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Efficiency and benchmarking with directional distances. A data driven approach

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  • Cinzia Daraio

    () (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

  • Leopold Simar

    () (Institute of Statistics, Biostatistics et Actuarial Sciences, Universite' Catholique de Louvain, Louvain-la-Neuve, Belgium)

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.

Suggested Citation

  • Cinzia Daraio & Leopold Simar, 2014. "Efficiency and benchmarking with directional distances. A data driven approach," DIAG Technical Reports 2014-07, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
  • Handle: RePEc:aeg:report:2014-07
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    References listed on IDEAS

    as
    1. Simar, Léopold & Vanhems, Anne, 2012. "Probabilistic characterization of directional distances and their robust versions," Journal of Econometrics, Elsevier, vol. 166(2), pages 342-354.
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    Cited by:

    1. Pedro Macedo & Elvira Silva, 2017. "Sensitivity of directional technical inefficiency measures to the choice of the direction vector: a simulation study," Economics Bulletin, AccessEcon, vol. 37(1), pages 52-62.
    2. repec:eee:ejores:v:262:y:2017:i:2:p:792-801 is not listed on IDEAS
    3. Duygun, Meryem & Sena, Vania & Shaban, Mohamed, 2016. "Trademarking activities and total factor productivity: Some evidence for British commercial banks using a metafrontier approach," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 70-80.
    4. repec:eee:ejores:v:262:y:2017:i:1:p:361-369 is not listed on IDEAS
    5. Paolo Liberati & Raffaele Lagravinese & Giuliano Resce, 2017. "How Does Economic Social And Cultural Status Affect The Efficiency Of Educational Attainments? A Comparative Analysis On Pisa Results," Departmental Working Papers of Economics - University 'Roma Tre' 0217, Department of Economics - University Roma Tre.
    6. Ang, Frederic & Kerstens, Pieter Jan, 2017. "Decomposing the Luenberger–Hicks–Moorsteen Total Factor Productivity indicator: An application to U.S. agriculture," European Journal of Operational Research, Elsevier, vol. 260(1), pages 359-375.

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    Keywords

    DEA; benchmarking; directional distance functions; nonparametric estimation; heterogeneity; performance; productivity; organizational studies;

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