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Measuring potential sub-unit efficiency to counter the aggregation bias in benchmarking

Author

Listed:
  • Heinz Ahn

    (Technische Universität Braunschweig)

  • Peter Bogetoft

    (Copenhagen Business School)

  • Ana Lopes

    (Universidade Federal de Minas Gerais)

Abstract

The paper deals with benchmarking cases where highly aggregated decision making units are in the data set. It is shown that these units—consisting of sub-units which are not further known by the evaluator—are likely to receive an unjustifiable harsh evaluation, here referred to as aggregation bias. To counter this bias, we present an approach which allows to calculate the potential sub-unit efficiency of a decision making unit by taking into account the possible impact of its sub-units’ aggregation without having disaggregated sub-unit data. Based on data envelopment analysis, the approach is operationalized in several ways. Finally, we apply our method to the benchmarking model actually used by the Brazilian Electricity Regulator to measure the cost efficiency of the Brazilian distribution system operators. For this case, our results reveal that the potential effect of the aggregation bias on the operators’ efficiency scores is enormous.

Suggested Citation

  • Heinz Ahn & Peter Bogetoft & Ana Lopes, 2019. "Measuring potential sub-unit efficiency to counter the aggregation bias in benchmarking," Journal of Business Economics, Springer, vol. 89(1), pages 53-77, February.
    • Handle: RePEc:spr:jbecon:v:89:y:2019:i:1:d:10.1007_s11573-018-0901-0
    DOI: 10.1007/s11573-018-0901-0
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    References listed on IDEAS

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    Cited by:

    1. Afsharian, Mohsen & Bogetoft, Peter, 2023. "Limiting flexibility in nonparametric efficiency evaluations: An ex post k-centroid clustering approach," European Journal of Operational Research, Elsevier, vol. 311(2), pages 633-647.
    2. Núñez, F. & Arcos-Vargas, A. & Villa, G., 2020. "Efficiency benchmarking and remuneration of Spanish electricity distribution companies," Utilities Policy, Elsevier, vol. 67(C).
    3. Camanho, Ana Santos & Silva, Maria Conceicao & Piran, Fabio Sartori & Lacerda, Daniel Pacheco, 2024. "A literature review of economic efficiency assessments using Data Envelopment Analysis," European Journal of Operational Research, Elsevier, vol. 315(1), pages 1-18.
    4. An, Qingxian & Tao, Xiangyang & Chen, Xiaohong, 2023. "Nested frontier-based best practice regulation under asymmetric information in a principal–agent framework," European Journal of Operational Research, Elsevier, vol. 306(1), pages 269-285.

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    More about this item

    Keywords

    Benchmarking; Data envelopment analysis; DEA; Aggregation bias; Potential sub-unit efficiency; Regulation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C67 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Input-Output Models

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