Efficiency and benchmarking with directional distances. A data driven approach

Cinzia Daraio () and Leopold Simar
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Cinzia Daraio: Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza"

No 2014-07, DIAG Technical Reports from Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza"

Abstract: In efficiency analysis the assessment of the performance of Decision Making Units (DMUs) relays on the selection of the direction along which the distance from the efficient frontier is measured. Directional Distance Functions (DDFs) represent a flexible way to gauge the inefficiency of DMUs. Permitting the selection of a direction towards the efficient frontier is often useful in empirical applications. As a matter of fact, many papers in the literature have proposed specific DDFs suitable for different contexts of application. Nevertheless, the selection of a direction implies the choice of an efficiency target which is imposed to all the analyzed DMUs. Moreover, there exist many situations in which there is no a priori economic or managerial rationale to impose a subjective efficiency target. In this paper we propose a data-driven approach to find out an “objective†direction along which to gauge the inefficiency of each DMU. Our approach permits to take into account for the heterogeneity of DMUs and their diverse contexts that may influence their input and/or output mixes. Our method is also a data driven technique for benchmarking each DMU. We describe how to implement our framework and illustrate its usefulness with simulated and real datasets.

Keywords: DEA; benchmarking; directional distance functions; nonparametric estimation; heterogeneity; performance; productivity; organizational studies (search for similar items in EconPapers)
Date: 2014
New Economics Papers: this item is included in nep-cdm, nep-cse, nep-ecm and nep-eff
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Efficiency and benchmarking with directional distances: a data-driven approach (2016)
Working Paper: Efficiency and benchmarking with directional distances: a data-driven approach (2016)
Working Paper: Efficiency and benchmarking with directional distances: a data driven approach (2014)
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