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Does Weather Have an Impact on Electricity Distribution Efficiency? Evidence from South America

Author

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  • Karim L. Anaya

    (Energy Policy Research Group, Judge Business School, University of Cambridge)

  • Michael G. Pollitt

    (Energy Policy Research Group, Judge Business School, University of Cambridge)

Abstract

This paper analyses the influence of weather variables on the efficiency of electricity distribution utilities in Argentina, Brazil, Chile and Peru. The data covers 82 firms that operate in the previously mentioned countries which represent more than 90 per cent of the distribution market of energy delivered for the period 1998-2008. The stochastic frontier analysis (SFA) is applied with a translog input distance function approach. A combination of cost and cost-quality models is proposed to create better discussions. Weather data are collected from 429 meteorological stations and lightning data (flash rate) are collected from 3,423 coordinates provided by NASA. A geographic information system (GIS) is used for locating the firms' service areas and for allocating their respective meteorological stations and coordinates. Results suggest that on average under cost models there is a significant increase in efficiency when weather is incorporated in the production function. Firms from Brazil and Peru are those which operate in less favourable weather conditions. Under the cost-quality models, on average the effect of weather is much lower. From this, it appears to be that firms have internalised the effects of weather and have adapted their networks with consideration to the environment in which they operate. A company-level analysis indicates that across models an important number of companies are affected by weather. Regulators are advised to make the case for the proper adjustments of efficiency scores when specific firms face important efficiency changes due to weather.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Karim L. Anaya & Michael G. Pollitt, 2014. "Does Weather Have an Impact on Electricity Distribution Efficiency? Evidence from South America," Working Papers EPRG 1404, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg1404
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    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. William Yu & Michael G. Pollitt, 2009. "Does Liberalisation cause more electricity blackouts? Evidence from a global study of newspaper reports," Working Papers EPRG 0902, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    3. Antonio Estache & MartÌn A. Rossi & Christian A. Ruzzier, 2004. "The Case for International Coordination of Electricity Regulation: Evidence from the Measurement of Efficiency in South America," Journal of Regulatory Economics, Springer, vol. 25(3), pages 271-295, May.
    4. Yang, Hongliang & Pollitt, Michael, 2009. "Incorporating both undesirable outputs and uncontrollable variables into DEA: The performance of Chinese coal-fired power plants," European Journal of Operational Research, Elsevier, vol. 197(3), pages 1095-1105, September.
    5. Tim Coelli & Antonio Estache & Sergio Perelman & Lourdes Trujillo, 2003. "A Primer on Efficiency Measurement for Utilities and Transport Regulators," World Bank Publications - Books, The World Bank Group, number 15149, December.
    6. 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.
    7. Tim Coelli & Sergio Perelman & Elliot Romano, 1999. "Accounting for Environmental Influences in Stochastic Frontier Models: With Application to International Airlines," Journal of Productivity Analysis, Springer, vol. 11(3), pages 251-273, June.
    8. 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, October.
    9. Mota, R.L., 2004. "‘Comparing Brazil and USA electricity performance; what was the impact of privatisation?’," Cambridge Working Papers in Economics 0423, Faculty of Economics, University of Cambridge.
    10. 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.
    11. 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.
    12. World Bank, 2008. "Benchmarking Analysis of the Electricity Distribution Sector in the Latin American and Caribbean Region," World Bank Publications - Reports 12675, The World Bank Group.
    13. 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.
    14. Growitsch, Christian & Jamasb, Tooraj & Wetzel, Heike, 2010. "Efficiency effects of quality of service and environmental factors: experience from Norwegian electricity distribution," EWI Working Papers 2010-3, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    15. 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.
    16. 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.
    17. 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.
    18. Timothy J. Coelli & D.S. Prasada Rao & Christopher J. O’Donnell & George E. Battese, 2005. "An Introduction to Efficiency and Productivity Analysis," Springer Books, Springer, edition 0, number 978-0-387-25895-9, September.
    19. Omar O. Chisari (ed.), 2007. "Regulatory Economics and Quantitative Methods," Books, Edward Elgar Publishing, number 12517.
    20. 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.
    21. 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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    More about this item

    Keywords

    technical efficiency; stocastic frontier analysis; electricity distribution markets; weather; South America;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General

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