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Efficiency benchmarking and remuneration of Spanish electricity distribution companies

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  • Núñez, F.
  • Arcos-Vargas, A.
  • Villa, G.

Abstract

In Spain, the average remuneration of large electric distribution companies is much lower than that of smaller ones, which leads us to wonder if the smaller ones are managed efficiently. A meta-frontier DEA model is calculated in order to benchmark, in terms of remuneration and quality, different clusters of distributors that present different technologies. The sample is composed of 236 distributors (including the five largest units). The savings for the system would be approximately 174 million Euros per year. Moreover, compensation regulation should aim to guide distributors towards their respective intra-cluster frontiers.

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  • Núñez, F. & Arcos-Vargas, A. & Villa, G., 2020. "Efficiency benchmarking and remuneration of Spanish electricity distribution companies," Utilities Policy, Elsevier, vol. 67(C).
    • Handle: RePEc:eee:juipol:v:67:y:2020:i:c:s0957178720301211
    DOI: 10.1016/j.jup.2020.101127
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    as
    1. Afsharian, Mohsen & Ahn, Heinz & Thanassoulis, Emmanuel, 2017. "A DEA-based incentives system for centrally managed multi-unit organisations," European Journal of Operational Research, Elsevier, vol. 259(2), pages 587-598.
    2. Kerstens, Kristiaan & O’Donnell, Christopher & Van de Woestyne, Ignace, 2019. "Metatechnology frontier and convexity: A restatement," European Journal of Operational Research, Elsevier, vol. 275(2), pages 780-792.
    3. 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.
    4. Jean-Jacques Laffont & Jean Tirole, 1993. "A Theory of Incentives in Procurement and Regulation," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262121743, December.
    5. Bădin, Luiza & Daraio, Cinzia & Simar, Léopold, 2012. "How to measure the impact of environmental factors in a nonparametric production model," European Journal of Operational Research, Elsevier, vol. 223(3), pages 818-833.
    6. Heinz Ahn & Peter Bogetoft & Ana Lopes, 2019. "Measuring potential sub-unit efficiency to counter the aggregation bias in benchmarking," Journal of Business Economics, Springer, vol. 89(1), pages 53-77, February.
    7. Léopold Simar & Paul W. Wilson, 2015. "Statistical Approaches for Non-parametric Frontier Models: A Guided Tour," International Statistical Review, International Statistical Institute, vol. 83(1), pages 77-110, April.
    8. Paul L. Joskow, 2014. "Incentive Regulation in Theory and Practice: Electricity Distribution and Transmission Networks," NBER Chapters, in: Economic Regulation and Its Reform: What Have We Learned?, pages 291-344, National Bureau of Economic Research, Inc.
    9. 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.
    10. 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.
    11. Jesús T. Pastor & José L. Ruiz, 2007. "Variables With Negative Values In Dea," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 63-84, Springer.
    12. Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & de Pinho Matos, Giordano Bruno Braz, 2015. "Statistical evaluation of Data Envelopment Analysis versus COLS Cobb–Douglas benchmarking models for the 2011 Brazilian tariff revision," Socio-Economic Planning Sciences, Elsevier, vol. 49(C), pages 47-60.
    13. Peter Bogetoft, 1997. "DEA-based yardstick competition: The optimality of best practice regulation," Annals of Operations Research, Springer, vol. 73(0), pages 277-298, October.
    14. 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.
    15. Sebastián Lozano & Gabriel Villa, 2004. "Centralized Resource Allocation Using Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 22(1), pages 143-161, July.
    16. Juan Aparicio & C. A. Knox Lovell & Jesus T. Pastor (ed.), 2016. "Advances in Efficiency and Productivity," International Series in Operations Research and Management Science, Springer, number 978-3-319-48461-7, July-Dece.
    17. 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.
    18. Cinzia Daraio & Léopold Simar, 2005. "Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach," Journal of Productivity Analysis, Springer, vol. 24(1), pages 93-121, September.
    19. Li, Hong-Zhou & Kopsakangas-Savolainen, Maria & Xiao, Xing-Zhi & Lau, Sim-Yee, 2017. "Have regulatory reforms improved the efficiency levels of the Japanese electricity distribution sector? A cost metafrontier-based analysis," Energy Policy, Elsevier, vol. 108(C), pages 606-616.
    20. Jin, Qianying & Kerstens, Kristiaan & Van de Woestyne, Ignace, 2020. "Metafrontier productivity indices: Questioning the common convexification strategy," European Journal of Operational Research, Elsevier, vol. 283(2), pages 737-747.
