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Improving the National Flood Insurance Program

Published online by Cambridge University Press:  09 July 2018

HOWARD KUNREUTHER
HOWARD KUNREUTHER*
Affiliation:
Wharton School, University of Pennsylvania, Philadelphia, PA, USA
*
Email: kunreuth@wharton.upenn.edu
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Abstract

This paper highlights factors that need to be considered for improving the National Flood Insurance Program in the USA to address the biases that lead individuals to not protect themselves against low-probability, high-consequence flood events. The errors that individuals exhibit in deciding not to purchase insurance or invest in loss reduction measures prior to a disaster can be traced to the effects of six biases: myopia, amnesia, optimism, inertia, simplification and herding. Along with two guiding principles for insurance, a behavioral risk audit can assist in designing a strategy using concepts from choice architecture coupled with economic incentives to encourage property owners in hazard-prone areas to purchase insurance and invest in cost-effective adaptation measures to protect themselves against future disaster losses.

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Type
Article
Information
Behavioural Public Policy , Volume 5 , Issue 3 , July 2021 , pp. 318 - 332
Copyright
Copyright © Cambridge University Press 2018

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References

ASFPM (2013), Flood Mapping for the Nation: A Cost Analysis for the Nation's Flood Map Inventory. Association of State Floodplain Managers, March 1.Google Scholar
Dixon, L., Clancy, N., Miller, B., Hoegberg, S., Lewis, M. M., Bender, B., Ebinger, S., Hodges, M., Syck, G. M., Nagy, C. and Choquette, S. R. (2017), The Cost and Affordability of Flood Insurance in New York City, Santa Monica, CA: RAND Corporation.Google Scholar
Dixon, L., Clancy, N., Seabury, S. A. and Overton, A. (2006), The National Flood Insurance Program's Market Penetration Rate: Estimates and Policy Implications, Santa Monica, California, RAND Corporation, February.Google Scholar
Gneezy, U. and Potters, J. (1997), ‘An experiment on risk taking and evaluation periods’, The Quarterly Journal of Economics, 112(2): 631–645.CrossRefGoogle Scholar
Hertwig, R., Barron, G., Weber, E. U. and Erev, I. (2004), ‘Decisions from Experience and the Effect of Rare Events in Risky Choice’, Psychological Science, 15(8): 534–539.CrossRefGoogle ScholarPubMed
Insurance Institute for Property Loss Reduction (IIPLR) (1995), Homes and Hurricanes: Public Opinion Concerning Various Issues Relating to Home Builders, Building Codes and Damage Mitigation. Boston, MA: IIPLR.Google Scholar
Johnson, E., Hershey, J., Meszaros, J. and Kunreuther, H. (1993), ‘Framing, Probability Distortions and Insurance Decisions’, Journal of Risk and Uncertainty, 7:35–51.CrossRefGoogle Scholar
Kahneman, D. and Lovallo, D. (1993), ‘Timid Choices and Bold Forecasts: A Cognitive Perspective on Risk Taking’, Management Science, 39(1): 17–31.CrossRefGoogle Scholar
Kousky, C. (2018), ‘Financing Flood Losses: A Discussion of the National Flood Insurance Program’, Risk Management and Insurance Review 21(1).CrossRefGoogle Scholar
Kousky, C. and Kunreuther, H. (2014), ‘Addressing Affordability in the National Flood Insurance Program’, Journal of Extreme Events 1(01):1–28.CrossRefGoogle Scholar
Kunreuther, H. (2015), ‘The role of insurance in reducing losses from extreme events: The need for public-private partnerships’, The Geneva Papers on Risk and Insurance-Issues and Practice, 40(4): 741–762.CrossRefGoogle Scholar
Kunreuther, H., Ginsberg, R., Miller, L., Sagi, P., Slovic, P., Borkan, B. and Katz, N. (1978), Disaster Insurance Protection: Public Policy Lessons, New York: Wiley.Google Scholar
Kunreuther, H., Meyer, R. J. and Michel-Kerjan, E. (2013a), ‘Overcoming Decision Biases to Reduce Losses from Natural Catastrophes’, in: Shafir, E. (ed.), Behavioral Foundations of Policy, Princeton University Press.Google Scholar
