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An efficiency measure satisfying the Dmitruk–Koshevoy criteria on DEA technologies

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  • Hirofumi Fukuyama
  • Kazuyuki Sekitani

Abstract

The purpose of this paper is to develop an efficiency measurement model by enhancing a CCR (Charnes–Cooper–Rhodes) model and then to prove that the enhanced model satisfies five desirable properties: indication, strict monotonicity, homogeneity, continuity and units unvariance. In order for our model to be empirically tractable, we also provide an algorithm aimed at estimating efficiency scores. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Hirofumi Fukuyama & Kazuyuki Sekitani, 2012. "An efficiency measure satisfying the Dmitruk–Koshevoy criteria on DEA technologies," Journal of Productivity Analysis, Springer, vol. 38(2), pages 131-143, October.
    • Handle: RePEc:kap:jproda:v:38:y:2012:i:2:p:131-143
    DOI: 10.1007/s11123-011-0248-9
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    References listed on IDEAS

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    1. Dmitruk, Andrei V. & Koshevoy, Gleb A., 1991. "On the existence of a technical efficiency criterion," Journal of Economic Theory, Elsevier, vol. 55(1), pages 121-144, October.
    2. Chang, Kuo-Ping & Guh, Yeah-Yuh, 1991. "Linear production functions and the data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 52(2), pages 215-223, May.
    3. 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.
    4. R. Russell & William Schworm, 2009. "Axiomatic foundations of efficiency measurement on data-generated technologies," Journal of Productivity Analysis, Springer, vol. 31(2), pages 77-86, April.
    5. 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.
    6. Robert Russell, R., 1990. "Continuity of measures of technical efficiency," Journal of Economic Theory, Elsevier, vol. 51(2), pages 255-267, August.
    7. W. Cooper & Z. Huang & S. Li & J. Zhu, 2008. "A response to the critiques of DEA by Dmitruk and Koshevoy, and Bol," Journal of Productivity Analysis, Springer, vol. 29(1), pages 15-21, February.
    8. O. B. Olesen & N. C. Petersen, 1996. "Indicators of Ill-Conditioned Data Sets and Model Misspecification in Data Envelopment Analysis: An Extended Facet Approach," Management Science, INFORMS, vol. 42(2), pages 205-219, February.
    9. Cooper, William W. & Ruiz, Jose L. & Sirvent, Inmaculada, 2007. "Choosing weights from alternative optimal solutions of dual multiplier models in DEA," European Journal of Operational Research, Elsevier, vol. 180(1), pages 443-458, July.
    10. A. Bessent & W. Bessent & J. Elam & T. Clark, 1988. "Efficiency Frontier Determination by Constrained Facet Analysis," Operations Research, INFORMS, vol. 36(5), pages 785-796, October.
    11. Bol, Georg, 1986. "On technical efficiency measures: A remark," Journal of Economic Theory, Elsevier, vol. 38(2), pages 380-385, April.
    12. Robert Russell, R., 1985. "Measures of technical efficiency," Journal of Economic Theory, Elsevier, vol. 35(1), pages 109-126, February.
    13. Ole Olesen & N. Petersen, 2003. "Identification and Use of Efficient Faces and Facets in DEA," Journal of Productivity Analysis, Springer, vol. 20(3), pages 323-360, November.
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    Cited by:

    1. Fukuyama, Hirofumi & Maeda, Yasunobu & Sekitani, Kazuyuki & Shi, Jianming, 2014. "Input–output substitutability and strongly monotonic p-norm least distance DEA measures," European Journal of Operational Research, Elsevier, vol. 237(3), pages 997-1007.
    2. Zhu, Qingyuan & Wu, Jie & Ji, Xiang & Li, Feng, 2018. "A simple MILP to determine closest targets in non-oriented DEA model satisfying strong monotonicity," Omega, Elsevier, vol. 79(C), pages 1-8.

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    More about this item

    Keywords

    DEA; CCR; Dmitruk–Koshevoy criteria; Färe–Lovell axioms; Pseudo input requirement set; C43; C61; D21; D24;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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