The representative heuristic and catastrophe-related risk behaviors

Abstract

Building on the work by Volkman-Wise Journal of Risk and Uncertainty, 51, 267–290 (2015) and Dumm et al. (The Geneva Risk and Insurance Review, 42, 117–139 (2017), we examine behavioral aspects of risk through the representative heuristic’s impact on catastrophe-related probability assessment and insurance demand of Florida homeowners. The representative heuristic models individuals as underweighting prior probabilities and overweighting posterior probabilities, thereby overweighting the probability of a loss from a disaster after a disaster occurs. Using data for homeowners’ insurance purchases through Florida’s residual market over a time period (2003-2008) that includes a sub-period of many losses (2004-2005) and sub-period of few catastrophic losses (2003, 2006-2008), we find increases in demand at the individual policyholder level for coverage following losses. Also consistent with the representative heuristic, we find that the effect attenuates as the losses fade from memory. That is, the effect of losses on demand is much higher for more recent losses. We also are able to parameterize the representative heuristic model showing that individual policy holders overweight the probability of another catastrophic event occurring by nearly 50%, after one has occurred.

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Notes

  1. 1.

    Note that if no events have occurred recently, individuals still overweight the current state (no events), leading to a belief that would underweight the true probability of an event.

  2. 2.

    For example, a consumer that chooses not to insure an automobile worth $5,000 because s∖he has not had a recent loss faces a different potential financial impact when deciding to not insure (or underinsure) a house.

  3. 3.

    Hurricane Katrina also occurred during this window, but its impact in Florida was minimal.

  4. 4.

    In fact, the average tenure for a flood insurance policy is only three years (Michel-Kerjan 2010).

  5. 5.

    NFIP premiums do not change much from year to year though and the premiums charged do not accurately reflect the true probability of a loss. Therefore, using this data to estimate probability updating for natural disasters is somewhat limited.

  6. 6.

    An indirect loss would be a loss by a neighbor, acquaintance, etc.

  7. 7.

    AIR Worldwide. (2016) reports that the exposure now exceeds $4 trillion.

  8. 8.

    According to the Insurance Information Institute. (2004), approximately one-fifth of homes in Florida were damaged during the 2004 hurricane season.

  9. 9.

    Loss data is from the Florida Office of Insurance Regulation.

  10. 10.

    Citizens was the largest insurer during the period examined in this paper.

  11. 11.

    HRA accounts also have a separate funding mechanism.

  12. 12.

    To control for size effects, we use the natural log of these variables.

  13. 13.

    Since the demand models will have an estimated regressor, we bootstrap the standard error (Pagan 1984).

  14. 14.

    Virtually all deductible changes were from 2% to $500 or 2% to 5%, representing an increase or decrease in demand.

  15. 15.

    The claims data and underwriting data were matched by policy number.

  16. 16.

    Citizens codes each catastrophe loss by the catastrophe name.

  17. 17.

    If property changes ownership a new policy is issued with a new policy number. Similarly, if an insured leaves Citizens and returns at a later date, they are issued a new policy number.

  18. 18.

    We do not, however, know the demand (either increasing or decreasing) of individuals that do not purchase insurance from Citizens or those that changed carriers. Since we control for price in the models, we are not concerned that individuals are dropping out of the sample because of prices changes.

  19. 19.

    The standard deductible for wind damage is 2%. One problem with measuring the changes in demand is that prior to 2007 Citizens standard deductibles in the PLA for wind damage were not percentage deductibles but recorded as dollar deductibles ($250, $500, rather than 2% or 5%). These dollar deductibles were not always “round” numbers, but included values such as $1,267, or $5,345 which were percentage deductibles recorded as dollar values. That changed in 2007 where the standard deductible for wind damage in the PLA became 2%. Thus for 2007 only, if the deductible in the PLA went from a dollar amount to 2%, we considered that no change in demand.

  20. 20.

    The coefficients on the first and second lags (Log(CatLosst− 1) andLog(CatLosst− 2)) are significantly different from the third lag at the 1% level for all models. The coefficients between the first and second lags are never significantly different from each other.

  21. 21.

    Here, the coefficients on the first lag (Log(CatLosst− 1)) are (statistically) significantly different from the second and third lags at the 1% level for all models. The coefficients between the second and third lags are still never (statistically) significantly different from each other.

