Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-23T05:20:23.795Z Has data issue: false hasContentIssue false
  • English
  • Français

Comparing the effects of behaviorally informed interventions on flood insurance demand: an experimental analysis of ‘boosts’ and ‘nudges’

Published online by Cambridge University Press:  11 October 2019

JACOB BRADT Open the ORCID record for JACOB BRADT [Opens in a new window]
JACOB BRADT*
Affiliation:
Harvard University, Cambridge, MA, USA
*
*Correspondence to: John F. Kennedy School of Government, Harvard University, 79 John F. Kennedy St., Cambridge, MA 02138, USA. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper compares the effects of two types of behaviorally informed policy – nudges and boosts – that are designed to increase consumer demand for insurance against low-probability, high-consequence events. Using previous findings in the behavioral sciences literature, this paper constructs and implements two nudges (an ‘informational’ and an ‘affective’ nudge) and a statistical numeracy boost and then elicits individual risk beliefs and demand for flood insurance using a contingent valuation survey of 331 participants recruited from an online labor pool. Using a two-limit Tobit model to estimate willingness to pay (WTP) for flood insurance, this paper finds that the affective and informational nudges result in increases in WTP for flood insurance of roughly $21/month and $11/month relative to the boost, respectively. Taken together, the findings of this paper suggest that nudges are the more effective behaviorally informed policy in this setting, particularly when the nudge design targets the affect and availability heuristics; however, additional research is necessary to establish sufficient conditions for this conclusion.

Type
New Voices
Information
Behavioural Public Policy , Volume 6 , Issue 3 , July 2022 , pp. 485 - 515
Copyright
Copyright © Cambridge University Press 2019

