An analysis of outcomes for FEMA individual assistance recipients in the context of social vulnerability

Natural disasters such as hurricanes, floods, and wildfires have been increasing both in frequency and in the cost of damages. Different communities experience disasters differently, from the immediate losses to the long-term recovery process. Age, disability, race, housing status, and socioeconomic status are factors that can contribute to disparities in a person or community's vulnerability to disasters. Federal disaster aid policy has historically contributed to disaster vulnerability as well, at times enhancing disparities in survivors' ability to respond or recover. The Federal Emergency Management Agency (FEMA) has recently significantly expanded its efforts to individualize the survivor experience, and especially consider the survivor's social vulnerability. We received a large dataset with outcomes for Individual Assistance (IA) programs for registrants in eight different natural disasters from 2008-2017. My research has been to analyze these outcomes in order to explore potential disparities in the survivor experience based on demographic characteristics at the individual and community levels. This document covers my exploration of this data set in the context of social vulnerability. I employ high-level descriptive statistics as well as multi-level modeling techniques to depict the relationship between demographic variables associated with social vulnerability and outcomes in IA programs. Through these modeling techniques, I find that many individual and community-level demographic variables are strongly correlated with FEMA assistance outcomes including assistance amount and the number of contacts between the registrant and FEMA. Finally, I outline significant disparities in assistance levels for different demographic groups.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Institute for Data, Systems, and Society

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Bann, Gabriel(Gabriel T.)

Advisor dc:contributor.advisor

• Jarrod Goentzel.

• Institute for Data, Systems, and Society.
• Technology and Policy Program.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/129077

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/129077