  22. 22.

    The Wald test of differences in the coefficients is significant at the 1% level in all models.

  23. 23.

    The coefficient on Age is insignificant in models analyzing only PLA insureds.

  24. 24.

    State-level data is available from 1913, onward; county-level data is available beginning in 1960.

  25. 25.

    If this frequency parameter estimate understates (overstates) the true probability, it will overestimate (underestimate) the effect of the representative heuristic.

  26. 26.

    Hazards and Vulnerability Research Institute, Department of Geography, University of South Carolina, Columbia, South Carolina 29208.

  27. 27.

    Both the Poisson and Lognormal distributions were chosen based on the Akaike Information Criteria goodness of fit test.

  28. 28.

    These levels are chosen to be representative of the average home in our sample.

  29. 29.

    We focus on the 2% and $500 deductible as these are the two most frequently used deductibles in our sample.

  30. 30.

    The results here are, of course, sensitive to the assumptions made in our analysis. However, a 50% difference between the “true” probability and the estimated probability from a policyholder exhibiting behavior consistent with the representative heuristic is significant. With this distortion, the policyholder anticipates a catastrophic loss once every three years instead of once every five years.

References

  1. AIR Worldwide. (2008). The coastline at risk: 2008 update to the estimated insured value of U.S. coastal properties, http://www.air-worldwide.com/download.aspx?c=388&id=15836.

  2. AIR Worldwide. (2016). The coastline at risk: 2016 update to the estimated insured value of U.S. coastal properties, http://airww.co/coastlineatrisk.

  3. Aseervatham, V., Born, P., & Richter, A. (2013). Demand reactions in the aftermath of catastrophes and the need for behavioral approaches. Munich Risk and Insurance Center (Working Paper 13).

  4. Born, P., & Klimaszewski-Blettner, B. (2013). Should I stay or should I go? The impact of natural disasters and regulation on U.S. property insurers’ supply decisions. Journal of Risk and Insurance, 80, 1–36.

    Article  Google Scholar 

  5. Brown, J. R., Kling, J. R., Mullainathan, S., & Wrobel, M. V. (2008). Why don’t people insure late-life consumption? A framing explanation of the under-annuitization puzzle. American Economic Review, 98, 304–09.

    Article  Google Scholar 

  6. Browne, M. J., & Hoyt, R. E. (2000). The demand for flood insurance: Empirical evidence. Journal of Risk and Uncertainty, 20, 291–306.

    Article  Google Scholar 

  7. Doherty, N. A. (1997). Innovations in managing catastrophe risk. Journal of Risk and Insurance, 64, 713–718.

    Article  Google Scholar 

  8. Dumm, R. E., Eckles, D. L., Nyce, C., & Volkman-Wise, J. (2017). Demand for windstorm insurance coverage and the representative heuristic. The Geneva Risk and Insurance Review, 42, 117–139.

    Article  Google Scholar 

  9. Gallagher, J. (2014). Learning about an infrequent event: Evidence from flood insurance take-up in the United States. American Economic Journal: Applied Economics, 6, 206–233.

    Google Scholar 

  10. Grether, D. M. (1980). Bayes rule as a descriptive model: The representativeness heuristic. The Quarterly Journal of Economics, 95, 537–557.

    Article  Google Scholar 

  11. Hershfield, H. E., Daniel, G, Goldstein, W. F., Sharpe, J. F., Yeykelis, L., Carstensen, L. L., & Bailenson, J. N. (2011). Increasing saving behavior through age-progressed renderings of the future self. JMR Journal of Marketing Research, 48, S23–S37. 24634544[pmid] PMC3949005[pmcid].

    Article  Google Scholar 

  12. Insurance Information Institute. (2004). Insurance companies paying two million claims from four Florida hurricanes, http://www.iii.org/media/updates/archive/press.738077/.

  13. Jaffee, D. M., & Russell, T. (1997). Catastrophe insurance, capital markets, and uninsurable risks. The Journal of Risk and Insurance, 64, 205–230.

