Do efficiency scores depend on input mix? A statistical test and empirical illustration
In this paper we examine the possibility of using the standard Kruskal-Wallis rank test in order to evaluate whether the distribution of efficiency scores resulting from Data Envelopment Analysis (DEA) is independent of the input (or output) mix. Recently, a general data generating process (DGP) suiting the DEA methodology has been formulated and some asymptotic properties of the DEA estimators have been established. In line with this generally accepted DGP, we formulate a conditional test for the assumption of mix independence. Since the DEA frontier is estimated, many standard assumptions for evaluating the test statistic are violated. Therefore, we propose to explore its statistical properties by the use of simulation studies. The simulations are performed conditional on the observed input mixes. The method, as it is shown here, is applicable when comparing distributions of efficiency scores in two or more groups in models with multiple inputs and one output with constant returns to scale. The approach is illustrated in an empirical case of demolition projects where we reject the assumption of mix independence. This means that it, in this case, is not meaningful to perform a complete ranking of the projects based on their efficiency scores. Thus the example illustrates how common practice can be inappropriate.
|Date of creation:||2012|
|Date of revision:|
|Contact details of provider:|| Web page: http://www.ifro.ku.dk/english/|
More information through EDIRC
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Simar, L. & Wilson, P.W., 1998. "A General Methodology for Bootstrapping in Nonparametric Frontier Models," Papers 9811, Catholique de Louvain - Institut de statistique.
- Adler, Nicole & Friedman, Lea & Sinuany-Stern, Zilla, 2002. "Review of ranking methods in the data envelopment analysis context," European Journal of Operational Research, Elsevier, vol. 140(2), pages 249-265, July.
- Kneip, Alois & Simar, Léopold & Wilson, Paul W., 2008.
"Asymptotics And Consistent Bootstraps For Dea Estimators In Nonparametric Frontier Models,"
Cambridge University Press, vol. 24(06), pages 1663-1697, December.
- Alois Kneip & Léopold Simar & Paul W. Wilson, 2006. "Asymptotics and Consistent Bootstraps for DEA Estimators in Non-parametric Frontier Models," Bonn Econ Discussion Papers bgse12_2006, University of Bonn, Germany.
- Simar, L. & Wilson, P.W., 1999.
"Statistical Inference in Nonparametric Frontier Models: the State of the Art,"
9904, Catholique de Louvain - Institut de statistique.
- Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
- Leopold Simar & Valentin Zelenyuk, 2006.
"On Testing Equality of Distributions of Technical Efficiency Scores,"
Taylor & Francis Journals, vol. 25(4), pages 497-522.
- Simar, Leopold & Zelenyuk, Valentin, 2004. "On testing equality of distributions of technical efficiency scores," MPRA Paper 28003, University Library of Munich, Germany.
- Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
When requesting a correction, please mention this item's handle: RePEc:foi:msapwp:05_2012. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Geir Tveit)
If references are entirely missing, you can add them using this form.