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The impact of governmental assistance on insurance demand under ambiguity: a theoretical model and an experimental test

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  • Marielle Brunette
  • Laure Cabantous
  • Stéphane Couture
  • Anne Stenger

Abstract

This article deals with the impact of governmental assistance on insurance demand under ambiguity, i.e., in situations where probabilities are uncertain. First, using a model of insurance demand under ambiguity, we derive theoretical predictions about the impact of several governmental assistance programmes on optimal insurance demand. For example, governmental assistance through a fixed public support scheme implies that partial insurance is always optimal under fair insurance with ambiguity. Second, we present the results of an experiment designed to test these predictions. We find support for several of our theoretical predictions. For example, the presence of governmental assistance through a fixed public support scheme decreases individuals’ willingness to pay to be fully insured. Finally, we compare these results with those obtained for a risk situation. We find that, regardless of the form of governmental assistance, participants in the ambiguity context are consistently willing to pay more to be fully insured than participants in the risk situation. Copyright Springer Science+Business Media New York 2013

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  • Marielle Brunette & Laure Cabantous & Stéphane Couture & Anne Stenger, 2013. "The impact of governmental assistance on insurance demand under ambiguity: a theoretical model and an experimental test," Theory and Decision, Springer, vol. 75(2), pages 153-174, August.
    • Handle: RePEc:kap:theord:v:75:y:2013:i:2:p:153-174
    DOI: 10.1007/s11238-012-9321-8
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    Cited by:

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    2. Kousky, Carolyn & Michel-Kerjan, Erwann O. & Raschky, Paul A., 2018. "Does federal disaster assistance crowd out flood insurance?," Journal of Environmental Economics and Management, Elsevier, vol. 87(C), pages 150-164.
    3. Patrice Loisel & Marielle Brunette & Stéphane Couture, 2020. "Insurance and Forest Rotation Decisions Under Storm Risk," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 76(2), pages 347-367, July.
    4. Timo Goeschl & Shunsuke Managi, 2019. "Public in-Kind Relief and Private Self-Insurance," Economics of Disasters and Climate Change, Springer, vol. 3(1), pages 3-21, April.
    5. Michael Berlemann & Max Steinhardt & Jascha Tutt, 2015. "Do Natural Disasters Stimulate Individual Saving? Evidence from a Natural Experiment in a Highly Developed Country," SOEPpapers on Multidisciplinary Panel Data Research 763, DIW Berlin, The German Socio-Economic Panel (SOEP).
    6. Peter John Robinson & W. J. Wouter Botzen & Fujin Zhou, 2021. "An experimental study of charity hazard: The effect of risky and ambiguous government compensation on flood insurance demand," Journal of Risk and Uncertainty, Springer, vol. 63(3), pages 275-318, December.
    7. Teh, Tse-Ling, 2017. "Insurance design in the presence of safety nets," Journal of Public Economics, Elsevier, vol. 149(C), pages 47-58.
    8. Marielle Brunette & Robin Bourke & Marc Hanewinkel & Rasoul Yousefpour, 2018. "Adaptation to climate change in forestry: a multiple correspondence analysis (MCA)," Post-Print hal-02620990, HAL.
    9. Thornton, Philip K. & Lipper, Leslie, 2014. "How does climate change alter agricultural strategies to support food security?:," IFPRI discussion papers 1340, International Food Policy Research Institute (IFPRI).
    10. Fluhrer, Svenja, 2023. "Crowding-in or crowding-out: The effect of humanitarian aid on households’ investments in climate adaptation," MPRA Paper 117975, University Library of Munich, Germany.
    11. Andor, Mark A. & Osberghaus, Daniel & Simora, Michael, 2020. "Natural Disasters and Governmental Aid: Is there a Charity Hazard?," Ecological Economics, Elsevier, vol. 169(C).
    12. Sauter, Philipp A. & Möllmann, Torsten B. & Anastassiadis, Friederike & Mußhoff, Oliver & Möhring, Bernhard, 2016. "To insure or not to insure? Analysis of foresters' willingness-to-pay for fire and storm insurance," Forest Policy and Economics, Elsevier, vol. 73(C), pages 78-89.
    13. Tesselaar, Max & Botzen, W.J. Wouter & Robinson, Peter J. & Aerts, Jeroen C.J.H. & Zhou, Fujin, 2022. "Charity hazard and the flood insurance protection gap: An EU scale assessment under climate change," Ecological Economics, Elsevier, vol. 193(C).
    14. Johannes G. Jaspersen, 2016. "Hypothetical Surveys And Experimental Studies Of Insurance Demand: A Review," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 83(1), pages 217-255, January.
    15. Stéphane Couture & Marielle Brunette & François Pannequin & Anne Corcos, 2016. "The self-insurance clauses puzzle : risk versus ambiguity," Post-Print hal-02801783, HAL.
    16. Ridho Al Izzati & Daniel Suryadarma & Asep Suryahadi, "undated". "The Behavioral Effects of Unconditional Cash Transfers: Evidence from Indonesia," Working Papers 2376, Publications Department.
    17. M. Brunette & S. Couture & J. Foncel & S. Garcia, 2020. "The decision to insure against forest fire risk: an econometric analysis combining hypothetical real data," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 45(1), pages 111-133, January.
    18. Bastit, Félix & Brunette, Marielle & Montagné-Huck, Claire, 2023. "Pests, wind and fire: A multi-hazard risk review for natural disturbances in forests," Ecological Economics, Elsevier, vol. 205(C).
    19. Ridho Al Izzati & Daniel Suryadarma & Asep Suryahadi, "undated". "Dampak Bantuan Langsung Tunai terhadap Perilaku Penerima Bantuan: Bukti dari Indonesia," Working Papers 3621, Publications Department.
    20. Camille Tevenart & Marielle Brunette, 2021. "Role of Farmers’ Risk and Ambiguity Preferences on Fertilization Decisions: An Experiment," Sustainability, MDPI, vol. 13(17), pages 1-27, August.
    21. Yiling Deng & Ian A. Munn & Haibo Yao, 2021. "Attributes‐based conjoint analysis of landowner preferences for standing timber insurance," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 24(4), pages 421-444, December.
    22. Sandrine Brèteau-Amores & Marielle Brunette & Christophe François & Antoine Leblois & Nicolas Martin-StPaul, 2021. "Index insurance for coping with drought-induced risk of production losses in French forests," Working Papers of BETA 2021-44, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    23. Brunette, M. & Holecy, J. & Sedliak, M. & Tucek, J. & Hanewinkel, M., 2015. "An actuarial model of forest insurance against multiple natural hazards in fir (Abies Alba Mill.) stands in Slovakia," Forest Policy and Economics, Elsevier, vol. 55(C), pages 46-57.
    24. Qin, Tao & Gu, Xuesong & Tian, Zhiwei & Pan, Huanxue & Deng, Jing & Wan, Li, 2016. "An empirical analysis of the factors influencing farmer demand for forest insurance: Based on surveys from Lin’an County in Zhejiang Province of China," Journal of Forest Economics, Elsevier, vol. 24(C), pages 37-51.
    25. Marielle Brunette & Stéphane Couture & Jérôme Foncel & Serge S. Garcia, 2017. "Insurance decision against forest fire : An econometric analysis combining experimental and real data," Post-Print hal-02785187, HAL.

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