IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v76y2021ics0038012120308107.html
   My bibliography  Save this article

Analysis of electric distribution utilities efficiency levels by stochastic frontier in Brazilian power sector

Author

Listed:
  • Cardoso de Mendonça, Mário Jorge
  • Pereira, Amaro Olimpio
  • Medrano, Luis Alberto
  • Pessanha, José Francisco M.

Abstract

The regulatory power sector model in several countries determines tariff review forms based, among other things, on sharing efficiency gains with consumers. As these reviews have an important impact on consumers and distribution utilities, it is necessary that the adopted methodologies always be improved. To this end, this article assessed a Bayesian inference application in order to estimate a stochastic cost frontier considering temporal efficiency dynamics. Taking this point into consideration is essential, since studies carried out to assess power sector efficiency have neglected the fact that part of efficiency increases originate from scale gain due to market expansion, which occurs over time. The sample assessed herein is composed of panel data from 61 electric power utilities between 2003 and 2016. The results demonstrate that the tariff review is positively affected by distributor efficiency.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:soceps:v:76:y:2021:i:c:s0038012120308107
    DOI: 10.1016/j.seps.2020.100973
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012120308107
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.seps.2020.100973?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. Christian Growitsch & Tooraj Jamasb & Michael Pollitt, 2009. "Quality of service, efficiency and scale in network industries: an analysis of European electricity distribution," Applied Economics, Taylor & Francis Journals, vol. 41(20), pages 2555-2570.
    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. Haney, Aoife Brophy & Pollitt, Michael G., 2009. "Efficiency analysis of energy networks: An international survey of regulators," Energy Policy, Elsevier, vol. 37(12), pages 5814-5830, December.
    4. Antonio Estache & MartÌn A. Rossi & Christian A. Ruzzier, 2004. "The Case for International Coordination of Electricity Regulation: Evidence from the Measurement of Efficiency in South America," Journal of Regulatory Economics, Springer, vol. 25(3), pages 271-295, May.
    5. 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.
    6. Simões, Paulo Fernando Mahaz & Souza, Reinaldo Castro & Calili, Rodrigo Flora & Pessanha, José Francisco Moreira, 2020. "Analysis and short-term predictions of non-technical loss of electric power based on mixed effects models," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    7. Marcus Vinicius Pereira de Souza & Madiagne Diallo & Reinaldo Castro Souza & Tara Keshar Nanda Baidya, 2010. "The Cost Efficiency of the Brazilian Electricity Distribution Utilities: A Comparison of Bayesian SFA and DEA Models," Mathematical Problems in Engineering, Hindawi, vol. 2010, pages 1-20, August.
    8. Pereira de Souza, Marcus Vinicius & Souza, Reinaldo C. & Pessanha, José Francisco M. & da Costa Oliveira, Carlos Henrique & Diallo, Madiagne, 2014. "An application of data envelopment analysis to evaluate the efficiency level of the operational cost of Brazilian electricity distribution utilities," Socio-Economic Planning Sciences, Elsevier, vol. 48(3), pages 169-174.
    9. Massimo Filippini & Luis Orea, 2014. "Applications of the stochastic frontier approach in Energy Economics," Economics and Business Letters, Oviedo University Press, vol. 3(1), pages 35-42.
    10. Efthymios G. Tsionas, 2002. "Stochastic frontier models with random coefficients," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 127-147.
    11. Kuosmanen, Timo & Saastamoinen, Antti & Sipiläinen, Timo, 2013. "What is the best practice for benchmark regulation of electricity distribution? Comparison of DEA, SFA and StoNED methods," Energy Policy, Elsevier, vol. 61(C), pages 740-750.
    12. Toru Hattori, 2002. "Relative Performance of U.S. and Japanese Electricity Distribution: An Application of Stochastic Frontier Analysis," Journal of Productivity Analysis, Springer, vol. 18(3), pages 269-284, November.
    13. Koop, Gary & Osiewalski, Jacek & Steel, Mark F J, 2000. "A Stochastic Frontier Analysis of Output Level and Growth in Poland and Western Economies," Economic Change and Restructuring, Springer, vol. 33(3), pages 185-202.
    14. 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.
    15. Mehdi Farsi & Massimo Filippini & William Greene, 2006. "Application Of Panel Data Models In Benchmarking Analysis Of The Electricity Distribution Sector," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 77(3), pages 271-290, September.
    16. Filippini, Massimo & Hrovatin, Nevenka & Zoric, Jelena, 2004. "Efficiency and regulation of the Slovenian electricity distribution companies," Energy Policy, Elsevier, vol. 32(3), pages 335-344, February.
    17. Galán, Jorge E. & Veiga, Helena & Wiper, Michael P., 2015. "Dynamic effects in inefficiency: Evidence from the Colombian banking sector," European Journal of Operational Research, Elsevier, vol. 240(2), pages 562-571.
    18. Burns, Philip & Weyman-Jones, Thomas G, 1996. "Cost Functions and Cost Efficiency in Electricity Distribution: A Stochastic Frontier Approach," Bulletin of Economic Research, Wiley Blackwell, vol. 48(1), pages 41-64, January.
    19. 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.
    20. KOOP , Gary & OSIEWALSKI, Jacek & STEEL , Mark, 1995. "Measuring the Sources of Output Growth in a Panel of Countries," LIDAM Discussion Papers CORE 1995042, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    21. Jamasb, Tooraj & Pollitt, Michael, 2003. "International benchmarking and regulation: an application to European electricity distribution utilities," Energy Policy, Elsevier, vol. 31(15), pages 1609-1622, December.
    22. Christopher R. Knittel, 2002. "Alternative Regulatory Methods And Firm Efficiency: Stochastic Frontier Evidence From The U.S. Electricity Industry," The Review of Economics and Statistics, MIT Press, vol. 84(3), pages 530-540, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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).

