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Integrating categorical variables in Data Envelopment Analysis models: A simple solution technique

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  • Löber, Gerrit
  • Staat, Matthias

Abstract

This paper introduces a novel method to incorporate categorical non-discretionary variables in Data Envelopment Analysis (DEA) models. While solutions to this problem have been introduced before, they have rarely been employed in applied work. We surmise that existing solution concepts pose problems for applied researchers and develop a simple and straightforward alternative based on indicator variables. We thereby provide a flexible tool for models with categorical variables that-unlike the approaches mentioned above-can be solved with standard DEA software irrespective of scale assumptions even if no option for non-discretionary variables is available. Furthermore, there is no need to split the data and run multiple DEA, one for each data set generated. The model is extensible to categorical discretionary variables and in addition to non-hierarchical data.

Suggested Citation

  • Löber, Gerrit & Staat, Matthias, 2010. "Integrating categorical variables in Data Envelopment Analysis models: A simple solution technique," European Journal of Operational Research, Elsevier, vol. 202(3), pages 810-818, May.
    • Handle: RePEc:eee:ejores:v:202:y:2010:i:3:p:810-818
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    3. Francisco Javier Santos Arteaga & Debora Di Caprio & David Cucchiari & Josep M Campistol & Federico Oppenheimer & Fritz Diekmann & Ignacio Revuelta, 2021. "Modeling patients as decision making units: evaluating the efficiency of kidney transplantation through data envelopment analysis," Health Care Management Science, Springer, vol. 24(1), pages 55-71, March.
    4. Podinovski, Victor V. & Bouzdine-Chameeva, Tatiana, 2011. "The impossibility of convex constant returns-to-scale production technologies with exogenously fixed factors," European Journal of Operational Research, Elsevier, vol. 213(1), pages 119-123, August.
    5. Kalinichenko, Olena & Amado, Carla A.F. & Santos, Sérgio P., 2022. "Exploring the potential of Data Envelopment Analysis for enhancing pay-for-performance programme design in primary health care," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1084-1100.
    6. Julie Harrison & Paul Rouse & Jamie Armstrong, 2012. "Categorical and continuous non-discretionary variables in data envelopment analysis: a comparison of two single-stage models," Journal of Productivity Analysis, Springer, vol. 37(3), pages 261-276, June.

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    More about this item

    Keywords

    C61 D24 Non-discretionary variables Categorical variables Returns to scale;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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