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Local capacity and resilience to flooding: community responsiveness to the community ratings system program incentives

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Abstract

To incentivize more community flood risks mitigation, the US Congress implemented the community rating system (CRS) in 1990. The CRS seeks to help communities build capacity to address flood risks and become more resilient to future flood disasters. Communities participating in CRS can reduce their flood risks and enjoy discounted premiums (up to 45 %) on federally required flood insurance commensurate with their community’s CRS score. A participant community is placed into one of the ten classes depending on its CRS score. Although previous research finds that the program’s structure creates opportunities for communities participating in CRS to respond to its incentives, no study has examined the characteristics of communities that changed their mitigation behavior due to this incentive scheme. In order to evaluate the performance of CRS and its tiered incentive structure, this study investigates the extent to which communities are responding strategically to CRS incentives and the characteristics of those communities behaving strategically. This study uses a regression discontinuity approach to compare the characteristics of communities above and below CRS class thresholds. The results show strategic behavior of communities participating in CRS. Communities with more information-based flood management activities, lower property values, lower flood risk, and lower population densities are more likely to respond strategically with respect to smaller CRS subsidies. For larger subsidies, the results indicate that CRS communities with higher property values are more likely to respond strategically to the policy incentives. The study concludes with a discussion of the implications of these results for the CRS program.

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Notes

  1. “EM-DAT: The OFDA/CRED International Disaster Database, University catholique de

    Louvain, Brussels, Bel.” Available at: http://www.preventionweb.net/english/hazards/statistics/?hid=62.

  2. “EM-DAT: The OFDA/CRED International Disaster Database, University catholique de

    Louvain, Brussels, Bel.” Available at: http://www.preventionweb.net/english/hazards/statistics/?hid=62.

  3. Although communities can choose which programs to implement and, consequently, have some control over the CRS scores they obtain, the ultimate decision on their actual and final CRS scores is in the hands of a CRS specialist after implementation. This argument is corroborated by the CRS Coordination Manual: “Only the final, verified credit calculated by the ISO/CRS Specialist after the verification visit determines a community’s total points.” (FEMA 2013a, b: 110–7).

  4. Basically, a kernel-weighted local regression is fit using only data to the left of the threshold, another kernel-weighted local regressions is fit using only data to the right, and the difference in their predicted values at the threshold is α.

  5. Zahran et al. (2010) use 50 points above thresholds as a cutoff to capture those relevant observations.

  6. We did not include the new activity 370 “Flood Insurance Promotions” because we have no information on it.

  7. Each 1-km grid cell raster map from FEMA (1996) is mapped to over 200,000 Census block groups to obtain a mean flood risk value for each of these neighborhood-scale units in the nation. Flood Risk is set to the maximum (block-group mean) flood risk value within each county. Other aggregations of localized flood risk measures are, of course, possible. The max-mean function is selected because it provided the strongest and most consistent predictor of overall CRS participation among a sample of 28,147 US places.

  8. Tests for the first, lowest threshold at 500 points are not possible because communities and their CRS scores are not observed if the score falls below 500.

  9. Alternate measures of government capacity were also explored, including total payroll outlays, total property tax revenues, property tax revenues per capita, and capital outlays for sewage, solid waste management, and water transport. Each returned qualitatively the same results.

  10. We also examined the share of housing units in the top-coded value category of $500,000 or above. The share of very large, expensive homes in the community also fell discontinuously at the 1000-point threshold. It appeared evenly distributed around the 1500-point threshold.

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Authors and Affiliations

  1. Indiana University-Purdue University Indianapolis, Indianapolis, IN, USA

    Abdul-Akeem Sadiq & Douglas Noonan

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  1. Abdul-Akeem Sadiq
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Correspondence to Abdul-Akeem Sadiq.

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Sadiq, AA., Noonan, D. Local capacity and resilience to flooding: community responsiveness to the community ratings system program incentives. Nat Hazards 78, 1413–1428 (2015). https://doi.org/10.1007/s11069-015-1776-9

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