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Willingness-to-Pay for Energy Conservation and Free-Ridership on Subsidization – Evidence from Germany

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  • Grösche, Peter
  • Vance, Colin

Abstract

Understanding the determinants of home-efficiency improvements is significant to a range of energy policy issues, including the reduction of fossil fuel use and environmental protection. This paper analyzes retrofit choices by assembling a unique data set merging a nationwide household survey from Germany with regional data on wages and construction costs. To explore the influence of both heterogeneous preferences and correlation among the utility of alternatives, conditional-, random parameters-, and error components logit models are estimated that parameterize the influence of costs, energy savings, and household-level socioeconomic attributes on the likelihood of undertaking one of 16 renovation options. We use the model coefficients to derive household-specific marginal willingness-to-pay estimates, and with these assess the extent to which free-ridership may undermine the effectiveness of recently implemented programs that subsidize the costs of retrofits.

Suggested Citation

  • Grösche, Peter & Vance, Colin, 2008. "Willingness-to-Pay for Energy Conservation and Free-Ridership on Subsidization – Evidence from Germany," Ruhr Economic Papers 58, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  • Handle: RePEc:zbw:rwirep:58
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    References listed on IDEAS

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    1. Gilbert E. Metcalf & Kevin A. Hassett, 1999. "Measuring The Energy Savings From Home Improvement Investments: Evidence From Monthly Billing Data," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 516-528, August.
    2. Quigley, John M & Rubinfeld, Daniel L, 1989. "Unobservables in Consumer Choice: Residential Energy and the Demand for Comfort," The Review of Economics and Statistics, MIT Press, vol. 71(3), pages 416-425, August.
    3. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
    4. Banfi, Silvia & Farsi, Mehdi & Filippini, Massimo & Jakob, Martin, 2008. "Willingness to pay for energy-saving measures in residential buildings," Energy Economics, Elsevier, vol. 30(2), pages 503-516, March.
    5. Paul L. Joskow & Donald B. Marron, 1992. "What Does a Negawatt Really Cost? Evidence from Utility Conservation Programs," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 41-74.
    6. King, Gary & Honaker, James & Joseph, Anne & Scheve, Kenneth, 2001. "Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation," American Political Science Review, Cambridge University Press, vol. 95(1), pages 49-69, March.
    7. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    8. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    9. Train, Kenneth E., 1994. "Estimation of net savings from energy-conservation programs," Energy, Elsevier, vol. 19(4), pages 423-441.
    10. David A. Hensher & Stewart Jones & William H. Greene, 2007. "An Error Component Logit Analysis of Corporate Bankruptcy and Insolvency Risk in Australia," The Economic Record, The Economic Society of Australia, vol. 83(260), pages 86-103, March.
    11. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    12. Cameron, Trudy Ann, 1985. "A Nested Logit Model of Energy Conservation Activity by Owners of Existing Single Family Dwellings," The Review of Economics and Statistics, MIT Press, vol. 67(2), pages 205-211, May.
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    More about this item

    Keywords

    Heterogenous preferences; residential sector; revealed-preference data;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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