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Measuring Inefficiency Via Potential Improvements

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Listed:
  • Mette Asmild
  • Jens Hougaard
  • Dorte Kronborg
  • Hans Kvist

Abstract

In a recent paper Bogetoft and Hougaard (1999) suggest the use of a new potential improvements approach to efficiency evaluation which has the advantage of separating the issue of benchmark selection from the issue of efficiency measurement. In the present paper the potential improvements inefficiency index is reexamined and a DEA-like approach for its determination is suggested. The approach is called Multi-directional Efficiency Analysis (MEA). An empirical example on Danish dairy farms is used for illustrative purposes and comparisons with various versions of DEA. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • Mette Asmild & Jens Hougaard & Dorte Kronborg & Hans Kvist, 2003. "Measuring Inefficiency Via Potential Improvements," Journal of Productivity Analysis, Springer, vol. 19(1), pages 59-76, January.
  • Handle: RePEc:kap:jproda:v:19:y:2003:i:1:p:59-76
    DOI: 10.1023/A:1021822103696
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    References listed on IDEAS

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    1. Peter Bogetoft & Jens Hougaard, 1999. "Efficiency Evaluations Based on Potential (Non-Proportional) Improvements," Journal of Productivity Analysis, Springer, vol. 12(3), pages 233-247, November.
    2. 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.
    3. Peter Bogetoft, 2000. "DEA and Activity Planning under Asymmetric Information," Journal of Productivity Analysis, Springer, vol. 13(1), pages 7-48, January.
    4. Luenberger, David G., 1992. "Benefit functions and duality," Journal of Mathematical Economics, Elsevier, vol. 21(5), pages 461-481.
    5. Peter Bogetoft, 1997. "DEA-based yardstick competition: The optimality of best practice regulation," Annals of Operations Research, Springer, vol. 73(0), pages 277-298, October.
    6. 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.
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    Citations

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    Cited by:

    1. Ke Wang & Shiwei Yu & Mo-Jie Li & Yi-Ming Wei, 2015. "Multi-directional efficiency analysis-based regional industrial environmental performance evaluation of China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 273-299, February.
    2. Manevska-Tasevska, Gordana & Hansson, Helena & Asmild, Mette & Surry, Yves, 2018. "Assessing the regional efficiency of Swedish agriculture under the CAP ‒ a multidirectional efficiency approach," 162nd Seminar, April 26-27, 2018, Budapest, Hungary 271971, European Association of Agricultural Economists.
    3. Tziogkidis, Panagiotis & Philippas, Dionisis & Leontitsis, Alexandros & Sickles, Robin C., 2020. "A data envelopment analysis and local partial least squares approach for identifying the optimal innovation policy direction," European Journal of Operational Research, Elsevier, vol. 285(3), pages 1011-1024.
    4. Leontitsis, Alexandros & Philippas, Dionisis & Sickles, Robin C. & Tziogkidis, Panagiotis, 2018. "Evaluating countries’ innovation potential: an international perspective," Working Papers 18-011, Rice University, Department of Economics.
    5. Asmild, Mette & Kronborg, Dorte & Matthews, Kent, 2016. "Introducing and modeling inefficiency contributions," European Journal of Operational Research, Elsevier, vol. 248(2), pages 725-730.
    6. M C A Silva Portela & E Thanassoulis & G Simpson, 2004. "Negative data in DEA: a directional distance approach applied to bank branches," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(10), pages 1111-1121, October.
    7. Asmild, Mette & Kronborg, Dorte & Mahbub, Tasmina & Matthews, Kent, 2019. "The efficiency patterns of Islamic banks during the global financial crisis: The case of Bangladesh," The Quarterly Review of Economics and Finance, Elsevier, vol. 74(C), pages 67-74.
    8. Mette Asmild & Torben Holvad & Jens Hougaard & Dorte Kronborg, 2009. "Railway reforms: do they influence operating efficiency?," Transportation, Springer, vol. 36(5), pages 617-638, September.
    9. Mette Asmild & Tomas Baležentis & Jens Leth Hougaard, 2016. "Multi-directional productivity change: MEA-Malmquist," Journal of Productivity Analysis, Springer, vol. 46(2), pages 109-119, December.
    10. Baležentis, Tomas & De Witte, Kristof, 2015. "One- and multi-directional conditional efficiency measurement – Efficiency in Lithuanian family farms," European Journal of Operational Research, Elsevier, vol. 245(2), pages 612-622.
    11. Aparicio, Juan & Cordero, Jose M. & Gonzalez, Martin & Lopez-Espin, Jose J., 2018. "Using non-radial DEA to assess school efficiency in a cross-country perspective: An empirical analysis of OECD countries," Omega, Elsevier, vol. 79(C), pages 9-20.
    12. Boris Bravo-Ureta & Daniel Solís & Víctor Moreira López & José Maripani & Abdourahmane Thiam & Teodoro Rivas, 2007. "Technical efficiency in farming: a meta-regression analysis," Journal of Productivity Analysis, Springer, vol. 27(1), pages 57-72, February.
    13. Asmild, Mette & Matthews, Kent, 2012. "Multi-directional efficiency analysis of efficiency patterns in Chinese banks 1997–2008," European Journal of Operational Research, Elsevier, vol. 219(2), pages 434-441.
    14. Kapelko, Magdalena & Oude Lansink, Alfons, 2017. "Dynamic multi-directional inefficiency analysis of European dairy manufacturing firms," European Journal of Operational Research, Elsevier, vol. 257(1), pages 338-344.
    15. Magdalena Kapelko, 2018. "Measuring inefficiency for specific inputs using data envelopment analysis: evidence from construction industry in Spain and Portugal," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 26(1), pages 43-66, March.
    16. Mette Asmild & Tomas Baležentis & Jens Hougaard, 2016. "Multi-directional program efficiency: the case of Lithuanian family farms," Journal of Productivity Analysis, Springer, vol. 45(1), pages 23-33, February.
    17. Schulte, Hinrich D. & Armbrecht, Linda & Bürger, Rasmus & Gauly, Matthias & Musshoff, Oliver & Hüttel, Silke, 2018. "Let the cows graze: An empirical investigation on the trade-off between efficiency and farm animal welfare in milk production," Land Use Policy, Elsevier, vol. 79(C), pages 375-385.
    18. Tziogkidis, Panagiotis & Philippas, Dionisis & Tsionas, Mike G., 2020. "Multidirectional conditional convergence in European banking," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 88-106.
    19. Lozano, S. & Hinojosa, M.A. & Mármol, A.M., 2019. "Extending the bargaining approach to DEA target setting," Omega, Elsevier, vol. 85(C), pages 94-102.
    20. Magdalena Kapelko & Alfons Oude Lansink, 2018. "Managerial and program inefficiency for European meat manufacturing firms: A dynamic multidirectional inefficiency analysis approach," Journal of Productivity Analysis, Springer, vol. 49(1), pages 25-36, February.
    21. Wang, Ke & Wei, Yi-Ming & Zhang, Xian, 2013. "Energy and emissions efficiency patterns of Chinese regions: A multi-directional efficiency analysis," Applied Energy, Elsevier, vol. 104(C), pages 105-116.
    22. Asmild, Mette & Pastor, Jesús T., 2010. "Slack free MEA and RDM with comprehensive efficiency measures," Omega, Elsevier, vol. 38(6), pages 475-483, December.
    23. Zhu, Ning & Wu, Yanrui & Wang, Bing & Yu, Zhiqian, 2019. "Risk preference and efficiency in Chinese banking," China Economic Review, Elsevier, vol. 53(C), pages 324-341.

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