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Identifying Free-riding in Home Renovation Programs Using Revealed Preference Data

Author

Listed:
  • Grösche Peter

    () (Hochschule Anhalt, Fachbereich Wirtschaft, Strenzfelder Allee 28, 06406 Bernburg, Germany)

  • Schmidt Christoph M.

    () (Rheinisch-Westfälisches Institut für Wirtschaftsforschung (RWI), Hohenzollernstr. 1-3, 45128 Essen, Germany, and Ruhr-Universität Bochum, Universitätstraße 150, 44801 Bochum, Germany)

  • Vance Colin

    () (Rheinisch-Westfälisches Institut für Wirtschaftsforschung (RWI), Hohenzollernstr. 1-3, 45128 Essen, Germany, and Jacobs University Bremen, Campus Ring 1, 28759 Bremen, Germany)

Abstract

Identifying free-ridership is significant to several issues relevant to program evaluation, including the calculation of net program benefits and assessments of political acceptability. Despite the potential of free-ridership to seriously undermine the economic efficiency of a program intervention, for instance to foster energy efficiency, the issue remains largely absent from contemporary environmental and energy policy discussions in Europe. One reason for this neglect is the inherent difficulty of assessing which households would have undertaken the energy conservation activity even without the program. This paper proposes a procedure to calculate the free-rider share using revealed preference data on home renovations from Germany’s residential sector.We employ a discrete-choice model to analyze the effect of grants on renovation choices, the output from which is used to assess the extent of free-ridership under a subsidy program very akin to an implemented grants program in Germany. Our empirical results suggest only very moderate energy savings induced by the program, making free-riding a problem of outstanding importance.

Suggested Citation

  • Grösche Peter & Schmidt Christoph M. & Vance Colin, 2013. "Identifying Free-riding in Home Renovation Programs Using Revealed Preference Data," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 233(5-6), pages 600-618, October.
  • Handle: RePEc:jns:jbstat:v:233:y:2013:i:5-6:p:600-618
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    References listed on IDEAS

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    1. Train, Kenneth E., 1994. "Estimation of net savings from energy-conservation programs," Energy, Elsevier, vol. 19(4), pages 423-441.
    2. Eric Malm, 1996. "An Actions-Based Estimate of the Free Rider Fraction in Electric Utility DSM Programs," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 41-48.
    3. Farsi, Mehdi, 2010. "Risk aversion and willingness to pay for energy efficient systems in rental apartments," Energy Policy, Elsevier, pages 3078-3088.
    4. Peter Grosche & Colin Vance, 2009. "Willingness to Pay for Energy Conservation and Free-Ridership on Subsidization: Evidence from Germany," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 135-154.
    5. Franz Wirl, 2000. "Lessons from Utility Conservation Programs," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 87-108.
    6. David S. Loughran and Jonathan Kulick, 2004. "Demand-Side Management and Energy Efficiency in the United States," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 19-44.
    7. 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.
    8. 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.
    9. 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.
    10. 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, pages 205-211.
    11. Jakob, Martin, 2006. "Marginal costs and co-benefits of energy efficiency investments: The case of the Swiss residential sector," Energy Policy, Elsevier, vol. 34(2), pages 172-187, January.
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    Cited by:

    1. Anna Alberini, Will Gans, and Charles Towe, 2016. "Free Riding, Upsizing, and Energy Efficiency Incentives in Maryland Homes," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    2. Nauleau, Marie-Laure, 2014. "Free-riding on tax credits for home insulation in France: An econometric assessment using panel data," Energy Economics, Elsevier, vol. 46(C), pages 78-92.

    More about this item

    Keywords

    Energy efficiency; residential sector; random utility model; discrete choice simulation;

    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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