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Controlling for environmental conditions in regulatory benchmarking

Author

Listed:
  • Emil Heesche

    (Department of Food and Resource Economics, University of Copenhagen)

  • Mette Asmild

    (Department of Food and Resource Economics, University of Copenhagen)

Abstract

Data Envelopment Analysis (DEA) is often used by regulators to create a pseudo-competitive environment for sectors with natural monopolies. In addition to develop a theoretically well-behaved model, regulators need to take into account several other factors, such as the political agenda and the historical context of the regulation. This sometimes results in some unconventional approaches, which furthermore are not easily changed. In this paper, we discuss the model used for DEA-based benchmark regulation of the Danish water sector. More specifically, we look at the characteristics of the method the regulator uses to take into account differences in the companies’ environmental conditions. We show how the approach currently used to control for differences in environmental conditions seemingly does not sufficiently control for the actual differences as intended since second stage analysis still reveals significant correlations between the efficiency scores and these external factors. To explain this, we reconsider the second stage analysis, using permutation-based approaches and also accounting for the fact that only those companies that in the DEA assign weights to those output measures adjusted for environmental conditions, will benefit from the adjustments.

Suggested Citation

  • Emil Heesche & Mette Asmild, 2020. "Controlling for environmental conditions in regulatory benchmarking," IFRO Working Paper 2020/03, University of Copenhagen, Department of Food and Resource Economics.
    • Handle: RePEc:foi:wpaper:2020_03
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    References listed on IDEAS

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    Cited by:

    1. Heesche, Emil & Asmild, Mette, 2022. "Incorporating quality in economic regulatory benchmarking," Omega, Elsevier, vol. 110(C).
    2. Emil Heesche & Mette Asmild, 2022. "Implications of Aggregation Uncertainty in DEA," IFRO Working Paper 2022/02, University of Copenhagen, Department of Food and Resource Economics.
    3. Emil Heesche & Mette Asmild, 2020. "Incorporating quality in economic regulatory benchmarking," IFRO Working Paper 2020/13, University of Copenhagen, Department of Food and Resource Economics.

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    More about this item

    Keywords

    Data envelopment analysis; Second Stage Analysis; Environmental Variables; Regulation; Permutation;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • 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
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation

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