IDEAS home Printed from https://ideas.repec.org/p/cam/camdae/1318.html
   My bibliography  Save this paper

Efficiency and Environmental Factors in the US Electricity Transmission Industry

Author

Listed:
  • Llorca, Manuel
  • Orea, Luis
  • Pollitt, Michael

Abstract

The electricity industry in most developed countries has been restructured over recent decades with the aim of improving both service quality and firms’ performance. Regulated segments (e.g. transmission) still provide the infrastructure for the competitive segments and represent a notable amount of the total price paid by final customers. However there is a lack of empirical studies that analyze firms’ performance in the electricity transmission sector. We conduct an empirical analysis of the US electricity transmission companies for the period 2001-2009. We use stochastic frontier models that allow us to identify determinants of firms’ inefficiency and to control for weather conditions, potentially one of the most decisive uncontrollable factors in electricity transportation. Our results suggest that there is room for improvement in the performance of the US electricity transmission system. Regulators should also take into account that more adverse conditions generate higher levels of inefficiency and that achieving long-term efficiency improvements tends to deteriorate firms’ short-term relative performance.

Suggested Citation

  • Llorca, Manuel & Orea, Luis & Pollitt, Michael, 2013. "Efficiency and Environmental Factors in the US Electricity Transmission Industry," Cambridge Working Papers in Economics 1318, Faculty of Economics, University of Cambridge.
  • Handle: RePEc:cam:camdae:1318
    as