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Amemiya, T. (1984), ‘Tobit models: A survey’, Journal of Econometrics, 24(1–2): 3–61.CrossRefGoogle Scholar
Atreya, A., Ferreira, S. and Michel-Kerjan, E. (2015), ‘What drives households to buy flood insurance? New evidence from Georgia’, Ecological Economics, 117: 153–161.CrossRefGoogle Scholar
Benjamin, D. J. (2018), Errors in probabilistic reasoning and judgment biases. Working Paper 25200, National Bureau of Economic Research.CrossRefGoogle Scholar
Berinsky, A. J., Huber, G. A. and Lenz, G. S. (2012), ‘Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk’, Political Analysis, 20(3): 351– 368.CrossRefGoogle Scholar
Botzen, W., de Boer, J. and Terpstra, T. (2013), ‘Framing of risk and preferences for annual and multi-year flood insurance’, Journal of Economic Psychology, 39: 357–375.CrossRefGoogle Scholar
Botzen, W. and van den Bergh, J. (2012), ‘Risk attitudes to low-probability climate change risks: WTP for flood insurance’, Journal of Economic Behavior & Organization, 82(1): 151– 166.CrossRefGoogle Scholar
Browne, M. J., Knoller, C. and Richter, A. (2015), ‘Behavioral bias and the demand for bicycle and flood insurance’, Journal of Risk and Uncertainty, 50(2): 141–160.CrossRefGoogle Scholar
Cameron, T. (2005), ‘Updating Subjective Risks in the Presence of Conflicting Information: An Application to Climate Change’, Journal of Risk and Uncertainty, 30(1): 63–97.CrossRefGoogle Scholar
Chetty, R. (2015), ‘Behavioral Economics and Public Policy: A Pragmatic Perspective’, American Economic Review, 105(5): 1–33.CrossRefGoogle Scholar
Clifford, S., Jewell, R. M. and Waggoner, P. D. (2015), ‘Are samples drawn from Mechanical Turk valid for research on political ideology? Research & Politics, 2(4): 1–9.CrossRefGoogle Scholar
de Boer, J., Botzen, W. J. and Terpstra, T. (2015), ‘More Than Fear Induction: Toward an Understanding of People's Motivation to Be Well-Prepared for Emergencies in Flood- Prone Areas: More Than Fear Induction’, Risk Analysis, 35(3): 518–535.CrossRefGoogle ScholarPubMed
de Boer, J., Botzen, W. J. W. and Terpstra, T. (2014), ‘Improving flood risk communication by focusing on prevention-focused motivation’, Risk Analysis, 34(2): 309–322.CrossRefGoogle ScholarPubMed
Finucane, M. L., Alhakami, A., Slovic, P. and Johnson, S. M. (2000), ‘The Affect Heuristic in Judgements of Risks and Benefits’, Journal of Behavioral Decision Making, 13(1): 1–17.3.0.CO;2-S>CrossRefGoogle Scholar
Fong, G., Krantz, H, and Nisbett, D., R. (1986), ‘The Effects of Statistical Training on Thinking About Everyday Problems’, Cognitive Psychology, 18: 253–292.CrossRefGoogle Scholar
Fong, G. T. and Nisbett, R. E. (1991), ‘Immediate and delayed transfer of training effects in statistical reasoning’, Journal of Experimental Psychology: General, 120(1): 34–45.CrossRefGoogle ScholarPubMed
Gigerenzer, G. (1996), ‘Why do frequency formats improve Bayesian reasoning? Cognitive algorithms work on information, which needs representation’, Behavioral and Brain Sciences, 19(01): 23.CrossRefGoogle Scholar
Gigerenzer, G. (2014), ‘Breast cancer screening pamphlets mislead women’, BMJ (Clinical research ed.), 348:g2636.Google Scholar
Gigerenzer, G. and Brighton, H. (2009), ‘Homo Heuristicus: Why Biased Minds Make Better Inferences’, Topics in Cognitive Science, 1(1): 107–143.CrossRefGoogle ScholarPubMed
Gigerenzer, G. and Hoffrage, U. (1995), ‘How to Improve Bayesian Reasoning Without Instruction: Frequency Formats’, Psychological Review, 102(4): 684–704.CrossRefGoogle Scholar
Gigerenzer, G. and Todd, P. M. (1999), ‘Fast and frugal heuristics: The adaptive toolbox’, in Simple heuristics that make us smart, Evolution and cognition, New York, NY, US: Oxford University Press, 3–34.Google Scholar
Grune-Yanoff, T. and Hertwig, R. (2016), ‘Nudge versus boost: How coherent are policy and theory? Minds and Machines: Journal for Artificial Intelligence, Philosophy and Cognitive Science, 26(1-2): 149–183.Google Scholar
Grune-Yanoff, T., Marchionni, C. and Feufel, M. A. (2018), ‘Toward a Framework for Selecting Behavioral Policies: How to Choose between Boosts and Nudges’, Economics and Philosophy, 34(2): 243–266.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
Higgins, E. (1998), ‘Promotion and Prevention: Regulatory Focus as a Motivational Principle’, Advances in Experimental Social Psychology, 30: 1–46.CrossRefGoogle Scholar
Johnson, E. J., Hershey, J. and Meszaros, J. (1993), ‘Framing, Probability Distortions, and Insurance Decisions’, Journal of Risk & Uncertainty, 7(1): 35–51.CrossRefGoogle Scholar
Kahneman, D., Slovic, P. and Tversky, A. (1982), Judgment under uncertainty: heuristics and biases, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Kahneman, D. and Tversky, A. (1996), ‘On the reality of cognitive illusions’, Psychological Review, 103(3): 582–591.CrossRefGoogle ScholarPubMed
Keller, C., Siegrist, M. and Gutscher, H. (2006), ‘The Role of the Affect and Availability Heuristics in Risk Communication’, Risk Analysis, 26(3): 631–639.CrossRefGoogle ScholarPubMed
Knight, F. (1921), Risk, Uncertainty, and Profit. Number No. 31 in Hart, Schaffner, and Marx Prize Essays. Houghton Mifflin, Boston and New York.Google Scholar
Kousky, C. (2018), ‘Financing Flood Losses: A Discussion of the National Flood Insurance Program’, Risk Management and Insurance Review, 21(1): 11–32.CrossRefGoogle Scholar