Kunreuther, H., Pauly, M. V. and McMorrow, S. (2013b), Insurance and Behavioral Economics: Improving Decisions in the Most Misunderstood Industry, New York: Cambridge University Press.Google Scholar
Kunreuther, H., Dorman, J., Edelman, S., Jones, C., Montgomery, M. and Sperger, J. (2017), ‘Structure Specific Flood Risk Based Insurance ’, Journal of Extreme Events, 4(3): 1750011 [21 pages]Google Scholar
Lo, A. (2013), ‘The role of social norms in climate adaptation: Mediating risk perception and flood insurance purchase’, Global Environmental Change, 23(5): 1249–57.CrossRefGoogle Scholar
Long, H. (2017), ‘Where Harvey is hitting hardest, 80 percent lack flood insurance’, The Washington Post, August 29.Google Scholar
McClelland, G. H., Schulze, W. D. and Coursey, D. L. (1993) ‘Insurance for low-probability hazards: A bimodal response to unlikely events’, Journal of Risk and Uncertainty, 7(1): 95–116.CrossRefGoogle Scholar
Meyer, R., Broad, K., Orlove, B. and Petrovic, N. (2013), ‘Dynamic simulation as an approach to understanding hurricane risk response: insights from the Stormview lab’, Risk Analysis, 33(8): 1532–1552.CrossRefGoogle ScholarPubMed
Meyer, R. and Kunreuther, H. (2017), The Ostrich Paradox: Why We Underprepare for Disasters, Wharton Digital Press.Google Scholar
Multihazard Mitigation Council (2015), Developing Pre-Disaster Resilience Based on Public and Private Incentivization, Washington, DC, National Institute of Building and Home Safety, Multihazard Mitigation Council in conjunction with the Council on Finance, Insurance and Real Estate, October.Google Scholar
National Research Council (2015a), Affordability of National Flood Insurance Program Premiums—Report 1, Washington, DC: The National Academies Press.Google Scholar
National Research Council (2015b), Tying Flood Insurance to Flood Risk for Low Lying Structures in the Flood Plain, Washington, DC: National Academies Press.Google Scholar
National Research Council (2016), Affordability of National Flood Insurance Program Premiums—Report 2, Washington, DC: The National Academies Press.Google Scholar
Office of Management and Budget (2016), Standards and Finance to Support Community Resilience, Washington, DC: Office of Management and Budget, The White House, December.Google Scholar
Redelmeier, D. A. and Tversky, A. (1992), ‘On the framing of multiple prospects’, Psychological Science, 3(3): 191–193.CrossRefGoogle Scholar
Samuelson, W. and Zeckhauser, R. (1988), ‘Status quo bias in decision making’, Journal of Risk and Uncertainty, 1: 7–59.CrossRefGoogle Scholar
Simmons, K., Czajkowski, J. and Done, J. (2017), ‘Economic Effectiveness of Implementing a Statewide Building Code: The Case of Florida’, Land Economics, Volume 94:2Google Scholar
Slovic, P. (2000), The Perception of Risk, London and Sterling, VA: Earthscan.Google Scholar
Slovic, P., Fischhoff, B., Lichtenstein, S., Corrigan, B. and Combs, B. (1977), ‘Preference for insuring against probable small losses: insurance implications’, Journal of Risk and Insurance, 44(2): 237–258.CrossRefGoogle Scholar
Slovic, P., Fischhoff, B. and Lichtenstein, S. (1978), ‘Accident probabilities and seat belt usage: A psychological perspective’, Accident Analysis & Prevention, 10(4): 281–285.CrossRefGoogle Scholar
Swiss Re Institute (2017), Sigma No. 2, Natural catastrophes and man-made disasters in 2016: A year of widespread damages, Zurich, Switzerland: Swiss Re.Google Scholar
Thaler, R. and Sunstein, C. (2008), Nudge: The Gentle Power of Choice Architecture, New Haven, CT: Yale University Press.Google Scholar
Thaler, R. H., Tversky, A., Kahneman, D. and Schwartz, A. (1997), ‘The Effect of Myopia and Loss Aversion on Risk Taking: An Experimental Test’, The Quarterly Journal of Economics, 112(2): 647–661.CrossRefGoogle Scholar
TMAC (2015), Technical Mapping Advisory Council Annual Report for 2015. FEMA, Washington DC. https://www.fema.gov/media-library-data/1454954097105-a94df962a0cce0eef5f84c0e2c814a1f/TMAC_2015_Annual_Report.pdfGoogle Scholar
Tversky, A. and Kahneman, D.. (1973), ‘Availability: a heuristic for judging frequency and probability’, Cognitive Psychology, 5: 207–232.CrossRefGoogle Scholar