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

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  • Grösche, Peter
  • Schmidt, Christoph M.
  • Vance, Colin

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 energyconservation 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 fromwhich 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," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 233(5+6), pages 600-618.
    • Handle: RePEc:zbw:espost:125162
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    References listed on IDEAS

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    Cited by:

    1. Bofinger, Peter & Feld, Lars P. & Schmidt, Christoph M. & Schnabel, Isabel & Wieland, Volker, 2018. "Vor wichtigen wirtschaftspolitischen Weichenstellungen. Jahresgutachten 2018/19 [Setting the Right Course for Economic Policy. Annual Report 2018/19]," Annual Economic Reports / Jahresgutachten, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, volume 127, number 201819.
    2. Serge Nyawa & Christian Gnekpe & Dieudonné Tchuente, 2025. "Transparent machine learning models for predicting decisions to undertake energy retrofits in residential buildings," Annals of Operations Research, Springer, vol. 354(1), pages 459-487, November.
    3. Henningsen, Geraldine & Wiese, Catharina, 2019. "Do Household Characteristics Really Matter? A Meta-Analysis on the Determinants of Households’ Energy-Efficiency Investments," MPRA Paper 101701, University Library of Munich, Germany.
    4. Spyridon Karytsas & Eleni Theodoropoulou, 2023. "Awareness and Utilization of Incentive Programs for Household Energy-Saving Renovations: Empirical Findings from Greece," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    5. Bofinger, Peter & Schnabel, Isabel & Feld, Lars P. & Schmidt, Christoph M. & Wieland, Volker, 2017. "Für eine zukunftsorientierte Wirtschaftspolitik. Jahresgutachten 2017/18 [Towards a Forward-Looking Economic Policy. Annual Report 2017/18]," Annual Economic Reports / Jahresgutachten, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, volume 127, number 201718.
    6. Risch, Anna, 2020. "Are environmental fiscal incentives effective in inducing energy-saving renovations? An econometric evaluation of the French energy tax credit," Energy Economics, Elsevier, vol. 90(C).
    7. 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.
    8. repec:aen:journl:ej37-1-alberini is not listed on IDEAS
    9. Huang, Fu-Wei & Chen, Shi & Lin, Jyh-Horng, 2022. "Free riding and insurer carbon-linked investment," Energy Economics, Elsevier, vol. 107(C).
    10. Jean-Sébastien Broc & Catherine Guermont & Christian Deconninck & Marie-Laure Nauleau, 2017. "Impacts and cost-effectiveness of major energy efficiency policies for existing buildings: what do we exactly know and what can we learn?," Post-Print hal-01577516, HAL.

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    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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