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State-Level Electricity Generation Efficiency: Do Restructuring and Regulatory Institutions Matter in the US?

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  • Ajayi, V.
  • Weyman-Jones, T.

Abstract

This paper examines the impact of deregulation and the political support for it on the electric power industry using a consistent state-level electricity generation dataset for the US contiguous states from 1997-2014. Recent analyses of productivity growth suggests that institutional factors are important and we wish to study the role of deregulation as a statelevel institutional change through two measures: (a) restructuring and (b) the political support for it, measured by the majority political affiliation of public utility commissions. We find evidence of positive impacts of deregulation (both restructuring and the political support for it) on technical efficiency across the models estimated. Our preferred model which allows for the control for deregulation variables on the mean and variance of the inefficiency shows an average technical efficiency of 73.1 percent. The results of the marginal effects reveal that the impact of deregulation including its political support on inefficiency is negative and monotonic, with the potential reduction of 8.4 percent in the mean of technical inefficiency, thereby suggesting a compelling evidence for generation efficiency improvement via deregulation.

Suggested Citation

  • 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.
    • Handle: RePEc:cam:camdae:2166
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    1. Massimo Filippini & William Greene, 2016. "Persistent and transient productive inefficiency: a maximum simulated likelihood approach," Journal of Productivity Analysis, Springer, vol. 45(2), pages 187-196, April.
    2. Magali Delmas & Yesim Tokat, 2005. "Deregulation, governance structures, and efficiency: the U.S. electric utility sector," Strategic Management Journal, Wiley Blackwell, vol. 26(5), pages 441-460, May.
    3. Caudill, Steven B. & Ford, Jon M., 1993. "Biases in frontier estimation due to heteroscedasticity," Economics Letters, Elsevier, vol. 41(1), pages 17-20.
    4. Acemoglu, Daron & Johnson, Simon & Robinson, James A., 2005. "Institutions as a Fundamental Cause of Long-Run Growth," Handbook of Economic Growth, in: Philippe Aghion & Steven Durlauf (ed.), Handbook of Economic Growth, edition 1, volume 1, chapter 6, pages 385-472, Elsevier.
    5. Severin Borenstein & James Bushnell, 2015. "The US Electricity Industry After 20 Years of Restructuring," Annual Review of Economics, Annual Reviews, vol. 7(1), pages 437-463, August.
    6. 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.
    7. Lucas W. Davis & Catherine Wolfram, 2012. "Deregulation, Consolidation, and Efficiency: Evidence from US Nuclear Power," American Economic Journal: Applied Economics, American Economic Association, vol. 4(4), pages 194-225, October.
    8. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    9. Andrew N. Kleit & Dek Terrell, 2001. "Measuring Potential Efficiency Gains From Deregulation Of Electricity Generation: A Bayesian Approach," The Review of Economics and Statistics, MIT Press, vol. 83(3), pages 523-530, August.
    10. Llorca, Manuel & Orea, Luis & Pollitt, Michael G., 2016. "Efficiency and environmental factors in the US electricity transmission industry," Energy Economics, Elsevier, vol. 55(C), pages 234-246.
    11. Kira R. Fabrizio & Nancy L. Rose & Catherine D. Wolfram, 2007. "Do Markets Reduce Costs? Assessing the Impact of Regulatory Restructuring on US Electric Generation Efficiency," American Economic Review, American Economic Association, vol. 97(4), pages 1250-1277, September.
    12. Ajayi, Victor & Weyman-Jones, Thomas & Glass, Anthony, 2017. "Cost efficiency and electricity market structure: A case study of OECD countries," Energy Economics, Elsevier, vol. 65(C), pages 283-291.
    13. Subal Kumbhakar & Gudbrand Lien & J. Hardaker, 2014. "Technical efficiency in competing panel data models: a study of Norwegian grain farming," Journal of Productivity Analysis, Springer, vol. 41(2), pages 321-337, April.
    14. Fan, Yanqin & Li, Qi & Weersink, Alfons, 1996. "Semiparametric Estimation of Stochastic Production Frontier Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 460-468, October.
    15. 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.
    16. Silvio Daidone & Francesco D’Amico, 2009. "Technical efficiency, specialization and ownership form: evidences from a pooling of Italian hospitals," Journal of Productivity Analysis, Springer, vol. 32(3), pages 203-216, December.
