Implications of aggregation uncertainty in Data Envelopment Analysis: An application in incentive regulation

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Researchers and practitioners who use Data Envelopment Analysis often want to incorporate several inputs and outputs in their model to consider as much relevant information as possible. However, too many inputs and outputs can result in the well-known dimensionality problem referred to as the “curse of dimensionality”. Several studies suggest how to solve, or at least reduce, this problem. One solution is to aggregate the inputs and outputs before using them in the model. This paper examines the implications when the methods used to aggregate the inputs and outputs contain uncertainty. The uncertainty can, for example, be price uncertainty if we use input and/or output prices for the aggregation. We show that the implications for a given unit depend entirely on its input and output mixes relative to those of its peers, and that the implications are higher the more heterogeneous the sector is. As an example, we use the Danish benchmarking regulation of the waste water companies. We find that uncertainty in the regulator's aggregation scheme does not, on average, influence the companies’ efficiency scores a lot. Still, individual companies can be greatly affected by this uncertainty.

Original languageEnglish
Article number100103
JournalDecision Analytics Journal
Volume4
Number of pages15
DOIs
Publication statusPublished - 2022

Bibliographical note

Funding Information:
One or more of the authors of this paper have disclosed potential or pertinent conflicts of interest, which may include receipt of payment, either direct or indirect, institutional support, or association with an entity in the biomedical field which may be perceived to have potential conflict of interest with this work. For full disclosure statements refer to https://doi.org/10.1016/j.dajour.2022.100103 .Emil Heesche reports financial support was provided by Danish Competition and Consumer Authority.

Publisher Copyright:
© 2022 The Author(s)

    Research areas

  • Aggregation uncertainty, Data Envelopment Analysis, Permutation tests, Regulation

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