Improving discrimination in data envelopment analysis:some practical suggestions

Podinovski, Victor V. and Thanassoulis, Emmanuel Improving discrimination in data envelopment analysis:some practical suggestions. Journal of Productivity Analysis, 28 (1-2), pp. 117-126.

Abstract

In some contexts data envelopment analysis (DEA) gives poor discrimination on the performance of units. While this may reflect genuine uniformity of performance between units, it may also reflect lack of sufficient observations or other factors limiting discrimination on performance between units. In this paper, we present an overview of the main approaches that can be used to improve the discrimination of DEA. This includes simple methods such as the aggregation of inputs or outputs, the use of longitudinal data, more advanced methods such as the use of weight restrictions, production trade-offs and unobserved units, and a relatively new method based on the use of selective proportionality between the inputs and outputs. © 2007 Springer Science+Business Media, LLC.

Publication DOI: https://doi.org/10.1007/s11123-007-0042-x
Divisions: Aston Business School > Operations & information management
Aston Business School > Operations & information management research group
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Uncontrolled Keywords: data envelopment analysis,efficiency,productivity,selective proportionality,unobserved DMUs,weight restrictions,Business and International Management,Economics and Econometrics,Social Sciences (miscellaneous)

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