Skip to main content
SpringerLink
Log in

Risk awareness and adverse selection in catastrophe insurance: Evidence from California’s residential earthquake insurance market

  • Published:
Journal of Risk and Uncertainty Aims and scope Submit manuscript

Abstract

How does catastrophe-risk awareness affect purchase decisions and selection patterns in the insurance market? I study this issue using data on take-up rates of earthquake insurance among homeowners in California, where a semi-public insurer coexists with private insurers. The public insurance policy charges cross-subsidized premiums and is offered through specific homeowners insurance agents. I find that for those offered the public earthquake policy, a positive demand-risk correlation exists within the insurer’s pricing territory, suggesting that people have some meaningful awareness of their risk and act on it in ways consistent with adverse selection. However, despite private insurers having the opportunity to select the lower risks by pricing at a finer level, the positive demand-risk correlation persists for them. I interpret this as a result of market friction that limits homeowners’ ability to comparison shop, and their relative insensitivity to price as compared to risk.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. Kousky (2011) surveys 10 state catastrophe insurance programs and finds cross-subsidization both in the form of ex-ante premiums and ex-post assessments and aid. At the federal level, the National Flood Insurance Program (NFIP) also involves heavy subsidization from low-risks to high-risks (Kousky and Shabman 2014). Such cross-subsidization is often a result of political concerns. The type of subsidization this paper focuses on is ex-ante premium subsidization from low-risk areas to high-risk areas.

  2. Both rational (Kunreuther and Pauly 2004) and behavioral (Johnson et al. 1993; Browne et al. 2015) models have been used to explain the very low take-up for catastrophe insurance despite favorable or even subsidized premiums.

  3. Some examples of CEA participating insurers (PIs) include State Farm, Liberty Mutual, USAA, etc. Some examples of non-PIs include Geico, Chubb, etc.

  4. Quote from the email correspondence between the California Department of Insurance and GeoVera (a private earthquake insurer) documented in the public rate filings by GeoVera in 1998.

  5. The standard test uses ex-post claims as proxies for risk types (e.g. Chiappori and Salanie 2000). A few studies have used other proxies for risk type. For example: Browne (1992) uses predicted claims instead of realized ex-post claims; Finkelstein and McGarry (2006) use subjective survey response to determine individuals’ risk types.

  6. See Zanjani (2008): “California has a “mandatory offer” law, dating from 1985, requiring that earthquake coverage be offered along with homeowners insurance.”

  7. Based on the author’s conversations with individuals who participated in the process of forming the CEA, 70% is the political threshold that led to the CEA’s formation. Zanjani (2008) discussed the characteristics of the insurers participating in the CEA. He found that insurers who suffered more losses in the Northridge Earthquake, and those with larger exposure to California’s homeowners insurance market are more likely to participate. The CEA reimburses its PIs for distributing and servicing CEA policies. In essence, the PIs take no earthquake risk exposure in selling earthquake insurance along with their homeowners insurance.

  8. This is a common assumption in discrete choice models in the random utility framework. Extreme value distributed error terms will yield the multinomial logistic choice model, which is directly derived from the random utility assumption (Train 2003; Frees 2009).

  9. Further explanation is in the data section on constructing seismic risk measures.

  10. Details on a few California earthquake insurers’ (including the CEA’s) policy option, limit, and deductible are in Appendix B: Other Data Sources. These insurers’ earthquake insurance policy forms and menus are broadly similar.

  11. Details on two earthquake insurers’ pricing schemes are in Appendix B: Geographic Risk Classification for Earthquake Risk by Different Insurers.

  12. See summary statistics in the Data section.

  13. Insurers with written premiums of at least $5 million in 2009.

  14. The maps incorporate information on potential earthquakes and associated ground shaking and are derived from science and engineering workshops involving hundreds of participants.

  15. 0.05-degree latitude is approximately 3.45 miles, while 0.05-degree longitude varies between 3.45 miles at the equator to 0 at the poles. In any case, these grids are generally much smaller than a zip-code area. The grids start from a northwest point at 50°N 125°W and ending at a southeast point at 24.6°N 65°W, covering the conterminous 48 states.

  16. During a potential earthquake, ground acceleration is measured in three directions: vertically (V or UD, for up-down) and two perpendicular horizontal directions (H1 and H2), often north-south (NS) and east-west (EW). The peak acceleration in each of these directions is recorded, with the highest individual value reported as PGA.

  17. See https://earthquake.usgs.gov/hazards/learn/technical.php.

  18. A figure can also be generated for the CEA policies among all (PI and non-PI) homeowners insurance policyholders, which is very similar to the one presented here.

  19. Alternative regressions using different dependent variables are also estimated. Table C.2 in the Online Appendix shows regression results using the share of CEA coverage limits over PI homeowners’ coverage limits as the dependent variable; Table C.1 in the Online Appendix shows regression results using the share of CEA policy count over all homeowners policy count (both PI and non-PI) as the dependent variable. Both additional results are similar to the result presented in the main text.

