobs,Rowena & Smith,Peter C. & Street,Andrew, 2006. "Measuring Efficiency in Health Care," Cambridge Books, Cambridge University Press, number 9780521851442, October.
    7. Rajiv D. Banker & Richard C. Morey, 1986. "The Use of Categorical Variables in Data Envelopment Analysis," Management Science, INFORMS, vol. 32(12), pages 1613-1627, December.
    8. Kalyan Chakraborty & Basudeb Biswas & W. Cris Lewis, 2001. "Measurement of Technical Efficiency in Public Education: A Stochastic and Nonstochastic Production Function Approach," Southern Economic Journal, Southern Economic Association, vol. 67(4), pages 889-905, April.
    9. Alene, Arega D. & Manyong, Victor M. & Gockowski, James, 2006. "The production efficiency of intercropping annual and perennial crops in southern Ethiopia: A comparison of distance functions and production frontiers," Agricultural Systems, Elsevier, vol. 91(1-2), pages 51-70, November.
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