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Using the latent class approach to cluster firms in benchmarking: An application to the US electricity transmission industry

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  • Llorca, Manuel
  • Orea, Luis
  • Pollitt, Michael G.

Abstract

In this paper we advocate using the latent class model (LCM) approach to control for technological differences in traditional efficiency analysis of regulated electricity networks. Our proposal relies on the fact that latent class models are designed to cluster firms by uncovering differences in technology parameters. Moreover, it can be viewed as a supervised method for clustering data that takes into account the same (production or cost) relationship that is analysed later, often using nonparametric frontier techniques. The simulation exercises show that the proposed approach outperforms other sample selection procedures. The proposed methodology is illustrated with an application to a sample of US electricity transmission firms for the period 2001–2009.

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  • Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2014. "Using the latent class approach to cluster firms in benchmarking: An application to the US electricity transmission industry," Operations Research Perspectives, Elsevier, vol. 1(1), pages 6-17.
  • Handle: RePEc:eee:oprepe:v:1:y:2014:i:1:p:6-17
    DOI: 10.1016/j.orp.2014.03.002
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    Cited by:

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    5. Francisco Benita & Serhad Sarica & Garvit Bansal, 2020. "Testing the static and dynamic performance of statistical methods for the detection of national industrial clusters," Papers in Regional Science, Wiley Blackwell, vol. 99(4), pages 1137-1157, August.
    6. Christian Grovermann & Tesfamicheal Wossen & Adrian Muller & Karin Nichterlein, 2019. "Eco-efficiency and agricultural innovation systems in developing countries: Evidence from macro-level analysis," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-16, April.
    7. 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.
    8. Orea, L. & Álvarez, I & Jamasb, T., 2016. "Using a spatial econometric approach to mitigate omitted variables in stochastic frontier models: An application to Norwegian electricity distribution networks," Cambridge Working Papers in Economics 1673, Faculty of Economics, University of Cambridge.
    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. Agrell, P & Brea-Solís, H., 2015. "Stationarity of Heterogeneity in Production Technology using Latent Class Modelling," LIDAM Discussion Papers CORE 2015047, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    11. 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.
    12. Rita, Rui & Marques, Vitor & Lúcia Costa, Ana & Matos Chaves, Inês & Gomes, Joana & Paulino, Paulo, 2018. "Efficiency performance and cost structure of Portuguese energy “utilities” – Non-parametric and parametric analysis," Energy, Elsevier, vol. 155(C), pages 35-45.
    13. Luis Orea & Tooraj Jamasb, 2017. "Regulating Heterogeneous Utilities: A New Latent Class Approach with Application to the Norwegian Electricity Distribution Networks," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    14. Maria Nieswand & Stefan Seifert, 2016. "Operational Conditions in Regulatory Benchmarking Models: A Monte Carlo Analysis," Discussion Papers of DIW Berlin 1585, DIW Berlin, German Institute for Economic Research.
    15. Nieswand, Maria & Seifert, Stefan, 2018. "Environmental factors in frontier estimation – A Monte Carlo analysis," European Journal of Operational Research, Elsevier, vol. 265(1), pages 133-148.
    16. Feder, Christophe, 2018. "The effects of disruptive innovations on productivity," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 186-193.
    17. Anaya, Karim L. & Pollitt, Michael G., 2017. "Using stochastic frontier analysis to measure the impact of weather on the efficiency of electricity distribution businesses in developing economies," European Journal of Operational Research, Elsevier, vol. 263(3), pages 1078-1094.
    18. da Silva, Aline Veronese & Costa, Marcelo Azevedo & Lopes, Ana Lúcia Miranda & do Carmo, Gabriela Miranda, 2019. "A close look at second stage data envelopment analysis using compound error models and the Tobit model," Socio-Economic Planning Sciences, Elsevier, vol. 65(C), pages 111-126.
    19. Ronald G. McGarvey & Andreas Thorsen & Maggie L. Thorsen & Rohith Madhi Reddy, 2019. "Measuring efficiency of community health centers: a multi-model approach considering quality of care and heterogeneous operating environments," Health Care Management Science, Springer, vol. 22(3), pages 489-511, September.

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    More about this item

    Keywords

    Electricity transmission; Utilities regulation; Latent class model approach; Nonparametric analysis;
    All these keywords.

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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