  20. Alternative equation assuming variable slope by territory is estimated in Table C.3 in the Online Appendix: 7 out of 8 largest territories have significantly positive slopes. The magnitude of the slope is the largest for the largest territory (territory 27).

  21. I conduct the following exercise: I rank zip-code areas in California by their risk (PGA) and divide them into 84 homogeneous risk groups in which the range of their PGA values does not exceed 0.01. The PGA values for all zip-codes range from 0.0481 to 0.8760; the number of zip-codes in each homogeneous group ranges from 12 to 49. I then run weighted simple linear regressions using non-PI market shares as the explanatory variable and non-CEA market shares as the dependent variable (weights are the number of homeowners in each zip-code area). Figure C.1 in the Online Appendix shows the distribution of the R-squared statistics for all of those regressions. The coefficient estimates for 70 (out of 84) regressions are significantly positive at 0.05 level, and that 28 (out of 84) have R-squared larger than 50%. Overall, the variation in default market share alone explains a significant portion of variation in earthquake insurance market share.

  22. For example, Kamiya and Yanase (2019) investigate the effect of experiencing an extreme catastrophes; Lin (2020) investigate the effect of experiencing moderate feelings of earthquake shakings that do not result in insured losses. However, they do not look at the impact of experience on the selection of insurance policies.

References

  • Abaluck, J., & Adams, A. (2017). What do consumers consider before they choose? Identification from asymmetric demand responses. NBER Working Paper No. 23566.

  • Akerlof, G. A. (1970). The market for “lemons”: Quality uncertainty and the market mechanism. Quarterly Journal of Economics, 84(3), 488–500.

    Article  Google Scholar 

  • Bordalo, P., Gennaioli, N., & Shleifer, A. (2013). Salience and consumer choice. Journal of Political Economy, 121(5), 803–843.

    Article  Google Scholar 

  • Browne, M. J. (1992). Evidence of adverse selection in the individual health insurance market. Journal of Risk and Insurance, 59(1), 13–33.

    Article  Google Scholar 

  • Browne, M. J., Knoller, C., & Richter, A. (2015). Behavioral bias and the demand for bicycle and flood insurance. Journal of Risk and Uncertainty, 50, 141–160.

    Article  Google Scholar 

  • Chiappori, P.-A., & Salanie, B. (2000). Testing for asymmetric information in insurance markets. Journal of Political Economy, 108(1), 56–78.

    Article  Google Scholar 

  • Cutler, D. M., & Reber, S. J. (1998). Paying for health insurance: The trade-off between competition and adverse selection. Quarterly Journal of Economics, 113(2), 433–466.

    Article  Google Scholar 

  • Czajkowski, J., Kunreuther, H., & Michel-Kerjan, E. (2012). A methodological approach for pricing flood insurance and evaluating loss reduction measures: Application to Texas. CoreLogic: Risk Management and Decision Processes Center, the Wharton School, University of Pennsylvania.

    Google Scholar 

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

    Article  Google Scholar 

  • Dumm, R. E., Eckles, D. L., Nyce, C., & Volkman-Wise, J. (2020). The representative heuristic and catastrophe-related risk behaviors. Journal of Risk and Uncertainty, 60, 157–185.

    Article  Google Scholar 

  • Finkelstein, A., & Poterba, J. (2004). Adverse selection in insurance markets: Policyholder evidence from the U.K. Annuity market. Journal of Political Economy, 112(1), 183–208.

    Article  Google Scholar 

  • Finkelstein, A., & McGarry, K. (2006). Multiple dimensions of private information: Evidence from the long-term care insurance market. American Economic Review, 96(4), 938–958.

    Article  Google Scholar 

  • Frees, E. (2009). Regression Modeling with Actuarial and Financial Applications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Gabaix, X. (2019). Behavioral inattention. In Bernheim, B. D., DellaVigna, S., & Laibson, D. (Eds.) Chapter 4 in Handbook of Behavioral Economics: Applications and Foundations 1, (Vol. 2 pp. 261–343). North-Holland.

  • Gallagher, J. (2014). Learning about an infrequent event: Evidence from flood insurance take-up in the United States. American Economic Journal: Applied Economics, 6(3), 206–233.

    Google Scholar 

  • Jaffee, D. M., & Russell, T. (2000). Behavioral models of insurance: The case of the California Earthquake authority. Boston: NBER Insurance Conference.

    Google Scholar 

  • Johnson, E. J., Hershey, J., Meszaros, J., & Kunreuther, H. (1993). Framing, probability distortions, and insurance decisions. Journal of Risk and Uncertainty, 7, 35–51.

    Article  Google Scholar 

  • Kahneman, D. (2003). Maps of bounded rationality: Psychology for behavioral economics. American Economic Review, 93(5), 1449–1475.

    Article  Google Scholar 

  • Kamiya, S., & Yanase, N. (2019). Learning from extreme catastrophes. Journal of Risk and Uncertainty, 59, 85–124.

