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Journal of Productivity Analysis

, Volume 28, Issue 1–2, pp 141–147 | Cite as

A cautionary note on methods of comparing programmatic efficiency between two or more groups of DMUs in data envelopment analysis

  • Gary SimpsonEmail author
Article

Abstract

In some applications of data envelopment analysis (DEA) there may be doubt as to whether all the DMUs form a single group with a common efficiency distribution. The Mann–Whitney rank statistic has been used to evaluate if two groups of DMUs come from a common efficiency distribution under the assumption of them sharing a common frontier and to test if the two groups have a common frontier. These procedures have subsequently been extended using the Kruskal–Wallis rank statistic to consider more than two groups. This technical note identifies problems with the second of these applications of both the Mann–Whitney and Kruskal–Wallis rank statistics. It also considers possible alternative methods of testing if groups have a common frontier, and the difficulties of disaggregating managerial and programmatic efficiency within a non-parametric framework.

Keywords

Data envelopment analysis (DEA) Statistics Programmatic efficiency 

JEL Classification

C12 C14 C61 C67 

References

  1. Bardhan R, Cooper WW, Kumbhakar SC (1998) A simulation study of joint uses of data envelopment analysis and statistical regressions for production function estimation and efficiency evaluation. J Prod Anal 9:249–278Google Scholar
  2. Brockett PL, Golany B (1996) Using rank statistics for determining programmatic efficiency differences in data envelopment analysis. Manage Sci 42(3):466–472Google Scholar
  3. Brockett PL, Cooper WW, Golden LL, Rousseau JJ, Wang YY (2005) Financial intermediary versus production approach to efficiency of marketing distribution systems and organizational structure of insurance companies. J Risk Insur 72(3):393–412CrossRefGoogle Scholar
  4. Charnes A, Cooper WW, Rhodes E (1981) Evaluating program and managerial efficiency: an application of data envelopment analysis to program follow through. Manage Sci 27(6):668–697Google Scholar
  5. Cooper WW, Seiford LM, Tone IL (2000) Data envelopment analysis: a comprehensive text with models, applications, references and DEA-Solver software, p 201. ISBN 0-7923-8693-0Google Scholar
  6. Golany B, Storbeck JE (1999) A data envelopment analysis of the operational efficiency of bank branches. Interfaces 29(3):14–26Google Scholar
  7. Revilla E, Sarkis J, Modrego A (2003) Evaluating performance of public-private research collaborations: a DEA analysis. J Opnl Res Soc 54:165–174CrossRefGoogle Scholar
  8. Ross AD, Droge C (2004) An analysis of operations efficiency in large-scale distribution systems. J Oper Manage 21(6):673–688CrossRefGoogle Scholar
  9. Simar L, Wilson PW (1998) Sensitivity analysis of efficiency scores: How to bootstrap in nonparametric models. Manage Sci 44:49–61CrossRefGoogle Scholar
  10. Simar L, Wilson PW (2000) Statistical inference in nonparametric frontier models: the state of the art. J Prod Anal 13:49–78CrossRefGoogle Scholar
  11. Simpson G (2005) Programmatic efficiency comparisons between unequally sized groups of DMUs in DEA. J Oper Res Soc 56:1431–1438CrossRefGoogle Scholar
  12. Sueyoshi T, Aoki S (2001) A use of a nonparametric statistic for DEA frontier shift: the Kruskal and Wallis rank test. Omega Int J Manage Sci 29:1–18CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Aston Business SchoolAston UniversityBirminghamUK

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