IDEAS home Printed from https://ideas.repec.org/a/eee/juipol/v80y2023ics095717872200145x.html
   My bibliography  Save this article

Service quality performance indicators for electricity distribution in Brazil

Author

Listed:
  • de Mendonça, Mário Jorge Cardoso
  • Pereira, Amaro Olimpio
  • Bellido, Marlon Max H.
  • Medrano, Luis Alberto
  • Pessanha, José Francisco Moreira

Abstract

One key aspect of electricity quality is the continuity of supply, assessed based on duration and interruption frequency indicators. In Brazil, these indicators are used to assess utility performance, including tariff reviews. This paper proposes adopting a stochastic frontier analysis to benchmark the continuity indicators. Based on Bayesian inference, the model can consider the temporal dynamics of utility performance in terms of gains from economies of scale due to market expansion. The results show that performance targets defined by the proposed methodology are compatible with those defined by the regulatory body.

Suggested Citation

  • de Mendonça, Mário Jorge Cardoso & Pereira, Amaro Olimpio & Bellido, Marlon Max H. & Medrano, Luis Alberto & Pessanha, José Francisco Moreira, 2023. "Service quality performance indicators for electricity distribution in Brazil," Utilities Policy, Elsevier, vol. 80(C).
  • Handle: RePEc:eee:juipol:v:80:y:2023:i:c:s095717872200145x
    DOI: 10.1016/j.jup.2022.101481
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S095717872200145X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jup.2022.101481?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cardoso de Mendonça, Mário Jorge & Pereira, Amaro Olimpio & Medrano, Luis Alberto & Pessanha, José Francisco M., 2021. "Analysis of electric distribution utilities efficiency levels by stochastic frontier in Brazilian power sector," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).
    2. Efthymios G. Tsionas, 2006. "Inference in dynamic stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 669-676, July.
    3. Christian Growitsch & Tooraj Jamasb & Christine Müller & Matthias Wissner, 2016. "Social Cost Efficient Service Quality: Integrating Customer Valuation in Incentive Regulation—Evidence from the Case of Norway," International Series in Operations Research & Management Science, in: Joe Zhu (ed.), Data Envelopment Analysis, chapter 0, pages 71-91, Springer.
    4. Fernandez, Carmen & Koop, Gary & Steel, Mark, 2000. "A Bayesian analysis of multiple-output production frontiers," Journal of Econometrics, Elsevier, vol. 98(1), pages 47-79, September.
    5. Janjic, Aleksandar & Velimirovic, Lazar Z. & Vranic, Petar, 2021. "Designing an electricity distribution reward-penalty scheme based on spatial reliability statistics," Utilities Policy, Elsevier, vol. 70(C).
    6. Prettico, G. & Marinopoulos, A. & Vitiello, S., 2022. "Guiding electricity distribution system investments to improve service quality: A European study," Utilities Policy, Elsevier, vol. 77(C).
    7. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    8. Jim Griffin & Mark Steel, 2007. "Bayesian stochastic frontier analysis using WinBUGS," Journal of Productivity Analysis, Springer, vol. 27(3), pages 163-176, June.
    9. van den Broeck, Julien & Koop, Gary & Osiewalski, Jacek & Steel, Mark F. J., 1994. "Stochastic frontier models : A Bayesian perspective," Journal of Econometrics, Elsevier, vol. 61(2), pages 273-303, April.
    10. Battese, George E. & Corra, Greg S., 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 21(3), pages 1-11, December.
    11. Giannakis, Dimitrios & Jamasb, Tooraj & Pollitt, Michael, 2005. "Benchmarking and incentive regulation of quality of service: an application to the UK electricity distribution networks," Energy Policy, Elsevier, vol. 33(17), pages 2256-2271, November.
    12. Fernandez, Carmen & Osiewalski, Jacek & Steel, Mark F. J., 1997. "On the use of panel data in stochastic frontier models with improper priors," Journal of Econometrics, Elsevier, vol. 79(1), pages 169-193, July.
    13. George E. Battese & Greg S. Corra, 1977. "Estimation Of A Production Frontier Model: With Application To The Pastoral Zone Of Eastern Australia," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 21(3), pages 169-179, December.
    14. Fernandez, Carmen & Koop, Gary & Steel, Mark F.J., 2005. "Alternative efficiency measures for multiple-output production," Journal of Econometrics, Elsevier, vol. 126(2), pages 411-444, June.
    15. Barbosa, Ailson de Souza & Shayani, Rafael Amaral & Oliveira, Marco Aurélio Gonçalves de, 2018. "A multi-criteria decision analysis method for regulatory evaluation of electricity distribution service quality," Utilities Policy, Elsevier, vol. 53(C), pages 38-48.