    Article  Google Scholar 

  14. Kahneman, D. (2011). Thinking, fast and slow. Farrar Straus and Giroux, New York.

  15. Kahneman, D., & Tversky, A. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3, 430–454.

    Article  Google Scholar 

  16. Kahneman, D. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–291.

    Article  Google Scholar 

  17. Kunreuther, H. (1978). Disaster insurance protection: Public policy lessons. New York: Wiley.

    Google Scholar 

  18. Kunreuther, H. (2006). Has the time come for comprehensive natural disaster insurance? Philadelphia: University of Pennsylvania Press.

    Google Scholar 

  19. Kunreuther, H., & Pauly, M. (2004). Neglecting disaster: Why don’t people insure against large losses? Journal of Risk and Uncertainty, 28, 5–21.

    Article  Google Scholar 

  20. Kunreuther, H. (2006). Rules rather than discretion: Lessons from Hurricane Katrina. Journal of Risk and Uncertainty, 33, 101–116.

    Article  Google Scholar 

  21. Kunreuther, H. C., Pauly, M. V., & McMorrow, S. (2013). Insurance and behavioral economics: Improving decisions in the most misunderstood industry. Cambridge: Cambridge University Press.

    Google Scholar 

  22. Laury, S. K., McInnes, M. M., & Swarthout, J.T. (2009). Insurance decisions for low-probability losses. Journal of Risk and Uncertainty, 39, 17–44.

    Article  Google Scholar 

  23. Loubergé, H., & Schlesinger, H. (2001). Optimal catastrophe insurance. Working Paper.

  24. Madrian, B., & Shea, D. F. (2001). The power of suggestion: Inertia in 401(k) participation and savings behavior. The Quarterly Journal of Economics, 116, 1149–1187.

    Article  Google Scholar 

  25. Meyer, R., & Kunreuther, H. (2017). The ostrich paradox: Why we underprepare for disasters. La Vergne: Wharton Digital Press.

    Google Scholar 

  26. Michel-Kerjan, E. O. (2010). Flood insurance in the United States: Past, present and future. Journal of Economic Perspectives, 24, 165–186.

    Article  Google Scholar 

  27. Pagan, A. (1984). Econometric issues in the analysis of regressions with generated regressors. International Economic Review, 1, 221–247.

    Article  Google Scholar 

  28. Schlesinger, H. (1999). Decomposing catastrophe risk. Insurance: Mathematics and Economics, 24, 95–101.

    Google Scholar 

  29. Seog, S. H. (2008). Informational cascade in the insurance market. The Journal of Risk and Insurance, 75, 145–165.

    Article  Google Scholar 

  30. Sunstein, C. R., & Zeckhauser, R. (2008). Overreaction to fearsome risks. Harvard Kennedy School Working Paper RWP08-079.

  31. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness . Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Pres.

    Google Scholar 

  32. Tversky, A., & Kahneman, D. (1973). Availability: a heuristic for judging frequency and probability. Cognitive Psychology, 5, 207–232.

    Article  Google Scholar 

  33. Tversky, A., Sattath, S., & Slovic, P. (1988). Contingent weighting in judgment and choice. Psychological Review, 95, 371–384.

    Article  Google Scholar 

  34. Viscusi, W. K. (2006). Natural disaster risks: an introduction. Journal of Risk and Uncertainty, 33, 5–11.

    Article  Google Scholar 

  35. Viscusi, W. K., & Zeckhauser, R. (2014). The relative weights of direct and indirect expereinces in the formation of environmental risk beliefs. Vanderbilt University Law School Working Paper, 14–6.

  36. Viscusi, W. K., & Zeckhauser, R. J. (2006). National survey evidence on disasters and relief: Risk beliefs, self-interest, and compassion. Journal of Risk and Uncertainty, 33, 13–36.

    Article  Google Scholar 

  37. Volkman-Wise, J. (2015). Representativeness and managing catastrophe risk. Journal of Risk and Uncertainty, 51, 267–290.

    Article  Google Scholar 

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Correspondence to David L. Eckles.

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Dumm, R.E., Eckles, D.L., Nyce, C. et al. The representative heuristic and catastrophe-related risk behaviors. J Risk Uncertain 60, 157–185 (2020). https://doi.org/10.1007/s11166-020-09324-7

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Keywords

  • Natural disasters
  • Insurance demand
  • Catastrophic Risk
  • Risk beliefs
  • Heuristics

JEL Classification

  • D03
  • D81
  • D83
  • G02
  • G22