    Download full text from publisher

    File URL: http://www.econ.cam.ac.uk/research-files/repec/cam/pdf/cwpe1318.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Jamasb, T. & Orea, L. & Pollitt, M.G., 2010. "Weather Factors and Performance of Network Utilities: A Methodology and Application to Electricity Distribution," Cambridge Working Papers in Economics 1042, Faculty of Economics, University of Cambridge.
    2. Brunekreeft, Gert & Neuhoff, Karsten & Newbery, David, 2005. "Electricity transmission: An overview of the current debate," Utilities Policy, Elsevier, vol. 13(2), pages 73-93, June.
    3. Li, Qi, et al, 2002. "Semiparametric Smooth Coefficient Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 412-422, July.
    4. 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.
    5. Nancy L. Rose, 2014. "Economic Regulation and Its Reform: What Have We Learned?," NBER Books, National Bureau of Economic Research, Inc, number rose05-1.
    6. Jamasb, Tooraj & Orea, Luis & Pollitt, Michael, 2012. "Estimating the marginal cost of quality improvements: The case of the UK electricity distribution companies," Energy Economics, Elsevier, vol. 34(5), pages 1498-1506.
    7. Caudill, Steven B. & Ford, Jon M., 1993. "Biases in frontier estimation due to heteroscedasticity," Economics Letters, Elsevier, vol. 41(1), pages 17-20.
    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. P. Nillesen & M. Pollitt, 2010. "Using Regulatory Benchmarking Techniques to Set Company Performance Targets: The Case of Us Electricity," Competition and Regulation in Network Industries, Intersentia, vol. 11(1), pages 50-85, March.
    10. Yu, William & Jamasb, Tooraj & Pollitt, Michael, 2009. "Does weather explain cost and quality performance? An analysis of UK electricity distribution companies," Energy Policy, Elsevier, vol. 37(11), pages 4177-4188, November.
    11. Johnson, Andrew L. & Kuosmanen, Timo, 2012. "One-stage and two-stage DEA estimation of the effects of contextual variables," European Journal of Operational Research, Elsevier, vol. 220(2), pages 559-570.
    12. Carlos Martins-Filho & Feng Yao, 2015. "Semiparametric Stochastic Frontier Estimation via Profile Likelihood," Econometric Reviews, Taylor & Francis Journals, vol. 34(4), pages 413-451, April.
    13. Hung-pin Lai & Cliff Huang, 2010. "Likelihood ratio tests for model selection of stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 34(1), pages 3-13, August.
    14. Philipp Geymueller, 2009. "Static versus dynamic DEA in electricity regulation: the case of US transmission system operators," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 17(4), pages 397-413, December.
    15. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    16. Ray, Subhash C., 1988. "Data envelopment analysis, nondiscretionary inputs and efficiency: an alternative interpretation," Socio-Economic Planning Sciences, Elsevier, vol. 22(4), pages 167-176.
    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. Rahmatallah Poudineh & Tooraj Jamasb, 2016. "A New Perspective: Investment and Efficiency under Incentive Regulation," The Energy Journal, , vol. 37(1), pages 158-182, January.
    19. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    20. Daniel Greenfield & John Kwoka, 2011. "The Cost Structure of Regional Transmission Organizations," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 159-182.
    21. Tooraj Jamasb & Michael Pollitt, 2005. "Electricity Market Reform in the European Union: Review of Progress toward Liberalization &Integration," The Energy Journal, , vol. 26(1_suppl), pages 11-41, June.
    22. Jamasb, Tooraj & Pollitt, Michael, 2007. "Incentive regulation of electricity distribution networks: Lessons of experience from Britain," Energy Policy, Elsevier, vol. 35(12), pages 6163-6187, December.
    23. Antonio Alvarez & Christine Amsler & Luis Orea & Peter Schmidt, 2006. "Interpreting and Testing the Scaling Property in Models where Inefficiency Depends on Firm Characteristics," Journal of Productivity Analysis, Springer, vol. 25(3), pages 201-212, June.
    24. Haney, A.B. & Pollitt, M.G., 2012. "International benchmarking of Electricity Transmission by Regulators: Theory and Practice," Cambridge Working Papers in Economics 1254, Faculty of Economics, University of Cambridge.
    25. Reifschneider, David & Stevenson, Rodney, 1991. "Systematic Departures from the Frontier: A Framework for the Analysis of Firm Inefficiency," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 32(3), pages 715-723, August.
    26. Kumbhakar, Subal C & Ghosh, Soumendra & McGuckin, J Thomas, 1991. "A Generalized Production Frontier Approach for Estimating Determinants of Inefficiency in U.S. Dairy Farms," Journal of Business & Economic Statistics, American Statistical Association, vol. 9(3), pages 279-286, July.
    27. Growitsch, Christian & Jamasb, Tooraj & Wetzel, Heike, 2012. "Efficiency effects of observed and unobserved heterogeneity: Evidence from Norwegian electricity distribution networks," Energy Economics, Elsevier, vol. 34(2), pages 542-548.
    28. 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.
    29. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    30. 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.
    31. Caudill, Steven B & Ford, Jon M & Gropper, Daniel M, 1995. "Frontier Estimation and Firm-Specific Inefficiency Measures in the Presence of Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(1), pages 105-111, January.
    32. Hung-Jen Wang, 2002. "Heteroscedasticity and Non-Monotonic Efficiency Effects of a Stochastic Frontier Model," Journal of Productivity Analysis, Springer, vol. 18(3), pages 241-253, November.
    33. Kumbhakar, Subal C. & Park, Byeong U. & Simar, Leopold & Tsionas, Efthymios G., 2007. "Nonparametric stochastic frontiers: A local maximum likelihood approach," Journal of Econometrics, Elsevier, vol. 137(1), pages 1-27, March.