    16. Daniel Leite & José Pessanha & Paulo Simões & Rodrigo Calili & Reinaldo Souza, 2020. "A Stochastic Frontier Model for Definition of Non-Technical Loss Targets," Energies, MDPI, vol. 13(12), pages 1-20, June.
    17. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    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. Luis R. Murillo‐Zamorano, 2004. "Economic Efficiency and Frontier Techniques," Journal of Economic Surveys, Wiley Blackwell, vol. 18(1), pages 33-77, February.
    2. Arabinda Das, 2015. "Copula-based Stochastic Frontier Model with Autocorrelated Inefficiency," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 7(2), pages 111-126, June.
    3. Mike Tsionas & Marwan Izzeldin & Arne Henningsen & Evaggelos Paravalos, 2022. "Addressing endogeneity when estimating stochastic ray production frontiers: a Bayesian approach," Empirical Economics, Springer, vol. 62(3), pages 1345-1363, March.
    4. Kamil Makieła & Błażej Mazur, 2020. "Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis," Econometrics, MDPI, vol. 8(2), pages 1-22, April.
    5. Goto, Mika & Makhija, Anil K., 2007. "The Impact of Competition and Corporate Structure on Productive Efficiency: The Case of the U.S. Electric Utility Industry, 1990-2004," Working Paper Series 2007-10, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    6. Sickles, Robin C. & Hao, Jiaqi & Shang, Chenjun, 2015. "Panel Data and Productivity Measurement," Working Papers 15-018, Rice University, Department of Economics.
    7. 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.
    8. Galán, Jorge E. & Pollitt, Michael G., 2014. "Inefficiency persistence and heterogeneity in Colombian electricity utilities," Energy Economics, Elsevier, vol. 46(C), pages 31-44.
    9. Tecles, Patricia Langsch & Tabak, Benjamin M., 2010. "Determinants of bank efficiency: The case of Brazil," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1587-1598, December.
    10. Carta, Alessandro & Steel, Mark F.J., 2012. "Modelling multi-output stochastic frontiers using copulas," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3757-3773.
    11. Efthymios G. Tsionas, 2006. "Inference in dynamic stochastic frontier models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(5), pages 669-676, July.
    12. Sickles, Robin C., 2005. "Panel estimators and the identification of firm-specific efficiency levels in parametric, semiparametric and nonparametric settings," Journal of Econometrics, Elsevier, vol. 126(2), pages 305-334, June.
    13. Kamil Makie{l}a & B{l}a.zej Mazur, 2020. "Stochastic Frontier Analysis with Generalized Errors: inference, model comparison and averaging," Papers 2003.07150, arXiv.org, revised Oct 2020.
    14. A. Tonini, 2012. "A Bayesian stochastic frontier: an application to agricultural productivity growth in European countries," Economic Change and Restructuring, Springer, vol. 45(4), pages 247-269, November.
    15. Li, Hong-Zhou & Kopsakangas-Savolainen, Maria & Xiao, Xing-Zhi & Tian, Zhen-Zhen & Yang, Xiao-Yuan & Wang, Jian-Lin, 2016. "Cost efficiency of electric grid utilities in China: A comparison of estimates from SFA–MLE, SFA–Bayes and StoNED–CNLS," Energy Economics, Elsevier, vol. 55(C), pages 272-283.
    16. Kamil Makieła & Błażej Mazur, 2022. "Model uncertainty and efficiency measurement in stochastic frontier analysis with generalized errors," Journal of Productivity Analysis, Springer, vol. 58(1), pages 35-54, August.
    17. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    18. Skevas, Ioannis & Skevas, Theodoros, 2021. "A generalized true random-effects model with spatially autocorrelated persistent and transient inefficiency," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1131-1142.
    19. Antonio Ramos Andrade & Julian Stow, 2017. "Assessing the efficiency of maintenance operators: A case study of turning railway wheelsets on an under-floor wheel lathe," Journal of Risk and Reliability, , vol. 231(2), pages 155-163, April.
    20. Galán, Jorge & Ramos, Sofía B. & Veiga, Helena, 2015. "An analysis of the dynamics of efficiency of mutual funds," DES - Working Papers. Statistics and Econometrics. WS ws1517, Universidad Carlos III de Madrid. Departamento de Estadística.

    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:eee:juipol:v:80:y:2023:i:c:s095717872200145x. 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: Catherine Liu (email available below). General contact details of provider: https://www.sciencedirect.com/journal/utilities-policy .

    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.