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An experimental study of charity hazard: The effect of risky and ambiguous government compensation on flood insurance demand

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  • Peter John Robinson
  • W.J.W. Botzen
  • F. Zhou

Abstract

This paper examines the problem of “charity hazard†, which is the crowding out of private insurance demand by government compensation. In the context of flood insurance and disaster financing, charity hazard is particularly worrisome given current trends of increasing flood risks as a result of climate change and more people choosing to locate in high-risk areas. We conduct an experimental analysis of the influence on flood insurance demand of risk and ambiguity preferences and the availability of different forms of government compensation for disaster damage. Certain and risky government compensation crowd out demand, confirming charity hazard, but this is not observed for ambiguous compensation. Ambiguity averse subjects have higher insurance demand when government compensation is ambiguous relative to risky. Policy recommendations are discussed to overcome charity hazard

Suggested Citation

  • Peter John Robinson & W.J.W. Botzen & F. Zhou, 2019. "An experimental study of charity hazard: The effect of risky and ambiguous government compensation on flood insurance demand," Working Papers 19-19, Utrecht School of Economics.
    • Handle: RePEc:use:tkiwps:1919
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    File URL: https://dspace.library.uu.nl/bitstream/handle/1874/394043/19_19.pdf
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    References listed on IDEAS

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    1. Tucci, Marco P. & Kendrick, David A. & Amman, Hans M., 2010. "The parameter set in an adaptive control Monte Carlo experiment: Some considerations," Journal of Economic Dynamics and Control, Elsevier, vol. 34(9), pages 1531-1549, September.
    2. Kiefer, Nicholas M., 1989. "A value function arising in the economics of information," Journal of Economic Dynamics and Control, Elsevier, vol. 13(2), pages 201-223, April.
    3. Amman, Hans, 1996. "Numerical methods for linear-quadratic models," Handbook of Computational Economics, in: H. M. Amman & D. A. Kendrick & J. Rust (ed.), Handbook of Computational Economics, edition 1, volume 1, chapter 13, pages 587-618, Elsevier.
    4. Beck, Gunter W. & Wieland, Volker, 2002. "Learning and control in a changing economic environment," Journal of Economic Dynamics and Control, Elsevier, vol. 26(9-10), pages 1359-1377, August.
    5. Amman, Hans M & Kendrick, David A, 1995. "Nonconvexities in Stochastic Control Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 36(2), pages 455-475, May.
    6. H. M. Amman & D. A. Kendrick & J. Rust (ed.), 1996. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 1, number 1.
    7. Prescott, Edward C, 1972. "The Multi-Period Control Problem Under Uncertainty," Econometrica, Econometric Society, vol. 40(6), pages 1043-1058, November.
    8. Taylor, John B, 1974. "Asymptotic Properties of Multiperiod Control Rules in the Linear Regression Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 15(2), pages 472-484, June.
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    Cited by:

    1. Peter John Robinson & W. J. Wouter Botzen, 2022. "Setting descriptive norm nudges to promote demand for insurance against increasing climate change risk," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 47(1), pages 27-49, January.
    2. Osberghaus, Daniel & Reif, Christiane, 2021. "How do different compensation schemes and loss experience affect insurance decisions? Experimental evidence from two independent and heterogeneous samples," Ecological Economics, Elsevier, vol. 187(C).

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    More about this item

    Keywords

    Ambiguity preferences; charity hazard; economic experiment; flood insurance demand; risk preferences;
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