    Article  Google Scholar 

  • Koszegi, B., & Rabin, M. (2006). A model of reference-dependent preferences. Quarterly Journal of Economics, 121(4), 1133–1165.

    Google Scholar 

  • Kousky, C. (2011). Managing natural catastrophe risk: State insurance programs in the United States. Review of Environmental Economics and Policy, 5(1), 153–171.

    Article  Google Scholar 

  • Kousky, C., & Shabman, L. (2014). Pricing flood insurance: How and why the NFIP differs from a private insurance company. Resources for the Future Discussion Paper: 14–37.

  • Kunreuther, H., Ginsberg, R., Miller, L., Sagi, P., Slovic, P., Borkan, B., & Katz, N. (1978). Disaster insurance protection: Public policy lessons. John Wiley & Sons: Wiley-Interscience.

    Google Scholar 

  • 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 

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

    Article  Google Scholar 

  • Kunreuther, H., & Roth Sr, R. (1998). Paying the price: The status and role of insurance against natural disasters in the United States. Washington: Joseph Henry Press.

    Google Scholar 

  • Lin, X. (2020). Feeling is believing? Evidence from earthquake shaking experience and insurance demand. Journal Risk and Insurance, 87(2), 351–380.

    Article  Google Scholar 

  • McClelland, G. H., Schulze, W. D., & Coursey, D. L. (1993). Insurance for low-probability hazards: A bimodal response to unlikely events. Journal of Risk and Uncertainty, 7, 95–116.

    Article  Google Scholar 

  • McFadden, D. (2001). Economic choices. American Economic Review, 91(3), 351–378. Nobel lecture, December 2000.

    Article  Google Scholar 

  • Meyer, R. (2012). Failing to learn from experience about catastrophes: The case of hurricane preparedness. Journal of Risk and Uncertainty, 45, 25–50.

    Article  Google Scholar 

  • Naoi, M., Seko, M., & Sumita, K. (2010). Community rating, cross subsidies and underinsurance: Why so many households in Japan do not purchase earthquake insurance. Journal of Real Estate Finance and Economics, 40(4), 544–561.

    Article  Google Scholar 

  • Petersen, M. D., Frankel, A. D., Harmsen, S. C., Mueller, C. S., Haller, K. M., Wheeler, R.L., Wesson, R. L., Zeng, Y., Boyd, O. S., Perkins, D. M., Luco, N., Field, E. H., Wills, C. J., & Rukstales, K. S. (2008). Documentation for the 2008 update of the United States national seismic hazard maps. U.S. Geological Survey Open-File Report 2008/11/28, available at http://pubs.usgs.gov/of/2008/1128/.

  • Slovic, P. (1987). Perception of risk. Science, 236(4799), 280–285.

    Article  Google Scholar 

  • Sutter, D., & Poitras, M. (2010). Do people respond to low probability risks? Evidence from tornado risk and manufactured homes. Journal of Risk and Uncertainty, 40(2), 181–196.

    Article  Google Scholar 

  • Train, K. E. (2003). Discrete choice methods with simulation. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Tversky, A., Sattath, S., & Slovic, P. (1988). Contingent weighting in judgment and choice. Psychological Review, 95(3), 371–384.

    Article  Google Scholar 

  • Viscusi, W. K. (1985). Are individuals Bayesian decision makers? American Economic Review: Papers & Proceedings, 75(2), 381–385.

    Google Scholar 

  • Viscusi, W. K. (1989). Prospective reference theory: Toward an explanation of the paradoxes. Journal of Risk and Uncertainty, 2, 235–263.

    Article  Google Scholar 

  • 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 

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

    Article  Google Scholar 

  • Zanjani, G. (2008). Public versus private underwriting of catastrophe risk: Lessons from the California Earthquake Authority. In Quigley, J. M., & Rosenthal, L. A (Eds.) Risking House and Home: Disasters, Cities, Public Policy. Berkeley: Berkeley Public Policy Press.

Download references

Acknowledgments

I thank Justin Sydnor, Mark Browne, Jed Frees, Jennifer Alix-Garcia, Magie Rosenberg, Joan Schmits, Martin Halek, Rexford Santerre, participants at the University of Wisconsin-Madison department seminars, the anonymous reviewer, the Editor and the editorial office of the JRU for very helpful comments, suggestions, and discussions. Data support from the California Department of Insurance is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Maurice R. Greenberg School of Risk Management, Insurance and Actuarial Science, St. John’s University, Tobin College of Business, 101 Astor place, New York, NY, 10003, USA

    Xiao Lin

Authors
  1. Xiao Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiao Lin.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

(PDF 460 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, X. Risk awareness and adverse selection in catastrophe insurance: Evidence from California’s residential earthquake insurance market. J Risk Uncertain 61, 43–65 (2020). https://doi.org/10.1007/s11166-020-09335-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11166-020-09335-4

Keywords

JEL Classification

Search

Navigation