    34. 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.
    35. Sun, Kai & Kumbhakar, Subal C., 2013. "Semiparametric smooth-coefficient stochastic frontier model," Economics Letters, Elsevier, vol. 120(2), pages 305-309.
    36. Hung-jen Wang & Peter Schmidt, 2002. "One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels," Journal of Productivity Analysis, Springer, vol. 18(2), pages 129-144, September.
    37. William A. Barnett & Milka Kirova & Meenakshi Pasupathy & Piyu Yue, 1996. "Estimating Policy-Invariant Technology and Taste Parameters in the Financial Sector, When Risk and Growth Matter," Macroeconomics 9602002, University Library of Munich, Germany.
    38. Kumbhakar, Subal C. & Parmeter, Christopher F. & Tsionas, Efthymios G., 2013. "A zero inefficiency stochastic frontier model," Journal of Econometrics, Elsevier, vol. 172(1), pages 66-76.
    39. Wang, Hung-Jen & Ho, Chia-Wen, 2010. "Estimating fixed-effect panel stochastic frontier models by model transformation," Journal of Econometrics, Elsevier, vol. 157(2), pages 286-296, August.
    40. Muniz, M. A., 2002. "Separating managerial inefficiency and external conditions in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(3), pages 625-643, December.
    41. Haney, Aoife Brophy & Pollitt, Michael G., 2013. "International benchmarking of electricity transmission by regulators: A contrast between theory and practice?," Energy Policy, Elsevier, vol. 62(C), pages 267-281.
    42. 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.
    43. Catherine J. Morrison, 1985. "On the Economic Interpretation and Measurement of Optimal Capacity Utilization with Anticipatory Expectations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 52(2), pages 295-309.
    44. Jamasb, T. & Pollitt, M., 2000. "Benchmarking and regulation: international electricity experience," Utilities Policy, Elsevier, vol. 9(3), pages 107-130, September.
    45. 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.
    46. 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.
    47. Ruggiero, John, 1996. "On the measurement of technical efficiency in the public sector," European Journal of Operational Research, Elsevier, vol. 90(3), pages 553-565, May.
    48. Coelli, Tim & Perelman, Sergio, 1999. "A comparison of parametric and non-parametric distance functions: With application to European railways," European Journal of Operational Research, Elsevier, vol. 117(2), pages 326-339, September.
    49. Greene, William & Orea, Luis & Wall, Alan, 2011. "A one-stage random effect counterpart of the fixed-effect vector decomposition model with an application to UK electricity distribution utilities," Efficiency Series Papers 2011/01, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    50. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    51. 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.
    52. Pekka Korhonen & Mikko Syrjänen, 2003. "Evaluation of Cost Efficiency in Finnish Electricity Distribution," Annals of Operations Research, Springer, vol. 121(1), pages 105-122, July.
    53. Dismukes, David E. & Cope III, Robert F. & Mesyanzhinov, Dmitry, 1998. "Capacity and economies of scale in electric power transmission," Utilities Policy, Elsevier, vol. 7(3), pages 155-162, November.
    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. Ajayi, Victor & Anaya, Karim & Pollitt, Michael, 2022. "Incentive regulation, productivity growth and environmental effects: the case of electricity networks in Great Britain," Energy Economics, Elsevier, vol. 115(C).
    2. Piotr F Borowski, 2019. "Adaptation strategy on regulated markets of power companies in Poland," Energy & Environment, , vol. 30(1), pages 3-26, February.
    3. Jamasb, Tooraj & Llorca, Manuel & Khetrapal, Pavan & Thakur, Tripta, 2021. "Institutions and performance of regulated firms: Evidence from electricity distribution in India," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 68-82.
    4. Xie, Bai-Chen & Zhang, Zhen-Jiang & Anaya, Karim L., 2021. "Has the unbundling reform improved the service efficiency of China's power grid firms?," Energy Economics, Elsevier, vol. 95(C).
    5. Soroush, Golnoush & Cambini, Carlo & Jamasb, Tooraj & Llorca, Manuel, 2021. "Network utilities performance and institutional quality: Evidence from the Italian electricity sector," Energy Economics, Elsevier, vol. 96(C).
    6. Orea, Luis & Álvarez, Inmaculada C., 2019. "A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms," Journal of Econometrics, Elsevier, vol. 213(2), pages 556-577.
    7. Nie, S. & Li, Y.P. & Liu, J. & Huang, Charley Z., 2017. "Risk management of energy system for identifying optimal power mix with financial-cost minimization and environmental-impact mitigation under uncertainty," Energy Economics, Elsevier, vol. 61(C), pages 313-329.
    8. Xie, Bai-Chen & Ni, Kang-Kang & O'Neill, Eoghan & Li, Hong-Zhou, 2021. "The scale effect in China's power grid sector from the perspective of malmquist total factor productivity analysis," Utilities Policy, Elsevier, vol. 69(C).
    9. Hung-pin Lai & Subal C. Kumbhakar, 2023. "Indirect inference estimation of stochastic production frontier models with skew-normal noise," Empirical Economics, Springer, vol. 64(6), pages 2771-2793, June.
    10. 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.
    11. Adwoa Asantewaa & Tooraj Jamasb & Manuel Llorca, 2022. "Electricity Sector Reform Performance in Sub-Saharan Africa: A Parametric Distance Function Approach," Energies, MDPI, vol. 15(6), pages 1-29, March.
    12. Tong, Q. & Swallow, B. & Zhang, L. & Zhang, J., 2018. "Risk Attitude, Technical Efficiency and Adoption: An Integrated Approach to Climate-Smart Rice Production in the Jianghan Plain, China," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277311, International Association of Agricultural Economists.
