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What people do when they say they are conserving electricity

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  • Woods, James

Abstract

Econometric practitioners must always make the case that existing data may be used to forecast future responses to price changes. In residential electricity markets this means providing assurances that either territories with different prices are similar enough to be used as a guide, or that households are still able to react to price changes with the same conservation measures they have in the past. This article presents the results of a conservation behavior survey conducted both concurrent with and immediately after the last California electricity crisis in 2000-2001. The survey used open-ended questions that provide some assurance that there are still conservation behaviors that may be performed, as well as raw data that may be used to construct new closed-ended questions. The prevalence of conservation behaviors is modeled with a forgetfulness process, necessary when using data from open-ended questions, and implemented with a generalized method of moments (GMM) estimator.

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Bibliographic Info

Article provided by Elsevier in its journal Energy Policy.

Volume (Year): 36 (2008)
Issue (Month): 6 (June)
Pages: 1945-1956

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Handle: RePEc:eee:enepol:v:36:y:2008:i:6:p:1945-1956

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Web page: http://www.elsevier.com/locate/enpol

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References

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  1. Halvorsen, Bente & Larsen, Bodil M., 2001. "The flexibility of household electricity demand over time," Resource and Energy Economics, Elsevier, vol. 23(1), pages 1-18, January.
  2. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
  3. Trevor Young & Thomas H. Stevens & Cleve Willis, 1983. "Asymmetry in the Residential Demand for Electricity," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
  4. Tversky, Amos & Kahneman, Daniel, 1991. "Loss Aversion in Riskless Choice: A Reference-Dependent Model," The Quarterly Journal of Economics, MIT Press, vol. 106(4), pages 1039-61, November.
  5. Bidwell, Miles O, Jr & Wang, Bruce X & Zona, J Douglas, 1995. "An Analysis of Asymmetric Demand Response to Price Changes: The Case of Local Telephone Calls," Journal of Regulatory Economics, Springer, vol. 8(3), pages 285-98, November.
  6. Aigner, Dennis J. & Leamer, Edward E., 1984. "Estimation of time-of-use pricing response in the absence of experimental data : An application of the methodology of data transferability," Journal of Econometrics, Elsevier, vol. 26(1-2), pages 205-227.
  7. Bruce G. S. Hardie & Eric J. Johnson & Peter S. Fader, 1993. "Modeling Loss Aversion and Reference Dependence Effects on Brand Choice," Marketing Science, INFORMS, vol. 12(4), pages 378-394.
  8. Garcia-Cerrutti, L. Miguel, 2000. "Estimating elasticities of residential energy demand from panel county data using dynamic random variables models with heteroskedastic and correlated error terms," Resource and Energy Economics, Elsevier, vol. 22(4), pages 355-366, October.
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Cited by:
  1. Xavier Labandeira Villot & Pedro Linares, 2009. "Energy Efficiency: Economics and Policy," Economic Reports 06-09, FEDEA.
  2. Diffney, Seán & Lyons, Seán & Malaguzzi Valeri, Laura, 2013. "Evaluation of the effect of the Power of One campaign on natural gas consumption," Energy Policy, Elsevier, vol. 62(C), pages 978-988.
  3. Leighty, Wayne & Meier, Alan, 2011. "Accelerated electricity conservation in Juneau, Alaska: A study of household activities that reduced demand 25%," Energy Policy, Elsevier, vol. 39(5), pages 2299-2309, May.

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