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/|
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- Alois Kneip & Léopold Simar & Paul W. Wilson, 2006.
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Bonn Econ Discussion Papers
bgse12_2006, University of Bonn, Germany.
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"On testing equality of distributions of technical efficiency scores,"
28003, University Library of Munich, Germany.
- Leopold Simar & Valentin Zelenyuk, 2006. "On Testing Equality of Distributions of Technical Efficiency Scores," Econometric Reviews, Taylor & Francis Journals, vol. 25(4), pages 497-522.
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"A General Methodology for Bootstrapping in Nonparametric Frontier Models,"
9811, Catholique de Louvain - Institut de statistique.
- 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., 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.
- Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
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