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Benchmarking Heterogeneous Distribution System Operators: Evidence from Norway

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  • George Elias

Abstract

Regulatory authorities in the European electricity sector use benchmarking techniques to determine the cost-e_cient production level for an incentive regulation of distribution system operators (DSOs). With nearly 900 DSOs operating in the German electricity sector, of which 200 subject to incentive regulation, the issue of heterogeneity of DSOs has to be addressed. Using publicly available data of 133 Norwegian DSOs and replicating the model employed by the German regulator (who refuses access to the data), I show its assumption of homogeneous technology cannot be maintained. Quantile regressions (QR) across the cost distribution reveal heterogeneity in the coe_cients of the explanatory variables, resulting in biased e_ciency scores derived from stochastic frontier analysis. To correct for this heterogeneity in coe_cients, I propose a Bayesian estimation of a more flexible SFA with latent classes for selected parameters that reflect variation in technologies. This estimation has better goodness of fit, reduced variance of all coe_cients, and higher e_ciency scores for nearly all DSOs, compared to the conventional alternative.

Suggested Citation

  • George Elias, 2016. "Benchmarking Heterogeneous Distribution System Operators: Evidence from Norway," Diskussionsschriften dp1606, Universitaet Bern, Departement Volkswirtschaft.
  • Handle: RePEc:ube:dpvwib:dp1606
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    References listed on IDEAS

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    1. Mehdi Farsi & Massimo Filippini, 2004. "Regulation and Measuring Cost-Efficiency with Panel Data Models: Application to Electricity Distribution Utilities," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 25(1), pages 1-19, August.
    2. Kopsakangas-Savolainen, Maria & Svento, Rauli, 2011. "Observed and unobserved heterogeneity in stochastic frontier models: An application to the electricity distribution industry," Energy Economics, Elsevier, vol. 33(2), pages 304-310, March.
    3. Eric W. Christensen, 2004. "Scale and scope economies in nursing homes: A quantile regression approach," Health Economics, John Wiley & Sons, Ltd., vol. 13(4), pages 363-377.
    4. Astrid Cullman & Mehdi Farsi & Massimo Filippini, 2009. "Unobserved Heterogeneity and International Benchmarking in Public Transport," CEPE Working paper series 09-65, CEPE Center for Energy Policy and Economics, ETH Zurich.
    5. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    6. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    7. William Rogers, 1993. "Quantile regression standard errors," Stata Technical Bulletin, StataCorp LP, vol. 2(9).
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    More about this item

    Keywords

    E_ciency measurement; cost function; incentive regulation; electricity sector;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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