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Observed and unobserved heterogeneity in stochastic frontier models: An application to the electricity distribution industry


  • Kopsakangas-Savolainen, Maria
  • Svento, Rauli


In this study we combine different possibilities to model firm level heterogeneity in stochastic frontier analysis. We show that both observed and unobserved heterogeneities cause serious biases in inefficiency results. Modelling observed and unobserved heterogeneities treat individual firms in different ways and even though the expected mean inefficiency scores in both cases diminish the firm level efficiency rank orders turn out to be very different. The best fit with the data is obtained by modelling unobserved heterogeneity through randomizing frontier parameters and at the same time explicitly modelling the observed heterogeneity into the inefficiency distribution. These results are obtained by using data from Finnish electricity distribution utilities and the results are relevant in relation to electricity distribution pricing and regulation.

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  • 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.
  • Handle: RePEc:eee:eneeco:v:33:y:2011:i:2:p:304-310

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    References listed on IDEAS

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    7. William Greene, 2004. "Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization's panel data on national health care systems," Health Economics, John Wiley & Sons, Ltd., vol. 13(10), pages 959-980.
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    Cited by:

    1. 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.
    2. 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.
    3. Bostian, Moriah & Färe, Rolf & Grosskopf, Shawna & Lundgren, Tommy, 2016. "Environmental investment and firm performance: A network approach," Energy Economics, Elsevier, vol. 57(C), pages 243-255.
    4. Galán, Jorge E. & Pollitt, Michael G., 2014. "Inefficiency persistence and heterogeneity in Colombian electricity utilities," Energy Economics, Elsevier, vol. 46(C), pages 31-44.
    5. George Elias, 2016. "Benchmarking Heterogeneous Distribution System Operators: Evidence from Norway," Diskussionsschriften dp1606, Universitaet Bern, Departement Volkswirtschaft.
    6. 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.
    7. repec:eee:enepol:v:108:y:2017:i:c:p:606-616 is not listed on IDEAS
    8. 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.
    9. Li, Hong-Zhou & Tian, Xian-Liang & Zou, Tao, 2015. "Impact analysis of coal-electricity pricing linkage scheme in China based on stochastic frontier cost function," Applied Energy, Elsevier, vol. 151(C), pages 296-305.
    10. 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.
    11. Saastamoinen, Antti & Kuosmanen, Timo, 2016. "Quality frontier of electricity distribution: Supply security, best practices, and underground cabling in Finland," Energy Economics, Elsevier, vol. 53(C), pages 281-292.


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