Krantz, D. H. and Kunreuther, H. C. (1998), ‘Goals and Plands in Decision Making’, Judge- ment and Decision Making, 2(3): 137–168.Google Scholar
Kunreuther, H. (2018), Improving the National Flood Insurance Program. Behavioural Public Policy, https://doi.org/10.1017/bpp.2018.26.CrossRefGoogle Scholar
Kunreuther, H., Meyer, R., Zeckhauser, R., Slovic, P., Schwartz, B., Schade, C., Luce, M. F., Lippman, S., Krantz, D., Kahn, B. and Hogarth, R. (2002), ‘High Stakes Decision Making: Normative, Descriptive and Prescriptive Considerations’, Marketing Letters, 13(3): 259–268.CrossRefGoogle Scholar
Kunreuther, H., Novemsky, N. and Kahneman, D. (2001), ‘Making Low Probabilities Useful’, The Journal of Risk and Uncertainty, 23(2): 103–120.CrossRefGoogle Scholar
Kunreuther, H. and Slovic, P. (1978), ‘Economics, Psychology, and Protective Behavior’, American Economic Review, 68(2): 64–69.Google Scholar
Lichtenstein, S., Slovic, P., Fischhoff, B., Layman, M. and Combs, B. (1978), ‘Judged Frequency of Lethal Events’, Journal of Experimental Psychology: Human Learning and Memory, 4(6): 551–578.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: 95–116.CrossRefGoogle Scholar
Morewedge, C. K., Yoon, H., Scopelliti, I., Symborski, C. W., Korris, J. H. and Kassam, K. S. (2015), ‘Debiasing Decisions: Improved Decision Making With a Single Training Intervention’, Policy Insights from the Behavioral and Brain Sciences, 2(1): 129–140.CrossRefGoogle Scholar
Nisbett, R. E., Fong, G. T., Lehman, D. R. and Cheng, P. W. (1987), ‘Teaching reasoning’, Science, 238(4827): 625–631.CrossRefGoogle Scholar
Oliver, A. E. (2013), Behavioural Public Policy, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Rothschild, M. and Stiglitz, J. (1976), ‘Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information’, The Quarterly Journal of Economics, 90(4): 629–649.CrossRefGoogle Scholar
Schade, C., Kunreuther, H. and Koellinger, P. (2012), ‘Protecting Against Low-Probability Disasters: The Role of Worry: Protecting Against Low-Probability Disasters’, Journal of Behavioral Decision Making, 25(5): 534–543.CrossRefGoogle Scholar
Sedlmeier, P. and Gigerenzer, G. (2001), ‘Teaching Bayesian Reasoning in Less Than Two Hours’, Journal of Experimental Psychology: General, 130(3): 380–400.CrossRefGoogle ScholarPubMed
Shafir, E. (2013), The Behavioral Foundations of Public Policy, Princeton: Princeton University Press.CrossRefGoogle Scholar
Sims, A. and Muller, T. M. (2018), ‘Nudge Versus Boost: A Distinction without a Difference’, Economics and Philosophy, pages 1–28.Google Scholar
Slovic, P., editor (2000), The perception of risk, London, England: Earthscan Publications.Google Scholar
Slovic, P., Finucane, M., Peters, E. and MacGregor, D. G. (2002), ‘Rational actors or rational fools: implications of the affect heuristic for behavioral economics’, The Journal of Socio-Economics, 31(4): 329–342.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
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 (pre-1986); Malvern, 44(2):237–258.CrossRefGoogle Scholar
Sunstein, C. (2015), ‘Nudges, Agency, and Abstraction: A Reply to Critics’, Review of Philosophy and Psychology, 6(3): 511–529.CrossRefGoogle Scholar
Sunstein, C. R. (2002), ‘Probability Neglect: Emotions, Worst Cases, and Law’, The Yale Law Journal, 112(1): 61–107.CrossRefGoogle Scholar
Thaler, R., 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
Thaler, R. H. and Sunstein, C. R. (2008), Nudge: Improving decisions about health, wealth, and happiness, New Haven: Yale University Press.Google Scholar
Tobin, J. (1958), ‘Estimation of Relationships for Limited Dependent Variables’, Economet- rica, 26(1): 24–36.CrossRefGoogle Scholar
Tom, S. M., Fox, C. R., Trepel, C. and Poldrack, R. A. (2007), ‘The neural basis of loss aversion in decision-making under risk’, Science, 315(5811): 515–518.CrossRefGoogle ScholarPubMed
Tukey, J. W. (1949), ‘Comparing Individual Means in the Analysis of Variance’, Biometrics, 5(2): 99–114.CrossRefGoogle ScholarPubMed
Tversky, A. and Kahneman, D. (1973), ‘Availability: A heuristic for judging frequency and probability’, Cognitive Psychology, 5(2): 207–232.CrossRefGoogle Scholar
Tversky, A. and Kahneman, D. (1986), ‘Rational Choice and the Framing of Decisions’, Journal of Business, 59(4): S251–S278.CrossRefGoogle Scholar
Tversky, A. and Shafir, E. (1992), ‘The Disjunction Effect in Choice under Uncertainty’, Psychological Science, 3(5): 305–310.CrossRefGoogle Scholar
US Census Bureau and US Department of Housing and Urban Development (2019), Average Sales Price of Houses Sold for the United States [ASPUS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/ASPUS.Google Scholar
Viscusi, W. K. (1985), ‘Are Individuals Bayesian Decision Makers? The American Economic Review, 75(2): 381–385.Google Scholar
Viscusi, W. K. and O'Connor, C. J. (1984), ‘Adaptive Responses to Chemical Labeling: Are Workers Bayesian Decision Makers? The American Economic Review, 74(5): 942–956.Google Scholar
Wooldridge, J. M. (2010), Econometric Analysis of Cross Section and Panel Data, 2nd edn., volume 1. Cambridge: The MIT Press.Google Scholar
Supplementary material: File

Bradt supplementary material

Appendix A

Download Bradt supplementary material(File)
File 881.4 KB