    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. 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.
    2. Cambini, Carlo & Croce, Annalisa & Fumagalli, Elena, 2014. "Output-based incentive regulation in electricity distribution: Evidence from Italy," Energy Economics, Elsevier, vol. 45(C), pages 205-216.
    3. Huang, Yi-Ju & Chen, Ku-Hsieh & Yang, Chih-Hai, 2010. "Cost efficiency and optimal scale of electricity distribution firms in Taiwan: An application of metafrontier analysis," Energy Economics, Elsevier, vol. 32(1), pages 15-23, January.
    4. 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.
    5. 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.
    6. Goto, Mika & Sueyoshi, Toshiyuki, 2009. "Productivity growth and deregulation of Japanese electricity distribution," Energy Policy, Elsevier, vol. 37(8), pages 3130-3138, August.
    7. Elvira Silva & Pedro Macedo & Isabel Soares, 2019. "Maximum entropy: a stochastic frontier approach for electricity distribution regulation," Journal of Regulatory Economics, Springer, vol. 55(3), pages 237-257, June.
    8. Assaf, A. George & Barros, Carlos Pestana & Managi, Shunsuke, 2011. "Cost efficiency of Japanese steam power generation companies: A Bayesian comparison of random and fixed frontier models," Applied Energy, Elsevier, vol. 88(4), pages 1441-1446, April.
    9. Zakaria, Muhammad & Noureen, Rabia, 2016. "Benchmarking and regulation of power distribution companies in Pakistan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1095-1099.
    10. 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.
    11. Barros, Carlos Pestana, 2008. "Efficiency analysis of hydroelectric generating plants: A case study for Portugal," Energy Economics, Elsevier, vol. 30(1), pages 59-75, January.
    12. Agrell, Per J. & Brea-Solís, Humberto, 2017. "Capturing heterogeneity in electricity distribution operations: A critical review of latent class modelling," Energy Policy, Elsevier, vol. 104(C), pages 361-372.
    13. Christian von Hirschhausen & Astrid Cullmann & Andreas Kappeler, 2006. "Efficiency analysis of German electricity distribution utilities - non-parametric and parametric tests," Applied Economics, Taylor & Francis Journals, vol. 38(21), pages 2553-2566.
    14. Arcos-Vargas, A. & Núñez-Hernández, F. & Villa-Caro, Gabriel, 2017. "A DEA analysis of electricity distribution in Spain: An industrial policy recommendation," Energy Policy, Elsevier, vol. 102(C), pages 583-592.
    15. Janda, Karel & Krska, Stepan, 2014. "Benchmarking Methods in the Regulation of Electricity Distribution System Operators," MPRA Paper 59442, University Library of Munich, Germany.
    16. Mehdi Farsi & Massimo Filippini & William Greene, 2006. "Application Of Panel Data Models In Benchmarking Analysis Of The Electricity Distribution Sector," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 77(3), pages 271-290, September.
    17. Galán, Jorge E. & Pollitt, Michael G., 2014. "Inefficiency persistence and heterogeneity in Colombian electricity utilities," Energy Economics, Elsevier, vol. 46(C), pages 31-44.
    18. Barros, Carlos Pestana & Managi, Shunsuke, 2009. "Regulation, pollution and heterogeneity in Japanese steam power generation companies," Energy Policy, Elsevier, vol. 37(8), pages 3109-3114, August.
    19. Shrivastava, Naveen & Sharma, Seema & Chauhan, Kavita, 2012. "Efficiency assessment and benchmarking of thermal power plants in India," Energy Policy, Elsevier, vol. 40(C), pages 159-176.
    20. Astrid Cullmann & Christian Hirschhausen, 2008. "Efficiency analysis of East European electricity distribution in transition: legacy of the past?," Journal of Productivity Analysis, Springer, vol. 29(2), pages 155-167, April.

    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:soceps:v:76:y:2021:i:c:s0038012120308107. 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: http://www.elsevier.com/locate/seps .

    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.