Improving discrimination in data envelopment analysis: some practical suggestions

Victor V. Podinovski, Emmanuel Thanassoulis*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)117-126
Number of pages10
JournalJournal of Productivity Analysis
Volume28
Issue number1-2
Early online date10 Apr 2007
DOIs
Publication statusPublished - Oct 2007

Keywords

  • data envelopment analysis
  • efficiency
  • productivity
  • selective proportionality
  • unobserved DMUs
  • weight restrictions

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