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Empirical tools to assess the sensitivity of directional distance functions to direction selection

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  • Antonio Peyrache
  • Cinzia Daraio

Abstract

Directional Distance Functions (DDFs) are becoming a popular way of measuring efficiency as they encompass the Shephard output and input distance functions as special cases. However, the most critical and still unsolved issue related to DDF remains the selection of the direction along which to measure the distance from the efficient frontier. In this article, we propose some empirical tools which allow to quantify the sensitivity of the efficiency measurement to the selection of the direction. The proposed tools are applied on a dataset on the Italian agricultural sector.

Suggested Citation

  • Antonio Peyrache & Cinzia Daraio, 2012. "Empirical tools to assess the sensitivity of directional distance functions to direction selection," Applied Economics, Taylor & Francis Journals, vol. 44(8), pages 933-943, March.
    • Handle: RePEc:taf:applec:44:y:2012:i:8:p:933-943
    DOI: 10.1080/00036846.2010.526582
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    References listed on IDEAS

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    1. Paola Dongili & Angelo Zago, 2005. "Bad loans and efficiency in Italian Banks," Working Papers 28/2005, University of Verona, Department of Economics.
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    Cited by:

    1. Wang, Ke & Yang, Kexin & Wei, Yi-Ming & Zhang, Chi, 2018. "Shadow prices of direct and overall carbon emissions in China’s construction industry: A parametric directional distance function-based sensitive estimation," Structural Change and Economic Dynamics, Elsevier, vol. 47(C), pages 180-193.
    2. Minyan Zhu & Antonio Peyrache, 2017. "The quality and efficiency of public service delivery in the UK and China," Regional Studies, Taylor & Francis Journals, vol. 51(2), pages 285-296, February.
    3. Fangqing Wei & Junfei Chu & Jiayun Song & Feng Yang, 2019. "A cross-bargaining game approach for direction selection in the directional distance function," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(3), pages 787-807, September.
    4. Hampf, Benjamin & Krüger, Jens J., 2013. "Optimal Directions for Directional Distance Functions: An Exploration of Potential Reductions of Greenhouse Gases," VfS Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79699, Verein für Socialpolitik / German Economic Association.
    5. Färe, Rolf & Pasurka, Carl & Vardanyan, Michael, 2017. "On endogenizing direction vectors in parametric directional distance function-based models," European Journal of Operational Research, Elsevier, vol. 262(1), pages 361-369.
    6. Fusco, Elisa & Maggi, Bernardo & Rizzuto, Livia, 2022. "Alternative indicators for the evaluation of renewables in Europe: An efficiency approach," Renewable Energy, Elsevier, vol. 190(C), pages 48-65.
    7. Vardanyan, Michael & Valdmanis, Vivian G. & Leleu, Hervé & Ferrier, Gary D., 2022. "Estimating technology characteristics of the U.S. hospital industry using directional distance functions with optimal directions," Omega, Elsevier, vol. 113(C).
    8. Cinzia Daraio & Léopold Simar, 2016. "Efficiency and benchmarking with directional distances: a data-driven approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(7), pages 928-944, July.
    9. Tsionas, Mike G., 2024. "A generalized inefficiency model with input and output dependence," European Journal of Operational Research, Elsevier, vol. 312(1), pages 315-323.
    10. Jorge Bonilla & Jessica Coria & Thomas Sterner, 2018. "Technical Synergies and Trade-Offs Between Abatement of Global and Local Air Pollution," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 70(1), pages 191-221, May.
    11. Ke Wang & Yujiao Xian & Yi-Ming Wei & Zhimin Huang, 2016. "Sources of carbon productivity change: A decomposition and disaggregation analysis based on global Luenberger productivity indicator and endogenous directional distance function," CEEP-BIT Working Papers 91, Center for Energy and Environmental Policy Research (CEEP), Beijing Institute of Technology.
    12. 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.
    13. Hampf, Benjamin & Krüger, Jens, 2013. "Optimal Directions for Directional Distance Functions: An Exploration of Potential Reductions of Greenhouse Gases," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 60952, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    14. Thyago Celso Cavalcante Nepomuceno & Katarina Tatiana Marques Santiago & Cinzia Daraio & Ana Paula Cabral Seixas Costa, 2022. "Exogenous crimes and the assessment of public safety efficiency and effectiveness," Annals of Operations Research, Springer, vol. 316(2), pages 1349-1382, September.

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