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Disentangling Costs of Persistent and Transient Technical Inefficiency and Input Misallocation: The Case of Norwegian Electricity Distribution Firms

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  • Subal C. Kumbhakar
  • Ørjan Mydland
  • Andrew Musau
  • Gudbrand Lien

Abstract

Numerous studies have focused on estimating technical inefficiency in electricity distribution firms. However, most of these studies did not distinguish between persistent and transient technical inefficiency. Furthermore, almost none of the studies estimated the cost of input misallocation arising from non-optimal use of inputs. One reason is that the cost function (input distance function) typically used in the literature does not allow for the separation of technical inefficiency and allocative inefficiency. In this study, we estimate both the persistent and transient components of technical inefficiency and input misallocation of Norwegian electricity distribution firms, using panel data from 2000 to 2016. Our modeling and estimation strategy is to use a system approach, consisting of the production function and the first-order conditions of cost minimization. Input misallocation for each pair of inputs is modeled via the first-order conditions of cost minimization. We also estimate the costs of each component of technical inefficiency and input misallocation by deriving the cost function for a multi-output separable production technology. Our modeling and estimation strategy handles endogeneity of inputs. Finally, we allow for inclusion of determinants of persistent and transient technical inefficiency. Our results show that the costs of input misallocation of Norwegian electricity distribution firms are non-negligible.

Suggested Citation

  • Subal C. Kumbhakar & Ørjan Mydland & Andrew Musau & Gudbrand Lien, 2020. "Disentangling Costs of Persistent and Transient Technical Inefficiency and Input Misallocation: The Case of Norwegian Electricity Distribution Firms," The Energy Journal, , vol. 41(3), pages 143-160, May.
    • Handle: RePEc:sae:enejou:v:41:y:2020:i:3:p:143-160
    DOI: 10.5547/01956574.41.3.skum
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    References listed on IDEAS

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    1. Finn Førsund & Lennart Hjalmarsson, 2004. "Are all Scales Optimal in DEA? Theory and Empirical Evidence," Journal of Productivity Analysis, Springer, vol. 21(1), pages 25-48, January.
    2. Henderson,Daniel J. & Parmeter,Christopher F., 2015. "Applied Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521279680.
    3. Henderson,Daniel J. & Parmeter,Christopher F., 2015. "Applied Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9781107010253.
    4. Subal Kumbhakar & Roar Amundsveen & Hilde Kvile & Gudbrand Lien, 2015. "Scale economies, technical change and efficiency in Norwegian electricity distribution, 1998–2010," Journal of Productivity Analysis, Springer, vol. 43(3), pages 295-305, June.
    5. Yatchew,Adonis, 2003. "Semiparametric Regression for the Applied Econometrician," Cambridge Books, Cambridge University Press, number 9780521012263.
    6. Jiro Nemoto & Mika Goto, 2006. "Measurement of technical and allocative efficiencies using a CES cost frontier: a benchmarking study of Japanese transmission-distribution electricity," Empirical Economics, Springer, vol. 31(1), pages 31-48, March.
    7. Lai, Hung-pin & Kumbhakar, Subal C., 2018. "Endogeneity in panel data stochastic frontier model with determinants of persistent and transient inefficiency," Economics Letters, Elsevier, vol. 162(C), pages 5-9.
    8. Yatchew,Adonis, 2003. "Semiparametric Regression for the Applied Econometrician," Cambridge Books, Cambridge University Press, number 9780521812832.
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    1. Liu, Tung, 2021. "Measuring cost inefficiency: A dual approach," Economic Modelling, Elsevier, vol. 99(C).
    2. Musau, Andrew & Kumbhakar, Subal C. & Mydland, Ørjan & Lien, Gudbrand, 2021. "Determinants of allocative and technical inefficiency in stochastic frontier models: An analysis of Norwegian electricity distribution firms," European Journal of Operational Research, Elsevier, vol. 288(3), pages 983-991.
    3. Just, Lisa & Wetzel, Heike, 2020. "Distributed Generation and Cost Efficiency of German Electricity Distribution Network Operators," EWI Working Papers 2020-9, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    4. Mike G. Tsionas & Subal C. Kumbhakar, 2023. "Efficiency Measurement in Norwegian Electricity Distribution: A Generalized Four-Way-Error-Component Stochastic Frontier Model," The Energy Journal, , vol. 44(2), pages 181-204, March.
    5. Hou, Zheng & Roseta-Palma, Catarina & Ramalho, Joaquim J.S., 2024. "Can operational efficiency in the Portuguese electricity sector be improved? Yes, but..," Energy Policy, Elsevier, vol. 190(C).

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