Statistical learning to identify salient factors influencing FEMA public assistance outlays

Hamed Ghaedi, Kelsea Best (), Allison Reilly and Deb Niemeier
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Hamed Ghaedi: University of Maryland
Kelsea Best: The Ohio State University
Allison Reilly: University of Maryland
Deb Niemeier: University of Maryland

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 12, No 5, 10559-10582

Abstract: Abstract Both the number of disasters in the U.S. and federal outlays following disasters are rising. FEMA’s Public Assistance (PA) is a key program for rebuilding damaged public infrastructure and aiding local and state governments in recovery. It is the primary post-disaster source of recovery funds. Between 2000 and 2019, more than $125B (adjusted, 2020 dollars) was awarded through PA. While all who qualify for PA should have equal opportunity to receive aid, not all do, and the factors influencing how the program has been administered are complex and multifaceted. Lacking an understanding of the factors positively associated with historical receipt of aid, there is little way to objectively evaluate the efficacy of the PA program. In this work, we evaluate the salient features that contribute to the number of county-level PA applicants and projects following disasters. We use statistical learning theory applied to repetitive flooding events in the upper Midwest between 2003 and 2018 as a case study. The results suggest that many non-disaster related indicators are key predictors of PA outlays, including the state in which the disaster occurred, the county’s prior experience with disasters, the county’s median income, and the length of time between the end of the disaster and the date when a disaster is declared. Our work suggests that indicators of PA aid are tied to exposure, bureaucratic attributes, and human behavior. For equitable distribution of aid, policymakers should explore more disaster-relevant indicators for PA distribution.

Keywords: FEMA’s public assistance; Public infrastructure; Repetitive flood losses; Statistical learning theory (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-024-06620-2

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