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Improving discrimination in data envelopment analysis: some practical suggestions

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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.

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Notes

  1. For a comprehensive introduction to DEA, see Cooper et al. (2000) and Thanassoulis (2001).

  2. For example, in the assessment of school performance, the overall number of students may be highly correlated with the number of students achieving good grades in exams. However, omitting one of these indicators from the model may change not only the efficiency measures but also the ‘flavour’ of the model used in the analysis.

  3. The assumption that the trade-offs should be applicable globally in the entire technology means that they cannot equate directly to the marginal rates of substitution. Indeed, the latter are generally different over the efficient frontier and reflect different production patterns exhibited by efficient DMUs. (For example, the marginal rates of substitution between teaching staff and students, or students and publications will generally be different in a high-ranked research-driven and average-ranked university department.) In non-CRS technologies, the marginal rates of substitution also depend on the type of local returns to scale. These observations mean that the production trade-offs should be sufficiently relaxed and non-demanding (Podinovski 2004c, 2007b).

  4. The HRS technology is a subset of the CRS technology if all inputs are included in the selective proportionality assumption (Podinovski 2004b).

  5. We cannot specify by how much the publications will increase. Leaving this unchanged is a safe option.

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Authors and Affiliations

  1. Warwick Business School, University of Warwick, Coventry, CV4 7AL, UK

    Victor V. Podinovski

  2. Aston Business School, Aston University, Birmingham, B4 7ET, UK

    Emmanuel Thanassoulis

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  1. Victor V. Podinovski
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  2. Emmanuel Thanassoulis
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Correspondence to Emmanuel Thanassoulis.

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Podinovski, V.V., Thanassoulis, E. Improving discrimination in data envelopment analysis: some practical suggestions. J Prod Anal 28, 117–126 (2007). https://doi.org/10.1007/s11123-007-0042-x

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