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Flood Insurance Market Penetration and Expectations of Disaster Assistance

Abstract

Concern over resilience to natural disasters often focuses on moral hazard; expectations of disaster assistance may lead households in hazard-prone communities to forego insurance. This has been dubbed “charity hazard” in the literature on natural disasters. We examine flood insurance uptake using household level survey data and employ instrumental variables (related to local history of aid distribution and political economy) to address endogeneity of individual expectations of eligibility for disaster assistance. To avoid potential problems with reverse causation, we drop any households that could have received payments in the past (triggering mandatory flood insurance purchase). We find coastal households that exhibit positive expectations of disaster aid eligibility are 25 to 42 percent less likely to hold flood insurance. We estimate that charity hazard could be responsible for 817,000 uninsured homes in the United States corresponding to a loss of $526 million in forgone annual revenue for the National Flood Insurance Program.

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Fig. 1

Notes

  1. In this setting, “extensive margin” refers to the binary decision to hold a flood insurance policy, where as the “intensive margin” references the choice concerning the level of coverage for an existing flood insurance policy.

  2. Citing a study by the Insurance Information Institute (2017) that reports 43% of US households erroneously perceive that flooding is covered by their homeowner’s policy, an anonymous reviewer raises concern that our measure of flood insurance may be inaccurate. We note that our measure of flood insurance market penetration (35% overall; 65% in the SFHA) is consistent with previous studies of the South and Gulf coastal zone. Using NFIP policy data, Dixon et al. (2006) report market penetration rates as high as 60% (80% in the SFHA) in the US south. Using survey data, Landry and Jahan-Parvar (2011) report market penetration in the coastal zone SFHA of 50%. Moreover, our survey instrument (Table A1) expressly measured whether the property was covered by a flood insurance policy [emphasis added] (not whether they were covered for flood damage more generally). Lastly, flood risk and the limits of coverage are likely to be more salient in the coastal zone, where standard homeowners policies often do not cover windstorm damage.

  3. The full text for survey questions that are used to construct our key variables can be found in Table 10.

  4. As an additional robustness check we run the non-parametric Mann–Whitney–Wilcoxon (MWW) test on all of our variables which is arguably more appropriate for ordinal data. All of the tests produce qualitatively equivalent results.

  5. For 2010, we only include IA grants that were awarded in response to events that occurred before our survey was distributed. The latest event to be included was Hurricane Alex which occurred in late June of 2010 which was approximately 2 months before our survey was distributed.

  6. 2009 was a very mild hurricane season (Berg and Avila 2011); thus, the insignificance of our aid instrument in 2009 is unsurprising.

  7. To address the possibility that the political variables (and consequently the IA payments) might be correlated with other, unobservable county-level characteristics, an additional robustness check involves adding county-level covariates that measure education, race, and age, in addition to a dummy variable indicating majority vote for Republican presidential candidate in the 2008 election. None of these covariates were statistically significant, and primary results remain unchanged.

  8. The 30% metric is unrealistically high for several reasons and is a defensible assumption to generate a lower bound on lost revenue. For one, most households that incur damage will not receive any form of federal disaster aid. Secondly, 30% of households in our full sample sustained damage in the past, but this is likely to be significantly lower for households that are not located near the Gulf Coast.

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Correspondence to Craig E. Landry.

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Appendix

Appendix

See Table 10 and Fig 2.

Table 10 Key Variables and Corresponding Survey Question Text
Fig. 2
figure 2

Question used to elicit risk preferences with example choice

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Landry, C.E., Turner, D. & Petrolia, D. Flood Insurance Market Penetration and Expectations of Disaster Assistance. Environ Resource Econ 79, 357–386 (2021). https://doi.org/10.1007/s10640-021-00565-x

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Keywords

  • Charity hazard
  • Flood insurance
  • Natural hazards

JEL Classification

  • Q54
  • G22