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Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes

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  • Andor, Mark A.
  • Parmeter, Christopher
  • Sommer, Stephan

Abstract

Data envelopment analysis (DEA) and stochastic frontier analysis (SFA), as well as combinations thereof, are widely applied in incentive regulation practice, where the assessment of efficiency plays a major role in regulation design and benchmarking. Using a Monte Carlo simulation experiment, this paper compares the performance of six alternative methods commonly applied by regulators. Our results demonstrate that combination approaches, such as taking the maximum or the mean over DEA and SFA efficiency scores, have certain practical merits and might offer an useful alternative to strict reliance on a singular method. In particular, the results highlight that taking the maximum not only minimizes the risk of underestimation, but can also improve the precision of efficiency estimation. Based on our results, we give recommendations for the estimation of individual efficiencies for regulation purposes and beyond.

Suggested Citation

  • Andor, Mark A. & Parmeter, Christopher & Sommer, Stephan, 2018. "Combining uncertainty with uncertainty to get certainty? Efficiency analysis for regulation purposes," Ruhr Economic Papers 770, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    • Handle: RePEc:zbw:rwirep:770
    DOI: 10.4419/86788898
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    6. Ahn, Heinz & Clermont, Marcel & Langner, Julia, 2023. "Comparative performance analysis of frontier-based efficiency measurement methods – A Monte Carlo simulation," European Journal of Operational Research, Elsevier, vol. 307(1), pages 294-312.
    7. Otsuka, Akihiro, 2023. "Industrial electricity consumption efficiency and energy policy in Japan," Utilities Policy, Elsevier, vol. 81(C).
    8. Wang, Derek D. & Ren, Yaoyao, 2024. "Accuracy of Deterministic Nonparametric Frontier Models with Undesirable Outputs," European Journal of Operational Research, Elsevier, vol. 315(2), pages 596-612.
    9. Imad Bou-Hamad & Abdel Latef Anouze & Ibrahim H. Osman, 2022. "A cognitive analytics management framework to select input and output variables for data envelopment analysis modeling of performance efficiency of banks using random forest and entropy of information," Annals of Operations Research, Springer, vol. 308(1), pages 63-92, January.
    10. Akihiro Otsuka, 2023. "Stochastic demand frontier analysis of residential electricity demands in Japan," Asia-Pacific Journal of Regional Science, Springer, vol. 7(1), pages 179-195, March.
    11. Xian’En Wang & Shimeng Wang & Xipan Wang & Wenbo Li & Junnian Song & Haiyan Duan & Shuo Wang, 2019. "The Assessment of Carbon Performance under the Region-Sector Perspective based on the Nonparametric Estimation: A Case Study of the Northern Province in China," Sustainability, MDPI, vol. 11(21), pages 1-23, October.
    12. Marcos Gonçalves Perroni & Claudimar Pereira da Veiga & Zhaohui Su & Fernando Maciel Ramos & Wesley Vieira da Silva, 2023. "Dynamic Equilibrium of Sustainable Ecosystem Variables: An Experiment," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    13. Rita, Rui & Marques, Vitor & Bárbara, Diogo & Chaves, Inês & Macedo, Pedro & Moutinho, Victor & Pereira, Mariana, 2023. "Crossing non-parametric and parametric techniques for measuring the efficiency: Evidence from 65 European electricity Distribution System Operators," Energy, Elsevier, vol. 283(C).
    14. Zangin Zeebari & Kristofer Månsson & Pär Sjölander & Magnus Söderberg, 2023. "Regularized conditional estimators of unit inefficiency in stochastic frontier analysis, with application to electricity distribution market," Journal of Productivity Analysis, Springer, vol. 59(1), pages 79-97, February.
    15. Tsionas, Mike G., 2021. "Optimal combinations of stochastic frontier and data envelopment analysis models," European Journal of Operational Research, Elsevier, vol. 294(2), pages 790-800.
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    More about this item

    Keywords

    data envelopment analysis; stochastic frontier analysis; efficiency analysis; regulation; network operators;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L50 - Industrial Organization - - Regulation and Industrial Policy - - - General

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