    13. Shamsuzzoha & Makoto Tanaka, 2021. "The role of human capital on the performance of manufacturing firms in Bangladesh," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(1), pages 21-33, January.
    14. Karim L. Anaya & Michael G. Pollitt, 2014. "Does Weather Have an Impact on Electricity Distribution Efficiency? Evidence from South America," Cambridge Working Papers in Economics 1424, Faculty of Economics, University of Cambridge.
    15. Tooraj Jamasb & Manuel Llorca & Pavan Khetrapal & Tripta Thakur, 2018. "Institutions and Performance of Regulated Firms: Evidence from Electric Utilities in the Indian States," Working Papers EPRG 1809, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    16. Liu, Xiao-Yan & Pollitt, Michael G. & Xie, Bai-Chen & Liu, Li-Qiu, 2019. "Does environmental heterogeneity affect the productive efficiency of grid utilities in China?," Energy Economics, Elsevier, vol. 83(C), pages 333-344.
    17. Ajayi, V. & Weyman-Jones, T., 2021. "State-Level Electricity Generation Efficiency: Do Restructuring and Regulatory Institutions Matter in the US?," Cambridge Working Papers in Economics 2166, Faculty of Economics, University of Cambridge.
    18. 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.
    19. Llorca, Manuel & Orea, Luis & Pollit, Michael G., 2013. "Using in the latent class approach as a supervised method to cluster firms in DEA: An application to the US electricity transmission industry," Efficiency Series Papers 2013/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    20. Wenche Tobiasson & Manuel Llorca & Tooraj Jamasb, 2021. "Performance Effects of Network Structure and Ownership: The Norwegian Electricity Distribution Sector," Energies, MDPI, vol. 14(21), pages 1-15, November.
    21. Haney, Aoife Brophy & Pollitt, Michael G., 2013. "International benchmarking of electricity transmission by regulators: A contrast between theory and practice?," Energy Policy, Elsevier, vol. 62(C), pages 267-281.
    22. da Silva, Aline Veronese & Costa, Marcelo Azevedo & Ahn, Heinz & Lopes, Ana Lúcia Miranda, 2019. "Performance benchmarking models for electricity transmission regulation: Caveats concerning the Brazilian case," Utilities Policy, Elsevier, vol. 60(C), pages 1-1.
    23. Zhang, Tao & Li, Hong-Zhou & Xie, Bai-Chen, 2022. "Have renewables and market-oriented reforms constrained the technical efficiency improvement of China's electric grid utilities?," Energy Economics, Elsevier, vol. 114(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. Jamasb, Tooraj & Llorca, Manuel & Khetrapal, Pavan & Thakur, Tripta, 2021. "Institutions and performance of regulated firms: Evidence from electricity distribution in India," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 68-82.
    2. 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.
    3. Jamasb, Tooraj & Llorca, Manuel & Khetrapal, Pavan & Thakur, Tripta, 2018. "Institutions and Performance of Regulated Firms: Evidence from Electric Utilities in the Indian States," Efficiency Series Papers 2018/03, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    4. 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).
    5. 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.
    6. Antti Saastamoinen, 2015. "Heteroscedasticity Or Production Risk? A Synthetic View," Journal of Economic Surveys, Wiley Blackwell, vol. 29(3), pages 459-478, July.
    7. 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.
    8. 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).
    9. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    10. Christopher F. Parmeter & Hung-Jen Wang & Subal C. Kumbhakar, 2017. "Nonparametric estimation of the determinants of inefficiency," Journal of Productivity Analysis, Springer, vol. 47(3), pages 205-221, June.
    11. Saastamoinen, Antti & Bjørndal, Endre & Bjørndal, Mette, 2016. "Specification of merger gains in the Norwegian electricity distribution industry," Discussion Papers 2016/7, Norwegian School of Economics, Department of Business and Management Science.
    12. Seunghwa Rho & Peter Schmidt, 2015. "Are all firms inefficient?," Journal of Productivity Analysis, Springer, vol. 43(3), pages 327-349, June.
    13. 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.
    14. Paul, Satya & Shankar, Sriram, 2018. "Modelling Efficiency Effects in a True Fixed Effects Stochastic Frontier," MPRA Paper 87437, University Library of Munich, Germany.
    15. Satya Paul & Sriram Shankar, 2020. "Estimating efficiency effects in a panel data stochastic frontier model," Journal of Productivity Analysis, Springer, vol. 53(2), pages 163-180, April.
    16. 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.
    17. Saastamoinen, Antti & Bjørndal, Endre & Bjørndal, Mette, 2017. "Specification of merger gains in the Norwegian electricity distribution industry," Energy Policy, Elsevier, vol. 102(C), pages 96-107.
    18. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    19. Jorge Galán & Helena Veiga & Michael Wiper, 2014. "Bayesian estimation of inefficiency heterogeneity in stochastic frontier models," Journal of Productivity Analysis, Springer, vol. 42(1), pages 85-101, August.
    20. repec:cte:wsrepe:ws121007 is not listed on IDEAS
    21. Ajayi, V. & Weyman-Jones, T., 2021. "State-Level Electricity Generation Efficiency: Do Restructuring and Regulatory Institutions Matter in the US?," Cambridge Working Papers in Economics 2166, Faculty of Economics, University of Cambridge.

    More about this item

    Keywords

    electricity transmission; heteroscedastic stochastic cost frontiers; inefficiency determinants;
    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    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:cam:camdae:1318. 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: Jake Dyer (email available below). General contact details of provider: https://www.econ.cam.ac.uk/ .

    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.