Improving discrimination in data envelopment analysis: some practical suggestions.

Podinovski, Victor V.; Thanassoulis, Emmanuel

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.

DATA envelopment analysis; LINEAR programming; MATHEMATICAL programming; GROUP decision making; DISCRIMINANT analysis; MULTIVARIATE analysis; ANALYSIS of variance; PRODUCTION possibility curve; PRODUCTION (Economic theory)

Journal of Productivity Analysis, 2007, Vol 28, Issue 1/2, p117

0895-562X

Academic Journal

10.1007/s11123-007-0042-x