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Programmatic efficiency comparisons between unequally sized groups of DMUs in DEA

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  • G Simpson

    (Aston University)

Abstract

Data envelopment analysis (DEA) is a popular non-parametric technique for determining the efficiency of a homogeneous set of decision-making units (DMUs). In many practical cases, there is some doubt if the all the DMUs form a single group with a common efficiency distribution. The Mann–Whitney rank statistic has been used in DEA both to test if two groups of DMUs come from a common efficiency distribution and also to test if the two groups have a common frontier, each of which are likely to have important but different policy implications for the management of the groups. In this paper it is demonstrated that where the Mann–Whitney rank statistic is used for the second of these it is likely to overestimate programmatic inefficiency, particularly of the smaller group. A new non-parametric statistic is proposed for the case of comparing the efficient frontiers of two groups, which overcomes the problems we identify in the use of the Mann–Whitney rank statistic for this purpose.

Suggested Citation

  • G Simpson, 2005. "Programmatic efficiency comparisons between unequally sized groups of DMUs in DEA," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(12), pages 1431-1438, December.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:12:d:10.1057_palgrave.jors.2601961
    DOI: 10.1057/palgrave.jors.2601961
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    References listed on IDEAS

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    1. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
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    4. Paul Wilson, 2003. "Testing Independence in Models of Productive Efficiency," Journal of Productivity Analysis, Springer, vol. 20(3), pages 361-390, November.
    5. W F Bowlin & C J Renner & J M Rives, 2003. "A DEA study of gender equity in executive compensation," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(7), pages 751-757, July.
    6. A. Charnes & W. W. Cooper & E. Rhodes, 1981. "Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through," Management Science, INFORMS, vol. 27(6), pages 668-697, June.
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    Cited by:

    1. Hampf, Benjamin & Krüger, Jens J., 2010. "Technical efficiency of automobiles: A nonparametric approach incorporating carbon dioxide emissions," Darmstadt Discussion Papers in Economics 198, Darmstadt University of Technology, Department of Law and Economics.
    2. Hampf, Benjamin & Krüger, Jens, 2010. "Technical Efficiency of Automobiles – A Nonparametric Approach Incorporating Carbon Dioxide Emissions," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 43177, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. Alexandr Gedranovich & Mykhaylo Salnykov, 2012. "Productivity analysis of Belarusian higher education system," BEROC Working Paper Series 16, Belarusian Economic Research and Outreach Center (BEROC).
    4. Thanassoulis, Emmanuel & Silva Portela, Maria & Graveney, Mike, 2016. "Identifying the scope for savings at inpatient episode level: An illustration applying DEA to chronic obstructive pulmonary disease," European Journal of Operational Research, Elsevier, vol. 255(2), pages 570-582.
    5. Seyed Ali Rakhshan, 2017. "Efficiency ranking of decision making units in data envelopment analysis by using TOPSIS-DEA method," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(8), pages 906-918, August.
    6. Julian H. Tice & António C. Moreira & Cláudia Sarrico, 2008. "Economic Performance Of Portuguese Companies 1989-1999: A Dea Study," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 0(1), pages 111-128.

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    data envelopment analysis; statistics;

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