    17. Fan Zhang, 2007. "Does Electricity Restructuring Work? Evidence From The U.S. Nuclear Energy Industry," Journal of Industrial Economics, Wiley Blackwell, vol. 55(3), pages 397-418, September.
    18. Kodde, David A & Palm, Franz C, 1986. "Wald Criteria for Jointly Testing Equality and Inequality Restriction s," Econometrica, Econometric Society, vol. 54(5), pages 1243-1248, September.
    19. John Kwoka, 2008. "Restructuring the U.S. Electric Power Sector: A Review of Recent Studies," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 32(3), pages 165-196, May.
    20. Burtraw, Dallas & Palmer, Karen, 2005. "The Environmental Impacts of Electricity Restructuring: Looking Back and Looking Forward," RFF Working Paper Series dp-05-07, Resources for the Future.
    21. 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.
    22. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    23. 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.
    24. Philippe Aghion, 2005. "Growth and Institutions," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 32(1), pages 3-18, March.
    25. Palmer, Karen & Ando, Amy, 1998. "Getting on the Map: The Political Economy of State-Level Electricity Restructuring," RFF Working Paper Series dp-98-19-rev, Resources for the Future.
    26. Willam Greene, 2005. "Fixed and Random Effects in Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 23(1), pages 7-32, January.
    27. Subal C. Kumbhakar & Almas Heshmati, 1995. "Efficiency Measurement in Swedish Dairy Farms: An Application of Rotating Panel Data, 1976–88," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(3), pages 660-674.
    28. Goto, Mika & Tsutsui, Miki, 1998. "Comparison of Productive and Cost Efficiencies Among Japanese and US Electric Utilities," Omega, Elsevier, vol. 26(2), pages 177-194, April.
    29. 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.
    30. J. Dean Craig and Scott J. Savage, 2013. "Market Restructuring, Competition and the Efficiency of Electricity Generation: Plant-level Evidence from the United States 1996 to 2006," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    31. L. Dean Hiebert, 2002. "The Determinants of the Cost Efficiency of Electric Generating Plants: A Stochastic Frontier Approach," Southern Economic Journal, John Wiley & Sons, vol. 68(4), pages 935-946, April.
    32. Yanyan Liu & Robert Myers, 2009. "Model selection in stochastic frontier analysis with an application to maize production in Kenya," Journal of Productivity Analysis, Springer, vol. 31(1), pages 33-46, February.
    33. Paul L. Joskow, 1997. "Restructuring, Competition and Regulatory Reform in the U.S. Electricity Sector," Journal of Economic Perspectives, American Economic Association, vol. 11(3), pages 119-138, Summer.
    34. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    35. Unknown, 2005. "Forward," 2005 Conference: Slovenia in the EU - Challenges for Agriculture, Food Science and Rural Affairs, November 10-11, 2005, Moravske Toplice, Slovenia 183804, Slovenian Association of Agricultural Economists (DAES).
    36. See, Kok Fong & Coelli, Tim, 2013. "Estimating and decomposing productivity growth of the electricity generation industry in Malaysia: A stochastic frontier analysis," Energy Policy, Elsevier, vol. 62(C), pages 207-214.
    37. Wang, Hung-Jen, 2003. "A Stochastic Frontier Analysis of Financing Constraints on Investment: The Case of Financial Liberalization in Taiwan," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(3), pages 406-419, July.
    38. 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.
    39. Sharabaroff, Alexander & Boyd, Roy & Chimeli, Ariaster, 2009. "The environmental and efficiency effects of restructuring on the electric power sector in the United States: An empirical analysis," Energy Policy, Elsevier, vol. 37(11), pages 4884-4893, November.
    40. 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.
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    1. Wang, Chang & Guo, Yue & Yang, Yu & Chen, Shiyi, 2022. "The environmental benefits of electricity industry restructuring in China: Ownership mixing vs. vertical unbundling," Energy Economics, Elsevier, vol. 115(C).

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    More about this item

    Keywords

    Electricity generation; technical efficiency; marginal effect; restructuring; regulatory institutions;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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