TY - JOUR

T1 - Implications of aggregation uncertainty in Data Envelopment Analysis

T2 - An application in incentive regulation

AU - Heesche, Emil

AU - Asmild, Mette

N1 - 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)

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Aggregation uncertainty

KW - Data Envelopment Analysis

KW - Permutation tests

KW - Regulation

U2 - 10.1016/j.dajour.2022.100103

DO - 10.1016/j.dajour.2022.100103

M3 - Journal article

AN - SCOPUS:85136623284

VL - 4

JO - Decision Analytics Journal

JF - Decision Analytics Journal

SN - 2772-6622

M1 - 100103

ER -