    21. Luis Orea & Subal C. Kumbhakar, 2004. "Efficiency measurement using a latent class stochastic frontier model," Empirical Economics, Springer, vol. 29(1), pages 169-183, January.
    22. 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.
    23. Bjørndal, Endre & Bjørndal, Mette & Cullmann, Astrid & Nieswand, Maria, 2016. "Finding the Right Yardstick: Regulation under Heterogeneous Environments," Discussion Papers 2016/4, Norwegian School of Economics, Department of Business and Management Science.
    24. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    25. Varmaz, Armin & Varwig, Andreas & Poddig, Thorsten, 2013. "Centralized resource planning and Yardstick competition," Omega, Elsevier, vol. 41(1), pages 112-118.
    26. Cinzia Daraio & Léopold Simar, 2007. "Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach," Journal of Productivity Analysis, Springer, vol. 28(1), pages 13-32, October.
    27. Andrew Johnson & Timo Kuosmanen, 2011. "One-stage estimation of the effects of operational conditions and practices on productive performance: asymptotically normal and efficient, root-n consistent StoNEZD method," Journal of Productivity Analysis, Springer, vol. 36(2), pages 219-230, October.
    28. Afsharian, Mohsen, 2017. "Metafrontier efficiency analysis with convex and non-convex metatechnologies by stochastic nonparametric envelopment of data," Economics Letters, Elsevier, vol. 160(C), pages 1-3.
    29. Anna Makles, 2012. "Stata tip 110: How to get the optimal k-means cluster solution," Stata Journal, StataCorp LP, vol. 12(2), pages 347-351, June.
    30. Daraio, Cinzia & Simar, Leopold & Wilson, Paul, 2015. "Testing the "Separability" Condition in Two-Stage Nonparametric Models of Production," LIDAM Discussion Papers ISBA 2015018, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    31. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    32. Christine Amsler & Christopher J. O’Donnell & Peter Schmidt, 2017. "Stochastic metafrontiers," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 1007-1020, October.
    33. Per J. Agrell & Peter Bogetoft, 2016. "Endogenous Common Weights as a Collusive Instrument in Frontier-Based Regulation," International Series in Operations Research & Management Science, in: Juan Aparicio & C. A. Knox Lovell & Jesus T. Pastor (ed.), Advances in Efficiency and Productivity, chapter 0, pages 181-194, Springer.
    34. KNEIP, Alois & SIMAR, Léopold, 1995. "A General Framework for Frontier Estimation with Panel Data," LIDAM Discussion Papers CORE 1995060, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    35. Hayami, Yujiro & Ruttan, Vernon W, 1970. "Agricultural Productivity Differences Among Countries," American Economic Review, American Economic Association, vol. 60(5), pages 895-911, December.
    36. Wade D. Cook & Joe Zhu, 2007. "Data Irregularities And Structural Complexities In Dea," Springer Books, in: Joe Zhu & Wade D. Cook (ed.), Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, chapter 0, pages 1-11, Springer.
    37. Kuosmanen, Timo, 2012. "Stochastic semi-nonparametric frontier estimation of electricity distribution networks: Application of the StoNED method in the Finnish regulatory model," Energy Economics, Elsevier, vol. 34(6), pages 2189-2199.
    38. Yun Zhang & Robert Bartels, 1998. "The Effect of Sample Size on the Mean Efficiency in DEA with an Application to Electricity Distribution in Australia, Sweden and New Zealand," Journal of Productivity Analysis, Springer, vol. 9(3), pages 187-204, March.
    39. Orea, Luis & Jamasb, Tooraj, 2014. "Identifying efficient regulated firms with unobserved technological heterogeneity: A nested latent class approach to Norwegian electricity distribution networks," Efficiency Series Papers 2014/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    40. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January.
    41. Rajiv D. Banker & Richard C. Morey, 1986. "Efficiency Analysis for Exogenously Fixed Inputs and Outputs," Operations Research, INFORMS, vol. 34(4), pages 513-521, August.
    42. Christopher O’Donnell & D. Rao & George Battese, 2008. "Metafrontier frameworks for the study of firm-level efficiencies and technology ratios," Empirical Economics, Springer, vol. 34(2), pages 231-255, March.
    43. 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.
    44. 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.
    45. Afsharian, Mohsen & Podinovski, Victor V., 2018. "A linear programming approach to efficiency evaluation in nonconvex metatechnologies," European Journal of Operational Research, Elsevier, vol. 268(1), pages 268-280.
    46. Walheer, Barnabé, 2018. "Aggregation of metafrontier technology gap ratios: the case of European sectors in 1995–2015," European Journal of Operational Research, Elsevier, vol. 269(3), pages 1013-1026.
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