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An Imperfect Storm: Fat-Tailed Hurricane Damages, Insurance and Climate Policy

Listed author(s):
  • Marc N. Conte

    (Department of Economics, Fordham University)

  • David L. Kelly

    (Department of Economics, University of Miami)

We perform two tests that estimate the thickness of the tails of the distribution of aggregate US hurricane damages. Both tests reject the hypothesis that the distribution of damages is thin tailed at the 95% confidence level, even after correcting for inflation, population, and per capita income growth. Our point estimates of the shape parameter of the damage distribution indicate that the distribution has finite mean, but infinite variance. In the second part of the paper, we develop a microfoundations model of insurance and storm size that generates fat tails in aggregate hurricane damages. In the model, the distribution of the number properties within a random geographical area that lies in the path of a hurricane drives fat tails in hurricane damages, and we confirm that the distribution of coastal city population is fat tailed in the US. We show empirically and theoretically that other random variation, such as the distribution of hurricane strength and the distribution of damages across individual properties do not generate fat tails. We consider policy options such as climate change mitigation, policies which encourage adaptation, reducing subsidies for coastal development, and disaster relief policies, which distort insurance markets. Such policies can reduce the thickness of the tail, but do not affect the shape parameter or the existence of the fat tail.

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File URL: http://bus.miami.edu/_assets/files/repec/WP2016-01.pdf
File Function: First version, 2016
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Paper provided by University of Miami, Department of Economics in its series Working Papers with number 2016-01.

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Date of creation: 02 Feb 2016
Handle: RePEc:mia:wpaper:2016-01
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  1. Kelly, David L. & Kolstad, Charles D. & Mitchell, Glenn T., 2005. "Adjustment costs from environmental change," Journal of Environmental Economics and Management, Elsevier, vol. 50(3), pages 468-495, November.
  2. Adam Smith & Richard Katz, 2013. "US billion-dollar weather and climate disasters: data sources, trends, accuracy and biases," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 67(2), pages 387-410, June.
  3. Justin Gallagher, 2014. "Learning about an Infrequent Event: Evidence from Flood Insurance Take-Up in the United States," American Economic Journal: Applied Economics, American Economic Association, vol. 6(3), pages 206-233, July.
  4. Xavier Gabaix & Rustam Ibragimov, 2011. "Rank - 1 / 2: A Simple Way to Improve the OLS Estimation of Tail Exponents," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 24-39, January.
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  7. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
  8. Rafael González-Val & Arturo Ramos & Fernando Sanz-Gracia & María Vera-Cabello, 2015. "Size distributions for all cities: Which one is best?," Papers in Regional Science, Wiley Blackwell, vol. 94(1), pages 177-196, 03.
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  11. Gabaix, Xavier & Ibragimov, Rustam, 2011. "Rank − 1 / 2: A Simple Way to Improve the OLS Estimation of Tail Exponents," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 24-39.
  12. Martin L. Weitzman, 2014. "Fat Tails and the Social Cost of Carbon," American Economic Review, American Economic Association, vol. 104(5), pages 544-546, May.
  13. Kelly, David L. & Tan, Zhuo, 2015. "Learning and climate feedbacks: Optimal climate insurance and fat tails," Journal of Environmental Economics and Management, Elsevier, vol. 72(C), pages 98-122.
  14. Kousky, Carolyn & Cooke, Roger M., 2009. "The Unholy Trinity: Fat Tails, Tail Dependence, and Micro-Correlations," Discussion Papers dp-09-36-rev.pdf, Resources For the Future.
  15. Brazauskas, Vytaras & Kleefeld, Andreas, 2009. "Robust and efficient fitting of the generalized Pareto distribution with actuarial applications in view," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 424-435, December.
  16. Hallstrom, Daniel G. & Smith, V. Kerry, 2005. "Market responses to hurricanes," Journal of Environmental Economics and Management, Elsevier, vol. 50(3), pages 541-561, November.
  17. Okmyung Biny & Stephen Polasky, 2004. "Effects of Flood Hazards on Property Values: Evidence Before and After Hurricane Floyd," Land Economics, University of Wisconsin Press, vol. 80(4).
  18. Xavier Gabaix, 1999. "Zipf's Law for Cities: An Explanation," The Quarterly Journal of Economics, Oxford University Press, vol. 114(3), pages 739-767.
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