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Sensitivity and Stability of the Classifications of Returns to Scale in Data Envelopment Analysis

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Abstract

Sensitivity of the returns to scale (RTS) classifications in data envelopment analysis is studied by means of linear programming problems. The stability region for an observation preserving its current RTS classification (constant, increasing or decreasing returns to scale) can be easily investigated by the optimal values to a set of particular DEA-type formulations. Necessary and sufficient conditions are determined for preserving the RTS classifications when input or output data perturbations are non-proportional. It is shown that the sensitivity analysis method under proportional data perturbations can also be used to estimate the RTS classifications and discover the identical RTS regions yielded by the input-based and the output-based DEA methods. Thus, our approach provides information on both the RTS classifications and the stability of the classifications. This sensitivity analysis method can easily be applied via existing DEA codes.

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, University of Massachusetts, Amherst, MA, 01003

    Lawrence M. Seiford

  2. Department of Management, Worcester Polytechnic Institute, Worcester, MA, 01609

    Joe Zhu

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  1. Lawrence M. Seiford
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  2. Joe Zhu
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Seiford, L.M., Zhu, J. Sensitivity and Stability of the Classifications of Returns to Scale in Data Envelopment Analysis. Journal of Productivity Analysis 12, 55–75 (1999). https://doi.org/10.1023/A:1007803207538

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  • DOI: https://doi.org/10.1023/A:1007803207538

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