IDEAS home Printed from https://ideas.repec.org/p/ube/dpvwib/dp1606.html
   My bibliography  Save this paper

Benchmarking Heterogeneous Distribution System Operators: Evidence from Norway

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://repec.vwiit.ch/dp/dp1606.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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, April.
    3. 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.
    4. 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.
    5. William Rogers, 1993. "Quantile regression standard errors," Stata Technical Bulletin, StataCorp LP, vol. 2(9).
    6. 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.
    7. Shuttleworth, Graham, 2005. "Benchmarking of electricity networks: Practical problems with its use for regulation," Utilities Policy, Elsevier, vol. 13(4), pages 310-317, December.
    8. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mehdi Farsi & Aurelio Fetz & Massimo Filippini, 2007. "Benchmarking and Regulation in the Electricity Distribution Sector," CEPE Working paper series 07-54, CEPE Center for Energy Policy and Economics, ETH Zurich.
    2. Per J. Agrell & Mehdi Farsi & Massimo Filippini & Martin Koller, 2013. "Unobserved heterogeneous effects in the cost efficiency analysis of electricity distribution systems," Working Papers 0038, Swiss Economics.
    3. Astrid Cullmann, 2012. "Benchmarking and firm heterogeneity: a latent class analysis for German electricity distribution companies," Empirical Economics, Springer, vol. 42(1), pages 147-169, February.
    4. Lundgren, Tommy & Marklund, Per-Olov & Zhang, Shanshan, 2016. "Industrial energy demand and energy efficiency – Evidence from Sweden," Resource and Energy Economics, Elsevier, vol. 43(C), pages 130-152.
    5. Zhang, Qizheng & Qian, Zesen & Wang, Shuo & Yuan, Lingran & Gong, Binlei, 2022. "Productivity drain or productivity gain? The effect of new technology adoption in the oilfield market," Energy Economics, Elsevier, vol. 108(C).
    6. Eri Nakamura & Fumitoshi Mizutani, 2019. "Necessary demand and extra demand of public utility product: identification using the stochastic frontier model," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 46(1), pages 45-64, March.
    7. Chen, Zhongfei & Barros, Carlos Pestana & Borges, Maria Rosa, 2015. "A Bayesian stochastic frontier analysis of Chinese fossil-fuel electricity generation companies," Energy Economics, Elsevier, vol. 48(C), pages 136-144.
    8. Liu, Xiao-Yan & Pollitt, Michael G. & Xie, Bai-Chen & Liu, Li-Qiu, 2019. "Does environmental heterogeneity affect the productive efficiency of grid utilities in China?," Energy Economics, Elsevier, vol. 83(C), pages 333-344.
    9. Shimshack, Jay P. & Ward, Michael B., 2008. "Enforcement and over-compliance," Journal of Environmental Economics and Management, Elsevier, vol. 55(1), pages 90-105, January.
    10. Medina, Eva & Vicéns, José, 2011. "Factores determinantes de la demanda eléctrica de los hogares en España: una aproximación mediante regresión cuantílica/Determinants of Household Electricity Demand in Spain: An Approach through Quant," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 29, pages 515-538, Agosto.
    11. Farsi, Mehdi & Filippini, Massimo & Kuenzle, Michael, 2007. "Cost efficiency in the Swiss gas distribution sector," Energy Economics, Elsevier, vol. 29(1), pages 64-78, January.
    12. E. Fusco & R. Benedetti & F. Vidoli, 2023. "Stochastic frontier estimation through parametric modelling of quantile regression coefficients," Empirical Economics, Springer, vol. 64(2), pages 869-896, February.
    13. Barros, Carlos Pestana & Chen, Zhongfei & Managi, Shunsuke & Antunes, Olinda Sequeira, 2013. "Examining the cost efficiency of Chinese hydroelectric companies using a finite mixture model," Energy Economics, Elsevier, vol. 36(C), pages 511-517.
    14. Joachim Zietz & Emily Zietz & G. Sirmans, 2008. "Determinants of House Prices: A Quantile Regression Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 317-333, November.
    15. Maria Letizia Giorgetti, 2001. "Quantile Regression in Lower Bound Estimation," STICERD - Economics of Industry Papers 29, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    16. Chen Lin & Sanford Berg, 2008. "Incorporating Service Quality into Yardstick Regulation: An Application to the Peru Water Sector," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 32(1), pages 53-75, February.
    17. Lowry, Mark Newton & Getachew, Lullit, 2009. "Statistical benchmarking in utility regulation: Role, standards and methods," Energy Policy, Elsevier, vol. 37(4), pages 1323-1330, April.
    18. Xie, Bai-Chen & Ni, Kang-Kang & O'Neill, Eoghan & Li, Hong-Zhou, 2021. "The scale effect in China's power grid sector from the perspective of malmquist total factor productivity analysis," Utilities Policy, Elsevier, vol. 69(C).
    19. Jorge E. Galán & Michael G. Pollitt, 2014. "Inefficiency persistence and heterogeneity in Colombian electricity distribution utilities," Cambridge Working Papers in Economics 1423, Faculty of Economics, University of Cambridge.
    20. Charles-Olivier Amédée-Manesme & Michel Baroni & Fabrice Barthélémy & Francois des Rosiers, 2017. "Market heterogeneity and the determinants of Paris apartment prices: A quantile regression approach," Urban Studies, Urban Studies Journal Limited, vol. 54(14), pages 3260-3280, November.

    More about this item

    Keywords

    E_ciency measurement; cost function; incentive regulation; electricity sector;
    All these keywords.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ube:dpvwib:dp1606. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Franz Koelliker (email available below). General contact details of provider: https://edirc.repec.org/data/vwibech.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.