Naturally Resilient to Natural Hazards? Urban–Rural Disparities in Hazard Mitigation Grant Program Assistance

ABSTRACT

The American public generally sees its rural communities as autonomous and self-sufficient—inherently resilient. Accordingly, research on federally funded hazard mitigation has disproportionately focused on urban areas, as rural communities rebuild largely by themselves. Our exploratory research challenges this overarching narrative on rural communities by examining disparities in the mitigation process—specifically, the amount of Hazard Mitigation Grant Program (HMGP) assistance awarded per recipient and the duration of HMGP projects—between urban and rural counties from 1989 to 2018. Our analysis reveals vast inequities in the distribution and duration of HMGP assistance between urban and rural counties. Controlling for characteristics of the mitigated properties and corresponding counties, social and physical vulnerability, and climate change factors, we find (a) the amount of HMGP assistance awarded per recipient is higher in urban counties, and (b) projects are completed more quickly in rural counties. Ultimately, our findings indicate that the current structure of the HMGP leaves rural counties in the dust.

KEYWORDS:

• Hazard Mitigation Grant Program
• rural communities
• disaster policy
• social vulnerability
• disaster resilience
• FEMA buyouts

Acknowledgments

We appreciate the comments of the anonymous reviewers, who improved the article and its message.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

1. FEMA (2020), Hazard Mitigation Grants, retrieved from https://www.fema.gov/grants/mitigation

2. We assume the definition of rural developed by the U.S. Department of Agriculture (USDA): nonmetropolitan (nonmetro) areas, based on proximity to metropolitan areas or the degree of urbanization. This study identifies rural counties as a combination of (a) open countryside; (b) rural towns, which are places with fewer than 2,500 people; and (c) urban areas with a population ranging from 2,500 to 49,999 that are not part of larger labor market areas (metropolitan areas). See USDA ERS (2019), Overview of Rural Classification, retrieved from https://www.ers.usda.gov/topics/rural-economy-population/rural-classifications/

3. Through the buyout process, existing structures are demolished, and the underlying land is then converted into open space, recreational, or wetland management uses.

4. States and local governments or nonprofit organizations generally cover the remaining portion of funds.

5. A total of 1,045 counties were divided into 10 categories according to the following criteria: (1) whether the county is urban or rural; and (2) how many properties have been assisted for three decades compared with the overall housing units in the county. To compare the urban–rural difference, we chose to use the ratio of HMA-assisted properties to total housing units, because it allows us to compare urban and rural areas by changing the absolute value to scale to control the number of housing units, which could be relatively higher in urban areas. The ratio of HMA-assisted properties to total housing units was multiplied by 1,000, thereby showing a range from 0.001 to 64.54. Then it was divided into five percentiles, each percentile including about 210 counties.

6. Seven counties were omitted because (a) they performed other types of mitigation actions (neither relocation nor staying actions) or (b) mitigation actions were undefined in the data set.

7. Data accessed from https://www.fema.gov/openfema-dataset-hazard-mitigation-assistance-mitigated-properties-v2

8. This study only includes counties and county equivalents of the United States situated on the mainland, meaning the 48 adjoining U.S. states on the continent of North America. The study excludes counties and county-equivalents situated entirely or predominantly on islands, including Alaska, Hawaii, and the 100 county-equivalents in the U.S. territories.

9. The discrepancy in units between the damage variable (ratio) and the amount paid (log-adjusted value) indicates that the interpretation of the damage variable coefficients may cause misinterpretation, underestimating the actual value loss when the predisaster home value is high. To control for this issue, we tested the model with an interaction term using the county’s median home value and the damage variable and found that the amount of HMGP assistance awarded per recipient is more likely to be underestimated when the property is completely destroyed, especially in neighborhoods that lie in counties with higher median home values. However, because the focus of the study is to examine the urban–rural disparity in the amount of HMGP assistance, we excluded the interaction term from the final models.

10. As for socioeconomic factors, we modified our MLM models by introducing interaction terms to plot predictive margins. Different interaction terms (minority#minority#rural; svisum#svisum#rural) were chosen based on statistical significance. We used quadratic interaction terms with a rural dummy variable to observe the different rates of change in slopes over the minority status and the sum of SVI, respectively.

11. FEMA (2020), Building Resilient Infrastructure and Communities (BRIC), retrieved from https://www.fema.gov/grants/mitigation/building-resilient-infrastructure-communities

Additional information

Notes on contributors

Kijin Seong

Kijin Seong is a PhD candidate in the Department of Landscape Architecture and Urban Planning at Texas A&M University. Kijin earned a bachelor of architecture from Yonsei University, South Korea, a master of architecture from Seoul National University, South Korea, and a master of community and regional planning from the University of Texas at Austin. Her ongoing research focuses on postdisaster relocation, floodplain buyouts, and hazard mitigation tools. She works as a graduate research assistant at the Hazard Reduction and Recovery Center (HRRC) at Texas A&M University.

Clare Losey

Clare Losey is a PhD candidate in the Department of Landscape Architecture and Urban Planning at Texas A&M University. She earned a bachelor of arts in geography and urban studies from the University of Texas at Austin and a master of real estate finance from Texas A&M University. Her ongoing research focuses on housing affordability and neighborhood opportunity, as well as the long-term recovery of homeowners following disasters. She works as a graduate research assistant at the Real Estate Research Center at Texas A&M University.

Donghwan Gu

Donghwan Gu is a PhD candidate in the Department of Landscape Architecture and Urban Planning at Texas A&M University. He earned a bachelor of science in civil, urban, and geosystems engineering from Seoul National University, South Korea, a master of science from Seoul National University, South Korea, and a master of urban planning from Texas A&M University. His ongoing research focuses on urban vacant land, postdisaster redevelopment, and urban heat island mitigation strategies. He works as a graduate research assistant at the Hazard Reduction and Recovery Center (HRRC) at Texas A&M University.